pandas.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Pandas Leson](./images/Pandas.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  Pandas\n",
    "\n",
    "<small><em>By Asabeneh Yetayeh</em></small>\n",
    "\n",
    "Pandas is an open source, high-performance, easy-to-use data structures and data analysis Python library.\n",
    "Pandas adds data structures and tools designed to work with table-like data which is *Series* and *Data Frames*.\n",
    "Pandas provides tools for data manipulation: \n",
    "\n",
    "- cleaning\n",
    "- exploring\n",
    "- analysing\n",
    "- reshaping\n",
    "- merging\n",
    "- sorting\n",
    "- slicing\n",
    "- aggregation\n",
    "- imputation\n",
    "\n",
    "If you are using anaconda, you do not have to install pandas.\n",
    "<p>If you are in favor of videos, you can watch the video tutorial from <a href=\"https://youtu.be/91ghb-msEVU\">here.</a>."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Installing Pandas\n",
    "\n",
    "For Mac:\n",
    "```py\n",
    "pip install conda\n",
    "conda install pandas\n",
    "```\n",
    "\n",
    "For Windows:\n",
    "```py\n",
    "pip install conda\n",
    "pip install pandas\n",
    "```\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pandas Series and DataFrames\n",
    "Pandas data structure is based on *Series* and *DataFrames*. \n",
    "\n",
    "\n",
    "### Pandas Series\n",
    "\n",
    "A *series* is a *column* and a DataFrame is a *multidimensional table* made up of collection of *series*. In order to create a pandas series we should use numpy to create a one dimensional arrays or a python list.\n",
    "Let us see an example of a series:\n",
    "\n",
    "Names Pandas Series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Names Pandas Series\n",
    "\n",
    "![pandas series](./images/pandas-series-1.png) \n",
    "\n",
    "Countries Series\n",
    "\n",
    "![pandas series](./images/pandas-series-2.png) \n",
    "\n",
    "Cities Series\n",
    "\n",
    "![pandas series](./images/pandas-series-3.png)\n",
    "\n",
    "As you can see, pandas series is just one column of data. If we want to have multiple columns we use data frames. The example below shows pandas DataFrames.\n",
    "\n",
    "Let us see, an example of a pandas data frame:\n",
    "\n",
    "![Pandas data frame](./images/pandas-dataframe-1.png)\n",
    "\n",
    "Data frame is a collection of rows and columns. Look at the table below; it has many more columns than the example above:\n",
    "\n",
    "![Pandas data frame](./images/pandas-dataframe-2.png)\n",
    "\n",
    "Next, we will see how to import pandas and how to create Series and DataFrames using pandas\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing Pandas\n",
    "\n",
    "```python\n",
    "import pandas as pd # importing pandas as pd\n",
    "import numpy  as np # importing numpy as np\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd # importing pandas as pd\n",
    "import numpy  as np # importing numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Pandas Version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.1.3'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating Pandas Series from list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    1\n",
      "1    2\n",
      "2    3\n",
      "3    4\n",
      "4    5\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "nums = [1, 2, 3, 4,5]\n",
    "s = pd.Series(nums)\n",
    "print(s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Getting the index from the Pandas Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=5, step=1)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 2, 3, 4]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(s.index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating  Pandas Series with custom index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1    1\n",
      "2    2\n",
      "3    3\n",
      "4    4\n",
      "5    5\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "nums = [1, 2, 3, 4, 5]\n",
    "s = pd.Series(nums, index=[1, 2, 3, 4, 5])\n",
    "print(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([1, 2, 3, 4, 5], dtype='int64')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A    1\n",
      "B    2\n",
      "C    3\n",
      "D    4\n",
      "E    5\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "nums = [1, 2, 3, 4, 5]\n",
    "s = pd.Series(nums, index=['A', 'B', 'C', 'D', 'E'])\n",
    "print(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['A', 'B', 'C', 'D', 'E'], dtype='object')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1    Orange\n",
      "2    Banana\n",
      "3     Mango\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "fruits = ['Orange','Banana','Mango']\n",
    "fruits = pd.Series(fruits, index=[1, 2, 3])\n",
    "print(fruits)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "O    Orange\n",
      "B    Banana\n",
      "M     Mango\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "fruits = ['Orange','Banana','Mango']\n",
    "fruits = pd.Series(fruits, index=['O', 'B', 'M'])\n",
    "print(fruits)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating Pandas Series from a Dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "name       Asabeneh\n",
      "country     Finland\n",
      "city       Helsinki\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "dct = {'name':'Asabeneh','country':'Finland','city':'Helsinki'}\n",
    "s = pd.Series(dct)\n",
    "print(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "name       Asabeneh\n",
      "country     Finland\n",
      "city       Helsinki\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "dct = {'name':'Asabeneh','country':'Finland','city':'Helsinki'}\n",
    "s = pd.Series(dct)\n",
    "print(s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating a Constant Pandas Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1    10\n",
      "2    10\n",
      "3    10\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "s = pd.Series(10, index = [1, 2, 3])\n",
    "print(s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating a  Pandas Series Using Linspace"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0     5.000000\n",
      "1     6.666667\n",
      "2     8.333333\n",
      "3    10.000000\n",
      "4    11.666667\n",
      "5    13.333333\n",
      "6    15.000000\n",
      "7    16.666667\n",
      "8    18.333333\n",
      "9    20.000000\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "s = pd.Series(np.linspace(5, 20, 10)) # linspace(starting, end, items)\n",
    "print(s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Accessing specific item from Pandas Series\n",
    "We can use the label to access Pandas series values. We can give labels to each item using the index argument\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A    1\n",
      "B    2\n",
      "C    3\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "nums = [1, 2, 3]\n",
    "s = pd.Series(nums, index =['A','B','C'])\n",
    "print(s)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s['A']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s['C']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    1\n",
      "1    2\n",
      "2    3\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# if we don't pass value to the index argument, the index of the pandas eries will start from zero\n",
    "nums = [1, 2, 3]\n",
    "s = pd.Series(nums)\n",
    "print(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Copying a Panda Series with copy method\n",
    "Working on the copy does not affect the original data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "O    Orange\n",
       "B    Banana\n",
       "M     Mango\n",
       "dtype: object"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "copied_fruits = fruits.copy()\n",
    "copied_fruits"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## DataFrames\n",
    "\n",
    "Pandas data frame has both rows and columns that has two dimenstional data structure like 2-dimensional numpy array or table. It can be created in different ways. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating DataFrames from List of Lists"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Names</th>\n",
       "      <th>Country</th>\n",
       "      <th>City</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Asabeneh</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Helsink</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>David</td>\n",
       "      <td>UK</td>\n",
       "      <td>London</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>John</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>Stockholm</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Names  Country       City\n",
       "0  Asabeneh  Finland    Helsink\n",
       "1     David       UK     London\n",
       "2      John   Sweden  Stockholm"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = [\n",
    "    ['Asabeneh', 'Finland', 'Helsink'], \n",
    "    ['David', 'UK', 'London'],\n",
    "    ['John', 'Sweden', 'Stockholm']\n",
    "]\n",
    "df = pd.DataFrame(data, columns=['Names','Country','City'])\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating DataFrame Using Dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Country</th>\n",
       "      <th>City</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Asabeneh</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Helsiki</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>David</td>\n",
       "      <td>UK</td>\n",
       "      <td>London</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>John</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>Stockholm</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Name  Country       City\n",
       "0  Asabeneh  Finland    Helsiki\n",
       "1     David       UK     London\n",
       "2      John   Sweden  Stockholm"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = {'Name': ['Asabeneh', 'David', 'John'], 'Country':[\n",
    "    'Finland', 'UK', 'Sweden'], 'City': ['Helsiki', 'London', 'Stockholm']}\n",
    "df = pd.DataFrame(data)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating DataFrames from a List of Dictionaries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Country</th>\n",
       "      <th>City</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Asabeneh</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Helsinki</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>David</td>\n",
       "      <td>UK</td>\n",
       "      <td>London</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>John</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>Stockholm</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Name  Country       City\n",
       "0  Asabeneh  Finland   Helsinki\n",
       "1     David       UK     London\n",
       "2      John   Sweden  Stockholm"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = [\n",
    "    {'Name': 'Asabeneh', 'Country': 'Finland', 'City': 'Helsinki'},\n",
    "    {'Name': 'David', 'Country': 'UK', 'City': 'London'},\n",
    "    {'Name': 'John', 'Country': 'Sweden', 'City': 'Stockholm'}]\n",
    "df = pd.DataFrame(data)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading different file formats Using Pandas\n",
    "We can read txt, json, csv, tsv, xls file formats using pandas reading methods"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reading CSV File Using Pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Loading a CSV file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Male</td>\n",
       "      <td>73.847017</td>\n",
       "      <td>241.893563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Male</td>\n",
       "      <td>68.781904</td>\n",
       "      <td>162.310473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Male</td>\n",
       "      <td>74.110105</td>\n",
       "      <td>212.740856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Male</td>\n",
       "      <td>71.730978</td>\n",
       "      <td>220.042470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Male</td>\n",
       "      <td>69.881796</td>\n",
       "      <td>206.349801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9995</th>\n",
       "      <td>Female</td>\n",
       "      <td>66.172652</td>\n",
       "      <td>136.777454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9996</th>\n",
       "      <td>Female</td>\n",
       "      <td>67.067155</td>\n",
       "      <td>170.867906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9997</th>\n",
       "      <td>Female</td>\n",
       "      <td>63.867992</td>\n",
       "      <td>128.475319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9998</th>\n",
       "      <td>Female</td>\n",
       "      <td>69.034243</td>\n",
       "      <td>163.852461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999</th>\n",
       "      <td>Female</td>\n",
       "      <td>61.944246</td>\n",
       "      <td>113.649103</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10000 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Gender     Height      Weight\n",
       "0       Male  73.847017  241.893563\n",
       "1       Male  68.781904  162.310473\n",
       "2       Male  74.110105  212.740856\n",
       "3       Male  71.730978  220.042470\n",
       "4       Male  69.881796  206.349801\n",
       "...      ...        ...         ...\n",
       "9995  Female  66.172652  136.777454\n",
       "9996  Female  67.067155  170.867906\n",
       "9997  Female  63.867992  128.475319\n",
       "9998  Female  69.034243  163.852461\n",
       "9999  Female  61.944246  113.649103\n",
       "\n",
       "[10000 rows x 3 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv('./datasets/weight-height.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data Exploration\n",
    "\n",
    "Data exploration is an initial stage of data analysis used to explore and visualize data to get insights from the beginning of data analysis or identifing some patterns for further analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Reading the first few records of a dataset using head()\n",
    "The head() method gives 5 records by default, however, an agrument can be passed to the head() method. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Male</td>\n",
       "      <td>73.847017</td>\n",
       "      <td>241.893563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Male</td>\n",
       "      <td>68.781904</td>\n",
       "      <td>162.310473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Male</td>\n",
       "      <td>74.110105</td>\n",
       "      <td>212.740856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Male</td>\n",
       "      <td>71.730978</td>\n",
       "      <td>220.042470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Male</td>\n",
       "      <td>69.881796</td>\n",
       "      <td>206.349801</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Gender     Height      Weight\n",
       "0   Male  73.847017  241.893563\n",
       "1   Male  68.781904  162.310473\n",
       "2   Male  74.110105  212.740856\n",
       "3   Male  71.730978  220.042470\n",
       "4   Male  69.881796  206.349801"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The head() method with argument provides as large as the size of the argument. If we pass 10 in the head() as argument will get 10 records."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Male</td>\n",
       "      <td>73.847017</td>\n",
       "      <td>241.893563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Male</td>\n",
       "      <td>68.781904</td>\n",
       "      <td>162.310473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Male</td>\n",
       "      <td>74.110105</td>\n",
       "      <td>212.740856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Male</td>\n",
       "      <td>71.730978</td>\n",
       "      <td>220.042470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Male</td>\n",
       "      <td>69.881796</td>\n",
       "      <td>206.349801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Male</td>\n",
       "      <td>67.253016</td>\n",
       "      <td>152.212156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Male</td>\n",
       "      <td>68.785081</td>\n",
       "      <td>183.927889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Male</td>\n",
       "      <td>68.348516</td>\n",
       "      <td>167.971110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Male</td>\n",
       "      <td>67.018950</td>\n",
       "      <td>175.929440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Male</td>\n",
       "      <td>63.456494</td>\n",
       "      <td>156.399676</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Gender     Height      Weight\n",
       "0   Male  73.847017  241.893563\n",
       "1   Male  68.781904  162.310473\n",
       "2   Male  74.110105  212.740856\n",
       "3   Male  71.730978  220.042470\n",
       "4   Male  69.881796  206.349801\n",
       "5   Male  67.253016  152.212156\n",
       "6   Male  68.785081  183.927889\n",
       "7   Male  68.348516  167.971110\n",
       "8   Male  67.018950  175.929440\n",
       "9   Male  63.456494  156.399676"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reading the last records of a dataset\n",
    "\n",
    "To explore the last five records of the data set we use the tail() method. However, we can get fewer or larger records by changing the argument we pass to the tail() method.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9995</th>\n",
       "      <td>Female</td>\n",
       "      <td>66.172652</td>\n",
       "      <td>136.777454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9996</th>\n",
       "      <td>Female</td>\n",
       "      <td>67.067155</td>\n",
       "      <td>170.867906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9997</th>\n",
       "      <td>Female</td>\n",
       "      <td>63.867992</td>\n",
       "      <td>128.475319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9998</th>\n",
       "      <td>Female</td>\n",
       "      <td>69.034243</td>\n",
       "      <td>163.852461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999</th>\n",
       "      <td>Female</td>\n",
       "      <td>61.944246</td>\n",
       "      <td>113.649103</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Gender     Height      Weight\n",
       "9995  Female  66.172652  136.777454\n",
       "9996  Female  67.067155  170.867906\n",
       "9997  Female  63.867992  128.475319\n",
       "9998  Female  69.034243  163.852461\n",
       "9999  Female  61.944246  113.649103"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9990</th>\n",
       "      <td>Female</td>\n",
       "      <td>63.179498</td>\n",
       "      <td>141.266100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9991</th>\n",
       "      <td>Female</td>\n",
       "      <td>62.636675</td>\n",
       "      <td>102.853563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9992</th>\n",
       "      <td>Female</td>\n",
       "      <td>62.077832</td>\n",
       "      <td>138.691680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9993</th>\n",
       "      <td>Female</td>\n",
       "      <td>60.030434</td>\n",
       "      <td>97.687432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9994</th>\n",
       "      <td>Female</td>\n",
       "      <td>59.098250</td>\n",
       "      <td>110.529686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9995</th>\n",
       "      <td>Female</td>\n",
       "      <td>66.172652</td>\n",
       "      <td>136.777454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9996</th>\n",
       "      <td>Female</td>\n",
       "      <td>67.067155</td>\n",
       "      <td>170.867906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9997</th>\n",
       "      <td>Female</td>\n",
       "      <td>63.867992</td>\n",
       "      <td>128.475319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9998</th>\n",
       "      <td>Female</td>\n",
       "      <td>69.034243</td>\n",
       "      <td>163.852461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999</th>\n",
       "      <td>Female</td>\n",
       "      <td>61.944246</td>\n",
       "      <td>113.649103</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Gender     Height      Weight\n",
       "9990  Female  63.179498  141.266100\n",
       "9991  Female  62.636675  102.853563\n",
       "9992  Female  62.077832  138.691680\n",
       "9993  Female  60.030434   97.687432\n",
       "9994  Female  59.098250  110.529686\n",
       "9995  Female  66.172652  136.777454\n",
       "9996  Female  67.067155  170.867906\n",
       "9997  Female  63.867992  128.475319\n",
       "9998  Female  69.034243  163.852461\n",
       "9999  Female  61.944246  113.649103"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail(10) # tail() method with an argument"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Number of Columns\n",
    "\n",
    "Knowing the fields or attributes of the dataset is one part of data exploration. In this dataset there are only three columns but most of the time, the size of columns is larger than this. Therefore, it is good to know how to get the columns and the size of the columns.\n",
    "We will use the .columns DataFrame attribute to get a column list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Gender', 'Height', 'Weight'], dtype='object')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DataFrame shape\n",
    "\n",
    "The DataFrame shape allows to understand the dataset better. It tells the number of rows and columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 3)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Descriptive Statistics\n",
    "\n",
    "Descriptive statistics summarizes a given data set that can be either a representation of the entire or a sample of a population. Descriptive statistics are divided into measures of central tendency and measures of variability (spread).\n",
    "\n",
    "Measures of central tendency includes:\n",
    "\n",
    "- mean\n",
    "- median\n",
    "- mode\n",
    "\n",
    "Measures of variability include:\n",
    "\n",
    "- standard deviation\n",
    "- variance\n",
    "- minimum\n",
    "- maximum\n",
    "- kurtosis\n",
    "- skewness\n",
    "\n",
    "Pandas describe() provides a descriptive statistics of a dataset. The method takes a couple of arguments\n",
    "```py\n",
    "DataFrame.describe(percentiles=None, include=None, exclude=None, datetime_is_numeric=False)\n",
    "```\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>66.367560</td>\n",
       "      <td>161.440357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.847528</td>\n",
       "      <td>32.108439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>54.263133</td>\n",
       "      <td>64.700127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>63.505620</td>\n",
       "      <td>135.818051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>66.318070</td>\n",
       "      <td>161.212928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>69.174262</td>\n",
       "      <td>187.169525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>78.998742</td>\n",
       "      <td>269.989699</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Height        Weight\n",
       "count  10000.000000  10000.000000\n",
       "mean      66.367560    161.440357\n",
       "std        3.847528     32.108439\n",
       "min       54.263133     64.700127\n",
       "25%       63.505620    135.818051\n",
       "50%       66.318070    161.212928\n",
       "75%       69.174262    187.169525\n",
       "max       78.998742    269.989699"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe() # without any argument"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>66.367560</td>\n",
       "      <td>161.440357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3.847528</td>\n",
       "      <td>32.108439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>54.263133</td>\n",
       "      <td>64.700127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>63.505620</td>\n",
       "      <td>135.818051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>66.318070</td>\n",
       "      <td>161.212928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>69.174262</td>\n",
       "      <td>187.169525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>70.577106</td>\n",
       "      <td>197.536443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>78.998742</td>\n",
       "      <td>269.989699</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Gender        Height        Weight\n",
       "count    10000  10000.000000  10000.000000\n",
       "unique       2           NaN           NaN\n",
       "top     Female           NaN           NaN\n",
       "freq      5000           NaN           NaN\n",
       "mean       NaN     66.367560    161.440357\n",
       "std        NaN      3.847528     32.108439\n",
       "min        NaN     54.263133     64.700127\n",
       "25%        NaN     63.505620    135.818051\n",
       "50%        NaN     66.318070    161.212928\n",
       "75%        NaN     69.174262    187.169525\n",
       "85%        NaN     70.577106    197.536443\n",
       "max        NaN     78.998742    269.989699"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(include='all', percentiles=[0.25, 0.5, 0.75, 0.85])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get information about the dataset\n",
    "\n",
    "It also possible to get some information about the dataset using the method info(). The info() takes a couple of arguments.\n",
    "\n",
    "```py\n",
    "DataFrame.info(verbose=None, buf=None, max_cols=None, memory_usage=None, show_counts=None, null_counts=None\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10000 entries, 0 to 9999\n",
      "Data columns (total 3 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   Gender  10000 non-null  object \n",
      " 1   Height  10000 non-null  float64\n",
      " 2   Weight  10000 non-null  float64\n",
      "dtypes: float64(2), object(1)\n",
      "memory usage: 234.5+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10000 entries, 0 to 9999\n",
      "Data columns (total 3 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   Gender  10000 non-null  object \n",
      " 1   Height  10000 non-null  float64\n",
      " 2   Weight  10000 non-null  float64\n",
      "dtypes: float64(2), object(1)\n",
      "memory usage: 234.5+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info(verbose=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modifying a DataFrame\n",
    "\n",
    "Modifying a DataFrame:\n",
    "\n",
    "    - We can create a new DataFrame\n",
    "    - We can create a new column and add it to the DataFrame, \n",
    "    - we can remove an existing column from a DataFrame, \n",
    "    - we can modify an existing column in a DataFrame, \n",
    "    - we can change the data type of column values in the DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating a DataFrame\n",
    "\n",
    "As we have seen before, it is possible to create DataFrame from list of lists, list of dictionaries or dictionaries.\n",
    "\n",
    "As always, first we import the necessary packages. Now, lets import pandas and numpy, two best friends ever."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Country</th>\n",
       "      <th>City</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Asabeneh</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Helsinki</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>David</td>\n",
       "      <td>UK</td>\n",
       "      <td>London</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>John</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>Stockholm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Eyob</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Espoo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Pawel</td>\n",
       "      <td>Poland</td>\n",
       "      <td>Warsaw</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Mahlet</td>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>Addis Ababa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Thomas</td>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>Bahir Dar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Lidiya</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Name   Country         City\n",
       "0  Asabeneh   Finland     Helsinki\n",
       "1     David        UK       London\n",
       "2      John    Sweden    Stockholm\n",
       "3      Eyob   Finland        Espoo\n",
       "4     Pawel    Poland       Warsaw\n",
       "5    Mahlet  Ethiopia  Addis Ababa\n",
       "6    Thomas  Ethiopia    Bahir Dar\n",
       "7    Lidiya       NaN          NaN"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "data = [\n",
    "    {\"Name\": \"Asabeneh\", \"Country\":\"Finland\",\"City\":\"Helsinki\"},\n",
    "    {\"Name\": \"David\", \"Country\":\"UK\",\"City\":\"London\"},\n",
    "    {\"Name\": \"John\", \"Country\":\"Sweden\",\"City\":\"Stockholm\"},\n",
    "    {\"Name\": \"Eyob\", \"Country\":\"Finland\",\"City\":\"Espoo\"},\n",
    "    {\"Name\": \"Pawel\", \"Country\":\"Poland\",\"City\":\"Warsaw\"},\n",
    "    {\"Name\": \"Mahlet\", \"Country\":\"Ethiopia\",\"City\":\"Addis Ababa\"},\n",
    "    {\"Name\": \"Thomas\", \"Country\":\"Ethiopia\",\"City\":\"Bahir Dar\"},\n",
    "    {\"Name\": \"Lidiya\", \"Country\":float('NaN'),\"City\":float('NaN')},\n",
    "]\n",
    "df = pd.DataFrame(data)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adding a New Column\n",
    "Let's add a weight column in the DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Country</th>\n",
       "      <th>City</th>\n",
       "      <th>Weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Asabeneh</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>74.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>David</td>\n",
       "      <td>UK</td>\n",
       "      <td>London</td>\n",
       "      <td>78.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>John</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>Stockholm</td>\n",
       "      <td>68.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Eyob</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Espoo</td>\n",
       "      <td>108.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Pawel</td>\n",
       "      <td>Poland</td>\n",
       "      <td>Warsaw</td>\n",
       "      <td>102.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Mahlet</td>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>Addis Ababa</td>\n",
       "      <td>58.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Thomas</td>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>Bahir Dar</td>\n",
       "      <td>52.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Lidiya</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Name   Country         City  Weight\n",
       "0  Asabeneh   Finland     Helsinki    74.0\n",
       "1     David        UK       London    78.0\n",
       "2      John    Sweden    Stockholm    68.0\n",
       "3      Eyob   Finland        Espoo   108.0\n",
       "4     Pawel    Poland       Warsaw   102.0\n",
       "5    Mahlet  Ethiopia  Addis Ababa    58.0\n",
       "6    Thomas  Ethiopia    Bahir Dar    52.0\n",
       "7    Lidiya       NaN          NaN     NaN"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weights = [74, 78, 68, 108, 102, 58, 52, float('NaN')]\n",
    "df['Weight'] = weights\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's add a height column into the DataFrame as well"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Country</th>\n",
       "      <th>City</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Height</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Asabeneh</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>74.0</td>\n",
       "      <td>173.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>David</td>\n",
       "      <td>UK</td>\n",
       "      <td>London</td>\n",
       "      <td>78.0</td>\n",
       "      <td>175.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>John</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>Stockholm</td>\n",
       "      <td>68.0</td>\n",
       "      <td>169.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Eyob</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Espoo</td>\n",
       "      <td>108.0</td>\n",
       "      <td>173.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Pawel</td>\n",
       "      <td>Poland</td>\n",
       "      <td>Warsaw</td>\n",
       "      <td>102.0</td>\n",
       "      <td>195.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Mahlet</td>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>Addis Ababa</td>\n",
       "      <td>58.0</td>\n",
       "      <td>168.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Thomas</td>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>Bahir Dar</td>\n",
       "      <td>52.0</td>\n",
       "      <td>170.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Lidiya</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Name   Country         City  Weight  Height\n",
       "0  Asabeneh   Finland     Helsinki    74.0   173.0\n",
       "1     David        UK       London    78.0   175.0\n",
       "2      John    Sweden    Stockholm    68.0   169.0\n",
       "3      Eyob   Finland        Espoo   108.0   173.0\n",
       "4     Pawel    Poland       Warsaw   102.0   195.0\n",
       "5    Mahlet  Ethiopia  Addis Ababa    58.0   168.0\n",
       "6    Thomas  Ethiopia    Bahir Dar    52.0   170.0\n",
       "7    Lidiya       NaN          NaN     NaN     NaN"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heights = [173, 175, 169, 173, 195,168, 170, float('NaN')]\n",
    "df['Height'] = heights\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see in the DataFrame above, we did add new columns, Weight and Height. Let's add one additional column called BMI(Body Mass Index) by calculating their BMI using thier mass and height. BMI is mass divided by height squared (in meters) - Weight/Height * Height.\n",
    "\n",
    "As you can see, the height is in centimeters, so we shoud change it to meters. Let's modify the height row."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Modifying column values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Country</th>\n",
       "      <th>City</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Height</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Asabeneh</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>74.0</td>\n",
       "      <td>1.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>David</td>\n",
       "      <td>UK</td>\n",
       "      <td>London</td>\n",
       "      <td>78.0</td>\n",
       "      <td>1.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>John</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>Stockholm</td>\n",
       "      <td>68.0</td>\n",
       "      <td>1.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Eyob</td>\n",
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       "      <td>Espoo</td>\n",
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       "      <td>1.73</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Pawel</td>\n",
       "      <td>Poland</td>\n",
       "      <td>Warsaw</td>\n",
       "      <td>102.0</td>\n",
       "      <td>1.95</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Mahlet</td>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>Addis Ababa</td>\n",
       "      <td>58.0</td>\n",
       "      <td>1.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Thomas</td>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>Bahir Dar</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Lidiya</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Name   Country         City  Weight  Height\n",
       "0  Asabeneh   Finland     Helsinki    74.0    1.73\n",
       "1     David        UK       London    78.0    1.75\n",
       "2      John    Sweden    Stockholm    68.0    1.69\n",
       "3      Eyob   Finland        Espoo   108.0    1.73\n",
       "4     Pawel    Poland       Warsaw   102.0    1.95\n",
       "5    Mahlet  Ethiopia  Addis Ababa    58.0    1.68\n",
       "6    Thomas  Ethiopia    Bahir Dar    52.0    1.70\n",
       "7    Lidiya       NaN          NaN     NaN     NaN"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Height'] = df['Height'] * 0.01\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Using functions makes our code clean, but you can calculate the bmi without a function\n",
    "def calculate_bmi ():\n",
    "    weights = df['Weight']\n",
    "    heights = df['Height']\n",
    "    bmi = []\n",
    "    for w,h in zip(weights, heights):\n",
    "        b = w/(h*h)\n",
    "        bmi.append(b)\n",
    "    return bmi\n",
    "    \n",
    "bmi = calculate_bmi()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>Country</th>\n",
       "      <th>City</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Height</th>\n",
       "      <th>BMI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Asabeneh</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>74.0</td>\n",
       "      <td>1.73</td>\n",
       "      <td>24.725183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>David</td>\n",
       "      <td>UK</td>\n",
       "      <td>London</td>\n",
       "      <td>78.0</td>\n",
       "      <td>1.75</td>\n",
       "      <td>25.469388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>John</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>Stockholm</td>\n",
       "      <td>68.0</td>\n",
       "      <td>1.69</td>\n",
       "      <td>23.808690</td>\n",
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       "      <th>3</th>\n",
       "      <td>Eyob</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Espoo</td>\n",
       "      <td>108.0</td>\n",
       "      <td>1.73</td>\n",
       "      <td>36.085402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Pawel</td>\n",
       "      <td>Poland</td>\n",
       "      <td>Warsaw</td>\n",
       "      <td>102.0</td>\n",
       "      <td>1.95</td>\n",
       "      <td>26.824458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Mahlet</td>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>Addis Ababa</td>\n",
       "      <td>58.0</td>\n",
       "      <td>1.68</td>\n",
       "      <td>20.549887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Thomas</td>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>Bahir Dar</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1.70</td>\n",
       "      <td>17.993080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Lidiya</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Name   Country         City  Weight  Height        BMI\n",
       "0  Asabeneh   Finland     Helsinki    74.0    1.73  24.725183\n",
       "1     David        UK       London    78.0    1.75  25.469388\n",
       "2      John    Sweden    Stockholm    68.0    1.69  23.808690\n",
       "3      Eyob   Finland        Espoo   108.0    1.73  36.085402\n",
       "4     Pawel    Poland       Warsaw   102.0    1.95  26.824458\n",
       "5    Mahlet  Ethiopia  Addis Ababa    58.0    1.68  20.549887\n",
       "6    Thomas  Ethiopia    Bahir Dar    52.0    1.70  17.993080\n",
       "7    Lidiya       NaN          NaN     NaN     NaN        NaN"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['BMI'] = bmi\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Formating DataFrame columns\n",
    "The BMI column values of the DataFrame are float with many significant digits after decimal. Let's change it to one significant digit after point."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Country</th>\n",
       "      <th>City</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Height</th>\n",
       "      <th>BMI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Asabeneh</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>74.0</td>\n",
       "      <td>1.73</td>\n",
       "      <td>24.7</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>David</td>\n",
       "      <td>UK</td>\n",
       "      <td>London</td>\n",
       "      <td>78.0</td>\n",
       "      <td>1.75</td>\n",
       "      <td>25.5</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>John</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>Stockholm</td>\n",
       "      <td>68.0</td>\n",
       "      <td>1.69</td>\n",
       "      <td>23.8</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Eyob</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Espoo</td>\n",
       "      <td>108.0</td>\n",
       "      <td>1.73</td>\n",
       "      <td>36.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Pawel</td>\n",
       "      <td>Poland</td>\n",
       "      <td>Warsaw</td>\n",
       "      <td>102.0</td>\n",
       "      <td>1.95</td>\n",
       "      <td>26.8</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Mahlet</td>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>Addis Ababa</td>\n",
       "      <td>58.0</td>\n",
       "      <td>1.68</td>\n",
       "      <td>20.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Thomas</td>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>Bahir Dar</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1.70</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Lidiya</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Name   Country         City  Weight  Height   BMI\n",
       "0  Asabeneh   Finland     Helsinki    74.0    1.73  24.7\n",
       "1     David        UK       London    78.0    1.75  25.5\n",
       "2      John    Sweden    Stockholm    68.0    1.69  23.8\n",
       "3      Eyob   Finland        Espoo   108.0    1.73  36.1\n",
       "4     Pawel    Poland       Warsaw   102.0    1.95  26.8\n",
       "5    Mahlet  Ethiopia  Addis Ababa    58.0    1.68  20.5\n",
       "6    Thomas  Ethiopia    Bahir Dar    52.0    1.70  18.0\n",
       "7    Lidiya       NaN          NaN     NaN     NaN   NaN"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['BMI'] = round(df['BMI'], 1)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>City</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Height</th>\n",
       "      <th>BMI</th>\n",
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       "      <th>0</th>\n",
       "      <td>Asabeneh</td>\n",
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       "      <td>Helsinki</td>\n",
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       "      <td>1.73</td>\n",
       "      <td>24.7</td>\n",
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       "      <th>1</th>\n",
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       "      <td>1.75</td>\n",
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       "      <th>2</th>\n",
       "      <td>John</td>\n",
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       "      <td>1.69</td>\n",
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       "      <td>Espoo</td>\n",
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       "      <td>1.73</td>\n",
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       "    <tr>\n",
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       "      <td>Pawel</td>\n",
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       "      <td>Warsaw</td>\n",
       "      <td>102.0</td>\n",
       "      <td>1.95</td>\n",
       "      <td>26.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Mahlet</td>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>Addis Ababa</td>\n",
       "      <td>58.0</td>\n",
       "      <td>1.68</td>\n",
       "      <td>20.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Thomas</td>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>Bahir Dar</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1.70</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Lidiya</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Name   Country         City  Weight  Height   BMI\n",
       "0  Asabeneh   Finland     Helsinki    74.0    1.73  24.7\n",
       "1     David        UK       London    78.0    1.75  25.5\n",
       "2      John    Sweden    Stockholm    68.0    1.69  23.8\n",
       "3      Eyob   Finland        Espoo   108.0    1.73  36.1\n",
       "4     Pawel    Poland       Warsaw   102.0    1.95  26.8\n",
       "5    Mahlet  Ethiopia  Addis Ababa    58.0    1.68  20.5\n",
       "6    Thomas  Ethiopia    Bahir Dar    52.0    1.70  18.0\n",
       "7    Lidiya       NaN          NaN     NaN     NaN   NaN"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(25)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "The information in the DataFrame seems not yet complete, let's add birth year and current year columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Country</th>\n",
       "      <th>City</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Height</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Birth Year</th>\n",
       "      <th>Current Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Asabeneh</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>74.0</td>\n",
       "      <td>1.73</td>\n",
       "      <td>24.7</td>\n",
       "      <td>1769</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>David</td>\n",
       "      <td>UK</td>\n",
       "      <td>London</td>\n",
       "      <td>78.0</td>\n",
       "      <td>1.75</td>\n",
       "      <td>25.5</td>\n",
       "      <td>1985</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>John</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>Stockholm</td>\n",
       "      <td>68.0</td>\n",
       "      <td>1.69</td>\n",
       "      <td>23.8</td>\n",
       "      <td>1990</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Eyob</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Espoo</td>\n",
       "      <td>108.0</td>\n",
       "      <td>1.73</td>\n",
       "      <td>36.1</td>\n",
       "      <td>1983</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Pawel</td>\n",
       "      <td>Poland</td>\n",
       "      <td>Warsaw</td>\n",
       "      <td>102.0</td>\n",
       "      <td>1.95</td>\n",
       "      <td>26.8</td>\n",
       "      <td>1985</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Mahlet</td>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>Addis Ababa</td>\n",
       "      <td>58.0</td>\n",
       "      <td>1.68</td>\n",
       "      <td>20.5</td>\n",
       "      <td>1995</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Thomas</td>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>Bahir Dar</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1.70</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1999</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Lidiya</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Name   Country         City  Weight  Height   BMI Birth Year  \\\n",
       "0  Asabeneh   Finland     Helsinki    74.0    1.73  24.7       1769   \n",
       "1     David        UK       London    78.0    1.75  25.5       1985   \n",
       "2      John    Sweden    Stockholm    68.0    1.69  23.8       1990   \n",
       "3      Eyob   Finland        Espoo   108.0    1.73  36.1       1983   \n",
       "4     Pawel    Poland       Warsaw   102.0    1.95  26.8       1985   \n",
       "5    Mahlet  Ethiopia  Addis Ababa    58.0    1.68  20.5       1995   \n",
       "6    Thomas  Ethiopia    Bahir Dar    52.0    1.70  18.0       1999   \n",
       "7    Lidiya       NaN          NaN     NaN     NaN   NaN        NaN   \n",
       "\n",
       "   Current Year  \n",
       "0          2021  \n",
       "1          2021  \n",
       "2          2021  \n",
       "3          2021  \n",
       "4          2021  \n",
       "5          2021  \n",
       "6          2021  \n",
       "7          2021  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "birth_year = ['1769', '1985', '1990', '1983', '1985','1995', '1999',float('NaN')]\n",
    "df['Birth Year'] = birth_year\n",
    "df['Current Year'] = 2021\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Copying Dataframe\n",
    "Sometimes, we may be interested to work on the copy of the original file and we want to keep the original file intact."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Country</th>\n",
       "      <th>City</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Height</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Birth Year</th>\n",
       "      <th>Current Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Asabeneh</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>74.0</td>\n",
       "      <td>1.73</td>\n",
       "      <td>24.7</td>\n",
       "      <td>1769</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>David</td>\n",
       "      <td>UK</td>\n",
       "      <td>London</td>\n",
       "      <td>78.0</td>\n",
       "      <td>1.75</td>\n",
       "      <td>25.5</td>\n",
       "      <td>1985</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>John</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>Stockholm</td>\n",
       "      <td>68.0</td>\n",
       "      <td>1.69</td>\n",
       "      <td>23.8</td>\n",
       "      <td>1990</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Eyob</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Espoo</td>\n",
       "      <td>108.0</td>\n",
       "      <td>1.73</td>\n",
       "      <td>36.1</td>\n",
       "      <td>1983</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Pawel</td>\n",
       "      <td>Poland</td>\n",
       "      <td>Warsaw</td>\n",
       "      <td>102.0</td>\n",
       "      <td>1.95</td>\n",
       "      <td>26.8</td>\n",
       "      <td>1985</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Mahlet</td>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>Addis Ababa</td>\n",
       "      <td>58.0</td>\n",
       "      <td>1.68</td>\n",
       "      <td>20.5</td>\n",
       "      <td>1995</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Thomas</td>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>Bahir Dar</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1.70</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1999</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Lidiya</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Name   Country         City  Weight  Height   BMI Birth Year  \\\n",
       "0  Asabeneh   Finland     Helsinki    74.0    1.73  24.7       1769   \n",
       "1     David        UK       London    78.0    1.75  25.5       1985   \n",
       "2      John    Sweden    Stockholm    68.0    1.69  23.8       1990   \n",
       "3      Eyob   Finland        Espoo   108.0    1.73  36.1       1983   \n",
       "4     Pawel    Poland       Warsaw   102.0    1.95  26.8       1985   \n",
       "5    Mahlet  Ethiopia  Addis Ababa    58.0    1.68  20.5       1995   \n",
       "6    Thomas  Ethiopia    Bahir Dar    52.0    1.70  18.0       1999   \n",
       "7    Lidiya       NaN          NaN     NaN     NaN   NaN        NaN   \n",
       "\n",
       "   Current Year  \n",
       "0          2021  \n",
       "1          2021  \n",
       "2          2021  \n",
       "3          2021  \n",
       "4          2021  \n",
       "5          2021  \n",
       "6          2021  \n",
       "7          2021  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_copy = df.copy()\n",
    "df_copy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Deleting a DataFrame Column\n",
    "#### Deleting Columns\n",
    "\n",
    "To delete a DataFrame column(s), we use the name of the columns and the axis as 1. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>City</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Height</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Birth Year</th>\n",
       "      <th>Current Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Finland</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>74.0</td>\n",
       "      <td>1.73</td>\n",
       "      <td>24.7</td>\n",
       "      <td>1769</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>UK</td>\n",
       "      <td>London</td>\n",
       "      <td>78.0</td>\n",
       "      <td>1.75</td>\n",
       "      <td>25.5</td>\n",
       "      <td>1985</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sweden</td>\n",
       "      <td>Stockholm</td>\n",
       "      <td>68.0</td>\n",
       "      <td>1.69</td>\n",
       "      <td>23.8</td>\n",
       "      <td>1990</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Finland</td>\n",
       "      <td>Espoo</td>\n",
       "      <td>108.0</td>\n",
       "      <td>1.73</td>\n",
       "      <td>36.1</td>\n",
       "      <td>1983</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Poland</td>\n",
       "      <td>Warsaw</td>\n",
       "      <td>102.0</td>\n",
       "      <td>1.95</td>\n",
       "      <td>26.8</td>\n",
       "      <td>1985</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>Addis Ababa</td>\n",
       "      <td>58.0</td>\n",
       "      <td>1.68</td>\n",
       "      <td>20.5</td>\n",
       "      <td>1995</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>Bahir Dar</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1.70</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1999</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Country         City  Weight  Height   BMI Birth Year  Current Year\n",
       "0   Finland     Helsinki    74.0    1.73  24.7       1769          2021\n",
       "1        UK       London    78.0    1.75  25.5       1985          2021\n",
       "2    Sweden    Stockholm    68.0    1.69  23.8       1990          2021\n",
       "3   Finland        Espoo   108.0    1.73  36.1       1983          2021\n",
       "4    Poland       Warsaw   102.0    1.95  26.8       1985          2021\n",
       "5  Ethiopia  Addis Ababa    58.0    1.68  20.5       1995          2021\n",
       "6  Ethiopia    Bahir Dar    52.0    1.70  18.0       1999          2021\n",
       "7       NaN          NaN     NaN     NaN   NaN        NaN          2021"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let us imagine the Name column is not important.\n",
    "# This does not affect the original data frame. To change from the original data frame we should add the inpplace argument\n",
    "df.drop('Name', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Country</th>\n",
       "      <th>City</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Height</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Birth Year</th>\n",
       "      <th>Current Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Asabeneh</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>74.0</td>\n",
       "      <td>1.73</td>\n",
       "      <td>24.7</td>\n",
       "      <td>1769</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>David</td>\n",
       "      <td>UK</td>\n",
       "      <td>London</td>\n",
       "      <td>78.0</td>\n",
       "      <td>1.75</td>\n",
       "      <td>25.5</td>\n",
       "      <td>1985</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>John</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>Stockholm</td>\n",
       "      <td>68.0</td>\n",
       "      <td>1.69</td>\n",
       "      <td>23.8</td>\n",
       "      <td>1990</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Eyob</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Espoo</td>\n",
       "      <td>108.0</td>\n",
       "      <td>1.73</td>\n",
       "      <td>36.1</td>\n",
       "      <td>1983</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Pawel</td>\n",
       "      <td>Poland</td>\n",
       "      <td>Warsaw</td>\n",
       "      <td>102.0</td>\n",
       "      <td>1.95</td>\n",
       "      <td>26.8</td>\n",
       "      <td>1985</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Mahlet</td>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>Addis Ababa</td>\n",
       "      <td>58.0</td>\n",
       "      <td>1.68</td>\n",
       "      <td>20.5</td>\n",
       "      <td>1995</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Thomas</td>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>Bahir Dar</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1.70</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1999</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Lidiya</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Name   Country         City  Weight  Height   BMI Birth Year  \\\n",
       "0  Asabeneh   Finland     Helsinki    74.0    1.73  24.7       1769   \n",
       "1     David        UK       London    78.0    1.75  25.5       1985   \n",
       "2      John    Sweden    Stockholm    68.0    1.69  23.8       1990   \n",
       "3      Eyob   Finland        Espoo   108.0    1.73  36.1       1983   \n",
       "4     Pawel    Poland       Warsaw   102.0    1.95  26.8       1985   \n",
       "5    Mahlet  Ethiopia  Addis Ababa    58.0    1.68  20.5       1995   \n",
       "6    Thomas  Ethiopia    Bahir Dar    52.0    1.70  18.0       1999   \n",
       "7    Lidiya       NaN          NaN     NaN     NaN   NaN        NaN   \n",
       "\n",
       "   Current Year  \n",
       "0          2021  \n",
       "1          2021  \n",
       "2          2021  \n",
       "3          2021  \n",
       "4          2021  \n",
       "5          2021  \n",
       "6          2021  \n",
       "7          2021  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#The original dataframe has not beeen changes\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Country</th>\n",
       "      <th>City</th>\n",
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       "      <td>Finland</td>\n",
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       "      <td>74.0</td>\n",
       "      <td>1.73</td>\n",
       "      <td>24.7</td>\n",
       "      <td>1769</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>UK</td>\n",
       "      <td>London</td>\n",
       "      <td>78.0</td>\n",
       "      <td>1.75</td>\n",
       "      <td>25.5</td>\n",
       "      <td>1985</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sweden</td>\n",
       "      <td>Stockholm</td>\n",
       "      <td>68.0</td>\n",
       "      <td>1.69</td>\n",
       "      <td>23.8</td>\n",
       "      <td>1990</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Finland</td>\n",
       "      <td>Espoo</td>\n",
       "      <td>108.0</td>\n",
       "      <td>1.73</td>\n",
       "      <td>36.1</td>\n",
       "      <td>1983</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Poland</td>\n",
       "      <td>Warsaw</td>\n",
       "      <td>102.0</td>\n",
       "      <td>1.95</td>\n",
       "      <td>26.8</td>\n",
       "      <td>1985</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>Addis Ababa</td>\n",
       "      <td>58.0</td>\n",
       "      <td>1.68</td>\n",
       "      <td>20.5</td>\n",
       "      <td>1995</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>Bahir Dar</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1.70</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1999</td>\n",
       "      <td>2021</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Country         City  Weight  Height   BMI Birth Year  Current Year\n",
       "0   Finland     Helsinki    74.0    1.73  24.7       1769          2021\n",
       "1        UK       London    78.0    1.75  25.5       1985          2021\n",
       "2    Sweden    Stockholm    68.0    1.69  23.8       1990          2021\n",
       "3   Finland        Espoo   108.0    1.73  36.1       1983          2021\n",
       "4    Poland       Warsaw   102.0    1.95  26.8       1985          2021\n",
       "5  Ethiopia  Addis Ababa    58.0    1.68  20.5       1995          2021\n",
       "6  Ethiopia    Bahir Dar    52.0    1.70  18.0       1999          2021\n",
       "7       NaN          NaN     NaN     NaN   NaN        NaN          2021"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let us imagine the Name column is not important.\n",
    "# This does not affect the original data frame. To change from the original data frame we should add the inpplace argument\n",
    "df.drop('Name', axis=1, inplace=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>City</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Height</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Birth Year</th>\n",
       "      <th>Current Year</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>Helsinki</td>\n",
       "      <td>74.0</td>\n",
       "      <td>1.73</td>\n",
       "      <td>24.7</td>\n",
       "      <td>1769</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>London</td>\n",
       "      <td>78.0</td>\n",
       "      <td>1.75</td>\n",
       "      <td>25.5</td>\n",
       "      <td>1985</td>\n",
       "      <td>2021</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Stockholm</td>\n",
       "      <td>68.0</td>\n",
       "      <td>1.69</td>\n",
       "      <td>23.8</td>\n",
       "      <td>1990</td>\n",
       "      <td>2021</td>\n",
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       "      <th>3</th>\n",
       "      <td>Espoo</td>\n",
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       "      <td>1.73</td>\n",
       "      <td>36.1</td>\n",
       "      <td>1983</td>\n",
       "      <td>2021</td>\n",
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       "      <th>4</th>\n",
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       "      <td>26.8</td>\n",
       "      <td>1985</td>\n",
       "      <td>2021</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Addis Ababa</td>\n",
       "      <td>58.0</td>\n",
       "      <td>1.68</td>\n",
       "      <td>20.5</td>\n",
       "      <td>1995</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Bahir Dar</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1.70</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1999</td>\n",
       "      <td>2021</td>\n",
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       "      <th>7</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          City  Weight  Height   BMI Birth Year  Current Year\n",
       "0     Helsinki    74.0    1.73  24.7       1769          2021\n",
       "1       London    78.0    1.75  25.5       1985          2021\n",
       "2    Stockholm    68.0    1.69  23.8       1990          2021\n",
       "3        Espoo   108.0    1.73  36.1       1983          2021\n",
       "4       Warsaw   102.0    1.95  26.8       1985          2021\n",
       "5  Addis Ababa    58.0    1.68  20.5       1995          2021\n",
       "6    Bahir Dar    52.0    1.70  18.0       1999          2021\n",
       "7          NaN     NaN     NaN   NaN        NaN          2021"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We can also use the columns attribute to delete a column. Let us remove the country column\n",
    "# To change the original we should make the inplace argument True\n",
    "df.drop(columns = 'Country', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Weight</th>\n",
       "      <th>Height</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Birth Year</th>\n",
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       "      <td>2021</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>78.0</td>\n",
       "      <td>1.75</td>\n",
       "      <td>25.5</td>\n",
       "      <td>1985</td>\n",
       "      <td>2021</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>68.0</td>\n",
       "      <td>1.69</td>\n",
       "      <td>23.8</td>\n",
       "      <td>1990</td>\n",
       "      <td>2021</td>\n",
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       "      <th>3</th>\n",
       "      <td>108.0</td>\n",
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       "      <td>2021</td>\n",
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       "      <th>4</th>\n",
       "      <td>102.0</td>\n",
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       "      <td>26.8</td>\n",
       "      <td>1985</td>\n",
       "      <td>2021</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>58.0</td>\n",
       "      <td>1.68</td>\n",
       "      <td>20.5</td>\n",
       "      <td>1995</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>52.0</td>\n",
       "      <td>1.70</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1999</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Weight  Height   BMI Birth Year  Current Year\n",
       "0    74.0    1.73  24.7       1769          2021\n",
       "1    78.0    1.75  25.5       1985          2021\n",
       "2    68.0    1.69  23.8       1990          2021\n",
       "3   108.0    1.73  36.1       1983          2021\n",
       "4   102.0    1.95  26.8       1985          2021\n",
       "5    58.0    1.68  20.5       1995          2021\n",
       "6    52.0    1.70  18.0       1999          2021\n",
       "7     NaN     NaN   NaN        NaN          2021"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#### Removing multiple columns\n",
    "df.drop(['Country','City'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Deleting Rows\n",
    "\n",
    "The seventh row does not have full information and it is not important to keep in the dataset. Let's remove the sventh row."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>24.7</td>\n",
       "      <td>1769</td>\n",
       "      <td>2021</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>UK</td>\n",
       "      <td>London</td>\n",
       "      <td>78.0</td>\n",
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       "      <td>25.5</td>\n",
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       "      <td>23.8</td>\n",
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       "      <td>2021</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Finland</td>\n",
       "      <td>Espoo</td>\n",
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       "      <td>1.73</td>\n",
       "      <td>36.1</td>\n",
       "      <td>1983</td>\n",
       "      <td>2021</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Poland</td>\n",
       "      <td>Warsaw</td>\n",
       "      <td>102.0</td>\n",
       "      <td>1.95</td>\n",
       "      <td>26.8</td>\n",
       "      <td>1985</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>Bahir Dar</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1.70</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1999</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Country       City  Weight  Height   BMI Birth Year  Current Year\n",
       "0   Finland   Helsinki    74.0    1.73  24.7       1769          2021\n",
       "1        UK     London    78.0    1.75  25.5       1985          2021\n",
       "2    Sweden  Stockholm    68.0    1.69  23.8       1990          2021\n",
       "3   Finland      Espoo   108.0    1.73  36.1       1983          2021\n",
       "4    Poland     Warsaw   102.0    1.95  26.8       1985          2021\n",
       "6  Ethiopia  Bahir Dar    52.0    1.70  18.0       1999          2021"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To delete it from the original dataframe the inplace=True should be included\n",
    "df.drop([7,5], axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
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       "      <td>1985</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sweden</td>\n",
       "      <td>Stockholm</td>\n",
       "      <td>68.0</td>\n",
       "      <td>1.69</td>\n",
       "      <td>23.8</td>\n",
       "      <td>1990</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Finland</td>\n",
       "      <td>Espoo</td>\n",
       "      <td>108.0</td>\n",
       "      <td>1.73</td>\n",
       "      <td>36.1</td>\n",
       "      <td>1983</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Poland</td>\n",
       "      <td>Warsaw</td>\n",
       "      <td>102.0</td>\n",
       "      <td>1.95</td>\n",
       "      <td>26.8</td>\n",
       "      <td>1985</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>Addis Ababa</td>\n",
       "      <td>58.0</td>\n",
       "      <td>1.68</td>\n",
       "      <td>20.5</td>\n",
       "      <td>1995</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>Bahir Dar</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1.70</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1999</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Country         City  Weight  Height   BMI Birth Year  Current Year\n",
       "0   Finland     Helsinki    74.0    1.73  24.7       1769          2021\n",
       "1        UK       London    78.0    1.75  25.5       1985          2021\n",
       "2    Sweden    Stockholm    68.0    1.69  23.8       1990          2021\n",
       "3   Finland        Espoo   108.0    1.73  36.1       1983          2021\n",
       "4    Poland       Warsaw   102.0    1.95  26.8       1985          2021\n",
       "5  Ethiopia  Addis Ababa    58.0    1.68  20.5       1995          2021\n",
       "6  Ethiopia    Bahir Dar    52.0    1.70  18.0       1999          2021\n",
       "7       NaN          NaN     NaN     NaN   NaN        NaN          2021"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Renaming Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>city</th>\n",
       "      <th>weight</th>\n",
       "      <th>height</th>\n",
       "      <th>bmi</th>\n",
       "      <th>birth_year</th>\n",
       "      <th>current_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Finland</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>74.0</td>\n",
       "      <td>1.73</td>\n",
       "      <td>24.7</td>\n",
       "      <td>1769</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>UK</td>\n",
       "      <td>London</td>\n",
       "      <td>78.0</td>\n",
       "      <td>1.75</td>\n",
       "      <td>25.5</td>\n",
       "      <td>1985</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sweden</td>\n",
       "      <td>Stockholm</td>\n",
       "      <td>68.0</td>\n",
       "      <td>1.69</td>\n",
       "      <td>23.8</td>\n",
       "      <td>1990</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Finland</td>\n",
       "      <td>Espoo</td>\n",
       "      <td>108.0</td>\n",
       "      <td>1.73</td>\n",
       "      <td>36.1</td>\n",
       "      <td>1983</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Poland</td>\n",
       "      <td>Warsaw</td>\n",
       "      <td>102.0</td>\n",
       "      <td>1.95</td>\n",
       "      <td>26.8</td>\n",
       "      <td>1985</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>Addis Ababa</td>\n",
       "      <td>58.0</td>\n",
       "      <td>1.68</td>\n",
       "      <td>20.5</td>\n",
       "      <td>1995</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>Bahir Dar</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1.70</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1999</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    country         city  weight  height   bmi birth_year  current_year\n",
       "0   Finland     Helsinki    74.0    1.73  24.7       1769          2021\n",
       "1        UK       London    78.0    1.75  25.5       1985          2021\n",
       "2    Sweden    Stockholm    68.0    1.69  23.8       1990          2021\n",
       "3   Finland        Espoo   108.0    1.73  36.1       1983          2021\n",
       "4    Poland       Warsaw   102.0    1.95  26.8       1985          2021\n",
       "5  Ethiopia  Addis Ababa    58.0    1.68  20.5       1995          2021\n",
       "6  Ethiopia    Bahir Dar    52.0    1.70  18.0       1999          2021\n",
       "7       NaN          NaN     NaN     NaN   NaN        NaN          2021"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To modifiy original dataframe the inplace=True should be included\n",
    "df.rename(\n",
    "    columns={\n",
    "        \"Country\": \"country\",\n",
    "        \"City\": \"city\",\n",
    "        \"Weight\":\"weight\",\n",
    "        \"Height\":\"height\",\n",
    "        \"BMI\":'bmi',\n",
    "        'Birth Year':'birth_year',\n",
    "        'Current Year':'current_year'\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>city</th>\n",
       "      <th>weight</th>\n",
       "      <th>height</th>\n",
       "      <th>bmi</th>\n",
       "      <th>birth year</th>\n",
       "      <th>current year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Finland</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>74.0</td>\n",
       "      <td>1.73</td>\n",
       "      <td>24.7</td>\n",
       "      <td>1769</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>UK</td>\n",
       "      <td>London</td>\n",
       "      <td>78.0</td>\n",
       "      <td>1.75</td>\n",
       "      <td>25.5</td>\n",
       "      <td>1985</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sweden</td>\n",
       "      <td>Stockholm</td>\n",
       "      <td>68.0</td>\n",
       "      <td>1.69</td>\n",
       "      <td>23.8</td>\n",
       "      <td>1990</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Finland</td>\n",
       "      <td>Espoo</td>\n",
       "      <td>108.0</td>\n",
       "      <td>1.73</td>\n",
       "      <td>36.1</td>\n",
       "      <td>1983</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Poland</td>\n",
       "      <td>Warsaw</td>\n",
       "      <td>102.0</td>\n",
       "      <td>1.95</td>\n",
       "      <td>26.8</td>\n",
       "      <td>1985</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>Addis Ababa</td>\n",
       "      <td>58.0</td>\n",
       "      <td>1.68</td>\n",
       "      <td>20.5</td>\n",
       "      <td>1995</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>Bahir Dar</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1.70</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1999</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    country         city  weight  height   bmi birth year  current year\n",
       "0   Finland     Helsinki    74.0    1.73  24.7       1769          2021\n",
       "1        UK       London    78.0    1.75  25.5       1985          2021\n",
       "2    Sweden    Stockholm    68.0    1.69  23.8       1990          2021\n",
       "3   Finland        Espoo   108.0    1.73  36.1       1983          2021\n",
       "4    Poland       Warsaw   102.0    1.95  26.8       1985          2021\n",
       "5  Ethiopia  Addis Ababa    58.0    1.68  20.5       1995          2021\n",
       "6  Ethiopia    Bahir Dar    52.0    1.70  18.0       1999          2021\n",
       "7       NaN          NaN     NaN     NaN   NaN        NaN          2021"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.rename(columns = str.lower)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>City</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Height</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Birth Year</th>\n",
       "      <th>Current Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Finland</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>74.0</td>\n",
       "      <td>1.73</td>\n",
       "      <td>24.7</td>\n",
       "      <td>1769</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>UK</td>\n",
       "      <td>London</td>\n",
       "      <td>78.0</td>\n",
       "      <td>1.75</td>\n",
       "      <td>25.5</td>\n",
       "      <td>1985</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sweden</td>\n",
       "      <td>Stockholm</td>\n",
       "      <td>68.0</td>\n",
       "      <td>1.69</td>\n",
       "      <td>23.8</td>\n",
       "      <td>1990</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Finland</td>\n",
       "      <td>Espoo</td>\n",
       "      <td>108.0</td>\n",
       "      <td>1.73</td>\n",
       "      <td>36.1</td>\n",
       "      <td>1983</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Poland</td>\n",
       "      <td>Warsaw</td>\n",
       "      <td>102.0</td>\n",
       "      <td>1.95</td>\n",
       "      <td>26.8</td>\n",
       "      <td>1985</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>Addis Ababa</td>\n",
       "      <td>58.0</td>\n",
       "      <td>1.68</td>\n",
       "      <td>20.5</td>\n",
       "      <td>1995</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>Bahir Dar</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1.70</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1999</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Country         City  Weight  Height   BMI Birth Year  Current Year\n",
       "0   Finland     Helsinki    74.0    1.73  24.7       1769          2021\n",
       "1        UK       London    78.0    1.75  25.5       1985          2021\n",
       "2    Sweden    Stockholm    68.0    1.69  23.8       1990          2021\n",
       "3   Finland        Espoo   108.0    1.73  36.1       1983          2021\n",
       "4    Poland       Warsaw   102.0    1.95  26.8       1985          2021\n",
       "5  Ethiopia  Addis Ababa    58.0    1.68  20.5       1995          2021\n",
       "6  Ethiopia    Bahir Dar    52.0    1.70  18.0       1999          2021\n",
       "7       NaN          NaN     NaN     NaN   NaN        NaN          2021"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Checking data types of Column values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Weight.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('O')"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Birth Year'].dtype # it gives string object , we should change this to "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "int32\n"
     ]
    }
   ],
   "source": [
    "df.drop([7, 5], axis = 0, inplace=True)\n",
    "df['Birth Year'] = df['Birth Year'].astype('int')\n",
    "\n",
    "print(df['Birth Year'].dtype)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1769\n",
       "1    1985\n",
       "2    1990\n",
       "3    1983\n",
       "4    1985\n",
       "6    1999\n",
       "Name: Birth Year, dtype: int32"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Birth Year']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now same for the current year:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int32')"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Current Year'] = df['Current Year'].astype('int')\n",
    "df['Current Year'].dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, the column values of birth year and current year are integers. We can calculate the age."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    252\n",
       "1     36\n",
       "2     31\n",
       "3     38\n",
       "4     36\n",
       "6     22\n",
       "dtype: int32"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ages = df['Current Year'] - df['Birth Year']\n",
    "ages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1769\n",
       "1    1985\n",
       "2    1990\n",
       "3    1983\n",
       "4    1985\n",
       "6    1999\n",
       "Name: Birth Year, dtype: int32"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Birth Year']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>City</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Height</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Birth Year</th>\n",
       "      <th>Current Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>Finland</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>74.0</td>\n",
       "      <td>1.73</td>\n",
       "      <td>24.7</td>\n",
       "      <td>1769</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>UK</td>\n",
       "      <td>London</td>\n",
       "      <td>78.0</td>\n",
       "      <td>1.75</td>\n",
       "      <td>25.5</td>\n",
       "      <td>1985</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sweden</td>\n",
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       "      <td>1.69</td>\n",
       "      <td>23.8</td>\n",
       "      <td>1990</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Finland</td>\n",
       "      <td>Espoo</td>\n",
       "      <td>108.0</td>\n",
       "      <td>1.73</td>\n",
       "      <td>36.1</td>\n",
       "      <td>1983</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Poland</td>\n",
       "      <td>Warsaw</td>\n",
       "      <td>102.0</td>\n",
       "      <td>1.95</td>\n",
       "      <td>26.8</td>\n",
       "      <td>1985</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>Bahir Dar</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1.70</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1999</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Country       City  Weight  Height   BMI  Birth Year  Current Year\n",
       "0   Finland   Helsinki    74.0    1.73  24.7        1769          2021\n",
       "1        UK     London    78.0    1.75  25.5        1985          2021\n",
       "2    Sweden  Stockholm    68.0    1.69  23.8        1990          2021\n",
       "3   Finland      Espoo   108.0    1.73  36.1        1983          2021\n",
       "4    Poland     Warsaw   102.0    1.95  26.8        1985          2021\n",
       "6  Ethiopia  Bahir Dar    52.0    1.70  18.0        1999          2021"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Ages'] = ages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>1769</td>\n",
       "      <td>2021</td>\n",
       "      <td>252</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>UK</td>\n",
       "      <td>London</td>\n",
       "      <td>78.0</td>\n",
       "      <td>1.75</td>\n",
       "      <td>25.5</td>\n",
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       "      <td>68.0</td>\n",
       "      <td>1.69</td>\n",
       "      <td>23.8</td>\n",
       "      <td>1990</td>\n",
       "      <td>2021</td>\n",
       "      <td>31</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Finland</td>\n",
       "      <td>Espoo</td>\n",
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       "      <td>1.73</td>\n",
       "      <td>36.1</td>\n",
       "      <td>1983</td>\n",
       "      <td>2021</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Poland</td>\n",
       "      <td>Warsaw</td>\n",
       "      <td>102.0</td>\n",
       "      <td>1.95</td>\n",
       "      <td>26.8</td>\n",
       "      <td>1985</td>\n",
       "      <td>2021</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>Bahir Dar</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1.70</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1999</td>\n",
       "      <td>2021</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Country       City  Weight  Height   BMI  Birth Year  Current Year  Ages\n",
       "0   Finland   Helsinki    74.0    1.73  24.7        1769          2021   252\n",
       "1        UK     London    78.0    1.75  25.5        1985          2021    36\n",
       "2    Sweden  Stockholm    68.0    1.69  23.8        1990          2021    31\n",
       "3   Finland      Espoo   108.0    1.73  36.1        1983          2021    38\n",
       "4    Poland     Warsaw   102.0    1.95  26.8        1985          2021    36\n",
       "6  Ethiopia  Bahir Dar    52.0    1.70  18.0        1999          2021    22"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The person in the first row lived so far for 251 years. It is unlikely for someone to live so long. Either it is a typo or the data is cooked. So lets fill that data with average of the columns without including outlier. \n",
    "\n",
    "mean = (35 + 30) / 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean:  32.5\n"
     ]
    }
   ],
   "source": [
    "mean = (35 + 30) / 2\n",
    "print('Mean: ',mean) #it is good to add some description to the output, so we know what is what"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>Weight</th>\n",
       "      <th>Height</th>\n",
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       "      <th>Birth Year</th>\n",
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       "  <tbody>\n",
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       "      <td>74.0</td>\n",
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       "      <th>1</th>\n",
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       "      <td>78.0</td>\n",
       "      <td>1.75</td>\n",
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       "      <td>1983</td>\n",
       "      <td>2021</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Poland</td>\n",
       "      <td>Warsaw</td>\n",
       "      <td>102.0</td>\n",
       "      <td>1.95</td>\n",
       "      <td>26.8</td>\n",
       "      <td>1985</td>\n",
       "      <td>2021</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>Bahir Dar</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1.70</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1999</td>\n",
       "      <td>2021</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Country       City  Weight  Height   BMI  Birth Year  Current Year  Ages\n",
       "0   Finland   Helsinki    74.0    1.73  24.7        1769          2021   252\n",
       "1        UK     London    78.0    1.75  25.5        1985          2021    36\n",
       "2    Sweden  Stockholm    68.0    1.69  23.8        1990          2021    31\n",
       "3   Finland      Espoo   108.0    1.73  36.1        1983          2021    38\n",
       "4    Poland     Warsaw   102.0    1.95  26.8        1985          2021    36\n",
       "6  Ethiopia  Bahir Dar    52.0    1.70  18.0        1999          2021    22"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use the iloc method to impute the value.\n",
    "DataFrame.iloc(row, col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.iloc[0, 7] = (35 + 30) / 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
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       "      <th>Country</th>\n",
       "      <th>City</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Height</th>\n",
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       "      <th>0</th>\n",
       "      <td>Finland</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>74.0</td>\n",
       "      <td>1.73</td>\n",
       "      <td>24.7</td>\n",
       "      <td>1769</td>\n",
       "      <td>2021</td>\n",
       "      <td>32.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>UK</td>\n",
       "      <td>London</td>\n",
       "      <td>78.0</td>\n",
       "      <td>1.75</td>\n",
       "      <td>25.5</td>\n",
       "      <td>1985</td>\n",
       "      <td>2021</td>\n",
       "      <td>36.0</td>\n",
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       "      <th>2</th>\n",
       "      <td>Sweden</td>\n",
       "      <td>Stockholm</td>\n",
       "      <td>68.0</td>\n",
       "      <td>1.69</td>\n",
       "      <td>23.8</td>\n",
       "      <td>1990</td>\n",
       "      <td>2021</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Finland</td>\n",
       "      <td>Espoo</td>\n",
       "      <td>108.0</td>\n",
       "      <td>1.73</td>\n",
       "      <td>36.1</td>\n",
       "      <td>1983</td>\n",
       "      <td>2021</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Poland</td>\n",
       "      <td>Warsaw</td>\n",
       "      <td>102.0</td>\n",
       "      <td>1.95</td>\n",
       "      <td>26.8</td>\n",
       "      <td>1985</td>\n",
       "      <td>2021</td>\n",
       "      <td>36.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>Bahir Dar</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1.70</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1999</td>\n",
       "      <td>2021</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Country       City  Weight  Height   BMI  Birth Year  Current Year  Ages\n",
       "0   Finland   Helsinki    74.0    1.73  24.7        1769          2021  32.5\n",
       "1        UK     London    78.0    1.75  25.5        1985          2021  36.0\n",
       "2    Sweden  Stockholm    68.0    1.69  23.8        1990          2021  31.0\n",
       "3   Finland      Espoo   108.0    1.73  36.1        1983          2021  38.0\n",
       "4    Poland     Warsaw   102.0    1.95  26.8        1985          2021  36.0\n",
       "6  Ethiopia  Bahir Dar    52.0    1.70  18.0        1999          2021  22.0"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Selecting Column(s)\n",
    "We can select a specified column(s) from a pandas dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     Finland\n",
       "1          UK\n",
       "2      Sweden\n",
       "3     Finland\n",
       "4      Poland\n",
       "6    Ethiopia\n",
       "Name: Country, dtype: object"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's select the country column\n",
    "df['Country']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     Helsinki\n",
       "1       London\n",
       "2    Stockholm\n",
       "3        Espoo\n",
       "4       Warsaw\n",
       "6    Bahir Dar\n",
       "Name: City, dtype: object"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let us select the city column\n",
    "df['City']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>City</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Finland</td>\n",
       "      <td>Helsinki</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>UK</td>\n",
       "      <td>London</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sweden</td>\n",
       "      <td>Stockholm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Finland</td>\n",
       "      <td>Espoo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Poland</td>\n",
       "      <td>Warsaw</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>Bahir Dar</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Country       City\n",
       "0   Finland   Helsinki\n",
       "1        UK     London\n",
       "2    Sweden  Stockholm\n",
       "3   Finland      Espoo\n",
       "4    Poland     Warsaw\n",
       "6  Ethiopia  Bahir Dar"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let us select Country and City columns\n",
    "df[['Country','City']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Ages</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.73</td>\n",
       "      <td>74.0</td>\n",
       "      <td>24.7</td>\n",
       "      <td>32.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.75</td>\n",
       "      <td>78.0</td>\n",
       "      <td>25.5</td>\n",
       "      <td>36.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.69</td>\n",
       "      <td>68.0</td>\n",
       "      <td>23.8</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.73</td>\n",
       "      <td>108.0</td>\n",
       "      <td>36.1</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.95</td>\n",
       "      <td>102.0</td>\n",
       "      <td>26.8</td>\n",
       "      <td>36.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.70</td>\n",
       "      <td>52.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Height  Weight   BMI  Ages\n",
       "0    1.73    74.0  24.7  32.5\n",
       "1    1.75    78.0  25.5  36.0\n",
       "2    1.69    68.0  23.8  31.0\n",
       "3    1.73   108.0  36.1  38.0\n",
       "4    1.95   102.0  26.8  36.0\n",
       "6    1.70    52.0  18.0  22.0"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let us select the height, weight, BMI, ages column\n",
    "df[['Height','Weight','BMI','Ages']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Boolean Indexing\n",
    "We can use boolean indexing to select some part of the rows. In the example below, we are selecting rows with birth year less than 1900. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>City</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Height</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Birth Year</th>\n",
       "      <th>Current Year</th>\n",
       "      <th>Ages</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Finland</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>74.0</td>\n",
       "      <td>1.73</td>\n",
       "      <td>24.7</td>\n",
       "      <td>1769</td>\n",
       "      <td>2021</td>\n",
       "      <td>32.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Country      City  Weight  Height   BMI  Birth Year  Current Year  Ages\n",
       "0  Finland  Helsinki    74.0    1.73  24.7        1769          2021  32.5"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# people that were born before 1990\n",
    "df[df['Birth Year'] < 1900]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>City</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Height</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Birth Year</th>\n",
       "      <th>Current Year</th>\n",
       "      <th>Ages</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Finland</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>74.0</td>\n",
       "      <td>1.73</td>\n",
       "      <td>24.7</td>\n",
       "      <td>1769</td>\n",
       "      <td>2021</td>\n",
       "      <td>32.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sweden</td>\n",
       "      <td>Stockholm</td>\n",
       "      <td>68.0</td>\n",
       "      <td>1.69</td>\n",
       "      <td>23.8</td>\n",
       "      <td>1990</td>\n",
       "      <td>2021</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>Bahir Dar</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1.70</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1999</td>\n",
       "      <td>2021</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Country       City  Weight  Height   BMI  Birth Year  Current Year  Ages\n",
       "0   Finland   Helsinki    74.0    1.73  24.7        1769          2021  32.5\n",
       "2    Sweden  Stockholm    68.0    1.69  23.8        1990          2021  31.0\n",
       "6  Ethiopia  Bahir Dar    52.0    1.70  18.0        1999          2021  22.0"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# BMI if BMI is 18.5 to 25, it is a healthy weight \n",
    "df[df['BMI'] <= 25]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>City</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Height</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Birth Year</th>\n",
       "      <th>Current Year</th>\n",
       "      <th>Ages</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Finland</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>74.0</td>\n",
       "      <td>1.73</td>\n",
       "      <td>24.7</td>\n",
       "      <td>1769</td>\n",
       "      <td>2021</td>\n",
       "      <td>32.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Finland</td>\n",
       "      <td>Espoo</td>\n",
       "      <td>108.0</td>\n",
       "      <td>1.73</td>\n",
       "      <td>36.1</td>\n",
       "      <td>1983</td>\n",
       "      <td>2021</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Country      City  Weight  Height   BMI  Birth Year  Current Year  Ages\n",
       "0  Finland  Helsinki    74.0    1.73  24.7        1769          2021  32.5\n",
       "3  Finland     Espoo   108.0    1.73  36.1        1983          2021  38.0"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Let's select peopel from Finland\n",
    "df[df['Country'] =='Finland']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>City</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Height</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Birth Year</th>\n",
       "      <th>Current Year</th>\n",
       "      <th>Ages</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Finland</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>74.0</td>\n",
       "      <td>1.73</td>\n",
       "      <td>24.7</td>\n",
       "      <td>1769</td>\n",
       "      <td>2021</td>\n",
       "      <td>32.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>UK</td>\n",
       "      <td>London</td>\n",
       "      <td>78.0</td>\n",
       "      <td>1.75</td>\n",
       "      <td>25.5</td>\n",
       "      <td>1985</td>\n",
       "      <td>2021</td>\n",
       "      <td>36.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Finland</td>\n",
       "      <td>Espoo</td>\n",
       "      <td>108.0</td>\n",
       "      <td>1.73</td>\n",
       "      <td>36.1</td>\n",
       "      <td>1983</td>\n",
       "      <td>2021</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Poland</td>\n",
       "      <td>Warsaw</td>\n",
       "      <td>102.0</td>\n",
       "      <td>1.95</td>\n",
       "      <td>26.8</td>\n",
       "      <td>1985</td>\n",
       "      <td>2021</td>\n",
       "      <td>36.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Country      City  Weight  Height   BMI  Birth Year  Current Year  Ages\n",
       "0  Finland  Helsinki    74.0    1.73  24.7        1769          2021  32.5\n",
       "1       UK    London    78.0    1.75  25.5        1985          2021  36.0\n",
       "3  Finland     Espoo   108.0    1.73  36.1        1983          2021  38.0\n",
       "4   Poland    Warsaw   102.0    1.95  26.8        1985          2021  36.0"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Let's select people taller than 1.75 meter\n",
    "df[df['Height'] > 1.7]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using apply method to modify pandas data frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "def classify_bmi(bmi):\n",
    "    if bmi >= 30:\n",
    "        return 'Obesity Range'\n",
    "    elif bmi < 30 and bmi > 25:\n",
    "        return 'Overweight Range'\n",
    "    elif bmi < 25 and bmi >= 18.5:\n",
    "        return 'Healthy Range'\n",
    "    elif bmi < 18.5:\n",
    "        return 'Underweight Range'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['BMI class'] = df['BMI'].apply(lambda x: classify_bmi(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>City</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Height</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Birth Year</th>\n",
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       "      <th>Ages</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Finland</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>74.0</td>\n",
       "      <td>1.73</td>\n",
       "      <td>24.7</td>\n",
       "      <td>1769</td>\n",
       "      <td>2021</td>\n",
       "      <td>32.5</td>\n",
       "      <td>Healthy Range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>UK</td>\n",
       "      <td>London</td>\n",
       "      <td>78.0</td>\n",
       "      <td>1.75</td>\n",
       "      <td>25.5</td>\n",
       "      <td>1985</td>\n",
       "      <td>2021</td>\n",
       "      <td>36.0</td>\n",
       "      <td>Overweight Range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sweden</td>\n",
       "      <td>Stockholm</td>\n",
       "      <td>68.0</td>\n",
       "      <td>1.69</td>\n",
       "      <td>23.8</td>\n",
       "      <td>1990</td>\n",
       "      <td>2021</td>\n",
       "      <td>31.0</td>\n",
       "      <td>Healthy Range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Finland</td>\n",
       "      <td>Espoo</td>\n",
       "      <td>108.0</td>\n",
       "      <td>1.73</td>\n",
       "      <td>36.1</td>\n",
       "      <td>1983</td>\n",
       "      <td>2021</td>\n",
       "      <td>38.0</td>\n",
       "      <td>Obesity Range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Poland</td>\n",
       "      <td>Warsaw</td>\n",
       "      <td>102.0</td>\n",
       "      <td>1.95</td>\n",
       "      <td>26.8</td>\n",
       "      <td>1985</td>\n",
       "      <td>2021</td>\n",
       "      <td>36.0</td>\n",
       "      <td>Overweight Range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>Bahir Dar</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1.70</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1999</td>\n",
       "      <td>2021</td>\n",
       "      <td>22.0</td>\n",
       "      <td>Underweight Range</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Country       City  Weight  Height   BMI  Birth Year  Current Year  Ages  \\\n",
       "0   Finland   Helsinki    74.0    1.73  24.7        1769          2021  32.5   \n",
       "1        UK     London    78.0    1.75  25.5        1985          2021  36.0   \n",
       "2    Sweden  Stockholm    68.0    1.69  23.8        1990          2021  31.0   \n",
       "3   Finland      Espoo   108.0    1.73  36.1        1983          2021  38.0   \n",
       "4    Poland     Warsaw   102.0    1.95  26.8        1985          2021  36.0   \n",
       "6  Ethiopia  Bahir Dar    52.0    1.70  18.0        1999          2021  22.0   \n",
       "\n",
       "           BMI class  \n",
       "0      Healthy Range  \n",
       "1   Overweight Range  \n",
       "2      Healthy Range  \n",
       "3      Obesity Range  \n",
       "4   Overweight Range  \n",
       "6  Underweight Range  "
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Accessing row(s) using loc attribute\n",
    "Pandas has loc attribute uses the pandas dataframe location or rows to return one or more row(s). If the dataframe does not have name indexes we can use positive number indexes that starts from zero. However, if the dataframe has a named indexes, we can use the name use the loc attribute to access the row(s)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Accessing a single row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country               Finland\n",
       "City                 Helsinki\n",
       "Weight                     74\n",
       "Height                   1.73\n",
       "BMI                      24.7\n",
       "Birth Year               1769\n",
       "Current Year             2021\n",
       "Ages                     32.5\n",
       "BMI class       Healthy Range\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Accessing only the first row\n",
    "df.loc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country                       UK\n",
       "City                      London\n",
       "Weight                        78\n",
       "Height                      1.75\n",
       "BMI                         25.5\n",
       "Birth Year                  1985\n",
       "Current Year                2021\n",
       "Ages                          36\n",
       "BMI class       Overweight Range\n",
       "Name: 1, dtype: object"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Second row\n",
    "df.loc[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country                   Poland\n",
       "City                      Warsaw\n",
       "Weight                       102\n",
       "Height                      1.95\n",
       "BMI                         26.8\n",
       "Birth Year                  1985\n",
       "Current Year                2021\n",
       "Ages                          36\n",
       "BMI class       Overweight Range\n",
       "Name: 4, dtype: object"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Accessing the fifth column\n",
    "df.iloc[4]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Accessing Multiple Rows\n",
    "\n",
    "To access multiple rows, we pass list indexes of the rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>City</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Height</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Birth Year</th>\n",
       "      <th>Current Year</th>\n",
       "      <th>Ages</th>\n",
       "      <th>BMI class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Finland</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>74.0</td>\n",
       "      <td>1.73</td>\n",
       "      <td>24.7</td>\n",
       "      <td>1769</td>\n",
       "      <td>2021</td>\n",
       "      <td>32.5</td>\n",
       "      <td>Healthy Range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>UK</td>\n",
       "      <td>London</td>\n",
       "      <td>78.0</td>\n",
       "      <td>1.75</td>\n",
       "      <td>25.5</td>\n",
       "      <td>1985</td>\n",
       "      <td>2021</td>\n",
       "      <td>36.0</td>\n",
       "      <td>Overweight Range</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Country      City  Weight  Height   BMI  Birth Year  Current Year  Ages  \\\n",
       "0  Finland  Helsinki    74.0    1.73  24.7        1769          2021  32.5   \n",
       "1       UK    London    78.0    1.75  25.5        1985          2021  36.0   \n",
       "\n",
       "          BMI class  \n",
       "0     Healthy Range  \n",
       "1  Overweight Range  "
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Accessing the first and second row\n",
    "df.loc[[0,1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>City</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Height</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Birth Year</th>\n",
       "      <th>Current Year</th>\n",
       "      <th>Ages</th>\n",
       "      <th>BMI class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Finland</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>74.0</td>\n",
       "      <td>1.73</td>\n",
       "      <td>24.7</td>\n",
       "      <td>1769</td>\n",
       "      <td>2021</td>\n",
       "      <td>32.5</td>\n",
       "      <td>Healthy Range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sweden</td>\n",
       "      <td>Stockholm</td>\n",
       "      <td>68.0</td>\n",
       "      <td>1.69</td>\n",
       "      <td>23.8</td>\n",
       "      <td>1990</td>\n",
       "      <td>2021</td>\n",
       "      <td>31.0</td>\n",
       "      <td>Healthy Range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Poland</td>\n",
       "      <td>Warsaw</td>\n",
       "      <td>102.0</td>\n",
       "      <td>1.95</td>\n",
       "      <td>26.8</td>\n",
       "      <td>1985</td>\n",
       "      <td>2021</td>\n",
       "      <td>36.0</td>\n",
       "      <td>Overweight Range</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Country       City  Weight  Height   BMI  Birth Year  Current Year  Ages  \\\n",
       "0  Finland   Helsinki    74.0    1.73  24.7        1769          2021  32.5   \n",
       "2   Sweden  Stockholm    68.0    1.69  23.8        1990          2021  31.0   \n",
       "4   Poland     Warsaw   102.0    1.95  26.8        1985          2021  36.0   \n",
       "\n",
       "          BMI class  \n",
       "0     Healthy Range  \n",
       "2     Healthy Range  \n",
       "4  Overweight Range  "
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Accessing the first, third and fifth\n",
    "df.loc[[0, 2, 4]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Name indexes Pandas DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = [\n",
    "    {\"Name\": \"Asabeneh\", \"Country\":\"Finland\",\"City\":\"Helsinki\"},\n",
    "    {\"Name\": \"David\", \"Country\":\"UK\",\"City\":\"London\"},\n",
    "    {\"Name\": \"John\", \"Country\":\"Sweden\",\"City\":\"Stockholm\"},\n",
    "    {\"Name\": \"Eyob\", \"Country\":\"Finland\",\"City\":\"Espoo\"},\n",
    "    {\"Name\": \"Pawel\", \"Country\":\"Poland\",\"City\":\"Warsaw\"},\n",
    "    {\"Name\": \"Mahlet\", \"Country\":\"Ethiopia\",\"City\":\"Addis Ababa\"},\n",
    "    {\"Name\": \"Thomas\", \"Country\":\"Ethiopia\",\"City\":\"Bahir Dar\"},\n",
    "\n",
    "]\n",
    "df = pd.DataFrame(data, index = ['A','B','C','D','E','F','G'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Country</th>\n",
       "      <th>City</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>Asabeneh</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Helsinki</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>David</td>\n",
       "      <td>UK</td>\n",
       "      <td>London</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>John</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>Stockholm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>Eyob</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Espoo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>Pawel</td>\n",
       "      <td>Poland</td>\n",
       "      <td>Warsaw</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <td>Mahlet</td>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>Addis Ababa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G</th>\n",
       "      <td>Thomas</td>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>Bahir Dar</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Name   Country         City\n",
       "A  Asabeneh   Finland     Helsinki\n",
       "B     David        UK       London\n",
       "C      John    Sweden    Stockholm\n",
       "D      Eyob   Finland        Espoo\n",
       "E     Pawel    Poland       Warsaw\n",
       "F    Mahlet  Ethiopia  Addis Ababa\n",
       "G    Thomas  Ethiopia    Bahir Dar"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name       Asabeneh\n",
       "Country     Finland\n",
       "City       Helsinki\n",
       "Name: A, dtype: object"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Accessing a signle row that is named B\n",
    "df.loc['A']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name        David\n",
       "Country        UK\n",
       "City       London\n",
       "Name: B, dtype: object"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Accessing a single row that is named B\n",
    "df.loc['B']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Country</th>\n",
       "      <th>City</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>Asabeneh</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Helsinki</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>David</td>\n",
       "      <td>UK</td>\n",
       "      <td>London</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>Pawel</td>\n",
       "      <td>Poland</td>\n",
       "      <td>Warsaw</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G</th>\n",
       "      <td>Thomas</td>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>Bahir Dar</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Name   Country       City\n",
       "A  Asabeneh   Finland   Helsinki\n",
       "B     David        UK     London\n",
       "E     Pawel    Poland     Warsaw\n",
       "G    Thomas  Ethiopia  Bahir Dar"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[['A','B', 'E','G']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Country</th>\n",
       "      <th>City</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>Asabeneh</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Helsinki</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>David</td>\n",
       "      <td>UK</td>\n",
       "      <td>London</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>John</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>Stockholm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>Eyob</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Espoo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>Pawel</td>\n",
       "      <td>Poland</td>\n",
       "      <td>Warsaw</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <td>Mahlet</td>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>Addis Ababa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G</th>\n",
       "      <td>Thomas</td>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>Bahir Dar</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Name   Country         City\n",
       "A  Asabeneh   Finland     Helsinki\n",
       "B     David        UK       London\n",
       "C      John    Sweden    Stockholm\n",
       "D      Eyob   Finland        Espoo\n",
       "E     Pawel    Poland       Warsaw\n",
       "F    Mahlet  Ethiopia  Addis Ababa\n",
       "G    Thomas  Ethiopia    Bahir Dar"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Asabeneh'"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We can also access specific cell \n",
    "\n",
    "df.loc['A', 'Name']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'John'"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['C','Name']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Stockholm'"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['C','City']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Accessing DataFrame values using iloc method\n",
    "\n",
    ".iloc[] is primarily integer position based (from 0 to length-1 of the axis), but may also be used with a boolean array.\n",
    "\n",
    "Allowed inputs are:\n",
    "\n",
    "An integer, e.g. 5.\n",
    "\n",
    "A list or array of integers, e.g. [4, 3, 0].\n",
    "\n",
    "A slice object with ints, e.g. 1:7.\n",
    "\n",
    "A boolean array.\n",
    "\n",
    "A callable function with one argument (the calling Series or DataFrame) and that returns valid output for indexing (one of the above). This is useful in method chains, when you don’t have a reference to the calling object, but would like to base your selection on some value.\n",
    "\n",
    ".iloc will raise IndexError if a requested indexer is out-of-bounds, except slice indexers which allow out-of-bounds indexing (this conforms with python/numpy slice semantics)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "# re-assigning the copied file, the df variable easy easy to write\n",
    "df = df_copy\n",
    "df['BMI class'] = df['BMI'].apply(lambda x : classify_bmi(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name                 Asabeneh\n",
       "Country               Finland\n",
       "City                 Helsinki\n",
       "Weight                     74\n",
       "Height                   1.73\n",
       "BMI                      24.7\n",
       "Birth Year               1769\n",
       "Current Year             2021\n",
       "BMI class       Healthy Range\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# first row\n",
    "\n",
    "df.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Country</th>\n",
       "      <th>City</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Height</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Birth Year</th>\n",
       "      <th>Current Year</th>\n",
       "      <th>BMI class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Asabeneh</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>74.0</td>\n",
       "      <td>1.73</td>\n",
       "      <td>24.7</td>\n",
       "      <td>1769</td>\n",
       "      <td>2021</td>\n",
       "      <td>Healthy Range</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Name  Country      City  Weight  Height   BMI Birth Year  Current Year  \\\n",
       "0  Asabeneh  Finland  Helsinki    74.0    1.73  24.7       1769          2021   \n",
       "\n",
       "       BMI class  \n",
       "0  Healthy Range  "
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[[0]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name                       David\n",
       "Country                       UK\n",
       "City                      London\n",
       "Weight                        78\n",
       "Height                      1.75\n",
       "BMI                         25.5\n",
       "Birth Year                  1985\n",
       "Current Year                2021\n",
       "BMI class       Overweight Range\n",
       "Name: 1, dtype: object"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# second row\n",
    "df.iloc[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Country</th>\n",
       "      <th>City</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Height</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Birth Year</th>\n",
       "      <th>Current Year</th>\n",
       "      <th>BMI class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>David</td>\n",
       "      <td>UK</td>\n",
       "      <td>London</td>\n",
       "      <td>78.0</td>\n",
       "      <td>1.75</td>\n",
       "      <td>25.5</td>\n",
       "      <td>1985</td>\n",
       "      <td>2021</td>\n",
       "      <td>Overweight Range</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Name Country    City  Weight  Height   BMI Birth Year  Current Year  \\\n",
       "1  David      UK  London    78.0    1.75  25.5       1985          2021   \n",
       "\n",
       "          BMI class  \n",
       "1  Overweight Range  "
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[[1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Country</th>\n",
       "      <th>City</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Height</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Birth Year</th>\n",
       "      <th>Current Year</th>\n",
       "      <th>BMI class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Asabeneh</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>74.0</td>\n",
       "      <td>1.73</td>\n",
       "      <td>24.7</td>\n",
       "      <td>1769</td>\n",
       "      <td>2021</td>\n",
       "      <td>Healthy Range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>John</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>Stockholm</td>\n",
       "      <td>68.0</td>\n",
       "      <td>1.69</td>\n",
       "      <td>23.8</td>\n",
       "      <td>1990</td>\n",
       "      <td>2021</td>\n",
       "      <td>Healthy Range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Pawel</td>\n",
       "      <td>Poland</td>\n",
       "      <td>Warsaw</td>\n",
       "      <td>102.0</td>\n",
       "      <td>1.95</td>\n",
       "      <td>26.8</td>\n",
       "      <td>1985</td>\n",
       "      <td>2021</td>\n",
       "      <td>Overweight Range</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Name  Country       City  Weight  Height   BMI Birth Year  \\\n",
       "0  Asabeneh  Finland   Helsinki    74.0    1.73  24.7       1769   \n",
       "2      John   Sweden  Stockholm    68.0    1.69  23.8       1990   \n",
       "4     Pawel   Poland     Warsaw   102.0    1.95  26.8       1985   \n",
       "\n",
       "   Current Year         BMI class  \n",
       "0          2021     Healthy Range  \n",
       "2          2021     Healthy Range  \n",
       "4          2021  Overweight Range  "
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# some selected rows\n",
    "df.iloc[[0, 2, 4]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Country</th>\n",
       "      <th>City</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Height</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Birth Year</th>\n",
       "      <th>Current Year</th>\n",
       "      <th>BMI class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Asabeneh</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>74.0</td>\n",
       "      <td>1.73</td>\n",
       "      <td>24.7</td>\n",
       "      <td>1769</td>\n",
       "      <td>2021</td>\n",
       "      <td>Healthy Range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>David</td>\n",
       "      <td>UK</td>\n",
       "      <td>London</td>\n",
       "      <td>78.0</td>\n",
       "      <td>1.75</td>\n",
       "      <td>25.5</td>\n",
       "      <td>1985</td>\n",
       "      <td>2021</td>\n",
       "      <td>Overweight Range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>John</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>Stockholm</td>\n",
       "      <td>68.0</td>\n",
       "      <td>1.69</td>\n",
       "      <td>23.8</td>\n",
       "      <td>1990</td>\n",
       "      <td>2021</td>\n",
       "      <td>Healthy Range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Eyob</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Espoo</td>\n",
       "      <td>108.0</td>\n",
       "      <td>1.73</td>\n",
       "      <td>36.1</td>\n",
       "      <td>1983</td>\n",
       "      <td>2021</td>\n",
       "      <td>Obesity Range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Pawel</td>\n",
       "      <td>Poland</td>\n",
       "      <td>Warsaw</td>\n",
       "      <td>102.0</td>\n",
       "      <td>1.95</td>\n",
       "      <td>26.8</td>\n",
       "      <td>1985</td>\n",
       "      <td>2021</td>\n",
       "      <td>Overweight Range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Mahlet</td>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>Addis Ababa</td>\n",
       "      <td>58.0</td>\n",
       "      <td>1.68</td>\n",
       "      <td>20.5</td>\n",
       "      <td>1995</td>\n",
       "      <td>2021</td>\n",
       "      <td>Healthy Range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Thomas</td>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>Bahir Dar</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1.70</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1999</td>\n",
       "      <td>2021</td>\n",
       "      <td>Underweight Range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Lidiya</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Name   Country         City  Weight  Height   BMI Birth Year  \\\n",
       "0  Asabeneh   Finland     Helsinki    74.0    1.73  24.7       1769   \n",
       "1     David        UK       London    78.0    1.75  25.5       1985   \n",
       "2      John    Sweden    Stockholm    68.0    1.69  23.8       1990   \n",
       "3      Eyob   Finland        Espoo   108.0    1.73  36.1       1983   \n",
       "4     Pawel    Poland       Warsaw   102.0    1.95  26.8       1985   \n",
       "5    Mahlet  Ethiopia  Addis Ababa    58.0    1.68  20.5       1995   \n",
       "6    Thomas  Ethiopia    Bahir Dar    52.0    1.70  18.0       1999   \n",
       "7    Lidiya       NaN          NaN     NaN     NaN   NaN        NaN   \n",
       "\n",
       "   Current Year          BMI class  \n",
       "0          2021      Healthy Range  \n",
       "1          2021   Overweight Range  \n",
       "2          2021      Healthy Range  \n",
       "3          2021      Obesity Range  \n",
       "4          2021   Overweight Range  \n",
       "5          2021      Healthy Range  \n",
       "6          2021  Underweight Range  \n",
       "7          2021               None  "
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# display all the data\n",
    "df.iloc[:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Country</th>\n",
       "      <th>City</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Height</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Birth Year</th>\n",
       "      <th>Current Year</th>\n",
       "      <th>BMI class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Asabeneh</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>74.0</td>\n",
       "      <td>1.73</td>\n",
       "      <td>24.7</td>\n",
       "      <td>1769</td>\n",
       "      <td>2021</td>\n",
       "      <td>Healthy Range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>David</td>\n",
       "      <td>UK</td>\n",
       "      <td>London</td>\n",
       "      <td>78.0</td>\n",
       "      <td>1.75</td>\n",
       "      <td>25.5</td>\n",
       "      <td>1985</td>\n",
       "      <td>2021</td>\n",
       "      <td>Overweight Range</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Name  Country      City  Weight  Height   BMI Birth Year  Current Year  \\\n",
       "0  Asabeneh  Finland  Helsinki    74.0    1.73  24.7       1769          2021   \n",
       "1     David       UK    London    78.0    1.75  25.5       1985          2021   \n",
       "\n",
       "          BMI class  \n",
       "0     Healthy Range  \n",
       "1  Overweight Range  "
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# slicing the first second row\n",
    "df.iloc[0:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Country</th>\n",
       "      <th>City</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Height</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Birth Year</th>\n",
       "      <th>Current Year</th>\n",
       "      <th>BMI class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Asabeneh</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>74.0</td>\n",
       "      <td>1.73</td>\n",
       "      <td>24.7</td>\n",
       "      <td>1769</td>\n",
       "      <td>2021</td>\n",
       "      <td>Healthy Range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>David</td>\n",
       "      <td>UK</td>\n",
       "      <td>London</td>\n",
       "      <td>78.0</td>\n",
       "      <td>1.75</td>\n",
       "      <td>25.5</td>\n",
       "      <td>1985</td>\n",
       "      <td>2021</td>\n",
       "      <td>Overweight Range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>John</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>Stockholm</td>\n",
       "      <td>68.0</td>\n",
       "      <td>1.69</td>\n",
       "      <td>23.8</td>\n",
       "      <td>1990</td>\n",
       "      <td>2021</td>\n",
       "      <td>Healthy Range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Eyob</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Espoo</td>\n",
       "      <td>108.0</td>\n",
       "      <td>1.73</td>\n",
       "      <td>36.1</td>\n",
       "      <td>1983</td>\n",
       "      <td>2021</td>\n",
       "      <td>Obesity Range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Pawel</td>\n",
       "      <td>Poland</td>\n",
       "      <td>Warsaw</td>\n",
       "      <td>102.0</td>\n",
       "      <td>1.95</td>\n",
       "      <td>26.8</td>\n",
       "      <td>1985</td>\n",
       "      <td>2021</td>\n",
       "      <td>Overweight Range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Mahlet</td>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>Addis Ababa</td>\n",
       "      <td>58.0</td>\n",
       "      <td>1.68</td>\n",
       "      <td>20.5</td>\n",
       "      <td>1995</td>\n",
       "      <td>2021</td>\n",
       "      <td>Healthy Range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Thomas</td>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>Bahir Dar</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1.70</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1999</td>\n",
       "      <td>2021</td>\n",
       "      <td>Underweight Range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Lidiya</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Name   Country         City  Weight  Height   BMI Birth Year  \\\n",
       "0  Asabeneh   Finland     Helsinki    74.0    1.73  24.7       1769   \n",
       "1     David        UK       London    78.0    1.75  25.5       1985   \n",
       "2      John    Sweden    Stockholm    68.0    1.69  23.8       1990   \n",
       "3      Eyob   Finland        Espoo   108.0    1.73  36.1       1983   \n",
       "4     Pawel    Poland       Warsaw   102.0    1.95  26.8       1985   \n",
       "5    Mahlet  Ethiopia  Addis Ababa    58.0    1.68  20.5       1995   \n",
       "6    Thomas  Ethiopia    Bahir Dar    52.0    1.70  18.0       1999   \n",
       "7    Lidiya       NaN          NaN     NaN     NaN   NaN        NaN   \n",
       "\n",
       "   Current Year          BMI class  \n",
       "0          2021      Healthy Range  \n",
       "1          2021   Overweight Range  \n",
       "2          2021      Healthy Range  \n",
       "3          2021      Obesity Range  \n",
       "4          2021   Overweight Range  \n",
       "5          2021      Healthy Range  \n",
       "6          2021  Underweight Range  \n",
       "7          2021               None  "
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <td>Asabeneh</td>\n",
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       "      <td>Helsinki</td>\n",
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       "      <td>1.73</td>\n",
       "      <td>24.7</td>\n",
       "      <td>1769</td>\n",
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       "      <td>1.95</td>\n",
       "      <td>26.8</td>\n",
       "      <td>1985</td>\n",
       "      <td>2021</td>\n",
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       "      <th>6</th>\n",
       "      <td>Thomas</td>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>Bahir Dar</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1.70</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1999</td>\n",
       "      <td>2021</td>\n",
       "      <td>Underweight Range</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Name   Country       City  Weight  Height   BMI Birth Year  \\\n",
       "0  Asabeneh   Finland   Helsinki    74.0    1.73  24.7       1769   \n",
       "2      John    Sweden  Stockholm    68.0    1.69  23.8       1990   \n",
       "4     Pawel    Poland     Warsaw   102.0    1.95  26.8       1985   \n",
       "6    Thomas  Ethiopia  Bahir Dar    52.0    1.70  18.0       1999   \n",
       "\n",
       "   Current Year          BMI class  \n",
       "0          2021      Healthy Range  \n",
       "2          2021      Healthy Range  \n",
       "4          2021   Overweight Range  \n",
       "6          2021  Underweight Range  "
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Boolean indexing\n",
    "df.iloc[lambda x : x.index % 2 == 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>BMI class</th>\n",
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       "  <tbody>\n",
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       "      <th>1</th>\n",
       "      <td>David</td>\n",
       "      <td>UK</td>\n",
       "      <td>London</td>\n",
       "      <td>78.0</td>\n",
       "      <td>1.75</td>\n",
       "      <td>25.5</td>\n",
       "      <td>1985</td>\n",
       "      <td>2021</td>\n",
       "      <td>Overweight Range</td>\n",
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       "      <td>Eyob</td>\n",
       "      <td>Finland</td>\n",
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       "      <td>108.0</td>\n",
       "      <td>1.73</td>\n",
       "      <td>36.1</td>\n",
       "      <td>1983</td>\n",
       "      <td>2021</td>\n",
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       "      <td>Mahlet</td>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>Addis Ababa</td>\n",
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       "      <td>1.68</td>\n",
       "      <td>20.5</td>\n",
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       "      <td>Healthy Range</td>\n",
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       "      <th>7</th>\n",
       "      <td>Lidiya</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Name   Country         City  Weight  Height   BMI Birth Year  \\\n",
       "1   David        UK       London    78.0    1.75  25.5       1985   \n",
       "3    Eyob   Finland        Espoo   108.0    1.73  36.1       1983   \n",
       "5  Mahlet  Ethiopia  Addis Ababa    58.0    1.68  20.5       1995   \n",
       "7  Lidiya       NaN          NaN     NaN     NaN   NaN        NaN   \n",
       "\n",
       "   Current Year         BMI class  \n",
       "1          2021  Overweight Range  \n",
       "3          2021     Obesity Range  \n",
       "5          2021     Healthy Range  \n",
       "7          2021              None  "
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Boolean indexing\n",
    "df.iloc[lambda x : x.index % 2 != 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Finland'"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# index and column ,[index, col] to get a value in a cell\n",
    "\n",
    "df.iloc[0, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>2</th>\n",
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       "      <td>68.0</td>\n",
       "      <td>1.69</td>\n",
       "      <td>23.8</td>\n",
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       "      <td>2021</td>\n",
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       "      <td>2021</td>\n",
       "      <td>Obesity Range</td>\n",
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       "      <td>Pawel</td>\n",
       "      <td>Poland</td>\n",
       "      <td>Warsaw</td>\n",
       "      <td>102.0</td>\n",
       "      <td>1.95</td>\n",
       "      <td>26.8</td>\n",
       "      <td>1985</td>\n",
       "      <td>2021</td>\n",
       "      <td>Overweight Range</td>\n",
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       "      <td>58.0</td>\n",
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       "      <td>Thomas</td>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>Bahir Dar</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1.70</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1999</td>\n",
       "      <td>2021</td>\n",
       "      <td>Underweight Range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Lidiya</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Name   Country         City  Weight  Height   BMI Birth Year  \\\n",
       "0  Asabeneh   Finland     Helsinki    74.0    1.73  24.7       1769   \n",
       "1     David        UK       London    78.0    1.75  25.5       1985   \n",
       "2      John    Sweden    Stockholm    68.0    1.69  23.8       1990   \n",
       "3      Eyob   Finland        Espoo   108.0    1.73  36.1       1983   \n",
       "4     Pawel    Poland       Warsaw   102.0    1.95  26.8       1985   \n",
       "5    Mahlet  Ethiopia  Addis Ababa    58.0    1.68  20.5       1995   \n",
       "6    Thomas  Ethiopia    Bahir Dar    52.0    1.70  18.0       1999   \n",
       "7    Lidiya       NaN          NaN     NaN     NaN   NaN        NaN   \n",
       "\n",
       "   Current Year          BMI class  \n",
       "0          2021      Healthy Range  \n",
       "1          2021   Overweight Range  \n",
       "2          2021      Healthy Range  \n",
       "3          2021      Obesity Range  \n",
       "4          2021   Overweight Range  \n",
       "5          2021      Healthy Range  \n",
       "6          2021  Underweight Range  \n",
       "7          2021               None  "
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "       Name  Country       City  Weight  Height   BMI Birth Year  \\\n",
       "0  Asabeneh  Finland   Helsinki    74.0    1.73  24.7       1769   \n",
       "1     David       UK     London    78.0    1.75  25.5       1985   \n",
       "2      John   Sweden  Stockholm    68.0    1.69  23.8       1990   \n",
       "\n",
       "   Current Year         BMI class  \n",
       "0          2021     Healthy Range  \n",
       "1          2021  Overweight Range  \n",
       "2          2021     Healthy Range  "
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# slicing three rows\n",
    "df.iloc[0:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Country</th>\n",
       "      <th>City</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Asabeneh</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Helsinki</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>David</td>\n",
       "      <td>UK</td>\n",
       "      <td>London</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>John</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>Stockholm</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Name  Country       City\n",
       "0  Asabeneh  Finland   Helsinki\n",
       "1     David       UK     London\n",
       "2      John   Sweden  Stockholm"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Slicing some part of a data frame\n",
    "df.iloc[0:3,0:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Weight</th>\n",
       "      <th>Height</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Birth Year</th>\n",
       "      <th>Current Year</th>\n",
       "      <th>BMI class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>74.0</td>\n",
       "      <td>1.73</td>\n",
       "      <td>24.7</td>\n",
       "      <td>1769</td>\n",
       "      <td>2021</td>\n",
       "      <td>Healthy Range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>78.0</td>\n",
       "      <td>1.75</td>\n",
       "      <td>25.5</td>\n",
       "      <td>1985</td>\n",
       "      <td>2021</td>\n",
       "      <td>Overweight Range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>68.0</td>\n",
       "      <td>1.69</td>\n",
       "      <td>23.8</td>\n",
       "      <td>1990</td>\n",
       "      <td>2021</td>\n",
       "      <td>Healthy Range</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Weight  Height   BMI Birth Year  Current Year         BMI class\n",
       "0    74.0    1.73  24.7       1769          2021     Healthy Range\n",
       "1    78.0    1.75  25.5       1985          2021  Overweight Range\n",
       "2    68.0    1.69  23.8       1990          2021     Healthy Range"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Slicing some part of a data frame\n",
    "df.iloc[0:3,3:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Concating DataFrames\n",
    "We can use the concat() method to join different dataframes horizontally."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Country</th>\n",
       "      <th>City</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Height</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Birth Year</th>\n",
       "      <th>Current Year</th>\n",
       "      <th>BMI class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Asabeneh</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>74.0</td>\n",
       "      <td>1.73</td>\n",
       "      <td>24.7</td>\n",
       "      <td>1769</td>\n",
       "      <td>2021</td>\n",
       "      <td>Healthy Range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>David</td>\n",
       "      <td>UK</td>\n",
       "      <td>London</td>\n",
       "      <td>78.0</td>\n",
       "      <td>1.75</td>\n",
       "      <td>25.5</td>\n",
       "      <td>1985</td>\n",
       "      <td>2021</td>\n",
       "      <td>Overweight Range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>John</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>Stockholm</td>\n",
       "      <td>68.0</td>\n",
       "      <td>1.69</td>\n",
       "      <td>23.8</td>\n",
       "      <td>1990</td>\n",
       "      <td>2021</td>\n",
       "      <td>Healthy Range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Eyob</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Espoo</td>\n",
       "      <td>108.0</td>\n",
       "      <td>1.73</td>\n",
       "      <td>36.1</td>\n",
       "      <td>1983</td>\n",
       "      <td>2021</td>\n",
       "      <td>Obesity Range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Pawel</td>\n",
       "      <td>Poland</td>\n",
       "      <td>Warsaw</td>\n",
       "      <td>102.0</td>\n",
       "      <td>1.95</td>\n",
       "      <td>26.8</td>\n",
       "      <td>1985</td>\n",
       "      <td>2021</td>\n",
       "      <td>Overweight Range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Mahlet</td>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>Addis Ababa</td>\n",
       "      <td>58.0</td>\n",
       "      <td>1.68</td>\n",
       "      <td>20.5</td>\n",
       "      <td>1995</td>\n",
       "      <td>2021</td>\n",
       "      <td>Healthy Range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Thomas</td>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>Bahir Dar</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1.70</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1999</td>\n",
       "      <td>2021</td>\n",
       "      <td>Underweight Range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Lidiya</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Name   Country         City  Weight  Height   BMI Birth Year  \\\n",
       "0  Asabeneh   Finland     Helsinki    74.0    1.73  24.7       1769   \n",
       "1     David        UK       London    78.0    1.75  25.5       1985   \n",
       "2      John    Sweden    Stockholm    68.0    1.69  23.8       1990   \n",
       "3      Eyob   Finland        Espoo   108.0    1.73  36.1       1983   \n",
       "4     Pawel    Poland       Warsaw   102.0    1.95  26.8       1985   \n",
       "5    Mahlet  Ethiopia  Addis Ababa    58.0    1.68  20.5       1995   \n",
       "6    Thomas  Ethiopia    Bahir Dar    52.0    1.70  18.0       1999   \n",
       "7    Lidiya       NaN          NaN     NaN     NaN   NaN        NaN   \n",
       "\n",
       "   Current Year          BMI class  \n",
       "0          2021      Healthy Range  \n",
       "1          2021   Overweight Range  \n",
       "2          2021      Healthy Range  \n",
       "3          2021      Obesity Range  \n",
       "4          2021   Overweight Range  \n",
       "5          2021      Healthy Range  \n",
       "6          2021  Underweight Range  \n",
       "7          2021               None  "
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <th>0</th>\n",
       "      <td>Asabeneh</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>74.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>David</td>\n",
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       "      <td>78.0</td>\n",
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       "      <td>John</td>\n",
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       "      <td>68.0</td>\n",
       "      <td>1.69</td>\n",
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       "      <td>1990</td>\n",
       "      <td>2021</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Eyob</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Espoo</td>\n",
       "      <td>108.0</td>\n",
       "      <td>1.73</td>\n",
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       "      <td>2021</td>\n",
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       "      <td>Pawel</td>\n",
       "      <td>Poland</td>\n",
       "      <td>Warsaw</td>\n",
       "      <td>102.0</td>\n",
       "      <td>1.95</td>\n",
       "      <td>26.8</td>\n",
       "      <td>1985</td>\n",
       "      <td>2021</td>\n",
       "      <td>Overweight Range</td>\n",
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       "      <td>Mahlet</td>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>Addis Ababa</td>\n",
       "      <td>58.0</td>\n",
       "      <td>1.68</td>\n",
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       "      <th>6</th>\n",
       "      <td>Thomas</td>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>Bahir Dar</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1.70</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1999</td>\n",
       "      <td>2021</td>\n",
       "      <td>Underweight Range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Lidiya</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Asabeneh</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>74.0</td>\n",
       "      <td>1.73</td>\n",
       "      <td>24.7</td>\n",
       "      <td>1769</td>\n",
       "      <td>2021</td>\n",
       "      <td>Healthy Range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>David</td>\n",
       "      <td>UK</td>\n",
       "      <td>London</td>\n",
       "      <td>78.0</td>\n",
       "      <td>1.75</td>\n",
       "      <td>25.5</td>\n",
       "      <td>1985</td>\n",
       "      <td>2021</td>\n",
       "      <td>Overweight Range</td>\n",
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       "      <th>2</th>\n",
       "      <td>John</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>Stockholm</td>\n",
       "      <td>68.0</td>\n",
       "      <td>1.69</td>\n",
       "      <td>23.8</td>\n",
       "      <td>1990</td>\n",
       "      <td>2021</td>\n",
       "      <td>Healthy Range</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Eyob</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Espoo</td>\n",
       "      <td>108.0</td>\n",
       "      <td>1.73</td>\n",
       "      <td>36.1</td>\n",
       "      <td>1983</td>\n",
       "      <td>2021</td>\n",
       "      <td>Obesity Range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Pawel</td>\n",
       "      <td>Poland</td>\n",
       "      <td>Warsaw</td>\n",
       "      <td>102.0</td>\n",
       "      <td>1.95</td>\n",
       "      <td>26.8</td>\n",
       "      <td>1985</td>\n",
       "      <td>2021</td>\n",
       "      <td>Overweight Range</td>\n",
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       "      <th>5</th>\n",
       "      <td>Mahlet</td>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>Addis Ababa</td>\n",
       "      <td>58.0</td>\n",
       "      <td>1.68</td>\n",
       "      <td>20.5</td>\n",
       "      <td>1995</td>\n",
       "      <td>2021</td>\n",
       "      <td>Healthy Range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Thomas</td>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>Bahir Dar</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1.70</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1999</td>\n",
       "      <td>2021</td>\n",
       "      <td>Underweight Range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Lidiya</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Name   Country         City  Weight  Height   BMI Birth Year  \\\n",
       "0  Asabeneh   Finland     Helsinki    74.0    1.73  24.7       1769   \n",
       "1     David        UK       London    78.0    1.75  25.5       1985   \n",
       "2      John    Sweden    Stockholm    68.0    1.69  23.8       1990   \n",
       "3      Eyob   Finland        Espoo   108.0    1.73  36.1       1983   \n",
       "4     Pawel    Poland       Warsaw   102.0    1.95  26.8       1985   \n",
       "5    Mahlet  Ethiopia  Addis Ababa    58.0    1.68  20.5       1995   \n",
       "6    Thomas  Ethiopia    Bahir Dar    52.0    1.70  18.0       1999   \n",
       "7    Lidiya       NaN          NaN     NaN     NaN   NaN        NaN   \n",
       "0  Asabeneh   Finland     Helsinki    74.0    1.73  24.7       1769   \n",
       "1     David        UK       London    78.0    1.75  25.5       1985   \n",
       "2      John    Sweden    Stockholm    68.0    1.69  23.8       1990   \n",
       "3      Eyob   Finland        Espoo   108.0    1.73  36.1       1983   \n",
       "4     Pawel    Poland       Warsaw   102.0    1.95  26.8       1985   \n",
       "5    Mahlet  Ethiopia  Addis Ababa    58.0    1.68  20.5       1995   \n",
       "6    Thomas  Ethiopia    Bahir Dar    52.0    1.70  18.0       1999   \n",
       "7    Lidiya       NaN          NaN     NaN     NaN   NaN        NaN   \n",
       "\n",
       "   Current Year          BMI class  \n",
       "0          2021      Healthy Range  \n",
       "1          2021   Overweight Range  \n",
       "2          2021      Healthy Range  \n",
       "3          2021      Obesity Range  \n",
       "4          2021   Overweight Range  \n",
       "5          2021      Healthy Range  \n",
       "6          2021  Underweight Range  \n",
       "7          2021               None  \n",
       "0          2021      Healthy Range  \n",
       "1          2021   Overweight Range  \n",
       "2          2021      Healthy Range  \n",
       "3          2021      Obesity Range  \n",
       "4          2021   Overweight Range  \n",
       "5          2021      Healthy Range  \n",
       "6          2021  Underweight Range  \n",
       "7          2021               None  "
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joined_df = pd.concat([df, df])\n",
    "joined_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cleaning Data\n",
    "\n",
    "Data cleaning takes most of the time in the data analysis work flow. The data cleaning is the first stage in the data analysis work flow. \n",
    "\n",
    "- Removing duplicates\n",
    "- Imputation(Filling missing data)\n",
    "- Removing null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Country</th>\n",
       "      <th>City</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Height</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Birth Year</th>\n",
       "      <th>Current Year</th>\n",
       "      <th>BMI class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Asabeneh</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>74.0</td>\n",
       "      <td>1.73</td>\n",
       "      <td>24.7</td>\n",
       "      <td>1769</td>\n",
       "      <td>2021</td>\n",
       "      <td>Healthy Range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>David</td>\n",
       "      <td>UK</td>\n",
       "      <td>London</td>\n",
       "      <td>78.0</td>\n",
       "      <td>1.75</td>\n",
       "      <td>25.5</td>\n",
       "      <td>1985</td>\n",
       "      <td>2021</td>\n",
       "      <td>Overweight Range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>John</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>Stockholm</td>\n",
       "      <td>68.0</td>\n",
       "      <td>1.69</td>\n",
       "      <td>23.8</td>\n",
       "      <td>1990</td>\n",
       "      <td>2021</td>\n",
       "      <td>Healthy Range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Eyob</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Espoo</td>\n",
       "      <td>108.0</td>\n",
       "      <td>1.73</td>\n",
       "      <td>36.1</td>\n",
       "      <td>1983</td>\n",
       "      <td>2021</td>\n",
       "      <td>Obesity Range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Pawel</td>\n",
       "      <td>Poland</td>\n",
       "      <td>Warsaw</td>\n",
       "      <td>102.0</td>\n",
       "      <td>1.95</td>\n",
       "      <td>26.8</td>\n",
       "      <td>1985</td>\n",
       "      <td>2021</td>\n",
       "      <td>Overweight Range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Mahlet</td>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>Addis Ababa</td>\n",
       "      <td>58.0</td>\n",
       "      <td>1.68</td>\n",
       "      <td>20.5</td>\n",
       "      <td>1995</td>\n",
       "      <td>2021</td>\n",
       "      <td>Healthy Range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Thomas</td>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>Bahir Dar</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1.70</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1999</td>\n",
       "      <td>2021</td>\n",
       "      <td>Underweight Range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Lidiya</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Name   Country         City  Weight  Height   BMI Birth Year  \\\n",
       "0  Asabeneh   Finland     Helsinki    74.0    1.73  24.7       1769   \n",
       "1     David        UK       London    78.0    1.75  25.5       1985   \n",
       "2      John    Sweden    Stockholm    68.0    1.69  23.8       1990   \n",
       "3      Eyob   Finland        Espoo   108.0    1.73  36.1       1983   \n",
       "4     Pawel    Poland       Warsaw   102.0    1.95  26.8       1985   \n",
       "5    Mahlet  Ethiopia  Addis Ababa    58.0    1.68  20.5       1995   \n",
       "6    Thomas  Ethiopia    Bahir Dar    52.0    1.70  18.0       1999   \n",
       "7    Lidiya       NaN          NaN     NaN     NaN   NaN        NaN   \n",
       "\n",
       "   Current Year          BMI class  \n",
       "0          2021      Healthy Range  \n",
       "1          2021   Overweight Range  \n",
       "2          2021      Healthy Range  \n",
       "3          2021      Obesity Range  \n",
       "4          2021   Overweight Range  \n",
       "5          2021      Healthy Range  \n",
       "6          2021  Underweight Range  \n",
       "7          2021               None  "
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 8 entries, 0 to 7\n",
      "Data columns (total 9 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   Name          8 non-null      object \n",
      " 1   Country       7 non-null      object \n",
      " 2   City          7 non-null      object \n",
      " 3   Weight        7 non-null      float64\n",
      " 4   Height        7 non-null      float64\n",
      " 5   BMI           7 non-null      float64\n",
      " 6   Birth Year    7 non-null      object \n",
      " 7   Current Year  8 non-null      int64  \n",
      " 8   BMI class     7 non-null      object \n",
      "dtypes: float64(3), int64(1), object(5)\n",
      "memory usage: 704.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "# the info provides information about the data\n",
    "# Only the Name and Current Year columns are complete data values\n",
    "# The last row has many NaN value, we can fill it with some value\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>Country</th>\n",
       "      <th>City</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>Asabeneh</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>74</td>\n",
       "      <td>1.73</td>\n",
       "      <td>24.7</td>\n",
       "      <td>1769</td>\n",
       "      <td>2021</td>\n",
       "      <td>Healthy Range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>David</td>\n",
       "      <td>UK</td>\n",
       "      <td>London</td>\n",
       "      <td>78</td>\n",
       "      <td>1.75</td>\n",
       "      <td>25.5</td>\n",
       "      <td>1985</td>\n",
       "      <td>2021</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>John</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>Stockholm</td>\n",
       "      <td>68</td>\n",
       "      <td>1.69</td>\n",
       "      <td>23.8</td>\n",
       "      <td>1990</td>\n",
       "      <td>2021</td>\n",
       "      <td>Healthy Range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Eyob</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Espoo</td>\n",
       "      <td>108</td>\n",
       "      <td>1.73</td>\n",
       "      <td>36.1</td>\n",
       "      <td>1983</td>\n",
       "      <td>2021</td>\n",
       "      <td>Obesity Range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Pawel</td>\n",
       "      <td>Poland</td>\n",
       "      <td>Warsaw</td>\n",
       "      <td>102</td>\n",
       "      <td>1.95</td>\n",
       "      <td>26.8</td>\n",
       "      <td>1985</td>\n",
       "      <td>2021</td>\n",
       "      <td>Overweight Range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Mahlet</td>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>Addis Ababa</td>\n",
       "      <td>58</td>\n",
       "      <td>1.68</td>\n",
       "      <td>20.5</td>\n",
       "      <td>1995</td>\n",
       "      <td>2021</td>\n",
       "      <td>Healthy Range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Thomas</td>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>Bahir Dar</td>\n",
       "      <td>52</td>\n",
       "      <td>1.7</td>\n",
       "      <td>18</td>\n",
       "      <td>1999</td>\n",
       "      <td>2021</td>\n",
       "      <td>Underweight Range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Lidiya</td>\n",
       "      <td>sample data</td>\n",
       "      <td>sample data</td>\n",
       "      <td>sample data</td>\n",
       "      <td>sample data</td>\n",
       "      <td>sample data</td>\n",
       "      <td>sample data</td>\n",
       "      <td>2021</td>\n",
       "      <td>sample data</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Name      Country         City       Weight       Height          BMI  \\\n",
       "0  Asabeneh      Finland     Helsinki           74         1.73         24.7   \n",
       "1     David           UK       London           78         1.75         25.5   \n",
       "2      John       Sweden    Stockholm           68         1.69         23.8   \n",
       "3      Eyob      Finland        Espoo          108         1.73         36.1   \n",
       "4     Pawel       Poland       Warsaw          102         1.95         26.8   \n",
       "5    Mahlet     Ethiopia  Addis Ababa           58         1.68         20.5   \n",
       "6    Thomas     Ethiopia    Bahir Dar           52          1.7           18   \n",
       "7    Lidiya  sample data  sample data  sample data  sample data  sample data   \n",
       "\n",
       "    Birth Year  Current Year          BMI class  \n",
       "0         1769          2021      Healthy Range  \n",
       "1         1985          2021   Overweight Range  \n",
       "2         1990          2021      Healthy Range  \n",
       "3         1983          2021      Obesity Range  \n",
       "4         1985          2021   Overweight Range  \n",
       "5         1995          2021      Healthy Range  \n",
       "6         1999          2021  Underweight Range  \n",
       "7  sample data          2021        sample data  "
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# to modify it from the originial we should use inplace=True argument\n",
    "df.fillna('sample data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>Name</th>\n",
       "      <th>Country</th>\n",
       "      <th>City</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Height</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Birth Year</th>\n",
       "      <th>Current Year</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Asabeneh</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>74.0</td>\n",
       "      <td>1.73</td>\n",
       "      <td>24.7</td>\n",
       "      <td>1769</td>\n",
       "      <td>2021</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>David</td>\n",
       "      <td>UK</td>\n",
       "      <td>London</td>\n",
       "      <td>78.0</td>\n",
       "      <td>1.75</td>\n",
       "      <td>25.5</td>\n",
       "      <td>1985</td>\n",
       "      <td>2021</td>\n",
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       "      <th>2</th>\n",
       "      <td>John</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>Stockholm</td>\n",
       "      <td>68.0</td>\n",
       "      <td>1.69</td>\n",
       "      <td>23.8</td>\n",
       "      <td>1990</td>\n",
       "      <td>2021</td>\n",
       "      <td>Healthy Range</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Eyob</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Espoo</td>\n",
       "      <td>108.0</td>\n",
       "      <td>1.73</td>\n",
       "      <td>36.1</td>\n",
       "      <td>1983</td>\n",
       "      <td>2021</td>\n",
       "      <td>Obesity Range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Pawel</td>\n",
       "      <td>Poland</td>\n",
       "      <td>Warsaw</td>\n",
       "      <td>102.0</td>\n",
       "      <td>1.95</td>\n",
       "      <td>26.8</td>\n",
       "      <td>1985</td>\n",
       "      <td>2021</td>\n",
       "      <td>Overweight Range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Mahlet</td>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>Addis Ababa</td>\n",
       "      <td>58.0</td>\n",
       "      <td>1.68</td>\n",
       "      <td>20.5</td>\n",
       "      <td>1995</td>\n",
       "      <td>2021</td>\n",
       "      <td>Healthy Range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Thomas</td>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>Bahir Dar</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1.70</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1999</td>\n",
       "      <td>2021</td>\n",
       "      <td>Underweight Range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Lidiya</td>\n",
       "      <td>Finland</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Name   Country         City  Weight  Height   BMI Birth Year  \\\n",
       "0  Asabeneh   Finland     Helsinki    74.0    1.73  24.7       1769   \n",
       "1     David        UK       London    78.0    1.75  25.5       1985   \n",
       "2      John    Sweden    Stockholm    68.0    1.69  23.8       1990   \n",
       "3      Eyob   Finland        Espoo   108.0    1.73  36.1       1983   \n",
       "4     Pawel    Poland       Warsaw   102.0    1.95  26.8       1985   \n",
       "5    Mahlet  Ethiopia  Addis Ababa    58.0    1.68  20.5       1995   \n",
       "6    Thomas  Ethiopia    Bahir Dar    52.0    1.70  18.0       1999   \n",
       "7    Lidiya   Finland          NaN     NaN     NaN   NaN        NaN   \n",
       "\n",
       "   Current Year          BMI class  \n",
       "0          2021      Healthy Range  \n",
       "1          2021   Overweight Range  \n",
       "2          2021      Healthy Range  \n",
       "3          2021      Obesity Range  \n",
       "4          2021   Overweight Range  \n",
       "5          2021      Healthy Range  \n",
       "6          2021  Underweight Range  \n",
       "7          2021               None  "
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We can also replace only a certain columns\n",
    "df['Country'].fillna('Finland', inplace=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dropping null value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Country</th>\n",
       "      <th>City</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Height</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Birth Year</th>\n",
       "      <th>Current Year</th>\n",
       "      <th>BMI class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Asabeneh</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>74.0</td>\n",
       "      <td>1.73</td>\n",
       "      <td>24.7</td>\n",
       "      <td>1769</td>\n",
       "      <td>2021</td>\n",
       "      <td>Healthy Range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>David</td>\n",
       "      <td>UK</td>\n",
       "      <td>London</td>\n",
       "      <td>78.0</td>\n",
       "      <td>1.75</td>\n",
       "      <td>25.5</td>\n",
       "      <td>1985</td>\n",
       "      <td>2021</td>\n",
       "      <td>Overweight Range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>John</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>Stockholm</td>\n",
       "      <td>68.0</td>\n",
       "      <td>1.69</td>\n",
       "      <td>23.8</td>\n",
       "      <td>1990</td>\n",
       "      <td>2021</td>\n",
       "      <td>Healthy Range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Eyob</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Espoo</td>\n",
       "      <td>108.0</td>\n",
       "      <td>1.73</td>\n",
       "      <td>36.1</td>\n",
       "      <td>1983</td>\n",
       "      <td>2021</td>\n",
       "      <td>Obesity Range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Pawel</td>\n",
       "      <td>Poland</td>\n",
       "      <td>Warsaw</td>\n",
       "      <td>102.0</td>\n",
       "      <td>1.95</td>\n",
       "      <td>26.8</td>\n",
       "      <td>1985</td>\n",
       "      <td>2021</td>\n",
       "      <td>Overweight Range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Mahlet</td>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>Addis Ababa</td>\n",
       "      <td>58.0</td>\n",
       "      <td>1.68</td>\n",
       "      <td>20.5</td>\n",
       "      <td>1995</td>\n",
       "      <td>2021</td>\n",
       "      <td>Healthy Range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Thomas</td>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>Bahir Dar</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1.70</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1999</td>\n",
       "      <td>2021</td>\n",
       "      <td>Underweight Range</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Name   Country         City  Weight  Height   BMI Birth Year  \\\n",
       "0  Asabeneh   Finland     Helsinki    74.0    1.73  24.7       1769   \n",
       "1     David        UK       London    78.0    1.75  25.5       1985   \n",
       "2      John    Sweden    Stockholm    68.0    1.69  23.8       1990   \n",
       "3      Eyob   Finland        Espoo   108.0    1.73  36.1       1983   \n",
       "4     Pawel    Poland       Warsaw   102.0    1.95  26.8       1985   \n",
       "5    Mahlet  Ethiopia  Addis Ababa    58.0    1.68  20.5       1995   \n",
       "6    Thomas  Ethiopia    Bahir Dar    52.0    1.70  18.0       1999   \n",
       "\n",
       "   Current Year          BMI class  \n",
       "0          2021      Healthy Range  \n",
       "1          2021   Overweight Range  \n",
       "2          2021      Healthy Range  \n",
       "3          2021      Obesity Range  \n",
       "4          2021   Overweight Range  \n",
       "5          2021      Healthy Range  \n",
       "6          2021  Underweight Range  "
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We can also remove the row containing the null\n",
    "# We can use inplace=True, to make change to the original\n",
    "\n",
    "df.dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Imputation\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We can use mean, median, mode methods to calculate descriptive statistics from data using pandas\n",
    "mean = df.iloc[1:]['Birth Year'].astype(float).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1769'"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Replacing outliers\n",
    "# Birth year 1769 does sould plausible, lets replace or imput that vaue with avarage birth year\n",
    "# To target 1769 data we can use loc or iloc method\n",
    "df.loc[0, 'Birth Year']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1769'"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[0, 6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Country</th>\n",
       "      <th>City</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Height</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Birth Year</th>\n",
       "      <th>Current Year</th>\n",
       "      <th>BMI class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Asabeneh</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>74.0</td>\n",
       "      <td>1.73</td>\n",
       "      <td>24.7</td>\n",
       "      <td>1990</td>\n",
       "      <td>2021</td>\n",
       "      <td>Healthy Range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>David</td>\n",
       "      <td>UK</td>\n",
       "      <td>London</td>\n",
       "      <td>78.0</td>\n",
       "      <td>1.75</td>\n",
       "      <td>25.5</td>\n",
       "      <td>1985</td>\n",
       "      <td>2021</td>\n",
       "      <td>Overweight Range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>John</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>Stockholm</td>\n",
       "      <td>68.0</td>\n",
       "      <td>1.69</td>\n",
       "      <td>23.8</td>\n",
       "      <td>1990</td>\n",
       "      <td>2021</td>\n",
       "      <td>Healthy Range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Eyob</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Espoo</td>\n",
       "      <td>108.0</td>\n",
       "      <td>1.73</td>\n",
       "      <td>36.1</td>\n",
       "      <td>1983</td>\n",
       "      <td>2021</td>\n",
       "      <td>Obesity Range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Pawel</td>\n",
       "      <td>Poland</td>\n",
       "      <td>Warsaw</td>\n",
       "      <td>102.0</td>\n",
       "      <td>1.95</td>\n",
       "      <td>26.8</td>\n",
       "      <td>1985</td>\n",
       "      <td>2021</td>\n",
       "      <td>Overweight Range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Mahlet</td>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>Addis Ababa</td>\n",
       "      <td>58.0</td>\n",
       "      <td>1.68</td>\n",
       "      <td>20.5</td>\n",
       "      <td>1995</td>\n",
       "      <td>2021</td>\n",
       "      <td>Healthy Range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Thomas</td>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>Bahir Dar</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1.70</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1999</td>\n",
       "      <td>2021</td>\n",
       "      <td>Underweight Range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Lidiya</td>\n",
       "      <td>Finland</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Name   Country         City  Weight  Height   BMI Birth Year  \\\n",
       "0  Asabeneh   Finland     Helsinki    74.0    1.73  24.7       1990   \n",
       "1     David        UK       London    78.0    1.75  25.5       1985   \n",
       "2      John    Sweden    Stockholm    68.0    1.69  23.8       1990   \n",
       "3      Eyob   Finland        Espoo   108.0    1.73  36.1       1983   \n",
       "4     Pawel    Poland       Warsaw   102.0    1.95  26.8       1985   \n",
       "5    Mahlet  Ethiopia  Addis Ababa    58.0    1.68  20.5       1995   \n",
       "6    Thomas  Ethiopia    Bahir Dar    52.0    1.70  18.0       1999   \n",
       "7    Lidiya   Finland          NaN     NaN     NaN   NaN        NaN   \n",
       "\n",
       "   Current Year          BMI class  \n",
       "0          2021      Healthy Range  \n",
       "1          2021   Overweight Range  \n",
       "2          2021      Healthy Range  \n",
       "3          2021      Obesity Range  \n",
       "4          2021   Overweight Range  \n",
       "5          2021      Healthy Range  \n",
       "6          2021  Underweight Range  \n",
       "7          2021               None  "
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[0, 6] = round( mean)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Removing Duplicates"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Checking if there is duplicates using the duplicated() method. If there is a duplicate, it returns True otherwise False. The data below has a duplicate. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Country</th>\n",
       "      <th>City</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Height</th>\n",
       "      <th>BMI</th>\n",
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       "      <th>0</th>\n",
       "      <td>Asabeneh</td>\n",
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       "    <tr>\n",
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       "      <td>2021</td>\n",
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       "      <td>Eyob</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Espoo</td>\n",
       "      <td>108.0</td>\n",
       "      <td>1.73</td>\n",
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       "      <td>1983</td>\n",
       "      <td>2021</td>\n",
       "      <td>Obesity Range</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Pawel</td>\n",
       "      <td>Poland</td>\n",
       "      <td>Warsaw</td>\n",
       "      <td>102.0</td>\n",
       "      <td>1.95</td>\n",
       "      <td>26.8</td>\n",
       "      <td>1985</td>\n",
       "      <td>2021</td>\n",
       "      <td>Overweight Range</td>\n",
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       "      <td>Addis Ababa</td>\n",
       "      <td>58.0</td>\n",
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       "      <td>2021</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Thomas</td>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>Bahir Dar</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1.70</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1999</td>\n",
       "      <td>2021</td>\n",
       "      <td>Underweight Range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Lidiya</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Asabeneh</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>74.0</td>\n",
       "      <td>1.73</td>\n",
       "      <td>24.7</td>\n",
       "      <td>1769</td>\n",
       "      <td>2021</td>\n",
       "      <td>Healthy Range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>David</td>\n",
       "      <td>UK</td>\n",
       "      <td>London</td>\n",
       "      <td>78.0</td>\n",
       "      <td>1.75</td>\n",
       "      <td>25.5</td>\n",
       "      <td>1985</td>\n",
       "      <td>2021</td>\n",
       "      <td>Overweight Range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>John</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>Stockholm</td>\n",
       "      <td>68.0</td>\n",
       "      <td>1.69</td>\n",
       "      <td>23.8</td>\n",
       "      <td>1990</td>\n",
       "      <td>2021</td>\n",
       "      <td>Healthy Range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Eyob</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Espoo</td>\n",
       "      <td>108.0</td>\n",
       "      <td>1.73</td>\n",
       "      <td>36.1</td>\n",
       "      <td>1983</td>\n",
       "      <td>2021</td>\n",
       "      <td>Obesity Range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Pawel</td>\n",
       "      <td>Poland</td>\n",
       "      <td>Warsaw</td>\n",
       "      <td>102.0</td>\n",
       "      <td>1.95</td>\n",
       "      <td>26.8</td>\n",
       "      <td>1985</td>\n",
       "      <td>2021</td>\n",
       "      <td>Overweight Range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Mahlet</td>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>Addis Ababa</td>\n",
       "      <td>58.0</td>\n",
       "      <td>1.68</td>\n",
       "      <td>20.5</td>\n",
       "      <td>1995</td>\n",
       "      <td>2021</td>\n",
       "      <td>Healthy Range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Thomas</td>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>Bahir Dar</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1.70</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1999</td>\n",
       "      <td>2021</td>\n",
       "      <td>Underweight Range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Lidiya</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Name   Country         City  Weight  Height   BMI Birth Year  \\\n",
       "0  Asabeneh   Finland     Helsinki    74.0    1.73  24.7       1769   \n",
       "1     David        UK       London    78.0    1.75  25.5       1985   \n",
       "2      John    Sweden    Stockholm    68.0    1.69  23.8       1990   \n",
       "3      Eyob   Finland        Espoo   108.0    1.73  36.1       1983   \n",
       "4     Pawel    Poland       Warsaw   102.0    1.95  26.8       1985   \n",
       "5    Mahlet  Ethiopia  Addis Ababa    58.0    1.68  20.5       1995   \n",
       "6    Thomas  Ethiopia    Bahir Dar    52.0    1.70  18.0       1999   \n",
       "7    Lidiya       NaN          NaN     NaN     NaN   NaN        NaN   \n",
       "0  Asabeneh   Finland     Helsinki    74.0    1.73  24.7       1769   \n",
       "1     David        UK       London    78.0    1.75  25.5       1985   \n",
       "2      John    Sweden    Stockholm    68.0    1.69  23.8       1990   \n",
       "3      Eyob   Finland        Espoo   108.0    1.73  36.1       1983   \n",
       "4     Pawel    Poland       Warsaw   102.0    1.95  26.8       1985   \n",
       "5    Mahlet  Ethiopia  Addis Ababa    58.0    1.68  20.5       1995   \n",
       "6    Thomas  Ethiopia    Bahir Dar    52.0    1.70  18.0       1999   \n",
       "7    Lidiya       NaN          NaN     NaN     NaN   NaN        NaN   \n",
       "\n",
       "   Current Year          BMI class  \n",
       "0          2021      Healthy Range  \n",
       "1          2021   Overweight Range  \n",
       "2          2021      Healthy Range  \n",
       "3          2021      Obesity Range  \n",
       "4          2021   Overweight Range  \n",
       "5          2021      Healthy Range  \n",
       "6          2021  Underweight Range  \n",
       "7          2021               None  \n",
       "0          2021      Healthy Range  \n",
       "1          2021   Overweight Range  \n",
       "2          2021      Healthy Range  \n",
       "3          2021      Obesity Range  \n",
       "4          2021   Overweight Range  \n",
       "5          2021      Healthy Range  \n",
       "6          2021  Underweight Range  \n",
       "7          2021               None  "
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joined_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1    False\n",
       "2    False\n",
       "3    False\n",
       "4    False\n",
       "5    False\n",
       "6    False\n",
       "7    False\n",
       "0     True\n",
       "1     True\n",
       "2     True\n",
       "3     True\n",
       "4     True\n",
       "5     True\n",
       "6     True\n",
       "7     True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joined_df.duplicated()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Country</th>\n",
       "      <th>City</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Height</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Birth Year</th>\n",
       "      <th>Current Year</th>\n",
       "      <th>BMI class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Asabeneh</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>74.0</td>\n",
       "      <td>1.73</td>\n",
       "      <td>24.7</td>\n",
       "      <td>1769</td>\n",
       "      <td>2021</td>\n",
       "      <td>Healthy Range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>David</td>\n",
       "      <td>UK</td>\n",
       "      <td>London</td>\n",
       "      <td>78.0</td>\n",
       "      <td>1.75</td>\n",
       "      <td>25.5</td>\n",
       "      <td>1985</td>\n",
       "      <td>2021</td>\n",
       "      <td>Overweight Range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>John</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>Stockholm</td>\n",
       "      <td>68.0</td>\n",
       "      <td>1.69</td>\n",
       "      <td>23.8</td>\n",
       "      <td>1990</td>\n",
       "      <td>2021</td>\n",
       "      <td>Healthy Range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Eyob</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Espoo</td>\n",
       "      <td>108.0</td>\n",
       "      <td>1.73</td>\n",
       "      <td>36.1</td>\n",
       "      <td>1983</td>\n",
       "      <td>2021</td>\n",
       "      <td>Obesity Range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Pawel</td>\n",
       "      <td>Poland</td>\n",
       "      <td>Warsaw</td>\n",
       "      <td>102.0</td>\n",
       "      <td>1.95</td>\n",
       "      <td>26.8</td>\n",
       "      <td>1985</td>\n",
       "      <td>2021</td>\n",
       "      <td>Overweight Range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Mahlet</td>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>Addis Ababa</td>\n",
       "      <td>58.0</td>\n",
       "      <td>1.68</td>\n",
       "      <td>20.5</td>\n",
       "      <td>1995</td>\n",
       "      <td>2021</td>\n",
       "      <td>Healthy Range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Thomas</td>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>Bahir Dar</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1.70</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1999</td>\n",
       "      <td>2021</td>\n",
       "      <td>Underweight Range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Lidiya</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Name   Country         City  Weight  Height   BMI Birth Year  \\\n",
       "0  Asabeneh   Finland     Helsinki    74.0    1.73  24.7       1769   \n",
       "1     David        UK       London    78.0    1.75  25.5       1985   \n",
       "2      John    Sweden    Stockholm    68.0    1.69  23.8       1990   \n",
       "3      Eyob   Finland        Espoo   108.0    1.73  36.1       1983   \n",
       "4     Pawel    Poland       Warsaw   102.0    1.95  26.8       1985   \n",
       "5    Mahlet  Ethiopia  Addis Ababa    58.0    1.68  20.5       1995   \n",
       "6    Thomas  Ethiopia    Bahir Dar    52.0    1.70  18.0       1999   \n",
       "7    Lidiya       NaN          NaN     NaN     NaN   NaN        NaN   \n",
       "\n",
       "   Current Year          BMI class  \n",
       "0          2021      Healthy Range  \n",
       "1          2021   Overweight Range  \n",
       "2          2021      Healthy Range  \n",
       "3          2021      Obesity Range  \n",
       "4          2021   Overweight Range  \n",
       "5          2021      Healthy Range  \n",
       "6          2021  Underweight Range  \n",
       "7          2021               None  "
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# removing dupliacates\n",
    "joined_df.drop_duplicates(inplace=True)\n",
    "joined_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using descriptive Statistics methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Country</th>\n",
       "      <th>City</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Height</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Birth Year</th>\n",
       "      <th>Current Year</th>\n",
       "      <th>BMI class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Asabeneh</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>74.0</td>\n",
       "      <td>1.73</td>\n",
       "      <td>24.7</td>\n",
       "      <td>1769</td>\n",
       "      <td>2021</td>\n",
       "      <td>Healthy Range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>David</td>\n",
       "      <td>UK</td>\n",
       "      <td>London</td>\n",
       "      <td>78.0</td>\n",
       "      <td>1.75</td>\n",
       "      <td>25.5</td>\n",
       "      <td>1985</td>\n",
       "      <td>2021</td>\n",
       "      <td>Overweight Range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>John</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>Stockholm</td>\n",
       "      <td>68.0</td>\n",
       "      <td>1.69</td>\n",
       "      <td>23.8</td>\n",
       "      <td>1990</td>\n",
       "      <td>2021</td>\n",
       "      <td>Healthy Range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Eyob</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Espoo</td>\n",
       "      <td>108.0</td>\n",
       "      <td>1.73</td>\n",
       "      <td>36.1</td>\n",
       "      <td>1983</td>\n",
       "      <td>2021</td>\n",
       "      <td>Obesity Range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Pawel</td>\n",
       "      <td>Poland</td>\n",
       "      <td>Warsaw</td>\n",
       "      <td>102.0</td>\n",
       "      <td>1.95</td>\n",
       "      <td>26.8</td>\n",
       "      <td>1985</td>\n",
       "      <td>2021</td>\n",
       "      <td>Overweight Range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Mahlet</td>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>Addis Ababa</td>\n",
       "      <td>58.0</td>\n",
       "      <td>1.68</td>\n",
       "      <td>20.5</td>\n",
       "      <td>1995</td>\n",
       "      <td>2021</td>\n",
       "      <td>Healthy Range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Thomas</td>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>Bahir Dar</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1.70</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1999</td>\n",
       "      <td>2021</td>\n",
       "      <td>Underweight Range</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Name   Country         City  Weight  Height   BMI Birth Year  \\\n",
       "0  Asabeneh   Finland     Helsinki    74.0    1.73  24.7       1769   \n",
       "1     David        UK       London    78.0    1.75  25.5       1985   \n",
       "2      John    Sweden    Stockholm    68.0    1.69  23.8       1990   \n",
       "3      Eyob   Finland        Espoo   108.0    1.73  36.1       1983   \n",
       "4     Pawel    Poland       Warsaw   102.0    1.95  26.8       1985   \n",
       "5    Mahlet  Ethiopia  Addis Ababa    58.0    1.68  20.5       1995   \n",
       "6    Thomas  Ethiopia    Bahir Dar    52.0    1.70  18.0       1999   \n",
       "\n",
       "   Current Year          BMI class  \n",
       "0          2021      Healthy Range  \n",
       "1          2021   Overweight Range  \n",
       "2          2021      Healthy Range  \n",
       "3          2021      Obesity Range  \n",
       "4          2021   Overweight Range  \n",
       "5          2021      Healthy Range  \n",
       "6          2021  Underweight Range  "
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Lets drop the null and do descriptive statistics\n",
    "joined_df.dropna(inplace=True)\n",
    "joined_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Weight</th>\n",
       "      <th>Height</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Current Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>77.142857</td>\n",
       "      <td>1.747143</td>\n",
       "      <td>25.057143</td>\n",
       "      <td>2021.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>21.066787</td>\n",
       "      <td>0.092864</td>\n",
       "      <td>5.736682</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>52.000000</td>\n",
       "      <td>1.680000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>2021.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>63.000000</td>\n",
       "      <td>1.695000</td>\n",
       "      <td>22.150000</td>\n",
       "      <td>2021.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>74.000000</td>\n",
       "      <td>1.730000</td>\n",
       "      <td>24.700000</td>\n",
       "      <td>2021.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>90.000000</td>\n",
       "      <td>1.740000</td>\n",
       "      <td>26.150000</td>\n",
       "      <td>2021.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>108.000000</td>\n",
       "      <td>1.950000</td>\n",
       "      <td>36.100000</td>\n",
       "      <td>2021.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Weight    Height        BMI  Current Year\n",
       "count    7.000000  7.000000   7.000000           7.0\n",
       "mean    77.142857  1.747143  25.057143        2021.0\n",
       "std     21.066787  0.092864   5.736682           0.0\n",
       "min     52.000000  1.680000  18.000000        2021.0\n",
       "25%     63.000000  1.695000  22.150000        2021.0\n",
       "50%     74.000000  1.730000  24.700000        2021.0\n",
       "75%     90.000000  1.740000  26.150000        2021.0\n",
       "max    108.000000  1.950000  36.100000        2021.0"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joined_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Weight          7.714286e+01\n",
       "Height          1.747143e+00\n",
       "BMI             2.505714e+01\n",
       "Birth Year      2.527426e+26\n",
       "Current Year    2.021000e+03\n",
       "dtype: float64"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We can also use mean, median mode separately\n",
    "\n",
    "joined_df.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Weight            74.00\n",
       "Height             1.73\n",
       "BMI               24.70\n",
       "Birth Year      1985.00\n",
       "Current Year    2021.00\n",
       "dtype: float64"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joined_df.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Ethiopia\n",
       "1     Finland\n",
       "dtype: object"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# mode of the Country column\n",
    "joined_df['Country'].mode()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Weight          21.066787\n",
       "Height           0.092864\n",
       "BMI              5.736682\n",
       "Current Year     0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Standard Deviation of the numeric columns\n",
    "joined_df.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Weight          443.809524\n",
       "Height            0.008624\n",
       "BMI              32.909524\n",
       "Current Year      0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Varience of the numeric columns\n",
    "joined_df.var()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Weight</th>\n",
       "      <th>Height</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Current Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Weight</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.644298</td>\n",
       "      <td>0.912597</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Height</th>\n",
       "      <td>0.644298</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.275980</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BMI</th>\n",
       "      <td>0.912597</td>\n",
       "      <td>0.275980</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Current Year</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Weight    Height       BMI  Current Year\n",
       "Weight        1.000000  0.644298  0.912597           NaN\n",
       "Height        0.644298  1.000000  0.275980           NaN\n",
       "BMI           0.912597  0.275980  1.000000           NaN\n",
       "Current Year       NaN       NaN       NaN           NaN"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Correlation shows the relationship between two variables. The value is between -1 and 1. \n",
    "# 0 No correlation at all \n",
    "# 1 mean a perfect relation\n",
    "# BMI has high correlation with Weight, however Height has relatively low correlation with BMI\n",
    "joined_df.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Use value_counts method "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Finland     2\n",
       "Ethiopia    2\n",
       "UK          1\n",
       "Sweden      1\n",
       "Poland      1\n",
       "Name: Country, dtype: int64"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joined_df['Country'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Stockholm      1\n",
       "Addis Ababa    1\n",
       "Warsaw         1\n",
       "Espoo          1\n",
       "Helsinki       1\n",
       "London         1\n",
       "Bahir Dar      1\n",
       "Name: City, dtype: int64"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joined_df['City'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using groupby method to group columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Weight</th>\n",
       "      <th>Height</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Current Year</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ethiopia</th>\n",
       "      <td>55.0</td>\n",
       "      <td>1.69</td>\n",
       "      <td>19.25</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Finland</th>\n",
       "      <td>91.0</td>\n",
       "      <td>1.73</td>\n",
       "      <td>30.40</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Poland</th>\n",
       "      <td>102.0</td>\n",
       "      <td>1.95</td>\n",
       "      <td>26.80</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sweden</th>\n",
       "      <td>68.0</td>\n",
       "      <td>1.69</td>\n",
       "      <td>23.80</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UK</th>\n",
       "      <td>78.0</td>\n",
       "      <td>1.75</td>\n",
       "      <td>25.50</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Weight  Height    BMI  Current Year\n",
       "Country                                      \n",
       "Ethiopia    55.0    1.69  19.25          2021\n",
       "Finland     91.0    1.73  30.40          2021\n",
       "Poland     102.0    1.95  26.80          2021\n",
       "Sweden      68.0    1.69  23.80          2021\n",
       "UK          78.0    1.75  25.50          2021"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('Country').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Weight</th>\n",
       "      <th>Height</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Current Year</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ethiopia</th>\n",
       "      <td>55.0</td>\n",
       "      <td>1.69</td>\n",
       "      <td>19.25</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Finland</th>\n",
       "      <td>91.0</td>\n",
       "      <td>1.73</td>\n",
       "      <td>30.40</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Poland</th>\n",
       "      <td>102.0</td>\n",
       "      <td>1.95</td>\n",
       "      <td>26.80</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sweden</th>\n",
       "      <td>68.0</td>\n",
       "      <td>1.69</td>\n",
       "      <td>23.80</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UK</th>\n",
       "      <td>78.0</td>\n",
       "      <td>1.75</td>\n",
       "      <td>25.50</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Weight  Height    BMI  Current Year\n",
       "Country                                      \n",
       "Ethiopia    55.0    1.69  19.25          2021\n",
       "Finland     91.0    1.73  30.40          2021\n",
       "Poland     102.0    1.95  26.80          2021\n",
       "Sweden      68.0    1.69  23.80          2021\n",
       "UK          78.0    1.75  25.50          2021"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('Country', sort=True).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Country</th>\n",
       "      <th>City</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Height</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Birth Year</th>\n",
       "      <th>Current Year</th>\n",
       "      <th>BMI class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Mahlet</td>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>Addis Ababa</td>\n",
       "      <td>58.0</td>\n",
       "      <td>1.68</td>\n",
       "      <td>20.5</td>\n",
       "      <td>1995</td>\n",
       "      <td>2021</td>\n",
       "      <td>Healthy Range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Thomas</td>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>Bahir Dar</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1.70</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1999</td>\n",
       "      <td>2021</td>\n",
       "      <td>Underweight Range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Asabeneh</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>74.0</td>\n",
       "      <td>1.73</td>\n",
       "      <td>24.7</td>\n",
       "      <td>1990</td>\n",
       "      <td>2021</td>\n",
       "      <td>Healthy Range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Eyob</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Espoo</td>\n",
       "      <td>108.0</td>\n",
       "      <td>1.73</td>\n",
       "      <td>36.1</td>\n",
       "      <td>1983</td>\n",
       "      <td>2021</td>\n",
       "      <td>Obesity Range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Lidiya</td>\n",
       "      <td>Finland</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Pawel</td>\n",
       "      <td>Poland</td>\n",
       "      <td>Warsaw</td>\n",
       "      <td>102.0</td>\n",
       "      <td>1.95</td>\n",
       "      <td>26.8</td>\n",
       "      <td>1985</td>\n",
       "      <td>2021</td>\n",
       "      <td>Overweight Range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>John</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>Stockholm</td>\n",
       "      <td>68.0</td>\n",
       "      <td>1.69</td>\n",
       "      <td>23.8</td>\n",
       "      <td>1990</td>\n",
       "      <td>2021</td>\n",
       "      <td>Healthy Range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>David</td>\n",
       "      <td>UK</td>\n",
       "      <td>London</td>\n",
       "      <td>78.0</td>\n",
       "      <td>1.75</td>\n",
       "      <td>25.5</td>\n",
       "      <td>1985</td>\n",
       "      <td>2021</td>\n",
       "      <td>Overweight Range</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Name   Country         City  Weight  Height   BMI Birth Year  \\\n",
       "5    Mahlet  Ethiopia  Addis Ababa    58.0    1.68  20.5       1995   \n",
       "6    Thomas  Ethiopia    Bahir Dar    52.0    1.70  18.0       1999   \n",
       "0  Asabeneh   Finland     Helsinki    74.0    1.73  24.7       1990   \n",
       "3      Eyob   Finland        Espoo   108.0    1.73  36.1       1983   \n",
       "7    Lidiya   Finland          NaN     NaN     NaN   NaN        NaN   \n",
       "4     Pawel    Poland       Warsaw   102.0    1.95  26.8       1985   \n",
       "2      John    Sweden    Stockholm    68.0    1.69  23.8       1990   \n",
       "1     David        UK       London    78.0    1.75  25.5       1985   \n",
       "\n",
       "   Current Year          BMI class  \n",
       "5          2021      Healthy Range  \n",
       "6          2021  Underweight Range  \n",
       "0          2021      Healthy Range  \n",
       "3          2021      Obesity Range  \n",
       "7          2021               None  \n",
       "4          2021   Overweight Range  \n",
       "2          2021      Healthy Range  \n",
       "1          2021   Overweight Range  "
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values('Country')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>Country</th>\n",
       "      <th>City</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Height</th>\n",
       "      <th>BMI</th>\n",
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       "      <th>Current Year</th>\n",
       "      <th>BMI class</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>David</td>\n",
       "      <td>UK</td>\n",
       "      <td>London</td>\n",
       "      <td>78.0</td>\n",
       "      <td>1.75</td>\n",
       "      <td>25.5</td>\n",
       "      <td>1985</td>\n",
       "      <td>2021</td>\n",
       "      <td>Overweight Range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>John</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>Stockholm</td>\n",
       "      <td>68.0</td>\n",
       "      <td>1.69</td>\n",
       "      <td>23.8</td>\n",
       "      <td>1990</td>\n",
       "      <td>2021</td>\n",
       "      <td>Healthy Range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Pawel</td>\n",
       "      <td>Poland</td>\n",
       "      <td>Warsaw</td>\n",
       "      <td>102.0</td>\n",
       "      <td>1.95</td>\n",
       "      <td>26.8</td>\n",
       "      <td>1985</td>\n",
       "      <td>2021</td>\n",
       "      <td>Overweight Range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Asabeneh</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>74.0</td>\n",
       "      <td>1.73</td>\n",
       "      <td>24.7</td>\n",
       "      <td>1990</td>\n",
       "      <td>2021</td>\n",
       "      <td>Healthy Range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Eyob</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Espoo</td>\n",
       "      <td>108.0</td>\n",
       "      <td>1.73</td>\n",
       "      <td>36.1</td>\n",
       "      <td>1983</td>\n",
       "      <td>2021</td>\n",
       "      <td>Obesity Range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Lidiya</td>\n",
       "      <td>Finland</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Mahlet</td>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>Addis Ababa</td>\n",
       "      <td>58.0</td>\n",
       "      <td>1.68</td>\n",
       "      <td>20.5</td>\n",
       "      <td>1995</td>\n",
       "      <td>2021</td>\n",
       "      <td>Healthy Range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Thomas</td>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>Bahir Dar</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1.70</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1999</td>\n",
       "      <td>2021</td>\n",
       "      <td>Underweight Range</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Name   Country         City  Weight  Height   BMI Birth Year  \\\n",
       "1     David        UK       London    78.0    1.75  25.5       1985   \n",
       "2      John    Sweden    Stockholm    68.0    1.69  23.8       1990   \n",
       "4     Pawel    Poland       Warsaw   102.0    1.95  26.8       1985   \n",
       "0  Asabeneh   Finland     Helsinki    74.0    1.73  24.7       1990   \n",
       "3      Eyob   Finland        Espoo   108.0    1.73  36.1       1983   \n",
       "7    Lidiya   Finland          NaN     NaN     NaN   NaN        NaN   \n",
       "5    Mahlet  Ethiopia  Addis Ababa    58.0    1.68  20.5       1995   \n",
       "6    Thomas  Ethiopia    Bahir Dar    52.0    1.70  18.0       1999   \n",
       "\n",
       "   Current Year          BMI class  \n",
       "1          2021   Overweight Range  \n",
       "2          2021      Healthy Range  \n",
       "4          2021   Overweight Range  \n",
       "0          2021      Healthy Range  \n",
       "3          2021      Obesity Range  \n",
       "7          2021               None  \n",
       "5          2021      Healthy Range  \n",
       "6          2021  Underweight Range  "
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values('Country' , ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Country</th>\n",
       "      <th>City</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Height</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Birth Year</th>\n",
       "      <th>Current Year</th>\n",
       "      <th>BMI class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Lidiya</td>\n",
       "      <td>Finland</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Pawel</td>\n",
       "      <td>Poland</td>\n",
       "      <td>Warsaw</td>\n",
       "      <td>102.0</td>\n",
       "      <td>1.95</td>\n",
       "      <td>26.8</td>\n",
       "      <td>1985</td>\n",
       "      <td>2021</td>\n",
       "      <td>Overweight Range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>John</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>Stockholm</td>\n",
       "      <td>68.0</td>\n",
       "      <td>1.69</td>\n",
       "      <td>23.8</td>\n",
       "      <td>1990</td>\n",
       "      <td>2021</td>\n",
       "      <td>Healthy Range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>David</td>\n",
       "      <td>UK</td>\n",
       "      <td>London</td>\n",
       "      <td>78.0</td>\n",
       "      <td>1.75</td>\n",
       "      <td>25.5</td>\n",
       "      <td>1985</td>\n",
       "      <td>2021</td>\n",
       "      <td>Overweight Range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Asabeneh</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>74.0</td>\n",
       "      <td>1.73</td>\n",
       "      <td>24.7</td>\n",
       "      <td>1990</td>\n",
       "      <td>2021</td>\n",
       "      <td>Healthy Range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Eyob</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Espoo</td>\n",
       "      <td>108.0</td>\n",
       "      <td>1.73</td>\n",
       "      <td>36.1</td>\n",
       "      <td>1983</td>\n",
       "      <td>2021</td>\n",
       "      <td>Obesity Range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Thomas</td>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>Bahir Dar</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1.70</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1999</td>\n",
       "      <td>2021</td>\n",
       "      <td>Underweight Range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Mahlet</td>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>Addis Ababa</td>\n",
       "      <td>58.0</td>\n",
       "      <td>1.68</td>\n",
       "      <td>20.5</td>\n",
       "      <td>1995</td>\n",
       "      <td>2021</td>\n",
       "      <td>Healthy Range</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Name   Country         City  Weight  Height   BMI Birth Year  \\\n",
       "7    Lidiya   Finland          NaN     NaN     NaN   NaN        NaN   \n",
       "4     Pawel    Poland       Warsaw   102.0    1.95  26.8       1985   \n",
       "2      John    Sweden    Stockholm    68.0    1.69  23.8       1990   \n",
       "1     David        UK       London    78.0    1.75  25.5       1985   \n",
       "0  Asabeneh   Finland     Helsinki    74.0    1.73  24.7       1990   \n",
       "3      Eyob   Finland        Espoo   108.0    1.73  36.1       1983   \n",
       "6    Thomas  Ethiopia    Bahir Dar    52.0    1.70  18.0       1999   \n",
       "5    Mahlet  Ethiopia  Addis Ababa    58.0    1.68  20.5       1995   \n",
       "\n",
       "   Current Year          BMI class  \n",
       "7          2021               None  \n",
       "4          2021   Overweight Range  \n",
       "2          2021      Healthy Range  \n",
       "1          2021   Overweight Range  \n",
       "0          2021      Healthy Range  \n",
       "3          2021      Obesity Range  \n",
       "6          2021  Underweight Range  \n",
       "5          2021      Healthy Range  "
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values('City' , ascending=False, na_position='first')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Country</th>\n",
       "      <th>City</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Height</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Birth Year</th>\n",
       "      <th>Current Year</th>\n",
       "      <th>BMI class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Asabeneh</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>74.0</td>\n",
       "      <td>1.73</td>\n",
       "      <td>24.7</td>\n",
       "      <td>1990</td>\n",
       "      <td>2021</td>\n",
       "      <td>Healthy Range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>John</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>Stockholm</td>\n",
       "      <td>68.0</td>\n",
       "      <td>1.69</td>\n",
       "      <td>23.8</td>\n",
       "      <td>1990</td>\n",
       "      <td>2021</td>\n",
       "      <td>Healthy Range</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Mahlet</td>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>Addis Ababa</td>\n",
       "      <td>58.0</td>\n",
       "      <td>1.68</td>\n",
       "      <td>20.5</td>\n",
       "      <td>1995</td>\n",
       "      <td>2021</td>\n",
       "      <td>Healthy Range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Eyob</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Espoo</td>\n",
       "      <td>108.0</td>\n",
       "      <td>1.73</td>\n",
       "      <td>36.1</td>\n",
       "      <td>1983</td>\n",
       "      <td>2021</td>\n",
       "      <td>Obesity Range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>David</td>\n",
       "      <td>UK</td>\n",
       "      <td>London</td>\n",
       "      <td>78.0</td>\n",
       "      <td>1.75</td>\n",
       "      <td>25.5</td>\n",
       "      <td>1985</td>\n",
       "      <td>2021</td>\n",
       "      <td>Overweight Range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Pawel</td>\n",
       "      <td>Poland</td>\n",
       "      <td>Warsaw</td>\n",
       "      <td>102.0</td>\n",
       "      <td>1.95</td>\n",
       "      <td>26.8</td>\n",
       "      <td>1985</td>\n",
       "      <td>2021</td>\n",
       "      <td>Overweight Range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Thomas</td>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>Bahir Dar</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1.70</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1999</td>\n",
       "      <td>2021</td>\n",
       "      <td>Underweight Range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Lidiya</td>\n",
       "      <td>Finland</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Name   Country         City  Weight  Height   BMI Birth Year  \\\n",
       "0  Asabeneh   Finland     Helsinki    74.0    1.73  24.7       1990   \n",
       "2      John    Sweden    Stockholm    68.0    1.69  23.8       1990   \n",
       "5    Mahlet  Ethiopia  Addis Ababa    58.0    1.68  20.5       1995   \n",
       "3      Eyob   Finland        Espoo   108.0    1.73  36.1       1983   \n",
       "1     David        UK       London    78.0    1.75  25.5       1985   \n",
       "4     Pawel    Poland       Warsaw   102.0    1.95  26.8       1985   \n",
       "6    Thomas  Ethiopia    Bahir Dar    52.0    1.70  18.0       1999   \n",
       "7    Lidiya   Finland          NaN     NaN     NaN   NaN        NaN   \n",
       "\n",
       "   Current Year          BMI class  \n",
       "0          2021      Healthy Range  \n",
       "2          2021      Healthy Range  \n",
       "5          2021      Healthy Range  \n",
       "3          2021      Obesity Range  \n",
       "1          2021   Overweight Range  \n",
       "4          2021   Overweight Range  \n",
       "6          2021  Underweight Range  \n",
       "7          2021               None  "
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values('BMI class', key=lambda col: col.str.lower())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visulization by plotting data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "joined_df.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Weight</th>\n",
       "      <th>Height</th>\n",
       "      <th>BMI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>74.0</td>\n",
       "      <td>1.73</td>\n",
       "      <td>24.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>78.0</td>\n",
       "      <td>1.75</td>\n",
       "      <td>25.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>68.0</td>\n",
       "      <td>1.69</td>\n",
       "      <td>23.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>108.0</td>\n",
       "      <td>1.73</td>\n",
       "      <td>36.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>102.0</td>\n",
       "      <td>1.95</td>\n",
       "      <td>26.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>58.0</td>\n",
       "      <td>1.68</td>\n",
       "      <td>20.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>52.0</td>\n",
       "      <td>1.70</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Weight  Height   BMI\n",
       "0    74.0    1.73  24.7\n",
       "1    78.0    1.75  25.5\n",
       "2    68.0    1.69  23.8\n",
       "3   108.0    1.73  36.1\n",
       "4   102.0    1.95  26.8\n",
       "5    58.0    1.68  20.5\n",
       "6    52.0    1.70  18.0"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joined_df[['Weight','Height','BMI']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "joined_df[['Weight','Height','BMI']].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "joined_df[['Height','BMI']].plot(kind='scatter', x='Height', y = 'BMI', title = 'Height vs BMI')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "joined_df[['Weight','BMI']].plot(kind='scatter', x='Weight', y = 'BMI', title = 'Weight vs BMI')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "joined_df[['Weight']].plot(kind='bar')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "joined_df[['Weight']].plot(kind='hist')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    " # Let's read a datas set and modify the height and weight value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "df = pd.read_csv('./datasets/weight-height.csv')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Male</td>\n",
       "      <td>73.847017</td>\n",
       "      <td>241.893563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Male</td>\n",
       "      <td>68.781904</td>\n",
       "      <td>162.310473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Male</td>\n",
       "      <td>74.110105</td>\n",
       "      <td>212.740856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Male</td>\n",
       "      <td>71.730978</td>\n",
       "      <td>220.042470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Male</td>\n",
       "      <td>69.881796</td>\n",
       "      <td>206.349801</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Gender     Height      Weight\n",
       "0   Male  73.847017  241.893563\n",
       "1   Male  68.781904  162.310473\n",
       "2   Male  74.110105  212.740856\n",
       "3   Male  71.730978  220.042470\n",
       "4   Male  69.881796  206.349801"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9995</th>\n",
       "      <td>Female</td>\n",
       "      <td>66.172652</td>\n",
       "      <td>136.777454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9996</th>\n",
       "      <td>Female</td>\n",
       "      <td>67.067155</td>\n",
       "      <td>170.867906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9997</th>\n",
       "      <td>Female</td>\n",
       "      <td>63.867992</td>\n",
       "      <td>128.475319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9998</th>\n",
       "      <td>Female</td>\n",
       "      <td>69.034243</td>\n",
       "      <td>163.852461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999</th>\n",
       "      <td>Female</td>\n",
       "      <td>61.944246</td>\n",
       "      <td>113.649103</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Gender     Height      Weight\n",
       "9995  Female  66.172652  136.777454\n",
       "9996  Female  67.067155  170.867906\n",
       "9997  Female  63.867992  128.475319\n",
       "9998  Female  69.034243  163.852461\n",
       "9999  Female  61.944246  113.649103"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10000 entries, 0 to 9999\n",
      "Data columns (total 3 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   Gender  10000 non-null  object \n",
      " 1   Height  10000 non-null  float64\n",
      " 2   Weight  10000 non-null  float64\n",
      "dtypes: float64(2), object(1)\n",
      "memory usage: 234.5+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 3)"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Gender', 'Height', 'Weight'], dtype='object')"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "# changing inch to centimenter\n",
    "# pounds to killogram\n",
    "df['Height in Cm'] = df['Height'].apply(lambda x : x * 2.54 )\n",
    "df['Weight in Kg'] = df['Weight'].apply(lambda x : x * 0.453592 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['BMI']  = df['Weight in Kg'] / (df['Height in Cm'] * 0.01) ** 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Height in Cm</th>\n",
       "      <th>Weight in Kg</th>\n",
       "      <th>BMI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Male</td>\n",
       "      <td>73.847017</td>\n",
       "      <td>241.893563</td>\n",
       "      <td>187.571423</td>\n",
       "      <td>109.720985</td>\n",
       "      <td>31.185761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Male</td>\n",
       "      <td>68.781904</td>\n",
       "      <td>162.310473</td>\n",
       "      <td>174.706036</td>\n",
       "      <td>73.622732</td>\n",
       "      <td>24.121044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Male</td>\n",
       "      <td>74.110105</td>\n",
       "      <td>212.740856</td>\n",
       "      <td>188.239668</td>\n",
       "      <td>96.497550</td>\n",
       "      <td>27.232906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Male</td>\n",
       "      <td>71.730978</td>\n",
       "      <td>220.042470</td>\n",
       "      <td>182.196685</td>\n",
       "      <td>99.809504</td>\n",
       "      <td>30.067059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Male</td>\n",
       "      <td>69.881796</td>\n",
       "      <td>206.349801</td>\n",
       "      <td>177.499761</td>\n",
       "      <td>93.598619</td>\n",
       "      <td>29.708033</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Gender     Height      Weight  Height in Cm  Weight in Kg        BMI\n",
       "0   Male  73.847017  241.893563    187.571423    109.720985  31.185761\n",
       "1   Male  68.781904  162.310473    174.706036     73.622732  24.121044\n",
       "2   Male  74.110105  212.740856    188.239668     96.497550  27.232906\n",
       "3   Male  71.730978  220.042470    182.196685     99.809504  30.067059\n",
       "4   Male  69.881796  206.349801    177.499761     93.598619  29.708033"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Height in Cm</th>\n",
       "      <th>Weight in Kg</th>\n",
       "      <th>BMI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>66.367560</td>\n",
       "      <td>161.440357</td>\n",
       "      <td>168.573602</td>\n",
       "      <td>73.228054</td>\n",
       "      <td>25.475457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.847528</td>\n",
       "      <td>32.108439</td>\n",
       "      <td>9.772721</td>\n",
       "      <td>14.564131</td>\n",
       "      <td>2.693810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>54.263133</td>\n",
       "      <td>64.700127</td>\n",
       "      <td>137.828359</td>\n",
       "      <td>29.347460</td>\n",
       "      <td>15.448750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>63.505620</td>\n",
       "      <td>135.818051</td>\n",
       "      <td>161.304276</td>\n",
       "      <td>61.605982</td>\n",
       "      <td>23.464807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>66.318070</td>\n",
       "      <td>161.212928</td>\n",
       "      <td>168.447898</td>\n",
       "      <td>73.124894</td>\n",
       "      <td>25.733316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>69.174262</td>\n",
       "      <td>187.169525</td>\n",
       "      <td>175.702625</td>\n",
       "      <td>84.898599</td>\n",
       "      <td>27.558535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>78.998742</td>\n",
       "      <td>269.989699</td>\n",
       "      <td>200.656806</td>\n",
       "      <td>122.465167</td>\n",
       "      <td>33.028103</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Height        Weight  Height in Cm  Weight in Kg           BMI\n",
       "count  10000.000000  10000.000000  10000.000000  10000.000000  10000.000000\n",
       "mean      66.367560    161.440357    168.573602     73.228054     25.475457\n",
       "std        3.847528     32.108439      9.772721     14.564131      2.693810\n",
       "min       54.263133     64.700127    137.828359     29.347460     15.448750\n",
       "25%       63.505620    135.818051    161.304276     61.605982     23.464807\n",
       "50%       66.318070    161.212928    168.447898     73.124894     25.733316\n",
       "75%       69.174262    187.169525    175.702625     84.898599     27.558535\n",
       "max       78.998742    269.989699    200.656806    122.465167     33.028103"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Height in Cm</th>\n",
       "      <th>Weight in Kg</th>\n",
       "      <th>BMI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Height</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.924756</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.924756</td>\n",
       "      <td>0.669199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Weight</th>\n",
       "      <td>0.924756</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.924756</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.896438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Height in Cm</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.924756</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.924756</td>\n",
       "      <td>0.669199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Weight in Kg</th>\n",
       "      <td>0.924756</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.924756</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.896438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BMI</th>\n",
       "      <td>0.669199</td>\n",
       "      <td>0.896438</td>\n",
       "      <td>0.669199</td>\n",
       "      <td>0.896438</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Height    Weight  Height in Cm  Weight in Kg       BMI\n",
       "Height        1.000000  0.924756      1.000000      0.924756  0.669199\n",
       "Weight        0.924756  1.000000      0.924756      1.000000  0.896438\n",
       "Height in Cm  1.000000  0.924756      1.000000      0.924756  0.669199\n",
       "Weight in Kg  0.924756  1.000000      0.924756      1.000000  0.896438\n",
       "BMI           0.669199  0.896438      0.669199      0.896438  1.000000"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = df['Height in Cm']\n",
    "y = df['Weight in Kg']\n",
    "\n",
    "\n",
    "m, b = np.polyfit(x, y, 1)\n",
    "\n",
    "df[['Height in Cm', 'Weight in Kg']].plot(kind='scatter', x = 'Height in Cm', y='Weight in Kg', title='Height Versus Weight')\n",
    "plt.plot(x, m*x + b, c='r', linewidth=5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[['Height in Cm']].plot(kind='hist', grid=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Frequency'>"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[['Weight in Kg']].plot(kind='hist', grid=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[['Height in Cm']].hist(grid=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cleaning Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercises\n",
    "\n",
    "1. Read the hacker_news.csv file from data directory \n",
    "1. Get the first five rows\n",
    "1. Get the last five rows\n",
    "1. Get the title column as pandas series\n",
    "1. Count the number of rows and columns\n",
    "    - Filter the titles which contain python\n",
    "    - Filter the titles which contain JavaScript\n",
    "    - Explore the data and make sense of it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}