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@@ -0,0 +1,1110 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 📘 Day 25\n",
+    "## Pandas\n",
+    "\n",
+    "Pandas is an open source,high-performance, easy-to-use data structures and data analysis tools for the Python programming language.\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: reshaping, merging, sorting, slicing, aggregation and imputation.\n",
+    "```py\n",
+    "pip install conda\n",
+    "conda install pandas\n",
+    "```\n",
+    "Pandas data structure is based on *Series* and *DataFrames*\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.\n",
+    "An example of a series, names\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 data. If we want to have multiple columns we use data frames. The example below shows pandas DataFrames.\n",
+    "\n",
+    "Let's see, an example of a pandas data frame:\n",
+    "\n",
+    "![Pandas data frame](images/pandas-dataframe-1.png)\n",
+    "\n",
+    "Data from is a collection of rows and columns. Look at the table below it has many columns than the above\n",
+    "\n",
+    "\n",
+    "![Pandas data frame](images/pandas-dataframe-2.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Importing pandas"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy  as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Creating Pandas Series with default index"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nums = [1, 2, 3,4,5]\n",
+    "s = pd.Series(nums)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    1\n",
+       "1    2\n",
+       "2    3\n",
+       "3    4\n",
+       "4    5\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Creating  Pandas Series with custom index"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fruits = ['Orange','Banana','Mangao']\n",
+    "fruits = pd.Series(fruits, index=[1, 2,3])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1    Orange\n",
+       "2    Banana\n",
+       "3    Mangao\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fruits"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Creating Pandas Series from a dictionary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dct = {'name':'Asabeneh','country':'Finland','city':'Helsinki'}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "s = pd.Series(dct)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "name       Asabeneh\n",
+       "country     Finland\n",
+       "city       Helsinki\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Creating a constant pandas series"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "s = pd.Series(10, index = [1, 2,3])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1    10\n",
+       "2    10\n",
+       "3    10\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Creating a  pandas series using linspace"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "s = pd.Series(np.linspace(5, 20, 10))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "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"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## DataFrames\n",
+    "\n",
+    "Pandas data frames can be created in different ways.\n",
+    "\n",
+    "### Creating DataFrames from list of lists"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = [[\"Asabeneh\", \"Finland\", \"Helsink\"], [\n",
+    "    \"David\", \"UK\", \"London\"], [\"John\", \"Sweden\", \"Stockholm\"]]\n",
+    "df = pd.DataFrame(data, columns=['Names','Country','City'])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "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>Names</th>\n",
+       "      <th>Country</th>\n",
+       "      <th>City</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>0</td>\n",
+       "      <td>Asabeneh</td>\n",
+       "      <td>Finland</td>\n",
+       "      <td>Helsink</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>1</td>\n",
+       "      <td>David</td>\n",
+       "      <td>UK</td>\n",
+       "      <td>London</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2</td>\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": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Creating DataFrame using Dictionary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = {\"Name\": [\"Asabeneh\", \"David\", \"John\"], \"Country\":[\n",
+    "    \"Finland\", \"UK\", \"Sweden\"], \"City\": [\"Helsiki\", \"London\", \"Stockholm\"]}\n",
+    "df = pd.DataFrame(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "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",
+       "      <td>0</td>\n",
+       "      <td>Asabeneh</td>\n",
+       "      <td>Finland</td>\n",
+       "      <td>Helsiki</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>1</td>\n",
+       "      <td>David</td>\n",
+       "      <td>UK</td>\n",
+       "      <td>London</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2</td>\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": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Creating DataFrams from list of dictionaries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "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",
+    "df = pd.DataFrame(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "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",
+       "      <td>0</td>\n",
+       "      <td>Asabeneh</td>\n",
+       "      <td>Finland</td>\n",
+       "      <td>Helsinki</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>1</td>\n",
+       "      <td>David</td>\n",
+       "      <td>UK</td>\n",
+       "      <td>London</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2</td>\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": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Reading CSV File using pandas"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "df = pd.read_csv('./data/weight-height.csv')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Data Exploration\n",
+    "Let's read only the first 5 rows using head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "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",
+       "      <td>0</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>73.847017</td>\n",
+       "      <td>241.893563</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>1</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>68.781904</td>\n",
+       "      <td>162.310473</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>74.110105</td>\n",
+       "      <td>212.740856</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>3</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>71.730978</td>\n",
+       "      <td>220.042470</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>4</td>\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": 84,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head() # give five rows we can increase the number of rows by passing argument to the head() method"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As you can see the csv file has three rows:Gender, Height and Weight. But we don't know the number of rows. Let's use shape meathod."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(10000, 3)"
+      ]
+     },
+     "execution_count": 85,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.shape # as you can see 10000 rows and three columns"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's get all the columns using columns.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['Gender', 'Height', 'Weight'], dtype='object')"
+      ]
+     },
+     "execution_count": 86,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.columns"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's read only the last 5 rows using tail()"
+   ]
+  },
+  {
+   "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>Gender</th>\n",
+       "      <th>Height</th>\n",
+       "      <th>Weight</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>9995</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>66.172652</td>\n",
+       "      <td>136.777454</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>9996</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>67.067155</td>\n",
+       "      <td>170.867906</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>9997</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>63.867992</td>\n",
+       "      <td>128.475319</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>9998</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>69.034243</td>\n",
+       "      <td>163.852461</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>9999</td>\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": 87,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.tail() # tails give the last five rows, we can increase the rows by passing argument to tail method"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, lets get specif colums using the column key\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "heights = df['Height'] # this is now a a series"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0       73.847017\n",
+       "1       68.781904\n",
+       "2       74.110105\n",
+       "3       71.730978\n",
+       "4       69.881796\n",
+       "          ...    \n",
+       "9995    66.172652\n",
+       "9996    67.067155\n",
+       "9997    63.867992\n",
+       "9998    69.034243\n",
+       "9999    61.944246\n",
+       "Name: Height, Length: 10000, dtype: float64"
+      ]
+     },
+     "execution_count": 92,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "heights"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "weights = df['Weight'] # this is now a series"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0       241.893563\n",
+       "1       162.310473\n",
+       "2       212.740856\n",
+       "3       220.042470\n",
+       "4       206.349801\n",
+       "           ...    \n",
+       "9995    136.777454\n",
+       "9996    170.867906\n",
+       "9997    128.475319\n",
+       "9998    163.852461\n",
+       "9999    113.649103\n",
+       "Name: Weight, Length: 10000, dtype: float64"
+      ]
+     },
+     "execution_count": 103,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "weights"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 104,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(heights) == len(weights)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "count    10000.000000\n",
+       "mean        66.367560\n",
+       "std          3.847528\n",
+       "min         54.263133\n",
+       "25%         63.505620\n",
+       "50%         66.318070\n",
+       "75%         69.174262\n",
+       "max         78.998742\n",
+       "Name: Height, dtype: float64"
+      ]
+     },
+     "execution_count": 105,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "heights.describe() # give statisical information about height data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "count    10000.000000\n",
+       "mean       161.440357\n",
+       "std         32.108439\n",
+       "min         64.700127\n",
+       "25%        135.818051\n",
+       "50%        161.212928\n",
+       "75%        187.169525\n",
+       "max        269.989699\n",
+       "Name: Weight, dtype: float64"
+      ]
+     },
+     "execution_count": 106,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "weights.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "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",
+       "      <td>count</td>\n",
+       "      <td>10000.000000</td>\n",
+       "      <td>10000.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>mean</td>\n",
+       "      <td>66.367560</td>\n",
+       "      <td>161.440357</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>std</td>\n",
+       "      <td>3.847528</td>\n",
+       "      <td>32.108439</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>min</td>\n",
+       "      <td>54.263133</td>\n",
+       "      <td>64.700127</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>25%</td>\n",
+       "      <td>63.505620</td>\n",
+       "      <td>135.818051</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>50%</td>\n",
+       "      <td>66.318070</td>\n",
+       "      <td>161.212928</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>75%</td>\n",
+       "      <td>69.174262</td>\n",
+       "      <td>187.169525</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>max</td>\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": 109,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.describe()  # describe can also give statistical information from a datafrom"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercises: Day 25\n",
+    "1. Read the hacker_ness.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 the data"
+   ]
+  },
+  {
+   "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": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/readme.md b/readme.md
index 9eef73cd..f010ffac 100644
--- a/readme.md
+++ b/readme.md
@@ -7,8 +7,8 @@
 🧳 [Part 5: Day 13 - 15](https://github.com/Asabeneh/30-Days-Of-Python/blob/master/readme13-15.md)  
 🧳 [Part 6: Day 16 - 18](https://github.com/Asabeneh/30-Days-Of-Python/blob/master/readme16-18.md)  
 🧳 [Part 7: Day 19 - 21](https://github.com/Asabeneh/30-Days-Of-Python/blob/master/readme19-21.md)  
-🧳 [Part 8: Day 22 - 24](https://github.com/Asabeneh/30-Days-Of-Python/blob/master/readme22-24.md)   
-🧳 [Part 9: Day 25 - 27](#) 🔒  
+🧳 [Part 8: Day 22 - 24](https://github.com/Asabeneh/30-Days-Of-Python/blob/master/readme22-24.md)  
+🧳 [Part 9: Day 25 - 27](https://github.com/Asabeneh/30-Days-Of-Python/blob/master/readme25-27.md)  
 🧳 [Part 10: Day 28 - 30](#) 🔒
 
 ---
diff --git a/readme10-12.md b/readme10-12.md
index 584fb3d8..fa65eefb 100644
--- a/readme10-12.md
+++ b/readme10-12.md
@@ -7,11 +7,9 @@
 🧳 [Part 5: Day 13 - 15](https://github.com/Asabeneh/30-Days-Of-Python/blob/master/readme13-15.md)  
 🧳 [Part 6: Day 16 - 18](https://github.com/Asabeneh/30-Days-Of-Python/blob/master/readme16-18.md)  
 🧳 [Part 7: Day 19 - 21](https://github.com/Asabeneh/30-Days-Of-Python/blob/master/readme19-21.md)  
-🧳 [Part 8: Day 22 - 24](https://github.com/Asabeneh/30-Days-Of-Python/blob/master/readme22-24.md)   
-🧳 [Part 9: Day 25 - 27](#) 🔒  
+🧳 [Part 8: Day 22 - 24](https://github.com/Asabeneh/30-Days-Of-Python/blob/master/readme22-24.md)  
+🧳 [Part 9: Day 25 - 27](https://github.com/Asabeneh/30-Days-Of-Python/blob/master/readme25-27.md)  
 🧳 [Part 10: Day 28 - 30](#) 🔒
-
----
 ---
 
 - [📘 Day 10](#%f0%9f%93%98-day-10)
diff --git a/readme13-15.md b/readme13-15.md
index 06b738af..da63381b 100644
--- a/readme13-15.md
+++ b/readme13-15.md
@@ -7,8 +7,8 @@
 🧳 [Part 5: Day 13 - 15](https://github.com/Asabeneh/30-Days-Of-Python/blob/master/readme13-15.md)  
 🧳 [Part 6: Day 16 - 18](https://github.com/Asabeneh/30-Days-Of-Python/blob/master/readme16-18.md)  
 🧳 [Part 7: Day 19 - 21](https://github.com/Asabeneh/30-Days-Of-Python/blob/master/readme19-21.md)  
-🧳 [Part 8: Day 22 - 24](https://github.com/Asabeneh/30-Days-Of-Python/blob/master/readme22-24.md)   
-🧳 [Part 9: Day 25 - 27](#) 🔒  
+🧳 [Part 8: Day 22 - 24](https://github.com/Asabeneh/30-Days-Of-Python/blob/master/readme22-24.md)  
+🧳 [Part 9: Day 25 - 27](https://github.com/Asabeneh/30-Days-Of-Python/blob/master/readme25-27.md)  
 🧳 [Part 10: Day 28 - 30](#) 🔒
 
 ---
diff --git a/readme16-18.md b/readme16-18.md
index be2e9cfe..a4f0ad19 100644
--- a/readme16-18.md
+++ b/readme16-18.md
@@ -7,8 +7,8 @@
 🧳 [Part 5: Day 13 - 15](https://github.com/Asabeneh/30-Days-Of-Python/blob/master/readme13-15.md)  
 🧳 [Part 6: Day 16 - 18](https://github.com/Asabeneh/30-Days-Of-Python/blob/master/readme16-18.md)  
 🧳 [Part 7: Day 19 - 21](https://github.com/Asabeneh/30-Days-Of-Python/blob/master/readme19-21.md)  
-🧳 [Part 8: Day 22 - 24](https://github.com/Asabeneh/30-Days-Of-Python/blob/master/readme22-24.md)   
-🧳 [Part 9: Day 25 - 27](#) 🔒  
+🧳 [Part 8: Day 22 - 24](https://github.com/Asabeneh/30-Days-Of-Python/blob/master/readme22-24.md)  
+🧳 [Part 9: Day 25 - 27](https://github.com/Asabeneh/30-Days-Of-Python/blob/master/readme25-27.md)  
 🧳 [Part 10: Day 28 - 30](#) 🔒
 
 ---
diff --git a/readme19-21.md b/readme19-21.md
index dd9669a7..505d6266 100644
--- a/readme19-21.md
+++ b/readme19-21.md
@@ -8,7 +8,7 @@
 🧳 [Part 6: Day 16 - 18](https://github.com/Asabeneh/30-Days-Of-Python/blob/master/readme16-18.md)  
 🧳 [Part 7: Day 19 - 21](https://github.com/Asabeneh/30-Days-Of-Python/blob/master/readme19-21.md)  
 🧳 [Part 8: Day 22 - 24](https://github.com/Asabeneh/30-Days-Of-Python/blob/master/readme22-24.md)  
-🧳 [Part 9: Day 25 - 27](#) 🔒  
+🧳 [Part 9: Day 25 - 27](https://github.com/Asabeneh/30-Days-Of-Python/blob/master/readme25-27.md)  
 🧳 [Part 10: Day 28 - 30](#) 🔒
 
 ---
diff --git a/readme22-24.md b/readme22-24.md
index 361f19ee..92e140f4 100644
--- a/readme22-24.md
+++ b/readme22-24.md
@@ -8,7 +8,7 @@
 🧳 [Part 6: Day 16 - 18](https://github.com/Asabeneh/30-Days-Of-Python/blob/master/readme16-18.md)  
 🧳 [Part 7: Day 19 - 21](https://github.com/Asabeneh/30-Days-Of-Python/blob/master/readme19-21.md)  
 🧳 [Part 8: Day 22 - 24](https://github.com/Asabeneh/30-Days-Of-Python/blob/master/readme22-24.md)  
-🧳 [Part 9: Day 25 - 27](#) 🔒  
+🧳 [Part 9: Day 25 - 27](https://github.com/Asabeneh/30-Days-Of-Python/blob/master/readme25-27.md)  
 🧳 [Part 10: Day 28 - 30](#) 🔒
 
 ---
diff --git a/readme25-27.md b/readme25-27.md
new file mode 100644
index 00000000..3ec4282d
--- /dev/null
+++ b/readme25-27.md
@@ -0,0 +1,784 @@
+![30DaysOfPython](./images/30DaysOfPython_banner3@2x.png)
+
+🧳 [Part 1: Day 1 - 3](https://github.com/Asabeneh/30-Days-Of-Python)  
+🧳 [Part 2: Day 4 - 6](https://github.com/Asabeneh/30-Days-Of-Python/blob/master/readme4-6.md)  
+🧳 [Part 3: Day 7 - 9](https://github.com/Asabeneh/30-Days-Of-Python/blob/master/readme7-9.md)  
+🧳 [Part 4: Day 10 - 12](https://github.com/Asabeneh/30-Days-Of-Python/blob/master/readme10-12.md)  
+🧳 [Part 5: Day 13 - 15](https://github.com/Asabeneh/30-Days-Of-Python/blob/master/readme13-15.md)  
+🧳 [Part 6: Day 16 - 18](https://github.com/Asabeneh/30-Days-Of-Python/blob/master/readme16-18.md)  
+🧳 [Part 7: Day 19 - 21](https://github.com/Asabeneh/30-Days-Of-Python/blob/master/readme19-21.md)  
+🧳 [Part 8: Day 22 - 24](https://github.com/Asabeneh/30-Days-Of-Python/blob/master/readme22-24.md)  
+🧳 [Part 9: Day 25 - 27](https://github.com/Asabeneh/30-Days-Of-Python/blob/master/readme25-27.md)  
+🧳 [Part 10: Day 28 - 30](#) 🔒
+
+- [📘 Day 25](#%f0%9f%93%98-day-25)
+  - [Pandas](#pandas)
+  - [Importing pandas](#importing-pandas)
+    - [Creating Pandas Series with default index](#creating-pandas-series-with-default-index)
+    - [Creating Pandas Series with custom index](#creating-pandas-series-with-custom-index)
+    - [Creating Pandas Series from a dictionary](#creating-pandas-series-from-a-dictionary)
+    - [Creating a constant pandas series](#creating-a-constant-pandas-series)
+    - [Creating a pandas series using linspace](#creating-a-pandas-series-using-linspace)
+  - [DataFrames](#dataframes)
+    - [Creating DataFrames from list of lists](#creating-dataframes-from-list-of-lists)
+    - [Creating DataFrame using Dictionary](#creating-dataframe-using-dictionary)
+    - [Creating DataFrams from list of dictionaries](#creating-dataframs-from-list-of-dictionaries)
+  - [Reading CSV File using pandas](#reading-csv-file-using-pandas)
+    - [Data Exploration](#data-exploration)
+  - [Exercises: Day 25](#exercises-day-25)
+# 📘 Day 25
+## Pandas
+
+Pandas is an open source,high-performance, easy-to-use data structures and data analysis tools for the Python programming language.
+Pandas adds data structures and tools designed to work with table-like data which is Series and Data Frames
+Pandas provides tools for data manipulation: reshaping, merging, sorting, slicing, aggregation and imputation.
+```py
+pip install conda
+conda install pandas
+```
+Pandas data structure is based on *Series* and *DataFrames*
+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.
+An example of a series, names
+
+![pandas series](images/pandas-series-1.png) 
+
+Countries Series
+
+![pandas series](images/pandas-series-2.png) 
+
+Cities Series
+
+![pandas series](images/pandas-series-3.png)
+
+As you can see, pandas series is just one column data. If we want to have multiple columns we use data frames. The example below shows pandas DataFrames.
+
+Let's see, an example of a pandas data frame:
+
+![Pandas data frame](images/pandas-dataframe-1.png)
+
+Data from is a collection of rows and columns. Look at the table below it has many columns than the above
+
+
+![Pandas data frame](images/pandas-dataframe-2.png)
+
+## Importing pandas
+
+
+```python
+import pandas as pd
+import numpy  as np
+```
+
+### Creating Pandas Series with default index
+
+
+```python
+nums = [1, 2, 3,4,5]
+s = pd.Series(nums)
+```
+
+
+```python
+s
+```
+
+
+
+
+    0    1
+    1    2
+    2    3
+    3    4
+    4    5
+    dtype: int64
+
+
+
+### Creating  Pandas Series with custom index
+
+
+```python
+fruits = ['Orange','Banana','Mangao']
+fruits = pd.Series(fruits, index=[1, 2,3])
+```
+
+
+```python
+fruits
+```
+
+
+
+
+    1    Orange
+    2    Banana
+    3    Mangao
+    dtype: object
+
+
+
+### Creating Pandas Series from a dictionary
+
+
+```python
+dct = {'name':'Asabeneh','country':'Finland','city':'Helsinki'}
+```
+
+
+```python
+s = pd.Series(dct)
+```
+
+
+```python
+s
+```
+
+
+
+
+    name       Asabeneh
+    country     Finland
+    city       Helsinki
+    dtype: object
+
+
+
+### Creating a constant pandas series
+
+
+```python
+s = pd.Series(10, index = [1, 2,3])
+```
+
+
+```python
+s
+```
+
+
+
+
+    1    10
+    2    10
+    3    10
+    dtype: int64
+
+
+
+### Creating a  pandas series using linspace
+
+
+```python
+s = pd.Series(np.linspace(5, 20, 10))
+```
+
+
+```python
+s
+```
+
+
+
+
+    0     5.000000
+    1     6.666667
+    2     8.333333
+    3    10.000000
+    4    11.666667
+    5    13.333333
+    6    15.000000
+    7    16.666667
+    8    18.333333
+    9    20.000000
+    dtype: float64
+
+
+
+## DataFrames
+
+Pandas data frames can be created in different ways.
+
+### Creating DataFrames from list of lists
+
+
+```python
+data = [["Asabeneh", "Finland", "Helsink"], [
+    "David", "UK", "London"], ["John", "Sweden", "Stockholm"]]
+df = pd.DataFrame(data, columns=['Names','Country','City'])
+
+```
+
+
+```python
+df
+```
+
+
+
+
+<div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>Names</th>
+      <th>Country</th>
+      <th>City</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td>0</td>
+      <td>Asabeneh</td>
+      <td>Finland</td>
+      <td>Helsink</td>
+    </tr>
+    <tr>
+      <td>1</td>
+      <td>David</td>
+      <td>UK</td>
+      <td>London</td>
+    </tr>
+    <tr>
+      <td>2</td>
+      <td>John</td>
+      <td>Sweden</td>
+      <td>Stockholm</td>
+    </tr>
+  </tbody>
+</table>
+</div>
+
+
+
+### Creating DataFrame using Dictionary
+
+
+```python
+data = {"Name": ["Asabeneh", "David", "John"], "Country":[
+    "Finland", "UK", "Sweden"], "City": ["Helsiki", "London", "Stockholm"]}
+df = pd.DataFrame(data)
+```
+
+
+```python
+df
+```
+
+
+
+
+<div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>Name</th>
+      <th>Country</th>
+      <th>City</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td>0</td>
+      <td>Asabeneh</td>
+      <td>Finland</td>
+      <td>Helsiki</td>
+    </tr>
+    <tr>
+      <td>1</td>
+      <td>David</td>
+      <td>UK</td>
+      <td>London</td>
+    </tr>
+    <tr>
+      <td>2</td>
+      <td>John</td>
+      <td>Sweden</td>
+      <td>Stockholm</td>
+    </tr>
+  </tbody>
+</table>
+</div>
+
+
+
+### Creating DataFrams from list of dictionaries
+
+
+```python
+data = [
+    {"Name": "Asabeneh", "Country":"Finland","City":"Helsinki"},
+    {"Name": "David", "Country":"UK","City":"London"},
+    {"Name": "John", "Country":"Sweden","City":"Stockholm"}]
+df = pd.DataFrame(data)
+```
+
+
+```python
+df
+```
+
+
+
+
+<div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>Name</th>
+      <th>Country</th>
+      <th>City</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td>0</td>
+      <td>Asabeneh</td>
+      <td>Finland</td>
+      <td>Helsinki</td>
+    </tr>
+    <tr>
+      <td>1</td>
+      <td>David</td>
+      <td>UK</td>
+      <td>London</td>
+    </tr>
+    <tr>
+      <td>2</td>
+      <td>John</td>
+      <td>Sweden</td>
+      <td>Stockholm</td>
+    </tr>
+  </tbody>
+</table>
+</div>
+
+
+
+## Reading CSV File using pandas
+
+
+```python
+import pandas as pd
+
+df = pd.read_csv('./data/weight-height.csv')
+```
+
+### Data Exploration
+Let's read only the first 5 rows using head()
+
+
+```python
+df.head() # give five rows we can increase the number of rows by passing argument to the head() method
+```
+
+
+
+
+<div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>Gender</th>
+      <th>Height</th>
+      <th>Weight</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td>0</td>
+      <td>Male</td>
+      <td>73.847017</td>
+      <td>241.893563</td>
+    </tr>
+    <tr>
+      <td>1</td>
+      <td>Male</td>
+      <td>68.781904</td>
+      <td>162.310473</td>
+    </tr>
+    <tr>
+      <td>2</td>
+      <td>Male</td>
+      <td>74.110105</td>
+      <td>212.740856</td>
+    </tr>
+    <tr>
+      <td>3</td>
+      <td>Male</td>
+      <td>71.730978</td>
+      <td>220.042470</td>
+    </tr>
+    <tr>
+      <td>4</td>
+      <td>Male</td>
+      <td>69.881796</td>
+      <td>206.349801</td>
+    </tr>
+  </tbody>
+</table>
+</div>
+
+
+
+As you can see the csv file has three rows:Gender, Height and Weight. But we don't know the number of rows. Let's use shape meathod.
+
+
+```python
+df.shape # as you can see 10000 rows and three columns
+```
+
+
+
+
+    (10000, 3)
+
+
+
+Let's get all the columns using columns.
+
+
+
+```python
+df.columns
+```
+
+
+
+
+    Index(['Gender', 'Height', 'Weight'], dtype='object')
+
+
+
+Let's read only the last 5 rows using tail()
+
+
+```python
+df.tail() # tails give the last five rows, we can increase the rows by passing argument to tail method
+```
+
+
+
+
+<div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>Gender</th>
+      <th>Height</th>
+      <th>Weight</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td>9995</td>
+      <td>Female</td>
+      <td>66.172652</td>
+      <td>136.777454</td>
+    </tr>
+    <tr>
+      <td>9996</td>
+      <td>Female</td>
+      <td>67.067155</td>
+      <td>170.867906</td>
+    </tr>
+    <tr>
+      <td>9997</td>
+      <td>Female</td>
+      <td>63.867992</td>
+      <td>128.475319</td>
+    </tr>
+    <tr>
+      <td>9998</td>
+      <td>Female</td>
+      <td>69.034243</td>
+      <td>163.852461</td>
+    </tr>
+    <tr>
+      <td>9999</td>
+      <td>Female</td>
+      <td>61.944246</td>
+      <td>113.649103</td>
+    </tr>
+  </tbody>
+</table>
+</div>
+
+
+
+Now, lets get specif colums using the column key
+
+
+
+```python
+heights = df['Height'] # this is now a a series
+```
+
+
+```python
+heights
+```
+
+
+
+
+    0       73.847017
+    1       68.781904
+    2       74.110105
+    3       71.730978
+    4       69.881796
+              ...    
+    9995    66.172652
+    9996    67.067155
+    9997    63.867992
+    9998    69.034243
+    9999    61.944246
+    Name: Height, Length: 10000, dtype: float64
+
+
+
+
+```python
+weights = df['Weight'] # this is now a series
+```
+
+
+```python
+weights
+```
+
+
+
+
+    0       241.893563
+    1       162.310473
+    2       212.740856
+    3       220.042470
+    4       206.349801
+               ...    
+    9995    136.777454
+    9996    170.867906
+    9997    128.475319
+    9998    163.852461
+    9999    113.649103
+    Name: Weight, Length: 10000, dtype: float64
+
+
+
+
+```python
+len(heights) == len(weights)
+```
+
+
+
+
+    True
+
+
+
+
+```python
+heights.describe() # give statisical information about height data
+```
+
+
+
+
+    count    10000.000000
+    mean        66.367560
+    std          3.847528
+    min         54.263133
+    25%         63.505620
+    50%         66.318070
+    75%         69.174262
+    max         78.998742
+    Name: Height, dtype: float64
+
+
+
+
+```python
+weights.describe()
+```
+
+
+
+
+    count    10000.000000
+    mean       161.440357
+    std         32.108439
+    min         64.700127
+    25%        135.818051
+    50%        161.212928
+    75%        187.169525
+    max        269.989699
+    Name: Weight, dtype: float64
+
+
+
+
+```python
+df.describe()  # describe can also give statistical information from a datafrom
+```
+
+
+
+
+<div>
+<style scoped>
+    .dataframe tbody tr th:only-of-type {
+        vertical-align: middle;
+    }
+
+    .dataframe tbody tr th {
+        vertical-align: top;
+    }
+
+    .dataframe thead th {
+        text-align: right;
+    }
+</style>
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>Height</th>
+      <th>Weight</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td>count</td>
+      <td>10000.000000</td>
+      <td>10000.000000</td>
+    </tr>
+    <tr>
+      <td>mean</td>
+      <td>66.367560</td>
+      <td>161.440357</td>
+    </tr>
+    <tr>
+      <td>std</td>
+      <td>3.847528</td>
+      <td>32.108439</td>
+    </tr>
+    <tr>
+      <td>min</td>
+      <td>54.263133</td>
+      <td>64.700127</td>
+    </tr>
+    <tr>
+      <td>25%</td>
+      <td>63.505620</td>
+      <td>135.818051</td>
+    </tr>
+    <tr>
+      <td>50%</td>
+      <td>66.318070</td>
+      <td>161.212928</td>
+    </tr>
+    <tr>
+      <td>75%</td>
+      <td>69.174262</td>
+      <td>187.169525</td>
+    </tr>
+    <tr>
+      <td>max</td>
+      <td>78.998742</td>
+      <td>269.989699</td>
+    </tr>
+  </tbody>
+</table>
+</div>
+
+
+
+## Exercises: Day 25
+1. Read the hacker_ness.csv file from data directory 
+1. Get the first five rows
+1. Get the last five rows
+1. Get the title column as pandas series
+1. Count the number of rows and columns
+    * Filter the titles which contain python
+    * Filter the titles which contain JavaScript
+    * Explore the data and make sense of the data
+
+
+
+
+[<< Part 8 ](https://github.com/Asabeneh/30-Days-Of-Python/blob/master/readme22-24.md) | [Part 10 >>](https://github.com/Asabeneh/30-Days-Of-Python/blob/master/readme28-30.md)
+
+---
+
+
diff --git a/readme4-6.md b/readme4-6.md
index a666a506..4a8154a9 100644
--- a/readme4-6.md
+++ b/readme4-6.md
@@ -8,7 +8,7 @@
 🧳 [Part 6: Day 16 - 18](https://github.com/Asabeneh/30-Days-Of-Python/blob/master/readme16-18.md)  
 🧳 [Part 7: Day 19 - 21](https://github.com/Asabeneh/30-Days-Of-Python/blob/master/readme19-21.md)  
 🧳 [Part 8: Day 22 - 24](https://github.com/Asabeneh/30-Days-Of-Python/blob/master/readme22-24.md)  
-🧳 [Part 9: Day 25 - 27](#) 🔒  
+🧳 [Part 9: Day 25 - 27](https://github.com/Asabeneh/30-Days-Of-Python/blob/master/readme25-27.md)  
 🧳 [Part 10: Day 28 - 30](#) 🔒
 
 ---
diff --git a/readme7-9.md b/readme7-9.md
index 252ca3ea..1a339659 100644
--- a/readme7-9.md
+++ b/readme7-9.md
@@ -8,7 +8,7 @@
 🧳 [Part 6: Day 16 - 18](https://github.com/Asabeneh/30-Days-Of-Python/blob/master/readme16-18.md)  
 🧳 [Part 7: Day 19 - 21](https://github.com/Asabeneh/30-Days-Of-Python/blob/master/readme19-21.md)  
 🧳 [Part 8: Day 22 - 24](https://github.com/Asabeneh/30-Days-Of-Python/blob/master/readme22-24.md)  
-🧳 [Part 9: Day 25 - 27](#) 🔒  
+🧳 [Part 9: Day 25 - 27](https://github.com/Asabeneh/30-Days-Of-Python/blob/master/readme25-27.md)  
 🧳 [Part 10: Day 28 - 30](#) 🔒
 
 ---