main/libraries/pandas/pandas.ipynb

{
 "cells": [
  {
   "attachments": {},
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   "metadata": {},
   "source": [
    "### Pandas / Package\n",
    "\n",
    "Pandas is the main package for working with `relational` or labeled data.  \n",
    "It is built on '`top'` of NumPy package. [<u>more details</u>](https://www.learndatasci.com/tutorials/python-pandas-tutorial-complete-introduction-for-beginners/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pip install pandas\n",
    "\n",
    "import pandas as pd"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DataFrame\n",
    "\n",
    "A DataFrame is a multi-dimensional `table` containing a collection of Series.  \n",
    "A Pandas Series is like a `column` in a table.  \n",
    "It is an one-dimensional array holding data of `any` type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>apples</th>\n",
       "      <th>oranges</th>\n",
       "      <th>available</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   apples  oranges available\n",
       "0       3        0       yes\n",
       "1       2        3        no\n",
       "2       0        7       yes\n",
       "3       1        2        no"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = {\n",
    "    'apples': [3, 2, 0, 1],\n",
    "    'oranges': [0, 3, 7, 2],\n",
    "    'available': ['yes', 'no', 'yes', 'no'],\n",
    "}\n",
    "\n",
    "df = pd.DataFrame(data)\n",
    "display(df)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read CSV\n",
    "\n",
    "Import a comma-separated values (csv) file into a `DataFrame`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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>PClass</th>\n",
       "      <th>Age</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Survived</th>\n",
       "      <th>SexCode</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Allen, Miss Elisabeth Walton</td>\n",
       "      <td>1st</td>\n",
       "      <td>29.00</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Allison, Miss Helen Loraine</td>\n",
       "      <td>1st</td>\n",
       "      <td>2.00</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Allison, Mr Hudson Joshua Creighton</td>\n",
       "      <td>1st</td>\n",
       "      <td>30.00</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Allison, Mrs Hudson JC (Bessie Waldo Daniels)</td>\n",
       "      <td>1st</td>\n",
       "      <td>25.00</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Allison, Master Hudson Trevor</td>\n",
       "      <td>1st</td>\n",
       "      <td>0.92</td>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            Name PClass    Age     Sex  \\\n",
       "0                   Allen, Miss Elisabeth Walton    1st  29.00  female   \n",
       "1                    Allison, Miss Helen Loraine    1st   2.00  female   \n",
       "2            Allison, Mr Hudson Joshua Creighton    1st  30.00    male   \n",
       "3  Allison, Mrs Hudson JC (Bessie Waldo Daniels)    1st  25.00  female   \n",
       "4                  Allison, Master Hudson Trevor    1st   0.92    male   \n",
       "\n",
       "   Survived  SexCode  \n",
       "0         1        1  \n",
       "1         0        1  \n",
       "2         0        0  \n",
       "3         0        1  \n",
       "4         1        0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape: (1313, 6)\n",
      "Columns: Index(['Name', 'PClass', 'Age', 'Sex', 'Survived', 'SexCode'], dtype='object')\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv(\"_data/titanic.csv\")\n",
    "display(df.head())\n",
    "\n",
    "print(\"Shape:\", df.shape)\n",
    "print(\"Columns:\", df.columns)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Location\n",
    "\n",
    "Both `iloc` and loc are very useful during search and data cleaning."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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>PClass</th>\n",
       "      <th>Age</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Survived</th>\n",
       "      <th>SexCode</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Allison, Miss Helen Loraine</td>\n",
       "      <td>1st</td>\n",
       "      <td>2.0</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Allison, Mr Hudson Joshua Creighton</td>\n",
       "      <td>1st</td>\n",
       "      <td>30.0</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Allison, Mrs Hudson JC (Bessie Waldo Daniels)</td>\n",
       "      <td>1st</td>\n",
       "      <td>25.0</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            Name PClass   Age     Sex  \\\n",
       "1                    Allison, Miss Helen Loraine    1st   2.0  female   \n",
       "2            Allison, Mr Hudson Joshua Creighton    1st  30.0    male   \n",
       "3  Allison, Mrs Hudson JC (Bessie Waldo Daniels)    1st  25.0  female   \n",
       "\n",
       "   Survived  SexCode  \n",
       "1         0        1  \n",
       "2         0        0  \n",
       "3         0        1  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Name        Allen, Miss Elisabeth Walton\n",
       "PClass                               1st\n",
       "Age                                 29.0\n",
       "Sex                               female\n",
       "Survived                               1\n",
       "SexCode                                1\n",
       "Name: Allen, Miss Elisabeth Walton, dtype: object"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(df.iloc[1:4]) # 2nd to 4th row\n",
    "\n",
    "# Select by index (name):\n",
    "df = df.set_index(df['Name'])\n",
    "display(df.loc['Allen, Miss Elisabeth Walton'])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conditional\n",
    "\n",
    "Conditional selecting and `filtering` data are common tasks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Females: 2772\n"
     ]
    },
    {
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       "      <th>Allen, Miss Elisabeth Walton</th>\n",
       "      <td>Allen, Miss Elisabeth Walton</td>\n",
       "      <td>1st</td>\n",
       "      <td>29.0</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
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       "      <th>Allison, Miss Helen Loraine</th>\n",
       "      <td>Allison, Miss Helen Loraine</td>\n",
       "      <td>1st</td>\n",
       "      <td>2.0</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Allison, Mrs Hudson JC (Bessie Waldo Daniels)</th>\n",
       "      <td>Allison, Mrs Hudson JC (Bessie Waldo Daniels)</td>\n",
       "      <td>1st</td>\n",
       "      <td>25.0</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andrews, Miss Kornelia Theodosia</th>\n",
       "      <td>Andrews, Miss Kornelia Theodosia</td>\n",
       "      <td>1st</td>\n",
       "      <td>63.0</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Appleton, Mrs Edward Dale (Charlotte Lamson)</th>\n",
       "      <td>Appleton, Mrs Edward Dale (Charlotte Lamson)</td>\n",
       "      <td>1st</td>\n",
       "      <td>58.0</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                        Name  \\\n",
       "Name                                                                                           \n",
       "Allen, Miss Elisabeth Walton                                    Allen, Miss Elisabeth Walton   \n",
       "Allison, Miss Helen Loraine                                      Allison, Miss Helen Loraine   \n",
       "Allison, Mrs Hudson JC (Bessie Waldo Daniels)  Allison, Mrs Hudson JC (Bessie Waldo Daniels)   \n",
       "Andrews, Miss Kornelia Theodosia                            Andrews, Miss Kornelia Theodosia   \n",
       "Appleton, Mrs Edward Dale (Charlotte Lamson)    Appleton, Mrs Edward Dale (Charlotte Lamson)   \n",
       "\n",
       "                                              PClass   Age     Sex  Survived  \\\n",
       "Name                                                                           \n",
       "Allen, Miss Elisabeth Walton                     1st  29.0  female         1   \n",
       "Allison, Miss Helen Loraine                      1st   2.0  female         0   \n",
       "Allison, Mrs Hudson JC (Bessie Waldo Daniels)    1st  25.0  female         0   \n",
       "Andrews, Miss Kornelia Theodosia                 1st  63.0  female         1   \n",
       "Appleton, Mrs Edward Dale (Charlotte Lamson)     1st  58.0  female         1   \n",
       "\n",
       "                                               SexCode  \n",
       "Name                                                    \n",
       "Allen, Miss Elisabeth Walton                         1  \n",
       "Allison, Miss Helen Loraine                          1  \n",
       "Allison, Mrs Hudson JC (Bessie Waldo Daniels)        1  \n",
       "Andrews, Miss Kornelia Theodosia                     1  \n",
       "Appleton, Mrs Edward Dale (Charlotte Lamson)         1  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Males age 60+: 108\n"
     ]
    },
    {
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       "      <th>Name</th>\n",
       "      <th>PClass</th>\n",
       "      <th>Age</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Survived</th>\n",
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       "  </thead>\n",
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       "    <tr>\n",
       "      <th>Artagaveytia, Mr Ramon</th>\n",
       "      <td>Artagaveytia, Mr Ramon</td>\n",
       "      <td>1st</td>\n",
       "      <td>71.0</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Crosby, Captain Edward Gifford</th>\n",
       "      <td>Crosby, Captain Edward Gifford</td>\n",
       "      <td>1st</td>\n",
       "      <td>70.0</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fortune, Mr Mark</th>\n",
       "      <td>Fortune, Mr Mark</td>\n",
       "      <td>1st</td>\n",
       "      <td>64.0</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Frolicher-Stehli, Mr Maxmillian</th>\n",
       "      <td>Frolicher-Stehli, Mr Maxmillian</td>\n",
       "      <td>1st</td>\n",
       "      <td>60.0</td>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Goldschmidt, Mr George B</th>\n",
       "      <td>Goldschmidt, Mr George B</td>\n",
       "      <td>1st</td>\n",
       "      <td>71.0</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                            Name PClass   Age  \\\n",
       "Name                                                                            \n",
       "Artagaveytia, Mr Ramon                    Artagaveytia, Mr Ramon    1st  71.0   \n",
       "Crosby, Captain Edward Gifford    Crosby, Captain Edward Gifford    1st  70.0   \n",
       "Fortune, Mr Mark                                Fortune, Mr Mark    1st  64.0   \n",
       "Frolicher-Stehli, Mr Maxmillian  Frolicher-Stehli, Mr Maxmillian    1st  60.0   \n",
       "Goldschmidt, Mr George B                Goldschmidt, Mr George B    1st  71.0   \n",
       "\n",
       "                                  Sex  Survived  SexCode  \n",
       "Name                                                      \n",
       "Artagaveytia, Mr Ramon           male         0        0  \n",
       "Crosby, Captain Edward Gifford   male         0        0  \n",
       "Fortune, Mr Mark                 male         0        0  \n",
       "Frolicher-Stehli, Mr Maxmillian  male         1        0  \n",
       "Goldschmidt, Mr George B         male         0        0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "females = df[df['Sex'] == 'female']\n",
    "males_60 = df[(df['Sex'] == 'male') & (df['Age'] >= 60)]\n",
    "\n",
    "print(\"Females:\", females.size)\n",
    "display(females.head())\n",
    "\n",
    "print(\"Males age 60+:\", males_60.size)\n",
    "display(males_60.head())"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Replace\n",
    "\n",
    "Replace accepts `regex` regular expressions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
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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>PClass</th>\n",
       "      <th>Age</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Survived</th>\n",
       "      <th>SexCode</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Allen, Miss Elisabeth Walton</th>\n",
       "      <td>Allen, Miss Elisabeth Walton</td>\n",
       "      <td>First</td>\n",
       "      <td>29.00</td>\n",
       "      <td>Woman</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Allison, Miss Helen Loraine</th>\n",
       "      <td>Allison, Miss Helen Loraine</td>\n",
       "      <td>First</td>\n",
       "      <td>2.00</td>\n",
       "      <td>Woman</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Allison, Mr Hudson Joshua Creighton</th>\n",
       "      <td>Allison, Mr Hudson Joshua Creighton</td>\n",
       "      <td>First</td>\n",
       "      <td>30.00</td>\n",
       "      <td>Man</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Allison, Mrs Hudson JC (Bessie Waldo Daniels)</th>\n",
       "      <td>Allison, Mrs Hudson JC (Bessie Waldo Daniels)</td>\n",
       "      <td>First</td>\n",
       "      <td>25.00</td>\n",
       "      <td>Woman</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Allison, Master Hudson Trevor</th>\n",
       "      <td>Allison, Master Hudson Trevor</td>\n",
       "      <td>First</td>\n",
       "      <td>0.92</td>\n",
       "      <td>Man</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                        Name  \\\n",
       "Name                                                                                           \n",
       "Allen, Miss Elisabeth Walton                                    Allen, Miss Elisabeth Walton   \n",
       "Allison, Miss Helen Loraine                                      Allison, Miss Helen Loraine   \n",
       "Allison, Mr Hudson Joshua Creighton                      Allison, Mr Hudson Joshua Creighton   \n",
       "Allison, Mrs Hudson JC (Bessie Waldo Daniels)  Allison, Mrs Hudson JC (Bessie Waldo Daniels)   \n",
       "Allison, Master Hudson Trevor                                  Allison, Master Hudson Trevor   \n",
       "\n",
       "                                              PClass    Age    Sex  Survived  \\\n",
       "Name                                                                           \n",
       "Allen, Miss Elisabeth Walton                   First  29.00  Woman         1   \n",
       "Allison, Miss Helen Loraine                    First   2.00  Woman         0   \n",
       "Allison, Mr Hudson Joshua Creighton            First  30.00    Man         0   \n",
       "Allison, Mrs Hudson JC (Bessie Waldo Daniels)  First  25.00  Woman         0   \n",
       "Allison, Master Hudson Trevor                  First   0.92    Man         1   \n",
       "\n",
       "                                               SexCode  \n",
       "Name                                                    \n",
       "Allen, Miss Elisabeth Walton                         1  \n",
       "Allison, Miss Helen Loraine                          1  \n",
       "Allison, Mr Hudson Joshua Creighton                  0  \n",
       "Allison, Mrs Hudson JC (Bessie Waldo Daniels)        1  \n",
       "Allison, Master Hudson Trevor                        0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Replace female/male\n",
    "df['Sex'] = df['Sex'].replace(['female', 'male'], ['Woman', 'Man'])\n",
    "df['PClass'] = df['PClass'].replace(r'1st', 'First', regex=True)\n",
    "display(df.head())"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Statistics\n",
    "\n",
    "Pandas has multiple `built-in methods` for descriptive statistics."
   ]
  },
  {
   "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>max</th>\n",
       "      <th>min</th>\n",
       "      <th>avg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>71.0</td>\n",
       "      <td>0.17</td>\n",
       "      <td>30.397989</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    max   min        avg\n",
       "0  71.0  0.17  30.397989"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>PClass</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3rd</th>\n",
       "      <td>711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>First</th>\n",
       "      <td>322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2nd</th>\n",
       "      <td>279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>*</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PClass\n",
       "3rd       711\n",
       "First     322\n",
       "2nd       279\n",
       "*           1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>unique_value</th>\n",
       "      <th>total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Woman</td>\n",
       "      <td>851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Man</td>\n",
       "      <td>462</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  unique_value  total\n",
       "0        Woman    851\n",
       "1          Man    462"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing values (Age): 3342\n"
     ]
    },
    {
     "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>PClass</th>\n",
       "      <th>Age</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Survived</th>\n",
       "      <th>SexCode</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Aubert, Mrs Leontine Pauline</th>\n",
       "      <td>Aubert, Mrs Leontine Pauline</td>\n",
       "      <td>First</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Woman</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Barkworth, Mr Algernon H</th>\n",
       "      <td>Barkworth, Mr Algernon H</td>\n",
       "      <td>First</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Man</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Baumann, Mr John D</th>\n",
       "      <td>Baumann, Mr John D</td>\n",
       "      <td>First</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Man</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Borebank, Mr John James</th>\n",
       "      <td>Borebank, Mr John James</td>\n",
       "      <td>First</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Man</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bradley, Mr George</th>\n",
       "      <td>Bradley, Mr George</td>\n",
       "      <td>First</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Man</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                      Name PClass  Age    Sex  \\\n",
       "Name                                                                            \n",
       "Aubert, Mrs Leontine Pauline  Aubert, Mrs Leontine Pauline  First  NaN  Woman   \n",
       "Barkworth, Mr Algernon H          Barkworth, Mr Algernon H  First  NaN    Man   \n",
       "Baumann, Mr John D                      Baumann, Mr John D  First  NaN    Man   \n",
       "Borebank, Mr John James            Borebank, Mr John James  First  NaN    Man   \n",
       "Bradley, Mr George                      Bradley, Mr George  First  NaN    Man   \n",
       "\n",
       "                              Survived  SexCode  \n",
       "Name                                             \n",
       "Aubert, Mrs Leontine Pauline         1        1  \n",
       "Barkworth, Mr Algernon H             1        0  \n",
       "Baumann, Mr John D                   0        0  \n",
       "Borebank, Mr John James              0        0  \n",
       "Bradley, Mr George                   1        0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Statistics (by Age)\n",
    "A = pd.DataFrame()\n",
    "A['max'] = [df['Age'].max()]\n",
    "A['min'] = [df['Age'].min()]\n",
    "A['avg'] = [df['Age'].mean()]\n",
    "display(A)\n",
    "\n",
    "# Value counts (by PClass)\n",
    "A = pd.DataFrame()\n",
    "A['PClass'] = df['PClass'].value_counts()\n",
    "display(A)\n",
    "\n",
    "# Unique values (by Sex)\n",
    "A = pd.DataFrame()\n",
    "A['unique_value'] = df['Sex'].unique()\n",
    "A['total'] = [df['Sex'].value_counts()[0], df['Sex'].value_counts()[1]]\n",
    "display(A)\n",
    "\n",
    "# Missing values (by Agge)\n",
    "A = pd.DataFrame()\n",
    "A = df[df['Age'].isnull()]\n",
    "\n",
    "print(\"Missing values (Age):\", A.size)\n",
    "display(A.head())\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GroupBy\n",
    "\n",
    "Groupby is one of the `most powerful` feature in pandas."
   ]
  },
  {
   "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",
       "    }\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>PClass</th>\n",
       "      <th>Age</th>\n",
       "      <th>Survived</th>\n",
       "      <th>SexCode</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Man</th>\n",
       "      <td>851</td>\n",
       "      <td>851</td>\n",
       "      <td>468</td>\n",
       "      <td>851</td>\n",
       "      <td>851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Woman</th>\n",
       "      <td>462</td>\n",
       "      <td>462</td>\n",
       "      <td>288</td>\n",
       "      <td>462</td>\n",
       "      <td>462</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Name  PClass  Age  Survived  SexCode\n",
       "Sex                                        \n",
       "Man     851     851  468       851      851\n",
       "Woman   462     462  288       462      462"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>Age</th>\n",
       "      <th>Survived</th>\n",
       "      <th>SexCode</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Man</th>\n",
       "      <td>31.014338</td>\n",
       "      <td>0.166863</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Woman</th>\n",
       "      <td>29.396424</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Age  Survived  SexCode\n",
       "Sex                                \n",
       "Man    31.014338  0.166863      0.0\n",
       "Woman  29.396424  0.666667      1.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Sex\n",
       "Man      851\n",
       "Woman    462\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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></th>\n",
       "      <th>Age</th>\n",
       "      <th>SexCode</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <th>Survived</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Man</th>\n",
       "      <th>0</th>\n",
       "      <td>32.320780</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25.951875</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Woman</th>\n",
       "      <th>0</th>\n",
       "      <td>24.901408</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>30.867143</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Age  SexCode\n",
       "Sex   Survived                    \n",
       "Man   0         32.320780      0.0\n",
       "      1         25.951875      0.0\n",
       "Woman 0         24.901408      1.0\n",
       "      1         30.867143      1.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "A = df.groupby('Sex').count()\n",
    "display(A)\n",
    "\n",
    "A = df.groupby('Sex').mean(numeric_only=True)\n",
    "display(A)\n",
    "\n",
    "A = df.groupby('Sex')['Survived'].count()\n",
    "display(A)\n",
    "\n",
    "A = df.groupby(['Sex', 'Survived']).mean(numeric_only=True)\n",
    "display(A)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting\n",
    "\n",
    "Pandas `integrates` with Matplotlib, so we can directly plot DataFrames."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: title={'center': 'Age'}, xlabel='Age', ylabel='Frequency'>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "df.plot(kind='scatter', x='Age', y='PClass')\n",
    "df['Age'].plot(kind='hist', title='Age')\n"
   ]
  }
 ],
 "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": "3.9.7"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}