07 Lesson Pandas/Lesson7 (Pandas).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# What is pandas (Python data analysis library)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "pandas is a fast, powerful, flexible and easy to use open source data analysis and manipulation tool,\n",
    "built on top of the Python programming language."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pandas is well suited for many different kinds of data:\n",
    "\n",
    "* Tabular data with heterogeneously-typed columns, as in an SQL table or Excel spreadsheet\n",
    "* Ordered and unordered (not necessarily fixed-frequency) time series data.\n",
    "* Arbitrary matrix data with row and column labels\n",
    "* Any other form of observational / statistical data sets."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Getting started¶"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Installation\n",
    "\n",
    "Pandas is an easy package to install. Open up your terminal program (for Mac users) or command line (for PC users) and install it using either of the following commands:\n",
    "\n",
    "### Working with conda?\n",
    "   \n",
    "   conda install pandas\n",
    "    \n",
    "### Prefer pip?\n",
    "   pip install pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Alternatively, if you're currently viewing this article in a Jupyter notebook you can run this cell:\n",
    "!pip install pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.1.3\n"
     ]
    }
   ],
   "source": [
    "# To load the pandas package and start working with it, import the package. \n",
    "# we usually import it with a shorter name pd since it's used so much:\n",
    "import pandas as pd\n",
    "\n",
    "#check  the version of pandas \n",
    "print(pd.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# What kind of data does Pandas handle?\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When working with tabular data, such as data stored in spreadsheets or databases, Pandas is the right tool for you. Pandas will help you to explore, clean and process your data. In Pandas, a data table is called a DataFrame.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![dataframe](img/pandas_data_structure.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* The primary two components of pandas are the Series and DataFrame.\n",
    "* A Series is essentially a column, and a DataFrame is a multi-dimensional table made up of a collection of Series."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### There are three main data structures in pandas:\n",
    "* Series — 1D.\n",
    "* DataFrame — 2D.\n",
    "* Panel — 3D."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Creating DataFrames from scratch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
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   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Marks</th>\n",
       "      <th>Sex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>22</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>35</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bonnell, Miss. Elizabeth</td>\n",
       "      <td>58</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "                       Name  Marks     Sex\n",
       "0   Braund, Mr. Owen Harris     22    male\n",
       "1  Allen, Mr. William Henry     35    male\n",
       "2  Bonnell, Miss. Elizabeth     58  female"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How  to create a dataframe \n",
    "students = {\"Name\": [\"Braund, Mr. Owen Harris\", \"Allen, Mr. William Henry\", \"Bonnell, Miss. Elizabeth\"], \n",
    "            \"Marks\": [22, 35, 58], \n",
    "            \"Sex\": [\"male\", \"male\", \"female\"]}\n",
    "\n",
    "# pass studnet  to the pandas DataFrame constructor:\n",
    "df = pd.DataFrame(data=students)\n",
    "df "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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   "outputs": [
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      "text/plain": [
       "Empty DataFrame\n",
       "Columns: []\n",
       "Index: []"
      ]
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     "execution_count": 3,
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     "output_type": "execute_result"
    }
   ],
   "source": [
    "# creating empty dataframe \n",
    "df1 = pd.DataFrame()\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    22\n",
       "1    35\n",
       "2    58\n",
       "Name: Marks, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Each column in a DataFrame is a Series¶\n",
    "df[\"Marks\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    22\n",
       "1    35\n",
       "2    58\n",
       "Name: Age, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#You can create a Series from scratch as well:\n",
    "marks = pd.Series([22, 35, 58], name=\"Age\")\n",
    "marks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Do something with a DataFrame or Series\n",
    "* View the first 2 rows \n",
    "* view the last 3 rows \n",
    "* I want to know the maximum Marks  of the Students ?\n",
    "* I’m interested in some basic statistics of the numerical data of my data tabled ?\n",
    "* datatypes ?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Marks</th>\n",
       "      <th>Sex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>22</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>35</td>\n",
       "      <td>male</td>\n",
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      "text/plain": [
       "                       Name  Marks   Sex\n",
       "0   Braund, Mr. Owen Harris     22  male\n",
       "1  Allen, Mr. William Henry     35  male"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#View the first 2 columns \n",
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Marks</th>\n",
       "      <th>Sex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bonnell, Miss. Elizabeth</td>\n",
       "      <td>58</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "                       Name  Marks     Sex\n",
       "2  Bonnell, Miss. Elizabeth     58  female"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#view the last 3 columns \n",
    "df.tail(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I want to know the maximum Marks  of the Students ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "58"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Marks\"].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name     Braund, Mr. Owen Harris\n",
       "Marks                         58\n",
       "Sex                         male\n",
       "dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "58"
      ]
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     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Marks.max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " I’m interested in some basic statistics of the numerical data of my data table ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Marks</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>38.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>18.230012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>22.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>28.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>35.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>46.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>58.000000</td>\n",
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      ],
      "text/plain": [
       "           Marks\n",
       "count   3.000000\n",
       "mean   38.333333\n",
       "std    18.230012\n",
       "min    22.000000\n",
       "25%    28.500000\n",
       "50%    35.000000\n",
       "75%    46.500000\n",
       "max    58.000000"
      ]
     },
     "execution_count": 11,
     "metadata": {},
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   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#datatypes ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3 entries, 0 to 2\n",
      "Data columns (total 3 columns):\n",
      " #   Column  Non-Null Count  Dtype \n",
      "---  ------  --------------  ----- \n",
      " 0   Name    3 non-null      object\n",
      " 1   Marks   3 non-null      int64 \n",
      " 2   Sex     3 non-null      object\n",
      "dtypes: int64(1), object(2)\n",
      "memory usage: 200.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# How do I read and write tabular data?\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas supports the integration with many file formats or data sources out of the box (csv, excel, sql, json, parquet, html,…). Importing data from each of these data sources is provided by function with the prefix read_*. Similarly, the to_* methods are used to store data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![title](img/panda_data.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read from CSV \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read telcom data from csv \n",
    "telcom = pd.read_csv('data/Telco_Churn.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customerID</th>\n",
       "      <th>gender</th>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <th>Partner</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>tenure</th>\n",
       "      <th>PhoneService</th>\n",
       "      <th>MultipleLines</th>\n",
       "      <th>InternetService</th>\n",
       "      <th>OnlineSecurity</th>\n",
       "      <th>...</th>\n",
       "      <th>DeviceProtection</th>\n",
       "      <th>TechSupport</th>\n",
       "      <th>StreamingTV</th>\n",
       "      <th>StreamingMovies</th>\n",
       "      <th>Contract</th>\n",
       "      <th>PaperlessBilling</th>\n",
       "      <th>PaymentMethod</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>TotalCharges</th>\n",
       "      <th>Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7590-VHVEG</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>29.85</td>\n",
       "      <td>29.85</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5575-GNVDE</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>34</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>56.95</td>\n",
       "      <td>1889.5</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3668-QPYBK</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>53.85</td>\n",
       "      <td>108.15</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7795-CFOCW</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>45</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>42.30</td>\n",
       "      <td>1840.75</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9237-HQITU</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>70.70</td>\n",
       "      <td>151.65</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7038</th>\n",
       "      <td>6840-RESVB</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>24</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>One year</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>84.80</td>\n",
       "      <td>1990.5</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7039</th>\n",
       "      <td>2234-XADUH</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>72</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>One year</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Credit card (automatic)</td>\n",
       "      <td>103.20</td>\n",
       "      <td>7362.9</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7040</th>\n",
       "      <td>4801-JZAZL</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>11</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>29.60</td>\n",
       "      <td>346.45</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7041</th>\n",
       "      <td>8361-LTMKD</td>\n",
       "      <td>Male</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>4</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>74.40</td>\n",
       "      <td>306.6</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7042</th>\n",
       "      <td>3186-AJIEK</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>66</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Two year</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>105.65</td>\n",
       "      <td>6844.5</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7043 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      customerID  gender  SeniorCitizen Partner Dependents  tenure  \\\n",
       "0     7590-VHVEG  Female              0     Yes         No       1   \n",
       "1     5575-GNVDE    Male              0      No         No      34   \n",
       "2     3668-QPYBK    Male              0      No         No       2   \n",
       "3     7795-CFOCW    Male              0      No         No      45   \n",
       "4     9237-HQITU  Female              0      No         No       2   \n",
       "...          ...     ...            ...     ...        ...     ...   \n",
       "7038  6840-RESVB    Male              0     Yes        Yes      24   \n",
       "7039  2234-XADUH  Female              0     Yes        Yes      72   \n",
       "7040  4801-JZAZL  Female              0     Yes        Yes      11   \n",
       "7041  8361-LTMKD    Male              1     Yes         No       4   \n",
       "7042  3186-AJIEK    Male              0      No         No      66   \n",
       "\n",
       "     PhoneService     MultipleLines InternetService OnlineSecurity  ...  \\\n",
       "0              No  No phone service             DSL             No  ...   \n",
       "1             Yes                No             DSL            Yes  ...   \n",
       "2             Yes                No             DSL            Yes  ...   \n",
       "3              No  No phone service             DSL            Yes  ...   \n",
       "4             Yes                No     Fiber optic             No  ...   \n",
       "...           ...               ...             ...            ...  ...   \n",
       "7038          Yes               Yes             DSL            Yes  ...   \n",
       "7039          Yes               Yes     Fiber optic             No  ...   \n",
       "7040           No  No phone service             DSL            Yes  ...   \n",
       "7041          Yes               Yes     Fiber optic             No  ...   \n",
       "7042          Yes                No     Fiber optic            Yes  ...   \n",
       "\n",
       "     DeviceProtection TechSupport StreamingTV StreamingMovies        Contract  \\\n",
       "0                  No          No          No              No  Month-to-month   \n",
       "1                 Yes          No          No              No        One year   \n",
       "2                  No          No          No              No  Month-to-month   \n",
       "3                 Yes         Yes          No              No        One year   \n",
       "4                  No          No          No              No  Month-to-month   \n",
       "...               ...         ...         ...             ...             ...   \n",
       "7038              Yes         Yes         Yes             Yes        One year   \n",
       "7039              Yes          No         Yes             Yes        One year   \n",
       "7040               No          No          No              No  Month-to-month   \n",
       "7041               No          No          No              No  Month-to-month   \n",
       "7042              Yes         Yes         Yes             Yes        Two year   \n",
       "\n",
       "     PaperlessBilling              PaymentMethod MonthlyCharges  TotalCharges  \\\n",
       "0                 Yes           Electronic check          29.85         29.85   \n",
       "1                  No               Mailed check          56.95        1889.5   \n",
       "2                 Yes               Mailed check          53.85        108.15   \n",
       "3                  No  Bank transfer (automatic)          42.30       1840.75   \n",
       "4                 Yes           Electronic check          70.70        151.65   \n",
       "...               ...                        ...            ...           ...   \n",
       "7038              Yes               Mailed check          84.80        1990.5   \n",
       "7039              Yes    Credit card (automatic)         103.20        7362.9   \n",
       "7040              Yes           Electronic check          29.60        346.45   \n",
       "7041              Yes               Mailed check          74.40         306.6   \n",
       "7042              Yes  Bank transfer (automatic)         105.65        6844.5   \n",
       "\n",
       "     Churn  \n",
       "0       No  \n",
       "1       No  \n",
       "2      Yes  \n",
       "3       No  \n",
       "4      Yes  \n",
       "...    ...  \n",
       "7038    No  \n",
       "7039    No  \n",
       "7040    No  \n",
       "7041   Yes  \n",
       "7042    No  \n",
       "\n",
       "[7043 rows x 21 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Make sure to always have a check on the data after reading in the data.\n",
    "#When displaying a DataFrame, the first and last 5 rows will be shown by default:\n",
    "telcom"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read data from HTML  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "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>Code</th>\n",
       "      <th>County</th>\n",
       "      <th>FormerProvince</th>\n",
       "      <th>Area(km2)</th>\n",
       "      <th>Population(2009 Census)</th>\n",
       "      <th>Population(2019 Census)</th>\n",
       "      <th>Capital</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Mombasa (County)</td>\n",
       "      <td>Coast</td>\n",
       "      <td>212.5</td>\n",
       "      <td>939370</td>\n",
       "      <td>1208333</td>\n",
       "      <td>Mombasa (City)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>Kwale</td>\n",
       "      <td>Coast</td>\n",
       "      <td>8270.3</td>\n",
       "      <td>649931</td>\n",
       "      <td>866820</td>\n",
       "      <td>Kwale</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>Kilifi</td>\n",
       "      <td>Coast</td>\n",
       "      <td>12245.9</td>\n",
       "      <td>1109735</td>\n",
       "      <td>1453787</td>\n",
       "      <td>Kilifi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>Tana River</td>\n",
       "      <td>Coast</td>\n",
       "      <td>35375.8</td>\n",
       "      <td>240075</td>\n",
       "      <td>315943</td>\n",
       "      <td>Hola</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>Lamu</td>\n",
       "      <td>Coast</td>\n",
       "      <td>6497.7</td>\n",
       "      <td>191539</td>\n",
       "      <td>143920</td>\n",
       "      <td>Lamu</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Code            County FormerProvince  Area(km2)  Population(2009 Census)  \\\n",
       "0   1.0  Mombasa (County)          Coast      212.5                   939370   \n",
       "1   2.0             Kwale          Coast     8270.3                   649931   \n",
       "2   3.0            Kilifi          Coast    12245.9                  1109735   \n",
       "3   4.0        Tana River          Coast    35375.8                   240075   \n",
       "4   5.0              Lamu          Coast     6497.7                   191539   \n",
       "\n",
       "   Population(2019 Census)         Capital  \n",
       "0                  1208333  Mombasa (City)  \n",
       "1                   866820           Kwale  \n",
       "2                  1453787          Kilifi  \n",
       "3                   315943            Hola  \n",
       "4                   143920            Lamu  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "htdata=pd.read_html('https://en.wikipedia.org/wiki/Counties_of_Kenya')[2]\n",
    "htdata.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read data from Excel "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>customerID</th>\n",
       "      <th>gender</th>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <th>Partner</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>tenure</th>\n",
       "      <th>PhoneService</th>\n",
       "      <th>MultipleLines</th>\n",
       "      <th>InternetService</th>\n",
       "      <th>OnlineSecurity</th>\n",
       "      <th>...</th>\n",
       "      <th>DeviceProtection</th>\n",
       "      <th>TechSupport</th>\n",
       "      <th>StreamingTV</th>\n",
       "      <th>StreamingMovies</th>\n",
       "      <th>Contract</th>\n",
       "      <th>PaperlessBilling</th>\n",
       "      <th>PaymentMethod</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>TotalCharges</th>\n",
       "      <th>Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7590-VHVEG</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>29.85</td>\n",
       "      <td>29.85</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5575-GNVDE</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>34</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>56.95</td>\n",
       "      <td>1889.5</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3668-QPYBK</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>53.85</td>\n",
       "      <td>108.15</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7795-CFOCW</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>45</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>42.30</td>\n",
       "      <td>1840.75</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9237-HQITU</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>70.70</td>\n",
       "      <td>151.65</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   customerID  gender  SeniorCitizen Partner Dependents  tenure PhoneService  \\\n",
       "0  7590-VHVEG  Female              0     Yes         No       1           No   \n",
       "1  5575-GNVDE    Male              0      No         No      34          Yes   \n",
       "2  3668-QPYBK    Male              0      No         No       2          Yes   \n",
       "3  7795-CFOCW    Male              0      No         No      45           No   \n",
       "4  9237-HQITU  Female              0      No         No       2          Yes   \n",
       "\n",
       "      MultipleLines InternetService OnlineSecurity  ... DeviceProtection  \\\n",
       "0  No phone service             DSL             No  ...               No   \n",
       "1                No             DSL            Yes  ...              Yes   \n",
       "2                No             DSL            Yes  ...               No   \n",
       "3  No phone service             DSL            Yes  ...              Yes   \n",
       "4                No     Fiber optic             No  ...               No   \n",
       "\n",
       "  TechSupport StreamingTV StreamingMovies        Contract PaperlessBilling  \\\n",
       "0          No          No              No  Month-to-month              Yes   \n",
       "1          No          No              No        One year               No   \n",
       "2          No          No              No  Month-to-month              Yes   \n",
       "3         Yes          No              No        One year               No   \n",
       "4          No          No              No  Month-to-month              Yes   \n",
       "\n",
       "               PaymentMethod MonthlyCharges  TotalCharges Churn  \n",
       "0           Electronic check          29.85         29.85    No  \n",
       "1               Mailed check          56.95        1889.5    No  \n",
       "2               Mailed check          53.85        108.15   Yes  \n",
       "3  Bank transfer (automatic)          42.30       1840.75    No  \n",
       "4           Electronic check          70.70        151.65   Yes  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#The equivalent read function to_excel() will reload the data to a DataFrame:\n",
    "telcom_excel= pd.read_excel('data/telcom.xlsx', sheet_name='customers')\n",
    "telcom_excel.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read Json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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>Marks</th>\n",
       "      <th>Sex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>22</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>35</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bonnell, Miss. Elizabeth</td>\n",
       "      <td>58</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Name  Marks     Sex\n",
       "0   Braund, Mr. Owen Harris     22    male\n",
       "1  Allen, Mr. William Henry     35    male\n",
       "2  Bonnell, Miss. Elizabeth     58  female"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_json('data/testapi.json')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read data from SQL "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you’re working with data from a MySQL database you need to first establish a connection using an appropriate Python library, then pass a query to pandas. Here we'll use SQLite to demonstrate."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we need  PyMySQL installed, so run this command in your terminal:\n",
    "\n",
    "* pip install PyMySQL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Or run this cell if you're in a notebook:\n",
    "! pip install PyMySQL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# sqlite3 is used to create a connection to a database which we can then use to generate a DataFrame through a SELECT query.\n",
    "import pymysql"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# So first we'll make a connection to a SQLite database file:\n",
    "conn = pymysql.connect(\n",
    "    host='localhost',\n",
    "    port=int(3306),\n",
    "    user=\"root\",\n",
    "    passwd='1234',\n",
    "    db=\"jumiaa_store\",\n",
    "    charset='utf8mb4')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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>customer_id</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>birth_date</th>\n",
       "      <th>phone</th>\n",
       "      <th>address</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>points</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Joe</td>\n",
       "      <td>Mutale</td>\n",
       "      <td>1986-03-28</td>\n",
       "      <td>781-932-9754</td>\n",
       "      <td>0 Saace</td>\n",
       "      <td>Nairobi</td>\n",
       "      <td>MA</td>\n",
       "      <td>2273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Fred</td>\n",
       "      <td>Vilakati</td>\n",
       "      <td>1986-04-13</td>\n",
       "      <td>804-427-9456</td>\n",
       "      <td>Coml</td>\n",
       "      <td>Cape</td>\n",
       "      <td>VA</td>\n",
       "      <td>947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Brian</td>\n",
       "      <td>Muyuda</td>\n",
       "      <td>1985-02-07</td>\n",
       "      <td>719-724-7869</td>\n",
       "      <td>Junction</td>\n",
       "      <td>Dodoma</td>\n",
       "      <td>CO</td>\n",
       "      <td>2967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>John</td>\n",
       "      <td>Muchiri</td>\n",
       "      <td>1974-04-14</td>\n",
       "      <td>407-231-8017</td>\n",
       "      <td>Terrace</td>\n",
       "      <td>Mombasa</td>\n",
       "      <td>FL</td>\n",
       "      <td>457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Clemmie</td>\n",
       "      <td>Banda</td>\n",
       "      <td>1973-11-07</td>\n",
       "      <td>None</td>\n",
       "      <td>5 Spohn</td>\n",
       "      <td>Kisumu</td>\n",
       "      <td>Ks</td>\n",
       "      <td>3675</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   customer_id first_name  last_name  birth_date         phone    address  \\\n",
       "0            1        Joe     Mutale  1986-03-28  781-932-9754    0 Saace   \n",
       "1            2       Fred  Vilakati   1986-04-13  804-427-9456       Coml   \n",
       "2            3      Brian     Muyuda  1985-02-07  719-724-7869   Junction   \n",
       "3            4       John    Muchiri  1974-04-14  407-231-8017    Terrace   \n",
       "4            5    Clemmie      Banda  1973-11-07          None    5 Spohn   \n",
       "\n",
       "      city state  points  \n",
       "0  Nairobi    MA    2273  \n",
       "1     Cape    VA     947  \n",
       "2   Dodoma    CO    2967  \n",
       "3  Mombasa    FL     457  \n",
       "4   Kisumu    Ks    3675  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# In Our  SQLite database we have a table called user_details\n",
    "\n",
    "# By passing a SELECT query and our con, we can read from the user_details table:\n",
    "\n",
    "df = pd.read_sql_query(\"SELECT * FROM customers\",conn)\n",
    "df.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you have data in PostgreSQL,  or some other SQL server, you'll need to obtain the right Python library to make a connection. For example, psycopg2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Converting back to a CSV, JSON, or SQL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# to csv\n",
    "df.to_csv('new_purchases.csv')\n",
    "# to Json\n",
    "df.to_json('new_purchases.json')\n",
    "# to SQL \n",
    "df.to_sql('new_purchases', con)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# My colleague requested the telcom data as a spreadsheet.\n",
    "telcom.to_excel('data/telcom.xlsx', sheet_name='customers', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### i’m interested in a technical summary of a DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 7043 entries, 0 to 7042\n",
      "Data columns (total 21 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   customerID        7043 non-null   object \n",
      " 1   gender            7043 non-null   object \n",
      " 2   SeniorCitizen     7043 non-null   int64  \n",
      " 3   Partner           7043 non-null   object \n",
      " 4   Dependents        7043 non-null   object \n",
      " 5   tenure            7043 non-null   int64  \n",
      " 6   PhoneService      7043 non-null   object \n",
      " 7   MultipleLines     7043 non-null   object \n",
      " 8   InternetService   7043 non-null   object \n",
      " 9   OnlineSecurity    7043 non-null   object \n",
      " 10  OnlineBackup      7043 non-null   object \n",
      " 11  DeviceProtection  7043 non-null   object \n",
      " 12  TechSupport       7043 non-null   object \n",
      " 13  StreamingTV       7043 non-null   object \n",
      " 14  StreamingMovies   7043 non-null   object \n",
      " 15  Contract          7043 non-null   object \n",
      " 16  PaperlessBilling  7043 non-null   object \n",
      " 17  PaymentMethod     7043 non-null   object \n",
      " 18  MonthlyCharges    7043 non-null   float64\n",
      " 19  TotalCharges      7043 non-null   object \n",
      " 20  Churn             7043 non-null   object \n",
      "dtypes: float64(1), int64(2), object(18)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "#I’m interested in a technical summary of a DataFrame\n",
    "telcom_excel.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A check on how pandas interpreted each of the column data types "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "customerID           object\n",
       "gender               object\n",
       "SeniorCitizen         int64\n",
       "Partner              object\n",
       "Dependents           object\n",
       "tenure                int64\n",
       "PhoneService         object\n",
       "MultipleLines        object\n",
       "InternetService      object\n",
       "OnlineSecurity       object\n",
       "OnlineBackup         object\n",
       "DeviceProtection     object\n",
       "TechSupport          object\n",
       "StreamingTV          object\n",
       "StreamingMovies      object\n",
       "Contract             object\n",
       "PaperlessBilling     object\n",
       "PaymentMethod        object\n",
       "MonthlyCharges      float64\n",
       "TotalCharges         object\n",
       "Churn                object\n",
       "dtype: object"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "telcom.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# How do I select a subset of a table?\n",
    "Selecting or filtering specific rows and/or columns? Filtering the data on a condition? Methods for slicing, selecting, and extracting the data you need are available in Pandas."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![title](img/subset.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### I’m interested in MonthlyCharges of the telecom customers?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    29.85\n",
       "1    56.95\n",
       "2    53.85\n",
       "3    42.30\n",
       "4    70.70\n",
       "Name: MonthlyCharges, dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#To select a single column, use square brackets [] with the column name of the column of interest.\n",
    "MonthlyCharges= telcom['MonthlyCharges']\n",
    "# view first 5\n",
    "MonthlyCharges.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'>\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(7043,)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#And have a look at the shape of the output:\n",
    "print(type(telcom['MonthlyCharges']))\n",
    "telcom['MonthlyCharges'].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### I’m interested in the MonthlyCharges and Totalcharges  of the telcom customers ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>MonthlyCharges</th>\n",
       "      <th>TotalCharges</th>\n",
       "      <th>Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>29.85</td>\n",
       "      <td>29.85</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>56.95</td>\n",
       "      <td>1889.5</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>53.85</td>\n",
       "      <td>108.15</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>42.30</td>\n",
       "      <td>1840.75</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>70.70</td>\n",
       "      <td>151.65</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   MonthlyCharges TotalCharges Churn\n",
       "0           29.85        29.85    No\n",
       "1           56.95       1889.5    No\n",
       "2           53.85       108.15   Yes\n",
       "3           42.30      1840.75    No\n",
       "4           70.70       151.65   Yes"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#To select multiple columns, use a list of column names within the selection brackets [].\n",
    "revenue = telcom[[\"MonthlyCharges\", \"TotalCharges\",\"Churn\"]]\n",
    "revenue.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### I’m interested in the customers  who paid  more than 100 in MonthlyCharges    ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>customerID</th>\n",
       "      <th>gender</th>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <th>Partner</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>tenure</th>\n",
       "      <th>PhoneService</th>\n",
       "      <th>MultipleLines</th>\n",
       "      <th>InternetService</th>\n",
       "      <th>OnlineSecurity</th>\n",
       "      <th>...</th>\n",
       "      <th>DeviceProtection</th>\n",
       "      <th>TechSupport</th>\n",
       "      <th>StreamingTV</th>\n",
       "      <th>StreamingMovies</th>\n",
       "      <th>Contract</th>\n",
       "      <th>PaperlessBilling</th>\n",
       "      <th>PaymentMethod</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>TotalCharges</th>\n",
       "      <th>Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>7892-POOKP</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>28</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>104.80</td>\n",
       "      <td>3046.05</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>8091-TTVAX</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>58</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Credit card (automatic)</td>\n",
       "      <td>100.35</td>\n",
       "      <td>5681.1</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0280-XJGEX</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>49</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>103.70</td>\n",
       "      <td>5036.3</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>5129-JLPIS</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>25</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>105.50</td>\n",
       "      <td>2686.05</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>3655-SNQYZ</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>69</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Two year</td>\n",
       "      <td>No</td>\n",
       "      <td>Credit card (automatic)</td>\n",
       "      <td>113.25</td>\n",
       "      <td>7895.15</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    customerID  gender  SeniorCitizen Partner Dependents  tenure PhoneService  \\\n",
       "8   7892-POOKP  Female              0     Yes         No      28          Yes   \n",
       "12  8091-TTVAX    Male              0     Yes         No      58          Yes   \n",
       "13  0280-XJGEX    Male              0      No         No      49          Yes   \n",
       "14  5129-JLPIS    Male              0      No         No      25          Yes   \n",
       "15  3655-SNQYZ  Female              0     Yes        Yes      69          Yes   \n",
       "\n",
       "   MultipleLines InternetService OnlineSecurity  ... DeviceProtection  \\\n",
       "8            Yes     Fiber optic             No  ...              Yes   \n",
       "12           Yes     Fiber optic             No  ...              Yes   \n",
       "13           Yes     Fiber optic             No  ...              Yes   \n",
       "14            No     Fiber optic            Yes  ...              Yes   \n",
       "15           Yes     Fiber optic            Yes  ...              Yes   \n",
       "\n",
       "   TechSupport StreamingTV StreamingMovies        Contract PaperlessBilling  \\\n",
       "8          Yes         Yes             Yes  Month-to-month              Yes   \n",
       "12          No         Yes             Yes        One year               No   \n",
       "13          No         Yes             Yes  Month-to-month              Yes   \n",
       "14         Yes         Yes             Yes  Month-to-month              Yes   \n",
       "15         Yes         Yes             Yes        Two year               No   \n",
       "\n",
       "                PaymentMethod MonthlyCharges  TotalCharges Churn  \n",
       "8            Electronic check         104.80       3046.05   Yes  \n",
       "12    Credit card (automatic)         100.35        5681.1    No  \n",
       "13  Bank transfer (automatic)         103.70        5036.3   Yes  \n",
       "14           Electronic check         105.50       2686.05    No  \n",
       "15    Credit card (automatic)         113.25       7895.15    No  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "paid_above_100 = telcom[telcom[\"MonthlyCharges\"] >100]\n",
    "paid_above_100.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### how many customers  paid above 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "customerID          902\n",
       "gender              902\n",
       "SeniorCitizen       902\n",
       "Partner             902\n",
       "Dependents          902\n",
       "tenure              902\n",
       "PhoneService        902\n",
       "MultipleLines       902\n",
       "InternetService     902\n",
       "OnlineSecurity      902\n",
       "OnlineBackup        902\n",
       "DeviceProtection    902\n",
       "TechSupport         902\n",
       "StreamingTV         902\n",
       "StreamingMovies     902\n",
       "Contract            902\n",
       "PaperlessBilling    902\n",
       "PaymentMethod       902\n",
       "MonthlyCharges      902\n",
       "TotalCharges        902\n",
       "Churn               902\n",
       "dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#\n",
    "paid_above_100.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### I’m interested in  customers  who are paying for  monthy and one year contract "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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>customerID</th>\n",
       "      <th>gender</th>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <th>Partner</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>tenure</th>\n",
       "      <th>PhoneService</th>\n",
       "      <th>MultipleLines</th>\n",
       "      <th>InternetService</th>\n",
       "      <th>OnlineSecurity</th>\n",
       "      <th>...</th>\n",
       "      <th>DeviceProtection</th>\n",
       "      <th>TechSupport</th>\n",
       "      <th>StreamingTV</th>\n",
       "      <th>StreamingMovies</th>\n",
       "      <th>Contract</th>\n",
       "      <th>PaperlessBilling</th>\n",
       "      <th>PaymentMethod</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>TotalCharges</th>\n",
       "      <th>Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7590-VHVEG</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>29.85</td>\n",
       "      <td>29.85</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5575-GNVDE</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>34</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>56.95</td>\n",
       "      <td>1889.5</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3668-QPYBK</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>53.85</td>\n",
       "      <td>108.15</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7795-CFOCW</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>45</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>42.30</td>\n",
       "      <td>1840.75</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9237-HQITU</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>70.70</td>\n",
       "      <td>151.65</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   customerID  gender  SeniorCitizen Partner Dependents  tenure PhoneService  \\\n",
       "0  7590-VHVEG  Female              0     Yes         No       1           No   \n",
       "1  5575-GNVDE    Male              0      No         No      34          Yes   \n",
       "2  3668-QPYBK    Male              0      No         No       2          Yes   \n",
       "3  7795-CFOCW    Male              0      No         No      45           No   \n",
       "4  9237-HQITU  Female              0      No         No       2          Yes   \n",
       "\n",
       "      MultipleLines InternetService OnlineSecurity  ... DeviceProtection  \\\n",
       "0  No phone service             DSL             No  ...               No   \n",
       "1                No             DSL            Yes  ...              Yes   \n",
       "2                No             DSL            Yes  ...               No   \n",
       "3  No phone service             DSL            Yes  ...              Yes   \n",
       "4                No     Fiber optic             No  ...               No   \n",
       "\n",
       "  TechSupport StreamingTV StreamingMovies        Contract PaperlessBilling  \\\n",
       "0          No          No              No  Month-to-month              Yes   \n",
       "1          No          No              No        One year               No   \n",
       "2          No          No              No  Month-to-month              Yes   \n",
       "3         Yes          No              No        One year               No   \n",
       "4          No          No              No  Month-to-month              Yes   \n",
       "\n",
       "               PaymentMethod MonthlyCharges  TotalCharges Churn  \n",
       "0           Electronic check          29.85         29.85    No  \n",
       "1               Mailed check          56.95        1889.5    No  \n",
       "2               Mailed check          53.85        108.15   Yes  \n",
       "3  Bank transfer (automatic)          42.30       1840.75    No  \n",
       "4           Electronic check          70.70        151.65   Yes  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Contract_month_and_1year= telcom[ telcom[\"Contract\"].isin(['Month-to-month','One year'])  ]\n",
    "Contract_month_and_1year.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### I want to work with customer  data for which the Partner status  is known.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>customerID</th>\n",
       "      <th>gender</th>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <th>Partner</th>\n",
       "      <th>Dependents</th>\n",
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       "      <th>PhoneService</th>\n",
       "      <th>MultipleLines</th>\n",
       "      <th>InternetService</th>\n",
       "      <th>OnlineSecurity</th>\n",
       "      <th>...</th>\n",
       "      <th>DeviceProtection</th>\n",
       "      <th>TechSupport</th>\n",
       "      <th>StreamingTV</th>\n",
       "      <th>StreamingMovies</th>\n",
       "      <th>Contract</th>\n",
       "      <th>PaperlessBilling</th>\n",
       "      <th>PaymentMethod</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>TotalCharges</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7590-VHVEG</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>29.85</td>\n",
       "      <td>29.85</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5575-GNVDE</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>34</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>56.95</td>\n",
       "      <td>1889.5</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3668-QPYBK</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>53.85</td>\n",
       "      <td>108.15</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7795-CFOCW</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>45</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>42.30</td>\n",
       "      <td>1840.75</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9237-HQITU</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>70.70</td>\n",
       "      <td>151.65</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   customerID  gender  SeniorCitizen Partner Dependents  tenure PhoneService  \\\n",
       "0  7590-VHVEG  Female              0     Yes         No       1           No   \n",
       "1  5575-GNVDE    Male              0      No         No      34          Yes   \n",
       "2  3668-QPYBK    Male              0      No         No       2          Yes   \n",
       "3  7795-CFOCW    Male              0      No         No      45           No   \n",
       "4  9237-HQITU  Female              0      No         No       2          Yes   \n",
       "\n",
       "      MultipleLines InternetService OnlineSecurity  ... DeviceProtection  \\\n",
       "0  No phone service             DSL             No  ...               No   \n",
       "1                No             DSL            Yes  ...              Yes   \n",
       "2                No             DSL            Yes  ...               No   \n",
       "3  No phone service             DSL            Yes  ...              Yes   \n",
       "4                No     Fiber optic             No  ...               No   \n",
       "\n",
       "  TechSupport StreamingTV StreamingMovies        Contract PaperlessBilling  \\\n",
       "0          No          No              No  Month-to-month              Yes   \n",
       "1          No          No              No        One year               No   \n",
       "2          No          No              No  Month-to-month              Yes   \n",
       "3         Yes          No              No        One year               No   \n",
       "4          No          No              No  Month-to-month              Yes   \n",
       "\n",
       "               PaymentMethod MonthlyCharges  TotalCharges Churn  \n",
       "0           Electronic check          29.85         29.85    No  \n",
       "1               Mailed check          56.95        1889.5    No  \n",
       "2               Mailed check          53.85        108.15   Yes  \n",
       "3  Bank transfer (automatic)          42.30       1840.75    No  \n",
       "4           Electronic check          70.70        151.65   Yes  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "status_no_na = telcom[ telcom[\"Partner\"].notna()]\n",
    "status_no_na.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# How do I select specific rows and columns from a DataFrame?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "look at getting data.We have two options:\n",
    "\n",
    " * .loc - locates by name\n",
    " * .iloc- locates by numerical index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### I’m interested in rows 10 till 25 and columns 3 to 5."
   ]
  },
  {
   "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",
       "    }\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>SeniorCitizen</th>\n",
       "      <th>Partner</th>\n",
       "      <th>Dependents</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    SeniorCitizen Partner Dependents\n",
       "10              0     Yes        Yes\n",
       "11              0      No         No\n",
       "12              0     Yes         No\n",
       "13              0      No         No\n",
       "14              0      No         No\n",
       "15              0     Yes        Yes\n",
       "16              0      No         No\n",
       "17              0      No        Yes\n",
       "18              0     Yes        Yes\n",
       "19              0      No         No\n",
       "20              1      No         No\n",
       "21              0     Yes         No\n",
       "22              0      No         No\n",
       "23              0     Yes         No\n",
       "24              0     Yes        Yes\n",
       "25              0      No         No"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "telcom.iloc[10:26, 2:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### row 0 till 3 and column 3  and change the cells to \"anonymous\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>customerID</th>\n",
       "      <th>gender</th>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <th>Partner</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>tenure</th>\n",
       "      <th>PhoneService</th>\n",
       "      <th>MultipleLines</th>\n",
       "      <th>InternetService</th>\n",
       "      <th>OnlineSecurity</th>\n",
       "      <th>...</th>\n",
       "      <th>DeviceProtection</th>\n",
       "      <th>TechSupport</th>\n",
       "      <th>StreamingTV</th>\n",
       "      <th>StreamingMovies</th>\n",
       "      <th>Contract</th>\n",
       "      <th>PaperlessBilling</th>\n",
       "      <th>PaymentMethod</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>TotalCharges</th>\n",
       "      <th>Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7590-VHVEG</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>anonymous</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>29.85</td>\n",
       "      <td>29.85</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5575-GNVDE</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>anonymous</td>\n",
       "      <td>No</td>\n",
       "      <td>34</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>56.95</td>\n",
       "      <td>1889.5</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3668-QPYBK</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>anonymous</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>53.85</td>\n",
       "      <td>108.15</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7795-CFOCW</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>45</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>42.30</td>\n",
       "      <td>1840.75</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9237-HQITU</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>70.70</td>\n",
       "      <td>151.65</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   customerID  gender  SeniorCitizen    Partner Dependents  tenure  \\\n",
       "0  7590-VHVEG  Female              0  anonymous         No       1   \n",
       "1  5575-GNVDE    Male              0  anonymous         No      34   \n",
       "2  3668-QPYBK    Male              0  anonymous         No       2   \n",
       "3  7795-CFOCW    Male              0         No         No      45   \n",
       "4  9237-HQITU  Female              0         No         No       2   \n",
       "\n",
       "  PhoneService     MultipleLines InternetService OnlineSecurity  ...  \\\n",
       "0           No  No phone service             DSL             No  ...   \n",
       "1          Yes                No             DSL            Yes  ...   \n",
       "2          Yes                No             DSL            Yes  ...   \n",
       "3           No  No phone service             DSL            Yes  ...   \n",
       "4          Yes                No     Fiber optic             No  ...   \n",
       "\n",
       "  DeviceProtection TechSupport StreamingTV StreamingMovies        Contract  \\\n",
       "0               No          No          No              No  Month-to-month   \n",
       "1              Yes          No          No              No        One year   \n",
       "2               No          No          No              No  Month-to-month   \n",
       "3              Yes         Yes          No              No        One year   \n",
       "4               No          No          No              No  Month-to-month   \n",
       "\n",
       "  PaperlessBilling              PaymentMethod MonthlyCharges  TotalCharges  \\\n",
       "0              Yes           Electronic check          29.85         29.85   \n",
       "1               No               Mailed check          56.95        1889.5   \n",
       "2              Yes               Mailed check          53.85        108.15   \n",
       "3               No  Bank transfer (automatic)          42.30       1840.75   \n",
       "4              Yes           Electronic check          70.70        151.65   \n",
       "\n",
       "  Churn  \n",
       "0    No  \n",
       "1    No  \n",
       "2   Yes  \n",
       "3    No  \n",
       "4   Yes  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "telcom.iloc[0:3, 3] = \"anonymous\"\n",
    "telcom.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### intrested in row  10, 1, 5, 700, 60, 26 and column 18, 19, 20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>TotalCharges</th>\n",
       "      <th>Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>49.95</td>\n",
       "      <td>587.45</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>56.95</td>\n",
       "      <td>1889.5</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>99.65</td>\n",
       "      <td>820.5</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>700</th>\n",
       "      <td>63.30</td>\n",
       "      <td>4189.7</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>100.50</td>\n",
       "      <td>4707.1</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>99.35</td>\n",
       "      <td>4749.15</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     MonthlyCharges TotalCharges Churn\n",
       "10            49.95       587.45    No\n",
       "1             56.95       1889.5    No\n",
       "5             99.65        820.5   Yes\n",
       "700           63.30       4189.7    No\n",
       "60           100.50       4707.1    No\n",
       "26            99.35      4749.15   Yes"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "telcom.iloc[[10, 1, 5, 700, 60, 26], [18, 19, 20]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using .loc  intrested in row 10, 1, 5, 700, 60, 26 and column 18, 19, 20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
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       "  <tbody>\n",
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       "      <td>No</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>56.95</td>\n",
       "      <td>1889.5</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>99.65</td>\n",
       "      <td>820.5</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>700</th>\n",
       "      <td>63.30</td>\n",
       "      <td>4189.7</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>100.50</td>\n",
       "      <td>4707.1</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>99.35</td>\n",
       "      <td>4749.15</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     MonthlyCharges TotalCharges Churn\n",
       "10            49.95       587.45    No\n",
       "1             56.95       1889.5    No\n",
       "5             99.65        820.5   Yes\n",
       "700           63.30       4189.7    No\n",
       "60           100.50       4707.1    No\n",
       "26            99.35      4749.15   Yes"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "telcom.loc[[10, 1, 5, 700, 60, 26], [\"MonthlyCharges\", \"TotalCharges\",\"Churn\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# How to create plots in pandas?\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![title](img/plot.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The .plot.* methods are applicable on both Series and DataFrames\n",
    "\n",
    "By default, each of the columns is plotted as a different element (line, boxplot,…)\n",
    "\n",
    "Any plot created by pandas is a Matplotlib object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 7043 entries, 0 to 7042\n",
      "Data columns (total 21 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   customerID        7043 non-null   object \n",
      " 1   gender            7043 non-null   object \n",
      " 2   SeniorCitizen     7043 non-null   int64  \n",
      " 3   Partner           7043 non-null   object \n",
      " 4   Dependents        7043 non-null   object \n",
      " 5   tenure            7043 non-null   int64  \n",
      " 6   PhoneService      7043 non-null   object \n",
      " 7   MultipleLines     7043 non-null   object \n",
      " 8   InternetService   7043 non-null   object \n",
      " 9   OnlineSecurity    7043 non-null   object \n",
      " 10  OnlineBackup      7043 non-null   object \n",
      " 11  DeviceProtection  7043 non-null   object \n",
      " 12  TechSupport       7043 non-null   object \n",
      " 13  StreamingTV       7043 non-null   object \n",
      " 14  StreamingMovies   7043 non-null   object \n",
      " 15  Contract          7043 non-null   object \n",
      " 16  PaperlessBilling  7043 non-null   object \n",
      " 17  PaymentMethod     7043 non-null   object \n",
      " 18  MonthlyCharges    7043 non-null   float64\n",
      " 19  TotalCharges      7043 non-null   object \n",
      " 20  Churn             7043 non-null   object \n",
      "dtypes: float64(1), int64(2), object(18)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "telcom.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>SeniorCitizen</th>\n",
       "      <th>tenure</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>7043.000000</td>\n",
       "      <td>7043.000000</td>\n",
       "      <td>7043.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.162147</td>\n",
       "      <td>32.371149</td>\n",
       "      <td>64.761692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.368612</td>\n",
       "      <td>24.559481</td>\n",
       "      <td>30.090047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>18.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>35.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>70.350000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>89.850000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>118.750000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       SeniorCitizen       tenure  MonthlyCharges\n",
       "count    7043.000000  7043.000000     7043.000000\n",
       "mean        0.162147    32.371149       64.761692\n",
       "std         0.368612    24.559481       30.090047\n",
       "min         0.000000     0.000000       18.250000\n",
       "25%         0.000000     9.000000       35.500000\n",
       "50%         0.000000    29.000000       70.350000\n",
       "75%         0.000000    55.000000       89.850000\n",
       "max         1.000000    72.000000      118.750000"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "telcom.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAE/CAYAAACq6RM2AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAAbNUlEQVR4nO3df7RdZX3n8ffHBCEKURguTMxNTXRCZ5KMhOY2jTK6/NUS0WVC19gJVskoq1EKRWdca0za1dFOJxZXFTrMFGbFAQnWIY1TXEQRHczoqC2CNxoJATJkAMk1Kbn+aqMdYwmf+WM/qYfL4d5z8+PsXJ7Pa62zzj7fs/c93+vVT7bPefZ+ZJuIiKjDc9puICIi+iehHxFRkYR+RERFEvoRERVJ6EdEVCShHxFRkem97ihpGjAMfNf2mySdAfw5MBd4FPgN2z8s+64DLgUOAVfa/kKpLwFuAmYAnwPe4wnmjJ555pmeO3fupH6piIjabdu27Xu2B8bWew594D3AA8DM8notsNX2VZLWltfvl7QAWAUsBF4EfFHSObYPAdcDa4Cv04T+cuCO8T507ty5DA8PT6LNiIiQ9J1u9Z6GdyQNAm8E/ltHeQWwsWxvBFZ21DfZPmj7EWA3sFTSLGCm7bvK2f3NHcdEREQf9Dqm/yfAvwOe7KidbXsfQHk+q9RnA3s69hsptdlle2z9aSStkTQsaXh0dLTHFiMiYiIThr6kNwH7bW/r8WeqS83j1J9etDfYHrI9NDDwtCGpiIg4Qr2M6Z8PvFnShcApwExJfwY8LmmW7X1l6GZ/2X8EmNNx/CCwt9QHu9QjIqJPJjzTt73O9qDtuTRf0P4v228DtgCry26rgdvK9hZglaSTJc0D5gP3lCGgA5KWSRJwSccxERHRB5OZvTPWVcBmSZcCjwFvAbC9U9Jm4H7gCeDyMnMH4DJ+PmXzDiaYuRMREceWTvRbKw8NDTlTNiMiJkfSNttDY+u5IjcioiJHM7zzrDR37e1tt3DcPHrVG9tuISJaljP9iIiKJPQjIiqS0I+IqEhCPyKiIgn9iIiKJPQjIiqS0I+IqEhCPyKiIgn9iIiKJPQjIiqS0I+IqEhCPyKiIgn9iIiKJPQjIiqS0I+IqEhCPyKiIhOGvqRTJN0j6duSdkr6g1L/oKTvStpeHhd2HLNO0m5JuyRd0FFfImlHee/askB6RET0SS8rZx0EXmv7x5JOAr4m6fCC5tfY/kjnzpIWAKuAhcCLgC9KOqcsjn49sAb4OvA5YDlZHD0iom8mPNN348fl5UnlMd5q6iuATbYP2n4E2A0slTQLmGn7Ljersd8MrDyq7iMiYlJ6GtOXNE3SdmA/cKftu8tbV0i6V9KNkk4vtdnAno7DR0ptdtkeW+/2eWskDUsaHh0d7f23iYiIcfUU+rYP2V4MDNKctS+iGap5KbAY2Ad8tOzebZze49S7fd4G20O2hwYGBnppMSIiejCp2Tu2fwR8GVhu+/Hyj8GTwMeApWW3EWBOx2GDwN5SH+xSj4iIPull9s6ApBeW7RnA64EHyxj9YRcB95XtLcAqSSdLmgfMB+6xvQ84IGlZmbVzCXDbsftVIiJiIr3M3pkFbJQ0jeYfic22PyvpE5IW0wzRPAq8C8D2TkmbgfuBJ4DLy8wdgMuAm4AZNLN2MnMnIqKPJgx92/cC53Wpv32cY9YD67vUh4FFk+wxIiKOkVyRGxFRkYR+RERFEvoRERVJ6EdEVCShHxFRkYR+RERFEvoRERVJ6EdEVCShHxFRkYR+RERFEvoRERVJ6EdEVCShHxFRkYR+RERFEvoRERVJ6EdEVCShHxFRkYR+RERFelkY/RRJ90j6tqSdkv6g1M+QdKekh8rz6R3HrJO0W9IuSRd01JdI2lHeu7YskB4REX3Sy5n+QeC1ts8FFgPLJS0D1gJbbc8HtpbXSFoArAIWAsuB68qi6gDXA2uA+eWx/Nj9KhERMZEJQ9+NH5eXJ5WHgRXAxlLfCKws2yuATbYP2n4E2A0slTQLmGn7LtsGbu44JiIi+qCnMX1J0yRtB/YDd9q+Gzjb9j6A8nxW2X02sKfj8JFSm122x9a7fd4aScOShkdHRyfx60RExHh6Cn3bh2wvBgZpztoXjbN7t3F6j1Pv9nkbbA/ZHhoYGOilxYiI6MGkZu/Y/hHwZZqx+MfLkA3leX/ZbQSY03HYILC31Ae71CMiok96mb0zIOmFZXsG8HrgQWALsLrsthq4rWxvAVZJOlnSPJovbO8pQ0AHJC0rs3Yu6TgmIiL6YHoP+8wCNpYZOM8BNtv+rKS7gM2SLgUeA94CYHunpM3A/cATwOW2D5WfdRlwEzADuKM8IiKiTyYMfdv3Aud1qX8feN0zHLMeWN+lPgyM931AREQcR7kiNyKiIgn9iIiKJPQjIiqS0I+IqEhCPyKiIgn9iIiKJPQjIiqS0I+IqEhCPyKiIgn9iIiKJPQjIiqS0I+IqEhCPyKiIgn9iIiKJPQjIiqS0I+IqEhCPyKiIr2skTtH0pckPSBpp6T3lPoHJX1X0vbyuLDjmHWSdkvaJemCjvoSSTvKe9eWtXIjIqJPelkj9wngfba/Kek0YJukO8t719j+SOfOkhYAq4CFwIuAL0o6p6yTez2wBvg68DlgOVknNyKibyY807e9z/Y3y/YB4AFg9jiHrAA22T5o+xFgN7BU0ixgpu27bBu4GVh5tL9ARET0blJj+pLm0iySfncpXSHpXkk3Sjq91GYDezoOGym12WV7bL3b56yRNCxpeHR0dDItRkTEOHoOfUmnAn8BvNf239IM1bwUWAzsAz56eNcuh3uc+tOL9gbbQ7aHBgYGem0xIiIm0FPoSzqJJvA/aftWANuP2z5k+0ngY8DSsvsIMKfj8EFgb6kPdqlHRESf9DJ7R8ANwAO2r+6oz+rY7SLgvrK9BVgl6WRJ84D5wD229wEHJC0rP/MS4LZj9HtEREQPepm9cz7wdmCHpO2l9rvAxZIW0wzRPAq8C8D2TkmbgftpZv5cXmbuAFwG3ATMoJm1k5k7ERF9NGHo2/4a3cfjPzfOMeuB9V3qw8CiyTQYERHHTq7IjYioSEI/IqIiCf2IiIok9CMiKpLQj4ioSEI/IqIiCf2IiIok9CMiKpLQj4ioSEI/IqIiCf2IiIok9CMiKpLQj4ioSEI/IqIiCf2IiIok9CMiKpLQj4ioSEI/IqIivSyMPkfSlyQ9IGmnpPeU+hmS7pT0UHk+veOYdZJ2S9ol6YKO+hJJO8p715YF0iMiok96OdN/Anif7X8GLAMul7QAWAtstT0f2FpeU95bBSwElgPXSZpWftb1wBpgfnksP4a/S0RETGDC0Le9z/Y3y/YB4AFgNrAC2Fh22wisLNsrgE22D9p+BNgNLJU0C5hp+y7bBm7uOCYiIvpgUmP6kuYC5wF3A2fb3gfNPwzAWWW32cCejsNGSm122R5b7/Y5ayQNSxoeHR2dTIsRETGOnkNf0qnAXwDvtf234+3apeZx6k8v2htsD9keGhgY6LXFiIiYQE+hL+kkmsD/pO1bS/nxMmRDed5f6iPAnI7DB4G9pT7YpR4REX3Sy+wdATcAD9i+uuOtLcDqsr0auK2jvkrSyZLm0Xxhe08ZAjogaVn5mZd0HBMREX0wvYd9zgfeDuyQtL3Ufhe4Ctgs6VLgMeAtALZ3StoM3E8z8+dy24fKcZcBNwEzgDvKIyIi+mTC0Lf9NbqPxwO87hmOWQ+s71IfBhZNpsGIiDh2ckVuRERFEvoRERVJ6EdEVCShHxFRkYR+RERFEvoRERVJ6EdEVCShHxFRkYR+RERFEvoRERXp5d47EVPC3LW3t93CcfXoVW9su4V4FsiZfkRERRL6EREVSehHRFQkoR8RUZGEfkRERRL6EREVSehHRFSkl4XRb5S0X9J9HbUPSvqupO3lcWHHe+sk7Za0S9IFHfUlknaU964ti6NHREQf9XKmfxOwvEv9GtuLy+NzAJIWAKuAheWY6yRNK/tfD6wB5pdHt58ZERHH0YShb/srwA96/HkrgE22D9p+BNgNLJU0C5hp+y7bBm4GVh5hzxERcYSOZkz/Ckn3luGf00ttNrCnY5+RUptdtsfWu5K0RtKwpOHR0dGjaDEiIjod6b13rgf+EHB5/ijwTqDbOL3HqXdlewOwAWBoaOgZ94uIZ4/cO6k/juhM3/bjtg/ZfhL4GLC0vDUCzOnYdRDYW+qDXeoREdFHRxT6ZYz+sIuAwzN7tgCrJJ0saR7NF7b32N4HHJC0rMzauQS47Sj6joiIIzDh8I6kW4BXA2dKGgE+ALxa0mKaIZpHgXcB2N4paTNwP/AEcLntQ+VHXUYzE2gGcEd5REREH00Y+rYv7lK+YZz91wPru9SHgUWT6i4iIo6pXJEbEVGRhH5EREUS+hERFUnoR0RUJKEfEVGRhH5EREUS+hERFUnoR0RUJKEfEVGRhH5EREUS+hERFUnoR0RUJKEfEVGRhH5EREUS+hERFUnoR0RUJKEfEVGRhH5EREUmDH1JN0raL+m+jtoZku6U9FB5Pr3jvXWSdkvaJemCjvoSSTvKe9eWBdIjIqKPejnTvwlYPqa2Fthqez6wtbxG0gJgFbCwHHOdpGnlmOuBNcD88hj7MyMi4jibMPRtfwX4wZjyCmBj2d4IrOyob7J90PYjwG5gqaRZwEzbd9k2cHPHMRER0SdHOqZ/tu19AOX5rFKfDezp2G+k1GaX7bH1riStkTQsaXh0dPQIW4yIiLGO9Re53cbpPU69K9sbbA/ZHhoYGDhmzUVE1O5IQ//xMmRDed5f6iPAnI79BoG9pT7YpR4REX10pKG/BVhdtlcDt3XUV0k6WdI8mi9s7ylDQAckLSuzdi7pOCYiIvpk+kQ7SLoFeDVwpqQR4APAVcBmSZcCjwFvAbC9U9Jm4H7gCeBy24fKj7qMZibQDOCO8oiIiD6aMPRtX/wMb73uGfZfD6zvUh8GFk2qu4iIOKZyRW5EREUS+hERFUnoR0RUJKEfEVGRhH5EREUS+hERFUnoR0RUJKEfEVGRhH5EREUS+hERFUnoR0RUJKEfEVGRhH5EREUS+hERFUnoR0RUJKEfEVGRhH5EREWOKvQlPSpph6TtkoZL7QxJd0p6qDyf3rH/Okm7Je2SdMHRNh8REZNzLM70X2N7se2h8notsNX2fGBreY2kBcAqYCGwHLhO0rRj8PkREdGj4zG8swLYWLY3Ais76ptsH7T9CLAbWHocPj8iIp7B0Ya+gf8paZukNaV2tu19AOX5rFKfDezpOHak1J5G0hpJw5KGR0dHj7LFiIg4bPpRHn++7b2SzgLulPTgOPuqS83ddrS9AdgAMDQ01HWfiIiYvKM607e9tzzvBz5NM1zzuKRZAOV5f9l9BJjTcfggsPdoPj8iIibniENf0vMlnXZ4G/g14D5gC7C67LYauK1sbwFWSTpZ0jxgPnDPkX5+RERM3tEM75wNfFrS4Z/z321/XtI3gM2SLgUeA94CYHunpM3A/cATwOW2Dx1V9xERMSlHHPq2HwbO7VL/PvC6ZzhmPbD+SD8zIiKOTq7IjYioSEI/IqIiCf2IiIok9CMiKpLQj4ioSEI/IqIiCf2IiIok9CMiKpLQj4ioSEI/IqIiCf2IiIok9CMiKpLQj4ioSEI/IqIiCf2IiIok9CMiKpLQj4ioSEI/IqIifQ99Scsl7ZK0W9Lafn9+RETN+hr6kqYBfwq8AVgAXCxpQT97iIioWb/P9JcCu20/bPtnwCZgRZ97iIio1vQ+f95sYE/H6xHgV8buJGkNsKa8/LGkXX3orS1nAt/rxwfpw/34lKr07W8H+fsdB8/2v9+LuxX7HfrqUvPTCvYGYMPxb6d9koZtD7XdR0xe/nZTW61/v34P74wAczpeDwJ7+9xDRES1+h363wDmS5on6bnAKmBLn3uIiKhWX4d3bD8h6QrgC8A04EbbO/vZwwmoimGsZ6n87aa2Kv9+sp82pB4REc9SuSI3IqIiCf2IiIok9CMiKpLQj+iBpGmSvth2H3Fkyt/vj9vu40SQ0I/oge1DwN9JekHbvcTklb/fEkndLhCtSr+vyA1A0vnAB2kuk55Oc6Wybb+kzb5iQj8Fdki6E/jJ4aLtK9trKSbhW8Btkj7FU/9+t7bXUv8l9NtxA/BvgG3AoZZ7id7dXh4xNZ0BfB94bUfNQFWhn3n6LZB0t+2n3WguIuJ4S+j3kaRfKpu/QXNF8q3AwcPv2/5mG31FbyTNB/6IZi2IUw7XMyw3NUg6BbgUWMhT/37vbK2pFmR4p78+OuZ15x3+zFP/b2eceD4OfAC4BngN8A663zk2TkyfAB4ELgD+A/CbwAOtdtSCnOm3QNJLbD88US1OLJK22V4iaYftf15qX7X9yrZ7i4lJ+pbt8yTda/tlkk4CvmC7qpOtTNlsx//oUvtU37uIyfqppOcAD0m6QtJFwFltNxU9+/vy/CNJi4AXAHPba6cdGd7pI0n/lGY88QWSfr3jrZl0jDHGCeu9wPOAK4E/pBniWd1mQzEpGySdDvw+zS3dTwX+fbst9V+Gd/pI0gpgJfBmnrqOwAFgk+2/aqOvmBxJz7f9k4n3jDjxJPRbIOnltu9qu4+YHEkvp7nG4lTbvyDpXOBdtn+75daiB5LOBj4EvMj2GyQtAF5u+4aWW+urhH4LJA0Av0UznvgPQ2y1TR2baiTdDfxLYIvt80rtPtuL2u0seiHpDpoZWL9n+1xJ04FvHf5SvhYZ02/HbcBXgS+SK3KnFNt7xty+JX+/qeNM25slrYN/WMmvur9fQr8dz7P9/rabiEnbI+kVgMsaz1dS4TzvKewnkv4RzTUxSFoG/E27LfVfpmy247OSLmy7iZi0dwOXA7OBEWBxeR1Tw/toJlC8VNJfAjcDv9NuS/2XMf0WSDoAPB/4GT+fO2zbM9vrKiYi6QzbP2i7jzhyZRz/F2mupN5l++8nOORZJ6Ef0SNJDwHbgRuBzzv/45lSJA3T/O1usf3DtvtpS4Z3WiLpzZI+Uh5varuf6Mk5wAbgEmC3pA9JOqflnqJ3q2iG5r4haZOkC2pcVCVn+i2QdBXwy8AnS+liYJvtte11FZMh6TXAn9EM030bWJtrL6aGciuNNwHXA0/SnP3/p1qG7hL6LZB0L7DY9pPl9TSa+cIva7ezGE+Z+fE24O3A4zQXam2h+UL3U7bntddd9ELSy2jujnoh8AWaE69/Abzd9uIWW+ubTNlszwuBw2cWWXd1ariL5va8K22PdNSHJf3XlnqKHknaBvyI5h/rtbYPr2Vxd1nCtAo502+BpIuBq4Av0cwieBWwzvamVhuLcUlSvrydunL78kZCvyWSZtGM6wu42/Zft9xSRFQgs3faM1CepwGvGHOr5YiI4yJj+i2QdCPwMmAnzewBaC4Nv7W1piKiChneaYGk+20vaLuPmBxJg8B/ppnt8STwNeA9Y77UjROUpOfR3IrhF2z/Vlno/hdtf7bl1voqwzvtuKvcyzumlo/TTNGcRXORz2dKLaaGjwMHgZeX1yPAf2yvnXbkTL8Fkl5FExh/TfNfQtHceyfz9E9gkraPncvdrRYnJknDtocOL5Beat+2fW7bvfVTxvTbcSPNBT47+PmYfpz4vifpbcAt5fXFwPdb7Ccm52eSZvDzWyu/lOakqyoJ/XY8ZnvLxLvFCeadwH8BrqEJjr8qtZgaPgB8Hpgj6ZPA+cC/brWjFmR4pwWSrqO5IvczdJxp2M7snROYpFNs/7TtPuLIlVtpLKMZUv267e+13FLf5Uy/HTNowv7XOmqZsnniu0/S4zRLXX4F+Evb1a28NMWdAvyQJvsWSML2V1ruqa9yph8xCZJ+AXglzdDAhcCP8kXu1CDpw8C/Ysz1Mbbf3F5X/Zcz/ZZJ+qbtX2q7j5hYmad/Pk3on0sTHl9rtamYjJU08/Kr+/K2U0K/fdUt4jCFPQZ8A/iQ7Xe33UxM2sPASVQ4Y6dTQr99t7fdQIxP0nTbTwDn0VyN+1ZJa4GHgP9t+4ZWG4xe/R2wXdJWnjqB4sr2Wuq/jOm3RNJzaZbfg0oXaJ4qOofgJJ1KE/yvpFlQxbbntthe9EjS6m512xv73UubcqbfAkmvBjYCj9IM78yRtLq2WQRTTVlY+2Sa+flfBV5l+zvtdhWT8OfAP6GZKfd/a51+mzP9FpQVfN5qe1d5fQ5wi+0l7XYW3UgaAa6muQ12511Rmw376jb6it5Img58iOZCuu/Q3HNskOZePL9X2//Lzg3X2nHS4cAHsP1/aL5gihPTNOBU4Hnl+VTgtI5HnNj+GDgDmGd7SbnvzktpLpD8SJuNtSFn+i0o99M3zXqrAL8JTLf9jva6imeSabVTm6SHgHPGLnUpaRrwoO357XTWjozpt+My4HLgSpox/a8Af9pqRzGeTKud2txtbWPbhyRVd9ab4Z12vNv21bZ/3fZFtq+h+YcgTkyva7uBOCr3S7pkbLHcMfXBFvppVYZ3WtBtuKDzHt8RcexImk1zX6v/B2yjGVr9ZZp7YF1k+7stttd3Cf0+knQx8Faaed5f7XjrNOCQ7de30lhEBSS9FlhIM1y30/bWlltqRUK/jyS9GJgH/BGwtuOtA8C95arPiIjjJqHfMklvqm1h5ohoT0K/ZZkOGBH9lNk77ct0wIjom4R++97VdgMRUY8M77RE0iuAuXRcIGf75tYaiogq5IrcFkj6BM29P7YDh0rZQEI/Io6rnOm3QNIDwIJul4ZHRBxPGdNvx33AP267iYioT4Z3+kjSZ2iGcU6juR/IPTx12bY3t9VbRNQhod9f1d27OyJOLBnTb4GkD9t+/0S1iIhjLWP67fjVLrU39L2LiKhOhnf6SNJlwG8DL5F0b8dbp9Esth0RcVxleKePJL0AOJ0ud9m0/YN2uoqImiT0W1LW5zybp16R+1h7HUVEDTK80wJJVwAfBB4HnixlAy9rq6eIqEPO9FsgaTfwK7a/33YvEVGXzN5pxx7gb9puIiLqk+GddjwMfFnS7Tz1ityr22spImqQ0G/HY+Xx3PKIiOiLjOm3SNJpgG3/uO1eIqIOGdNvgaRFkr5Fc7fNnZK2SVrYdl8R8eyX0G/HBuDf2n6x7RcD7wM+1nJPEVGBhH47nm/7S4df2P4y8Pz22omIWuSL3HY8LOn3gU+U128DHmmxn4ioRM702/FOYAC4Ffh02X5Hqx1FRBUyeycioiIZ3ukjSVvGez/LJUbE8ZbQ76+X09yC4RbgbkDtthMRtcnwTh+V2yn/KnAxzR01bwdusb2z1cYiohr5IrePbB+y/Xnbq4FlwG6ae/D8TsutRUQlMrzTZ5JOBt5Ic7Y/F7iWZhZPRMRxl+GdPpK0EVgE3AFssn1fyy1FRGUS+n0k6UngJ+Vl53/wornx2sz+dxURNUnoR0RUJF/kRkRUJKEfEVGRhH5EREUS+hERFfn//k9CNbqDndwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "telcom['Contract'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 34,
     "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": [
    "telcom[\"MonthlyCharges\"].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 35,
     "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": [
    "telcom.tenure.plot.box()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# How to create new columns derived from existing columns?¶"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![cols](img/colums.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a new column by assigning the output to the DataFrame with a new column name in between the [].\n",
    "\n",
    "Operations are element-wise, no need to loop over rows.\n",
    "\n",
    "Use rename with a dictionary or function to rename row labels or column names."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### I want forecast montly sales in the next 10months "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>customerID</th>\n",
       "      <th>gender</th>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <th>Partner</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>tenure</th>\n",
       "      <th>PhoneService</th>\n",
       "      <th>MultipleLines</th>\n",
       "      <th>InternetService</th>\n",
       "      <th>OnlineSecurity</th>\n",
       "      <th>...</th>\n",
       "      <th>TechSupport</th>\n",
       "      <th>StreamingTV</th>\n",
       "      <th>StreamingMovies</th>\n",
       "      <th>Contract</th>\n",
       "      <th>PaperlessBilling</th>\n",
       "      <th>PaymentMethod</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>TotalCharges</th>\n",
       "      <th>Churn</th>\n",
       "      <th>ForecastMonthly</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7590-VHVEG</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>anonymous</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>29.85</td>\n",
       "      <td>29.85</td>\n",
       "      <td>No</td>\n",
       "      <td>298.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5575-GNVDE</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>anonymous</td>\n",
       "      <td>No</td>\n",
       "      <td>34</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>56.95</td>\n",
       "      <td>1889.5</td>\n",
       "      <td>No</td>\n",
       "      <td>569.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3668-QPYBK</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>anonymous</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>53.85</td>\n",
       "      <td>108.15</td>\n",
       "      <td>Yes</td>\n",
       "      <td>538.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7795-CFOCW</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>45</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>42.30</td>\n",
       "      <td>1840.75</td>\n",
       "      <td>No</td>\n",
       "      <td>423.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9237-HQITU</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>70.70</td>\n",
       "      <td>151.65</td>\n",
       "      <td>Yes</td>\n",
       "      <td>707.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   customerID  gender  SeniorCitizen    Partner Dependents  tenure  \\\n",
       "0  7590-VHVEG  Female              0  anonymous         No       1   \n",
       "1  5575-GNVDE    Male              0  anonymous         No      34   \n",
       "2  3668-QPYBK    Male              0  anonymous         No       2   \n",
       "3  7795-CFOCW    Male              0         No         No      45   \n",
       "4  9237-HQITU  Female              0         No         No       2   \n",
       "\n",
       "  PhoneService     MultipleLines InternetService OnlineSecurity  ...  \\\n",
       "0           No  No phone service             DSL             No  ...   \n",
       "1          Yes                No             DSL            Yes  ...   \n",
       "2          Yes                No             DSL            Yes  ...   \n",
       "3           No  No phone service             DSL            Yes  ...   \n",
       "4          Yes                No     Fiber optic             No  ...   \n",
       "\n",
       "  TechSupport StreamingTV StreamingMovies        Contract PaperlessBilling  \\\n",
       "0          No          No              No  Month-to-month              Yes   \n",
       "1          No          No              No        One year               No   \n",
       "2          No          No              No  Month-to-month              Yes   \n",
       "3         Yes          No              No        One year               No   \n",
       "4          No          No              No  Month-to-month              Yes   \n",
       "\n",
       "               PaymentMethod MonthlyCharges TotalCharges  Churn  \\\n",
       "0           Electronic check          29.85        29.85     No   \n",
       "1               Mailed check          56.95       1889.5     No   \n",
       "2               Mailed check          53.85       108.15    Yes   \n",
       "3  Bank transfer (automatic)          42.30      1840.75     No   \n",
       "4           Electronic check          70.70       151.65    Yes   \n",
       "\n",
       "  ForecastMonthly  \n",
       "0           298.5  \n",
       "1           569.5  \n",
       "2           538.5  \n",
       "3           423.0  \n",
       "4           707.0  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "telcom[\"ForecastMonthly\"] =telcom[\"MonthlyCharges\"] * 10\n",
    "telcom.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### I want to rename the data columns "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "telcom_renamed=telcom.rename(\n",
    "       columns={\"ForecastMonthly\": \"Forecast\",\"Churn\":\"ChurnedCustomer\"})"
   ]
  },
  {
   "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>customerID</th>\n",
       "      <th>gender</th>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <th>Partner</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>tenure</th>\n",
       "      <th>PhoneService</th>\n",
       "      <th>MultipleLines</th>\n",
       "      <th>InternetService</th>\n",
       "      <th>OnlineSecurity</th>\n",
       "      <th>...</th>\n",
       "      <th>TechSupport</th>\n",
       "      <th>StreamingTV</th>\n",
       "      <th>StreamingMovies</th>\n",
       "      <th>Contract</th>\n",
       "      <th>PaperlessBilling</th>\n",
       "      <th>PaymentMethod</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>TotalCharges</th>\n",
       "      <th>ChurnedCustomer</th>\n",
       "      <th>Forecast</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7590-VHVEG</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>anonymous</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>29.85</td>\n",
       "      <td>29.85</td>\n",
       "      <td>No</td>\n",
       "      <td>298.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5575-GNVDE</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>anonymous</td>\n",
       "      <td>No</td>\n",
       "      <td>34</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>56.95</td>\n",
       "      <td>1889.5</td>\n",
       "      <td>No</td>\n",
       "      <td>569.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   customerID  gender  SeniorCitizen    Partner Dependents  tenure  \\\n",
       "0  7590-VHVEG  Female              0  anonymous         No       1   \n",
       "1  5575-GNVDE    Male              0  anonymous         No      34   \n",
       "\n",
       "  PhoneService     MultipleLines InternetService OnlineSecurity  ...  \\\n",
       "0           No  No phone service             DSL             No  ...   \n",
       "1          Yes                No             DSL            Yes  ...   \n",
       "\n",
       "  TechSupport StreamingTV StreamingMovies        Contract PaperlessBilling  \\\n",
       "0          No          No              No  Month-to-month              Yes   \n",
       "1          No          No              No        One year               No   \n",
       "\n",
       "      PaymentMethod MonthlyCharges TotalCharges  ChurnedCustomer Forecast  \n",
       "0  Electronic check          29.85        29.85               No    298.5  \n",
       "1      Mailed check          56.95       1889.5               No    569.5  \n",
       "\n",
       "[2 rows x 22 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "telcom_renamed.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# How to calculate summary statistics?\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![agg](img/agg.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Aggregation statistics can be calculated on entire columns or rows\n",
    "\n",
    "groupby provides the power of the split-apply-combine pattern\n",
    "\n",
    "value_counts is a convenient shortcut to count the number of entries in each category of a variable\n",
    "\n"
   ]
  },
  {
   "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",
       "    }\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>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "      <th>class</th>\n",
       "      <th>who</th>\n",
       "      <th>adult_male</th>\n",
       "      <th>deck</th>\n",
       "      <th>embark_town</th>\n",
       "      <th>alive</th>\n",
       "      <th>alone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   survived  pclass     sex   age  sibsp  parch     fare embarked  class  \\\n",
       "0         0       3    male  22.0      1      0   7.2500        S  Third   \n",
       "1         1       1  female  38.0      1      0  71.2833        C  First   \n",
       "2         1       3  female  26.0      0      0   7.9250        S  Third   \n",
       "3         1       1  female  35.0      1      0  53.1000        S  First   \n",
       "4         0       3    male  35.0      0      0   8.0500        S  Third   \n",
       "\n",
       "     who  adult_male deck  embark_town alive  alone  \n",
       "0    man        True  NaN  Southampton    no  False  \n",
       "1  woman       False    C    Cherbourg   yes  False  \n",
       "2  woman       False  NaN  Southampton   yes   True  \n",
       "3  woman       False    C  Southampton   yes  False  \n",
       "4    man        True  NaN  Southampton    no   True  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get dataset # https://github.com/mwaskom/seaborn-data\n",
    "import seaborn as sns \n",
    "titanic= sns.load_dataset('titanic')\n",
    "titanic.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What is the average age of the titanic passengers?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "29.69911764705882"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic[\"age\"].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What is the median age and ticket fare price of the titanic passengers?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age     28.0000\n",
       "fare    14.4542\n",
       "dtype: float64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " titanic[[\"age\", \"fare\"]].median()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The aggregating statistic can be calculated for multiple columns at the same time. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>age</th>\n",
       "      <th>fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>29.699118</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>14.526497</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>20.125000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>28.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>38.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>80.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              age        fare\n",
       "count  714.000000  891.000000\n",
       "mean    29.699118   32.204208\n",
       "std     14.526497   49.693429\n",
       "min      0.420000    0.000000\n",
       "25%     20.125000    7.910400\n",
       "50%     28.000000   14.454200\n",
       "75%     38.000000   31.000000\n",
       "max     80.000000  512.329200"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "titanic[[\"age\", \"fare\"]].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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>age</th>\n",
       "      <th>fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>80.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median</th>\n",
       "      <td>28.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>skew</th>\n",
       "      <td>0.389108</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              age        fare\n",
       "max     80.000000  512.329200\n",
       "mean          NaN   32.204208\n",
       "median  28.000000   14.454200\n",
       "min      0.420000    0.000000\n",
       "skew     0.389108         NaN"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Instead of the predefined statistics, \n",
    "#specific combinations of aggregating statistics for given columns can be defined using the DataFrame.agg() method:\n",
    "titanic.agg({'age': ['min', 'max', 'median', 'skew'],\n",
    "             'fare': ['min', 'max', 'median', 'mean']})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What is the average age for male versus female titanic passengers?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>age</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>female</th>\n",
       "      <td>27.915709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>30.726645</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              age\n",
       "sex              \n",
       "female  27.915709\n",
       "male    30.726645"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic[[\"sex\", \"age\"]].groupby(\"sex\").mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What is the mean ticket fare price for each of the sex and cabin class combinations?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sex     pclass\n",
       "female  1         106.125798\n",
       "        2          21.970121\n",
       "        3          16.118810\n",
       "male    1          67.226127\n",
       "        2          19.741782\n",
       "        3          12.661633\n",
       "Name: fare, dtype: float64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.groupby([\"sex\", \"pclass\"])[\"fare\"].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What is the number of passengers in each of the cabin classes?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    491\n",
       "1    216\n",
       "2    184\n",
       "Name: pclass, dtype: int64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic[\"pclass\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pclass\n",
       "1    216\n",
       "2    184\n",
       "3    491\n",
       "Name: pclass, dtype: int64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.groupby(\"pclass\")[\"pclass\"].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# How to reshape the layout of tables?\n",
    "### Sort table rows¶"
   ]
  },
  {
   "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>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "      <th>class</th>\n",
       "      <th>who</th>\n",
       "      <th>adult_male</th>\n",
       "      <th>deck</th>\n",
       "      <th>embark_town</th>\n",
       "      <th>alive</th>\n",
       "      <th>alone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>803</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8.5167</td>\n",
       "      <td>C</td>\n",
       "      <td>Third</td>\n",
       "      <td>child</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>755</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>0.67</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>14.5000</td>\n",
       "      <td>S</td>\n",
       "      <td>Second</td>\n",
       "      <td>child</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>644</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>0.75</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>19.2583</td>\n",
       "      <td>C</td>\n",
       "      <td>Third</td>\n",
       "      <td>child</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>469</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>0.75</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>19.2583</td>\n",
       "      <td>C</td>\n",
       "      <td>Third</td>\n",
       "      <td>child</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>29.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>Second</td>\n",
       "      <td>child</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     survived  pclass     sex   age  sibsp  parch     fare embarked   class  \\\n",
       "803         1       3    male  0.42      0      1   8.5167        C   Third   \n",
       "755         1       2    male  0.67      1      1  14.5000        S  Second   \n",
       "644         1       3  female  0.75      2      1  19.2583        C   Third   \n",
       "469         1       3  female  0.75      2      1  19.2583        C   Third   \n",
       "78          1       2    male  0.83      0      2  29.0000        S  Second   \n",
       "\n",
       "       who  adult_male deck  embark_town alive  alone  \n",
       "803  child       False  NaN    Cherbourg   yes  False  \n",
       "755  child       False  NaN  Southampton   yes  False  \n",
       "644  child       False  NaN    Cherbourg   yes  False  \n",
       "469  child       False  NaN    Cherbourg   yes  False  \n",
       "78   child       False  NaN  Southampton   yes  False  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#I want to sort the titanic data according to the age of the passengers.\n",
    "titanic.sort_values(by=\"age\").head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### want to sort the titanic data according to the cabin class and age in descending order."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "      <th>class</th>\n",
       "      <th>who</th>\n",
       "      <th>adult_male</th>\n",
       "      <th>deck</th>\n",
       "      <th>embark_town</th>\n",
       "      <th>alive</th>\n",
       "      <th>alone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>851</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>74.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.7750</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>70.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>Q</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Queenstown</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>65.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>Q</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Queenstown</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>483</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>63.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9.5875</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>326</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>61.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6.2375</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     survived  pclass     sex   age  sibsp  parch    fare embarked  class  \\\n",
       "851         0       3    male  74.0      0      0  7.7750        S  Third   \n",
       "116         0       3    male  70.5      0      0  7.7500        Q  Third   \n",
       "280         0       3    male  65.0      0      0  7.7500        Q  Third   \n",
       "483         1       3  female  63.0      0      0  9.5875        S  Third   \n",
       "326         0       3    male  61.0      0      0  6.2375        S  Third   \n",
       "\n",
       "       who  adult_male deck  embark_town alive  alone  \n",
       "851    man        True  NaN  Southampton    no   True  \n",
       "116    man        True  NaN   Queenstown    no   True  \n",
       "280    man        True  NaN   Queenstown    no   True  \n",
       "483  woman       False  NaN  Southampton   yes   True  \n",
       "326    man        True  NaN  Southampton    no   True  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.sort_values(by=['pclass', 'age'], ascending=False).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  Read More \n",
    "\n",
    "https://github.com/LearnDataSci/articles/blob/master/Python%20Pandas%20Tutorial%20A%20Complete%20Introduction%20for%20Beginners/notebook.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n"
   ]
  }
 ],
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