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using Pandas and NumPy.

$1.$ Importing Necessary Libraries

# First, let's import the necessary libraries:

# Pandas for data manipulation and analysis

# NumPy for numerical operations.

import pandas as pd

import numpy as np

$2.$ Loading the Dataset

We'll start by loading the dataset "CableTV Subscriber Data.csv$"$ into a Pandas DataFrame. A DataFrame is a core data structure in the pandas library, widely used in data manipulation and analysis in Python. It can be thought of as a table, similar to an Excel spreadsheet or a SQL table, where data is organized in rows and columns.

# Please ensure you download the dataset and place it in the same folder as your Jupyter notebook file.

# If it's in a different location, remember to add the correct file path when loading the dataset.

# creating the dataframe

data $=$ pd$.$read$\_$csv$("$CableTVSubscribersData$.$csv$")$

# Display the first few rows of the dataset to get an overview

# 'dataframe.head$()\text{'}$ is used to quickly preview the first few rows of a DataFrame. By default, it returns the first $5$ rows.

data.head$()$

age gender income kids ownHome subscribe Segment

$047\mathrm{.}316133$ Male $49482\mathrm{.}810442$ ownNo subNo Suburb mix

$131\mathrm{.}386839$ Male $35546\mathrm{.}288301$ ownYes subNo Suburb mix

$243\mathrm{.}200342$ Male $44169\mathrm{.}186380$ ownYes subNo Suburb mix

$337\mathrm{.}316995$ Female $81041\mathrm{.}986391$ ownNo subNo Suburb mix

$440\mathrm{.}954390$ Female $79353\mathrm{.}014443$ ownYes subNo Suburb mix

$3.$ Data Overview and Description

Let's take a closer look at the dataset by using some of Pandas' descriptive functions.

# Get basic information about the dataset using 'dataframe.info$()\text{'}.$

# 'dataframe.info$()\text{'}$ is a method used to get a concise summary of a DataFrame.

data.info$()$

RangeIndex: $300$ entries, $0$ to $299$

Data columns $($total $7$ columns$)$:

# Column Non$-$Null Count Dtype

$----------------------------$

$0$ age $300$ non$-$null float$64$

$1$ gender $300$ non$-$null object

$2$ income $300$ non$-$null float$64$

$3$ kids $300$ non$-$null int$64$

$4$ ownHome $300$ non$-$null object

$5$ subscribe $300$ non$-$null object

$6$ Segment $300$ non$-$null object

dtypes: float$64(2),$ int$64(1),$ object$(4)$

memory usage: $16\mathrm{.}5+$ KB

# Get statistical summary of numerical columns

# 'dataframe.describe$()\text{'}$ provides a summary of the statistical information for a DataFrame's numerical columns.

# It is a quick way to get an overview of the central tendencies, dispersions, and shape of the distribution of a dataset's values.

data.describe$()$

age income kids

count $300\mathrm{.}000000300\mathrm{.}000000300\mathrm{.}000000$

mean $41\mathrm{.}19965050936\mathrm{.}5361841\mathrm{.}270000$

std $12\mathrm{.}70742720137\mathrm{.}5494311\mathrm{.}408443$

min $19\mathrm{.}259932-5183\mathrm{.}3542430\mathrm{.}000000$

$25\%33\mathrm{.}01205939656\mathrm{.}2836250\mathrm{.}000000$

$50\%39\mathrm{.}48767452014\mathrm{.}3524501\mathrm{.}000000$

$75\%47\mathrm{.}89565761403\mathrm{.}1762652\mathrm{.}000000$

max $80\mathrm{.}486179114278\mathrm{.}2556007\mathrm{.}000000$

$4.$ Data Cleaning

Data cleaning is an essential step in data processing. Let's check for missing values and handle them accordingly.

# Check for missing values

data.isnull$().$sum$()$

age $0$

gender $0$

income $0$

kids $0$

ownHome $0$

subscribe $0$

Segment $0$

dtype: int$64$

Suppose there are some missing values, we can decide to fill them or drop them depending on the context. For demonstration, we'll fill missing values in the 'Income' column with the median income.

# Fill missing values in 'income' column with median

data$[\text{'}$income$\text{'}].$fillna$($data$[\text{'}$income$\text{'}].$median$())$

$049482\mathrm{.}81044$

$135546\mathrm{.}28830$

$244169\mathrm{.}18638$

$381041\mathrm{.}98639$

$479353\mathrm{.}01444$

$...$

$29543882\mathrm{.}42561$

$29664197\mathrm{.}08688$

$29747580\mathrm{.}92678$

$29860747\mathrm{.}33640$

$29953674\mathrm{.}93137$

Name: income, Length: $300,$ dtype: float$64$

# Drop any remaining rows with missing values

data.dropna$($inplace$=$True$)$

# Verify that there are no missing values left

data.isnull$().$sum$()$

age $0$

gender $0$

income $0$

kids $0$

ownHome $0$

subscribe $0$

Segment $0$

dtype: int$64$

$5.$ Some Common Pandas Operations and Practices

We'll explore some example tasks that are frequently used in data processing with Pandas.

$5\mathrm{.}1$ Filtering Data

#filter the dataset to find all subscribers who own a home and have more than $2$ kids.

homeowners$\_$with$\_$kids $=$ data$[($data$[\text{'}$ownHome$\text{'}]==$ 'ownYes'$)$ & $($data$[\text{'}$kids$\text{'}]>2)]$

homeowners$\_$with$\_$kids.head$()$

age gender income kids ownHome subscribe Segment

$440\mathrm{.}954390$ Female $79353\mathrm{.}014443$ ownYes subNo Suburb mix

$543\mathrm{.}033865$ Male $58143\mathrm{.}363324$ ownYes subNo Suburb mix

$1343\mathrm{.}181091$ Male $57200\mathrm{.}145643$ ownYes subNo Suburb mix

$1542\mathrm{.}030555$ Male $57036\mathrm{.}837333$ ownYes subNo Suburb mix

$2136\mathrm{.}701617$ Female $46163\mathrm{.}383326$ ownYes subNo Suburb mix

$5\mathrm{.}2$ Grouping and Aggregating Data

# Group the data by the 'segment' and calculate the average income for each segment.

average$\_$income$\_$by$\_$segment $=$ data.groupby$(\text{'}$Segment$\text{'})[\text{'}$income$\text{'}].$mean$()$

average$\_$income$\_$by$\_$segment

Segment

Moving up $53090\mathrm{.}965254$

Suburb mix $55033\mathrm{.}815593$

Travelers $62213\mathrm{.}941787$

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