Ex-01_DS_Data_Cleansing AIM To read the given data and perform data cleaning and save the cleaned data to a file.

Explanation Data cleaning is the process of preparing data for analysis by removing or modifying data that is incorrect ,incompleted , irrelevant , duplicated or improperly formatted. Data cleaning is not simply about erasing data ,but rather finding a way to maximize datasets accuracy without necessarily deleting the information.

ALGORITHM STEP 1 Read the given Data

STEP 2 Get the information about the data

STEP 3 Remove the null values from the data

STEP 4 Save the Clean data to the file

CODE

import pandas as pd import numpy as np from google.colab import files uploaded=files.upload() df=pd.read_csv('/content/Data_set (1).csv') df.info() df.head() df=df.dropna(subset=['show_name']) df.head() df['aired_on']=df['aired_on'].fillna(df['aired_on'].mode()[0]) df.head() df['original_network']=df['original_network'].fillna(df['original_network'].mode()[0]) df.head() df['rating']=df['rating'].fillna(df['rating'].median()) df.head() df=df.dropna(subset=['current_overall_rank']) df.head() df['watchers']=df['watchers'].fillna(df['watchers'].mean()) df.head() df.info()

import pandas as pd import numpy as np from google.colab import files uploaded=files.upload() df=pd.read_csv('Loan_data.csv') df.info() df.head() df['Gender']=df['Gender'].fillna('Female') df.head() df['Dependents']=df['Dependents'].fillna(method='ffill') df.head() df['Self_Employed']=df['Self_Employed'].fillna(df['Self_Employed'].mode()[0]) df.head() df=df.dropna(subset=['LoanAmount']) df.head() df['Loan_Amount_Term']=df['Loan_Amount_Term'].fillna(df['Loan_Amount_Term'].median()) df.head() df['Credit_History']=df['Credit_History'].fillna(df['Credit_History'].min()) df.head() df.info() OUTPUT Data_set.csv : non null before: 261836958-ad3b2517-f2e6-4d96-87dc-49810fe0a36c

261836963-af56a5e6-27a8-4380-b012-2d9e20a983df

261836968-37d0a314-ee3b-4d39-addd-8cb6795f7623

261836973-9e0e6cc2-f09d-4898-9cf9-76790c818b7e

261836979-ffaace5a-4a9d-47a0-b00f-6f63d540143e

261836988-837aeda2-4ca6-489c-b23c-fc5c1ef8c1c6

261836997-775e4f8d-0748-4cd2-80cd-989947c8828f

non null after: 261837110-af870054-b99f-4fd3-af78-c861f5ca3c1d

Loan_data.csv: non null before: 261837537-e0607ba4-f472-447e-9c9b-33abe5e213b2

261838856-57b6ec7b-9706-4f74-831a-f857cc6d0b64

261838863-430771e5-e43c-4907-99c6-befaefb7433c

261838867-dd98d932-ba16-4d00-8c37-50d1b517ce5c

261838880-9ada9191-d888-4e3d-bc6f-8852791081b8

261838881-6653be15-acd1-4d58-a4bd-7e0a32e880bd

261838884-608c0170-ba81-4b50-af26-c5df77c74452

non null after: 261838894-231e324c-6a95-41ed-bc8d-d3c48d4354b9

Result: Thus the given data is read,cleansed and the cleaned data is saved into the fil