anuayshh / data_cleaning

Data_Cleaning

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 and OUTPUT

DATA SET:

import pandas as pd
df=pd.read_csv("Data_set.csv")
print(df)
df.head(10)
df.info()
df.isnull()
df.isnull().sum()
df['show_name']=df['show_name'].fillna(df['aired_on'].mode()[0])
df['aired_on']=df['aired_on'].fillna (df['aired_on'].mode()[0])
df['original_network']=df[ 'original_network'].fillna (df['aired_on'].mode()[0])
df.head()
df['rating']=df['rating'].fillna (df['rating'].mean())
df['current_overall_rank']=df['current_overall_rank'].fillna(df['current_overall_rank'].mean())
df.head()
df['watchers']=df['watchers'].fillna (df['watchers'].median())
df.head()
df.info()
df.isnull().sum()

LOAN DATA:

import pandas as pd
df=pd.read_csv("Loan_data.csv")
print(df)
df.head(10)
df.info()
df.isnull()
df.isnull().sum()
df['Loan_ID']=df['Loan_ID'].fillna(df['LoanAmount'].mode()[0])
df.head()
df['Dependents']=df['Dependents'].fillna (df['Dependents'].mode()[0])
df.head()
df['Gender']=df['Gender'].fillna (df['Gender'].mode()[0])
df.head()
df['Education']=df['Education'].fillna (df['Dependents'].mode()[0])
df.head()
df['Self_Employed']=df['Self_Employed'].fillna (df['Self_Employed'].mode()[0])
df.head()
df['LoanAmount']=df['LoanAmount'].fillna (df['LoanAmount'].mean())
df.head()
df['Loan_Amount_Term']=df['Loan_Amount_Term'].fillna(df['Loan_Amount_Term'].mean())
df.head()
df['Credit_History']=df['Credit_History'].fillna (df['Credit_History'].median())
df.head()
df.info()
df.isnull().sum()

OUTPUT:

DATA SET:

DATA:

NON NULL BEFORE:

NON NULL AFTER:

LOAN DATA:

DATA:

NON NULL BEFORE:

MODE:

MEAN:

MEDIAN:

NON NULL AFTER: