rekhadivay / Data_cleaning_using_Pandas

Data Cleaning Steps

Load Data:

Read the dataset from an Excel file into a Pandas DataFrame.

Remove Duplicates:

Eliminate duplicate rows to ensure unique records in the dataset.

Remove Unnecessary Column:

Drop a column that is not useful for the analysis.

Clean "Last_Name" Column:

Strip extraneous characters from the "Last_Name" entries.

Clean "Phone_Number" Column:

Standardize phone numbers by removing non-numeric characters and formatting them as xxx-xxx-xxxx.

Split "Address" Column:

Divide the "Address" column into separate columns for street address, state, and zip code.

Clean "Do_Not_Contact" Column:

Standardize values in the "Do_Not_Contact" column, replacing 'Yes' with 'Y' and 'No' with 'N'.

Remove Rows Based on Conditions:

Remove rows where "Do_Not_Contact" is 'Y' or the "Phone_Number" is empty.

Reset Index:

Reset the DataFrame index for a clean representation of the cleaned dataset.