{"id":20892499,"url":"https://github.com/prajjwol09/datacleaning-using-pandas","last_synced_at":"2026-05-17T17:32:57.157Z","repository":{"id":256728898,"uuid":"856248829","full_name":"Prajjwol09/DataCleaning-Using-Pandas","owner":"Prajjwol09","description":"This project focuses on data cleaning and preprocessing a customer call list using Python's Pandas library. 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The primary focus is on preparing the data for analysis by removing unnecessary values, standardizing formats, handling missing values, and ensuring consistency throughout the dataset.\n\nProject Overview:\n\nThe dataset contains customer information such as phone numbers, addresses, and preferences regarding communication. The cleaning process involves:\n\nStripping unwanted characters from the last names.\n\nCleaning and reformatting phone numbers to a standard xxx-xxx-xxxx format.\n\nSplitting the address into separate columns (street name, state, and zip code).\n\nStandardizing responses for customer preferences.\n\nHandling missing values by either filling them with empty strings or dropping records.\n\nRemoving duplicates and irrelevant columns.\n\nSteps Performed:\n\nImporting Required Libraries:\n\nUsed pandas for data manipulation.\n\nLoading Data:\n\nThe dataset is loaded from an Excel file.\n\nCleaning Last Names:\n\nStripped special characters (123./_) from last names using str.strip().\n\nCleaning and Formatting Phone Numbers:\n\nRemoved all non-alphanumeric characters from the phone number column.\n\nReformatted the cleaned phone numbers to the format xxx-xxx-xxxx.\n\nHandled cases where phone numbers were missing.\n\nSplitting the Address Column:\n\nSplit the full address into separate columns for Street Name, State, and Zip Code.\n\nStandardizing Customer Preferences:\n\nConverted the values in the Paying Customer and Do Not Contact columns to 'Y' (Yes) and 'N' (No).\n\nHandling Missing Data:\n\nReplaced missing values (NaN) with empty strings.\n\nRemoved rows where important fields, such as Phone Number, were missing.\n\nRemoving Duplicates:\n\nIdentified and removed duplicate records.\n\nDropping Unnecessary Columns:\n\nRemoved irrelevant columns, such as Address and Not Useful Column.\n\nFinal Dataset:\n\nReset the index and ensured the cleaned dataset is ready for further analysis or export.\n\nPrerequisites\n\nPython \n\nPandas library\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fprajjwol09%2Fdatacleaning-using-pandas","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fprajjwol09%2Fdatacleaning-using-pandas","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fprajjwol09%2Fdatacleaning-using-pandas/lists"}