@loresowhat: Data cleaning isn’t sexy but it’s literally what separates analysts who get promoted from those who don’t 🧹   This Python workflow takes messy flight booking data and transforms it into analysis ready gold:   Standardize text (strip whitespace, uppercase airport codes, title case names). Handle missing values (fillna for numerical zeros, placeholders for missing identities). Remove duplicates (drop_duplicates keeps first occurrence, removes system errors). Convert dates to datetime objects (pd.to_datetime with errors=‘coerce’ for time series analysis).   The analysts who skip this step are the ones constantly fixing broken dashboards at midnight. Don’t be that person.   Comment “CODE” for the full Python script and save this before your next messy dataset 📊   #DataCleaning #PythonForDataScience #PandasTutorial #DataAnalyticsProjects

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Sunday 21 June 2026 10:00:12 GMT