Скачать с ютуб #6 Mastering Data Cleaning in Pandas: Detecting, Dropping, Filling Missing Data &Handling Duplicates в хорошем качестве

#6 Mastering Data Cleaning in Pandas: Detecting, Dropping, Filling Missing Data &Handling Duplicates

#learnpython #Pandas #Python #DataScience #DataAnalysis #PythonProgramming #PandasTutorial #LearnPandas #PythonDataScience #DataManipulation #PythonForBeginners#pandaslibrary #pandas #pandaspythontutorial #pythonpandas #pandasDataFrame #DataFrameTutorial #PythonDataAnalysis #pandasTutorial #ImportDataInPandas #ExportDataInPandas #CreateDataFrameInPython #PythonPandasDataFrame #CSVtoDataFrame #ExcelToDataFrame #SQLtoDataFrame #JSONtoDataFrame #DataScienceWithPython #PythonDataManipulation #pandasforbeginners ========================================================== Numpy playlist -    • Numpy in Python   =============================================== pandas part1 -    • #1 Beginner’s Guide to pandas: What, ...   pandas part-2 -    • #2 Complete Guide to pandas Series: C...   pandas part-3 -   • #3 Complete Guide to DataFrames:Impor...   pandas part-4 -    • #4 Pandas Data Selection .loc[] vs .i...   pandas part-5 -    • #5 How to Add, Remove, Rename & sort ...   =================================================== In this video, you'll learn how to clean and prepare your data for analysis using powerful Pandas methods in Python. We'll cover essential techniques for handling missing data and duplicates, ensuring your datasets are accurate and reliable for further insights. Topics Covered: Detecting Missing Values: Use .isna() and .notna() to identify gaps in your dataset. Dropping Missing Data: Learn how to use .dropna() to remove rows or columns with missing values. Filling Missing Data: Fill missing values with .fillna() using placeholders or calculated values to maintain data integrity. Handling Duplicates: Identify and remove duplicate records with .drop_duplicates() to ensure a clean dataset. Whether you're cleaning customer data, sales records, or any dataset, these methods will help you prepare your data for more accurate analysis. Code Examples Included! 🔔 Don’t forget to like, subscribe, and hit the bell icon for more data science tutorials!