Real World Data Cleaning in Python Pandas (Step By Step)

Step-by-Step Tutorial: Real World Data Cleaning in Python Pandas

1. Accessing the Data:

   • Open a new tab in your browser and load the CSV data file provided in the video description.
   • Download the data into an Excel file.
   • Open the data in Excel by clicking on "From Web" and entering the URL.
   • Load the data into the spreadsheet.

2. Cleaning the Data:

   • Replace blank cells with null values to make data manipulation easier.
   • Create duplicate entries to demonstrate how to drop duplicate data.
   • Save the cleaned data as a CSV file.

3. Importing Data into Pandas:

   • Open Jupyter Notebook and import Pandas.
   • Import the CSV file using pd.read_csv() and assign it to a DataFrame (DF).

4. Renaming Columns:

   • Use DF.rename() to rename columns to more meaningful names.
   • Ensure columns have appropriate labels for better understanding.

5. Handling Null Values:

   • Check for null values using DF.isnull() and identify columns with null values.
   • Replace null values with zeros in specific columns to prepare for calculations.

6. Dropping Duplicate Rows:

   • Identify and drop duplicate rows using DF.duplicated() and DF.drop_duplicates().
   • Ensure only unique rows remain in the DataFrame.

7. Manipulating Columns:

   • Split the 'span' column into 'rookie year' and 'final year' columns for better analysis.
   • Create new columns for 'player' and 'country' to enhance data analysis.

8. Handling Data Types:

   • Check the data types of columns using DF.dtypes.
   • Convert columns to appropriate data types (e.g., integers, floats) using astype().

9. Data Analysis:

   • Calculate the average career length of cricketers in the dataset.
   • Calculate the average batting strike rate for cricketers who played over 10 years.
   • Count the number of cricketers who played before 1960.
   • Find the highest ending score by country using groupby and max functions.
   • Calculate the average of 150s and Ducks by country using mean function.

10. Final Steps:

    • Analyze the data based on the questions provided in the video.
    • Save and export the cleaned and analyzed data for further use.

By following these steps, you can effectively clean, manipulate, and analyze real-world data using Python Pandas, as demonstrated in the video tutorial.