Research Breakthrough Possible @S-Logix pro@slogix.in

Office Address

Social List

How to Handle Missing Values in a Dataset Using Pandas

Handling Missing Values in a Dataset

Condition for Handling Missing Values in a Dataset

  • Description:
    In data analysis, missing values refer to the absence of data in certain cells of a dataset. Handling missing values is crucial for ensuring the quality of data, as they can affect statistical analyses and machine learning models.
Step-by-Step Process
  • Import Libraries:
    Import the required library (Pandas).
  • Load the Data:
    Load your dataset (from a CSV file, Excel, or other sources).
  • Find Missing Values:
    Use the isna() or isnull() method to identify missing values in the dataset.
  • Analyze Missing Data:
    Also, use sum() or describe() to check how many missing values are present in each column.
  • Drop Missing Values:
    Use dropna() to remove rows or columns with missing values.
Sample Source Code
  • # Code for Finding Missing Values and Dropping It

    import pandas as pd

    data = {
    'Product': ['A', 'B', 'C', 'D', None],
    'Price': [10.5, 20.5, None, 40.0, 15.0],
    'Units Sold': [100, None, 200, 150, 50]
    }

    df = pd.DataFrame(data)

    # Show the original dataset with missing values
    print("Original DataFrame:")
    print(df)

    # Find missing values using isna() method
    missing_values = df.isna()

    # Display the missing values
    print("\nMissing Values (True indicates missing data):")
    print(missing_values)

    # Drop rows with missing values
    df_cleaned = df.dropna() # Drops rows where any column has a missing value

    # Show the DataFrame after dropping rows with missing values
    print("\nDataFrame after Dropping Rows with Missing Values:")
    print(df_cleaned)

    # Now drop columns with missing values
    df_cleaned_columns = df.dropna(axis=1) # Drops columns with any missing value

    # Show the DataFrame after dropping columns with missing values
    print("\nDataFrame after Dropping Columns with Missing Values:")
    print(df_cleaned_columns)
Screenshots
  • Handling Missing Values Output