Counting the number of missing/NaN in each row in python

Counting the number of missing/NaN in each row in python

You can count the number of missing (NaN) values in each row of a Pandas DataFrame by using the isna() or isnull() method along with the sum() function. Here's how you can do it:

import pandas as pd # Create a sample DataFrame data = {'A': [1, 2, None, 4], 'B': [None, 2, 3, 4], 'C': [1, None, 3, 4]} df = pd.DataFrame(data) # Count the number of missing values (NaN) in each row missing_counts = df.isna().sum(axis=1) # Print the result print(missing_counts)

Output:

0 1 1 1 2 2 3 0 dtype: int64

In this example, df.isna() creates a DataFrame of the same shape as the original DataFrame but with True in the places where the values are missing (NaN), and False where they are not. Calling sum(axis=1) then sums the True values for each row, effectively giving you the count of missing values in each row.

You can also use df.isnull() instead of df.isna(); they are equivalent and provide the same result.

Examples

  1. How to count the number of missing values (NaN) in each row of a pandas DataFrame in Python?

    Description: This query looks for methods to count the number of missing values (NaN) present in each row of a pandas DataFrame, a common preprocessing step in data analysis.

    import pandas as pd # Sample DataFrame df = pd.DataFrame({'A': [1, 2, None], 'B': [None, 5, 6], 'C': [7, 8, 9]}) # Count missing values in each row missing_values_per_row = df.isnull().sum(axis=1) print(missing_values_per_row)

  2. Python code to count NaN values in each row of a pandas DataFrame and create a new column

    Description: This query demonstrates how to count NaN values in each row of a pandas DataFrame and append the count as a new column for further analysis.

    import pandas as pd # Sample DataFrame df = pd.DataFrame({'A': [1, None, 3], 'B': [None, 5, None], 'C': [7, 8, 9]}) # Count NaN values in each row and create a new column df['NaN_count'] = df.isnull().sum(axis=1) print(df)

  3. How to calculate the percentage of missing values in each row of a pandas DataFrame?

    Description: This query aims to compute the percentage of missing values in each row of a pandas DataFrame, offering insights into data completeness.

    import pandas as pd # Sample DataFrame df = pd.DataFrame({'A': [1, None, 3], 'B': [None, 5, None], 'C': [7, 8, 9]}) # Calculate percentage of missing values in each row total_cols = len(df.columns) missing_percentage_per_row = (df.isnull().sum(axis=1) / total_cols) * 100 print(missing_percentage_per_row)

  4. Python code to count missing values in each row of a pandas DataFrame ignoring certain columns

    Description: This query addresses counting missing values in each row of a pandas DataFrame while ignoring specific columns, providing flexibility in analysis.

    import pandas as pd # Sample DataFrame df = pd.DataFrame({'A': [1, None, 3], 'B': [None, 5, None], 'C': [7, 8, 9]}) # Define columns to ignore ignore_columns = ['C'] # Count missing values in each row, ignoring specified columns missing_values_per_row = df.drop(columns=ignore_columns).isnull().sum(axis=1) print(missing_values_per_row)

  5. How to count missing values in each row of a pandas DataFrame with conditionals?

    Description: This query explores counting missing values in each row of a pandas DataFrame based on conditional logic, allowing customized analysis.

    import pandas as pd # Sample DataFrame df = pd.DataFrame({'A': [1, None, 3], 'B': [None, 5, None], 'C': [7, 8, 9]}) # Define condition condition = df['A'].isnull() | df['B'].isnull() # Count missing values in each row based on condition missing_values_per_row = condition.sum() print(missing_values_per_row)

  6. Python code to count NaN values in each row of a pandas DataFrame and find rows with specific number of missing values

    Description: This query demonstrates counting NaN values in each row of a pandas DataFrame and identifies rows with a specific number of missing values.

    import pandas as pd # Sample DataFrame df = pd.DataFrame({'A': [1, None, None], 'B': [None, None, 6], 'C': [None, 8, None]}) # Count NaN values in each row df['NaN_count'] = df.isnull().sum(axis=1) # Filter rows with a specific number of missing values specific_missing_values_rows = df[df['NaN_count'] == 2] print(specific_missing_values_rows)

  7. How to count missing values in each row of a pandas DataFrame and drop rows with a certain threshold of missing values?

    Description: This query addresses counting missing values in each row of a pandas DataFrame and dropping rows exceeding a certain threshold of missing values.

    import pandas as pd # Sample DataFrame df = pd.DataFrame({'A': [1, None, 3], 'B': [None, 5, None], 'C': [7, 8, None]}) # Define threshold for missing values threshold = 1 # Count missing values in each row df['NaN_count'] = df.isnull().sum(axis=1) # Drop rows exceeding threshold of missing values filtered_df = df[df['NaN_count'] <= threshold] print(filtered_df)

  8. Python code to count missing values in each row of a pandas DataFrame and fill missing values with a specific value

    Description: This query demonstrates counting missing values in each row of a pandas DataFrame and filling missing values with a specified value for data imputation.

    import pandas as pd # Sample DataFrame df = pd.DataFrame({'A': [1, None, 3], 'B': [None, 5, None], 'C': [7, 8, None]}) # Fill missing values with a specific value (e.g., 0) filled_df = df.fillna(0) print(filled_df)

  9. How to count missing values in each row of a pandas DataFrame and drop rows with any missing value?

    Description: This query addresses counting missing values in each row of a pandas DataFrame and dropping rows containing any missing value for data cleaning purposes.

    import pandas as pd # Sample DataFrame df = pd.DataFrame({'A': [1, None, 3], 'B': [None, 5, None], 'C': [7, 8, 9]}) # Drop rows with any missing value cleaned_df = df.dropna() print(cleaned_df)

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