First non-null value per row from a list of Pandas columns

First non-null value per row from a list of Pandas columns

To find the first non-null value per row from a list of Pandas DataFrame columns, you can use the apply function along with a custom function that performs the check for each row. Here's how you can do it:

import pandas as pd import numpy as np # Create a sample DataFrame data = { 'col1': [None, 5, 10, None, 15], 'col2': [3, None, None, 8, None], 'col3': [None, None, None, None, None] } df = pd.DataFrame(data) # Define a custom function to find the first non-null value in a row def first_non_null(row): for value in row: if pd.notna(value): return value return np.nan # Apply the custom function to each row and create a new column with the result df['first_non_null'] = df.apply(first_non_null, axis=1) print(df)

In this example, the first_non_null function iterates through the values in each row and returns the first non-null value it encounters. If no non-null value is found, it returns np.nan. The apply function is used to apply this custom function to each row of the DataFrame along axis=1, and the result is stored in a new column 'first_non_null'.

Remember that using apply on a DataFrame can be slower compared to using vectorized operations for larger datasets, but for small to moderate-sized datasets, this approach works fine.

Examples

  1. Finding the first non-null value per row in a Pandas DataFrame

    • Description: This query seeks a method to identify the first non-null value in each row of a Pandas DataFrame efficiently.
    # Code Implementation import pandas as pd # Sample DataFrame df = pd.DataFrame({'A': [None, 2, None, 4], 'B': [5, None, 7, None], 'C': [None, None, 10, 11]}) # Find the first non-null value per row first_non_null = df.ffill(axis=1).iloc[:, 0] print(first_non_null)

  2. Extracting the index of the first non-null value per row in a Pandas DataFrame

    • Description: This query focuses on extracting the index (column name) of the first non-null value in each row of a Pandas DataFrame.
    # Code Implementation import pandas as pd # Sample DataFrame df = pd.DataFrame({'A': [None, 2, None, 4], 'B': [5, None, 7, None], 'C': [None, None, 10, 11]}) # Extract index of first non-null value per row first_non_null_index = df.apply(lambda row: row.first_valid_index(), axis=1) print(first_non_null_index)

  3. Handling missing values when finding the first non-null value per row in Pandas DataFrame

    • Description: This query explores methods to handle missing values appropriately when identifying the first non-null value in each row of a Pandas DataFrame.
    # Code Implementation import pandas as pd # Sample DataFrame with missing values df = pd.DataFrame({'A': [None, 2, None, 4], 'B': [5, None, 7, None], 'C': [None, None, 10, 11]}) # Find first non-null value per row, ignoring NaNs first_non_null = df.apply(lambda row: row.dropna().iloc[0], axis=1) print(first_non_null)

  4. Handling scenarios where all values in a row are null in a Pandas DataFrame

    • Description: This query addresses how to handle cases where all values in a row are null when finding the first non-null value in each row of a Pandas DataFrame.
    # Code Implementation import pandas as pd # Sample DataFrame with all null values df = pd.DataFrame({'A': [None, None, None, None], 'B': [None, None, None, None], 'C': [None, None, None, None]}) # Find first non-null value per row, returning NaN if all values are null first_non_null = df.apply(lambda row: row.dropna().iloc[0] if not row.isnull().all() else pd.NA, axis=1) print(first_non_null)

  5. Identifying the first non-null value per row across specific columns in a Pandas DataFrame

    • Description: This query explores methods to find the first non-null value per row across specific columns in a Pandas DataFrame.
    # Code Implementation import pandas as pd # Sample DataFrame df = pd.DataFrame({'A': [None, 2, None, 4], 'B': [5, None, 7, None], 'C': [None, None, 10, 11]}) # Find first non-null value per row across specific columns first_non_null = df[['A', 'B', 'C']].ffill(axis=1).iloc[:, 0] print(first_non_null)

  6. Using numpy to find the first non-null value per row in a Pandas DataFrame

    • Description: This query investigates utilizing numpy operations to efficiently find the first non-null value in each row of a Pandas DataFrame.
    # Code Implementation import pandas as pd import numpy as np # Sample DataFrame df = pd.DataFrame({'A': [None, 2, None, 4], 'B': [5, None, 7, None], 'C': [None, None, 10, 11]}) # Find first non-null value per row using numpy first_non_null = df.to_numpy().argmin(axis=1) print(df.columns[first_non_null])

  7. Using list comprehension to find the first non-null value per row in Pandas DataFrame

    • Description: This query explores a concise approach using list comprehension to find the first non-null value in each row of a Pandas DataFrame.
    # Code Implementation import pandas as pd # Sample DataFrame df = pd.DataFrame({'A': [None, 2, None, 4], 'B': [5, None, 7, None], 'C': [None, None, 10, 11]}) # Find first non-null value per row using list comprehension first_non_null = [row[row.first_valid_index()] for index, row in df.iterrows()] print(first_non_null)

  8. Using iterrows to find the first non-null value per row in Pandas DataFrame

    • Description: This query explores leveraging the iterrows method to iterate through rows and find the first non-null value in each row of a Pandas DataFrame.
    # Code Implementation import pandas as pd # Sample DataFrame df = pd.DataFrame({'A': [None, 2, None, 4], 'B': [5, None, 7, None], 'C': [None, None, 10, 11]}) # Find first non-null value per row using iterrows first_non_null = [row.dropna().iloc[0] if not row.isnull().all() else pd.NA for index, row in df.iterrows()] print(first_non_null)

  9. Using iloc to find the first non-null value per row in Pandas DataFrame

    • Description: This query investigates using integer-based indexing (iloc) to find the first non-null value in each row of a Pandas DataFrame.
    # Code Implementation import pandas as pd # Sample DataFrame df = pd.DataFrame({'A': [None, 2, None, 4], 'B': [5, None, 7, None], 'C': [None, None, 10, 11]}) # Find first non-null value per row using iloc first_non_null = df.ffill(axis=1).iloc[:, 0] print(first_non_null)

  10. Dealing with datetime columns when finding the first non-null value per row in Pandas DataFrame

    • Description: This query addresses considerations when dealing with datetime columns while finding the first non-null value in each row of a Pandas DataFrame.
    # Code Implementation import pandas as pd # Sample DataFrame with datetime column df = pd.DataFrame({'A': [None, pd.Timestamp('2022-01-01'), None, pd.Timestamp('2022-01-04')], 'B': [pd.Timestamp('2022-01-05'), None, pd.Timestamp('2022-01-07'), None], 'C': [None, None, pd.Timestamp('2022-01-10'), pd.Timestamp('2022-01-11')]}) # Find first non-null value per row, handling datetime columns first_non_null = df.ffill(axis=1).iloc[:, 0] print(first_non_null)

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