Select non-null rows from a specific column in a DataFrame and take a sub-selection of other columns in python

Select non-null rows from a specific column in a DataFrame and take a sub-selection of other columns in python

To select non-null rows from a specific column in a pandas DataFrame and take a sub-selection of other columns, you can use boolean indexing along with column selection. Here's how you can achieve this:

Assuming you have a DataFrame named df, and you want to select rows where the values in column 'column_name' are not null, and you want to take a sub-selection of columns 'col1' and 'col2':

import pandas as pd # Sample DataFrame data = { 'column_name': [1, None, 3, 4, None], 'col1': [10, 20, 30, 40, 50], 'col2': ['A', 'B', 'C', 'D', 'E'] } df = pd.DataFrame(data) # Select non-null rows from 'column_name' and sub-select columns 'col1' and 'col2' selected_rows = df[df['column_name'].notnull()][['col1', 'col2']] print(selected_rows)

In this example, the df['column_name'].notnull() creates a boolean mask that is True for non-null values in the specified column. This mask is used to index the DataFrame, selecting only the rows with non-null values in the 'column_name' column. Then, ['col1', 'col2'] selects the desired subset of columns.

Remember to replace 'column_name', 'col1', and 'col2' with the actual column names you're working with.

Examples

  1. Selecting Non-Null Rows from a Specific Column in a DataFrame

    • Description: This query is about selecting rows where a specific column is not null in a DataFrame.
    • Code:
      import pandas as pd # Create a sample DataFrame with some NaN values df = pd.DataFrame({ 'A': [1, 2, None, 4], 'B': ['x', 'y', 'z', None], 'C': [10, 20, 30, 40] }) # Select non-null rows from column 'A' non_null_rows = df[df['A'].notna()] print("Rows with non-null 'A':") print(non_null_rows)

  2. Selecting Rows with Non-Null Values in a Specific Column and Subset of Other Columns

    • Description: This query is about selecting rows with non-null values in a specific column and getting a subset of other columns.
    • Code:
      import pandas as pd df = pd.DataFrame({ 'A': [1, None, 3, 4], 'B': ['x', 'y', 'z', 'w'], 'C': [10, 20, 30, 40] }) # Select non-null rows from column 'A' and only subset columns 'A' and 'C' selected_rows = df[df['A'].notna()][['A', 'C']] print("Rows with non-null 'A', selecting 'A' and 'C':") print(selected_rows)

  3. Filtering Rows Based on Non-Null Values in Multiple Columns

    • Description: This query involves selecting rows where multiple specified columns have non-null values.
    • Code:
      import pandas as pd df = pd.DataFrame({ 'A': [1, None, 3, 4], 'B': ['x', 'y', None, 'w'], 'C': [10, 20, 30, 40] }) # Select rows where 'A' and 'B' are both non-null, with specific columns selected_rows = df[df[['A', 'B']].notna().all(axis=1)][['A', 'B']] print("Rows with non-null 'A' and 'B', selecting 'A' and 'B':") print(selected_rows)

  4. Selecting Rows with Non-Null Values in a Specific Column and Resetting the Index

    • Description: This query is about selecting rows where a specific column is not null and then resetting the DataFrame index.
    • Code:
      import pandas as pd df = pd.DataFrame({ 'A': [1, None, 3, 4], 'B': ['x', 'y', 'z', 'w'], 'C': [10, 20, 30, 40] }) # Select non-null rows from column 'A' and reset the index non_null_rows = df[df['A'].notna()].reset_index(drop=True) print("Rows with non-null 'A', index reset:") print(non_null_rows)

  5. Selecting Non-Null Rows from a Specific Column and Renaming Columns

    • Description: This query involves selecting rows with non-null values in a specific column and renaming other column headers.
    • Code:
      import pandas as pd df = pd.DataFrame({ 'A': [1, None, 3, 4], 'B': ['x', 'y', 'z', 'w'], 'C': [10, 20, 30, 40] }) # Select non-null rows from column 'A' and rename columns non_null_rows = df[df['A'].notna()][['A', 'B']] non_null_rows.rename(columns={'A': 'Column1', 'B': 'Column2'}, inplace=True) print("Rows with non-null 'A' and renamed columns:") print(non_null_rows)

  6. Selecting Non-Null Rows from a Specific Column and Filtering Based on a Condition

    • Description: This query is about selecting rows with non-null values in a specific column and applying additional filtering based on another condition.
    • Code:
      import pandas as pd df = pd.DataFrame({ 'A': [1, None, 3, 4], 'B': ['x', 'y', 'z', 'w'], 'C': [10, 20, 30, 40] }) # Select non-null rows from column 'A' and where 'C' > 20 selected_rows = df[df['A'].notna() & (df['C'] > 20)][['A', 'C']] print("Rows with non-null 'A' and 'C' > 20, selecting 'A' and 'C':") print(selected_rows)

  7. Selecting Rows with Non-Null Values in a Specific Column and Grouping by Another Column

    • Description: This query involves selecting rows with non-null values in a specific column and then grouping the DataFrame by another column.
    • Code:
      import pandas as pd df = pd.DataFrame({ 'A': [1, None, 3, 4], 'B': ['x', 'y', 'z', 'w'], 'C': [10, 20, 30, 40] }) # Select non-null rows from column 'A' and group by column 'B' non_null_rows = df[df['A'].notna()] grouped = non_null_rows.groupby('B') for name, group in grouped: print(f"Group '{name}':") print(group[['A', 'C']])

  8. Selecting Rows with Non-Null Values in a Specific Column and Applying a Custom Function

    • Description: This query is about selecting rows with non-null values in a specific column and applying a custom function to the DataFrame.
    • Code:
      import pandas as pd df = pd.DataFrame({ 'A': [1, None, 3, 4], 'B': ['x', 'y', 'z', 'w'], 'C': [10, 20, 30, 40] }) # Define a custom function to add a new column def add_new_column(row): row['D'] = row['C'] * 2 return row # Select non-null rows from column 'A' and apply the custom function non_null_rows = df[df['A'].notna()].apply(add_new_column, axis=1) print("Rows with non-null 'A' after applying custom function:") print(non_null_rows[['A', 'C', 'D']])

  9. Selecting Rows with Non-Null Values in a Specific Column and Changing Data Types

    • Description: This query involves selecting rows with non-null values in a specific column and changing the data type of other columns.
    • Code:
      import pandas as pd df = pd.DataFrame({ 'A': [1, None, 3, 4], 'B': ['x', 'y', 'z', 'w'], 'C': [10, 20, 30, 40] }) # Select non-null rows from column 'A' and convert 'C' to string non_null_rows = df[df['A'].notna()] non_null_rows['C'] = non_null_rows['C'].astype(str) print("Rows with non-null 'A', 'C' converted to string:") print(non_null_rows[['A', 'C']])

  10. Selecting Non-Null Rows from a Specific Column and Reordering Columns

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