Sum all columns with a wildcard name search using Python Pandas

Sum all columns with a wildcard name search using Python Pandas

To sum all columns with a wildcard name search using Python Pandas, you can filter the columns based on a wildcard pattern and then sum them using the sum() function. You can achieve this with the help of list comprehension and the filter() function. Here's how you can do it:

import pandas as pd # Create a sample dataframe data = { 'col1': [1, 2, 3], 'col2_x': [4, 5, 6], 'col2_y': [7, 8, 9], 'col3': [10, 11, 12] } df = pd.DataFrame(data) # Filter columns with wildcard pattern 'col2_*' and sum them sum_of_cols = sum(df[col] for col in filter(lambda x: x.startswith('col2_'), df.columns)) print(sum_of_cols)

Output:

0 12 1 15 2 18 dtype: int64

In this example:

  • We create a sample dataframe df.
  • We filter the columns based on the wildcard pattern 'col2_*' using the filter() function along with a lambda function that checks if the column name starts with 'col2_'.
  • We then use a list comprehension to access each filtered column and sum them using the sum() function.
  • The result is a pandas Series containing the sum of values for each row across the filtered columns.

Examples

  1. Sum all columns matching a wildcard pattern in a Pandas DataFrame

    Description: This query sums all columns in a Pandas DataFrame that match a wildcard pattern using list comprehension.

    Code:

    import pandas as pd # Sample DataFrame df = pd.DataFrame({ 'col1_a': [1, 2, 3], 'col1_b': [4, 5, 6], 'col2_a': [7, 8, 9], 'col2_b': [10, 11, 12] }) # Sum columns with names matching 'col1_*' sum_col1 = df[[col for col in df.columns if col.startswith('col1_')]].sum().sum() print("Sum of columns matching 'col1_*':", sum_col1)

  2. Sum columns dynamically with wildcard in Pandas

    Description: This query dynamically sums columns in a Pandas DataFrame based on a wildcard pattern using filter function.

    Code:

    import pandas as pd # Sample DataFrame df = pd.DataFrame({ 'col1_a': [1, 2, 3], 'col1_b': [4, 5, 6], 'col2_a': [7, 8, 9], 'col2_b': [10, 11, 12] }) # Sum columns with names matching 'col1_*' sum_col1 = df.filter(like='col1_').sum().sum() print("Sum of columns matching 'col1_*':", sum_col1)

  3. Sum all columns with a specific wildcard pattern using Pandas

    Description: This query sums all columns in a Pandas DataFrame that match a specific wildcard pattern using filter function.

    Code:

    import pandas as pd # Sample DataFrame df = pd.DataFrame({ 'col1_a': [1, 2, 3], 'col1_b': [4, 5, 6], 'col2_a': [7, 8, 9], 'col2_b': [10, 11, 12] }) # Sum columns with names matching 'col1_*' sum_col1 = df.filter(regex='^col1_').sum().sum() print("Sum of columns matching 'col1_*':", sum_col1)

  4. Sum columns with a wildcard pattern using Pandas and regular expressions

    Description: This query sums columns in a Pandas DataFrame based on a wildcard pattern using regular expressions with filter function.

    Code:

    import pandas as pd # Sample DataFrame df = pd.DataFrame({ 'col1_a': [1, 2, 3], 'col1_b': [4, 5, 6], 'col2_a': [7, 8, 9], 'col2_b': [10, 11, 12] }) # Sum columns with names matching 'col1_*' sum_col1 = df.filter(regex=r'^col1_').sum().sum() print("Sum of columns matching 'col1_*':", sum_col1)

  5. Sum all columns with a wildcard pattern ignoring case in Pandas

    Description: This query sums all columns in a Pandas DataFrame that match a wildcard pattern while ignoring case sensitivity using filter function.

    Code:

    import pandas as pd # Sample DataFrame df = pd.DataFrame({ 'Col1_a': [1, 2, 3], 'Col1_b': [4, 5, 6], 'Col2_a': [7, 8, 9], 'Col2_b': [10, 11, 12] }) # Sum columns with names matching 'Col1_*' ignoring case sum_col1 = df.filter(regex='(?i)^Col1_').sum().sum() print("Sum of columns matching 'Col1_*' ignoring case:", sum_col1)

  6. Sum columns with a wildcard pattern using Pandas and column selection

    Description: This query sums columns in a Pandas DataFrame based on a wildcard pattern using column selection with loc function.

    Code:

    import pandas as pd # Sample DataFrame df = pd.DataFrame({ 'col1_a': [1, 2, 3], 'col1_b': [4, 5, 6], 'col2_a': [7, 8, 9], 'col2_b': [10, 11, 12] }) # Sum columns with names matching 'col1_*' sum_col1 = df.loc[:, df.columns.str.startswith('col1_')].sum().sum() print("Sum of columns matching 'col1_*':", sum_col1)

  7. Sum all columns with a wildcard pattern using Pandas and iteration

    Description: This query iteratively sums columns in a Pandas DataFrame that match a wildcard pattern using a loop.

    Code:

    import pandas as pd # Sample DataFrame df = pd.DataFrame({ 'col1_a': [1, 2, 3], 'col1_b': [4, 5, 6], 'col2_a': [7, 8, 9], 'col2_b': [10, 11, 12] }) # Initialize sum total_sum = 0 # Iterate over columns matching 'col1_*' for col in df.columns: if col.startswith('col1_'): total_sum += df[col].sum() print("Sum of columns matching 'col1_*':", total_sum)

  8. Sum columns with a wildcard pattern and handle missing values in Pandas

    Description: This query sums columns in a Pandas DataFrame based on a wildcard pattern and handles missing values using fillna function.

    Code:

    import pandas as pd import numpy as np # Sample DataFrame with missing values df = pd.DataFrame({ 'col1_a': [1, np.nan, 3], 'col1_b': [4, 5, 6], 'col2_a': [7, 8, np.nan], 'col2_b': [10, 11, 12] }) # Sum columns with names matching 'col1_*' and handle missing values sum_col1 = df.filter(regex='^col1_').fillna(0).sum().sum() print("Sum of columns matching 'col1_*' with handling missing values:", sum_col1)

  9. Sum columns dynamically with wildcard pattern and handle NaN in Pandas

    Description: This query dynamically sums columns in a Pandas DataFrame based on a wildcard pattern and handles NaN values using sum function.

    Code:

    import pandas as pd import numpy as np # Sample DataFrame with NaN values df = pd.DataFrame({ 'col1_a': [1, np.nan, 3], 'col1_b': [4, 5, 6], 'col2_a': [7, 8, np.nan], 'col2_b': [10, 11, 12] }) # Sum columns with names matching 'col1_*' and handle NaN values sum_col1 = df.filter(like='col1_').sum().sum() print("Sum of columns matching 'col1_*' with handling NaN values:", sum_col1)

  10. Sum all columns with a wildcard pattern using Pandas and handle exceptions

    Description: This query sums all columns in a Pandas DataFrame that match a wildcard pattern and handles exceptions using try-except block.

    Code:

    import pandas as pd # Sample DataFrame df = pd.DataFrame({ 'col1_a': [1, 2, 3], 'col1_b': [4, 5, 6], 'col2_a': [7, 8, 9], 'col2_b': [10, 11, 12] }) try: # Sum columns with names matching 'col1_*' sum_col1 = df.filter(regex='^col1_').sum().sum() print("Sum of columns matching 'col1_*':", sum_col1) except KeyError as e: print(f"Error: {e}. No columns matching the pattern found.")

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