How to create a sum row and sum column in pandas?

How to create a sum row and sum column in pandas?

To create a sum row and sum column in a pandas DataFrame, you can use the .sum() method along with .loc indexing. Here's how you can do it:

Let's say you have the following DataFrame:

import pandas as pd data = { 'A': [1, 2, 3], 'B': [4, 5, 6], 'C': [7, 8, 9] } df = pd.DataFrame(data) print(df)

Output:

 A B C 0 1 4 7 1 2 5 8 2 3 6 9

Now, let's create a sum column and sum row:

1. Sum Column:

You can use the .sum() method along the rows (axis 0) to calculate the sum of each column and then append the results as a new row.

sum_row = df.sum() df_with_sum_column = df.append(sum_row, ignore_index=True) print(df_with_sum_column)

Output:

 A B C 0 1 4 7 1 2 5 8 2 3 6 9 3 6 15 24

2. Sum Row:

You can use the .sum() method along the columns (axis 1) to calculate the sum of each row and then add the results as a new column.

df['Sum'] = df.sum(axis=1) print(df)

Output:

 A B C Sum 0 1 4 7 12 1 2 5 8 15 2 3 6 9 18

In both cases, the .sum() method is used to calculate the sum along the specified axis (either rows or columns), and then the results are added either as a new row or a new column. The ignore_index=True parameter in the append method is used to reset the index after appending the sum row.

Choose the method that suits your needs: adding a sum row or a sum column.

Examples

  1. How to create a sum row in pandas DataFrame?

    • Description: Users often want to compute the sum of values across rows in a pandas DataFrame. This is commonly required for creating summary statistics or aggregating data.
    • Code:
      import pandas as pd # Create a sample DataFrame data = {'A': [1, 2, 3], 'B': [4, 5, 6], 'C': [7, 8, 9]} df = pd.DataFrame(data) # Add a sum row at the bottom df.loc['Total'] = df.sum()

  2. How to create a sum column in pandas DataFrame?

    • Description: Users often need to compute the sum of values down columns in a pandas DataFrame. This is useful for obtaining totals across different categories or features.
    • Code:
      import pandas as pd # Create a sample DataFrame data = {'A': [1, 2, 3], 'B': [4, 5, 6], 'C': [7, 8, 9]} df = pd.DataFrame(data) # Add a sum column to the DataFrame df['Total'] = df.sum(axis=1)

  3. How to sum specific columns in pandas DataFrame?

    • Description: Sometimes, users may want to sum only specific columns in a pandas DataFrame instead of all columns. This is helpful when dealing with a large dataset with many columns.
    • Code:
      import pandas as pd # Create a sample DataFrame data = {'A': [1, 2, 3], 'B': [4, 5, 6], 'C': [7, 8, 9]} df = pd.DataFrame(data) # Sum specific columns and store the result in a new column df['Sum_AB'] = df[['A', 'B']].sum(axis=1)

  4. How to create a sum row for a specific subset of columns in pandas DataFrame?

    • Description: Users may want to compute the sum of values across a specific subset of columns rather than all columns in a pandas DataFrame.
    • Code:
      import pandas as pd # Create a sample DataFrame data = {'A': [1, 2, 3], 'B': [4, 5, 6], 'C': [7, 8, 9]} df = pd.DataFrame(data) # Add a sum row for a specific subset of columns df.loc['Total_AB'] = df[['A', 'B']].sum()

  5. How to sum rows based on conditions in pandas DataFrame?

    • Description: Users may need to sum rows in a pandas DataFrame based on certain conditions, such as values meeting specific criteria.
    • Code:
      import pandas as pd # Create a sample DataFrame data = {'A': [1, 2, 3], 'B': [4, 5, 6], 'C': [7, 8, 9]} df = pd.DataFrame(data) # Sum rows based on a condition df['Sum_A_gt_1'] = df[df['A'] > 1].sum(axis=1)

  6. How to calculate row-wise sum ignoring NaN values in pandas DataFrame?

    • Description: Users may want to compute the sum of values across rows while ignoring NaN (Not a Number) values in a pandas DataFrame.
    • Code:
      import pandas as pd import numpy as np # Create a sample DataFrame with NaN values data = {'A': [1, np.nan, 3], 'B': [4, 5, np.nan], 'C': [7, np.nan, 9]} df = pd.DataFrame(data) # Calculate row-wise sum ignoring NaN values df['Sum_ignore_nan'] = df.sum(axis=1, skipna=True)

  7. How to compute column-wise sum excluding certain columns in pandas DataFrame?

    • Description: Users may want to exclude specific columns while computing the sum across columns in a pandas DataFrame.
    • Code:
      import pandas as pd # Create a sample DataFrame data = {'A': [1, 2, 3], 'B': [4, 5, 6], 'C': [7, 8, 9]} df = pd.DataFrame(data) # Compute column-wise sum excluding column 'C' df['Sum_excluding_C'] = df.drop(columns=['C']).sum(axis=1)

  8. How to create a sum row for each group in a pandas DataFrame?

    • Description: Users may need to compute the sum of values for each group within a pandas DataFrame based on a categorical variable.
    • Code:
      import pandas as pd # Create a sample DataFrame with a categorical column 'Group' data = {'Group': ['A', 'A', 'B', 'B'], 'Value': [1, 2, 3, 4]} df = pd.DataFrame(data) # Compute sum row for each group sum_by_group = df.groupby('Group').sum()

  9. How to calculate cumulative sum row in pandas DataFrame?

    • Description: Users may want to compute the cumulative sum of values across rows in a pandas DataFrame.
    • Code:
      import pandas as pd # Create a sample DataFrame data = {'A': [1, 2, 3], 'B': [4, 5, 6], 'C': [7, 8, 9]} df = pd.DataFrame(data) # Calculate cumulative sum row df.loc['Cumulative_Sum'] = df.cumsum(axis=0).iloc[-1]

  10. How to create a sum row with customized aggregation functions in pandas DataFrame?

    • Description: Users may want to apply customized aggregation functions while computing the sum row in a pandas DataFrame.
    • Code:
      import pandas as pd # Create a sample DataFrame data = {'A': [1, 2, 3], 'B': [4, 5, 6], 'C': [7, 8, 9]} df = pd.DataFrame(data) # Define a customized aggregation function def custom_sum(row): return row.sum() * 2 # Customized aggregation, multiplying sum by 2 # Apply the customized aggregation function to create a sum row df.loc['Custom_Sum'] = df.apply(custom_sum)

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