Pandas DataFrame merge summing column

Pandas DataFrame merge summing column

You can use the pd.merge() function in combination with the .groupby() and .sum() methods to merge two Pandas DataFrames while summing a specific column based on a shared key. Here's how you can do it:

import pandas as pd # Sample DataFrames data1 = {'key': ['A', 'B', 'C'], 'value1': [10, 20, 30]} data2 = {'key': ['B', 'C', 'D'], 'value2': [40, 50, 60]} df1 = pd.DataFrame(data1) df2 = pd.DataFrame(data2) # Merge DataFrames on the 'key' column and sum the 'value2' column merged_df = pd.merge(df1, df2, on='key', how='left') merged_df['sum_value2'] = merged_df['value2'].fillna(0) # Display the merged DataFrame print(merged_df)

In this example, the pd.merge() function is used to merge the two DataFrames (df1 and df2) based on the 'key' column. We use a left merge to retain all rows from the left DataFrame (df1) and fill missing values in the 'value2' column with zeros. Finally, a new column 'sum_value2' is created to store the summed values of the 'value2' column.

The resulting merged_df DataFrame will contain the merged data with summed values from 'value2' for matching keys.

Remember to adjust column names and data according to your specific use case.

Examples

  1. "Pandas DataFrame merge summing column"
    • Description: How to merge two Pandas DataFrames and sum a specific column during the merge operation.
    • Code:
      import pandas as pd # Sample DataFrames df1 = pd.DataFrame({'key': ['A', 'B', 'C'], 'value': [1, 2, 3]}) df2 = pd.DataFrame({'key': ['A', 'B', 'D'], 'value': [4, 5, 6]}) # Merge and sum values of 'value' column merged_df = df1.merge(df2, on='key', how='outer', suffixes=('_left', '_right')) merged_df['sum_value'] = merged_df['value_left'].fillna(0) + merged_df['value_right'].fillna(0) print(merged_df)
  2. "Pandas merge with summing values"
    • Description: How to merge two Pandas DataFrames and sum the corresponding values from a specific column.
    • Code:
      import pandas as pd # Sample DataFrames df1 = pd.DataFrame({'key': ['A', 'B', 'C'], 'value': [1, 2, 3]}) df2 = pd.DataFrame({'key': ['A', 'B', 'D'], 'value': [4, 5, 6]}) # Merge and sum values of 'value' column merged_df = df1.merge(df2, on='key', how='outer', suffixes=('_left', '_right')) merged_df['sum_value'] = merged_df[['value_left', 'value_right']].sum(axis=1) print(merged_df)
  3. "Pandas merge and sum columns"
    • Description: How to merge two Pandas DataFrames and sum specific columns.
    • Code:
      import pandas as pd # Sample DataFrames df1 = pd.DataFrame({'key': ['A', 'B', 'C'], 'value_x': [1, 2, 3]}) df2 = pd.DataFrame({'key': ['A', 'B', 'D'], 'value_y': [4, 5, 6]}) # Merge and sum specific columns merged_df = df1.merge(df2, on='key', how='outer') merged_df['sum_values'] = merged_df[['value_x', 'value_y']].sum(axis=1) print(merged_df)
  4. "Pandas merge sum values from multiple columns"
    • Description: How to merge two Pandas DataFrames and sum values from multiple columns during the merge.
    • Code:
      import pandas as pd # Sample DataFrames df1 = pd.DataFrame({'key': ['A', 'B', 'C'], 'value1': [1, 2, 3]}) df2 = pd.DataFrame({'key': ['A', 'B', 'D'], 'value2': [4, 5, 6]}) # Merge and sum values from multiple columns merged_df = df1.merge(df2, on='key', how='outer', suffixes=('_left', '_right')) merged_df['sum_values'] = merged_df[['value1', 'value2']].sum(axis=1) print(merged_df)
  5. "Pandas DataFrame merge and aggregate sum"
    • Description: How to merge Pandas DataFrames and aggregate the sum of a column.
    • Code:
      import pandas as pd # Sample DataFrames df1 = pd.DataFrame({'key': ['A', 'B', 'C'], 'value': [1, 2, 3]}) df2 = pd.DataFrame({'key': ['A', 'B', 'D'], 'value': [4, 5, 6]}) # Merge and aggregate sum of 'value' column merged_df = df1.merge(df2, on='key', how='outer', suffixes=('_left', '_right')) sum_value = merged_df['value'].sum() print(sum_value)
  6. "Pandas merge sum column values based on condition"
    • Description: How to merge two Pandas DataFrames and sum column values based on a condition.
    • Code:
      import pandas as pd # Sample DataFrames df1 = pd.DataFrame({'key': ['A', 'B', 'C'], 'value': [1, 2, 3]}) df2 = pd.DataFrame({'key': ['A', 'B', 'D'], 'value': [4, 5, 6]}) # Merge and sum 'value' column based on condition merged_df = df1.merge(df2, on='key', how='outer', suffixes=('_left', '_right')) condition = merged_df['key'].isin(['A', 'B']) sum_value = merged_df.loc[condition, 'value'].sum() print(sum_value)
  7. "Pandas merge and aggregate sum of grouped values"
    • Description: How to merge Pandas DataFrames and aggregate the sum of grouped values.
    • Code:
      import pandas as pd # Sample DataFrames df1 = pd.DataFrame({'key': ['A', 'B', 'C'], 'value': [1, 2, 3]}) df2 = pd.DataFrame({'key': ['A', 'B', 'D'], 'value': [4, 5, 6]}) # Merge and aggregate sum of grouped values merged_df = df1.merge(df2, on='key', how='outer', suffixes=('_left', '_right')) grouped_sum = merged_df.groupby('key')['value'].sum() print(grouped_sum)
  8. "Pandas DataFrame merge and sum column with groupby"
    • Description: How to merge Pandas DataFrames and sum a column using groupby.
    • Code:
      import pandas as pd # Sample DataFrames df1 = pd.DataFrame({'key': ['A', 'B', 'C'], 'value': [1, 2, 3]}) df2 = pd.DataFrame({'key': ['A', 'B', 'D'], 'value': [4, 5, 6]}) # Merge and sum 'value' column with groupby merged_df = df1.merge(df2, on='key', how='outer', suffixes=('_left', '_right')) sum_by_group = merged_df.groupby('key')['value'].sum() print(sum_by_group)
  9. "Pandas merge and sum column values with null handling"
    • Description: How to merge Pandas DataFrames and sum column values while handling null values.
    • Code:
      import pandas as pd # Sample DataFrames df1 = pd.DataFrame({'key': ['A', 'B', 'C'], 'value': [1, 2, 3]}) df2 = pd.DataFrame({'key': ['A', 'B', 'D'], 'value': [4, 5, 6]}) # Merge and sum column values with null handling merged_df = df1.merge(df2, on='key', how='outer', suffixes=('_left', '_right')) merged_df['sum_value'] = merged_df['value_left'].fillna(0) + merged_df['value_right'].fillna(0) print(merged_df)
  10. "Pandas merge and sum columns with different names"
    • Description: How to merge Pandas DataFrames with columns having different names and summing them.
    • Code:
      import pandas as pd # Sample DataFrames df1 = pd.DataFrame({'key': ['A', 'B', 'C'], 'value1': [1, 2, 3]}) df2 = pd.DataFrame({'key': ['A', 'B', 'D'], 'value2': [4, 5, 6]}) # Merge and sum columns with different names merged_df = df1.merge(df2, on='key', how='outer') merged_df['sum_values'] = merged_df[['value1', 'value2']].sum(axis=1) print(merged_df)

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