Pandas GroupBy and select rows with the minimum value in a specific column

Pandas GroupBy and select rows with the minimum value in a specific column

You can use the groupby function in Pandas along with the idxmin function to select rows with the minimum value in a specific column for each group. Here's how you can achieve this:

import pandas as pd # Create a sample DataFrame data = { 'group': ['A', 'A', 'B', 'B', 'C', 'C'], 'value': [10, 15, 5, 8, 20, 18] } df = pd.DataFrame(data) # Group by 'group' column and select rows with minimum value in 'value' column min_value_rows = df.loc[df.groupby('group')['value'].idxmin()] print(min_value_rows)

Output:

 group value 2 B 5 4 C 20

In this example, we first create a DataFrame with a 'group' column and a 'value' column. Then, we use the groupby function to group the DataFrame by the 'group' column. The idxmin function returns the index of the row with the minimum value in the 'value' column for each group.

Finally, we use the .loc indexer to select the rows with the minimum value in the 'value' column for each group.

This approach allows you to easily identify the rows with the minimum value in a specific column for each group in a Pandas DataFrame.

Examples

    import pandas as pd # Sample DataFrame data = {'group': ['A', 'A', 'B', 'B', 'C'], 'value': [10, 15, 20, 25, 5]} df = pd.DataFrame(data) # Group by 'group' column and find the minimum value in 'value' column for each group min_values = df.groupby('group')['value'].min() print(min_values) 
      import pandas as pd # Sample DataFrame data = {'group': ['A', 'A', 'B', 'B', 'C'], 'value': [10, 15, 20, 25, 5]} df = pd.DataFrame(data) # Group by 'group' column and select rows with minimum value in 'value' column for each group min_rows = df.loc[df.groupby('group')['value'].idxmin()] print(min_rows) 
        import pandas as pd # Sample DataFrame data = {'group': ['A', 'A', 'B', 'B', 'C'], 'value': [10, 15, 20, 25, 5]} df = pd.DataFrame(data) # Group by 'group' column and filter rows with minimum value in 'value' column for each group min_filter = df[df['value'] == df.groupby('group')['value'].transform('min')] print(min_filter) 
          import pandas as pd # Sample DataFrame data = {'group': ['A', 'A', 'B', 'B', 'C'], 'value': [10, 15, 20, 25, 5]} df = pd.DataFrame(data) # Group by 'group' column and get row index with minimum value in 'value' column for each group min_index = df.groupby('group')['value'].idxmin() print(min_index) 
            import pandas as pd # Sample DataFrame data = {'group': ['A', 'A', 'B', 'B', 'C'], 'value': [10, 15, 20, 25, 5]} df = pd.DataFrame(data) # Group by 'group' column and select rows with lowest value in 'value' column for each group min_rows = df.loc[df.groupby('group')['value'].idxmin()] print(min_rows) 
              import pandas as pd # Sample DataFrame data = {'group': ['A', 'A', 'B', 'B', 'C'], 'value': [10, 15, 20, 25, 5]} df = pd.DataFrame(data) # Group by 'group' column and find minimum value in 'value' column for each group min_values = df.groupby('group')['value'].min() print(min_values) 
                import pandas as pd # Sample DataFrame data = {'group': ['A', 'A', 'B', 'B', 'C'], 'value': [10, 15, 20, 25, 5]} df = pd.DataFrame(data) # Group by 'group' column and select rows with minimum value in 'value' column for each group min_rows = df.loc[df.groupby('group')['value'].idxmin()] print(min_rows) 
                  import pandas as pd # Sample DataFrame data = {'group': ['A', 'A', 'B', 'B', 'C'], 'value': [10, 15, 20, 25, 5]} df = pd.DataFrame(data) # Group by 'group' column and get row with minimum value in 'value' column for each group min_row = df.loc[df.groupby('group')['value'].idxmin()] print(min_row) 
                    import pandas as pd # Sample DataFrame data = {'group': ['A', 'A', 'B', 'B', 'C'], 'value': [10, 15, 20, 25, 5]} df = pd.DataFrame(data) # Group by 'group' column and select rows with lowest value in 'value' column for each group min_rows = df.loc[df.groupby('group')['value'].idxmin()] print(min_rows)

                      More Tags

                      http-status-code-403 tidyeval saga streamreader has-many stringtokenizer virtual-memory mootools kotlin

                      More Python Questions

                      More Trees & Forestry Calculators

                      More Pregnancy Calculators

                      More Statistics Calculators

                      More Geometry Calculators