Pandas dataframe: how to apply describe() to each group and add to new columns?

Pandas dataframe: how to apply describe() to each group and add to new columns?

To apply the describe() method to each group in a Pandas DataFrame and add the results as new columns, you can use the groupby() function along with the apply() function. Here's how you can achieve this:

Let's assume you have a DataFrame named df and you want to group it by a column named 'group_column' and add the descriptive statistics as new columns for each group.

import pandas as pd

# Create a sample DataFrame
data = {
    'group_column': ['A', 'A', 'B', 'B', 'A', 'B'],
    'value': [10, 20, 15, 25, 30, 35]
}
df = pd.DataFrame(data)

# Group by 'group_column' and apply describe() to each group
grouped = df.groupby('group_column')['value'].apply(lambda x: x.describe())

# Reset the index to move the group_column back to a regular column
grouped = grouped.reset_index()

# Rename columns to make them unique
grouped.rename(columns={'value': 'group_stats'}, inplace=True)

# Merge the group statistics back to the original DataFrame
df = df.merge(grouped, on='group_column')

print(df)

In this example, the groupby() function is used to group the DataFrame by the 'group_column'. The apply() function is then used to apply the describe() method to each group of the 'value' column. The result is a new DataFrame grouped containing the group statistics.

After calculating the group statistics, the index is reset to move the 'group_column' back to a regular column. The columns are then renamed to ensure uniqueness, and the group statistics are merged back into the original DataFrame using the 'group_column' as the merge key.

As a result, the df DataFrame will have new columns containing the descriptive statistics for each group.

Remember to adjust the column names and DataFrame structure to match your specific use case.

Examples

  1. How to apply describe() to each group in Pandas DataFrame and add results to new columns?

    • Description: This query seeks a method to compute descriptive statistics for each group in a Pandas DataFrame using the describe() function and add the results to new columns.
    import pandas as pd

# Apply describe() to each group and add results to new columns
grouped_stats = df.groupby('group_column').apply(lambda x: x.describe())

  2. Pandas DataFrame: apply describe() to each group and append results as rows?

    • Description: This query looks for a way to apply the describe() function to each group in a Pandas DataFrame and append the results as rows.
    import pandas as pd

# Apply describe() to each group and append results as rows
grouped_stats = df.groupby('group_column').apply(lambda x: x.describe().reset_index(drop=True))

  3. How to compute group statistics with describe() in Pandas DataFrame and add as new columns?

    • Description: This query aims to compute group-level statistics using the describe() function in a Pandas DataFrame and add the results as new columns.
    import pandas as pd

# Compute group statistics with describe() and add as new columns
grouped_stats = df.groupby('group_column').describe().add_prefix('group_stats_')

  4. Pandas dataframe: apply describe() to each group and concatenate results horizontally?

    • Description: This query seeks to apply the describe() function to each group in a Pandas DataFrame and concatenate the results horizontally.
    import pandas as pd

# Apply describe() to each group and concatenate results horizontally
grouped_stats = df.groupby('group_column').apply(lambda x: x.describe()).unstack()

  5. How to add group statistics from describe() to Pandas DataFrame as new columns?

    • Description: This query looks for a way to add group-level statistics computed using the describe() function to a Pandas DataFrame as new columns.
    import pandas as pd

# Add group statistics from describe() as new columns
grouped_stats = df.groupby('group_column').describe().add_prefix('group_stats_')

  6. Pandas DataFrame: apply describe() to each group and merge results with original data?

    • Description: This query aims to apply the describe() function to each group in a Pandas DataFrame and merge the results with the original data.
    import pandas as pd

# Apply describe() to each group and merge results with original data
grouped_stats = df.groupby('group_column').apply(lambda x: x.describe()).reset_index(level=1, drop=True)

  7. How to apply describe() to each group in Pandas DataFrame and store results as separate DataFrames?

    • Description: This query seeks a method to apply the describe() function to each group in a Pandas DataFrame and store the results as separate DataFrames.
    import pandas as pd

# Apply describe() to each group and store results as separate DataFrames
grouped_stats = df.groupby('group_column').apply(lambda x: x.describe())

  8. Pandas groupby: apply describe() to each group and save results to new columns?

    • Description: This query looks for a way to apply the describe() function to each group in a Pandas DataFrame using groupby and save the results to new columns.
    import pandas as pd

# Apply describe() to each group and save results to new columns
grouped_stats = df.groupby('group_column').describe().add_suffix('_stats')

  9. How to compute group-level summary statistics with describe() in Pandas DataFrame?

    • Description: This query aims to compute group-level summary statistics using the describe() function in a Pandas DataFrame.
    import pandas as pd

# Compute group-level summary statistics with describe()
grouped_stats = df.groupby('group_column').describe()

  10. Pandas DataFrame: apply describe() to each group and append results to original data?

    • Description: This query seeks to apply the describe() function to each group in a Pandas DataFrame and append the results to the original data.
    import pandas as pd

# Apply describe() to each group and append results to original data
grouped_stats = df.groupby('group_column').apply(lambda x: x.describe().add_prefix('group_stats_'))

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