Applying a Function to Specific Columns in a Pandas DataFrame

When working with pandas DataFrames, it’s often necessary to apply functions to specific columns. In this scenario, we have a MultiIndexed DataFrame where each row is associated with two keys: ‘body_part’ and ‘y’. We want to apply a function to every row under the ‘y’ key, normalize and/or invert the values using a given y_max value, and then repackage the DataFrame with the output from the function.

Reading the CSV File

First, let’s read our CSV file into a pandas DataFrame:

import pandas as pd


csv_file = pd.read_csv('hello.csv', engine='c', delimiter=',', index_col=0,
                       skiprows=1, header=[0, 1])

This code assumes that the CSV file has an index column and two header rows.

Understanding MultiIndexed DataFrames

The resulting DataFrame is a MultiIndexed DataFrame with two levels:

tuple(('body_part1', 'body_part2', ..., 'body_partn'), ('x', 'y', 'likelihood')

This means that each row in the DataFrame has multiple indices: one for the body part and one for the coordinate (x or y).

Grouping by the Second Level of MultiIndex

When we group by y using:

csv_file.groupby('y', axis=1, level=1)

We might expect this to return a grouped DataFrame with ‘y’ as its index. However, pandas raises a KeyError: 'y' because the first level of the MultiIndex is not recognized.

Reaching for the Second Level

To access the second level of the MultiIndex (which corresponds to the ‘y’ key), we need to use the .reset_index(level=1) method:

csv_file.groupby('y', axis=0, level=1).reset_index()

This code groups by y in the first column and resets its index.

Applying the Function

Now that we have our data grouped by ‘y’, let’s apply a function to each value. In this case, we want to normalize and/or invert the values using a given y_max value:

def normalize_y(y_values, y_max):
    # Normalize values between 0 and 1
    normalized_y = (y_values - y_values.min()) / (y_values.max() - y_values.min())
    
    # Invert values if specified
    if invert:
        return 1 - normalized_y
    
    return normalized_y

# Define the function to be applied to each group
def apply_function(group):
    return normalize_y(group['y'], y_max)

# Apply the function to each group and reindex
grouped_df = csv_file.groupby('y', axis=0, level=1).apply(apply_function).reset_index(drop=True)

In this example, normalize_y takes a list of y values, normalizes them between 0 and 1 using the formula (y - min(y)) / (max(y) - min(y)), and optionally inverts the values if specified.

Rebuilding the DataFrame

Now that we have our data normalized and grouped by ‘y’, let’s rebuild the original DataFrame structure:

# Create a new MultiIndex with body part index
body_part_idx = pd.MultiIndex.from_product(csv_file.index.get_level_values(0), names='body_part')

# Combine the 'x' and 'likelihood' columns into a single column
grouped_df = grouped_df.reset_index()[['y']].join(body_part_idx).set_index('y')

# Create the desired output DataFrame structure
output_df = pd.DataFrame(index=body_part_idx, columns=['x', 'y', 'likelihood'])

# Copy data from input group to output DataFrame
for index in output_df.index:
    output_df.loc[index] = grouped_df.groupby('y')[index].apply(lambda x: apply_function(x), raw=True).values

output_df

In this final step, we create a new MultiIndex with the body part indices and combine the ‘x’ and ’likelihood’ columns into a single column. We then rebuild the original DataFrame structure by copying data from each group to the output DataFrame.

Conclusion

By following these steps, you can apply a function to every row under the ‘y’ key in your pandas MultiIndexed DataFrame, normalize and/or invert the values using a given y_max value, and then repackage the DataFrame with the output from the function.

Last modified on 2024-12-27