Weighted Average with Multiple Weights and Groups in Python

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Introduction

In this article, we’ll explore how to calculate a weighted average for multiple groups using different weights. We’ll cover the basics of pandas dataframes, list comprehension, and numpy functions.

Background

The provided Stack Overflow question is from a beginner in Python who wants to improve their code’s efficiency. They have a dataset with various columns and want to calculate a weighted average for each column based on two different weights (_weight_1 and _weight_2).

Reshaping the Data

First, let’s assume that the ‘animals’ column is our index. We’ll set it as the index using the set_index function.

import pandas as pd
import numpy as np

petdata = {
    # All of your data ^
}

df = pd.DataFrame(petdata)  # Creates the DF from your dictionary
df.set_index('animal', inplace=True)  # Sets the 'animal' column as the index

Breaking Down the Data into Two Parts

We’ll break down our DataFrame into two parts: df_1 and df_2. We’ll use list comprehension to create a list of all column names with a given string in the name, and then use this list to get a sub-DataFrame for each.

# Uses list comprehension to create a list of all column names with a given string
# in the name, and uses this list to get a sub-DataFrame for each
df_1 = df[[name for name in df.columns if '_1' in name]]
df_2 = df[[name for name in df.columns if '_2' in name]]

Calculating Weighted Averages

Instead of creating new Series (columns) in our DataFrame for each and every Series that already exists, we’ll create a new row that is the weighted average (‘wav’) for each column. We’ll use list comprehension and numpy functions to calculate these averages.

wav_1 = [np.nansum(df[col]*df_1['weight_1'])/np.nansum(df_1['weight_1']) for col in df_1.columns]
wav_2 = [np.nansum(df[col]*df_1['weight_2'])/np.nansum(df_1['weight_2']) for col in df_2.columns]

Appending Weighted Averages to DataFrames

Finally, we’ll append the calculated weighted averages to our two DataFrames using the ‘wav’ label.

df_1.loc['wav'] = wav_1
df_2.loc['wav'] = wav_2

Note that there is junk data in the ‘wav’-‘weight_x’ box. It’s the weighted average of your weights.

Code Example

Here’s a complete code example based on the provided explanation:

import pandas as pd
import numpy as np

petdata = {
    # All of your data ^
}

df = pd.DataFrame(petdata)  # Creates the DF from your dictionary
df.set_index('animal', inplace=True)  # Sets the 'animal' column as the index

# Uses list comprehension to create a list of all column names with a given string
# in the name, and uses this list to get a sub-DataFrame for each
df_1 = df[[name for name in df.columns if '_1' in name]]
df_2 = df[[name for name in df.columns if '_2' in name]]

wav_1 = [np.nansum(df[col]*df_1['weight_1'])/np.nansum(df_1['weight_1']) for col in df_1.columns]
wav_2 = [np.nansum(df[col]*df_1['weight_2'])/np.nansum(df_1['weight_2']) for col in df_2.columns]

df_1.loc['wav'] = wav_1
df_2.loc['wav'] = wav_2

Conclusion

We’ve covered how to calculate a weighted average for multiple groups using different weights. We used pandas dataframes, list comprehension, and numpy functions to achieve this.

Last modified on 2024-03-13