Merging and Computing Averages Across DataFrames in Pandas

Introduction

The pandas library is a powerful tool for data manipulation and analysis in Python. One of its key features is the ability to easily merge and manipulate dataframes, which are two-dimensional labeled data structures with columns of potentially different types. In this article, we’ll explore how to average one dataframe based on conditions from another dataframe.

Problem Statement

The problem presented involves taking a binary-valued dataframe (df1) and averaging it according to the values in another float-valued dataframe (df2), where only values greater than or equal to 0.5 are considered. The solution requires that both dataframes have the same column names and index.

Solution Overview

There are several ways to achieve this, but we’ll focus on three main approaches:

1. Using DataFrame.where(), which sets missing values in a dataframe to false values according to a mask.
2. Utilizing numpy.nanmean() for computing mean while excluding missing values.
3. Converting dataframes into series with DataFrame.stack() and then calculating the mean.

Approach 1: Using DataFrame.where() for Averaging

where() is used to set elements in a dataframe based on a boolean mask, effectively allowing us to filter out specific values.

# Import necessary libraries
import pandas as pd

# Create df1 with binary data and df2 with float data
df1 = pd.DataFrame({
    'col1': [0.0, 0.0, 0.0, 0.0, 0.0],
    'col2': [1.0, 0.0, 1.0, 0.0, 1.0]
})

df2 = pd.DataFrame({
    'col1': [0.068467, 0.091651, 0.070380, 0.052239, 0.087564],
    'col2': [0.099870, 0.084946, 0.104353, 0.123760, 0.104460]
})

# Define the condition for averaging (df2 > 0.5)
condition = df2 >= 0.5

# Use DataFrame.where() to filter values in df1 according to the condition
filtered_df = df1.where(condition)

# Calculate the mean of filtered dataframe
mean = filtered_df.mean()

print(mean)

Approach 2: Using numpy.nanmean() for Averaging While Excluding Missing Values

numpy.nanmean() calculates the mean of a dataset excluding missing values (nan).

import pandas as pd
import numpy as np

# Create df1 with binary data and df2 with float data
df1 = pd.DataFrame({
    'col1': [0.0, 0.0, 0.0, 0.0, 0.0],
    'col2': [1.0, 0.0, 1.0, 0.0, 1.0]
})

df2 = pd.DataFrame({
    'col1': [0.068467, 0.091651, 0.070380, 0.052239, 0.087564],
    'col2': [0.099870, 0.084946, 0.104353, 0.123760, 0.104460]
})

# Define the condition for averaging (df2 > 0.5)
condition = df2 >= 0.5

# Filter values in df1 according to the condition
filtered_df = df1.where(condition)

# Calculate the mean of filtered dataframe using numpy.nanmean()
mean = np.nanmean(filtered_df.values)

print(mean)

Approach 3: Converting DataFrames into Series with DataFrame.stack() for Averaging

Converting dataframes into series allows us to perform operations similar to those on individual columns.

import pandas as pd
import numpy as np

# Create df1 with binary data and df2 with float data
df1 = pd.DataFrame({
    'col1': [0.0, 0.0, 0.0, 0.0, 0.0],
    'col2': [1.0, 0.0, 1.0, 0.0, 1.0]
})

df2 = pd.DataFrame({
    'col1': [0.068467, 0.091651, 0.070380, 0.052239, 0.087564],
    'col2': [0.099870, 0.084946, 0.104353, 0.123760, 0.104460]
})

# Define the condition for averaging (df2 > 0.5)
condition = df2 >= 0.5

# Convert dataframes into series and apply the condition
series1 = df1.stack()
filtered_series1 = series1.where(condition)

# Calculate the mean of filtered series
mean = filtered_series1.mean()

print(mean)

Conclusion

In this article, we explored different methods for averaging a dataframe based on conditions from another dataframe. Whether using DataFrame.where(), numpy.nanmean(), or converting dataframes into series with DataFrame.stack(), the key takeaway is that these approaches allow us to leverage pandas’ powerful data manipulation capabilities.

Last modified on 2024-09-25