Combining Multiple DataFrames with Pandas in Python

In this article, we’ll explore how to combine multiple data frames using pandas in Python. We’ll take a closer look at the provided code and walk through the steps necessary to achieve the desired output.

Understanding the Problem

The problem involves combining two separate data frames: df3 and df4. These data frames contain aggregated values for certain columns, with each hour of the day represented by a unique index. The goal is to merge these two data frames into one new data frame (df2) that includes both aggregated values.

Overview of Pandas DataFrames

Before we dive into the solution, let’s quickly review how pandas data frames work. A pandas DataFrame is a 2-dimensional labeled data structure with columns of potentially different types. Each column represents a variable, and each row represents an observation.

The key components of a pandas DataFrame are:

• Index: The unique values in each row.
• Columns: The variables represented by the data.
• Data: The actual values stored in the DataFrame.

Provided Code Analysis

Let’s take a closer look at the provided code and understand what it does:

1. We start with reading a CSV file into a pandas DataFrame (df) using pd.read_csv().
2. We select specific columns from df and convert the ’timestamp’ column to datetime format using parse_dates=True.
3. We create a new DataFrame (df2) that includes only the selected columns.
4. We set the ’timestamp’ column as the index of df2.
5. We calculate the mean of two specific columns (‘avg_hr’ and ‘avg_rr’) for each hour using groupby().

However, when trying to create an aggregated column from these groupby results, we encounter a problem:

df2['AVG_HR'] = df2.groupby(df2.index.map(lambda t: t.hour))['avg_hr'].transofrm('mean')

The issue here is that the transform() function returns a Series aligned with the original DataFrame (df2). This can lead to unexpected results when trying to merge this Series with other DataFrames.

Solution Overview

To solve this problem, we’ll explore three approaches:

1. Using transform()
2. Simplifying the groupby operation using datetime attributes
3. Resampling by hour and then resetting the index

We’ll examine each approach in detail and provide example code to illustrate how they work.

Approach 1: Using transform()

The first approach involves using the transform() function to create an aggregated column from the groupby results. Here’s the modified code:

df2['AVG_HR'] = df2.groupby(df2.index.map(lambda t: t.hour))['avg_hr'].transofrm('mean')

However, as mentioned earlier, this can lead to unexpected results due to alignment issues.

Approach 2: Simplifying the Groupby Operation

We can simplify the groupby operation by directly accessing the datetime attributes of the index. This allows us to perform a more straightforward aggregation:

df2['AVG_HR'] = df2.groupby(df2.index.hour)['avg_hr'].transform('mean')

By using hour instead of map(), we can avoid alignment issues and achieve the desired result.

Approach 3: Resampling by Hour

If you want to resample your data by hour, you can use the groupby() function followed by reset_index(). Here’s an example:

resampled_df = df.groupby(df.index.hour).mean().reset_index()

This approach allows you to easily merge the resampled DataFrame with other DataFrames.

Final Solution

To achieve our final solution, we can combine the first two approaches. We’ll use transform() to create an aggregated column from the groupby results, but this time, we’ll use it in a more controlled manner:

df2['AVG_HR'] = df2.groupby(df2.index.hour)['avg_hr'].transform('mean')

This approach ensures that we’re working with the correct DataFrame and achieving the desired result.

Example Usage

Here’s an example of how to use these approaches in practice:

import pandas as pd
import numpy as np

# Create a sample DataFrame
np.random.seed(0)
df = pd.DataFrame({
    'timestamp': pd.date_range('2022-01-01', periods=24, freq='H'),
    'avg_hr': np.random.randint(50, 60, size=24),
    'avg_rr': np.random.randint(20, 30, size=24)
})

# Create a new DataFrame (df2) with selected columns
df2 = df[['timestamp', 'avg_hr']]

# Set the timestamp column as the index of df2
df2.set_index('timestamp', inplace=True)

# Approach 1: Using transform()
df2['AVG_HR'] = df2.groupby(df2.index.map(lambda t: t.hour))['avg_hr'].transform('mean')

print("Transform Approach:")
print(df2.head())

# Approach 2: Simplifying the groupby operation
df2['AVG_HR'] = df2.groupby(df2.index.hour)['avg_hr'].transform('mean')

print("\nSimplified Groupby Approach:")
print(df2.head())

# Approach 3: Resampling by hour and then resetting the index
resampled_df = df.groupby(df.index.hour).mean().reset_index()
df2 = resampled_df

print("\nResampled DataFrame:")
print(df2)

This example demonstrates how to use these approaches in practice, including transforming an aggregated column using transform() and simplifying the groupby operation by directly accessing datetime attributes.

Conclusion

Combining multiple data frames is a common task when working with pandas in Python. By leveraging techniques like transform(), groupby(), and resampling, we can easily create aggregated columns from our data. In this article, we explored three approaches to achieve the desired result: using transform() with caution, simplifying the groupby operation by accessing datetime attributes directly, and resampling by hour followed by resetting the index.

Each approach has its own strengths and weaknesses, and choosing the right one depends on your specific use case. By understanding these techniques and applying them in practice, you’ll be better equipped to handle complex data manipulation tasks with pandas.

Last modified on 2025-03-05