Merging DataFrames from Functions Using Python's Pandas Library

Merging DataFrames from a Function in Python

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In this article, we will explore how to merge multiple DataFrames into one DataFrame using Python’s pandas library. Specifically, we’ll examine how to achieve this when working with functions that produce multiple DataFrames.

Introduction

When working with data in Python, it’s often necessary to process large datasets from various sources. In many cases, these datasets are available as APIs or web scraping tasks, which can result in multiple small DataFrames being returned. While these individual DataFrames might be manageable, merging them into a single DataFrame for analysis or export becomes a pressing concern.

This article will provide a comprehensive guide on how to merge DataFrames from functions using the pandas library in Python.

Background

Before we dive into the solution, let’s take a look at some background information. The pandas library provides efficient data structures and operations for working with structured data, such as tabular data.

DataFrames

A DataFrame is similar to an Excel spreadsheet or a table in a relational database. It consists of rows and columns, where each column represents a variable, and the index represents the row. DataFrames are particularly useful when dealing with messy data that needs to be cleaned, transformed, or analyzed.

Using Functions to Produce Data

In many cases, functions can produce multiple DataFrames as output. This might happen when working with APIs, web scraping tasks, or other external data sources.

Here’s an example of a simple function that produces two separate DataFrames:

def f(name):
    url = "https://apiurl"
    response = requests.get(url, params = {'page': 1}) 
    records = []
    for page_number in range(1, response.json().get("pages")+1):
        response = requests.get(url, params = {'page': page_number})
        records += response.json().get('records')
        df = pd.DataFrame(records)
    return df

In this example, the function f produces two separate DataFrames: one for each page of data returned by the API.

Merging DataFrames

When working with multiple DataFrames, merging them into a single DataFrame can be a challenge. This is where pandas comes to the rescue!

Using pd.concat()

One common approach to merging DataFrames is using the pd.concat() function. This function allows you to concatenate (join) multiple DataFrames together.

Here’s an example of how to use pd.concat() to merge two separate DataFrames:

import pandas as pd

# Create two separate DataFrames
df1 = pd.DataFrame({'Name': ['John', 'Mary'], 'Age': [25, 31]})
df2 = pd.DataFrame({'Name': ['Jane', 'Bob'], 'Age': [22, 35]})

# Merge the two DataFrames using pd.concat()
merged_df = pd.concat([df1, df2])

print(merged_df)

Output:

     Name  Age
0    John   25
1    Mary   31
0    Jane   22
1     Bob   35

As we can see, pd.concat() has successfully merged the two separate DataFrames into a single DataFrame.

Using `dfs` List to Store Multiple DataFrames

Another approach to merging multiple DataFrames is by storing them in a list and using pd.concat() on the entire list. This method is particularly useful when working with large numbers of DataFrames.

Here’s an example:

import pandas as pd

# Create multiple separate DataFrames
dfs = []
for row in valdf.itertuples():
    name = valdf.loc[row.Index, 'Account_ID']
    df1 = f(name) 
    dfs.append(df1)

# Merge the list of DataFrames using pd.concat()
all_df = pd.concat(dfs)

print(all_df)

In this example, we create a list dfs to store multiple DataFrames. We then use pd.concat() on the entire list to merge all the DataFrames into a single DataFrame.

Handling Inconsistent Data

One potential issue when merging DataFrames is dealing with inconsistent data formats or structures. This can happen when working with APIs or external data sources that return data in different formats.

To handle this, you can use various techniques such as:

Data cleaning: using pandas’ built-in functions to clean and preprocess the data
Data transformation: using pandas’ built-in functions to transform the data into a consistent format
Data merging: using pd.concat() or other merge functions to combine DataFrames with different structures

Best Practices

When working with multiple DataFrames, keep the following best practices in mind:

Use dfs list to store multiple DataFrames: this approach allows you to easily merge multiple DataFrames into a single DataFrame.
Use pd.concat() with caution: be aware of potential issues such as inconsistent data formats or structures when using pd.concat().
Clean and preprocess data: use pandas’ built-in functions to clean and preprocess the data before merging it into a single DataFrame.

Conclusion

Merging DataFrames from functions can be a complex task, especially when working with large numbers of DataFrames. By using the dfs list approach or pd.concat() function, you can easily merge multiple DataFrames into a single DataFrame. Additionally, by following best practices and handling potential issues such as inconsistent data formats or structures, you can ensure that your code is efficient and effective.

Example Use Cases

Here are some example use cases for merging DataFrames from functions:

API integration: when working with APIs that return multiple DataFrames, using the dfs list approach or pd.concat() function allows you to easily merge the DataFrames into a single DataFrame.
Web scraping tasks: when working with web scraping tasks that return multiple DataFrames, using the dfs list approach or pd.concat() function allows you to easily merge the DataFrames into a single DataFrame.
Data analysis: when working with large datasets that require merging and analysis, using the dfs list approach or pd.concat() function allows you to efficiently merge multiple DataFrames into a single DataFrame.

Last modified on 2025-03-22