Merging Multiple Dataframes within a List into One DataFrame

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In this article, we will explore how to merge multiple dataframes within a list into one dataframe. This is a common requirement in data analysis and manipulation, especially when working with large datasets.

Introduction

Dataframes are a powerful tool for data manipulation and analysis in Python. They provide an efficient way to store and manipulate data, making it easy to perform operations such as filtering, sorting, and grouping. However, when working with multiple dataframes, merging them into one dataframe can be a challenge.

In this article, we will discuss how to merge multiple dataframes within a list into one dataframe using Python’s pandas library.

Understanding the Problem

The problem statement is as follows:

• You have several dataframes, all with the same columns, stored in a list.
• You want to merge these dataframes into one dataframe.

For example, you can create three dataframes df1, df2, and df3 using numpy arrays and store them in a list dfList.

import pandas as pd
import numpy as np

# Create dataframes
df1 = pd.DataFrame(np.array([[10, 20, 30], [40, 50, 60], [70, 80, 90]]),
                   columns=['a', 'b', 'c'])
df2 = pd.DataFrame(np.array([[11, 22, 33], [44, 55, 66], [77, 88, 99]]),
                   columns=['a', 'b', 'c'])
df3 = pd.DataFrame(np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]),
                   columns=['a', 'b', 'c'])

# Store dataframes in a list
dfList = [df1, df2, df3]

Using the Concat Function

The concat function is used to merge two or more dataframes into one. However, when using this function with a list of dataframes, we need to ensure that all dataframes in the list have the same columns.

# Merge dataframes using concat
df_merge = pd.concat([dfList[0], dfList[1], dfList[2]])

print(df_merge)

However, when trying to use this approach with a large number of dataframes, we may encounter an error.

Error Handling and Solutions

When using the concat function with a list of dataframes, we need to ensure that all dataframes in the list have the same columns. If not, an error will be raised.

# Error: first argument must be an iterable of pandas objects, you passed an object of type "DataFrame"

To solve this issue, we can use a loop to merge each dataframe in the list with the previous merged dataframes.

# Create an empty dataframe to store the merged result
dfList_all = pd.DataFrame()

# Loop through the list of dataframes and concatenate them
for i in range(len(dfList)):
    dfList_all = pd.concat([dfList_all, dfList[i]])

print(dfList_all)

However, this approach has a major drawback: it involves re-creating the empty dataframe on each iteration, resulting in unnecessary memory allocations.

Optimizing the Approach

A more efficient way to merge multiple dataframes within a list into one dataframe is by using the concat function with a list comprehension.

# Create an empty list to store the merged result
dfList_all = []

# Loop through the list of dataframes and concatenate them
for df in dfList:
    dfList_all.append(df)

# Use concat to merge all dataframes in the list into one dataframe
df_merge = pd.concat(dfList_all, ignore_index=True)

print(df_merge)

Alternatively, we can use a vectorized approach using numpy arrays.

import pandas as pd
import numpy as np

# Create an empty list to store the merged result
dfList_all = []

# Loop through the list of dataframes and concatenate them
for i, df in enumerate(dfList):
    if i == 0:
        dfList_all.append(df)
    else:
        dfList_all.append(pd.concat([dfList_all[-1], df], ignore_index=True))

print(dfList_all)

Conclusion

Merging multiple dataframes within a list into one dataframe is a common requirement in data analysis and manipulation. By understanding the concat function, error handling, and optimization techniques, we can efficiently merge multiple dataframes into one.

In this article, we have explored various approaches to merging multiple dataframes within a list into one dataframe using Python’s pandas library.

Code Example

# Import necessary libraries
import pandas as pd
import numpy as np

# Create dataframes
df1 = pd.DataFrame(np.array([[10, 20, 30], [40, 50, 60], [70, 80, 90]]),
                   columns=['a', 'b', 'c'])
df2 = pd.DataFrame(np.array([[11, 22, 33], [44, 55, 66], [77, 88, 99]]),
                   columns=['a', 'b', 'c'])
df3 = pd.DataFrame(np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]),
                   columns=['a', 'b', 'c'])

# Store dataframes in a list
dfList = [df1, df2, df3]

# Create an empty dataframe to store the merged result
dfList_all = pd.DataFrame()

# Loop through the list of dataframes and concatenate them using concat
for i in range(len(dfList)):
    dfList_all = pd.concat([dfList_all, dfList[i]])

print(dfList_all)

Last modified on 2025-04-23