Concatenate More Than Two Pandas DataFrames

In this article, we will explore how to concatenate more than two pandas DataFrames using the pandas and numpy modules. Typically, the pandas.concat() method is used to concatenate multiple data frames. This method allows for concatenation along rows (axis=0) or columns (axis=1), providing flexibility in combining data efficiently.

A DataFrame in Python's pandas library is a two-dimensional labeled data structure that is used for data manipulation and analysis. It can handle different data types such as integers, floats, and strings. Each column has a unique label, and each row is labeled with a unique index value, which helps in accessing specific rows.

Various ways to Concatenate two data frames

Following are different ways to concatenate more than two pandas data frames ?

• Using concat() Method
• Using join() Method
• Using merge() Method

Creating data frames

Before concatenating data frames, let us create two data frames. In the following example we have created two data frames using numpy module and pandas ?

import pandas
import numpy 

data_frame1 = pandas.DataFrame({'Student': ["Ram", "Neha", "John", "Sakshi"],
                   'Marks': [95, 80, 90, 89]})

data_frame2 = pandas.DataFrame({'Student': ["Akash", "Bhanu", "krishna", "Rani"],
                    'Marks': [79, 85, 96, 80]})

print("DataFrame 1:")
print(data_frame1)
print()
print("DataFrame 2:")
print(data_frame2)

Output

Following is the output of the above code ?

DataFrame 1:
  Student  Marks
0     Ram     95
1    Neha     80
2    John     90
3  Sakshi     89

DataFrame 2:
   Student  Marks
0    Akash     79
1    Bhanu     85
2  krishna     96
3     Rani     80

Using concat() Method

The concat() method in pandas is used to combine two or more DataFrames either vertically (along rows) or horizontally (along columns). When axis=0, the DataFrames are concatenated vertically by stacking rows. When axis=1, they are concatenated horizontally by aligning columns. The sort=False parameter can be used to enhance performance by skipping the sorting of non-concatenation axis labels in the resulting data frame.

Example

Following is an example of concatenating data frames using the concat() method ?

import pandas
import numpy 

data_frame1 = pandas.DataFrame({'Students': ["Ram", "Neha", "John", "Sakshi"],
                   'Marks': [95, 80, 90, 89]})

data_frame2 = pandas.DataFrame({'Students': ["Akash", "Bhanu", "krishna", "Rani"],
                    'Marks': [79, 85, 96, 80]})
					
concat_dataframe = pandas.concat([data_frame1, data_frame2], ignore_index=True, sort=False)
print(concat_dataframe)

Following is the output of the above code ?

Students  Marks
0      Ram     95
1     Neha     80
2     John     90
3   Sakshi     89
4    Akash     79
5    Bhanu     85
6  krishna     96
7     Rani     80

Using join() Method

The join() method in pandas is used to combine DataFrames based on their indices or specified columns. It aligns rows or columns from two DataFrames using common index values or column keys, supporting various types of joins such as inner, outer, left, or right. This method provides a flexible way to merge data while preserving the specified join logic.

Example

In the following example, we have combined two data frames using the join() method ?

import pandas
import numpy 

data_frame1 = pandas.DataFrame({'Class1': ["Ram", "Neha", "John","Sam"],'Maths_Marks': [95, 80, 90, 89]})

data_frame2 = pandas.DataFrame({'Class2': ["Akash", "Bhanu","krishna", "Rani"],
                    'Physics_Marks': [79, 85, 96, 80]})
					
concat_dataframe = data_frame2.join(data_frame1)
print(concat_dataframe)

Following is the output of the above code ?

Class2  Physics_Marks Class1  Maths_Marks
0    Akash             79    Ram           95
1    Bhanu             85   Neha           80
2  krishna             96   John           90
3     Rani             80    Sam           89

Using merge() Method

The merge() method in the pandas module is used to concatenate data frames either vertically or horizontally. Based on common columns in the given dataframes it combines using a merge operation. The how parameter in merge() specifies the type of merge(inner, outer, left, or right), determining how the data frames are combined.

Example

In the following example, we have created two data frames and concatenated the first data frame to the second data frame using the merge() method ?

import pandas
import numpy 

data_frame1 = pandas.DataFrame({'Course1': ["Python", "C++", "Java","Javasript"],'Price1': [15000, 20000, 30000, 35000]})

data_frame2 = pandas.DataFrame({'Course2': ["HTML", "CSS","C", "React"],
                    'Price2': [5000, 8000, 10000, 35000]})
					
concat_dataframe = data_frame2.join(data_frame1)
print(concat_dataframe)

Following is the output of the above code ?

  Course2  Price2    Course1  Price1
0    HTML    5000     Python   15000
1     CSS    8000        C++   20000
2       C   10000       Java   30000
3   React   35000  Javasript   35000