paint-brush
How to Use the Concat Function in Pandas for Horizontal or Vertical Table Concatenationby@luca1iu
830 reads
830 reads

How to Use the Concat Function in Pandas for Horizontal or Vertical Table Concatenation

by Luca LiuMarch 13th, 2024
Read on Terminal Reader
Read this story w/o Javascript
tldt arrow

Too Long; Didn't Read

In data analysis and manipulation, it is common to combine or concatenate multiple tables to create a single data structure for analysis. Pandas, a popular Python library for data manipulation and analysis, provides a `concat()` function that allows you to combine tables either horizontally or vertically. In this article, we will demonstrate how to use the `conCat()' function in pandas to concaternate tables horizontally and vertically.
featured image - How to Use the Concat Function in Pandas for Horizontal or Vertical Table Concatenation
Luca Liu HackerNoon profile picture

Introduction

In data analysis and manipulation, it is common to combine or concatenate multiple tables to create a single data structure for analysis. Pandas, a popular Python library for data manipulation and analysis, provides a concat() function that allows you to combine tables either horizontally or vertically. In this article, we will demonstrate how to use the concat() function in pandas to concatenate tables horizontally and vertically.

Horizontal Concatenation

Horizontal concatenation involves merging two tables by columns, effectively extending the tables by columns. This can be achieved using the concat() function in pandas by setting the axis parameter to 1.

Example: Horizontal Concatenation

import pandas as pd

# Create two DataFrames
df1 = pd.DataFrame({'A': ['A0', 'A1', 'A2', 'A3'],
                    'B': ['B0', 'B1', 'B2', 'B3'],
                    'C': ['C0', 'C1', 'C2', 'C3'],
                    'D': ['D0', 'D1', 'D2', 'D3']})

df2 = pd.DataFrame({'A': ['A4', 'A5', 'A6', 'A7'],
                    'B': ['B4', 'B5', 'B6', 'B7'],
                    'C': ['C4', 'C5', 'C6', 'C7'],
                    'D': ['D4', 'D5', 'D6', 'D7']})

# Merge the two tables horizontally.
result = pd.concat([df1, df2], axis=1)

print(result)

Vertical Concatenation

Vertical concatenation involves stacking tables on top of each other, effectively extending the tables by rows. This can be achieved using the concat() function in pandas by setting the axis parameter to 0.

Example: Vertical Concatenation

import pandas as pd

# Create two DataFrames
df1 = pd.DataFrame({'A': ['A0', 'A1', 'A2', 'A3'],
                    'B': ['B0', 'B1', 'B2', 'B3'],
                    'C': ['C0', 'C1', 'C2', 'C3'],
                    'D': ['D0', 'D1', 'D2', 'D3']})

df2 = pd.DataFrame({'A': ['A4', 'A5', 'A6', 'A7'],
                    'B': ['B4', 'B5', 'B6', 'B7'],
                    'C': ['C4', 'C5', 'C6', 'C7'],
                    'D': ['D4', 'D5', 'D6', 'D7']})

# Concatenate the two tables vertically
result = pd.concat([df1, df2], axis=0)

print(result)

Conclusion

In this article, we have discussed how to use the concat() function in the pandas library to concatenate tables horizontally or vertically by specifying the axis parameter. By following the examples provided in this article, you should be able to effectively merge tables to create a single, comprehensive dataset for further data analysis and manipulation. Happy coding!


Thank you for taking the time to explore data-related insights with me. I appreciate your engagement. If you find this information helpful, I invite you to follow me or connect with me on LinkedIn or X(@Luca_DataTeam). You can also catch glimpses of my personal life on Instagram, Happy exploring!👋