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The concat() function in pandas is used to append either columns or rows from one DataFrame to another. The concat() function does all the heavy lifting of performing concatenation operations along an axis while performing optional set logic (union or intersection) of the indexes (if any) on the other axes.

To concatenate dataframes with different columns, we use the concat() function in Pandas. The concat() function takes two or more dataframes as arguments and returns a new dataframe that combines them. Let's break down each argument: [df1, df2, df3, ...] : A list of dataframes to concatenate.

Pandas concat() function Syntax axis: axis to concatenate along; default = 0. join: way to handle indexes on other axis; default = 'outer' ignore_index: if True, do not use the index values along the concatenation axis; default = False. keys: sequence to add an identifier to the result indexes; default = None.

Using Series.str.cat() Function to Concat Columns By using series.str.cat() function you can concatenate two Series by a delimiter/separator. You can apply this with DataFrame as below. Here df["courses"] & df["Duration"] returns series. Yields same output as above.

By use + operator simply you can concatenate two or multiple text/string columns in pandas DataFrame. Note that when you apply + operator on numeric columns it actually does addition instead of concatenation.

Concatenating DataFrames import pandas as pd #importing pandas library. ? #DataFrame 1. data=[['Cars',5],['Motorbikes',10],['Trucks',2],['Minivans',1]] ... print('DataFrame 1 \n' , df1) ? #DataFrame 2. ... print('\n DataFrame 2 \n' ,df2) ? #DataFrame 3. ... print('\n DataFrame 3 \n' ,df3) ? #concatenation.