concat() function does all of the heavy liftings of performing concatenation operations along an axis while performing optional set logic (union or intersection) of the indexes (if any) on the other axes.
The concat() function combines data frames in one of two ways:
- Stacked: Axis = 0 (This is the default option).
Axis=0
- Side by Side: Axis = 1
Axis=1
Steps to Union Pandas DataFrames using Concat:
- Create the first DataFrame
Python3
import pandas as pd students1 = {'Class': ['10','10','10'], 'Name': ['Hari','Ravi','Aditi'], 'Marks': [80,85,93] } df1 = pd.DataFrame(students1, columns= ['Class','Name','Marks']) df1 |
Output:
- Create the second DataFrame
Python3
import pandas as pd students2 = {'Class': ['10','10','10'], 'Name': ['Tanmay','Akshita','Rashi'], 'Marks': [89,91,87] } df2 = pd.DataFrame(students2, columns= ['Class','Name','Marks']) df2 |
Output:
- Union Pandas DataFrames using Concat
Python3
pd.concat([df1,df2]) |
Output:
Note: You’ll need to keep the same column names across all the DataFrames to avoid any ‘NaN’ values.
