In order to merge two data frames with the same column names, we are going to use the pandas.concat(). This function does all the heavy lifting of performing concatenation operations along with an axis of Pandas objects while performing optional set logic (union or intersection) of the indexes (if any) on the other axes.
Syntax: concat(objs, axis, join, ignore_index, keys, levels, names, verify_integrity, sort, copy)
Approach
- Import module
- Create or load first dataframe
- Create or load second dataframe
- Concatenate on the basis of same column names
- Display result
Below are various examples that depict how to merge two data frames with the same column names:
Example 1:
Python3
# import moduleimport pandas as pd # assign dataframesdata1 = pd.DataFrame([[1, 2, 3], [4, 5, 6], [7, 8, 9]], columns=['A', 'B', 'C']) data2 = pd.DataFrame([[3, 4], [5, 6]], columns=['A', 'C']) # display dataframesprint('Dataframes:')display(data1)display(data2) # merge two data framesprint('After merging:')pd.concat([data1, data2], axis=0) |
Output:
Example 2:
Python3
# import moduleimport pandas as pd # assign dataframesdata1 = pd.DataFrame([[25, 77.5, 'A'], [30, 60.2, 'B']], columns=['Students', 'Avg Marks', 'Section']) data2 = pd.DataFrame([[52, 'C'], [25, 'A']], columns=['Students', 'Section']) # display dataframesprint('Dataframes:')display(data1)display(data2) # merge two data framesprint('After merging:')pd.concat([data1, data2], axis=0) |
Output:
Example 3:
Python3
# import moduleimport pandas as pd # assign dataframesdata1 = pd.DataFrame([[25, 77.5, 'A'], [30, 60.2, 'B'], [25, 70.7, 'C']], columns=['Students', 'Avg Marks', 'Section']) data2 = pd.DataFrame([[30, 70.2, 'A'], [25, 65.2, 'B'], [35, 77.7, 'C']], columns=['Students', 'Avg Marks', 'Section']) # display dataframesprint('Dataframes:')display(data1)display(data2) # merge two data framesprint('After merging:')pd.concat([data1, data2], axis=0) |
Output:
