Concatenate Two pandas DataFrames with Different Columns in Python (Example Code)
In this tutorial, I’ll show how to combine two pandas DataFrames with different column names in the Python programming language.
Setting up the Example
import pandas as pd # Load pandas library |
import pandas as pd # Load pandas library
my_df1 = pd.DataFrame({"A":["yes", "yes", "no", "yes", "yes"], # Construct two pandas DataFrames "B":range(10, 15), "C":["hey", "hi", "huhu", "huhu", "hello"]}) print(my_df1) # A B C # 0 yes 10 hey # 1 yes 11 hi # 2 no 12 huhu # 3 yes 13 huhu # 4 yes 14 hello |
my_df1 = pd.DataFrame({"A":["yes", "yes", "no", "yes", "yes"], # Construct two pandas DataFrames "B":range(10, 15), "C":["hey", "hi", "huhu", "huhu", "hello"]}) print(my_df1) # A B C # 0 yes 10 hey # 1 yes 11 hi # 2 no 12 huhu # 3 yes 13 huhu # 4 yes 14 hello
my_df2 = pd.DataFrame({"A":["foo", "bar", "foo", "foo"], "X":range(11, 15)}) print(my_df2) # A X # 0 foo 11 # 1 bar 12 # 2 foo 13 # 3 foo 14 |
my_df2 = pd.DataFrame({"A":["foo", "bar", "foo", "foo"], "X":range(11, 15)}) print(my_df2) # A X # 0 foo 11 # 1 bar 12 # 2 foo 13 # 3 foo 14
Example: Appending Two pandas DataFrames with Different Variables
my_df_combined = pd.concat([my_df1, my_df2]) # Combine two DataFrames print(my_df_combined) # A B C X # 0 yes 10.0 hey NaN # 1 yes 11.0 hi NaN # 2 no 12.0 huhu NaN # 3 yes 13.0 huhu NaN # 4 yes 14.0 hello NaN # 0 foo NaN NaN 11.0 # 1 bar NaN NaN 12.0 # 2 foo NaN NaN 13.0 # 3 foo NaN NaN 14.0 |
my_df_combined = pd.concat([my_df1, my_df2]) # Combine two DataFrames print(my_df_combined) # A B C X # 0 yes 10.0 hey NaN # 1 yes 11.0 hi NaN # 2 no 12.0 huhu NaN # 3 yes 13.0 huhu NaN # 4 yes 14.0 hello NaN # 0 foo NaN NaN 11.0 # 1 bar NaN NaN 12.0 # 2 foo NaN NaN 13.0 # 3 foo NaN NaN 14.0
Related Articles & Further Resources
Here, you can find some further resources on topics such as data conversion and lists: