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Merge, Join and Concatenate DataFrames using Pandas
In this tutorial, we are going to learn to merge, join, and concat the DataFrames using pandas library. I think you are already familiar with dataframes and pandas library. Let's see the three operations one by one.
Merge
We have a method called pandas.merge() that merges dataframes similar to the database join operations. Follow the below steps to achieve the desired output. Merge method uses the common column for the merge operation.
Initialize the Dataframes.
Call the method pandas.merge() with three arguments dataframes, how (defines the database join operation), on (common field of the dataframes).
Example
Let's see an example.
# importing the pandas library
import pandas
# creating dataframes
dataframe_1 = pandas.DataFrame({"Common": ["A", "B", "C", "D", "E"],
"Name": ["John", "Alice", "Emma", "Watson", "Harry"], "Age": [18, 19, 20, 21, 15]}) dataframe_2 = pandas.DataFrame({"Common": ["A", "B", "C", "D", "E"], "Sport": ["Cricket", "Football", "Table Tennis", "Badminton", "Chess"], "Movie": ["Jumanji", "Black Widow", "End Game", "Mr. Robot", "Matrix"]})
# merging using merge method
# how = left or right or inner
new_df = pandas.merge(dataframe_1, dataframe_2, how="left", on="Common") # printing the resultant dataframe print(new_df)Output
If you run the above code, you will get the following results.
Common Name Age Sport Movie 0 A John 18 Cricket Jumanji 1 B Alice 19 Football Black Widow 2 C Emma 20 Table Tennis End Game 3 D Watson 21 Badminton Mr. Robot 4 E Harry 15 Chess Matrix
Join
Similar to the merge method, we have a method called dataframe.join(dataframe) for joining the dataframes. Let's see steps to join two dataframes into one. The join method uses the index of the dataframe.
Initialize the dataframes.
Write a statment dataframe_1.join(dataframe_2) to join.
Example
Let's try it with the coding example.
# importing the pandas library
import pandas
# creating dataframes
dataframe_1 = pandas.DataFrame({"Name": ["John", "Alice", "Emma", "Watson", "Harry"], "Age": [18, 19, 20, 21, 15]}, index = ["A", "B", "C", "D", "E"])dataframe_2 = pandas.DataFrame({"Sport": ["Cricket", "Football", "Table Tennis", "Badminton", "Chess"], "Movie": ["Jumanji", "Black Widow", "End Game", "Mr. Robot", "Matrix"]}, index = ["A", "B", "C", "D", "E"])
# joining
new_df = dataframe_1.join(dataframe_2)
# printing the new dataframe
print(new_df)If you run the above program, you will get the following output
Output
Name Age Sport Movie A John 18 Cricket Jumanji B Alice 19 Football Black Widow C Emma 20 Table Tennis End Game D Watson 21 Badminton Mr. Robot E Harry 15 Chess Matrix
Concatenation
Similar to the merge and join methods, we have a method called pandas.concat(list->dataframes) for concatenation of dataframes. Let's see steps to concatenate dataframes. Concatenation combines dataframes into one.
Initialize the dataframes.
Concatenate dataframes using pandas.concat([df_1, df_2, ..]). Print the result.
Example
Let's try it with the coding example.
# importing the pandas library
import pandas
# creating dataframes dataframe_1 = pandas.DataFrame({"Name": ["John","Alice","Emma","Watson","Harry"], "Age": [18, 19, 20, 21, 15]}, index = ["A", "B", "C", "D", "E"]) dataframe_2 = pandas.DataFrame({"Name": ["Wick", "Robert", "Elliot", "Baby", "Cruise"], "Age": [22, 20, 45, 15, 42]}, index = ["F", "G", "H", "I", "J"])
# concatenating -> you can pass any number of new_df = pandas.concat([dataframe_1, dataframe_2])
# printing the new dataframe
print(new_df)Output
If you run the above program, you will get the following output.
Name Age A John 18 B Alice 19 C Emma 20 D Watson 21 E Harry 15 F Wick 22 G Robert 20 H Elliot 45 I Baby 15 J Cruise 42
Conclusion
If you have any doubts in the tutorial, mention them in the comment section.
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