How to find the uncommon rows between two DataFrames in pandas
Overview
A DataFrame is a two-dimensional data structure where data is aligned in rows and columns. In this answer, we'll learn to find the uncommon rows between the two data frames using pandas.
To find out uncommon rows, we follow the steps below:
- Merge the two DataFrames.
- Remove the duplicates from the resulted DataFrame in the above step.
We can merge two DataFrames using the concat() method and remove duplicates using the drop_duplicates() method.
Syntax
concat([list of dataframes separated by commas])drop_duplicates()
Parameters
The concat() method takes a list of DataFrames as a parameter.
Return value
It returns a new DataFrame appending all of them.
The drop_duplicates() method takes optional parameters like keep, which decides
Example
import pandas as pd#data frame 1classA = pd.DataFrame({"Student": ['John', 'Lexi', 'Augustin', 'Jane', 'Kate'],"Age": [18, 17, 19, 17, 18]})#data frame 2classB = pd.DataFrame({"Student": ['John', 'Lexi', 'Bob', 'karl', 'Kate'],"Age": [18, 17, 16, 19, 18]})#get uncommon rowsprint(pd.concat([classA,classB]).drop_duplicates(keep=False))
Explanation
- Line 1: We import the
pandasmodule, which contains methods to create DataFrames and modify them. - Line 4–9: We create a DataFrame that represents
class A, which containsStudentand theirAge. - Line 13–18: We create DataFrame that represents
class B, which containsStudentand theirAge. - Line 21: We filter the uncommon rows from the above two DataFrames. We use the
concat()method to do so. In this method, we input DataFrames in a list as a parameter to it and remove duplicate rows from the resultant data frame using thedrop_duplicates()method.
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