Pandas is the open-source Python library used for Data analysis and manipulation. It is fast, powerful, flexible, and easy to use. In this article, we will discuss how to find columns that are common between two Data Frames. Below is the different approach that can be used to find common columns.
Method 1: Using Numpy intersect1d method
In this example, we will create Pandas Dataframe from the list, and then we will use Numpy’s intersect1d() method which will return columns that are common between two Dataframes.
Python3
# Importing librariesimport pandas as pdimport numpy as np# Creating Dataframesa = [{'Name': 'abc', 'Age': 8, 'Grade': 3},{'Name': 'xyz', 'Age': 9, 'Grade': 3}]df1 = pd.DataFrame(a)b = [{'ID': 1,'Name': 'abc', 'Age': 8},{'ID': 2,'Name': 'xyz', 'Age': 9}]df2 = pd.DataFrame(b)# Printing Dataframesdisplay(df1)display(df2)# Finding Common columnsa = np.intersect1d(df2.columns, df1.columns)# Printing common columnsprint ("Common Columns:",a) |
Output:
Method 2: Using Pandas intersection method
In this example, we will create Pandas Dataframe from the list, and then we will use Panda’s intersection() method which will return columns that are common between two Dataframes.
Python3
# Importing librariesimport pandas as pd# Creating Dataframesa = [{'Name': 'abc', 'Age': 8, 'Grade': 3}, {'Name': 'xyz', 'Age': 9, 'Grade': 3}]df1 = pd.DataFrame(a)b = [{'ID': 1,'Name': 'abc', 'Age': 8}, {'ID': 2,'Name': 'xyz', 'Age': 9}]df2 = pd.DataFrame(b)# Printing Dataframesdisplay(df1)display(df2)# Finding Common columnsa = df2.columns.intersection(df1.columns)# Printing common columnsprint ("Common Columns:",a) |
Output:
Method 3: In this example, we will use & operator to find common columns.
Python3
# Importing librariesimport pandas as pd# Creating Dataframesa = [{'Name': 'abc', 'Age': 8, 'Grade': 3}, {'Name': 'xyz', 'Age': 9, 'Grade': 3}]df1 = pd.DataFrame(a)b = [{'ID': 1,'Name': 'abc', 'Age': 8}, {'ID': 2,'Name': 'xyz', 'Age': 9}]df2 = pd.DataFrame(b)# printing Dataframesdisplay(df1)display(df2)# Finding Common columnsa = df1.columns & df2.columns# Printing common columnsprint ("Common Columns:",a) |
Output:
