Let’s discuss how to compare values in the Pandas dataframe. Here are the steps for comparing values in two pandas Dataframes:
Step 1 Dataframe Creation: The dataframes for the two datasets can be created using the following code:
Python3
import pandas as pd# elements of first datasetfirst_Set = {'Prod_1': ['Laptop', 'Mobile Phone', 'Desktop', 'LED'], 'Price_1': [25000, 8000, 20000, 35000] }# creation of Dataframe 1df1 = pd.DataFrame(first_Set, columns=['Prod_1', 'Price_1'])print(df1)# elements of second datasetsecond_Set = {'Prod_2': ['Laptop', 'Mobile Phone', 'Desktop', 'LED'], 'Price_2': [25000, 10000, 15000, 30000] }# creation of Dataframe 2df2 = pd.DataFrame(second_Set, columns=['Prod_2', 'Price_2'])print(df2) |
Output:
Step 2 Comparison of values: You need to import numpy for the successful execution of this step. Here is the general template to perform the comparison:
df1[‘new column for the comparison results’] = np.where(condition, ‘value if true’, ‘value if false’)
Example: After execution of this code, the new column with the name Price_Matching will be formed under df1. Columns result will be displayed according to the following conditions:
- If Price_1 is equal to Price_2, then assign the value of True
- Otherwise, assign the value of False.
Python3
import numpy as np# add the Price2 column from # df2 to df1df1['Price_2'] = df2['Price_2'] # create new column in df1 to # check if prices matchdf1['Price_Matching'] = np.where(df1['Price_1'] == df2['Price_2'], 'True', 'False') df1 |
Output:
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