There are possibilities of filtering data from Pandas dataframe with multiple conditions during the entire software development. Create column using np.where () Pass the condition to the np.where () function, followed by the value you want if the condition evaluates to True and then the value you want if the condition doesn’t evaluate to True. Given a dictionary which contains Employee entity as keys and list of those entity as values. Now, we can see list comprehension using multiple if statement in Python. Ways to apply an if condition in Pandas DataFrame I want to create a new column based on the other columns. By using df [] & pandas.DataFrame.loc [] you can select multiple columns by names or labels. If you want to use the data I used to test out these methods of selecting columns from a pandas data frame, use the code snippet below to get the wine dataset into your IDE or a notebook. To print the numbers I have used print (num). import numpy as np import pandas as pd data = pd.read_csv ('some.csv') syntax: df [‘column_name’].mask ( df [‘column_name’] == ‘some_value’, value , inplace=True ) How to apply the “if else” condition on multiple columns … SQLite pandas

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