pandas.Series.map () to Create New DataFrame Columns Based on a Given Condition in Pandas We can create the DataFrame columns based on a given condition in Pandas using list comprehension, NumPy methods, apply () method, and map () method of the DataFrame object. Otherwise, if the number is greater than 4, then assign the value of 'False'. With this, we can use conditional filtering, by passing into multiple conditions. Method 2 : Query Function. Pandas' loc creates a boolean mask, based on a condition. Python - Create a new column in a Pandas dataframe Let's assume that we ant to filter the rows realted to the Swift language. # If you only have one condition use numpy.where () # Example usage with np.where: df = pd.DataFrame({'Type':list('ABBC'), 'Set':list('ZZXY')}) # Define df print(df) Type Set 0 A Z 1 B Z 2 B X 3 C Y # Add new column based on single condition: df['color'] = np.where(df['Set . df['Is_eligible'] = np.where(df['Age'] >= 18, True, False) pandas create new column based on row value (condition) Otherwise, if the number is greater than 4, then assign the value of 'False'. show_id type title director cast s1 Movie Duck the Halls Dave Wasson Chris Diamantopoulos, Tony Anselmo, Tress MacNeille, Bill Farmer, I need to be able to break down the 'cast'' field in such a way that it is in several rows Example: show_id type title director cast s1 Movie Duck the Halls Dave . In pandas package, there are multiple ways to perform filtering. This a subset of the data group by symbol. In this tutorial, we will be focusing on how to update rows and columns in python using pandas. In some cases, the new columns are created according to some conditions on the other columns. 3 Methods to Create Conditional Columns with Python Pandas and Numpy Select the columns from the original DataFrame and copy it to create a new DataFrame using copy () function. Filter Pandas Dataframe with multiple conditions - GeeksforGeeks Create a new column in Pandas DataFrame based on the existing columns