change pandas column value based on condition
In [41]: df.loc[df['First Season'] > 1990, 'First Season'] = 1 df Out[41]: Team First Season Total Games 0 Dallas Cowboys 1960 894 1 Chicago Bears 1920 1357 2 Green Bay Packers 1921 1339 3 Miami Dolphins 1966 792 4 Baltimore Ravens 1 326 5 San Franciso 49ers 1950 1003
python conditionally create new column in pandas dataframe
# 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']=='Z', 'green', 'red') print(df) Type Set color 0 A Z green 1 B Z green 2 B X red 3 C Y red # If you have multiple conditions use numpy.select() # Example usage with np.select: 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 # Set the conditions for determining values in new column: conditions = [ (df['Set'] == 'Z') & (df['Type'] == 'A'), (df['Set'] == 'Z') & (df['Type'] == 'B'), (df['Type'] == 'B')] # Set the new column values in order of the conditions they should # correspond to: choices = ['yellow', 'blue', 'purple'] # Add new column based on conditions and choices: df['color'] = np.select(conditions, choices, default='black') print(df) # Returns: Set Type color 0 Z A yellow 1 Z B blue 2 X B purple 3 Y C black
Pandas conditional collumn
import pandas as pd import numpy as np df = pd.DataFrame({'Type':list('ABBC'), 'Set':list('ZZXY')}) df['color'] = np.where(df['Set']=='Z', 'green', 'red') print(df)
Copyright © 2021 Codeinu
Forgot your account's password or having trouble logging into your Account? Don't worry, we'll help you to get back your account. Enter your email address and we'll send you a recovery link to reset your password. If you are experiencing problems resetting your password contact us
