dataframe if else column

pandas if else new column

# Method 1:
df.loc[df['column name'] condition, 'new column name'] = 'value if condition is met'
#or
df.loc[df['set_of_numbers'] <= 4, 'equal_or_lower_than_4?'] = 'True' 

# Method 2:
df['new column name'] = df['column name'].apply(lambda x: 'value if condition is met' if x condition else 'value if condition is not met')
#or
df['name_match'] = df['First_name'].apply(lambda x: 'Match' if x == 'Bill' else 'Mismatch')

# or
df.loc[(df['First_name'] == 'Bill') | (df['First_name'] == 'Emma'), 'name_match'] = 'Match'  
df.loc[(df['First_name'] != 'Bill') & (df['First_name'] != 'Emma'), 'name_match'] = 'Mismatch'

Posted by: Guest on April-13-2021

Source

import pandas as pd numbers = {'set_of_numbers': [1,2,3,4,5,6,7,8,9,10]} df = pd.DataFrame(numbers,columns=['set_of_numbers']) df['equal_or_lower_than_4?'] = df['set_of_numbers'].apply(lambda x: 'True' if x <= 4 else 'False') print (df)

# create a list of our conditions conditions = [ (df['likes_count'] <= 2), (df['likes_count'] > 2) & (df['likes_count'] <= 9), (df['likes_count'] > 9) & (df['likes_count'] <= 15), (df['likes_count'] > 15) ] # create a list of the values we want to assign for each condition values = ['tier_4', 'tier_3', 'tier_2', 'tier_1'] # create a new column and use np.select to assign values to it using our lists as arguments df['tier'] = np.select(conditions, values) # display updated DataFrame df.head()

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