pandas create column from another column
# Creates a new column 'blue_yn' based on the existing 'color' column
# If the 'color' column value is 'blue' then the new column value is 'YES'
df['blue_yn'] = np.where(df['color'] == 'blue', 'YES', 'NO')
# Can also do this using .apply and a lambda function
df['blue_yn']= df['color'].apply(lambda x: 'YES' if (x == 'blue') else 'NO')pandas create new column conditional on other columns
# For creating new column with multiple conditions
conditions = [
(df['Base Column 1'] == 'A') & (df['Base Column 2'] == 'B'),
(df['Base Column 3'] == 'C')]
choices = ['Conditional Value 1', 'Conditional Value 2']
df['New Column'] = np.select(conditions, choices, default='Conditional Value 1')create new columns pandas from another column
def label_race (row):
if row['eri_hispanic'] == 1 :
return 'Hispanic'
if row['eri_afr_amer'] + row['eri_asian'] + row['eri_hawaiian'] + row['eri_nat_amer'] + row['eri_white'] > 1 :
return 'Two Or More'
if row['eri_nat_amer'] == 1 :
return 'A/I AK Native'
if row['eri_asian'] == 1:
return 'Asian'
if row['eri_afr_amer'] == 1:
return 'Black/AA'
if row['eri_hawaiian'] == 1:
return 'Haw/Pac Isl.'
if row['eri_white'] == 1:
return 'White'
return 'Other'
df.apply(lambda row: label_race(row), axis=1)Copyright © 2021 Codeinu
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