Answers for "dataframe create new column based on other columns"

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')

pandas create a new column based on condition of two columns

conditions = [
    df['gender'].eq('male') & df['pet1'].eq(df['pet2']),
    df['gender'].eq('female') & df['pet1'].isin(['cat', 'dog'])
]

choices = [5,5]

df['points'] = np.select(conditions, choices, default=0)

print(df)
     gender      pet1      pet2  points
0      male       dog       dog       5
1      male       cat       cat       5
2      male       dog       cat       0
3    female       cat  squirrel       5
4    female       dog       dog       5
5    female  squirrel       cat       0
6  squirrel       dog       cat       0

create new dataframe with columns from another dataframe pandas

new = old[['A', 'C', 'D']].copy()