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')select columns to include in new dataframe in python
new = old.filter(['A','B','D'], axis=1)How to Create new dataframe columns from existing column
new_df = df.filter(like='n_')
.replace(0., np.inf)
.apply(lambda x: sorted(x), axis=1, result_type='expand')
.replace(np.inf, 0.0)
new_df.columns = ['new_1', 'new_2', 'new_3']
out = pd.concat([df, new_df], axis=1)Copyright © 2021 Codeinu
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