python conditionally create new column in pandas dataframe

1# For creating new column with multiple conditions
2conditions = [
3    (df['Base Column 1'] == 'A') & (df['Base Column 2'] == 'B'),
4    (df['Base Column 3'] == 'C')]
5choices = ['Conditional Value 1', 'Conditional Value 2']
6df['New Column'] = np.select(conditions, choices, default='Conditional Value 1')

1# If you only have one condition use numpy.where()
2# Example usage with np.where:
3df = pd.DataFrame({'Type':list('ABBC'), 'Set':list('ZZXY')}) # Define df
4print(df)
5  Type Set
60    A   Z
71    B   Z
82    B   X
93    C   Y
10
11# Add new column based on single condition:
12df['color'] = np.where(df['Set']=='Z', 'green', 'red')
13print(df)
14  Type Set  color
150    A   Z  green
161    B   Z  green
172    B   X    red
183    C   Y    red
19
20
21# If you have multiple conditions use numpy.select()
22# Example usage with np.select:
23df = pd.DataFrame({'Type':list('ABBC'), 'Set':list('ZZXY')}) # Define df
24print(df)
25  Type Set
260    A   Z
271    B   Z
282    B   X
293    C   Y
30
31# Set the conditions for determining values in new column:
32conditions = [
33    (df['Set'] == 'Z') & (df['Type'] == 'A'),
34    (df['Set'] == 'Z') & (df['Type'] == 'B'),
35    (df['Type'] == 'B')]
36
37# Set the new column values in order of the conditions they should
38#	correspond to:
39choices = ['yellow', 'blue', 'purple']
40
41# Add new column based on conditions and choices:
42df['color'] = np.select(conditions, choices, default='black')
43
44print(df)
45# Returns:
46  Set Type   color
470   Z    A  yellow
481   Z    B    blue
492   X    B  purple
503   Y    C   black

1# create a list of our conditions
2conditions = [
3    (df['likes_count'] <= 2),
4    (df['likes_count'] > 2) & (df['likes_count'] <= 9),
5    (df['likes_count'] > 9) & (df['likes_count'] <= 15),
6    (df['likes_count'] > 15)
7    ]
8
9# create a list of the values we want to assign for each condition
10values = ['tier_4', 'tier_3', 'tier_2', 'tier_1']
11
12# create a new column and use np.select to assign values to it using our lists as arguments
13df['tier'] = np.select(conditions, values)
14
15# display updated DataFrame
16df.head()