PYTHON

adding a pandas column with multiple conditions

# create a list of  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
choices = ['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, choices)

# display updated DataFrame
df.head()

pandas set condition multi columns

conditions = [
   .....:     (df['col2'] == 'Z') & (df['col1'] == 'A'),
   .....:     (df['col2'] == 'Z') & (df['col1'] == 'B'),
   .....:     (df['col1'] == 'B')
   .....: ]
   .....: 

In [214]: choices = ['yellow', 'blue', 'purple']

In [215]: df['color'] = np.select(conditions, choices, default='black')

In [216]: df
Out[216]: 
  col1 col2   color
0    A    Z  yellow
1    B    Z    blue
2    B    X  purple
3    C    Y   black

python multiple conditions in dataframe column values

df.loc[(df['Salary_in_1000']>=100) & (df['Age']< 60) & (df['FT_Team'].str.startswith('S')),['Name','FT_Team']]

pandas set condition multi columns

conditions = [
            (df['col2'] == 'Z') & (df['col1'] == 'A'),
            (df['col2'] == 'Z') & (df['col1'] == 'B'),
            (df['col1'] == 'B')
			]


choices = ['yellow', 'blue', 'purple']

df['color'] = np.select(conditions, choices, default='black')

df
# Out[216]: 
#  col1 col2   color
# 0    A    Z  yellow
# 1    B    Z    blue
# 2    B    X  purple
# 3    C    Y   black

pandas operations with multiple columns

df.query('A in @mylist')