Tags
query
Good for method chaining, i.e. adding more methods or filters without assigning a new variable.
# is skus.query('AVAILABILITY == " AVAILABLE"') # is not skus.query('AVAILABILITY != " AVAILABLE"')
masking
general purpose, this is probably the most common method you see in training/examples
# is skus[skus['AVAILABILITY'] == 'AVAILABLE'] # is not skus[~skus['AVAILABILITY'] == 'AVAILABLE']
isin
capable of including multiple strings to include
# is in
df[df.AVAILABILITY.isin(['AVAILABLE', 'AVL'])]
# is not in
df[~df.AVAILABILITY.isin(['AVAILABLE', 'AVL'])]
contains
Good For partial matches
# contains
df[df.AVAILABILITY.str.contains('AVA')]
# not contains
df[~df.AVAILABILITY.str.contains('AVA')]
MASKS
anything that we put inside of square brackets can be set as a variable then passed in.
service_mask = skus['AVAILABILITY'] == 'AVAILABLE'
name_mask = skus['NAME'] == 'Dell chromebook 11'
Operators
& - and ~ - not | - or
AVAILABLE and NAME
df[service_mask & name_mask]
AVAILABLE or NAME
df[service_mask | name_mask]
AVAILABLE and not NAME
df[service_mask & ~name_mask]