Filtering Pandas
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Filtering pandas DataFrames many different ways.

Date: September 24, 2019

query
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Good for method chaining, i.e. adding more methods or filters without assigning a new variable.

[code]
  # is
  skus.query('AVAILABILITY == " AVAILABLE"')
  # is not
  skus.query('AVAILABILITY != " AVAILABLE"')

masking
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general purpose, this is probably the most common method you see in training/examples

[code]
  # is
  skus[skus['AVAILABILITY'] == 'AVAILABLE']
  # is not
  skus[~skus['AVAILABILITY'] == 'AVAILABLE']

isin
────

capable of including multiple strings to include

[code]
  # is in
  df[df.AVAILABILITY.isin(['AVAILABLE', 'AVL'])]
  # is not in
  df[~df.AVAILABILITY.isin(['AVAILABLE', 'AVL'])]

contains
────────

Good For partial matches

[code]
  # 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.

[code]
  service_mask = skus['AVAILABILITY'] == 'AVAILABLE'
  name_mask = skus['NAME'] == 'Dell chromebook 11'

### Operators

& - and ~ - not | - or

### AVAILABLE and NAME

[code]
  df[service_mask & name_mask]

### AVAILABLE or NAME

[code]
  df[service_mask | name_mask]

### AVAILABLE and not NAME

[code]
  df[service_mask & ~name_mask]