Filtering Pandas
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Filtering pandas DataFrames many different ways.

Date: September 24, 2019

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]
```