# Filter rows

## By regex

You can use Python’s builtin `re` module to introduce a regular expression into a `Table.where()` query.

For example, here we find all states that start with “C”.

    import re

    new_table = table.where(lambda row: re.match('^C', row['state']))

This can also be useful for finding values that don’t match your expectations. For example, finding all values in the “phone number” column that don’t look like phone numbers:

    new_table = table.where(lambda row: not re.match('\d{3}-\d{3}-\d{4}', row['phone']))

## By glob

Hate regexes? You can use glob (`fnmatch`) syntax too!

    from fnmatch import fnmatch

    new_table = table.where(lambda row: fnmatch('C*', row['state']))

## Values within a range

This snippet filters the dataset to incomes between 100,000 and 200,000.

    new_table = table.where(lambda row: 100000 < row['income'] < 200000)

## Dates within a range

This snippet filters the dataset to events during the summer of 2015:

    import datetime

    new_table = table.where(lambda row: datetime.datetime(2015, 6, 1) <= row['date'] <= datetime.datetime(2015, 8, 31))

If you want to filter to events during the summer of any year:

    new_table = table.where(lambda row: 6 <= row['date'].month <= 8)

## Top N percent

To filter a dataset to the top 10% percent of values we first compute the percentiles for the column and then use the result in the `Table.where()` truth test:

    percentiles = table.aggregate(agate.Percentiles('salary'))
    top_ten_percent = table.where(lambda r: r['salary'] >= percentiles[90])

## Random sample

By combining a random sort with limiting, we can effectively get a random sample from a table.

    import random

    randomized = table.order_by(lambda row: random.random())
    sampled = table.limit(10)

## Ordered sample

With can also get an ordered sample by simply using the `step` parameter of the `Table.limit()` method to get every Nth row.

    sampled = table.limit(step=10)

## Distinct values

You can retrieve a distinct list of values in a column using `Column.values_distinct()` or `Table.distinct()`.

`Table.distinct()` returns the entire row so it’s necessary to chain a select on the specific column.

    columns = ('value',)
    rows = ([1],[2],[2],[5])
    new_table = agate.Table(rows, columns)

    new_table.columns['value'].values_distinct()
    # or
    new_table.distinct('value').columns['value'].values()
    (Decimal('1'), Decimal('2'), Decimal('5'))