python/source/data.rst - arrow-cookbook - Git at Google

 =================
 Data Manipulation
 =================

 Recipes related to filtering or transforming data in
 arrays and tables.

 .. contents::

 See :ref:`compute` for a complete list of all available compute functions

 Computing Mean/Min/Max values of an array
 =========================================

 Arrow provides compute functions that can be applied to arrays.
 Those compute functions are exposed through the :mod:`arrow.compute`
 module.

 .. testsetup::

   import numpy as np
   import pyarrow as pa

   arr = pa.array(np.arange(100))

 Given an array with 100 numbers, from 0 to 99

 .. testcode::

   print(f"{arr[0]} .. {arr[-1]}")

 .. testoutput::

   0 .. 99

 We can compute the ``mean`` using the :func:`arrow.compute.mean`
 function

 .. testcode::

   import pyarrow.compute as pc

   mean = pc.mean(arr)
   print(mean)

 .. testoutput::

   49.5

 And the ``min`` and ``max`` using the :func:`arrow.compute.min_max`
 function

 .. testcode::

   import pyarrow.compute as pc

   min_max = pc.min_max(arr)
   print(min_max)

 .. testoutput::

   [('min', 0), ('max', 99)]

 Counting Occurrences of Elements
 ================================

 Arrow provides compute functions that can be applied to arrays,
 those compute functions are exposed through the :mod:`arrow.compute`
 module.

 .. testsetup::

   import pyarrow as pa

   nums_arr = pa.array(list(range(10))*10)

 Given an array with all numbers from 0 to 9 repeated 10 times

 .. testcode::

   print(f"LEN: {len(nums_arr)}, MIN/MAX: {nums_arr[0]} .. {nums_arr[-1]}")

 .. testoutput::

   LEN: 100, MIN/MAX: 0 .. 9

 We can count occurences of all entries in the array using the
 :func:`arrow.compute.value_counts` function

 .. testcode::

   import pyarrow.compute as pc

   counts = pc.value_counts(nums_arr)
   for pair in counts:
       print(pair)

 .. testoutput::

   [('values', 0), ('counts', 10)]
   [('values', 1), ('counts', 10)]
   [('values', 2), ('counts', 10)]
   [('values', 3), ('counts', 10)]
   [('values', 4), ('counts', 10)]
   [('values', 5), ('counts', 10)]
   [('values', 6), ('counts', 10)]
   [('values', 7), ('counts', 10)]
   [('values', 8), ('counts', 10)]
   [('values', 9), ('counts', 10)]

 Applying arithmetic functions to arrays.
 =========================================

 The compute functions in :mod:`arrow.compute` also include
 common transformations such as arithmetic functions.

 Given an array with 100 numbers, from 0 to 99

 .. testcode::

   print(f"{arr[0]} .. {arr[-1]}")

 .. testoutput::

   0 .. 99

 We can multiply all values by 2 using the :func:`arrow.compute.multiply`
 function

 .. testcode::

   import pyarrow.compute as pc

   doubles = pc.multiply(arr, 2)
   print(f"{doubles[0]} .. {doubles[-1]}")

 .. testoutput::

   0 .. 198

 Appending tables to an existing table
 =====================================

 If you have data split across two different tables, it is possible
 to concatenate their rows into a single table.

 If we have the list of Oscar nominations divided between two different tables:

 .. testcode::

   import pyarrow as pa

   oscar_nominations_1 = pa.table([
     ["Meryl Streep", "Katharine Hepburn"],
     [21, 12]
   ], names=["actor", "nominations"])

   oscar_nominations_2 = pa.table([
     ["Jack Nicholson", "Bette Davis"],
     [12, 10]
   ], names=["actor", "nominations"])

 We can combine them into a single table using :func:`pyarrow.concat_tables`:

 .. testcode::

   oscar_nominations = pa.concat_tables([oscar_nominations_1,
                                         oscar_nominations_2])
   print(oscar_nominations)

 .. testoutput::

     pyarrow.Table
     actor: string
     nominations: int64
     ----
     actor: [["Meryl Streep","Katharine Hepburn"],["Jack Nicholson","Bette Davis"]]
     nominations: [[21,12],[12,10]]

 .. note::

   By default, appending two tables is a zero-copy operation that doesn't need to
   copy or rewrite data. As tables are made of :class:`pyarrow.ChunkedArray`,
   the result will be a table with multiple chunks, each pointing to the original
   data that has been appended. Under some conditions, Arrow might have to
   cast data from one type to another (if `promote=True`).  In such cases the data
   will need to be copied and an extra cost will occur.

 Adding a column to an existing Table
 ====================================

 If you have a table it is possible to extend its columns using
 :meth:`pyarrow.Table.append_column`

 Suppose we have a table with oscar nominations for each actress

 .. testcode::

   import pyarrow as pa

   oscar_nominations = pa.table([
     ["Meryl Streep", "Katharine Hepburn"],
     [21, 12]
   ], names=["actor", "nominations"])

   print(oscar_nominations)

 .. testoutput::

     pyarrow.Table
     actor: string
     nominations: int64
     ----
     actor: [["Meryl Streep","Katharine Hepburn"]]
     nominations: [[21,12]]

 it's possible to append an additional column to track the years the
 nomination was won using :meth:`pyarrow.Table.append_column`

 .. testcode::

   oscar_nominations = oscar_nominations.append_column(
     "wonyears",
     pa.array([
       [1980, 1983, 2012],
       [1934, 1968, 1969, 1982]
     ])
   )

   print(oscar_nominations)

 .. testoutput::

     pyarrow.Table
     actor: string
     nominations: int64
     wonyears: list<item: int64>
       child 0, item: int64
     ----
     actor: [["Meryl Streep","Katharine Hepburn"]]
     nominations: [[21,12]]
     wonyears: [[[1980,1983,2012],[1934,1968,1969,1982]]]

 Searching for values matching a predicate in Arrays
 ===================================================

 If you have to look for values matching a predicate in Arrow arrays
 the :mod:`arrow.compute` module provides several methods that
 can be used to find the values you are looking for.

 For example, given an array with numbers from 0 to 9, if we
 want to look only for those greater than 5 we could use the
 func:`arrow.compute.greater` method and get back the elements
 that fit our predicate

 .. testcode::

   import pyarrow as pa
   import pyarrow.compute as pc

   arr = pa.array(range(10))
   gtfive = pc.greater(arr, 5)

   print(gtfive.to_string())

 .. testoutput::

   [
     false,
     false,
     false,
     false,
     false,
     false,
     true,
     true,
     true,
     true
   ]

 Furthermore we can filter the array to get only the entries
 that match our predicate

 .. testcode::

   filtered_array = pc.filter(arr, gtfive)
   print(filtered_array)

 .. testoutput::

   [
     6,
     7,
     8,
     9
   ]

 Filtering Arrays using a mask
 =============================

 In many cases, when you are searching for something in an array
 you will end up with a mask that tells you the positions at which
 your search matched the values.

 For example in an array of four items, we might have a mask that
 matches the first and the last items only:

 .. testcode::

   import pyarrow as pa

   array = pa.array([1, 2, 3, 4])
   mask = pa.array([True, False, False, True])

 We can then filter the array according to the mask using
 :meth:`pyarrow.Array.filter` to get back a new array with
 only the values matching the mask:

 .. testcode::

   filtered_array = array.filter(mask)
   print(filtered_array)

 .. testoutput::

   [
     1,
     4
   ]

 Most search functions in :mod:`pyarrow.compute` will produce
 a mask as the output, so you can use them to filter your arrays
 for the values that have been found by the function.

 For example we might filter our arrays for the values equal to ``2``
 using :func:`pyarrow.compute.equal`:

 .. testcode::

   import pyarrow.compute as pc

   filtered_array = array.filter(pc.equal(array, 2))
   print(filtered_array)

 .. testoutput::

   [
     2
   ]
	=================
	Data Manipulation
	=================

	Recipes related to filtering or transforming data in
	arrays and tables.

	.. contents::

	See :ref:`compute` for a complete list of all available compute functions

	Computing Mean/Min/Max values of an array
	=========================================

	Arrow provides compute functions that can be applied to arrays.
	Those compute functions are exposed through the :mod:`arrow.compute`
	module.

	.. testsetup::

	import numpy as np
	import pyarrow as pa

	arr = pa.array(np.arange(100))

	Given an array with 100 numbers, from 0 to 99

	.. testcode::

	print(f"{arr[0]} .. {arr[-1]}")

	.. testoutput::

	0 .. 99

	We can compute the ``mean`` using the :func:`arrow.compute.mean`
	function

	.. testcode::

	import pyarrow.compute as pc

	mean = pc.mean(arr)
	print(mean)

	.. testoutput::

	49.5

	And the ``min`` and ``max`` using the :func:`arrow.compute.min_max`
	function

	.. testcode::

	import pyarrow.compute as pc

	min_max = pc.min_max(arr)
	print(min_max)

	.. testoutput::

	[('min', 0), ('max', 99)]

	Counting Occurrences of Elements
	================================

	Arrow provides compute functions that can be applied to arrays,
	those compute functions are exposed through the :mod:`arrow.compute`
	module.

	.. testsetup::

	import pyarrow as pa

	nums_arr = pa.array(list(range(10))*10)

	Given an array with all numbers from 0 to 9 repeated 10 times

	.. testcode::

	print(f"LEN: {len(nums_arr)}, MIN/MAX: {nums_arr[0]} .. {nums_arr[-1]}")

	.. testoutput::

	LEN: 100, MIN/MAX: 0 .. 9

	We can count occurences of all entries in the array using the
	:func:`arrow.compute.value_counts` function

	.. testcode::

	import pyarrow.compute as pc

	counts = pc.value_counts(nums_arr)
	for pair in counts:
	print(pair)

	.. testoutput::

	[('values', 0), ('counts', 10)]
	[('values', 1), ('counts', 10)]
	[('values', 2), ('counts', 10)]
	[('values', 3), ('counts', 10)]
	[('values', 4), ('counts', 10)]
	[('values', 5), ('counts', 10)]
	[('values', 6), ('counts', 10)]
	[('values', 7), ('counts', 10)]
	[('values', 8), ('counts', 10)]
	[('values', 9), ('counts', 10)]

	Applying arithmetic functions to arrays.
	=========================================

	The compute functions in :mod:`arrow.compute` also include
	common transformations such as arithmetic functions.

	Given an array with 100 numbers, from 0 to 99

	.. testcode::

	print(f"{arr[0]} .. {arr[-1]}")

	.. testoutput::

	0 .. 99

	We can multiply all values by 2 using the :func:`arrow.compute.multiply`
	function

	.. testcode::

	import pyarrow.compute as pc

	doubles = pc.multiply(arr, 2)
	print(f"{doubles[0]} .. {doubles[-1]}")

	.. testoutput::

	0 .. 198

	Appending tables to an existing table
	=====================================

	If you have data split across two different tables, it is possible
	to concatenate their rows into a single table.

	If we have the list of Oscar nominations divided between two different tables:

	.. testcode::

	import pyarrow as pa

	oscar_nominations_1 = pa.table([
	["Meryl Streep", "Katharine Hepburn"],
	[21, 12]
	], names=["actor", "nominations"])

	oscar_nominations_2 = pa.table([
	["Jack Nicholson", "Bette Davis"],
	[12, 10]
	], names=["actor", "nominations"])

	We can combine them into a single table using :func:`pyarrow.concat_tables`:

	.. testcode::

	oscar_nominations = pa.concat_tables([oscar_nominations_1,
	oscar_nominations_2])
	print(oscar_nominations)

	.. testoutput::

	pyarrow.Table
	actor: string
	nominations: int64
	----
	actor: [["Meryl Streep","Katharine Hepburn"],["Jack Nicholson","Bette Davis"]]
	nominations: [[21,12],[12,10]]

	.. note::

	By default, appending two tables is a zero-copy operation that doesn't need to
	copy or rewrite data. As tables are made of :class:`pyarrow.ChunkedArray`,
	the result will be a table with multiple chunks, each pointing to the original
	data that has been appended. Under some conditions, Arrow might have to
	cast data from one type to another (if `promote=True`). In such cases the data
	will need to be copied and an extra cost will occur.

	Adding a column to an existing Table
	====================================

	If you have a table it is possible to extend its columns using
	:meth:`pyarrow.Table.append_column`

	Suppose we have a table with oscar nominations for each actress

	.. testcode::

	import pyarrow as pa

	oscar_nominations = pa.table([
	["Meryl Streep", "Katharine Hepburn"],
	[21, 12]
	], names=["actor", "nominations"])

	print(oscar_nominations)

	.. testoutput::

	pyarrow.Table
	actor: string
	nominations: int64
	----
	actor: [["Meryl Streep","Katharine Hepburn"]]
	nominations: [[21,12]]

	it's possible to append an additional column to track the years the
	nomination was won using :meth:`pyarrow.Table.append_column`

	.. testcode::

	oscar_nominations = oscar_nominations.append_column(
	"wonyears",
	pa.array([
	[1980, 1983, 2012],
	[1934, 1968, 1969, 1982]
	])
	)

	print(oscar_nominations)

	.. testoutput::

	pyarrow.Table
	actor: string
	nominations: int64
	wonyears: list<item: int64>
	child 0, item: int64
	----
	actor: [["Meryl Streep","Katharine Hepburn"]]
	nominations: [[21,12]]
	wonyears: [[[1980,1983,2012],[1934,1968,1969,1982]]]

	Searching for values matching a predicate in Arrays
	===================================================

	If you have to look for values matching a predicate in Arrow arrays
	the :mod:`arrow.compute` module provides several methods that
	can be used to find the values you are looking for.

	For example, given an array with numbers from 0 to 9, if we
	want to look only for those greater than 5 we could use the
	func:`arrow.compute.greater` method and get back the elements
	that fit our predicate

	.. testcode::

	import pyarrow as pa
	import pyarrow.compute as pc

	arr = pa.array(range(10))
	gtfive = pc.greater(arr, 5)

	print(gtfive.to_string())

	.. testoutput::

	[
	false,
	false,
	false,
	false,
	false,
	false,
	true,
	true,
	true,
	true
	]

	Furthermore we can filter the array to get only the entries
	that match our predicate

	.. testcode::

	filtered_array = pc.filter(arr, gtfive)
	print(filtered_array)

	.. testoutput::

	[
	6,
	7,
	8,
	9
	]

	Filtering Arrays using a mask
	=============================

	In many cases, when you are searching for something in an array
	you will end up with a mask that tells you the positions at which
	your search matched the values.

	For example in an array of four items, we might have a mask that
	matches the first and the last items only:

	.. testcode::

	import pyarrow as pa

	array = pa.array([1, 2, 3, 4])
	mask = pa.array([True, False, False, True])

	We can then filter the array according to the mask using
	:meth:`pyarrow.Array.filter` to get back a new array with
	only the values matching the mask:

	.. testcode::

	filtered_array = array.filter(mask)
	print(filtered_array)

	.. testoutput::

	[
	1,
	4
	]

	Most search functions in :mod:`pyarrow.compute` will produce
	a mask as the output, so you can use them to filter your arrays
	for the values that have been found by the function.

	For example we might filter our arrays for the values equal to ``2``
	using :func:`pyarrow.compute.equal`:

	.. testcode::

	import pyarrow.compute as pc

	filtered_array = array.filter(pc.equal(array, 2))
	print(filtered_array)

	.. testoutput::

	[
	2
	]