Compute Functions#
Arrow supports logical compute operations over inputs of possibly varying types.
The standard compute operations are provided by the pyarrow.compute
module and can be used directly:
>>> import pyarrow as pa
>>> import pyarrow.compute as pc
>>> a = pa.array([1, 1, 2, 3])
>>> pc.sum(a)
<pyarrow.Int64Scalar: 7>
The grouped aggregation functions raise an exception instead
and need to be used through the pyarrow.Table.group_by() capabilities.
See Grouped Aggregations for more details.
Standard Compute Functions#
Many compute functions support both array (chunked or not)
and scalar inputs, but some will mandate either. For example,
sort_indices requires its first and only input to be an array.
Below are a few simple examples:
>>> import pyarrow as pa
>>> import pyarrow.compute as pc
>>> a = pa.array([1, 1, 2, 3])
>>> b = pa.array([4, 1, 2, 8])
>>> pc.equal(a, b)
<pyarrow.lib.BooleanArray object at 0x7f686e4eef30>
[
false,
true,
true,
false
]
>>> x, y = pa.scalar(7.8), pa.scalar(9.3)
>>> pc.multiply(x, y)
<pyarrow.DoubleScalar: 72.54>
These functions can do more than just element-by-element operations. Here is an example of sorting a table:
>>> import pyarrow as pa
>>> import pyarrow.compute as pc
>>> t = pa.table({'x':[1,2,3],'y':[3,2,1]})
>>> i = pc.sort_indices(t, sort_keys=[('y', 'ascending')])
>>> i
<pyarrow.lib.UInt64Array object at 0x7fcee5df75e8>
[
2,
1,
0
]
For a complete list of the compute functions that PyArrow provides you can refer to Compute Functions reference.
Grouped Aggregations#
PyArrow supports grouped aggregations over pyarrow.Table through the
pyarrow.Table.group_by() method.
The method will return a grouping declaration
to which the hash aggregation functions can be applied:
>>> import pyarrow as pa
>>> t = pa.table([
... pa.array(["a", "a", "b", "b", "c"]),
... pa.array([1, 2, 3, 4, 5]),
... ], names=["keys", "values"])
>>> t.group_by("keys").aggregate([("values", "sum")])
pyarrow.Table
values_sum: int64
keys: string
----
values_sum: [[3,7,5]]
keys: [["a","b","c"]]
The "sum" aggregation passed to the aggregate method in the previous
example is the hash_sum compute function.
Multiple aggregations can be performed at the same time by providing them
to the aggregate method:
>>> import pyarrow as pa
>>> t = pa.table([
... pa.array(["a", "a", "b", "b", "c"]),
... pa.array([1, 2, 3, 4, 5]),
... ], names=["keys", "values"])
>>> t.group_by("keys").aggregate([
... ("values", "sum"),
... ("keys", "count")
... ])
pyarrow.Table
values_sum: int64
keys_count: int64
keys: string
----
values_sum: [[3,7,5]]
keys_count: [[2,2,1]]
keys: [["a","b","c"]]
Aggregation options can also be provided for each aggregation function,
for example we can use CountOptions to change how we count
null values:
>>> import pyarrow as pa
>>> import pyarrow.compute as pc
>>> table_with_nulls = pa.table([
... pa.array(["a", "a", "a"]),
... pa.array([1, None, None])
... ], names=["keys", "values"])
>>> table_with_nulls.group_by(["keys"]).aggregate([
... ("values", "count", pc.CountOptions(mode="all"))
... ])
pyarrow.Table
values_count: int64
keys: string
----
values_count: [[3]]
keys: [["a"]]
>>> table_with_nulls.group_by(["keys"]).aggregate([
... ("values", "count", pc.CountOptions(mode="only_valid"))
... ])
pyarrow.Table
values_count: int64
keys: string
----
values_count: [[1]]
keys: [["a"]]
Following is a list of all supported grouped aggregation functions.
You can use them with or without the "hash_" prefix.
hash_all |
Whether all elements in each group evaluate to true |
|
hash_any |
Whether any element in each group evaluates to true |
|
hash_approximate_median |
Compute approximate medians of values in each group |
|
hash_count |
Count the number of null / non-null values in each group |
|
hash_count_distinct |
Count the distinct values in each group |
|
hash_distinct |
Keep the distinct values in each group |
|
hash_list |
List all values in each group |
|
hash_max |
Compute the minimum or maximum of values in each group |
|
hash_mean |
Compute the mean of values in each group |
|
hash_min |
Compute the minimum or maximum of values in each group |
|
hash_min_max |
Compute the minimum and maximum of values in each group |
|
hash_one |
Get one value from each group |
|
hash_product |
Compute the product of values in each group |
|
hash_stddev |
Compute the standard deviation of values in each group |
|
hash_sum |
Sum values in each group |
|
hash_tdigest |
Compute approximate quantiles of values in each group |
|
hash_variance |
Compute the variance of values in each group |
Table and Dataset Joins#
Both Table and Dataset support
join operations through Table.join()
and Dataset.join() methods.
The methods accept a right table or dataset that will be joined to the initial one and one or more keys that should be used from the two entities to perform the join.
By default a left outer join is performed, but it’s possible
to ask for any of the supported join types:
left semi
right semi
left anti
right anti
inner
left outer
right outer
full outer
A basic join can be performed just by providing a table and a key on which the join should be performed:
import pyarrow as pa
table1 = pa.table({'id': [1, 2, 3],
'year': [2020, 2022, 2019]})
table2 = pa.table({'id': [3, 4],
'n_legs': [5, 100],
'animal': ["Brittle stars", "Centipede"]})
joined_table = table1.join(table2, keys="id")
The result will be a new table created by joining table1 with
table2 on the id key with a left outer join:
pyarrow.Table
id: int64
year: int64
n_legs: int64
animal: string
----
id: [[3,1,2]]
year: [[2019,2020,2022]]
n_legs: [[5,null,null]]
animal: [["Brittle stars",null,null]]
We can perform additional type of joins, like full outer join by
passing them to the join_type argument:
table1.join(table2, keys='id', join_type="full outer")
In that case the result would be:
pyarrow.Table
id: int64
year: int64
n_legs: int64
animal: string
----
id: [[3,1,2],[4]]
year: [[2019,2020,2022],[null]]
n_legs: [[5,null,null],[100]]
animal: [["Brittle stars",null,null],["Centipede"]]
It’s also possible to provide additional join keys, so that the
join happens on two keys instead of one. For example we can add
an year column to table2 so that we can join on ('id', 'year'):
table2_withyear = table2.append_column("year", pa.array([2019, 2022]))
table1.join(table2_withyear, keys=["id", "year"])
The result will be a table where only entries with id=3 and year=2019
have data, the rest will be null:
pyarrow.Table
id: int64
year: int64
animal: string
n_legs: int64
----
id: [[3,1,2]]
year: [[2019,2020,2022]]
animal: [["Brittle stars",null,null]]
n_legs: [[5,null,null]]
The same capabilities are available for Dataset.join() too, so you can
take two datasets and join them:
import pyarrow.dataset as ds
ds1 = ds.dataset(table1)
ds2 = ds.dataset(table2)
joined_ds = ds1.join(ds2, key="id")
The resulting dataset will be an InMemoryDataset containing the joined data:
>>> joined_ds.head(5)
pyarrow.Table
id: int64
year: int64
animal: string
n_legs: int64
----
id: [[3,1,2]]
year: [[2019,2020,2022]]
animal: [["Brittle stars",null,null]]
n_legs: [[5,null,null]]