# 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: []
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: []
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 `ScalarAggregateOptions` hash_any Whether any element in each group evaluates to true `ScalarAggregateOptions` hash_approximate_median Compute approximate medians of values in each group `ScalarAggregateOptions` hash_count Count the number of null / non-null values in each group `CountOptions` hash_count_all Count the number of rows in each group hash_count_distinct Count the distinct values in each group `CountOptions` hash_distinct Keep the distinct values in each group `CountOptions` hash_first Compute the first value in each group `ScalarAggregateOptions` hash_first_last Compute the first and last of values in each group `ScalarAggregateOptions` hash_last Compute the first value in each group `ScalarAggregateOptions` hash_list List all values in each group hash_max Compute the minimum or maximum of values in each group `ScalarAggregateOptions` hash_mean Compute the mean of values in each group `ScalarAggregateOptions` hash_min Compute the minimum or maximum of values in each group `ScalarAggregateOptions` hash_min_max Compute the minimum and maximum of values in each group `ScalarAggregateOptions` hash_one Get one value from each group hash_product Compute the product of values in each group `ScalarAggregateOptions` hash_stddev Compute the standard deviation of values in each group hash_sum Sum values in each group `ScalarAggregateOptions` hash_tdigest Compute approximate quantiles of values in each group `TDigestOptions` 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]]
```

## Filtering by Expressions#

`Table` and `Dataset` can both be filtered using a boolean `Expression`.

The expression can be built starting from a `pyarrow.compute.field()`. Comparisons and transformations can then be applied to one or more fields to build the filter expression you care about.

Most Compute Functions can be used to perform transformations on a `field`.

For example we could build a filter to find all rows that are even in column `"nums"`

```import pyarrow.compute as pc
even_filter = (pc.bit_wise_and(pc.field("nums"), pc.scalar(1)) == pc.scalar(0))
```

Note

The filter finds even numbers by performing a bitwise and operation between the number and `1`. As `1` is to `00000001` in binary form, only numbers that have the last bit set to `1` will return a non-zero result from the `bit_wise_and` operation. This way we are identifying all odd numbers. Given that we are interested in the even ones, we then check that the number returned by the `bit_wise_and` operation equals `0`. Only the numbers where the last bit was `0` will return a `0` as the result of `num & 1` and as all numbers where the last bit is `0` are multiples of `2` we will be filtering for the even numbers only.

Once we have our filter, we can provide it to the `Table.filter()` method to filter our table only for the matching rows:

```>>> table = pa.table({'nums': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
...                   'chars': ["a", "b", "c", "d", "e", "f", "g", "h", "i", "l"]})
>>> table.filter(even_filter)
pyarrow.Table
nums: int64
chars: string
----
nums: [[2,4,6,8,10]]
chars: [["b","d","f","h","l"]]
```

Multiple filters can be joined using `&`, `|`, `~` to perform `and`, `or` and `not` operations. For example using `~even_filter` will actually end up filtering for all numbers that are odd:

```>>> table.filter(~even_filter)
pyarrow.Table
nums: int64
chars: string
----
nums: [[1,3,5,7,9]]
chars: [["a","c","e","g","i"]]
```

and we could build a filter that finds all even numbers greater than 5 by combining our `even_filter` with a `pc.field("nums") > 5` filter:

```>>> table.filter(even_filter & (pc.field("nums") > 5))
pyarrow.Table
nums: int64
chars: string
----
nums: [[6,8,10]]
chars: [["f","h","l"]]
```

`Dataset` can similarly be filtered with the `Dataset.filter()` method. The method will return an instance of `Dataset` which will lazily apply the filter as soon as actual data of the dataset is accessed:

```>>> dataset = ds.dataset(table)
>>> filtered = dataset.filter(pc.field("nums") < 5).filter(pc.field("nums") > 2)
>>> filtered.to_table()
pyarrow.Table
nums: int64
chars: string
----
nums: [[3,4]]
chars: [["c","d"]]
```

## User-Defined Functions#

Warning

This API is experimental.

PyArrow allows defining and registering custom compute functions. These functions can then be called from Python as well as C++ (and potentially any other implementation wrapping Arrow C++, such as the R `arrow` package) using their registered function name.

UDF support is limited to scalar functions. A scalar function is a function which executes elementwise operations on arrays or scalars. In general, the output of a scalar function does not depend on the order of values in the arguments. Note that such functions have a rough correspondence to the functions used in SQL expressions, or to NumPy universal functions.

To register a UDF, a function name, function docs, input types and output type need to be defined. Using `pyarrow.compute.register_scalar_function()`,

```import numpy as np

import pyarrow as pa
import pyarrow.compute as pc

function_name = "numpy_gcd"
function_docs = {
"summary": "Calculates the greatest common divisor",
"description":
"Given 'x' and 'y' find the greatest number that divides\n"
"evenly into both x and y."
}

input_types = {
"x" : pa.int64(),
"y" : pa.int64()
}

output_type = pa.int64()

def to_np(val):
if isinstance(val, pa.Scalar):
return val.as_py()
else:
return np.array(val)

def gcd_numpy(ctx, x, y):
np_x = to_np(x)
np_y = to_np(y)
return pa.array(np.gcd(np_x, np_y))

pc.register_scalar_function(gcd_numpy,
function_name,
function_docs,
input_types,
output_type)
```

The implementation of a user-defined function always takes a first context parameter (named `ctx` in the example above) which is an instance of `pyarrow.compute.ScalarUdfContext`. This context exposes several useful attributes, particularly a `memory_pool` to be used for allocations in the context of the user-defined function.

You can call a user-defined function directly using `pyarrow.compute.call_function()`:

```>>> pc.call_function("numpy_gcd", [pa.scalar(27), pa.scalar(63)])
<pyarrow.Int64Scalar: 9>
>>> pc.call_function("numpy_gcd", [pa.scalar(27), pa.array([81, 12, 5])])
<pyarrow.lib.Int64Array object at 0x7fcfa0e7b100>
[
27,
3,
1
]
```

### Working with Datasets#

More generally, user-defined functions are usable everywhere a compute function can be referred by its name. For example, they can be called on a dataset’s column using `Expression._call()`.

Consider an instance where the data is in a table and we want to compute the GCD of one column with the scalar value 30. We will be re-using the “numpy_gcd” user-defined function that was created above:

```>>> import pyarrow.dataset as ds
>>> data_table = pa.table({'category': ['A', 'B', 'C', 'D'], 'value': [90, 630, 1827, 2709]})
>>> dataset = ds.dataset(data_table)
>>> func_args = [pc.scalar(30), ds.field("value")]
>>> dataset.to_table(
...             columns={
...                 'gcd_value': ds.field('')._call("numpy_gcd", func_args),
...                 'value': ds.field('value'),
...                 'category': ds.field('category')
...             })
pyarrow.Table
gcd_value: int64
value: int64
category: string
----
gcd_value: [[30,30,3,3]]
value: [[90,630,1827,2709]]
category: [["A","B","C","D"]]
```

Note that `ds.field('')._call(...)` returns a `pyarrow.compute.Expression()`. The arguments passed to this function call are expressions, not scalar values (notice the difference between `pyarrow.scalar()` and `pyarrow.compute.scalar()`, the latter produces an expression). This expression is evaluated when the projection operator executes it.

#### Projection Expressions#

In the above example we used an expression to add a new column (`gcd_value`) to our table. Adding new, dynamically computed, columns to a table is known as “projection” and there are limitations on what kinds of functions can be used in projection expressions. A projection function must emit a single output value for each input row. That output value should be calculated entirely from the input row and should not depend on any other row. For example, the “numpy_gcd” function that we’ve been using as an example above is a valid function to use in a projection. A “cumulative sum” function would not be a valid function since the result of each input row depends on the rows that came before. A “drop nulls” function would also be invalid because it doesn’t emit a value for some rows.