# Data Manipulation¶

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

See Compute Functions 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 `pyarrow.compute`

module.

Given an array with 100 numbers, from 0 to 99

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

```
0 .. 99
```

We can compute the `mean`

using the `pyarrow.compute.mean()`

function

```
import pyarrow.compute as pc
mean = pc.mean(arr)
print(mean)
```

```
49.5
```

And the `min`

and `max`

using the `pyarrow.compute.min_max()`

function

```
import pyarrow.compute as pc
min_max = pc.min_max(arr)
print(min_max)
```

```
[('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 `pyarrow.compute`

module.

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

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

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

We can count occurrences of all entries in the array using the
`pyarrow.compute.value_counts()`

function

```
import pyarrow.compute as pc
counts = pc.value_counts(nums_arr)
for pair in counts:
print(pair)
```

```
[('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 `pyarrow.compute`

also include
common transformations such as arithmetic functions.

Given an array with 100 numbers, from 0 to 99

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

```
0 .. 99
```

We can multiply all values by 2 using the `pyarrow.compute.multiply()`

function

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

```
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:

```
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 `pyarrow.concat_tables()`

:

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

```
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 `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
`pyarrow.Table.append_column()`

Suppose we have a table with oscar nominations for each actress

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

```
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 `pyarrow.Table.append_column()`

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

```
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]]]
```

## Replacing a column in an existing Table¶

If you have a table it is possible to replace an existing column using
`pyarrow.Table.set_column()`

Suppose we have a table with information about items sold at a supermarket on a particular day.

```
import pyarrow as pa
sales_data = pa.table([
["Potato", "Bean", "Cucumber", "Eggs"],
[21, 12, 10, 30]
], names=["item", "amount"])
print(sales_data)
```

```
pyarrow.Table
item: string
amount: int64
----
item: [["Potato","Bean","Cucumber","Eggs"]]
amount: [[21,12,10,30]]
```

it’s possible to replace the existing column amount
in index 1 to update the sales
using `pyarrow.Table.set_column()`

```
new_sales_data = sales_data.set_column(
1,
"new_amount",
pa.array([30, 20, 15, 40])
)
print(new_sales_data)
```

```
pyarrow.Table
item: string
new_amount: int64
----
item: [["Potato","Bean","Cucumber","Eggs"]]
new_amount: [[30,20,15,40]]
```

## Group a Table¶

If you have a table which needs to be grouped by a particular key,
you can use `pyarrow.Table.group_by()`

followed by an aggregation
operation `pyarrow.TableGroupBy.aggregate()`

. Learn more about
groupby operations here.

For example, let’s say we have some data with a particular set of keys and values associated with that key. And we want to group the data by those keys and apply an aggregate function like sum to evaluate how many items are for each unique key.

```
import pyarrow as pa
table = pa.table([
pa.array(["a", "a", "b", "b", "c", "d", "e", "c"]),
pa.array([11, 20, 3, 4, 5, 1, 4, 10]),
], names=["keys", "values"])
print(table)
```

```
pyarrow.Table
keys: string
values: int64
----
keys: [["a","a","b","b","c","d","e","c"]]
values: [[11,20,3,4,5,1,4,10]]
```

Now we let’s apply a groupby operation. The table will be grouped
by the field `key`

and an aggregation operation, `sum`

is applied
on the column `values`

. Note that, an aggregation operation pairs with a column name.

```
aggregated_table = table.group_by("keys").aggregate([("values", "sum")])
print(aggregated_table)
```

```
pyarrow.Table
values_sum: int64
keys: string
----
values_sum: [[31,7,15,1,4]]
keys: [["a","b","c","d","e"]]
```

If you observe carefully, the new table returns the aggregated column
as `values_sum`

which is formed by the column name and aggregation operation name.

Aggregation operations can be applied with options. Let’s take a case where we have null values included in our dataset, but we want to take the count of the unique groups excluding the null values.

A sample dataset can be formed as follows.

```
import pyarrow as pa
table = pa.table([
pa.array(["a", "a", "b", "b", "b", "c", "d", "d", "e", "c"]),
pa.array([None, 20, 3, 4, 5, 6, 10, 1, 4, None]),
], names=["keys", "values"])
print(table)
```

```
pyarrow.Table
keys: string
values: int64
----
keys: [["a","a","b","b","b","c","d","d","e","c"]]
values: [[null,20,3,4,5,6,10,1,4,null]]
```

Let’s apply an aggregation operation `count`

with the option to exclude
null values.

```
import pyarrow.compute as pc
grouped_table = table.group_by("keys").aggregate(
[("values",
"count",
pc.CountOptions(mode="only_valid"))]
)
print(grouped_table)
```

```
pyarrow.Table
values_count: int64
keys: string
----
values_count: [[1,3,1,2,1]]
keys: [["a","b","c","d","e"]]
```

## Sort a Table¶

Let’s discusse how to sort a table. We can sort a table,
based on values of a given column. Data can be either sorted `ascending`

or `descending`

.

Prepare data;

```
import pyarrow as pa
table = pa.table([
pa.array(["a", "a", "b", "b", "b", "c", "d", "d", "e", "c"]),
pa.array([15, 20, 3, 4, 5, 6, 10, 1, 14, 123]),
], names=["keys", "values"])
print(table)
```

```
pyarrow.Table
keys: string
values: int64
----
keys: [["a","a","b","b","b","c","d","d","e","c"]]
values: [[15,20,3,4,5,6,10,1,14,123]]
```

Then applying sort with `pyarrow.Table.sort_by()`

;

```
sorted_table = table.sort_by([("values", "ascending")])
print(sorted_table)
```

```
pyarrow.Table
keys: string
values: int64
----
keys: [["d","b","b","b","c","d","e","a","a","c"]]
values: [[1,3,4,5,6,10,14,15,20,123]]
```

## Searching for values matching a predicate in Arrays¶

If you have to look for values matching a predicate in Arrow arrays
the `pyarrow.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
`pyarrow.compute.greater()`

method and get back the elements
that fit our predicate

```
import pyarrow as pa
import pyarrow.compute as pc
arr = pa.array(range(10))
gtfive = pc.greater(arr, 5)
print(gtfive.to_string())
```

```
[
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 with `pyarrow.compute.filter()`

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

```
[
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:

```
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
`pyarrow.Array.filter()`

to get back a new array with
only the values matching the mask:

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

```
[
1,
4
]
```

Most search functions in `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 `pyarrow.compute.equal()`

:

```
import pyarrow.compute as pc
filtered_array = array.filter(pc.equal(array, 2))
print(filtered_array)
```

```
[
2
]
```