# Transform and apply a function¶

There are many APIs that allow users to apply a function against Koalas DataFrame such as
`DataFrame.transform()`

, `DataFrame.apply()`

, `DataFrame.koalas.transform_batch()`

,
`DataFrame.koalas.apply_batch()`

, `Series.koalas.transform_batch()`

, etc. Each has a distinct
purpose and works differently internally. This section describes the differences among
them where users are confused often.

`transform`

and `apply`

¶

The main difference between `DataFrame.transform()`

and `DataFrame.apply()`

is that the former requires
to return the same length of the input and the latter does not require this. See the example below:

```
>>> kdf = ks.DataFrame({'a': [1,2,3], 'b':[4,5,6]})
>>> def pandas_plus(pser):
... return pser + 1 # should always return the same length as input.
...
>>> kdf.transform(pandas_plus)
```

```
>>> kdf = ks.DataFrame({'a': [1,2,3], 'b':[5,6,7]})
>>> def pandas_plus(pser):
... return pser[pser % 2 == 1] # allows an arbitrary length
...
>>> kdf.apply(pandas_plus)
```

In this case, each function takes a pandas Series, and Koalas computes the functions in a distributed manner as below.

In case of ‘column’ axis, the function takes each row as a pandas Series.

```
>>> kdf = ks.DataFrame({'a': [1,2,3], 'b':[4,5,6]})
>>> def pandas_plus(pser):
... return sum(pser) # allows an arbitrary length
...
>>> kdf.apply(pandas_plus, axis='columns')
```

The example above calculates the summation of each row as a pandas Series. See below:

In the examples above, the type hints were not used for simplicity but it is encouraged to use to avoid performance penalty. Please refer the API documentations.

`koalas.transform_batch`

and `koalas.apply_batch`

¶

In `DataFrame.koalas.transform_batch()`

, `DataFrame.koalas.apply_batch()`

, `Series.koalas.transform_batch()`

, etc., the `batch`

postfix means each chunk in Koalas DataFrame or Series. The APIs slice the Koalas DataFrame or Series, and
then applies the given function with pandas DataFrame or Series as input and output. See the examples below:

```
>>> kdf = ks.DataFrame({'a': [1,2,3], 'b':[4,5,6]})
>>> def pandas_plus(pdf):
... return pdf + 1 # should always return the same length as input.
...
>>> kdf.koalas.transform_batch(pandas_plus)
```

```
>>> kdf = ks.DataFrame({'a': [1,2,3], 'b':[4,5,6]})
>>> def pandas_plus(pdf):
... return pdf[pdf.a > 1] # allow arbitrary length
...
>>> kdf.koalas.apply_batch(pandas_plus)
```

The functions in both examples take a pandas DataFrame as a chunk of Koalas DataFrame, and output a pandas DataFrame. Koalas combines the pandas DataFrames as a Koalas DataFrame.

Note that `DataFrame.koalas.transform_batch()`

has the length restriction - the length of input and output should be
the same whereas `DataFrame.koalas.apply_batch()`

does not. However, it is important to know that
the output belongs to the same DataFrame when `DataFrame.koalas.transform_batch()`

can a Series, and
you can avoid a shuffle by the operations between different DataFrames. In case of `DataFrame.koalas.apply_batch()`

, its output is always
treated that it belongs to a new different DataFrame. See also
Operations on different DataFrames for more details.

In case of `Series.koalas.transform_batch()`

, it is also similar with `DataFrame.koalas.transform_batch()`

; however, it takes
a pandas Series as a chunk of Koalas Series.

```
>>> kdf = ks.DataFrame({'a': [1,2,3], 'b':[4,5,6]})
>>> def pandas_plus(pser):
... return pser + 1 # should always return the same length as input.
...
>>> kdf.a.koalas.transform_batch(pandas_plus)
```

Under the hood, each batch of Koalas Series is split to multiple pandas Series, and each function computes on that as below:

There are more details such as the type inference and preventing its performance penalty. Please refer the API documentations.