Hello Guys, How are you all? Hope You all Are Fine. Today We Are Going To learn about **How to transform Dask.DataFrame to pd.DataFrame** **in Python**. So Here I am Explain to you all the possible Methods here.

Without wasting your time, Let’s start This Article.

Table of Contents

## How to transform Dask.DataFrame to pd.DataFrame?

**How to transform Dask.DataFrame to pd.DataFrame?**You can call the .compute() method to transform a dask.dataframe to a pandas dataframe:

`df = df.compute()`

**transform Dask.DataFrame to pd.DataFrame**You can call the .compute() method to transform a dask.dataframe to a pandas dataframe:

`df = df.compute()`

## Method 1

You can call the .compute() method to transform a dask.dataframe to a pandas dataframe:

df = df.compute()

## Method 2

This answer gives more details on when it’s appropriate to convert from a Dask DataFrame to and Pandas DataFrame (and how to predict when it’ll cause problems).

Each partition in a Dask DataFrame is a Pandas DataFrame. Running `df.compute()`

will coalesce all the underlying partitions in the Dask DataFrame into a single Pandas DataFrame. That’ll cause problems if the size of the Pandas DataFrame is bigger than the RAM on your machine.

If `df`

has 30 GB of data and your computer has 16 GB of RAM, then `df.compute()`

will blow up with a memory error. If `df`

only has 1 GB of data, then you’ll be fine.

You can run `df.memory_usage(deep=True).sum()`

to compute the amount of memory that your DataFrame is using. This’ll let you know if your DataFrame is sufficiently small to be coalesced into a single Pandas DataFrame.

Repartioning changes the number of underlying partitions in a Dask DataFrame. `df.repartition(1).partitions[0]`

is conceptually similar to `df.compute()`

.

Converting to a Pandas DataFrame is especially possible after performing a big filtering operation. If you filter a 100 billion row dataset down to 10 thousand rows, then you can probably just switch to the Pandas API.

**Summery**

It’s all About this issue. Hope all Methods helped you a lot. Comment below Your thoughts and your queries. Also, Comment below which Method worked for you? Thank You.

**Also, Read**