[Python, pandas, Dask, parallel computing, partitions, data preparation, big data, large datasets, memory, RAM, dataframe, numpy, array]


Discover Dask, a valuable solution to handle large datasets in Python that provides parallel computing functionalities to popular libraries such as Pandas and Numpy. In this building block, you will be introduced to the package Dask. Moreover, you will learn some of Dask’s fundamental operations that will allow you to handle and work with large datasets in Python in a much more effective and efficient manner.

Memory Errors when working with large datasets

When trying to import a large dataset to dataframe format with pandas (for example, using the read_csv function), you are likely to run into MemoryError. This error indicates that you have run out of memory in your RAM. Pandas uses in-memory analytics, so larger-than-memory datasets won’t load. Additionally, any operations performed on the dataframe require memory as well.

Wes McKinney - the creator of the Python pandas project, noted in his 2017 blog post:


pandas rule of thumb: have 5 to 10 times as much RAM as the size of your dataset

You can check the memory usage of each column of the pandas DataFrame (including the dataframe’s index) with the following line of code:


Moreover, pandas only uses a single CPU core to perform computations, so it is relatively slow, especially when working with larger datasets.

Dask library

One of the solutions to memory errors is to use another library. Here Dask comes in handy. Dask is a Python library for parallel computing, which can perform computations on large datasets while scaling well-known Python libraries such as pandas, NumPy, and scikit-learn.

Dask splits the dataset into a number of partitions. Unlike pandas, each Dask partition is sent to a separate CPU core. This feature allows us to work on a larger-than-memory dataset but also speeds up the computations on that dataset.


Dask is included by default in the Anaconda distribution. Otherwise, you can also use pip to install everything required for the most common uses of Dask or choose to only install the Dask library:

python -m pip install "dask[complete]"    # Install everything
python -m pip install dask                # Install only core parts of Dask

Alternatively, see other installing options here.

Dask DataFrame

Dask DataFrame is a collection of smaller pandas DataFrames, split along the index.

The following are the fundamental operations on the Dask DataFrame:

# import the Dask DataFrame module
import dask.dataframe as dd

# read a single CSV file
df = dd.read_csv('/path/example-01.csv')
# read multiple CSV files at once
df = dd.read_csv('/path/example-*.csv')

# check the number of partitions
# change the number of partitions
df = df.repartition(npartitions=10)  

# save the Dask DataFrame to CSV files (one file per partition)
# save the Dask DataFrame to a single CSV file (by converting it to pandas DataFrame first)

Dask DataFrame utilizes a great portion of the pandas API; therefore, there are a lot of similarities in use. However, Dask DataFrame doesn’t support all pandas' features.

For the full list of operations, see the Dask Dataframe documentation.

Dask vs pandas

The main difference between Dask and pandas is that in Dask, each computation requires you to call the compute() function. This is because Dask uses so-called lazy evaluation, meaning that the evaluation of an expression will not be executed unless explicitly requested to do so.

For example, this is how you would get column mean in pandas and Dask:

# get column mean in pandas

# get column mean in Dask

To convert a Dask DataFrame to a pandas DataFrame, you call the compute() function on that dataframe:

df = df.compute()

After that, you continue working with pandas. You usually want to do this after reducing the large dataset with Dask (for example, by selecting a subsection) to a manageable level.


The entire dataset must fit in the memory before applying the compute() function.

Dask Array

Dask Array implements a large subset of NumPy API, breaking up the large array into many small arrays. You can use Dask Array instead of NumPy if you are out of RAM or experiencing performance issues.

You can see the list of functionalities here.

  • With Dask you can handle and manage efficiently large datasets in Python.

  • Dask divides datasets into partitions and distributes the work across multiple CPU cores.

-Working with Dask DataFrames is user-friendly for pandas users due to extensive use of the pandas API.

  • However, Dask DataFrame doesn’t support all pandas functionalities. This makes it an interesting option to Reduce datasets with Dask first, and then switch to pandas for detailed analysis.

See Also

Practical Example Using Dask

Contributed by Paulina Ambroziak