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Spark dataframes support - Spark Dataframes profiling is available from ydata-profiling version 4.0.0 onwards

Data Profiling is a core step in the process of developing AI solutions. For small datasets, the data can be loaded into memory and easily accessed with Python and pandas dataframes. However, for larger datasets what can be done?

Big data engines, that distribute the workload through different machines, are the answer. Particularly, Spark rose as one of the most used and adopted engines by the data community. ydata-profiling provides an ease-to-use interface to generate complete and comprehensive data profiling out of your Spark dataframes with a single line of code.

Getting started

Installing Pyspark for Linux and Windows


Ensure that you first install the system requirements (spark and java).
export SPARK_VERSION=3.3.0
export SPARK_DIRECTORY=/opt/spark
sudo apt-get update
sudo apt-get -y install openjdk-8-jdk
curl${SPARK_VERSION}/spark-${SPARK_VERSION}-bin-hadoop${HADOOP_VERSION}.tgz \
--output ${SPARK_DIRECTORY}/spark.tgz
cd ${SPARK_DIRECTORY} && tar -xvzf spark.tgz && mv spark-${SPARK_VERSION}-bin-hadoop${HADOOP_VERSION} sparkenv

A more detailed tutorial for the installation can be found here.

Installing Pyspark for MacOS

Use Homebrew to ensure that the system requirements are installed (java and scala (optional))

console brew
install <openjdk@11>
#Install scala is optional
brew install scala

Install pyspark

brew install apache-spark

After successful installation of Apache Spark run pyspark from the command line to launch PySpark shell and confirm both python and pyspark versions. A more detailed tutorial for the installation can be found here

Install ydata-profiling

Create a pip virtual environment or a conda environment and install ydata-profiling with pyspark as a dependency

pip install ydata-profiling[pyspark]

Profiling with Spark - Supported Features

Minimal mode

This mode was introduced in version v4.0.0

ydata-profiling now supports Spark Dataframes profiling. You can find an example of the integration here.

Features supported:
  • Univariate variables' analysis
  • Head and Tail dataset sample
  • Correlation matrices: Pearson and Spearman
Coming soon
  • Missing values analysis
  • Interactions
  • Improved histogram computation

Profiling with Spark DataFrames

A quickstart example to profile data from a CSV leveraging Pyspark engine and ydata-profiling.

Profiling with Spark Dataframes
from pyspark.sql import SparkSession
spark = SparkSession.builder().master("local[1]")

df ="{insert-file-path}")


a = ProfileReport(df)

ydata-profiling in Databricks

Yes! We have fantastic new coming with a full tutorial on how you can use ydata-profiling in Databricks Notebooks.

The notebook example can be found here.

Stay tuned - we are going to update the documentation soon!