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Profiling large datasets

By default, ydata-profiling comprehensively summarizes the input dataset in a way that gives the most insights for data analysis. For small datasets, these computations can be performed in quasi real-time. For larger datasets, deciding upfront which calculations to make might be required. Whether a computation scales to a large datasets not only depends on the exact size of the dataset, but also on its complexity and on whether fast computations are available. If the computation time of the profiling becomes a bottleneck, ydata-profiling offers several alternatives to overcome it.

Scale in a fully managed system

Looking for an fully managed system that is able to scale the profiling for large datasets? Sign up Fabric community for distributed data profiling.



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

Keep an eye on the GitHub page to follow the updates on the implementation of Pyspark Dataframes support.

Minimal mode

Minimal mode

This mode was introduced in version v2.4.0

ydata-profiling includes a minimal configuration file where the most expensive computations are turned off by default. This is the recommended starting point for larger datasets.

profile = ProfileReport(large_dataset, minimal=True)

This configuration file can be found here: config_minimal.yaml. More details on settings and configuration are available in ../advanced_usage/available_settings.

Sample the dataset

An alternative way to handle really large datasets is to use a portion of it to generate the profiling report. Several users report this is a good way to scale back the computation time while maintaining representativity.

Sampling a large dataset
# Sample 10.000 rows
sample = large_dataset.sample(10000)

profile = ProfileReport(sample, minimal=True)

The reader of the report might want to know that the profile is generated using a sample from the data. This can be done by adding a description to the report (see metadata for details).

Sample 5% of your dataset
description = "Disclaimer: this profiling report was generated using a sample of 5% of the original dataset."
sample = large_dataset.sample(frac=0.05)

profile = sample.profile_report(dataset={"description": description}, minimal=True)

Disable expensive computations

To decrease the computational burden in particularly large datasets but still maintain some information of interest that may stem from them, some computations can be filtered only for certain columns. Particularly, a list of targets can be provided to Interactions, so that only the interactions with these variables in specific are computed.

Disable expensive computations
from ydata_profiling import ProfileReport
import pandas as pd

# Reading the data
data = pd.read_csv(

# Creating the profile without specifying the data source, to allow editing the configuration
profile = ProfileReport()
profile.config.interactions.targets = ["Name", "Sex", "Age"]

# Assigning a DataFrame and exporting to a file, triggering computation
profile.df = data

The setting controlling this, ├Čnteractions.targets, can be changed via multiple interfaces (configuration files or environment variables). For details, see ../advanced_usage/changing_settings{.interpreted-text role="doc"}.


ydata-profiling is a project under active development. One of the highly desired features is the addition of a scalable backend such as Modin or Dask.

Keep an eye on the GitHub page to follow the updates on the implementation of a concurrent and highly scalable backend. Specifically, development of a Spark backend is currently underway.