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pandas-profiling generates profile reports from a pandas DataFrame. The pandas df.describe() function is handy yet a little basic for exploratory data analysis. pandas-profiling extends pandas DataFrame with df.profile_report(), which automatically generates a standardized univariate and multivariate report for data understanding.
For each column, the following information (whenever relevant for the column type) is presented in an interactive HTML report:
- Type inference: detect the types of columns in a DataFrame
- Essentials: type, unique values, indication of missing values
- Quantile statistics: minimum value, Q1, median, Q3, maximum, range, interquartile range
- Descriptive statistics: mean, mode, standard deviation, sum, median absolute deviation, coefficient of variation, kurtosis, skewness
- Most frequent and extreme values
- Histograms: categorical and numerical
- Correlations: high correlation warnings, based on different correlation metrics (Spearman, Pearson, Kendall, Cramér’s V, Phik)
- Missing values: through counts, matrix and heatmap
- Duplicate rows: list of the most common duplicated rows
- Text analysis: most common categories (uppercase, lowercase, separator), scripts (Latin, Cyrillic) and blocks (ASCII, Cyrilic)
- File and Image analysis: file sizes, creation dates, dimensions, indication of truncated images and existence of EXIF metadata
The report contains three additional sections:
- Overview: mostly global details about the dataset (number of records, number of variables, overall missigness and duplicates, memory footprint)
- Alerts: a comprehensive and automatic list of potential data quality issues (high correlation, skewness, uniformity, zeros, missing values, constant values, between others)
- Reproduction: technical details about the analysis (time, version and configuration)
⚡ Looking for a Spark backend to profile large datasets? It's work in progress.
⌛ Interested in uncovering temporal patterns? Check out popmon.
Start by loading your pandas DataFrame as you normally would, e.g. by using:
import numpy as np
import pandas as pd
from pandas_profiling import ProfileReport
df = pd.DataFrame(np.random.rand(100, 5), columns=["a", "b", "c", "d", "e"])To generate the standard profiling report, merely run:
profile = ProfileReport(df, title="Pandas Profiling Report")There are two interfaces to consume the report inside a Jupyter notebook: through widgets and through an embedded HTML report.
The above is achieved by simply displaying the report as a set of widgets. In a Jupyter Notebook, run:
profile.to_widgets()The HTML report can be directly embedded in a cell in a similar fashion:
profile.to_notebook_iframe()
To generate a HTML report file, save the ProfileReport to an object and use the to_file() function:
profile.to_file("your_report.html")Alternatively, the report's data can be obtained as a JSON file:
# As a JSON string
json_data = profile.to_json()
# As a file
profile.to_file("your_report.json")For standard formatted CSV files (which can be read directly by pandas without additional settings), the pandas_profiling executable can be used in the command line. The example below generates a report named Example Profiling Report, using a configuration file called default.yaml, in the file report.html by processing a data.csv dataset.
pandas_profiling --title "Example Profiling Report" --config_file default.yaml data.csv report.htmlAdditional details on the CLI are available on the documentation.
The following example reports showcase the potentialities of the package across a wide range of dataset and data types:
- Census Income (US Adult Census data relating income with other demographic properties)
- NASA Meteorites (comprehensive set of meteorite landing - object properties and locations)
- Titanic (the "Wonderwall" of datasets)
- NZA (open data from the Dutch Healthcare Authority)
- Stata Auto (1978 Automobile data)
- Colors (a simple colors dataset)
- Vektis (Vektis Dutch Healthcare data)
- UCI Bank Dataset (marketing dataset from a bank)
- Russian Vocabulary (100 most common Russian words, showcasing unicode text analysis)
- Website Inaccessibility (website accessibility analysis, showcasing support for URL data)
- Orange prices and Coal prices (simple pricing evolution datasets, showcasing the theming options)
Additional details, including information about widget support, are available on the documentation.
You can install using the pip package manager by running:
pip install -U pandas-profilingThe package declares "extras", sets of additional dependencies.
[notebook]: support for rendering the report in Jupyter notebook widgets.[unicode]: support for more detailed Unicode analysis, at the expense of additional disk space.
Install these with e.g.
pip install -U pandas-profiling[notebook,unicode]
You can install using the conda package manager by running:
conda install -c conda-forge pandas-profilingDownload the source code by cloning the repository or click on Download ZIP to download the latest stable version.
Install it by navigating to the proper directory and running:
pip install -e .The profiling report is written in HTML and CSS, which means a modern browser is required.
You need Python 3 to run the package. Other dependencies can be found in the requirements files:
| Filename | Requirements |
|---|---|
| requirements.txt | Package requirements |
| requirements-dev.txt | Requirements for development |
| requirements-test.txt | Requirements for testing |
| setup.py | Requirements for widgets etc. |
The documentation includes guides, tips and tricks for tackling common use cases:
| Use case | Description |
|---|---|
| Profiling large datasets | Tips on how to prepare data and configure pandas-profiling for working with large datasets |
| Handling sensitive data | Generating reports which are mindful about sensitive data in the input dataset |
| Comparing datasets | Comparing multiple version of the same dataset |
| Dataset metadata and data dictionaries | Complementing the report with dataset details and column-specific data dictionaries |
| Customizing the report's appearance | Changing the appearance of the report's page and of the contained visualizations |
To maximize its usefulness in real world contexts, pandas-profiling has a set of implicit and explicit integrations with a variety of other actors in the Data Science ecosystem:
| Integration type | Description |
|---|---|
| Other DataFrame libraries | How to compute the profiling of data stored in libraries other than pandas |
| Great Expectations | Generating Great Expectations expectations suites directly from a profiling report |
| Interactive applications | Embedding profiling reports in Streamlit, Dash or Panel applications |
| Pipelines | Integration with DAG workflow execution tools like Airflow or Kedro |
| Cloud services | Using pandas-profiling in hosted computation services like Lambda, Google Cloud or Kaggle |
| IDEs | Using pandas-profiling directly from integrated development environments such as PyCharm |
Need help? Want to share a perspective? Report a bug? Ideas for collaborations? Reach out via the following channels:
- Stack Overflow: ideal for asking questions on how to use the package
- GitHub Issues: bugs, proposals for changes, feature requests
- Slack: general chat, questions, collaborations
- Email: project collaborations or sponsoring
❗ Before reporting an issue on GitHub, check out Common Issues.
Learn how to get involved in the Contribution Guide.
A low-threshold place to ask questions or start contributing is the Data Centric AI Community's Slack.