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`ydata-profiling` primary goal is to provide a one-line Exploratory Data Analysis (EDA) experience in a consistent and fast solution. Like pandas `df.describe()` function, that is so handy, ydata-profiling delivers an extended analysis of a DataFrame while allowing the data analysis to be exported in different formats such as **html** and **json**. The package outputs a simple and digested analysis of a dataset, including **time-series** and **text**. > **Looking for a scalable solution that can fully integrate with your database systems?**
The above is achieved by simply displaying the report as a set of widgets. In a Jupyter Notebook, run:
```python
profile.to_widgets()
```
The HTML report can be directly embedded in a cell in a similar fashion:
```python
profile.to_notebook_iframe()
```
### Exporting the report to a file
To generate a HTML report file, save the `ProfileReport` to an object and use the `to_file()` function:
```python
profile.to_file("your_report.html")
```
Alternatively, the report's data can be obtained as a JSON file:
```python
# As a JSON string
json_data = profile.to_json()
# As a file
profile.to_file("your_report.json")
```
### Using in the command line
For standard formatted CSV files (which can be read directly by pandas without additional settings), the `ydata_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.
```sh
ydata_profiling --title "Example Profiling Report" --config_file default.yaml data.csv report.html
```
Additional details on the CLI are available [on the documentation](https://ydata-profiling.ydata.ai/docs/master/pages/getting_started/quickstart.html#command-line-usage).
## 👀 Examples
The following example reports showcase the potentialities of the package across a wide range of dataset and data types:
* [Census Income](https://ydata-profiling.ydata.ai/examples/master/census/census_report.html) (US Adult Census data relating income with other demographic properties)
* [NASA Meteorites](https://ydata-profiling.ydata.ai/examples/master/meteorites/meteorites_report.html) (comprehensive set of meteorite landing - object properties and locations) [](https://colab.research.google.com/github/ydataai/pandas-profiling/blob/master/examples/meteorites/meteorites_cloud.ipynb) [](https://mybinder.org/v2/gh/ydataai/pandas-profiling/master?filepath=examples%2Fmeteorites%2Fmeteorites%5Fcloud.ipynb)
* [Titanic](https://ydata-profiling.ydata.ai/examples/master/titanic/titanic_report.html) (the "Wonderwall" of datasets) [](https://colab.research.google.com/github/ydataai/pandas-profiling/blob/master/examples/titanic/titanic_cloud.ipynb) [](https://mybinder.org/v2/gh/ydataai/pandas-profiling/master?filepath=examples%2Ftitanic%2Ftitanic%5Fcloud.ipynb)
* [NZA](https://ydata-profiling.ydata.ai/examples/master/nza/nza_report.html) (open data from the Dutch Healthcare Authority)
* [Stata Auto](https://ydata-profiling.ydata.ai/examples/master/stata_auto/stata_auto_report.html) (1978 Automobile data)
* [Colors](https://ydata-profiling.ydata.ai/examples/master/colors/colors_report.html) (a simple colors dataset)
* [Vektis](https://ydata-profiling.ydata.ai/examples/master/vektis/vektis_report.html) (Vektis Dutch Healthcare data)
* [UCI Bank Dataset](https://ydata-profiling.ydata.ai/examples/master/bank_marketing_data/uci_bank_marketing_report.html) (marketing dataset from a bank)
* [Russian Vocabulary](https://ydata-profiling.ydata.ai/examples/master/features/russian_vocabulary.html) (100 most common Russian words, showcasing unicode text analysis)
* [Website Inaccessibility](https://ydata-profiling.ydata.ai/examples/master/features/website_inaccessibility_report.html) (website accessibility analysis, showcasing support for URL data)
* [Orange prices](https://ydata-profiling.ydata.ai/examples/master/features/united_report.html) and
* [Coal prices](https://ydata-profiling.ydata.ai/examples/master/features/flatly_report.html) (simple pricing evolution datasets, showcasing the theming options)
* [USA Air Quality](https://github.com/ydataai/pandas-profiling/tree/master/examples/usaairquality) (Time-series air quality dataset EDA example)
* [HCC](https://github.com/ydataai/pandas-profiling/tree/master/examples/hcc) (Open dataset from healthcare, showcasing compare between two sets of data, before and after preprocessing)
## 🛠️ Installation
Additional details, including information about widget support, are available [on the documentation](https://ydata-profiling.ydata.ai/docs/master/pages/getting_started/installation.html).
### Using pip
[](https://pepy.tech/project/ydata-profiling)
[](https://pepy.tech/project/ydata-profiling/month)
[](https://pypi.org/project/ydata-profiling/)
You can install using the `pip` package manager by running:
```sh
pip install -U ydata-profiling
```
#### Extras
The 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.
* `[pyspark]`: support for pyspark for big dataset analysis
Install these with e.g.
```sh
pip install -U ydata-profiling[notebook,unicode,pyspark]
```
### Using conda
[](https://anaconda.org/conda-forge/pandas-profiling)
[](https://anaconda.org/conda-forge/pandas-profiling)
You can install using the `conda` package manager by running:
```sh
conda install -c conda-forge ydata-profiling
```
### From source (development)
Download the source code by cloning the repository or click on [Download ZIP](https://github.com/ydataai/pandas-profiling/archive/master.zip) to download the latest stable version.
Install it by navigating to the proper directory and running:
```sh
pip install -e .
```
The profiling report is written in HTML and CSS, which means a modern browser is required.
You need [Python 3](https://python3statement.github.io/) to run the package. Other dependencies can be found in the requirements files:
| Filename | Requirements|
|----------|-------------|
| [requirements.txt](https://github.com/ydataai/pandas-profiling/blob/master/requirements.txt) | Package requirements|
| [requirements-dev.txt](https://github.com/ydataai/pandas-profiling/blob/master/requirements-dev.txt) | Requirements for development|
| [requirements-test.txt](https://github.com/ydataai/pandas-profiling/blob/master/requirements-test.txt) | Requirements for testing|
| [setup.py](https://github.com/ydataai/pandas-profiling/blob/master/setup.py) | Requirements for widgets etc. |
## 🔗 Integrations
To maximize its usefulness in real world contexts, `ydata-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](https://docs.profiling.ydata.ai/latest/integrations/other_dataframe_libraries) | How to compute the profiling of data stored in libraries other than pandas |
| [Great Expectations](https://ydata-profiling.ydata.ai/docs/master/pages/integrations/great_expectations.html) | Generating [Great Expectations](https://greatexpectations.io) expectations suites directly from a profiling report |
| [Interactive applications](https://docs.profiling.ydata.ai/latest/integrations/interactive_applications) | Embedding profiling reports in [Streamlit](http://streamlit.io), [Dash](http://dash.plotly.com) or [Panel](https://panel.holoviz.org) applications |
| [Pipelines](https://ydata-profiling.ydata.ai/docs/master/pages/integrations/pipelines.html) | Integration with DAG workflow execution tools like [Airflow](https://airflow.apache.org) or [Kedro](https://kedro.org) |
| [Cloud services](https://ydata-profiling.ydata.ai/docs/master/pages/integrations/cloud_services.html) | Using `ydata-profiling` in hosted computation services like [Lambda](https://lambdalabs.com), [Google Cloud](https://github.com/GoogleCloudPlatform/analytics-componentized-patterns/blob/master/retail/propensity-model/bqml/bqml_kfp_retail_propensity_to_purchase.ipynb) or [Kaggle](https://www.kaggle.com/code) |
| [IDEs](https://ydata-profiling.ydata.ai/docs/master/pages/integrations/ides.html) | Using `ydata-profiling` directly from integrated development environments such as [PyCharm](https://www.jetbrains.com/pycharm/) |
## 🙋 Support
Need help? Want to share a perspective? Report a bug? Ideas for collaborations? Reach out via the following channels:
- [Stack Overflow](https://stackoverflow.com/questions/tagged/pandas-profiling+or+ydata-profiling): ideal for asking questions on how to use the package
- [GitHub Issues](https://github.com/ydataai/ydata-profiling/issues): bugs, proposals for changes, feature requests
- [Discord](https://tiny.ydata.ai/dcai-ydata-profiling): ideal for projects discussions, ask questions, collaborations, general chat
> **Need Help?**