[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-9636":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":10,"language":11,"languages":10,"totalLinesOfCode":10,"stars":12,"forks":13,"watchers":14,"openIssues":15,"contributorsCount":16,"subscribersCount":16,"size":16,"stars1d":16,"stars7d":17,"stars30d":18,"stars90d":16,"forks30d":16,"starsTrendScore":19,"compositeScore":20,"rankGlobal":10,"rankLanguage":10,"license":21,"archived":22,"fork":22,"defaultBranch":23,"hasWiki":24,"hasPages":24,"topics":25,"createdAt":10,"pushedAt":10,"updatedAt":46,"readmeContent":47,"aiSummary":48,"trendingCount":16,"starSnapshotCount":16,"syncStatus":49,"lastSyncTime":50,"discoverSource":51},9636,"fg-data-profiling","Data-Centric-AI-Community\u002Ffg-data-profiling","Data-Centric-AI-Community","1 Line of code data quality profiling & exploratory data analysis for Pandas and Spark DataFrames. ","https:\u002F\u002Fdocs.sdk.ydata.ai",null,"Python",13596,1791,148,263,0,12,54,7,44.76,"MIT License",false,"develop",true,[26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45],"big-data-analytics","data-analysis","data-exploration","data-profiling","data-quality","data-science","deep-learning","eda","exploration","exploratory-data-analysis","hacktoberfest","html-report","jupyter","jupyter-notebook","machine-learning","pandas","pandas-dataframe","pandas-profiling","python","statistics","2026-06-12 02:02:10","# fg-data-profiling\n\n> **`ydata-profiling` is now `fg-data-profiling`.** This package has been renamed to `fg-data-profiling`. Please follow the [Migration Guide](#migration-guide) as soon as possible — the old package will no longer receive updates or bug fixes.\n\n[![Build Status](https:\u002F\u002Fgithub.com\u002Fydataai\u002Fpandas-profiling\u002Factions\u002Fworkflows\u002Ftests.yml\u002Fbadge.svg?branch=master)](https:\u002F\u002Fgithub.com\u002Fydataai\u002Fpandas-profiling\u002Factions\u002Fworkflows\u002Ftests.yml)\n[![PyPI download month](https:\u002F\u002Fimg.shields.io\u002Fpypi\u002Fdm\u002Ffg-data-profiling.svg)](https:\u002F\u002Fpypi.python.org\u002Fpypi\u002Ffg-data-profiling\u002F)\n[![](https:\u002F\u002Fpepy.tech\u002Fbadge\u002Fpandas-profiling)](https:\u002F\u002Fpypi.org\u002Fproject\u002Ffg-data-profiling\u002F)\n[![Code Coverage](https:\u002F\u002Fcodecov.io\u002Fgh\u002Fydataai\u002Fpandas-profiling\u002Fbranch\u002Fmaster\u002Fgraph\u002Fbadge.svg?token=gMptB4YUnF)](https:\u002F\u002Fcodecov.io\u002Fgh\u002Fydataai\u002Fpandas-profiling)\n[![Release Version](https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Frelease\u002Fydataai\u002Fpandas-profiling.svg)](https:\u002F\u002Fgithub.com\u002Fydataai\u002Fpandas-profiling\u002Freleases)\n[![Python Version](https:\u002F\u002Fimg.shields.io\u002Fpypi\u002Fpyversions\u002Ffg-data-profiling)](https:\u002F\u002Fpypi.org\u002Fproject\u002Ffg-data-profiling\u002F)\n[![Code style: black](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fcode%20style-black-000000.svg)](https:\u002F\u002Fgithub.com\u002Fpython\u002Fblack)\n\u003Cimg referrerpolicy=\"no-referrer-when-downgrade\" src=\"https:\u002F\u002Fstatic.scarf.sh\u002Fa.png?x-pxid=cb7e69df-af81-4352-809a-d4251756affc\" \u002F>\n\n\u003Cp align=\"center\">\u003Cimg width=\"300\" src=\"https:\u002F\u002Fassets.ydata.ai\u002Foss\u002Fydata-profiling_black.png\" alt=\"Data Profiling Logo\">\u003C\u002Fp>\n\n\u003Cp align=\"center\">\n  \u003Ca href=\"https:\u002F\u002Fydata-profiling.ydata.ai\u002Fdocs\u002Fmaster\u002F\">Documentation\u003C\u002Fa>\n  |\n  \u003Ca href=\"https:\u002F\u002Ftiny.ydata.ai\u002Fdcai-data-profiling\">Discord\u003C\u002Fa>\n  | \n  \u003Ca href=\"https:\u002F\u002Fstackoverflow.com\u002Fquestions\u002Ftagged\u002Fpandas-profiling+or+data-profiling\">Stack Overflow\u003C\u002Fa>\n  |\n  \u003Ca href=\"https:\u002F\u002Fydata-profiling.ydata.ai\u002Fdocs\u002Fmaster\u002Fpages\u002Freference\u002Fchangelog.html#changelog\">Latest changelog\u003C\u002Fa>\n\n\u003C\u002Fp>\n\n\u003Cp align=\"center\">\n  Do you like this project? Show us your love and \u003Ca href=\"https:\u002F\u002Fengage.ydata.ai\">give feedback!\u003C\u002Fa>\n\u003C\u002Fp>\n\n`fg-data-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, fg-data-profiling delivers an extended analysis of a DataFrame while allowing the data analysis to be exported in different formats such as **html** and **json**.\n\nThe package outputs a simple and digested analysis of a dataset, including **time-series** and **text**.\n\n> **Looking for a scalable solution that can fully integrate with your database systems?**\u003Cbr>\n> Leverage YData Fabric Data Catalog to connect to different databases and storages (Oracle, snowflake, PostGreSQL, GCS, S3, etc.) and leverage an interactive and guided profiling experience in Fabric. Check out the [Community Version](http:\u002F\u002Fydata.ai\u002Fregister?utm_source=data-profiling&utm_medium=documentation&utm_campaign=YData%20Fabric%20Community).\n\n## Migration Guide\n \n### 1. Uninstall the old package\n \n```bash\npip uninstall ydata-profiling\n```\n \n### 2. Install the new package\n \n```bash\npip install fg-data-profiling\n```\n \n### 3. Update your imports\n \nFind and replace all occurrences of the old import in your codebase:\n \n```python\n# Before\nimport ydata_profiling\nfrom ydata_profiling import ProfileReport\n\n# After\nimport data_profiling\nfrom data_profiling import ProfileReport\n```\n \nYou can use this one-liner to find all affected files:\n \n```bash\ngrep -r \"ydata_profiling\" . --include=\"*.py\"\n```\n\n## ▶️ Quickstart\n\n### Install\n```cmd\npip install fg-data-profiling\n```\nor\n```cmd\nconda install -c conda-forge fg-data-profiling\n```\n### Start profiling\n\nStart by loading your pandas `DataFrame` as you normally would, e.g. by using:\n\n```python\nimport numpy as np\nimport pandas as pd\nfrom data_profiling import ProfileReport\n\ndf = pd.DataFrame(np.random.rand(100, 5), columns=[\"a\", \"b\", \"c\", \"d\", \"e\"])\n```\n\nTo generate the standard profiling report, merely run:\n\n```python\nprofile = ProfileReport(df, title=\"Profiling Report\")\n```\n\n## 📊 Key features\n\n- **Type inference**: automatic detection of columns' data types (*Categorical*, *Numerical*, *Date*, etc.)\n- **Warnings**: A summary of the problems\u002Fchallenges in the data that you might need to work on (*missing data*, *inaccuracies*, *skewness*, etc.)\n- **Univariate analysis**: including descriptive statistics (mean, median, mode, etc) and informative visualizations such as distribution histograms\n- **Multivariate analysis**: including correlations, a detailed analysis of missing data, duplicate rows, and visual support for variables pairwise interaction\n- **Time-Series**: including different statistical information relative to time dependent data such as auto-correlation and seasonality, along ACF and PACF plots.\n- **Text analysis**: most common categories (uppercase, lowercase, separator), scripts (Latin, Cyrillic) and blocks (ASCII, Cyrilic)\n- **File and Image analysis**: file sizes, creation dates, dimensions, indication of truncated images and existence of EXIF metadata\n- **Compare datasets**: one-line solution to enable a fast and complete report on the comparison of datasets\n- **Flexible output formats**: all analysis can be exported to an HTML report that can be easily shared with different parties, as JSON for an easy integration in automated systems and as a widget in a Jupyter Notebook.\n\nThe report contains three additional sections:\n\n- **Overview**: mostly global details about the dataset (number of records, number of variables, overall missigness and duplicates, memory footprint)\n- **Alerts**: a comprehensive and automatic list of potential data quality issues (high correlation, skewness, uniformity, zeros, missing values, constant values, between others)\n- **Reproduction**: technical details about the analysis (time, version and configuration)\n\n### 🎁 Latest features\n\n- Want to scale? Check the latest release with ⭐⚡[Spark support](https:\u002F\u002Fydata-profiling.ydata.ai\u002Fdocs\u002Fmaster\u002Fpages\u002Fintegrations\u002Fpypspark.html)! \n- Looking for how you can do an EDA for Time-Series 🕛 ? Check [this blogpost](https:\u002F\u002Ftowardsdatascience.com\u002Fhow-to-do-an-eda-for-time-series-cbb92b3b1913).\n- You want to compare 2 datasets and get a report? Check [this blogpost](https:\u002F\u002Fmedium.com\u002Ftowards-artificial-intelligence\u002Fhow-to-compare-2-dataset-with-pandas-profiling-2ae3a9d7695e)\n\n### ✨ Spark\n\nSpark support has been released, but we are always looking for an extra pair of hands 👐.\n[Check current work in progress!](https:\u002F\u002Fgithub.com\u002FData-Centric-AI-Community\u002Ffg-data-profiling\u002Fprojects\u002F3).\n\n## 📝 Use cases\nfg-data-profiling can be used to deliver a variety of different use-case. The documentation includes guides, tips and tricks for tackling them:\n\n| Use case | Description                                                                                 |\n|----------|---------------------------------------------------------------------------------------------|\n| [Comparing datasets](https:\u002F\u002Fdocs.profiling.ydata.ai\u002Flatest\u002Ffeatures\u002Fcomparing_datasets)                        | Comparing multiple version of the same dataset                                              |\n| [Profiling a Time-Series dataset](https:\u002F\u002Fdocs.profiling.ydata.ai\u002Flatest\u002Ffeatures\u002Ftime_series_datasets)               | Generating a report for a time-series dataset with a single line of code                    |\n|[Profiling large datasets](https:\u002F\u002Fdocs.profiling.ydata.ai\u002Flatest\u002Ffeatures\u002Fbig_data)                            | Tips on how to prepare data and configure `fg-data-profiling` for working with large datasets |\n| [Handling sensitive data](https:\u002F\u002Fdocs.profiling.ydata.ai\u002Flatest\u002Ffeatures\u002Fsensitive_data)                       | Generating reports which are mindful about sensitive data in the input dataset              |\n| [Dataset metadata and data dictionaries](https:\u002F\u002Fdocs.profiling.ydata.ai\u002Flatest\u002Ffeatures\u002Fmetadata)               | Complementing the report with dataset details and column-specific data dictionaries         |\n| [Customizing the report's appearance](https:\u002F\u002Fdocs.profiling.ydata.ai\u002Flatest\u002Ffeatures\u002Fcustom_reports) | Changing the appearance of the report's page and of the contained visualizations            |\n| [Profiling Databases](https:\u002F\u002Fdocs.profiling.ydata.ai\u002Flatest\u002Ffeatures\u002Fcollaborative_data_profiling) | For a seamless profiling experience in your organization's databases, check [Fabric Data Catalog](https:\u002F\u002Fydata.ai\u002Fproducts\u002Fdata_catalog), which allows to consume data from different types of storages such as RDBMs (Azure SQL, PostGreSQL, Oracle, etc.) and object storages (Google Cloud Storage, AWS S3, Snowflake, etc.), among others. |\n### Using inside Jupyter Notebooks\n\nThere are two interfaces to consume the report inside a Jupyter notebook: through widgets and through an embedded HTML report.\n\n\u003Cimg alt=\"Notebook Widgets\" src=\"https:\u002F\u002Fdata-profiling.ydata.ai\u002Fdocs\u002Fmaster\u002Fassets\u002Fwidgets.gif\" width=\"800\" \u002F>\n\nThe above is achieved by simply displaying the report as a set of widgets. In a Jupyter Notebook, run:\n\n```python\nprofile.to_widgets()\n```\n\nThe HTML report can be directly embedded in a cell in a similar fashion:\n\n```python\nprofile.to_notebook_iframe()\n```\n\n\u003Cimg alt=\"HTML\" src=\"https:\u002F\u002Fydata-profiling.ydata.ai\u002Fdocs\u002Fmaster\u002Fassets\u002Fiframe.gif\" width=\"800\" \u002F>\n\n### Exporting the report to a file\n\nTo generate a HTML report file, save the `ProfileReport` to an object and use the `to_file()` function:\n\n```python\nprofile.to_file(\"your_report.html\")\n```\n\nAlternatively, the report's data can be obtained as a JSON file:\n\n```python\n# As a JSON string\njson_data = profile.to_json()\n\n# As a file\nprofile.to_file(\"your_report.json\")\n```\n\n### Using in the command line\n\nFor standard formatted CSV files (which can be read directly by pandas without additional settings), the `data_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.\n\n```sh\ndata_profiling --title \"Example Profiling Report\" --config_file default.yaml data.csv report.html\n```\n\nAdditional details on the CLI are available [on the documentation](https:\u002F\u002Fydata-profiling.ydata.ai\u002Fdocs\u002Fmaster\u002Fpages\u002Fgetting_started\u002Fquickstart.html#command-line-usage).\n\n## 👀 Examples\n\nThe following example reports showcase the potentialities of the package across a wide range of dataset and data types:\n\n* [Census Income](https:\u002F\u002Fydata-profiling.ydata.ai\u002Fexamples\u002Fmaster\u002Fcensus\u002Fcensus_report.html) (US Adult Census data relating income with other demographic properties)\n* [NASA Meteorites](https:\u002F\u002Fydata-profiling.ydata.ai\u002Fexamples\u002Fmaster\u002Fmeteorites\u002Fmeteorites_report.html) (comprehensive set of meteorite landing - object properties and locations) [![Open In Colab](https:\u002F\u002Fcamo.githubusercontent.com\u002F52feade06f2fecbf006889a904d221e6a730c194\u002F68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667)](https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Fydataai\u002Fpandas-profiling\u002Fblob\u002Fmaster\u002Fexamples\u002Fmeteorites\u002Fmeteorites_cloud.ipynb) [![Binder](https:\u002F\u002Fcamo.githubusercontent.com\u002F483bae47a175c24dfbfc57390edd8b6982ac5fb3\u002F68747470733a2f2f6d7962696e6465722e6f72672f62616467655f6c6f676f2e737667)](https:\u002F\u002Fmybinder.org\u002Fv2\u002Fgh\u002Fydataai\u002Fpandas-profiling\u002Fmaster?filepath=examples%2Fmeteorites%2Fmeteorites%5Fcloud.ipynb)\n* [Titanic](https:\u002F\u002Fydata-profiling.ydata.ai\u002Fexamples\u002Fmaster\u002Ftitanic\u002Ftitanic_report.html) (the \"Wonderwall\" of datasets) [![Open In Colab](https:\u002F\u002Fcamo.githubusercontent.com\u002F52feade06f2fecbf006889a904d221e6a730c194\u002F68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667)](https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Fydataai\u002Fpandas-profiling\u002Fblob\u002Fmaster\u002Fexamples\u002Ftitanic\u002Ftitanic_cloud.ipynb) [![Binder](https:\u002F\u002Fcamo.githubusercontent.com\u002F483bae47a175c24dfbfc57390edd8b6982ac5fb3\u002F68747470733a2f2f6d7962696e6465722e6f72672f62616467655f6c6f676f2e737667)](https:\u002F\u002Fmybinder.org\u002Fv2\u002Fgh\u002Fydataai\u002Fpandas-profiling\u002Fmaster?filepath=examples%2Ftitanic%2Ftitanic%5Fcloud.ipynb)\n* [NZA](https:\u002F\u002Fydata-profiling.ydata.ai\u002Fexamples\u002Fmaster\u002Fnza\u002Fnza_report.html) (open data from the Dutch Healthcare Authority)\n* [Stata Auto](https:\u002F\u002Fydata-profiling.ydata.ai\u002Fexamples\u002Fmaster\u002Fstata_auto\u002Fstata_auto_report.html) (1978 Automobile data)\n* [Colors](https:\u002F\u002Fydata-profiling.ydata.ai\u002Fexamples\u002Fmaster\u002Fcolors\u002Fcolors_report.html) (a simple colors dataset)\n* [Vektis](https:\u002F\u002Fydata-profiling.ydata.ai\u002Fexamples\u002Fmaster\u002Fvektis\u002Fvektis_report.html) (Vektis Dutch Healthcare data)\n* [UCI Bank Dataset](https:\u002F\u002Fydata-profiling.ydata.ai\u002Fexamples\u002Fmaster\u002Fbank_marketing_data\u002Fuci_bank_marketing_report.html) (marketing dataset from a bank)\n* [Russian Vocabulary](https:\u002F\u002Fydata-profiling.ydata.ai\u002Fexamples\u002Fmaster\u002Ffeatures\u002Frussian_vocabulary.html) (100 most common Russian words, showcasing unicode text analysis)\n* [Website Inaccessibility](https:\u002F\u002Fydata-profiling.ydata.ai\u002Fexamples\u002Fmaster\u002Ffeatures\u002Fwebsite_inaccessibility_report.html) (website accessibility analysis, showcasing support for URL data)\n* [Orange prices](https:\u002F\u002Fydata-profiling.ydata.ai\u002Fexamples\u002Fmaster\u002Ffeatures\u002Funited_report.html) and \n* [Coal prices](https:\u002F\u002Fydata-profiling.ydata.ai\u002Fexamples\u002Fmaster\u002Ffeatures\u002Fflatly_report.html) (simple pricing evolution datasets, showcasing the theming options)\n* [USA Air Quality](https:\u002F\u002Fgithub.com\u002FData-Centric-AI-Community\u002Ffg-data-profiling\u002Ftree\u002Fmaster\u002Fexamples\u002Fusaairquality) (Time-series air quality dataset EDA example)\n* [HCC](https:\u002F\u002Fgithub.com\u002FData-Centric-AI-Community\u002Ffg-data-profiling\u002Ftree\u002Fmaster\u002Fexamples\u002Fhcc) (Open dataset from healthcare, showcasing compare between two sets of data, before and after preprocessing)\n\n## 🛠️ Installation\nAdditional details, including information about widget support, are available [on the documentation](https:\u002F\u002Fydata-profiling.ydata.ai\u002Fdocs\u002Fmaster\u002Fpages\u002Fgetting_started\u002Finstallation.html).\n\n### Using pip\n[![PyPi Downloads](https:\u002F\u002Fpepy.tech\u002Fbadge\u002Ffg-data-profiling)](https:\u002F\u002Fpepy.tech\u002Fproject\u002Ffg-data-profiling)\n[![PyPi Monthly Downloads](https:\u002F\u002Fpepy.tech\u002Fbadge\u002Fpandas-profiling\u002Fmonth)](https:\u002F\u002Fpepy.tech\u002Fproject\u002Ffg-data-profiling\u002Fmonth)\n[![PyPi Version](https:\u002F\u002Fbadge.fury.io\u002Fpy\u002Ffg-data-profiling.svg)](https:\u002F\u002Fpypi.org\u002Fproject\u002Ffg-data-profiling\u002F)\n\nYou can install using the `pip` package manager by running:\n\n```sh\npip install -U fg-data-profiling\n```\n\n#### Extras\n\nThe package declares \"extras\", sets of additional dependencies.\n\n* `[notebook]`: support for rendering the report in Jupyter notebook widgets.\n* `[unicode]`: support for more detailed Unicode analysis, at the expense of additional disk space.\n* `[pyspark]`: support for pyspark for big dataset analysis\n\nInstall these with e.g.\n\n```sh\npip install -U fg-data-profiling[notebook,unicode,pyspark]\n```\n\n\n### Using conda\n[![Conda Downloads](https:\u002F\u002Fimg.shields.io\u002Fconda\u002Fdn\u002Fconda-forge\u002Fpandas-profiling.svg)](https:\u002F\u002Fanaconda.org\u002Fconda-forge\u002Fpandas-profiling)\n[![Conda Version](https:\u002F\u002Fimg.shields.io\u002Fconda\u002Fvn\u002Fconda-forge\u002Fpandas-profiling.svg)](https:\u002F\u002Fanaconda.org\u002Fconda-forge\u002Fpandas-profiling) \n\n\nYou can install using the `conda` package manager by running:\n\n```sh\nconda install -c conda-forge fg-data-profiling\n```\n\n### From source (development)\n\nDownload the source code by cloning the repository or click on [Download ZIP](https:\u002F\u002Fgithub.com\u002Fydataai\u002Fpandas-profiling\u002Farchive\u002Fmaster.zip) to download the latest stable version.\n\nInstall it by navigating to the proper directory and running:\n\n```sh\npip install -e .\n```\n\nThe profiling report is written in HTML and CSS, which means a modern browser is required. \n\nYou need [Python 3](https:\u002F\u002Fpython3statement.github.io\u002F) to run the package. Other dependencies can be found in the requirements files:\n\n| Filename | Requirements|\n|----------|-------------|\n| [requirements.txt](https:\u002F\u002Fgithub.com\u002Fydataai\u002Fpandas-profiling\u002Fblob\u002Fmaster\u002Frequirements.txt) | Package requirements|\n| [requirements-dev.txt](https:\u002F\u002Fgithub.com\u002Fydataai\u002Fpandas-profiling\u002Fblob\u002Fmaster\u002Frequirements-dev.txt)  |  Requirements for development|\n| [requirements-test.txt](https:\u002F\u002Fgithub.com\u002Fydataai\u002Fpandas-profiling\u002Fblob\u002Fmaster\u002Frequirements-test.txt) | Requirements for testing|\n| [setup.py](https:\u002F\u002Fgithub.com\u002Fydataai\u002Fpandas-profiling\u002Fblob\u002Fmaster\u002Fsetup.py) | Requirements for widgets etc. |\n\n## 🔗 Integrations\n\nTo maximize its usefulness in real world contexts, `fg-data-profiling` has a set of implicit and explicit integrations with a variety of other actors in the Data Science ecosystem: \n\n| Integration type | Description |\n|---|---|\n| [Other DataFrame libraries](https:\u002F\u002Fdocs.profiling.ydata.ai\u002Flatest\u002Fintegrations\u002Fother_dataframe_libraries) | How to compute the profiling of data stored in libraries other than pandas |\n| [Great Expectations](https:\u002F\u002Fydata-profiling.ydata.ai\u002Fdocs\u002Fmaster\u002Fpages\u002Fintegrations\u002Fgreat_expectations.html) | Generating [Great Expectations](https:\u002F\u002Fgreatexpectations.io) expectations suites directly from a profiling report |\n| [Interactive applications](https:\u002F\u002Fdocs.profiling.ydata.ai\u002Flatest\u002Fintegrations\u002Finteractive_applications) | Embedding profiling reports in [Streamlit](http:\u002F\u002Fstreamlit.io), [Dash](http:\u002F\u002Fdash.plotly.com) or [Panel](https:\u002F\u002Fpanel.holoviz.org) applications |\n| [Pipelines](https:\u002F\u002Fydata-profiling.ydata.ai\u002Fdocs\u002Fmaster\u002Fpages\u002Fintegrations\u002Fpipelines.html) | Integration with DAG workflow execution tools like [Airflow](https:\u002F\u002Fairflow.apache.org) or [Kedro](https:\u002F\u002Fkedro.org) |\n| [Cloud services](https:\u002F\u002Fydata-profiling.ydata.ai\u002Fdocs\u002Fmaster\u002Fpages\u002Fintegrations\u002Fcloud_services.html) | Using `fg-data-profiling` in hosted computation services like [Lambda](https:\u002F\u002Flambdalabs.com), [Google Cloud](https:\u002F\u002Fgithub.com\u002FGoogleCloudPlatform\u002Fanalytics-componentized-patterns\u002Fblob\u002Fmaster\u002Fretail\u002Fpropensity-model\u002Fbqml\u002Fbqml_kfp_retail_propensity_to_purchase.ipynb) or [Kaggle](https:\u002F\u002Fwww.kaggle.com\u002Fcode) |\n| [IDEs](https:\u002F\u002Fydata-profiling.ydata.ai\u002Fdocs\u002Fmaster\u002Fpages\u002Fintegrations\u002Fides.html) | Using `fg-data-profiling` directly from integrated development environments such as [PyCharm](https:\u002F\u002Fwww.jetbrains.com\u002Fpycharm\u002F) |\n\n## 🙋 Support\nNeed help? Want to share a perspective? Report a bug? Ideas for collaborations? Reach out via the following channels:\n\n- [Stack Overflow](https:\u002F\u002Fstackoverflow.com\u002Fquestions\u002Ftagged\u002Fpandas-profiling+or+data-profiling): ideal for asking questions on how to use the package\n- [GitHub Issues](https:\u002F\u002Fgithub.com\u002FData-Centric-AI-Community\u002Ffg-data-profiling\u002Fissues): bugs, proposals for changes, feature requests\n- [Discord](https:\u002F\u002Ftiny.ydata.ai\u002Fdcai-data-profiling): ideal for projects discussions, ask questions, collaborations, general chat\n\n> **Need Help?**\u003Cbr>\n> Get your questions answered with a product owner by [booking a Pawsome chat](https:\u002F\u002Fmeetings.hubspot.com\u002Ffabiana-clemente)! 🐼\n\n> ❗ Before reporting an issue on GitHub, check out [Common Issues](https:\u002F\u002Fdocs.profiling.ydata.ai\u002Flatest\u002Fsupport-contribution\u002Fcommon_issues).\n\n## 🤝🏽 Contributing\nLearn how to get involved in the [Contribution Guide](https:\u002F\u002Fydata-profiling.ydata.ai\u002Fdocs\u002Fmaster\u002Fpages\u002Fsupport_contrib\u002Fcontribution_guidelines.html).\n\nA low-threshold place to ask questions or start contributing is the [Data Centric AI Community's Discord](https:\u002F\u002Ftiny.ydata.ai\u002Fdcai-data-profiling).\n\n\nA big thank you to all our amazing contributors! \n\n\u003Ca href=\"https:\u002F\u002Fgithub.com\u002FData-Centric-AI-Community\u002Ffg-data-profiling\u002Fgraphs\u002Fcontributors\">\n  \u003Cimg src=\"https:\u002F\u002Fcontrib.rocks\u002Fimage?repo=Data-Centric-AI-Community\u002Ffg-data-profiling\" \u002F>\n\u003C\u002Fa>\n\nContributors wall made with [contrib.rocks](https:\u002F\u002Fcontrib.rocks).\n","`fg-data-profiling` 是一个用于Pandas和Spark DataFrame的数据质量分析和探索性数据分析的工具，仅需一行代码即可完成。其核心功能包括生成数据集的详细统计报告，并支持以HTML和JSON格式导出分析结果，涵盖时间序列和文本数据的处理。该工具非常适合需要快速了解数据集特征、进行初步数据探索或评估数据质量的场景，如数据科学项目初期、机器学习模型训练前的数据准备等。采用Python编写，遵循MIT许可证，确保了广泛的适用性和灵活性。",2,"2026-06-11 03:23:53","top_topic"]