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GitHub - sfu-db/dataprep: Open-source low code data preparation library in python. Collect, clean and visualization your data in python with a few lines of code.
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12.25 14h-8.5A1.75 1.75 0 0 1 2 12.25v-8.5C2 2.784 2.784 2 3.75 2Zm6.854-1h4.146a.25.25 0 0 1 .25.25v4.146a.25.25 0 0 1-.427.177L13.03 4.03 9.28 7.78a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042l3.75-3.75-1.543-1.543A.25.25 0 0 1 10.604 1Z"></path> </svg> </a></li> </ul> </div> </div> </div> </li> <li class="HeaderMenu-item position-relative flex-wrap flex-justify-between flex-items-center d-block d-lg-flex flex-lg-nowrap flex-lg-items-center js-details-container js-header-menu-item"> <button type="button" class="HeaderMenu-link border-0 width-full width-lg-auto px-0 px-lg-2 py-lg-2 no-wrap d-flex flex-items-center flex-justify-between js-details-target" aria-expanded="false"> Solutions <svg opacity="0.5" aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-chevron-down HeaderMenu-icon ml-1"> <path d="M12.78 5.22a.749.749 0 0 1 0 1.06l-4.25 4.25a.749.749 0 0 1-1.06 0L3.22 6.28a.749.749 0 1 1 1.06-1.06L8 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Link--secondary" data-analytics-event="{"location":"navbar","action":"view_all_industries","context":"solutions","tag":"link","label":"view_all_industries_link_solutions_navbar"}" href="/solutions/industry"> View all industries </a></li> </ul> </div> </div> <div class="HeaderMenu-trailing-link rounded-bottom-2 flex-shrink-0 mt-lg-4 px-lg-4 py-4 py-lg-3 f5 text-semibold"> <a href="/solutions"> View all solutions <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-chevron-right HeaderMenu-trailing-link-icon"> <path d="M6.22 3.22a.75.75 0 0 1 1.06 0l4.25 4.25a.75.75 0 0 1 0 1.06l-4.25 4.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042L9.94 8 6.22 4.28a.75.75 0 0 1 0-1.06Z"></path> </svg> </a> </div> </div> </li> <li class="HeaderMenu-item position-relative flex-wrap flex-justify-between flex-items-center d-block d-lg-flex flex-lg-nowrap flex-lg-items-center js-details-container js-header-menu-item"> <button type="button" class="HeaderMenu-link border-0 width-full width-lg-auto px-0 px-lg-2 py-lg-2 no-wrap d-flex flex-items-center flex-justify-between js-details-target" aria-expanded="false"> Resources <svg opacity="0.5" aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-chevron-down HeaderMenu-icon ml-1"> <path d="M12.78 5.22a.749.749 0 0 1 0 1.06l-4.25 4.25a.749.749 0 0 1-1.06 0L3.22 6.28a.749.749 0 1 1 1.06-1.06L8 8.939l3.72-3.719a.749.749 0 0 1 1.06 0Z"></path> </svg> </button> <div class="HeaderMenu-dropdown dropdown-menu rounded m-0 p-0 pt-2 pt-lg-4 position-relative position-lg-absolute left-0 left-lg-n3 pb-2 pb-lg-4 d-lg-flex flex-wrap dropdown-menu-wide"> <div class="HeaderMenu-column px-lg-4 border-lg-right mb-4 mb-lg-0 pr-lg-7"> <div class="border-bottom pb-3 pb-lg-0 border-lg-bottom-0"> <span class="d-block h4 color-fg-default my-1" id="resources-topics-heading">Topics</span> <ul 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data-view-component="true" class="octicon octicon-link-external HeaderMenu-external-icon color-fg-subtle"> <path d="M3.75 2h3.5a.75.75 0 0 1 0 1.5h-3.5a.25.25 0 0 0-.25.25v8.5c0 .138.112.25.25.25h8.5a.25.25 0 0 0 .25-.25v-3.5a.75.75 0 0 1 1.5 0v3.5A1.75 1.75 0 0 1 12.25 14h-8.5A1.75 1.75 0 0 1 2 12.25v-8.5C2 2.784 2.784 2 3.75 2Zm6.854-1h4.146a.25.25 0 0 1 .25.25v4.146a.25.25 0 0 1-.427.177L13.03 4.03 9.28 7.78a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042l3.75-3.75-1.543-1.543A.25.25 0 0 1 10.604 1Z"></path> </svg> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{"location":"navbar","action":"executive_insights","context":"resources","tag":"link","label":"executive_insights_link_resources_navbar"}" href="https://github.com/solutions/executive-insights"> Executive Insights </a></li> </ul> </div> </div> </div> </li> <li class="HeaderMenu-item position-relative flex-wrap flex-justify-between flex-items-center d-block d-lg-flex flex-lg-nowrap flex-lg-items-center js-details-container js-header-menu-item"> <button type="button" class="HeaderMenu-link border-0 width-full width-lg-auto px-0 px-lg-2 py-lg-2 no-wrap d-flex flex-items-center flex-justify-between js-details-target" aria-expanded="false"> Open Source <svg opacity="0.5" aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-chevron-down HeaderMenu-icon ml-1"> <path d="M12.78 5.22a.749.749 0 0 1 0 1.06l-4.25 4.25a.749.749 0 0 1-1.06 0L3.22 6.28a.749.749 0 1 1 1.06-1.06L8 8.939l3.72-3.719a.749.749 0 0 1 1.06 0Z"></path> </svg> </button> <div class="HeaderMenu-dropdown dropdown-menu rounded m-0 p-0 pt-2 pt-lg-4 position-relative position-lg-absolute left-0 left-lg-n3 pb-2 pb-lg-4 px-lg-4"> <div class="HeaderMenu-column"> <div class="border-bottom pb-3 pb-lg-0 pb-lg-3 mb-3 mb-lg-0 mb-lg-3"> <ul class="list-style-none f5" > <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description" data-analytics-event="{"location":"navbar","action":"github_sponsors","context":"open_source","tag":"link","label":"github_sponsors_link_open_source_navbar"}" href="/sponsors"> <div> <div class="color-fg-default h4">GitHub Sponsors</div> Fund open source developers </div> </a></li> </ul> </div> <div class="border-bottom pb-3 pb-lg-0 pb-lg-3 mb-3 mb-lg-0 mb-lg-3"> <ul class="list-style-none f5" > <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description" data-analytics-event="{"location":"navbar","action":"the_readme_project","context":"open_source","tag":"link","label":"the_readme_project_link_open_source_navbar"}" href="https://github.com/readme"> <div> <div class="color-fg-default h4">The ReadME Project</div> GitHub community articles </div> </a></li> </ul> </div> <div class="border-bottom pb-3 pb-lg-0 border-bottom-0"> <span class="d-block h4 color-fg-default my-1" id="open-source-repositories-heading">Repositories</span> <ul class="list-style-none f5" aria-labelledby="open-source-repositories-heading"> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{"location":"navbar","action":"topics","context":"open_source","tag":"link","label":"topics_link_open_source_navbar"}" href="https://github.com/topics"> Topics </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{"location":"navbar","action":"trending","context":"open_source","tag":"link","label":"trending_link_open_source_navbar"}" href="https://github.com/trending"> Trending </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" 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6.94l3.22-3.22a.749.749 0 0 1 1.275.326.749.749 0 0 1-.215.734L9.06 8l3.22 3.22a.749.749 0 0 1-.326 1.275.749.749 0 0 1-.734-.215L8 9.06l-3.22 3.22a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042L6.94 8 3.72 4.78a.75.75 0 0 1 0-1.06Z"></path> </svg></button> </div> </div> </div> <scrollable-region data-labelled-by="custom-scopes-dialog-title"> <div data-view-component="true" class="Overlay-body"> <div data-target="custom-scopes.customScopesModalDialogFlash"></div> <div hidden class="create-custom-scope-form" data-target="custom-scopes.createCustomScopeForm"> <!-- '"` --><!-- </textarea></xmp> --></option></form><form id="custom-scopes-dialog-form" data-turbo="false" action="/search/custom_scopes" accept-charset="UTF-8" method="post"><input type="hidden" data-csrf="true" name="authenticity_token" value="PoDNBbB6o2uCxeHbawAOOoDYNZMfT6A5oagGvcLTcFneRZXFF3KArHw4+erASDp3GKX14n/dLz59z2t9EbVhcg==" /> <div data-target="custom-scopes.customScopesModalDialogFlash"></div> <input type="hidden" 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src=\"https://camo.githubusercontent.com/6a87a85d41f8c5c9cbad44944e6ee272a844af6fc1d686bc188996e530e017f4/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f64796e616d69632f6a736f6e3f636f6c6f723d626c7565266c6162656c3d646f6373267072656669783d762671756572793d2532342e696e666f2e76657273696f6e2675726c3d6874747073253341253246253246707970692e6f72672532467079706925324664617461707265702532466a736f6e267374796c653d666c61742d737175617265\" data-canonical-src=\"https://img.shields.io/badge/dynamic/json?color=blue\u0026amp;label=docs\u0026amp;prefix=v\u0026amp;query=%24.info.version\u0026amp;url=https%3A%2F%2Fpypi.org%2Fpypi%2Fdataprep%2Fjson\u0026amp;style=flat-square\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n \u003ca href=\"https://pypi.org/project/dataprep/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e536ca1203b38911813b05adc2c825138c227b4d5cfeb1f5ecb20774568ff504/68747470733a2f2f696d672e736869656c64732e696f2f707970692f707976657273696f6e732f64617461707265703f7374796c653d666c61742d737175617265\" data-canonical-src=\"https://img.shields.io/pypi/pyversions/dataprep?style=flat-square\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n \n\n \n\u003c/p\u003e\n\u003cp align=\"center\" dir=\"auto\"\u003e\n \u003ca href=\"https://sfu-db.github.io/dataprep/\" rel=\"nofollow\"\u003eDocumentation\u003c/a\u003e\n | \n \u003ca href=\"https://github.com/sfu-db/dataprep/discussions\"\u003eForum\u003c/a\u003e\n\u003c/p\u003e\n\u003cp align=\"center\" dir=\"auto\"\u003e\u003cb\u003eLow code data preparation\u003c/b\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eCurrently, you can use DataPrep to:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eCollect data from common data sources (through \u003ca href=\"#connector\"\u003e\u003ccode\u003edataprep.connector\u003c/code\u003e\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003eDo your exploratory data analysis (through \u003ca href=\"#eda\"\u003e\u003ccode\u003edataprep.eda\u003c/code\u003e\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003eClean and standardize data (through \u003ca href=\"#clean\"\u003e\u003ccode\u003edataprep.clean\u003c/code\u003e\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003e...more modules are coming\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eReleases\u003c/h2\u003e\u003ca id=\"user-content-releases\" class=\"anchor\" aria-label=\"Permalink: Releases\" href=\"#releases\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv align=\"center\" dir=\"auto\"\u003e\n \u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n \u003ctbody\u003e\u003ctr\u003e\n \u003cth\u003eRepo\u003c/th\u003e\n \u003cth\u003eVersion\u003c/th\u003e\n \u003cth\u003eDownloads\u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003ePyPI\u003c/td\u003e\n \u003ctd\u003e\u003ca href=\"https://pypi.org/project/dataprep/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/da466d5e731b31d9f799366ff965fc789c369f31cdde85c6df3619cbc22c5e13/68747470733a2f2f696d672e736869656c64732e696f2f707970692f762f64617461707265703f7374796c653d666c61742d737175617265\" data-canonical-src=\"https://img.shields.io/pypi/v/dataprep?style=flat-square\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n \u003ctd\u003e\u003ca href=\"https://pepy.tech/project/dataprep\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/a57329ad3f9b13f255c24b28cbc34bc98f3fa5d44f66ac7dc11b3b2c068558fb/68747470733a2f2f706570792e746563682f62616467652f6461746170726570\" data-canonical-src=\"https://pepy.tech/badge/dataprep\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e \n \u003ctd\u003econda-forge\u003c/td\u003e\n \u003ctd\u003e\u003ca href=\"https://anaconda.org/conda-forge/dataprep\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/cae571666e5846411946e0a1a6b8a47bb71d6d0aeaa53e5759a2f90c006943d1/68747470733a2f2f696d672e736869656c64732e696f2f636f6e64612f766e2f636f6e64612d666f7267652f64617461707265702e737667\" data-canonical-src=\"https://img.shields.io/conda/vn/conda-forge/dataprep.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n \u003ctd\u003e\u003ca href=\"https://anaconda.org/conda-forge/dataprep\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/03056fdc83b6d54063969ed314b97800d2feda6615f8fe2280984c9d7c794c00/68747470733a2f2f696d672e736869656c64732e696f2f636f6e64612f646e2f636f6e64612d666f7267652f64617461707265702e737667\" data-canonical-src=\"https://img.shields.io/conda/dn/conda-forge/dataprep.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\u003c/table\u003e\u003c/markdown-accessiblity-table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"pip install -U dataprep\"\u003e\u003cpre\u003epip install -U dataprep\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eEDA\u003c/h2\u003e\u003ca id=\"user-content-eda\" class=\"anchor\" aria-label=\"Permalink: EDA\" href=\"#eda\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eDataPrep.EDA is the fastest and the easiest EDA (Exploratory Data Analysis) tool in Python. It allows you to understand a Pandas/Dask DataFrame with a few lines of code in seconds.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eCreate Profile Reports, Fast\u003c/h4\u003e\u003ca id=\"user-content-create-profile-reports-fast\" class=\"anchor\" aria-label=\"Permalink: Create Profile Reports, Fast\" href=\"#create-profile-reports-fast\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eYou can create a beautiful profile report from a Pandas/Dask DataFrame with the \u003ccode\u003ecreate_report\u003c/code\u003e function. DataPrep.EDA has the following advantages compared to other tools:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003cstrong\u003e\u003ca href=\"https://arxiv.org/abs/2104.00841\" rel=\"nofollow\"\u003e10X Faster\u003c/a\u003e\u003c/strong\u003e: DataPrep.EDA can be 10X faster than Pandas-based profiling tools due to its highly optimized Dask-based computing module.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eInteractive Visualization\u003c/strong\u003e: DataPrep.EDA generates interactive visualizations in a report, which makes the report look more appealing to end users.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eBig Data Support\u003c/strong\u003e: DataPrep.EDA naturally supports big data stored in a Dask cluster by accepting a Dask dataframe as input.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003eThe following code demonstrates how to use DataPrep.EDA to create a profile report for the titanic dataset.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"from dataprep.datasets import load_dataset\nfrom dataprep.eda import create_report\ndf = load_dataset(\u0026quot;titanic\u0026quot;)\ncreate_report(df).show_browser()\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003edataprep\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003edatasets\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eload_dataset\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003edataprep\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eeda\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ecreate_report\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003edf\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eload_dataset\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\"titanic\"\u003c/span\u003e)\n\u003cspan class=\"pl-en\"\u003ecreate_report\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003edf\u003c/span\u003e).\u003cspan class=\"pl-c1\"\u003eshow_browser\u003c/span\u003e()\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eClick \u003ca href=\"https://docs.dataprep.ai/_downloads/1a61c6aebb3ecbe9dc9742bd6ca78ddb/titanic_dp.html\" rel=\"nofollow\"\u003ehere\u003c/a\u003e to see the generated report of the above code.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eClick \u003ca href=\"https://docs.dataprep.ai/dev/bench/index.html\" rel=\"nofollow\"\u003ehere\u003c/a\u003e to see the benchmark result.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eTry DataPrep.EDA Online: \u003ca href=\"https://colab.research.google.com/drive/1U_-pAMcne3hK1HbMB3kuEt-093Np_7Uk?usp=sharing\" rel=\"nofollow\"\u003eDataPrep.EDA Demo in Colab\u003c/a\u003e\u003c/h4\u003e\u003ca id=\"user-content-try-dataprepeda-online-dataprepeda-demo-in-colab\" class=\"anchor\" aria-label=\"Permalink: Try DataPrep.EDA Online: DataPrep.EDA Demo in Colab\" href=\"#try-dataprepeda-online-dataprepeda-demo-in-colab\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eInnovative System Design\u003c/h4\u003e\u003ca id=\"user-content-innovative-system-design\" class=\"anchor\" aria-label=\"Permalink: Innovative System Design\" href=\"#innovative-system-design\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eDataPrep.EDA is the \u003cstrong\u003e\u003cem\u003eonly\u003c/em\u003e\u003c/strong\u003e task-centric EDA system in Python. It is carefully designed to improve usability.\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003cstrong\u003eTask-Centric API Design\u003c/strong\u003e: You can declaratively specify a wide range of EDA tasks in different granularity with a single function call. All needed visualizations will be automatically and intelligently generated for you.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eAuto-Insights\u003c/strong\u003e: DataPrep.EDA automatically detects and highlights the insights (e.g., a column has many outliers) to facilitate pattern discovery about the data.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eHow-to Guide\u003c/strong\u003e: A how-to guide is provided to show the configuration of each plot function. With this feature, you can easily customize the generated visualizations.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eLearn DataPrep.EDA in 2 minutes:\u003c/h4\u003e\u003ca id=\"user-content-learn-dataprepeda-in-2-minutes\" class=\"anchor\" aria-label=\"Permalink: Learn DataPrep.EDA in 2 minutes:\" href=\"#learn-dataprepeda-in-2-minutes\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"https://youtu.be/nSkQy3ew3EI\" rel=\"nofollow\"\u003e\u003cimg src=\"/sfu-db/dataprep/raw/develop/assets/eda_video_cover.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eClick \u003ca href=\"https://sfu-db.github.io/dataprep/user_guide/eda/introduction.html\" rel=\"nofollow\"\u003ehere\u003c/a\u003e to check all the supported tasks.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eCheck \u003ca href=\"https://sfu-db.github.io/dataprep/user_guide/eda/plot.html\" rel=\"nofollow\"\u003eplot\u003c/a\u003e, \u003ca href=\"https://sfu-db.github.io/dataprep/user_guide/eda/plot_correlation.html\" rel=\"nofollow\"\u003eplot_correlation\u003c/a\u003e, \u003ca href=\"https://sfu-db.github.io/dataprep/user_guide/eda/plot_missing.html\" rel=\"nofollow\"\u003eplot_missing\u003c/a\u003e and \u003ca href=\"https://sfu-db.github.io/dataprep/user_guide/eda/create_report.html\" rel=\"nofollow\"\u003ecreate_report\u003c/a\u003e to see how each function works.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eClean\u003c/h2\u003e\u003ca id=\"user-content-clean\" class=\"anchor\" aria-label=\"Permalink: Clean\" href=\"#clean\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eDataPrep.Clean contains about \u003cstrong\u003e140+\u003c/strong\u003e functions designed for cleaning and validating data in a DataFrame. It provides\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003cstrong\u003eA Convenient GUI\u003c/strong\u003e: incorporated into Jupyter Notebook, users can clean their own DataFrame without any coding (see the video below).\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eA Unified API\u003c/strong\u003e: each function follows the syntax \u003ccode\u003eclean_{type}(df, 'column name')\u003c/code\u003e (see an example below).\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eSpeed\u003c/strong\u003e: the computations are parallelized using Dask. It can clean \u003cstrong\u003e50K rows per second\u003c/strong\u003e on a dual-core laptop (that means cleaning 1 million rows in only 20 seconds).\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eTransparency\u003c/strong\u003e: a report is generated that summarizes the alterations to the data that occured during cleaning.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003eThe following video shows how to use GUI of Dataprep.Clean\n\u003ca href=\"https://youtu.be/WtJaVBIVoxQ\" rel=\"nofollow\"\u003e\u003cimg src=\"/sfu-db/dataprep/raw/develop/assets/clean_video_cover.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eThe following example shows how to clean and standardize a column of country names.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"from dataprep.clean import clean_country\nimport pandas as pd\ndf = pd.DataFrame({'country': ['USA', 'country: Canada', '233', ' tr ', 'NA']})\ndf2 = clean_country(df, 'country')\ndf2\n country country_clean\n0 USA United States\n1 country: Canada Canada\n2 233 Estonia\n3 tr Turkey\n4 NA NaN\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003edataprep\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eclean\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eclean_country\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003epandas\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eas\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003epd\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003edf\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003epd\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eDataFrame\u003c/span\u003e({\u003cspan class=\"pl-s\"\u003e'country'\u003c/span\u003e: [\u003cspan class=\"pl-s\"\u003e'USA'\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e'country: Canada'\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e'233'\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e' tr '\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e'NA'\u003c/span\u003e]})\n\u003cspan class=\"pl-s1\"\u003edf2\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eclean_country\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003edf\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e'country'\u003c/span\u003e)\n\u003cspan class=\"pl-s1\"\u003edf2\u003c/span\u003e\n \u003cspan class=\"pl-s1\"\u003ecountry\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ecountry_clean\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e0\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003eUSA\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eUnited\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eStates\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ecountry\u003c/span\u003e: \u003cspan class=\"pl-v\"\u003eCanada\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eCanada\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e2\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e233\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eEstonia\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e3\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003etr\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eTurkey\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e4\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003eNA\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eNaN\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eType validation is also supported:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"from dataprep.clean import validate_country\nseries = validate_country(df['country'])\nseries\n0 True\n1 False\n2 True\n3 True\n4 False\nName: country, dtype: bool\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003edataprep\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eclean\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003evalidate_country\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003eseries\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003evalidate_country\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003edf\u003c/span\u003e[\u003cspan class=\"pl-s\"\u003e'country'\u003c/span\u003e])\n\u003cspan class=\"pl-s1\"\u003eseries\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e0\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003eFalse\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e2\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e3\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e4\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003eFalse\u003c/span\u003e\n\u003cspan class=\"pl-v\"\u003eName\u003c/span\u003e: \u003cspan class=\"pl-smi\"\u003ecountry\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003edtype\u003c/span\u003e: \u003cspan class=\"pl-smi\"\u003ebool\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eCheck \u003ca href=\"https://docs.dataprep.ai/user_guide/clean/introduction.html\" rel=\"nofollow\"\u003eDocumentation of Dataprep.Clean\u003c/a\u003e to see how each function works.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eConnector\u003c/h2\u003e\u003ca id=\"user-content-connector\" class=\"anchor\" aria-label=\"Permalink: Connector\" href=\"#connector\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eConnector now supports loading data from both web API and databases.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eWeb API\u003c/h3\u003e\u003ca id=\"user-content-web-api\" class=\"anchor\" aria-label=\"Permalink: Web API\" href=\"#web-api\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eConnector is an intuitive, open-source API wrapper that speeds up development by standardizing calls to multiple APIs as a simple workflow.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eConnector provides a simple wrapper to collect structured data from different Web APIs (e.g., Twitter, Spotify), making web data collection easy and efficient, without requiring advanced programming skills.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eDo you want to leverage the growing number of websites that are opening their data through public APIs? Connector is for you!\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eLet's check out the several benefits that Connector offers:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003cstrong\u003eA unified API:\u003c/strong\u003e You can fetch data using one or two lines of code to get data from \u003ca href=\"https://github.com/sfu-db/DataConnectorConfigs\"\u003etens of popular websites\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eAuto Pagination:\u003c/strong\u003e Do you want to invoke a Web API that could return a large result set and need to handle it through pagination? Connector automatically does the pagination for you! Just specify the desired number of returned results (argument \u003ccode\u003e_count\u003c/code\u003e) without getting into unnecessary detail about a specific pagination scheme.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eSpeed:\u003c/strong\u003e Do you want to fetch results more quickly by making concurrent requests to Web APIs? Through the \u003ccode\u003e_concurrency\u003c/code\u003e argument, Connector simplifies concurrency, issuing API requests in parallel while respecting the API's rate limit policy.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eHow to fetch all publications of Andrew Y. Ng?\u003c/h4\u003e\u003ca id=\"user-content-how-to-fetch-all-publications-of-andrew-y-ng\" class=\"anchor\" aria-label=\"Permalink: How to fetch all publications of Andrew Y. Ng?\" href=\"#how-to-fetch-all-publications-of-andrew-y-ng\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"from dataprep.connector import connect\nconn_dblp = connect(\u0026quot;dblp\u0026quot;, _concurrency = 5)\ndf = await conn_dblp.query(\u0026quot;publication\u0026quot;, author = \u0026quot;Andrew Y. Ng\u0026quot;, _count = 2000)\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003edataprep\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003econnector\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003econnect\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003econn_dblp\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003econnect\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\"dblp\"\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003e_concurrency\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e5\u003c/span\u003e)\n\u003cspan class=\"pl-s1\"\u003edf\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eawait\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003econn_dblp\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003equery\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\"publication\"\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003eauthor\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\"Andrew Y. Ng\"\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003e_count\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e2000\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eHere, you can find detailed \u003ca href=\"https://github.com/sfu-db/dataprep/tree/develop/examples\"\u003eExamples.\u003c/a\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eConnector is designed to be easy to extend. If you want to connect with your own web API, you just have to write a simple \u003ca href=\"https://github.com/sfu-db/DataConnectorConfigs/blob/develop/CONTRIBUTING.md#add-configuration-files\"\u003econfiguration file\u003c/a\u003e to support it. This configuration file describes the API's main attributes like the URL, query parameters, authorization method, pagination properties, etc.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eDatabase\u003c/h3\u003e\u003ca id=\"user-content-database\" class=\"anchor\" aria-label=\"Permalink: Database\" href=\"#database\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eConnector now has adopted \u003ca href=\"https://github.com/sfu-db/connector-x\"\u003econnectorx\u003c/a\u003e in order to enable loading data from databases (Postgres, Mysql, SQLServer, etc.) into Python dataframes (pandas, dask, modin, arrow, polars) in the fastest and most memory efficient way. \u003ca href=\"https://github.com/sfu-db/connector-x/blob/main/Benchmark.md#benchmark-result-on-aws-r54xlarge\"\u003e[Benchmark]\u003c/a\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eWhat you need to do is just install \u003ccode\u003econnectorx\u003c/code\u003e (\u003ccode\u003epip install -U connectorx\u003c/code\u003e) and run one line of code:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"from dataprep.connector import read_sql\nread_sql(\u0026quot;postgresql://username:password@server:port/database\u0026quot;, \u0026quot;SELECT * FROM lineitem\u0026quot;)\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003edataprep\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003econnector\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eread_sql\u003c/span\u003e\n\u003cspan class=\"pl-en\"\u003eread_sql\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\"postgresql://username:password@server:port/database\"\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e\"SELECT * FROM lineitem\"\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eCheck out \u003ca href=\"https://github.com/sfu-db/connector-x#supported-sources--destinations\"\u003ehere\u003c/a\u003e for supported databases and dataframes and more examples usages.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eLineage\u003c/h2\u003e\u003ca id=\"user-content-lineage\" class=\"anchor\" aria-label=\"Permalink: Lineage\" href=\"#lineage\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eA Column Level Lineage Graph for SQL. This tool is intended to help you by creating an interactive graph on a webpage to explore the column level lineage among them.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eThe lineage module offers:\u003c/h3\u003e\u003ca id=\"user-content-the-lineage-module-offers\" class=\"anchor\" aria-label=\"Permalink: The lineage module offers:\" href=\"#the-lineage-module-offers\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eA general introduction of the project can be found in this \u003ca href=\"https://medium.com/@shz1/lineagex-the-python-library-for-your-lineage-needs-5e51b77a0032\" rel=\"nofollow\"\u003eblog post\u003c/a\u003e.\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003cstrong\u003eAutomatic dependency creation\u003c/strong\u003e: When there are dependency among the SQL files, and those tables are not yet in the database, the lineage module will automatically tries to find the dependency table and creates it.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eClean and simple but very interactive user interface\u003c/strong\u003e: The user interface is very simple to use with minimal clutters on the page while showing all of the necessary information.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eVariety of SQL statements\u003c/strong\u003e: The lineage module supports a variety of SQL statements, aside from the typical \u003ccode\u003eSELECT\u003c/code\u003e statement, it also supports \u003ccode\u003eCREATE TABLE/VIEW [IF NOT EXISTS]\u003c/code\u003e statement as well as the \u003ccode\u003eINSERT\u003c/code\u003e and \u003ccode\u003eDELETE\u003c/code\u003e statement.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003e\u003ca href=\"https://docs.getdbt.com/\" rel=\"nofollow\"\u003edbt\u003c/a\u003e support\u003c/strong\u003e: The lineage module is also implemented in the \u003ca href=\"https://github.com/sfu-db/dbt-lineagex\"\u003edbt-LineageX\u003c/a\u003e, it is added into a dbt project and by using the dbt library \u003ca href=\"https://github.com/fal-ai/fal\"\u003efal\u003c/a\u003e, it is able to reuse the Python core and create the similar output from the dbt project.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eUses and Demo\u003c/h3\u003e\u003ca id=\"user-content-uses-and-demo\" class=\"anchor\" aria-label=\"Permalink: Uses and Demo\" href=\"#uses-and-demo\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThe interactive graph looks like this:\n\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://raw.githubusercontent.com/sfu-db/lineagex/main/docs/example.gif\"\u003e\u003cimg src=\"https://raw.githubusercontent.com/sfu-db/lineagex/main/docs/example.gif\" data-animated-image=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\nHere is a \u003ca href=\"https://zshandy.github.io/lineagex-demo/\" rel=\"nofollow\"\u003elive demo\u003c/a\u003e with the \u003ca href=\"https://github.com/MIT-LCP/mimic-code/tree/main/mimic-iv/concepts_postgres\"\u003emimic-iv concepts_postgres\u003c/a\u003e files(\u003ca href=\"https://sfu-db.github.io/lineagex/output.html#how-to-navigate-the-webpage\" rel=\"nofollow\"\u003enavigation instructions\u003c/a\u003e) and that is created with one line of code:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"from dataprep.lineage import lineagex\nlineagex(sql=path/to/sql, target_schema=\u0026quot;schema1\u0026quot;, conn_string=\u0026quot;postgresql://username:password@server:port/database\u0026quot;, search_path_schema=\u0026quot;schema1, public\u0026quot;)\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003edataprep\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003elineage\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003elineagex\u003c/span\u003e\n\u003cspan class=\"pl-en\"\u003elineagex\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003esql\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003epath\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e/\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003eto\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e/\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003esql\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003etarget_schema\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"schema1\"\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003econn_string\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"postgresql://username:password@server:port/database\"\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003esearch_path_schema\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"schema1, public\"\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eCheck out more detailed usage and examples \u003ca href=\"https://sfu-db.github.io/lineagex/intro.html\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eDocumentation\u003c/h2\u003e\u003ca id=\"user-content-documentation\" class=\"anchor\" aria-label=\"Permalink: Documentation\" href=\"#documentation\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThe following documentation can give you an impression of what DataPrep can do:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://docs.dataprep.ai/user_guide/connector/introduction.html\" rel=\"nofollow\"\u003eConnector\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://docs.dataprep.ai/user_guide/eda/introduction.html\" rel=\"nofollow\"\u003eEDA\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://docs.dataprep.ai/user_guide/clean/introduction.html\" rel=\"nofollow\"\u003eClean\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://sfu-db.github.io/lineagex/intro.html\" rel=\"nofollow\"\u003eLineage\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eContribute\u003c/h2\u003e\u003ca id=\"user-content-contribute\" class=\"anchor\" aria-label=\"Permalink: Contribute\" href=\"#contribute\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThere are many ways to contribute to DataPrep.\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eSubmit bugs and help us verify fixes as they are checked in.\u003c/li\u003e\n\u003cli\u003eReview the source code changes.\u003c/li\u003e\n\u003cli\u003eEngage with other DataPrep users and developers on StackOverflow.\u003c/li\u003e\n\u003cli\u003eAsk questions \u0026amp; propose new ideas in our \u003ca href=\"https://github.com/sfu-db/dataprep/discussions\"\u003eForum\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://twitter.com/dataprepai\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/6e368096f0f844722f409f617c1150316e6afed9a7f17fb72e984c213498ba08/68747470733a2f2f696d672e736869656c64732e696f2f747769747465722f666f6c6c6f772f646174617072657061693f7374796c653d736f6369616c\" alt=\"Twitter\" data-canonical-src=\"https://img.shields.io/twitter/follow/dataprepai?style=social\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eContribute bug fixes.\u003c/li\u003e\n\u003cli\u003eProviding use cases and writing down your user experience.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003ePlease take a look at our \u003ca href=\"https://github.com/sfu-db/dataprep/wiki\"\u003ewiki\u003c/a\u003e for development documentations!\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eAcknowledgement\u003c/h2\u003e\u003ca id=\"user-content-acknowledgement\" class=\"anchor\" aria-label=\"Permalink: Acknowledgement\" href=\"#acknowledgement\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eSome functionalities of DataPrep are inspired by the following packages.\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"https://github.com/pandas-profiling/pandas-profiling\"\u003ePandas Profiling\u003c/a\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eInspired the report functionality and insights provided in \u003ccode\u003edataprep.eda\u003c/code\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"https://github.com/ResidentMario/missingno\"\u003emissingno\u003c/a\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eInspired the missing value analysis in \u003ccode\u003edataprep.eda\u003c/code\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eCiting DataPrep\u003c/h2\u003e\u003ca id=\"user-content-citing-dataprep\" class=\"anchor\" aria-label=\"Permalink: Citing DataPrep\" href=\"#citing-dataprep\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eIf you use DataPrep, please consider citing the following paper:\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eJinglin Peng, Weiyuan Wu, Brandon Lockhart, Song Bian, Jing Nathan Yan, Linghao Xu, Zhixuan Chi, Jeffrey M. Rzeszotarski, and Jiannan Wang. \u003ca href=\"https://arxiv.org/abs/2104.00841\" rel=\"nofollow\"\u003eDataPrep.EDA: Task-Centric Exploratory Data Analysis for Statistical\nModeling in Python.\u003c/a\u003e \u003cem\u003eSIGMOD 2021\u003c/em\u003e.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eBibTeX entry:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-text-bibtex notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"@inproceedings{dataprepeda2021,\n author = {Jinglin Peng and Weiyuan Wu and Brandon Lockhart and Song Bian and Jing Nathan Yan and Linghao Xu and Zhixuan Chi and Jeffrey M. Rzeszotarski and Jiannan Wang},\n title = {DataPrep.EDA: Task-Centric Exploratory Data Analysis for Statistical Modeling in Python},\n booktitle = {Proceedings of the 2021 International Conference on Management of Data (SIGMOD '21), June 20--25, 2021, Virtual Event, China},\n year = {2021}\n}\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003e@inproceedings\u003c/span\u003e{\u003cspan class=\"pl-en\"\u003edataprepeda2021\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003eauthor\u003c/span\u003e = \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003eJinglin Peng and Weiyuan Wu and Brandon Lockhart and Song Bian and Jing Nathan Yan and Linghao Xu and Zhixuan Chi and Jeffrey M. Rzeszotarski and Jiannan Wang\u003cspan class=\"pl-pds\"\u003e}\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003etitle\u003c/span\u003e = \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003eDataPrep.EDA: Task-Centric Exploratory Data Analysis for Statistical Modeling in Python\u003cspan class=\"pl-pds\"\u003e}\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003ebooktitle\u003c/span\u003e = \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003eProceedings of the 2021 International Conference on Management of Data (SIGMOD '21), June 20--25, 2021, Virtual Event, China\u003cspan class=\"pl-pds\"\u003e}\u003c/span\u003e\u003c/span\u003e,\n \u003cspan class=\"pl-s\"\u003eyear\u003c/span\u003e = \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e{\u003c/span\u003e2021\u003cspan class=\"pl-pds\"\u003e}\u003c/span\u003e\u003c/span\u003e\n}\u003c/pre\u003e\u003c/div\u003e\n\u003c/article\u003e","loaded":true,"timedOut":false,"errorMessage":null,"headerInfo":{"toc":[{"level":2,"text":"Releases","anchor":"releases","htmlText":"Releases"},{"level":2,"text":"Installation","anchor":"installation","htmlText":"Installation"},{"level":2,"text":"EDA","anchor":"eda","htmlText":"EDA"},{"level":4,"text":"Create Profile Reports, Fast","anchor":"create-profile-reports-fast","htmlText":"Create Profile Reports, Fast"},{"level":4,"text":"Try DataPrep.EDA Online: DataPrep.EDA Demo in Colab","anchor":"try-dataprepeda-online-dataprepeda-demo-in-colab","htmlText":"Try DataPrep.EDA Online: DataPrep.EDA Demo in Colab"},{"level":4,"text":"Innovative System Design","anchor":"innovative-system-design","htmlText":"Innovative System Design"},{"level":4,"text":"Learn DataPrep.EDA in 2 minutes:","anchor":"learn-dataprepeda-in-2-minutes","htmlText":"Learn DataPrep.EDA in 2 minutes:"},{"level":2,"text":"Clean","anchor":"clean","htmlText":"Clean"},{"level":2,"text":"Connector","anchor":"connector","htmlText":"Connector"},{"level":3,"text":"Web API","anchor":"web-api","htmlText":"Web API"},{"level":4,"text":"How to fetch all publications of Andrew Y. Ng?","anchor":"how-to-fetch-all-publications-of-andrew-y-ng","htmlText":"How to fetch all publications of Andrew Y. Ng?"},{"level":3,"text":"Database","anchor":"database","htmlText":"Database"},{"level":2,"text":"Lineage","anchor":"lineage","htmlText":"Lineage"},{"level":3,"text":"The lineage module offers:","anchor":"the-lineage-module-offers","htmlText":"The lineage module offers:"},{"level":3,"text":"Uses and Demo","anchor":"uses-and-demo","htmlText":"Uses and Demo"},{"level":2,"text":"Documentation","anchor":"documentation","htmlText":"Documentation"},{"level":2,"text":"Contribute","anchor":"contribute","htmlText":"Contribute"},{"level":2,"text":"Acknowledgement","anchor":"acknowledgement","htmlText":"Acknowledgement"},{"level":2,"text":"Citing DataPrep","anchor":"citing-dataprep","htmlText":"Citing DataPrep"}],"siteNavLoginPath":"/login?return_to=https%3A%2F%2Fgithub.com%2Fsfu-db%2Fdataprep"}},{"displayName":"LICENSE","repoName":"dataprep","refName":"develop","path":"LICENSE","preferredFileType":"license","tabName":"MIT","richText":null,"loaded":false,"timedOut":false,"errorMessage":null,"headerInfo":{"toc":null,"siteNavLoginPath":"/login?return_to=https%3A%2F%2Fgithub.com%2Fsfu-db%2Fdataprep"}}],"overviewFilesProcessingTime":0}},"appPayload":{"helpUrl":"https://docs.github.com","findFileWorkerPath":"/assets-cdn/worker/find-file-worker-7d7eb7c71814.js","findInFileWorkerPath":"/assets-cdn/worker/find-in-file-worker-96e76d5fdb2c.js","githubDevUrl":null,"enabled_features":{"copilot_workspace":null,"code_nav_ui_events":false,"overview_shared_code_dropdown_button":false,"react_blob_overlay":false,"accessible_code_button":true,"github_models_repo_integration":false}}}}</script> <div data-target="react-partial.reactRoot"><style data-styled="true" 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style="max-width: 100%;"></a></div> <hr> <p align="center" dir="auto"> <a href="/sfu-db/dataprep/blob/develop/LICENSE"><img src="https://camo.githubusercontent.com/ede0b982511d7daddcff522ef9515b048e120a1e062c18ee3c5dc11894fc7d64/68747470733a2f2f696d672e736869656c64732e696f2f707970692f6c2f64617461707265703f7374796c653d666c61742d737175617265" data-canonical-src="https://img.shields.io/pypi/l/dataprep?style=flat-square" style="max-width: 100%;"></a> <a href="https://sfu-db.github.io/dataprep/" rel="nofollow"><img src="https://camo.githubusercontent.com/6a87a85d41f8c5c9cbad44944e6ee272a844af6fc1d686bc188996e530e017f4/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f64796e616d69632f6a736f6e3f636f6c6f723d626c7565266c6162656c3d646f6373267072656669783d762671756572793d2532342e696e666f2e76657273696f6e2675726c3d6874747073253341253246253246707970692e6f72672532467079706925324664617461707265702532466a736f6e267374796c653d666c61742d737175617265" data-canonical-src="https://img.shields.io/badge/dynamic/json?color=blue&label=docs&prefix=v&query=%24.info.version&url=https%3A%2F%2Fpypi.org%2Fpypi%2Fdataprep%2Fjson&style=flat-square" style="max-width: 100%;"></a> <a href="https://pypi.org/project/dataprep/" rel="nofollow"><img src="https://camo.githubusercontent.com/e536ca1203b38911813b05adc2c825138c227b4d5cfeb1f5ecb20774568ff504/68747470733a2f2f696d672e736869656c64732e696f2f707970692f707976657273696f6e732f64617461707265703f7374796c653d666c61742d737175617265" data-canonical-src="https://img.shields.io/pypi/pyversions/dataprep?style=flat-square" style="max-width: 100%;"></a> </p> <p align="center" dir="auto"> <a href="https://sfu-db.github.io/dataprep/" rel="nofollow">Documentation</a> | <a href="https://github.com/sfu-db/dataprep/discussions">Forum</a> </p> <p align="center" dir="auto"><b>Low code data preparation</b></p> <p dir="auto">Currently, you can use DataPrep to:</p> <ul dir="auto"> <li>Collect data from common data sources (through <a href="#connector"><code>dataprep.connector</code></a>)</li> <li>Do your exploratory data analysis (through <a href="#eda"><code>dataprep.eda</code></a>)</li> <li>Clean and standardize data (through <a href="#clean"><code>dataprep.clean</code></a>)</li> <li>...more modules are coming</li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Releases</h2><a id="user-content-releases" class="anchor" aria-label="Permalink: Releases" href="#releases"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div align="center" dir="auto"> <markdown-accessiblity-table><table> <tbody><tr> <th>Repo</th> <th>Version</th> <th>Downloads</th> </tr> <tr> <td>PyPI</td> <td><a href="https://pypi.org/project/dataprep/" rel="nofollow"><img src="https://camo.githubusercontent.com/da466d5e731b31d9f799366ff965fc789c369f31cdde85c6df3619cbc22c5e13/68747470733a2f2f696d672e736869656c64732e696f2f707970692f762f64617461707265703f7374796c653d666c61742d737175617265" data-canonical-src="https://img.shields.io/pypi/v/dataprep?style=flat-square" style="max-width: 100%;"></a></td> <td><a href="https://pepy.tech/project/dataprep" rel="nofollow"><img src="https://camo.githubusercontent.com/a57329ad3f9b13f255c24b28cbc34bc98f3fa5d44f66ac7dc11b3b2c068558fb/68747470733a2f2f706570792e746563682f62616467652f6461746170726570" data-canonical-src="https://pepy.tech/badge/dataprep" style="max-width: 100%;"></a></td> </tr> <tr> <td>conda-forge</td> <td><a href="https://anaconda.org/conda-forge/dataprep" rel="nofollow"><img src="https://camo.githubusercontent.com/cae571666e5846411946e0a1a6b8a47bb71d6d0aeaa53e5759a2f90c006943d1/68747470733a2f2f696d672e736869656c64732e696f2f636f6e64612f766e2f636f6e64612d666f7267652f64617461707265702e737667" data-canonical-src="https://img.shields.io/conda/vn/conda-forge/dataprep.svg" style="max-width: 100%;"></a></td> <td><a href="https://anaconda.org/conda-forge/dataprep" rel="nofollow"><img src="https://camo.githubusercontent.com/03056fdc83b6d54063969ed314b97800d2feda6615f8fe2280984c9d7c794c00/68747470733a2f2f696d672e736869656c64732e696f2f636f6e64612f646e2f636f6e64612d666f7267652f64617461707265702e737667" data-canonical-src="https://img.shields.io/conda/dn/conda-forge/dataprep.svg" style="max-width: 100%;"></a></td> </tr> </tbody></table></markdown-accessiblity-table> </div> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Installation</h2><a id="user-content-installation" class="anchor" aria-label="Permalink: Installation" href="#installation"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="pip install -U dataprep"><pre>pip install -U dataprep</pre></div> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">EDA</h2><a id="user-content-eda" class="anchor" aria-label="Permalink: EDA" href="#eda"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">DataPrep.EDA is the fastest and the easiest EDA (Exploratory Data Analysis) tool in Python. It allows you to understand a Pandas/Dask DataFrame with a few lines of code in seconds.</p> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">Create Profile Reports, Fast</h4><a id="user-content-create-profile-reports-fast" class="anchor" aria-label="Permalink: Create Profile Reports, Fast" href="#create-profile-reports-fast"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">You can create a beautiful profile report from a Pandas/Dask DataFrame with the <code>create_report</code> function. DataPrep.EDA has the following advantages compared to other tools:</p> <ul dir="auto"> <li><strong><a href="https://arxiv.org/abs/2104.00841" rel="nofollow">10X Faster</a></strong>: DataPrep.EDA can be 10X faster than Pandas-based profiling tools due to its highly optimized Dask-based computing module.</li> <li><strong>Interactive Visualization</strong>: DataPrep.EDA generates interactive visualizations in a report, which makes the report look more appealing to end users.</li> <li><strong>Big Data Support</strong>: DataPrep.EDA naturally supports big data stored in a Dask cluster by accepting a Dask dataframe as input.</li> </ul> <p dir="auto">The following code demonstrates how to use DataPrep.EDA to create a profile report for the titanic dataset.</p> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="from dataprep.datasets import load_dataset from dataprep.eda import create_report df = load_dataset("titanic") create_report(df).show_browser()"><pre><span class="pl-k">from</span> <span class="pl-s1">dataprep</span>.<span class="pl-s1">datasets</span> <span class="pl-k">import</span> <span class="pl-s1">load_dataset</span> <span class="pl-k">from</span> <span class="pl-s1">dataprep</span>.<span class="pl-s1">eda</span> <span class="pl-k">import</span> <span class="pl-s1">create_report</span> <span class="pl-s1">df</span> <span class="pl-c1">=</span> <span class="pl-en">load_dataset</span>(<span class="pl-s">"titanic"</span>) <span class="pl-en">create_report</span>(<span class="pl-s1">df</span>).<span class="pl-c1">show_browser</span>()</pre></div> <p dir="auto">Click <a href="https://docs.dataprep.ai/_downloads/1a61c6aebb3ecbe9dc9742bd6ca78ddb/titanic_dp.html" rel="nofollow">here</a> to see the generated report of the above code.</p> <p dir="auto">Click <a href="https://docs.dataprep.ai/dev/bench/index.html" rel="nofollow">here</a> to see the benchmark result.</p> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">Try DataPrep.EDA Online: <a href="https://colab.research.google.com/drive/1U_-pAMcne3hK1HbMB3kuEt-093Np_7Uk?usp=sharing" rel="nofollow">DataPrep.EDA Demo in Colab</a></h4><a id="user-content-try-dataprepeda-online-dataprepeda-demo-in-colab" class="anchor" aria-label="Permalink: Try DataPrep.EDA Online: DataPrep.EDA Demo in Colab" href="#try-dataprepeda-online-dataprepeda-demo-in-colab"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">Innovative System Design</h4><a id="user-content-innovative-system-design" class="anchor" aria-label="Permalink: Innovative System Design" href="#innovative-system-design"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">DataPrep.EDA is the <strong><em>only</em></strong> task-centric EDA system in Python. It is carefully designed to improve usability.</p> <ul dir="auto"> <li><strong>Task-Centric API Design</strong>: You can declaratively specify a wide range of EDA tasks in different granularity with a single function call. All needed visualizations will be automatically and intelligently generated for you.</li> <li><strong>Auto-Insights</strong>: DataPrep.EDA automatically detects and highlights the insights (e.g., a column has many outliers) to facilitate pattern discovery about the data.</li> <li><strong>How-to Guide</strong>: A how-to guide is provided to show the configuration of each plot function. With this feature, you can easily customize the generated visualizations.</li> </ul> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">Learn DataPrep.EDA in 2 minutes:</h4><a id="user-content-learn-dataprepeda-in-2-minutes" class="anchor" aria-label="Permalink: Learn DataPrep.EDA in 2 minutes:" href="#learn-dataprepeda-in-2-minutes"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><a href="https://youtu.be/nSkQy3ew3EI" rel="nofollow"><img src="/sfu-db/dataprep/raw/develop/assets/eda_video_cover.png" style="max-width: 100%;"></a></p> <p dir="auto">Click <a href="https://sfu-db.github.io/dataprep/user_guide/eda/introduction.html" rel="nofollow">here</a> to check all the supported tasks.</p> <p dir="auto">Check <a href="https://sfu-db.github.io/dataprep/user_guide/eda/plot.html" rel="nofollow">plot</a>, <a href="https://sfu-db.github.io/dataprep/user_guide/eda/plot_correlation.html" rel="nofollow">plot_correlation</a>, <a href="https://sfu-db.github.io/dataprep/user_guide/eda/plot_missing.html" rel="nofollow">plot_missing</a> and <a href="https://sfu-db.github.io/dataprep/user_guide/eda/create_report.html" rel="nofollow">create_report</a> to see how each function works.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Clean</h2><a id="user-content-clean" class="anchor" aria-label="Permalink: Clean" href="#clean"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">DataPrep.Clean contains about <strong>140+</strong> functions designed for cleaning and validating data in a DataFrame. It provides</p> <ul dir="auto"> <li><strong>A Convenient GUI</strong>: incorporated into Jupyter Notebook, users can clean their own DataFrame without any coding (see the video below).</li> <li><strong>A Unified API</strong>: each function follows the syntax <code>clean_{type}(df, 'column name')</code> (see an example below).</li> <li><strong>Speed</strong>: the computations are parallelized using Dask. It can clean <strong>50K rows per second</strong> on a dual-core laptop (that means cleaning 1 million rows in only 20 seconds).</li> <li><strong>Transparency</strong>: a report is generated that summarizes the alterations to the data that occured during cleaning.</li> </ul> <p dir="auto">The following video shows how to use GUI of Dataprep.Clean <a href="https://youtu.be/WtJaVBIVoxQ" rel="nofollow"><img src="/sfu-db/dataprep/raw/develop/assets/clean_video_cover.png" style="max-width: 100%;"></a></p> <p dir="auto">The following example shows how to clean and standardize a column of country names.</p> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="from dataprep.clean import clean_country import pandas as pd df = pd.DataFrame({'country': ['USA', 'country: Canada', '233', ' tr ', 'NA']}) df2 = clean_country(df, 'country') df2 country country_clean 0 USA United States 1 country: Canada Canada 2 233 Estonia 3 tr Turkey 4 NA NaN"><pre><span class="pl-k">from</span> <span class="pl-s1">dataprep</span>.<span class="pl-s1">clean</span> <span class="pl-k">import</span> <span class="pl-s1">clean_country</span> <span class="pl-k">import</span> <span class="pl-s1">pandas</span> <span class="pl-k">as</span> <span class="pl-s1">pd</span> <span class="pl-s1">df</span> <span class="pl-c1">=</span> <span class="pl-s1">pd</span>.<span class="pl-c1">DataFrame</span>({<span class="pl-s">'country'</span>: [<span class="pl-s">'USA'</span>, <span class="pl-s">'country: Canada'</span>, <span class="pl-s">'233'</span>, <span class="pl-s">' tr '</span>, <span class="pl-s">'NA'</span>]}) <span class="pl-s1">df2</span> <span class="pl-c1">=</span> <span class="pl-en">clean_country</span>(<span class="pl-s1">df</span>, <span class="pl-s">'country'</span>) <span class="pl-s1">df2</span> <span class="pl-s1">country</span> <span class="pl-s1">country_clean</span> <span class="pl-c1">0</span> <span class="pl-c1">USA</span> <span class="pl-v">United</span> <span class="pl-v">States</span> <span class="pl-c1">1</span> <span class="pl-s1">country</span>: <span class="pl-v">Canada</span> <span class="pl-smi">Canada</span> <span class="pl-c1">2</span> <span class="pl-c1">233</span> <span class="pl-v">Estonia</span> <span class="pl-c1">3</span> <span class="pl-s1">tr</span> <span class="pl-v">Turkey</span> <span class="pl-c1">4</span> <span class="pl-c1">NA</span> <span class="pl-v">NaN</span></pre></div> <p dir="auto">Type validation is also supported:</p> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="from dataprep.clean import validate_country series = validate_country(df['country']) series 0 True 1 False 2 True 3 True 4 False Name: country, dtype: bool"><pre><span class="pl-k">from</span> <span class="pl-s1">dataprep</span>.<span class="pl-s1">clean</span> <span class="pl-k">import</span> <span class="pl-s1">validate_country</span> <span class="pl-s1">series</span> <span class="pl-c1">=</span> <span class="pl-en">validate_country</span>(<span class="pl-s1">df</span>[<span class="pl-s">'country'</span>]) <span class="pl-s1">series</span> <span class="pl-c1">0</span> <span class="pl-c1">True</span> <span class="pl-c1">1</span> <span class="pl-c1">False</span> <span class="pl-c1">2</span> <span class="pl-c1">True</span> <span class="pl-c1">3</span> <span class="pl-c1">True</span> <span class="pl-c1">4</span> <span class="pl-c1">False</span> <span class="pl-v">Name</span>: <span class="pl-smi">country</span>, <span class="pl-s1">dtype</span>: <span class="pl-smi">bool</span></pre></div> <p dir="auto">Check <a href="https://docs.dataprep.ai/user_guide/clean/introduction.html" rel="nofollow">Documentation of Dataprep.Clean</a> to see how each function works.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Connector</h2><a id="user-content-connector" class="anchor" aria-label="Permalink: Connector" href="#connector"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Connector now supports loading data from both web API and databases.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Web API</h3><a id="user-content-web-api" class="anchor" aria-label="Permalink: Web API" href="#web-api"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Connector is an intuitive, open-source API wrapper that speeds up development by standardizing calls to multiple APIs as a simple workflow.</p> <p dir="auto">Connector provides a simple wrapper to collect structured data from different Web APIs (e.g., Twitter, Spotify), making web data collection easy and efficient, without requiring advanced programming skills.</p> <p dir="auto">Do you want to leverage the growing number of websites that are opening their data through public APIs? Connector is for you!</p> <p dir="auto">Let's check out the several benefits that Connector offers:</p> <ul dir="auto"> <li><strong>A unified API:</strong> You can fetch data using one or two lines of code to get data from <a href="https://github.com/sfu-db/DataConnectorConfigs">tens of popular websites</a>.</li> <li><strong>Auto Pagination:</strong> Do you want to invoke a Web API that could return a large result set and need to handle it through pagination? Connector automatically does the pagination for you! Just specify the desired number of returned results (argument <code>_count</code>) without getting into unnecessary detail about a specific pagination scheme.</li> <li><strong>Speed:</strong> Do you want to fetch results more quickly by making concurrent requests to Web APIs? Through the <code>_concurrency</code> argument, Connector simplifies concurrency, issuing API requests in parallel while respecting the API's rate limit policy.</li> </ul> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">How to fetch all publications of Andrew Y. Ng?</h4><a id="user-content-how-to-fetch-all-publications-of-andrew-y-ng" class="anchor" aria-label="Permalink: How to fetch all publications of Andrew Y. Ng?" href="#how-to-fetch-all-publications-of-andrew-y-ng"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="from dataprep.connector import connect conn_dblp = connect("dblp", _concurrency = 5) df = await conn_dblp.query("publication", author = "Andrew Y. Ng", _count = 2000)"><pre><span class="pl-k">from</span> <span class="pl-s1">dataprep</span>.<span class="pl-s1">connector</span> <span class="pl-k">import</span> <span class="pl-s1">connect</span> <span class="pl-s1">conn_dblp</span> <span class="pl-c1">=</span> <span class="pl-en">connect</span>(<span class="pl-s">"dblp"</span>, <span class="pl-s1">_concurrency</span> <span class="pl-c1">=</span> <span class="pl-c1">5</span>) <span class="pl-s1">df</span> <span class="pl-c1">=</span> <span class="pl-k">await</span> <span class="pl-s1">conn_dblp</span>.<span class="pl-c1">query</span>(<span class="pl-s">"publication"</span>, <span class="pl-s1">author</span> <span class="pl-c1">=</span> <span class="pl-s">"Andrew Y. Ng"</span>, <span class="pl-s1">_count</span> <span class="pl-c1">=</span> <span class="pl-c1">2000</span>)</pre></div> <p dir="auto">Here, you can find detailed <a href="https://github.com/sfu-db/dataprep/tree/develop/examples">Examples.</a></p> <p dir="auto">Connector is designed to be easy to extend. If you want to connect with your own web API, you just have to write a simple <a href="https://github.com/sfu-db/DataConnectorConfigs/blob/develop/CONTRIBUTING.md#add-configuration-files">configuration file</a> to support it. This configuration file describes the API's main attributes like the URL, query parameters, authorization method, pagination properties, etc.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Database</h3><a id="user-content-database" class="anchor" aria-label="Permalink: Database" href="#database"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Connector now has adopted <a href="https://github.com/sfu-db/connector-x">connectorx</a> in order to enable loading data from databases (Postgres, Mysql, SQLServer, etc.) into Python dataframes (pandas, dask, modin, arrow, polars) in the fastest and most memory efficient way. <a href="https://github.com/sfu-db/connector-x/blob/main/Benchmark.md#benchmark-result-on-aws-r54xlarge">[Benchmark]</a></p> <p dir="auto">What you need to do is just install <code>connectorx</code> (<code>pip install -U connectorx</code>) and run one line of code:</p> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="from dataprep.connector import read_sql read_sql("postgresql://username:password@server:port/database", "SELECT * FROM lineitem")"><pre><span class="pl-k">from</span> <span class="pl-s1">dataprep</span>.<span class="pl-s1">connector</span> <span class="pl-k">import</span> <span class="pl-s1">read_sql</span> <span class="pl-en">read_sql</span>(<span class="pl-s">"postgresql://username:password@server:port/database"</span>, <span class="pl-s">"SELECT * FROM lineitem"</span>)</pre></div> <p dir="auto">Check out <a href="https://github.com/sfu-db/connector-x#supported-sources--destinations">here</a> for supported databases and dataframes and more examples usages.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Lineage</h2><a id="user-content-lineage" class="anchor" aria-label="Permalink: Lineage" href="#lineage"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">A Column Level Lineage Graph for SQL. This tool is intended to help you by creating an interactive graph on a webpage to explore the column level lineage among them.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">The lineage module offers:</h3><a id="user-content-the-lineage-module-offers" class="anchor" aria-label="Permalink: The lineage module offers:" href="#the-lineage-module-offers"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">A general introduction of the project can be found in this <a href="https://medium.com/@shz1/lineagex-the-python-library-for-your-lineage-needs-5e51b77a0032" rel="nofollow">blog post</a>.</p> <ul dir="auto"> <li><strong>Automatic dependency creation</strong>: When there are dependency among the SQL files, and those tables are not yet in the database, the lineage module will automatically tries to find the dependency table and creates it.</li> <li><strong>Clean and simple but very interactive user interface</strong>: The user interface is very simple to use with minimal clutters on the page while showing all of the necessary information.</li> <li><strong>Variety of SQL statements</strong>: The lineage module supports a variety of SQL statements, aside from the typical <code>SELECT</code> statement, it also supports <code>CREATE TABLE/VIEW [IF NOT EXISTS]</code> statement as well as the <code>INSERT</code> and <code>DELETE</code> statement.</li> <li><strong><a href="https://docs.getdbt.com/" rel="nofollow">dbt</a> support</strong>: The lineage module is also implemented in the <a href="https://github.com/sfu-db/dbt-lineagex">dbt-LineageX</a>, it is added into a dbt project and by using the dbt library <a href="https://github.com/fal-ai/fal">fal</a>, it is able to reuse the Python core and create the similar output from the dbt project.</li> </ul> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Uses and Demo</h3><a id="user-content-uses-and-demo" class="anchor" aria-label="Permalink: Uses and Demo" href="#uses-and-demo"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">The interactive graph looks like this: <a target="_blank" rel="noopener noreferrer nofollow" href="https://raw.githubusercontent.com/sfu-db/lineagex/main/docs/example.gif"><img src="https://raw.githubusercontent.com/sfu-db/lineagex/main/docs/example.gif" data-animated-image="" style="max-width: 100%;"></a> Here is a <a href="https://zshandy.github.io/lineagex-demo/" rel="nofollow">live demo</a> with the <a href="https://github.com/MIT-LCP/mimic-code/tree/main/mimic-iv/concepts_postgres">mimic-iv concepts_postgres</a> files(<a href="https://sfu-db.github.io/lineagex/output.html#how-to-navigate-the-webpage" rel="nofollow">navigation instructions</a>) and that is created with one line of code:</p> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="from dataprep.lineage import lineagex lineagex(sql=path/to/sql, target_schema="schema1", conn_string="postgresql://username:password@server:port/database", search_path_schema="schema1, public")"><pre><span class="pl-k">from</span> <span class="pl-s1">dataprep</span>.<span class="pl-s1">lineage</span> <span class="pl-k">import</span> <span class="pl-s1">lineagex</span> <span class="pl-en">lineagex</span>(<span class="pl-s1">sql</span><span class="pl-c1">=</span><span class="pl-s1">path</span><span class="pl-c1">/</span><span class="pl-s1">to</span><span class="pl-c1">/</span><span class="pl-s1">sql</span>, <span class="pl-s1">target_schema</span><span class="pl-c1">=</span><span class="pl-s">"schema1"</span>, <span class="pl-s1">conn_string</span><span class="pl-c1">=</span><span class="pl-s">"postgresql://username:password@server:port/database"</span>, <span class="pl-s1">search_path_schema</span><span class="pl-c1">=</span><span class="pl-s">"schema1, public"</span>)</pre></div> <p dir="auto">Check out more detailed usage and examples <a href="https://sfu-db.github.io/lineagex/intro.html" rel="nofollow">here</a>.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Documentation</h2><a id="user-content-documentation" class="anchor" aria-label="Permalink: Documentation" href="#documentation"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">The following documentation can give you an impression of what DataPrep can do:</p> <ul dir="auto"> <li><a href="https://docs.dataprep.ai/user_guide/connector/introduction.html" rel="nofollow">Connector</a></li> <li><a href="https://docs.dataprep.ai/user_guide/eda/introduction.html" rel="nofollow">EDA</a></li> <li><a href="https://docs.dataprep.ai/user_guide/clean/introduction.html" rel="nofollow">Clean</a></li> <li><a href="https://sfu-db.github.io/lineagex/intro.html" rel="nofollow">Lineage</a></li> <li></li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Contribute</h2><a id="user-content-contribute" class="anchor" aria-label="Permalink: Contribute" href="#contribute"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">There are many ways to contribute to DataPrep.</p> <ul dir="auto"> <li>Submit bugs and help us verify fixes as they are checked in.</li> <li>Review the source code changes.</li> <li>Engage with other DataPrep users and developers on StackOverflow.</li> <li>Ask questions & propose new ideas in our <a href="https://github.com/sfu-db/dataprep/discussions">Forum</a>.</li> <li><a href="https://twitter.com/dataprepai" rel="nofollow"><img src="https://camo.githubusercontent.com/6e368096f0f844722f409f617c1150316e6afed9a7f17fb72e984c213498ba08/68747470733a2f2f696d672e736869656c64732e696f2f747769747465722f666f6c6c6f772f646174617072657061693f7374796c653d736f6369616c" alt="Twitter" data-canonical-src="https://img.shields.io/twitter/follow/dataprepai?style=social" style="max-width: 100%;"></a></li> <li>Contribute bug fixes.</li> <li>Providing use cases and writing down your user experience.</li> </ul> <p dir="auto">Please take a look at our <a href="https://github.com/sfu-db/dataprep/wiki">wiki</a> for development documentations!</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Acknowledgement</h2><a id="user-content-acknowledgement" class="anchor" aria-label="Permalink: Acknowledgement" href="#acknowledgement"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Some functionalities of DataPrep are inspired by the following packages.</p> <ul dir="auto"> <li> <p dir="auto"><a href="https://github.com/pandas-profiling/pandas-profiling">Pandas Profiling</a></p> <p dir="auto">Inspired the report functionality and insights provided in <code>dataprep.eda</code>.</p> </li> <li> <p dir="auto"><a href="https://github.com/ResidentMario/missingno">missingno</a></p> <p dir="auto">Inspired the missing value analysis in <code>dataprep.eda</code>.</p> </li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Citing DataPrep</h2><a id="user-content-citing-dataprep" class="anchor" aria-label="Permalink: Citing DataPrep" href="#citing-dataprep"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">If you use DataPrep, please consider citing the following paper:</p> <p dir="auto">Jinglin Peng, Weiyuan Wu, Brandon Lockhart, Song Bian, Jing Nathan Yan, Linghao Xu, Zhixuan Chi, Jeffrey M. Rzeszotarski, and Jiannan Wang. <a href="https://arxiv.org/abs/2104.00841" rel="nofollow">DataPrep.EDA: Task-Centric Exploratory Data Analysis for Statistical Modeling in Python.</a> <em>SIGMOD 2021</em>.</p> <p dir="auto">BibTeX entry:</p> <div class="highlight highlight-text-bibtex notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="@inproceedings{dataprepeda2021, author = {Jinglin Peng and Weiyuan Wu and Brandon Lockhart and Song Bian and Jing Nathan Yan and Linghao Xu and Zhixuan Chi and Jeffrey M. Rzeszotarski and Jiannan Wang}, title = {DataPrep.EDA: Task-Centric Exploratory Data Analysis for Statistical Modeling in Python}, booktitle = {Proceedings of the 2021 International Conference on Management of Data (SIGMOD '21), June 20--25, 2021, Virtual Event, China}, year = {2021} }"><pre><span class="pl-k">@inproceedings</span>{<span class="pl-en">dataprepeda2021</span>, <span class="pl-s">author</span> = <span class="pl-s"><span class="pl-pds">{</span>Jinglin Peng and Weiyuan Wu and Brandon Lockhart and Song Bian and Jing Nathan Yan and Linghao Xu and Zhixuan Chi and Jeffrey M. Rzeszotarski and Jiannan Wang<span class="pl-pds">}</span></span>, <span class="pl-s">title</span> = <span class="pl-s"><span class="pl-pds">{</span>DataPrep.EDA: Task-Centric Exploratory Data Analysis for Statistical Modeling in Python<span class="pl-pds">}</span></span>, <span class="pl-s">booktitle</span> = <span class="pl-s"><span class="pl-pds">{</span>Proceedings of the 2021 International Conference on Management of Data (SIGMOD '21), June 20--25, 2021, Virtual Event, China<span class="pl-pds">}</span></span>, <span class="pl-s">year</span> = <span class="pl-s"><span class="pl-pds">{</span>2021<span class="pl-pds">}</span></span> }</pre></div> </article></div></div></div></div></div> <!-- --> <!-- --> <script type="application/json" id="__PRIMER_DATA_:R0:__">{"resolvedServerColorMode":"day"}</script></div> </react-partial> <input type="hidden" data-csrf="true" value="+RzBIlmBtBGLMwEt5EfHqyqyl5s1iBCbgXWDDrpXo+JGCsgBNvIi3cZsXvvtuIrHoGo42NMxusNFe9BM5GXU7A==" /> </div> <div data-view-component="true" class="Layout-sidebar"> <div class="BorderGrid about-margin" data-pjax> <div class="BorderGrid-row"> <div class="BorderGrid-cell"> <div class="hide-sm hide-md"> <h2 class="mb-3 h4">About</h2> <p class="f4 my-3"> Open-source low code data preparation library in python. Collect, clean and visualization your data in python with a few lines of code. </p> <div class="my-3 d-flex flex-items-center"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-link flex-shrink-0 mr-2"> <path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path> </svg> <span class="flex-auto min-width-0 css-truncate css-truncate-target width-fit"> <a title="http://dataprep.ai" role="link" target="_blank" rel="noopener noreferrer nofollow" class="text-bold" href="http://dataprep.ai">dataprep.ai</a> </span> </div> <h3 class="sr-only">Topics</h3> <div class="my-3"> <div class="f6"> <a href="/topics/data-science" title="Topic: data-science" data-view-component="true" class="topic-tag topic-tag-link"> data-science </a> <a href="/topics/connector" title="Topic: connector" data-view-component="true" class="topic-tag topic-tag-link"> connector </a> <a href="/topics/exploratory-data-analysis" title="Topic: exploratory-data-analysis" data-view-component="true" class="topic-tag topic-tag-link"> exploratory-data-analysis </a> <a href="/topics/eda" title="Topic: eda" data-view-component="true" class="topic-tag topic-tag-link"> eda </a> <a href="/topics/apis" title="Topic: apis" data-view-component="true" class="topic-tag topic-tag-link"> apis </a> <a href="/topics/data-exploration" title="Topic: data-exploration" data-view-component="true" class="topic-tag topic-tag-link"> data-exploration </a> <a href="/topics/cleaning" title="Topic: cleaning" data-view-component="true" class="topic-tag topic-tag-link"> cleaning </a> <a href="/topics/dataprep" title="Topic: dataprep" data-view-component="true" class="topic-tag topic-tag-link"> dataprep </a> <a href="/topics/datacleaning" title="Topic: datacleaning" data-view-component="true" class="topic-tag topic-tag-link"> datacleaning </a> <a href="/topics/dataconnector" title="Topic: dataconnector" data-view-component="true" class="topic-tag topic-tag-link"> dataconnector </a> <a href="/topics/apiwrapper" title="Topic: apiwrapper" data-view-component="true" class="topic-tag topic-tag-link"> apiwrapper </a> <a href="/topics/webconnector" title="Topic: webconnector" data-view-component="true" class="topic-tag topic-tag-link"> webconnector </a> <a href="/topics/datapreparation" title="Topic: datapreparation" data-view-component="true" class="topic-tag topic-tag-link"> datapreparation </a> </div> </div> <h3 class="sr-only">Resources</h3> <div class="mt-2"> <a 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height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-star mr-2"> <path d="M8 .25a.75.75 0 0 1 .673.418l1.882 3.815 4.21.612a.75.75 0 0 1 .416 1.279l-3.046 2.97.719 4.192a.751.751 0 0 1-1.088.791L8 12.347l-3.766 1.98a.75.75 0 0 1-1.088-.79l.72-4.194L.818 6.374a.75.75 0 0 1 .416-1.28l4.21-.611L7.327.668A.75.75 0 0 1 8 .25Zm0 2.445L6.615 5.5a.75.75 0 0 1-.564.41l-3.097.45 2.24 2.184a.75.75 0 0 1 .216.664l-.528 3.084 2.769-1.456a.75.75 0 0 1 .698 0l2.77 1.456-.53-3.084a.75.75 0 0 1 .216-.664l2.24-2.183-3.096-.45a.75.75 0 0 1-.564-.41L8 2.694Z"></path> </svg> <strong>2.1k</strong> stars</a> </div> <h3 class="sr-only">Watchers</h3> <div class="mt-2"> <a href="/sfu-db/dataprep/watchers" data-view-component="true" class="Link Link--muted"><svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-eye mr-2"> <path d="M8 2c1.981 0 3.671.992 4.933 2.078 1.27 1.091 2.187 2.345 2.637 3.023a1.62 1.62 0 0 1 0 1.798c-.45.678-1.367 1.932-2.637 3.023C11.67 13.008 9.981 14 8 14c-1.981 0-3.671-.992-4.933-2.078C1.797 10.83.88 9.576.43 8.898a1.62 1.62 0 0 1 0-1.798c.45-.677 1.367-1.931 2.637-3.022C4.33 2.992 6.019 2 8 2ZM1.679 7.932a.12.12 0 0 0 0 .136c.411.622 1.241 1.75 2.366 2.717C5.176 11.758 6.527 12.5 8 12.5c1.473 0 2.825-.742 3.955-1.715 1.124-.967 1.954-2.096 2.366-2.717a.12.12 0 0 0 0-.136c-.412-.621-1.242-1.75-2.366-2.717C10.824 4.242 9.473 3.5 8 3.5c-1.473 0-2.825.742-3.955 1.715-1.124.967-1.954 2.096-2.366 2.717ZM8 10a2 2 0 1 1-.001-3.999A2 2 0 0 1 8 10Z"></path> </svg> <strong>25</strong> watching</a> </div> <h3 class="sr-only">Forks</h3> <div class="mt-2"> <a href="/sfu-db/dataprep/forks" data-view-component="true" class="Link Link--muted"><svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-repo-forked mr-2"> <path d="M5 5.372v.878c0 .414.336.75.75.75h4.5a.75.75 0 0 0 .75-.75v-.878a2.25 2.25 0 1 1 1.5 0v.878a2.25 2.25 0 0 1-2.25 2.25h-1.5v2.128a2.251 2.251 0 1 1-1.5 0V8.5h-1.5A2.25 2.25 0 0 1 3.5 6.25v-.878a2.25 2.25 0 1 1 1.5 0ZM5 3.25a.75.75 0 1 0-1.5 0 .75.75 0 0 0 1.5 0Zm6.75.75a.75.75 0 1 0 0-1.5.75.75 0 0 0 0 1.5Zm-3 8.75a.75.75 0 1 0-1.5 0 .75.75 0 0 0 1.5 0Z"></path> </svg> <strong>213</strong> forks</a> </div> <div class="mt-2"> <a class="Link--muted" href="/contact/report-content?content_url=https%3A%2F%2Fgithub.com%2Fsfu-db%2Fdataprep&report=sfu-db+%28user%29"> Report repository </a> </div> </div> </div> </div> <div class="BorderGrid-row"> <div class="BorderGrid-cell"> <h2 class="h4 mb-3" data-pjax="#repo-content-pjax-container" data-turbo-frame="repo-content-turbo-frame"> <a href="/sfu-db/dataprep/releases" data-view-component="true" class="Link--primary no-underline Link">Releases <span title="23" data-view-component="true" class="Counter">23</span></a></h2> <a class="Link--primary d-flex no-underline" data-pjax="#repo-content-pjax-container" data-turbo-frame="repo-content-turbo-frame" href="/sfu-db/dataprep/releases/tag/v0.4.4-alpha.1"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-tag flex-shrink-0 mt-1 color-fg-success"> <path d="M1 7.775V2.75C1 1.784 1.784 1 2.75 1h5.025c.464 0 .91.184 1.238.513l6.25 6.25a1.75 1.75 0 0 1 0 2.474l-5.026 5.026a1.75 1.75 0 0 1-2.474 0l-6.25-6.25A1.752 1.752 0 0 1 1 7.775Zm1.5 0c0 .066.026.13.073.177l6.25 6.25a.25.25 0 0 0 .354 0l5.025-5.025a.25.25 0 0 0 0-.354l-6.25-6.25a.25.25 0 0 0-.177-.073H2.75a.25.25 0 0 0-.25.25ZM6 5a1 1 0 1 1 0 2 1 1 0 0 1 0-2Z"></path> </svg> <div class="ml-2 min-width-0"> <div class="d-flex"> <span class="css-truncate css-truncate-target text-bold mr-2" style="max-width: none;">v0.4.4-alpha.1</span> <span title="Label: Latest" data-view-component="true" class="Label Label--success flex-shrink-0"> Latest </span> </div> <div class="text-small color-fg-muted"><relative-time datetime="2022-07-08T00:57:11Z" class="no-wrap">Jul 8, 2022</relative-time></div> </div> </a> <div data-view-component="true" class="mt-3"> <a text="small" data-pjax="#repo-content-pjax-container" data-turbo-frame="repo-content-turbo-frame" href="/sfu-db/dataprep/releases" data-view-component="true" class="Link">+ 22 releases</a></div> </div> </div> <div class="BorderGrid-row"> <div class="BorderGrid-cell"> <h2 class="h4 mb-3"> <a href="/orgs/sfu-db/packages?repo_name=dataprep" data-view-component="true" class="Link--primary no-underline Link d-flex flex-items-center">Packages <span title="0" hidden="hidden" data-view-component="true" class="Counter ml-1">0</span></a></h2> <div class="text-small color-fg-muted" > No packages published <br> </div> </div> </div> <div class="BorderGrid-row" > <div class="BorderGrid-cell"> <h2 class="h4 mb-3"> <a href="/sfu-db/dataprep/network/dependents" data-view-component="true" class="Link--primary no-underline Link">Used by <span title="628" data-view-component="true" class="Counter">628</span></a> </h2> <a class="d-flex flex-items-center" href="/sfu-db/dataprep/network/dependents"> <ul class="hx_flex-avatar-stack list-style-none min-width-0"> <li class="hx_flex-avatar-stack-item"> <img class="avatar" src="https://avatars.githubusercontent.com/u/180925766?s=64&v=4" width="32" height="32" alt="@UpstageAILab5" /> </li> <li class="hx_flex-avatar-stack-item"> <img class="avatar avatar-user" src="https://avatars.githubusercontent.com/u/96465764?s=64&v=4" width="32" height="32" alt="@mnazaar" /> </li> <li class="hx_flex-avatar-stack-item"> <img class="avatar avatar-user" src="https://avatars.githubusercontent.com/u/133800795?s=64&v=4" width="32" height="32" alt="@marcelopinho82" /> </li> <li class="hx_flex-avatar-stack-item"> <img class="avatar avatar-user" src="https://avatars.githubusercontent.com/u/60332634?s=64&v=4" width="32" height="32" alt="@HanNguyen0910" /> </li> <li class="hx_flex-avatar-stack-item"> <img class="avatar avatar-user" src="https://avatars.githubusercontent.com/u/156856994?s=64&v=4" width="32" height="32" alt="@santigonzalez13" /> </li> <li class="hx_flex-avatar-stack-item"> <img class="avatar avatar-user" src="https://avatars.githubusercontent.com/u/119647680?s=64&v=4" width="32" height="32" alt="@minhpnh" /> </li> <li class="hx_flex-avatar-stack-item"> <img class="avatar avatar-user" src="https://avatars.githubusercontent.com/u/103023274?s=64&v=4" width="32" height="32" alt="@vishvaspatel" /> </li> <li class="hx_flex-avatar-stack-item"> <img class="avatar avatar-user" src="https://avatars.githubusercontent.com/u/26376312?s=64&v=4" width="32" height="32" alt="@praveenkaushik80" /> </li> </ul> <span class="px-2 text-bold text-small no-wrap"> + 620 </span> </a> </div> </div> <div class="BorderGrid-row"> <div class="BorderGrid-cell"> <h2 class="h4 mb-3"> <a href="/sfu-db/dataprep/graphs/contributors" data-view-component="true" class="Link--primary no-underline Link d-flex flex-items-center">Contributors <span title="36" data-view-component="true" class="Counter ml-1">36</span></a></h2> <ul class="list-style-none d-flex flex-wrap mb-n2"> <li class="mb-2 mr-2" > <a href="https://github.com/dovahcrow" class="" data-hovercard-type="user" data-hovercard-url="/users/dovahcrow/hovercard" data-octo-click="hovercard-link-click" data-octo-dimensions="link_type:self" > <img src="https://avatars.githubusercontent.com/u/998606?s=64&v=4" alt="@dovahcrow" size="32" height="32" width="32" data-view-component="true" class="avatar circle" /> </a> </li> <li class="mb-2 mr-2" > <a href="https://github.com/jinglinpeng" class="" data-hovercard-type="user" data-hovercard-url="/users/jinglinpeng/hovercard" data-octo-click="hovercard-link-click" data-octo-dimensions="link_type:self" > <img src="https://avatars.githubusercontent.com/u/18078770?s=64&v=4" alt="@jinglinpeng" size="32" height="32" width="32" data-view-component="true" class="avatar circle" /> </a> </li> <li class="mb-2 mr-2" > <a href="https://github.com/brandonlockhart" class="" data-hovercard-type="user" data-hovercard-url="/users/brandonlockhart/hovercard" data-octo-click="hovercard-link-click" data-octo-dimensions="link_type:self" > <img src="https://avatars.githubusercontent.com/u/25874021?s=64&v=4" alt="@brandonlockhart" size="32" height="32" width="32" data-view-component="true" class="avatar circle" /> </a> </li> <li class="mb-2 mr-2" > <a href="https://github.com/qidanrui" class="" data-hovercard-type="user" data-hovercard-url="/users/qidanrui/hovercard" data-octo-click="hovercard-link-click" data-octo-dimensions="link_type:self" > <img src="https://avatars.githubusercontent.com/u/10005367?s=64&v=4" alt="@qidanrui" size="32" height="32" width="32" data-view-component="true" class="avatar circle" /> </a> </li> <li class="mb-2 mr-2" > <a href="https://github.com/peiwangdb" class="" data-hovercard-type="user" data-hovercard-url="/users/peiwangdb/hovercard" data-octo-click="hovercard-link-click" data-octo-dimensions="link_type:self" > <img src="https://avatars.githubusercontent.com/u/15167104?s=64&v=4" alt="@peiwangdb" size="32" height="32" width="32" data-view-component="true" class="avatar circle" /> </a> </li> <li class="mb-2 mr-2" > <a href="https://github.com/Waterpine" class="" data-hovercard-type="user" data-hovercard-url="/users/Waterpine/hovercard" data-octo-click="hovercard-link-click" data-octo-dimensions="link_type:self" > <img src="https://avatars.githubusercontent.com/u/29000790?s=64&v=4" alt="@Waterpine" size="32" height="32" width="32" data-view-component="true" class="avatar circle" /> </a> </li> <li class="mb-2 mr-2" > <a href="https://github.com/yxie66" class="" data-hovercard-type="user" data-hovercard-url="/users/yxie66/hovercard" data-octo-click="hovercard-link-click" data-octo-dimensions="link_type:self" > <img 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