{"product_id":"pandas-workout","title":"Pandas Workout","description":"\u003cdiv class=\"product-page-section\" style='box-sizing: border-box; margin-bottom: 36px; color: rgb(51, 51, 51); font-family: Lato, -apple-system, BlinkMacSystemFont, \"avenir next\", avenir, \"helvetica neue\", helvetica, Ubuntu, roboto, noto, \"segoe ui\", arial, sans-serif; font-size: 17.25px;'\u003e  \u003cdiv class=\"product-page-section\" style=\"box-sizing: border-box; margin-bottom: 0px; padding-bottom: 0px;\"\u003e   \u003cb style=\"box-sizing: border-box;\"\u003ePractice makes perfect pandas. Work out your pandas skills against dozens of real-world challenges, each carefully designed to build an intuitive knowledge of essential pandas tasks.\u003c\/b\u003e   \u003cbr style=\"box-sizing: border-box;\"\u003e   \u003cbr style=\"box-sizing: border-box;\"\u003eIn    \u003ci style=\"box-sizing: border-box;\"\u003ePandas Workout\u003c\/i\u003e you''ll learn how to:   \u003cbr style=\"box-sizing: border-box;\"\u003e   \u003cbr style=\"box-sizing: border-box;\"\u003e   \u003cul style=\"box-sizing: border-box; margin-top: 0px; margin-bottom: 10.5px; padding-left: 17.5px;\"\u003e    \u003cli style=\"box-sizing: border-box;\"\u003eClean your data for accurate analysis\u003c\/li\u003e    \u003cli style=\"box-sizing: border-box;\"\u003eWork with rows and columns for retrieving and assigning data\u003c\/li\u003e    \u003cli style=\"box-sizing: border-box;\"\u003eHandle indexes, including hierarchical indexes\u003c\/li\u003e    \u003cli style=\"box-sizing: border-box;\"\u003eRead and write data with a number of common formats, such as CSV and JSON\u003c\/li\u003e    \u003cli style=\"box-sizing: border-box;\"\u003eProcess and manipulate textual data from within pandas\u003c\/li\u003e    \u003cli style=\"box-sizing: border-box;\"\u003eWork with dates and times in pandas\u003c\/li\u003e    \u003cli style=\"box-sizing: border-box;\"\u003ePerform aggregate calculations on selected subsets of data\u003c\/li\u003e    \u003cli style=\"box-sizing: border-box;\"\u003eProduce attractive and useful visualizations that make your data come alive\u003c\/li\u003e   \u003c\/ul\u003e   \u003cbr style=\"box-sizing: border-box; margin-bottom: 0px; padding-bottom: 0px;\"\u003eDiscover 50 exercises that will strengthen your pandas skills to a level of automatic fluency. You''ll test yourself against common pandas challenges such as data cleaning, and explore real-world datasets such as New York Taxis, Kickstarter projects, and global tourist spending. Detailed explanations help guide your success and make your new skills stick. You''ll even get a big boost to productivity, with tasks that used to mean a trip to StackOverflow now a natural part of your skillset.  \u003c\/div\u003e \u003c\/div\u003e \u003cdiv class=\"product-page-section\" style='box-sizing: border-box; margin-bottom: 36px; color: rgb(51, 51, 51); font-family: Lato, -apple-system, BlinkMacSystemFont, \"avenir next\", avenir, \"helvetica neue\", helvetica, Ubuntu, roboto, noto, \"segoe ui\", arial, sans-serif; font-size: 17.25px;'\u003e  \u003ch2 style=\"box-sizing: border-box; line-height: 1.1; color: inherit; margin: 0px 0px 10px; font-size: 27px; text-transform: lowercase;\"\u003eabout the technology\u003c\/h2\u003e  \u003ca name=\"about-the-technology\" class=\"anchor\" style=\"box-sizing: border-box; color: rgb(64, 127, 191); visibility: hidden; display: block; position: relative; margin-bottom: 0px; padding-bottom: 0px;\"\u003e\u003c\/a\u003eMastering pandas means working out your new skills until they become like reflexes. This book gives you lots of pandas practice by working through the kind of scenarios you''ll face in the real world. Whether you''re a data scientist or a programmer handling large quantities of data, you''ll soon overcome pandas''s learning curve and start solving complex problems in less time. \u003c\/div\u003e \u003cdiv class=\"product-page-section\" style='box-sizing: border-box; margin-bottom: 36px; color: rgb(51, 51, 51); font-family: Lato, -apple-system, BlinkMacSystemFont, \"avenir next\", avenir, \"helvetica neue\", helvetica, Ubuntu, roboto, noto, \"segoe ui\", arial, sans-serif; font-size: 17.25px;'\u003e  \u003ch2 style=\"box-sizing: border-box; line-height: 1.1; color: inherit; margin: 0px 0px 10px; font-size: 27px; text-transform: lowercase;\"\u003eabout the book\u003c\/h2\u003e  \u003ca name=\"about-the-book\" class=\"anchor\" style=\"box-sizing: border-box; color: rgb(64, 127, 191); visibility: hidden; display: block; position: relative;\"\u003e\u003c\/a\u003e  \u003ci style=\"box-sizing: border-box; margin-bottom: 0px; padding-bottom: 0px;\"\u003ePandas Workout\u003c\/i\u003e hones your pandas skills to a professional-level through 50 hands-on exercises, along with 150 bonus challenges to really test your skills. Expert Python trainer Reuven Lerner coaches you through essentials like data frames and reveals pandas''s rich functionality for string and date\/time handling, complex indexing, and visualization. Clear explanations and detailed Jupyter Notebooks accompany every exercise, along with comparisons of different possible solutions. Work through this book, and you''ll be ready to flex your muscles against even the trickiest pandas problems! \u003c\/div\u003e \u003cdiv class=\"product-page-section\" style='box-sizing: border-box; margin-bottom: 36px; color: rgb(51, 51, 51); font-family: Lato, -apple-system, BlinkMacSystemFont, \"avenir next\", avenir, \"helvetica neue\", helvetica, Ubuntu, roboto, noto, \"segoe ui\", arial, sans-serif;'\u003e  \u003cfont size=\"5\"\u003e\u003cb\u003eRETAIL SELLING POINTS \u003c\/b\u003e\u003c\/font\u003e \u003c\/div\u003e \u003cdiv class=\"product-page-section\" style='box-sizing: border-box; margin-bottom: 36px; color: rgb(51, 51, 51); font-family: Lato, -apple-system, BlinkMacSystemFont, \"avenir next\", avenir, \"helvetica neue\", helvetica, Ubuntu, roboto, noto, \"segoe ui\", arial, sans-serif; font-size: 17.25px;'\u003e   • Clean your data for accurate analysis  \u003c\/div\u003e \u003cdiv class=\"product-page-section\" style='box-sizing: border-box; margin-bottom: 36px; color: rgb(51, 51, 51); font-family: Lato, -apple-system, BlinkMacSystemFont, \"avenir next\", avenir, \"helvetica neue\", helvetica, Ubuntu, roboto, noto, \"segoe ui\", arial, sans-serif; font-size: 17.25px;'\u003e  • Work with rows and columns for retrieving and assigning data• Handle indexes, including hierarchical indexes \u003c\/div\u003e \u003cdiv class=\"product-page-section\" style='box-sizing: border-box; margin-bottom: 36px; color: rgb(51, 51, 51); font-family: Lato, -apple-system, BlinkMacSystemFont, \"avenir next\", avenir, \"helvetica neue\", helvetica, Ubuntu, roboto, noto, \"segoe ui\", arial, sans-serif; font-size: 17.25px;'\u003e   • Read and write data with a number of common formats, such as CSV and JSON  \u003c\/div\u003e \u003cdiv class=\"product-page-section\" style='box-sizing: border-box; margin-bottom: 36px; color: rgb(51, 51, 51); font-family: Lato, -apple-system, BlinkMacSystemFont, \"avenir next\", avenir, \"helvetica neue\", helvetica, Ubuntu, roboto, noto, \"segoe ui\", arial, sans-serif; font-size: 17.25px;'\u003e  • Process and manipulate textual data from within pandas \u003c\/div\u003e \u003cdiv class=\"product-page-section\" style='box-sizing: border-box; margin-bottom: 36px; color: rgb(51, 51, 51); font-family: Lato, -apple-system, BlinkMacSystemFont, \"avenir next\", avenir, \"helvetica neue\", helvetica, Ubuntu, roboto, noto, \"segoe ui\", arial, sans-serif; font-size: 17.25px;'\u003e   • Work with dates and times in pandas  \u003c\/div\u003e \u003cdiv class=\"product-page-section\" style='box-sizing: border-box; margin-bottom: 36px; color: rgb(51, 51, 51); font-family: Lato, -apple-system, BlinkMacSystemFont, \"avenir next\", avenir, \"helvetica neue\", helvetica, Ubuntu, roboto, noto, \"segoe ui\", arial, sans-serif; font-size: 17.25px;'\u003e  • Perform aggregate calculations on selected subsets of data  \u003c\/div\u003e \u003cdiv class=\"product-page-section\" style='box-sizing: border-box; margin-bottom: 36px; color: rgb(51, 51, 51); font-family: Lato, -apple-system, BlinkMacSystemFont, \"avenir next\", avenir, \"helvetica neue\", helvetica, Ubuntu, roboto, noto, \"segoe ui\", arial, sans-serif;'\u003e  \u003cfont size=\"5\"\u003e\u003cb\u003eAUDIENCE \u003c\/b\u003e\u003c\/font\u003e \u003c\/div\u003e \u003cdiv class=\"product-page-section\" style='box-sizing: border-box; margin-bottom: 36px; color: rgb(51, 51, 51); font-family: Lato, -apple-system, BlinkMacSystemFont, \"avenir next\", avenir, \"helvetica neue\", helvetica, Ubuntu, roboto, noto, \"segoe ui\", arial, sans-serif; font-size: 17.25px;'\u003e  For Python programmers and data analysts, with basic knowledge of pandas.  \u003c\/div\u003e","brand":"MediaPlace","offers":[{"title":"Default Title","offer_id":57312092881278,"sku":"NW9781617299728","price":47.95,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1379\/1261\/files\/9781617299728.jpg?v=1778530590","url":"https:\/\/mediaplace.com\/en-eu\/products\/pandas-workout","provider":"MediaPlace","version":"1.0","type":"link"}