[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"sanity-ogF26Jtef26GdqbOHVvXMPhLQ721qbQfVwNhiOuGh70":3,"sanity-ZsQPIJo6sX3E87UoGVfZk8W0r0dU6raWM57cilhgm8k":299},{"data":4,"sourceMap":-1},{"latestPodcast":5,"latestReleases":14,"post":39,"recent":274},[6],{"_id":7,"publishedAt":8,"slug":9,"sponsored":12,"title":13},"d53f9358-3bb2-4f69-aebe-d31d19522cd4","2026-07-10T07:40:00.000Z",{"_type":10,"current":11},"slug","building-more-than-just-an-agent-harness",null,"Building more than just an agent harness",[15,21,27,33],{"_id":16,"publishedAt":17,"slug":18,"title":20},"eb5b66eb-9410-4329-83bb-22bbff39402a","2026-04-28T13:00:00.000Z",{"_type":10,"current":19},"turn-scattered-knowledge-into-trusted-intelligence","Turning scattered knowledge into trusted intelligence: Stack Internal 2026.3",{"_id":22,"publishedAt":23,"slug":24,"title":26},"369c2401-b62e-4a37-8ff8-bf603023ecad","2026-03-02T15:03:00.988Z",{"_type":10,"current":25},"what-s-new-at-stack-overflow-march-2026","What’s new at Stack Overflow: March 2026",{"_id":28,"publishedAt":29,"slug":30,"title":32},"5e9053a4-07ea-447c-91ea-29e0b6228537","2026-02-02T15:00:00.000Z",{"_type":10,"current":31},"what-s-new-at-stack-overflow-february-2026","What’s new at Stack Overflow: February 2026",{"_id":34,"publishedAt":35,"slug":36,"title":38},"a1b538eb-a8a6-46d0-80a1-ac70ec9bb935","2026-01-05T10:00:00.000-05:00",{"_type":10,"current":37},"what-s-new-at-stack-overflow-january-2026","What’s new at Stack Overflow: January 2026",{"_createdAt":40,"_id":41,"_rev":42,"_type":43,"_updatedAt":44,"author":45,"body":76,"comments":234,"dateUrl":235,"excerpt":236,"image":237,"legacyBody":240,"product":12,"publishedAt":243,"slug":244,"sponsored":12,"tags":246,"title":273,"visible":234},"2023-05-25T09:39:17Z","wp-post-17503","9HpbCsT2tq0xwozQfkgBTj","blogPost","2023-07-13T14:55:53Z",[46,64],{"_createdAt":47,"_id":48,"_rev":49,"_system":50,"_type":53,"_updatedAt":54,"avatar":55,"employee":60,"name":61,"slug":62},"2023-05-23T16:27:18Z","wp-author-213","nRfzWrvFg3DIXOd15U9uv8",{"base":51},{"id":48,"rev":52},"9xJoPFf2DISyAMMJXP7Ct6","blogAuthor","2025-07-29T19:37:47Z",{"_type":56,"asset":57},"image",{"_ref":58,"_type":59},"image-e81c84dcaeb58be1002795a6544b595bd6fc8071-1024x1024-jpg","reference","former","Ben Popper",{"current":63},"benpopper",{"_createdAt":47,"_id":65,"_rev":66,"_type":53,"_updatedAt":67,"avatar":68,"bio":71,"employee":72,"name":73,"slug":74},"wp-author-cap-17508","07ZbrKPSUrjrV4wQ6fDpaa","2023-06-20T15:05:10Z",{"_type":56,"asset":69},{"_ref":70,"_type":59},"image-8c28cb2ef9d5c7ef909c7685c3808e5c66f17aeb-400x400-jpg","Curriculum Developer, Codecademy","none","Sophie Sommer",{"current":75},"sophie-sommer",[77,101,109,117,125,129,137,148,159,170,199,216],{"_key":78,"_type":79,"children":80,"markDefs":96,"style":100},"05219fc027df","block",[81,87,92],{"_key":82,"_type":83,"marks":84,"text":86},"05219fc027df0","span",[85],"em","Welcome back! This is the second class in our Level Up series. If you're just tuning in, you can catch up on what we're doing and review the first lesson ",{"_key":88,"_type":83,"marks":89,"text":91},"05219fc027df1",[85,90],"7e81bb9d58dd","here",{"_key":93,"_type":83,"marks":94,"text":95},"05219fc027df2",[85],".",[97],{"_key":90,"_type":98,"href":99,"reference":12},"link","https:\u002F\u002Fstackoverflow.blog\u002F2021\u002F02\u002F16\u002Flevel-up-mastering-statistics-with-python\u002F","normal",{"_key":102,"_type":79,"children":103,"markDefs":108,"style":100},"eafcdd2d981a",[104],{"_key":105,"_type":83,"marks":106,"text":107},"eafcdd2d981a0",[],"In this session, we'll continue to investigate a dataset with summary statistics and some basic data visualizations. We'll be using the Python libraries NumPy, pandas, matplotlib, and Seaborn.",[],{"_key":110,"_type":79,"children":111,"markDefs":116,"style":100},"60565bbff863",[112],{"_key":113,"_type":83,"marks":114,"text":115},"60565bbff8630",[],"We're using a fun new dataset for this session—New York City housing data. We begin by looking at summary statistics for a quantitative variable, like rent. What does the mean rent for an apartment in New York City tell us compared to the median rent? How about the trimmed mean? How does a histogram relate to those statistics? We'll also look into the spread of the data. What can we learn from looking at the minimum, maximum, 25th percentile, and 75th percentile?",[],{"_key":118,"_type":79,"children":119,"markDefs":124,"style":100},"22554fec0650",[120],{"_key":121,"_type":83,"marks":122,"text":123},"22554fec06500",[],"This session is particularly fun as we get to do some data investigation on the fly. As we begin plotting our data, we see some surprising irregularities. Why is there a block of apartments in New York that are 40 minutes away from the nearest subway station? Follow along as we try to solve this mystery!",[],{"_key":126,"_type":127,"markDefs":12,"url":128},"39f868056902","embed","https:\u002F\u002Fwww.youtube.com\u002Fembed\u002FVNOdmBdes68?start=30",{"_key":130,"_type":79,"children":131,"markDefs":136,"style":100},"f55703484972",[132],{"_key":133,"_type":83,"marks":134,"text":135},"f557034849720",[],"Here are some Stack Overflow questions related to the work we did in today's session:",[],{"_key":138,"_type":79,"children":139,"markDefs":145,"style":100},"35402de51330",[140],{"_key":141,"_type":83,"marks":142,"text":144},"35402de513300",[143],"a7fb2bd4e964","Finding the average of a dataframe column",[146],{"_key":143,"_type":98,"href":147,"reference":12},"https:\u002F\u002Fstackoverflow.com\u002Fquestions\u002F31037298\u002Fpandas-get-column-average-mean",{"_key":149,"_type":79,"children":150,"markDefs":156,"style":100},"3a7dcd01ea03",[151],{"_key":152,"_type":83,"marks":153,"text":155},"3a7dcd01ea030",[154],"dc1a82a93eb0","Creating a histogram using matplotlib",[157],{"_key":154,"_type":98,"href":158,"reference":12},"https:\u002F\u002Fstackoverflow.com\u002Fquestions\u002F33203645\u002Fhow-to-plot-a-histogram-using-matplotlib-in-python-with-a-list-of-data",{"_key":160,"_type":79,"children":161,"markDefs":167,"style":100},"60ed92a00a9a",[162],{"_key":163,"_type":83,"marks":164,"text":166},"60ed92a00a9a0",[165],"6a8df24a0cca","Customizing the output from pandas describe function",[168],{"_key":165,"_type":98,"href":169,"reference":12},"https:\u002F\u002Fstackoverflow.com\u002Fquestions\u002F19124148\u002Fmodify-output-from-python-pandas-describe",{"_key":171,"_type":79,"children":172,"markDefs":194,"style":100},"6a7ccca95f8e",[173,177,182,186,191],{"_key":174,"_type":83,"marks":175,"text":176},"6a7ccca95f8e0",[],"If you enjoyed this lesson, you can catch up on the ",{"_key":178,"_type":83,"marks":179,"text":181},"6a7ccca95f8e1",[180],"91191f018708","rest of the series",{"_key":183,"_type":83,"marks":184,"text":185},"6a7ccca95f8e2",[]," on YouTube. If you’d like to watch a session live, follow the ",{"_key":187,"_type":83,"marks":188,"text":190},"6a7ccca95f8e3",[189],"82265552cddf","Codecademy YouTube channel",{"_key":192,"_type":83,"marks":193,"text":95},"6a7ccca95f8e4",[],[195,197],{"_key":180,"_type":98,"href":196,"reference":12},"https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLFzsFUO-y0HDWkdsBMtufEThI2I3c9WlZ",{"_key":189,"_type":98,"href":198,"reference":12},"https:\u002F\u002Fwww.youtube.com\u002Fc\u002Fcodecademy\u002Ffeatured",{"_key":200,"_type":79,"children":201,"markDefs":213,"style":100},"aef3f2f3af4c",[202,206,210],{"_key":203,"_type":83,"marks":204,"text":205},"aef3f2f3af4c0",[],"Every Tuesday from now until March 2nd, we’ll be streaming a new session at 4PM EST. You can set a reminder for the stream for February 23rd ",{"_key":207,"_type":83,"marks":208,"text":91},"aef3f2f3af4c1",[209],"ad00430d86ed",{"_key":211,"_type":83,"marks":212,"text":95},"aef3f2f3af4c2",[],[214],{"_key":209,"_type":98,"href":215,"reference":12},"https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=w-7dSKL0UHs",{"_key":217,"_type":79,"children":218,"markDefs":231,"style":100},"c084c249861b",[219,223,227],{"_key":220,"_type":83,"marks":221,"text":222},"c084c249861b0",[],"Finally, if you want even more stats content, you can sign up for the interactive course this series was based on ",{"_key":224,"_type":83,"marks":225,"text":91},"c084c249861b1",[226],"152835d7b7df",{"_key":228,"_type":83,"marks":229,"text":230},"c084c249861b2",[],". This course was developed by Sophie and has many more quizzes, projects, and helpful nuggets that we can’t fit into our streams!",[232],{"_key":226,"_type":98,"href":233,"reference":12},"https:\u002F\u002Fwww.codecademy.com\u002Flearn\u002Fpaths\u002Fmaster-statistics-with-python?utm_source=stack_overflow&utm_medium=partners&utm_content=cclive_stats_1",true,"2021\u002F02\u002F23","Investigate a dataset with summary statistics and some basic data visualizations using the Python libraries NumPy, pandas, matplotlib, and Seaborn. ",{"_type":56,"asset":238},{"_ref":239,"_type":59},"image-07fdf2b4ad01dc1e53caef7199bf7141906ad2d2-4166x1750-png",{"code":241,"language":242},"\u003C!-- wp:paragraph -->\n\u003Cp>\u003Cem>Welcome back! This is the second class in our Level Up series. If you're just tuning in, you can catch up on what we're doing and review the first lesson \u003Ca href=\"https:\u002F\u002Fstackoverflow.blog\u002F2021\u002F02\u002F16\u002Flevel-up-mastering-statistics-with-python\u002F\">here\u003C\u002Fa>. \u003C\u002Fem>\u003C\u002Fp>\n\u003C!-- \u002Fwp:paragraph -->\n\n\u003C!-- wp:paragraph -->\n\u003Cp>In this session, we'll continue to investigate a dataset with summary statistics and some basic data visualizations. We'll be using the Python libraries NumPy, pandas, matplotlib, and Seaborn. \u003C\u002Fp>\n\u003C!-- \u002Fwp:paragraph -->\n\n\u003C!-- wp:paragraph -->\n\u003Cp>We're using a fun new dataset for this session—New York City housing data. We begin by looking at summary statistics for a quantitative variable, like rent. What does the mean rent for an apartment in New York City tell us compared to the median rent? How about the trimmed mean? How does a histogram relate to those statistics? We'll also look into the spread of the data. What can we learn from looking at the minimum, maximum, 25th percentile, and 75th percentile?\u003C\u002Fp>\n\u003C!-- \u002Fwp:paragraph -->\n\n\u003C!-- wp:paragraph -->\n\u003Cp>This session is particularly fun as we get to do some data investigation on the fly. As we begin plotting our data, we see some surprising irregularities. Why is there a block of apartments in New York that are 40 minutes away from the nearest subway station? Follow along as we try to solve this mystery!\u003C\u002Fp>\n\u003C!-- \u002Fwp:paragraph -->\n\n\u003C!-- wp:html -->\n\u003Ciframe width=\"560\" height=\"315\" src=\"https:\u002F\u002Fwww.youtube.com\u002Fembed\u002FVNOdmBdes68?start=30\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen=\"\">\u003C\u002Fiframe>\n\u003C!-- \u002Fwp:html -->\n\n\u003C!-- wp:paragraph -->\n\u003Cp>Here are some Stack Overflow questions related to the work we did in today's session:\u003C\u002Fp>\n\u003C!-- \u002Fwp:paragraph -->\n\n\u003C!-- wp:paragraph -->\n\u003Cp>\u003Ca href=\"https:\u002F\u002Fstackoverflow.com\u002Fquestions\u002F31037298\u002Fpandas-get-column-average-mean\">Finding the average of a dataframe column\u003C\u002Fa>\u003C\u002Fp>\n\u003C!-- \u002Fwp:paragraph -->\n\n\u003C!-- wp:paragraph -->\n\u003Cp>\u003Ca href=\"https:\u002F\u002Fstackoverflow.com\u002Fquestions\u002F33203645\u002Fhow-to-plot-a-histogram-using-matplotlib-in-python-with-a-list-of-data\">Creating a histogram using matplotlib\u003C\u002Fa>\u003C\u002Fp>\n\u003C!-- \u002Fwp:paragraph -->\n\n\u003C!-- wp:paragraph -->\n\u003Cp>\u003Ca href=\"https:\u002F\u002Fstackoverflow.com\u002Fquestions\u002F19124148\u002Fmodify-output-from-python-pandas-describe\">Customizing the output from pandas describe function\u003C\u002Fa>\u003C\u002Fp>\n\u003C!-- \u002Fwp:paragraph -->\n\n\u003C!-- wp:paragraph -->\n\u003Cp>If you enjoyed this lesson, you can catch up on the \u003Ca href=\"https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLFzsFUO-y0HDWkdsBMtufEThI2I3c9WlZ\">rest of the series\u003C\u002Fa> on YouTube. If you’d like to watch a session live, follow the \u003Ca href=\"https:\u002F\u002Fwww.youtube.com\u002Fc\u002Fcodecademy\u002Ffeatured\">Codecademy YouTube channel\u003C\u002Fa>.\u003C\u002Fp>\n\u003C!-- \u002Fwp:paragraph -->\n\n\u003C!-- wp:paragraph -->\n\u003Cp>Every Tuesday from now until March 2nd, we’ll be streaming a new session at 4PM EST. You can set a reminder for the stream for February 23rd \u003Ca href=\"https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=w-7dSKL0UHs\">here\u003C\u002Fa>.\u003C\u002Fp>\n\u003C!-- \u002Fwp:paragraph -->\n\n\u003C!-- wp:paragraph -->\n\u003Cp>Finally, if you want even more stats content, you can sign up for the interactive course this series was based on \u003Ca href=\"https:\u002F\u002Fwww.codecademy.com\u002Flearn\u002Fpaths\u002Fmaster-statistics-with-python?utm_source=stack_overflow&utm_medium=partners&utm_content=cclive_stats_1\">here\u003C\u002Fa>. This course was developed by Sophie and has many more quizzes, projects, and helpful nuggets that we can’t fit into our streams!\u003C\u002Fp>\n\u003C!-- \u002Fwp:paragraph -->","html","2021-02-23T19:03:17.000Z",{"current":245},"level-up-mastering-statistics-with-python-part-2",[247,255,259,264,269],{"_createdAt":248,"_id":249,"_rev":250,"_type":251,"_updatedAt":248,"slug":252,"title":254},"2023-05-23T16:43:21Z","wp-tagcat-code-for-a-living","9HpbCsT2tq0xwozQfkc4ih","blogTag",{"current":253},"code-for-a-living","Code for a Living",{"_createdAt":248,"_id":256,"_rev":250,"_type":251,"_updatedAt":248,"slug":257,"title":258},"wp-tagcat-codecademy",{"current":258},"codecademy",{"_createdAt":248,"_id":260,"_rev":250,"_type":251,"_updatedAt":248,"slug":261,"title":263},"wp-tagcat-data-science",{"current":262},"data-science","data science",{"_createdAt":248,"_id":265,"_rev":250,"_type":251,"_updatedAt":248,"slug":266,"title":268},"wp-tagcat-engineering",{"current":267},"engineering","Engineering",{"_createdAt":248,"_id":270,"_rev":250,"_type":251,"_updatedAt":248,"slug":271,"title":272},"wp-tagcat-statistics",{"current":272},"statistics","Level Up: Mastering statistics with Python - part 2",[275,281,287,293],{"_id":276,"publishedAt":277,"slug":278,"sponsored":12,"title":280},"76c9771b-34e6-4d98-8641-ecefc711f0ef","2026-07-06T15:23:34.559Z",{"_type":10,"current":279},"when-the-sensor-starts-thinking-snortml-agentic-ai-and-the-evolving-architecture-of-intrusion-detection","When the sensor starts thinking: SnortML, agentic AI, and the evolving architecture of intrusion detection",{"_id":282,"publishedAt":283,"slug":284,"sponsored":12,"title":286},"28e560af-f0aa-4d46-bd90-f435ad604aa7","2026-06-26T14:00:27.102Z",{"_type":10,"current":285},"paging-charity-how-can-engineering-leaders-avoid-becoming-bond-villains","Paging Charity! How can engineering leaders avoid becoming Bond villains?",{"_id":288,"publishedAt":289,"slug":290,"sponsored":12,"title":292},"4b22c2a3-3779-4966-93eb-5230391dbdce","2026-06-23T14:08:58.595Z",{"_type":10,"current":291},"your-ai-shipped-a-backend-that-boots-that-is-the-whole-problem","Your AI shipped a backend that boots. That is the whole problem.",{"_id":294,"publishedAt":295,"slug":296,"sponsored":12,"title":298},"5cf362e1-fe7b-45af-b69c-914731c6a052","2026-06-23T14:00:00.000Z",{"_type":10,"current":297},"the-2026-developer-survey-is-now-open-for-human-developers-only","The 2026 Developer Survey is now open (for human developers only)!",{"data":300,"sourceMap":-1},{"count":301,"lastTimestamp":12},0]