{"id":9257,"date":"2025-12-21T07:04:22","date_gmt":"2025-12-21T07:04:22","guid":{"rendered":"https:\/\/mailitics.com\/index.php\/2025\/12\/21\/eda-in-public-part-2-product-deep-dive-time-series-analysis-in-pandas\/"},"modified":"2025-12-21T07:04:22","modified_gmt":"2025-12-21T07:04:22","slug":"eda-in-public-part-2-product-deep-dive-time-series-analysis-in-pandas","status":"publish","type":"post","link":"https:\/\/mailitics.com\/index.php\/2025\/12\/21\/eda-in-public-part-2-product-deep-dive-time-series-analysis-in-pandas\/","title":{"rendered":"EDA in Public (Part 2): Product Deep Dive &amp; Time-Series Analysis in\u00a0Pandas"},"content":{"rendered":"\n<div>EDA in Public (Part 2): Product Deep Dive &#038; Time-Series Analysis in\u00a0Pandas<\/div>\n<p> \t<BR><br \/>\n<BR><\/BR><br \/>\n    <!-- no image --><br \/>\n \t<BR><br \/>\n<BR><\/BR><\/p>\n<div>\n<p>Learn how to analyze product performance, extract time-series features, and uncover key seasonal trends in your sales\u00a0data.<\/p>\n<p>The post <a href=\"https:\/\/towardsdatascience.com\/eda-in-public-part-2-product-deep-dive-time-series-analysis-in-pandas\/\">EDA in Public (Part 2): Product Deep Dive &amp; Time-Series Analysis in\u00a0Pandas<\/a> appeared first on <a href=\"https:\/\/towardsdatascience.com\/\">Towards Data Science<\/a>.<\/p>\n<\/div>\n<p> \t<BR><br \/>\n <BR><\/BR><br \/>\n    Ibrahim Salami<br \/>\n \t<BR><br \/>\n<BR><\/BR><br \/>\n<a href=\"https:\/\/towardsdatascience.com\/eda-in-public-part-2-product-deep-dive-time-series-analysis-in-pandas\/\">Go to original source<\/a><br \/>\n \t<BR><br \/>\n <BR><\/BR><\/p>\n","protected":false},"excerpt":{"rendered":"<p>EDA in Public (Part 2): Product Deep Dive &#038; Time-Series Analysis in\u00a0Pandas Learn how to analyze product performance, extract time-series features, and uncover key seasonal trends in your sales\u00a0data. The post EDA in Public (Part 2): Product Deep Dive &amp; Time-Series Analysis in\u00a0Pandas appeared first on Towards Data Science. Ibrahim Salami Go to original source<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[62,83,4415,70,937,160,157],"tags":[993,325,15],"class_list":["post-9257","post","type-post","status-publish","format-standard","hentry","category-aimldsaimlds","category-data-science","category-exploratory-data-analysis","category-machine-learning","category-pandas","category-programming","category-python","tag-product","tag-series","tag-time"],"_links":{"self":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts\/9257"}],"collection":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/comments?post=9257"}],"version-history":[{"count":0,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts\/9257\/revisions"}],"wp:attachment":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/media?parent=9257"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/categories?post=9257"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/tags?post=9257"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}