{"id":24696,"date":"2022-12-05T01:29:13","date_gmt":"2022-12-04T19:59:13","guid":{"rendered":"https:\/\/jassweb.com\/solved\/solved-parsing-date-from-fetched-dataframe-python\/"},"modified":"2022-12-05T01:29:13","modified_gmt":"2022-12-04T19:59:13","slug":"solved-parsing-date-from-fetched-dataframe-python","status":"publish","type":"post","link":"https:\/\/jassweb.com\/solved\/solved-parsing-date-from-fetched-dataframe-python\/","title":{"rendered":"[Solved] Parsing date from fetched dataframe &#8211; Python"},"content":{"rendered":"<p> [ad_1]<br \/>\n<\/p>\n<div id=\"answer-62299102\" class=\"answer js-answer accepted-answer js-accepted-answer\" data-answerid=\"62299102\" data-parentid=\"62298226\" data-score=\"1\" data-position-on-page=\"1\" data-highest-scored=\"1\" data-question-has-accepted-highest-score=\"1\" itemprop=\"acceptedAnswer\" itemscope itemtype=\"https:\/\/schema.org\/Answer\">\n<div class=\"post-layout\">\n<div class=\"votecell post-layout--left\"><\/div>\n<div class=\"answercell post-layout--right\">\n<div class=\"s-prose js-post-body\" itemprop=\"text\">\n<p>Here are some potential solutions you could use:<\/p>\n<pre><code>import pandas as pd\nfrom yahooquery import Ticker\nfrom datetime import datetime, date\n\naapl = Ticker('aapl')\ndf = aapl.history(period='max', interval=\"1d\")\n<\/code><\/pre>\n<p>df<\/p>\n<pre><code>                      volume       low      high     close      open      adjclose  dividends  splits\n1980-12-12 14:30:00  117258400.0  0.513393  0.515625  0.513393  0.513393  0.405683        0.0     0.0\n1980-12-15 14:30:00   43971200.0  0.486607  0.488839  0.486607  0.488839  0.384517        0.0     0.0\n1980-12-16 14:30:00   26432000.0  0.450893  0.453125  0.450893  0.453125  0.356296        0.0     0.0\n1980-12-17 14:30:00   21610400.0  0.462054  0.464286  0.462054  0.462054  0.365115        0.0     0.0\n1980-12-18 14:30:00   18362400.0  0.475446  0.477679  0.475446  0.475446  0.375698        0.0     0.0\n<\/code><\/pre>\n<p>Option 1. Get rid of the the timestamp and rename the index:<\/p>\n<pre><code>df.index = df.index.normalize()\ndf.index.name=\"Date\"\n\nDate        volume       low      high     close      open       adjclose  dividends  splits                                                                                \n1980-12-12  117258400.0  0.513393  0.515625  0.513393  0.513393  0.405683        0.0     0.0\n1980-12-15   43971200.0  0.486607  0.488839  0.486607  0.488839  0.384517        0.0     0.0\n1980-12-16   26432000.0  0.450893  0.453125  0.450893  0.453125  0.356296        0.0     0.0\n1980-12-17   21610400.0  0.462054  0.464286  0.462054  0.462054  0.365115        0.0     0.0\n1980-12-18   18362400.0  0.475446  0.477679  0.475446  0.475446  0.375698        0.0     0.0\n<\/code><\/pre>\n<p>Option 2: Just add a date column in front<\/p>\n<pre><code>df.insert(0, 'Date', df.index.normalize())\n\n                          Date      open     close      high       volume  \\\n1980-12-12 14:30:00 1980-12-12  0.513393  0.513393  0.515625  117258400.0   \n1980-12-15 14:30:00 1980-12-15  0.488839  0.486607  0.488839   43971200.0   \n1980-12-16 14:30:00 1980-12-16  0.453125  0.450893  0.453125   26432000.0   \n1980-12-17 14:30:00 1980-12-17  0.462054  0.462054  0.464286   21610400.0   \n1980-12-18 14:30:00 1980-12-18  0.475446  0.475446  0.477679   18362400.0   \n<\/code><\/pre>\n<p>Option 3: Add a Date column in front and then reset the index<\/p>\n<pre><code>df.insert(0, 'Date', df.index.normalize())\ndf.reset_index(inplace=True, drop=True)\n\n        Date      high       volume     close      open       low  adjclose  \\\n0 1980-12-12  0.515625  117258400.0  0.513393  0.513393  0.513393  0.405683   \n1 1980-12-15  0.488839   43971200.0  0.486607  0.488839  0.486607  0.384517   \n2 1980-12-16  0.453125   26432000.0  0.450893  0.453125  0.450893  0.356296   \n3 1980-12-17  0.464286   21610400.0  0.462054  0.462054  0.462054  0.365115   \n4 1980-12-18  0.477679   18362400.0  0.475446  0.475446  0.475446  0.375698  \n<\/code><\/pre>\n<p>It&#8217;s hard to be sure what you&#8217;re after, but maybe a quick tutorial in pandas wouldn&#8217;t hurt<br \/>\n<a rel=\"nofollow noopener\" target=\"_blank\" href=\"https:\/\/www.tutorialspoint.com\/python_pandas\/index.htm\">https:\/\/www.tutorialspoint.com\/python_pandas\/index.htm<\/a> since you seem to be thinking that Excel knowledge is directly translated into python pandas without a learning curve.<\/p>\n<\/p><\/div>\n<div class=\"mt24\"><\/div>\n<\/div>\n<p>            <span class=\"d-none\" itemprop=\"commentCount\"><\/span> <\/p><\/div>\n<\/div>\n<p>[ad_2]<\/p>\n<p>solved Parsing date from fetched dataframe &#8211; Python <\/p>\n","protected":false},"excerpt":{"rendered":"<p>[ad_1] Here are some potential solutions you could use: import pandas as pd from yahooquery import Ticker from datetime import datetime, date aapl = Ticker(&#8216;aapl&#8217;) df = aapl.history(period=&#8217;max&#8217;, interval=&#8221;1d&#8221;) df volume low high close open adjclose dividends splits 1980-12-12 14:30:00 117258400.0 0.513393 0.515625 0.513393 0.513393 0.405683 0.0 0.0 1980-12-15 14:30:00 43971200.0 0.486607 0.488839 0.486607 0.488839 &#8230; <a title=\"[Solved] Parsing date from fetched dataframe &#8211; Python\" class=\"read-more\" href=\"https:\/\/jassweb.com\/solved\/solved-parsing-date-from-fetched-dataframe-python\/\" aria-label=\"More on [Solved] Parsing date from fetched dataframe &#8211; Python\">Read more<\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[320],"tags":[415,349],"class_list":["post-24696","post","type-post","status-publish","format-standard","hentry","category-solved","tag-pandas","tag-python"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>[Solved] Parsing date from fetched dataframe - Python - JassWeb<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/jassweb.com\/solved\/solved-parsing-date-from-fetched-dataframe-python\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"[Solved] Parsing date from fetched dataframe - Python - JassWeb\" \/>\n<meta property=\"og:description\" content=\"[ad_1] Here are some potential solutions you could use: import pandas as pd from yahooquery import Ticker from datetime import datetime, date aapl = Ticker(&#039;aapl&#039;) df = aapl.history(period=&#039;max&#039;, interval=&quot;1d&quot;) df volume low high close open adjclose dividends splits 1980-12-12 14:30:00 117258400.0 0.513393 0.515625 0.513393 0.513393 0.405683 0.0 0.0 1980-12-15 14:30:00 43971200.0 0.486607 0.488839 0.486607 0.488839 ... 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