{"id":971,"date":"2020-07-06T07:00:00","date_gmt":"2020-07-06T04:00:00","guid":{"rendered":"https:\/\/www.dataplatform.gr\/?p=971"},"modified":"2026-02-24T11:21:31","modified_gmt":"2026-02-24T08:21:31","slug":"vlookup-me-chrisi-python-choris-microsoft-excel-functions","status":"publish","type":"post","link":"https:\/\/www.dataplatform.gr\/en\/vlookup-me-chrisi-python-choris-microsoft-excel-functions\/","title":{"rendered":"Vlookup with Python without using Microsoft Excel functions"},"content":{"rendered":"<p>The <strong>Microsoft Excel<\/strong> it is very efficient when handling small amount of data. But it requires the use of functions which are very easy to modify by mistake or to forget to remove them before saving the result.<\/p>\n\n\n\n<p>Somewhere there comes the <strong>Python<\/strong> which with a few lines of code can perform the tasks we want and export the data to a file <strong>Excel<\/strong>.<\/p>\n\n\n\n<p>One of the most useful and used functions is the function <strong>vlookup<\/strong> (vertical lookup). THE <strong>vlookup<\/strong> provides cross-linking of relationships between different Excel files or different friends in Excel. This action can be done easily by using <strong>Python<\/strong>.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\">Let&#039;s look at the problem we want to solve.<\/h4>\n\n\n\n<p>We have an Excel file with the total sales named sales.xlsx.<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"410\" height=\"453\" src=\"https:\/\/www.dataplatform.gr\/wp-content\/uploads\/2020\/06\/1-px.png\" alt=\"\" class=\"wp-image-974\" srcset=\"https:\/\/www.dataplatform.gr\/wp-content\/uploads\/2020\/06\/1-px.png 410w, https:\/\/www.dataplatform.gr\/wp-content\/uploads\/2020\/06\/1-px-272x300.png 272w\" sizes=\"auto, (max-width: 410px) 100vw, 410px\" \/><\/figure>\n\n\n\n<p>There is a second Excel file with the customer names called pelates.xlsx.<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"465\" height=\"384\" src=\"https:\/\/www.dataplatform.gr\/wp-content\/uploads\/2020\/06\/2-px.png\" alt=\"\" class=\"wp-image-975\" srcset=\"https:\/\/www.dataplatform.gr\/wp-content\/uploads\/2020\/06\/2-px.png 465w, https:\/\/www.dataplatform.gr\/wp-content\/uploads\/2020\/06\/2-px-300x248.png 300w\" sizes=\"auto, (max-width: 465px) 100vw, 465px\" \/><\/figure>\n\n\n\n<p>We would like to have the names of the customers in the sales.xlsx file, let&#039;s see how this is done.<\/p>\n\n\n\n<p>Apart from having Python installed, we will need the following libraries which are easily installed by running the following commands in the command prompt.<\/p>\n\n\n\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"python\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\" data-no-translation=\"\" data-no-auto-translation=\"\">pip install xlrd\npip install pandas\npip install numpy\npip install openpyxl<\/pre>\n\n\n\n<p>We import the libraries<\/p>\n\n\n\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"python\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\" data-no-translation=\"\" data-no-auto-translation=\"\">import pandas as pd\nimport numpy as np<\/pre>\n\n\n\n<p>We fill 2 dataframe variables with the records of each Excel. With the sheet_name parameter we can also choose the gender where the data is located.<\/p>\n\n\n\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"python\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\" data-no-translation=\"\" data-no-auto-translation=\"\">df_pelates = pd.read_excel(\u2018pelates.xlsx\u2019,sheet_name=\u2019Sheet1\u2032)\n\ndf_sales = pd.read_excel(\u2018sales.xlsx\u2019,sheet_name=\u2019Sheet1\u2032)<\/pre>\n\n\n\n<p>We should rename the id field to customer_id so that it has the same name as it has in the sales dataframe.<\/p>\n\n\n\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"python\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\" data-no-translation=\"\" data-no-auto-translation=\"\">df_pelates.rename(columns={'id':'customer_id'},\u00a0inplace=True)<\/pre>\n\n\n\n<p>In this step the <strong>vlookup<\/strong>, we merge both dataframes into a new one (df_final) in the customer_id field. In the how parameter we declare the way it will be done <strong>join<\/strong> as in SQL we have options left, right, outer, inner.<\/p>\n\n\n\n<p>By choice <strong>right<\/strong> we declare that we want all the records from the second dataframe, linking to those linked to the first. <\/p>\n\n\n\n<p>If there is no common customer_id it will have the value NaN\/Null or else the blank.<\/p>\n\n\n\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"python\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\" data-no-translation=\"\" data-no-auto-translation=\"\">df_final = pd.merge(df_pelates, df_sales, on='customer_id', how='right')<\/pre>\n\n\n\n<p>Let&#039;s see the fields that the merged dataframe now has.<\/p>\n\n\n\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"python\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\" data-no-translation=\"\" data-no-auto-translation=\"\">print(df_final.columns)<\/pre>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"518\" height=\"57\" src=\"https:\/\/www.dataplatform.gr\/wp-content\/uploads\/2020\/06\/1_1-px.png\" alt=\"\" class=\"wp-image-1233\" srcset=\"https:\/\/www.dataplatform.gr\/wp-content\/uploads\/2020\/06\/1_1-px.png 518w, https:\/\/www.dataplatform.gr\/wp-content\/uploads\/2020\/06\/1_1-px-300x33.png 300w\" sizes=\"auto, (max-width: 518px) 100vw, 518px\" \/><\/figure>\n\n\n\n<p>The result so far is this.<\/p>\n\n\n\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"python\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\" data-no-translation=\"\" data-no-auto-translation=\"\">print(df_final)<\/pre>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"568\" height=\"192\" src=\"https:\/\/www.dataplatform.gr\/wp-content\/uploads\/2020\/06\/1_2-px.png\" alt=\"\" class=\"wp-image-1232\" srcset=\"https:\/\/www.dataplatform.gr\/wp-content\/uploads\/2020\/06\/1_2-px.png 568w, https:\/\/www.dataplatform.gr\/wp-content\/uploads\/2020\/06\/1_2-px-300x101.png 300w\" sizes=\"auto, (max-width: 568px) 100vw, 568px\" \/><\/figure>\n\n\n\n<p>In the case that the client does not exist and we do not want the NaN values to come to us in their place and we just want to have an empty cell, we execute the following.<\/p>\n\n\n\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"python\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\" data-no-translation=\"\" data-no-auto-translation=\"\">df_final = df_final.replace(np.nan, '', regex=True)<\/pre>\n\n\n\n<p>We can also choose which fields of these to keep.<\/p>\n\n\n\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"python\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\" data-no-translation=\"\" data-no-auto-translation=\"\">df_final = df_final[['onoma','epitheto','eidos','posotita','kostos']]<\/pre>\n\n\n\n<h4 class=\"wp-block-heading has-vivid-cyan-blue-color has-text-color\">The end result<\/h4>\n\n\n\n<p>And finally to export the results in a new excel.<\/p>\n\n\n\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"python\" data-enlighter-theme=\"\" data-enlighter-highlight=\"\" data-enlighter-linenumbers=\"\" data-enlighter-lineoffset=\"\" data-enlighter-title=\"\" data-enlighter-group=\"\" data-no-translation=\"\" data-no-auto-translation=\"\">df_final.to_excel('vlookuped.xlsx', index=False)<\/pre>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"446\" height=\"502\" src=\"https:\/\/www.dataplatform.gr\/wp-content\/uploads\/2020\/06\/3-px.png\" alt=\"\" class=\"wp-image-973\" srcset=\"https:\/\/www.dataplatform.gr\/wp-content\/uploads\/2020\/06\/3-px.png 446w, https:\/\/www.dataplatform.gr\/wp-content\/uploads\/2020\/06\/3-px-267x300.png 267w\" sizes=\"auto, (max-width: 446px) 100vw, 446px\" \/><\/figure>","protected":false},"excerpt":{"rendered":"<p>Microsoft Excel is very efficient when managing a small amount of data. But it requires the use of functions which are very easy to modify by mistake or to forget to remove them before saving the result. Somewhere there comes Python which with a few lines of code can perform the tasks we want and export [...]<\/p>","protected":false},"author":1,"featured_media":694,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[12,13,17,14],"tags":[43,23,38,92,9],"class_list":["post-971","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-business-intelligence","category-datascience_ai","category-ms-excel","category-python","tag-microsoft-excel","tag-microsoft","tag-office-365","tag-programming","tag-python"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Vlookup \u03bc\u03b5 Python \u03c7\u03c9\u03c1\u03af\u03c2 \u03c4\u03b7 \u03c7\u03c1\u03ae\u03c3\u03b7 Microsoft Excel functions - DataPlatform.gr<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, 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functions \u03bf\u03b9 \u03bf\u03c0\u03bf\u03af\u03b5\u03c2 \u03b5\u03af\u03bd\u03b1\u03b9 \u03c0\u03bf\u03bb\u03cd \u03b5\u03cd\u03ba\u03bf\u03bb\u03bf \u03bd\u03b1 \u03c4\u03c1\u03bf\u03c0\u03bf\u03c0\u03bf\u03b9\u03b7\u03b8\u03bf\u03cd\u03bd \u03ba\u03b1\u03c4\u03ac \u03bb\u03ac\u03b8\u03bf\u03c2 \u03ae \u03bd\u03b1 \u03be\u03b5\u03c7\u03ac\u03c3\u03bf\u03c5\u03bc\u03b5 \u03bd\u03b1 \u03c4\u03b9\u03c2 \u03b1\u03c6\u03b1\u03b9\u03c1\u03ad\u03c3\u03bf\u03c5\u03bc\u03b5 \u03c0\u03c1\u03b9\u03bd \u03b1\u03c0\u03bf\u03b8\u03b7\u03ba\u03b5\u03cd\u03c3\u03bf\u03c5\u03bc\u03b5 \u03c4\u03bf \u03b1\u03c0\u03bf\u03c4\u03ad\u03bb\u03b5\u03c3\u03bc\u03b1. \u039a\u03ac\u03c0\u03bf\u03c5 \u03b5\u03ba\u03b5\u03af \u03ad\u03c1\u03c7\u03b5\u03c4\u03b1\u03b9 \u03b7 Python \u03c0\u03bf\u03c5 \u03bc\u03b5 \u03bc\u03b5\u03c1\u03b9\u03ba\u03ad\u03c2 \u03b3\u03c1\u03b1\u03bc\u03bc\u03ad\u03c2 \u03ba\u03ce\u03b4\u03b9\u03ba\u03b1 \u03b3\u03af\u03bd\u03b5\u03c4\u03b1\u03b9 \u03bd\u03b1 \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03bf\u03c0\u03bf\u03b9\u03ae\u03c3\u03b5\u03b9 \u03c4\u03b9\u03c2 \u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b5\u03c2 \u03c0\u03bf\u03c5 \u03b8\u03ad\u03bb\u03bf\u03c5\u03bc\u03b5 \u03ba\u03b1\u03b9 \u03bd\u03b1 \u03b5\u03be\u03ac\u03b3\u03b5\u03b9 [&hellip;]\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.dataplatform.gr\/en\/vlookup-me-chrisi-python-choris-microsoft-excel-functions\/\" \/>\n<meta property=\"og:site_name\" content=\"DataPlatform.gr\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/dataplatform.gr\/\" \/>\n<meta property=\"article:published_time\" content=\"2020-07-06T04:00:00+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2026-02-24T08:21:31+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/www.dataplatform.gr\/wp-content\/uploads\/2020\/06\/dp_excel_python.png\" \/>\n\t<meta property=\"og:image:width\" content=\"1280\" \/>\n\t<meta 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