{"id":2557,"date":"2015-02-27T13:15:23","date_gmt":"2015-02-27T11:15:23","guid":{"rendered":"http:\/\/www.webcodegeeks.com\/?p=2557"},"modified":"2015-02-26T16:14:53","modified_gmt":"2015-02-26T14:14:53","slug":"python-pandas-column-value-list","status":"publish","type":"post","link":"https:\/\/www.webcodegeeks.com\/python\/python-pandas-column-value-list\/","title":{"rendered":"Python\/pandas: Column value in list"},"content":{"rendered":"

I\u2019ve been using Python\u2019s pandas<\/a> library while exploring some CSV files and although for the most part I\u2019ve found it intuitive to use, I had trouble filtering a data frame based on checking whether a column value was in a list.<\/p>\n

A subset of one of the CSV files I\u2019ve been working with looks like this:
\n 
\n 
\n 
\n 
\n 
\n <\/p>\n

$ cat foo.csv\r\n'Foo'\r\n1\r\n2\r\n3\r\n4\r\n5\r\n6\r\n7\r\n8\r\n9\r\n10<\/pre>\n
Loading it into a pandas data frame is reasonably simple:<\/p>\n
import pandas as pd\r\ndf = pd.read_csv('foo.csv', index_col=False, header=0)\r\n>>> df\r\n   Foo\r\n0    1\r\n1    2\r\n2    3\r\n3    4\r\n4    5\r\n5    6\r\n6    7\r\n7    8\r\n8    9\r\n9   10<\/pre>\n
If we want to find the rows which have a value of 1 we\u2019d write the following:<\/p>\n
>>> df[df['Foo'] == 1]\r\n   Foo\r\n0    1<\/pre>\n
Finding the rows with a value less than 7 is as you\u2019d expect too:<\/p>\n
>>> df[df['Foo'] < 7]\r\n   Foo\r\n0    1\r\n1    2\r\n2    3\r\n3    4\r\n4    5\r\n5    6<\/pre>\n
Next I wanted to filter out the rows containing odd numbers which I initially tried to do like this:<\/p>\n
odds = [i for i in range(1,10) if i % 2 <> 0]\r\n>>> odds\r\n[1, 3, 5, 7, 9]\r\n \r\n>>> df[df['Foo'] in odds]\r\nTraceback (most recent call last):\r\n  File '<stdin>', line 1, in <module>\r\n  File '\/Users\/markneedham\/projects\/neo4j-himym\/himym\/lib\/python2.7\/site-packages\/pandas\/core\/generic.py', line 698, in __nonzero__\r\n    .format(self.__class__.__name__))\r\nValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().<\/pre>\n
Unfortunately that doesn\u2019t work and I couldn\u2019t get any of the suggestions from the error message to work either. Luckily pandas has a special isin<\/a><\/cite> function for this use case which we can call like this:<\/p>\n
>>> df[df['Foo'].isin(odds)]\r\n   Foo\r\n0    1\r\n2    3\r\n4    5\r\n6    7\r\n8    9<\/pre>\n
Much better!<\/p>\n
\n\n\n\n
Reference: <\/span><\/td>\nPython\/pandas: Column value in list<\/a> from our WCG partner<\/a> Mark Needham at the Mark Needham Blog<\/a> blog.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n","protected":false},"excerpt":{"rendered":"
I\u2019ve been using Python\u2019s pandas library while exploring some CSV files and although for the most part I\u2019ve found it intuitive to use, I had trouble filtering a data frame based on checking whether a column value was in a list. A subset of one of the CSV files I\u2019ve been working with looks like …<\/p>\n","protected":false},"author":48,"featured_media":1651,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[53],"tags":[109],"class_list":["post-2557","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-python","tag-pandas"],"yoast_head":"\nPython\/pandas: Column value in list - Web Code Geeks - 2026<\/title>\n<meta name=\"description\" content=\"I\u2019ve been using Python\u2019s pandas library while exploring some CSV files and although for the most part I\u2019ve found it intuitive to use, I had trouble\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.webcodegeeks.com\/python\/python-pandas-column-value-list\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Python\/pandas: Column value in list - 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