{"id":3395,"date":"2021-07-23T12:18:24","date_gmt":"2021-07-23T12:18:24","guid":{"rendered":"https:\/\/www.bearloga.space\/?page_id=3395"},"modified":"2021-07-23T12:18:25","modified_gmt":"2021-07-23T12:18:25","slug":"python-pandas-groupby","status":"publish","type":"page","link":"https:\/\/www.bearloga.space\/uk\/python-pandas-groupby\/","title":{"rendered":"Python Pandas \u2014 GroupBy"},"content":{"rendered":"\n<pre class=\"wp-block-code\"><code>\r\u041b\u044e\u0431\u0430\u044f \u0433\u0440\u0443\u043f\u043f\u043e\u0432\u0430\u044f \u043e\u043f\u0435\u0440\u0430\u0446\u0438\u044f \u0432\u043a\u043b\u044e\u0447\u0430\u0435\u0442 \u0432 \u0441\u0435\u0431\u044f \u043e\u0434\u043d\u0443 \u0438\u0437 \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0438\u0445 \u043e\u043f\u0435\u0440\u0430\u0446\u0438\u0439 \u0441 \u0438\u0441\u0445\u043e\u0434\u043d\u044b\u043c \u043e\u0431\u044a\u0435\u043a\u0442\u043e\u043c. \u041e\u043d\u0438 \u2014\r\n\r\n\u0420\u0430\u0437\u0434\u0435\u043b\u0435\u043d\u0438\u0435 \u043e\u0431\u044a\u0435\u043a\u0442\u0430\r\n\r\n\u041f\u0440\u0438\u043c\u0435\u043d\u0435\u043d\u0438\u0435 \u0444\u0443\u043d\u043a\u0446\u0438\u0438\r\n\r\n\u041e\u0431\u044a\u0435\u0434\u0438\u043d\u0435\u043d\u0438\u0435 \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442\u043e\u0432\r\n\r\n\u0420\u0430\u0437\u0434\u0435\u043b\u0435\u043d\u0438\u0435 \u043e\u0431\u044a\u0435\u043a\u0442\u0430\r\n\r\n\u041f\u0440\u0438\u043c\u0435\u043d\u0435\u043d\u0438\u0435 \u0444\u0443\u043d\u043a\u0446\u0438\u0438\r\n\r\n\u041e\u0431\u044a\u0435\u0434\u0438\u043d\u0435\u043d\u0438\u0435 \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442\u043e\u0432\r\n\r\n\u0412\u043e \u043c\u043d\u043e\u0433\u0438\u0445 \u0441\u0438\u0442\u0443\u0430\u0446\u0438\u044f\u0445 \u043c\u044b \u0440\u0430\u0437\u0434\u0435\u043b\u044f\u0435\u043c \u0434\u0430\u043d\u043d\u044b\u0435 \u043d\u0430 \u043d\u0430\u0431\u043e\u0440\u044b \u0438 \u043f\u0440\u0438\u043c\u0435\u043d\u044f\u0435\u043c \u043d\u0435\u043a\u043e\u0442\u043e\u0440\u044b\u0435 \u0444\u0443\u043d\u043a\u0446\u0438\u043e\u043d\u0430\u043b\u044c\u043d\u044b\u0435 \u0432\u043e\u0437\u043c\u043e\u0436\u043d\u043e\u0441\u0442\u0438 \u043a \u043a\u0430\u0436\u0434\u043e\u043c\u0443 \u043f\u043e\u0434\u043c\u043d\u043e\u0436\u0435\u0441\u0442\u0432\u0443. \u0412 \u0444\u0443\u043d\u043a\u0446\u0438\u043e\u043d\u0430\u043b\u044c\u043d\u043e\u0441\u0442\u0438 \u043f\u0440\u0438\u043c\u0435\u043d\u0435\u043d\u0438\u044f \u043c\u044b \u043c\u043e\u0436\u0435\u043c \u0432\u044b\u043f\u043e\u043b\u043d\u0438\u0442\u044c \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0438\u0435 \u043e\u043f\u0435\u0440\u0430\u0446\u0438\u0438:\r\n\r\n\u0410\u0433\u0440\u0435\u0433\u0430\u0446\u0438\u044f \u2014 \u0432\u044b\u0447\u0438\u0441\u043b\u0435\u043d\u0438\u0435 \u0441\u0432\u043e\u0434\u043d\u043e\u0439 \u0441\u0442\u0430\u0442\u0438\u0441\u0442\u0438\u043a\u0438\r\n\r\n\u041f\u0440\u0435\u043e\u0431\u0440\u0430\u0437\u043e\u0432\u0430\u043d\u0438\u0435 \u2014 \u0432\u044b\u043f\u043e\u043b\u043d\u0438\u0442\u044c \u043d\u0435\u043a\u043e\u0442\u043e\u0440\u0443\u044e \u0433\u0440\u0443\u043f\u043f\u043e\u0432\u0443\u044e \u043e\u043f\u0435\u0440\u0430\u0446\u0438\u044e\r\n\r\n\u0424\u0438\u043b\u044c\u0442\u0440\u0430\u0446\u0438\u044f \u2014 \u043e\u0442\u0431\u0440\u0430\u0441\u044b\u0432\u0430\u043d\u0438\u0435 \u0434\u0430\u043d\u043d\u044b\u0445 \u0441 \u043d\u0435\u043a\u043e\u0442\u043e\u0440\u044b\u043c \u0443\u0441\u043b\u043e\u0432\u0438\u0435\u043c\r\n\r\n\u0410\u0433\u0440\u0435\u0433\u0430\u0446\u0438\u044f \u2014 \u0432\u044b\u0447\u0438\u0441\u043b\u0435\u043d\u0438\u0435 \u0441\u0432\u043e\u0434\u043d\u043e\u0439 \u0441\u0442\u0430\u0442\u0438\u0441\u0442\u0438\u043a\u0438\r\n\r\n\u041f\u0440\u0435\u043e\u0431\u0440\u0430\u0437\u043e\u0432\u0430\u043d\u0438\u0435 \u2014 \u0432\u044b\u043f\u043e\u043b\u043d\u0438\u0442\u044c \u043d\u0435\u043a\u043e\u0442\u043e\u0440\u0443\u044e \u0433\u0440\u0443\u043f\u043f\u043e\u0432\u0443\u044e \u043e\u043f\u0435\u0440\u0430\u0446\u0438\u044e\r\n\r\n\u0424\u0438\u043b\u044c\u0442\u0440\u0430\u0446\u0438\u044f \u2014 \u043e\u0442\u0431\u0440\u0430\u0441\u044b\u0432\u0430\u043d\u0438\u0435 \u0434\u0430\u043d\u043d\u044b\u0445 \u0441 \u043d\u0435\u043a\u043e\u0442\u043e\u0440\u044b\u043c \u0443\u0441\u043b\u043e\u0432\u0438\u0435\u043c\r\n\r\n\u0422\u0435\u043f\u0435\u0440\u044c \u0434\u0430\u0432\u0430\u0439\u0442\u0435 \u0441\u043e\u0437\u0434\u0430\u0434\u0438\u043c \u043e\u0431\u044a\u0435\u043a\u0442 DataFrame \u0438 \u0432\u044b\u043f\u043e\u043b\u043d\u0438\u043c \u0432\u0441\u0435 \u043e\u043f\u0435\u0440\u0430\u0446\u0438\u0438 \u043d\u0430\u0434 \u043d\u0438\u043c:\r\n\r\n Live Demo\r\n\r\n#import the pandas library\r\nimport pandas as pd\r\n\r\nipl_data = {'Team': &#91;'Riders', 'Riders', 'Devils', 'Devils', 'Kings',\r\n   'kings', 'Kings', 'Kings', 'Riders', 'Royals', 'Royals', 'Riders'],\r\n   'Rank': &#91;1, 2, 2, 3, 3,4 ,1 ,1,2 , 4,1,2],\r\n   'Year': &#91;2014,2015,2014,2015,2014,2015,2016,2017,2016,2014,2015,2017],\r\n   'Points':&#91;876,789,863,673,741,812,756,788,694,701,804,690]}\r\ndf = pd.DataFrame(ipl_data)\r\n\r\nprint df\r\n\u0415\u0433\u043e \u0432\u044b\u0432\u043e\u0434 \u0432\u044b\u0433\u043b\u044f\u0434\u0438\u0442 \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0438\u043c \u043e\u0431\u0440\u0430\u0437\u043e\u043c \u2014\r\n\r\n    Points   Rank     Team   Year\r\n0      876      1   Riders   2014\r\n1      789      2   Riders   2015\r\n2      863      2   Devils   2014\r\n3      673      3   Devils   2015\r\n4      741      3    Kings   2014\r\n5      812      4    kings   2015\r\n6      756      1    Kings   2016\r\n7      788      1    Kings   2017\r\n8      694      2   Riders   2016\r\n9      701      4   Royals   2014\r\n10     804      1   Royals   2015\r\n11     690      2   Riders   2017\r\n\u0420\u0430\u0437\u0434\u0435\u043b\u0438\u0442\u044c \u0434\u0430\u043d\u043d\u044b\u0435 \u043d\u0430 \u0433\u0440\u0443\u043f\u043f\u044b\r\n\u041e\u0431\u044a\u0435\u043a\u0442 \u041f\u0430\u043d\u0434\u044b \u043c\u043e\u0436\u043d\u043e \u0440\u0430\u0437\u0431\u0438\u0442\u044c \u043d\u0430 \u043b\u044e\u0431\u043e\u0439 \u0438\u0437 \u0441\u0432\u043e\u0438\u0445 \u043e\u0431\u044a\u0435\u043a\u0442\u043e\u0432. \u0415\u0441\u0442\u044c \u043d\u0435\u0441\u043a\u043e\u043b\u044c\u043a\u043e \u0441\u043f\u043e\u0441\u043e\u0431\u043e\u0432 \u0440\u0430\u0437\u0431\u0438\u0442\u044c \u043e\u0431\u044a\u0435\u043a\u0442, \u043a\u0430\u043a \u2014\r\n\r\nobj.groupby ( \u2018\u043a\u043b\u044e\u0447\u2019)\r\nobj.groupby (&#91; \u2018\u043a\u043b\u044e\u04471\u2019, \u2018\u043a\u043b\u044e\u04472\u2019])\r\nobj.groupby (\u043a\u043b\u044e\u0447, \u043e\u0441\u044c = 1)\r\n\u0414\u0430\u0432\u0430\u0439\u0442\u0435 \u0442\u0435\u043f\u0435\u0440\u044c \u043f\u043e\u0441\u043c\u043e\u0442\u0440\u0438\u043c, \u043a\u0430\u043a \u043e\u0431\u044a\u0435\u043a\u0442\u044b \u0433\u0440\u0443\u043f\u043f\u0438\u0440\u043e\u0432\u043a\u0438 \u043c\u043e\u0433\u0443\u0442 \u043f\u0440\u0438\u043c\u0435\u043d\u044f\u0442\u044c\u0441\u044f \u043a \u043e\u0431\u044a\u0435\u043a\u0442\u0443 DataFrame.\r\n\r\n\u043f\u0440\u0438\u043c\u0435\u0440\r\n Live Demo\r\n\r\n# import the pandas library\r\nimport pandas as pd\r\n\r\nipl_data = {'Team': &#91;'Riders', 'Riders', 'Devils', 'Devils', 'Kings',\r\n   'kings', 'Kings', 'Kings', 'Riders', 'Royals', 'Royals', 'Riders'],\r\n   'Rank': &#91;1, 2, 2, 3, 3,4 ,1 ,1,2 , 4,1,2],\r\n   'Year': &#91;2014,2015,2014,2015,2014,2015,2016,2017,2016,2014,2015,2017],\r\n   'Points':&#91;876,789,863,673,741,812,756,788,694,701,804,690]}\r\ndf = pd.DataFrame(ipl_data)\r\n\r\nprint df.groupby('Team')\r\n\u0415\u0433\u043e \u0432\u044b\u0432\u043e\u0434 \u0432\u044b\u0433\u043b\u044f\u0434\u0438\u0442 \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0438\u043c \u043e\u0431\u0440\u0430\u0437\u043e\u043c \u2014\r\n\r\n&lt;pandas.core.groupby.DataFrameGroupBy object at 0x7fa46a977e50>\r\n\u041f\u0440\u043e\u0441\u043c\u043e\u0442\u0440 \u0433\u0440\u0443\u043f\u043f\r\n Live Demo\r\n\r\n# import the pandas library\r\nimport pandas as pd\r\n\r\nipl_data = {'Team': &#91;'Riders', 'Riders', 'Devils', 'Devils', 'Kings',\r\n   'kings', 'Kings', 'Kings', 'Riders', 'Royals', 'Royals', 'Riders'],\r\n   'Rank': &#91;1, 2, 2, 3, 3,4 ,1 ,1,2 , 4,1,2],\r\n   'Year': &#91;2014,2015,2014,2015,2014,2015,2016,2017,2016,2014,2015,2017],\r\n   'Points':&#91;876,789,863,673,741,812,756,788,694,701,804,690]}\r\ndf = pd.DataFrame(ipl_data)\r\n\r\nprint df.groupby('Team').groups\r\n\u0415\u0433\u043e \u0432\u044b\u0432\u043e\u0434 \u0432\u044b\u0433\u043b\u044f\u0434\u0438\u0442 \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0438\u043c \u043e\u0431\u0440\u0430\u0437\u043e\u043c \u2014\r\n\r\n{'Kings': Int64Index(&#91;4, 6, 7],      dtype='int64'),\r\n'Devils': Int64Index(&#91;2, 3],         dtype='int64'),\r\n'Riders': Int64Index(&#91;0, 1, 8, 11],  dtype='int64'),\r\n'Royals': Int64Index(&#91;9, 10],        dtype='int64'),\r\n'kings' : Int64Index(&#91;5],            dtype='int64')}\r\n\u043f\u0440\u0438\u043c\u0435\u0440\r\n\u0413\u0440\u0443\u043f\u043f\u0438\u0440\u043e\u0432\u0430\u0442\u044c \u043f\u043e \u043d\u0435\u0441\u043a\u043e\u043b\u044c\u043a\u0438\u043c \u0441\u0442\u043e\u043b\u0431\u0446\u0430\u043c \u2014\r\n\r\r\n\r\n# import the pandas library\r\nimport pandas as pd\r\n\r\nipl_data = {'Team': &#91;'Riders', 'Riders', 'Devils', 'Devils', 'Kings',\r\n   'kings', 'Kings', 'Kings', 'Riders', 'Royals', 'Royals', 'Riders'],\r\n   'Rank': &#91;1, 2, 2, 3, 3,4 ,1 ,1,2 , 4,1,2],\r\n   'Year': &#91;2014,2015,2014,2015,2014,2015,2016,2017,2016,2014,2015,2017],\r\n   'Points':&#91;876,789,863,673,741,812,756,788,694,701,804,690]}\r\ndf = pd.DataFrame(ipl_data)\r\n\r\nprint df.groupby(&#91;'Team','Year']).groups\r\n\u0415\u0433\u043e \u0432\u044b\u0432\u043e\u0434 \u0432\u044b\u0433\u043b\u044f\u0434\u0438\u0442 \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0438\u043c \u043e\u0431\u0440\u0430\u0437\u043e\u043c \u2014\r\n\r\n{('Kings', 2014): Int64Index(&#91;4], dtype='int64'),\r\n ('Royals', 2014): Int64Index(&#91;9], dtype='int64'),\r\n ('Riders', 2014): Int64Index(&#91;0], dtype='int64'),\r\n ('Riders', 2015): Int64Index(&#91;1], dtype='int64'),\r\n ('Kings', 2016): Int64Index(&#91;6], dtype='int64'),\r\n ('Riders', 2016): Int64Index(&#91;8], dtype='int64'),\r\n ('Riders', 2017): Int64Index(&#91;11], dtype='int64'),\r\n ('Devils', 2014): Int64Index(&#91;2], dtype='int64'),\r\n ('Devils', 2015): Int64Index(&#91;3], dtype='int64'),\r\n ('kings', 2015): Int64Index(&#91;5], dtype='int64'),\r\n ('Royals', 2015): Int64Index(&#91;10], dtype='int64'),\r\n ('Kings', 2017): Int64Index(&#91;7], dtype='int64')}\r\n\u0418\u0442\u0435\u0440\u0430\u0446\u0438\u044f \u043f\u043e \u0433\u0440\u0443\u043f\u043f\u0430\u043c\r\n\u0418\u043c\u0435\u044f \u043e\u0431\u044a\u0435\u043a\u0442 groupby , \u043c\u044b \u043c\u043e\u0436\u0435\u043c \u0432\u044b\u043f\u043e\u043b\u043d\u0438\u0442\u044c \u0438\u0442\u0435\u0440\u0430\u0446\u0438\u044e \u043f\u043e \u043e\u0431\u044a\u0435\u043a\u0442\u0443, \u043f\u043e\u0445\u043e\u0436\u0435\u043c\u0443 \u043d\u0430 itertools.obj.\r\n\r\n Live Demo\r\n\r\n# import the pandas library\r\nimport pandas as pd\r\n\r\nipl_data = {'Team': &#91;'Riders', 'Riders', 'Devils', 'Devils', 'Kings',\r\n   'kings', 'Kings', 'Kings', 'Riders', 'Royals', 'Royals', 'Riders'],\r\n   'Rank': &#91;1, 2, 2, 3, 3,4 ,1 ,1,2 , 4,1,2],\r\n   'Year': &#91;2014,2015,2014,2015,2014,2015,2016,2017,2016,2014,2015,2017],\r\n   'Points':&#91;876,789,863,673,741,812,756,788,694,701,804,690]}\r\ndf = pd.DataFrame(ipl_data)\r\n\r\ngrouped = df.groupby('Year')\r\n\r\nfor name,group in grouped:\r\n   print name\r\n   print group\r\n\u0415\u0433\u043e \u0432\u044b\u0432\u043e\u0434 \u0432\u044b\u0433\u043b\u044f\u0434\u0438\u0442 \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0438\u043c \u043e\u0431\u0440\u0430\u0437\u043e\u043c \u2014\r\n\r\n2014\r\n   Points  Rank     Team   Year\r\n0     876     1   Riders   2014\r\n2     863     2   Devils   2014\r\n4     741     3   Kings    2014\r\n9     701     4   Royals   2014\r\n\r\n2015\r\n   Points  Rank     Team   Year\r\n1     789     2   Riders   2015\r\n3     673     3   Devils   2015\r\n5     812     4    kings   2015\r\n10    804     1   Royals   2015\r\n\r\n2016\r\n   Points  Rank     Team   Year\r\n6     756     1    Kings   2016\r\n8     694     2   Riders   2016\r\n\r\n2017\r\n   Points  Rank    Team   Year\r\n7     788     1   Kings   2017\r\n11    690     2  Riders   2017\r\n\u041f\u043e \u0443\u043c\u043e\u043b\u0447\u0430\u043d\u0438\u044e \u043e\u0431\u044a\u0435\u043a\u0442 groupby \u0438\u043c\u0435\u0435\u0442 \u0442\u043e \u0436\u0435 \u0438\u043c\u044f \u043c\u0435\u0442\u043a\u0438, \u0447\u0442\u043e \u0438 \u0438\u043c\u044f \u0433\u0440\u0443\u043f\u043f\u044b.\r\n\r\n\u0412\u044b\u0431\u0435\u0440\u0438\u0442\u0435 \u0433\u0440\u0443\u043f\u043f\u0443\r\n\u0418\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u044f \u043c\u0435\u0442\u043e\u0434 get_group () , \u043c\u044b \u043c\u043e\u0436\u0435\u043c \u0432\u044b\u0431\u0440\u0430\u0442\u044c \u043e\u0434\u043d\u0443 \u0433\u0440\u0443\u043f\u043f\u0443.\r\n\r\n Live Demo\r\n\r\n# import the pandas library\r\nimport pandas as pd\r\n\r\nipl_data = {'Team': &#91;'Riders', 'Riders', 'Devils', 'Devils', 'Kings',\r\n   'kings', 'Kings', 'Kings', 'Riders', 'Royals', 'Royals', 'Riders'],\r\n   'Rank': &#91;1, 2, 2, 3, 3,4 ,1 ,1,2 , 4,1,2],\r\n   'Year': &#91;2014,2015,2014,2015,2014,2015,2016,2017,2016,2014,2015,2017],\r\n   'Points':&#91;876,789,863,673,741,812,756,788,694,701,804,690]}\r\ndf = pd.DataFrame(ipl_data)\r\n\r\ngrouped = df.groupby('Year')\r\nprint grouped.get_group(2014)\r\n\u0415\u0433\u043e \u0432\u044b\u0432\u043e\u0434 \u0432\u044b\u0433\u043b\u044f\u0434\u0438\u0442 \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0438\u043c \u043e\u0431\u0440\u0430\u0437\u043e\u043c \u2014\r\n\r\n   Points  Rank     Team    Year\r\n0     876     1   Riders    2014\r\n2     863     2   Devils    2014\r\n4     741     3   Kings     2014\r\n9     701     4   Royals    2014\r\n\u0421\u043a\u043e\u043f\u043b\u0435\u043d\u0438\u044f\r\n\u0410\u0433\u0440\u0435\u0433\u0438\u0440\u043e\u0432\u0430\u043d\u043d\u0430\u044f \u0444\u0443\u043d\u043a\u0446\u0438\u044f \u0432\u043e\u0437\u0432\u0440\u0430\u0449\u0430\u0435\u0442 \u043e\u0434\u043d\u043e \u0430\u0433\u0440\u0435\u0433\u0438\u0440\u043e\u0432\u0430\u043d\u043d\u043e\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435 \u0434\u043b\u044f \u043a\u0430\u0436\u0434\u043e\u0439 \u0433\u0440\u0443\u043f\u043f\u044b. \u041f\u043e\u0441\u043b\u0435 \u0441\u043e\u0437\u0434\u0430\u043d\u0438\u044f \u0433\u0440\u0443\u043f\u043f\u044b \u043f\u043e \u043e\u0431\u044a\u0435\u043a\u0442\u0443 \u043c\u043e\u0436\u043d\u043e \u0432\u044b\u043f\u043e\u043b\u043d\u0438\u0442\u044c \u043d\u0435\u0441\u043a\u043e\u043b\u044c\u043a\u043e \u043e\u043f\u0435\u0440\u0430\u0446\u0438\u0439 \u0430\u0433\u0440\u0435\u0433\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u044f \u0441\u0433\u0440\u0443\u043f\u043f\u0438\u0440\u043e\u0432\u0430\u043d\u043d\u044b\u0445 \u0434\u0430\u043d\u043d\u044b\u0445.\r\n\r\n\u041e\u0447\u0435\u0432\u0438\u0434\u043d\u044b\u043c \u044f\u0432\u043b\u044f\u0435\u0442\u0441\u044f \u0430\u0433\u0440\u0435\u0433\u0430\u0446\u0438\u044f \u0441 \u043f\u043e\u043c\u043e\u0449\u044c\u044e \u0430\u0433\u0440\u0435\u0433\u0430\u0442\u043d\u043e\u0433\u043e \u0438\u043b\u0438 \u044d\u043a\u0432\u0438\u0432\u0430\u043b\u0435\u043d\u0442\u043d\u043e\u0433\u043e \u043c\u0435\u0442\u043e\u0434\u0430 \u0430\u0433\u0433.\r\n\r\n\r\n\r\n# import the pandas library\r\nimport pandas as pd\r\nimport numpy as np\r\n\r\nipl_data = {'Team': &#91;'Riders', 'Riders', 'Devils', 'Devils', 'Kings',\r\n   'kings', 'Kings', 'Kings', 'Riders', 'Royals', 'Royals', 'Riders'],\r\n   'Rank': &#91;1, 2, 2, 3, 3,4 ,1 ,1,2 , 4,1,2],\r\n   'Year': &#91;2014,2015,2014,2015,2014,2015,2016,2017,2016,2014,2015,2017],\r\n   'Points':&#91;876,789,863,673,741,812,756,788,694,701,804,690]}\r\ndf = pd.DataFrame(ipl_data)\r\n\r\ngrouped = df.groupby('Year')\r\nprint grouped&#91;'Points'].agg(np.mean)\r\n\u0415\u0433\u043e \u0432\u044b\u0432\u043e\u0434 \u0432\u044b\u0433\u043b\u044f\u0434\u0438\u0442 \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0438\u043c \u043e\u0431\u0440\u0430\u0437\u043e\u043c \u2014\r\n\r\nYear\r\n2014   795.25\r\n2015   769.50\r\n2016   725.00\r\n2017   739.00\r\nName: Points, dtype: float64\r\n\u0415\u0449\u0435 \u043e\u0434\u0438\u043d \u0441\u043f\u043e\u0441\u043e\u0431 \u0443\u0432\u0438\u0434\u0435\u0442\u044c \u0440\u0430\u0437\u043c\u0435\u0440 \u043a\u0430\u0436\u0434\u043e\u0439 \u0433\u0440\u0443\u043f\u043f\u044b \u2014 \u043f\u0440\u0438\u043c\u0435\u043d\u0438\u0442\u044c \u0444\u0443\u043d\u043a\u0446\u0438\u044e size () \u2014\r\n\r\n Live Demo\r\n\r\nimport pandas as pd\r\nimport numpy as np\r\n\r\nipl_data = {'Team': &#91;'Riders', 'Riders', 'Devils', 'Devils', 'Kings',\r\n   'kings', 'Kings', 'Kings', 'Riders', 'Royals', 'Royals', 'Riders'],\r\n   'Rank': &#91;1, 2, 2, 3, 3,4 ,1 ,1,2 , 4,1,2],\r\n   'Year': &#91;2014,2015,2014,2015,2014,2015,2016,2017,2016,2014,2015,2017],\r\n   'Points':&#91;876,789,863,673,741,812,756,788,694,701,804,690]}\r\ndf = pd.DataFrame(ipl_data)\r\n\r\nAttribute Access in Python Pandas\r\ngrouped = df.groupby('Team')\r\nprint grouped.agg(np.size)\r\n\u0415\u0433\u043e \u0432\u044b\u0432\u043e\u0434 \u0432\u044b\u0433\u043b\u044f\u0434\u0438\u0442 \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0438\u043c \u043e\u0431\u0440\u0430\u0437\u043e\u043c \u2014\r\n\r\n         Points   Rank   Year\r\nTeam\r\nDevils        2      2      2\r\nKings         3      3      3\r\nRiders        4      4      4\r\nRoyals        2      2      2\r\nkings         1      1      1\r\n\u041f\u0440\u0438\u043c\u0435\u043d\u0435\u043d\u0438\u0435 \u043d\u0435\u0441\u043a\u043e\u043b\u044c\u043a\u0438\u0445 \u0444\u0443\u043d\u043a\u0446\u0438\u0439 \u0430\u0433\u0440\u0435\u0433\u0430\u0446\u0438\u0438 \u043e\u0434\u043d\u043e\u0432\u0440\u0435\u043c\u0435\u043d\u043d\u043e\r\n\u0412 \u0441\u0433\u0440\u0443\u043f\u043f\u0438\u0440\u043e\u0432\u0430\u043d\u043d\u044b\u0445 \u0441\u0435\u0440\u0438\u044f\u0445 \u0432\u044b \u0442\u0430\u043a\u0436\u0435 \u043c\u043e\u0436\u0435\u0442\u0435 \u043f\u0435\u0440\u0435\u0434\u0430\u0442\u044c \u0441\u043f\u0438\u0441\u043e\u043a \u0438\u043b\u0438 \u043d\u0430\u0431\u043e\u0440 \u0444\u0443\u043d\u043a\u0446\u0438\u0439 \u0434\u043b\u044f \u0430\u0433\u0440\u0435\u0433\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u044f \u0438 \u0441\u0433\u0435\u043d\u0435\u0440\u0438\u0440\u043e\u0432\u0430\u0442\u044c DataFrame \u0432 \u043a\u0430\u0447\u0435\u0441\u0442\u0432\u0435 \u0432\u044b\u0432\u043e\u0434\u0430 \u2014\r\n\r\n Live Demo\r\n\r\n# import the pandas library\r\nimport pandas as pd\r\nimport numpy as np\r\n\r\nipl_data = {'Team': &#91;'Riders', 'Riders', 'Devils', 'Devils', 'Kings',\r\n   'kings', 'Kings', 'Kings', 'Riders', 'Royals', 'Royals', 'Riders'],\r\n   'Rank': &#91;1, 2, 2, 3, 3,4 ,1 ,1,2 , 4,1,2],\r\n   'Year': &#91;2014,2015,2014,2015,2014,2015,2016,2017,2016,2014,2015,2017],\r\n   'Points':&#91;876,789,863,673,741,812,756,788,694,701,804,690]}\r\ndf = pd.DataFrame(ipl_data)\r\n\r\ngrouped = df.groupby('Team')\r\nprint grouped&#91;'Points'].agg(&#91;np.sum, np.mean, np.std])\r\n\u0415\u0433\u043e \u0432\u044b\u0432\u043e\u0434 \u0432\u044b\u0433\u043b\u044f\u0434\u0438\u0442 \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0438\u043c \u043e\u0431\u0440\u0430\u0437\u043e\u043c \u2014\r\n\r\nTeam      sum      mean          std\r\nDevils   1536   768.000000   134.350288\r\nKings    2285   761.666667    24.006943\r\nRiders   3049   762.250000    88.567771\r\nRoyals   1505   752.500000    72.831998\r\nkings     812   812.000000          NaN\r\n\u0422\u0440\u0430\u043d\u0441\u0444\u043e\u0440\u043c\u0430\u0446\u0438\u0438\r\n\u041f\u0440\u0435\u043e\u0431\u0440\u0430\u0437\u043e\u0432\u0430\u043d\u0438\u0435 \u0434\u043b\u044f \u0433\u0440\u0443\u043f\u043f\u044b \u0438\u043b\u0438 \u0441\u0442\u043e\u043b\u0431\u0446\u0430 \u0432\u043e\u0437\u0432\u0440\u0430\u0449\u0430\u0435\u0442 \u043e\u0431\u044a\u0435\u043a\u0442, \u0438\u043d\u0434\u0435\u043a\u0441\u0438\u0440\u043e\u0432\u0430\u043d\u043d\u044b\u0439 \u043f\u043e \u0442\u043e\u043c\u0443 \u0436\u0435 \u0440\u0430\u0437\u043c\u0435\u0440\u0443, \u0447\u0442\u043e \u0438 \u0441\u0433\u0440\u0443\u043f\u043f\u0438\u0440\u043e\u0432\u0430\u043d\u043d\u044b\u0439. \u0422\u0430\u043a\u0438\u043c \u043e\u0431\u0440\u0430\u0437\u043e\u043c, \u043f\u0440\u0435\u043e\u0431\u0440\u0430\u0437\u043e\u0432\u0430\u043d\u0438\u0435 \u0434\u043e\u043b\u0436\u043d\u043e \u0432\u043e\u0437\u0432\u0440\u0430\u0449\u0430\u0442\u044c \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442 \u0442\u043e\u0433\u043e \u0436\u0435 \u0440\u0430\u0437\u043c\u0435\u0440\u0430, \u0447\u0442\u043e \u0438 \u0434\u043b\u044f \u0433\u0440\u0443\u043f\u043f\u043e\u0432\u043e\u0433\u043e \u0444\u0440\u0430\u0433\u043c\u0435\u043d\u0442\u0430.\r\n\r\n Live Demo\r\n\r\n# import the pandas library\r\nimport pandas as pd\r\nimport numpy as np\r\n\r\nipl_data = {'Team': &#91;'Riders', 'Riders', 'Devils', 'Devils', 'Kings',\r\n   'kings', 'Kings', 'Kings', 'Riders', 'Royals', 'Royals', 'Riders'],\r\n   'Rank': &#91;1, 2, 2, 3, 3,4 ,1 ,1,2 , 4,1,2],\r\n   'Year': &#91;2014,2015,2014,2015,2014,2015,2016,2017,2016,2014,2015,2017],\r\n   'Points':&#91;876,789,863,673,741,812,756,788,694,701,804,690]}\r\ndf = pd.DataFrame(ipl_data)\r\n\r\ngrouped = df.groupby('Team')\r\nscore = lambda x: (x - x.mean()) \/ x.std()*10\r\nprint grouped.transform(score)\r\n\u0415\u0433\u043e \u0432\u044b\u0432\u043e\u0434 \u0432\u044b\u0433\u043b\u044f\u0434\u0438\u0442 \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0438\u043c \u043e\u0431\u0440\u0430\u0437\u043e\u043c \u2014\r\n\r\n       Points        Rank        Year\r\n0   12.843272  -15.000000  -11.618950\r\n1   3.020286     5.000000   -3.872983\r\n2   7.071068    -7.071068   -7.071068\r\n3  -7.071068     7.071068    7.071068\r\n4  -8.608621    11.547005  -10.910895\r\n5        NaN          NaN         NaN\r\n6  -2.360428    -5.773503    2.182179\r\n7  10.969049    -5.773503    8.728716\r\n8  -7.705963     5.000000    3.872983\r\n9  -7.071068     7.071068   -7.071068\r\n10  7.071068    -7.071068    7.071068\r\n11 -8.157595     5.000000   11.618950\r\n\u0444\u0438\u043b\u044c\u0442\u0440\u043e\u0432\u0430\u043d\u0438\u0435\r\n\u0424\u0438\u043b\u044c\u0442\u0440\u0430\u0446\u0438\u044f \u0444\u0438\u043b\u044c\u0442\u0440\u0443\u0435\u0442 \u0434\u0430\u043d\u043d\u044b\u0435 \u043f\u043e \u043e\u043f\u0440\u0435\u0434\u0435\u043b\u0435\u043d\u043d\u044b\u043c \u043a\u0440\u0438\u0442\u0435\u0440\u0438\u044f\u043c \u0438 \u0432\u043e\u0437\u0432\u0440\u0430\u0449\u0430\u0435\u0442 \u043f\u043e\u0434\u043c\u043d\u043e\u0436\u0435\u0441\u0442\u0432\u043e \u0434\u0430\u043d\u043d\u044b\u0445. \u0424\u0443\u043d\u043a\u0446\u0438\u044f filter () \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u0435\u0442\u0441\u044f \u0434\u043b\u044f \u0444\u0438\u043b\u044c\u0442\u0440\u0430\u0446\u0438\u0438 \u0434\u0430\u043d\u043d\u044b\u0445.\r\n\r\n Live Demo\r\n\r\nimport pandas as pd\r\nimport numpy as np\r\n\r\nipl_data = {'Team': &#91;'Riders', 'Riders', 'Devils', 'Devils', 'Kings',\r\n   'kings', 'Kings', 'Kings', 'Riders', 'Royals', 'Royals', 'Riders'],\r\n   'Rank': &#91;1, 2, 2, 3, 3,4 ,1 ,1,2 , 4,1,2],\r\n   'Year': &#91;2014,2015,2014,2015,2014,2015,2016,2017,2016,2014,2015,2017],\r\n   'Points':&#91;876,789,863,673,741,812,756,788,694,701,804,690]}\r\ndf = pd.DataFrame(ipl_data)\r\n\r\nprint df.groupby('Team').filter(lambda x: len(x) >= 3)\r\n\u0415\u0433\u043e \u0432\u044b\u0432\u043e\u0434 \u0432\u044b\u0433\u043b\u044f\u0434\u0438\u0442 \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0438\u043c \u043e\u0431\u0440\u0430\u0437\u043e\u043c \u2014\r\n\r\n    Points  Rank     Team   Year\r\n0      876     1   Riders   2014\r\n1      789     2   Riders   2015\r\n4      741     3   Kings    2014\r\n6      756     1   Kings    2016\r\n7      788     1   Kings    2017\r\n8      694     2   Riders   2016\r\n11     690     2   Riders   2017\r\n\u0412 \u0432\u044b\u0448\u0435\u0443\u043f\u043e\u043c\u044f\u043d\u0443\u0442\u043e\u043c \u0443\u0441\u043b\u043e\u0432\u0438\u0438 \u0444\u0438\u043b\u044c\u0442\u0440\u0430 \u043c\u044b \u043f\u0440\u043e\u0441\u0438\u043c \u0432\u0435\u0440\u043d\u0443\u0442\u044c \u043a\u043e\u043c\u0430\u043d\u0434\u044b, \u043a\u043e\u0442\u043e\u0440\u044b\u0435 \u0443\u0447\u0430\u0441\u0442\u0432\u043e\u0432\u0430\u043b\u0438 \u0442\u0440\u0438 \u0438\u043b\u0438 \u0431\u043e\u043b\u0435\u0435 \u0440\u0430\u0437 \u0432 IPL.<\/code><\/pre>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":2,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"categories":[109],"tags":[],"class_list":["post-3395","page","type-page","status-publish","hentry","category-python-pandas"],"jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/www.bearloga.space\/uk\/wp-json\/wp\/v2\/pages\/3395","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.bearloga.space\/uk\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.bearloga.space\/uk\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.bearloga.space\/uk\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.bearloga.space\/uk\/wp-json\/wp\/v2\/comments?post=3395"}],"version-history":[{"count":0,"href":"https:\/\/www.bearloga.space\/uk\/wp-json\/wp\/v2\/pages\/3395\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.bearloga.space\/uk\/wp-json\/wp\/v2\/media?parent=3395"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.bearloga.space\/uk\/wp-json\/wp\/v2\/categories?post=3395"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.bearloga.space\/uk\/wp-json\/wp\/v2\/tags?post=3395"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}