{"id":1728,"date":"2023-07-25T05:15:38","date_gmt":"2023-07-25T05:15:38","guid":{"rendered":"https:\/\/statorials.org\/uk\/%d0%bf%d0%b0%d0%bd%d0%b4%d0%b8-%d0%bf%d0%be%d1%94%d0%b4%d0%bd%d1%83%d1%8e%d1%82%d1%8c-%d0%b4%d0%b2%d1%96-%d0%ba%d0%be%d0%bb%d0%be%d0%bd%d0%b8\/"},"modified":"2023-07-25T05:15:38","modified_gmt":"2023-07-25T05:15:38","slug":"%d0%bf%d0%b0%d0%bd%d0%b4%d0%b8-%d0%bf%d0%be%d1%94%d0%b4%d0%bd%d1%83%d1%8e%d1%82%d1%8c-%d0%b4%d0%b2%d1%96-%d0%ba%d0%be%d0%bb%d0%be%d0%bd%d0%b8","status":"publish","type":"post","link":"https:\/\/statorials.org\/uk\/%d0%bf%d0%b0%d0%bd%d0%b4%d0%b8-%d0%bf%d0%be%d1%94%d0%b4%d0%bd%d1%83%d1%8e%d1%82%d1%8c-%d0%b4%d0%b2%d1%96-%d0%ba%d0%be%d0%bb%d0%be%d0%bd%d0%b8\/","title":{"rendered":"\u042f\u043a \u043f\u043e\u0454\u0434\u043d\u0430\u0442\u0438 \u0434\u0432\u0430 \u0441\u0442\u043e\u0432\u043f\u0446\u0456 \u0432 pandas (\u0437 \u043f\u0440\u0438\u043a\u043b\u0430\u0434\u0430\u043c\u0438)"},"content":{"rendered":"

<\/p>\n

\n

\u0412\u0438 \u043c\u043e\u0436\u0435\u0442\u0435 \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0432\u0430\u0442\u0438 \u0442\u0430\u043a\u0438\u0439 \u0441\u0438\u043d\u0442\u0430\u043a\u0441\u0438\u0441, \u0449\u043e\u0431 \u043e\u0431\u2019\u0454\u0434\u043d\u0430\u0442\u0438 \u0434\u0432\u0430 \u0442\u0435\u043a\u0441\u0442\u043e\u0432\u0456 \u0441\u0442\u043e\u0432\u043f\u0446\u0456 \u0432 \u043e\u0434\u0438\u043d \u0443 pandas DataFrame:<\/span><\/p>\n

 df[' new_column<\/span> '] = df[' column1<\/span> '] + df[' column2<\/span> ']\n<\/strong><\/pre>\n
 \u042f\u043a\u0449\u043e \u043e\u0434\u0438\u043d \u0437\u0456 \u0441\u0442\u043e\u0432\u043f\u0446\u0456\u0432 \u0449\u0435 \u043d\u0435 \u0454 \u0440\u044f\u0434\u043a\u043e\u043c, \u0432\u0438 \u043c\u043e\u0436\u0435\u0442\u0435 \u043f\u0435\u0440\u0435\u0442\u0432\u043e\u0440\u0438\u0442\u0438 \u0439\u043e\u0433\u043e \u0437\u0430 \u0434\u043e\u043f\u043e\u043c\u043e\u0433\u043e\u044e \u043a\u043e\u043c\u0430\u043d\u0434\u0438 astype(str)<\/strong> :<\/span><\/p>\n
 df[' new_column<\/span> '] = df[' column1<\/span> ']. astype<\/span> ( str<\/span> )+df[' column2<\/span> ']<\/strong><\/pre>\n
 \u0406 \u0432\u0438 \u043c\u043e\u0436\u0435\u0442\u0435 \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0432\u0430\u0442\u0438 \u0442\u0430\u043a\u0438\u0439 \u0441\u0438\u043d\u0442\u0430\u043a\u0441\u0438\u0441, \u0449\u043e\u0431 \u043e\u0431\u2019\u0454\u0434\u043d\u0430\u0442\u0438 \u043a\u0456\u043b\u044c\u043a\u0430 \u0442\u0435\u043a\u0441\u0442\u043e\u0432\u0438\u0445 \u0441\u0442\u043e\u0432\u043f\u0446\u0456\u0432 \u0432 \u043e\u0434\u0438\u043d:<\/span><\/p>\n
 df[' new_column<\/span> '] = df[[' col1<\/span> ', ' col2<\/span> ', ' col3<\/span> ', ...]]. agg<\/span> (' '. join<\/span> , axis= 1<\/span> )<\/strong><\/pre>\n
 \u041d\u0430\u0432\u0435\u0434\u0435\u043d\u0456 \u043d\u0438\u0436\u0447\u0435 \u043f\u0440\u0438\u043a\u043b\u0430\u0434\u0438 \u043f\u043e\u043a\u0430\u0437\u0443\u044e\u0442\u044c, \u044f\u043a \u043d\u0430 \u043f\u0440\u0430\u043a\u0442\u0438\u0446\u0456 \u043e\u0431\u2019\u0454\u0434\u043d\u0430\u0442\u0438 \u0442\u0435\u043a\u0441\u0442\u043e\u0432\u0456 \u0441\u0442\u043e\u0432\u043f\u0446\u0456.<\/span><\/p>\n
 \u041f\u0440\u0438\u043a\u043b\u0430\u0434 1: \u043e\u0431\u2019\u0454\u0434\u043d\u0430\u0439\u0442\u0435 \u0434\u0432\u0456 \u043a\u043e\u043b\u043e\u043d\u043a\u0438<\/strong><\/span><\/h3>\n
 \u041d\u0430\u0441\u0442\u0443\u043f\u043d\u0438\u0439 \u043a\u043e\u0434 \u043f\u043e\u043a\u0430\u0437\u0443\u0454, \u044f\u043a \u043e\u0431\u2019\u0454\u0434\u043d\u0430\u0442\u0438 \u0434\u0432\u0430 \u0442\u0435\u043a\u0441\u0442\u043e\u0432\u0456 \u0441\u0442\u043e\u0432\u043f\u0446\u0456 \u0432 \u043e\u0434\u0438\u043d \u0443 pandas DataFrame:<\/span><\/p>\n
 import<\/span> pandas as<\/span> pd\n\n#create dataFrame<\/span>\ndf = pd. DataFrame<\/span> ({' team<\/span> ': ['Mavs', 'Lakers', 'Spurs', 'Cavs'],\n                   ' first<\/span> ': ['Dirk', 'Kobe', 'Tim', 'Lebron'],\n                   ' last<\/span> ': ['Nowitzki', 'Bryant', 'Duncan', 'James'],\n                   ' points<\/span> ': [26, 31, 22, 29]})\n\n#combine first and last name column into new column, with space in between<\/span>\ndf[' full_name<\/span> '] = df[' first<\/span> '] + ' ' + df[' last<\/span> ']\n\n#view resulting dataFrame\ndf<\/span>\n\nteam first last<\/span> points full_name\n0 Mavs Dirk Nowitzki<\/span> 26 Dirk Nowitzki\n1 Lakers Kobe Bryant<\/span> 31 Kobe Bryant\n2 Spurs Tim Duncan<\/span> 22 Tim Duncan\n3 Cavs LeBron James<\/span> 29 LeBron James\n<\/span><\/span><\/strong><\/pre>\n
 \u041c\u0438 \u043e\u0431\u2019\u0454\u0434\u043d\u0430\u043b\u0438 \u0441\u0442\u043e\u0432\u043f\u0435\u0446\u044c \u0456\u043c\u0435\u043d\u0456 \u0442\u0430 \u043f\u0440\u0456\u0437\u0432\u0438\u0449\u0430 \u043f\u0440\u043e\u0431\u0456\u043b\u043e\u043c \u043c\u0456\u0436 \u043d\u0438\u043c\u0438, \u0430\u043b\u0435 \u043c\u0438 \u0442\u0430\u043a\u043e\u0436 \u043c\u043e\u0436\u0435\u043c\u043e \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u0430\u0442\u0438 \u0456\u043d\u0448\u0438\u0439 \u0440\u043e\u0437\u0434\u0456\u043b\u044c\u043d\u0438\u043a, \u043d\u0430\u043f\u0440\u0438\u043a\u043b\u0430\u0434 \u0434\u0435\u0444\u0456\u0441:<\/span><\/p>\n
 #combine first and last name column into new column, with dash in between<\/span>\ndf[' full_name<\/span> '] = df[' first<\/span> '] + ' -<\/span> ' + df[' last<\/span> ']\n\n#view resulting dataFrame\ndf<\/span>\n\nteam first last<\/span> points full_name\n0 Mavs Dirk Nowitzki<\/span> 26 Dirk<\/span> - Nowitzki\n1 Lakers Kobe Bryant<\/span> 31 Kobe<\/span> - Bryant\n2 Spurs Tim Duncan<\/span> 22 Tim<\/span> - Duncan\n3 Cavs Lebron James<\/span> 29 Lebron<\/span> - James<\/span><\/span><\/strong><\/pre>\n
 \u041f\u0440\u0438\u043a\u043b\u0430\u0434 2: \u041f\u0435\u0440\u0435\u0442\u0432\u043e\u0440\u0435\u043d\u043d\u044f \u043d\u0430 \u0442\u0435\u043a\u0441\u0442 \u0456 \u043e\u0431\u2019\u0454\u0434\u043d\u0430\u043d\u043d\u044f \u0434\u0432\u043e\u0445 \u0441\u0442\u043e\u0432\u043f\u0446\u0456\u0432<\/strong><\/span><\/h3>\n
 \u041d\u0430\u0441\u0442\u0443\u043f\u043d\u0438\u0439 \u043a\u043e\u0434 \u043f\u043e\u043a\u0430\u0437\u0443\u0454, \u044f\u043a \u043f\u0435\u0440\u0435\u0442\u0432\u043e\u0440\u0438\u0442\u0438 \u0441\u0442\u043e\u0432\u043f\u0435\u0446\u044c \u043d\u0430 \u0442\u0435\u043a\u0441\u0442, \u0430 \u043f\u043e\u0442\u0456\u043c \u043f\u0440\u0438\u0454\u0434\u043d\u0430\u0442\u0438 \u0439\u043e\u0433\u043e \u0434\u043e \u0456\u043d\u0448\u043e\u0433\u043e \u0441\u0442\u043e\u0432\u043f\u0446\u044f:<\/span><\/p>\n
 import<\/span> pandas as<\/span> pd\n\n#create dataFrame<\/span>\ndf = pd. DataFrame<\/span> ({' team<\/span> ': ['Mavs', 'Lakers', 'Spurs', 'Cavs'],\n                   ' first<\/span> ': ['Dirk', 'Kobe', 'Tim', 'Lebron'],\n                   ' last<\/span> ': ['Nowitzki', 'Bryant', 'Duncan', 'James'],\n                   ' points<\/span> ': [26, 31, 22, 29]})\n\n#convert points to text, then join to last name column<\/span>\ndf[' name_points<\/span> '] = df[' last<\/span> '] + df[' points<\/span> ']. astype<\/span> ( str<\/span> )\n\n#view resulting dataFrame\ndf<\/span>\n\n        team first last<\/span> points name_points\n0 Mavs Dirk Nowitzki<\/span> 26 Nowitzki26\n1 Lakers Kobe Bryant<\/span> 31 Bryant31\n2 Spurs Tim Duncan<\/span> 22 Duncan22\n3 Cavs LeBron James<\/span> 29 James29<\/span><\/span><\/strong><\/pre>\n
 \u041f\u0440\u0438\u043a\u043b\u0430\u0434 3: \u043e\u0431\u2019\u0454\u0434\u043d\u0430\u0439\u0442\u0435 \u0431\u0456\u043b\u044c\u0448\u0435 \u0434\u0432\u043e\u0445 \u0441\u0442\u043e\u0432\u043f\u0446\u0456\u0432<\/strong><\/span><\/h3>\n
 \u041d\u0430\u0441\u0442\u0443\u043f\u043d\u0438\u0439 \u043a\u043e\u0434 \u043f\u043e\u043a\u0430\u0437\u0443\u0454, \u044f\u043a \u043e\u0431\u2019\u0454\u0434\u043d\u0430\u0442\u0438 \u043a\u0456\u043b\u044c\u043a\u0430 \u0441\u0442\u043e\u0432\u043f\u0446\u0456\u0432 \u0432 \u043e\u0434\u0438\u043d:<\/span><\/p>\n
 import<\/span> pandas as<\/span> pd\n\n#create dataFrame<\/span>\ndf = pd. DataFrame<\/span> ({' team<\/span> ': ['Mavs', 'Lakers', 'Spurs', 'Cavs'],\n                   ' first<\/span> ': ['Dirk', 'Kobe', 'Tim', 'Lebron'],\n                   ' last<\/span> ': ['Nowitzki', 'Bryant', 'Duncan', 'James'],\n                   ' points<\/span> ': [26, 31, 22, 29]})\n\n#join team, first name, and last name into one column<\/span>\ndf[' team_and_name<\/span> '] = df[[' team<\/span> ', ' first<\/span> ', ' last<\/span> ']]. agg<\/span> (' '. join<\/span> , axis= 1<\/span> )\n\n#view resulting dataFrame\ndf<\/span>\n\nteam first last points team_name\n0 Mavs Dirk Nowitzki 26 Mavs Dirk Nowitzki\n1 Lakers Kobe Bryant 31 Lakers Kobe Bryant\n2 Spurs Tim Duncan 22 Spurs Tim Duncan\n3 Cavs Lebron James 29 Cavs Lebron James<\/span><\/span><\/strong><\/pre>\n
 \u0414\u043e\u0434\u0430\u0442\u043a\u043e\u0432\u0456 \u0440\u0435\u0441\u0443\u0440\u0441\u0438<\/strong><\/span><\/h3>\n
 \u041f\u0430\u043d\u0434\u0438: \u044f\u043a \u0437\u043d\u0430\u0439\u0442\u0438 \u0440\u0456\u0437\u043d\u0438\u0446\u044e \u043c\u0456\u0436 \u0434\u0432\u043e\u043c\u0430 \u043a\u043e\u043b\u043e\u043d\u043a\u0430\u043c\u0438<\/a>
 \u041f\u0430\u043d\u0434\u0438: \u044f\u043a \u0437\u043d\u0430\u0439\u0442\u0438 \u0440\u0456\u0437\u043d\u0438\u0446\u044e \u043c\u0456\u0436 \u0434\u0432\u043e\u043c\u0430 \u043b\u0456\u043d\u0456\u044f\u043c\u0438<\/a>
 Pandas: \u044f\u043a \u0441\u043e\u0440\u0442\u0443\u0432\u0430\u0442\u0438 \u0441\u0442\u043e\u0432\u043f\u0446\u0456 \u0437\u0430 \u043d\u0430\u0437\u0432\u043e\u044e<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
\u0412\u0438 \u043c\u043e\u0436\u0435\u0442\u0435 \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0432\u0430\u0442\u0438 \u0442\u0430\u043a\u0438\u0439 \u0441\u0438\u043d\u0442\u0430\u043a\u0441\u0438\u0441, \u0449\u043e\u0431 \u043e\u0431\u2019\u0454\u0434\u043d\u0430\u0442\u0438 \u0434\u0432\u0430 \u0442\u0435\u043a\u0441\u0442\u043e\u0432\u0456 \u0441\u0442\u043e\u0432\u043f\u0446\u0456 \u0432 \u043e\u0434\u0438\u043d \u0443 pandas DataFrame: df[‘ new_column ‘] = df[‘ column1 ‘] + df[‘ column2 ‘] \u042f\u043a\u0449\u043e \u043e\u0434\u0438\u043d \u0437\u0456 \u0441\u0442\u043e\u0432\u043f\u0446\u0456\u0432 \u0449\u0435 \u043d\u0435 \u0454 \u0440\u044f\u0434\u043a\u043e\u043c, \u0432\u0438 \u043c\u043e\u0436\u0435\u0442\u0435 \u043f\u0435\u0440\u0435\u0442\u0432\u043e\u0440\u0438\u0442\u0438 \u0439\u043e\u0433\u043e \u0437\u0430 \u0434\u043e\u043f\u043e\u043c\u043e\u0433\u043e\u044e \u043a\u043e\u043c\u0430\u043d\u0434\u0438 astype(str) : df[‘ new_column ‘] = df[‘ column1 ‘]. astype ( str )+df[‘ column2 […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[3],"tags":[],"yoast_head":"\n\u042f\u043a \u043f\u043e\u0454\u0434\u043d\u0430\u0442\u0438 \u0434\u0432\u0430 \u0441\u0442\u043e\u0432\u043f\u0446\u0456 \u0432 Pandas (\u0437 \u043f\u0440\u0438\u043a\u043b\u0430\u0434\u0430\u043c\u0438)<\/title>\n<meta name=\"description\" content=\"\u0426\u0435\u0439 \u043f\u0456\u0434\u0440\u0443\u0447\u043d\u0438\u043a \u043f\u043e\u044f\u0441\u043d\u044e\u0454, \u044f\u043a \u043e\u0431\u2019\u0454\u0434\u043d\u0430\u0442\u0438 \u0434\u0432\u0430 \u0441\u0442\u043e\u0432\u043f\u0446\u0456 \u0432 pandas DataFrame, \u0437 \u043a\u0456\u043b\u044c\u043a\u043e\u043c\u0430 \u043f\u0440\u0438\u043a\u043b\u0430\u0434\u0430\u043c\u0438.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, 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