{"id":3932,"date":"2023-07-14T16:17:06","date_gmt":"2023-07-14T16:17:06","guid":{"rendered":"https:\/\/statorials.org\/ko\/%e1%84%91%e1%85%a2%e1%86%ab%e1%84%83%e1%85%a5-%e1%84%91%e1%85%b3%e1%86%af%e1%84%85%e1%85%a9%e1%86%ba-%e1%84%80%e1%85%a1%e1%86%b9-%e1%84%80%e1%85%a2%e1%84%89%e1%85%ae\/"},"modified":"2023-07-14T16:17:06","modified_gmt":"2023-07-14T16:17:06","slug":"%e1%84%91%e1%85%a2%e1%86%ab%e1%84%83%e1%85%a5-%e1%84%91%e1%85%b3%e1%86%af%e1%84%85%e1%85%a9%e1%86%ba-%e1%84%80%e1%85%a1%e1%86%b9-%e1%84%80%e1%85%a2%e1%84%89%e1%85%ae","status":"publish","type":"post","link":"https:\/\/statorials.org\/ko\/%e1%84%91%e1%85%a2%e1%86%ab%e1%84%83%e1%85%a5-%e1%84%91%e1%85%b3%e1%86%af%e1%84%85%e1%85%a9%e1%86%ba-%e1%84%80%e1%85%a1%e1%86%b9-%e1%84%80%e1%85%a2%e1%84%89%e1%85%ae\/","title":{"rendered":"Pandas: \uac12\uc758 \uac1c\uc218\ub97c \uadf8\ub9ac\ub294 \ubc29\ubc95(\uc608\uc81c \ud3ec\ud568)"},"content":{"rendered":"

<\/p>\n

\n

\ud32c\ub354\uc758 value_counts()<\/strong> \ud568\uc218\ub97c \uc0ac\uc6a9\ud558\uc5ec DataFrame\uc758 \ud2b9\uc815 \uc5f4\uc5d0\uc11c \uac12\uc758 \ubc1c\uc0dd \ud69f\uc218\ub97c \uacc4\uc0b0\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n

\ub2e4\uc74c \ubc29\ubc95 \uc911 \ud558\ub098\ub97c \uc0ac\uc6a9\ud558\uc5ec value_counts()<\/strong> \ud568\uc218\uc5d0 \uc758\ud574 \uc0dd\uc131\ub41c \uac12\uc744 \ud50c\ub86f\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n

\ubc29\ubc95 1: \uac12\uc758 \uac1c\uc218\ub97c \ub0b4\ub9bc\ucc28\uc21c\uc73c\ub85c \ud50c\ub86f<\/strong><\/span><\/p>\n

 df. my_column<\/span> . value_counts<\/span> (). plot<\/span> (kind=' bar<\/span> ')\n<\/strong><\/pre>\n
 \ubc29\ubc95 2: \uac12\uc758 \uac1c\uc218\ub97c \uc624\ub984\ucc28\uc21c\uc73c\ub85c \ud50c\ub86f<\/strong><\/span><\/p>\n
 df. my_column<\/span> . value_counts<\/span> (). sort_values<\/span> (). plot<\/span> (kind=' bar<\/span> ')<\/strong><\/pre>\n
 \ubc29\ubc95 3: DataFrame\uc5d0 \ub098\ud0c0\ub098\ub294 \uc21c\uc11c\ub300\ub85c \uac12 \uac1c\uc218\ub97c \ud45c\uc2dc\ud569\ub2c8\ub2e4.<\/strong><\/span><\/p>\n
 df. my_column<\/span> . value_counts<\/span> ()[df. my_column<\/span> . unique<\/span> ()]. plot<\/span> (kind=' bar<\/span> ')<\/strong><\/pre>\n
 \ub2e4\uc74c \uc608\uc5d0\uc11c\ub294 \ub2e4\uc74c pandas DataFrame\uc5d0\uc11c \uc2e4\uc81c\ub85c \uac01 \uba54\uc11c\ub4dc\ub97c \uc0ac\uc6a9\ud558\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/span><\/p>\n
 import<\/span> pandas as<\/span> pd\n\n#createDataFrame\ndf = pd. DataFrame<\/span> ({' team<\/span> ': ['A', 'A', 'B', 'B', 'B', 'B', 'B', 'C'],\n                   ' points<\/span> ': [15, 12, 18, 20, 22, 28, 35, 40]})\n\n#view DataFrame\n<\/span>print<\/span> (df)\n\n  team points\n0 to 15\n1 to 12\n2 B 18\n3 B 20\n4 B 22\n5 B 28\n6 B 35\n7 C 40\n\n#calculate occurrences of each value in 'team' column\n<\/span>df. team<\/span> . value_counts<\/span> ()\n\nB5\nAt 2\nC 1\nName: team, dtype: int64\n<\/span><\/span><\/strong><\/pre>\n
 \uc608\uc2dc 1: \uac12\uc758 \uac1c\uc218\ub97c \ub0b4\ub9bc\ucc28\uc21c\uc73c\ub85c \ud50c\ub86f<\/strong><\/span><\/h2>\n
 \ub2e4\uc74c \ucf54\ub4dc\ub294 \ub9c9\ub300 \ucc28\ud2b8\uc758 \uac12 \uc218\ub97c \ub0b4\ub9bc\ucc28\uc21c\uc73c\ub85c \ud45c\uc2dc\ud558\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/span> <\/p>\n
 #plot value counts of team in descending order\n<\/span>df. team<\/span> . value_counts<\/span> (). plot<\/span> (kind=' bar<\/span> ')<\/strong> <\/pre>\n
\"\"<\/p>\n
 x\ucd95\uc740 \ud300 \uc774\ub984\uc744 \ud45c\uc2dc\ud558\uace0 y\ucd95\uc740 \uac01 \ud300\uc758 \ube48\ub3c4\ub97c \ud45c\uc2dc\ud569\ub2c8\ub2e4.<\/span><\/p>\n
 \ub9c9\ub300\ub294 \uae30\ubcf8\uc801\uc73c\ub85c \ub0b4\ub9bc\ucc28\uc21c\uc73c\ub85c \uc815\ub82c\ub429\ub2c8\ub2e4.<\/span><\/p>\n
 \ucc38\uace0<\/strong> : \uac00\ub85c \ub9c9\ub300 \ucc28\ud2b8\ub97c \ub300\uc2e0 \uc0dd\uc131\ud558\ub824\uba74 kind<\/strong> \uc778\uc218\uc5d0\uc11c bar\ub97c<\/strong> barh<\/strong> \ub85c \ubc14\uafb8\uba74 \ub429\ub2c8\ub2e4.<\/span><\/p>\n
 \uc608\uc2dc 2: \uac12\uc758 \uac1c\uc218\ub97c \uc624\ub984\ucc28\uc21c\uc73c\ub85c \ud50c\ub86f<\/strong><\/span><\/h2>\n
 \ub2e4\uc74c \ucf54\ub4dc\ub294 \ub9c9\ub300 \ucc28\ud2b8\uc758 \uac12 \uc218\ub97c \uc624\ub984\ucc28\uc21c\uc73c\ub85c \ud45c\uc2dc\ud558\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/span> <\/p>\n
 #plot value counts of team in descending order\n<\/span>df. team<\/span> . value_counts<\/span> (). sort_values<\/span> (). plot<\/span> (kind=' bar<\/span> ')\n<\/strong><\/pre>\n
\"\"<\/p>\n
 \uc774\uc81c \ub9c9\ub300\uac00 \uc624\ub984\ucc28\uc21c\uc73c\ub85c \uc815\ub82c\ub429\ub2c8\ub2e4.<\/span><\/p>\n
 \uc608\uc2dc 3: DataFrame\uc5d0 \ub098\ud0c0\ub098\ub294 \uc21c\uc11c\ub300\ub85c \uac12 \uac1c\uc218\ub97c \ud45c\uc2dc\ud569\ub2c8\ub2e4.<\/strong><\/span><\/h2>\n
 \ub2e4\uc74c \ucf54\ub4dc\ub294 DataFrame\uc5d0 \ub098\ud0c0\ub098\ub294 \uc21c\uc11c\uc5d0 \ub530\ub77c \ub9c9\ub300 \ucc28\ud2b8\uc758 \uac12 \uc218\ub97c \ud45c\uc2dc\ud558\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/span> <\/p>\n
 #plot value counts of team in order they appear in DataFrame\n<\/span>df. team<\/span> . value_counts<\/span> ()[df. team<\/span> . single<\/span> ()]. plot<\/span> (kind=' bar<\/span> ')\n<\/strong><\/pre>\n
\"\"<\/p>\n
 \uc774\uc81c \ub9c9\ub300\ub294 DataFrame\uc5d0 \ub098\ud0c0\ub098\ub294 \uc21c\uc11c\uc5d0 \ub530\ub77c \uc815\ub82c\ub429\ub2c8\ub2e4.<\/span><\/p>\n
 \uc608\ub97c \ub4e4\uc5b4 \ud300<\/strong> \uc5f4\uc5d0 \uac12 “A”\uac00 \uba3c\uc800 \ud45c\uc2dc\ub418\uace0 \uadf8 \ub2e4\uc74c “B”, “C”\uac00 \ud45c\uc2dc\ub429\ub2c8\ub2e4.<\/span><\/p>\n
 \ub530\ub77c\uc11c \ub9c9\ub300 \ucc28\ud2b8\uc5d0 \ub9c9\ub300\uac00 \ubc30\uce58\ub418\ub294 \uc21c\uc11c\ub294 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 \ucd94\uac00 \ub9ac\uc18c\uc2a4<\/strong><\/span><\/h2>\n
 \ub2e4\uc74c \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 \ub2e4\ub978 \uc77c\ubc18\uc801\uc778 Panda \uc791\uc5c5\uc744 \uc218\ud589\ud558\ub294 \ubc29\ubc95\uc744 \uc124\uba85\ud569\ub2c8\ub2e4.<\/span><\/p>\n
 Pandas: \ud50c\ub86f\uc5d0 \uc81c\ubaa9\uc744 \ucd94\uac00\ud558\ub294 \ubc29\ubc95<\/a>
 Pandas: \ud50c\ub86f \ucea1\uc158\uc744 \ub9cc\ub4dc\ub294 \ubc29\ubc95<\/a>
 Pandas: GroupBy\uc5d0\uc11c \ub9c9\ub300 \uadf8\ub798\ud504\ub97c \ub9cc\ub4dc\ub294 \ubc29\ubc95<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
\ud32c\ub354\uc758 value_counts() \ud568\uc218\ub97c \uc0ac\uc6a9\ud558\uc5ec DataFrame\uc758 \ud2b9\uc815 \uc5f4\uc5d0\uc11c \uac12\uc758 \ubc1c\uc0dd \ud69f\uc218\ub97c \uacc4\uc0b0\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ub2e4\uc74c \ubc29\ubc95 \uc911 \ud558\ub098\ub97c \uc0ac\uc6a9\ud558\uc5ec value_counts() \ud568\uc218\uc5d0 \uc758\ud574 \uc0dd\uc131\ub41c \uac12\uc744 \ud50c\ub86f\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ubc29\ubc95 1: \uac12\uc758 \uac1c\uc218\ub97c \ub0b4\ub9bc\ucc28\uc21c\uc73c\ub85c \ud50c\ub86f df. my_column . value_counts (). plot (kind=’ bar ‘) \ubc29\ubc95 2: \uac12\uc758 \uac1c\uc218\ub97c \uc624\ub984\ucc28\uc21c\uc73c\ub85c \ud50c\ub86f df. my_column . value_counts (). sort_values (). plot […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[20],"tags":[],"class_list":["post-3932","post","type-post","status-publish","format-standard","hentry","category-20"],"yoast_head":"\nPandas: \uac12 \uac1c\uc218\ub97c \ud45c\uc2dc\ud558\ub294 \ubc29\ubc95(\uc608\uc81c \ud3ec\ud568) - 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