{"id":4040,"date":"2023-07-14T00:01:15","date_gmt":"2023-07-14T00:01:15","guid":{"rendered":"https:\/\/statorials.org\/ko\/%e1%84%91%e1%85%a2%e1%86%ab%e1%84%83%e1%85%a5-%e1%84%87%e1%85%b5%e1%86%ab%e1%84%83%e1%85%a9-%e1%84%90%e1%85%a6%e1%84%8b%e1%85%b5%e1%84%87%e1%85%b3%e1%86%af-%e1%84%8b%e1%85%a7%e1%84%85%e1%85%a5\/"},"modified":"2023-07-14T00:01:15","modified_gmt":"2023-07-14T00:01:15","slug":"%e1%84%91%e1%85%a2%e1%86%ab%e1%84%83%e1%85%a5-%e1%84%87%e1%85%b5%e1%86%ab%e1%84%83%e1%85%a9-%e1%84%90%e1%85%a6%e1%84%8b%e1%85%b5%e1%84%87%e1%85%b3%e1%86%af-%e1%84%8b%e1%85%a7%e1%84%85%e1%85%a5","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%87%e1%85%b5%e1%86%ab%e1%84%83%e1%85%a9-%e1%84%90%e1%85%a6%e1%84%8b%e1%85%b5%e1%84%87%e1%85%b3%e1%86%af-%e1%84%8b%e1%85%a7%e1%84%85%e1%85%a5\/","title":{"rendered":"Pandas: \uc5ec\ub7ec \uc5f4\uc744 \uae30\ubc18\uc73c\ub85c \ube48\ub3c4\ud45c \ub9cc\ub4e4\uae30"},"content":{"rendered":"

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\ub2e4\uc74c \uae30\ubcf8 \uad6c\ubb38\uc744 \uc0ac\uc6a9\ud558\uc5ec \uc5ec\ub7ec \uc5f4\uc744 \uae30\ubc18\uc73c\ub85c \ud32c\ub354\uc5d0\uc11c \ube48\ub3c4 \ud14c\uc774\ube14\uc744 \ub9cc\ub4e4 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n

 df. value_counts<\/span> ([' column1<\/span> ',' column2<\/span> '])\n<\/strong><\/pre>\n
 \ub2e4\uc74c \uc608\uc5d0\uc11c\ub294 \uc2e4\uc81c\ub85c \uc774 \uad6c\ubb38\uc744 \uc0ac\uc6a9\ud558\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/span><\/p>\n
 \uc608: Pandas\uc5d0\uc11c \uc5ec\ub7ec \uc5f4\uc744 \uae30\ubc18\uc73c\ub85c \ube48\ub3c4\ud45c \ub9cc\ub4e4\uae30<\/strong><\/span><\/h2>\n
 \ud300 \uc774\ub984, \uc704\uce58, \ub2e4\uc591\ud55c \ub18d\uad6c \uc120\uc218\uac00 \ub4dd\uc810\ud55c \uc810\uc218\uc5d0 \ub300\ud55c \uc815\ubcf4\uac00 \ud3ec\ud568\ub41c \ub2e4\uc74c\uacfc \uac19\uc740 pandas DataFrame\uc774 \uc788\ub2e4\uace0 \uac00\uc815\ud569\ub2c8\ub2e4.<\/span><\/p>\n
 import<\/span> pandas as<\/span> pd\n\n#createDataFrame<\/span>\ndf = pd. DataFrame<\/span> ({' team<\/span> ': ['A', 'A', 'A', 'A', 'B', 'B', 'B', 'B'],\n                   ' position<\/span> ': ['G', 'G', 'G', 'F', 'G', 'G', 'F', 'F'],\n                   ' points<\/span> ': [24, 33, 20, 15, 16, 16, 29, 25]})\n\n#view DataFrame\n<\/span>print<\/span> (df)\n\n  team position points\n0 AG 24\n1 AG 33\n2 AG 20\n3 AF15\n4 BG 16\n5 BG 16\n6 BF 29\n7 BF 25<\/strong><\/pre>\n
 value_counts()<\/strong> \ud568\uc218\ub97c \uc0ac\uc6a9\ud558\uc5ec \ud300<\/strong> \ubc0f \ud3ec\uc9c0\uc158<\/strong> \uc5f4\uc5d0\uc11c \uac01 \uac12 \uc870\ud569\uc758 \ubc1c\uc0dd\uc744 \ubcf4\uc5ec\uc8fc\ub294 \ube48\ub3c4 \ud14c\uc774\ube14\uc744 \ub9cc\ub4e4 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 #count frequency of values in team and position columns\ndf. value_counts<\/span> ([' team<\/span> ',' position<\/span> '])\n\nteam position\nGA 3\nBF 2\n      G2\nAF1\ndtype: int64<\/span><\/span>\n<\/strong><\/pre>\n
 \uacb0\uacfc\uc5d0\uc11c \ub2e4\uc74c\uc744 \ud655\uc778\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n