{"id":3649,"date":"2023-07-16T08:13:17","date_gmt":"2023-07-16T08:13:17","guid":{"rendered":"https:\/\/statorials.org\/ko\/%e1%84%8b%e1%85%b5%e1%86%af%e1%84%8e%e1%85%b5%e1%84%92%e1%85%a1%e1%84%82%e1%85%b3%e1%86%ab-%e1%84%91%e1%85%a2%e1%86%ab%e1%84%83%e1%85%a5\/"},"modified":"2023-07-16T08:13:17","modified_gmt":"2023-07-16T08:13:17","slug":"%e1%84%8b%e1%85%b5%e1%86%af%e1%84%8e%e1%85%b5%e1%84%92%e1%85%a1%e1%84%82%e1%85%b3%e1%86%ab-%e1%84%91%e1%85%a2%e1%86%ab%e1%84%83%e1%85%a5","status":"publish","type":"post","link":"https:\/\/statorials.org\/ko\/%e1%84%8b%e1%85%b5%e1%86%af%e1%84%8e%e1%85%b5%e1%84%92%e1%85%a1%e1%84%82%e1%85%b3%e1%86%ab-%e1%84%91%e1%85%a2%e1%86%ab%e1%84%83%e1%85%a5\/","title":{"rendered":"Pandas\uc5d0\uc11c corrwith()\ub97c \uc0ac\uc6a9\ud558\ub294 \ubc29\ubc95(\uc608\uc81c \ud3ec\ud568)"},"content":{"rendered":"

<\/p>\n

\n

Pandas\uc5d0\uc11c corrwith()<\/strong> \ud568\uc218\ub97c \uc0ac\uc6a9\ud558\uc5ec \ub450 \uac1c\uc758 \uc11c\ub85c \ub2e4\ub978 Pandas DataFrame\uc5d0\uc11c \ub3d9\uc77c\ud55c \uc774\ub984\uc744 \uac00\uc9c4<\/em> \uc22b\uc790 \uc5f4 \uac04\uc758 \uc30d\ubcc4 \uc0c1\uad00 \uad00\uacc4\ub97c \uacc4\uc0b0\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n

\uc774 \ud568\uc218\ub294 \ub2e4\uc74c \uae30\ubcf8 \uad6c\ubb38\uc744 \uc0ac\uc6a9\ud569\ub2c8\ub2e4.<\/span><\/p>\n

 df1. corrwith<\/span> (df2)<\/span><\/span>\n<\/strong><\/pre>\n
 \ucc38\uace0<\/strong> : \uc774 \ud568\uc218\ub294 \ub3d9\uc77c\ud55c DataFrame \ub0b4\uc758 \ub450 \uc22b\uc790 \uc5f4 \uac04\uc758 \uc0c1\uad00 \uad00\uacc4\ub97c \uacc4\uc0b0\ud558\ub294 corr()<\/strong> \ud568\uc218\uc640 \ub2e4\ub985\ub2c8\ub2e4.<\/span><\/p>\n
 \ub2e4\uc74c \uc608\uc81c\uc5d0\uc11c\ub294 corrwith()<\/strong> \ud568\uc218\ub97c \uc2e4\uc81c\ub85c \uc0ac\uc6a9\ud558\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/span><\/p>\n
 \uc608: Pandas\uc5d0\uc11c corrwith()\ub97c \uc0ac\uc6a9\ud558\ub294 \ubc29\ubc95<\/strong><\/span><\/h2>\n
 \ub2e4\uc74c \ub450 \uac1c\uc758 \ud32c\ub354 DataFrame\uc774 \uc788\ub2e4\uace0 \uac00\uc815\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 import<\/span> pandas as<\/span> pd\n\n#create first DataFrame\n<\/span>df1 = pd. DataFrame<\/span> ({' team<\/span> ': ['A', 'B', 'C', 'D', 'E', 'F'],\n                    ' points<\/span> ': [18, 22, 29, 25, 14, 11],\n                    ' assists<\/span> ': [4, 5, 5, 4, 8, 12],\n                    ' rebounds<\/span> ': [10, 6, 4, 6, 3, 5]})\n\nprint<\/span> (df1)\n\n  team points assists rebounds\n0 to 18 4 10\n1 B 22 5 6\n2 C 29 5 4\n3 D 25 4 6\n4 E 14 8 3\n5 F 11 12 5\n\n#create second DataFrame<\/span>\ndf2 = pd. DataFrame<\/span> ({' team<\/span> ': ['A', 'B', 'C', 'D', 'E', 'F'],\n                    ' points<\/span> ': [22, 25, 27, 35, 25, 20],\n                    ' assists<\/span> ': [15, 13, 8, 8, 5, 8],\n                    ' rebs<\/span> ': [4, 11, 12, 8, 7, 10]})\n\nprint<\/span> (df2)\n\n  team points assists rebs\n0 A 22 15 4\n1 B 25 13 11\n2 C 27 8 12\n3 D 35 8 8\n4 E 25 5 7\n5 F 20 8 10<\/b><\/pre>\n
 corrwith()<\/strong> \ud568\uc218\ub97c \uc0ac\uc6a9\ud558\uc5ec \ub450 DataFrame\uc5d0\uc11c \ub3d9\uc77c\ud55c \uc774\ub984\uc744 \uac00\uc9c4 \uc22b\uc790 \uc5f4 \uac04\uc758 \uc0c1\uad00 \uad00\uacc4\ub97c \uacc4\uc0b0\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 #calculate correlation between numeric columns with same names in each DataFrame\n<\/span>df1. corrwith<\/span> (df2)\n\npoints 0.677051\nassists -0.478184\nNaN rebounds\nrebs NaN\ndtype:float64<\/span><\/span><\/strong><\/pre>\n
 \uacb0\uacfc\uc5d0\uc11c \uc6b0\ub9ac\ub294 \ub2e4\uc74c\uc744 \ubcfc \uc218 \uc788\uc2b5\ub2c8\ub2e4:<\/span><\/p>\n