{"id":3722,"date":"2023-07-15T22:26:06","date_gmt":"2023-07-15T22:26:06","guid":{"rendered":"https:\/\/statorials.org\/ko\/pandas%e1%84%82%e1%85%b3%e1%86%ab-%e1%84%92%e1%85%a2%e1%86%bc%e1%84%8b%e1%85%b5-%e1%84%83%e1%85%a1%e1%84%85%e1%85%b3%e1%86%ab-%e1%84%83%e1%85%a6%e1%84%8b%e1%85%b5%e1%84%90%e1%85%a5-%e1%84%91%e1%85%b3\/"},"modified":"2023-07-15T22:26:06","modified_gmt":"2023-07-15T22:26:06","slug":"pandas%e1%84%82%e1%85%b3%e1%86%ab-%e1%84%92%e1%85%a2%e1%86%bc%e1%84%8b%e1%85%b5-%e1%84%83%e1%85%a1%e1%84%85%e1%85%b3%e1%86%ab-%e1%84%83%e1%85%a6%e1%84%8b%e1%85%b5%e1%84%90%e1%85%a5-%e1%84%91%e1%85%b3","status":"publish","type":"post","link":"https:\/\/statorials.org\/ko\/pandas%e1%84%82%e1%85%b3%e1%86%ab-%e1%84%92%e1%85%a2%e1%86%bc%e1%84%8b%e1%85%b5-%e1%84%83%e1%85%a1%e1%84%85%e1%85%b3%e1%86%ab-%e1%84%83%e1%85%a6%e1%84%8b%e1%85%b5%e1%84%90%e1%85%a5-%e1%84%91%e1%85%b3\/","title":{"rendered":"Pandas: \ud55c dataframe\uc758 \ud589\uc774 \ub2e4\ub978 dataframe\uc5d0 \uc788\ub294\uc9c0 \ud655\uc778"},"content":{"rendered":"

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\ub2e4\uc74c \uad6c\ubb38\uc744 \uc0ac\uc6a9\ud558\uc5ec \uac01 \ud589\uc774 \ub2e4\ub978 DataFrame\uc5d0 \uc788\ub294\uc9c0 \uc5ec\ubd80\ub97c \ub098\ud0c0\ub0b4\ub294 pandas DataFrame\uc5d0 \uc0c8 \uc5f4\uc744 \ucd94\uac00\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n

 #merge two DataFrames on specific columns<\/span>\nall_df = pd. merge<\/span> (df1, df2, on=[' column1<\/span> ', ' column2<\/span> '], how=' left<\/span> ', indicator=' exists<\/span> ')\n\n#drop unwanted columns<\/span>\nall_df = all_df. drop<\/span> (' column3<\/span> ', axis= 1<\/span> )\n\n#add column that shows if each row in one DataFrame exists in another<\/span>\nall_df[' exists<\/span> '] = np. where<\/span> (all_df. exists<\/span> == ' both<\/span> ', True<\/span> , False<\/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: \ud55c Pandas DataFrame\uc758 \ud589\uc774 \ub2e4\ub978 \ud589\uc5d0 \uc788\ub294\uc9c0 \ud655\uc778<\/strong><\/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\ndf1 = pd. DataFrame<\/span> ({' team<\/span> ': ['A', 'B', 'C', 'D', 'E'], \n                    ' points<\/span> ': [12, 15, 22, 29, 24]}) \n\nprint<\/span> (df1)\n\n  team points\n0 to 12\n1 B 15\n2 C 22\n3 D 29\n4 E 24\n\n#create second DataFrame\n<\/span>df2 = pd. DataFrame<\/span> ({' team<\/span> ': ['A', 'D', 'F', 'G', 'H'],\n                    ' points<\/span> ': [12, 29, 15, 19, 10],\n                    ' assists<\/span> ': [4, 7, 7, 10, 12]})\n\nprint<\/span> (df2)\n\n  team points assists\n0 to 12 4\n1 D 29 7\n2 F 15 7\n3 G 19 10\n4:10:12\n<\/span><\/span><\/strong><\/pre>\n
 \ub2e4\uc74c \uad6c\ubb38\uc744 \uc0ac\uc6a9\ud558\uc5ec \ud300<\/strong> \uc758 \uac01 \uac12\uacfc \uac01 \ud589\uc758 \ud3ec\uc778\ud2b8<\/strong> \uc5f4\uc774 \ub450 \ubc88\uc9f8 DataFrame\uc5d0 \uc874\uc7ac\ud558\ub294\uc9c0 \uc5ec\ubd80\ub97c \ub098\ud0c0\ub0b4\ub294 \uc874\uc7ac\ub77c\ub294<\/strong> \uc5f4\uc744 \uccab \ubc88\uc9f8 DataFrame\uc5d0 \ucd94\uac00\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 import<\/span> numpy as<\/span> np\n\n#merge two dataFrames and add indicator column\n<\/span>all_df = pd. merge<\/span> (df1, df2, on=[' team<\/span> ', ' points<\/span> '], how=' left<\/span> ', indicator=' exists<\/span> ')\n\n#drop assists columns\n<\/span>all_df = all_df. drop<\/span> (' assists<\/span> ', axis= 1<\/span> )\n\n#add column to show if each row in first DataFrame exists in second\n<\/span>all_df[' exists<\/span> '] = np. where<\/span> (all_df. exists<\/span> == ' both<\/span> ', True<\/span> , False<\/span> )\n\n#view updated DataFrame\n<\/span>print<\/span> (all_df)\n\n  team points exists\n0 A 12 True\n1 B 15 False\n2 C 22 False\n3 D 29 True\n4 E 24 False\n<\/strong><\/span><\/pre>\n
 \uc0c8\ub85c\uc6b4 \uc5f4 \uc874\uc7ac\ub294<\/strong> \uac01 \ud589\uc758 \ud300<\/strong> \ubc0f \ud3ec\uc778\ud2b8<\/strong> \uc5f4\uc5d0 \uc788\ub294 \uac01 \uac12\uc774 \ub450 \ubc88\uc9f8 DataFrame\uc5d0 \uc874\uc7ac\ud558\ub294\uc9c0 \uc5ec\ubd80\ub97c \ub098\ud0c0\ub0c5\ub2c8\ub2e4.<\/span><\/span><\/p>\n
 \uacb0\uacfc\uc5d0\uc11c \uc6b0\ub9ac\ub294 \ub2e4\uc74c\uc744 \ubcfc \uc218 \uc788\uc2b5\ub2c8\ub2e4:<\/span><\/p>\n