{"id":2569,"date":"2023-07-21T16:37:26","date_gmt":"2023-07-21T16:37:26","guid":{"rendered":"https:\/\/statorials.org\/ko\/%e1%84%91%e1%85%a2%e1%86%ab%e1%84%83%e1%85%a5-%e1%84%82%e1%85%ae%e1%84%8c%e1%85%a5%e1%86%a8-%e1%84%87%e1%85%a2%e1%86%a8%e1%84%87%e1%85%ae%e1%86%ab%e1%84%8b%e1%85%b2%e1%86%af\/"},"modified":"2023-07-21T16:37:26","modified_gmt":"2023-07-21T16:37:26","slug":"%e1%84%91%e1%85%a2%e1%86%ab%e1%84%83%e1%85%a5-%e1%84%82%e1%85%ae%e1%84%8c%e1%85%a5%e1%86%a8-%e1%84%87%e1%85%a2%e1%86%a8%e1%84%87%e1%85%ae%e1%86%ab%e1%84%8b%e1%85%b2%e1%86%af","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%82%e1%85%ae%e1%84%8c%e1%85%a5%e1%86%a8-%e1%84%87%e1%85%a2%e1%86%a8%e1%84%87%e1%85%ae%e1%86%ab%e1%84%8b%e1%85%b2%e1%86%af\/","title":{"rendered":"\ud32c\ub354\uc5d0\uc11c \ub204\uc801 \ubc31\ubd84\uc728\uc744 \uacc4\uc0b0\ud558\ub294 \ubc29\ubc95"},"content":{"rendered":"

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\ub2e4\uc74c \uae30\ubcf8 \uad6c\ubb38\uc744 \uc0ac\uc6a9\ud558\uc5ec Pandas DataFrame \uc5f4\uc5d0 \uc788\ub294 \uac12\uc758 \ub204\uc801 \ubc31\ubd84\uc728\uc744 \uacc4\uc0b0\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n

 #calculate cumulative sum of column\n<\/span>df[' cum_sum<\/span> '] = df[' col1<\/span> ']. cumsum<\/span> ()\n\n#calculate cumulative percentage of column (rounded to 2 decimal places)\n<\/span>df[' cum_percent<\/span> '] = round( 100<\/span> *df. cum_sum<\/span> \/df[' col1<\/span> ']. sum<\/span> (), 2<\/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: \ud32c\ub354 \uac04\uc758 \ub204\uc801 \ubc31\ubd84\uc728 \uacc4\uc0b0<\/strong><\/span><\/h3>\n
 \ud68c\uc0ac\uac00 \uc5f0\uc18d\uc801\uc73c\ub85c \ud310\ub9e4\ud55c \ub2e8\uc704 \uc218\ub97c \ud45c\uc2dc\ud558\ub294 \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\ndf = pd. DataFrame<\/span> ({' year<\/span> ': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n                   ' units_sold<\/span> ': [60, 75, 77, 87, 104, 134, 120, 125, 140, 150]})\n\n#view DataFrame\n<\/span>print<\/span> (df)\n\n   year units_sold\n0 1 60\n1 2 75\n2 3 77\n3 4 87\n4 5 104\n5 6 134\n6 7 120\n7 8 125\n8 9 140\n9 10 150\n<\/span><\/span><\/strong><\/pre>\n
 \ub2e4\uc74c\uc73c\ub85c, \ub2e4\uc74c \ucf54\ub4dc\ub97c \uc0ac\uc6a9\ud558\uc5ec \ub204\uc801 \ud310\ub9e4 \ub2e8\uc704 \uc218\uc640 \ud310\ub9e4 \ub2e8\uc704\uc758 \ub204\uc801 \ubc31\ubd84\uc728\uc744 \ud45c\uc2dc\ud558\ub294 \uc5f4\uc744 \ucd94\uac00\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/span><\/p>\n
 #calculate cumulative sum of units sold\n<\/span>df[' cum_sum<\/span> '] = df[' units_sold<\/span> ']. cumsum<\/span> ()\n\n#calculate cumulative percentage of units sold\n<\/span>df[' cum_percent<\/span> '] = round( 100<\/span> *df. cum_sum<\/span> \/df[' units_sold<\/span> ']. sum<\/span> (), 2<\/span> )\n\n#view updated DataFrame\n<\/span>print<\/span> (df)\n\n   year units_sold cum_sum cum_percent\n0 1 60 60 5.60\n1 2 75 135 12.59\n2 3 77 212 19.78\n3 4 87 299 27.89\n4 5 104 403 37.59\n5 6 134 537 50.09\n6 7 120 657 61.29\n7 8 125 782 72.95\n8 9 140 922 86.01\n9 10 150 1072 100.00\n<\/span><\/span><\/strong><\/pre>\n
 \ub204\uc801 \ubc31\ubd84\uc728\uc744 \ub2e4\uc74c\uacfc \uac19\uc774 \ud574\uc11d\ud569\ub2c8\ub2e4.<\/span><\/p>\n