{"id":3717,"date":"2023-07-15T23:07:56","date_gmt":"2023-07-15T23:07:56","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%b3%e1%86%ab-%e1%84%90%e1%85%a1%e1%84%8b%e1%85%b5%e1%86%b7-%e1%84%89%e1%85%b3%e1%84%90%e1%85%a2%e1%86%b7%e1%84%91%e1%85%b3%e1%84%85%e1%85%a9\/"},"modified":"2023-07-15T23:07:56","modified_gmt":"2023-07-15T23:07:56","slug":"%e1%84%91%e1%85%a2%e1%86%ab%e1%84%83%e1%85%a5%e1%84%82%e1%85%b3%e1%86%ab-%e1%84%90%e1%85%a1%e1%84%8b%e1%85%b5%e1%86%b7-%e1%84%89%e1%85%b3%e1%84%90%e1%85%a2%e1%86%b7%e1%84%91%e1%85%b3%e1%84%85%e1%85%a9","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%b3%e1%86%ab-%e1%84%90%e1%85%a1%e1%84%8b%e1%85%b5%e1%86%b7-%e1%84%89%e1%85%b3%e1%84%90%e1%85%a2%e1%86%b7%e1%84%91%e1%85%b3%e1%84%85%e1%85%a9\/","title":{"rendered":"Pandas: \ud0c0\uc784\uc2a4\ud0ec\ud504\ub97c \uae30\uc900\uc73c\ub85c dataframe \ud589\uc744 \uc120\ud0dd\ud558\ub294 \ubc29\ubc95"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\">\ub2e4\uc74c \uae30\ubcf8 \uad6c\ubb38\uc744 \uc0ac\uc6a9\ud558\uc5ec Pandas DataFrame\uc758 \ub450 \ud0c0\uc784\uc2a4\ud0ec\ud504 \uc0ac\uc774\uc5d0\uc11c \ud589\uc744 \uc120\ud0dd\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong>df[(df[' <span style=\"color: #ff0000;\">tstamp<\/span> '] &gt; ' <span style=\"color: #ff0000;\">2022-10-25 04:30:00<\/span> ') <span style=\"color: #800080;\">&amp;<\/span> (df[' <span style=\"color: #ff0000;\">tstamp<\/span> '] &lt; ' <span style=\"color: #ff0000;\">2022-10-27 11:00:00<\/span> ')]\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">\uc774 \uad6c\ubb38\uc740 <b>tstamp<\/b> \uc5d0 \uc774\ubbf8 datetime \uc720\ud615\uc774 \uc788\ub2e4\uace0 \uac00\uc815\ud569\ub2c8\ub2e4.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\uadf8\ub807\uc9c0 \uc54a\uc740 \uacbd\uc6b0 \ub2e4\uc74c \uad6c\ubb38\uc744 \uc0ac\uc6a9\ud558\uc5ec \ub0a0\uc9dc\/\uc2dc\uac04 \uc5f4\ub85c \ubcc0\ud658\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong>df[' <span style=\"color: #ff0000;\">tstamp<\/span> '] = pd. <span style=\"color: #3366ff;\">to_datetime<\/span> (df[' <span style=\"color: #ff0000;\">tstamp<\/span> '])\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">\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<h2> <span style=\"color: #000000;\"><strong>\uc608: \ud0c0\uc784\uc2a4\ud0ec\ud504\ub97c \uae30\uc900\uc73c\ub85c Pandas DataFrame \ud589 \uc120\ud0dd<\/strong><\/span><\/h2>\n<p> <span style=\"color: #000000;\">\uc18c\ub9e4\uc810 \ud310\ub9e4 \uc815\ubcf4\uac00 \ud3ec\ud568\ub41c \ub2e4\uc74c\uacfc \uac19\uc740 pandas DataFrame\uc774 \uc788\ub2e4\uace0 \uac00\uc815\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008000;\">import<\/span> pandas <span style=\"color: #008000;\">as<\/span> pd\n\n<span style=\"color: #008080;\">#createDataFrame\n<\/span>df = pd. <span style=\"color: #3366ff;\">DataFrame<\/span> ({' <span style=\"color: #ff0000;\">tstamp<\/span> ': ['2022-10-25 04:00:00', '2022-10-25 11:55:12',\n                                 '2022-10-26 02:00:00', '2022-10-27 10:30:00',\n                                 '2022-10-27 14:25:00', '2022-10-28 01:15:27'],\n                   ' <span style=\"color: #ff0000;\">sales<\/span> ': [18, 22, 19, 14, 14, 11]})\n\n<span style=\"color: #008080;\">#view DataFrame\n<\/span><span style=\"color: #008000;\">print<\/span> (df)\n\n                tstamp sales\n0 2022-10-25 04:00:00 18\n1 2022-10-25 11:55:12 22\n2 2022-10-26 02:00:00 19\n3 2022-10-27 10:30:00 14\n4 2022-10-27 14:25:00 14\n5 2022-10-28 01:15:27 11\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">\ub2e4\uc74c \ub450 \ud0c0\uc784\uc2a4\ud0ec\ud504 \uc0ac\uc774\uc758 \ud589\ub9cc \uc120\ud0dd\ud55c\ub2e4\uace0 \uac00\uc815\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">2022-10-25 04:30:00<\/span><\/li>\n<li> <span style=\"color: #000000;\">2022-10-27 11:00:00<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">\uc774\ub97c \uc704\ud574 \ub2e4\uc74c \uad6c\ubb38\uc744 \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#convert timestamp column to datetime dtype\n<\/span>df[' <span style=\"color: #ff0000;\">tstamp<\/span> '] = pd. <span style=\"color: #3366ff;\">to_datetime<\/span> (df[' <span style=\"color: #ff0000;\">tstamp<\/span> '])\n\n<span style=\"color: #008080;\">#select rows between two timestamps\n<\/span>df[(df[' <span style=\"color: #ff0000;\">tstamp<\/span> '] &gt; ' <span style=\"color: #ff0000;\">2022-10-25 04:30:00<\/span> ') <span style=\"color: #800080;\">&amp;<\/span> (df[' <span style=\"color: #ff0000;\">tstamp<\/span> '] &lt; ' <span style=\"color: #ff0000;\">2022-10-27 11:00:00<\/span> ')]\n\n\ttstamp sales\n1 2022-10-25 11:55:12 22\n2 2022-10-26 02:00:00 19\n3 2022-10-27 10:30:00 14<\/strong>\n<\/pre>\n<p> <span style=\"color: #000000;\">\uc6b0\ub9ac\uac00 \uc9c0\uc815\ud55c \ub450 \ud0c0\uc784\uc2a4\ud0ec\ud504 \uc0ac\uc774\uc758 \uc904\ub9cc \uc120\ud0dd\ub41c\ub2e4\ub294 \uc810\uc5d0 \uc720\uc758\ud558\uc138\uc694.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\ub610\ud55c \ub0a0\uc9dc \uac12\ub9cc \uc0ac\uc6a9\ud558\uc5ec \ud0c0\uc784\uc2a4\ud0ec\ud504\ubcc4\ub85c \ud589\uc744 \uc120\ud0dd\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\uc608\ub97c \ub4e4\uc5b4 \ub2e4\uc74c \ucf54\ub4dc\ub97c \uc0ac\uc6a9\ud558\uc5ec \ud0c0\uc784\uc2a4\ud0ec\ud504\uac00 2022-10-27\ubcf4\ub2e4 \ud070 \ubaa8\ub4e0 \ud589\uc744 \uc120\ud0dd\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#convert timestamp column to datetime dtype\n<\/span>df[' <span style=\"color: #ff0000;\">tstamp<\/span> '] = pd. <span style=\"color: #3366ff;\">to_datetime<\/span> (df[' <span style=\"color: #ff0000;\">tstamp<\/span> '])\n\n<span style=\"color: #008080;\">#select rows with timestamp after 2022-10-27\n<\/span>df[df[' <span style=\"color: #ff0000;\">tstamp<\/span> '] &gt; ' <span style=\"color: #ff0000;\">2022-10-27<\/span> ']\n\n\ttstamp sales\n3 2022-10-27 10:30:00 14\n4 2022-10-27 14:25:00 14\n5 2022-10-28 01:15:27 11<\/strong><\/pre>\n<p> <span style=\"color: #000000;\"><strong>tsamp<\/strong> \uc5f4\uc758 \uac12\uc774 2022-10-27\ubcf4\ub2e4 \uc774\ud6c4\uc778 \ud589\ub9cc \uc120\ud0dd\ub429\ub2c8\ub2e4.<\/span><\/p>\n<h2> <span style=\"color: #000000;\"><strong>\ucd94\uac00 \ub9ac\uc18c\uc2a4<\/strong><\/span><\/h2>\n<p> <span style=\"color: #000000;\">\ub2e4\uc74c \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 Pandas\uc5d0\uc11c \ub2e4\ub978 \uc77c\ubc18\uc801\uc778 \uc791\uc5c5\uc744 \uc218\ud589\ud558\ub294 \ubc29\ubc95\uc744 \uc124\uba85\ud569\ub2c8\ub2e4.<\/span><\/p>\n<p> <a href=\"https:\/\/statorials.org\/ko\/\u1102\u1161\u11af\u110d\u1161-\u1109\u1175\u1100\u1161\u11ab\u110b\u1173\u11af-\u1102\u1161\u11af\u110d\u1161-\u1111\u1162\u11ab\u1103\u1165\u1105\u1169-\u1107\u1167\u11ab\u1112\u116a\u11ab\/\" target=\"_blank\" rel=\"noopener\">Pandas\uc5d0\uc11c DateTime\uc744 \ub0a0\uc9dc\ub85c \ubcc0\ud658\ud558\ub294 \ubc29\ubc95<\/a><br \/> Pandas\uc5d0\uc11c \uc5f4\uc744 DateTime\uc73c\ub85c \ubcc0\ud658\ud558\ub294 \ubc29\ubc95<br \/> <a href=\"https:\/\/statorials.org\/ko\/\u1111\u1162\u11ab\u1103\u1165\u1102\u1173\u11ab-\u1102\u1161\u11af\u110d\u1161\u1107\u1167\u11af\u1105\u1169-\u110c\u1165\u11bc\u1105\u1167\u11af\u1112\u1161\u11b8\u1102\u1175\u1103\u1161.\/\" target=\"_blank\" rel=\"noopener\">Pandas DataFrame\uc744 \ub0a0\uc9dc\ubcc4\ub85c \uc815\ub82c\ud558\ub294 \ubc29\ubc95<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\ub2e4\uc74c \uae30\ubcf8 \uad6c\ubb38\uc744 \uc0ac\uc6a9\ud558\uc5ec Pandas DataFrame\uc758 \ub450 \ud0c0\uc784\uc2a4\ud0ec\ud504 \uc0ac\uc774\uc5d0\uc11c \ud589\uc744 \uc120\ud0dd\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. df[(df[&#8216; tstamp &#8216;] &gt; &#8216; 2022-10-25 04:30:00 &#8216;) &amp; (df[&#8216; tstamp &#8216;] &lt; &#8216; 2022-10-27 11:00:00 &#8216;)] \uc774 \uad6c\ubb38\uc740 tstamp \uc5d0 \uc774\ubbf8 datetime \uc720\ud615\uc774 \uc788\ub2e4\uace0 \uac00\uc815\ud569\ub2c8\ub2e4. \uadf8\ub807\uc9c0 \uc54a\uc740 \uacbd\uc6b0 \ub2e4\uc74c \uad6c\ubb38\uc744 \uc0ac\uc6a9\ud558\uc5ec \ub0a0\uc9dc\/\uc2dc\uac04 \uc5f4\ub85c \ubcc0\ud658\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. df[&#8216; tstamp &#8216;] = pd. 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