{"id":3193,"date":"2023-07-18T18:13:36","date_gmt":"2023-07-18T18:13:36","guid":{"rendered":"https:\/\/statorials.org\/ko\/python-verite-%e1%84%8b%e1%85%b5%e1%86%af%e1%84%85%e1%85%a7%e1%86%ab-%e1%84%80%e1%85%a1%e1%86%b9%e1%84%8b%e1%85%b5-%e1%84%86%e1%85%a9%e1%84%92%e1%85%a9%e1%84%92%e1%85%a1%e1%86%b8%e1%84%82%e1%85%b5\/"},"modified":"2023-07-18T18:13:36","modified_gmt":"2023-07-18T18:13:36","slug":"python-verite-%e1%84%8b%e1%85%b5%e1%86%af%e1%84%85%e1%85%a7%e1%86%ab-%e1%84%80%e1%85%a1%e1%86%b9%e1%84%8b%e1%85%b5-%e1%84%86%e1%85%a9%e1%84%92%e1%85%a9%e1%84%92%e1%85%a1%e1%86%b8%e1%84%82%e1%85%b5","status":"publish","type":"post","link":"https:\/\/statorials.org\/ko\/python-verite-%e1%84%8b%e1%85%b5%e1%86%af%e1%84%85%e1%85%a7%e1%86%ab-%e1%84%80%e1%85%a1%e1%86%b9%e1%84%8b%e1%85%b5-%e1%84%86%e1%85%a9%e1%84%92%e1%85%a9%e1%84%92%e1%85%a1%e1%86%b8%e1%84%82%e1%85%b5\/","title":{"rendered":"Pandas\uc5d0\uc11c \uc218\uc815\ud558\ub294 \ubc29\ubc95: \uc2dc\ub9ac\uc988\uc758 \uc9c4\ub9ac\uac12\uc774 \ubaa8\ud638\ud569\ub2c8\ub2e4."},"content":{"rendered":"

<\/p>\n

\n

Python\uc5d0\uc11c \ubc1c\uc0dd\ud560 \uc218 \uc788\ub294 \uc624\ub958\ub294 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n

 ValueError<\/span> : The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(),\n            a.any() or a.all().\n<\/strong><\/pre>\n
 \uc774 \uc624\ub958\ub294 \uc77c\ubc18\uc801\uc73c\ub85c &<\/strong> \ubc0f |<\/strong> \ubb38\uc790\ub97c \uc0ac\uc6a9\ud558\ub294 \ub300\uc2e0 \ubc0f<\/strong> \ubc0f \ub610\ub294<\/strong> \ub2e8\uc5b4\ub97c \uc0ac\uc6a9\ud558\uc5ec pandas DataFrame\uc744 \ud544\ud130\ub9c1\ud558\ub824\uace0 \ud560 \ub54c \ubc1c\uc0dd\ud569\ub2c8\ub2e4. \uc6b4\uc601\uc790.<\/span><\/p>\n
 \uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 \uc2e4\uc81c\ub85c \uc774 \uc624\ub958\ub97c \ud574\uacb0\ud558\ub294 \ubc29\ubc95\uc744 \uc124\uba85\ud569\ub2c8\ub2e4.<\/span><\/p>\n
 \uc624\ub958\ub97c \uc7ac\ud604\ud558\ub294 \ubc29\ubc95<\/strong><\/h3>\n
 \ub2e4\uc74c\uacfc \uac19\uc740 pandas DataFrame\uc744 \uc0dd\uc131\ud55c\ub2e4\uace0 \uac00\uc815\ud569\ub2c8\ub2e4.<\/span><\/p>\n
 import<\/span> pandas as<\/span> pd\n\n#createDataFrame\n<\/span>df = pd. DataFrame<\/span> ({' team<\/span> ': ['A', 'A', 'A', 'A', 'B', 'B', 'B', 'B'],\n                   ' points<\/span> ': [18, 22, 19, 14, 14, 11, 20, 28],\n                   ' assists<\/span> ': [5, 7, 7, 9, 12, 9, 9, 4],\n                   ' rebounds<\/span> ': [11, 8, 10, 6, 6, 5, 9, 12]})\n\n#view DataFrame\n<\/span>print<\/span> (df)\n\n  team points assists rebounds\n0 A 18 5 11\n1 to 22 7 8\n2 A 19 7 10\n3 A 14 9 6\n4 B 14 12 6\n5 B 11 9 5\n6 B 20 9 9\n7 B 28 4 12\n<\/strong><\/span><\/pre>\n
 \uc774\uc81c \ud300\uc774 “A” \uc774\uace0<\/strong> \uc810\uc218\uac00 20\ubcf4\ub2e4 \uc791\uc740 \ud589\uc744 \ud544\ud130\ub9c1\ud55c\ub2e4\uace0 \uac00\uc815\ud569\ub2c8\ub2e4.<\/span><\/p>\n
 #attempt to filter DataFrame\n<\/span>df[(df[' team<\/span> '] == ' A<\/span> ') and<\/span> (df[' points<\/span> '] < 20<\/span> )]\n\nValueError<\/span> : The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(),\n            a.any() or a.all().\n<\/strong><\/span><\/pre>\n
 \ub610\ub294 \ud300\uc774 “A” \uc774\uac70\ub098<\/b> \uc810\uc218\uac00 20\ubcf4\ub2e4 \uc791\uc740 \ud589\uc744 \ud544\ud130\ub9c1\ud55c\ub2e4\uace0 \uac00\uc815\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 #attempt to filter DataFrame\n<\/span>df[(df['<\/span> team<\/span> '] == '<\/span> A<\/span> ')<\/span> or<\/span> (df['<\/span> points<\/span> '] <<\/span> 20<\/span> )]\n\n<\/span>ValueError<\/span> : The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(),\n            a.any() or a.all().<\/span><\/strong><\/span><\/pre>\n
 \ub450 \uc2dc\ub098\ub9ac\uc624 \ubaa8\ub450 \uacc4\uc5f4\uc758 \uc9c4\ub9ac\uac12\uc774 \ubaa8\ud638\ud558\ub2e4\ub294 \uc624\ub958\ub97c \uc218\uc2e0\ud569\ub2c8\ub2e4.<\/span><\/p>\n
 \uc624\ub958\ub97c \uc218\uc815\ud558\ub294 \ubc29\ubc95<\/strong><\/h3>\n
 \ud544\ud130\ub9c1\ud560 \ub54c \uc774 \uc624\ub958\ub97c \ubc29\uc9c0\ud558\ub824\uba74 &<\/strong> \ubc0f |<\/strong> \uac15\uc694. \uc6b4\uc601\uc790.<\/span><\/p>\n
 \uc608\ub97c \ub4e4\uc5b4, \ub2e4\uc74c \ucf54\ub4dc\ub97c \uc0ac\uc6a9\ud558\uc5ec \ud300\uc774 “A” \uc774\uace0<\/strong> \ud3ec\uc778\ud2b8\uac00 20 \ubbf8\ub9cc\uc778 \ud589\uc744 \ud544\ud130\ub9c1\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 #filter DataFrame\n<\/span>df[(df['<\/span> team<\/span> '] == '<\/span> A<\/span> ')<\/span> &<\/span> (df['<\/span> points<\/span> '] <<\/span> 20<\/span> )]\n\n<\/span>team points assists rebounds\n0 A 18 5 11\n2 A 19 7 10\n3 A 14 9 6<\/span><\/strong><\/span><\/pre>\n
 \ub610\ub294 \ub2e4\uc74c \ucf54\ub4dc\ub97c \uc0ac\uc6a9\ud558\uc5ec \ud300\uc774 “A” \uc774\uac70\ub098<\/b> \uc810\uc218\uac00 20\ubcf4\ub2e4 \uc791\uc740 \ud589\uc744 \ud544\ud130\ub9c1\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 #filter DataFrame\n<\/span>df[(df[' team<\/span> '] == ' A<\/span> ') |<\/span> (df[' points<\/span> '] < 20<\/span> )]\n\n        team points assists rebounds\n0 A 18 5 11\n1 to 22 7 8\n2 A 19 7 10\n3 A 14 9 6\n4 B 14 12 6\n5 B 11 9 5<\/strong><\/span><\/pre>\n
 \ub450 \uc2dc\ub098\ub9ac\uc624 \ubaa8\ub450 &<\/strong> \ubc0f |\ub97c<\/strong> \uc0ac\uc6a9\ud588\uae30 \ub54c\ubb38\uc5d0 \uc624\ub958\uac00 \ubc1c\uc0dd\ud558\uc9c0 \uc54a\uc2b5\ub2c8\ub2e4. \uac15\uc694. \uc6b4\uc601\uc790.<\/span><\/p>\n
 \ucc38\uace0<\/strong> : \uc5ec\ub7ec \uc870\uac74\uc73c\ub85c pandas DataFrame\uc744 \ud544\ud130\ub9c1\ud560 \ub54c \uac01 \uac1c\ubcc4 \uc870\uac74 \uc8fc\uc704\uc5d0 \uad04\ud638\ub97c \ud3ec\ud568\ud558\ub294 \uac83\uc774 \uc911\uc694\ud569\ub2c8\ub2e4. \uadf8\ub807\uc9c0 \uc54a\uc73c\uba74 \uc624\ub958\uac00 \ubc1c\uc0dd\ud569\ub2c8\ub2e4.<\/span><\/p>\n
 \ucd94\uac00 \ub9ac\uc18c\uc2a4<\/strong><\/span><\/h3>\n
 \ub2e4\uc74c \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 Python\uc758 \ub2e4\ub978 \uc77c\ubc18\uc801\uc778 \uc624\ub958\ub97c \uc218\uc815\ud558\ub294 \ubc29\ubc95\uc744 \uc124\uba85\ud569\ub2c8\ub2e4.<\/span><\/p>\n
 \ud574\uacb0 \ubc29\ubc95: “pandas” \ubaa8\ub4c8\uc5d0 “dataframe” \uc18d\uc131\uc774 \uc5c6\uc2b5\ub2c8\ub2e4.<\/a>
 Pandas\uc5d0\uc11c \uc218\uc815\ud558\ub294 \ubc29\ubc95:SettingWithCopyWarning<\/a>
 Pandas\uc5d0\uc11c \uc218\uc815\ud558\ub294 \ubc29\ubc95: TypeError: \ud50c\ub86f\ud560 \uc22b\uc790 \ub370\uc774\ud130\uac00 \uc5c6\uc2b5\ub2c8\ub2e4.<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
Python\uc5d0\uc11c \ubc1c\uc0dd\ud560 \uc218 \uc788\ub294 \uc624\ub958\ub294 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4. ValueError : The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all(). \uc774 \uc624\ub958\ub294 \uc77c\ubc18\uc801\uc73c\ub85c & \ubc0f | \ubb38\uc790\ub97c \uc0ac\uc6a9\ud558\ub294 \ub300\uc2e0 \ubc0f \ubc0f \ub610\ub294 \ub2e8\uc5b4\ub97c \uc0ac\uc6a9\ud558\uc5ec pandas DataFrame\uc744 \ud544\ud130\ub9c1\ud558\ub824\uace0 \ud560 \ub54c \ubc1c\uc0dd\ud569\ub2c8\ub2e4. \uc6b4\uc601\uc790. \uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 \uc2e4\uc81c\ub85c \uc774 \uc624\ub958\ub97c \ud574\uacb0\ud558\ub294 \ubc29\ubc95\uc744 \uc124\uba85\ud569\ub2c8\ub2e4. \uc624\ub958\ub97c \uc7ac\ud604\ud558\ub294 […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[20],"tags":[],"class_list":["post-3193","post","type-post","status-publish","format-standard","hentry","category-20"],"yoast_head":"\nPandas\uc5d0\uc11c \uc218\uc815\ud558\ub294 \ubc29\ubc95: \uc2dc\ub9ac\uc988\uc758 \uc9c4\ub9ac\uac12\uc774 \ubaa8\ud638\ud569\ub2c8\ub2e4. - Statorials<\/title>\n<meta name=\"description\" content=\"\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 Python\uc5d0\uc11c \ub2e4\uc74c \uc624\ub958\ub97c \uc218\uc815\ud558\ub294 \ubc29\ubc95\uc744 \uc124\uba85\ud569\ub2c8\ub2e4. ValueError: The true value of a series is ambiguous. a.empty, a.bool(), a.item(), a.any() \ub610\ub294 a.all()\uc744 \uc0ac\uc6a9\ud558\uc2ed\uc2dc\uc624.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/statorials.org\/ko\/python-verite-\u110b\u1175\u11af\u1105\u1167\u11ab-\u1100\u1161\u11b9\u110b\u1175-\u1106\u1169\u1112\u1169\u1112\u1161\u11b8\u1102\u1175\/\" \/>\n<meta property=\"og:locale\" content=\"ko_KR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Pandas\uc5d0\uc11c \uc218\uc815\ud558\ub294 \ubc29\ubc95: \uc2dc\ub9ac\uc988\uc758 \uc9c4\ub9ac\uac12\uc774 \ubaa8\ud638\ud569\ub2c8\ub2e4. - Statorials\" \/>\n<meta property=\"og:description\" content=\"\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 Python\uc5d0\uc11c \ub2e4\uc74c \uc624\ub958\ub97c \uc218\uc815\ud558\ub294 \ubc29\ubc95\uc744 \uc124\uba85\ud569\ub2c8\ub2e4. ValueError: The true value of a series is ambiguous. a.empty, a.bool(), a.item(), a.any() \ub610\ub294 a.all()\uc744 \uc0ac\uc6a9\ud558\uc2ed\uc2dc\uc624.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/statorials.org\/ko\/python-verite-\u110b\u1175\u11af\u1105\u1167\u11ab-\u1100\u1161\u11b9\u110b\u1175-\u1106\u1169\u1112\u1169\u1112\u1161\u11b8\u1102\u1175\/\" \/>\n<meta property=\"og:site_name\" content=\"Statorials\" \/>\n<meta property=\"article:published_time\" content=\"2023-07-18T18:13:36+00:00\" \/>\n<meta name=\"author\" content=\"\ubca4\uc790\ubbfc \uc564\ub354\uc2a8\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"\ubca4\uc790\ubbfc \uc564\ub354\uc2a8\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"1\ubd84\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/statorials.org\/ko\/python-verite-%e1%84%8b%e1%85%b5%e1%86%af%e1%84%85%e1%85%a7%e1%86%ab-%e1%84%80%e1%85%a1%e1%86%b9%e1%84%8b%e1%85%b5-%e1%84%86%e1%85%a9%e1%84%92%e1%85%a9%e1%84%92%e1%85%a1%e1%86%b8%e1%84%82%e1%85%b5\/\",\"url\":\"https:\/\/statorials.org\/ko\/python-verite-%e1%84%8b%e1%85%b5%e1%86%af%e1%84%85%e1%85%a7%e1%86%ab-%e1%84%80%e1%85%a1%e1%86%b9%e1%84%8b%e1%85%b5-%e1%84%86%e1%85%a9%e1%84%92%e1%85%a9%e1%84%92%e1%85%a1%e1%86%b8%e1%84%82%e1%85%b5\/\",\"name\":\"Pandas\uc5d0\uc11c \uc218\uc815\ud558\ub294 \ubc29\ubc95: \uc2dc\ub9ac\uc988\uc758 \uc9c4\ub9ac\uac12\uc774 \ubaa8\ud638\ud569\ub2c8\ub2e4. - Statorials\",\"isPartOf\":{\"@id\":\"https:\/\/statorials.org\/ko\/#website\"},\"datePublished\":\"2023-07-18T18:13:36+00:00\",\"dateModified\":\"2023-07-18T18:13:36+00:00\",\"author\":{\"@id\":\"https:\/\/statorials.org\/ko\/#\/schema\/person\/600556789b7d79cbaa9d9f0c721b74f4\"},\"description\":\"\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 Python\uc5d0\uc11c \ub2e4\uc74c \uc624\ub958\ub97c \uc218\uc815\ud558\ub294 \ubc29\ubc95\uc744 \uc124\uba85\ud569\ub2c8\ub2e4. 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