{"id":1115,"date":"2023-07-27T14:58:19","date_gmt":"2023-07-27T14:58:19","guid":{"rendered":"https:\/\/statorials.org\/my\/%e1%80%9a%e1%80%b0%e1%80%80%e1%80%9c%e1%80%85%e1%80%ba-%e1%80%a1%e1%80%80%e1%80%bd%e1%80%ac%e1%80%a1%e1%80%9d%e1%80%b1%e1%80%b8-%e1%80%85%e1%80%95%e1%80%ab%e1%80%b8%e1%80%a1%e1%80%af%e1%80%b6%e1%80%b8\/"},"modified":"2023-07-27T14:58:19","modified_gmt":"2023-07-27T14:58:19","slug":"%e1%80%9a%e1%80%b0%e1%80%80%e1%80%9c%e1%80%85%e1%80%ba-%e1%80%a1%e1%80%80%e1%80%bd%e1%80%ac%e1%80%a1%e1%80%9d%e1%80%b1%e1%80%b8-%e1%80%85%e1%80%95%e1%80%ab%e1%80%b8%e1%80%a1%e1%80%af%e1%80%b6%e1%80%b8","status":"publish","type":"post","link":"https:\/\/statorials.org\/my\/%e1%80%9a%e1%80%b0%e1%80%80%e1%80%9c%e1%80%85%e1%80%ba-%e1%80%a1%e1%80%80%e1%80%bd%e1%80%ac%e1%80%a1%e1%80%9d%e1%80%b1%e1%80%b8-%e1%80%85%e1%80%95%e1%80%ab%e1%80%b8%e1%80%a1%e1%80%af%e1%80%b6%e1%80%b8\/","title":{"rendered":"Python \u1010\u103d\u1004\u103a euclidean \u1021\u1000\u103d\u102c\u1021\u101d\u1031\u1038\u1000\u102d\u102f \u1010\u103d\u1000\u103a\u1001\u103b\u1000\u103a\u1014\u100a\u103a\u1038 (\u1014\u1019\u1030\u1014\u102c\u1019\u103b\u102c\u1038\u1016\u103c\u1004\u1037\u103a)"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\">Vector A \u1014\u103e\u1004\u1037\u103a B \u1014\u103e\u1005\u103a\u1001\u102f\u1000\u103c\u102c\u1038\u101b\u103e\u102d <strong>Euclidean \u1021\u1000\u103d\u102c\u1021\u101d\u1031\u1038\u1000\u102d\u102f<\/strong> \u1021\u1031\u102c\u1000\u103a\u1015\u102b\u1021\u1010\u102d\u102f\u1004\u103a\u1038 \u1010\u103d\u1000\u103a\u1001\u103b\u1000\u103a\u101e\u100a\u103a\u104b<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>\u101a\u1030\u1000\u101c\u1005\u103a\u1021\u1000\u103d\u102c\u1021\u101d\u1031\u1038 = \u221a <span style=\"border-top: 1px solid black;\">\u03a3(A <sub>i<\/sub> -B <sub>i<\/sub> ) <sup>\u1042<\/sup><\/span><\/strong><\/span><\/p>\n<p> <span style=\"color: #000000;\">Python \u101b\u103e\u102d vector \u1014\u103e\u1005\u103a\u1001\u102f\u1000\u103c\u102c\u1038\u101b\u103e\u102d Euclidean \u1021\u1000\u103d\u102c\u1021\u101d\u1031\u1038\u1000\u102d\u102f \u1010\u103d\u1000\u103a\u1001\u103b\u1000\u103a\u101b\u1014\u103a <strong>numpy.linalg.norm<\/strong> \u101c\u102f\u1015\u103a\u1006\u1031\u102c\u1004\u103a\u1001\u103b\u1000\u103a\u1000\u102d\u102f \u1000\u103b\u103d\u1014\u103a\u102f\u1015\u103a\u1010\u102d\u102f\u1037 \u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u1014\u102d\u102f\u1004\u103a\u1015\u102b\u101e\u100a\u103a\u104b<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008000;\"><span style=\"color: #008080;\">#import functions<\/span>\nimport <span style=\"color: #000000;\">numpy<\/span> as <span style=\"color: #000000;\">np<\/span>\nfrom<\/span> numpy. <span style=\"color: #008000;\"><span style=\"color: #3366ff;\">linalg<\/span> import<\/span> norm\n\n<span style=\"color: #008080;\">#define two vectors\n<\/span>a = np.array([2, 6, 7, 7, 5, 13, 14, 17, 11, 8])\nb = np.array([3, 5, 5, 3, 7, 12, 13, 19, 22, 7])\n\n<span style=\"color: #008080;\">#calculate Euclidean distance between the two vectors<\/span> \nnorm(ab)\n\n12.409673645990857<\/strong>\n<\/pre>\n<p> <span style=\"color: #000000;\">vector \u1014\u103e\u1005\u103a\u1001\u102f\u1000\u103c\u102c\u1038\u101b\u103e\u102d Euclidean \u1021\u1000\u103d\u102c\u1021\u101d\u1031\u1038\u101e\u100a\u103a <strong>12.40967<\/strong> \u1016\u103c\u1005\u103a\u101e\u103d\u102c\u1038\u101e\u100a\u103a\u104b<\/span><\/p>\n<p> <span style=\"color: #000000;\">Vector \u1014\u103e\u1005\u103a\u1001\u102f\u101e\u100a\u103a \u1021\u101b\u103e\u100a\u103a\u1019\u1010\u1030\u100a\u102e\u1015\u102b\u1000\u104a \u1024\u101c\u102f\u1015\u103a\u1006\u1031\u102c\u1004\u103a\u1001\u103b\u1000\u103a\u101e\u100a\u103a \u101e\u1010\u102d\u1015\u1031\u1038\u1001\u103b\u1000\u103a\u1019\u1000\u103a\u1006\u1031\u1037\u1001\u103b\u103a\u1000\u102d\u102f \u1011\u102f\u1010\u103a\u1015\u1031\u1038\u1019\u100a\u103a\u1016\u103c\u1005\u103a\u1000\u103c\u1031\u102c\u1004\u103a\u1038 \u101e\u1010\u102d\u1015\u103c\u102f\u1015\u102b\u104b<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008000;\"><span style=\"color: #008080;\">#import functions<\/span>\nimport <span style=\"color: #000000;\">numpy<\/span> as <span style=\"color: #000000;\">np<\/span>\nfrom<\/span> numpy. <span style=\"color: #008000;\"><span style=\"color: #3366ff;\">linalg<\/span> import<\/span> norm\n\n<span style=\"color: #008080;\">#define two vectors\n<\/span>a = np.array([2, 6, 7, 7, 5, 13, 14])\nb = np.array([3, 5, 5, 3, 7, 12, 13, 19, 22, 7])\n\n<span style=\"color: #008080;\">#calculate Euclidean distance between the two vectors<\/span> \nnorm(ab)\n\n<span style=\"color: #993300;\">ValueError<\/span> : operands could not be broadcast together with shapes (7,) (10,) \n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">\u1015\u1014\u103a\u1012\u102b DataFrame \u1000\u1031\u102c\u103a\u101c\u1036\u1014\u103e\u1005\u103a\u1001\u102f\u1000\u103c\u102c\u1038\u101b\u103e\u102d Euclidean \u1021\u1000\u103d\u102c\u1021\u101d\u1031\u1038\u1000\u102d\u102f \u1010\u103d\u1000\u103a\u1001\u103b\u1000\u103a\u101b\u1014\u103a \u1024\u101c\u102f\u1015\u103a\u1006\u1031\u102c\u1004\u103a\u1001\u103b\u1000\u103a\u1000\u102d\u102f\u101c\u100a\u103a\u1038 \u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u1014\u102d\u102f\u1004\u103a\u1000\u103c\u1031\u102c\u1004\u103a\u1038 \u101e\u1010\u102d\u1015\u103c\u102f\u1015\u102b\u104b<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008000;\"><span style=\"color: #008080;\">#import functions\n<\/span>import <span style=\"color: #000000;\">pandas<\/span> as <span style=\"color: #000000;\">pd<\/span> \nimport <span style=\"color: #000000;\">numpy<\/span> as <span style=\"color: #000000;\">np<\/span>\nfrom<\/span> numpy. <span style=\"color: #008000;\"><span style=\"color: #3366ff;\">linalg<\/span> import<\/span> norm\n\n<span style=\"color: #008080;\">#define DataFrame with three columns\n<\/span>df = pd.DataFrame({'points': [25, 12, 15, 14, 19, 23, 25, 29],\n                   'assists': [5, 7, 7, 9, 12, 9, 9, 4],\n                   'rebounds': [11, 8, 10, 6, 6, 5, 9, 12]})\n\n<span style=\"color: #008080;\">#calculate Euclidean distance between 'points' and 'assists'<\/span> \nnorm(df[' <span style=\"color: #008000;\">points<\/span> '] - df[' <span style=\"color: #008000;\">assists<\/span> '])\n\n40.496913462633174\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">\u1000\u1031\u102c\u103a\u101c\u1036\u1014\u103e\u1005\u103a\u1001\u102f\u1000\u103c\u102c\u1038\u101b\u103e\u102d Euclidean \u1021\u1000\u103d\u102c\u1021\u101d\u1031\u1038\u101e\u100a\u103a <strong>40.49691<\/strong> \u1016\u103c\u1005\u103a\u101c\u102c\u101e\u100a\u103a\u104b<\/span><\/p>\n<h3> <strong><span style=\"color: #000000;\">\u1019\u103e\u1010\u103a\u1001\u103b\u1000\u103a\u1019\u103b\u102c\u1038<\/span><\/strong><\/h3>\n<p> <span style=\"color: #000000;\"><strong>1.<\/strong> Python \u1010\u103d\u1004\u103a Euclidean \u1021\u1000\u103d\u102c\u1021\u101d\u1031\u1038\u1000\u102d\u102f \u1010\u103d\u1000\u103a\u1001\u103b\u1000\u103a\u101b\u1014\u103a \u1014\u100a\u103a\u1038\u101c\u1019\u103a\u1038\u1019\u103b\u102c\u1038\u1005\u103d\u102c \u101b\u103e\u102d\u101e\u100a\u103a\u104a \u101e\u102d\u102f\u1037\u101e\u1031\u102c\u103a <a href=\"https:\/\/stackoverflow.com\/questions\/1401712\/how-can-the-euclidean-distance-be-calculated-with-numpy\" target=\"_blank\" rel=\"noopener noreferrer\">\u1024 Stack Overflow thread \u1000 \u101b\u103e\u1004\u103a\u1038\u1015\u103c\u1011\u102c\u1038\u101e\u100a\u1037\u103a<\/a> \u1021\u1010\u102d\u102f\u1004\u103a\u1038\u104a \u1024\u1014\u1031\u101b\u102c\u1010\u103d\u1004\u103a \u101b\u103e\u1004\u103a\u1038\u1015\u103c\u1011\u102c\u1038\u101e\u1031\u102c \u1014\u100a\u103a\u1038\u101c\u1019\u103a\u1038\u101e\u100a\u103a \u1021\u1019\u103c\u1014\u103a\u1006\u102f\u1036\u1038 \u1016\u103c\u1005\u103a\u101c\u102c\u1015\u102b\u101e\u100a\u103a\u104b<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>2.<\/strong> <strong>numpy.linalg.norm<\/strong> \u101c\u102f\u1015\u103a\u1006\u1031\u102c\u1004\u103a\u1001\u103b\u1000\u103a\u104f \u1005\u102c\u101b\u103d\u1000\u103a\u1005\u102c\u1010\u1019\u103a\u1038\u1021\u1015\u103c\u100a\u1037\u103a\u1021\u1005\u102f\u1036\u1000\u102d\u102f <a href=\"https:\/\/numpy.org\/doc\/stable\/reference\/generated\/numpy.linalg.norm.html\" target=\"_blank\" rel=\"noopener noreferrer\">\u1024\u1014\u1031\u101b\u102c\u1010\u103d\u1004\u103a<\/a> \u101b\u103e\u102c\u1016\u103d\u1031\u1014\u102d\u102f\u1004\u103a\u1015\u102b\u101e\u100a\u103a\u104b<\/span><\/p>\n<p> 3. <span style=\"color: #000000;\">Euclidean \u1021\u1000\u103d\u102c\u1021\u101d\u1031\u1038\u1021\u1000\u103c\u1031\u102c\u1004\u103a\u1038 \u1015\u102d\u102f\u1019\u102d\u102f\u101c\u1031\u1037\u101c\u102c\u101b\u1014\u103a<\/span> <a href=\"https:\/\/en.wikipedia.org\/wiki\/Euclidean_distance\" target=\"_blank\" rel=\"noopener noreferrer\">\u1024 Wikipedia \u1005\u102c\u1019\u103b\u1000\u103a\u1014\u103e\u102c<\/a> <span style=\"color: #000000;\"><strong>\u1000\u102d\u102f<\/strong> \u1000\u102d\u102f\u1038\u1000\u102c\u1038\u1014\u102d\u102f\u1004\u103a\u1015\u102b\u101e\u100a\u103a<\/span> \u104b<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Vector A \u1014\u103e\u1004\u1037\u103a B \u1014\u103e\u1005\u103a\u1001\u102f\u1000\u103c\u102c\u1038\u101b\u103e\u102d Euclidean \u1021\u1000\u103d\u102c\u1021\u101d\u1031\u1038\u1000\u102d\u102f \u1021\u1031\u102c\u1000\u103a\u1015\u102b\u1021\u1010\u102d\u102f\u1004\u103a\u1038 \u1010\u103d\u1000\u103a\u1001\u103b\u1000\u103a\u101e\u100a\u103a\u104b \u101a\u1030\u1000\u101c\u1005\u103a\u1021\u1000\u103d\u102c\u1021\u101d\u1031\u1038 = \u221a \u03a3(A i -B i ) \u1042 Python \u101b\u103e\u102d vector \u1014\u103e\u1005\u103a\u1001\u102f\u1000\u103c\u102c\u1038\u101b\u103e\u102d Euclidean \u1021\u1000\u103d\u102c\u1021\u101d\u1031\u1038\u1000\u102d\u102f \u1010\u103d\u1000\u103a\u1001\u103b\u1000\u103a\u101b\u1014\u103a numpy.linalg.norm \u101c\u102f\u1015\u103a\u1006\u1031\u102c\u1004\u103a\u1001\u103b\u1000\u103a\u1000\u102d\u102f \u1000\u103b\u103d\u1014\u103a\u102f\u1015\u103a\u1010\u102d\u102f\u1037 \u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u1014\u102d\u102f\u1004\u103a\u1015\u102b\u101e\u100a\u103a\u104b #import functions import numpy as np from numpy. linalg import norm #define two vectors a = np.array([2, 6, 7, 7, 5, 13, 14, 17, 11, 8]) [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[3],"tags":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Python \u1010\u103d\u1004\u103a Euclidean \u1021\u1000\u103d\u102c\u1021\u101d\u1031\u1038\u1000\u102d\u102f \u1010\u103d\u1000\u103a\u1001\u103b\u1000\u103a\u1014\u100a\u103a\u1038 (\u1025\u1015\u1019\u102c\u1019\u103b\u102c\u1038\u1016\u103c\u1004\u1037\u103a) - Statorials<\/title>\n<meta name=\"description\" content=\"\u1024\u101e\u1004\u103a\u1001\u1014\u103a\u1038\u1005\u102c\u1010\u103d\u1004\u103a \u1025\u1015\u1019\u102c\u1019\u103b\u102c\u1038\u1005\u103d\u102c\u1016\u103c\u1004\u1037\u103a Python \u101b\u103e\u102d Euclidean \u1021\u1000\u103d\u102c\u1021\u101d\u1031\u1038\u1000\u102d\u102f 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content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Benjamin Anderson\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"1 minute\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/statorials.org\/my\/%e1%80%9a%e1%80%b0%e1%80%80%e1%80%9c%e1%80%85%e1%80%ba-%e1%80%a1%e1%80%80%e1%80%bd%e1%80%ac%e1%80%a1%e1%80%9d%e1%80%b1%e1%80%b8-%e1%80%85%e1%80%95%e1%80%ab%e1%80%b8%e1%80%a1%e1%80%af%e1%80%b6%e1%80%b8\/\",\"url\":\"https:\/\/statorials.org\/my\/%e1%80%9a%e1%80%b0%e1%80%80%e1%80%9c%e1%80%85%e1%80%ba-%e1%80%a1%e1%80%80%e1%80%bd%e1%80%ac%e1%80%a1%e1%80%9d%e1%80%b1%e1%80%b8-%e1%80%85%e1%80%95%e1%80%ab%e1%80%b8%e1%80%a1%e1%80%af%e1%80%b6%e1%80%b8\/\",\"name\":\"Python \u1010\u103d\u1004\u103a Euclidean \u1021\u1000\u103d\u102c\u1021\u101d\u1031\u1038\u1000\u102d\u102f 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