{"id":641,"date":"2020-12-29T13:31:34","date_gmt":"2020-12-29T13:31:34","guid":{"rendered":"https:\/\/web.htk.tlu.ee\/digitaru\/tarkvara2\/?post_type=chapter&#038;p=641"},"modified":"2020-12-29T13:40:15","modified_gmt":"2020-12-29T13:40:15","slug":"seeria-tukeldamine","status":"publish","type":"chapter","link":"https:\/\/web.htk.tlu.ee\/digitaru\/tarkvara2\/chapter\/seeria-tukeldamine\/","title":{"raw":"Seeria t\u00fckeldamine","rendered":"Seeria t\u00fckeldamine"},"content":{"raw":"Andmete t\u00f6\u00f6tlemisel tuleb sageli ette seda, et tervet rida v\u00f5i veergu ei ole vaja anal\u00fc\u00fcsida, soovitakse teha arvutusi vaid n\u00e4iteks mingile osale veerust. Loome seeria, kus on andmed \u00f5pilaste arvude kohta klassis.\r\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"python\" data-enlighter-theme=\"bootstrap4\" data-enlighter-linenumbers=\"false\">andmed = [24, 23, 21, 22, 28, 26, 30, 28, 31, 35, 33, 32, 29, 27, 25, 30, 26, 31, 22]\r\nklassid = ['1a', '1b', '2a', '3a', '3b', '4a', '4b', '5a', '6a', '6b', '7a', '8a', '9a', '10 reaal', '10 sotsiaal', '11 reaal', '11 sotsiaal', '12 reaal', '12 sotsiaal']\r\n\r\nopilaste_arv = pd.Series(andmed, index = klassid, name = '\u00d5pilaste arv klassis')\r\n\r\nprint(opilaste_arv)<\/pre>\r\n<pre><span style=\"color: #3366ff\"><strong>&gt;&gt;&gt;<\/strong><\/span> <span style=\"color: #999999\">%Run guido.py<\/span>\r\n  1a         \t24\r\n  1b         \t23\r\n  2a          \t21\r\n  3a         \t22\r\n  3b         \t28\r\n  4a         \t26\r\n  4b         \t30\r\n  5a          \t28\r\n  6a         \t31\r\n  6b         \t35\r\n  7a          \t33\r\n  8a          \t32\r\n  9a         \t29\r\n  10 reaal   \t27\r\n  10 sotsiaal\t25\r\n  11 reaal   \t30\r\n  11 sotsiaal\t26\r\n  12 reaal   \t31\r\n  12 sotsiaal\t22\r\n  Name: \u00d5pilaste arv klassis, dtype: int64\r\n<\/pre>\r\nOletame, et meil on vaja eraldada ainult 1. - 4. klass \u00f5pilaste arvud. Seda saab teha kahel moel. Esiteks saab kasutada indekseid. Seda saab teha sarnaselt Pythoni j\u00e4rjendi viilutamisele, kus j\u00e4rjendi muutujanime j\u00e4rel lisatakse nurksulgude vahele soovitud vahemiku algusindeks ja l\u00f5ppindeks (ei ole kaasa arvatud). Indeksid eraldatakse kooloni abil. N\u00e4ites kasutatud seerias on algindeksiks 0 ja l\u00f5ppindeks on 7.\r\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"python\" data-enlighter-theme=\"bootstrap4\" data-enlighter-linenumbers=\"false\">klassid_1_4 = opilaste_arv[0:7]\r\n\r\nprint(klassid_1_4)<\/pre>\r\n<pre><span style=\"color: #3366ff\"><strong>&gt;&gt;&gt;<\/strong><\/span> <span style=\"color: #999999\">%Run guido.py<\/span>\r\n  1a\t 24\r\n  1b\t 23\r\n  2a\t 21\r\n  3a\t 22\r\n  3b\t 28\r\n  4a\t 26\r\n  4b\t 30\r\n  Name: \u00d5pilaste arv klassis, dtype: int64<\/pre>\r\nTeine v\u00f5imalus on kasutada silte. Sarnaselt eelnevas n\u00e4ites, kus lisasime vastavad indeksid nurksulgude vahele, lisame seekord j\u00e4rjendi, kus elementideks on siltide nimed. Nii on v\u00f5imalik korraga eraldada andmed erinevate siltide nimega. Loome j\u00e4rjendi, kus elemendid on 1. - 4. klasside sildid ja kasutame seda, sarnaselt eelmisele n\u00e4itele, nurksulgude vahel.\r\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"python\" data-enlighter-theme=\"bootstrap4\" data-enlighter-linenumbers=\"false\">sildid_1_4 = ['1a', '1b', '2a', '3a', '3b', '4a', '4b']\r\nklassid_1_4 = opilaste_arv[sildid_1_4]\r\n\r\nprint(klassid_1_4)<\/pre>\r\n<pre><span style=\"color: #3366ff\"><strong>&gt;&gt;&gt;<\/strong><\/span> <span style=\"color: #999999\">%Run guido.py<\/span>\r\n  1a\t  24\r\n  1b\t  23\r\n  2a\t  21\r\n  3a\t  22\r\n  3b\t  28\r\n  4a\t  26\r\n  4b\t  30\r\n  Name: \u00d5pilaste arv klassis, dtype: int64<\/pre>\r\nTulemus tuleb m\u00f5lemal juhul t\u00e4pselt sama.","rendered":"<p>Andmete t\u00f6\u00f6tlemisel tuleb sageli ette seda, et tervet rida v\u00f5i veergu ei ole vaja anal\u00fc\u00fcsida, soovitakse teha arvutusi vaid n\u00e4iteks mingile osale veerust. Loome seeria, kus on andmed \u00f5pilaste arvude kohta klassis.<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"python\" data-enlighter-theme=\"bootstrap4\" data-enlighter-linenumbers=\"false\">andmed = [24, 23, 21, 22, 28, 26, 30, 28, 31, 35, 33, 32, 29, 27, 25, 30, 26, 31, 22]\r\nklassid = ['1a', '1b', '2a', '3a', '3b', '4a', '4b', '5a', '6a', '6b', '7a', '8a', '9a', '10 reaal', '10 sotsiaal', '11 reaal', '11 sotsiaal', '12 reaal', '12 sotsiaal']\r\n\r\nopilaste_arv = pd.Series(andmed, index = klassid, name = '\u00d5pilaste arv klassis')\r\n\r\nprint(opilaste_arv)<\/pre>\n<pre><span style=\"color: #3366ff\"><strong>&gt;&gt;&gt;<\/strong><\/span> <span style=\"color: #999999\">%Run guido.py<\/span>\r\n  1a         \t24\r\n  1b         \t23\r\n  2a          \t21\r\n  3a         \t22\r\n  3b         \t28\r\n  4a         \t26\r\n  4b         \t30\r\n  5a          \t28\r\n  6a         \t31\r\n  6b         \t35\r\n  7a          \t33\r\n  8a          \t32\r\n  9a         \t29\r\n  10 reaal   \t27\r\n  10 sotsiaal\t25\r\n  11 reaal   \t30\r\n  11 sotsiaal\t26\r\n  12 reaal   \t31\r\n  12 sotsiaal\t22\r\n  Name: \u00d5pilaste arv klassis, dtype: int64\r\n<\/pre>\n<p>Oletame, et meil on vaja eraldada ainult 1. &#8211; 4. klass \u00f5pilaste arvud. Seda saab teha kahel moel. Esiteks saab kasutada indekseid. Seda saab teha sarnaselt Pythoni j\u00e4rjendi viilutamisele, kus j\u00e4rjendi muutujanime j\u00e4rel lisatakse nurksulgude vahele soovitud vahemiku algusindeks ja l\u00f5ppindeks (ei ole kaasa arvatud). Indeksid eraldatakse kooloni abil. N\u00e4ites kasutatud seerias on algindeksiks 0 ja l\u00f5ppindeks on 7.<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"python\" data-enlighter-theme=\"bootstrap4\" data-enlighter-linenumbers=\"false\">klassid_1_4 = opilaste_arv[0:7]\r\n\r\nprint(klassid_1_4)<\/pre>\n<pre><span style=\"color: #3366ff\"><strong>&gt;&gt;&gt;<\/strong><\/span> <span style=\"color: #999999\">%Run guido.py<\/span>\r\n  1a\t 24\r\n  1b\t 23\r\n  2a\t 21\r\n  3a\t 22\r\n  3b\t 28\r\n  4a\t 26\r\n  4b\t 30\r\n  Name: \u00d5pilaste arv klassis, dtype: int64<\/pre>\n<p>Teine v\u00f5imalus on kasutada silte. Sarnaselt eelnevas n\u00e4ites, kus lisasime vastavad indeksid nurksulgude vahele, lisame seekord j\u00e4rjendi, kus elementideks on siltide nimed. Nii on v\u00f5imalik korraga eraldada andmed erinevate siltide nimega. Loome j\u00e4rjendi, kus elemendid on 1. &#8211; 4. klasside sildid ja kasutame seda, sarnaselt eelmisele n\u00e4itele, nurksulgude vahel.<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"python\" data-enlighter-theme=\"bootstrap4\" data-enlighter-linenumbers=\"false\">sildid_1_4 = ['1a', '1b', '2a', '3a', '3b', '4a', '4b']\r\nklassid_1_4 = opilaste_arv[sildid_1_4]\r\n\r\nprint(klassid_1_4)<\/pre>\n<pre><span style=\"color: #3366ff\"><strong>&gt;&gt;&gt;<\/strong><\/span> <span style=\"color: #999999\">%Run guido.py<\/span>\r\n  1a\t  24\r\n  1b\t  23\r\n  2a\t  21\r\n  3a\t  22\r\n  3b\t  28\r\n  4a\t  26\r\n  4b\t  30\r\n  Name: \u00d5pilaste arv klassis, dtype: int64<\/pre>\n<p>Tulemus tuleb m\u00f5lemal juhul t\u00e4pselt sama.<\/p>\n","protected":false},"author":16,"menu_order":3,"template":"","meta":{"pb_show_title":"on","pb_short_title":"","pb_subtitle":"","pb_authors":[],"pb_section_license":""},"chapter-type":[],"contributor":[],"license":[],"class_list":["post-641","chapter","type-chapter","status-publish","hentry"],"part":93,"_links":{"self":[{"href":"https:\/\/web.htk.tlu.ee\/digitaru\/tarkvara2\/wp-json\/pressbooks\/v2\/chapters\/641","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/web.htk.tlu.ee\/digitaru\/tarkvara2\/wp-json\/pressbooks\/v2\/chapters"}],"about":[{"href":"https:\/\/web.htk.tlu.ee\/digitaru\/tarkvara2\/wp-json\/wp\/v2\/types\/chapter"}],"author":[{"embeddable":true,"href":"https:\/\/web.htk.tlu.ee\/digitaru\/tarkvara2\/wp-json\/wp\/v2\/users\/16"}],"version-history":[{"count":3,"href":"https:\/\/web.htk.tlu.ee\/digitaru\/tarkvara2\/wp-json\/pressbooks\/v2\/chapters\/641\/revisions"}],"predecessor-version":[{"id":652,"href":"https:\/\/web.htk.tlu.ee\/digitaru\/tarkvara2\/wp-json\/pressbooks\/v2\/chapters\/641\/revisions\/652"}],"part":[{"href":"https:\/\/web.htk.tlu.ee\/digitaru\/tarkvara2\/wp-json\/pressbooks\/v2\/parts\/93"}],"metadata":[{"href":"https:\/\/web.htk.tlu.ee\/digitaru\/tarkvara2\/wp-json\/pressbooks\/v2\/chapters\/641\/metadata\/"}],"wp:attachment":[{"href":"https:\/\/web.htk.tlu.ee\/digitaru\/tarkvara2\/wp-json\/wp\/v2\/media?parent=641"}],"wp:term":[{"taxonomy":"chapter-type","embeddable":true,"href":"https:\/\/web.htk.tlu.ee\/digitaru\/tarkvara2\/wp-json\/pressbooks\/v2\/chapter-type?post=641"},{"taxonomy":"contributor","embeddable":true,"href":"https:\/\/web.htk.tlu.ee\/digitaru\/tarkvara2\/wp-json\/wp\/v2\/contributor?post=641"},{"taxonomy":"license","embeddable":true,"href":"https:\/\/web.htk.tlu.ee\/digitaru\/tarkvara2\/wp-json\/wp\/v2\/license?post=641"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}