{"id":223,"date":"2021-05-12T17:48:28","date_gmt":"2021-05-12T14:48:28","guid":{"rendered":"http:\/\/is42-2018.susu.ru\/spiridonovani\/?p=223"},"modified":"2021-05-12T17:48:28","modified_gmt":"2021-05-12T14:48:28","slug":"opisatelnaya-statistika-na-python","status":"publish","type":"post","link":"https:\/\/is42-2018.susu.ru\/spiridonovani\/2021\/05\/12\/opisatelnaya-statistika-na-python\/","title":{"rendered":"\u041e\u043f\u0438\u0441\u0430\u0442\u0435\u043b\u044c\u043d\u0430\u044f \u0441\u0442\u0430\u0442\u0438\u0441\u0442\u0438\u043a\u0430 \u043d\u0430 Python"},"content":{"rendered":"<p>\u0414\u043b\u044f \u0438\u0437\u0443\u0447\u0435\u043d\u0438\u044f \u0441\u0442\u0430\u0442\u0438\u0441\u0442\u0438\u043a\u0438 \u0437\u0430\u0433\u0440\u0443\u0436\u0430\u0435\u043c \u0431\u0438\u0431\u043b\u0438\u043e\u0442\u0435\u043a\u0438: math, numpy, pandas, statistics, scipy.stats. \u041f\u043e\u0441\u043c\u043e\u0442\u0440\u0438\u043c, \u043a\u0430\u043a\u0438\u043c \u043e\u0431\u0440\u0430\u0437\u043e\u043c \u043c\u043e\u0436\u043d\u043e \u0440\u0430\u0441\u0441\u0447\u0438\u0442\u0430\u0442\u044c \u0446\u0435\u043d\u0442\u0440\u0430\u043b\u044c\u043d\u044b\u0435 \u043c\u0435\u0442\u0440\u0438\u043a\u0438, \u0441\u0440\u0435\u0434\u043d\u0435\u0432\u0437\u0432\u0435\u0448\u0435\u043d\u043d\u043e\u0435, \u0433\u0430\u0440\u043c\u043e\u043d\u0438\u0447\u0435\u0441\u043a\u043e\u0435 \u0441\u0440\u0435\u0434\u043d\u0435\u0435, \u0441\u0440\u0435\u0434\u043d\u0435\u0435 \u0433\u0435\u043e\u043c\u0435\u0442\u0440\u0438\u0447\u0435\u0441\u043a\u043e\u0435, \u043c\u0435\u0434\u0438\u0430\u043d\u0443, \u043c\u043e\u0434\u0443, \u0434\u0438\u0441\u043f\u0435\u0440\u0441\u0438\u044e, \u0441\u0440\u0435\u0434\u043d\u0435\u043a\u0432\u0430\u0434\u0440\u0430\u0442\u0438\u0447\u043d\u043e\u0435 \u043e\u0442\u043a\u043b\u043e\u043d\u0435\u043d\u0438\u0435, \u0441\u043c\u0435\u0449\u0435\u043d\u0438\u0435, \u043f\u0440\u043e\u0446\u0435\u043d\u0442\u0438\u043b\u0438, \u0434\u0438\u0430\u043f\u0430\u0437\u043e\u043d. \u041f\u0440\u043e\u0433\u0440\u0430\u043c\u043c\u043d\u044b\u0439 \u043a\u043e\u0434 \u0434\u043b\u044f \u0440\u0430\u0441\u0447\u0451\u0442\u0430 \u0434\u0430\u043d\u043d\u044b\u0445 \u043f\u043e\u043a\u0430\u0437\u0430\u0442\u0435\u043b\u0435\u0439 \u043f\u0440\u0438\u0432\u0435\u0434\u0451\u043d \u043d\u0438\u0436\u0435.<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"python\">#\r\nimport math\r\nimport statistics\r\nimport numpy as np\r\nimport scipy.stats\r\nimport pandas as pd\r\nprint(\"\u0418\u0441\u0445\u043e\u0434\u043d\u044b\u0435 \u0434\u0430\u043d\u043d\u044b\u0435\")\r\nx = [8.0, 1, 2.5, 4, 28.0]\r\nx_with_nan = [8.0, 1, 2.5, math.nan, 4, 28.0]\r\ny, y_with_nan = np.array(x), np.array(x_with_nan)\r\nz, z_with_nan = pd.Series(x), pd.Series(x_with_nan)\r\nprint(y)\r\nprint(y_with_nan)\r\nprint(z)\r\nprint(z_with_nan)\r\n\r\n# \u0421\u0440\u0435\u0434\u043d\u0435\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435\r\nprint(\"\u0421\u0440\u0435\u0434\u043d\u0435\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435\")\r\nmean_=sum(x)\/len(x)\r\nprint(mean_)\r\nmean_=statistics.mean(x)\r\nprint(mean_)\r\nm=np.nanmean(y_with_nan)\r\nprint(m)\r\n\r\n\r\n# \u0421\u0440\u0435\u0434\u043d\u0435\u0432\u0437\u0432\u0435\u0448\u0435\u043d\u043d\u043e\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435\r\nprint(\"\u0421\u0440\u0435\u0434\u043d\u0435\u0432\u0437\u0432\u0435\u0448\u0435\u043d\u043d\u043e\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435\")\r\nx = [8.0, 1, 2.5, 4, 28.0]\r\nw = [0.1, 0.2, 0.3, 0.25, 0.15]\r\nwmean = sum(w[i] * x[i] for i in range(len(x))) \/ sum(w)\r\nprint(wmean)\r\nwmean = sum(x_ * w_ for (x_, w_) in zip(x, w)) \/ sum(w)\r\nprint(wmean)\r\n# \u0421\u0440\u0435\u0434\u043d\u0435\u0432\u0437\u0432\u0435\u0448\u0435\u043d\u043d\u043e\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435, \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043d\u0438\u0435 \u043c\u0430\u0441\u0441\u0438\u0432\u043e\u0432 Numpy \u0438 Pandas\r\nx = [8.0, 1, 2.5, 4, 28.0]\r\ny, z, w = np.array(x), pd.Series(x), np.array(w)\r\nwmean = np.average(y, weights=w)\r\nprint(wmean)\r\nwmean = np.average(z, weights=w)\r\nprint(wmean)\r\n\r\n# \u0413\u0430\u0440\u043c\u043e\u043d\u0438\u0447\u0435\u0441\u043a\u043e\u0435 \u0441\u0440\u0435\u0434\u043d\u0435\u0435\r\nprint(\"\u0413\u0430\u0440\u043c\u043e\u043d\u0438\u0447\u0435\u0441\u043a\u043e\u0435 \u0441\u0440\u0435\u0434\u043d\u0435\u0435\")\r\nhmean = len(x) \/ sum(1 \/ item for item in x)\r\nprint(hmean)\r\nhmean==scipy.stats.hmean(y)\r\nprint(hmean)\r\n\r\n# \u0421\u0440\u0435\u0434\u043d\u0435\u0435 \u0433\u0435\u043e\u043c\u0435\u0442\u0440\u0438\u0447\u0435\u0441\u043a\u043e\u0435\r\nprint(\"\u0421\u0440\u0435\u0434\u043d\u0435\u0435 \u0433\u0435\u043e\u043c\u0435\u0442\u0440\u0438\u0447\u0435\u0441\u043a\u043e\u0435\")\r\ngmean = 1\r\nfor item in x:\r\n    gmean *= item\r\ngmean **= 1 \/ len(x)\r\nprint(gmean)\r\n\r\n# \u041c\u0435\u0434\u0438\u0430\u043d\u0430\r\nprint(\"\u041c\u0435\u0434\u0438\u0430\u043d\u0430\")\r\nn = len(x)\r\nif n % 2:\r\n    median_ = sorted(x)[round(0.5*(n-1))]\r\nelse:\r\n    x_ord, index = sorted(x), round(0.5 * n)\r\n    median_ = 0.5 * (x_ord[index-1] + x_ord[index])\r\nprint(median_)\r\nprint(z.median())\r\nprint(z_with_nan.median())\r\n\r\n# \u041c\u0435\u0434\u0438\u0430\u043d\u0430\r\nu = [2, 3, 2, 8, 12]\r\nmode_ = max((u.count(item), item) for item in set(u))[1]\r\nprint(mode_)\r\n\r\n\r\n# \u0414\u0438\u0441\u043f\u0435\u0440\u0441\u0438\u044f\r\nprint(\"\u0414\u0438\u0441\u043f\u0435\u0440\u0441\u0438\u044f\")\r\nn = len(x)\r\nmean_ = sum(x) \/ n\r\nvar_ = sum((item - mean_)**2 for item in x) \/ (n - 1)\r\nprint(var_)\r\n\r\n# \u0421\u0440\u0435\u0434\u043d\u0435\u043a\u0432\u0430\u0434\u0440\u0430\u0442\u0438\u0447\u0435\u0441\u043a\u043e\u0435 \u043e\u0442\u043a\u043b\u043e\u043d\u0435\u043d\u0438\u0435\r\nprint(\"\u0421\u0440\u0435\u0434\u043d\u0435\u043a\u0432\u0430\u0434\u0440\u0430\u0442\u0438\u0447\u0435\u0441\u043a\u043e\u0435 \u043e\u0442\u043a\u043b\u043e\u043d\u0435\u043d\u0438\u0435\")\r\nstd_ = var_ ** 0.5\r\nprint(std_)\r\nstd_=np.std(y, ddof=1)\r\nprint(std_)\r\n\r\n# \u0421\u043c\u0435\u0449\u0435\u043d\u0438\u0435\r\nprint(\"\u0421\u043c\u0435\u0449\u0435\u043d\u0438\u0435\")\r\ny, y_with_nan = np.array(x), np.array(x_with_nan)\r\nprint(scipy.stats.skew(y, bias=False))\r\nprint(scipy.stats.skew(y_with_nan, bias=False))\r\n\r\n# \u041f\u0440\u043e\u0446\u0435\u043d\u0442\u0438\u043b\u0438\r\nprint(\"\u041f\u0440\u043e\u0446\u0435\u043d\u0442\u0438\u043b\u0438\")\r\ny = np.array(x)\r\nprint(np.percentile(y, 5))\r\nprint(np.percentile(y, 95))\r\n\r\n# \u0414\u0438\u0430\u043f\u0430\u0437\u043e\u043d\r\nprint(\"\u0414\u0438\u0430\u043f\u0430\u0437\u043e\u043d\")\r\nprint(np.amax(y) - np.amin(y))\r\nprint(np.nanmax(y_with_nan) - np.nanmin(y_with_nan))\r\nprint(y.max() - y.min())\r\nprint(z.max() - z.min())\r\nprint(z_with_nan.max() - z_with_nan.min())\r\n<\/pre>\n<p>\u0420\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442 \u0440\u0430\u0431\u043e\u0442\u044b \u043f\u0440\u043e\u0433\u0440\u0430\u043c\u043c\u044b \u043f\u0440\u0435\u0434\u0441\u0442\u0430\u0432\u043b\u0435\u043d \u043d\u0438\u0436\u0435.<\/p>\n<p><img loading=\"lazy\" src=\"http:\/\/is42-2018.susu.ru\/spiridonovani\/wp-content\/uploads\/sites\/25\/2021\/05\/Rezultat-raboty-e1620830751237-1024x909.png\" alt=\"\" width=\"840\" height=\"746\" class=\"aligncenter size-large wp-image-224\" srcset=\"https:\/\/is42-2018.susu.ru\/spiridonovani\/wp-content\/uploads\/sites\/25\/2021\/05\/Rezultat-raboty-e1620830751237-1024x909.png 1024w, https:\/\/is42-2018.susu.ru\/spiridonovani\/wp-content\/uploads\/sites\/25\/2021\/05\/Rezultat-raboty-e1620830751237-300x266.png 300w, https:\/\/is42-2018.susu.ru\/spiridonovani\/wp-content\/uploads\/sites\/25\/2021\/05\/Rezultat-raboty-e1620830751237-768x681.png 768w, https:\/\/is42-2018.susu.ru\/spiridonovani\/wp-content\/uploads\/sites\/25\/2021\/05\/Rezultat-raboty-e1620830751237.png 1162w\" sizes=\"(max-width: 709px) 85vw, (max-width: 909px) 67vw, (max-width: 1362px) 62vw, 840px\" \/><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u0414\u043b\u044f \u0438\u0437\u0443\u0447\u0435\u043d\u0438\u044f \u0441\u0442\u0430\u0442\u0438\u0441\u0442\u0438\u043a\u0438 \u0437\u0430\u0433\u0440\u0443\u0436\u0430\u0435\u043c \u0431\u0438\u0431\u043b\u0438\u043e\u0442\u0435\u043a\u0438: math, numpy, pandas, statistics, scipy.stats. \u041f\u043e\u0441\u043c\u043e\u0442\u0440\u0438\u043c, \u043a\u0430\u043a\u0438\u043c \u043e\u0431\u0440\u0430\u0437\u043e\u043c \u043c\u043e\u0436\u043d\u043e \u0440\u0430\u0441\u0441\u0447\u0438\u0442\u0430\u0442\u044c \u0446\u0435\u043d\u0442\u0440\u0430\u043b\u044c\u043d\u044b\u0435 \u043c\u0435\u0442\u0440\u0438\u043a\u0438, \u0441\u0440\u0435\u0434\u043d\u0435\u0432\u0437\u0432\u0435\u0448\u0435\u043d\u043d\u043e\u0435, \u0433\u0430\u0440\u043c\u043e\u043d\u0438\u0447\u0435\u0441\u043a\u043e\u0435 \u0441\u0440\u0435\u0434\u043d\u0435\u0435, \u0441\u0440\u0435\u0434\u043d\u0435\u0435 \u0433\u0435\u043e\u043c\u0435\u0442\u0440\u0438\u0447\u0435\u0441\u043a\u043e\u0435, \u043c\u0435\u0434\u0438\u0430\u043d\u0443, \u043c\u043e\u0434\u0443, \u0434\u0438\u0441\u043f\u0435\u0440\u0441\u0438\u044e, \u0441\u0440\u0435\u0434\u043d\u0435\u043a\u0432\u0430\u0434\u0440\u0430\u0442\u0438\u0447\u043d\u043e\u0435 \u043e\u0442\u043a\u043b\u043e\u043d\u0435\u043d\u0438\u0435, \u0441\u043c\u0435\u0449\u0435\u043d\u0438\u0435, \u043f\u0440\u043e\u0446\u0435\u043d\u0442\u0438\u043b\u0438, \u0434\u0438\u0430\u043f\u0430\u0437\u043e\u043d. \u041f\u0440\u043e\u0433\u0440\u0430\u043c\u043c\u043d\u044b\u0439 \u043a\u043e\u0434 \u0434\u043b\u044f \u0440\u0430\u0441\u0447\u0451\u0442\u0430 \u0434\u0430\u043d\u043d\u044b\u0445 \u043f\u043e\u043a\u0430\u0437\u0430\u0442\u0435\u043b\u0435\u0439 \u043f\u0440\u0438\u0432\u0435\u0434\u0451\u043d \u043d\u0438\u0436\u0435. # import math import statistics import numpy as np import scipy.stats import pandas as pd print(\"\u0418\u0441\u0445\u043e\u0434\u043d\u044b\u0435 \u0434\u0430\u043d\u043d\u044b\u0435\") &hellip; <a href=\"https:\/\/is42-2018.susu.ru\/spiridonovani\/2021\/05\/12\/opisatelnaya-statistika-na-python\/\" class=\"more-link\">\u0427\u0438\u0442\u0430\u0442\u044c \u0434\u0430\u043b\u0435\u0435<span class=\"screen-reader-text\"> \u00ab\u041e\u043f\u0438\u0441\u0430\u0442\u0435\u043b\u044c\u043d\u0430\u044f \u0441\u0442\u0430\u0442\u0438\u0441\u0442\u0438\u043a\u0430 \u043d\u0430 Python\u00bb<\/span><\/a><\/p>\n","protected":false},"author":22,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_bbp_topic_count":0,"_bbp_reply_count":0,"_bbp_total_topic_count":0,"_bbp_total_reply_count":0,"_bbp_voice_count":0,"_bbp_anonymous_reply_count":0,"_bbp_topic_count_hidden":0,"_bbp_reply_count_hidden":0,"_bbp_forum_subforum_count":0},"categories":[6],"tags":[],"_links":{"self":[{"href":"https:\/\/is42-2018.susu.ru\/spiridonovani\/wp-json\/wp\/v2\/posts\/223"}],"collection":[{"href":"https:\/\/is42-2018.susu.ru\/spiridonovani\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/is42-2018.susu.ru\/spiridonovani\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/is42-2018.susu.ru\/spiridonovani\/wp-json\/wp\/v2\/users\/22"}],"replies":[{"embeddable":true,"href":"https:\/\/is42-2018.susu.ru\/spiridonovani\/wp-json\/wp\/v2\/comments?post=223"}],"version-history":[{"count":3,"href":"https:\/\/is42-2018.susu.ru\/spiridonovani\/wp-json\/wp\/v2\/posts\/223\/revisions"}],"predecessor-version":[{"id":227,"href":"https:\/\/is42-2018.susu.ru\/spiridonovani\/wp-json\/wp\/v2\/posts\/223\/revisions\/227"}],"wp:attachment":[{"href":"https:\/\/is42-2018.susu.ru\/spiridonovani\/wp-json\/wp\/v2\/media?parent=223"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/is42-2018.susu.ru\/spiridonovani\/wp-json\/wp\/v2\/categories?post=223"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/is42-2018.susu.ru\/spiridonovani\/wp-json\/wp\/v2\/tags?post=223"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}