{"id":790,"date":"2021-04-22T11:35:38","date_gmt":"2021-04-22T08:35:38","guid":{"rendered":"http:\/\/is19-2017.susu.ru\/matveev\/?p=790"},"modified":"2021-04-22T12:33:38","modified_gmt":"2021-04-22T09:33:38","slug":"kontrolnoe-zadanie-po-mashinnomu-obucheniyu","status":"publish","type":"post","link":"https:\/\/is19-2017.susu.ru\/matveev\/2021\/04\/22\/kontrolnoe-zadanie-po-mashinnomu-obucheniyu\/","title":{"rendered":"\u041a\u043e\u043d\u0442\u0440\u043e\u043b\u044c\u043d\u043e\u0435 \u0437\u0430\u0434\u0430\u043d\u0438\u0435 \u043f\u043e \u043c\u0430\u0448\u0438\u043d\u043d\u043e\u043c\u0443 \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044e"},"content":{"rendered":"

1. \u041d\u0430\u043c \u043d\u0435\u043e\u0431\u0445\u043e\u0434\u0438\u043c\u043e \u043f\u0440\u0435\u0434\u0432\u0430\u0440\u0438\u0442\u0435\u043b\u044c\u043d\u043e \u0443\u0441\u0442\u0430\u043d\u043e\u0432\u0438\u0442\u044c, \u0438 \u0437\u0430\u0442\u0435\u043c \u043f\u043e\u0434\u043a\u043b\u044e\u0447\u0438\u0442\u044c \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0438\u0435 \u0431\u0438\u0431\u043b\u0438\u043e\u0442\u0435\u043a\u0438:<\/p>\n

\r\nimport pandas as pd\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\n<\/pre>\n
2. \u0414\u0430\u043b\u0435\u0435 \u043d\u0435\u043e\u0431\u0445\u043e\u0434\u0438\u043c\u043e \u043f\u043e\u0434\u043a\u043b\u044e\u0447\u0438\u0442\u044c \u0441\u0432\u043e\u0438 \u0434\u0430\u043d\u043d\u044b\u0435:<\/p>\n
\r\ntall = pd.read_csv("..\/matveev\/Data\/1heart4.csv")\r\ntall.head(8)\r\n<\/pre>\n
\"\"<\/p>\n
3. \u0421 \u043f\u043e\u043c\u043e\u0449\u044c\u044e \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0435\u0439 \u043a\u043e\u043c\u0430\u043d\u0434\u044b \u043c\u043e\u0436\u043d\u043e \u0443\u0437\u043d\u0430\u0442\u044c \u043a\u043e\u043b\u0438\u0447\u0435\u0441\u0442\u0432\u043e \u0441\u0442\u0440\u043e\u043a \u0438 \u0441\u0442\u043e\u043b\u0431\u0446\u043e\u0432:<\/p>\n
\r\nprint( "\u0447\u0438\u0441\u043b\u043e \u0441\u0442\u0440\u043e\u043a =", tall.shape[0], "\u0447\u0438\u0441\u043b\u043e \u0441\u0442\u043e\u043b\u0431\u0446\u043e\u0432 =", tall.shape[1])\r\n<\/pre>\n
\"\"<\/p>\n
4. \u0414\u0430\u043b\u0435\u0435 \u043f\u0440\u043e\u0430\u043d\u0430\u043b\u0438\u0437\u0438\u0440\u0443\u0435\u043c \u043d\u0430\u0448\u0438 \u0434\u0430\u043d\u043d\u044b\u0435:<\/p>\n
\r\ncols=tall.columns\r\nfor col in cols:\r\n    s=tall[col].dtypes\r\n    if s == 'object' or s == 'int64':\r\n        n=tall[col].nunique()\r\n        print(col, s, n)\r\n<\/pre>\n
\"\"
\n\u041a\u0430\u043a \u043c\u044b \u0432\u0438\u0434\u0438\u043c \u043f\u0435\u0440\u0432\u044b\u0435 2 \u0441\u0442\u043e\u043b\u0431\u0446\u0430 - \u044d\u0442\u043e \u0441\u0447\u0435\u0442\u0447\u0438\u043a\u0438, 39 \u0432\u0430\u0440\u0438\u0430\u043d\u0442\u043e\u0432 - \u0432\u043e\u0437\u0440\u0430\u0441\u0442\u043e\u0432, TenYearCHD - \u043d\u0430\u0448\u0430 \u0446\u0435\u043b\u0435\u0432\u0430\u044f \u043f\u0435\u0440\u0435\u043c\u0435\u043d\u043d\u0430\u044f, \u0441 \u043a\u043e\u0442\u043e\u0440\u043e\u0439 \u043c\u044b \u0431\u0443\u0434\u0435\u043c \u0440\u0430\u0431\u043e\u0442\u0430\u0442\u044c.<\/p>\n
5. \u0414\u0430\u043b\u0435\u0435 \u0437\u0430\u0434\u0430\u0434\u0438\u043c \u043d\u0430\u0448\u0435\u0439 \u0446\u0435\u043b\u0435\u0432\u043e\u0439 \u043f\u0435\u0440\u0435\u043c\u0435\u043d\u043d\u043e\u0439 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u044f \u0442\u0438\u043f\u0430 int, \u0438 \u043f\u0440\u043e\u0434\u0435\u043c\u043e\u043d\u0441\u0442\u0440\u0438\u0440\u0443\u0435\u043c \u0438\u0445 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u044f \u0441 \u043f\u043e\u043c\u043e\u0449\u044c\u044e \u0434\u0438\u0430\u0433\u0440\u0430\u043c\u043c\u044b:<\/p>\n
\r\nfrom sklearn.preprocessing import LabelEncoder\r\nla = LabelEncoder()\r\nmapped_TenYearCHD = pd.Series(la.fit_transform(tall['TenYearCHD']))\r\nmapped_TenYearCHD.value_counts().plot.barh()\r\nplt.show()\r\ndicts={}\r\ndicts=dict(enumerate(la.classes_))\r\nprint(dicts)\r\n<\/pre>\n
\"\"<\/p>\n
6. \u0422\u0435\u043f\u0435\u0440\u044c \u043f\u043e\u0441\u043c\u043e\u0442\u0440\u0438\u043c \u043d\u0430 \u0442\u043e, \u043a\u0430\u043a \u0438\u0437\u043c\u0435\u043d\u0438\u043b\u0438\u0441\u044c \u0434\u0430\u043d\u043d\u044b\u0435:<\/p>\n
\r\ntall['TenYearCHD'] = mapped_TenYearCHD\r\ntall.head(5)\r\n<\/pre>\n
\"\"<\/p>\n
7. \u0414\u0430\u043b\u0435\u0435 \u043c\u044b \u043c\u043e\u0436\u0435\u043c \u0432\u044b\u0447\u0438\u0441\u043b\u0438\u0442\u044c \u043a\u043e\u0440\u0440\u0435\u043b\u044f\u0446\u0438\u043e\u043d\u043d\u0443\u044e \u043c\u0430\u0442\u0440\u0438\u0446\u0443 \u0434\u043b\u044f \u0434\u0430\u043d\u043d\u044b\u0445 \u0438 \u043d\u0430\u0433\u043b\u044f\u0434\u043d\u043e \u043f\u0440\u043e\u0434\u0435\u043c\u043e\u043d\u0441\u0442\u0440\u0438\u0440\u043e\u0432\u0430\u0442\u044c \u0441\u0432\u044f\u0437\u044c \u043c\u0435\u0436\u0434\u0443 \u043f\u043e\u043a\u0430\u0437\u0430\u0442\u0435\u043b\u044f\u043c\u0438:<\/p>\n
\r\ncorr=tall.corr()\r\nimport seaborn as sns\r\nf, ax = plt.subplots(figsize=(20, 20))\r\nsns.heatmap(corr, annot=True, mask=np.zeros_like(corr, dtype=np.bool),\r\n    cmap=sns.diverging_palette(300, 10, as_cmap=True),\r\n        square=True, ax=ax)\r\n<\/pre>\n
\"\"<\/p>\n
8. \u0422\u0430\u043a\u0436\u0435 \u043d\u0430\u043c \u043d\u0435\u043e\u0431\u0445\u043e\u0434\u0438\u043c\u043e \u0443\u0434\u0430\u043b\u0438\u0442\u044c \u0434\u0443\u0431\u043b\u0438\u0440\u0443\u0435\u043c\u044b\u0439 \u0441\u0442\u043e\u043b\u0431\u0438\u043a:<\/p>\n
\r\ntall = tall.drop(['Unnamed: 0.1'], axis=1)\r\n<\/pre>\n
9. \u0414\u0430\u043b\u0435\u0435 \u043d\u0435\u043e\u0431\u0445\u043e\u0434\u0438\u043c\u043e \u043e\u043f\u0440\u0435\u0434\u0435\u043b\u0438\u0442\u044c\u0441\u044f \u0441 \u0442\u0435\u043c, \u043a\u0430\u043a\u0438\u0435 \u0434\u0430\u043d\u043d\u044b\u0435 \u043f\u043e\u043a\u0430\u0437\u044b\u0432\u0430\u044e\u0442 \u0437\u0430\u0432\u0438\u0441\u0438\u043c\u043e\u0441\u0442\u044c:<\/p>\n
\r\ntall_aggr1=tall.pivot_table('Unnamed: 0', 'age', 'TenYearCHD', 'count', fill_value=0)\r\ntall_aggr1.plot(kind='bar', stacked=True, figsize=(25,5), fontsize =18, rot=0, colormap='winter')\r\nplt.legend(labels=['\u0417\u0434\u043e\u0440\u043e\u0432\u044b\u0439','\u0411\u043e\u043b\u0435\u0437\u043d\u044c \u0441\u0435\u0440\u0434\u0446\u0430'], fontsize=18)\r\nplt.xlabel("\u0412\u043e\u0437\u0440\u0430\u0441\u0442", fontsize=15)\r\nplt.ylabel("\u0427\u0438\u0441\u043b\u043e \u043b\u044e\u0434\u0435\u0439", fontsize=18)\r\nplt.show()\r\n<\/pre>\n
\"\"<\/p>\n
\r\ntall_aggr2=tall.pivot_table('Unnamed: 0', 'diabetes', 'TenYearCHD', 'count')\r\ntall_aggr2.plot(kind='bar', stacked=True, figsize=(25,5), fontsize =18, rot=0, colormap='winter')\r\nplt.legend(labels=['\u0417\u0434\u043e\u0440\u043e\u0432\u044b\u0439','\u0411\u043e\u043b\u0435\u0437\u043d\u044c \u0441\u0435\u0440\u0434\u0446\u0430'], fontsize=18)\r\nplt.xlabel("\u0414\u0438\u0430\u0431\u0435\u0442", fontsize=15)\r\nplt.ylabel("\u0427\u0438\u0441\u043b\u043e \u043b\u044e\u0434\u0435\u0439", fontsize=18)\r\nplt.show()\r\n<\/pre>\n
\"\"<\/p>\n
\r\ntall_aggr3=tall.pivot_table('Unnamed: 0', 'prevalentStroke', 'TenYearCHD', 'count')\r\ntall_aggr3.plot(kind='bar', stacked=True, figsize=(25,5), fontsize =18, rot=0, colormap='winter')\r\nplt.legend(labels=['\u0417\u0434\u043e\u0440\u043e\u0432\u044b\u0439','\u0411\u043e\u043b\u0435\u0437\u043d\u044c \u0441\u0435\u0440\u0434\u0446\u0430'], fontsize=18)\r\nplt.xlabel("\u041f\u0443\u043b\u044c\u0441", fontsize=15)\r\nplt.ylabel("\u0427\u0438\u0441\u043b\u043e \u043b\u044e\u0434\u0435\u0439", fontsize=18)\r\nplt.show()\r\n<\/pre>\n
\"\"<\/p>\n
\r\ntall_aggr4=tall.pivot_table('Unnamed: 0', 'sysBP', 'TenYearCHD', 'count', fill_value=0)\r\ntall_aggr4.plot(kind='bar', stacked=True, figsize=(25,5), fontsize =18, rot=0, colormap='winter')\r\nplt.legend(labels=['\u0417\u0434\u043e\u0440\u043e\u0432\u044b\u0439','\u0411\u043e\u043b\u0435\u0437\u043d\u044c \u0441\u0435\u0440\u0434\u0446\u0430'], fontsize=18)\r\nplt.xlabel("\u0412\u0435\u0440\u0445\u043d\u0438\u0435 \u043a\u0440\u043e\u0432\u044f\u043d\u043d\u043e\u0435 \u0434\u0430\u0432\u043b\u0435\u043d\u0438\u0435", fontsize=15)\r\nplt.ylabel("\u0427\u0438\u0441\u043b\u043e \u043b\u044e\u0434\u0435\u0439", fontsize=18)\r\nplt.show()\r\n<\/pre>\n
\"\"<\/p>\n
\r\ntall_aggr5=tall.pivot_table('Unnamed: 0', 'diaBP', 'TenYearCHD', 'count', fill_value=0)\r\ntall_aggr5.plot(kind='bar', stacked=True, figsize=(25,5), fontsize =18, rot=0, colormap='winter')\r\nplt.legend(labels=['\u0417\u0434\u043e\u0440\u043e\u0432\u044b\u0439','\u0411\u043e\u043b\u0435\u0437\u043d\u044c \u0441\u0435\u0440\u0434\u0446\u0430'], fontsize=18)\r\nplt.xlabel("\u041d\u0438\u0436\u043d\u0435\u0435 \u043a\u0440\u043e\u0432\u044f\u043d\u043d\u043e\u0435 \u0434\u0430\u0432\u043b\u0435\u043d\u0438\u0435", fontsize=15)\r\nplt.ylabel("\u0427\u0438\u0441\u043b\u043e \u043b\u044e\u0434\u0435\u0439", fontsize=18)\r\nplt.show()\r\n<\/pre>\n
\"\"<\/p>\n
\r\ntall_aggr6=tall.pivot_table('Unnamed: 0', 'heartRate', 'TenYearCHD', 'count', fill_value=0)\r\ntall_aggr6.plot(kind='bar', stacked=True, figsize=(25,5), fontsize =18, rot=0, colormap='winter')\r\nplt.legend(labels=['\u0417\u0434\u043e\u0440\u043e\u0432\u044b\u0439','\u0411\u043e\u043b\u0435\u0437\u043d\u044c \u0441\u0435\u0440\u0434\u0446\u0430'], fontsize=18)\r\nplt.xlabel("\u041f\u0443\u043b\u044c\u0441", fontsize=15)\r\nplt.ylabel("\u0427\u0438\u0441\u043b\u043e \u043b\u044e\u0434\u0435\u0439", fontsize=18)\r\nplt.show()\r\n<\/pre>\n
\"\"<\/p>\n
\r\ntall_aggr7=tall.pivot_table('Unnamed: 0', 'education', 'TenYearCHD', 'count', fill_value=0)\r\ntall_aggr7.plot(kind='bar', stacked=True, figsize=(25,5), fontsize =18, rot=0, colormap='winter')\r\nplt.legend(labels=['\u0417\u0434\u043e\u0440\u043e\u0432\u044b\u0439','\u0411\u043e\u043b\u0435\u0437\u043d\u044c \u0441\u0435\u0440\u0434\u0446\u0430'], fontsize=18)\r\nplt.xlabel("\u0423\u0440\u043e\u0432\u0435\u043d\u044c \u043e\u0431\u0440\u0430\u0437\u043e\u0432\u0430\u043d\u0438\u044f", fontsize=15)\r\nplt.ylabel("\u0427\u0438\u0441\u043b\u043e \u043b\u044e\u0434\u0435\u0439", fontsize=18)\r\nplt.show()\r\n<\/pre>\n
\"\"<\/p>\n
10. \u0422\u0430\u043a\u0436\u0435 \u0441\u0443\u0449\u0435\u0441\u0442\u0432\u0443\u044e\u0442 \u0435\u0449\u0451 \u0441\u043f\u043e\u0441\u043e\u0431\u044b \u0434\u043b\u044f \u043d\u0430\u0445\u043e\u0436\u0434\u0435\u043d\u0438\u044f \u0441\u0432\u044f\u0437\u0435\u0439, \u043d\u043e \u0434\u043b\u044f \u043d\u0430\u0447\u0430\u043b\u0430 \u043d\u0435\u043e\u0431\u0445\u043e\u0434\u0438\u043c\u043e \u0443\u0434\u0430\u043b\u0438\u0442\u044c 1 \u0441\u0442\u043e\u043b\u0431\u0435\u0446 - \u0441\u0447\u0435\u0442\u0447\u0438\u043a:<\/p>\n
\r\ntall = tall.drop(['Unnamed: 0'], axis=1)\r\n<\/pre>\n
11. \u0422\u0435\u043f\u0435\u0440\u044c \u043d\u0435\u043e\u0431\u0445\u043e\u0434\u0438\u043c\u043e \u0440\u0430\u0437\u0434\u0435\u043b\u0438\u0442\u044c \u0432\u044b\u0431\u043e\u0440\u043a\u0443 \u043d\u0430 \u0442\u0435\u0441\u0442\u043e\u0432\u0443\u044e \u0438 \u043e\u0431\u0443\u0447\u0430\u044e\u0449\u0443\u044e:<\/p>\n
\r\nclDrop='TenYearCHD'\r\nytall=tall[clDrop].values\r\ntall.drop(clDrop, axis=1, inplace=True)\r\nxtall=tall.values\r\nMtest=int(len(tall)\/4)\r\nMtrain= int(len(tall)-Mtest)\r\nttrain = np.arange(Mtrain) \r\nttest = np.arange(Mtest) + Mtrain  \r\nprint (len(tall), Mtrain, Mtest)\r\n<\/pre>\n
\"\"<\/p>\n
12. \u0414\u0430\u043b\u0435\u0435 \u0432 \u0440\u0430\u0431\u043e\u0442\u0435 \u0438\u0434\u0435\u0442 \u043f\u0440\u043e\u0446\u0435\u0441\u0441 \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044f:<\/p>\n
\r\nttrain = np.arange(Mtrain) \r\nttest = np.arange(Mtest) + Mtrain  \r\nprint (ytall[ttrain])\r\n\r\nfrom sklearn.metrics import  roc_auc_score, accuracy_score, roc_curve\r\nfrom sklearn import tree\r\n\r\ndtc = tree.DecisionTreeClassifier(max_depth = 8, max_features = 8)\r\ndtc = dtc.fit(xtall[ttrain], ytall[ttrain])\r\n\r\nytrainDtc = dtc.predict_proba(xtall[ttrain])\r\nytestDtc = dtc.predict_proba(xtall[ttest])\r\nsc0= dtc.score(xtall[ttrain], ytall[ttrain])\r\nsc1= dtc.score(xtall[ttest], ytall[ttest])\r\nprint("DesicionTree", "i_fold =", " \u0422\u043e\u0447\u043d\u043e\u0441\u0442\u044c \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044f", sc0, " \u0422\u043e\u0447\u043d\u043e\u0441\u0442\u044c \u0442\u0435\u0441\u0442\u043e\u0432\u0430\u044f", sc1)\r\n<\/pre>\n
\"\"<\/p>\n
\r\ndef estim(y_prob,y_exact, pr):\r\n    kall=y_prob.shape[0]\r\n    tp=0\r\n    fp=0\r\n    tn=0\r\n    fn=0\r\n    kr=0\r\n    for i in np.arange(kall):\r\n        if(y_prob[i] &gt; pr):\r\n            if(y_exact[i] == 1 ):\r\n                tp+=1\r\n            else:\r\n                fp+=1\r\n        else:\r\n            if(y_exact[i] == 0 ):\r\n                tn+=1\r\n            else:\r\n                fn+=1   \r\n    kr=1.*(tp+tn)\/kall\r\n    kr1=1.*tp\/(tp+fp)\r\n    kr0=1.*tn\/(tn+fn)\r\n    rr1=1.*tp\/(tp+fn)\r\n    rr0=1.*tn\/(tn+fp)\r\n    print("Precission: ", kr0, "\u043a\u043b\u0430\u0441\u0441 0 \u0417\u0434\u043e\u0440\u043e\u0432\u044b", tn+fn, " \u0418\u0437 \u043d\u0438\u0445 \u043f\u0440\u0430\u0432\u0438\u043b\u044c\u043d\u043e \u0441\u043f\u0440\u043e\u0433\u043d\u043e\u0437\u0438\u0440\u043e\u0432\u0430\u043d\u043e =", tn, ", \u043e\u0448\u0438\u0431\u043e\u0447\u043d\u043e=", fn)\r\n    print("Precission: ", kr1,"\u043a\u043b\u0430\u0441\u0441 1 \u0411\u043e\u043b\u044c\u043d\u044b", tp+tn, " \u0418\u0437 \u043d\u0438\u0445 \u043f\u0440\u0430\u0432\u0438\u043b\u044c\u043d\u043e \u0441\u043f\u0440\u043e\u0433\u043d\u043e\u0437\u0438\u0440\u043e\u0432\u0430\u043d\u043e =", tp, ", \u043e\u0448\u0438\u0431\u043e\u0447\u043d\u043e=", tn)\r\n    print("Recall: ", rr0,"\u043a\u043b\u0430\u0441\u0441 0 \u0417\u0434\u043e\u0440\u043e\u0432\u044b")\r\n    print("Recall: ", rr1,"\u043a\u043b\u0430\u0441\u0441 1 \u0411\u043e\u043b\u044c\u043d\u044b")\r\n    print("\u0422\u043e\u0447\u043d\u043e\u0441\u0442\u044c \u0440\u0430\u0441\u043f\u043e\u0437\u043d\u0430\u0432\u0430\u043d\u0438\u044f ", kr, " precission", "recall")\r\n    return kr\r\nestim(ytestDtc[:,1], ytall[ttest], 0.5)\r\n<\/pre>\n
\"\"<\/p>\n
13. \u041c\u043d\u043e\u0433\u043e \u043a\u043e\u0434\u0430, \u043a\u043e\u0442\u043e\u0440\u044b\u0439 \u043d\u0435\u043e\u0431\u0445\u043e\u0434\u0438\u043c \u0434\u043b\u044f \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044f...<\/p>\n
14. \u041c\u0435\u0442\u043e\u0434 \"feature_importances\", \u043a\u043e\u0442\u043e\u0440\u044b\u0439 \u0432\u043e\u0437\u0432\u0440\u0430\u0449\u0430\u0435\u0442 \u0432\u0435\u043a\u0442\u043e\u0440 \"\u0432\u0430\u0436\u043d\u043e\u0441\u0442\u0435\u0439\" \u043f\u0440\u0438\u0437\u043d\u0430\u043a\u043e\u0432:<\/p>\n
\r\nimp= dtc.feature_importances_\r\nimp = dtc.feature_importances_\r\nrfImp = pd.DataFrame(imp, columns=['imp'])\r\ntitle1 = tall.columns\r\nrfImp['title']=title1\r\nrfImp = rfImp.sort_values('imp', ascending = False)\r\nrfImp.head(9)\r\n<\/pre>\n
\"\"<\/p>\n
15. \u0422\u0435\u043f\u0435\u0440\u044c \u0441\u043e\u0437\u0434\u0430\u0434\u0438\u043c \u0434\u0435\u0440\u0435\u0432\u043e \u0440\u0435\u0448\u0435\u043d\u0438\u0439:<\/p>\n
\r\nimport pydotplus\r\ndot_data = tree.export_graphviz(dtc, feature_names=title1,class_names=["\u0417\u0434\u043e\u0440\u043e\u0432\u044b", "\u0411\u043e\u043b\u044c\u043d\u044b\u0439"], out_file=None, filled=True, rounded=True)\r\ngraph = pydotplus.graph_from_dot_data(dot_data)\r\ngraph.write_png("..\/matveev\/DC.jpg")\r\n\r\nimport matplotlib.image as mpimg\r\nimg=mpimg.imread("..\/matveev\/DC.jpg")\r\n\r\nimg=mpimg.imread("..\/matveev\/DC.jpg")\r\nfig = plt.figure(figsize=(100,100))\r\nimgplot = plt.imshow(img)\r\n<\/pre>\n
\"\"<\/p>\n","protected":false},"excerpt":{"rendered":"
1. \u041d\u0430\u043c \u043d\u0435\u043e\u0431\u0445\u043e\u0434\u0438\u043c\u043e \u043f\u0440\u0435\u0434\u0432\u0430\u0440\u0438\u0442\u0435\u043b\u044c\u043d\u043e \u0443\u0441\u0442\u0430\u043d\u043e\u0432\u0438\u0442\u044c, \u0438 \u0437\u0430\u0442\u0435\u043c \u043f\u043e\u0434\u043a\u043b\u044e\u0447\u0438\u0442\u044c \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0438\u0435 \u0431\u0438\u0431\u043b\u0438\u043e\u0442\u0435\u043a\u0438: import pandas as pd import numpy as np import matplotlib.pyplot as plt 2. \u0414\u0430\u043b\u0435\u0435 \u043d\u0435\u043e\u0431\u0445\u043e\u0434\u0438\u043c\u043e…<\/p>\n","protected":false},"author":13,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[15],"tags":[],"_links":{"self":[{"href":"https:\/\/is19-2017.susu.ru\/matveev\/wp-json\/wp\/v2\/posts\/790"}],"collection":[{"href":"https:\/\/is19-2017.susu.ru\/matveev\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/is19-2017.susu.ru\/matveev\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/is19-2017.susu.ru\/matveev\/wp-json\/wp\/v2\/users\/13"}],"replies":[{"embeddable":true,"href":"https:\/\/is19-2017.susu.ru\/matveev\/wp-json\/wp\/v2\/comments?post=790"}],"version-history":[{"count":9,"href":"https:\/\/is19-2017.susu.ru\/matveev\/wp-json\/wp\/v2\/posts\/790\/revisions"}],"predecessor-version":[{"id":817,"href":"https:\/\/is19-2017.susu.ru\/matveev\/wp-json\/wp\/v2\/posts\/790\/revisions\/817"}],"wp:attachment":[{"href":"https:\/\/is19-2017.susu.ru\/matveev\/wp-json\/wp\/v2\/media?parent=790"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/is19-2017.susu.ru\/matveev\/wp-json\/wp\/v2\/categories?post=790"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/is19-2017.susu.ru\/matveev\/wp-json\/wp\/v2\/tags?post=790"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}