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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Лабораторная работа №1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 7. Основные возможности библиотеки Pandas"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"###\tЗагрузка и сохранение данных"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd "
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Pregnancies</th>\n",
" <th>Glucose</th>\n",
" <th>BloodPressure</th>\n",
" <th>SkinThickness</th>\n",
" <th>Insulin</th>\n",
" <th>BMI</th>\n",
" <th>DiabetesPedigreeFunction</th>\n",
" <th>Age</th>\n",
" <th>Outcome</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>6</td>\n",
" <td>148</td>\n",
" <td>72</td>\n",
" <td>35</td>\n",
" <td>0</td>\n",
" <td>33.6</td>\n",
" <td>0.627</td>\n",
" <td>50</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>85</td>\n",
" <td>66</td>\n",
" <td>29</td>\n",
" <td>0</td>\n",
" <td>26.6</td>\n",
" <td>0.351</td>\n",
" <td>31</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>8</td>\n",
" <td>183</td>\n",
" <td>64</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>23.3</td>\n",
" <td>0.672</td>\n",
" <td>32</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>89</td>\n",
" <td>66</td>\n",
" <td>23</td>\n",
" <td>94</td>\n",
" <td>28.1</td>\n",
" <td>0.167</td>\n",
" <td>21</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0</td>\n",
" <td>137</td>\n",
" <td>40</td>\n",
" <td>35</td>\n",
" <td>168</td>\n",
" <td>43.1</td>\n",
" <td>2.288</td>\n",
" <td>33</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Pregnancies Glucose BloodPressure SkinThickness Insulin BMI \\\n",
"0 6 148 72 35 0 33.6 \n",
"1 1 85 66 29 0 26.6 \n",
"2 8 183 64 0 0 23.3 \n",
"3 1 89 66 23 94 28.1 \n",
"4 0 137 40 35 168 43.1 \n",
"\n",
" DiabetesPedigreeFunction Age Outcome \n",
"0 0.627 50 1 \n",
"1 0.351 31 0 \n",
"2 0.672 32 1 \n",
"3 0.167 21 0 \n",
"4 2.288 33 1 "
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.read_csv(\"diabetes.csv\")\n",
"\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"df.to_csv(\"new.csv\", index=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Получение сведений о датафрейме с данными"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Pregnancies</th>\n",
" <th>Glucose</th>\n",
" <th>BloodPressure</th>\n",
" <th>SkinThickness</th>\n",
" <th>Insulin</th>\n",
" <th>BMI</th>\n",
" <th>DiabetesPedigreeFunction</th>\n",
" <th>Age</th>\n",
" <th>Outcome</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>768.000000</td>\n",
" <td>768.000000</td>\n",
" <td>768.000000</td>\n",
" <td>768.000000</td>\n",
" <td>768.000000</td>\n",
" <td>768.000000</td>\n",
" <td>768.000000</td>\n",
" <td>768.000000</td>\n",
" <td>768.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>3.845052</td>\n",
" <td>120.894531</td>\n",
" <td>69.105469</td>\n",
" <td>20.536458</td>\n",
" <td>79.799479</td>\n",
" <td>31.992578</td>\n",
" <td>0.471876</td>\n",
" <td>33.240885</td>\n",
" <td>0.348958</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>3.369578</td>\n",
" <td>31.972618</td>\n",
" <td>19.355807</td>\n",
" <td>15.952218</td>\n",
" <td>115.244002</td>\n",
" <td>7.884160</td>\n",
" <td>0.331329</td>\n",
" <td>11.760232</td>\n",
" <td>0.476951</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.078000</td>\n",
" <td>21.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>1.000000</td>\n",
" <td>99.000000</td>\n",
" <td>62.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>27.300000</td>\n",
" <td>0.243750</td>\n",
" <td>24.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>3.000000</td>\n",
" <td>117.000000</td>\n",
" <td>72.000000</td>\n",
" <td>23.000000</td>\n",
" <td>30.500000</td>\n",
" <td>32.000000</td>\n",
" <td>0.372500</td>\n",
" <td>29.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>6.000000</td>\n",
" <td>140.250000</td>\n",
" <td>80.000000</td>\n",
" <td>32.000000</td>\n",
" <td>127.250000</td>\n",
" <td>36.600000</td>\n",
" <td>0.626250</td>\n",
" <td>41.000000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>17.000000</td>\n",
" <td>199.000000</td>\n",
" <td>122.000000</td>\n",
" <td>99.000000</td>\n",
" <td>846.000000</td>\n",
" <td>67.100000</td>\n",
" <td>2.420000</td>\n",
" <td>81.000000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Pregnancies Glucose BloodPressure SkinThickness Insulin \\\n",
"count 768.000000 768.000000 768.000000 768.000000 768.000000 \n",
"mean 3.845052 120.894531 69.105469 20.536458 79.799479 \n",
"std 3.369578 31.972618 19.355807 15.952218 115.244002 \n",
"min 0.000000 0.000000 0.000000 0.000000 0.000000 \n",
"25% 1.000000 99.000000 62.000000 0.000000 0.000000 \n",
"50% 3.000000 117.000000 72.000000 23.000000 30.500000 \n",
"75% 6.000000 140.250000 80.000000 32.000000 127.250000 \n",
"max 17.000000 199.000000 122.000000 99.000000 846.000000 \n",
"\n",
" BMI DiabetesPedigreeFunction Age Outcome \n",
"count 768.000000 768.000000 768.000000 768.000000 \n",
"mean 31.992578 0.471876 33.240885 0.348958 \n",
"std 7.884160 0.331329 11.760232 0.476951 \n",
"min 0.000000 0.078000 21.000000 0.000000 \n",
"25% 27.300000 0.243750 24.000000 0.000000 \n",
"50% 32.000000 0.372500 29.000000 0.000000 \n",
"75% 36.600000 0.626250 41.000000 1.000000 \n",
"max 67.100000 2.420000 81.000000 1.000000 "
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.describe()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 768 entries, 0 to 767\n",
"Data columns (total 9 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 Pregnancies 768 non-null int64 \n",
" 1 Glucose 768 non-null int64 \n",
" 2 BloodPressure 768 non-null int64 \n",
" 3 SkinThickness 768 non-null int64 \n",
" 4 Insulin 768 non-null int64 \n",
" 5 BMI 768 non-null float64\n",
" 6 DiabetesPedigreeFunction 768 non-null float64\n",
" 7 Age 768 non-null int64 \n",
" 8 Outcome 768 non-null int64 \n",
"dtypes: float64(2), int64(7)\n",
"memory usage: 54.1 KB\n"
]
}
],
"source": [
"df.info()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"###\tПолучение сведений о колонках датафрейма"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin',\n",
" 'BMI', 'DiabetesPedigreeFunction', 'Age', 'Outcome'],\n",
" dtype='object')"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.columns"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"###\tВывод отельных строки и столбцов из датафрейма"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
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" </thead>\n",
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" <tr>\n",
" <th>0</th>\n",
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" <th>1</th>\n",
" <td>31</td>\n",
" <td>0</td>\n",
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" <tr>\n",
" <th>2</th>\n",
" <td>32</td>\n",
" <td>0</td>\n",
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" <tr>\n",
" <th>3</th>\n",
" <td>21</td>\n",
" <td>94</td>\n",
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" <tr>\n",
" <th>4</th>\n",
" <td>33</td>\n",
" <td>168</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>763</th>\n",
" <td>63</td>\n",
" <td>180</td>\n",
" </tr>\n",
" <tr>\n",
" <th>764</th>\n",
" <td>27</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>765</th>\n",
" <td>30</td>\n",
" <td>112</td>\n",
" </tr>\n",
" <tr>\n",
" <th>766</th>\n",
" <td>47</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>767</th>\n",
" <td>23</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>768 rows × 2 columns</p>\n",
"</div>"
],
"text/plain": [
" Age Insulin\n",
"0 50 0\n",
"1 31 0\n",
"2 32 0\n",
"3 21 94\n",
"4 33 168\n",
".. ... ...\n",
"763 63 180\n",
"764 27 0\n",
"765 30 112\n",
"766 47 0\n",
"767 23 0\n",
"\n",
"[768 rows x 2 columns]"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[[\"Age\", \"Insulin\"]]"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
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" <th>Age</th>\n",
" <th>Outcome</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>89</td>\n",
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" <tr>\n",
" <th>4</th>\n",
" <td>0</td>\n",
" <td>137</td>\n",
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" <td>168</td>\n",
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" <td>2.288</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>5</td>\n",
" <td>116</td>\n",
" <td>74</td>\n",
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" <td>0.201</td>\n",
" <td>30</td>\n",
" <td>0</td>\n",
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" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Pregnancies Glucose BloodPressure SkinThickness Insulin BMI \\\n",
"3 1 89 66 23 94 28.1 \n",
"4 0 137 40 35 168 43.1 \n",
"5 5 116 74 0 0 25.6 \n",
"\n",
" DiabetesPedigreeFunction Age Outcome \n",
"3 0.167 21 0 \n",
"4 2.288 33 1 \n",
"5 0.201 30 0 "
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.iloc[3:6]"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
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" <th>Age</th>\n",
" <th>Outcome</th>\n",
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" <tr>\n",
" <th>4</th>\n",
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" <tr>\n",
" <th>8</th>\n",
" <td>2</td>\n",
" <td>197</td>\n",
" <td>70</td>\n",
" <td>45</td>\n",
" <td>543</td>\n",
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" <td>0.158</td>\n",
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" <tr>\n",
" <th>13</th>\n",
" <td>1</td>\n",
" <td>189</td>\n",
" <td>60</td>\n",
" <td>23</td>\n",
" <td>846</td>\n",
" <td>30.1</td>\n",
" <td>0.398</td>\n",
" <td>59</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>5</td>\n",
" <td>166</td>\n",
" <td>72</td>\n",
" <td>19</td>\n",
" <td>175</td>\n",
" <td>25.8</td>\n",
" <td>0.587</td>\n",
" <td>51</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>0</td>\n",
" <td>118</td>\n",
" <td>84</td>\n",
" <td>47</td>\n",
" <td>230</td>\n",
" <td>45.8</td>\n",
" <td>0.551</td>\n",
" <td>31</td>\n",
" <td>1</td>\n",
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" <td>36.4</td>\n",
" <td>0.408</td>\n",
" <td>36</td>\n",
" <td>1</td>\n",
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" <tr>\n",
" <th>753</th>\n",
" <td>0</td>\n",
" <td>181</td>\n",
" <td>88</td>\n",
" <td>44</td>\n",
" <td>510</td>\n",
" <td>43.3</td>\n",
" <td>0.222</td>\n",
" <td>26</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>755</th>\n",
" <td>1</td>\n",
" <td>128</td>\n",
" <td>88</td>\n",
" <td>39</td>\n",
" <td>110</td>\n",
" <td>36.5</td>\n",
" <td>1.057</td>\n",
" <td>37</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>763</th>\n",
" <td>10</td>\n",
" <td>101</td>\n",
" <td>76</td>\n",
" <td>48</td>\n",
" <td>180</td>\n",
" <td>32.9</td>\n",
" <td>0.171</td>\n",
" <td>63</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>765</th>\n",
" <td>5</td>\n",
" <td>121</td>\n",
" <td>72</td>\n",
" <td>23</td>\n",
" <td>112</td>\n",
" <td>26.2</td>\n",
" <td>0.245</td>\n",
" <td>30</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>243 rows × 9 columns</p>\n",
"</div>"
],
"text/plain": [
" Pregnancies Glucose BloodPressure SkinThickness Insulin BMI \\\n",
"4 0 137 40 35 168 43.1 \n",
"8 2 197 70 45 543 30.5 \n",
"13 1 189 60 23 846 30.1 \n",
"14 5 166 72 19 175 25.8 \n",
"16 0 118 84 47 230 45.8 \n",
".. ... ... ... ... ... ... \n",
"748 3 187 70 22 200 36.4 \n",
"753 0 181 88 44 510 43.3 \n",
"755 1 128 88 39 110 36.5 \n",
"763 10 101 76 48 180 32.9 \n",
"765 5 121 72 23 112 26.2 \n",
"\n",
" DiabetesPedigreeFunction Age Outcome \n",
"4 2.288 33 1 \n",
"8 0.158 53 1 \n",
"13 0.398 59 1 \n",
"14 0.587 51 1 \n",
"16 0.551 31 1 \n",
".. ... ... ... \n",
"748 0.408 36 1 \n",
"753 0.222 26 1 \n",
"755 1.057 37 1 \n",
"763 0.171 63 0 \n",
"765 0.245 30 0 \n",
"\n",
"[243 rows x 9 columns]"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[df['Insulin'] > 100]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"###\tГруппировка и агрегация данных в датафрейме"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
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" <th>1</th>\n",
" <td>98.674074</td>\n",
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" <tr>\n",
" <th>2</th>\n",
" <td>85.844660</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>87.453333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>69.441176</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>57.298246</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>63.580000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>84.466667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>92.815789</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>62.428571</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>34.791667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>65.454545</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>112.555556</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>27.900000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>92.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>110.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>114.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Insulin\n",
"Pregnancies \n",
"0 81.675676\n",
"1 98.674074\n",
"2 85.844660\n",
"3 87.453333\n",
"4 69.441176\n",
"5 57.298246\n",
"6 63.580000\n",
"7 84.466667\n",
"8 92.815789\n",
"9 62.428571\n",
"10 34.791667\n",
"11 65.454545\n",
"12 112.555556\n",
"13 27.900000\n",
"14 92.000000\n",
"15 110.000000\n",
"17 114.000000"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"group = df.groupby(['Pregnancies'])['Insulin'].mean()\n",
"group.to_frame()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Сортировка данных в датафрейме"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Pregnancies</th>\n",
" <th>Glucose</th>\n",
" <th>BloodPressure</th>\n",
" <th>SkinThickness</th>\n",
" <th>Insulin</th>\n",
" <th>BMI</th>\n",
" <th>DiabetesPedigreeFunction</th>\n",
" <th>Age</th>\n",
" <th>Outcome</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>661</th>\n",
" <td>1</td>\n",
" <td>199</td>\n",
" <td>76</td>\n",
" <td>43</td>\n",
" <td>0</td>\n",
" <td>42.9</td>\n",
" <td>1.394</td>\n",
" <td>22</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>561</th>\n",
" <td>0</td>\n",
" <td>198</td>\n",
" <td>66</td>\n",
" <td>32</td>\n",
" <td>274</td>\n",
" <td>41.3</td>\n",
" <td>0.502</td>\n",
" <td>28</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>228</th>\n",
" <td>4</td>\n",
" <td>197</td>\n",
" <td>70</td>\n",
" <td>39</td>\n",
" <td>744</td>\n",
" <td>36.7</td>\n",
" <td>2.329</td>\n",
" <td>31</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>2</td>\n",
" <td>197</td>\n",
" <td>70</td>\n",
" <td>45</td>\n",
" <td>543</td>\n",
" <td>30.5</td>\n",
" <td>0.158</td>\n",
" <td>53</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>579</th>\n",
" <td>2</td>\n",
" <td>197</td>\n",
" <td>70</td>\n",
" <td>99</td>\n",
" <td>0</td>\n",
" <td>34.7</td>\n",
" <td>0.575</td>\n",
" <td>62</td>\n",
" <td>1</td>\n",
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" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
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" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>342</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>68</td>\n",
" <td>35</td>\n",
" <td>0</td>\n",
" <td>32.0</td>\n",
" <td>0.389</td>\n",
" <td>22</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>349</th>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>80</td>\n",
" <td>32</td>\n",
" <td>0</td>\n",
" <td>41.0</td>\n",
" <td>0.346</td>\n",
" <td>37</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>502</th>\n",
" <td>6</td>\n",
" <td>0</td>\n",
" <td>68</td>\n",
" <td>41</td>\n",
" <td>0</td>\n",
" <td>39.0</td>\n",
" <td>0.727</td>\n",
" <td>41</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>182</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>74</td>\n",
" <td>20</td>\n",
" <td>23</td>\n",
" <td>27.7</td>\n",
" <td>0.299</td>\n",
" <td>21</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>48</td>\n",
" <td>20</td>\n",
" <td>0</td>\n",
" <td>24.7</td>\n",
" <td>0.140</td>\n",
" <td>22</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>768 rows × 9 columns</p>\n",
"</div>"
],
"text/plain": [
" Pregnancies Glucose BloodPressure SkinThickness Insulin BMI \\\n",
"661 1 199 76 43 0 42.9 \n",
"561 0 198 66 32 274 41.3 \n",
"228 4 197 70 39 744 36.7 \n",
"8 2 197 70 45 543 30.5 \n",
"579 2 197 70 99 0 34.7 \n",
".. ... ... ... ... ... ... \n",
"342 1 0 68 35 0 32.0 \n",
"349 5 0 80 32 0 41.0 \n",
"502 6 0 68 41 0 39.0 \n",
"182 1 0 74 20 23 27.7 \n",
"75 1 0 48 20 0 24.7 \n",
"\n",
" DiabetesPedigreeFunction Age Outcome \n",
"661 1.394 22 1 \n",
"561 0.502 28 1 \n",
"228 2.329 31 0 \n",
"8 0.158 53 1 \n",
"579 0.575 62 1 \n",
".. ... ... ... \n",
"342 0.389 22 0 \n",
"349 0.346 37 1 \n",
"502 0.727 41 1 \n",
"182 0.299 21 0 \n",
"75 0.140 22 0 \n",
"\n",
"[768 rows x 9 columns]"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sorted_df = df.sort_values(by='Glucose', ascending = False)\n",
"sorted_df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"###\tУдаление строк/столбцов"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"df_dropped_columns = df.drop(columns=['Insulin', 'BMI']) # Удаление столбцов 'Insulin' и 'BMI'"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
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" <th>764</th>\n",
" <td>2</td>\n",
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" <td>27</td>\n",
" <td>0.340</td>\n",
" <td>27</td>\n",
" <td>0</td>\n",
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" <tr>\n",
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" <td>72</td>\n",
" <td>23</td>\n",
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" <td>30</td>\n",
" <td>0</td>\n",
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" <tr>\n",
" <th>766</th>\n",
" <td>1</td>\n",
" <td>126</td>\n",
" <td>60</td>\n",
" <td>0</td>\n",
" <td>0.349</td>\n",
" <td>47</td>\n",
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" <tr>\n",
" <th>767</th>\n",
" <td>1</td>\n",
" <td>93</td>\n",
" <td>70</td>\n",
" <td>31</td>\n",
" <td>0.315</td>\n",
" <td>23</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>768 rows × 7 columns</p>\n",
"</div>"
],
"text/plain": [
" Pregnancies Glucose BloodPressure SkinThickness \\\n",
"0 6 148 72 35 \n",
"1 1 85 66 29 \n",
"2 8 183 64 0 \n",
"3 1 89 66 23 \n",
"4 0 137 40 35 \n",
".. ... ... ... ... \n",
"763 10 101 76 48 \n",
"764 2 122 70 27 \n",
"765 5 121 72 23 \n",
"766 1 126 60 0 \n",
"767 1 93 70 31 \n",
"\n",
" DiabetesPedigreeFunction Age Outcome \n",
"0 0.627 50 1 \n",
"1 0.351 31 0 \n",
"2 0.672 32 1 \n",
"3 0.167 21 0 \n",
"4 2.288 33 1 \n",
".. ... ... ... \n",
"763 0.171 63 0 \n",
"764 0.340 27 0 \n",
"765 0.245 30 0 \n",
"766 0.349 47 1 \n",
"767 0.315 23 0 \n",
"\n",
"[768 rows x 7 columns]"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_dropped_columns"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
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" <td>40</td>\n",
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" <td>33</td>\n",
" <td>1</td>\n",
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" <tr>\n",
" <th>5</th>\n",
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" <td>74</td>\n",
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" <td>0</td>\n",
" <td>25.6</td>\n",
" <td>0.201</td>\n",
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" <td>0</td>\n",
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" <tr>\n",
" <th>6</th>\n",
" <td>3</td>\n",
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" <td>50</td>\n",
" <td>32</td>\n",
" <td>88</td>\n",
" <td>31.0</td>\n",
" <td>0.248</td>\n",
" <td>26</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
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" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>763</th>\n",
" <td>10</td>\n",
" <td>101</td>\n",
" <td>76</td>\n",
" <td>48</td>\n",
" <td>180</td>\n",
" <td>32.9</td>\n",
" <td>0.171</td>\n",
" <td>63</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>764</th>\n",
" <td>2</td>\n",
" <td>122</td>\n",
" <td>70</td>\n",
" <td>27</td>\n",
" <td>0</td>\n",
" <td>36.8</td>\n",
" <td>0.340</td>\n",
" <td>27</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>765</th>\n",
" <td>5</td>\n",
" <td>121</td>\n",
" <td>72</td>\n",
" <td>23</td>\n",
" <td>112</td>\n",
" <td>26.2</td>\n",
" <td>0.245</td>\n",
" <td>30</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>766</th>\n",
" <td>1</td>\n",
" <td>126</td>\n",
" <td>60</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>30.1</td>\n",
" <td>0.349</td>\n",
" <td>47</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>767</th>\n",
" <td>1</td>\n",
" <td>93</td>\n",
" <td>70</td>\n",
" <td>31</td>\n",
" <td>0</td>\n",
" <td>30.4</td>\n",
" <td>0.315</td>\n",
" <td>23</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>766 rows × 9 columns</p>\n",
"</div>"
],
"text/plain": [
" Pregnancies Glucose BloodPressure SkinThickness Insulin BMI \\\n",
"2 8 183 64 0 0 23.3 \n",
"3 1 89 66 23 94 28.1 \n",
"4 0 137 40 35 168 43.1 \n",
"5 5 116 74 0 0 25.6 \n",
"6 3 78 50 32 88 31.0 \n",
".. ... ... ... ... ... ... \n",
"763 10 101 76 48 180 32.9 \n",
"764 2 122 70 27 0 36.8 \n",
"765 5 121 72 23 112 26.2 \n",
"766 1 126 60 0 0 30.1 \n",
"767 1 93 70 31 0 30.4 \n",
"\n",
" DiabetesPedigreeFunction Age Outcome \n",
"2 0.672 32 1 \n",
"3 0.167 21 0 \n",
"4 2.288 33 1 \n",
"5 0.201 30 0 \n",
"6 0.248 26 1 \n",
".. ... ... ... \n",
"763 0.171 63 0 \n",
"764 0.340 27 0 \n",
"765 0.245 30 0 \n",
"766 0.349 47 1 \n",
"767 0.315 23 0 \n",
"\n",
"[766 rows x 9 columns]"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_dropped_rows = df.drop([0, 1]) # Удаление строк с индексами 0 и 1\n",
"df_dropped_rows"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"###\tСоздание новых столбцов на основе данных из существующих столбцов датафрейма"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"df['Glucose-BP'] = df['Glucose'] - df['BloodPressure']\n"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Pregnancies</th>\n",
" <th>Glucose</th>\n",
" <th>BloodPressure</th>\n",
" <th>SkinThickness</th>\n",
" <th>Insulin</th>\n",
" <th>BMI</th>\n",
" <th>DiabetesPedigreeFunction</th>\n",
" <th>Age</th>\n",
" <th>Outcome</th>\n",
" <th>Glucose-BP</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
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" <td>35</td>\n",
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" <td>0.627</td>\n",
" <td>50</td>\n",
" <td>1</td>\n",
" <td>76</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>85</td>\n",
" <td>66</td>\n",
" <td>29</td>\n",
" <td>0</td>\n",
" <td>26.6</td>\n",
" <td>0.351</td>\n",
" <td>31</td>\n",
" <td>0</td>\n",
" <td>19</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>8</td>\n",
" <td>183</td>\n",
" <td>64</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>23.3</td>\n",
" <td>0.672</td>\n",
" <td>32</td>\n",
" <td>1</td>\n",
" <td>119</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>89</td>\n",
" <td>66</td>\n",
" <td>23</td>\n",
" <td>94</td>\n",
" <td>28.1</td>\n",
" <td>0.167</td>\n",
" <td>21</td>\n",
" <td>0</td>\n",
" <td>23</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0</td>\n",
" <td>137</td>\n",
" <td>40</td>\n",
" <td>35</td>\n",
" <td>168</td>\n",
" <td>43.1</td>\n",
" <td>2.288</td>\n",
" <td>33</td>\n",
" <td>1</td>\n",
" <td>97</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>763</th>\n",
" <td>10</td>\n",
" <td>101</td>\n",
" <td>76</td>\n",
" <td>48</td>\n",
" <td>180</td>\n",
" <td>32.9</td>\n",
" <td>0.171</td>\n",
" <td>63</td>\n",
" <td>0</td>\n",
" <td>25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>764</th>\n",
" <td>2</td>\n",
" <td>122</td>\n",
" <td>70</td>\n",
" <td>27</td>\n",
" <td>0</td>\n",
" <td>36.8</td>\n",
" <td>0.340</td>\n",
" <td>27</td>\n",
" <td>0</td>\n",
" <td>52</td>\n",
" </tr>\n",
" <tr>\n",
" <th>765</th>\n",
" <td>5</td>\n",
" <td>121</td>\n",
" <td>72</td>\n",
" <td>23</td>\n",
" <td>112</td>\n",
" <td>26.2</td>\n",
" <td>0.245</td>\n",
" <td>30</td>\n",
" <td>0</td>\n",
" <td>49</td>\n",
" </tr>\n",
" <tr>\n",
" <th>766</th>\n",
" <td>1</td>\n",
" <td>126</td>\n",
" <td>60</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>30.1</td>\n",
" <td>0.349</td>\n",
" <td>47</td>\n",
" <td>1</td>\n",
" <td>66</td>\n",
" </tr>\n",
" <tr>\n",
" <th>767</th>\n",
" <td>1</td>\n",
" <td>93</td>\n",
" <td>70</td>\n",
" <td>31</td>\n",
" <td>0</td>\n",
" <td>30.4</td>\n",
" <td>0.315</td>\n",
" <td>23</td>\n",
" <td>0</td>\n",
" <td>23</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>768 rows × 10 columns</p>\n",
"</div>"
],
"text/plain": [
" Pregnancies Glucose BloodPressure SkinThickness Insulin BMI \\\n",
"0 6 148 72 35 0 33.6 \n",
"1 1 85 66 29 0 26.6 \n",
"2 8 183 64 0 0 23.3 \n",
"3 1 89 66 23 94 28.1 \n",
"4 0 137 40 35 168 43.1 \n",
".. ... ... ... ... ... ... \n",
"763 10 101 76 48 180 32.9 \n",
"764 2 122 70 27 0 36.8 \n",
"765 5 121 72 23 112 26.2 \n",
"766 1 126 60 0 0 30.1 \n",
"767 1 93 70 31 0 30.4 \n",
"\n",
" DiabetesPedigreeFunction Age Outcome Glucose-BP \n",
"0 0.627 50 1 76 \n",
"1 0.351 31 0 19 \n",
"2 0.672 32 1 119 \n",
"3 0.167 21 0 23 \n",
"4 2.288 33 1 97 \n",
".. ... ... ... ... \n",
"763 0.171 63 0 25 \n",
"764 0.340 27 0 52 \n",
"765 0.245 30 0 49 \n",
"766 0.349 47 1 66 \n",
"767 0.315 23 0 23 \n",
"\n",
"[768 rows x 10 columns]"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"###\tУдаление строк с пустыми значениями"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Pregnancies 0\n",
"Glucose 0\n",
"BloodPressure 0\n",
"SkinThickness 0\n",
"Insulin 0\n",
"BMI 0\n",
"DiabetesPedigreeFunction 0\n",
"Age 0\n",
"Outcome 0\n",
"Glucose-BP 0\n",
"dtype: int64\n"
]
}
],
"source": [
"print(df.isna().sum())"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Pregnancies</th>\n",
" <th>Glucose</th>\n",
" <th>BloodPressure</th>\n",
" <th>SkinThickness</th>\n",
" <th>Insulin</th>\n",
" <th>BMI</th>\n",
" <th>DiabetesPedigreeFunction</th>\n",
" <th>Age</th>\n",
" <th>Outcome</th>\n",
" <th>Glucose-BP</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>6</td>\n",
" <td>148</td>\n",
" <td>72</td>\n",
" <td>35</td>\n",
" <td>0</td>\n",
" <td>33.6</td>\n",
" <td>0.627</td>\n",
" <td>50</td>\n",
" <td>1</td>\n",
" <td>76</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>85</td>\n",
" <td>66</td>\n",
" <td>29</td>\n",
" <td>0</td>\n",
" <td>26.6</td>\n",
" <td>0.351</td>\n",
" <td>31</td>\n",
" <td>0</td>\n",
" <td>19</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>8</td>\n",
" <td>183</td>\n",
" <td>64</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>23.3</td>\n",
" <td>0.672</td>\n",
" <td>32</td>\n",
" <td>1</td>\n",
" <td>119</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>89</td>\n",
" <td>66</td>\n",
" <td>23</td>\n",
" <td>94</td>\n",
" <td>28.1</td>\n",
" <td>0.167</td>\n",
" <td>21</td>\n",
" <td>0</td>\n",
" <td>23</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0</td>\n",
" <td>137</td>\n",
" <td>40</td>\n",
" <td>35</td>\n",
" <td>168</td>\n",
" <td>43.1</td>\n",
" <td>2.288</td>\n",
" <td>33</td>\n",
" <td>1</td>\n",
" <td>97</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>763</th>\n",
" <td>10</td>\n",
" <td>101</td>\n",
" <td>76</td>\n",
" <td>48</td>\n",
" <td>180</td>\n",
" <td>32.9</td>\n",
" <td>0.171</td>\n",
" <td>63</td>\n",
" <td>0</td>\n",
" <td>25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>764</th>\n",
" <td>2</td>\n",
" <td>122</td>\n",
" <td>70</td>\n",
" <td>27</td>\n",
" <td>0</td>\n",
" <td>36.8</td>\n",
" <td>0.340</td>\n",
" <td>27</td>\n",
" <td>0</td>\n",
" <td>52</td>\n",
" </tr>\n",
" <tr>\n",
" <th>765</th>\n",
" <td>5</td>\n",
" <td>121</td>\n",
" <td>72</td>\n",
" <td>23</td>\n",
" <td>112</td>\n",
" <td>26.2</td>\n",
" <td>0.245</td>\n",
" <td>30</td>\n",
" <td>0</td>\n",
" <td>49</td>\n",
" </tr>\n",
" <tr>\n",
" <th>766</th>\n",
" <td>1</td>\n",
" <td>126</td>\n",
" <td>60</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>30.1</td>\n",
" <td>0.349</td>\n",
" <td>47</td>\n",
" <td>1</td>\n",
" <td>66</td>\n",
" </tr>\n",
" <tr>\n",
" <th>767</th>\n",
" <td>1</td>\n",
" <td>93</td>\n",
" <td>70</td>\n",
" <td>31</td>\n",
" <td>0</td>\n",
" <td>30.4</td>\n",
" <td>0.315</td>\n",
" <td>23</td>\n",
" <td>0</td>\n",
" <td>23</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>768 rows × 10 columns</p>\n",
"</div>"
],
"text/plain": [
" Pregnancies Glucose BloodPressure SkinThickness Insulin BMI \\\n",
"0 6 148 72 35 0 33.6 \n",
"1 1 85 66 29 0 26.6 \n",
"2 8 183 64 0 0 23.3 \n",
"3 1 89 66 23 94 28.1 \n",
"4 0 137 40 35 168 43.1 \n",
".. ... ... ... ... ... ... \n",
"763 10 101 76 48 180 32.9 \n",
"764 2 122 70 27 0 36.8 \n",
"765 5 121 72 23 112 26.2 \n",
"766 1 126 60 0 0 30.1 \n",
"767 1 93 70 31 0 30.4 \n",
"\n",
" DiabetesPedigreeFunction Age Outcome Glucose-BP \n",
"0 0.627 50 1 76 \n",
"1 0.351 31 0 19 \n",
"2 0.672 32 1 119 \n",
"3 0.167 21 0 23 \n",
"4 2.288 33 1 97 \n",
".. ... ... ... ... \n",
"763 0.171 63 0 25 \n",
"764 0.340 27 0 52 \n",
"765 0.245 30 0 49 \n",
"766 0.349 47 1 66 \n",
"767 0.315 23 0 23 \n",
"\n",
"[768 rows x 10 columns]"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.dropna() #Тк.пустых строк нет, мы ничего не удалили"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"###\tЗаполнение пустых значений на основе существующих данных"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"df.fillna(df.mean(), inplace=True)\n",
"df.fillna(df.median(), inplace=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Мы обрабатываем пустые значения для каждого столбца отдельно\n",
"\n",
"Мы можем заполнить пропуски средним или медианой, если это числовой столбец\n",
"\n",
"Мы заполняем средним, если в колонке нет выбросов\n",
"\n",
"Если столбец категориальный, то мы можем заполнить пропуски модой (самым часто встречающимся значением)\n",
"\n",
"Если пропусков мало, то их можно просто удалить."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 8. Возможности визуализации"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1000x500 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Линейная диаграмма\n",
"plt.figure(figsize=(10, 5))\n",
"df['Glucose'].plot(title='Line Plot (Glucose)')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<Figure size 800x500 with 0 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Гистограмма\n",
"plt.figure(figsize=(8, 5))\n",
"df.plot.hist(column=[\"Age\"], bins=80)\n",
"plt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 800x500 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(8, 5))\n",
"df['Age'].value_counts().plot(kind='bar', title='Bar Plot (Age)')\n",
"plt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 800x500 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(8, 5))\n",
"df[\"BMI\"].plot(kind = \"box\", title='Ящик с усами')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<Figure size 800x500 with 0 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(8, 5))\n",
"df[['Glucose', 'BMI']].plot(kind='area', alpha=0.2, title='Area Plot (Glucose, BMI)')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<Axes: xlabel='Age', ylabel='Glucose'>"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df.plot.scatter(x=\"Age\", y=\"Glucose\")"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 800x500 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(8, 5))\n",
"df['Outcome'].value_counts().plot(kind='pie', autopct='%1.1f%%', title='Pie Chart (Outcome)')\n",
"plt.show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.5"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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