{
"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": [
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" Pregnancies | \n",
" Glucose | \n",
" BloodPressure | \n",
" SkinThickness | \n",
" Insulin | \n",
" BMI | \n",
" DiabetesPedigreeFunction | \n",
" Age | \n",
" Outcome | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" 6 | \n",
" 148 | \n",
" 72 | \n",
" 35 | \n",
" 0 | \n",
" 33.6 | \n",
" 0.627 | \n",
" 50 | \n",
" 1 | \n",
"
\n",
" \n",
" 1 | \n",
" 1 | \n",
" 85 | \n",
" 66 | \n",
" 29 | \n",
" 0 | \n",
" 26.6 | \n",
" 0.351 | \n",
" 31 | \n",
" 0 | \n",
"
\n",
" \n",
" 2 | \n",
" 8 | \n",
" 183 | \n",
" 64 | \n",
" 0 | \n",
" 0 | \n",
" 23.3 | \n",
" 0.672 | \n",
" 32 | \n",
" 1 | \n",
"
\n",
" \n",
" 3 | \n",
" 1 | \n",
" 89 | \n",
" 66 | \n",
" 23 | \n",
" 94 | \n",
" 28.1 | \n",
" 0.167 | \n",
" 21 | \n",
" 0 | \n",
"
\n",
" \n",
" 4 | \n",
" 0 | \n",
" 137 | \n",
" 40 | \n",
" 35 | \n",
" 168 | \n",
" 43.1 | \n",
" 2.288 | \n",
" 33 | \n",
" 1 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"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": 1,
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'df' is not defined",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[1;32mIn[1], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mdf\u001b[49m\u001b[38;5;241m.\u001b[39mto_csv(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnew.csv\u001b[39m\u001b[38;5;124m\"\u001b[39m, index\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n",
"\u001b[1;31mNameError\u001b[0m: name 'df' is not defined"
]
}
],
"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": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" Pregnancies | \n",
" Glucose | \n",
" BloodPressure | \n",
" SkinThickness | \n",
" Insulin | \n",
" BMI | \n",
" DiabetesPedigreeFunction | \n",
" Age | \n",
" Outcome | \n",
"
\n",
" \n",
" \n",
" \n",
" count | \n",
" 768.000000 | \n",
" 768.000000 | \n",
" 768.000000 | \n",
" 768.000000 | \n",
" 768.000000 | \n",
" 768.000000 | \n",
" 768.000000 | \n",
" 768.000000 | \n",
" 768.000000 | \n",
"
\n",
" \n",
" mean | \n",
" 3.845052 | \n",
" 120.894531 | \n",
" 69.105469 | \n",
" 20.536458 | \n",
" 79.799479 | \n",
" 31.992578 | \n",
" 0.471876 | \n",
" 33.240885 | \n",
" 0.348958 | \n",
"
\n",
" \n",
" std | \n",
" 3.369578 | \n",
" 31.972618 | \n",
" 19.355807 | \n",
" 15.952218 | \n",
" 115.244002 | \n",
" 7.884160 | \n",
" 0.331329 | \n",
" 11.760232 | \n",
" 0.476951 | \n",
"
\n",
" \n",
" min | \n",
" 0.000000 | \n",
" 0.000000 | \n",
" 0.000000 | \n",
" 0.000000 | \n",
" 0.000000 | \n",
" 0.000000 | \n",
" 0.078000 | \n",
" 21.000000 | \n",
" 0.000000 | \n",
"
\n",
" \n",
" 25% | \n",
" 1.000000 | \n",
" 99.000000 | \n",
" 62.000000 | \n",
" 0.000000 | \n",
" 0.000000 | \n",
" 27.300000 | \n",
" 0.243750 | \n",
" 24.000000 | \n",
" 0.000000 | \n",
"
\n",
" \n",
" 50% | \n",
" 3.000000 | \n",
" 117.000000 | \n",
" 72.000000 | \n",
" 23.000000 | \n",
" 30.500000 | \n",
" 32.000000 | \n",
" 0.372500 | \n",
" 29.000000 | \n",
" 0.000000 | \n",
"
\n",
" \n",
" 75% | \n",
" 6.000000 | \n",
" 140.250000 | \n",
" 80.000000 | \n",
" 32.000000 | \n",
" 127.250000 | \n",
" 36.600000 | \n",
" 0.626250 | \n",
" 41.000000 | \n",
" 1.000000 | \n",
"
\n",
" \n",
" max | \n",
" 17.000000 | \n",
" 199.000000 | \n",
" 122.000000 | \n",
" 99.000000 | \n",
" 846.000000 | \n",
" 67.100000 | \n",
" 2.420000 | \n",
" 81.000000 | \n",
" 1.000000 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"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": [
"\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": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" Age | \n",
" Insulin | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" 50 | \n",
" 0 | \n",
"
\n",
" \n",
" 1 | \n",
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" 2 | \n",
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" 4 | \n",
" 33 | \n",
" 168 | \n",
"
\n",
" \n",
" ... | \n",
" ... | \n",
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" \n",
" 763 | \n",
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" \n",
" 764 | \n",
" 27 | \n",
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" \n",
" 765 | \n",
" 30 | \n",
" 112 | \n",
"
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" \n",
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\n",
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" 767 | \n",
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\n",
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768 rows × 2 columns
\n",
"
"
],
"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": {
"text/html": [
"\n",
"\n",
"
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" | \n",
" Pregnancies | \n",
" Glucose | \n",
" BloodPressure | \n",
" SkinThickness | \n",
" Insulin | \n",
" BMI | \n",
" DiabetesPedigreeFunction | \n",
" Age | \n",
" Outcome | \n",
"
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"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": {
"text/html": [
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" | \n",
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" SkinThickness | \n",
" Insulin | \n",
" BMI | \n",
" DiabetesPedigreeFunction | \n",
" Age | \n",
" Outcome | \n",
"
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\n",
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" 755 | \n",
" 1 | \n",
" 128 | \n",
" 88 | \n",
" 39 | \n",
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" 36.5 | \n",
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" 37 | \n",
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\n",
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" 763 | \n",
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" 63 | \n",
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\n",
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" 765 | \n",
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\n",
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243 rows × 9 columns
\n",
"
"
],
"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": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" Insulin | \n",
"
\n",
" \n",
" Pregnancies | \n",
" | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" 81.675676 | \n",
"
\n",
" \n",
" 1 | \n",
" 98.674074 | \n",
"
\n",
" \n",
" 2 | \n",
" 85.844660 | \n",
"
\n",
" \n",
" 3 | \n",
" 87.453333 | \n",
"
\n",
" \n",
" 4 | \n",
" 69.441176 | \n",
"
\n",
" \n",
" 5 | \n",
" 57.298246 | \n",
"
\n",
" \n",
" 6 | \n",
" 63.580000 | \n",
"
\n",
" \n",
" 7 | \n",
" 84.466667 | \n",
"
\n",
" \n",
" 8 | \n",
" 92.815789 | \n",
"
\n",
" \n",
" 9 | \n",
" 62.428571 | \n",
"
\n",
" \n",
" 10 | \n",
" 34.791667 | \n",
"
\n",
" \n",
" 11 | \n",
" 65.454545 | \n",
"
\n",
" \n",
" 12 | \n",
" 112.555556 | \n",
"
\n",
" \n",
" 13 | \n",
" 27.900000 | \n",
"
\n",
" \n",
" 14 | \n",
" 92.000000 | \n",
"
\n",
" \n",
" 15 | \n",
" 110.000000 | \n",
"
\n",
" \n",
" 17 | \n",
" 114.000000 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"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": [
{
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768 rows × 9 columns
\n",
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],
"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": [
{
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"765 5 121 72 23 \n",
"766 1 126 60 0 \n",
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"\n",
" DiabetesPedigreeFunction Age Outcome \n",
"0 0.627 50 1 \n",
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"4 2.288 33 1 \n",
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"\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": [
{
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766 rows × 9 columns
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"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": {
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" BMI | \n",
" DiabetesPedigreeFunction | \n",
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],
"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": {
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\n",
" \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
"
\n",
" \n",
" 763 | \n",
" 10 | \n",
" 101 | \n",
" 76 | \n",
" 48 | \n",
" 180 | \n",
" 32.9 | \n",
" 0.171 | \n",
" 63 | \n",
" 0 | \n",
" 25 | \n",
"
\n",
" \n",
" 764 | \n",
" 2 | \n",
" 122 | \n",
" 70 | \n",
" 27 | \n",
" 0 | \n",
" 36.8 | \n",
" 0.340 | \n",
" 27 | \n",
" 0 | \n",
" 52 | \n",
"
\n",
" \n",
" 765 | \n",
" 5 | \n",
" 121 | \n",
" 72 | \n",
" 23 | \n",
" 112 | \n",
" 26.2 | \n",
" 0.245 | \n",
" 30 | \n",
" 0 | \n",
" 49 | \n",
"
\n",
" \n",
" 766 | \n",
" 1 | \n",
" 126 | \n",
" 60 | \n",
" 0 | \n",
" 0 | \n",
" 30.1 | \n",
" 0.349 | \n",
" 47 | \n",
" 1 | \n",
" 66 | \n",
"
\n",
" \n",
" 767 | \n",
" 1 | \n",
" 93 | \n",
" 70 | \n",
" 31 | \n",
" 0 | \n",
" 30.4 | \n",
" 0.315 | \n",
" 23 | \n",
" 0 | \n",
" 23 | \n",
"
\n",
" \n",
"
\n",
"
768 rows × 10 columns
\n",
"
"
],
"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": {
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",
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