238 lines
78 KiB
Plaintext
238 lines
78 KiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Начало лабы\n",
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"\n",
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"Выгрузка данных из csv в датафрейм и вывел названия колонок"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Index(['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin',\n",
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" 'BMI', 'DiabetesPedigreeFunction', 'Age', 'Outcome'],\n",
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" dtype='object')\n"
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]
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}
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],
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"source": [
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"import pandas as pd\n",
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"df = pd.read_csv(\".//static//csv//diabetes.csv\")\n",
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"print(df.columns)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Вывод данных и первая диаграмма - гистограмма\n",
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"частота людей с диабетом в определенном возрасте"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 37,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" Pregnancies Glucose BloodPressure SkinThickness Insulin BMI \\\n",
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"0 6 148 72 35 0 33.6 \n",
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"1 1 85 66 29 0 26.6 \n",
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"2 8 183 64 0 0 23.3 \n",
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"3 1 89 66 23 94 28.1 \n",
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"4 0 137 40 35 168 43.1 \n",
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"\n",
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" DiabetesPedigreeFunction Age Outcome \n",
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"0 0.627 50 1 \n",
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"1 0.351 31 0 \n",
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"2 0.672 32 1 \n",
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"3 0.167 21 0 \n",
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"4 2.288 33 1 \n",
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" Pregnancies Glucose BloodPressure SkinThickness Insulin BMI \\\n",
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"763 10 101 76 48 180 32.9 \n",
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"764 2 122 70 27 0 36.8 \n",
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"765 5 121 72 23 112 26.2 \n",
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"766 1 126 60 0 0 30.1 \n",
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"767 1 93 70 31 0 30.4 \n",
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"\n",
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" DiabetesPedigreeFunction Age Outcome \n",
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"763 0.171 63 0 \n",
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"764 0.340 27 0 \n",
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"765 0.245 30 0 \n",
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"766 0.349 47 1 \n",
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"767 0.315 23 0 \n"
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]
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},
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{
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"data": {
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"text/plain": [
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"Text(0, 0.5, 'Частота')"
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]
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},
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"execution_count": 37,
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"metadata": {},
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"output_type": "execute_result"
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},
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{
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"data": {
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"image/png": "iVBORw0KGgoAAAANSUhEUgAAAjIAAAGwCAYAAACzXI8XAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8hTgPZAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAnkElEQVR4nO3df1SVdYLH8c9V5AIKKCi/FF3NUksxV10jW/PXhtY4mp6maZwZnTy6Gjr+WjPakmi3MGdnss6Wjv2w05Y5OWWOteoSCu6UmDJLRhmp2cCMgGYBgnH1yHf/mOM9oYBwvfDcL75f5zzncJ9ffL589fA5z/Nwr8sYYwQAAGChDk4HAAAA8BVFBgAAWIsiAwAArEWRAQAA1qLIAAAAa1FkAACAtSgyAADAWkFOB2htdXV1OnHihMLDw+VyuZyOAwAAmsEYozNnzighIUEdOjR+3aXdF5kTJ04oMTHR6RgAAMAHJSUl6tWrV6Pb232RCQ8Pl/S3H0RERITDaQAAQHNUVVUpMTHR+3u8Me2+yFy8nRQREUGRAQDAMld6LISHfQEAgLUoMgAAwFoUGQAAYC2KDAAAsBZFBgAAWIsiAwAArEWRAQAA1qLIAAAAa1FkAACAtSgyAADAWhQZAABgLYoMAACwFkUGAABYiyIDAACsRZEBAADWCnI6gM0yXBlOR/BJukl3OgIAAH7BFRkAAGAtigwAALAWRQYAAFiLIgMAAKxFkQEAANaiyAAAAGs5WmTWrVunpKQkRUREKCIiQsnJydqxY4d3e21trVJTUxUdHa0uXbpoxowZKi8vdzAxAAAIJI4WmV69emn16tXKz8/XwYMHNX78eE2dOlWffvqpJGnp0qXavn27tmzZotzcXJ04cULTp093MjIAAAggjr4h3pQpU+q9fuKJJ7Ru3Trl5eWpV69eeumll7Rp0yaNHz9ekrRx40YNGjRIeXl5uuWWW5yIDAAAAkjAPCNz4cIFbd68WTU1NUpOTlZ+fr7Onz+viRMnevcZOHCgevfurX379jV6Ho/Ho6qqqnoLAABonxwvMp988om6dOkit9ut+fPna+vWrbrxxhtVVlam4OBgde3atd7+sbGxKisra/R8mZmZioyM9C6JiYmtPAIAAOAUx4vMgAEDVFBQoP3792vBggWaNWuWPvvsM5/Pl5aWpsrKSu9SUlLix7QAACCQOP6hkcHBwerfv78kafjw4Tpw4ICeeeYZ3XvvvTp37pwqKirqXZUpLy9XXFxco+dzu91yu92tHRsAAAQAx6/IXKqurk4ej0fDhw9Xp06dlJ2d7d1WVFSk4uJiJScnO5gQAAAECkevyKSlpWny5Mnq3bu3zpw5o02bNiknJ0e7du1SZGSk5syZo2XLlikqKkoRERFatGiRkpOT+YslAAAgyeEic/LkSf385z9XaWmpIiMjlZSUpF27dumf/umfJElPP/20OnTooBkzZsjj8SglJUXPP/+8k5EBAEAAcRljjNMhWlNVVZUiIyNVWVmpiIgIv547w5Xh1/O1lXST7nQEAACa1Nzf3wH3jAwAAEBzUWQAAIC1KDIAAMBaFBkAAGAtigwAALAWRQYAAFiLIgMAAKxFkQEAANaiyAAAAGtRZAAAgLUoMgAAwFoUGQAAYC2KDAAAsBZFBgAAWIsiAwAArEWRAQAA1qLIAAAAa1FkAACAtSgyAADAWhQZAABgLYoMAACwFkUGAABYiyIDAACsRZEBAADWosgAAABrUWQAAIC1KDIAAMBaFBkAAGAtigwAALAWRQYAAFiLIgMAAKxFkQEAANaiyAAAAGtRZAAAgLUoMgAAwFoUGQAAYC2KDAAAsBZFBgAAWIsiAwAArEWRAQAA1qLIAAAAa1FkAACAtSgyAADAWhQZAABgLYoMAACwFkUGAABYy9Eik5mZqZEjRyo8PFwxMTGaNm2aioqK6u0zduxYuVyuesv8+fMdSgwAAAKJo0UmNzdXqampysvLU1ZWls6fP6877rhDNTU19fabO3euSktLvcuaNWscSgwAAAJJkJPffOfOnfVev/LKK4qJiVF+fr7GjBnjXR8WFqa4uLi2jgcAAAJcQD0jU1lZKUmKioqqt/71119X9+7dNXjwYKWlpens2bONnsPj8aiqqqreAgAA2idHr8h8X11dnZYsWaLRo0dr8ODB3vU/+clP1KdPHyUkJOjQoUNauXKlioqK9Pbbbzd4nszMTGVkZLRVbAAA4CCXMcY4HUKSFixYoB07duiPf/yjevXq1eh+u3fv1oQJE3T06FFdd911l233eDzyeDze11VVVUpMTFRlZaUiIiL8mjnDZWdhSjfpTkcAAKBJVVVVioyMvOLv74C4IrNw4UK9++672rt3b5MlRpJGjRolSY0WGbfbLbfb3So5AQBAYHG0yBhjtGjRIm3dulU5OTnq27fvFY8pKCiQJMXHx7dyOgAAEOgcLTKpqanatGmTtm3bpvDwcJWVlUmSIiMjFRoaqmPHjmnTpk268847FR0drUOHDmnp0qUaM2aMkpKSnIwOAAACgKNFZt26dZL+9qZ337dx40bNnj1bwcHBev/997V27VrV1NQoMTFRM2bM0COPPOJAWgAAEGgcv7XUlMTEROXm5rZRGgAAYJuAeh8ZAACAlqDIAAAAa1FkAACAtSgyAADAWhQZAABgLYoMAACwFkUGAABYiyIDAACsRZEBAADWosgAAABrUWQAAIC1KDIAAMBaFBkAAGAtigwAALAWRQYAAFiLIgMAAKxFkQEAANaiyAAAAGtRZAAAgLUoMgAAwFoUGQAAYC2KDAAAsBZFBgAAWIsiAwAArEWRAQAA1qLIAAAAa1FkAACAtSgyAADAWhQZAABgLYoMAACwFkUGAABYiyIDAACsRZEBAADWosgAAABrUWQAAIC1KDIAAMBaFBkAAGAtigwAALAWRQYAAFiLIgMAAKxFkQEAANaiyAAAAGtRZAAAgLUoMgAAwFoUGQAAYC2KDAAAsJajRSYzM1MjR45UeHi4YmJiNG3aNBUVFdXbp7a2VqmpqYqOjlaXLl00Y8YMlZeXO5QYAAAEEkeLTG5urlJTU5WXl6esrCydP39ed9xxh2pqarz7LF26VNu3b9eWLVuUm5urEydOaPr06Q6mBgAAgSLIyW++c+fOeq9feeUVxcTEKD8/X2PGjFFlZaVeeuklbdq0SePHj5ckbdy4UYMGDVJeXp5uueUWJ2IDAIAAEVDPyFRWVkqSoqKiJEn5+fk6f/68Jk6c6N1n4MCB6t27t/bt29fgOTwej6qqquotAACgfXL0isz31dXVacmSJRo9erQGDx4sSSorK1NwcLC6du1ab9/Y2FiVlZU1eJ7MzExlZGS0dlyrZbjs+/mkm3SnIwAAAlDAXJFJTU1VYWGhNm/efFXnSUtLU2VlpXcpKSnxU0IAABBoAuKKzMKFC/Xuu+9q79696tWrl3d9XFyczp07p4qKinpXZcrLyxUXF9fgudxut9xud2tHBgAAAcDRKzLGGC1cuFBbt27V7t271bdv33rbhw8frk6dOik7O9u7rqioSMXFxUpOTm7ruAAAIMA4ekUmNTVVmzZt0rZt2xQeHu597iUyMlKhoaGKjIzUnDlztGzZMkVFRSkiIkKLFi1ScnIyf7EEAACcLTLr1q2TJI0dO7be+o0bN2r27NmSpKefflodOnTQjBkz5PF4lJKSoueff76NkwIAgEDkaJExxlxxn5CQED333HN67rnn2iARAACwScD81RIAAEBLUWQAAIC1KDIAAMBaFBkAAGAtigwAALAWRQYAAFiLIgMAAKxFkQEAANaiyAAAAGtRZAAAgLV8/oiCmpoa5ebmqri4WOfOnau37Ze//OVVBwMAALgSn4rM//3f/+nOO+/U2bNnVVNTo6ioKH399dcKCwtTTEwMRQYAALQJn24tLV26VFOmTNG3336r0NBQ5eXl6c9//rOGDx+u//iP//B3RgAAgAb5VGQKCgq0fPlydejQQR07dpTH41FiYqLWrFmjhx9+2N8ZAQAAGuRTkenUqZM6dPjboTExMSouLpYkRUZGqqSkxH/pAAAAmuD
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"text/plain": [
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"<Figure size 640x480 with 1 Axes>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"import matplotlib.pyplot as plt\n",
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"\n",
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"print(df.head())\n",
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"print(df.tail())\n",
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"\n",
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"data = df[\"Age\"]\n",
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"data = data[20:120]\n",
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"\n",
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"plt.hist(data, color=\"purple\")\n",
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"plt.xlabel(\"Возраст\")\n",
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"plt.ylabel(\"Частота\")\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"На данной гистограмме отображена информация по наличию диабета у индейцев Пима в определенном возрасте. Можем сделать вывод что диабет преобладает у молодых людей в возрасте от 20 до 30 лет, а дальше частота падает, можно сделать вывод, что люди не доживают до старчества"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Вторая диаграмма - точечная диаграмма\n",
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"Зависимость инсулина и глюкозы\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 39,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"Text(0, 0.5, 'Инсулин')"
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]
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},
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"execution_count": 39,
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"metadata": {},
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"output_type": "execute_result"
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},
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{
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"data": {
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"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 640x480 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"data = df[[\"Insulin\", \"Glucose\"]].copy()\n",
|
|||
|
|
"plt.scatter(data[\"Glucose\"], data[\"Insulin\"])\n",
|
|||
|
|
"plt.xlabel(\"Глюкоза\")\n",
|
|||
|
|
"plt.ylabel(\"Инсулин\")"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"Существует положительная корреляция между уровнем инсулина и уровнем глюкозы. Это означает, что с увеличением уровня инсулина чаще наблюдаются более высокие уровни глюкозы. В диапозоне инсулина от 0 до 200 единиц наблюдается выскоая плотность точек с уровнями глюкозы от 50 до 125, это гвоорит о том, что большинство людей имеют инсулин и глюкозу в этих пределах."
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"## Третья диаграма - Круговая диаграмма\n",
|
|||
|
|
"Анализ смертности у индейцев Пима."
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 44,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"Text(0, 0.5, '')"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 44,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 800x800 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"plt.figure(figsize=(8, 8))\n",
|
|||
|
|
"df[\"Outcome\"][0:500].value_counts().plot.pie(autopct='%1.1f%%', startangle=90, pctdistance=1.25, labeldistance=.8)\n",
|
|||
|
|
"plt.ylabel('')"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"На данной круговой диаграмме видим высокую смертность из-за сахарного диабета, из-за него умерло треть населения. "
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"kernelspec": {
|
|||
|
|
"display_name": ".venv",
|
|||
|
|
"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
|
|||
|
|
}
|