diff --git a/README.md b/README.md index 15a99ef..066bf1d 100644 --- a/README.md +++ b/README.md @@ -1 +1,3 @@ -# AIM_PIbd-31_Tabeev_A.P \ No newline at end of file +# AIM_PIbd-31_Tabeev_A.P +# Вариант 3 +# https://clck.yandex.ru/redir/nWO_r1F33ck?data=NnBZTWRhdFZKOHRaTENSMFc4S0VQUGMtSXk0bDRzNnVpakFkYjNNRE5ZNFRuVk4yRGpQaHlFSHNPNVpscDY1RGxPdmF0UFlUU3V4cmpoNDBvcE5vQVAxUzRxUzFpU1YzejluV1ozdUpoS1ZMeWdjcHktYS1IT2diWFhTLWpZcVhkdzF4a25GUGRYZGtSQ3RsclBRV1RiTWJsdFlyNVFIV1MyVHp0NDJTY21Z&b64e=2&sign=0e97a68a5fb67b83ca9ea592b182bbae&keyno=17 \ No newline at end of file diff --git a/lab_1/image.png b/lab_1/image.png new file mode 100644 index 0000000..87e6758 Binary files /dev/null and b/lab_1/image.png differ diff --git a/lab_1/lab1.ipynb b/lab_1/lab1.ipynb new file mode 100644 index 0000000..c022d05 --- /dev/null +++ b/lab_1/lab1.ipynb @@ -0,0 +1,237 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Начало лабы\n", + "\n", + "Выгрузка данных из csv в датафрейм и вывел названия колонок" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Index(['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin',\n", + " 'BMI', 'DiabetesPedigreeFunction', 'Age', 'Outcome'],\n", + " dtype='object')\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "df = pd.read_csv(\".//static//csv//diabetes.csv\")\n", + "print(df.columns)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Вывод данных и первая диаграмма - гистограмма\n", + "частота людей с диабетом в определенном возрасте" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 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 \n", + " Pregnancies Glucose BloodPressure SkinThickness Insulin BMI \\\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", + "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" + ] + }, + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Частота')" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "print(df.head())\n", + "print(df.tail())\n", + "\n", + "data = df[\"Age\"]\n", + "data = data[20:120]\n", + "\n", + "plt.hist(data, color=\"purple\")\n", + "plt.xlabel(\"Возраст\")\n", + "plt.ylabel(\"Частота\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "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" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Вторая диаграмма - точечная диаграмма\n", + "Зависимость инсулина и глюкозы\n" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Инсулин')" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "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": [ + "
" + ] + }, + "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 +} diff --git a/lab_1/requirements.txt b/lab_1/requirements.txt new file mode 100644 index 0000000..ea03739 Binary files /dev/null and b/lab_1/requirements.txt differ diff --git a/lab_1/static/csv/diabetes.csv b/lab_1/static/csv/diabetes.csv new file mode 100644 index 0000000..9e6a362 --- /dev/null +++ b/lab_1/static/csv/diabetes.csv @@ -0,0 +1,769 @@ +Pregnancies,Glucose,BloodPressure,SkinThickness,Insulin,BMI,DiabetesPedigreeFunction,Age,Outcome +6,148,72,35,0,33.6,0.627,50,1 +1,85,66,29,0,26.6,0.351,31,0 +8,183,64,0,0,23.3,0.672,32,1 +1,89,66,23,94,28.1,0.167,21,0 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