AIM-PIbd-32-Kuzin-P-S/lab_1/lab1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Hello**  \n",
    "#Блок комментов     \n",
    "Толстый - ** **\n",
    "Курсив - * *\n",
    "Зачеркнутый - ~~ ~~ \n",
    "Выводок колонок: print(df.columns)\n",
    "\n",
    "df.info() - информация о всех колонках в таблице (сколько записей в каждой колонке, какой тип данных в записи)\n",
    "\n",
    ".transpose() - транспонировать матрицу\n",
    "df.drop() - возвращает таблицу с убраннами столбцами ()  axis = ось (0 или index - значит строки/1 или coloumns - столбцы) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Date', ylabel='High'>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 3000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Начало начал\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from datetime import date\n",
    "\n",
    "df = pd.read_csv(\".//static//csv//Starbucks Dataset.csv\")\n",
    "\n",
    "viborka = df.groupby(df.index // 500).head(1)\n",
    "viborka.plot.scatter(x=\"Date\", y=\"High\", figsize=(30, 4))\n",
    "# Диаграмма цен:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 1500x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_subset = df.groupby(df.index // 1000).head(1)\n",
    "\n",
    "value_counts = df_subset['Volume'].value_counts()\n",
    "year = df_subset[\"Date\"]\n",
    "\n",
    "plt.figure(figsize=(15, 15))\n",
    "plt.pie(\n",
    "    df_subset[\"Volume\"],\n",
    "    labels= df_subset[\"Date\"], # type: ignore\n",
    "    autopct=\"%1.1f%%\", rotatelabels=True\n",
    ")\n",
    "plt.show()\n",
    "#Сравнение объема продаж от общего числа:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Date'>"
      ]
     },
     "execution_count": 79,
     "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": [
    "from sqlite3 import Date\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# year_groups = pd.cut(dates, everyFiveYears, right=False)  # type: ignore\n",
    "\n",
    "# group_counts = year_groups.value_counts().sort_index()  # type: ignore\n",
    "\n",
    "\n",
    "# plt.figure(figsize=(10, 6))\n",
    "# plt.show()\n",
    "\n",
    "df.head(5).plot(x=\"Date\", y=[\"High\", \"Low\"])\n",
    "#ВЫсоты и впадины в зависимости от времени"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "aisenv",
   "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": "3.12.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}