AIM-PIbd-32-Smirnov-A-A/lab1/lab1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Начало лабораторной \n",
    "\n",
    "Выгрузка данных из csv файла в датафрейм"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['ID', 'Year_Birth', 'Education', 'Marital_Status', 'Income', 'Kidhome',\n",
      "       'Teenhome', 'Dt_Customer', 'Recency', 'MntWines', 'MntFruits',\n",
      "       'MntMeatProducts', 'MntFishProducts', 'MntSweetProducts',\n",
      "       'MntGoldProds', 'NumDealsPurchases', 'NumWebPurchases',\n",
      "       'NumCatalogPurchases', 'NumStorePurchases', 'NumWebVisitsMonth',\n",
      "       'AcceptedCmp3', 'AcceptedCmp4', 'AcceptedCmp5', 'AcceptedCmp1',\n",
      "       'AcceptedCmp2', 'Complain', 'Z_CostContact', 'Z_Revenue', 'Response'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "df = pd.read_csv(\"..//..//static//csv//marketing_campaign.csv\", sep=\"\\t\")\n",
    "\n",
    "print (df.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Эта вывод датасета по столбцам \"Year_Birth\", \"Marital_Status\", \"Income\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Year_Birth Marital_Status   Income\n",
      "0           1957         Single  58138.0\n",
      "1           1954         Single  46344.0\n",
      "2           1965       Together  71613.0\n",
      "3           1984       Together  26646.0\n",
      "4           1981        Married  58293.0\n",
      "...          ...            ...      ...\n",
      "2235        1967        Married  61223.0\n",
      "2236        1946       Together  64014.0\n",
      "2237        1981       Divorced  56981.0\n",
      "2238        1956       Together  69245.0\n",
      "2239        1954        Married  52869.0\n",
      "\n",
      "[2240 rows x 3 columns]\n"
     ]
    }
   ],
   "source": [
    "data = df[[\"Year_Birth\", \"Marital_Status\", \"Income\"]].copy()\n",
    "print(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Данная гистограмма отображает распределение значений года рождения среди клиентов. На оси X указаны годы рождения, разбитые на 100 интервалов, чтобы отобразить каждый год с большей детализацией. На оси Y показано количество записей (частота) для каждого года. Анализируя гистограмму по столбцу \"Year_Birth\", можно сделать выводы о возрастной структуре клиентов. Например, можно увидеть, что большинство клиентов родились между 1950 и 1980 годами, а количество рождений до 1940 года и после 1980 года значительно ниже."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='Frequency'>"
      ]
     },
     "execution_count": 13,
     "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": [
    "data.plot.hist(column=[\"Year_Birth\"], bins=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Данная гистограмма отображает распределение доходов среди клиентов. На оси X указаны значения доходов, разбитые на 50 интервалов, что позволяет увидеть, как доходы распределены по разным уровням. На оси Y указано количество записей (частота) в каждом интервале дохода. Анализируя гистограмму \"Income\", можно сделать вывод о том, как варьируются доходы. Например, мы можем увидеть, что большинство клиентов имеют доход в пределах определенного диапазона, и только небольшая часть имеет более высокий доход."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "data[\"Income\"].plot.hist(bins=50, color='skyblue', edgecolor='black')\n",
    "plt.title(\"Распределение доходов среди клиентов\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Данная круговая диаграмма отображает распределение клиентов по их семейному положению. На ней представлены различные категории семейного положения, такие как Married, Together, Single, Divorced и Widow, каждая из которых окрашена в отдельный цвет и подписана соответствующим процентом от общего числа клиентов. Анализируя эту диаграмму, можно сделать вывод о том, что наибольшую долю клиентов составляют Married (38.7%) и Together (26.0%), что указывает на преобладание семейных или живущих вместе клиентов. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "filtered_data = data[data[\"Marital_Status\"].isin([\"Married\", \"Together\", \"Single\", \"Divorced\", \"Widow\"])]\n",
    "\n",
    "plt.figure(figsize=(8, 8))  \n",
    "filtered_data[\"Marital_Status\"].value_counts().plot(\n",
    "    kind=\"pie\", \n",
    "    autopct='%1.1f%%', \n",
    "    colors=['salmon', 'skyblue', 'lightgreen', 'orange', 'purple'], \n",
    ")\n",
    "plt.title(\"Распределение клиентов по семейному положению (без YOLO и Absurd)\")\n",
    "plt.ylabel(\"\")  \n",
    "plt.show()\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "aimenv",
   "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": "3.12.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}