A7-Aczel problems practice (1-41, 1-42, 1-43, 1-44, 1-45).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# A7-Aczel problems practice (1-41, 1-42, 1-43, 1-44, 1-45)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 41. The following data are estimated worldwide appliance sales (in millions of dollars). Use the data to construct a pie chart for the worldwide appliance sales of the listed manufacturers."
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Electrolux          $5,100\n",
    "General Electric     4,350\n",
    "Matsushita Electric  4,180\n",
    "Whirlpool            3,950\n",
    "Bosch-Siemens        2,200\n",
    "Philips              2,000\n",
    "Maytag               1,580"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[5100, 4350, 4180, 3950, 2200, 2000, 1580]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales=[5100,4350,4180,3950,2200,2000,1580]\n",
    "sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Electrolux',\n",
       " 'General_Electric',\n",
       " 'Matsushita_Electric',\n",
       " 'Whirlpool',\n",
       " 'Bosch-Siemens',\n",
       " 'Philips',\n",
       " 'Maytag']"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "company=['Electrolux','General_Electric','Matsushita_Electric','Whirlpool','Bosch-Siemens','Philips','Maytag']\n",
    "company"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pie(sales, labels = company, autopct='%1.0f%%')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 42. Draw a bar graph for the data on the first five stocks in problem 1–14. Is any one of the three kinds of plot more appropriate than the others for these data? If so, why?\n",
    "\n",
    "12.5, 13, 14.8, 11, 16.7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "annualized_returns=[12.5, 13, 14.8, 11, 16.7]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'annualized_returns')"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(range(1,6),annualized_returns)\n",
    "plt.xlabel('stocks')\n",
    "plt.ylabel('annualized_returns')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.patches.Wedge at 0x1bc9457bc70>,\n",
       "  <matplotlib.patches.Wedge at 0x1bc94610640>,\n",
       "  <matplotlib.patches.Wedge at 0x1bc94678eb0>,\n",
       "  <matplotlib.patches.Wedge at 0x1bc94678700>,\n",
       "  <matplotlib.patches.Wedge at 0x1bc9466a460>],\n",
       " [Text(0.9216139214117318, 0.6005229220105511, '12.5'),\n",
       "  Text(-0.2021244576881282, 1.0812704118786753, '13'),\n",
       "  Text(-1.0943230377172501, 0.11161133061338366, '14.8'),\n",
       "  Text(-0.5084242439586235, -0.9754510690727148, '11'),\n",
       "  Text(0.7885229844873057, -0.7669625172948367, '16.7')],\n",
       " [Text(0.5026985025882172, 0.3275579574603006, '18%'),\n",
       "  Text(-0.11024970419352446, 0.589783861024732, '19%'),\n",
       "  Text(-0.5969034751185001, 0.06087890760730017, '22%'),\n",
       "  Text(-0.27732231488652187, -0.532064219494208, '16%'),\n",
       "  Text(0.4301034460839849, -0.41834319125172903, '25%')])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pie(annualized_returns,labels=annualized_returns,autopct='%1.0f%%')"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "In this case, Pie chart is more apprpriate, as it conveys which stock gave better annualized returns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 43. Draw a bar graph for the endowments (stated in billions of dollars) of each of the universities specified in the following list."
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Harvard    $3.4\n",
    "Texas       2.5\n",
    "Princeton   1.9\n",
    "Yale        1.7\n",
    "Stanford    1.4\n",
    "Columbia    1.3\n",
    "Texas A&M   1.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "Univ=['Harvard','Texas','Princeton','Yale','Stanford','Columbia','Texas A&M']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "Endow=[3.4,2.5,1.9,1.7,1.4,1.3,1.1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 7 artists>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure(figsize=(10,5))\n",
    "plt.bar(Univ,Endow)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 44. The following are the top 10 private equity deals of all time, in billions of dollars. Find the mean, median, and standard deviation. Draw a bar graph.\n",
    "\n",
    "38.9, 32.7, 31.1, 27.4, 25.7, 21.6, 17.6, 17.4, 15.0, 13.9\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    38.9\n",
       "1    32.7\n",
       "2    31.1\n",
       "3    27.4\n",
       "4    25.7\n",
       "5    21.6\n",
       "6    17.6\n",
       "7    17.4\n",
       "8    15.0\n",
       "9    13.9\n",
       "dtype: float64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deals=pd.Series([38.9, 32.7, 31.1, 27.4, 25.7, 21.6, 17.6, 17.4, 15.0, 13.9])\n",
    "deals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24.13"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deals.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23.65"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deals.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8.404238348727517"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deals.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 10 artists>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Q5EHgMuCsJC8Dnwd2AA8luYmFX2jXTSSbl9JLUps8hCJJjbLAJalRFrgkNcoCl6RGWeCS1CgLXJIaZYFLUqP+D0c1mDMgqLseAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(range(1,11),deals)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 45. The following data are credit default swap values:. Draw a pie chart of these amounts. Find the mean and median. Draw a pie chart of these amounts. Find the mean and median.\n",
    "\n",
    "6, 10, 12, 13, 18, 21 (in trillions of dollars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "swap=pd.Series([6, 10, 12, 13, 18, 21])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.patches.Wedge at 0x1bc9487e970>,\n",
       "  <matplotlib.patches.Wedge at 0x1bc9487ee20>,\n",
       "  <matplotlib.patches.Wedge at 0x1bc9488b4f0>,\n",
       "  <matplotlib.patches.Wedge at 0x1bc9488bb80>,\n",
       "  <matplotlib.patches.Wedge at 0x1bc94898250>,\n",
       "  <matplotlib.patches.Wedge at 0x1bc948988e0>],\n",
       " [Text(1.0696069100332037, 0.2567899102558786, '6'),\n",
       "  Text(0.7143928375004462, 0.8364465755372912, '10'),\n",
       "  Text(-0.17207795223283848, 1.086457168210212, '12'),\n",
       "  Text(-0.9989575084546003, 0.4605256738795106, '13'),\n",
       "  Text(-0.7778174593052025, -0.7778174593052023, '18'),\n",
       "  Text(0.7466807747097594, -0.8077548023248353, '21')],\n",
       " [Text(0.583421950927202, 0.14006722377593378, '8%'),\n",
       "  Text(0.3896688204547888, 0.45624358665670417, '12%'),\n",
       "  Text(-0.09386070121791189, 0.5926130008419338, '15%'),\n",
       "  Text(-0.5448859137025092, 0.25119582211609665, '16%'),\n",
       "  Text(-0.4242640687119286, -0.42426406871192845, '22%'),\n",
       "  Text(0.4072804225689596, -0.44059352854081923, '26%')])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pie(swap, labels=swap, autopct='%1.0f%%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13.333333333333334"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Mean\n",
    "swap.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12.5"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Median\n",
    "swap.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}