A9-Aczel problems practice (1-54, 1-57, 1-59, 1-60).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# A9-Aczel problems practice (1-54, 1-57, 1-59, 1-60)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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": "markdown",
   "metadata": {},
   "source": [
    "#### 54. The following data are the daily price quotations for a certain stock over a period of 45 days. Construct a stem-and-leaf display for these data. What can you conclude about the distribution of daily stock prices over the period under study?\n",
    "10, 11, 10, 11, 11, 12, 12, 13, 14, 16, 15, 11, 18, 19, 20, 15, 14, 14, 22, 25, 27, 23, 22, 26, 27,\n",
    "29, 28, 31, 32, 30, 32, 34, 33, 38, 41, 40, 42, 53, 52, 47, 37, 23, 11, 32, 23"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=pd.Series([10, 11, 10, 11, 11, 12, 12, 13, 14, 16, 15, 11, 18, 19, 20, 15, 14, 14, 22, 25, 27, 23, 22, 26, 27, 29, 28, 31, 32, 30, 32, 34, 33, 38, 41, 40, 42, 53, 52, 47, 37, 23, 11, 32, 23])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     10\n",
       "1     10\n",
       "2     11\n",
       "3     11\n",
       "4     11\n",
       "5     11\n",
       "6     11\n",
       "7     12\n",
       "8     12\n",
       "9     13\n",
       "10    14\n",
       "11    14\n",
       "12    14\n",
       "13    15\n",
       "14    15\n",
       "15    16\n",
       "16    18\n",
       "17    19\n",
       "18    20\n",
       "19    22\n",
       "20    22\n",
       "21    23\n",
       "22    23\n",
       "23    23\n",
       "24    25\n",
       "25    26\n",
       "26    27\n",
       "27    27\n",
       "28    28\n",
       "29    29\n",
       "30    30\n",
       "31    31\n",
       "32    32\n",
       "33    32\n",
       "34    32\n",
       "35    33\n",
       "36    34\n",
       "37    37\n",
       "38    38\n",
       "39    40\n",
       "40    41\n",
       "41    42\n",
       "42    47\n",
       "43    52\n",
       "44    53\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Sort=pd.Series(sorted(data)) # Just for verifying the data\n",
    "Sort"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23.0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.median() # Just for verification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: stemgraphic in c:\\users\\hitesh koli\\anaconda3\\lib\\site-packages (0.9.0)\n",
      "Requirement already satisfied: docopt in c:\\users\\hitesh koli\\anaconda3\\lib\\site-packages (from stemgraphic) (0.6.2)\n",
      "Requirement already satisfied: seaborn in c:\\users\\hitesh koli\\anaconda3\\lib\\site-packages (from stemgraphic) (0.10.1)\n",
      "Requirement already satisfied: matplotlib in c:\\users\\hitesh koli\\anaconda3\\lib\\site-packages (from stemgraphic) (3.2.2)\n",
      "Requirement already satisfied: pandas in c:\\users\\hitesh koli\\anaconda3\\lib\\site-packages (from stemgraphic) (1.0.5)\n",
      "Requirement already satisfied: scipy>=1.0.1 in c:\\users\\hitesh koli\\anaconda3\\lib\\site-packages (from seaborn->stemgraphic) (1.4.1)\n",
      "Requirement already satisfied: numpy>=1.13.3 in c:\\users\\hitesh koli\\anaconda3\\lib\\site-packages (from seaborn->stemgraphic) (1.18.5)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in c:\\users\\hitesh koli\\anaconda3\\lib\\site-packages (from matplotlib->stemgraphic) (2.4.7)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\users\\hitesh koli\\anaconda3\\lib\\site-packages (from matplotlib->stemgraphic) (1.2.0)\n",
      "Requirement already satisfied: python-dateutil>=2.1 in c:\\users\\hitesh koli\\anaconda3\\lib\\site-packages (from matplotlib->stemgraphic) (2.8.1)\n",
      "Requirement already satisfied: cycler>=0.10 in c:\\users\\hitesh koli\\anaconda3\\lib\\site-packages (from matplotlib->stemgraphic) (0.10.0)\n",
      "Requirement already satisfied: pytz>=2017.2 in c:\\users\\hitesh koli\\anaconda3\\lib\\site-packages (from pandas->stemgraphic) (2020.1)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\hitesh koli\\anaconda3\\lib\\site-packages (from python-dateutil>=2.1->matplotlib->stemgraphic) (1.15.0)\n"
     ]
    }
   ],
   "source": [
    "# Stem and Leaf display\n",
    "\n",
    "import sys\n",
    "!pip install stemgraphic\n",
    "import stemgraphic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 540x144 with 1 Axes>,\n",
       " <matplotlib.axes._axes.Axes at 0x20c17a696a0>)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 540x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "stemgraphic.stem_graphic(data, scale = 10) "
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Conclusion \n",
    "1. Highest freuency i.e Mode=11\n",
    "2. Distributin is right skewed\n",
    "3. Largest datapoints lie between 10 to 19\n",
    "4. min value=10\n",
    "5. max value=53\n",
    "6. Median=23"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 57. The following data are the number of ounces of silver per ton of ore for two mines. Construct a stem-and-leaf display for each data set and a box plot for each data set. Compare the two displays and the two box plots. Draw conclusions about the data.\n",
    "\n",
    "Mine A: 34, 32, 35, 37, 41, 42, 43, 45, 46, 45, 48, 49, 51, 52, 53, 60, 73, 76, 85\n",
    "\n",
    "Mine B: 23, 24, 28, 29, 32, 34, 35, 37, 38, 40, 43, 44, 47, 48, 49, 50, 51, 52, 59\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "MineA=[34, 32, 35, 37, 41, 42, 43, 45, 46, 45, 48, 49, 51, 52, 53, 60, 73, 76, 85]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 540x162 with 1 Axes>,\n",
       " <matplotlib.axes._axes.Axes at 0x20c18218580>)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 540x162 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "stemgraphic.stem_graphic(MineA,scale=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "fig,ax=plt.subplots()\n",
    "plt.boxplot(MineA)\n",
    "ax.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "MineB=[23, 24, 28, 29, 32, 34, 35, 37, 38, 40, 43, 44, 47, 48, 49, 50, 51, 52, 59]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 540x126 with 1 Axes>,\n",
       " <matplotlib.axes._axes.Axes at 0x20c1830dca0>)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 540x126 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "stemgraphic.stem_graphic(MineB,scale=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "fig.ax=plt.subplots()\n",
    "plt.boxplot(MineB)\n",
    "ax.grid()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Comparison and conclusions\n",
    "1. Mine A & B data are dissmilar\n",
    "2. There are 3 outliars in Mine A.\n",
    "3. There are no utliars in Mine B.\n",
    "4. Mina A: Min Value=32, Max Value=85, Median=46\n",
    "5. Mine B: Min Value=23, Max Value=59, Median=40"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 59. The following data are daily percentage changes in stock prices for 20 stocks called “The Favorites\". Draw a box plot of these data.\n",
    "\n",
    "-0.1, 0.5, 0.6, 0.7, 1.4, 0.7, 1.3, 0.3, 1.6, 0.6, -3.5, 0.6, 1.1, 1.3, -0.1, 2.5, -0.3, 0.3, 0.2, 0.4\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "Fav=[-0.1, 0.5, 0.6, 0.7, 1.4, 0.7, 1.3, 0.3, 1.6, 0.6, -3.5, 0.6, 1.1, 1.3, -0.1, 2.5, -0.3, 0.3, 0.2, 0.4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXIAAAD4CAYAAADxeG0DAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAKMUlEQVR4nO3dUYil91nH8d/jZGVBW92QgWDbdQVLmTIUhUEQF2Q1F1FEURHcCxUcWLxwUPBCy4CpyIJQ8GYVZGFDb+p4oyFiK20CA2GgirMllI1bpQjBoNApWYwgi5Pt34tsYzbZzZyZ82bPPjOfDyzsec+Z932uvrz8z/u+p8YYAaCv71r0AADMR8gBmhNygOaEHKA5IQdo7rFFHPSJJ54Y586dW8ShAdq6fv36t8YYy+/evpCQnzt3Lru7u4s4NEBbVfXq/bZbWgFoTsgBmhNygOaEHKA5IQdoTsghydbWVlZXV7O0tJTV1dVsbW0teiSY2UIuP4RHydbWVjY3N3Pt2rWcP38+Ozs7WV9fT5JcvHhxwdPBwWoRj7FdW1sbriPnUbG6uporV67kwoULb2/b3t7OxsZGbty4scDJ4F5VdX2Msfae7ULOSbe0tJTbt2/n1KlTb2/b39/P6dOnc+fOnQVOBvd6UMitkXPiraysZGdn555tOzs7WVlZWdBEcDhCzom3ubmZ9fX1bG9vZ39/P9vb21lfX8/m5uaiR4OZ+LKTE+87X2hubGzk5s2bWVlZyeXLl33RSRvWyAGasEYOcEwJOUBzQg7QnJADNCfkAM0JOUBzQg7QnJADNCfkAM3NHfKq+lhVbVfVzap6pap+Z4rBAJjNFM9aeTPJ740xvlpVH0pyvapeGGP88wT7BuAAc5+RjzH+c4zx1bv//+8kN5N8ZN79AjCbSdfIq+pckh9N8o/3ee9SVe1W1e7e3t6UhwU40SYLeVV9b5K/TvK7Y4w33v3+GOPqGGNtjLG2vLw81WEBTrxJQl5Vp/JWxD8/xvibKfYJwGymuGqlklxLcnOM8afzjwTAYUxxRv4TSX4tyU9V1ct3//3sBPsFYAZzX344xthJUhPMAsARuLMToDkhB2hOyAGaE3KA5oQcoDkhB2hOyAGam+IxtvDIeuvG4w/eGOOhHAfuR8g51g4b2KoSZdqxtALQnJADNCfkAM0JOUBzQg7QnJADNCfkAM0JOUBzQg7QnJADNCfkAM0JOUBzQg7QnKcf0sbjjz+eW7dufeDH+aAffXvmzJm8/vrrH+gxOFmEnDZu3bp1LB4x+7Cekc7JMcnSSlU9W1XfrKobU+wPgNlNtUb+uSRPT7QvAA5hkpCPMV5KYtEPYAEe2lUrVXWpqnarandvb+9hHRbg2HtoIR9jXB1jrI0x1paXlx/WYQGOPdeRAzQn5ADNTXX54VaSryT5RFW9VlXrU+wXgINNckPQGOPiFPsB4PAsrQA0J+QAzQk5QHNCDtCckAM0J+QAzQk5QHN+WII2xjMfTj7zfYseY27jmQ8vegSOGSGnjfqjN47NLwSNzyx6Co4TSysAzQk5QHNCDtCckAM0J+QAzQk5QHNCDtCckAM0J+QAzbmzk1aqatEjzO3MmTOLHoFjRshp42Hcnl9Vx+IxAJwsllYAmhNygOaEHKC5SUJeVU9X1b9U1Teq6g+m2CcAs5k75FW1lOTPk/xMkk8muVhVn5x3vwDMZooz8h9L8o0xxr+NMf43yV8l+YUJ9gvADKYI+UeS/Ps7Xr92dxsAD8EUIb/fHRrvuRC3qi5V1W5V7e7t7U1wWACSaUL+WpKPveP1R5P8x7s/NMa4OsZYG2OsLS8vT3BYAJJpQv5PST5eVT9UVd+d5FeT/O0E+wVgBnPfoj/GeLOqfjvJl5IsJXl2jPHK3JMBMJNJnrUyxvhiki9OsS8ADsednQDNCTlAc0IO0JyQAzTnhyU41o7yi0JH+Rs/RsEiCTnHmsByElhaAWhOyAGaE3KA5oQcoDkhB2hOyAGaE3KA5oQcoDkhB2hOyAGaE3KA5oQcoDkhB2hOyAGaE3KA5oQcoDkhB2hOyAGamyvkVfUrVfVKVX27qtamGgqA2c17Rn4jyS8leWmCWQA4grl+fHmMcTM52q+OAzANa+QAzR14Rl5VLyZ58j5vbY4xnp/1QFV1KcmlJDl79uzMAwLw/g4M+RjjqSkONMa4muRqkqytrY0p9gmApRWA9ua9/PAXq+q1JD+e5AtV9aVpxgJgVvNetfJckucmmgWAI7C0AtCckAM0J+QAzQk5QHNCDtCckAM0J+QAzQk5QHNCDtCckAM0J+QAzQk5QHNCDtCckAM0J+QAzQk5QHNCDtCckAM0J+QAzQk5QHNCDtCckAM0J+QAzQk5QHNzhbyqPltVX6+qr1XVc1X1/VMNBsBs5j0jfyHJ6hjjU0n+Ncmn5x8JgMOYK+RjjC+PMd68+/Ifknx0/pEAOIwp18h/M8nfP+jNqrpUVbtVtbu3tzfhYQFOtscO+kBVvZjkyfu8tTnGeP7uZzaTvJnk8w/azxjjapKrSbK2tjaONC0A73FgyMcYT73f+1X1G0l+LslPjzEEGuAhOzDk76eqnk7y+0l+cozxP9OMBMBhzLtG/mdJPpTkhap6uar+YoKZADiEuc7Ixxg/PNUgAByNOzsBmhNygOaEHKA5IQdoTsgBmhNygOaEHKA5IQdoTsgBmhNygOaEHKA5IQdoTsgBmhNygOaEHKA5IQdoTsgBmhNygOaEHKA5IQdoTsgBmhNygOaEHKA5IQdobq6QV9UfV9XXqurlqvpyVf3AVIMBMJt5z8g/O8b41BjjR5L8XZI/nGAmAA5hrpCPMd54x8vvSTLmGweAw3ps3h1U1eUkv57kv5JceJ/PXUpyKUnOnj0772EBuKvGeP+T6Kp6McmT93lrc4zx/Ds+9+kkp8cYzxx00LW1tbG7u3vYWQFOtKq6PsZYe/f2A8/IxxhPzXiMv0zyhSQHhhyA6cx71crH3/Hy55N8fb5xADisedfI/6SqPpHk20leTfJb848EwGHMFfIxxi9PNQgAR+POToDmhBygOSEHaE7IAZoTcoDmhBygOSEHaE7IAZoTckiytbWV1dXVLC0tZXV1NVtbW4seCWY292Nsobutra1sbm7m2rVrOX/+fHZ2drK+vp4kuXjx4oKng4Md+BjbD4LH2PIoWV1dzZUrV3Lhwv8/Tn97ezsbGxu5cePGAieDez3oMbZCzom3tLSU27dv59SpU29v29/fz+nTp3Pnzp0FTgb3elDIrZFz4q2srGRnZ+eebTs7O1lZWVnQRHA4Qs6Jt7m5mfX19Wxvb2d/fz/b29tZX1/P5ubmokeDmfiykxPvO19obmxs5ObNm1lZWcnly5d90Ukb1sgBmrBGDnBMCTlAc0IO0JyQAzQn5ADNLeSqlaraS/LqQz8wHOyJJN9a9BDwAD84xlh+98aFhBweVVW1e7/Lu+BRZmkFoDkhB2hOyOFeVxc9AByWNXKA5pyRAzQn5ADNCTkkqapnq+qbVeW33WhHyOEtn0vy9KKHgKMQckgyxngpyeuLngOOQsgBmhNygOaEHKA5IQdoTsghSVVtJflKkk9U1WtVtb7omWBWbtEHaM4ZOUBzQg7QnJADNCfkAM0JOUBzQg7QnJADNPd/JxsuuHyXQ8sAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "B=plt.boxplot(Fav)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([ 0.275, -0.3  ]), array([1.15, 1.6 ])]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[item.get_ydata() for item in B['whiskers']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.median(Fav)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Interpretation\n",
    "LH = LQ = 0.275\n",
    "LW = -0.3\n",
    "UH = UQ = 1.15\n",
    "UW = 1.6\n",
    "Median = 0.6"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 60. Consult the following data on a sports car 0 to 60 speed  (times, in seconds).Find the mean and the median. Compare the two. Also construct a box plot. Interpret your findings.\n",
    "\n",
    "4.9, 4.6, 4.2, 5.1, 5.2, 5.1, 4.8, 4.7, 4.9, 5.3\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "time=[4.9, 4.6, 4.2, 5.1, 5.2, 5.1, 4.8, 4.7, 4.9, 5.3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.88"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.9"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.median(time)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Interpretation:\n",
    "Data has same Mean and Median"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "B=plt.boxplot(time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([4.725, 4.2  ]), array([5.1, 5.3])]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[item.get_ydata() for item in B ['whiskers']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Interpretation:\n",
    "LH = 4.725\n",
    "LW = 4.2\n",
    "UH = 5.1\n",
    "UW = 5.3"
   ]
  }
 ],
 "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
}