python_dataviz_ncsu_data_wrangling_pt2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setup Complete\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "# using this to show stats such as the correlation coefficient between 2 variables\n",
    "import scipy.stats as stats\n",
    "# need this to explicitly register converters for future version of panda\n",
    "from pandas.plotting import register_matplotlib_converters\n",
    "print(\"Setup Complete\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done\n"
     ]
    }
   ],
   "source": [
    "# Path of the file to read.  Note to self: current path is the path of jupyter files(which is C:\\Users\\jklopfen)\n",
    "medmal_filepath = \"./Documents/AAAA-DATA-WRANGLING/2-Data-Viz-Homework/MedMalpractice.csv\"\n",
    "\n",
    "#explicitly register matplotlib converters - required in future versions of panda\n",
    "register_matplotlib_converters()\n",
    "\n",
    "# Read the file into a variable fifa_data\n",
    "medmal_data = pd.read_csv(medmal_filepath)\n",
    "print(\"Done\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Amount', 'Severity', 'Age', 'Private Attorney', 'Marital Status',\n",
       "       'Specialty', 'Insurance', 'Gender'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# See what variables/columns we're working with\n",
    "medmal_data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count       118.000000\n",
      "mean      91044.915254\n",
      "std      164501.795934\n",
      "min        1550.000000\n",
      "25%        7625.000000\n",
      "50%       22750.000000\n",
      "75%       90250.000000\n",
      "max      926500.000000\n",
      "Name: Amount, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Set the input variable X to 'Amount' only\n",
    "# and print the statistical summary for initial analysis\n",
    "\n",
    "X = medmal_data.Amount\n",
    "print(X.describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Amount</th>\n",
       "      <th>Severity</th>\n",
       "      <th>Age</th>\n",
       "      <th>Private Attorney</th>\n",
       "      <th>Marital Status</th>\n",
       "      <th>Specialty</th>\n",
       "      <th>Insurance</th>\n",
       "      <th>Gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>64000</td>\n",
       "      <td>4</td>\n",
       "      <td>66</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Plastic Surgeon</td>\n",
       "      <td>Medicare/Medicaid</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>86500</td>\n",
       "      <td>4</td>\n",
       "      <td>69</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>OBGYN</td>\n",
       "      <td>Medicare/Medicaid</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>161500</td>\n",
       "      <td>3</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Neurology/Neurosurgery</td>\n",
       "      <td>Medicare/Medicaid</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11500</td>\n",
       "      <td>5</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Family Practice</td>\n",
       "      <td>Medicare/Medicaid</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>76500</td>\n",
       "      <td>6</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Resident</td>\n",
       "      <td>Medicare/Medicaid</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Amount  Severity  Age  Private Attorney  Marital Status  \\\n",
       "0   64000         4   66                 1               2   \n",
       "1   86500         4   69                 1               2   \n",
       "2  161500         3   70                 1               2   \n",
       "3   11500         5   30                 1               0   \n",
       "4   76500         6   21                 1               1   \n",
       "\n",
       "                Specialty          Insurance  Gender  \n",
       "0         Plastic Surgeon  Medicare/Medicaid  Female  \n",
       "1                   OBGYN  Medicare/Medicaid  Female  \n",
       "2  Neurology/Neurosurgery  Medicare/Medicaid  Female  \n",
       "3         Family Practice  Medicare/Medicaid  Female  \n",
       "4                Resident  Medicare/Medicaid  Female  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print the first 5 rows of the data\n",
    "medmal_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x27a4dd46978>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set the width and height of the figure\n",
    "plt.figure(figsize=(16,6))\n",
    "\n",
    "# Seaborn Histogram showing amounts of lawsuits \n",
    "sns.distplot(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x27a4df4f6a0>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set the width and height of the figure\n",
    "plt.figure(figsize=(16,6))\n",
    "\n",
    "# Display the same in a bar chart for 'Insurance' type\n",
    "sns.countplot(x=medmal_data.Insurance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x27a4ddadc50>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set the width and height of the figure\n",
    "plt.figure(figsize=(16,6))\n",
    "\n",
    "# Display the same in a bar chart for 'Gender'\n",
    "sns.countplot(x=medmal_data.Gender)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x27a4e9570b8>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set the width and height of the figure\n",
    "plt.figure(figsize=(16,6))\n",
    "\n",
    "# Analyze the relationship between Gender and Amount\n",
    "sns.boxplot(x=medmal_data.Gender, y=medmal_data.Amount)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x27a4e983390>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set the width and height of the figure\n",
    "plt.figure(figsize=(16,6))\n",
    "\n",
    "# Analyze the relationship between Gender and Amount\n",
    "sns.boxplot(x=medmal_data.Private_Attorney, y=medmal_data.Amount)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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uHf7BTmaI20qYBgCAqWN4eDjvfve7c/nll2f27Nn9Loe9y5jC9HjfGgsAAGCvMzQ0lOXLl2doaKjfpTBJCdMAAMCUMjw8nBtuuCGttSxdutQtqHhWhGkAAGBKGRoaysjISJJkZGTE6DTPimumO66ZBgCAqeHEE0/Mxo0bty0feOCBWbFiRR8rYi/jmmkAAIBnWrBgQQYGBpIkAwMDWbhwYZ8rYjISpgEAgCllcHAw06ZtjULTpk3L4OBgnytiMhKmAQCAKWXOnDlZtGhRqiqLFy92ayyelRn9LgAAAGCiDQ4OZs2aNUaledZMQNYxARkAAAAxARkAAACMD2EaAAAAeiRMAwAAQI+EaQAAAOiRMA0AAAA9EqYBAACgR8I0AAAA9EiYBgAAgB4J0wAAANAjYRoAAAB6JEwDAABAj4RpAAAA6JEwDQAAAD0SpgEAAKBHwjQAAAD0SJgGAACAHgnTAAAA0CNhGgAAAHokTAMAAECPhGkAAADokTANAAAAPRKmAQAAoEfCNAAAAPRImAYAAIAeCdMAAADQI2EaAAAAeiRMAwAAQI+EaQAAAOiRMA0AAAA9EqYBAACgR8I0AAAA9EiYBgAAgB4J0wAAANAjYRoAAAB6JEwDAABAj4RpAAAA6JEwDQAAAD0SpgEAAKBHwjQAAAD0SJgGAACAHgnTAAAA0CNhGgAAAHokTAMAAECPhGkAAADokTANAAAAPRKmAQAAoEfjGqar6uCqur6qVlfVqqo6uap+qKpuqap7u78v6PpWVf1hVa2pqruq6oRR+3lz1//eqnrzqPYTq+rr3TZ/WFXVtW/3GAAAALAnjPfI9B8k+evW2rwkP5FkVZKLktzaWjsyya3dcpK8PsmR3ePCJH+abA3GSd6X5CeTvCrJ+0aF4z/t+j613Rld+46OAQAAALtt3MJ0VR2U5DVJPpIkrbUnWmuPJDkryRVdtyuSvKF7flaSv2xb3Z7k4Kp6UZLTk9zSWnu4tfZvSW5Jcka37qDW2t+11lqSv3zGvrZ3DAAAANht4zky/aNJ1if5WFV9rao+XFXPTfLDrbWHkqT7O6frf2iS+0dtv65r21n7uu20ZyfHAAAAgN02nmF6RpITkvxpa+34JP+enZ9uXdtpa8+ifcyq6sKqWl5Vy5O8sJdtAQAAmLrGM0yvS7KutfaVbvn6bA3X/9qdop3u7/Co/i8etf3cJA/uon3udtqzk2M8TWvtL1pr81tr85N8+9m8SAAAAKaecQvTrbVvJbm/qn68azolyT1JliV5akbuNye5sXu+LMkF3azeJyV5tDtF+3NJTquqF3QTj52W5HPduseq6qRuFu8LnrGv7R0DAAAAdtuMcd7/u5JcXVX7JfnnJG/N1gD/yap6e5J/SXJO1/czSc5MsibJpq5vWmsPV9UHk3y16/eB1trD3fNfSvLxJM9J8tnukSSX7uAYAAAAsNtq60TYVNXy7nTvXfGGAQAA7Lu2Nz/XDxjv+0wDAADAPkeYBgAAgB4J0wAAANAjYRoAAAB6JEwDAABAj4RpAAAA6JEwDQAAAD0SpgEAAKBHwjQAAAD0SJgGAACAHgnTAAAA0CNhGgAAmHKGh4ezZMmSrF+/vt+lMEkJ0wAAwJQzNDSU5cuXZ2hoqN+lMEkJ0wAAwJQyPDycpUuXprWWpUuXGp3mWRGmAQCAKWVoaChbtmxJkmzevNnoNM9Ktdb6XcNeoaqWt9bmj6GrNwwAACax448/Pps2bdq2fMABB+RrX/taHytiL1Nj6WRkGgAAmFIOOeSQpy0feuihfaqEyUyYBgAAppQHH3zwacsPPPBAnyphMhOmAQCAKeWss85K1dYzeasqb3jDG/pcEZORMA0AAEwpg4ODGRgYSJIMDAxkcHCwzxUxGQnTAADAlDJnzpwsXrw4VZWzzz47s2fP7ndJTEK7DNNVdetY2gAAACaLwcHBzJ8/36g0z9oOb41VVTOTHJDkC0lem+9PD35Qks+21o6aiAIniltjAQAAkDHeGmvGTtb9YpJfSXJIkhWjdvidJH+yW6UBAADAJLbDkeltHare1Vr7owmqp2+MTAMAAJA9MDKdJGmt/VFV/Yckh4/u31r7y2ddGgAAAExiuwzTVXVlkh9LsjLJk11zSyJMAwAAMCXtMkwnmZ/k6Lar88EBAABgihjLfabvTvIj410IAAAATBZjGZl+YZJ7quqOJI8/1dhaWzhuVQEAAMBebCxh+v3jXQQAAABMJmOZzft/T0QhAAAAMFmMZTbvx/L9eyvvl2Qgyb+31g4az8IAAABgbzWWkennjV6uqjckedW4VQQAAAB7ubHM5v00rbVPJ3ndONQCAAAAk8JYTvNeNGpxWrbed9o9pwEAAJiyxjKb94JRz7ckuS/JWeNSDQAAAEwCY7lm+q0TUQgAAABMFru8Zrqq5lbVp6pquKr+taqWVtXciSgOAAAA9kZjmYDsY0mWJTkkyaFJburaAAAAYEoaS5ie3Vr7WGttS/f4eJLZ41wXAAAA7LXGEqa/XVVLqmp691iSZMN4FwYAAAB7q7GE6bcl+b+SfCvJQ0nO7toAAABgShrLbN7/kmThBNQCAAAAk8Iuw3RVHZHkXUkOH92/tSZgAwAAMCXtMkwn+XSSj2TrLN4j41sOAAAA7P3GEqa/11r7w3GvBAAAACaJsUxA9gdV9b6qOrmqTnjqMe6VATBhhoeHs2TJkqxfv77fpQAATApjCdOvSPJfklya5EPd4/fGsygAJtbQ0FCWL1+eoaGhfpcCADApVGtt5x2qVic5trX2xMSU1B9Vtby1Nn8MXXf+hgFMMsPDwzn11FPz+OOPZ//998+tt96a2bNn97ssAIB+qbF0GsvI9N8nOXj3agFgbzU0NJSRka3zS46MjBidBgAYg7GMTH8xybFJvprk8a65tdbOGt/SJpaRaWCqOvHEE7Nx48ZtywceeGBWrFjRx4oAAPpqTCPTY5nN+33P2OlPJTnv2VQEwN5nwYIFuf7667N58+YMDAxk4cKF/S4JAGCvt8vTvFtr/zvJo0n+Y5KPJzklyZ+Nb1kATJTBwcFMm7b142DatGkZHBzsc0UAAHu/HYbpqnpZVf1mVa1K8sdJ7s/W08J/trX2RxNWIQDjas6cOVm0aFGqKosXLzb5GADAGOxsZHp1to5CL2it/VQXoJ/s9QBVNb2qvlZV/6tbPqKqvlJV91bVdVW1X9e+f7e8plt/+Kh9vLdr/0ZVnT6q/YyubU1VXTSqfbvHAGD7BgcHM3/+fKPSAABjtLMwvTjJt5J8oar+Z1WdkjFeiP0Mv5xk1ajl30lyeWvtyCT/luTtXfvbk/xba+2lSS7v+qWqjk5ybpKXJzkjyVAX0Kcn+ZMkr09ydJLzur47OwYA2zFnzpxcddVVRqUBAMZoh2G6tfap1tobk8xL8sUk/y3JD1fVn1bVaWPZeVXNzdZrrT/cLVeS1yW5vutyRZI3dM/P6pbTrT+l639Wkmtba4+31r6ZZE2SV3WPNa21f+7ugX1tkrN2cQwAAADYbWOZgOzfW2tXt9b+U5K5SVYmuWgXmz3lfyT59SQj3fKsJI+01rZ0y+uSHNo9PzRbr8tOt/7Rrv+29mdss6P2nR0DAAAAdtsuw/RorbWHW2t/3lp73a76VtV/SjLcWht9s9LtnSbedrFuT7Vvr8YLq2p5VS1P8sLt9QEAAIBnGst9pp+tVydZWFVnJpmZ5KBsHak+uKpmdCPHc5M82PVfl+TFSdZV1Ywkz0/y8Kj2p4zeZnvt397JMZ6mtfYXSf4iSbpADQAAALvU08h0L1pr722tzW2tHZ6tE4h9vrX2piRfSHJ21+3NSW7sni/rltOt/3xrrXXt53azfR+R5MgkdyT5apIju5m79+uOsazbZkfHAAAAgN02bmF6J96T5N1VtSZbr2/+SNf+kSSzuvZ3p7suu7X2D0k+meSeJH+d5B2ttSe7Ued3Jvlcts4W/smu786OAQAAALuttg7kUlXLW2vzx9DVGwbsc4aHh/Pud787l19+udtjAQBT3ZhuCd2PkWkA9jJDQ0NZvnx5hoaG+l0KAMCkIEwDTHHDw8O54YYb0lrL0qVLs379+n6XBACw1xOmAaa4oaGhjIyMJElGRkaMTgMAjIFrpjuumQamqhNPPDEbN27ctnzggQdmxYoVfawIAKCvXDMNwK4tWLAgM2bMSJLMmDEjCxcu7HNFAAB7PyPTHSPTwFQ1PDycn/mZn8nIyEimTZuWL33pS2b0BgCmMiPTAIxNVT3tLwAAOydMA0xxQ0NDmTZt68fBtGnTTEAG7NTw8HCWLFli5n9gyhOmAaa4m266KZs3b06SbN68OcuWLetzRcDezH3pAbYSpgGmuAULFmRgYCBJMjAwYAIyYIfclx7g+4RpgClucHDwaad5Dw4O9rkiYG/lvvQA3ydMA0xxc+bMyaJFi1JVWbx4sZm8gR1yWQjA9wnTAGRwcDDz5883Kg3slMtCAL7PfaY77jMNALBzw8PDOfXUU/P4449n//33z6233upsFmBf5D7TAADsOS4LAfi+Gf0uAACAyWNwcDBr1qxxWQgw5TnNu+M0bwAAAOI0bwAAABgfwjQAAAD0SJgGAACAHgnTAGR4eDhLlizJ+vXr+10KAMCkIEwDkKGhoSxfvjxDQ0P9LgUAYFIQpgGmuOHh4dxwww1prWXp0qVGpwEAxkCYBpjihoaGMjIykiQZGRkxOg0AMAbuM91xn2lgqjrxxBOzcePGbcsHHnhgVqxY0ceKAAD6yn2mAdi1BQsWZGBgIEkyMDCQhQsX9rkiAIC9nzANMMUNDg5m2rStHwfTpk3L4OBgnysCANj7zeh3AQD015w5c7Jo0aJce+21Wbx4cWbPnt3vkgCYhC655JKsXr2632WM2dq1a5Mkhx12WJ8rGZt58+bl4osv7ncZjCJMA5DBwcGsWbPGqDQAU8amTZv6XQKTnAnIOiYgAwCAqeP8889Pklx55ZV9roS9kAnIAAAAYDwI0wAAANAjYRoAAAB6JEwDAABAj4RpAAAA6JEwDQAAAD0SpgEAAKBHwjQAAAD0SJgGAACAHgnTAAAA0CNhGgAAAHokTAMAAECPZvS7AIB91SWXXJLVq1f3u4wxWbt2bZLksMMO63MlYzdv3rxcfPHF/S4DAJiihGkAsmnTpn6XAAAwqQjTAONkMo2ann/++UmSK6+8ss+VAABMDq6ZBgAAgB4J0wAAANAjYRoAAAB6JEwDAABAj4RpAAAA6JEwDQAAAD0SpgEAAKBHwjQAAAD0SJiG3TQ8PJwlS5Zk/fr1/S4FAACYIMI07KahoaEsX748Q0ND/S4FAACYIOMWpqvqxVX1hapaVVX/UFW/3LX/UFXdUlX3dn9f0LVXVf1hVa2pqruq6oRR+3pz1//eqnrzqPYTq+rr3TZ/WFW1s2PAnjY8PJwbbrghrbUsXbrU6DQAAEwR4zkyvSXJr7bWjkpyUpJ3VNXRSS5Kcmtr7cgkt3bLSfL6JEd2jwuT/GmyNRgneV+Sn0zyqiTvGxWO/7Tr+9R2Z3TtOzoG7FFDQ0MZGRlJkoyMjBidBgCAKWLcwnRr7aHW2p3d88eSrEpyaJKzklzRdbsiyRu652cl+cu21e1JDq6qFyU5PcktrbWHW2v/luSWJGd06w5qrf1da60l+ctn7Gt7x4A96qabbsrmzZuTJJs3b86yZcv6XBEAADARJuSa6ao6PMnxSb6S5Idbaw8lWwN3kjldt0OT3D9qs3Vd287a122nPTs5BuxRCxYsyMDAQJJkYGAgCxcu7HNFAADARBj3MF1VByZZmuRXWmvf2VnX7bS1Z9HeS20XVtXyqlqe5IW9bAtJMjg4mGnTtv7faNq0aRkcHOxzRQAAwEQY1zBdVQPZGqSvbq3d0DX/a3eKdrq/w137uiQvHrX53CQP7qJ97nbad3aMp2mt/UVrbX5rbX6Sbz+rF8mUNmfOnCxatChVlcWLF2f27Nn9LgkAAJgAM8Zrx93M2h9Jsqq19vujVi1L8uYkl3Z/bxzV/s6qujZbJxt7tLX2UFV9Lslvj5p07LQk722tPVxVj1XVSdl6+vgFSf5oF8eAPW5wcDBr1qwxKg0A7FGXXHJJVq9e3e8y9lnAAZYgAAAMgElEQVSrVq1Kkpx//vl9rmTfNG/evFx88cX9LmNcjVuYTvLqJOcn+XpVrezafiNbA+4nq+rtSf4lyTndus8kOTPJmiSbkrw1SbrQ/MEkX+36faC19nD3/JeSfDzJc5J8tntkJ8eAPW7OnDm56qqr+l0GALCPWb16dW6/8/ZsOWhLv0vZJ01/YnqS5LY1t/W5kn3PjO+MZ8zce4zbq2yt3ZbtX9ecJKdsp39L8o4d7OujST66nfblSY7ZTvuG7R0DAAAmky0HbckjJz3S7zKgJwfffnC/S5gQEzKbNwAAAOxLhGkAAADokTANAAAAPRKmAQAAoEfCNAAAAPRImAYAAIAeCdMAAADQI2EaAAAAeiRMAwAAQI+EaQAAAOiRMA0AAAA9EqYBAACgR8I0AAAA9EiYBgAAgB4J0wAAANAjYRoAAAB6JEwDAABAj4RpAAAA6JEwDQAAAD2a0e8CAMbqkksuyerVq/tdxj5p1apVSZLzzz+/z5Xsu+bNm5eLL76432UAAHuIMA1MGqtXr87td96eLQdt6Xcp+5zpT0xPkty25rY+V7JvmvEdH7cAsK/x6Q5MKlsO2pJHTnqk32VATw6+/eB+lwAA7GGumQYAAIAeCdOwm4aHh7NkyZKsX7++36UAAAATRJiG3TQ0NJTly5dnaGio36UAAAATRJiG3TA8PJzrr78+rbX81V/9ldFpAACYIoRp2A1DQ0PZvHlzkmTz5s1GpwEAYIoQpmE3fPrTn37a8qc+9ak+VQIAAEwkYRp2w8DAwNOW99tvvz5VAgAATCRhGnbDd77znactP/roo32qBAAAmEjCNOyGl770pU9bPvLII/tUCQAAMJGEadgNl1122dOWf+/3fq9PlQAAABNJmIbdcPTRR28bnT7yyCMzb968PlcEAABMBGEadtNll12WAw880Kg0AABMITP6XQBMdkcffXRWrFjR7zIAAIAJZGQaAAAAeiRMAwAAQI+EaQAAAOiRMA0AAAA9MgEZMGmsXbs2M74zIwfffnC/S4GezPjOjKxdu7bfZQAAe5CRaQAAAOiRkWlg0jjssMNy/+b788hJj/S7FOjJwbcfnMMOO6zfZQAAe5CRaQAAAOiRkWkAgD665JJLsnr16n6XMWZPXf8/mc62mDdvXi6++OJ+lwHsY4RpAADGbNOmTf0uAWCvIEwDAPTRZBsxPf/885MkV155ZZ8r2fe5iwWT1VS5i4VrpgEAAKBHRqYBAGAv5C4WTFZT5S4WRqYBAACgR8I0AAAA9EiYBgAAgB65ZnoKmIz3r3TbjfFzwAEHTKprWNwbFACAvZEwPQWsXr06d33lrvzIkz/S71LGZNO0TXminuh3GfuuR5PhB4b7XcWYfGv6t36gzS1Cxsf0f5+eJHnyuU/2uZJ904zv+LidaJPth+TJZNWqVUm+f4ss9jw/JMPk4NN9iviRJ38kb9301n6XAT352AEfe9ryvHnz+lTJvu+pL8dHvfSoPley7/Lvd2JNth+SJ5XuIsHh/zM5fpidbLb3QzKwdxKmgUnDr/Tj56kRpiuvvLLPlcCe44dkJqNn/pDsjKzx46ys8TNVzsiaGq8SAAAmGWe0jC9nZY2vqfDvd58N01V1RpI/SDI9yYdba5f2uaS+Wbt2bR6Z/sgP/NIJe7uHpj+U7679br/LAIC+cEbW+HJWFrtrnwzTVTU9yZ8k+bkk65J8taqWtdbu6W9lAMBE8EMyk9Vk/iF5sk38N9km0zMx3d5nnwzTSV6VZE1r7Z+TpKquTXJWkikZpg877LA8+q1H+13GmG2YtsFs3uNov7ZfZo3M6ncZY1KpSXUbr2eaTF8qJtsXisSXCnbtiTyRh6Y/1O8ydmlLtmQkI/0uY582LdMyY5J87X0ivgNNlAMOOKDfJTDJTY7/qvTu0CT3j1pel+Qnn9mpqi5McmG3OHMC6uqLyXa9wnfXfjdxm+lxc8ABB2TOYXP6XcaYzMmcSffvd7LyhYJ9zemnnz5pfsxau3ZtNm3ywTeeDjjggEn14+xk/ezzAydTTbXW+l3DHldV5yQ5vbX2C93y+Ule1Vp71x7Y/b73hgEAAPCUGkunaeNdRZ+sS/LiUctzkzzYp1oAAADYx+yrYfqrSY6sqiOqar8k5yZZ1ueaAAAA2Efsk9dMt9a2VNU7k3wuW2+N9dHW2j/0uSwAAAD2EfvkNdPjzBsGAACw75rS10wDAADAuBGmAQAAoEfCNAAAAPRImAYAAIAeCdMAAADQI2EaAAAAeiRMAwAAQI+EaQAAAOiRMA0AAAA9mtHvAiah6ncBAAAA9JeRaQAAAOiRMA0AAAA9EqYBAACgR8I0AAAA9EiYBgAAgB4J0wAAANAjYRoAAAB6JEwDAABAj4RpAAAA6NGMfhcAMBZVdXeS7/W7DgCYIDNba8f0uwhgx4RpYLL4Xmttfr+LAICJUFXL+10DsHNO8wYAAIAeCdMAAADQI2EamCz+ot8FAMAE8rkHe7lqrfW7BgAAAJhUjEwDAABAj4RpYNxV1ZNVtXLU4/BxPNZbquqPx2v/ALA7qqpV1ZWjlmdU1fqq+l+72O61u+oDTCy3xgImwndba8f1uwgA2Av8e5Jjquo5rbXvJvm5JA/0uSbgWTAyDfRFVU2vqsuq6qtVdVdV/WLX/tqq+t9V9cmq+sequrSq3lRVd1TV16vqx7p+C6rqK1X1tar6m6r64e0cY3ZVLe2O8dWqevVEv04A2I7PJvmP3fPzklzz1IqqelVV/Z/u8+3/VNWPP3PjqnpuVX20+2z7WlWdNUF1A6MI08BEeM6oU7w/1bW9PcmjrbVXJnllkv9SVUd0634iyS8neUWS85O8rLX2qiQfTvKurs9tSU5qrR2f5Nokv76d4/5Bksu7YyzutgeAfrs2yblVNTPJsUm+Mmrd6iSv6T7ffjPJb29n+4uTfL77fPvZJJdV1XPHuWbgGZzmDUyE7Z3mfVqSY6vq7G75+UmOTPJEkq+21h5Kkqr6pyQ3d32+nq1fGpJkbpLrqupFSfZL8s3tHPfUJEdX1VPLB1XV81prj+2B1wQAz0pr7a5u/pDzknzmGaufn+SKqjoySUsysJ1dnJZkYVX99255ZpKXJFk1LgUD2yVMA/1SSd7VWvvc0xqrXpvk8VFNI6OWR/L9/279UZLfb60t67Z5/3aOMS3Jyd01aQCwN1mW5PeSvDbJrFHtH0zyhdbaz3eB+4vb2baSLG6tfWN8SwR2xmneQL98LskvVdVAklTVy3o8Re35+f6ELW/eQZ+bk7zzqYWqMgkaAHuLjyb5QGvt689oH/359pYdbPu5JO+q7tSrqjp+XCoEdkqYBvrlw0nuSXJnVd2d5M/T29ky70/yV1X1t0m+vYM+/zXJ/G6Cs3uS/N+7US8A7DGttXWttT/YzqrfTfL/VtWXk0zfweYfzNbTv+/qPkM/OE5lAjtRrbV+1wAAAACTipFpAAAA6JEwDQAAAD0SpgEAAKBHwjQAAAD0SJgGAACAHgnTALCPqqofrqpPVNU/V9WKqvq7qvr5PbDf11bV/9oTNQLAZCVMA8A+qKoqyaeTfKm19qOttROTnJtkbh9q6eUe8gAwKQjTALBvel2SJ1prf/ZUQ2ttbWvtj6pqelVdVlVfraq7quoXk20jzl+squuranVVXd2F8lTVGV3bbUkWPbXPqnpuVX2029fXquqsrv0tVfVXVXVTkpsn9JUDwATwSzEA7JtenuTOHax7e5JHW2uvrKr9k3y5qp4KvMd32z6Y5MtJXl1Vy5P8z2wN6GuSXDdqXxcn+Xxr7W1VdXCSO6rqb7p1Jyc5trX28J58YQCwNxCmAWAKqKo/SfJTSZ5IsjbJsVV1drf6+UmO7Nbd0Vpb122zMsnhSTYm+WZr7d6u/aokF3bbnpZkYVX99255ZpKXdM9vEaQB2FcJ0wCwb/qHJIufWmitvaOqXphkeZJ/SfKu1trnRm9QVa9N8viopifz/e8KbQfHqSSLW2vfeMa+fjLJv+/OCwCAvZlrpgFg3/T5JDOr6pdGtR3Q/f1ckl+qqoEkqaqXVdVzd7Kv1UmOqKof65bPG7Xuc0neNera6uP3SPUAsJcTpgFgH9Raa0nekORnquqbVXVHkiuSvCfJh5Pck+TOqro7yZ9nJ2ertda+l62ndf9/3QRka0et/mCSgSR3dfv64Hi8HgDY29TWz1oAAABgrIxMAwAAQI+EaQAAAOiRMA0AAAA9EqYBAACgR8I0AAAA9EiYBgAAgB4J0wAAANAjYRoAAAB69P8D5L38qyOGXj0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set the width and height of the figure\n",
    "plt.figure(figsize=(16,6))\n",
    "\n",
    "sns.boxplot(x=medmal_data.Gender, y=medmal_data.Amount,\n",
    "            hue=medmal_data.Private_Attorney, palette=[\"m\", \"g\"],)\n",
    "sns.despine(offset=10, trim=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x27a4e929400>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set the width and height of the figure\n",
    "plt.figure(figsize=(16,6))\n",
    "\n",
    "# Analyze the relationship between Gender and Amount\n",
    "sns.scatterplot(x=medmal_data.Age, y=medmal_data.Amount)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x27a4ed17438>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1152x432 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Tried a jointplot\n",
    "\n",
    "# Set the width and height of the figure\n",
    "plt.figure(figsize=(16,6))\n",
    "\n",
    "# Analyze the relationship between Gender and Amount\n",
    "sns.jointplot(x=medmal_data.Age, y=medmal_data.Amount)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jklopfen\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\seaborn\\axisgrid.py:1847: UserWarning: JointGrid annotation is deprecated and will be removed in a future release.\n",
      "  warnings.warn(UserWarning(msg))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x27a4fb6cf60>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1152x720 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set the width and height of the figure\n",
    "plt.figure(figsize=(16,10))\n",
    "\n",
    "# Analyze the relationship between Gender and Amount\n",
    "j = sns.jointplot(x=medmal_data.Age, y=medmal_data.Amount)\n",
    "j.annotate(stats.pearsonr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x27a4fb2bf98>"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
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\n",
      "text/plain": [
       "<Figure size 2520x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Exploring the data\n",
    "# Set the width and height of the figure\n",
    "plt.figure(figsize=(35,6))\n",
    "\n",
    "# Display the same in a bar chart for 'Insurance' type\n",
    "sns.barplot(x=medmal_data.Specialty)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x27a503e08d0>"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2520x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set the width and height of the figure\n",
    "plt.figure(figsize=(35,6))\n",
    "\n",
    "# Analyze the relationship between Gender and Amount\n",
    "sns.scatterplot(x=medmal_data.Specialty, y=medmal_data.Amount)"
   ]
  }
 ],
 "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}