analyzeData.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import json\n",
    "import requests as req"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   0                                     1   2           3       4   \\\n",
      "0   1  17439B12-1A55-4B6A-980E-5BAC15841AC4   1  1426008064  525314   \n",
      "1   2  EA2E2977-1FBA-4254-A2A9-855FA012565E   2  1426008064  525314   \n",
      "2   3  08FF3561-CE31-4BEB-AFD8-6DC47AC3C557   3  1426008064  525314   \n",
      "3   4  AE0A5BEF-F80F-4BD6-938C-37A639AB5400   4  1426008064  525314   \n",
      "4   5  72AD83C0-C652-4580-B9C3-F7304CB396A4   5  1426008064  525314   \n",
      "\n",
      "           5       6     7                          8   \\\n",
      "0  1426008064  525314  {\\n}           Adario,Anthony J   \n",
      "1  1426008064  525314  {\\n}         Baker,Katherine E.   \n",
      "2  1426008064  525314  {\\n}             Braga,Ann Hess   \n",
      "3  1426008064  525314  {\\n}       Calobrisi,Jane Ellen   \n",
      "4  1426008064  525314  {\\n}  Castillo-Cook,Wendolyn M.   \n",
      "\n",
      "                              9                    10        11    12  \\\n",
      "0   Supvising Claims Agent (Asd)  ASD Human Resources  98538.32  0.00   \n",
      "1             Nurse Case Manager  ASD Human Resources  83942.21  0.00   \n",
      "2  HumanResourcesComplianceOffcr  ASD Human Resources  98538.34  0.00   \n",
      "3              Personnel Analyst  ASD Human Resources  70073.81  0.00   \n",
      "4              Alcholism Coord I  ASD Human Resources  79253.11  0.00   \n",
      "\n",
      "        13    14    15    16    17         18     19  \n",
      "0  1842.87  0.00  0.00  0.00  0.00  100381.19  02132  \n",
      "1   225.00  0.00  0.00  0.00  0.00   84167.21  02136  \n",
      "2     0.00  0.00  0.00  0.00  0.00   98538.34  02126  \n",
      "3   225.00  0.00  0.00  0.00  0.00   70298.81  02090  \n",
      "4     0.00  0.00  0.00  0.00  0.00   79253.11  02121  \n"
     ]
    }
   ],
   "source": [
    "# get json data from url with simple python request\n",
    "url = \"https://pollysnips.s3.amazonaws.com/bostonEmployeeSalaries.json\"\n",
    "response = req.get(url)\n",
    "data = response.json()\n",
    "# convert the data part of the json to pandas dataframe\n",
    "df = pd.DataFrame(data[\"data\"])\n",
    "# print first 5 rows of dataframe\n",
    "print(df.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_data():\n",
    "    # url = \"https://pollysnips.s3.amazonaws.com/bostonEmployeeSalaries.json\"\n",
    "    # get json data from local file bostonEmployeeSalaries.json\n",
    "    data = json.load(open('bostonEmployeeSalaries.json'))\n",
    "    return data\n",
    "\n",
    "\n",
    "data = get_data()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   0                                     1   2           3       4   \\\n",
      "0   1  17439B12-1A55-4B6A-980E-5BAC15841AC4   1  1426008064  525314   \n",
      "1   2  EA2E2977-1FBA-4254-A2A9-855FA012565E   2  1426008064  525314   \n",
      "2   3  08FF3561-CE31-4BEB-AFD8-6DC47AC3C557   3  1426008064  525314   \n",
      "3   4  AE0A5BEF-F80F-4BD6-938C-37A639AB5400   4  1426008064  525314   \n",
      "4   5  72AD83C0-C652-4580-B9C3-F7304CB396A4   5  1426008064  525314   \n",
      "\n",
      "           5       6     7                          8   \\\n",
      "0  1426008064  525314  {\\n}           Adario,Anthony J   \n",
      "1  1426008064  525314  {\\n}         Baker,Katherine E.   \n",
      "2  1426008064  525314  {\\n}             Braga,Ann Hess   \n",
      "3  1426008064  525314  {\\n}       Calobrisi,Jane Ellen   \n",
      "4  1426008064  525314  {\\n}  Castillo-Cook,Wendolyn M.   \n",
      "\n",
      "                              9                    10        11    12  \\\n",
      "0   Supvising Claims Agent (Asd)  ASD Human Resources  98538.32  0.00   \n",
      "1             Nurse Case Manager  ASD Human Resources  83942.21  0.00   \n",
      "2  HumanResourcesComplianceOffcr  ASD Human Resources  98538.34  0.00   \n",
      "3              Personnel Analyst  ASD Human Resources  70073.81  0.00   \n",
      "4              Alcholism Coord I  ASD Human Resources  79253.11  0.00   \n",
      "\n",
      "        13    14    15    16    17         18     19  \n",
      "0  1842.87  0.00  0.00  0.00  0.00  100381.19  02132  \n",
      "1   225.00  0.00  0.00  0.00  0.00   84167.21  02136  \n",
      "2     0.00  0.00  0.00  0.00  0.00   98538.34  02126  \n",
      "3   225.00  0.00  0.00  0.00  0.00   70298.81  02090  \n",
      "4     0.00  0.00  0.00  0.00  0.00   79253.11  02121  \n",
      "(22233, 20)\n"
     ]
    }
   ],
   "source": [
    "df = pd.DataFrame(data[\"data\"])\n",
    "print(df.head())\n",
    "print(df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "maxium salary is:  415709.53\n",
      "job title of max salary is:  Boston Police Department\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: ylabel='Frequency'>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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qPb1qdxIBAICGya9niBYvXqyioiLdcsstatOmjflasWKFWfP888/rtttu08iRIzVw4EC5XC69++675nhQUJDWrFmjoKAgud1u3X///RozZoyeffZZsyYmJkbp6enKyMhQr169tGDBAr322mtKTEys0/0FAACBKaCeQxSoeA5RRfWx56rgOUQAUP/V2+cQAQAA+AOBCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWB6BCAAAWJ5fv+0eVdfQv0OsNlX1+8kAANbBGSIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB5BCIAAGB51QpEX3/9dU33AQAA4DfVCkQdO3bUoEGD9Ne//lUXLlyo6Z4AAADqVLUC0T/+8Q/17NlTKSkpcrlc+s1vfqOdO3fWdG8AAAB1olqB6IYbbtCiRYt0/PhxvfHGG8rPz1f//v3VvXt3LVy4UCdPnqzpPgEAAGrNL5pUHRwcrBEjRmjVqlX64x//qK+++kqPP/64oqOjNWbMGOXn59dUnwAAALXmFwWiXbt26be//a3atGmjhQsX6vHHH9fhw4eVkZGh48eP64477qipPgEAAGpNcHV+aOHChVq6dKlyc3M1fPhwvfXWWxo+fLgaNfohX8XExCgtLU0dOnSoyV4BAABqRbUC0eLFizVu3Dg9+OCDatOmzWVrIiIi9Prrr/+i5gAAAOpCtQLRl19+edUau92usWPHVmf1AAAAdapac4iWLl2qVatWVVi+atUqvfnmm7+4KQAAgLpUrUCUmpqqVq1aVVgeERGhP/zhD7+4KQAAgLpUrUCUl5enmJiYCsvbt2+vvLy8X9wUAABAXapWIIqIiNDevXsrLN+zZ49atmz5i5sCAACoS9UKRPfee6/+53/+R5s3b1ZpaalKS0u1adMmTZ48WaNGjarpHgEAAGpVte4ye+6553T06FENHjxYwcE/rKKsrExjxoxhDhEAAKh3qhWI7Ha7VqxYoeeee0579uxRSEiIevToofbt29d0fwAAALWuWoGoXOfOndW5c+ea6gV+1mFWur9bAADAL6oViEpLS5WWlqbMzEydOHFCZWVlPuObNm2qkeYAAADqQrUC0eTJk5WWlqakpCR1795dNputpvsCAACoM9UKRMuXL9fKlSs1fPjwmu4HAACgzlXrtnu73a6OHTvWdC8AAAB+Ua1ANG3aNC1atEiGYdR0PwAAAHWuWpfMPv74Y23evFlr167V9ddfr8aNG/uMv/vuuzXSHAAAQF2oViAKDw/XXXfdVdO9AAAA+EW1AtHSpUtrug8AAAC/qdYcIkm6dOmSNm7cqD//+c86c+aMJOn48eM6e/ZsjTUHAABQF6p1huibb77R0KFDlZeXp+LiYv3qV79Ss2bN9Mc//lHFxcVasmRJTfcJAABQa6p1hmjy5Mnq27evTp8+rZCQEHP5XXfdpczMzBprDgAAoC5U6wzRRx99pE8//VR2u91neYcOHfTPf/6zRhoDAACoK9U6Q1RWVqbS0tIKy7/99ls1a9as0uvZtm2bbr/9dkVFRclms+m9997zGX/wwQdls9l8XkOHDvWpOXXqlEaPHi2n06nw8HCNHz++wjymvXv3asCAAWrSpImio6M1b968yu8sAABo8Kp1hmjIkCF64YUX9Je//EWSZLPZdPbsWT311FNV+jqPc+fOqVevXho3bpxGjBhx2ZqhQ4f63NXmcDh8xkePHq38/HxlZGSopKREDz30kCZMmKBly5ZJkrxer4YMGaKEhAQtWbJE+/bt07hx4xQeHq4JEyZUddfrFb69HgCAyqlWIFqwYIESExMVGxurCxcu6L777tOXX36pVq1a6Z133qn0eoYNG6Zhw4ZdscbhcMjlcl127PPPP9e6dev02WefqW/fvpKkl156ScOHD9ef/vQnRUVF6e2339bFixf1xhtvyG636/rrr1dOTo4WLlzY4AMRAAConGpdMmvbtq327NmjJ554QlOnTlXv3r01d+5c7d69WxERETXa4JYtWxQREaEuXbro0Ucf1XfffWeOZWVlKTw83AxDkpSQkKBGjRppx44dZs3AgQN95jslJiYqNzdXp0+fvuw2i4uL5fV6fV4AAKDhqtYZIkkKDg7W/fffX5O9VDB06FCNGDFCMTExOnz4sJ544gkNGzZMWVlZCgoKksfjqRDAgoOD1aJFC3k8HkmSx+NRTEyMT01kZKQ51rx58wrbTU1N1TPPPFNLewUAAAJNtQLRW2+9dcXxMWPGVKuZnxo1apT57x49eqhnz5667rrrtGXLFg0ePLhGtnE5s2fPVkpKivne6/UqOjq61rYHAAD8q1qBaPLkyT7vS0pK9P3338tutys0NLTGAtFPXXvttWrVqpW++uorDR48WC6XSydOnPCpuXTpkk6dOmXOO3K5XCooKPCpKX//c3OTHA5HhcnbAACg4arWHKLTp0/7vM6ePavc3Fz179+/SpOqq+rbb7/Vd999pzZt2kiS3G63CgsLlZ2dbdZs2rRJZWVlio+PN2u2bdumkpISsyYjI0NdunS57OUyAABgPdX+LrOf6tSpk+bOnVvh7NGVnD17Vjk5OcrJyZEkHTlyRDk5OcrLy9PZs2c1ffp0bd++XUePHlVmZqbuuOMOdezYUYmJiZKkbt26aejQoXrkkUe0c+dOffLJJ5o0aZJGjRqlqKgoSdJ9990nu92u8ePH68CBA1qxYoUWLVrkc0kMAABYW40FIumHCc3Hjx+vdP2uXbvUu3dv9e7dW5KUkpKi3r17a86cOQoKCtLevXv161//Wp07d9b48eMVFxenjz76yOdy1ttvv62uXbtq8ODBGj58uPr3728+H0mSwsLCtGHDBh05ckRxcXGaNm2a5syZwy33AADAVK05RO+//77Pe8MwlJ+fr5dfflk333xzpddzyy23yDCMnx1fv379VdfRokUL8yGMP6dnz5766KOPKt0XAACwlmoFojvvvNPnvc1mU+vWrXXrrbdqwYIFNdEXAABAnalWICorK6vpPgAAAPym2g9mBGpLVb6D7ejcpFrsBABgFdUKRFW5Q2vhwoXV2QQAAECdqVYg2r17t3bv3q2SkhJ16dJFkvTFF18oKChIffr0MetsNlvNdAkAAFCLqhWIbr/9djVr1kxvvvmm+XDD06dP66GHHtKAAQM0bdq0Gm0SAACgNlXrOUQLFixQamqqz5Oemzdvrt///vfcZQYAAOqdagUir9erkydPVlh+8uRJnTlz5hc3BQAAUJeqdcnsrrvu0kMPPaQFCxbopptukiTt2LFD06dP14gRI2q0QdR/VblrDAAAf6hWIFqyZIkef/xx3XfffeaXpgYHB2v8+PGaP39+jTYIAABQ26oViEJDQ/Xqq69q/vz5Onz4sCTpuuuu0zXXXFOjzQEAANSFX/Tlrvn5+crPz1enTp10zTXXXPF7yQAAAAJVtQLRd999p8GDB6tz584aPny48vPzJUnjx4/nlnsAAFDvVCsQTZ06VY0bN1ZeXp5CQ0PN5ffcc4/WrVtXY80BAADUhWrNIdqwYYPWr1+vtm3b+izv1KmTvvnmmxppDAAAoK5U6wzRuXPnfM4MlTt16pQcDscvbgoAAKAuVSsQDRgwQG+99Zb53mazqaysTPPmzdOgQYNqrDkAAIC6UK1LZvPmzdPgwYO1a9cuXbx4UTNmzNCBAwd06tQpffLJJzXdIwAAQK2q1hmi7t2764svvlD//v11xx136Ny5cxoxYoR2796t6667rqZ7BAAAqFVVPkNUUlKioUOHasmSJfrd735XGz0BAADUqSqfIWrcuLH27t1bG70AAAD4RbUumd1///16/fXXa7oXAAAAv6jWpOpLly7pjTfe0MaNGxUXF1fhO8wWLlxYI80BAADUhSoFoq+//lodOnTQ/v371adPH0nSF1984VNjs9lqrjsAAIA6UKVA1KlTJ+Xn52vz5s2SfviqjhdffFGRkZG10hwAAEBdqNIcop9+m/3atWt17ty5Gm0IAACgrlVrUnW5nwYkAACA+qhKgchms1WYI8ScIQAAUN9VaQ6RYRh68MEHzS9wvXDhgiZOnFjhLrN333235joEAACoZVUKRGPHjvV5f//999doM0BVdZiV7u8WAAANQJUC0dKlS2urDwAAAL/5RZOqAQAAGgICEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsDwCEQAAsLxgfzcANAQdZqVXuvbo3KRa7AQAUB2cIQIAAJZHIAIAAJbn10C0bds23X777YqKipLNZtN7773nM24YhubMmaM2bdooJCRECQkJ+vLLL31qTp06pdGjR8vpdCo8PFzjx4/X2bNnfWr27t2rAQMGqEmTJoqOjta8efNqe9cAAEA94tdAdO7cOfXq1UuvvPLKZcfnzZunF198UUuWLNGOHTt0zTXXKDExURcuXDBrRo8erQMHDigjI0Nr1qzRtm3bNGHCBHPc6/VqyJAhat++vbKzszV//nw9/fTT+stf/lLr+wcAAOoHm2EYhr+bkCSbzabVq1frzjvvlPTD2aGoqChNmzZNjz/+uCSpqKhIkZGRSktL06hRo/T5558rNjZWn332mfr27StJWrdunYYPH65vv/1WUVFRWrx4sX73u9/J4/HIbrdLkmbNmqX33ntPhw4dqlRvXq9XYWFhKioqktPprPF9r8qEXNR/TKoGgLpRlb/fATuH6MiRI/J4PEpISDCXhYWFKT4+XllZWZKkrKwshYeHm2FIkhISEtSoUSPt2LHDrBk4cKAZhiQpMTFRubm5On369GW3XVxcLK/X6/MCAAANV8AGIo/HI0mKjIz0WR4ZGWmOeTweRURE+IwHBwerRYsWPjWXW8ePt/FTqampCgsLM1/R0dG/fIcAAEDACthA5E+zZ89WUVGR+Tp27Ji/WwIAALUoYAORy+WSJBUUFPgsLygoMMdcLpdOnDjhM37p0iWdOnXKp+Zy6/jxNn7K4XDI6XT6vAAAQMMVsIEoJiZGLpdLmZmZ5jKv16sdO3bI7XZLktxutwoLC5WdnW3WbNq0SWVlZYqPjzdrtm3bppKSErMmIyNDXbp0UfPmzetobwAAQCDzayA6e/ascnJylJOTI+mHidQ5OTnKy8uTzWbTlClT9Pvf/17vv/++9u3bpzFjxigqKsq8E61bt24aOnSoHnnkEe3cuVOffPKJJk2apFGjRikqKkqSdN9998lut2v8+PE6cOCAVqxYoUWLFiklJcVPew0AAAKNX7/LbNeuXRo0aJD5vjykjB07VmlpaZoxY4bOnTunCRMmqLCwUP3799e6devUpEkT82fefvttTZo0SYMHD1ajRo00cuRIvfjii+Z4WFiYNmzYoOTkZMXFxalVq1aaM2eOz7OKAACAtQXMc4gCGc8hQk3iOUQAUDcaxHOIAAAA6gqBCAAAWJ5f5xABVlSVS6RcXgOAusEZIgAAYHkEIgAAYHkEIgAAYHkEIgAAYHkEIgAAYHkEIgAAYHkEIgAAYHkEIgAAYHkEIgAAYHkEIgAAYHkEIgAAYHkEIgAAYHkEIgAAYHkEIgAAYHkEIgAAYHkEIgAAYHkEIgAAYHkEIgAAYHkEIgAAYHkEIgAAYHkEIgAAYHkEIgAAYHkEIgAAYHkEIgAAYHkEIgAAYHkEIgAAYHkEIgAAYHkEIgAAYHkEIgAAYHkEIgAAYHkEIgAAYHkEIgAAYHkEIgAAYHkEIgAAYHkEIgAAYHkEIgAAYHkEIgAAYHkEIgAAYHnB/m4AwM/rMCu90rVH5ybVYicA0LBxhggAAFgegQgAAFgegQgAAFgegQgAAFgegQgAAFgegQgAAFgegQgAAFgegQgAAFgegQgAAFheQAeip59+WjabzefVtWtXc/zChQtKTk5Wy5Yt1bRpU40cOVIFBQU+68jLy1NSUpJCQ0MVERGh6dOn69KlS3W9KwAAIIAF/Fd3XH/99dq4caP5Pjj43y1PnTpV6enpWrVqlcLCwjRp0iSNGDFCn3zyiSSptLRUSUlJcrlc+vTTT5Wfn68xY8aocePG+sMf/lDn+wIAAAJTwAei4OBguVyuCsuLior0+uuva9myZbr11lslSUuXLlW3bt20fft29evXTxs2bNDBgwe1ceNGRUZG6oYbbtBzzz2nmTNn6umnn5bdbq/r3QEAAAEooC+ZSdKXX36pqKgoXXvttRo9erTy8vIkSdnZ2SopKVFCQoJZ27VrV7Vr105ZWVmSpKysLPXo0UORkZFmTWJiorxerw4cOPCz2ywuLpbX6/V5AQCAhiugA1F8fLzS0tK0bt06LV68WEeOHNGAAQN05swZeTwe2e12hYeH+/xMZGSkPB6PJMnj8fiEofLx8rGfk5qaqrCwMPMVHR1dszsGAAACSkBfMhs2bJj57549eyo+Pl7t27fXypUrFRISUmvbnT17tlJSUsz3Xq+XUAQAQAMW0GeIfio8PFydO3fWV199JZfLpYsXL6qwsNCnpqCgwJxz5HK5Ktx1Vv7+cvOSyjkcDjmdTp8XAABouOpVIDp79qwOHz6sNm3aKC4uTo0bN1ZmZqY5npubq7y8PLndbkmS2+3Wvn37dOLECbMmIyNDTqdTsbGxdd4/AAAITAF9yezxxx/X7bffrvbt2+v48eN66qmnFBQUpHvvvVdhYWEaP368UlJS1KJFCzmdTj322GNyu93q16+fJGnIkCGKjY3VAw88oHnz5snj8ejJJ59UcnKyHA6Hn/cOAAAEioAORN9++63uvfdefffdd2rdurX69++v7du3q3Xr1pKk559/Xo0aNdLIkSNVXFysxMREvfrqq+bPBwUFac2aNXr00Ufldrt1zTXXaOzYsXr22Wf9tUsAACAA2QzDMPzdRKDzer0KCwtTUVFRrcwn6jArvcbXCes5OjfJ3y0AQECpyt/vejWHCAAAoDYQiAAAgOURiAAAgOURiAAAgOURiAAAgOUF9G33ACqvqncrclcaAPwbZ4gAAIDlEYgAAIDlcckMsKiqXGLj8hqAho4zRAAAwPIIRAAAwPIIRAAAwPIIRAAAwPIIRAAAwPIIRAAAwPIIRAAAwPIIRAAAwPIIRAAAwPIIRAAAwPIIRAAAwPIIRAAAwPIIRAAAwPIIRAAAwPIIRAAAwPIIRAAAwPIIRAAAwPKC/d0AgMDXYVZ6pWuPzk2qxU4AoHZwhggAAFgegQgAAFgegQgAAFgegQgAAFgek6oB1KiqTMCWmIQNIDBwhggAAFgegQgAAFgegQgAAFgegQgAAFgegQgAAFgegQgAAFget90D8Cu+Jw1AICAQAag3CE8AaguXzAAAgOURiAAAgOURiAAAgOUxhwhAg8R8IwBVwRkiAABgeQQiAABgeQQiAABgeQQiAABgeUyqBmB5VZmALTEJG2iIOEMEAAAsz1JniF555RXNnz9fHo9HvXr10ksvvaSbbrrJ320BqGeqekapsjjzBPiPZQLRihUrlJKSoiVLlig+Pl4vvPCCEhMTlZubq4iICH+3BwA8OwnwI5thGIa/m6gL8fHxuvHGG/Xyyy9LksrKyhQdHa3HHntMs2bNuuLPer1ehYWFqaioSE6ns8Z7q63/2wSA6iBsoaGoyt9vS5whunjxorKzszV79mxzWaNGjZSQkKCsrKwK9cXFxSouLjbfFxUVSfrhg60NZcXf18p6AaA62k1dVena/c8k1mInwC9T/ne7Mud+LBGI/vWvf6m0tFSRkZE+yyMjI3Xo0KEK9ampqXrmmWcqLI+Ojq61HgGgPgp7wd8dAFd35swZhYWFXbHGEoGoqmbPnq2UlBTzfVlZmU6dOqWWLVvKZrPV6La8Xq+io6N17NixWrkch5rF8ap/OGb1C8er/gnkY2YYhs6cOaOoqKir1loiELVq1UpBQUEqKCjwWV5QUCCXy1Wh3uFwyOFw+CwLDw+vzRbldDoD7j8k/DyOV/3DMatfOF71T6Aes6udGSpniecQ2e12xcXFKTMz01xWVlamzMxMud1uP3YGAAACgSXOEElSSkqKxo4dq759++qmm27SCy+8oHPnzumhhx7yd2sAAMDPLBOI7rnnHp08eVJz5syRx+PRDTfcoHXr1lWYaF3XHA6HnnrqqQqX6BCYOF71D8esfuF41T8N5ZhZ5jlEAAAAP8cSc4gAAACuhEAEAAAsj0AEAAAsj0AEAAAsj0DkR6+88oo6dOigJk2aKD4+Xjt37vR3Sw3Ctm3bdPvttysqKko2m03vvfeez7hhGJozZ47atGmjkJAQJSQk6Msvv/SpOXXqlEaPHi2n06nw8HCNHz9eZ8+e9anZu3evBgwYoCZNmig6Olrz5s2r0MuqVavUtWtXNWnSRD169NCHH35Y5V4autTUVN14441q1qyZIiIidOeddyo3N9en5sKFC0pOTlbLli3VtGlTjRw5ssKDVvPy8pSUlKTQ0FBFRERo+vTpunTpkk/Nli1b1KdPHzkcDnXs2FFpaWkV+rna72VlemnIFi9erJ49e5oP4XO73Vq7dq05zrEKbHPnzpXNZtOUKVPMZRyz/2fAL5YvX27Y7XbjjTfeMA4cOGA88sgjRnh4uFFQUODv1uq9Dz/80Pjd735nvPvuu4YkY/Xq1T7jc+fONcLCwoz33nvP2LNnj/HrX//aiImJMc6fP2/WDB061OjVq5exfft246OPPjI6duxo3HvvveZ4UVGRERkZaYwePdrYv3+/8c477xghISHGn//8Z7Pmk08+MYKCgox58+YZBw8eNJ588kmjcePGxr59+6rUS0OXmJhoLF261Ni/f7+Rk5NjDB8+3GjXrp1x9uxZs2bixIlGdHS0kZmZaezatcvo16+f8Z//+Z/m+KVLl4zu3bsbCQkJxu7du40PP/zQaNWqlTF79myz5uuvvzZCQ0ONlJQU4+DBg8ZLL71kBAUFGevWrTNrKvN7ebVeGrr333/fSE9PN7744gsjNzfXeOKJJ4zGjRsb+/fvNwyDYxXIdu7caXTo0MHo2bOnMXnyZHM5x+wHBCI/uemmm4zk5GTzfWlpqREVFWWkpqb6sauG56eBqKyszHC5XMb8+fPNZYWFhYbD4TDeeecdwzAM4+DBg4Yk47PPPjNr1q5da9hsNuOf//ynYRiG8eqrrxrNmzc3iouLzZqZM2caXbp0Md//93//t5GUlOTTT3x8vPGb3/ym0r1Y0YkTJwxJxtatWw3D+OEzady4sbFq1Sqz5vPPPzckGVlZWYZh/BCCGzVqZHg8HrNm8eLFhtPpNI/RjBkzjOuvv95nW/fcc4+RmJhovr/a72VlerGi5s2bG6+99hrHKoCdOXPG6NSpk5GRkWH813/9lxmIOGb/xiUzP7h48aKys7OVkJBgLmvUqJESEhKUlZXlx84aviNHjsjj8fh89mFhYYqPjzc/+6ysLIWHh6tv375mTUJCgho1aqQdO3aYNQMHDpTdbjdrEhMTlZubq9OnT5s1P95OeU35dirTixUVFRVJklq0aCFJys7OVklJic/n1LVrV7Vr187nmPXo0cPnQauJiYnyer06cOCAWXOl41GZ38vK9GIlpaWlWr58uc6dOye3282xCmDJyclKSkqq8LlyzP7NMk+qDiT/+te/VFpaWuEp2ZGRkTp06JCfurIGj8cjSZf97MvHPB6PIiIifMaDg4PVokULn5qYmJgK6ygfa968uTwez1W3c7VerKasrExTpkzRzTffrO7du0v64XOy2+0VvmD5p5/l5T7H8rEr1Xi9Xp0/f16nT5++6u9lZXqxgn379sntduvChQtq2rSpVq9erdjYWOXk5HCsAtDy5cv1j3/8Q5999lmFMX6//o1ABCBgJCcna//+/fr444/93QquoEuXLsrJyVFRUZH+9re/aezYsdq6dau/28JlHDt2TJMnT1ZGRoaaNGni73YCGpfM/KBVq1YKCgqqMHO+oKBALpfLT11ZQ/nne6XP3uVy6cSJEz7jly5d0qlTp3xqLreOH2/j52p+PH61Xqxk0qRJWrNmjTZv3qy2bduay10uly5evKjCwkKf+p9+ltU9Hk6nUyEhIZX6vaxML1Zgt9vVsWNHxcXFKTU1Vb169dKiRYs4VgEoOztbJ06cUJ8+fRQcHKzg4GBt3bpVL774ooKDgxUZGckx+38EIj+w2+2Ki4tTZmamuaysrEyZmZlyu91+7Kzhi4mJkcvl8vnsvV6vduzYYX72brdbhYWFys7ONms2bdqksrIyxcfHmzXbtm1TSUmJWZORkaEuXbqoefPmZs2Pt1NeU76dyvRiBYZhaNKkSVq9erU2bdpU4VJkXFycGjdu7PM55ebmKi8vz+eY7du3zyfIZmRkyOl0KjY21qy50vGozO9lZXqxorKyMhUXF3OsAtDgwYO1b98+5eTkmK++fftq9OjR5r85Zv+v1qdt47KWL19uOBwOIy0tzTh48KAxYcIEIzw83GcWP6rnzJkzxu7du43du3cbkoyFCxcau3fvNr755hvDMH641T08PNz4+9//buzdu9e44447Lnvbfe/evY0dO3YYH3/8sdGpUyef2+4LCwuNyMhI44EHHjD2799vLF++3AgNDa1w231wcLDxpz/9yfj888+Np5566rK33V+tl4bu0UcfNcLCwowtW7YY+fn55uv77783ayZOnGi0a9fO2LRpk7Fr1y7D7XYbbrfbHC+/LXjIkCFGTk6OsW7dOqN169aXvS14+vTpxueff2688sorl70t+Gq/l1frpaGbNWuWsXXrVuPIkSPG3r17jVmzZhk2m83YsGGDYRgcq/rgx3eZGQbHrByByI9eeuklo127dobdbjduuukmY/v27f5uqUHYvHmzIanCa+zYsYZh/HC7+//+7/8akZGRhsPhMAYPHmzk5ub6rOO7774z7r33XqNp06aG0+k0HnroIePMmTM+NXv27DH69+9vOBwO4z/+4z+MuXPnVuhl5cqVRufOnQ273W5cf/31Rnp6us94ZXpp6C53rCQZS5cuNWvOnz9v/Pa3vzWaN29uhIaGGnfddZeRn5/vs56jR48aw4YNM0JCQoxWrVoZ06ZNM0pKSnxqNm/ebNxwww2G3W43rr32Wp9tlLva72VlemnIxo0bZ7Rv396w2+1G69atjcGDB5thyDA4VvXBTwMRx+wHNsMwjNo/DwUAABC4mEMEAAAsj0AEAAAsj0AEAAAsj0AEAAAsj0AEAAAsj0AEAAAsj0AEAAAsj0AEAAAsj0AEAAAsj0AEAAAsj0AEAAAsj0AEAAAs7/8AKe3fxl67bHoAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# column 10 is job title and column 18 is salary\n",
    "# change column 18 to int\n",
    "salaries = df[18].astype(float)\n",
    "# plot histogram of salaries in 40 bins\n",
    "print(\"maxium salary is: \", salaries.max())\n",
    "# job title of max salary\n",
    "print(\"job title of max salary is: \", df[10][salaries.idxmax()])\n",
    "salaries.plot.hist(bins=40)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: ylabel='Frequency'>"
      ]
     },
     "execution_count": 33,
     "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": [
    "# plot histogram of salaries > 200,000 in 10 bins\n",
    "salaries[salaries > 200000].plot.hist(bins=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Boston Police Department          2017\n",
      "Boston Fire Department            1345\n",
      "BPS Special Education               85\n",
      "BPS Boston Latin                    45\n",
      "Dpt of Innovation & Technology      44\n",
      "                                  ... \n",
      "BPS The Harbor School                1\n",
      "Office of Economic Development       1\n",
      "BPS Clap Elementary                  1\n",
      "Office of Finance & Budget           1\n",
      "BPS Equity                           1\n",
      "Name: 10, Length: 210, dtype: int64\n",
      "Boston Police Department    59\n",
      "Boston Fire Department       5\n",
      "BPS Kenny Elementary         1\n",
      "Superintendent               1\n",
      "Name: 10, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# number of police officers with salary > 100,000\n",
    "print(df[10][salaries > 100000].value_counts())\n",
    "# number of police officers with salary > 250,000\n",
    "print(df[10][salaries > 250000].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0             ASD Human Resources\n",
      "1     ASD Intergvernmtl Relations\n",
      "2    ASD Office of Budget Mangmnt\n",
      "3    ASD Office Of Labor Relation\n",
      "4         ASD Purchasing Division\n",
      "dtype: object\n",
      "(1,)\n"
     ]
    }
   ],
   "source": [
    "# find out unique job titles in column 10\n",
    "job_titles = df[10].unique()\n",
    "job_titles = pd.Series(job_titles)\n",
    "print(job_titles.head())\n",
    "# how many unique job titles with police in them\n",
    "print(job_titles[job_titles.str.contains('Police')].shape)  \n"
   ]
  }
 ],
 "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": "3.10.4"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "3ad933181bd8a04b432d3370b9dc3b0662ad032c4dfaa4e4f1596c548f763858"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}