final_vF.ipynb

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    "# Analyzing Arsenic Data from the NJ Private Well Testing Act\n",
    "## Tomer Solomon, December 2016 \n",
    "\n",
    "This project is analyzing data from the NJ Private Well Testing Act. In essence, this act requires NJ landowners as well as buyers/sellers of NJ property to disclose the quality of the water, and I am interested in seeing how Arsenic values have changed over time as well as which counties in NJ are most problematic when it comes to having arsenic in their water data. Hope you enjoy my project!\n",
    "\n",
    "The link to the data can be found here: http://njdep.maps.arcgis.com/apps/MapSeries/index.html?appid=826ec9fae77543caa582a787d5f088e7"
   ]
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   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pylab as plt\n",
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
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   "source": [
    "doc = 'ArsenicAllSamples_thruMar2014_for_BrianMailloux_Fe_Mn_GridID.csv'\n",
    "data = pd.read_csv(doc,index_col='SAMPLE_DATE',parse_dates=['SAMPLE_DATE'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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   "outputs": [
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     "data": {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DATA_SUBMISSION_ID</th>\n",
       "      <th>LOCATION_ID</th>\n",
       "      <th>MUNI_NAME</th>\n",
       "      <th>COUNTY_NAME</th>\n",
       "      <th>COLLECTION_PT_DESC</th>\n",
       "      <th>AnalyticLabName</th>\n",
       "      <th>ANALYTIC_METHOD</th>\n",
       "      <th>AS_QUALIFIER</th>\n",
       "      <th>AS_RESULT_VALUE</th>\n",
       "      <th>AS_RESULT_UNIT</th>\n",
       "      <th>...</th>\n",
       "      <th>FE_RESULT_UNIT</th>\n",
       "      <th>FE_REPORTING_DETECTION_LIMIT</th>\n",
       "      <th>FE_CHEMICAL_NAME</th>\n",
       "      <th>MN_QUALIFIER</th>\n",
       "      <th>MN_RESULT_VALUE</th>\n",
       "      <th>MN_RESULT_UNIT</th>\n",
       "      <th>MN_REPORTING_DETECTION_LIMIT</th>\n",
       "      <th>MN_CHEMICAL_NAME</th>\n",
       "      <th>PH_RESULT_VALUE</th>\n",
       "      <th>GRID_2M_ID</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SAMPLE_DATE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2002-09-18</th>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>HILLSBOROUGH TWP</td>\n",
       "      <td>SOMERSET</td>\n",
       "      <td>Before Treatment</td>\n",
       "      <td>QC INC.</td>\n",
       "      <td>SM3113B</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.40</td>\n",
       "      <td>ug/l</td>\n",
       "      <td>...</td>\n",
       "      <td>mg/l</td>\n",
       "      <td>0.03</td>\n",
       "      <td>Iron</td>\n",
       "      <td>&lt;</td>\n",
       "      <td>0.01</td>\n",
       "      <td>mg/l</td>\n",
       "      <td>0.01</td>\n",
       "      <td>Manganese</td>\n",
       "      <td>7.33</td>\n",
       "      <td>1183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-03-24</th>\n",
       "      <td>139584</td>\n",
       "      <td>10</td>\n",
       "      <td>HILLSBOROUGH TWP</td>\n",
       "      <td>SOMERSET</td>\n",
       "      <td>Alternative Location</td>\n",
       "      <td>QC INC.</td>\n",
       "      <td>SM3113B</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.20</td>\n",
       "      <td>ug/l</td>\n",
       "      <td>...</td>\n",
       "      <td>mg/l</td>\n",
       "      <td>0.03</td>\n",
       "      <td>Iron</td>\n",
       "      <td>&lt;</td>\n",
       "      <td>0.01</td>\n",
       "      <td>mg/l</td>\n",
       "      <td>0.01</td>\n",
       "      <td>Manganese</td>\n",
       "      <td>7.06</td>\n",
       "      <td>1183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-08-10</th>\n",
       "      <td>264009</td>\n",
       "      <td>10</td>\n",
       "      <td>HILLSBOROUGH TWP</td>\n",
       "      <td>SOMERSET</td>\n",
       "      <td>Bathroom Tap</td>\n",
       "      <td>AGRA ENVIRONMENTAL AND LABORATORY SERVICES</td>\n",
       "      <td>SM3113B</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.57</td>\n",
       "      <td>ug/l</td>\n",
       "      <td>...</td>\n",
       "      <td>mg/l</td>\n",
       "      <td>0.10</td>\n",
       "      <td>Iron</td>\n",
       "      <td>&lt;</td>\n",
       "      <td>0.03</td>\n",
       "      <td>mg/l</td>\n",
       "      <td>0.03</td>\n",
       "      <td>Manganese</td>\n",
       "      <td>7.32</td>\n",
       "      <td>1183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-03</th>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>FRANKLIN TWP</td>\n",
       "      <td>SOMERSET</td>\n",
       "      <td>Before Treatment</td>\n",
       "      <td>QC INC.</td>\n",
       "      <td>SM3113B</td>\n",
       "      <td>&lt;</td>\n",
       "      <td>2.00</td>\n",
       "      <td>ug/l</td>\n",
       "      <td>...</td>\n",
       "      <td>mg/l</td>\n",
       "      <td>0.03</td>\n",
       "      <td>Iron</td>\n",
       "      <td>&lt;</td>\n",
       "      <td>0.01</td>\n",
       "      <td>mg/l</td>\n",
       "      <td>0.01</td>\n",
       "      <td>Manganese</td>\n",
       "      <td>7.30</td>\n",
       "      <td>1181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-06</th>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>WEST MILFORD TWP</td>\n",
       "      <td>PASSAIC</td>\n",
       "      <td>Before Treatment</td>\n",
       "      <td>QC INC.</td>\n",
       "      <td>SM3113B</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.20</td>\n",
       "      <td>ug/l</td>\n",
       "      <td>...</td>\n",
       "      <td>mg/l</td>\n",
       "      <td>0.03</td>\n",
       "      <td>Iron</td>\n",
       "      <td>&lt;</td>\n",
       "      <td>0.01</td>\n",
       "      <td>mg/l</td>\n",
       "      <td>0.01</td>\n",
       "      <td>Manganese</td>\n",
       "      <td>7.06</td>\n",
       "      <td>1481</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             DATA_SUBMISSION_ID  LOCATION_ID         MUNI_NAME COUNTY_NAME  \\\n",
       "SAMPLE_DATE                                                                  \n",
       "2002-09-18                   10           10  HILLSBOROUGH TWP    SOMERSET   \n",
       "2008-03-24               139584           10  HILLSBOROUGH TWP    SOMERSET   \n",
       "2012-08-10               264009           10  HILLSBOROUGH TWP    SOMERSET   \n",
       "2002-09-03                   12           12      FRANKLIN TWP    SOMERSET   \n",
       "2002-09-06                   13           13  WEST MILFORD TWP     PASSAIC   \n",
       "\n",
       "               COLLECTION_PT_DESC                             AnalyticLabName  \\\n",
       "SAMPLE_DATE                                                                     \n",
       "2002-09-18       Before Treatment                                     QC INC.   \n",
       "2008-03-24   Alternative Location                                     QC INC.   \n",
       "2012-08-10           Bathroom Tap  AGRA ENVIRONMENTAL AND LABORATORY SERVICES   \n",
       "2002-09-03       Before Treatment                                     QC INC.   \n",
       "2002-09-06       Before Treatment                                     QC INC.   \n",
       "\n",
       "            ANALYTIC_METHOD AS_QUALIFIER  AS_RESULT_VALUE AS_RESULT_UNIT  \\\n",
       "SAMPLE_DATE                                                                \n",
       "2002-09-18          SM3113B          NaN             3.40           ug/l   \n",
       "2008-03-24          SM3113B          NaN             5.20           ug/l   \n",
       "2012-08-10          SM3113B          NaN             4.57           ug/l   \n",
       "2002-09-03          SM3113B            <             2.00           ug/l   \n",
       "2002-09-06          SM3113B          NaN             2.20           ug/l   \n",
       "\n",
       "               ...      FE_RESULT_UNIT FE_REPORTING_DETECTION_LIMIT  \\\n",
       "SAMPLE_DATE    ...                                                    \n",
       "2002-09-18     ...                mg/l                         0.03   \n",
       "2008-03-24     ...                mg/l                         0.03   \n",
       "2012-08-10     ...                mg/l                         0.10   \n",
       "2002-09-03     ...                mg/l                         0.03   \n",
       "2002-09-06     ...                mg/l                         0.03   \n",
       "\n",
       "            FE_CHEMICAL_NAME  MN_QUALIFIER MN_RESULT_VALUE  MN_RESULT_UNIT  \\\n",
       "SAMPLE_DATE                                                                  \n",
       "2002-09-18              Iron             <            0.01            mg/l   \n",
       "2008-03-24              Iron             <            0.01            mg/l   \n",
       "2012-08-10              Iron             <            0.03            mg/l   \n",
       "2002-09-03              Iron             <            0.01            mg/l   \n",
       "2002-09-06              Iron             <            0.01            mg/l   \n",
       "\n",
       "            MN_REPORTING_DETECTION_LIMIT MN_CHEMICAL_NAME  PH_RESULT_VALUE  \\\n",
       "SAMPLE_DATE                                                                  \n",
       "2002-09-18                          0.01        Manganese             7.33   \n",
       "2008-03-24                          0.01        Manganese             7.06   \n",
       "2012-08-10                          0.03        Manganese             7.32   \n",
       "2002-09-03                          0.01        Manganese             7.30   \n",
       "2002-09-06                          0.01        Manganese             7.06   \n",
       "\n",
       "            GRID_2M_ID  \n",
       "SAMPLE_DATE             \n",
       "2002-09-18        1183  \n",
       "2008-03-24        1183  \n",
       "2012-08-10        1183  \n",
       "2002-09-03        1181  \n",
       "2002-09-06        1481  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
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     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
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+wZIn56vr8zUeWDdvvU+3Ov2DJU+1/nVdzYAkyo9X3/1B7gLWjoiL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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1154ed7d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots()\n",
    "data['AS_RESULT_VALUE'].plot()\n",
    "ax.set_title('Arsenic Values over Time')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('ug/l')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1154ed410>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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mRV/BcPfUXkYJuhC9hBVWyK8ZTLQSxQW9Ya1aZrafmT1jZs+aWUqXhhCiGiTm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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1152dd890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.AS_RESULT_VALUE['2011':'2013'].plot()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x115c93050>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1156d5090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#not analyzing this\n",
    "data['FE_RESULT_VALUE'].plot()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x115b67bd0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11652bc50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#not analyzing this\n",
    "data['MN_RESULT_VALUE'].plot()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:5: FutureWarning: how in .resample() is deprecated\n",
      "the new syntax is .resample(...)..apply(<func>)\n",
      "/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:6: FutureWarning: how in .resample() is deprecated\n",
      "the new syntax is .resample(...)..apply(<func>)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x11822bcd0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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lqqo8If0icnDKzMwkMzMz1WFIA1TDSFOt7cxdd4+LpCfVMA4Cre3MPTs7O21j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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117a63c90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax=plt.subplots()\n",
    "props=dict(boxstyle='round',facecolor='white',alpha=0.5)\n",
    "\n",
    "\n",
    "x = data.AS_RESULT_VALUE.resample('A',how=np.mean).index.year\n",
    "y = data.AS_RESULT_VALUE.resample('A',how=np.mean)\n",
    "\n",
    "#calculate statistics\n",
    "results=stats.linregress(x,y)\n",
    "ax.plot(x,results[0]*x+results[1])\n",
    "textstr='m={:>12.3f}\\nb ={:>10.3f}\\n$r^2$={:>12.3f}\\np ={:>12.3f}'.format(results[0],results[1],results[2]**2,results[3])\n",
    "ax.text(0.75,0.95,textstr,transform=ax.transAxes,fontsize=10,verticalalignment='top',bbox=props)\n",
    "ax.scatter(x,y)\n",
    "\n",
    "ax.set_title('Average Arsenic Value per Year')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('ug/l')\n",
    "\n",
    "#old method\n",
    "#data['AS_RESULT_VALUE'].resample('A',how='mean').plot()#marker='8',color='r',linestyle='none')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "location BERGEN\n",
      "mean AS value 4.69929887185\n",
      "count 2739\n",
      "location ESSEX\n",
      "mean AS value 5.04656774194\n",
      "count 155\n",
      "location HUNTERDON\n",
      "mean AS value 3.98902951448\n",
      "count 10335\n",
      "location MERCER\n",
      "mean AS value 3.99264457695\n",
      "count 3028\n",
      "location MIDDLESEX\n",
      "mean AS value 3.18706954545\n",
      "count 858\n",
      "location MORRIS\n",
      "mean AS value 3.57112581913\n",
      "count 9919\n",
      "location PASSAIC\n",
      "mean AS value 4.12207133121\n",
      "count 4402\n",
      "location SOMERSET\n",
      "mean AS value 4.37061592535\n",
      "count 5787\n",
      "location SUSSEX\n",
      "mean AS value 2.91382444949\n",
      "count 3524\n",
      "location WARREN\n",
      "mean AS value 3.03657856343\n",
      "count 2144\n"
     ]
    },
    {
     "data": {
      "image/png": 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tySSPMx229LCcHm/FOHeUI6RA2bHzzVacJ2Iitq2Q5sbCVvUpdNLTSK7VH+qn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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117a63650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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3mTWlCifhMj1pZs9H5dphZmeEWh4zm1dQjp1mNmxmTWn8TvgYfdTM/m+0nv8S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\nBEjJW0QkQEreIiIBUvIWEQnQ/wcV1rvbVRWiNAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a5e8110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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sKkSc7OzayapNq3I6l9t915AmuTNG7xuQ1O85T89JGQsQTYGx/IPltLa19vjs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\n5maJflhGDR3l+txkcl+G+Ic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      "text/plain": [
       "<matplotlib.figure.Figure at 0x117a63a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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pRBacsYAVtSu6pGmyR02tmreKCeUT+MO+P1BZXsnMCTP55IOf7HI+ojxaTod3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\nJGT+P8owSMcWIU+IAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12bfb3fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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9XG/uev4u2oOJ7ZYePFww8ALOPuVsvB4vJ3c5GYCrn7g6IYFpi2/eSnZ9di5h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\nwVsppVxIg7dSSrmQBm+llHKh/w8C/LH0zZ+NjgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12bfb3690>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x12bfb31d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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IQltEJEAU2iIiAaLQFhEJEIW2iEiAKLRFRAJEoS0iEiAKbRGRAFFoi4gEiEJb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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119dba9d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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7wFMHxt8Dfx2Mwvsox/6Ezewg6M0Rnst7N44NtgHvT9pyM6hybZ0y\n7Y+ZNXv/jwauAO6sfNF9FbM/6XL1Eql275EB788Qiwelq3brbakP4GSSl7ArSfY+WA6cTbLXwVKS\nreJPAKPSlrmOZI5zLXCmN+1gkl+4lcDLwDwgVMP7M4xkz5GVwCpgLj69S2plf9JeWw8cXevHmzf9\nPuAvJE+4n6+h/dkAvEUy7/w6cIw3/RaS7Qlxb/o3a3V/hko8GOhDNyiJiARMradiREQkiwK7iEjA\nKLCLiASMAruISMAosIuIBIwCu4hIwCiwi4gEjAK7iEjA/H+/QMsoXMkVpQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x123c39d90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1241442d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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lnzazfwvWc3O+25JQj7TaZGYzgvmPmNltSev6hpntNbM3CtGWoA7ZbE/BY0LG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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12d041cd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# for county name\n",
    "\n",
    "for locat, df_group in data.groupby('COUNTY_NAME'):\n",
    "    if df_group.AS_RESULT_VALUE.count() > 100:\n",
    "        print 'location',locat\n",
    "        print \"mean AS value\", df_group.AS_RESULT_VALUE.mean()\n",
    "        print \"count\", df_group.AS_RESULT_VALUE.count()\n",
    "        #print df_group\n",
    "        fig,ax=plt.subplots()\n",
    "        ax.scatter(df_group.index,df_group.AS_RESULT_VALUE,color = 'g')\n",
    "        ax.set_title('{}'.format(locat))\n",
    "        \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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KGhURrwI7Aw91k//kiDg5H38f4FpJm0bEMzl9Qp6qZRlST8tLSNPBwNKDCS9V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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118360590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#for county name\n",
    "\n",
    "location_grid = []\n",
    "mean_grid = []\n",
    "for locat, df_group in data.groupby('COUNTY_NAME'):\n",
    "    if df_group.AS_RESULT_VALUE.count() > 100:\n",
    "        location_grid.append(locat)\n",
    "        mean_grid.append(df_group.AS_RESULT_VALUE.mean())\n",
    "        \n",
    "y_position = np.arange(len(location_grid))\n",
    "\n",
    " \n",
    "plt.barh(y_position, mean_grid, align='center', color = 'r', alpha=0.6,label=\"test\")\n",
    "plt.yticks(y_position, location_grid)\n",
    "plt.ylabel('Grids')\n",
    "plt.xlabel('Mean')\n",
    "\n",
    "plt.title('Arsenic Means from 2002 to 2013 of Different Grids')\n",
    " \n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [Root]",
   "language": "python",
   "name": "Python [Root]"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}