{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# imports\n",
    "# sqlalchemy imports\n",
    "from sqlalchemy import create_engine, Column, String, Integer, Numeric, MetaData, Table, type_coerce, ForeignKey, case\n",
    "from sqlalchemy.orm import create_session \n",
    "from sqlalchemy.ext.declarative import declarative_base\n",
    "from sqlalchemy.ext.automap import automap_base\n",
    "from sqlalchemy.sql import select, and_, or_, not_, func\n",
    "\n",
    "# other imports\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import datetime as dt\n",
    "import pandas as pd\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# definitions\n",
    "dbpath='/ocean/eolson/MEOPAR/obs/WODSelect_46_52_m130_m122_nitrate/WOD2.sqlite'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Base = automap_base()\n",
    "engine = create_engine('sqlite:///' + dbpath, echo = False)\n",
    "# reflect the tables in salish.sqlite:\n",
    "Base.prepare(engine, reflect=True)\n",
    "# mapped classes have been created"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "Sta=Base.classes.stationTBL\n",
    "Prof=Base.classes.profileTBL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "session = create_session(bind = engine, autocommit = False, autoflush = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "qry=session.query(Sta.Year, Sta.Month, Sta.Day, Sta.Latitude, Sta.Longitude, \n",
    "                   Prof.Depth, Prof.Temperatur, Prof.Salinity,Prof.Nitrate, Prof.Chlorophyl, \n",
    "                   Prof.Phosphate).select_from(Prof).join(Sta,Sta.CastID==Prof.CastID).\\\n",
    "                    filter(and_(\n",
    "                            Prof.Depth != None,\n",
    "                            Prof.Chlorophyl != None,\n",
    "                            Prof.Nitrate != None\n",
    "                            ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#for row in data.all():\n",
    "#    print(row)\n",
    "qrydf=pd.DataFrame(qry.all())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "session.close()\n",
    "engine.dispose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "qrydf"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "qrydf.to_csv('/ocean/eolson/MEOPAR/obs/WODSelect_46_52_m130_m122_nitrate/WOD2.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# alternative: load csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "qrydf=pd.read_csv('/ocean/eolson/MEOPAR/obs/WODSelect_46_52_m130_m122_nitrate/WOD2.csv', index_col=0, parse_dates=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "qrydf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Using Pandas:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "qrydf.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "qrydf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "qrydf.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "qrydf.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "qrydf.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "qrydf.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "qrydf.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "np.nanmean(qrydf['Salinity'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "qrydf.sort_values(by='Month').head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# both endpoints are included\n",
    "qrydf.loc[2:5,['Year','Month']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# endpoint not included\n",
    "qrydf.iloc[2:5,0:2]"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# FILTERING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "qrydf[qrydf.Day == 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "qrydf[qrydf.Chlorophyl > 35.0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "qrydf[qrydf['Month'].isin([1,4])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "binD=np.round(qrydf['Depth']/5.0)*5.0\n",
    "qrydf['binDepth']=binD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "qrydf.loc[10:15,['Depth','binDepth']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "qrydf[pd.isnull(qrydf['Salinity'])].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "qrydf.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "qrydf.apply(lambda x: x.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "qrydf.apply(lambda x: x.max() - x.min())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# merge:\n",
    "mondf = pd.DataFrame({'Month': [1,2,3,4,5,6,7,8,9,10,11,12], \n",
    "                      'strMon': ['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec']})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "qrydf2=pd.merge(qrydf, mondf, on='Month')\n",
    "qrydf2.head(30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# grouping\n",
    "qrydf.groupby(['Year','Month','binDepth']).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# grouping\n",
    "qrydf.groupby(['Year','Month']).count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "qrydf.hist(figsize=(10,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8e52541dd8>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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NwC8BVyU5E7gW2FZVZwDbgY0jqk+SNMdIAqOqnqiqB7r5Z4CdwDrgQmBz97bNwEWjqE+S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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8e52541518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "NtoP=qrydf['Nitrate'][qrydf['Phosphate']>0]/qrydf['Phosphate'][qrydf['Phosphate']>0]\n",
    "NtoP.plot(kind='hist',bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8e4ffbfa20>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8e4ff430b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "NtoP[(qrydf['Phosphate']>0) & (qrydf['Depth']>80)].plot(kind='hist', bins=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8e4f8fb320>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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fp059j/377+J1r/t5JiYm6O/vLxHI/f39nDgxgVU+64E1pFIJ/vqvdzM4OMg9\n9+zjpps+RlDgwtlA8JgnOX36+7zpTVdhzBKOH38Sz1sB2OtfffVVc35/KlH6Gx/h5Mlj9Pf3N3Rd\nzcaRI0c4cuRIfU42nSkCtAG/UqspU8X5Y8DfYxWG/92jFLunHi1zbP3sNUWZByq5bgpprf0urnCj\n+PO2E4lznPtonRRmg3dO68sPp/lu2fLuiPqOgmunrS3tXFR2oFNbW9oV1Y1KNTM5GoFmXM0c5iGm\n8a+1XqCKc/8F8JHQd7uAG9x7DYQrCwI/fuF5ayWV6ikKEvuCf9u26yWZXOYC1X5c48IS4W6rxf2g\neWkTwiCTk5Ny5ZW/WhS7uOaaa52CKo5dGJNwwfE+gQ5pa0u5HlClLdmbqWWGZk/NjNkoDWOPr4wx\n5nbgB8Aw8FzASnlmNlaOMeY1wP/BptuK224ExoFPAucBx7CWzo8ijpdq1q8ojWZqaoqXv3yAU6fG\n8N0o7e2vJh6P88ILfwpcjnVHXYo1rn+EjUGsAS4BlgHfCJxxOfAM1hFwPjBBKpXl05++h56eniJ3\n1aOPPsqFF74CG7Ow14ZLSacHeO65r2DjKBPY5g8/CO33KhKJGCdOfNr9fTT/N8/bxLFjj9U9TlEv\npqamyrruFjvGGETE1HRwNZoF+FbE9s1aNVW9NtTSUJqMck+8w8PDLkNKAtsZUjzz4rB74h/PZzcl\nElnZtu16KS622yWFOovgaFVPkslsURX3+Pi43HnnnRLVuyrcy8m6xM6V4imAA7Jt2/XieUskleoX\n27Nqbck8jeBvboanfk3DrQzzUdxXzXfzvanSUJqJSoLKCu6g4B+NcDn1iG2JboV2Or0+30XWCvge\nsR1sw8f5o1X7BbaI38LDFt+tc5Xnpb2rbrnlw27IkZ13Yd1Sfurv3rzi8tcQ1XAw/Ju3bt2W71Kb\nTGbzqbrziabhTs98KI2oLrcl3833pkpDaQTVTtvzvCUyPDwsIyMj8tnPflYgJoV2576AlqKn+uDY\n1vCQo1SqW1KpZVJaRT7ojgvWW1zr9rPzuW2X20IzwXh8Wd4asVZQqVLx26SXE7blixNvcorsYgnW\neMwXrdyyfL6YM6WBda6+ApvNNAhsdNsQZQru5nNTpaHMN+WsiejRo34VdlJsT6dVTqieIZB0k+7K\nz+OOmmkxMjISIaj9flVh4b1cfLdXNjvoBjz5mVAFxWQ74a4Nrd1aH5UEfvRvXi7hAVLJZHUV7fVC\nLY3pmUsY6zCcAAAgAElEQVSl8U5s5Oun7tXfPgO8pdaL1mtTpaHMJzNrDOi7m3ISrsROJLpkbGws\nMlW0mvhA+Li3vOUKKY1ZBGMjPfmBSVFP4LY7bXgmxxJJp9dWHMcaPcsjKVF9q+b7KV/TcCszH+6p\nK2q9wFxuqjSU+WQ6t4cvqIqn6VVOVa0UNA5bNcFeT8Hj7KjVsHupW6xryk4E3LFjZ0WlFzWTo9Iw\nJn9diUSXxOOZvHDevPk3StYyk6f8egbRmyEg36zMpaXxdvf6XuB3wlutF63XpkpDmU+qnfdQ/ARe\n2vMplerJB5erv5bfVbb4ydkXjAVhvcI97WfFnwgYi6VLAtflu+FmJZNZW/bpvNw9CP4ev6tusANu\ncK3lfrdmPM0fc6k0trjXW6K2Wi9ar02VhjLfVCsQDx48LPF4VmxcI+kEus18am/vKBH+YYqtmuj5\nFX4FeWfnoCST3XLLLR+Wd7zjXSVP+olEV9XNA6cT7NUGmctN/SunEOoZh1ALY3rm3D3VrJsqDWU+\n8QVfOJ20nEAcGxsTSAjc7WIbmyUcsK5uil3ppLxMZm1EHMKT7dtvdBXclYV6rdQi3Ks5ppIymokS\nUGulOubS0ri5wnZTrRet16ZKQ5kvygm+XC5XViCOj4+L5/kZTGudxVEs/CsFiX0BmMmU1mbEYmnJ\nZDYUnQvWSyKRmfPMoWqCzEFBX411Uu7+VjtfvNI51OIoZS6VxnsjtpuxrT2erfWi9dpUaSj1xo9J\nhGMO5VJqt2x5d8UnZCvERgUOiO0hVSzUpktH9ddTKO4bzMcpojKeMpm1smPHzjnPHJppAL8aYR5W\nRtUe56P1GdUzL+4poBPbGv1b2IaCZ9Z60XptqjSUemLjEJ1iM59WSDzemc86KpdSm0xmKwq2Umsh\nerhRJQrC0B/JOinZ7KBs3/4BKZfx1Ci//nQWw3SKbKYWSjXXVkujlDlVGsAS4PecsrgV6Kn1YvXe\nVGko9WJyctJlPPmZTofd+xV5IWdnene4p/0lAnslnV4p27d/oKJADLc+t/GI6ltsTJcqO13G01wR\npZjqFZvwzz9TJaD1GdUxl+6p3di54DcAmVovMlebKg2lXoyPj0s6vUpsmmp0tlIul5NkMitwm8Ad\neaWSSnXLtm3Xy5133im5XG5W2UnlqCQMG2FVlAs41/tpvxYlUK5PllJgLpXGS8BxVxH+k8D2U+An\ntV60XpsqDWUmTCfMC5bGvRIuyEun18t/+S9vlkJ3WU9sv6e9YmsiBgQ6pL3d9msKCtNyvapmKtCa\nJZV0OsVQ76f9Wn63ZlFVRlNuFWUaqhEiBw8elrY2T2xabIcUxy/K9XeK6lTb5awVWyORSnUXXbfV\nBVq12VCNUnAa25geVRrKomY6ATVdz6hga45CttNOsa04BtzrToGLigQlnB/x3aDYPlDj4mdYWcvF\nXjeV6m55gdbsQlmzqKZnNkqjDUVpYQ4dGqavbzWXX34dfX2rOXRouGSfiYkJEol+7MQ5gPXE433c\nc8++omPvuWef228IO0Dy68AJ7MDK/wp8EzvNDvf6feB7oe8mgKeBfvf5SexUPnvd9vYzaWs7r2Qt\nExMTs74XQaampjh69ChTU1N1PS9Ab28v+/ffhedtIpvdiOdtYv/+uxo+Hc//zZlMhhMnJgj+dzl5\n8hj9/f2NW9xColZt0wwbamksaqp94o3ar9wTv025DbugRp3l0OcC5H72VL8U5lgUxzR8f37xdLxR\nSSQykkr1VFzzbF078+X+apYYi0j5YVDVxFWa6XfMF6h7SlmMzMQNEQ7O7tixs+TYjo510t7uSbCI\nrr29Q1Kp7kCdha9ARsUOKeqUPXv2FhUEBrN3duzY6YYnLRc7KnWdxOMZSSS6IgWaP2wpnV4lqVT3\njAX+XLqOGtmBdrokhnLV+tNdo9XjS7WiSkNZlMxUQEbHL8LB7uUSLKLzvLUyMjIi4+Pjsnv3HVIo\npusRuFKSyey0VdGpVI9TRvfmA+T+fIuwhWEtnR7xO9RWmpwXxVz58+spXGd6run2r/U3N3tsZi5R\npaEsWmaT3ukfa11LPWLTZ4tdR+DJ2NiYiIgr7lsuhSD5RoEO2bFjZ9F5oxWSJ7BOgpP0wkJtZGRE\norK2RkZGqv5NcyEIZ6Oc63Gu6fav9Tcv5oC5Kg1lUVOumKuc8Ap+b6fWDTgLQMRWgneI7RPlCZwt\nyWS37Nmz19VxdJUolrArJLpPVXCSXlekhWKVRnj+98CMlIZI/eskanED1ssqqHb/WosA1dJQpaEs\nQKrxf5cLhPqzJsq1Mb/llg+L7T47Gni6Twp0Fn1nZ2isc1bG8rz7CkRSqeWSTHZXbNBnLQxfMa2Q\nWCwd2W4kkegqOi48C6Oe92wm56o14WC2VsFM9p9NEeBiazuiSkNZsFTj/y4VLKNO8N9X5B7avfuO\n0H67nDVxgXvtd8J9iftO8ltn5wbXQuQmt+/Fbj//c7kBSRuk0KgwqEBGI4VfcGTsfAixagVtNcJ1\nrqyCuRbsmj2lSkNZIFT7lFksrA4HhH63+ywC6yUeTwcGFJX2l7L7H5BEIiOJRLbkuoVAePCYpKRS\nayMFpS+Mdu++w6Xe+rM1Dld0y8yXEJtpQHq6nk5zaRUsRsE+l6jSUBYkMxktWqjkjnILjQp0S0fH\nCjeCNXoang2Ix8Tz1kki0VVUb3Hw4GEZHx8vmYqXTpdO0Ivqw2QHJnnOMqksUOeDWv350ymaxeru\naTVUaSgLkukEW/Dp8+DBw859tDKkCC5wrqo+AU/a2lJiA9ml0/DCfaM8b0lRWmzUehKJLonF0mKD\n5wOSSHRV7PgKXkPamIepJXNoJrENtQqam9koDW0jojSM6Vpd9Pb2snnz24FLgZXApWze/HZ6e3tL\n2ocA/MM/fI5E4mmK23p8FziMbe/xAC+91AZ8Fngf8JvAq4CNwCbgbuB8t69t79HT05NvjxG1ntOn\nT3Lq1D+7Y26jrc3wutf9PBDdvqSzcxX/63+9j2PHHuPqq6+a7S2smf7+/hm32ijXjiXcAqW3t5dL\nLrmk4W1FlDmiVm1Trw3Yj23W80jgux7gfuBxYAToKnNsPZWvMo/MLMA9Kn4Vtp/eGvXEb1uD+JXX\n1m2USJwnwUwnOMtZExtdDCMuwaI7a21MRj5Fl67nbgmnyAaf1huV0lnP4Hb4vIs1RXWhQSu7p4Cf\nBTaElMYu4P3u/Q3A7WWOreNtVOaLaoXPyMiIG4zkC/xJSadXyp133lkSWyjuJjsqyWTWpdMGi+pK\nM53a2tICCbGZUx3u/UDZGgObdusroUkJF+OFf8d8+fh9ReFnbc00uF2t4NeYxcKgpZWGXT99IaXx\nGLDUvT8LeKzMcfW7i8q8UY0/vVCtvcI9/V8v/qS8WKzTWQjhFiCT+fNlMmsDQW9/H09SqTWB6/qF\nfAPO+tgrfrru8PBwybr37Nkrxem2uyQez0wrROfax+/fK6vQStN/631djVm0PgtRaTwT+vszZY6r\n1z1U5pFqAtyldRfhoLUnkBZbae1Je3vxE38ikSlxHcGAtLen3X6TUtoyxC/AK63CLhfU3rNnr+Ry\nOTlw4ICMjY3NuzAtXldpRthiaYuhzIzZKI1Y/aMkc4KU+8Ott96afz80NMTQ0NA8LEeZDf48hs2b\nNxGP93Hy5LGieQx+wPX4cT/gmgbOJRiAhQuwwefdwHWcPv1d4FI8bwD4Ptdfv43bb/9jbKB3Pf5s\ni9OnT2CD3z3AGaFz9gFfIB6fYnBwsGjNpWtaTyazkqmpKV7xip8FzuH48SfwvLOAH7N//13zEugu\nXtcU8B2Cv1nnSCgAR44c4ciRI/U5Wa3app4bpZbGoxS7px4tc1z9VK8yI+rhoog6h98PqvD0PCk2\n4BwetdotdnLeHziL4z6BLkkkMpLL5ZwrKSHBNuf2830CI+64sPXSIeCVNCD01xVlaZTO3yhf7T0X\n97Z0XbbKvbNzg8YclLKwANxT/cBXAp93ATe49xoIbzJm0ya7kkAMnjcezzhXUofACmlvzzihv15s\nX6isi0V4AkvFH4qUTq8MKJ1dTrmscEqn0+230b2+zB2/xr2eKZDK96kKU4hprHfHb41wgQ0KjNfs\nFqrl3oaD0+H5HooSpqWVBnAQOzfzReDbwK9jfQdfxKbc3g90lzm2vndSmZbZpF1WEoiTk5Oui6yf\n/jpaYgnE41kXuwi3D1+S3z+ZzMrIyEioXci42KK/qLjIcrHFfxeJH9wu93uqyZ6ajaVR6731rTNf\nSSzWwUJK9bS00pjNpkpj/plJJXHQqphOINpZFR0BK2CnhKu7PW+txGJe2ad7GJDt22903WKzUlx/\nkZVwE8JCmm2x0M9k1pb9PeHf4GdPeZ6tME+l+msW1LVUaYcVRFSHXa2lUMKo0lDmjWqfhsPCLGq8\narCxX2m8IFvGMrhPyveXsqNZt2693qXbDjilkJGoGg3rrgrXe6wvO40v+LuCKbbTNfKr972ttH8y\nmS2pYdEMKiWMKg1lXpmuwKuc8LPup0IabTKZlVwu54r4SpsH/szPvDp/nWTSr/YWKe5k6wksc69+\nnUVpgDuV6pYrr7wq33bcKpO9EQrIk82bf6Pi7/dTbHO53Lzf2yBRlkkmU7mBoqKIqNJQGkC5zKfx\n8XEZHh6WZPKiEtfSjh07nfJYLpCSZHJAYrG0y0AKu4l6pL09Lfv27ZPh4WEZHh6OyKhKiHVVJQVu\ndMeOS6kbquC2GhkZkTvuuEMK2ViHxWZW+QV+N1UUsvMRL6g2e6qccvZdVFq1rZRDlYbScHxh6nnr\nnEAudS3lcjnJ5XIuKyorNmU2Snj7MycGBM4V20tqucTjGWlr8wPhK9w5trpzjOYtmOJrjwokpa3N\nywt72+nWz7gaFJthdbbYVNzKMZpwsN63lhpFOctEq7aVSqjSUOpKLQNySmMSGSeMrRKIx8+T8fFx\n2bdvn7MMesRWL3dIYVDSpFMGI3lroxDI7hZbWxGu1/Ckra3HjWPdIPF4Vozxp+8tl+LJfLvc+bLu\n3KPiN0K0549uVOhTHKzvcr9xpSST3Q19mlcFocwUVRpK3ajF/RLlW7dP8CNiM6DuFvDkLW+5UqBN\nolxRBeXgp8EGlYnfI2pt6HsRWCeJREa2bbvepeP6iuJ3nCIIB8xvdIrJj4sMCnSIMcmKI1ajFWP5\njriK0syo0lDqwmzqBEoFaqF2AtJiTKd7f46zPoIKZkDa25c5hXCHe4LvkvI9ooqVTCzm95nqccrg\ny2KtmXBsY507d0/g3PdKLJaRZDIrHR0DkkhkIov7yivGcdEMJaXVmI3S0CFMSp5qh+yE8XtJed4m\nYAV2SFEaeKPb40xETgEPAA8Bz1A8KOn7iDwFCPA64M+AU9geUYOU9oha4r5/FXCaU6duAr4BHAHe\nA5yN7U313dB1ngDOxA5b2gT8Z+BaRE7z4osf5Pnnf8iJEwNcd9313HPPvqLfGDW0yPa+6ifc42m6\n4VKK0tLUqm2aYWOBWxrz7aueqaUR7u76P/7H9e4Jf617ovcD0uNiW2/4T+i+u8mmvl50kd/S2491\nbBAbu9gsMBzhZvLEurx8a2OJQE6sO+x897duicXS4nlLpKNjnbvehwPnslZGIpGVdHqNhFNvk8nu\nsrUn2exg5Azx4D5aja00M6h7auHRKOHjX9fm+2fL9mHauvV6J7zPF0hKe/uSgJKYFLgy4Ia6wymE\n0YDg7xa4W+LxzkD9xk53jpVimxB2ORdTwn2/wr2eHXITXeiUgj8bwxNjEnLw4GHZs2evmx2+3Cmk\nbeJnafnpqfbvxXUinZ0bqqpyDyp1nWyntAqqNBYY8yF8KlkxVpB2S2dn4Sk6WNCWy+Wc8L5JbCbS\nRU4pXCiFAPPywD6e28cTP4XWb+Hxjne8KzCdb9Ipk2D/qaTYgUtdAqtCFoyvgDwpxEAkbymMjY25\nQrdRKWRJdUgymZUdO3bmf3uhEeHs7nctbUAUpRGo0lhgzLXwCVoxqVR3kQCNUlhtbV7AAvDkta+9\nXGydQ3CK3U1iXUrBoHWwdcekUwZZZzHcIO3tnnvKDwaxbWtvW7XtCbxNSrOt4iHLw+9cW7hfyeSF\nEo+nBV4uwc62icSykgFLIkFFWXtLcbU0lFZBlcYCYy6FT/G5/YK6FXlBWaqwfKsiKLSjivf8VuMD\nUugsOyLWveRbHxudAmh3xw9IccaTbfexefNvOIF/vjtHsEGh31l21F3jvghl5VsfByLW6ZUtxqtH\nDElnaCutgCqNBchcCZ+CUpiUcPDX85ZILpcLtevYIqWpq+dJe/uq0HfrxcYe/GC2XwBXTqCPhhTO\npKTT62Xfvn3O+vAbE45KsevpXintctvvru3HNLJuHSNi02wL+3pedAfbeqLFdkqzo0pjgTIXwqdg\nadwrYZeO7wI7ePBwoB/UMikNYqcklSpVBL/2a2+PeLKPSWldxgXi1zcU6h3ulXi808UgVjqFsc29\nni12VsaFkkp1uzYkwbbnXWIHKY24bdJd8+5IxajCXFnsqNJoIZrhKfTgwcMuY6kjUqAWFMsuJ3T9\nILYV3lu3bstbQp2dGySZ7Jbdu++Q9773vRFWwHJpbw+PRA1bGjY4nUh0SanL6z5JJDKyb98+2bdv\nn7zjHe+SWKxTCm3P/UB58W9JJLokleqWVKpfbO+qteouUhSHKo0WoZly+CcnJ/NdZ8OzIQ4cOBBZ\nuxCLdcrY2Fj+eH9a3O7dd0gymZV43M+YKlYQt9zyYdfd1lc+/pjVCwU82b79AzIyMhJRcX2BtLd3\nuJqIZWJdXeGgeIckEpl82/O5mHOhKAuN2SgNY49vTYwx0irrn5qaoq9vNcePj2Krmh/B8zZx7Nhj\n9Pb2zus6JiYm6O/vp7e3N/85k8nwqU/dx+///h3EYn389Kc5bHX3V/PHet6F7Nr1bhKJFL/929tJ\nJPp57rnHOXXqJWAlcAyIA6eBPuAYbW0eDzxwH5lMhvHxcb71rWPcfvsdxGIv59Sp7/PRj/4hW7Zc\nG3l/4vH/RFsbvPjip4ErgD8B7gD+Lb+mdPpi/vqvd/P617++5LcpihKNMQYRMTUdXKu2aYaNFrI0\nmiGHv+BSskON/MK9aHfVaMhq2CXFaa5+x9iwS8mfrjeeP8fu3XeUjCSNevIvtFe3o1MTiT63pp1i\n4y/RwXu1IBRlZqDuqeanETn8083oBk+2bbu+bGDc1mJ0is1OSklx8HmJ2KDzYOiYC12cYYPbZ6m0\nt1cunAuuM5fLueyp0cA6uwLKojB3I5Xq0RiFotSAKo0WYa5z+IPC9+DBw5JMZiWVWpavgA7PjrZp\nsnFnPZQ+xdun/GGBXinMkfAHJK13SiMqnfZt7m+jUki7nSyxsCYnJ2X79hvdXOt1ZWeJ26D3WQFL\nJyVXXnmVWhiKUiOqNFqI2WRPVTo2XOVdqJq2VdzGJF3WUVDAdwmsDriYip/i4/GMczWFg9s9ThEM\nOKuiU2xleNZd189sSkhhNsbWIktjz569EoulpbiqfFfELPGCVdPRsVpuvvnmhk7KU5SFgCqNRUCl\nzKti19Nesc3+Squ4bTuQHudS6nH7dbtjOiSdXl/UVsS3VqzgHw9YCwMCZ7hr9LrXlNi+UMFrFqbh\ngScdHedLKmVjKVYxlKbYplKr81ld4YrxxRC/aIaUbGXho0qjiZlOCFT6ezBlNCoeMjY2JgcOHJDh\n4WHn0rnDCfDbnIUhge1cSaXWSqHFh18At1Pgy5JKdcvw8LCMjIwUrWX3bv+cBWvACvtgbGPUWRvh\nIr7CkCJrvVyQd0HZJoUbQvuvF0hKLpfLpwSnUt2LpiVHM6VkKwsbVRpNynRCoNLfd+++Q+LxtKTT\naySZzIrnrQsJWN81tNIJ9bj7fLHAmERVcdtq63D8wXabbWvzJJHoyq9lx46dkcrKWhQpZ50EFYM/\nijXsxposee95S5wFU5p5lUyeV5RRVqkV+UJCmx0q84kqjSZkOiFQ6e/XXPObEoxHwFskuh34aOjz\ngHvi7xHbVsMTP4D8+tf/goshJAX6JR7Pus/3im1K6Ae0J8W230hIPJ52BXm+ovKHJ/lDk24KWBo9\nUmhhfqEUhir58Y3CXO/Ozg2yY8fOQExjnVhX1k1lBeVCfwpvhpRsZfGgSqMJmU4IlPv78PBwpIIw\nJhtQJJ1SOojIr5/ISDA1tr09Ix/5yEdcLyk/ntEl7e2edHauc/sdcIL7sBP6K6TQoiPY2jwqU8pm\nVrW3pyUeX+qE/yqBbonHz5SdO3dGBLY92bNnb1H2VCZTvs3HYngKXwy/UWkeVGk0IbVaGnfeeaeU\nxiMukEI7cf+pPBEhwFdJaWrsgLS19UhU+w1bP+ErkpRTRmH3Uod7PUdK+0qtFzhDkslzJJnsilzT\n8PBwIC6yXoJZUsF7UcnttFiewrWtujJfLFilAbwBeAz4OnBDxN/rdQ/nhHJCIJfLyZYt75b29sKI\n0njc1lKMjY1FWBpR8yv8jKULpFAbEa7G7nGWgz+M6Gax8Y5JgbNdt9hgy/GoQPZKsTUXN0aswT9/\ncMpeLnD8gKTTq1y9yBoJZmDNROgvpqfwhRy3UZqH2SiNpu09ZYxpwyqL1wLfB44CvyoijwX2kWZd\nv8+jjz7KF7/4RU6dOkUsFuOBB45y8OBfAecC38X2WTLAUVKpPuAZfu7nXsX9948CLwN+AGSALuCJ\nwJlXAD8B3g28HniN2/84kAUmgXbgFcA4kHTX/DYQA84CngT+HLgKmAL6gS/h936CIeAl4Btu30vc\nOV/uPp8GPu7W8x7gDHfde4A1wGXY/4RfA34ReIBa+24dOjTM5s3vIR7v4+TJY+zffxdXX31VVccq\nilLMguw9BVwKfD7weTsha4MWsDSsywaBNufiiernNCCFsaUZ98SelEIl9q9WsDQuEltU53eYTbhX\n3wJpd9/5vaKyofNkpZDhlHBWSaE4z5iU2AyrTrEZU6MCCfnIRz7iYhWjEl1J7olN57VWRyrVL8nk\n7NJn9SlcUeoDC9E9hW1rujfw+e3AnaF96nUP687k5KQU4hC+AG+TwhS8cm07smIHF3nOpeTPsYia\nTOfPu/AzpaJcSJ4UiviGpTQuMeAUjl+DkRLr6so4heQHwwfFj5P40+8KxX/hGMxat95RCbqTtEW5\nojQHs1EabbMxcZTy3HjjjUAC65L5unuNY11SjwATwHlYdw3udRXwIbff54AX3OuLWO/cBHAbIMBS\nYBcwCvwxcA7wf7EuqOA5z8G6t14GPI319D3i/v4I1s3029jQ0fvdmv4W+P/duTvc2h90n9+NyHfp\n7+/n6quv4qGHHiCZnAyd89/ZvPkdeN4VZLMb8bxN7N9/F2vWrOGSSy7RtuWK0sLEGr2ACnwPWBb4\nfK77rohbb701/35oaIihoaG5XldVjI6OUirAl2GVxqXYmMJTWCHrxxCOASl3XBobY/Bf/fO8DbgZ\nG5u40H3/FFYZ/ArwR6Fzfg9oA34M/CFwChur6McqoVPAOqDX7f9d4Hx3/FFgeeg3LOGDH7wmL/jX\nrFnDxz62h82bNxGLLePEiQk++tGPsmXLtfzP//n7Ot9CUZqAI0eOcOTIkfqcrFYTZa43bMT1Cew0\nnwTwMLAmtE/97LU6c9NNN5VxFSWd+ykmthiuW4p7LPkNAkelUDgXdmN1B1xffg1F2p3Dd235dRt+\nTGOJe590Lqhz3Gss7/Zqb8/I29729tB5i2szymUtabxBUVoHFmL2FIAx5g3AR7GPyvtF5PbQ36WZ\n159MdnDiBFgX0feAk9hspJh7fQmrDzuA54GX094+SSrVxnPPncRmTP0Ymw31E+BsrFVxCjhFJtPJ\ns8+ewGYzfRebhbUE64Y6zeDgRrZvv4FzzjmHBx98EM/zWLZsGeeddx7f+c53AEin0zz44IMsXbqU\nTZs20dvbyz337OP6699PItHPCy88gTHtpFLna9aSoiwQZpM91dRKYzqaXWkA3HzzzXziE59g1apV\nvPWtbwXgq1/9Kl//+td5/vnnSaVS/PCHP2TVqlW88Y1vzAvuu+++m0OHDjE0NMTAwABnnHEGX/rS\nl3jooYdoa2vjuuuu441vfCOPPvoo9913HwCXXXYZzz33HACDg4OzcgkFR6cC6mZSlAWEKg1FURSl\namajNDR7SlEURakaVRqKoihK1ajSUBRFUapGlYaiKIpSNao0FEVRlKpRpaEoiqJUjSoNRVEUpWpU\naSiKoihVo0pDURRFqRpVGoqiKErVqNJQFEVRqkaVhqIoilI1qjQURVGUqlGloSiKolSNKg1FURSl\nalRpKIqiKFWjSkNRFEWpGlUaiqIoStWo0lAURVGqRpWGoiiKUjWqNBRFUZSqUaWhKIqiVI0qDUVR\nFKVqVGkoiqIoVaNKQ1EURakaVRqKoihK1ajSUBRFUaqmYUrDGPNWY8xXjTGnjTEbQ3/7gDHmG8aY\nR40xr2/UGhVFUZRiGmlpfAX4r8A/Bb80xqwBfgVYA/wCcJcxxsz/8urHkSNHGr2EqtB11hddZ/1o\nhTVC66xzNjRMaYjI4yLyDSCsEN4EHBaRUyIyAXwDeOV8r6+etMo/JF1nfdF11o9WWCO0zjpnQzPG\nNM4BvhP4/D33naIoitJgYnN5cmPMF4Clwa8AAT4oIp+dy2sriqIo9ceISGMXYMwo8F4RedB93g6I\niOxyn/8euEVE/iXi2MYuXlEUpUURkZpixXNqacyA4OI/A9xrjPkjrFtqBTAedVCtP1pRFEWpjUam\n3L7ZGPMd4FLgb40xnwcQkRzwSSAHfA54jzTaHFIURVGAJnBPKYqiKK1DM2ZPlcUY02OMud8Y87gx\nZsQY01VmvwljzJeNMQ8ZYyJdW3O0vjcYYx4zxnzdGHNDmX3udIWLDxtjNszX2kJrqLhOY8xlxpgf\nGWMedNuHGrDG/caYp40xj1TYpxnuZcV1Nsm9PNcY84/GmK8ZY75ijNlWZr+G3s9q1tkk9zNpjPkX\nJw8ZCOcAAAZZSURBVF++Zoz5/TL7Nfp+TrvOmu6niLTMBuwC3u/e3wDcXma/bwI987y2NuAJoA+I\nAw8Dq0P7/ALwd+79zwAPNOAeVrPOy4DPNPi/9c8CG4BHyvy94feyynU2w708C9jg3meAx5v032Y1\n62z4/XTr6HCv7cADwGua7X5Wuc4Z38+WsjSwhX8fd+8/Dry5zH6G+beiXgl8Q0SOichJ4DB2vUHe\nBPwFgNhssC5jzFLml2rWCaVFl/OKiIwBP6ywSzPcy2rWCY2/l0+JyMPu/bPAo5TWPjX8fla5Tmjw\n/QQQkefd2yRW1oT/DTT8frprT7dOmOH9bDWlcaaIPA32HxhwZpn9BPiCMeaoMebaeVpbuCjxu5T+\ng2+GwsVq1gnwKmdW/50x5sL5WdqMaIZ7WS1Ncy+NMf1Yyyicwt5U97PCOqEJ7qcxps0Y8xDwFHBE\nbAJPkKa4n1WsE2Z4P5sl5TZPhYLAKF9buSj+a0TkSWNML1Z5POqeCJXq+DdgmYg8b4z5BeBvgJUN\nXlOr0jT30hiTAf4KuN49yTcl06yzKe6niLwEDBpjssD9xpjLROSfpjtuvqlinTO+n01naYjI5SKy\nPrCtc6+fAZ72TTxjzFnAZJlzPOlep4D7mJ/eVd8DlgU+n+u+C+9z3jT7zDXTrlNEnvXNWhH5PBA3\nxiyZvyVWRTPcy2lplntpjIlhBfEnROTTEbs0xf2cbp3Ncj8D6/kJ8HfA/xf6U1PcT59y66zlfjad\n0piGzwDvcu/fCZT8ozLGdLgnFYwxaeD1wFfnYW1HgRXGmD5jTAL4VbfeIJ8B3uHWdinwI9/dNo9M\nu86g79UY80psavYz87tMe3nK+1ub4V76lF1nE93LPwdyIvLRMn9vlvtZcZ3NcD+NMS8zLnPTGOMB\nl2MTSoI0/H5Ws85a7mfTuaemYRfwSWPMNcAxbAt1jDFnA/tE5I1Y19Z9xrYYiQH3isj9c70wETlt\njNkK3I9VxvtF5FFjzBb7Z9krIp8zxvyiMeYJ4Dng1+d6XbWsE3irMebdwEngOHDVfK/TGHMQGALO\nMMZ8G7gFSNBE97KaddIc9/I1wK8BX3H+bQFuxGbQNc39rGadNMH9BM4GPm6M8RNuPiEi/9Bs/69X\ns05quJ9a3KcoiqJUTau5pxRFUZQGokpDURRFqRpVGoqiKErVqNJQFEVRqkaVhqIoilI1qjQURVGU\nqlGloSxKjDGnXSvorxhjho0xKVfw+JU5vOZlxpjPzvCYd7ruB4rSFKjSUBYrz4nIRhFZhy1sus59\nP9eFSzM9/7to3kaMyiJElYaiwP/FzqIHiBlj9hpjvmqM+XtjTBLAGHOxMeZLrhvopwLtGbYZO+Dm\nYVcdjjHmFmPMXxhj/tnYgWG/EbhWpzHmfxtjHjXGfML/0hhzk7EDcx4xxuxx312B7RX0l84qShpj\nNhpjjhjbwfnzpgHttpXFjSoNZbFiIN8g7xcA3y11AfAnIrIW+DFwhfv+L4DfFZEN2F5mt7jvb8AO\nDtpAwVoBWIdtL/Jq4OaAi2kDsA24EBgwxrzaff8nIvIzIrIe6DDG/JKIfAr4V+BtIrIROA38CXCF\niFwCfAyInBqnKHOFKg1lseIZYx4ExrF9zPa7778pIr4C+Teg37WV7gq01/848HPu/ZeBg8aYX8MK\ndZ9Pi8gJEfkP4B8pdFoeF5EnxfbveRjod9+/1hjzgLFjYzcBFwXO5TdDXAWsxbb7fwj4IPDy2m+B\nosycVmtYqCj14nn39J7H9nXjxcBXp4GU/+cy5/klrAL5ZeCDxpi17vtg7MIEPofPH3MusD8FNorI\n940xtwSuW7RE4Ksi8ppKP0xR5hK1NJTFSjklUPK9m0XwjOvCCvDfAH+QzTI31GY7kMXOtgZ4kzEm\nYYw5AzuH+WiFtaSwSuU/XFv/twb+9lN3XrAzs3tdq22MMTHTnFMVlQWMWhrKYqVcFlO5798F7HFz\nCb4J/LqLh/ylc18Z4KMi8hNnsTwCHAHOAG4TkaeMMauiriUiPzbG/BnwNeBJrMvM54C77vPAq4Ar\ngTtdIL4d+GMgaoSnoswJ2hpdUeqMcy/9VEQ+0ui1KEq9UfeUoiiKUjVqaSiKoihVo5aGoiiKUjWq\nNBRFUZSqUaWhKIqiVI0qDUVRFKVqVGkoiqIoVaNKQ1EURama/weoNCpUQ7mqvwAAAABJRU5ErkJg\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8e7c02d550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "qrydf.plot(kind='scatter',x='Phosphate',y='Nitrate')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [default]",
   "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": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}