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       "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
       "     \"</p>\\n\"+\n",
       "     \"<ul>\\n\"+\n",
       "     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
       "     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
       "     \"</ul>\\n\"+\n",
       "     \"<code>\\n\"+\n",
       "     \"from bokeh.resources import INLINE\\n\"+\n",
       "     \"output_notebook(resources=INLINE)\\n\"+\n",
       "     \"</code>\\n\"+\n",
       "     \"</div>\"}};\n",
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     "metadata": {},
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   "source": [
    "import bokeh\n",
    "from bokeh.plotting import output_notebook\n",
    "output_notebook()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
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     "start_time": "2017-11-17T21:46:21.168776"
    },
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   "outputs": [],
   "source": [
    "## Get some data\n",
    "import pandas as pd\n",
    "#cashReserve18Nov2016 = pd.read_excel('https://rbidocs.rbi.org.in/rdocs/content/docs/PR130423112016_RM.xls')\n",
    "cashReserve18Nov2016 = pd.read_excel('/home/anand/DataScientist/data/PR130423112016_RM.xls')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-17T21:46:21.729196",
     "start_time": "2017-11-17T21:46:21.679293"
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   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Statement on Reserve Money</th>\n",
       "      <th>Unnamed: 2</th>\n",
       "      <th>Unnamed: 3</th>\n",
       "      <th>Unnamed: 4</th>\n",
       "      <th>Unnamed: 5</th>\n",
       "      <th>Unnamed: 6</th>\n",
       "      <th>Unnamed: 7</th>\n",
       "      <th>Unnamed: 8</th>\n",
       "      <th>Unnamed: 9</th>\n",
       "      <th>Unnamed: 10</th>\n",
       "      <th>Unnamed: 11</th>\n",
       "      <th>Unnamed: 12</th>\n",
       "      <th>Unnamed: 13</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>(` billion)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>ITEM</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Variations over</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016#</td>\n",
       "      <td>2016 #</td>\n",
       "      <td>Week</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Financial Year so far</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Year-on-year</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-03-31 00:00:00</td>\n",
       "      <td>2016-11-18 00:00:00</td>\n",
       "      <td>Amount</td>\n",
       "      <td>%</td>\n",
       "      <td>Amount</td>\n",
       "      <td>%</td>\n",
       "      <td>Amount</td>\n",
       "      <td>%</td>\n",
       "      <td>Amount</td>\n",
       "      <td>%</td>\n",
       "      <td>Amount</td>\n",
       "      <td>%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0 Statement on Reserve Money            Unnamed: 2  \\\n",
       "0         NaN                 (` billion)                  NaN   \n",
       "1         NaN                        ITEM                  NaN   \n",
       "2         NaN                         NaN                2016#   \n",
       "3         NaN                         NaN                  NaN   \n",
       "4         NaN                         NaN  2016-03-31 00:00:00   \n",
       "\n",
       "            Unnamed: 3       Unnamed: 4 Unnamed: 5             Unnamed: 6  \\\n",
       "0                  NaN              NaN        NaN                    NaN   \n",
       "1                  NaN  Variations over        NaN                    NaN   \n",
       "2               2016 #             Week        NaN  Financial Year so far   \n",
       "3                  NaN              NaN        NaN                2015-16   \n",
       "4  2016-11-18 00:00:00           Amount          %                 Amount   \n",
       "\n",
       "  Unnamed: 7           Unnamed: 8 Unnamed: 9   Unnamed: 10 Unnamed: 11  \\\n",
       "0        NaN                  NaN        NaN           NaN         NaN   \n",
       "1        NaN                  NaN        NaN           NaN         NaN   \n",
       "2        NaN                  NaN        NaN  Year-on-year         NaN   \n",
       "3        NaN              2016-17        NaN          2015         NaN   \n",
       "4          %               Amount          %        Amount           %   \n",
       "\n",
       "  Unnamed: 12 Unnamed: 13  \n",
       "0         NaN         NaN  \n",
       "1         NaN         NaN  \n",
       "2         NaN         NaN  \n",
       "3        2016         NaN  \n",
       "4      Amount           %  "
      ]
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    }
   ],
   "source": [
    "cashReserve18Nov2016.head()"
   ]
  },
  {
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     "end_time": "2017-11-17T21:46:24.702144",
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    {
     "ename": "ValueError",
     "evalue": "could not convert string to float: '%'",
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      "\u001b[1;32m<ipython-input-4-fb3da2322859>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mcashReserve18Nov2016\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfillna\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minplace\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m \u001b[0manalyze\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcorrelation_analyze\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcashReserve18Nov2016\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'Unnamed: 5'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'Unnamed: 0'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m/home/anand/anaconda3/envs/analytics/lib/python3.5/site-packages/datascienceutils-1.2.19-py3.5.egg/datascienceutils/analyze.py\u001b[0m in \u001b[0;36mcorrelation_analyze\u001b[1;34m(df, col1, col2, categories, measures, summary_only, check_linearity, trellis)\u001b[0m\n\u001b[0;32m    135\u001b[0m     \u001b[0mu\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mv\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcol1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcol2\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    136\u001b[0m     \u001b[0mplots\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mplotter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mscatterplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mu\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mv\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 137\u001b[1;33m     \u001b[0mplots\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mplotter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msb_jointplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mu\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mv\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    138\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mcheck_linearity\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    139\u001b[0m         \u001b[0mu_2diff\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgradient\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mu\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/anand/anaconda3/envs/analytics/lib/python3.5/site-packages/datascienceutils-1.2.19-py3.5.egg/datascienceutils/plotter.py\u001b[0m in \u001b[0;36msb_jointplot\u001b[1;34m(series1, series2)\u001b[0m\n\u001b[0;32m    535\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    536\u001b[0m     \u001b[1;31m# Show the joint distribution using kernel density estimation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 537\u001b[1;33m     \u001b[0msns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mjointplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mseries1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mseries2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"kde\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m7\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mspace\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    538\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    539\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mfacets_dashboard\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdataframe\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/anand/anaconda3/envs/analytics/lib/python3.5/site-packages/seaborn/distributions.py\u001b[0m in \u001b[0;36mjointplot\u001b[1;34m(x, y, data, kind, stat_func, color, size, ratio, space, dropna, xlim, ylim, joint_kws, marginal_kws, annot_kws, **kwargs)\u001b[0m\n\u001b[0;32m    823\u001b[0m         \u001b[0mjoint_kws\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msetdefault\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"shade\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    824\u001b[0m         \u001b[0mjoint_kws\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msetdefault\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"cmap\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcmap\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 825\u001b[1;33m         \u001b[0mgrid\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot_joint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkdeplot\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mjoint_kws\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    826\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    827\u001b[0m         \u001b[0mmarginal_kws\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msetdefault\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"shade\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/anand/anaconda3/envs/analytics/lib/python3.5/site-packages/seaborn/axisgrid.py\u001b[0m in \u001b[0;36mplot_joint\u001b[1;34m(self, func, **kwargs)\u001b[0m\n\u001b[0;32m   1704\u001b[0m         \"\"\"\n\u001b[0;32m   1705\u001b[0m         \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msca\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0max_joint\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1706\u001b[1;33m         \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1707\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1708\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/anand/anaconda3/envs/analytics/lib/python3.5/site-packages/seaborn/distributions.py\u001b[0m in \u001b[0;36mkdeplot\u001b[1;34m(data, data2, shade, vertical, kernel, bw, gridsize, cut, clip, legend, cumulative, shade_lowest, ax, **kwargs)\u001b[0m\n\u001b[0;32m    572\u001b[0m         \u001b[0max\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgca\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    573\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 574\u001b[1;33m     \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfloat64\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    575\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mdata2\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    576\u001b[0m         \u001b[0mdata2\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdata2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfloat64\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: could not convert string to float: '%'"
     ]
    }
   ],
   "source": [
    "import datascienceutils as dsu\n",
    "from datascienceutils import analyze\n",
    "cashReserve18Nov2016.fillna(0, inplace=True)\n",
    "\n",
    "analyze.correlation_analyze(cashReserve18Nov2016,'Unnamed: 5', 'Unnamed: 0')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-17T21:46:24.703891",
     "start_time": "2017-11-17T16:16:20.522Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cashReserve18Nov2016['Unnamed: 0']"
   ]
  }
 ],
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