diabetes_project.ipynb

{
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   "cell_type": "code",
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     "text": [
      "/Users/Johnny1/anaconda/lib/python2.7/site-packages/h5py/__init__.py:34: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib as mpl \n",
    "import seaborn as sns \n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
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   "source": [
    "data = pd.read_csv('data.csv')\n",
    "train = data.drop(['result'], axis = 1)\n",
    "result = data['result']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   preg  plas  pres  skin  insu  mass   pedi  age           result\n",
      "0     6   148    72    35     0  33.6  0.627   50  tested_positive\n",
      "1     1    85    66    29     0  26.6  0.351   31  tested_negative\n",
      "2     8   183    64     0     0  23.3  0.672   32  tested_positive\n",
      "3     1    89    66    23    94  28.1  0.167   21  tested_negative\n",
      "4     0   137    40    35   168  43.1  2.288   33  tested_positive\n"
     ]
    }
   ],
   "source": [
    "print(data.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "preg      0\n",
      "plas      0\n",
      "pres      0\n",
      "skin      0\n",
      "insu      0\n",
      "mass      0\n",
      "pedi      0\n",
      "age       0\n",
      "result    0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#Prints out the number of NULL entries in each feature\n",
    "print(data.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "ufunc 'isnan' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-7-8a7d2d893ca0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mheatmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/Users/Johnny1/anaconda/lib/python2.7/site-packages/seaborn/matrix.pyc\u001b[0m in \u001b[0;36mheatmap\u001b[0;34m(data, vmin, vmax, cmap, center, robust, annot, fmt, annot_kws, linewidths, linecolor, cbar, cbar_kws, cbar_ax, square, ax, xticklabels, yticklabels, mask, **kwargs)\u001b[0m\n\u001b[1;32m    483\u001b[0m     plotter = _HeatMapper(data, vmin, vmax, cmap, center, robust, annot, fmt,\n\u001b[1;32m    484\u001b[0m                           \u001b[0mannot_kws\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcbar\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcbar_kws\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxticklabels\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 485\u001b[0;31m                           yticklabels, mask)\n\u001b[0m\u001b[1;32m    486\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    487\u001b[0m     \u001b[0;31m# Add the pcolormesh kwargs here\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/Johnny1/anaconda/lib/python2.7/site-packages/seaborn/matrix.pyc\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, data, vmin, vmax, cmap, center, robust, annot, fmt, annot_kws, cbar, cbar_kws, xticklabels, yticklabels, mask)\u001b[0m\n\u001b[1;32m    165\u001b[0m         \u001b[0;31m# Determine good default values for the colormapping\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    166\u001b[0m         self._determine_cmap_params(plot_data, vmin, vmax,\n\u001b[0;32m--> 167\u001b[0;31m                                     cmap, center, robust)\n\u001b[0m\u001b[1;32m    168\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    169\u001b[0m         \u001b[0;31m# Sort out the annotations\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/Johnny1/anaconda/lib/python2.7/site-packages/seaborn/matrix.pyc\u001b[0m in \u001b[0;36m_determine_cmap_params\u001b[0;34m(self, plot_data, vmin, vmax, cmap, center, robust)\u001b[0m\n\u001b[1;32m    202\u001b[0m                                cmap, center, robust):\n\u001b[1;32m    203\u001b[0m         \u001b[0;34m\"\"\"Use some heuristics to set good defaults for colorbar and range.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 204\u001b[0;31m         \u001b[0mcalc_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplot_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m~\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misnan\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mplot_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    205\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mvmin\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    206\u001b[0m             \u001b[0mvmin\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpercentile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcalc_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mrobust\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mcalc_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: ufunc 'isnan' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''"
     ]
    }
   ],
   "source": [
    "sns.heatmap(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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