diff --git a/AliMehroze_lhr_assignment_1_Python.ipynb b/AliMehroze_lhr_assignment_1_Python.ipynb
new file mode 100644
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+++ b/AliMehroze_lhr_assignment_1_Python.ipynb
@@ -0,0 +1,420 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['age' 'bp' 'sg' 'al' 'su' 'rbc' 'pc' 'pcc' 'ba' 'bgr' 'bu' 'sc' 'sod'\n",
+      " 'pot' 'hemo' 'pcv' 'wbcc' 'rbcc' 'htn' 'dm' 'cad' 'appet' 'pe' 'ane'\n",
+      " 'class']\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib\n",
+    "import csv\n",
+    "\n",
+    "df=pd.DataFrame()\n",
+    "df=pd.read_csv(\"chronic_kidney_disease_updated.csv\")\n",
+    "\n",
+    "\n",
+    "print df.columns.values\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>age</th>\n",
+       "      <th>bp</th>\n",
+       "      <th>sg</th>\n",
+       "      <th>al</th>\n",
+       "      <th>su</th>\n",
+       "      <th>rbc</th>\n",
+       "      <th>pc</th>\n",
+       "      <th>pcc</th>\n",
+       "      <th>ba</th>\n",
+       "      <th>bgr</th>\n",
+       "      <th>...</th>\n",
+       "      <th>pcv</th>\n",
+       "      <th>wbcc</th>\n",
+       "      <th>rbcc</th>\n",
+       "      <th>htn</th>\n",
+       "      <th>dm</th>\n",
+       "      <th>cad</th>\n",
+       "      <th>appet</th>\n",
+       "      <th>pe</th>\n",
+       "      <th>ane</th>\n",
+       "      <th>class</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</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>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</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>48</td>\n",
+       "      <td>80</td>\n",
+       "      <td>1.020</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>?</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>notpresent</td>\n",
+       "      <td>notpresent</td>\n",
+       "      <td>121</td>\n",
+       "      <td>...</td>\n",
+       "      <td>44</td>\n",
+       "      <td>7800</td>\n",
+       "      <td>5.2</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>no</td>\n",
+       "      <td>good</td>\n",
+       "      <td>no</td>\n",
+       "      <td>no</td>\n",
+       "      <td>ckd</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>7</td>\n",
+       "      <td>50</td>\n",
+       "      <td>1.020</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>?</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>notpresent</td>\n",
+       "      <td>notpresent</td>\n",
+       "      <td>?</td>\n",
+       "      <td>...</td>\n",
+       "      <td>38</td>\n",
+       "      <td>6000</td>\n",
+       "      <td>?</td>\n",
+       "      <td>no</td>\n",
+       "      <td>no</td>\n",
+       "      <td>no</td>\n",
+       "      <td>good</td>\n",
+       "      <td>no</td>\n",
+       "      <td>no</td>\n",
+       "      <td>ckd</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>62</td>\n",
+       "      <td>80</td>\n",
+       "      <td>1.010</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>notpresent</td>\n",
+       "      <td>notpresent</td>\n",
+       "      <td>423</td>\n",
+       "      <td>...</td>\n",
+       "      <td>31</td>\n",
+       "      <td>7500</td>\n",
+       "      <td>?</td>\n",
+       "      <td>no</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>no</td>\n",
+       "      <td>poor</td>\n",
+       "      <td>no</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>ckd</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>48</td>\n",
+       "      <td>70</td>\n",
+       "      <td>1.005</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>abnormal</td>\n",
+       "      <td>present</td>\n",
+       "      <td>notpresent</td>\n",
+       "      <td>117</td>\n",
+       "      <td>...</td>\n",
+       "      <td>32</td>\n",
+       "      <td>6700</td>\n",
+       "      <td>3.9</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>no</td>\n",
+       "      <td>no</td>\n",
+       "      <td>poor</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>ckd</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 25 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   age   bp     sg   al   su     rbc        pc         pcc          ba  bgr  \\\n",
+       "0  NaN  NaN    NaN  NaN  NaN     NaN       NaN         NaN         NaN  NaN   \n",
+       "1   48   80  1.020    1    0       ?    normal  notpresent  notpresent  121   \n",
+       "2    7   50  1.020    4    0       ?    normal  notpresent  notpresent    ?   \n",
+       "3   62   80  1.010    2    3  normal    normal  notpresent  notpresent  423   \n",
+       "4   48   70  1.005    4    0  normal  abnormal     present  notpresent  117   \n",
+       "\n",
+       "   ...   pcv  wbcc rbcc  htn   dm  cad appet   pe  ane class  \n",
+       "0  ...   NaN   NaN  NaN  NaN  NaN  NaN   NaN  NaN  NaN   NaN  \n",
+       "1  ...    44  7800  5.2  yes  yes   no  good   no   no   ckd  \n",
+       "2  ...    38  6000    ?   no   no   no  good   no   no   ckd  \n",
+       "3  ...    31  7500    ?   no  yes   no  poor   no  yes   ckd  \n",
+       "4  ...    32  6700  3.9  yes   no   no  poor  yes  yes   ckd  \n",
+       "\n",
+       "[5 rows x 25 columns]"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#first five rows\n",
+    "df.head(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "##A function to replace \"?\"  with NaN, and trim other data\n",
+    "def cleanse(df):\n",
+    "    df.replace(\"?\", np.nan, inplace=True)\n",
+    "    df.replace(regex=True,inplace=True, to_replace=r'\\t',value=r'')\n",
+    "    df.replace(regex=True,inplace=True, to_replace=r' ',value=r'')\n",
+    "    return df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([nan, 'yes', 'no'], dtype=object)"
+      ]
+     },
+     "execution_count": 103,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "##calling function\n",
+    "# df.age.replace(\"?\", np.nan, inplace=True)\n",
+    "cleanse(df)\n",
+    "##df.head(5)\n",
+    "df.dm.unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[nan 'yes' 'no']\n"
+     ]
+    }
+   ],
+   "source": [
+    "print df.dm.unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "no     261\n",
+       "yes    137\n",
+       "Name: dm, dtype: int64"
+      ]
+     },
+     "execution_count": 105,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.dm.value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "('Unable to parse string', u'occurred at index age')",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0mTraceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-101-827cc6d5aad5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'age'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'bp'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'bgr'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'bu'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'sc'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'sod'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'pot'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'hemo'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'pcv'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'wbcc'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'rbcc'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'age'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'bp'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'bgr'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'bu'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'sc'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'sod'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'pot'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'hemo'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'pcv'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'wbcc'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'rbcc'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_numeric\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/pandas/core/frame.pyc\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self, func, axis, broadcast, raw, reduce, args, **kwds)\u001b[0m\n\u001b[1;32m   4059\u001b[0m                     \u001b[0;32mif\u001b[0m \u001b[0mreduce\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   4060\u001b[0m                         \u001b[0mreduce\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4061\u001b[0;31m                     \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_apply_standard\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreduce\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mreduce\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4062\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4063\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_apply_broadcast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/pandas/core/frame.pyc\u001b[0m in \u001b[0;36m_apply_standard\u001b[0;34m(self, func, axis, ignore_failures, reduce)\u001b[0m\n\u001b[1;32m   4155\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4156\u001b[0m                 \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mseries_gen\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4157\u001b[0;31m                     \u001b[0mresults\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4158\u001b[0m                     \u001b[0mkeys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4159\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/pandas/tools/util.pyc\u001b[0m in \u001b[0;36mto_numeric\u001b[0;34m(arg, errors)\u001b[0m\n\u001b[1;32m    113\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    114\u001b[0m             values = lib.maybe_convert_numeric(values, set(),\n\u001b[0;32m--> 115\u001b[0;31m                                                coerce_numeric=coerce_numeric)\n\u001b[0m\u001b[1;32m    116\u001b[0m         \u001b[0;32mexcept\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    117\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0merrors\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'raise'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32mpandas/src/inference.pyx\u001b[0m in \u001b[0;36mpandas.lib.maybe_convert_numeric (pandas/lib.c:53558)\u001b[0;34m()\u001b[0m\n",
+      "\u001b[0;32mpandas/src/inference.pyx\u001b[0m in \u001b[0;36mpandas.lib.maybe_convert_numeric (pandas/lib.c:53344)\u001b[0;34m()\u001b[0m\n",
+      "\u001b[0;31mValueError\u001b[0m: ('Unable to parse string', u'occurred at index age')"
+     ]
+    }
+   ],
+   "source": [
+    "df[['age', 'bp', 'bgr', 'bu', 'sc', 'sod', 'pot', 'hemo', 'pcv', 'wbcc', 'rbcc']] = df[['age', 'bp', 'bgr', 'bu', 'sc', 'sod', 'pot', 'hemo', 'pcv', 'wbcc', 'rbcc']].apply(pd.to_numeric)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "#df\n",
+    "##matplotlib.bar(df['rbcc'],df['class'], width = 0.5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0mTraceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-96-b6c5f495e390>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'bp'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'bp'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'class'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0;34m'ckd'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m/usr/local/lib/python2.7/dist-packages/pandas/core/generic.pyc\u001b[0m in \u001b[0;36m__nonzero__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    890\u001b[0m         raise ValueError(\"The truth value of a {0} is ambiguous. \"\n\u001b[1;32m    891\u001b[0m                          \u001b[0;34m\"Use a.empty, a.bool(), a.item(), a.any() or a.all().\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 892\u001b[0;31m                          .format(self.__class__.__name__))\n\u001b[0m\u001b[1;32m    893\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    894\u001b[0m     \u001b[0m__bool__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m__nonzero__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mValueError\u001b[0m: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all()."
+     ]
+    }
+   ],
+   "source": [
+    "(df['bp']==max(df['bp']) and df['class']=='ckd')==True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "df.to_csv(\"clean_chronic_kidney_disease.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "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"
+  },
+  "widgets": {
+   "state": {},
+   "version": "1.1.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/DIH b/DIH
new file mode 160000
index 0000000..c089def
--- /dev/null
+++ b/DIH
@@ -0,0 +1 @@
+Subproject commit c089defeacf966815508d06dec5fe5cab65c2b0d