diff --git a/02_end_to_end_machine_learning_project.ipynb b/02_end_to_end_machine_learning_project.ipynb
index dae1e4a..895a484 100644
--- a/02_end_to_end_machine_learning_project.ipynb
+++ b/02_end_to_end_machine_learning_project.ipynb
@@ -16,7 +16,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "**Note**: You may find little differences between the code outputs in the book and in these Jupyter notebooks: these slight differences are mostly due to the random nature of many training algorithms: although I have tried to make these notebooks' outputs as constant as possible, it is impossible to guarantee that they will produce the exact same output on every platform. Also, some data structures (such as dictionaries) do not preserve the item order. Finally, I fixed a few minor bugs (I added notes next to the concerned cells) which lead to slightly different results, without changing the ideas presented in the book."
    ]
@@ -245,6 +248,7 @@
    "source": [
     "import numpy as np\n",
     "\n",
+    "# For illustration only. Sklearn has train_test_split()\n",
     "def split_train_test(data, test_ratio):\n",
     "    shuffled_indices = np.random.permutation(len(data))\n",
     "    test_set_size = int(len(data) * test_ratio)\n",
@@ -395,7 +399,9 @@
    },
    "outputs": [],
    "source": [
+    "# Divide by 1.5 to limit the number of income categories\n",
     "housing[\"income_cat\"] = np.ceil(housing[\"median_income\"] / 1.5)\n",
+    "# Label those above 5 as 5\n",
     "housing[\"income_cat\"].where(housing[\"income_cat\"] < 5, 5.0, inplace=True)"
    ]
   },
@@ -416,7 +422,9 @@
    "cell_type": "code",
    "execution_count": 23,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -558,7 +566,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "The argument `sharex=False` fixes a display bug (the x-axis values and legend were not displayed). This is a temporary fix (see: https://github.com/pandas-dev/pandas/issues/10611). Thanks to Wilmer Arellano for pointing it out."
    ]
@@ -649,10 +660,12 @@
    },
    "outputs": [],
    "source": [
-    "housing.plot(kind=\"scatter\", x=\"median_income\", y=\"median_house_value\",\n",
-    "             alpha=0.1)\n",
-    "plt.axis([0, 16, 0, 550000])\n",
-    "save_fig(\"income_vs_house_value_scatterplot\")"
+    "from pandas.tools.plotting import scatter_matrix\n",
+    "\n",
+    "attributes = [\"median_house_value\", \"median_income\", \"total_rooms\",\n",
+    "              \"housing_median_age\"]\n",
+    "scatter_matrix(housing[attributes], figsize=(12, 8))\n",
+    "save_fig(\"scatter_matrix_plot\")"
    ]
   },
   {
@@ -665,12 +678,10 @@
    },
    "outputs": [],
    "source": [
-    "from pandas.tools.plotting import scatter_matrix\n",
-    "\n",
-    "attributes = [\"median_house_value\", \"median_income\", \"total_rooms\",\n",
-    "              \"housing_median_age\"]\n",
-    "scatter_matrix(housing[attributes], figsize=(12, 8))\n",
-    "save_fig(\"scatter_matrix_plot\")"
+    "housing.plot(kind=\"scatter\", x=\"median_income\", y=\"median_house_value\",\n",
+    "             alpha=0.1)\n",
+    "plt.axis([0, 16, 0, 550000])\n",
+    "save_fig(\"income_vs_house_value_scatterplot\")"
    ]
   },
   {
@@ -690,7 +701,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
    "source": [
     "Note: there was a bug in the previous cell, in the definition of the `rooms_per_household` attribute. This explains why the correlation value below differs slightly from the value in the book (unless you are reading the latest version)."
    ]
@@ -758,7 +772,7 @@
    },
    "outputs": [],
    "source": [
-    "housing = strat_train_set.drop(\"median_house_value\", axis=1)\n",
+    "housing = strat_train_set.drop(\"median_house_value\", axis=1) # drop labels for training set\n",
     "housing_labels = strat_train_set[\"median_house_value\"].copy()"
    ]
   },
@@ -772,7 +786,8 @@
    },
    "outputs": [],
    "source": [
-    "housing.iloc[21:24]"
+    "sample_incomplete_rows = housing[housing.isnull().any(axis=1)].head()\n",
+    "sample_incomplete_rows"
    ]
   },
   {
@@ -785,8 +800,7 @@
    },
    "outputs": [],
    "source": [
-    "housing_copy = housing.copy().iloc[21:24]\n",
-    "housing_copy.dropna(subset=[\"total_bedrooms\"])    # option 1"
+    "sample_incomplete_rows.dropna(subset=[\"total_bedrooms\"])    # option 1"
    ]
   },
   {
@@ -799,8 +813,7 @@
    },
    "outputs": [],
    "source": [
-    "housing_copy = housing.copy().iloc[21:24]\n",
-    "housing_copy.drop(\"total_bedrooms\", axis=1)       # option 2"
+    "sample_incomplete_rows.drop(\"total_bedrooms\", axis=1)       # option 2"
    ]
   },
   {
@@ -813,10 +826,9 @@
    },
    "outputs": [],
    "source": [
-    "housing_copy = housing.copy().iloc[21:24]\n",
-    "median = housing_copy[\"total_bedrooms\"].median()\n",
-    "housing_copy[\"total_bedrooms\"].fillna(median, inplace=True) # option 3\n",
-    "housing_copy"
+    "median = housing[\"total_bedrooms\"].median()\n",
+    "sample_incomplete_rows[\"total_bedrooms\"].fillna(median, inplace=True) # option 3\n",
+    "sample_incomplete_rows"
    ]
   },
   {
@@ -828,65 +840,22 @@
     "editable": true
    },
    "outputs": [],
-   "source": [
-    "housing_copy.drop(\"total_bedrooms\", axis=1)       # option 2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
-   "outputs": [],
-   "source": [
-    "median = housing_copy[\"total_bedrooms\"].median()\n",
-    "housing_copy[\"total_bedrooms\"].fillna(median, inplace=True) # option 3\n",
-    "housing_copy"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
-   "outputs": [],
-   "source": [
-    "# Summary...\n",
-    "housing_copy = housing.copy().iloc[21:24]\n",
-    "housing_copy.dropna(subset=[\"total_bedrooms\"])    # option 1\n",
-    "\n",
-    "housing_copy = housing.copy().iloc[21:24]\n",
-    "housing_copy.drop(\"total_bedrooms\", axis=1)       # option 2\n",
-    "\n",
-    "housing_copy = housing.copy().iloc[21:24]\n",
-    "median = housing_copy[\"total_bedrooms\"].median()\n",
-    "housing_copy[\"total_bedrooms\"].fillna(median, inplace=True) # option 3"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
-   "outputs": [],
    "source": [
     "from sklearn.preprocessing import Imputer\n",
     "\n",
     "imputer = Imputer(strategy=\"median\")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Remove the text attribute because median can only be calculated on numerical attributes:"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 48,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -899,7 +868,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 49,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -912,7 +881,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 50,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -923,9 +892,16 @@
     "imputer.statistics_"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Check that this is the same as manually computing the median of each attribute:"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 51,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -936,9 +912,16 @@
     "housing_num.median().values"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Transform the training set:"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 52,
    "metadata": {
     "collapsed": true,
     "deletable": true,
@@ -951,20 +934,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
-   "metadata": {
-    "collapsed": true,
-    "deletable": true,
-    "editable": true
-   },
-   "outputs": [],
-   "source": [
-    "housing_tr = pd.DataFrame(X, columns=housing_num.columns)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 53,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -972,12 +942,26 @@
    },
    "outputs": [],
    "source": [
-    "housing_tr.iloc[21:24]"
+    "housing_tr = pd.DataFrame(X, columns=housing_num.columns,\n",
+    "                          index = list(housing.index.values))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 54,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "housing_tr.loc[sample_incomplete_rows.index.values]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -990,7 +974,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 56,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -1004,7 +988,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 57,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -1022,7 +1006,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 58,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -1035,7 +1019,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 59,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -1052,7 +1036,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 60,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -1065,7 +1049,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 61,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -1082,7 +1066,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 62,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -1092,6 +1076,7 @@
    "source": [
     "from sklearn.base import BaseEstimator, TransformerMixin\n",
     "\n",
+    "# column index\n",
     "rooms_ix, bedrooms_ix, population_ix, household_ix = 3, 4, 5, 6\n",
     "\n",
     "class CombinedAttributesAdder(BaseEstimator, TransformerMixin):\n",
@@ -1115,7 +1100,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 63,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -1129,7 +1114,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 64,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -1151,7 +1136,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 65,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -1164,7 +1149,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 66,
    "metadata": {
     "collapsed": true,
     "deletable": true,
@@ -1174,6 +1159,8 @@
    "source": [
     "from sklearn.base import BaseEstimator, TransformerMixin\n",
     "\n",
+    "# Create a class to select numerical or categorical columns \n",
+    "# since Scikit-Learn doesn't handle DataFrames yet\n",
     "class DataFrameSelector(BaseEstimator, TransformerMixin):\n",
     "    def __init__(self, attribute_names):\n",
     "        self.attribute_names = attribute_names\n",
@@ -1185,7 +1172,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 67,
    "metadata": {
     "collapsed": true,
     "deletable": true,
@@ -1211,7 +1198,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 68,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -1229,7 +1216,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 69,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -1243,7 +1230,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 70,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -1266,7 +1253,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": 71,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -1282,7 +1269,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": 72,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -1298,9 +1285,16 @@
     "print(\"Predictions:\", lin_reg.predict(some_data_prepared))"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Compare against the actual values:"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": 73,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -1313,7 +1307,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": 74,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -1326,7 +1320,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": 75,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -1344,7 +1338,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": 76,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -1360,7 +1354,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
+   "execution_count": 77,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -1376,7 +1370,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
+   "execution_count": 78,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -1402,7 +1396,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
+   "execution_count": 79,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -1419,7 +1413,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
+   "execution_count": 80,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -1437,7 +1431,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 84,
+   "execution_count": 81,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -1453,7 +1447,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 85,
+   "execution_count": 82,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -1469,7 +1463,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 86,
+   "execution_count": 83,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -1485,7 +1479,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 87,
+   "execution_count": 84,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -1503,7 +1497,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 88,
+   "execution_count": 85,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -1517,7 +1511,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 89,
+   "execution_count": 86,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -1537,7 +1531,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 90,
+   "execution_count": 87,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -1548,19 +1542,29 @@
     "from sklearn.model_selection import GridSearchCV\n",
     "\n",
     "param_grid = [\n",
+    "    # try 12 (3×4) combinations of hyperparameters\n",
     "    {'n_estimators': [3, 10, 30], 'max_features': [2, 4, 6, 8]},\n",
+    "    # then try 6 (2×3) combinations with bootstrap set as False\n",
     "    {'bootstrap': [False], 'n_estimators': [3, 10], 'max_features': [2, 3, 4]},\n",
     "  ]\n",
     "\n",
     "forest_reg = RandomForestRegressor(random_state=42)\n",
+    "# train across 5 folds, that's a total of (12+6)*5=90 rounds of training \n",
     "grid_search = GridSearchCV(forest_reg, param_grid, cv=5,\n",
     "                           scoring='neg_mean_squared_error')\n",
     "grid_search.fit(housing_prepared, housing_labels)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The best hyperparameter combination found:"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 91,
+   "execution_count": 88,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -1573,7 +1577,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 92,
+   "execution_count": 89,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -1584,9 +1588,16 @@
     "grid_search.best_estimator_"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's look at the score of each hyperparameter combination tested during the grid search:"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 93,
+   "execution_count": 90,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -1601,7 +1612,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 94,
+   "execution_count": 91,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -1614,7 +1625,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 95,
+   "execution_count": 92,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -1638,7 +1649,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 96,
+   "execution_count": 93,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -1653,7 +1664,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 97,
+   "execution_count": 94,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -1667,7 +1678,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 98,
+   "execution_count": 95,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -1683,7 +1694,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 99,
+   "execution_count": 96,
    "metadata": {
     "collapsed": true,
     "deletable": true,
@@ -1705,7 +1716,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 100,
+   "execution_count": 97,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -1741,7 +1752,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 101,
+   "execution_count": 98,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -1778,7 +1789,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 102,
+   "execution_count": 99,
    "metadata": {
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     "deletable": true,
@@ -1791,7 +1802,7 @@
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   {
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-   "execution_count": 103,
+   "execution_count": 100,
    "metadata": {
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     "deletable": true,
@@ -1817,7 +1828,7 @@
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   {
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-   "execution_count": 104,
+   "execution_count": 101,
    "metadata": {
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     "deletable": true,
@@ -1867,7 +1878,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 105,
+   "execution_count": 102,
    "metadata": {
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     "deletable": true,
@@ -1900,7 +1911,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 106,
+   "execution_count": 103,
    "metadata": {
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     "deletable": true,
@@ -1925,7 +1936,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 107,
+   "execution_count": 104,
    "metadata": {
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     "deletable": true,
@@ -1968,7 +1979,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 108,
+   "execution_count": 105,
    "metadata": {
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     "deletable": true,
@@ -2008,7 +2019,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 109,
+   "execution_count": 106,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -2033,7 +2044,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 110,
+   "execution_count": 107,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -2066,7 +2077,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 111,
+   "execution_count": 108,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -2098,7 +2109,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 112,
+   "execution_count": 109,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -2150,7 +2161,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 113,
+   "execution_count": 110,
    "metadata": {
     "collapsed": true,
     "deletable": true,
@@ -2196,7 +2207,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 114,
+   "execution_count": 111,
    "metadata": {
     "collapsed": true,
     "deletable": true,
@@ -2219,7 +2230,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 115,
+   "execution_count": 112,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -2233,7 +2244,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 116,
+   "execution_count": 113,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -2256,7 +2267,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 117,
+   "execution_count": 114,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -2279,7 +2290,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 118,
+   "execution_count": 115,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -2295,7 +2306,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 119,
+   "execution_count": 116,
    "metadata": {
     "collapsed": true,
     "deletable": true,
@@ -2318,7 +2329,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 120,
+   "execution_count": 117,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -2341,7 +2352,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 121,
+   "execution_count": 118,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -2384,7 +2395,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 122,
+   "execution_count": 119,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -2401,7 +2412,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 123,
+   "execution_count": 120,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -2424,7 +2435,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 124,
+   "execution_count": 121,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -2471,7 +2482,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 125,
+   "execution_count": 122,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -2491,7 +2502,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 126,
+   "execution_count": 123,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -2514,7 +2525,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 127,
+   "execution_count": 124,
    "metadata": {
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     "deletable": true,