diff --git a/2-Working-With-Data/08-data-preparation/notebook.ipynb b/2-Working-With-Data/08-data-preparation/notebook.ipynb
index e45a5cb..93c37c3 100644
--- a/2-Working-With-Data/08-data-preparation/notebook.ipynb
+++ b/2-Working-With-Data/08-data-preparation/notebook.ipynb
@@ -3,28 +3,28 @@
     {
       "cell_type": "markdown",
       "source": [
-        "# Data Preparation\r\n",
-        "\r\n",
-        "[Original Notebook source from *Data Science: Introduction to Machine Learning for Data Science Python and Machine Learning Studio by Lee Stott*](https://github.com/leestott/intro-Datascience/blob/master/Course%20Materials/4-Cleaning_and_Manipulating-Reference.ipynb)\r\n",
-        "\r\n",
-        "## Exploring `DataFrame` information\r\n",
-        "\r\n",
-        "> **Learning goal:** By the end of this subsection, you should be comfortable finding general information about the data stored in pandas DataFrames.\r\n",
-        "\r\n",
-        "Once you have loaded your data into pandas, it will more likely than not be in a `DataFrame`. However, if the data set in your `DataFrame` has 60,000 rows and 400 columns, how do you even begin to get a sense of what you're working with? Fortunately, pandas provides some convenient tools to quickly look at overall information about a `DataFrame` in addition to the first few and last few rows.\r\n",
-        "\r\n",
-        "In order to explore this functionality, we will import the Python scikit-learn library and use an iconic dataset that every data scientist has seen hundreds of times: British biologist Ronald Fisher's *Iris* data set used in his 1936 paper \"The use of multiple measurements in taxonomic problems\":"
+        "# Data Preparation\n",
+        "\n",
+        "[Original Notebook source from *Data Science: Introduction to Machine Learning for Data Science Python and Machine Learning Studio by Lee Stott*](https://github.com/leestott/intro-Datascience/blob/master/Course%20Materials/4-Cleaning_and_Manipulating-Reference.ipynb)\n",
+        "\n",
+        "## Exploring `DataFrame` information\n",
+        "\n",
+        "> **Learning goal:** By the end of this subsection, you should be comfortable finding general information about the data stored in pandas DataFrames.\n",
+        "\n",
+        "Once you have loaded your data into pandas, it will more likely than not be in a `DataFrame`. \n",
+        "\n",
+        "In order to explore our `DataFramme`, we will import the Python `scikit-learn` library and use an iconic dataset that every data scientist has seen hundreds of times: British biologist Ronald Fisher's **Iris data set** used in his 1936 paper \"*The use of multiple measurements in taxonomic problems*\":"
       ],
       "metadata": {}
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 1,
       "source": [
-        "import pandas as pd\r\n",
-        "from sklearn.datasets import load_iris\r\n",
-        "\r\n",
-        "iris = load_iris()\r\n",
+        "import pandas as pd\n",
+        "from sklearn.datasets import load_iris\n",
+        "\n",
+        "iris = load_iris()\n",
         "iris_df = pd.DataFrame(data=iris['data'], columns=iris['feature_names'])"
       ],
       "outputs": [],
@@ -43,11 +43,29 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 2,
       "source": [
         "iris_df.info()"
       ],
-      "outputs": [],
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "<class 'pandas.core.frame.DataFrame'>\n",
+            "RangeIndex: 150 entries, 0 to 149\n",
+            "Data columns (total 4 columns):\n",
+            " #   Column             Non-Null Count  Dtype  \n",
+            "---  ------             --------------  -----  \n",
+            " 0   sepal length (cm)  150 non-null    float64\n",
+            " 1   sepal width (cm)   150 non-null    float64\n",
+            " 2   petal length (cm)  150 non-null    float64\n",
+            " 3   petal width (cm)   150 non-null    float64\n",
+            "dtypes: float64(4)\n",
+            "memory usage: 4.8 KB\n"
+          ]
+        }
+      ],
       "metadata": {
         "trusted": false
       }
@@ -69,11 +87,92 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 3,
       "source": [
         "iris_df.head()"
       ],
-      "outputs": [],
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>sepal length (cm)</th>\n",
+              "      <th>sepal width (cm)</th>\n",
+              "      <th>petal length (cm)</th>\n",
+              "      <th>petal width (cm)</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>5.1</td>\n",
+              "      <td>3.5</td>\n",
+              "      <td>1.4</td>\n",
+              "      <td>0.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>4.9</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>1.4</td>\n",
+              "      <td>0.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>4.7</td>\n",
+              "      <td>3.2</td>\n",
+              "      <td>1.3</td>\n",
+              "      <td>0.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>4.6</td>\n",
+              "      <td>3.1</td>\n",
+              "      <td>1.5</td>\n",
+              "      <td>0.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>5.0</td>\n",
+              "      <td>3.6</td>\n",
+              "      <td>1.4</td>\n",
+              "      <td>0.2</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)\n",
+              "0                5.1               3.5                1.4               0.2\n",
+              "1                4.9               3.0                1.4               0.2\n",
+              "2                4.7               3.2                1.3               0.2\n",
+              "3                4.6               3.1                1.5               0.2\n",
+              "4                5.0               3.6                1.4               0.2"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 3
+        }
+      ],
       "metadata": {
         "trusted": false
       }
@@ -89,7 +188,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 4,
       "source": [
         "# Hint: Consult the documentation by using iris_df.head?"
       ],
@@ -109,11 +208,92 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 5,
       "source": [
         "iris_df.tail()"
       ],
-      "outputs": [],
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>sepal length (cm)</th>\n",
+              "      <th>sepal width (cm)</th>\n",
+              "      <th>petal length (cm)</th>\n",
+              "      <th>petal width (cm)</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>145</th>\n",
+              "      <td>6.7</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>5.2</td>\n",
+              "      <td>2.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>146</th>\n",
+              "      <td>6.3</td>\n",
+              "      <td>2.5</td>\n",
+              "      <td>5.0</td>\n",
+              "      <td>1.9</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>147</th>\n",
+              "      <td>6.5</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>5.2</td>\n",
+              "      <td>2.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>148</th>\n",
+              "      <td>6.2</td>\n",
+              "      <td>3.4</td>\n",
+              "      <td>5.4</td>\n",
+              "      <td>2.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>149</th>\n",
+              "      <td>5.9</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>5.1</td>\n",
+              "      <td>1.8</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "     sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)\n",
+              "145                6.7               3.0                5.2               2.3\n",
+              "146                6.3               2.5                5.0               1.9\n",
+              "147                6.5               3.0                5.2               2.0\n",
+              "148                6.2               3.4                5.4               2.3\n",
+              "149                5.9               3.0                5.1               1.8"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 5
+        }
+      ],
       "metadata": {
         "trusted": false
       }
@@ -154,14 +334,25 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 6,
       "source": [
-        "import numpy as np\r\n",
-        "\r\n",
-        "example1 = np.array([2, None, 6, 8])\r\n",
+        "import numpy as np\n",
+        "\n",
+        "example1 = np.array([2, None, 6, 8])\n",
         "example1"
       ],
-      "outputs": [],
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([2, None, 6, 8], dtype=object)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 6
+        }
+      ],
       "metadata": {
         "trusted": false
       }
@@ -177,11 +368,24 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 7,
       "source": [
         "example1.sum()"
       ],
-      "outputs": [],
+      "outputs": [
+        {
+          "output_type": "error",
+          "ename": "TypeError",
+          "evalue": "unsupported operand type(s) for +: 'int' and 'NoneType'",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+            "\u001b[0;32m<ipython-input-7-ce9901ad18bd>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mexample1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+            "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/numpy/core/_methods.py\u001b[0m in \u001b[0;36m_sum\u001b[0;34m(a, axis, dtype, out, keepdims, initial, where)\u001b[0m\n\u001b[1;32m     45\u001b[0m def _sum(a, axis=None, dtype=None, out=None, keepdims=False,\n\u001b[1;32m     46\u001b[0m          initial=_NoValue, where=True):\n\u001b[0;32m---> 47\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mumr_sum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeepdims\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minitial\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwhere\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     48\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     49\u001b[0m def _prod(a, axis=None, dtype=None, out=None, keepdims=False,\n",
+            "\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for +: 'int' and 'NoneType'"
+          ]
+        }
+      ],
       "metadata": {
         "trusted": false
       }
@@ -204,22 +408,44 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 8,
       "source": [
         "np.nan + 1"
       ],
-      "outputs": [],
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "nan"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 8
+        }
+      ],
       "metadata": {
         "trusted": false
       }
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 9,
       "source": [
         "np.nan * 0"
       ],
-      "outputs": [],
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "nan"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 9
+        }
+      ],
       "metadata": {
         "trusted": false
       }
@@ -233,12 +459,23 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 10,
       "source": [
-        "example2 = np.array([2, np.nan, 6, 8]) \r\n",
+        "example2 = np.array([2, np.nan, 6, 8]) \n",
         "example2.sum(), example2.min(), example2.max()"
       ],
-      "outputs": [],
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(nan, nan, nan)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 10
+        }
+      ],
       "metadata": {
         "trusted": false
       }
@@ -252,9 +489,9 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 11,
       "source": [
-        "# What happens if you add np.nan and None together?\r\n"
+        "# What happens if you add np.nan and None together?\n"
       ],
       "outputs": [],
       "metadata": {
@@ -280,12 +517,26 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 12,
       "source": [
-        "int_series = pd.Series([1, 2, 3], dtype=int)\r\n",
+        "int_series = pd.Series([1, 2, 3], dtype=int)\n",
         "int_series"
       ],
-      "outputs": [],
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0    1\n",
+              "1    2\n",
+              "2    3\n",
+              "dtype: int64"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 12
+        }
+      ],
       "metadata": {
         "trusted": false
       }
@@ -299,11 +550,11 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 13,
       "source": [
-        "# Now set an element of int_series equal to None.\r\n",
-        "# How does that element show up in the Series?\r\n",
-        "# What is the dtype of the Series?\r\n"
+        "# Now set an element of int_series equal to None.\n",
+        "# How does that element show up in the Series?\n",
+        "# What is the dtype of the Series?\n"
       ],
       "outputs": [],
       "metadata": {
@@ -335,7 +586,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 14,
       "source": [
         "example3 = pd.Series([0, np.nan, '', None])"
       ],
@@ -347,11 +598,26 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 15,
       "source": [
         "example3.isnull()"
       ],
-      "outputs": [],
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0    False\n",
+              "1     True\n",
+              "2    False\n",
+              "3     True\n",
+              "dtype: bool"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 15
+        }
+      ],
       "metadata": {
         "trusted": false
       }
@@ -374,10 +640,10 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 16,
       "source": [
-        "# Try running example3[example3.notnull()].\r\n",
-        "# Before you do so, what do you expect to see?\r\n"
+        "# Try running example3[example3.notnull()].\n",
+        "# Before you do so, what do you expect to see?\n"
       ],
       "outputs": [],
       "metadata": {
@@ -403,12 +669,25 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 17,
       "source": [
-        "example3 = example3.dropna()\r\n",
+        "example3 = example3.dropna()\n",
         "example3"
       ],
-      "outputs": [],
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0    0\n",
+              "2     \n",
+              "dtype: object"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 17
+        }
+      ],
       "metadata": {
         "trusted": false
       }
@@ -424,14 +703,75 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 18,
       "source": [
-        "example4 = pd.DataFrame([[1,      np.nan, 7], \r\n",
-        "                         [2,      5,      8], \r\n",
-        "                         [np.nan, 6,      9]])\r\n",
+        "example4 = pd.DataFrame([[1,      np.nan, 7], \n",
+        "                         [2,      5,      8], \n",
+        "                         [np.nan, 6,      9]])\n",
         "example4"
       ],
-      "outputs": [],
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>0</th>\n",
+              "      <th>1</th>\n",
+              "      <th>2</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>1.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>2.0</td>\n",
+              "      <td>5.0</td>\n",
+              "      <td>8</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>NaN</td>\n",
+              "      <td>6.0</td>\n",
+              "      <td>9</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "     0    1  2\n",
+              "0  1.0  NaN  7\n",
+              "1  2.0  5.0  8\n",
+              "2  NaN  6.0  9"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 18
+        }
+      ],
       "metadata": {
         "trusted": false
       }
@@ -447,11 +787,58 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 19,
       "source": [
         "example4.dropna()"
       ],
-      "outputs": [],
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>0</th>\n",
+              "      <th>1</th>\n",
+              "      <th>2</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>2.0</td>\n",
+              "      <td>5.0</td>\n",
+              "      <td>8</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "     0    1  2\n",
+              "1  2.0  5.0  8"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 19
+        }
+      ],
       "metadata": {
         "trusted": false
       }
@@ -465,11 +852,64 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 20,
       "source": [
         "example4.dropna(axis='columns')"
       ],
-      "outputs": [],
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>2</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>8</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>9</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   2\n",
+              "0  7\n",
+              "1  8\n",
+              "2  9"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 20
+        }
+      ],
       "metadata": {
         "trusted": false
       }
@@ -485,12 +925,77 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 21,
       "source": [
-        "example4[3] = np.nan\r\n",
+        "example4[3] = np.nan\n",
         "example4"
       ],
-      "outputs": [],
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>0</th>\n",
+              "      <th>1</th>\n",
+              "      <th>2</th>\n",
+              "      <th>3</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>1.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>7</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>2.0</td>\n",
+              "      <td>5.0</td>\n",
+              "      <td>8</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>NaN</td>\n",
+              "      <td>6.0</td>\n",
+              "      <td>9</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "     0    1  2   3\n",
+              "0  1.0  NaN  7 NaN\n",
+              "1  2.0  5.0  8 NaN\n",
+              "2  NaN  6.0  9 NaN"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 21
+        }
+      ],
       "metadata": {
         "trusted": false
       }
@@ -504,10 +1009,10 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 22,
       "source": [
-        "# How might you go about dropping just column 3?\r\n",
-        "# Hint: remember that you will need to supply both the axis parameter and the how parameter.\r\n"
+        "# How might you go about dropping just column 3?\n",
+        "# Hint: remember that you will need to supply both the axis parameter and the how parameter.\n"
       ],
       "outputs": [],
       "metadata": {
@@ -524,11 +1029,60 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 23,
       "source": [
         "example4.dropna(axis='rows', thresh=3)"
       ],
-      "outputs": [],
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>0</th>\n",
+              "      <th>1</th>\n",
+              "      <th>2</th>\n",
+              "      <th>3</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>2.0</td>\n",
+              "      <td>5.0</td>\n",
+              "      <td>8</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "     0    1  2   3\n",
+              "1  2.0  5.0  8 NaN"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 23
+        }
+      ],
       "metadata": {
         "trusted": false
       }
@@ -551,12 +1105,28 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 24,
       "source": [
-        "example5 = pd.Series([1, np.nan, 2, None, 3], index=list('abcde'))\r\n",
+        "example5 = pd.Series([1, np.nan, 2, None, 3], index=list('abcde'))\n",
         "example5"
       ],
-      "outputs": [],
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "a    1.0\n",
+              "b    NaN\n",
+              "c    2.0\n",
+              "d    NaN\n",
+              "e    3.0\n",
+              "dtype: float64"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 24
+        }
+      ],
       "metadata": {
         "trusted": false
       }
@@ -570,11 +1140,27 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 25,
       "source": [
         "example5.fillna(0)"
       ],
-      "outputs": [],
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "a    1.0\n",
+              "b    0.0\n",
+              "c    2.0\n",
+              "d    0.0\n",
+              "e    3.0\n",
+              "dtype: float64"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 25
+        }
+      ],
       "metadata": {
         "trusted": false
       }
@@ -588,9 +1174,9 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 26,
       "source": [
-        "# What happens if you try to fill null values with a string, like ''?\r\n"
+        "# What happens if you try to fill null values with a string, like ''?\n"
       ],
       "outputs": [],
       "metadata": {
@@ -607,11 +1193,27 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 27,
       "source": [
         "example5.fillna(method='ffill')"
       ],
-      "outputs": [],
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "a    1.0\n",
+              "b    1.0\n",
+              "c    2.0\n",
+              "d    2.0\n",
+              "e    3.0\n",
+              "dtype: float64"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 27
+        }
+      ],
       "metadata": {
         "trusted": false
       }
@@ -625,11 +1227,27 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 28,
       "source": [
         "example5.fillna(method='bfill')"
       ],
-      "outputs": [],
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "a    1.0\n",
+              "b    2.0\n",
+              "c    2.0\n",
+              "d    3.0\n",
+              "e    3.0\n",
+              "dtype: float64"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 28
+        }
+      ],
       "metadata": {
         "trusted": false
       }
@@ -645,22 +1263,152 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 29,
       "source": [
         "example4"
       ],
-      "outputs": [],
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>0</th>\n",
+              "      <th>1</th>\n",
+              "      <th>2</th>\n",
+              "      <th>3</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>1.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>7</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>2.0</td>\n",
+              "      <td>5.0</td>\n",
+              "      <td>8</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>NaN</td>\n",
+              "      <td>6.0</td>\n",
+              "      <td>9</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "     0    1  2   3\n",
+              "0  1.0  NaN  7 NaN\n",
+              "1  2.0  5.0  8 NaN\n",
+              "2  NaN  6.0  9 NaN"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 29
+        }
+      ],
       "metadata": {
         "trusted": false
       }
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 30,
       "source": [
         "example4.fillna(method='ffill', axis=1)"
       ],
-      "outputs": [],
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>0</th>\n",
+              "      <th>1</th>\n",
+              "      <th>2</th>\n",
+              "      <th>3</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>7.0</td>\n",
+              "      <td>7.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>2.0</td>\n",
+              "      <td>5.0</td>\n",
+              "      <td>8.0</td>\n",
+              "      <td>8.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>NaN</td>\n",
+              "      <td>6.0</td>\n",
+              "      <td>9.0</td>\n",
+              "      <td>9.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "     0    1    2    3\n",
+              "0  1.0  1.0  7.0  7.0\n",
+              "1  2.0  5.0  8.0  8.0\n",
+              "2  NaN  6.0  9.0  9.0"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 30
+        }
+      ],
       "metadata": {
         "trusted": false
       }
@@ -681,11 +1429,11 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 31,
       "source": [
-        "# What output does example4.fillna(method='bfill', axis=1) produce?\r\n",
-        "# What about example4.fillna(method='ffill') or example4.fillna(method='bfill')?\r\n",
-        "# Can you think of a longer code snippet to write that can fill all of the null values in example4?\r\n"
+        "# What output does example4.fillna(method='bfill', axis=1) produce?\n",
+        "# What about example4.fillna(method='ffill') or example4.fillna(method='bfill')?\n",
+        "# Can you think of a longer code snippet to write that can fill all of the null values in example4?\n"
       ],
       "outputs": [],
       "metadata": {
@@ -702,11 +1450,76 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 32,
       "source": [
         "example4.fillna(example4.mean())"
       ],
-      "outputs": [],
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>0</th>\n",
+              "      <th>1</th>\n",
+              "      <th>2</th>\n",
+              "      <th>3</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>1.0</td>\n",
+              "      <td>5.5</td>\n",
+              "      <td>7</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>2.0</td>\n",
+              "      <td>5.0</td>\n",
+              "      <td>8</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>1.5</td>\n",
+              "      <td>6.0</td>\n",
+              "      <td>9</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "     0    1  2   3\n",
+              "0  1.0  5.5  7 NaN\n",
+              "1  2.0  5.0  8 NaN\n",
+              "2  1.5  6.0  9 NaN"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 32
+        }
+      ],
       "metadata": {
         "trusted": false
       }
@@ -742,24 +1555,109 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 33,
       "source": [
-        "example6 = pd.DataFrame({'letters': ['A','B'] * 2 + ['B'],\r\n",
-        "                         'numbers': [1, 2, 1, 3, 3]})\r\n",
+        "example6 = pd.DataFrame({'letters': ['A','B'] * 2 + ['B'],\n",
+        "                         'numbers': [1, 2, 1, 3, 3]})\n",
         "example6"
       ],
-      "outputs": [],
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>letters</th>\n",
+              "      <th>numbers</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>A</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>B</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>A</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>B</td>\n",
+              "      <td>3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>B</td>\n",
+              "      <td>3</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "  letters  numbers\n",
+              "0       A        1\n",
+              "1       B        2\n",
+              "2       A        1\n",
+              "3       B        3\n",
+              "4       B        3"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 33
+        }
+      ],
       "metadata": {
         "trusted": false
       }
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 34,
       "source": [
         "example6.duplicated()"
       ],
-      "outputs": [],
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0    False\n",
+              "1    False\n",
+              "2     True\n",
+              "3    False\n",
+              "4     True\n",
+              "dtype: bool"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 34
+        }
+      ],
       "metadata": {
         "trusted": false
       }
@@ -774,11 +1672,68 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 35,
       "source": [
         "example6.drop_duplicates()"
       ],
-      "outputs": [],
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>letters</th>\n",
+              "      <th>numbers</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>A</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>B</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>B</td>\n",
+              "      <td>3</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "  letters  numbers\n",
+              "0       A        1\n",
+              "1       B        2\n",
+              "3       B        3"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 35
+        }
+      ],
       "metadata": {
         "trusted": false
       }
@@ -792,11 +1747,62 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 36,
       "source": [
         "example6.drop_duplicates(['letters'])"
       ],
-      "outputs": [],
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>letters</th>\n",
+              "      <th>numbers</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>A</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>B</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "  letters  numbers\n",
+              "0       A        1\n",
+              "1       B        2"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 36
+        }
+      ],
       "metadata": {
         "trusted": false
       }
@@ -813,20 +1819,22 @@
     "anaconda-cloud": {},
     "kernelspec": {
       "name": "python3",
-      "display_name": "Python 3",
-      "language": "python"
+      "display_name": "Python 3.8.8 64-bit ('base': conda)"
     },
     "language_info": {
       "mimetype": "text/x-python",
       "nbconvert_exporter": "python",
       "name": "python",
       "file_extension": ".py",
-      "version": "3.5.4",
+      "version": "3.8.8",
       "pygments_lexer": "ipython3",
       "codemirror_mode": {
         "version": 3,
         "name": "ipython"
       }
+    },
+    "interpreter": {
+      "hash": "ac36fb7022a775f2750f61e1a6104d2d5a9eb3fb9bd004b80f1c771537b93945"
     }
   },
   "nbformat": 4,