diff --git a/tutorial/Pandas_tutorial_part2.ipynb b/tutorial/Pandas_tutorial_part2.ipynb
index 7fdea6a..a9c1dba 100644
--- a/tutorial/Pandas_tutorial_part2.ipynb
+++ b/tutorial/Pandas_tutorial_part2.ipynb
@@ -39,7 +39,7 @@
     "  - (9.2) Training a model\n",
     "- **(10) Misc tips and tricks**\n",
     "  - (10.1) Performance\n",
-    "- **(11) Problems and answers**"
+    "  - (10.2) Exporting data"
    ]
   },
   {
@@ -119,7 +119,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Today is 2019-03-04 21:37:06.735457\n",
+      "Today is 2019-03-11 12:24:04.996909\n",
       "----------------------------------------------------------------\n",
       "pandas       version 0.24.1\n",
       "numpy        version 1.15.4\n",
@@ -163,8 +163,8 @@
      "output_type": "stream",
      "text": [
       "Loaded data of size (5043, 6) into memory.\n",
-      "CPU times: user 31.8 ms, sys: 0 ns, total: 31.8 ms\n",
-      "Wall time: 32.5 ms\n"
+      "CPU times: user 68.6 ms, sys: 12.7 ms, total: 81.3 ms\n",
+      "Wall time: 235 ms\n"
      ]
     }
    ],
@@ -336,6 +336,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "--------------\n",
+    "\n",
     "# (5) Filtering and sorting"
    ]
   },
@@ -364,10 +366,145 @@
     "## (5.1) Equality, non-equality and logical operators"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- When using the `df[array-like]` syntax, pandas is smart enough to figure out what you want. \n",
+    "- For finer control, use the `df.loc[[row1, row2, ...], [col1, col2, ...]]` syntax instead.\n",
+    "\n",
+    "Below we create boolean vectors over the rows (*masks*) and select based on these."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 7,
    "metadata": {},
+   "outputs": [
+    {
+     "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>director_name</th>\n",
+       "      <th>gross</th>\n",
+       "      <th>movie_title</th>\n",
+       "      <th>country</th>\n",
+       "      <th>content_rating</th>\n",
+       "      <th>imdb_score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>James Cameron</td>\n",
+       "      <td>760505847.0</td>\n",
+       "      <td>Avatar</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>PG-13</td>\n",
+       "      <td>7.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>Bryan Singer</td>\n",
+       "      <td>200069408.0</td>\n",
+       "      <td>Superman Returns</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>PG-13</td>\n",
+       "      <td>6.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>Ridley Scott</td>\n",
+       "      <td>105219735.0</td>\n",
+       "      <td>Robin Hood</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>PG-13</td>\n",
+       "      <td>6.7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>33</th>\n",
+       "      <td>Tim Burton</td>\n",
+       "      <td>334185206.0</td>\n",
+       "      <td>Alice in Wonderland</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>PG</td>\n",
+       "      <td>6.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>44</th>\n",
+       "      <td>McG</td>\n",
+       "      <td>125320003.0</td>\n",
+       "      <td>Terminator Salvation</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>PG-13</td>\n",
+       "      <td>6.6</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    director_name        gross            movie_title country content_rating  \\\n",
+       "0   James Cameron  760505847.0                Avatar      USA          PG-13   \n",
+       "11   Bryan Singer  200069408.0      Superman Returns      USA          PG-13   \n",
+       "22   Ridley Scott  105219735.0            Robin Hood      USA          PG-13   \n",
+       "33     Tim Burton  334185206.0   Alice in Wonderland      USA             PG   \n",
+       "44            McG  125320003.0  Terminator Salvation      USA          PG-13   \n",
+       "\n",
+       "    imdb_score  \n",
+       "0          7.9  \n",
+       "11         6.1  \n",
+       "22         6.7  \n",
+       "33         6.5  \n",
+       "44         6.6  "
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[['gross', 'movie_title']].head()  # Selects columns\n",
+    "\n",
+    "# Select when the row numbers is divisible by 11\n",
+    "# The actual df.index need not be numeric here\n",
+    "mask = np.where(df.index % 11 == 0, True, False)\n",
+    "df.set_index('movie_title')[mask].head()  # Selects rows\n",
+    "\n",
+    "# Here the index needs to be numeric, to match the mask\n",
+    "mask = pd.Series(mask, index=df.index)\n",
+    "df[mask].head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's how we apply the above to filter out rows."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -442,7 +579,7 @@
        "4240         7.6  "
       ]
      },
-     "execution_count": 7,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -454,7 +591,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -535,7 +672,7 @@
        "4029              R         8.7  "
       ]
      },
-     "execution_count": 8,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -554,7 +691,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -652,7 +789,7 @@
        "3423              R         8.1  "
       ]
      },
-     "execution_count": 9,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -664,7 +801,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -740,7 +877,7 @@
        "2      UK          PG-13         6.8  "
       ]
      },
-     "execution_count": 10,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -752,7 +889,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -857,7 +994,7 @@
        "1974          PG-13         7.1  "
       ]
      },
-     "execution_count": 11,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -883,7 +1020,25 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## (6.1) The groupby operation"
+    "## (6.1) The groupby operation\n",
+    "\n",
+    "Very similar to Structured Query Language (SQL).\n",
+    "\n",
+    "**SQL - [The SQL GROUP BY Statement](https://www.w3schools.com/sql/sql_groupby.asp)**\n",
+    "\n",
+    "```sql\n",
+    "SELECT TOP 3 \n",
+    "    country, \n",
+    "    AVG(imdb_score) as mean_imdb_score\n",
+    "FROM df\n",
+    "GROUP BY country; \n",
+    "```\n",
+    "\n",
+    "**Pandas - [Group By: split-apply-combine](https://pandas.pydata.org/pandas-docs/stable/user_guide/groupby.html)**\n",
+    "\n",
+    "```python\n",
+    "df.groupby(df.country).mean().head(3)\n",
+    "```"
    ]
   },
   {
@@ -895,9 +1050,16 @@
     "*Image source is https://data36.com/sql-functions-beginners-tutorial-ep3/*"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Example.** Mean `imdb_score` per country."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -927,55 +1089,49 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>0</th>\n",
+       "      <th>3533</th>\n",
        "      <td>USA</td>\n",
-       "      <td>7.9</td>\n",
+       "      <td>6.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>USA</td>\n",
-       "      <td>7.1</td>\n",
+       "      <th>4024</th>\n",
+       "      <td>France</td>\n",
+       "      <td>7.6</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>UK</td>\n",
-       "      <td>6.8</td>\n",
+       "      <th>974</th>\n",
+       "      <td>USA</td>\n",
+       "      <td>5.8</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>3</th>\n",
+       "      <th>4784</th>\n",
        "      <td>USA</td>\n",
-       "      <td>8.5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>USA</td>\n",
-       "      <td>6.6</td>\n",
+       "      <td>6.2</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "  country  imdb_score\n",
-       "0     USA         7.9\n",
-       "1     USA         7.1\n",
-       "2      UK         6.8\n",
-       "3     USA         8.5\n",
-       "5     USA         6.6"
+       "     country  imdb_score\n",
+       "3533     USA         6.0\n",
+       "4024  France         7.6\n",
+       "974      USA         5.8\n",
+       "4784     USA         6.2"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df[['country', 'imdb_score']].head()"
+    "df[['country', 'imdb_score']].sample(4, random_state=1)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -1036,7 +1192,7 @@
        "Aruba        1.007614e+07         4.8"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1049,12 +1205,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Directors with the most movies."
+    "**Example.** Directors with the most movies."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -1120,33 +1276,56 @@
        "Ridley Scott               16"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
+    "top_n = 5  # Top people to show\n",
+    "\n",
     "(df\n",
     " .groupby(df.director_name)\n",
     " .nunique()\n",
     " .movie_title\n",
-    " .nlargest(5)\n",
+    " .nlargest(top_n)\n",
     " .to_frame()\n",
     ")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- `mean()` - Compute mean of groups\n",
+    "- `sum()` - Compute sum of group values\n",
+    "- `size()` - Compute group sizes\n",
+    "- `count()` - Compute count of group\n",
+    "- `std()` - Standard deviation of groups\n",
+    "- `var()` - Compute variance of groups\n",
+    "- `sem()` - Standard error of the mean of groups\n",
+    "- `describe()` - Generates descriptive statistics\n",
+    "- `first()` - Compute first of group values\n",
+    "- `last()` - Compute last of group values\n",
+    "- `nth()` - Take nth value, or a subset if n is a list\n",
+    "- `min()` - Compute min of group values\n",
+    "- `max()` - Compute max of group values\n",
+    "\n",
+    "*The above is from the [pandas docs](https://pandas.pydata.org/pandas-docs/stable/user_guide/groupby.html#aggregation).*"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "## (6.2) Several groups and aggregations\n",
     "\n",
-    "A group can be a combination of columns, e.g. [`country`, `content_rating`]."
+    "**Several groups (columns).** A group can be a combination of columns, e.g. [`country`, `content_rating`]."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -1245,26 +1424,33 @@
        "Brazil      R                     8.17"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df.groupby(['country', 'content_rating']).mean().imdb_score.round(2).to_frame().head(10)"
+    "(df\n",
+    " .groupby(['country', 'content_rating'])\n",
+    " .mean()\n",
+    " .imdb_score\n",
+    " .round(2)\n",
+    " .to_frame()\n",
+    " .head(10)\n",
+    ")"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Serveral aggregation functions may be used.   \n",
+    "**Several aggregation functions.** Serveral aggregation functions may be used.   \n",
     "Below we see directors and their `average`, `max` and `min` imdb_scores."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -1323,7 +1509,7 @@
        "Aaron Seltzer     2.7  2.7  2.7"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1339,7 +1525,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -1398,7 +1584,7 @@
        "Adam Shankman  5.962500     2.2        7"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1418,9 +1604,16 @@
     ")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Several groups (columns) and aggregation functions.**"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -1608,7 +1801,7 @@
        "            PG               5.520000  1.023230  5.192535e+07  113006880.0"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1623,33 +1816,41 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "## (6.3) Unstacking and stacking\n",
+    "\n",
+    "To motivate the methods, consider the following data set.\n",
+    "\n",
     "Data source: https://github.com/highcharts/highcharts/blob/master/samples/data/world-population-history.csv"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df_world = pd.read_csv(f'data/world_population_history.csv')"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
-    "df_world = (df_world\n",
-    "            .drop(columns=['Country Name', 'Country Code', 'Indicator Name', 'Indicator Code'])\n",
-    "            .dropna(axis=1, how='all'))"
+    "df_world = pd.read_csv(f'data/world_population_history.csv')"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 21,
    "metadata": {},
+   "outputs": [],
+   "source": [
+    "drop_cols = ['Country Name', 'Country Code', 'Indicator Name', \n",
+    "             'Indicator Code', 'World Development Indicators']\n",
+    "\n",
+    "df_world = (df_world\n",
+    "            .drop(columns=drop_cols)\n",
+    "            .dropna(axis=1, how='all')\n",
+    "           )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -1673,7 +1874,6 @@
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Data Source</th>\n",
-       "      <th>World Development Indicators</th>\n",
        "      <th>1960</th>\n",
        "      <th>1961</th>\n",
        "      <th>1962</th>\n",
@@ -1682,6 +1882,7 @@
        "      <th>1965</th>\n",
        "      <th>1966</th>\n",
        "      <th>1967</th>\n",
+       "      <th>1968</th>\n",
        "      <th>...</th>\n",
        "      <th>2006</th>\n",
        "      <th>2007</th>\n",
@@ -1699,7 +1900,6 @@
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>Aruba</td>\n",
-       "      <td>ABW</td>\n",
        "      <td>56225.0</td>\n",
        "      <td>56695.0</td>\n",
        "      <td>57032.0</td>\n",
@@ -1708,6 +1908,7 @@
        "      <td>58055.0</td>\n",
        "      <td>58386.0</td>\n",
        "      <td>58726.0</td>\n",
+       "      <td>59063.0</td>\n",
        "      <td>...</td>\n",
        "      <td>101353.0</td>\n",
        "      <td>101453.0</td>\n",
@@ -1723,7 +1924,6 @@
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>Afghanistan</td>\n",
-       "      <td>AFG</td>\n",
        "      <td>9345868.0</td>\n",
        "      <td>9533954.0</td>\n",
        "      <td>9731361.0</td>\n",
@@ -1732,6 +1932,7 @@
        "      <td>10372630.0</td>\n",
        "      <td>10604346.0</td>\n",
        "      <td>10854428.0</td>\n",
+       "      <td>11126123.0</td>\n",
        "      <td>...</td>\n",
        "      <td>27294031.0</td>\n",
        "      <td>28004331.0</td>\n",
@@ -1747,7 +1948,6 @@
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>Angola</td>\n",
-       "      <td>AGO</td>\n",
        "      <td>5866061.0</td>\n",
        "      <td>5980417.0</td>\n",
        "      <td>6093321.0</td>\n",
@@ -1756,6 +1956,7 @@
        "      <td>6414995.0</td>\n",
        "      <td>6523791.0</td>\n",
        "      <td>6642632.0</td>\n",
+       "      <td>6776381.0</td>\n",
        "      <td>...</td>\n",
        "      <td>21759420.0</td>\n",
        "      <td>22549547.0</td>\n",
@@ -1771,7 +1972,6 @@
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>Albania</td>\n",
-       "      <td>ALB</td>\n",
        "      <td>1711319.0</td>\n",
        "      <td>1762621.0</td>\n",
        "      <td>1814135.0</td>\n",
@@ -1780,6 +1980,7 @@
        "      <td>1965598.0</td>\n",
        "      <td>2022272.0</td>\n",
        "      <td>2081695.0</td>\n",
+       "      <td>2135479.0</td>\n",
        "      <td>...</td>\n",
        "      <td>2947314.0</td>\n",
        "      <td>2927519.0</td>\n",
@@ -1795,7 +1996,6 @@
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>Andorra</td>\n",
-       "      <td>AND</td>\n",
        "      <td>15370.0</td>\n",
        "      <td>16412.0</td>\n",
        "      <td>17469.0</td>\n",
@@ -1804,6 +2004,7 @@
        "      <td>20758.0</td>\n",
        "      <td>21890.0</td>\n",
        "      <td>23058.0</td>\n",
+       "      <td>24276.0</td>\n",
        "      <td>...</td>\n",
        "      <td>83861.0</td>\n",
        "      <td>84462.0</td>\n",
@@ -1818,23 +2019,23 @@
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>5 rows × 58 columns</p>\n",
+       "<p>5 rows × 57 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "   Data Source World Development Indicators       1960       1961       1962  \\\n",
-       "0        Aruba                          ABW    56225.0    56695.0    57032.0   \n",
-       "1  Afghanistan                          AFG  9345868.0  9533954.0  9731361.0   \n",
-       "2       Angola                          AGO  5866061.0  5980417.0  6093321.0   \n",
-       "3      Albania                          ALB  1711319.0  1762621.0  1814135.0   \n",
-       "4      Andorra                          AND    15370.0    16412.0    17469.0   \n",
+       "   Data Source       1960       1961       1962       1963        1964  \\\n",
+       "0        Aruba    56225.0    56695.0    57032.0    57360.0     57715.0   \n",
+       "1  Afghanistan  9345868.0  9533954.0  9731361.0  9938414.0  10152331.0   \n",
+       "2       Angola  5866061.0  5980417.0  6093321.0  6203299.0   6309770.0   \n",
+       "3      Albania  1711319.0  1762621.0  1814135.0  1864791.0   1914573.0   \n",
+       "4      Andorra    15370.0    16412.0    17469.0    18549.0     19647.0   \n",
        "\n",
-       "        1963        1964        1965        1966        1967  ...        2006  \\\n",
-       "0    57360.0     57715.0     58055.0     58386.0     58726.0  ...    101353.0   \n",
-       "1  9938414.0  10152331.0  10372630.0  10604346.0  10854428.0  ...  27294031.0   \n",
-       "2  6203299.0   6309770.0   6414995.0   6523791.0   6642632.0  ...  21759420.0   \n",
-       "3  1864791.0   1914573.0   1965598.0   2022272.0   2081695.0  ...   2947314.0   \n",
-       "4    18549.0     19647.0     20758.0     21890.0     23058.0  ...     83861.0   \n",
+       "         1965        1966        1967        1968  ...        2006  \\\n",
+       "0     58055.0     58386.0     58726.0     59063.0  ...    101353.0   \n",
+       "1  10372630.0  10604346.0  10854428.0  11126123.0  ...  27294031.0   \n",
+       "2   6414995.0   6523791.0   6642632.0   6776381.0  ...  21759420.0   \n",
+       "3   1965598.0   2022272.0   2081695.0   2135479.0  ...   2947314.0   \n",
+       "4     20758.0     21890.0     23058.0     24276.0  ...     83861.0   \n",
        "\n",
        "         2007        2008        2009        2010        2011        2012  \\\n",
        "0    101453.0    101669.0    102053.0    102577.0    103187.0    103795.0   \n",
@@ -1850,30 +2051,23 @@
        "3   2880703.0   2876101.0   2879000.0  \n",
        "4     78014.0     77281.0     77000.0  \n",
        "\n",
-       "[5 rows x 58 columns]"
+       "[5 rows x 57 columns]"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df_world.head()"
+    "df_world.head(5)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## (6.3) Unstacking and stacking"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's load a data set from [https://github.com/highcharts/highcharts](https://github.com/highcharts/highcharts/blob/master/samples/data/world-population-history.csv), which is not tidy.\n",
+    "The dataset from [https://github.com/highcharts/highcharts](https://github.com/highcharts/highcharts/blob/master/samples/data/world-population-history.csv) is **not tidy**.\n",
     "\n",
     ">  (1) Each variable you measure should be in one column.   \n",
     "   (2) Each different observation of that variable should be in a different row.   \n",
@@ -1886,121 +2080,10 @@
     "Read [\"Tidy Data\" by H Wickham](https://www.jstatsoft.org/article/view/v059i10/v59i10.pdf) for more information."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df_world = pd.read_csv(f'data/world_population_history.csv')\n",
-    "\n",
-    "drop_cols = ['Country Name', 'Country Code', 'Indicator Name', 'Indicator Code', 'World Development Indicators']\n",
-    "\n",
-    "df_world = (df_world\n",
-    "            .drop(columns=drop_cols)\n",
-    "            .dropna(axis=1, how='all'))"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 23,
    "metadata": {},
-   "outputs": [
-    {
-     "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>Data Source</th>\n",
-       "      <th>1960</th>\n",
-       "      <th>1961</th>\n",
-       "      <th>1962</th>\n",
-       "      <th>1963</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Aruba</td>\n",
-       "      <td>56225.0</td>\n",
-       "      <td>56695.0</td>\n",
-       "      <td>57032.0</td>\n",
-       "      <td>57360.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Afghanistan</td>\n",
-       "      <td>9345868.0</td>\n",
-       "      <td>9533954.0</td>\n",
-       "      <td>9731361.0</td>\n",
-       "      <td>9938414.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Angola</td>\n",
-       "      <td>5866061.0</td>\n",
-       "      <td>5980417.0</td>\n",
-       "      <td>6093321.0</td>\n",
-       "      <td>6203299.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Albania</td>\n",
-       "      <td>1711319.0</td>\n",
-       "      <td>1762621.0</td>\n",
-       "      <td>1814135.0</td>\n",
-       "      <td>1864791.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Andorra</td>\n",
-       "      <td>15370.0</td>\n",
-       "      <td>16412.0</td>\n",
-       "      <td>17469.0</td>\n",
-       "      <td>18549.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   Data Source       1960       1961       1962       1963\n",
-       "0        Aruba    56225.0    56695.0    57032.0    57360.0\n",
-       "1  Afghanistan  9345868.0  9533954.0  9731361.0  9938414.0\n",
-       "2       Angola  5866061.0  5980417.0  6093321.0  6203299.0\n",
-       "3      Albania  1711319.0  1762621.0  1814135.0  1864791.0\n",
-       "4      Andorra    15370.0    16412.0    17469.0    18549.0"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_world.iloc[:5, :5]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -2072,12 +2155,14 @@
        "4   Aruba 1964-01-01     57715.0"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
+    "# We put the data in tidy format as follows\n",
+    "\n",
     "df_world_tidy = (df_world\n",
     " .set_index(['Data Source'])\n",
     " .stack(0)\n",
@@ -2085,48 +2170,123 @@
     " .to_frame()\n",
     " .reset_index()\n",
     " .rename(columns={'level_1':'Year', 'Data Source':'Country'})\n",
-    " .assign(Year=lambda df:pd.to_datetime(df.Year)))\n",
+    " .assign(Year=lambda df:pd.to_datetime(df.Year))\n",
+    ")\n",
     "\n",
-    "df_world_tidy.iloc[:5,:5]"
+    "df_world_tidy.head(5)"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 25,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "to_plot = (df_world_tidy\n",
-    " .set_index(['Country', 'Year'])\n",
-    " .unstack(level=0)\n",
-    " .loc[:, (slice(None), ['Norway', 'Sweden'])])"
+    "> **Note.** What does it mean to have tidy data in relational databases? Read about [Boyce–Codd normal form](https://en.wikipedia.org/wiki/Boyce%E2%80%93Codd_normal_form)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plotting libraries might not want data in a tidy format. The following transformation is often required."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "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 tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr:last-of-type th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th colspan=\"2\" halign=\"left\">Population</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Country</th>\n",
+       "      <th>Norway</th>\n",
+       "      <th>Sweden</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Year</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1960-01-01</th>\n",
+       "      <td>3638918.0</td>\n",
+       "      <td>7561588.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1961-01-01</th>\n",
+       "      <td>3666537.0</td>\n",
+       "      <td>7604328.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1962-01-01</th>\n",
+       "      <td>3694339.0</td>\n",
+       "      <td>7661354.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1963-01-01</th>\n",
+       "      <td>3723168.0</td>\n",
+       "      <td>7733853.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1964-01-01</th>\n",
+       "      <td>3753012.0</td>\n",
+       "      <td>7807797.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
       "text/plain": [
-       "<Figure size 720x360 with 1 Axes>"
+       "           Population           \n",
+       "Country        Norway     Sweden\n",
+       "Year                            \n",
+       "1960-01-01  3638918.0  7561588.0\n",
+       "1961-01-01  3666537.0  7604328.0\n",
+       "1962-01-01  3694339.0  7661354.0\n",
+       "1963-01-01  3723168.0  7733853.0\n",
+       "1964-01-01  3753012.0  7807797.0"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "# Return Index with requested level removed\n",
-    "to_plot.columns = to_plot.columns.droplevel(0)\n",
+    "to_plot = (df_world_tidy\n",
+    "           .set_index(['Country', 'Year'])\n",
+    "           .unstack(level=0)\n",
+    "           .loc[:, (slice(None), ['Norway', 'Sweden'])]\n",
+    "          )\n",
     "\n",
-    "(to_plot / 10**6).plot(grid=True, figsize=(10, 5));\n",
-    "plt.ylabel('Population (millions)'); \n",
-    "plt.ylim([0, 11]); plt.show()"
+    "to_plot.head()"
    ]
   },
   {
@@ -2140,7 +2300,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
@@ -2199,7 +2359,7 @@
        "2   Aruba 1962-01-01     57032.0"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2216,6 +2376,124 @@
     "The `.pivot()` method is more powerful than unstack. Both move rows up to the columns."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "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>Country</th>\n",
+       "      <th>Afghanistan</th>\n",
+       "      <th>Albania</th>\n",
+       "      <th>Algeria</th>\n",
+       "      <th>American Samoa</th>\n",
+       "      <th>Andorra</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Year</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1960-01-01</th>\n",
+       "      <td>9345868.0</td>\n",
+       "      <td>1711319.0</td>\n",
+       "      <td>11690153.0</td>\n",
+       "      <td>21117.0</td>\n",
+       "      <td>15370.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1961-01-01</th>\n",
+       "      <td>9533954.0</td>\n",
+       "      <td>1762621.0</td>\n",
+       "      <td>11985136.0</td>\n",
+       "      <td>21882.0</td>\n",
+       "      <td>16412.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1962-01-01</th>\n",
+       "      <td>9731361.0</td>\n",
+       "      <td>1814135.0</td>\n",
+       "      <td>12295970.0</td>\n",
+       "      <td>22698.0</td>\n",
+       "      <td>17469.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1963-01-01</th>\n",
+       "      <td>9938414.0</td>\n",
+       "      <td>1864791.0</td>\n",
+       "      <td>12626952.0</td>\n",
+       "      <td>23520.0</td>\n",
+       "      <td>18549.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1964-01-01</th>\n",
+       "      <td>10152331.0</td>\n",
+       "      <td>1914573.0</td>\n",
+       "      <td>12980267.0</td>\n",
+       "      <td>24321.0</td>\n",
+       "      <td>19647.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Country     Afghanistan    Albania     Algeria  American Samoa  Andorra\n",
+       "Year                                                                   \n",
+       "1960-01-01    9345868.0  1711319.0  11690153.0         21117.0  15370.0\n",
+       "1961-01-01    9533954.0  1762621.0  11985136.0         21882.0  16412.0\n",
+       "1962-01-01    9731361.0  1814135.0  12295970.0         22698.0  17469.0\n",
+       "1963-01-01    9938414.0  1864791.0  12626952.0         23520.0  18549.0\n",
+       "1964-01-01   10152331.0  1914573.0  12980267.0         24321.0  19647.0"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Demonstrating the pivot method\n",
+    "# Return reshaped DataFrame organized by given index / column values.\n",
+    "df_world_pivot = df_world_tidy.pivot(index='Year', columns='Country', values='Population')\n",
+    "df_world_pivot.iloc[:5, :5]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Drop every country where there are any missing values\n",
+    "df_world_pivot = df_world_pivot.dropna(how='any', axis=1)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 28,
@@ -2260,56 +2538,56 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>1960-01-01</th>\n",
-       "      <td>9345868.0</td>\n",
-       "      <td>1711319.0</td>\n",
-       "      <td>11690153.0</td>\n",
-       "      <td>21117.0</td>\n",
-       "      <td>15370.0</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1961-01-01</th>\n",
-       "      <td>9533954.0</td>\n",
-       "      <td>1762621.0</td>\n",
-       "      <td>11985136.0</td>\n",
-       "      <td>21882.0</td>\n",
-       "      <td>16412.0</td>\n",
+       "      <td>1.020125</td>\n",
+       "      <td>1.029978</td>\n",
+       "      <td>1.025233</td>\n",
+       "      <td>1.036227</td>\n",
+       "      <td>1.067794</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1962-01-01</th>\n",
-       "      <td>9731361.0</td>\n",
-       "      <td>1814135.0</td>\n",
-       "      <td>12295970.0</td>\n",
-       "      <td>22698.0</td>\n",
-       "      <td>17469.0</td>\n",
+       "      <td>1.041247</td>\n",
+       "      <td>1.060080</td>\n",
+       "      <td>1.051823</td>\n",
+       "      <td>1.074869</td>\n",
+       "      <td>1.136565</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1963-01-01</th>\n",
-       "      <td>9938414.0</td>\n",
-       "      <td>1864791.0</td>\n",
-       "      <td>12626952.0</td>\n",
-       "      <td>23520.0</td>\n",
-       "      <td>18549.0</td>\n",
+       "      <td>1.063402</td>\n",
+       "      <td>1.089681</td>\n",
+       "      <td>1.080136</td>\n",
+       "      <td>1.113795</td>\n",
+       "      <td>1.206831</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1964-01-01</th>\n",
-       "      <td>10152331.0</td>\n",
-       "      <td>1914573.0</td>\n",
-       "      <td>12980267.0</td>\n",
-       "      <td>24321.0</td>\n",
-       "      <td>19647.0</td>\n",
+       "      <td>1.086291</td>\n",
+       "      <td>1.118770</td>\n",
+       "      <td>1.110359</td>\n",
+       "      <td>1.151726</td>\n",
+       "      <td>1.278269</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "Country     Afghanistan    Albania     Algeria  American Samoa  Andorra\n",
-       "Year                                                                   \n",
-       "1960-01-01    9345868.0  1711319.0  11690153.0         21117.0  15370.0\n",
-       "1961-01-01    9533954.0  1762621.0  11985136.0         21882.0  16412.0\n",
-       "1962-01-01    9731361.0  1814135.0  12295970.0         22698.0  17469.0\n",
-       "1963-01-01    9938414.0  1864791.0  12626952.0         23520.0  18549.0\n",
-       "1964-01-01   10152331.0  1914573.0  12980267.0         24321.0  19647.0"
+       "Country     Afghanistan   Albania   Algeria  American Samoa   Andorra\n",
+       "Year                                                                 \n",
+       "1960-01-01     1.000000  1.000000  1.000000        1.000000  1.000000\n",
+       "1961-01-01     1.020125  1.029978  1.025233        1.036227  1.067794\n",
+       "1962-01-01     1.041247  1.060080  1.051823        1.074869  1.136565\n",
+       "1963-01-01     1.063402  1.089681  1.080136        1.113795  1.206831\n",
+       "1964-01-01     1.086291  1.118770  1.110359        1.151726  1.278269"
       ]
      },
      "execution_count": 28,
@@ -2317,128 +2595,11 @@
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "# Demonstrating the pivot method\n",
-    "# Return reshaped DataFrame organized by given index / column values.\n",
-    "df_world_pivot = df_world_tidy.pivot(index='Year', columns='Country', values='Population')\n",
-    "df_world_pivot.iloc[:5, :5]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Drop every country where there are any missing values\n",
-    "df_world_pivot = df_world_pivot.dropna(how='any', axis=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "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>Country</th>\n",
-       "      <th>Afghanistan</th>\n",
-       "      <th>Albania</th>\n",
-       "      <th>Algeria</th>\n",
-       "      <th>American Samoa</th>\n",
-       "      <th>Andorra</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Year</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1960-01-01</th>\n",
-       "      <td>9345868.0</td>\n",
-       "      <td>1711319.0</td>\n",
-       "      <td>11690153.0</td>\n",
-       "      <td>21117.0</td>\n",
-       "      <td>15370.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1961-01-01</th>\n",
-       "      <td>9533954.0</td>\n",
-       "      <td>1762621.0</td>\n",
-       "      <td>11985136.0</td>\n",
-       "      <td>21882.0</td>\n",
-       "      <td>16412.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1962-01-01</th>\n",
-       "      <td>9731361.0</td>\n",
-       "      <td>1814135.0</td>\n",
-       "      <td>12295970.0</td>\n",
-       "      <td>22698.0</td>\n",
-       "      <td>17469.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1963-01-01</th>\n",
-       "      <td>9938414.0</td>\n",
-       "      <td>1864791.0</td>\n",
-       "      <td>12626952.0</td>\n",
-       "      <td>23520.0</td>\n",
-       "      <td>18549.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1964-01-01</th>\n",
-       "      <td>10152331.0</td>\n",
-       "      <td>1914573.0</td>\n",
-       "      <td>12980267.0</td>\n",
-       "      <td>24321.0</td>\n",
-       "      <td>19647.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "Country     Afghanistan    Albania     Algeria  American Samoa  Andorra\n",
-       "Year                                                                   \n",
-       "1960-01-01    9345868.0  1711319.0  11690153.0         21117.0  15370.0\n",
-       "1961-01-01    9533954.0  1762621.0  11985136.0         21882.0  16412.0\n",
-       "1962-01-01    9731361.0  1814135.0  12295970.0         22698.0  17469.0\n",
-       "1963-01-01    9938414.0  1864791.0  12626952.0         23520.0  18549.0\n",
-       "1964-01-01   10152331.0  1914573.0  12980267.0         24321.0  19647.0"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
     "# Compute the relative change since 1960\n",
-    "df_world_rel = (df_world_pivot / df_world_pivot.iloc[0, :])\n",
-    "df_world_pivot.iloc[:5, :5]"
+    "first_row = df_world_pivot.iloc[0, :]\n",
+    "df_world_rel = (df_world_pivot / first_row)\n",
+    "df_world_rel.iloc[:5, :5]"
    ]
   },
   {
@@ -2447,13 +2608,13 @@
    "source": [
     "The `.melt()` method is more powerful than stack. Both move columns up to the index.\n",
     "\n",
-    "> `unstack` and `stack` are inverses.   \n",
-    "  `pivot` and `melt` are inverses."
+    "> `unstack` and `stack` are inverse operations.   \n",
+    "  `pivot` and `melt` are inverse operations."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
@@ -2526,24 +2687,26 @@
        "4 1964-01-01  Afghanistan    1.086291"
       ]
      },
-     "execution_count": 31,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
+    "# When pivoted, computing the relative change is easy\n",
+    "# Converting back to tidy format can be achieved using `melt`\n",
     "df_world_rel = df_world_rel.reset_index().melt(id_vars='Year', value_name='Population')\n",
     "df_world_rel.iloc[:5, :5]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 720x360 with 1 Axes>"
       ]
@@ -2556,17 +2719,24 @@
    ],
    "source": [
     "top_n = 5\n",
+    "plot_title = f'Population growth of the {top_n} countries with largest relative change'\n",
+    "\n",
+    "# Countries with the largest relative change over time\n",
     "countries = (df_world_rel\n",
     "             .groupby('Country')\n",
     "             .max()\n",
     "             .Population\n",
     "             .nlargest(top_n)\n",
-    "             .index)\n",
+    "             .index\n",
+    "            )\n",
     "\n",
+    "\n",
+    "# Plot the population of the above countries\n",
     "(df_world_rel\n",
     " .pivot(index='Year', columns='Country', values='Population')\n",
-    " .loc[:, countries].plot(figsize=(10, 5)))\n",
-    "plt.legend(bbox_to_anchor=(1,1));"
+    " .loc[:, countries]\n",
+    " .plot(figsize=(10, 5), title=plot_title, grid=True, lw=2)\n",
+    ");"
    ]
   },
   {
@@ -2596,7 +2766,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
@@ -2632,6 +2802,22 @@
        "      <th>Aaron Schneider</th>\n",
        "      <td>7.1</td>\n",
        "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Aaron Seltzer</th>\n",
+       "      <td>2.7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Abel Ferrara</th>\n",
+       "      <td>6.6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Adam Brooks</th>\n",
+       "      <td>7.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Adam Goldberg</th>\n",
+       "      <td>5.4</td>\n",
+       "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
@@ -2639,24 +2825,32 @@
       "text/plain": [
        "                 director_imdb_score\n",
        "director_name                       \n",
-       "Aaron Schneider                  7.1"
+       "Aaron Schneider                  7.1\n",
+       "Aaron Seltzer                    2.7\n",
+       "Abel Ferrara                     6.6\n",
+       "Adam Brooks                      7.2\n",
+       "Adam Goldberg                    5.4"
       ]
      },
-     "execution_count": 33,
+     "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "director_means = (df.groupby(df.director_name).mean().round(1)\n",
+    "director_means = (df\n",
+    "                  .groupby(df.director_name)\n",
+    "                  .mean()\n",
+    "                  .round(1)\n",
     "                  .loc[:, ['imdb_score']]\n",
-    "                  .rename(columns={'imdb_score':'director_imdb_score'}))\n",
-    "director_means.head(1)"
+    "                  .rename(columns={'imdb_score':'director_imdb_score'})\n",
+    "                 )\n",
+    "director_means.head(5)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
     {
@@ -2760,7 +2954,7 @@
        "5     USA          PG-13         6.6                  7.7  "
       ]
      },
-     "execution_count": 34,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2778,7 +2972,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [
     {
@@ -2851,7 +3045,7 @@
        "Bob Clark                      2.5                6.2"
       ]
      },
-     "execution_count": 35,
+     "execution_count": 33,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2860,10 +3054,14 @@
     "# For every director which has made movies in the USA and Cananda\n",
     "# get average imdb scores for both locations\n",
     "\n",
-    "american_directors = (df[df.country == 'USA'].groupby('director_name')\n",
-    "                      .mean().imdb_score.to_frame())\n",
-    "canadian_directors = (df[df.country == 'Canada'].groupby('director_name')\n",
-    "                      .mean().imdb_score.to_frame())\n",
+    "american_directors = (df[df.country == 'USA']\n",
+    "                      .groupby('director_name')\n",
+    "                      .mean().imdb_score.to_frame()\n",
+    "                     )\n",
+    "canadian_directors = (df[df.country == 'Canada']\n",
+    "                      .groupby('director_name')\n",
+    "                      .mean().imdb_score.to_frame()\n",
+    "                     )\n",
     "\n",
     "(american_directors.merge(canadian_directors, how='inner', \n",
     "                          left_index=True, right_index=True, \n",
@@ -2913,12 +3111,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 720x360 with 1 Axes>"
       ]
@@ -2941,12 +3139,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 720x360 with 1 Axes>"
       ]
@@ -2970,22 +3168,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8acd7ed0b8>"
-      ]
-     },
-     "execution_count": 38,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 720x360 with 1 Axes>"
       ]
@@ -2999,7 +3187,7 @@
    "source": [
     "countries = set(['Norway', 'Sweden', 'Denmark'])\n",
     "df_world_pivot.loc[:, countries].plot(linewidth=3, title='Population growth', \n",
-    "                                      fontsize=14, figsize=(10, 5))"
+    "                                      fontsize=14, figsize=(10, 5), grid=True);"
    ]
   },
   {
@@ -3014,12 +3202,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 720x360 with 1 Axes>"
       ]
@@ -3031,10 +3219,13 @@
     }
    ],
    "source": [
+    "# Create figure and axis objects\n",
     "fig, ax = plt.subplots(figsize=(10, 5))\n",
     "\n",
+    "# Use the pandas plotting functionality, passing and returning the axis\n",
     "ax = (df_world_pivot.loc[:, countries] / 10**6).plot(ax=ax, lw=3, zorder=50)\n",
     "\n",
+    "# Tweak the axis object to your desire\n",
     "ax.set_title('Population growth', fontsize=17)\n",
     "ax.legend(fontsize=14, loc='upper left')\n",
     "ax.tick_params(axis='both', which='both', labelsize=14)\n",
@@ -3043,12 +3234,20 @@
     "ax.grid(True, zorder=-50, ls='--')\n",
     "ax.set_ylim([3, 11]);\n",
     "\n",
-    "#plt.savefig('my_figure.pdf')"
+    "# plt.savefig('my_figure.pdf')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Examine the distribution over the scores, unweighted and weighted by gross.   \n",
+    "Here we use [KDEpy](https://kdepy.readthedocs.io/en/latest/) to show how data can be sent from Pandas to other libraries."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [
     {
@@ -3073,10 +3272,13 @@
     "\n",
     "kde = FFTKDE(bw='ISJ', kernel='gaussian')\n",
     "\n",
+    "# This is the crucial line: to_numpy() converts the column for us\n",
     "x, y = kde.fit(data.imdb_score.to_numpy()).evaluate()\n",
     "ax.plot(x, y, label='imdb_score', lw=2)\n",
     "\n",
-    "y = kde.fit(data.imdb_score.to_numpy(), weights=data.gross.to_numpy()).evaluate(x)\n",
+    "# Same idea here as above, we use to_numpy()\n",
+    "y = kde.fit(data.imdb_score.to_numpy(), \n",
+    "            weights=data.gross.to_numpy()).evaluate(x)\n",
     "ax.plot(x, y, label='imdb_score weighted by gross', lw=2)\n",
     "\n",
     "ax.set_title('Score distribution', fontsize=17)\n",
@@ -3090,6 +3292,17 @@
     "#plt.savefig('my_figure.pdf')"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For more information about plotting, see:\n",
+    "\n",
+    "- [The matplotlib gallery](https://matplotlib.org/gallery.html)\n",
+    "- [seaborn: statistical data visualization](https://seaborn.pydata.org/)\n",
+    "- [PyCon 2017 - The Python Visualization Landscape](https://www.youtube.com/watch?v=FytuB8nFHPQ) [YouTube]"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -3108,7 +3321,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [
     {
@@ -3140,48 +3353,126 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>2013-12-22</td>\n",
-       "      <td>2</td>\n",
+       "      <th>51</th>\n",
+       "      <td>2014-12-14</td>\n",
+       "      <td>4</td>\n",
        "      <td>1</td>\n",
-       "      <td>53</td>\n",
+       "      <td>75</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2013-12-29</td>\n",
-       "      <td>2</td>\n",
+       "      <th>157</th>\n",
+       "      <td>2016-12-25</td>\n",
+       "      <td>9</td>\n",
+       "      <td>&lt;1</td>\n",
+       "      <td>41</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>216</th>\n",
+       "      <td>2018-02-11</td>\n",
+       "      <td>27</td>\n",
        "      <td>1</td>\n",
-       "      <td>62</td>\n",
+       "      <td>63</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "         Week  python pandas: (Worldwide) sas enterprise guide: (Worldwide)  \\\n",
-       "0  2013-12-22                           2                                 1   \n",
-       "1  2013-12-29                           2                                 1   \n",
+       "           Week  python pandas: (Worldwide) sas enterprise guide: (Worldwide)  \\\n",
+       "51   2014-12-14                           4                                 1   \n",
+       "157  2016-12-25                           9                                <1   \n",
+       "216  2018-02-11                          27                                 1   \n",
        "\n",
-       "   microsoft excel: (Worldwide)  \n",
-       "0                            53  \n",
-       "1                            62  "
+       "     microsoft excel: (Worldwide)  \n",
+       "51                             75  \n",
+       "157                            41  \n",
+       "216                            63  "
       ]
      },
-     "execution_count": 41,
+     "execution_count": 39,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "df_trends = pd.read_csv(r'data/google_trends.csv')\n",
-    "df_trends.head(2)"
+    "\n",
+    "df_trends.sample(3, random_state=13)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 40,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "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>python pandas</th>\n",
+       "      <th>sas enterprise guide</th>\n",
+       "      <th>microsoft excel</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Date</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2014-12-14</th>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2016-12-25</th>\n",
+       "      <td>9</td>\n",
+       "      <td>0</td>\n",
+       "      <td>41</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-02-11</th>\n",
+       "      <td>27</td>\n",
+       "      <td>1</td>\n",
+       "      <td>63</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            python pandas  sas enterprise guide  microsoft excel\n",
+       "Date                                                            \n",
+       "2014-12-14              4                     1               75\n",
+       "2016-12-25              9                     0               41\n",
+       "2018-02-11             27                     1               63"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# Some values are assigned `<1`, we set these to zero\n",
     "for col in df_trends.columns:\n",
@@ -3197,12 +3488,14 @@
     "# Change column names, set the 'Week' column to datetime, and use it as index\n",
     "df_trends.columns = [c.split(':')[0] if ':' in c else c for c in df_trends.columns]\n",
     "df_trends['Date'] = pd.to_datetime(df_trends['Week'])\n",
-    "df_trends = df_trends.set_index('Date').drop(columns='Week')"
+    "df_trends = df_trends.set_index('Date').drop(columns='Week')\n",
+    "\n",
+    "df_trends.sample(3, random_state=13)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [
     {
@@ -3225,21 +3518,18 @@
     "fig.savefig('pandas_vs_excel_vs_sas.png', dpi=100)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Take mean over the week of the each, looking for a *seasonal component*."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8acda54f28>"
-      ]
-     },
-     "execution_count": 44,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
     {
      "data": {
       "image/png": 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\n",
@@ -3258,24 +3548,22 @@
     " .assign(week_of_year=lambda df: df.index.to_series().dt.week)\n",
     " .groupby('week_of_year')\n",
     " .mean()\n",
-    ".plot(figsize=(10, 5)))"
+    " .plot(figsize=(10, 5))\n",
+    ");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The same as above, but grouping by **months** instead of **weeks**."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8acd72a908>"
-      ]
-     },
-     "execution_count": 45,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
     {
      "data": {
       "image/png": 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\n",
@@ -3294,7 +3582,8 @@
     " .assign(month=lambda df: df.index.to_series().dt.month)\n",
     " .groupby('month')\n",
     " .mean()\n",
-    ".plot(figsize=(10, 5)))"
+    " .plot(figsize=(10, 5))\n",
+    ");"
    ]
   },
   {
@@ -3306,7 +3595,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [
     {
@@ -3325,13 +3614,28 @@
    "source": [
     "window = 48\n",
     "fig, ax = plt.subplots(figsize=(10, 5))\n",
+    "\n",
+    "# Ploto the original data\n",
     "ax = df_trends.plot(legend=False, lw=1, ax=ax);\n",
-    "df_trends.rolling(window, win_type='triang', center=True).mean().plot(ax=ax, lw=3);"
+    "\n",
+    "# Ploto the rolling means, using a triangular window\n",
+    "(df_trends\n",
+    " .rolling(window, win_type='triang', center=True)\n",
+    " .mean()\n",
+    " .plot(ax=ax, lw=3)\n",
+    ");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Rolling **average** and **standard deviation**."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 45,
    "metadata": {},
    "outputs": [
     {
@@ -3356,6 +3660,8 @@
     "for column, color in zip(df_trends.columns, colors):\n",
     "    ax = df_trends[column].plot(ax=ax, lw=1, color=color, alpha=0.5);\n",
     "    avg = df_trends[column].rolling(window, center=True).mean()\n",
+    "    \n",
+    "    # Here an aggregation function can be used\n",
     "    std = df_trends[column].rolling(window, center=True).agg(pd.Series.std)\n",
     "    ax = avg.plot(ax=ax, lw=3, color=color, alpha=1);\n",
     "    ax = (avg + std).plot(ax=ax, lw=2, color=color, alpha=0.75, ls='--');\n",
@@ -3371,7 +3677,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 46,
    "metadata": {},
    "outputs": [
     {
@@ -3388,71 +3694,20 @@
     }
    ],
    "source": [
-    "ax = df_trends.resample('d').pad().plot(figsize=(10, 5))\n",
-    "df_trends.resample('M').mean().plot(ax=ax);"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x360 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = plt.subplots(figsize=(10, 5))\n",
-    "window = 12\n",
+    "# The original data is weekly. Here we resample to daily and monthly\n",
     "\n",
-    "agg_func = lambda ser: ser.max() - ser.min()\n",
+    "# Upsample\n",
+    "ax = (df_trends\n",
+    "      .resample('d')\n",
+    "      .pad()\n",
+    "      .plot(figsize=(10, 5))\n",
+    "     )\n",
     "\n",
-    "colors = plt.rcParams['axes.prop_cycle'].by_key()['color']\n",
-    "\n",
-    "for column, color in zip(df_trends.columns, colors):\n",
-    "    ax = df_trends[column].plot(ax=ax, lw=1, color=color, alpha=0.5);\n",
-    "    avg = df_trends[column].rolling(window, center=True).mean()\n",
-    "    std = df_trends[column].rolling(window, center=True).agg(agg_func)\n",
-    "    ax = avg.plot(ax=ax, lw=3, color=color, alpha=1);\n",
-    "    ax = (avg + std).plot(ax=ax, lw=2, color=color, alpha=0.75, ls='--');\n",
-    "    ax = (avg - std).plot(ax=ax, lw=2, color=color, alpha=0.75, ls='--');"
+    "# Downsample\n",
+    "(df_trends\n",
+    " .resample('M')\n",
+    " .mean()\n",
+    " .plot(ax=ax));"
    ]
   },
   {
@@ -3462,12 +3717,21 @@
     "# (9) Modeling and Machine Learning"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- How to create *dummy variables* (one-hot-encoding) of categorical data for machine learning algorithms.\n",
+    "- How to use [scikit-learn](https://scikit-learn.org/stable/) with pandas."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 47,
    "metadata": {},
    "outputs": [],
    "source": [
+    "# We want pandas to display more columns and rows\n",
     "pd.set_option('display.max_rows', 2**6)\n",
     "pd.set_option('display.max_columns', 2**6)"
    ]
@@ -3479,9 +3743,16 @@
     "## (9.1) Dummy variables for categorical data"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We start by re-loading the data."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 48,
    "metadata": {},
    "outputs": [
     {
@@ -3493,6 +3764,7 @@
     }
    ],
    "source": [
+    "# We reload the data\n",
     "cols_to_use = ['movie_title', 'duration', 'genres', 'content_rating', 'budget', 'gross']\n",
     "df_model = pd.read_csv(r'data/movie_metadata.csv', sep=',', usecols=cols_to_use)\n",
     "print(f'Loaded data of size {df_model.shape} into memory.')"
@@ -3500,29 +3772,207 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 49,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(3840, 6)"
+       "(3739, 6)"
       ]
      },
-     "execution_count": 52,
+     "execution_count": 49,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "# Drop any row with missing information\n",
-    "df_model = df_model.dropna(how='any')\n",
+    "df_model = df_model.dropna(axis=0, how='any').drop_duplicates(subset=None)\n",
     "df_model.shape"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "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>duration</th>\n",
+       "      <th>gross</th>\n",
+       "      <th>genres</th>\n",
+       "      <th>movie_title</th>\n",
+       "      <th>content_rating</th>\n",
+       "      <th>budget</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>74</th>\n",
+       "      <td>96.0</td>\n",
+       "      <td>100289690.0</td>\n",
+       "      <td>Comedy|Family|Fantasy</td>\n",
+       "      <td>Evan Almighty</td>\n",
+       "      <td>PG</td>\n",
+       "      <td>175000000.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1524</th>\n",
+       "      <td>138.0</td>\n",
+       "      <td>141340178.0</td>\n",
+       "      <td>Drama|Thriller</td>\n",
+       "      <td>A Few Good Men</td>\n",
+       "      <td>R</td>\n",
+       "      <td>40000000.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3032</th>\n",
+       "      <td>107.0</td>\n",
+       "      <td>50461335.0</td>\n",
+       "      <td>Drama|Fantasy|Music|Romance</td>\n",
+       "      <td>If I Stay</td>\n",
+       "      <td>PG-13</td>\n",
+       "      <td>11000000.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>332</th>\n",
+       "      <td>126.0</td>\n",
+       "      <td>63540020.0</td>\n",
+       "      <td>Action|Adventure|Sci-Fi</td>\n",
+       "      <td>The Fifth Element</td>\n",
+       "      <td>PG-13</td>\n",
+       "      <td>93000000.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1163</th>\n",
+       "      <td>122.0</td>\n",
+       "      <td>41252428.0</td>\n",
+       "      <td>Comedy|Drama|Romance</td>\n",
+       "      <td>The Mirror Has Two Faces</td>\n",
+       "      <td>PG-13</td>\n",
+       "      <td>42000000.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      duration        gross                       genres  \\\n",
+       "74        96.0  100289690.0        Comedy|Family|Fantasy   \n",
+       "1524     138.0  141340178.0               Drama|Thriller   \n",
+       "3032     107.0   50461335.0  Drama|Fantasy|Music|Romance   \n",
+       "332      126.0   63540020.0      Action|Adventure|Sci-Fi   \n",
+       "1163     122.0   41252428.0         Comedy|Drama|Romance   \n",
+       "\n",
+       "                    movie_title content_rating       budget  \n",
+       "74               Evan Almighty              PG  175000000.0  \n",
+       "1524            A Few Good Men               R   40000000.0  \n",
+       "3032                 If I Stay           PG-13   11000000.0  \n",
+       "332          The Fifth Element           PG-13   93000000.0  \n",
+       "1163  The Mirror Has Two Faces           PG-13   42000000.0  "
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_model.sample(5, random_state=3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "How to apply one-hot-encoding using `pandas.get_dummies`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "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>content_rating</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>74</th>\n",
+       "      <td>PG</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1524</th>\n",
+       "      <td>R</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3032</th>\n",
+       "      <td>PG-13</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     content_rating\n",
+       "74               PG\n",
+       "1524              R\n",
+       "3032          PG-13"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_model.content_rating.to_frame().sample(3, random_state=3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
    "metadata": {},
    "outputs": [
     {
@@ -3562,8 +4012,7 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0</td>\n",
+       "      <th>74</th>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
@@ -3575,9 +4024,25 @@
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
+       "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1</th>\n",
+       "      <th>1524</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3032</th>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
@@ -3596,24 +4061,25 @@
        "</div>"
       ],
       "text/plain": [
-       "   Approved  G  GP  M  NC-17  Not Rated  PG  PG-13  Passed  R  Unrated  X\n",
-       "0         0  0   0  0      0          0   0      1       0  0        0  0\n",
-       "1         0  0   0  0      0          0   0      1       0  0        0  0"
+       "      Approved  G  GP  M  NC-17  Not Rated  PG  PG-13  Passed  R  Unrated  X\n",
+       "74           0  0   0  0      0          0   1      0       0  0        0  0\n",
+       "1524         0  0   0  0      0          0   0      0       0  1        0  0\n",
+       "3032         0  0   0  0      0          0   0      1       0  0        0  0"
       ]
      },
-     "execution_count": 53,
+     "execution_count": 52,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "dummies = pd.get_dummies(df_model.content_rating)\n",
-    "dummies.head(2)"
+    "dummies.sample(3, random_state=3)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 53,
    "metadata": {},
    "outputs": [
     {
@@ -3658,12 +4124,52 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>178.0</td>\n",
-       "      <td>760505847.0</td>\n",
-       "      <td>Action|Adventure|Fantasy|Sci-Fi</td>\n",
-       "      <td>Avatar</td>\n",
-       "      <td>237000000.0</td>\n",
+       "      <th>74</th>\n",
+       "      <td>96.0</td>\n",
+       "      <td>100289690.0</td>\n",
+       "      <td>Comedy|Family|Fantasy</td>\n",
+       "      <td>Evan Almighty</td>\n",
+       "      <td>175000000.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1524</th>\n",
+       "      <td>138.0</td>\n",
+       "      <td>141340178.0</td>\n",
+       "      <td>Drama|Thriller</td>\n",
+       "      <td>A Few Good Men</td>\n",
+       "      <td>40000000.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3032</th>\n",
+       "      <td>107.0</td>\n",
+       "      <td>50461335.0</td>\n",
+       "      <td>Drama|Fantasy|Music|Romance</td>\n",
+       "      <td>If I Stay</td>\n",
+       "      <td>11000000.0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
@@ -3678,12 +4184,32 @@
        "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>169.0</td>\n",
-       "      <td>309404152.0</td>\n",
-       "      <td>Action|Adventure|Fantasy</td>\n",
-       "      <td>Pirates of the Caribbean: At World's End</td>\n",
-       "      <td>300000000.0</td>\n",
+       "      <th>332</th>\n",
+       "      <td>126.0</td>\n",
+       "      <td>63540020.0</td>\n",
+       "      <td>Action|Adventure|Sci-Fi</td>\n",
+       "      <td>The Fifth Element</td>\n",
+       "      <td>93000000.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1163</th>\n",
+       "      <td>122.0</td>\n",
+       "      <td>41252428.0</td>\n",
+       "      <td>Comedy|Drama|Romance</td>\n",
+       "      <td>The Mirror Has Two Faces</td>\n",
+       "      <td>42000000.0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
@@ -3702,28 +4228,38 @@
        "</div>"
       ],
       "text/plain": [
-       "   duration        gross                           genres  \\\n",
-       "0     178.0  760505847.0  Action|Adventure|Fantasy|Sci-Fi   \n",
-       "1     169.0  309404152.0         Action|Adventure|Fantasy   \n",
+       "      duration        gross                       genres  \\\n",
+       "74        96.0  100289690.0        Comedy|Family|Fantasy   \n",
+       "1524     138.0  141340178.0               Drama|Thriller   \n",
+       "3032     107.0   50461335.0  Drama|Fantasy|Music|Romance   \n",
+       "332      126.0   63540020.0      Action|Adventure|Sci-Fi   \n",
+       "1163     122.0   41252428.0         Comedy|Drama|Romance   \n",
        "\n",
-       "                                 movie_title       budget  Approved  G  GP  M  \\\n",
-       "0                                    Avatar   237000000.0         0  0   0  0   \n",
-       "1  Pirates of the Caribbean: At World's End   300000000.0         0  0   0  0   \n",
+       "                    movie_title       budget  Approved  G  GP  M  NC-17  \\\n",
+       "74               Evan Almighty   175000000.0         0  0   0  0      0   \n",
+       "1524            A Few Good Men    40000000.0         0  0   0  0      0   \n",
+       "3032                 If I Stay    11000000.0         0  0   0  0      0   \n",
+       "332          The Fifth Element    93000000.0         0  0   0  0      0   \n",
+       "1163  The Mirror Has Two Faces    42000000.0         0  0   0  0      0   \n",
        "\n",
-       "   NC-17  Not Rated  PG  PG-13  Passed  R  Unrated  X  \n",
-       "0      0          0   0      1       0  0        0  0  \n",
-       "1      0          0   0      1       0  0        0  0  "
+       "      Not Rated  PG  PG-13  Passed  R  Unrated  X  \n",
+       "74            0   1      0       0  0        0  0  \n",
+       "1524          0   0      0       0  1        0  0  \n",
+       "3032          0   0      1       0  0        0  0  \n",
+       "332           0   0      1       0  0        0  0  \n",
+       "1163          0   0      1       0  0        0  0  "
       ]
      },
-     "execution_count": 54,
+     "execution_count": 53,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
+    "# Merge the dummy variables into the original data set\n",
     "df_model = df_model.merge(dummies, how='left', left_index=True, right_index=True)\n",
     "df_model = df_model.drop(columns='content_rating', errors='ignore')\n",
-    "df_model.head(2)"
+    "df_model.sample(5, random_state=3)"
    ]
   },
   {
@@ -3732,25 +4268,36 @@
    "source": [
     "-----------------\n",
     "\n",
-    "A more advanced example follows."
+    "Notice now the **genres** column above. Here each row has a variable number of genres.\n",
+    "\n",
+    "We now show how to dummy-encode this categorical data."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 54,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'Mystery', 'Film-Noir', 'Animation', 'Adventure', 'Documentary', 'Musical', 'Fantasy', 'Western', 'Horror', 'Thriller', 'Family', 'Action', 'Comedy', 'Sport', 'Crime', 'Biography', 'History', 'War', 'Sci-Fi', 'Drama', 'Romance', 'Music'}\n"
+     ]
+    }
+   ],
    "source": [
     "from functools import reduce\n",
     "\n",
     "# Split the genres, take the union over every set to get every genre in the data set\n",
     "genres_sets = df_model.genres.str.split('|').apply(set)\n",
-    "genres = reduce(set.union, genres_sets)"
+    "genres = reduce(set.union, genres_sets)\n",
+    "print(genres)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 55,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3763,7 +4310,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 56,
    "metadata": {},
    "outputs": [
     {
@@ -3803,28 +4350,28 @@
        "      <th>R</th>\n",
        "      <th>Unrated</th>\n",
        "      <th>X</th>\n",
+       "      <th>Mystery</th>\n",
+       "      <th>Film-Noir</th>\n",
        "      <th>Animation</th>\n",
-       "      <th>Romance</th>\n",
-       "      <th>Comedy</th>\n",
-       "      <th>Horror</th>\n",
-       "      <th>Biography</th>\n",
-       "      <th>Musical</th>\n",
-       "      <th>Sci-Fi</th>\n",
-       "      <th>Fantasy</th>\n",
-       "      <th>Crime</th>\n",
-       "      <th>Western</th>\n",
-       "      <th>Thriller</th>\n",
-       "      <th>Sport</th>\n",
-       "      <th>War</th>\n",
-       "      <th>Family</th>\n",
-       "      <th>Drama</th>\n",
        "      <th>Adventure</th>\n",
        "      <th>Documentary</th>\n",
-       "      <th>Mystery</th>\n",
+       "      <th>Musical</th>\n",
+       "      <th>Fantasy</th>\n",
+       "      <th>Western</th>\n",
+       "      <th>Horror</th>\n",
+       "      <th>Thriller</th>\n",
+       "      <th>Family</th>\n",
        "      <th>Action</th>\n",
-       "      <th>Music</th>\n",
-       "      <th>Film-Noir</th>\n",
+       "      <th>Comedy</th>\n",
+       "      <th>Sport</th>\n",
+       "      <th>Crime</th>\n",
+       "      <th>Biography</th>\n",
        "      <th>History</th>\n",
+       "      <th>War</th>\n",
+       "      <th>Sci-Fi</th>\n",
+       "      <th>Drama</th>\n",
+       "      <th>Romance</th>\n",
+       "      <th>Music</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
@@ -3849,21 +4396,21 @@
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
        "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
        "      <td>1</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
        "      <td>1</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
        "      <td>1</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
@@ -3890,6 +4437,10 @@
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
@@ -3902,10 +4453,6 @@
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
@@ -3923,20 +4470,20 @@
        "0  237000000.0         0  0   0  0      0          0   0      1       0  0   \n",
        "1  300000000.0         0  0   0  0      0          0   0      1       0  0   \n",
        "\n",
-       "   Unrated  X  Animation  Romance  Comedy  Horror  Biography  Musical  Sci-Fi  \\\n",
-       "0        0  0          0        0       0       0          0        0       1   \n",
-       "1        0  0          0        0       0       0          0        0       0   \n",
+       "   Unrated  X  Mystery  Film-Noir  Animation  Adventure  Documentary  Musical  \\\n",
+       "0        0  0        0          0          0          1            0        0   \n",
+       "1        0  0        0          0          0          1            0        0   \n",
        "\n",
-       "   Fantasy  Crime  Western  Thriller  Sport  War  Family  Drama  Adventure  \\\n",
-       "0        1      0        0         0      0    0       0      0          1   \n",
-       "1        1      0        0         0      0    0       0      0          1   \n",
+       "   Fantasy  Western  Horror  Thriller  Family  Action  Comedy  Sport  Crime  \\\n",
+       "0        1        0       0         0       0       1       0      0      0   \n",
+       "1        1        0       0         0       0       1       0      0      0   \n",
        "\n",
-       "   Documentary  Mystery  Action  Music  Film-Noir  History  \n",
-       "0            0        0       1      0          0        0  \n",
-       "1            0        0       1      0          0        0  "
+       "   Biography  History  War  Sci-Fi  Drama  Romance  Music  \n",
+       "0          0        0    0       1      0        0      0  \n",
+       "1          0        0    0       0      0        0      0  "
       ]
      },
-     "execution_count": 57,
+     "execution_count": 56,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3949,174 +4496,130 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## (9.2) Training a model"
+    "## (9.2) Training a model\n",
+    "\n",
+    "Let's try to predict $y = \\log_{10}(1 + \\text{gross})$ based on the other features in the data set."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert (df_model['gross'] >= 0).all()\n",
+    "assert (df_model['budget'] >= 0).all()\n",
+    "\n",
+    "log10plus = lambda x: np.log10(1 + x)\n",
+    "\n",
+    "df_model['gross_log'] = df_model['gross'].apply(log10plus)\n",
+    "df_model['budget_log'] = df_model['budget'].apply(log10plus)\n",
+    "df_model = df_model.drop(columns=['gross', 'budget'])"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 58,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "type(X)=<class 'numpy.ndarray'>\n",
+      "type(y)=<class 'pandas.core.series.Series'>\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "0.9208172135283045"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "from sklearn.linear_model import LinearRegression\n",
-    "from sklearn.model_selection import train_test_split"
+    "from sklearn.model_selection import cross_val_score\n",
+    "\n",
+    "# Here's a very simple model. It only has a bias, so it uses the mean value to predict\n",
+    "X = np.ones((len(df_model), 1))\n",
+    "y = df_model['gross_log']\n",
+    "\n",
+    "reg = LinearRegression(fit_intercept=False)\n",
+    "\n",
+    "print(f'type(X)={type(X)}')\n",
+    "print(f'type(y)={type(y)}')\n",
+    "scores = -cross_val_score(reg, X, y, cv=10, scoring='neg_mean_squared_error')\n",
+    "scores = np.sqrt(scores)\n",
+    "\n",
+    "pd.Series(scores, name='Root mean squared error (bias model)').plot.box();\n",
+    "np.mean(scores)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 59,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Columns used: ['Action', 'Adventure', 'Animation', 'Approved', 'Biography', 'Comedy', 'Crime', 'Documentary', 'Drama', 'Family', 'Fantasy', 'Film-Noir', 'G', 'GP', 'History', 'Horror', 'M', 'Music', 'Musical', 'Mystery', 'NC-17', 'Not Rated', 'PG', 'PG-13', 'Passed', 'R', 'Romance', 'Sci-Fi', 'Sport', 'Thriller', 'Unrated', 'War', 'Western', 'X', 'budget_log', 'duration']\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "0.7029719639736746"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "df_model['gross_log'] = df_model['gross'].apply(np.log1p)\n",
-    "df_model['budget_log'] = df_model['budget'].apply(np.log1p)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 60,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "linreg = LinearRegression()\n",
-    "X = df_model.drop(columns=['movie_title', 'gross', 'budget', 'gross_log']).to_numpy()\n",
-    "y = df_model.gross_log.to_numpy()\n",
+    "X = df_model.drop(columns=['movie_title', 'gross_log'])\n",
+    "y = df_model.gross_log\n",
     "\n",
-    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=123)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 61,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
-       "         normalize=False)"
-      ]
-     },
-     "execution_count": 61,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "linreg.fit(X_train, y_train)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 62,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "y_pred = linreg.predict(X_test)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 63,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1.6093703080115105"
-      ]
-     },
-     "execution_count": 63,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pd.Series(y_test.mean() - y_test).abs().mean()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 64,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1.1842132628414064"
-      ]
-     },
-     "execution_count": 64,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pd.Series(y_pred - y_test).abs().mean()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 65,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8ac5266630>"
-      ]
-     },
-     "execution_count": 65,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x360 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pd.Series(y_test.mean() - y_test).plot(figsize=(10, 5))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 66,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8ac528af98>"
-      ]
-     },
-     "execution_count": 66,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x360 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pd.Series(y_pred - y_test).plot(figsize=(10, 5))"
+    "print('Columns used:', sorted(X.columns.tolist()))\n",
+    "\n",
+    "reg = LinearRegression()\n",
+    "\n",
+    "scores = -cross_val_score(reg, X, y, cv=10, scoring='neg_mean_squared_error')\n",
+    "scores = np.sqrt(scores)\n",
+    "\n",
+    "pd.Series(scores, name='Root mean squared error (linear model)').plot.box();\n",
+    "np.mean(scores)"
    ]
   },
   {
@@ -4130,7 +4633,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## (10.1) Performance"
+    "## (10.1) Performance\n",
+    "\n",
+    "Recall that:\n",
+    "- A millisecond `ms` is $10^{-3}$ seconds (*thousandth*)\n",
+    "- A microsecond `µs` is $10^{-6}$ seconds (*millionth*)\n",
+    "- A nanosecond `ns` is $10^{-9}$ seconds (*billionth*)"
    ]
   },
   {
@@ -4149,7 +4657,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 60,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4157,6 +4665,147 @@
     "ser = pd.Series(np.random.randn(1000000))"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(ser.nlargest(50) == ser.sort_values(ascending=False).head(50)).all()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "219 ms ± 5.02 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%timeit\n",
+    "\n",
+    "[ser.max() for i in range(50)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "192 ms ± 13.1 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%timeit\n",
+    "\n",
+    "ser.sort_values(ascending=False).head(50)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "14.3 ms ± 207 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%timeit\n",
+    "\n",
+    "ser.nlargest(50)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Filter *before* computing\n",
+    "\n",
+    "It's generally better to apply filters *before* computations, especially if the computations are expensive."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "49.4 ms ± 324 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%timeit\n",
+    "\n",
+    "df.mean().imdb_score"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "46 µs ± 288 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%timeit\n",
+    "\n",
+    "df.imdb_score.mean()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Setting a sorted index can speed up filtering "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = df.sort_values('gross').dropna(how='any')\n",
+    "df_sorted_index = df.set_index('director_name').sort_index()"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 68,
@@ -4174,7 +4823,7 @@
     }
    ],
    "source": [
-    "(ser.nlargest(50) == ser.sort_values(ascending=False).head(50)).all()"
+    "df_sorted_index.index.dropna().is_monotonic"
    ]
   },
   {
@@ -4186,14 +4835,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "6.57 ms ± 916 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
+      "721 µs ± 19.2 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
      ]
     }
    ],
    "source": [
     "%%timeit\n",
     "\n",
-    "ser.max()"
+    "df.loc[lambda df:df.director_name == 'o', :]"
    ]
   },
   {
@@ -4205,42 +4854,34 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "31.9 ms ± 7.85 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
+      "130 µs ± 352 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n"
      ]
     }
    ],
    "source": [
     "%%timeit\n",
     "\n",
-    "ser.nlargest(50)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 71,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "257 ms ± 37.9 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%timeit\n",
-    "\n",
-    "ser.sort_values(ascending=False).head(50)"
+    "df_sorted_index.loc[slice('o', None), :]"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Filter *before* computing\n",
+    "### Vectorize everything (if you can)\n",
     "\n",
-    "It's generally better to apply filters *before* computations, especially if the computations are expensive."
+    "Example with logarithms and `map` vs. `apply`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import math\n",
+    "\n",
+    "temp = df.gross.dropna()"
    ]
   },
   {
@@ -4252,157 +4893,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "57.1 ms ± 997 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%timeit\n",
-    "\n",
-    "df.mean().imdb_score"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 73,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "48.9 µs ± 1.28 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%timeit\n",
-    "\n",
-    "df.imdb_score.mean()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "-----------------"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 74,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df = df.sort_values('gross').dropna(how='any')\n",
-    "temp = df.set_index('director_name').sort_index()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 75,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "502 µs ± 41.7 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%timeit\n",
-    "\n",
-    "temp.loc[lambda df:df.index > 'o', :]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 76,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "718 µs ± 26.2 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%timeit\n",
-    "\n",
-    "df.loc[lambda df:df.director_name > 'o', :]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 77,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "137 µs ± 1.71 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%timeit\n",
-    "\n",
-    "temp.loc[slice('o', None), :]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 78,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 78,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "temp.index.dropna().is_monotonic"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Vectorize everything (if you can)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 79,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import math\n",
-    "\n",
-    "temp = df.gross.dropna()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 80,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "866 µs ± 18.6 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
+      "802 µs ± 3.15 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
      ]
     }
    ],
@@ -4414,14 +4905,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
+   "execution_count": 73,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "168 µs ± 2.3 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n"
+      "160 µs ± 309 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n"
      ]
     }
    ],
@@ -4435,12 +4926,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Another vectorization example"
+    "### Another vectorization example\n",
+    "\n",
+    "Computing the mean of differences."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
+   "execution_count": 74,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4451,26 +4944,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
+   "execution_count": 75,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2.01 ms ± 81.8 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
+      "1.64 ms ± 11.1 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
      ]
     }
    ],
    "source": [
     "%%timeit\n",
     "\n",
+    "# Fast NumPy solution\n",
     "np.mean(vector[1:] - vector[:-1])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 84,
+   "execution_count": 76,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4479,20 +4973,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 85,
+   "execution_count": 77,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "777 ms ± 10.5 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
+      "719 ms ± 1.85 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
      ]
     }
    ],
    "source": [
     "%%timeit\n",
     "\n",
+    "# Slow pure Python solution\n",
     "statistics.mean((i - j for (i, j) in zip(vector[1:], vector[:-1])))"
    ]
   },
@@ -4500,12 +4995,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "----------------"
+    "> **Note.** Learn more about the joys of vectorization in [From Python to Numpy](https://www.labri.fr/perso/nrougier/from-python-to-numpy/)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Vectorizing equality checks"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 86,
+   "execution_count": 78,
    "metadata": {},
    "outputs": [
     {
@@ -4543,7 +5045,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>France</td>\n",
+       "      <td>USA</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -4553,10 +5055,10 @@
        "  country\n",
        "0     USA\n",
        "1     USA\n",
-       "2  France"
+       "2     USA"
       ]
      },
-     "execution_count": 86,
+     "execution_count": 78,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4568,14 +5070,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 87,
+   "execution_count": 79,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "354 ms ± 40.4 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
+      "317 ms ± 3.98 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
      ]
     }
    ],
@@ -4587,14 +5089,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 88,
+   "execution_count": 80,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "1.38 s ± 38.1 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
+      "1.33 s ± 11.9 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
      ]
     }
    ],
@@ -4613,32 +5115,194 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# (11) Problems and answers"
+    "## (10.2) Exporting data"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 81,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "# Save data as a .csv file\n",
+    "df.to_csv(path_or_buf='movies_data_saved.csv', index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Save data as an Excel file\n",
+    "df.to_excel(excel_writer='movies_data_saved.xlsx', index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\\begin{tabular}{lrlllr}\n",
+      "\\toprule\n",
+      "         director\\_name &   gross &                 movie\\_title &   country & content\\_rating &  imdb\\_score \\\\\n",
+      "\\midrule\n",
+      "   Ekachai Uekrongtham &   162.0 &                 Skin Trade  &  Thailand &              R &         5.7 \\\\\n",
+      "          Frank Whaley &   703.0 &             The Jimmy Show  &       USA &              R &         5.4 \\\\\n",
+      " Brian Trenchard-Smith &   721.0 &        In Her Line of Fire  &   Germany &              R &         4.1 \\\\\n",
+      "        Ian Fitzgibbon &   828.0 &           Perrier's Bounty  &   Ireland &              R &         6.4 \\\\\n",
+      "           Ricki Stern &  1111.0 &  The Trials of Darryl Hunt  &       USA &          PG-13 &         7.7 \\\\\n",
+      "\\bottomrule\n",
+      "\\end{tabular}\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Generate LaTeX code\n",
+    "print(df.head().to_latex(index=False))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>director_name</th>\n",
+       "      <th>gross</th>\n",
+       "      <th>movie_title</th>\n",
+       "      <th>country</th>\n",
+       "      <th>content_rating</th>\n",
+       "      <th>imdb_score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>Ekachai Uekrongtham</td>\n",
+       "      <td>162.0</td>\n",
+       "      <td>Skin Trade</td>\n",
+       "      <td>Thailand</td>\n",
+       "      <td>R</td>\n",
+       "      <td>5.7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>Frank Whaley</td>\n",
+       "      <td>703.0</td>\n",
+       "      <td>The Jimmy Show</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>R</td>\n",
+       "      <td>5.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>Brian Trenchard-Smith</td>\n",
+       "      <td>721.0</td>\n",
+       "      <td>In Her Line of Fire</td>\n",
+       "      <td>Germany</td>\n",
+       "      <td>R</td>\n",
+       "      <td>4.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>Ian Fitzgibbon</td>\n",
+       "      <td>828.0</td>\n",
+       "      <td>Perrier's Bounty</td>\n",
+       "      <td>Ireland</td>\n",
+       "      <td>R</td>\n",
+       "      <td>6.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>Ricki Stern</td>\n",
+       "      <td>1111.0</td>\n",
+       "      <td>The Trials of Darryl Hunt</td>\n",
+       "      <td>USA</td>\n",
+       "      <td>PG-13</td>\n",
+       "      <td>7.7</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from IPython.core.display import display, HTML\n",
+    "\n",
+    "display(HTML(df.head().to_html(index=False)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<table border=\"1\" class=\"dataframe\">\n",
+      "  <thead>\n",
+      "    <tr style=\"text-align: right;\">\n",
+      "      <th>director_name</th>\n",
+      "      <th>gross</th>\n",
+      "      <th>movie_title</th>\n",
+      "      <th>country</th>\n",
+      "      <th>content_rating</th>\n",
+      "      <th>imdb_score</th>\n",
+      "    </tr>\n",
+      "  </thead>\n",
+      "  <tbody>\n",
+      "    <tr>\n",
+      "      <td>Ekachai Uekrongtham</td>\n",
+      "      <td>162.0</td>\n",
+      "      <td>Skin Trade</td>\n",
+      "      <td>Thailand</td>\n",
+      "      <td>R</td>\n",
+      "      <td>5.7</td>\n",
+      "    </tr>\n",
+      "  </tbody>\n",
+      "</table>\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df.head(1).to_html(index=False))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Can write directly to databases\n",
+    "\n",
+    "# df.to_sql()"
+   ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# References\n",
+    "# Resources\n",
     "\n",
     "- [Official Pandas Documentation](https://pandas.pydata.org/pandas-docs/stable/index.html)\n",
-    "- Videos at [awesome-pandas](https://github.com/tommyod/awesome-pandas), especially"
+    "- Videos at [awesome-pandas](https://github.com/tommyod/awesome-pandas), especially\n",
+    "- [pandas_exercises](https://github.com/guipsamora/pandas_exercises) repository if you want practice"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {