day73/Day73.ipynb

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 "cells": [
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   "cell_type": "markdown",
   "metadata": {
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    "dc": {
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    "id": "s9JLDE8NIquP",
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    "tags": [
     "context"
    ]
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   "source": [
    "# Introduction\n",
    "\n",
    "Today we'll dive deep into a dataset all about LEGO. From the dataset we can ask whole bunch of interesting questions about the history of the LEGO company, their product offering, and which LEGO set ultimately rules them all:\n",
    "\n",
    "<ul type=\"square\">\n",
    "<li>What is the most enormous LEGO set ever created and how many parts did it have?</li>\n",
    "\n",
    "<li>How did the LEGO company start out? In which year were the first LEGO sets released and how many sets did the company sell when it first launched?</li>\n",
    "\n",
    "<li>Which LEGO theme has the most sets? Is it one of LEGO's own themes like Ninjago or a theme they licensed liked Harry Potter or Marvel Superheroes?</li>\n",
    "\n",
    "<li>When did the LEGO company really expand its product offering? Can we spot a change in the company strategy based on how many themes and sets did it released year-on-year?</li>\n",
    "\n",
    "<li>Did LEGO sets grow in size and complexity over time? Do older LEGO \n",
    "sets tend to have more or fewer parts than newer sets?</li>\n",
    "</ul>\n",
    "\n",
    "**Data Source**\n",
    "\n",
    "[Rebrickable](https://rebrickable.com/downloads/) has compiled data on all the LEGO pieces in existence. I recommend you use download the .csv files provided in this lesson. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# using most recent CSV files from 2021-02-10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"assets/bricks.jpg\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "V0u2lGJuIquQ"
   },
   "source": [
    "# Import Statements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "z5Wk7rs-IquQ"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "R5NQpJ_KIquT"
   },
   "source": [
    "# Data Exploration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "dc": {
     "key": "044b2cef41"
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    "id": "ffaG-UFYIquT",
    "run_control": {
     "frozen": true
    },
    "tags": [
     "context"
    ]
   },
   "source": [
    "**Challenge**: How many different colours does the LEGO company produce? Read the colors.csv file in the data folder and find the total number of unique colours. Try using the [.nunique() method](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.nunique.html?highlight=nunique#pandas.DataFrame.nunique) to accomplish this."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"assets/rebrickable_schema.png\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "yd4G9pK7IquU"
   },
   "outputs": [],
   "source": [
    "# using only the highlighted tables\n",
    "colors_df = pd.read_csv(\"data/colors_20210210.csv\", header=0, names=[\"id\", \"name\", \"rgb\", \"is_trans\"])\n",
    "\n",
    "# prepared for later use\n",
    "# sets_df = pd.read_csv(\"data/sets_20210210.csv\", header=0, names=[\"set_num\", \"name\", \"year\", \"theme_id\", \"num_parts\"])\n",
    "# themes_df = pd.read_csv(\"data/themes_20210210.csv\", header=0, names=[\"id\", \"name\", \"parent_id\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>id</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>186.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>466.897849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>825.053266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>60.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>178.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1012.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9999.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                id\n",
       "count   186.000000\n",
       "mean    466.897849\n",
       "std     825.053266\n",
       "min      -1.000000\n",
       "25%      60.250000\n",
       "50%     178.500000\n",
       "75%    1012.750000\n",
       "max    9999.000000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# make sure the dataframe was loaded properly\n",
    "colors_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>name</th>\n",
       "      <th>rgb</th>\n",
       "      <th>is_trans</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1</td>\n",
       "      <td>[Unknown]</td>\n",
       "      <td>0033B2</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>Black</td>\n",
       "      <td>05131D</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Blue</td>\n",
       "      <td>0055BF</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>Green</td>\n",
       "      <td>237841</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>Dark Turquoise</td>\n",
       "      <td>008F9B</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id            name     rgb is_trans\n",
       "0  -1       [Unknown]  0033B2        f\n",
       "1   0           Black  05131D        f\n",
       "2   1            Blue  0055BF        f\n",
       "3   2           Green  237841        f\n",
       "4   3  Dark Turquoise  008F9B        f"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "colors_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
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    "colab_type": "code",
    "dc": {
     "key": "044b2cef41"
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    "id": "QmbAXax7IquW",
    "tags": [
     "sample_code"
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "186"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "colors_df[\"name\"].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "dc": {
     "key": "15c1e2ce38"
    },
    "id": "SMc2sKiIIquY",
    "tags": [
     "sample_code"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "160"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# should be colors that have a transparent variant too counted twice?\n",
    "colors_df[\"rgb\"].nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "dc": {
     "key": "a5723ae5c2"
    },
    "id": "PItRbqgcIqua",
    "run_control": {
     "frozen": true
    },
    "tags": [
     "context"
    ]
   },
   "source": [
    "**Challenge**: Find the number of transparent colours where <code>is_trans == 't'</code> versus the number of opaque colours where <code>is_trans == 'f'</code>. See if you can accomplish this in two different ways."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "1UZrfq82Iqub"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "\n",
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       "        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>id</th>\n",
       "      <th>name</th>\n",
       "      <th>rgb</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>is_trans</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>f</th>\n",
       "      <td>151</td>\n",
       "      <td>151</td>\n",
       "      <td>151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t</th>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "           id  name  rgb\n",
       "is_trans                \n",
       "f         151   151  151\n",
       "t          35    35   35"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# count transparent and opaque colors\n",
    "colors_df.groupby([\"is_trans\"]).count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "KQFf-leCIqud"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "f    151\n",
       "t     35\n",
       "Name: is_trans, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# alternatively\n",
    "colors_df[\"is_trans\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "TMqdhUYcusfy"
   },
   "source": [
    "**Challenge**: Change this into an h3 section heading: Understanding LEGO Themes vs. LEGO Sets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "y0kxCh63uwOv"
   },
   "source": [
    "### Walk into a LEGO store and you will see their products organised by theme. Their themes include Star Wars, Batman, Harry Potter and many more.\n",
    "\n",
    "**Challenge**: Display this image: https://i.imgur.com/aKcwkSx.png"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"assets/lego_themes.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "dc": {
     "key": "c9d0e58653"
    },
    "id": "u_xkZUF8Iqug",
    "run_control": {
     "frozen": true
    },
    "tags": [
     "context"
    ]
   },
   "source": [
    "### A lego set is a particular box of LEGO or product. Therefore, a single theme typically has many different sets.\n",
    "\n",
    "**Challenge**: Display this image https://i.imgur.com/whB1olq.png "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"assets/lego_sets.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "jJTAROe5unkx"
   },
   "source": [
    "The <code>sets.csv</code> data contains a list of sets over the years and the number of parts that each of these sets contained.\n",
    "\n",
    "**Challenge**: Read the sets.csv data and take a look at the first and last couple of rows. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "vGMOv-NRIquh"
   },
   "outputs": [],
   "source": [
    "sets_df = pd.read_csv(\"data/sets_20210210.csv\", header=0, names=[\"set_num\", \"name\", \"year\", \"theme_id\", \"num_parts\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "T3lLFvyZIqui"
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   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>theme_id</th>\n",
       "      <th>num_parts</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>16448.000000</td>\n",
       "      <td>16448.000000</td>\n",
       "      <td>16448.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2005.112901</td>\n",
       "      <td>391.444553</td>\n",
       "      <td>163.875912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>13.987155</td>\n",
       "      <td>201.039303</td>\n",
       "      <td>377.741904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1949.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1999.000000</td>\n",
       "      <td>227.000000</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2009.000000</td>\n",
       "      <td>447.000000</td>\n",
       "      <td>42.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2016.000000</td>\n",
       "      <td>524.000000</td>\n",
       "      <td>160.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2021.000000</td>\n",
       "      <td>712.000000</td>\n",
       "      <td>9987.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               year      theme_id     num_parts\n",
       "count  16448.000000  16448.000000  16448.000000\n",
       "mean    2005.112901    391.444553    163.875912\n",
       "std       13.987155    201.039303    377.741904\n",
       "min     1949.000000      1.000000      0.000000\n",
       "25%     1999.000000    227.000000      8.000000\n",
       "50%     2009.000000    447.000000     42.000000\n",
       "75%     2016.000000    524.000000    160.000000\n",
       "max     2021.000000    712.000000   9987.000000"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# confirm import\n",
    "sets_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "XprDBmzwIquk"
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>set_num</th>\n",
       "      <th>name</th>\n",
       "      <th>year</th>\n",
       "      <th>theme_id</th>\n",
       "      <th>num_parts</th>\n",
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       "      <th>0</th>\n",
       "      <td>001-1</td>\n",
       "      <td>Gears</td>\n",
       "      <td>1965</td>\n",
       "      <td>1</td>\n",
       "      <td>43</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0011-2</td>\n",
       "      <td>Town Mini-Figures</td>\n",
       "      <td>1978</td>\n",
       "      <td>84</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0011-3</td>\n",
       "      <td>Castle 2 for 1 Bonus Offer</td>\n",
       "      <td>1987</td>\n",
       "      <td>199</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0012-1</td>\n",
       "      <td>Space Mini-Figures</td>\n",
       "      <td>1979</td>\n",
       "      <td>143</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0013-1</td>\n",
       "      <td>Space Mini-Figures</td>\n",
       "      <td>1979</td>\n",
       "      <td>143</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  set_num                        name  year  theme_id  num_parts\n",
       "0   001-1                       Gears  1965         1         43\n",
       "1  0011-2           Town Mini-Figures  1978        84         12\n",
       "2  0011-3  Castle 2 for 1 Bonus Offer  1987       199          0\n",
       "3  0012-1          Space Mini-Figures  1979       143         12\n",
       "4  0013-1          Space Mini-Figures  1979       143         12"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sets_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>name</th>\n",
       "      <th>year</th>\n",
       "      <th>theme_id</th>\n",
       "      <th>num_parts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16443</th>\n",
       "      <td>XWING-1</td>\n",
       "      <td>Mini X-Wing Fighter</td>\n",
       "      <td>2019</td>\n",
       "      <td>158</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16444</th>\n",
       "      <td>XWING-2</td>\n",
       "      <td>X-Wing Trench Run</td>\n",
       "      <td>2019</td>\n",
       "      <td>158</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16445</th>\n",
       "      <td>YODACHRON-1</td>\n",
       "      <td>Yoda Chronicles Promotional Set</td>\n",
       "      <td>2013</td>\n",
       "      <td>158</td>\n",
       "      <td>413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16446</th>\n",
       "      <td>YTERRIER-1</td>\n",
       "      <td>Yorkshire Terrier</td>\n",
       "      <td>2018</td>\n",
       "      <td>598</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16447</th>\n",
       "      <td>ZX8000-1</td>\n",
       "      <td>ZX 8000 LEGO Sneaker</td>\n",
       "      <td>2020</td>\n",
       "      <td>501</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           set_num                             name  year  theme_id  num_parts\n",
       "16443      XWING-1              Mini X-Wing Fighter  2019       158         60\n",
       "16444      XWING-2                X-Wing Trench Run  2019       158         52\n",
       "16445  YODACHRON-1  Yoda Chronicles Promotional Set  2013       158        413\n",
       "16446   YTERRIER-1                Yorkshire Terrier  2018       598          0\n",
       "16447     ZX8000-1             ZX 8000 LEGO Sneaker  2020       501          0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sets_df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ez-UXSMUIqum"
   },
   "source": [
    "**Challenge**: In which year were the first LEGO sets released and what were these sets called?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "s2aL6qrGIqum"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1949"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sets_df[\"year\"].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>set_num</th>\n",
       "      <th>name</th>\n",
       "      <th>year</th>\n",
       "      <th>theme_id</th>\n",
       "      <th>num_parts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9959</th>\n",
       "      <td>700.2-1</td>\n",
       "      <td>Large Gift Set (ABB)</td>\n",
       "      <td>1949</td>\n",
       "      <td>365</td>\n",
       "      <td>178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9964</th>\n",
       "      <td>700.3-1</td>\n",
       "      <td>Medium Gift Set (ABB)</td>\n",
       "      <td>1949</td>\n",
       "      <td>365</td>\n",
       "      <td>142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9946</th>\n",
       "      <td>700.1-1</td>\n",
       "      <td>Extra-Large Gift Set (ABB)</td>\n",
       "      <td>1949</td>\n",
       "      <td>365</td>\n",
       "      <td>142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9970</th>\n",
       "      <td>700.B-1</td>\n",
       "      <td>Small Doors and Windows Set (ABB)</td>\n",
       "      <td>1949</td>\n",
       "      <td>371</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9969</th>\n",
       "      <td>700.A-1</td>\n",
       "      <td>Small Brick Set (ABB)</td>\n",
       "      <td>1949</td>\n",
       "      <td>371</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      set_num                               name  year  theme_id  num_parts\n",
       "9959  700.2-1               Large Gift Set (ABB)  1949       365        178\n",
       "9964  700.3-1              Medium Gift Set (ABB)  1949       365        142\n",
       "9946  700.1-1         Extra-Large Gift Set (ABB)  1949       365        142\n",
       "9970  700.B-1  Small Doors and Windows Set (ABB)  1949       371         12\n",
       "9969  700.A-1              Small Brick Set (ABB)  1949       371         24"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sets_df.sort_values(\"year\").head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "JJoK3M8TBAVU"
   },
   "source": [
    "**Challenge**: How many different sets did LEGO sell in their first year? How many types of LEGO products were on offer in the year the company started?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "h-Tf1w7IBBg9"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>set_num</th>\n",
       "      <th>name</th>\n",
       "      <th>year</th>\n",
       "      <th>theme_id</th>\n",
       "      <th>num_parts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9946</th>\n",
       "      <td>700.1-1</td>\n",
       "      <td>Extra-Large Gift Set (ABB)</td>\n",
       "      <td>1949</td>\n",
       "      <td>365</td>\n",
       "      <td>142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9959</th>\n",
       "      <td>700.2-1</td>\n",
       "      <td>Large Gift Set (ABB)</td>\n",
       "      <td>1949</td>\n",
       "      <td>365</td>\n",
       "      <td>178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9964</th>\n",
       "      <td>700.3-1</td>\n",
       "      <td>Medium Gift Set (ABB)</td>\n",
       "      <td>1949</td>\n",
       "      <td>365</td>\n",
       "      <td>142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9969</th>\n",
       "      <td>700.A-1</td>\n",
       "      <td>Small Brick Set (ABB)</td>\n",
       "      <td>1949</td>\n",
       "      <td>371</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9970</th>\n",
       "      <td>700.B-1</td>\n",
       "      <td>Small Doors and Windows Set (ABB)</td>\n",
       "      <td>1949</td>\n",
       "      <td>371</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      set_num                               name  year  theme_id  num_parts\n",
       "9946  700.1-1         Extra-Large Gift Set (ABB)  1949       365        142\n",
       "9959  700.2-1               Large Gift Set (ABB)  1949       365        178\n",
       "9964  700.3-1              Medium Gift Set (ABB)  1949       365        142\n",
       "9969  700.A-1              Small Brick Set (ABB)  1949       371         24\n",
       "9970  700.B-1  Small Doors and Windows Set (ABB)  1949       371         12"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# filter the df by a condition\n",
    "sets_df[sets_df[\"year\"] == 1949]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "RJMMYQYqIquo"
   },
   "source": [
    "**Challenge**: Find the top 5 LEGO sets with the most number of parts. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "toJvjRuQIqup"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>set_num</th>\n",
       "      <th>name</th>\n",
       "      <th>year</th>\n",
       "      <th>theme_id</th>\n",
       "      <th>num_parts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15668</th>\n",
       "      <td>BIGBOX-1</td>\n",
       "      <td>The Ultimate Battle for Chima</td>\n",
       "      <td>2015</td>\n",
       "      <td>571</td>\n",
       "      <td>9987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>318</th>\n",
       "      <td>10276-1</td>\n",
       "      <td>Colosseum</td>\n",
       "      <td>2020</td>\n",
       "      <td>673</td>\n",
       "      <td>9036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11666</th>\n",
       "      <td>75192-1</td>\n",
       "      <td>UCS Millennium Falcon</td>\n",
       "      <td>2017</td>\n",
       "      <td>171</td>\n",
       "      <td>7541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10993</th>\n",
       "      <td>71043-1</td>\n",
       "      <td>Hogwarts Castle</td>\n",
       "      <td>2018</td>\n",
       "      <td>246</td>\n",
       "      <td>6020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>10256-1</td>\n",
       "      <td>Taj Mahal</td>\n",
       "      <td>2017</td>\n",
       "      <td>673</td>\n",
       "      <td>5923</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        set_num                           name  year  theme_id  num_parts\n",
       "15668  BIGBOX-1  The Ultimate Battle for Chima  2015       571       9987\n",
       "318     10276-1                      Colosseum  2020       673       9036\n",
       "11666   75192-1          UCS Millennium Falcon  2017       171       7541\n",
       "10993   71043-1                Hogwarts Castle  2018       246       6020\n",
       "296     10256-1                      Taj Mahal  2017       673       5923"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sets_df.sort_values(\"num_parts\", ascending=False).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "uSyhOzAHIqur"
   },
   "source": [
    "**Challenge**: Use <code>.groupby()</code> and <code>.count()</code> to show the number of LEGO sets released year-on-year. How do the number of sets released in 1955 compare to the number of sets released in 2019? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "qjdrktZAIqus",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>set_num</th>\n",
       "      <th>name</th>\n",
       "      <th>theme_id</th>\n",
       "      <th>num_parts</th>\n",
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       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1949</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1953</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954</th>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1955</th>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>801</td>\n",
       "      <td>801</td>\n",
       "      <td>801</td>\n",
       "      <td>801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>834</td>\n",
       "      <td>834</td>\n",
       "      <td>834</td>\n",
       "      <td>834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>882</td>\n",
       "      <td>882</td>\n",
       "      <td>882</td>\n",
       "      <td>882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>826</td>\n",
       "      <td>826</td>\n",
       "      <td>826</td>\n",
       "      <td>826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>331</td>\n",
       "      <td>331</td>\n",
       "      <td>331</td>\n",
       "      <td>331</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>71 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      set_num  name  theme_id  num_parts\n",
       "year                                    \n",
       "1949        5     5         5          5\n",
       "1950        6     6         6          6\n",
       "1953        4     4         4          4\n",
       "1954       14    14        14         14\n",
       "1955       28    28        28         28\n",
       "...       ...   ...       ...        ...\n",
       "2017      801   801       801        801\n",
       "2018      834   834       834        834\n",
       "2019      882   882       882        882\n",
       "2020      826   826       826        826\n",
       "2021      331   331       331        331\n",
       "\n",
       "[71 rows x 4 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sets_df.groupby(\"year\").count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "tFInsHOkIqut",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "set_num      28\n",
       "name         28\n",
       "year         28\n",
       "theme_id     28\n",
       "num_parts    28\n",
       "dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sets_df[sets_df[\"year\"] == 1955].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "set_num      882\n",
       "name         882\n",
       "year         882\n",
       "theme_id     882\n",
       "num_parts    882\n",
       "dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sets_df[sets_df[\"year\"] == 2019].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "set_num      826\n",
       "name         826\n",
       "year         826\n",
       "theme_id     826\n",
       "num_parts    826\n",
       "dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# also 2020, for completeness' sake\n",
    "sets_df[sets_df[\"year\"] == 2020].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "xJrmIOULIquv"
   },
   "source": [
    "**Challenge**: Show the number of LEGO releases on a line chart using Matplotlib. <br>\n",
    "<br>\n",
    "Note that the .csv file is from late 2020, so to plot the full calendar years, you will have to exclude some data from your chart. Can you use the slicing techniques covered in Day 21 to avoid plotting the last two years? The same syntax will work on Pandas DataFrames. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Nckj4lSGIquw"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year\n",
       "1949     5\n",
       "1950     6\n",
       "1953     4\n",
       "1954    14\n",
       "1955    28\n",
       "Name: set_num, dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# save the above into a new df \n",
    "sets_by_year_df = sets_df.groupby(\"year\").count()\n",
    "sets_by_year_df[\"set_num\"].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "_ZqAN-5MIquz"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year\n",
       "2017    801\n",
       "2018    834\n",
       "2019    882\n",
       "2020    826\n",
       "2021    331\n",
       "Name: set_num, dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sets_by_year_df[\"set_num\"].tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1e81ec5d7f0>]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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5QLgDfGVREgBvu/CO1f2ldQAa7n0YVLiLSDJwDfC7bpvXABuczzcAX+62faMxpt0YUwDkAktdUq1SymvkVjSRGBXCuNCBR6kkx4azYno8b2SXumwJvpziOiwBwtzkaJd8nq8Z7Jn7U8DDQPfZ9ycYY8oBnI+Jzu1JQEm3/Uqd25RSPiSv8tw5ZfpzQ0YyRdUt7Myrdsnx95XUcd6EKMKDA13yeb5mwHAXkWuBCmNM1iA/U3rZds6vahFZJyKZIpJZWVk5yI9WSnkDYwx5Ff0Pgzzb1XMnERsexEu7ikZ8fLvdsL+kjgVT9Ky9L4M5c18JfElECoGNwGUi8jJwWkQmATgfu5ZdKQWmdHt/MnDy7A81xqw3xmQYYzISEhJG8EdQSo22isZ2GtutQzpzDw2y8LUlU/jLkdOcqm8b0fELqptpaLNqv70fA4a7MeZRY0yyMSYVx4XSvxpjbgU2Abc7d7sdeNf5fBOwVkRCRGQakA7scXnlSimP2ZTjOF8bSrgD3LI0BbsxvLqneETH3++8eWnBFP+es70/Ixnn/gRwhYicAK5wfo8x5jDwGnAE2Azcb4yxjbRQpZR7NLVbue253Rw+WT/gvja74cfvH+HxD46y6rwElk4b2mpHU+PD+dx5Cby6p5hO2/AX0M4pqSMi2DLkXy7+ZEjhboz5uzHmWufzamPM5caYdOdjTbf9HjfGpBljZhpj/uzqopVSrnOgtI6tJ6r42Qef9rtfY1sn92zYy3PbCrjjwlSeuz2DIMvQzw9vW5FCRWM7Hx05PdySySmpY15yDJaA3i7xKdA7VJXye11zsm/Lrepz9sbS2hau/9+dfHKiip98eQ7/8aXZBA4j2AFWnZdIcmwYL+0c3oXVtk4bR8sb9M7UAWi4K+Xn8iubCQ0KYHxkME99fPyc19utNtb9PouT9a1suHMpty5PGdHxLAHCLctS2JlfTW5F45Dff/hkA502oxdTB6DhrpSfK6hqYtr4SO5blcb23Gr2FPQ8e//lX45zpLyBX31tARelj3fJMb+WkUywJYCXdw39wmrXxdSFU2NcUouv0nBXys/lVzUzPSGCW5alMD4ypMfZ+468KtZvzefmZVP5/KwJLjtmfGQI18ybxJtZpTS3D20q4JySOiZFhzJBZ4Lsl4a7Un6s3WqjpKaFtPERhAVbuG/VdHbkVbM7v5r6lk6+89p+psVH8P1rLnD5sW9dnkJju5UnN/d/IfdsOSV12pIZBA13pfxYSU0LdgPTnXea3ro8hYSoEJ76+ASPvXOQysZ2fr12oVtu8V+cEss9F01jw84int9WMKj3VDe1U1zTouE+CDopg1J+rGva3mnjHXOyhwZZuG9VGj9+/wgA31s9060Tcz129QWU1rby4z8dISk2jNWzJ/a7f9fUBYtS9OalgeiZu1J+rGsY5PRuC27csmwqSTFhLJsWx32r0tx6/IAA4Vc3LmBecgwPbdx35mJpb97ZV8ZTH5/guoVJZGi4D0jDXSk/ll/ZREJUCFHdpu0NDbLwwUMX88o9y0blJqGwYAu/+3oGCVEh3L1hL0XVzefsszu/moffOMCyaXE88dW5iOjNSwPRcFfKjxVUNTN9/LnL5EWHBQ37JqXhSIgK4YU7ltJhtXPFrz7hX98+SHG1Y9WmvMom1r2UxZS4MNbflkFIoK79Mxjac1fKj+VXNbN6tuuGOI7EjMRI/vTNi3nm73m8nlnKq3uKuXbeZHJK6giyCC/euZTo8IEXBlEOGu5K+am6lg5qmjuYPt57Jt+aEhfOz74yl299Pp3nthXwyq4irHbDxnXLmRIX7unyxhQNd6X8VF4vF1O9xYRxoTx29QXc/7kZNLZ3khyrwT5UGu5K+amCqp7DIL1RdHiQtmKGSS+oKuWn8iubCAwQbXf4KA13pfxUfmUzU+PDhzUnu/J++l9VKT/lGAbpPRdTlWtpuCvlh2x2Q0F1s1deTFWuoeGulB86WddKh9Xe6w1MyjdouCvlh/Iqm4DPZoNUvkfDXSk/1DUMUtsyvkvDXSk/lF/ZTFRoIPERwZ4uRbmJhrtSfii/qonpCZE6u6IP03BXyg/lVzaTphdTfZqGu1J+pqXDSnl9m1dPO6BGTsNdKT/z2cVUHSnjyzTclfIzvS2tp3yPhrtSfiarqJaQwABty/g4DXel/IjdbvjgYDmfm5lAaJAuV+fLNNyV8iOZRbVUNLZzzbzJni5FuZmGu1J+5IOD5YQEBnD5+YmeLkW5mYa7Ui5QVtfK/a9k09Ru9XQpfbI5WzKXzkwkIkQXYfN1Gu5KucC7OWX86WA5+0vqPF1KnzILa5wtmUmeLkWNAg13pVxgZ141AKW1LR6upG9dLZnLtCXjFzTclRqhDqudzMJaAEprWz1cTe9sdsMHh05x2fnakvEXGu5KjdD+0jpaO20AlHlpuO8trKGysZ2r52pLxl8MGO4iMkVE/iYiR0XksIg85NweJyIficgJ52Nst/c8KiK5InJMRFa78w+glKftzKtGBGZOiPLaM/cPDpYTGqQtGX8ymDN3K/AdY8wFwHLgfhGZBTwCbDHGpANbnN/jfG0tMBu4CnhGRPRuCeWzduZVc8HEccyePM4re+6OUTLakvE3A4a7MabcGJPtfN4IHAWSgDXABuduG4AvO5+vATYaY9qNMQVALrDUxXUr5RXaOm1kFdeyIi2e5NgwTjW00Wmze7qsHvYU1FDVpC0ZfzOknruIpAILgd3ABGNMOTh+AQBd/95LAkq6va3UuU0pn7OvuI4Oq50V0+NJjg3HbuBUfZuny+rh3Zwybcn4oUGHu4hEAm8C3zLGNPS3ay/bTC+ft05EMkUks7KycrBlKOVVduZXEyCwdHocSbFhAJR4UWvmzwfL2bi3hOsXJxMerC0ZfzKocBeRIBzB/oox5i3n5tMiMsn5+iSgwrm9FJjS7e3JwMmzP9MYs94Yk2GMyUhISBhu/Up51K68auYkRTMuNIhkZ7h7y0XVQ2X1fPu1/SycGsP3r5nl6XLUKBvMaBkBngOOGmN+2e2lTcDtzue3A+92275WREJEZBqQDuxxXclKeYfWDhv7SmpZMT0egEnRYYh4x3DIisY21v0+k5jwIH5722KdAdIPDebfaSuB24CDIpLj3PYY8ATwmojcDRQDNwAYYw6LyGvAERwjbe43xthcXbhSnpZVVEunzbA8zRHuwYEBTIgK9fiZe1unjXtfyqK2pZPX71tBYlSoR+tRnjFguBtjttF7Hx3g8j7e8zjw+AjqUsrr7cyvwhIgLEmNO7MtOTbMo8MhWzts/MubB9hXXMf/3rKIOUnRHqtFeZZeYVFqEJrbrQRahJDAz9obO/OqmZccTWS3sePJsWFkFtWOen3GGN47UM4THxzlZH0b31s9ky/o0Ee/puGu1ACMMXzp6W00tln55uXp3LhkCh1WOwdK61l3yfQe+ybHhvPegXKsNjuBltGZ3eNgaT0/fO8wmUW1zJ48jl/duIBlzusAyn9puCs1gMLqFvIqm0mMCuH77xxi/Sf5XHZ+Ila7YUVazxBNig3DZjecamgjOTbc7bVtPnSKb7ySRVx4ME98ZS43ZEzBEtBXF1X5Ew13pQawI68KgFfXLae4uoUnPzzGizsKCbIIGSlxPfbtGg5ZVtvq9nA/Vd/Gv7x5gLlJ0bx8zzLGhQa59XhqbNFwV2oAO/KqmTgulOnjI0hLiGTVeQn8+dAprHY7YcE9hxh2BXppbSvL3FiT3W747uv76bDaeerGBRrs6hwa7kr1w2437MqrZtV5CThu+YCAAOlzNaNJ0Y5hh+4eDvn89gK25Vbx0+vmMj0h0q3HUmOTzueuVD+OVzRS3dxxTm+9L6FBFhKjQiirG/pwyKLqZpb/dAv3vZTFjrwqjDln1g4AjpY38OTmY3z+ggnctHRKr/sopWfuSvVjR65j+bzBhjt0jXUf+pn7b7bkUtPSwa6CajYfPkV6YiS3rUhhxfR4IkMDiQgJJCgggG9tzGFcWBA//+rcM/+aUOpsGu5K9WNHXjUp8eFDujiaFBve60LZFQ1t2IxhUnTYOa8VVjXzTk4Zd1yYyvdWz+S9/Sf5/c4i/u3dw70e44U7lxAfGTLompT/0XBXqg9Wm53d+dVcO39oNwMlx4ax+VA5NrvpMSzxn17KoryulT8/dPE5wfybv54gyCLcu2o6oUEWbsiYwvWLkzl8soGCqmaa2600Ob/OmxDFpTN1+l7VPw13pfpw+GQDje1Wlg/xhqDk2DA6bYaKxrYzZ+l5lU1nzua/8/p+nr99CQHO4C+oauadfWXctXJaj3lgRIQ5SdE6hYAaFr2gqlQfduQNvd8OkBRz7tS/7+acRATuvzSNvx+r5PntBWde+++/niA4MIB7V6W5oGqlHDTclerDzvxq0hMjhzyrYld/vmvqX2MMm3LKuDAtnu9eOZMrZ03g55s/5UBpHfmVTbyzr4zblqeQEKU9dOU6Gu5K9aLDamdvQQ0XDvGsHei2aIdjOOT+0noKq1tYMz8JEeHJ6+eREBnCg6/u48nNxwgODGDdJXrWrlxLw93HVTa285P3j9DWqVPqD8X+0jpaO22sSBs/5PeGBlkYHxl8pi3zbk4ZwZYAVs+ZCEBMeDC/vmkhJTUtbD58iq+vSNWzduVyGu4+7r39J/ndtgJ25ld7upRR44pfZDtyqxGB5dPjBt65F0mx4ZTVtWKzG97bX85l5ycSHfbZFAFLUuN45AvnMzk69JyZJZVyBQ13H3eorB6ArMLRn2N8pGx2w18On6Klwzqo/Q+V1XPnC3uY8+8f8lpmyYiOvSOvitmTxxETHjys93fdyLQjr4qqpnbWLJh8zj7rLklj279cxngdr67cQMPdxx066Qj3vYU1Hq5k6J7481HWvZTFvS9l0WG197lffmUTD/whm2v/exvZxXXMmjyOh984wAvdRqQMRUuHlX3FdVw4jJZMl+TYMMpqW3l7XxlRIYFcen7v49IDdHpe5SY6zt2HtXRYya1oItgSwP7SOjqsdoIDx8bv89cyS3h2awFLUmPZeqKK772xn199bUGPMGy32vjPzcd4YUchIYEBPHjZDO65eDqhQQF889V9/PC9IzS1WXngshmDvk3fbjc88uZBOmx2rpg1Ydj1J8eE0WGz8/7+ctYsmKwLVKtRp+Huw46WN2A3cO38SbyVXcbhk/UsnBrr6bIGlFlYw7++fZCVM+J58c6lPLs1nyc3HyM+IoQfXHsBIkJhVTMPvrqPg2X13LR0Kt++4rweFyX/5+ZFPPzGAf7ro+M0tlt58KyADw+y9HrW/MuPjrNp/0kevmpmj7VRh6prOGSHzc6aBUnD/hylhkvD3YcdLHW0ZO64MJW3ssvILKwdVLg3tnXy6anGEYXbcJXWtnDvS1kkx4bzzM2LCbIE8I1VaVQ2tvP89gISokKYHBPKY28dJNASwPrbFnPl7InnfE6gJYBf3DCfyNBA1n+Sz/pP8nu8nhofzmNXX8AVsyacCf0/7i3m6b/lctPSKXxjhDcUdQ2HTIgKGfJNUEq5goa7DztY1sD4yGDmJkWTEh9OZlEN/8TAIzN++sFRXt1TwsffvoQZiVGjUKlDc7uVezZk0mGz8+zXM4gOd4wuERF+cM0sqps6+PnmTwFYkhrLU2sXnrkbtDcBAcIPvzSbpdPiKK9rO7PdZgxvZpWy7qUsLpoxnn/74ixO1bfx2NuHuDh9PD9aM2fEsy0mxYYRZBG+NH+yLnunPELD3YcdKqtnTlI0IsLilFj+cawSY0y/wVXZ2M6b2WUAvLijkJ98ee6wj9/WaRtSr/m//nKc46cbeeHOpcxI7LkARUCA8Isb5hMWZGFyTBj3X5o2qAWoRYRr5507UuXui6bxyq4ifvnRcb7w660EWwJIT4zkmVsWEeSCha3DgwN58xsXnvPnUGq0jI2ra2rIWjtsnKhoZK5z0qklqXFUN3dQWN3/IhK/31lIp83O8ulxvJlVRn1L57CO/25OGYt+/BFZRYMbpVNe38rLu4u4fnEyq85L6HWf4MAAfn79PB76fPqggr0/QZYA7lg5jX9871JuWTaVtMQInr9jCVEuXK5uXnIM4cF6/qQ8Q8PdRx095biY2jWjYEaKo9fe35DIlg4rL+0q4ooLJvD9a2bR2mkb1njx+tZOfvTeEVo6bPzgncPY7L2vKNTd03/NxRjDg5elD/l4IxEbEcyP1szh/QcvZnI/LR6lxhoNdx/VdfNS15l7WkIkMeFB/d7M9EZWKXUtndy7ajpzkqJZmhrHhp2Fgwrn7n710XFqWjq4/9I0jpQ38IfdRf3uX1LTwh/3lnDjkilMiRv8ohhKqb5puPuog6X1xEcEn1mwOSBAWDw1lr19tElsdsPvthawaGoMi1Mco2TuXJlKaW0rHx05PejjHi1v4Pc7C7ll2VS+e+VMLkyL5z8/PEZ1U3uf7/n1lhMEBAgPXDq6Z+1K+TINdx91sNvF1C4ZqXHkVzb3GrQfHj5FcU1Lj3lOrpg1gaSYsHPu9KxoaOOeDZn86L0jtHZ8No+LMYZ/f/cw0WFBfPfKmYgIP1ozm5YOG09uPtZrnfmVTbyVXcpty1OYGD20qXWVUn3TcHeT3IpGtp2o8six2zptnKhoYk7SuB7bM1Idffesop6tGWMMv/0kn5T4cK6Y9dmY8UBLAF9fkcLughoOO6cxyCys4Zr/3sYnJxwLTlz9m61nPu/dnJPsKazh4avOPzMny4zEKO66aBp/zCwhp5d1RZ/6+AQhgRa+8Tmd8lYpV9Jwd4PKxnbWrt/NXS/upaKxbeA39KGupYPdw5jN8Wh5Aza7OdNv7zI3KZpgSwCZZ4V7ZlEt+0vquOeiaeeMyV67ZCphQRZe3F7Ii9sLWLt+FxHBFjY9sJI/3LOMDqudG/5vBz/74CiPf3CU+cnR3JgxpcdnfPPydBKjQvi3dw/16N9/eqqB9w6c5M6VqTp5llIupuO0XMxuN3z7tRwa2zrptNv5/Y4ivrt65pA/p7KxnZue3UVuRRM/uHYWd180bdDv7bqYevbam6FBFuYmR5PZbcRMbkUj//7uYWLDg7h+cc9QBogOD+Iri5J4ZXcxr2fB5y9I5L++tuDM9LWbv3UxP3n/KL/9JB8RePbrGefc1h8ZEsi/XnMBD23MYcEP/3Lm9XarjcjgQJ3yVik30HB3sfVb89l6oorHr5vD1uNVvLSriH++NG1I450rGtu4+dndlNW2snx6HD9+/wgRwRbWLp06qPcfKmsgNjyo17s3M1JjeX5bAQ1tnTy3tYBn/p5LeHAgP//qPMKCe7/h6O6LpvHx0dPcvDSFBy+b0SO8o0KD+Pn187h63iRqmztYMCWm18/40vzJ1PYyzv7i9PHDnlZXKdU3DfdBKKlpYX9pHVfPmdTvFK37imv5xYfHuHruRG5eOpXzJ45j8+FTvLa3hDtWDu7Mu6KxjZvW7+JkXRsv3LmEhVNjWPf7LB59+yBhwZZBTULV28XULktS4vjtP/K57Bf/ODPP+PevmdXvSkDTEyLZ/djn+z1mXzcedRGRQf8MlFIjpz33AWzaf5Iv/HorD/xhH/e9nEVjW+93bNa3dvLgq/uYMC6Un31l3plb/henxPLc9gKstr7nI+/SFezl9W28eOcSlk+PJyTQwv/dupilqXF8+7X9/OXwqX4/o63TxvHTjef027ssToklODCAsOAANty1lF+vXahLvCnlg/TMvQ8tHVZ+uOkIf8wsYXFKLJfOTOBXH59gzdPb+e1ti0mf4JhQyxhDTkkdv/zoOOX1bbx+34oey6n908XTue/lLD48fJpr5k3q83gVDW3c9Kwj2F+4YwnLpn82k2BYsIXn7ljCrb/bzf1/yGZxSiwzEiNJS3B8zU2KJjbC0do4dqoRq92c02/vEhsRzJZvryIhKkTnGFfKh2m49+LwyXoe2phDXmUTD1w6g2855zJZkhrH/X/Yx5r/2c5/fHE2FY1tvJVdRn5VMyGBAfzHF2ex6Kwpda+YNYHU+HDWf5LH1XMn9toqqWhoY+2zuzhV38aLdy5l6bRzp9qNDAlkw51L+c+/fMqRkw1syjlJQ5tj+TkRmDVpHBfNGE+Lc9x5X2fugN4FqpQfEGOGdmv5oD9Y5Crg14AF+J0x5om+9s3IyDCZmZluqWMoiqtbeOrj47ydU8b4yBCeunEBK2f0XGrtVH0b33gli33FdYBjAeWvLErmC3Mm9jnp1Eu7ivjBO4d47d4V5wT3YIK9N8YYqpo6OFHRSGZhLdtyq9hXXEunzRAbHkT2D64Y8bS1SinvJiJZxpiMXl9zR7iLiAU4DlwBlAJ7gZuMMUd623+44V7f2kl2cS3N7Vaa2qw0tVtpbrchAqFBAYQGWQgNtBDifB4S6NwWZHG8Huh43mG1s35rHhv3lGAJEO5Ymcp9l6SdaXWcrcNq52/HKpg1adygzoJbO2xc+MQWFk2NZf3XM86MJT/d4Oixn25o48W7lo54cYyWDit7C2sZFxo4JlZcUkqNTH/h7q62zFIg1xiT7yxgI7AG6DXch6ugqpk7X9jrks8KDBDWLp3Cg5elM2Fc/7fBBwcGsLqX1X/6EhZs4bYVqfxmywnSHvuA8GALESGBtHfasNkNG+5aSoYLVj0KDw4ccNSKUso/uCvck4Duc8WWAstcfZD0xEje+ucLiQoJJKLryzlWu81qp63T5vxyPG+3fvb8zKPVhtVm+NzMBFLiI1xd4hn3XjKd2PAg6lo6aW630txhpb3Tzm0rUvQsWynlcu4K996avT36PyKyDlgHMHXq4G7OOVtESOA5FzC7RFoCiAzxnuvFESGB3KnjvJVSo8Rd49xLge73sicDJ7vvYIxZb4zJMMZkJCRoK0EppVzJXeG+F0gXkWkiEgysBTa56VhKKaXO4pa+hTHGKiIPAB/iGAr5vDHmsDuOpZRS6lxua0obYz4APnDX5yullOqbzi2jlFI+SMNdKaV8kIa7Ukr5IA13pZTyQW6bOGxIRYhUAkUu/MjxgGdWpx46rdX1xkqdoLW6i7/UmmKM6fVGIa8Id1cTkcy+JtPxNlqr642VOkFrdRetVdsySinlkzTclVLKB/lquK/3dAFDoLW63lipE7RWd/H7Wn2y566UUv7OV8/clVLKr2m4K6WUDxoT4S4iz4tIhYgc6rZtvojsFJGDIvKeiIxzbk8VkVYRyXF+/V+39yx27p8rIr8RN6wgPZRana/Nc7522Pl6qDfWKiK3dPuZ5oiIXUQWeGmtQSKywbn9qIg82u093lZrsIi84Ny+X0Q+N1q1isgUEfmb82d0WEQecm6PE5GPROSE8zG223seddZzTERWe2utIhLv3L9JRJ4+67O8rdYrRCTLWVOWiFzmklqNMV7/BVwCLAIOddu2F1jlfH4X8GPn89Tu+531OXuAFThWivoz8AUP1xoIHADmO7+PByzeWOtZ75sL5Hvxz/VmYKPzeThQCKR6aa33Ay84nycCWUDAaNQKTAIWOZ9H4VjUfhbwJPCIc/sjwM+dz2cB+4EQYBqQN1p/X4dRawRwEXAf8PRZn+VttS4EJjufzwHKXFGrS/9Su/OLs0IbaOCzC8JTgCO97XfWD/zTbt/fBPzWw7VeDbw8Fmo96z0/BR731lqdNbyH45dnvPN/rjgvrfV/gFu77bcFxwLzo1Zrt2O8C1wBHAMmdfvve8z5/FHg0W77f+gMHq+rtdt+d9At3L25Vud2Aapx/AIdUa1joi3Th0PAl5zPb6Dnsn7TRGSfiPxDRC52bkvCsfxfl1LnttHQV63nAUZEPhSRbBF52LndG2vt7kbgVedzb6z1DaAZKAeKgV8YY2rwzlr3A2tEJFBEpgGLna+Naq0ikorjDHI3MMEYUw7gfEx07tbbwvdJXlprX7y91q8C+4wx7SOtdSyH+13A/SKSheOfPh3O7eXAVGPMQuDbwB+c/c0BF+12o75qDcTxT8dbnI/XicjleGetAIjIMqDFGNPVT/bGWpcCNmAyjvbBd0RkOt5Z6/M4/qfNBJ4CdgBWRrFWEYkE3gS+ZYxp6G/XPmryxlr7/IhetnlFrSIyG/g5cG/Xpl52G3StbluJyd2MMZ8CVwKIyHnANc7t7UC783mWiOThOEMuxbFQd5dzFu0e7VqdNf3DGFPlfO0DHL3al72w1i5r+eysHbzz53ozsNkY0wlUiMh2IAPY6m21GmOswP/r2k9EdgAngNrRqFVEgnAE0CvGmLecm0+LyCRjTLmITAIqnNv7Wvh+VP4ODLHWvnhlrSKSDLwNfN0Yk+eKWsfsmbuIJDofA4DvA//n/D5BRCzO59OBdBwX/8qBRhFZ7rzi/HUcvTCP1YqjZzlPRMJFJBBYhaMX6421dm27AdjYtc1Lay0GLhOHCGA5jt6l19Xq/G8f4Xx+BWA1xozK3wHn5z4HHDXG/LLbS5uA253Pb+923E3AWhEJcbaQ0oE9Xlprr7yxVhGJAf6E43rGdpfV6s4LCS68IPEqjnZLJ47fZncDD+G4UHYceILPLlZ9FTiMo5eZDXyx2+dk4Oh95gFPd73HU7U697/VWe8h4Ekvr/VzwK5ePseragUigdedP9cjwPe8uNZUHBfajgIf45jCdVRqxdEKNDhGbOU4v67GcRF6C45/QWwB4rq951+d9Ryj28gNL621EKgBmpz/HWZ5Y604ftk3d9s3B0gcaa06/YBSSvmgMduWUUop1TcNd6WU8kEa7kop5YM03JVSygdpuCullA/ScFdKKR+k4a6UUj7o/wMX3uBQhJK3owAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# don't include the year 2021, as it's not over yet\n",
    "plt.plot(sets_by_year_df.index[:-1], sets_by_year_df[\"set_num\"][:-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1e81f179ac0>]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# a little prettier\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.xticks(fontsize=13)\n",
    "plt.xlabel(\"Year\", fontsize=14)\n",
    "plt.yticks(fontsize=13)\n",
    "plt.ylabel(\"Number of Released Sets\", fontsize=14)\n",
    "plt.plot(sets_by_year_df.index[:-1], sets_by_year_df[\"set_num\"][:-1], color=\"green\", linewidth=2.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "xrDeNYYXIqu1"
   },
   "source": [
    "### Aggregate Data with the Python .agg() Function\n",
    "\n",
    "Let's work out the number of different themes shipped by year. This means we have to count the number of unique theme_ids per calendar year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "dc": {
     "key": "266a3f390c"
    },
    "id": "qx8pTau4Iqu2",
    "tags": [
     "sample_code"
    ]
   },
   "outputs": [],
   "source": [
    "# using the full dataframe\n",
    "themes_by_year_df = sets_df.groupby(\"year\").agg({\"theme_id\": pd.Series.nunique})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "BqF1o997Iqu3"
   },
   "outputs": [],
   "source": [
    "# rename columns\n",
    "themes_by_year_df.rename(columns={\"theme_id\": \"nr_themes\"}, inplace =True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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>nr_themes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1949</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1953</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1955</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      nr_themes\n",
       "year           \n",
       "1949          2\n",
       "1950          1\n",
       "1953          2\n",
       "1954          2\n",
       "1955          4"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "themes_by_year_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "immCqqw1Iqu5"
   },
   "source": [
    "**Challenge**: Plot the number of themes released by year on a line chart. Only include the full calendar years (i.e., exclude 2020 and 2021). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "r2pamQEkIqu5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1e81f2981c0>]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# again, skipping data for 2021\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.xticks(fontsize=13)\n",
    "plt.xlabel(\"Year\", fontsize=14)\n",
    "plt.yticks(fontsize=13)\n",
    "plt.ylabel(\"Number of Released Themes\", fontsize=14)\n",
    "plt.plot(themes_by_year_df.index[:-1], themes_by_year_df[\"nr_themes\"][:-1], color=\"blue\", linewidth=2.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "uBbt9-lJIqu7"
   },
   "source": [
    "### Line Charts with Two Seperate Axes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "j7lQ_amFIqu7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1e81f508c70>]"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# to share the same axis\n",
    "ax1 = plt.gca()\n",
    "ax2 = ax1.twinx() \n",
    "\n",
    "ax1.set_xlabel(\"Year\", fontsize=13)\n",
    "\n",
    "ax1.set_ylabel(\"Number of Released Sets\", fontsize=13, color=\"green\")\n",
    "ax1.plot(sets_by_year_df.index[:-1], sets_by_year_df[\"set_num\"][:-1], color=\"green\", linewidth=2)\n",
    "\n",
    "ax2.set_ylabel(\"Number of Released Themes\", fontsize=14, color=\"blue\")\n",
    "ax2.plot(themes_by_year_df.index[:-1], themes_by_year_df[\"nr_themes\"][:-1], color=\"blue\", linewidth=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "7BHYaUf-Iqu9"
   },
   "source": [
    "**Challenge**: Use the <code>.groupby()</code> and <code>.agg()</code> function together to figure out the average number of parts per set. How many parts did the average LEGO set released in 1954 compared to say, 2017?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "W7BcH9vuIqu9"
   },
   "outputs": [],
   "source": [
    "parts_per_set_df = sets_df.groupby(\"year\").agg({\"num_parts\": pd.Series.mean})\n",
    "# rename the colum\n",
    "parts_per_set_df.rename(columns={\"num_parts\": \"mean parts\"}, inplace =True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "fjbb3tZcIqu_"
   },
   "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>mean parts</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>221.791511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>205.324940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>198.007937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>254.486683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>161.761329</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      mean parts\n",
       "year            \n",
       "2017  221.791511\n",
       "2018  205.324940\n",
       "2019  198.007937\n",
       "2020  254.486683\n",
       "2021  161.761329"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parts_per_set_df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "bAeTe2XqIqvB"
   },
   "source": [
    "### Scatter Plots in Matplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "SAViZ_TYIqvB"
   },
   "source": [
    "**Challenge**: Has the size and complexity of LEGO sets increased over time based on the number of parts? Plot the average number of parts over time using a Matplotlib scatter plot. See if you can use the [scatter plot documentation](https://matplotlib.org/3.1.0/api/_as_gen/matplotlib.pyplot.scatter.html) before I show you the solution. Do you spot a trend in the chart? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "EQNZ0D7JIqvB"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1e81f335c40>"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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TmhEPJL2+lYzuM/o3pJrnD2cJ+DpSjeGtwN8lXUZKrAZJfffenj3fD3Ke/zbSNCxfJg2oeT1pIM7HIuIv8GjN0BtJf2s3SSpN47ID6VqeC3yQ+szef4ikKVW23xMRXy67v7+GXz7vkrKa7uNJtb4nkf7xuphUk7gd6TOj9A/Eo7Ww2es9gvQ+flXSa0irNKwj1dQfS3oPj4xtpy+q5jPAjOy9upl0bexJKut/Jg2eujl77r9KejvwP8Dy7G/4TlIrw9NJkwk/lS2raFxLqlH8GOkzbTOpj+OjA6+sIJo9rNW3zr6RmmgCePoIx91KajqbWrbtv7PHPlC+veJx3aQv4N+QPkzXkZKFC0gj+0rHHUzFVAUV53lCFusq0ofp9aTattOzx+1dcfwLSR+UD5MGHpxDGiEYwElVzv9GUh+WNdlj7iA1w7w2RxmWYt9megTSF+ZdVJkOJNt/IunDfgPpw/zN1J4OpPJ17k3FFCdl+34O3FGxLUhf2i8iddx/KCuf/6Zi+oiy+D9K+hJ7iJQwLCfNm3VA2XHbxJzz+usm1fYtz8rgXlJN3DbXI2ObDmSbMq9y3EGkPksPkq7xHwGzhym/mjGQ5ha8hS1TSpxH+ofhk9k1uzp7nXeQppqYOYrr6xjS39IKtvTXek+Nv5cvZ8+zMXvepVkce5Yddx5ZBeAoyrUUz3C3P+Q8LoAnV5xbpKbjRaT55zaSPjP+kJXXC4aJaQppAMi1pM+jh0lJ35cpm5Ijx2t7MWnOvRtJ/8ANZWV3Nelvc1KVxxyYxTuYxXtXdvwHgCllx/WQ/gm+l5S0bfP37FsxbsreUDNrMEmvIk2ye2REfKfZ8ZjlobSCxtXAsRFxXlODMTP3cTOrt2w+rikV27Yj9bUbItWkmJmZjZr7uJnV32TgTqXluW4lzUf2WlKflwURcXczgzMzs+Jy4mZWf4+Q+ikdTlqWRqQE7p0R8cVmBmZmZsXmPm5mZmZmBeE+bmZmZmYF0RFNpbvvvnvsvffezQ7DzMzMbERLly69JyL2qLavIxK3vffemyVLljQ7DDMzM7MRSbpzuH1uKjUzMzMrCCduZmZmZgXhxM3MzMysIJy4mZmZmRWEEzczMzOzgnDiZmZmZlYQTtzMzMzMCsKJm5mZmVlBOHEzMzMzK4iOWDnBzMzMbLwGlw2yesVqZsycQc/snqbE4MTNzMzMbASLz1jMwIIBJnVPYvPQZvpO6aP/o/0THoebSs3MzMxqGFw2yMCCAYbWD7FxzUaG1g8xMH+AwWWDEx6LEzczMzOzGlavWM2k7q1Tpq7uLlavWD3hsThxMzMzM6thxswZbB7avNW2TUObmDFzxoTHMmGJm6QFkn4vaY2kuyQtlLRb2f5jJG2WtLbsdmHFOXolXS9pvaSVko6aqPjNzMysM/XM7qHv5D66p3Yzedpkuqd203dKX1MGKEzk4IRNwFHAMmAX4Hzg68DhZcfcHhFPrvZgSdOBy4GzgecDBwGLJK2MiF83MG4zMzPrcP2n9jNr7qzOGVUaER8qu7tK0ueBb4/iFHOBh4AzIyKAqyQtAo4DnLiZmZlZQ/XM7mlawlbSzD5uhwA3VWzbU9Ldkv4i6TuS9inbty9wQ5a0ldyQbTczMzNre01J3CS9CpgHvKds8y+ApwP/BDwbeJhUq7Zjtn9n4IGKU90PTBvmOY6TtETSklWrVtUxejMzM7PmmPDETdIRwELgFRFxQ2l7RNweEbdFxOaIuJuU2P0T8NzskAeB6RWn2wVYU+15IuLciOiNiN499tij3i/DzMzMbMJNaOIm6VjgK8DLI+LqEQ6P7Kbs/o3AfhXH7JdtNzMzM2t7EzkdyLtJI0JfEhG/rLL/3yQ9XsluwBeAe4Brs0MWATtIOlHS9pIOIQ1YOHeCXoKZmZlZU01kjds5pP5oV5fP1Va2/2DgemAt8HtgBnBoRKwFiIj7gcOAI0h93RYCx3sqEDMzM+sUEzkdiEbYfyJw4gjH/AZ4Tj3jMjMzMysKL3llZmZmVhBO3MzMzMwKwombmZmZWUFM5FqlZmZmNgEGlw02fU1NawwnbmZmZm1k8RmLGVgwwKTuSWwe2kzfKX30f7S/2WFZnbip1MzMrE0MLhtkYMEAQ+uH2LhmI0PrhxiYP8DgssFmh2Z14sTNzMysTaxesZpJ3Vt/tXd1d7F6xeomRWT15sTNzMysTcyYOYPNQ5u32rZpaBMzZs5oUkRWb07czMzMCmhw2SDLFy3fqhm0Z3YPfSf30T21m8nTJtM9tZu+U/o8QKGNeHCCmZlZwdQagNB/aj+z5s7yqNI25cTNzMysQMoHIJQMzB9g1pxZjyZpPbN7nLC1KTeVmpmZFYgHIHQ2J25mZmYF4gEInc2Jm5mZWYF4AEJncx83MzOzgvEAhM7lxM3MzKyAPAChM7mp1MzMzKwgnLiZmZmZFYQTNzMzM7OCcOJmZmZmVhBO3MzMzMwKwombmZmZWUE4cTMzMzMrCM/jZmZm1qIGlw16kl3bihM3MzOzFrT4jMUMLBhgUvckNg9tpu+UPvo/2t/ssAqhnRNeJ25mZmYtZnDZIAMLBhhaP/TotoH5A8yaM6vtEpF6a/eE133czMzMWszqFauZ1L31V3RXdxerV6xuUkTFUJ7wblyzkaH1QwzMH2Bw2WCzQ6sbJ25mZmYtZsbMGWwe2rzVtk1Dm5gxc0aTIiqGTkh4nbiZmZm1mJ7ZPfSd3Ef31G4mT5tM99Ru+k7pczPpCDoh4XUfNzMzsxbUf2o/s+bOattO9o1QSngH5g/QtV0Xmx7Z1HYJryKi2TE0XG9vbyxZsqTZYZiZmdkEKPqoUklLI6K32j7XuJmZmVlb6ZndU8iELQ/3cTMzMzMrCCduZmZmZgXhxM3MzMysIJy4mZmZmRWEEzczMzOzgnDiZmZmZlYQTtzMzMzMCsKJm5mZmVlBeAJeMzMzA4q/4kAncOJmZmZmLD5jMQMLBpjUPYnNQ5vpO6WP/o/2Nzssq+CmUjMzsw43uGyQgQUDDK0fYuOajQytH2Jg/gCDywabHdqwBpcNsnzR8paOsRFc42ZmZtbhVq9YzaTuretyurq7WL1idUs2mY6ndrDozcFO3MzMzDrcjJkz2Dy0eattm4Y2MWPmjCZFNLzy2sGSgfkDzJoza8RErB2ag91UamZm1uF6ZvfQd3If3VO7mTxtMt1Tu+k7pa8la6Rq1Q7WUsTm4Gpc42ZmZmb0n9rPrLmzWr4Zcay1g0VrDh6Oa9zMzMwMSDVveZocm2mstYNFag6uxTVuZmZmVihjqR0sJXwD8wfo2q6LTY9satnm4FoUERPzRNIC4N+BPYG1wI+AkyPi3rJjjgZOAx4H3Ay8IyKWlu3vBb4IzAb+DpwWEd8a6bl7e3tjyZIldXw1ZmZmVkRFGFUqaWlE9FbbN6amUklTJb1I0hNG8bBNwFHADGBf4PHA18vO2Qd8CXg7sCtwEfBjSdOy/dOBy7PtuwLHA1+W9LyxvAYzMzPrPEVoDq4lV+Im6TxJ78h+3x64HrgSuFXSy/KcIyI+FBG/jYhHImIV8Hng4LJD5gEXR8SVEbEBOAvYAMzJ9s8FHgLOjIgNEXEVsAg4Ls/zm5mZmRVd3hq3lwDXZr+/AtgZeCxwenYbi0OAm8ru7ws82iwaqQ33t9n20v4bYuu23RvK9m9F0nGSlkhasmrVqjGGaGZmZp2iCKsx5B2csCtQehUvBS6KiEFJ3wE+PNonlfQqUg1b+ax3OwMPVBx6PzAt5/6tRMS5wLmQ+riNNkYzMzPrHEWZnDdvjdvdwGxJXaTat59k23cCHhnNE0o6AlgIvCIibijb9SAwveLwXYA1OfebmZmZjVqRJufNm7h9DfgusIw0yOCn2fYDgD/kfTJJxwJfAV4eEVdX7L4R2L/sWAHPzLaX9u9X8Zj9yvabmZmZjdpYV2NohlyJW0ScAbyZ1PTYFxEbs11DwII855D0buBs4CUR8csqhywE5ko6JBsA8QFgCmkAAtnPHSSdKGl7SYeQBiycm+f5zczMWk0R+lR1giJNzpurj5ukg4BLI2KoYtcFwL/mfK5zSIne1akyLYmInbKfA9nI1YVsmcftsIhYk+2/X9JhwBeAM0jzuB0fEb/O+fxmZmYtoyh9qjpBkSbnzTUBr6RNwOMiYrBi+wxgMCK6GhRfXXgCXjMzayWDywZZeMBChtZvqQ/p3qGbedfNa8lkoVO0yuS8tSbgzTuqVEC1DG8GsG6sgZmZmXWidlnwvN30zO5p+fKvmbhJuiz7NYBvSdpQtruLtPTUrxoUm5mZWVsqUp+qZmqVGrBWMlKNW2k4hYD7SCsXlGwEBkh90szMzCynZvapKkoy5D6A1Y3Yx03SJNJUIMdERCGbRd3HzczMWtFEJ1FFSYY6vQ/geBeZD+Bw0khPMzMzq5OJXPC8SJPMFmletYk2YuKWrQ16K7BH48MxMzOzRihSMuQ+gMPLu3LCScBZkp6p8knYzMzMrBCKlAyV+gB2T+1m8rTJdE/tbtl51SZa3ulAvkdaxWApMFQxupSIqLrQu5mZmbWGIk0yC9B/aj+z5s4qxECKiZQ3cXtXQ6MwMzOzhitaMlSEedUmWq7ELSK+0ehAzMzMrPGcDBVb3hq3R0l6LLB9+baI+HPdIjIzMzOzqvIuMj8d+BzwGiqStkxLr1VqZmZm1g7yjio9G9gXeCXwMPB64ETgr8BrGxKZmZmZmW0lb1Ppy4AjI+IaSZuApRHxXUl/B94GfL9hEZqZmZkZkL/GbRfgzuz3B4DSpC+/Bv61zjGZmZlZhxhcNsjyRctbcgWHVpS3xm0l8ETgz8By4HWSrgfmAvc2KDYzMzNrY0VZO7WV5K1xOw94Rvb7fFLz6EbgLGBB/cMyMzOzdlaktVNbSd553D5T9vvPJP0L0AusiIibGxWcmZmZtadaa6d6nrnh1UzcJE0CPkAaTbod8BPgjGzeNs/dZmZmZmNSpLVTW8lITaUnk5pG1wF/B95Pms/NrC7cKdXMrP1V+6z3QvJjo4gYfqd0K3BORHwxu/9S4BJgatR6YIvp7e2NJUuWNDsMq+BOqWZm7W+kz/rBZYOFWTt1okhaGhG91faNVOP2BOCHZfevAAT8U51isw7lTqlmZu0vz2d9z+weZs2Z5aQtp5ESt+2Bh0p3slq2jcDkRgZl7a9Wp1QzM2sP/qyvvzyjSj8paX3Z/e2B0yQ9UNoQEe+ue2TW1twp1cys/fmzvv5GqnH7BfAk4Ollt18Be5Xdn93IAK09uVOqmVn782d9/dUcnNAuPDihdblTqplZ+/Nn/ejUGpyQd8krs4bomd3jP2Izszbnz/r6ybvklZmZmZk1mRM3MzMzs4Jw4mZmZmZWEMMmbpK+Jmnn7PeDJLk/nJmZmVkT1apxOwrYMfv9amC3xodjZmZmZsOpVYt2B/Afkq4kLXP1PEn3VTswIn7RgNjMzMzMrEytxO1EYCHwQSCARcMcF0BXneMyM7Mx8pxZZu1r2MQtIi4FLpW0C3Av8DTAK4CbmbWwxWcsZmDBAJO6J7F5aDN9p/TR/9H+ZodlZnUy4oCDiLhf0guAFRExNAExmZnZGAwuG2RgwQBD67d8VA/MH2DWnFmueTNrE7lGikbEYkmTJR0NPJXUPHoL8O2I2NDIAM3MLJ/VK1YzqXvrMWdd3V2sXrHaiZtZm8g1j5ukpwK3AZ8GDgCeC3wGuE3SrMaFZ2Zmec2YOYPNQ5u32rZpaBMzZs5oUkRmVm95J+A9B/gdsFdEPD8ing/sBdwIfLYxoZmZ2Wj0zO6h7+Q+uqd2M3naZLqndtN3Sp9r28zaiCJi5IOk9cCzI+L3FdufDlwbETtWf2Rr6O3tjSVLljQ7DDOzCeFRpWbFJmlpRPRW25d3NYSHgV2qbJ+e7TMzsxbRM7vHCZtZm8rbVPoDYKGkAyV1Zbc+4CvAZY0Lz8zMzMxK8iZu7wFWANeQatgeBhaTBiy8tyGRmZmZmdlW8k4Hcj9wuKQnA7NIS2DdEhF/bGBsZmbW5twfz2x08vZxAyBL1JysmZnZuHmVh2Jxkt0aRpW4mZmZ1YNXeSgWJ9mtI28fNzMzs7qptcpDEQ0uG2T5ouUMLmu/Jb3Lk+yNazYytH6IgfkDW73Wdn79rcY1bmZmNuHaaZWHdq+NGmkptXZ//a1mxBo3Sd2S3iHpn8b7ZJJeJ+kaSWskDVXsO0bSZklry24XVhzTK+l6SeslrZR01HhjMjOzidcuqzzkqY0qulpJdie8/lYzYo1bRAxJOgv4UR2e7z7gi8BU4Nwq+2+PiCdXe6Ck6cDlwNnA84GDgEWSVkbEr+sQm5mZTaD+U/uZNXdWoTu8j1Qb1Q5KSfbA/AG6tuti0yObHk2yly9a3vavv9XkbSq9FtgfuHM8TxYRVwBIOngMD58LPAScGWmdrqskLQKOA5y4mZkVUNFXeWinJt9ahkuyO+X1t5K8gxMWAp+S9F5Jz5e0f/mtjvHsKeluSX+R9B1J+5Tt2xe4IbZeXPWGbLuZmdmEa5cm3zx6ZvdsM+q3k15/q8i7yPzmGrsjIrpG9aSpxu0nEdFdtu2JpBrAPwI9wHygD9g3ItZJ+irQHRFvKnvMscCHqzWvSjqOVBvHXnvt9aw77xxXZaGZmdmwOn2Os05//fVWj0Xm9xn5kPGJiNvL7t4taR7wAPBc4KfAg8DeFQ/bBVgzzPnOJetH19vbO3J2amZmNkZFb/Idr05//RMp75JXzaiuiuym7P6NwJyKY/bLtpuZmZm1vdwT8Ep6maQfSrpF0p7ZtrdKOmQU5+iSNAXYPrs/JbtJ0r9Jenz2+27AF4B7SAMjABYBO0g6UdL22fPOpfroVDMzM7O2kytxk/QG4HvAClKz6XbZri7gpFE83xtJI0OvyB77UHZ7AnAwcD2wFvg9MAM4NCLWwqML3R8GHEFqQl0IHO+pQMzMzKxT5B2ccCPwyYj4jqQHSQMGbpe0L3BlRDym0YGOR29vbyxZsqTZYZiZmZmNqB6DE2ZSfa60tcC0sQZmZu3BI8qsFl8fZvWTN3G7C/hntp2A9yBgZV0jMrNC8TqFVouvD7P6yjs44Vzgc5IOzO7vKelNwJnAlxoSmZm1PK9TaLX4+jCrv1yJW0ScCVwMXAXsCFwNfBn4ckR8oXHhmVkrq7VOo5mvj2Rw2SDLFy13wmp1kbeplIj4sKRPAE8lJXy3lEZ8mllnqsc6he7/1L68jqWbiq3+cs/jlgngYWA9sKn+4ZhZkYx3ncLFZyxm4QELueSYS1h4wEIWf2xxgyO2idTp61i6qdgaIVeNm6TJwALgbaTJcwVskHQucHJEPNy4EM2slfWf2s+subNGXWtW/qVWMjB/YJtFrK3Yxnp9tINaTcWdVA5WX3mbSr8EvBh4K1umBXke8ElgZ+DN9Q/NzIpiLOsU+kutc3TqOpZuKrZGyNtUegRwbERcEBG3Z7cLgLcAr25ceGbWrvylZu2u05uKrTHy1ritA/5WZfvfSEtWmZmNSulLbWD+AF3bdbHpkU3+UrO208lNxdYYeZe8+jDwDOCYiHgo2zYV+Brw+4j4eEOjHCcveWXWujyqdGK5vM1a35iWvJJ0WcWmg4G/Sbopu//07PE71iNIM+tMndr/qRnaaWoKJ6DWqWo1lVbOkHhRxf0/1TkWMzNrkHYaxdtOCajZaA2buEXEsRMZiJmZNU67jOJtpwTUbCxGOwGvmZkVULuM4vUyWtbpciVuknaVdI6kmyTdLWmw/NboIM3MOlE917hsl6kp2iUBNRurvNOBnA88DfgG8A/S0ldm1kHcGXxiNaIfVztMTeFpZKzT5Z0O5EGgPyJuaHxI9efpQMzGx53BJ9bgskEWHrBwq35c3Tt0M++6eU5QMv5HwtpZrelA8vZxWzmKY82sjXih7Innflwj65nd4wEJ1pHyJmPvAT4paV9JXY0MyMxai5OIied+XGY2nLyJ2x+BqcANwEZJm8pvjQvPzJrNScTEa5eBBGZWf3kHJ1wITAfejQcnmHUUdwZvjlYcSOB+ZWbNl3dwwnrgORGxrPEh1Z8HJ5iNn7+0O5sHqJhNnDGtVVrhFmBa/UIys6LxmqKdy6sVmLWOvH3cPgJ8WtKLJD1G0m7lt0YGaGZmzeUBKmatI2+N24+zn1eydf82Zfc90tTMrE15gIpZ68ibuL2goVGYmVnL8gAVs9aRK3GLiMWNDsTMzFpXK45yNetEuRI3SfvX2l/UpbDMzCw/D1Axa768TaVLSH3ZVLatvK+b+7iZtQFP+WFm1tryJm77VNzfDtgP+DDwwbpGZNZmipIMeZ4uM7PWl7eP251VNv9R0gPAacDldY3KrE0UJRnyPF1mZsWQdx634fwJeGYd4jBrO+XJ0MY1GxlaP8TA/AEGlw02O7RteJ4uM7NiyDs4oXKSXQGPA04Hbq1zTGZtoVYy1Gq1WJ6ny8ysGPLWuN0DrCq7DQI3Ac8G3tGY0MyKrUjJUGmeru6p3UyeNpnuqd0tP0/X4LJBli9a3pI1mFYffo/NtjXWCXg3kxK4P0bEUJXjzTpe0SYtLdI8XUXpO2hj5/fYrDpFxMhHFVxvb28sWbKk2WFYhyrKqNKiGFw2yMIDFm41kKJ7h27mXTfP5dsm/B5bp5O0NCJ6q+2rWeOWdwH5iLh3LIGZdQJPWlpfReo7aGPj99hseCM1ld7D1hPtVhM5zmNmVhdF6jtoY+P32Gx4Iw1OeAHwwmFuZwIbgPWNDNDMrFwRB1LY6Pg9NhveqPu4ZeuWLgAOAr4CfCwiVjUgtrpxHzez9uO+g+3P77F1qjH3cas4yT7AJ4AjgIuBp0bEyvqEaGY2Ou472P78Hptta8R53CTNkHQO8AfgscDzIuK1TtrMzKykE+Zc64TXaK1vpFGlHwJOAu4ADo+I/5uIoMzMiqaTm/U6Yc61TniNVgw1+7hJ2gw8BFxNmnS3qoh4Rf1Dqx/3cTOzRmqnL/XRJqCdMOdaJ7xGay3j6eN2PiNPB2Jm1rEGlw0ysGBgqy/1gfkDzJozq3Bf6mNJQDthzrVOeI1WHDUTt4g4ZoLiMDMrpHb5Uh9rAtoJc651wmu04si7yLyZmVXRLl/qtRLQWjphzrVOeI1WHF7xwKwNdXJH+YlW+lIfmD9A13ZdbHpkUyG/1MeTgPaf2s+subPa+prrhNdoxTChi8xLeh3wTmBfYIeI6K7YfzRwGvA44GbgHRGxtGx/L/BFYDbwd+C0iPjWSM/rwQnWaK2UKI3UT6mVYm0n7VCui89YvHUC+sHOGWRh1kpqDU6Y6MTtJcBuwFTg3PLETVIfcAUwB1gMvAf4ADAzItZImg78ETgb+Cxp5YZFwKER8etaz9voxM0fEJ2tlUYUjjT6rZVitdbUDp9nvs6t6GolbhPaxy0iroiIC4Hbq+yeB1wcEVdGxAbgLNJaqHOy/XNJU5OcGREbIuIqUuJ23ASEPqzFZyxm4QELueSYS1h4wEIWf2xxM8OxCVbeoXvjmo0MrR9iYP5A0yborNVPqdVitdbUM7unkCNiS3ydW7vLnbhJerqkz0u6XNLjsm2vlLRfnWLZF3i0WTRSVeBvs+2l/TfE1lWEN5Ttr4z3OElLJC1ZtaoxS6n6A8LG2qG7UWr1U2q1WPPwTPU2Wnmu81rXla85a3W5BidIejFwGXA58EJSUyfAk4BjgFfWIZadgQcqtt0PTMu5fysRcS5wLqSm0jrEt412mQbAxq7VRhSO1FG+lWIdiZu7bCxG+pusdV35mrMiyFvj9jHg/RExB9hYtv3nwHPqFMuDwPSKbbsAa3Lun3Ct9qVtE68VpwnoP7WfedfP4/DzDmfe9fMe/eJpxViH02612a7FmTi1rvNa11W7XXPWvvJOB/I04MdVtt9LGmxQDzcC+5fuSBLwTODisv1zKh6zX7a9KdplGgAbn1acJqBndk/VOFox1mraqTbbtTgTb7jrfKRm1Ha55qy95U3c7gP+H2mx+XL7A3/N+2SSuoDtgO2z+1OyXRuAhcD/SfoGcA3wbmAKaQAC2c8zJZ0InAM8nzRg4dC8z98IRfkitMYaLlFqRUWItVm12fUeUdlOy2EVTbXrfKTryi0oVgR5m0q/DZwl6fGktUu7JfWTpuY4fxTP90bSyNArgK7s94eAJ0TEAPAOUgL3APAa4LCIWAMQEfcDhwFHZPsXAsePNBXIRCj6KCyzVjPeZt2xNE02YoR4EQeEtLNa11WRuhJYZ8s1j5uk7YDzgNcBAjZnP78NHBMRmxoY47h5Al6zYhpLDdhYmiZHmv9uPPE34rw2PrWuq3aYx86Kr9Y8brmaSiPiEeANkk4l9SubBPw2IlbUL0wzs62Ntll3rE2TjepT536wranWdVWErgTW2Ua1VmlErARWNigWM7NxGWsC1sg+dY3qB+uaIbPOlHcet68NsyuAh0lLUX03Iu6qV2BmZqM11gSs0TVj9a7F8UhVs86Vt4/bD0ijODcDy7LNs0n93JaSpgvZCXh+RPyuIZGOg/u4mXWO8SyUXoRaLPebM2t/4+7jBvwSWAu8JSLWZyfdgTSy80bSaM/zgU8Bh4w7YjMrRBLRisbTNFmE/k3tNMedmY1e3sTtPcALS0kbQESsl/QJ4KcRcaakBcBPGhGkWadpp6awZiSgRUjAxsortph1trzzuO0EPK7K9sdm+yAtPTWqwQ5mtq12WnqnEXOjdTrPN2bW2fImWouAr0o6CfgNaVDCc4Az2bIk1XOA2+oeoVmHaZemMK8a0DhescWsc+VN3I4HPg18q+wxQ8DXgBOy+8uBeXWNzqwDtUtTWLskoK2qnZuDzWx4uZpKI2J9RBxPWlB+P9IapbtFxNsjYl12zO9acUSpWdG0S1NYuySgZmatZLQT8K4DbmpQLGaWaYemMK8aYGZWf7nmcQOQ9ALgSGAvYPvyfRHxwvqHVj+ex82seTytiZnZ6NSaxy1XU6mkY4DLgZ2Bg4FVwK6kJtNb6hKlmbWlntk9HpBgZlYneacDOQF4V0QcCTwCfDAi9iMNVljbqODMzMzMbIu8idsT2TK57ga2zN32eeCYOsdkZmZmZlXkTdxWk5pJAf5GWqcUYAYwtd5BmTXK4LJBli9aXsjJbM3MzPKOKr0GeDFwM/A94HOSDiWtS3pVg2Izq6t2WkbKJp4HWZhZK8ibuL0LmJL9/knS5LsHkpK4jzcgLrO68iz+Nh5O+s2sVYzYVCqpG3hd6X5EbI6IBRHxiog4ISLub2SAZvVQaxZ/s1raae1YMyu+ERO3iBgCzgK2a3w4Zo3RbrP4u6/exHHSb2atJG9T6bXAs4A7GxiLWcO00yz+brabWO2W9JtZseVN3BYCZ0vaC1gKrCvfGRE31Dsws3prh2Wk3Fdv4rVT0m9mxZc3cft29vPTVfYF0FWfcMwaq2d2T6G/cGs12xX5dbW6dkj6zaw95E3c9mloFGaWi5vtmqfoSb+ZtYdciVtEuG+bWQtws52ZWWfLW+OGpJcB7yQtf/WSiPiLpLcCf4qInzYqQDPbmpvtzMw6V64lryS9gTTZ7gpSs2lpapAu4KTGhGZmw+mZ3eMBCWZmHSjvWqUnAfMi4n2kVRNKrgWeWe+gzMzMzGxbeZtKZwK/rrJ9LTCtfuGYdRavf2lmZqORN3G7C/hntp2A9yBgZV0jMusQnkjXzMxGK29T6bnA5yQdmN3fU9KbgDOBLzUkMrM25vUvm8fLhZlZkeWdDuRMSdOBq4ApwNXABuDsiPhCA+Mza0ueSLc5WrGW083lZjYauacDiYgPS/oE8FRSTd0tEbG2YZGZtTFPpDvxWnG5sFZMJM2steWdDuQ9kvaIiPURsSQirnfSZjZ2pYl0u6d2M3naZLqndnsi3QarVcvZDG4uN7OxyFvj9gHgLEk/Bb4JXBIR6xsXlln780S6E6vVajndXG5mY5F3cMITgJcAfwU+D/xD0jclvURS3nOYWQVPpDtxWq2Ws9USSTMrBkXE6B4gbQ/8O/B64DDg/oj4pwbEVje9vb2xZMmSZodhZi2glQYDLD5j8dbrzn7QfdzMDCQtjYjeqvtGm7hlJ3wcKXF7K/CUiGjpWjcnbmbWqlopkTSz1lArcRvNIvPTgFcBbwD6SRPvfhv4Vj2CNDPrRD2ze5ywmVluuRI3Sd8nNYs+CHwX+FBEXN/IwMzMzMxsa3lr3DYCrwauiIhN5TskvSgiflL3yMxqaFTzkputzMysleVdOeH15fcl/T/gWOAtwF5AV/1DM6uuUZOWejJUMzNrdbkHFUjqkjRH0o+BO4A5pHVKn9yg2My20ahJSz0ZqpmZFcGIiZukp0g6C7gL+BRwQ7brjRFxZkT8qZEBmpVr1Oz3rTarvpmZWTU1EzdJ1wDXArsAr4mIJ0bERyYiMLNqGjVpqSdDNTOzIhipxu15wPnAORGxeALiMaupUbPft9qs+mZmZtXUnIBX0jOBeaTJdu8gJXEXAn8B9o2IWxof4vh5At7241GlZmbWrsa9coKkKcARpFGkB5Jq6k4B/ici7qtjrA3hxK2YnESZmVknqpW45RpVGhEPR8Q3I+JgYBZwFvA+4G5Jl9cpyPMkPSJpbdntHRXHHC1ppaT1kq6T9Kx6PLe1nsVnLGbhAQu55JhLWHjAQhZ/zC31ZmZmo15jNCL+GBGnAHsCryFNzlsv34iIncpuXyztkNRHmn7k7cCuwEXAj7OluKyNeGoOMzOz6sa8OHxEbIqISyPi8HoGVMM84OKIuDIiNpBq/TaQ5pOzNuKpOczMzKobc+LWIK+SdK+k2ySdJWmnsn37AktLdyJ1zvtttt3ayHin5hhcNsjyRctdQ2dmZm2nlRK3/wb+BdidVIvWDyws278z8EDFY+4HqjaVSjpO0hJJS1atWlX/aK1hxjM1h/vGmZlZO8s1qrQZJB0I/BzYKSI2SPodcF5EfLbsmEuBlRHx/lrn8qjSYhrtqNLBZYMsPGAhQ+uHHt3WvUM3866b51GpZmZWGLVGleZaZL5JSm1lyn7eCOxf2ilJwDOBiyc2LJsoPbN7RpVw1eob58TNzMzaQcs0lUp6naRdst9nktZFvSwiHs4OWQjMlXSIpO2BDwBTgEXNiNdaj5etMjOzdtcyiRtwPHC7pHXAlaQ1Uo8t7YyIAeAdpATuAdJUJIdFxJomxGotyMtWmZlZu2vZPm715D5uncUrLpiZWZEVtY+b2ZiMtm+cmZlZUThxs1xci2VmZtZ8TtxsRIvPWMzAggEmdU9i89Bm+k7po/+j/c0Oy8zMrOO00uAEa0FeN9TMzKx1OHGzmrxuqJmZWetw4mY1eW40MzOz1uHEzWry3GhmZmatw4MTbET9p/Yza+4sjyo1MzNrMidulovnRvOUKGZm1nxO3Mxy8JQoZmbWCtzHzWwEnhLFzMxahRM3sxF4ShQzM2sVTtzMRuApUczMrFU4cTMbgadEMTOzVuHBCWY5eEoUMzNrBU7czHLylChmZtZsbio1MzMzKwgnbmZmZmYF4cTNzMzMrCCcuJmZmZkVhBM3MzMzs4Jw4mZmZmZWEE7czMzMzArCiZuZmZlZQXgCXquLwWWDXlXAzMyswZy42bgtPmMxAwsGmNQ9ic1Dm+k7pY/+j/Y3OywzM7O246ZSG5fBZYMMLBhgaP0QG9dsZGj9EAPzBxhcNtjs0MzMzNqOEzd71OCyQZYvWj6qpGv1itVM6t76Murq7mL1itX1Ds/MzKzjuanUgLE3d86YOYPNQ5u32rZpaBMzZs5oVKhmZmYdyzVuNq7mzp7ZPfSd3Ef31G4mT5tM99Ru+k7p8wAFMzOzBnCNW4epNvqzVnNnngSs/9R+Zs2dNeyoUo84NTMzqw8nbh1kuObQejR39szuqZqUecSpmZlZ/biptEPUag5tVHOnR5yamZnVl2vcOsRIzaEjNXc24jnNzMxsdJy4dYg8zaHDNXc28jnNzMwsPzeVFtRo51xrxuhPjzg1MzOrL0VEs2NouN7e3liyZEmzw6ib8XT4b8YIT48qNTMzy0/S0ojorbrPiVuxDC4bZOEBCxlaP/Totu4dupl33TwnRWZmZm2gVuLmptKC8RJTZmZmncuDEwomT4d/N02amZm1JyduBVPq8D8wf4Cu7brY9MimrTr8e8JbMzOz9uU+bgVVrVbN/d/MzMyKr1YfN9e4FVS1Odc84a2ZmVl78+CENuIJb83MzNqbE7c24glvzczM2pubSttMI9YcNTMzs9bgxK0N1XvNUTMzM2sNbio1MzMzKwjXuDWYJ8M1MzOzeilc4iapC5gPHANMAa4E3hYR9zQzrmo8Ga6ZmZnVUxGbSk8BDgcOAB6fbftm88KpbnDZIAMLBhhaP8TGNRsZWj/EwPwBBpcNNjs0MzMzK6giJm7HAQsi4vaIeAA4CXippL2bG9bWvBi8mZmZ1VuhEjdJ04G9gKWlbRGxElgDPKPi2OMkLZG0ZNWqVRMbKJ4M18zMzOqvUIkbMC37+UDF9vvL9gEQEedGRG9E9O6xxx4TEdtWPBmumZmZ1VvRBic8mP2cXrF9F1KtW0vxZLhmZmZWT4VK3CLifkl/BvYHfgcg6Ymk2rabmhjasDwZrpmZmdVL0ZpKAc4FTpa0j6RpwALgioi4o7lhmZmZmTVWoWrcMvOBXYHfAJOBq4CjmhqRmZmZ2QQoXOIWEZuAE7KbmZmZWccoYlOpmZmZWUdy4mZmZmZWEE7czMzMzArCiZuZmZlZQThxMzMzMysIJ25mZmZmBeHEzczMzKwgFBHNjqHhJK0C7mxiCLsD9zTx+Vudy2d4LpvaXD7Dc9nU5vIZnsumtokonydExB7VdnRE4tZskpZERG+z42hVLp/huWxqc/kMz2VTm8tneC6b2ppdPm4qNTMzMysIJ25mZmZmBeHEbWKc2+wAWpzLZ3gum9pcPsNz2dTm8hmey6a2ppaP+7iZmZmZFYRr3MzMzMwKwombmZmZWUE4cctJ0uskXSNpjaShin1dkj4q6U+S1mbHPaNs/96SQtK6bP9aSX+tOEePpIslPShplaQFkgrz/oynfLJjdpT0OUl/z45ZLmm/sv2FLZ9xXjtvKLtmSrdNki4rO6awZQN1uXaOlvT77PXfKel0SSrbX9jyqUPZzJV0U7b/VklHVOwvctksyN73NZLukrRQ0m4VxxwtaaWk9ZKuk/Ssiv29kq7P9q+UdFTF/o4tH0lTJf2vpBWSNkv6SJXnKGT51KFsnivpR5L+IekBSUslvbLi8Y0rm4jwLccNeAlwJPBmYKhi34nArcATge2B/wL+Duyc7d8bCODxNc5/FXAxMD07z23Ayc1+3RNUPspe/2XAntm2JwKPaYfyGU/ZVDnXdGAd8Jp2KJs6XDv7AkPAK7Lr6F+y/fPaoXzGWTbPBdYDLyT9k/5yYCNwQJuUzX8B+wHbAXsAlwOXlu3vy/5WXgxMBk4C/gFMy/ZPB1YBJ2f7DwXWAs9z+QTAFOB9wAuAa4GPVHmOQpZPHcrmMODo7LGTgFcCDwHPnoiyaXoBFu0GHFzlA/R64D1l97fLPiCPzu7vTY3EDdgn2/+ksm1vAf7U7Nc7QeXzEtIXzG7tXD5jKZsq53gXcDewXTuVzTiunbnAXRWPuRD4fDuVzxjL5kxgUcVjrga+1k5lUxb7vwEPlN3/BvDNsvsC/gy8Kbt/bHZfZcd8E/i6y2ebx/6cisStncpnPGVTdsxvgPdNRNm0fJVmQUwivbHlBDyzYtt1WZXpzyUdXLZ9X9JFs7Js2w3A3pKm1TnWZhipfF4ArAROljSYVU//l6Ttsv3tXD55r52St5G+eB/J7rdz2cDI5XMFcJekOZImSZoNHESqvYX2Lp+Ryqba/kll+9utbA4Bbiq7vy+wtHQn0rfnb7Ptpf03ZNtLbqjY38nlM5J2Kp9xlY2kxwJPKztHQ8vGiVt9/AB4p6SZkqYAHwe6gNIbdA/wPFIWvjdwEXB5WX+UnYEHKs55f/azaH8A1YxUPrsDs7NtewEvBY4gVU9De5fPSGXzKEkHAk8FFpZtbueygRHKJyLWAV8n/Ye8EbiZ9J/yldnj27l8Rrp2fgi8VNKhkrolzQEOLNvfNmUj6VXAPOA9ZZuHe30jvX6XTz5tUT7jLRtJO5K+0y+LiJ+O8HiqnWO0nLjVx3xgEXAlqToVYDnZIrQRsTYiro2IjRGxLiL+GxggJScAD5LawcvtUrav6GqWD+k1bgI+HBEPR8QK4AvA4WX727V8Riqbcm8DroyIP5Vta+eygRHKR9KxwOmk/knbk/45OljSGdmx7Vw+I33u/Bw4Hvg0MAgcA3yHrf/uCl822YCLhcArIuKGsl3Dvb4149xf2lcI4yifkRS+fMZbNpJ2JvWPGyT1eRvp8aV94+LErQ4iYkNEnBQR+0RED3AWqTPiz2s8bDNbmjFuBKZLemLZ/v2AOyKiMmsvnBzl87vhHpr9bNvyyXvtZCOejgC+XHGKti0byFU+zwJ+FhHXRcTmiLgDuIDUER/auHzyXDsRcV5EPD0idouIw4GnlO0vfNlkiftXgJdHxNUVu28E9i87ttSMfGPZ/v0qHrNfxf5OLp+RFLp8xls22WfyT4G7gCMiYmPF4xtXNhPR8a8dbqQmiCmkUSZD2e9TSMnXY4G9s+P2JDVR/IwtK1M8l9QU2J095jjgYeBZZee/Cvg+qRp1H9JosVOa/bonqHx2Jo2Gm8+WWpNbKRuBU+TyGU/ZlJ3jfcBfgK4q5y9s2dTh2jmS9N/us8qOuQ74ajuUzzjLppv05dNF+u//Y6SBLZWjtYtaNu8GVlM2kq9ifx9plOgh2efKCWw9MnAX0qjSE7P9h1B9VGlHlk92zOTsevsFqWZ7CtnAqCKXTx2unceSumV8gyqfyY0um6YXYFFupGaGqHLbG+gFVpBGRv4D+DywU9ljjwT+SBpevBq4Bji04vw9pKHDD5KaMs4EJjX7dU9E+WSPf0ZWLuuAO7MPia6y/YUtn/GWTXaO5cBpw5y/sGVTp2vnxOyYB0n/AHwVmN4O5TPOz53JwJLsda8BLgWe3C7XTlYOj5C+YB+9VRxzNHA7aaqG6yn7Zznb/+xs+0PZcUe5fLbaf0eVa++8opfPeMsGOC07x7qKc3xoIsrGa5WamZmZFYT7uJmZmZkVhBM3MzMzs4Jw4mZmZmZWEE7czMzMzArCiZuZmZlZQThxMzMzMysIJ25mZmZmBeHEzcw6mqRvSfqdpO0rth8i6RFJ/9qs2MzMKjlxM7NO9y5gBmk2dAAkTQO+BpwVEb+q55NVJohmZqPhxM3MOlpE3A8cC5wk6TnZ5s8A9wFfkfQdSfdltx9Jmll6rKQnSbpU0t2S1km6QdK/l59f0h2STpf0NUn3AxdMzCszs3bkxM3MOl5E/AT4EnC+pFcDbwDeRFoo+mGgH3geaS3Un0jaIXvoTsDlwKHAvsBFwMWS/qXiKd4P/IG0vuiHGvtqzKydea1SMzNA0lTgt8BM4BRgNfBB4J8j+6CU1AUMAm+PiO8Nc55rgR9GxMez+3cAN0fEyxv+Isys7bnGzcwMiIiHgLOBDcCngGcB+wAPSloraS3wALAr8CQASTtKOlPSLVlT6lpSrdpeFadfMlGvw8zaW3ezAzAzayFDwOaI2CxpEvA74HVVjrs3+3k28FLgBGAFsB44H6gcgLCuIdGaWcdx4mZmVt0NwJHAPdkAhmr6gPMj4iIASVNItXG3TUiEZtZx3FRqZlbdBcA/gEsl9UvaR9JBkj5VNrL0NmCOpP0lPR34FjClWQGbWftz4mZmVkVErAcOAm4H/pc0KvQbpD5u92WHvZ80WOEa0ujSa7PfzcwawqNKzczMzArCNW5mZmZmBeHEzczMzKwgnLiZmZmZFYQTNzMzM7OCcOJmZmZmVhBO3MzMzMwKwombmZmZWUE4cTMzMzMrCCduZmZmZgXx/wHZUI3g1m9R3gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "plt.title(\"Average Number of Parts per LEGO Set\", fontsize=18) \n",
    "plt.xticks(fontsize=13)\n",
    "plt.xlabel(\"Year\", fontsize=14)\n",
    "plt.yticks(fontsize=13)\n",
    "plt.ylabel(\"Average Number of Parts\", fontsize=14)\n",
    "plt.scatter(parts_per_set_df.index[:-1], parts_per_set_df[\"mean parts\"][:-1], color=\"purple\", s=22)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "xK226Ip-IqvE"
   },
   "source": [
    "### Number of Sets per LEGO Theme"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "VKHa1FePIqvE"
   },
   "source": [
    "LEGO has licensed many hit franchises from Harry Potter to Marvel Super Heros to many others. But which theme has the largest number of individual sets? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "hOBcNrC9IqvE"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "501    906\n",
       "158    766\n",
       "494    425\n",
       "435    381\n",
       "503    373\n",
       "Name: theme_id, dtype: int64"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# theme_id and the number of sets\n",
    "set_theme_count_df = sets_df[\"theme_id\"].value_counts()\n",
    "set_theme_count_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "J-i6JULGIqvG"
   },
   "source": [
    "**Challenge** Use what you know about HTML markup and tags to display the database schema: https://i.imgur.com/Sg4lcjx.png"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "27oDwiPHIqvH"
   },
   "source": [
    "<img src=\"assets/rebrickable_schema.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "J_0iuerKIqvG"
   },
   "source": [
    "### Database Schemas, Foreign Keys and Merging DataFrames\n",
    "\n",
    "The themes.csv file has the actual theme names. The sets .csv has <code>theme_ids</code> which link to the <code>id</code> column in the themes.csv. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "cp1tMW6oIqvH"
   },
   "source": [
    "**Challenge**: Explore the themes.csv. How is it structured? Search for the name 'Star Wars'. How many <code>id</code>s correspond to this name in the themes.csv? Now use these <code>id</code>s and find the corresponding the sets in the sets.csv (Hint: you'll need to look for matches in the <code>theme_id</code> column)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "3uN3wN5sIqvH"
   },
   "outputs": [],
   "source": [
    "themes_df = pd.read_csv(\"data/themes_20210210.csv\", header=0, names=[\"id\", \"name\", \"parent_id\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "xAO2XlQGIqvJ"
   },
   "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>id</th>\n",
       "      <th>parent_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>598.000000</td>\n",
       "      <td>465.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>362.525084</td>\n",
       "      <td>306.167742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>211.823601</td>\n",
       "      <td>198.422262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>161.250000</td>\n",
       "      <td>112.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>370.500000</td>\n",
       "      <td>324.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>545.750000</td>\n",
       "      <td>504.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>713.000000</td>\n",
       "      <td>697.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               id   parent_id\n",
       "count  598.000000  465.000000\n",
       "mean   362.525084  306.167742\n",
       "std    211.823601  198.422262\n",
       "min      1.000000    1.000000\n",
       "25%    161.250000  112.000000\n",
       "50%    370.500000  324.000000\n",
       "75%    545.750000  504.000000\n",
       "max    713.000000  697.000000"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# confirm import\n",
    "themes_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "xYEDlY-_IqvL"
   },
   "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>id</th>\n",
       "      <th>name</th>\n",
       "      <th>parent_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Technic</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Arctic Technic</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Competition</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Expert Builder</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Model</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id            name  parent_id\n",
       "0   1         Technic        NaN\n",
       "1   2  Arctic Technic        1.0\n",
       "2   3     Competition        1.0\n",
       "3   4  Expert Builder        1.0\n",
       "4   5           Model        1.0"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "themes_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "qBhckawNIqvN",
    "scrolled": true
   },
   "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>id</th>\n",
       "      <th>name</th>\n",
       "      <th>parent_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>Star Wars</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>158</td>\n",
       "      <td>Star Wars</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>209</td>\n",
       "      <td>Star Wars</td>\n",
       "      <td>207.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209</th>\n",
       "      <td>261</td>\n",
       "      <td>Star Wars</td>\n",
       "      <td>258.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id       name  parent_id\n",
       "17    18  Star Wars        1.0\n",
       "149  158  Star Wars        NaN\n",
       "172  209  Star Wars      207.0\n",
       "209  261  Star Wars      258.0"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Start Wars theme\n",
    "themes_df[themes_df[\"name\"] == \"Star Wars\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "aqt07DSTIqvP",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>set_num</th>\n",
       "      <th>name</th>\n",
       "      <th>year</th>\n",
       "      <th>theme_id</th>\n",
       "      <th>num_parts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11496</th>\n",
       "      <td>75023-1</td>\n",
       "      <td>Star Wars Advent Calendar 2013</td>\n",
       "      <td>2013</td>\n",
       "      <td>209</td>\n",
       "      <td>254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11529</th>\n",
       "      <td>75056-1</td>\n",
       "      <td>Star Wars Advent Calendar 2014</td>\n",
       "      <td>2014</td>\n",
       "      <td>209</td>\n",
       "      <td>275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11563</th>\n",
       "      <td>75097-1</td>\n",
       "      <td>Star Wars Advent Calendar 2015</td>\n",
       "      <td>2015</td>\n",
       "      <td>209</td>\n",
       "      <td>291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11614</th>\n",
       "      <td>75146-1</td>\n",
       "      <td>Star Wars Advent Calendar 2016</td>\n",
       "      <td>2016</td>\n",
       "      <td>209</td>\n",
       "      <td>282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11656</th>\n",
       "      <td>75184-1</td>\n",
       "      <td>Star Wars Advent Calendar 2017</td>\n",
       "      <td>2017</td>\n",
       "      <td>209</td>\n",
       "      <td>309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11689</th>\n",
       "      <td>75213-1</td>\n",
       "      <td>Star Wars Advent Calendar 2018</td>\n",
       "      <td>2018</td>\n",
       "      <td>209</td>\n",
       "      <td>307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11728</th>\n",
       "      <td>75245-1</td>\n",
       "      <td>Star Wars Advent Calendar 2019</td>\n",
       "      <td>2019</td>\n",
       "      <td>209</td>\n",
       "      <td>280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11764</th>\n",
       "      <td>75279-1</td>\n",
       "      <td>Star Wars Advent Calendar 2020</td>\n",
       "      <td>2020</td>\n",
       "      <td>209</td>\n",
       "      <td>312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12532</th>\n",
       "      <td>7958-1</td>\n",
       "      <td>Star Wars Advent Calendar 2011</td>\n",
       "      <td>2011</td>\n",
       "      <td>209</td>\n",
       "      <td>267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14982</th>\n",
       "      <td>9509-1</td>\n",
       "      <td>Star Wars Advent Calendar 2012</td>\n",
       "      <td>2012</td>\n",
       "      <td>209</td>\n",
       "      <td>235</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       set_num                            name  year  theme_id  num_parts\n",
       "11496  75023-1  Star Wars Advent Calendar 2013  2013       209        254\n",
       "11529  75056-1  Star Wars Advent Calendar 2014  2014       209        275\n",
       "11563  75097-1  Star Wars Advent Calendar 2015  2015       209        291\n",
       "11614  75146-1  Star Wars Advent Calendar 2016  2016       209        282\n",
       "11656  75184-1  Star Wars Advent Calendar 2017  2017       209        309\n",
       "11689  75213-1  Star Wars Advent Calendar 2018  2018       209        307\n",
       "11728  75245-1  Star Wars Advent Calendar 2019  2019       209        280\n",
       "11764  75279-1  Star Wars Advent Calendar 2020  2020       209        312\n",
       "12532   7958-1  Star Wars Advent Calendar 2011  2011       209        267\n",
       "14982   9509-1  Star Wars Advent Calendar 2012  2012       209        235"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# look up a particular id in the sets dataframe\n",
    "sets_df[sets_df[\"theme_id\"] == 209]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "SmTCXWKKIqvQ"
   },
   "source": [
    "### Merging (i.e., Combining) DataFrames based on a Key\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "esKQULhcIqvR"
   },
   "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",
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       "\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>id</th>\n",
       "      <th>set_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>501</td>\n",
       "      <td>906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>158</td>\n",
       "      <td>766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>494</td>\n",
       "      <td>425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>435</td>\n",
       "      <td>381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>503</td>\n",
       "      <td>373</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    id  set_count\n",
       "0  501        906\n",
       "1  158        766\n",
       "2  494        425\n",
       "3  435        381\n",
       "4  503        373"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# updating the set_theme_count dataframe with column titles \n",
    "set_theme_count_df = pd.DataFrame({\"id\": set_theme_count_df.index, \"set_count\": set_theme_count_df.values})\n",
    "set_theme_count_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "i0LobgIvIqvT"
   },
   "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>id</th>\n",
       "      <th>set_count</th>\n",
       "      <th>name</th>\n",
       "      <th>parent_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>501</td>\n",
       "      <td>906</td>\n",
       "      <td>Gear</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>158</td>\n",
       "      <td>766</td>\n",
       "      <td>Star Wars</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>494</td>\n",
       "      <td>425</td>\n",
       "      <td>Friends</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>435</td>\n",
       "      <td>381</td>\n",
       "      <td>Ninjago</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>503</td>\n",
       "      <td>373</td>\n",
       "      <td>Key Chain</td>\n",
       "      <td>501.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    id  set_count       name  parent_id\n",
       "0  501        906       Gear        NaN\n",
       "1  158        766  Star Wars        NaN\n",
       "2  494        425    Friends        NaN\n",
       "3  435        381    Ninjago        NaN\n",
       "4  503        373  Key Chain      501.0"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# merging dataframes on the \"id\" column\n",
    "merged_df = pd.merge(set_theme_count_df, themes_df, on=\"id\")\n",
    "merged_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "I7UMP7VXIqvU"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 10 artists>"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# bar chart of top 10 themes\n",
    "plt.figure(figsize=(10, 6))\n",
    "# seems to include several other \"non-theme\" bars too\n",
    "plt.title(\"Top 10 Themes (incl. gear and misc. products)\", fontsize=18) \n",
    "# rotate the text to make it readable\n",
    "plt.xticks(fontsize=13, rotation=45)\n",
    "plt.xlabel(\"Number of Sets\", fontsize=14)\n",
    "plt.yticks(fontsize=13)\n",
    "plt.ylabel(\"Theme Name\", fontsize=14)\n",
    "# list of colors to use, no particular meaning for their order\n",
    "colors = [\"red\", \"blue\", \"green\", \"purple\", \"orange\", \"red\", \"blue\", \"green\", \"purple\", \"orange\"]\n",
    "# only show the first 10\n",
    "plt.bar(merged_df[\"name\"][:10], merged_df[\"set_count\"][:10], color=colors)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "name": "Lego Analysis for Course (start).ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}