Day73/Lego_Analysis_for_Course_(start).ipynb

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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": "markdown",
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      "source": [
        "<img src=\"https://i.imgur.com/49FNOHj.jpg\">"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
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      "source": [
        "# Import Statements"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z5Wk7rs-IquQ"
      },
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R5NQpJ_KIquT"
      },
      "source": [
        "# Data Exploration"
      ]
    },
    {
      "cell_type": "markdown",
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        "id": "ffaG-UFYIquT"
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      "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."
      ]
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      "source": [
        "colors = pd.read_csv('data/colors.csv')"
      ],
      "execution_count": 8,
      "outputs": []
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        "tags": [
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        "id": "QmbAXax7IquW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
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        "outputId": "600921a5-b9a8-41f2-a6c4-ec443592e2fd"
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      "source": [
        "colors.head()"
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      "execution_count": 9,
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        "**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."
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        "colors.groupby('is_trans').count() "
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      },
      "source": [
        "**<h3>Understanding LEGO Themes vs. LEGO Sets</h3>**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "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",
        "<img src=\"https://i.imgur.com/aKcwkSx.png\">"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
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          "key": "c9d0e58653"
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          "frozen": true
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        "tags": [
          "context"
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        "id": "u_xkZUF8Iqug"
      },
      "source": [
        "A lego **set** is a particular box of LEGO or product. Therefore, a single theme typically has many different sets.\n",
        "\n",
        "<img src=\"https://i.imgur.com/whB1olq.png\">"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "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",
      "metadata": {
        "id": "vGMOv-NRIquh"
      },
      "source": [
        "sets = pd.read_csv('data/sets.csv')"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
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      "source": [
        "sets.head()"
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      "execution_count": 15,
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          "data": {
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              "      <td>1965</td>\n",
              "      <td>1</td>\n",
              "      <td>43</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
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              "      <td>Town Mini-Figures</td>\n",
              "      <td>1978</td>\n",
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              "      <td>12</td>\n",
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              "      <th>2</th>\n",
              "      <td>0011-3</td>\n",
              "      <td>Castle 2 for 1 Bonus Offer</td>\n",
              "      <td>1987</td>\n",
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              "  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"
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      "source": [
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      "execution_count": 16,
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              "      <th>15705</th>\n",
              "      <td>wwgp1-1</td>\n",
              "      <td>Wild West Limited Edition Gift Pack</td>\n",
              "      <td>1996</td>\n",
              "      <td>476</td>\n",
              "      <td>0</td>\n",
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              "      <td>XMASTREE-1</td>\n",
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              "      <td>XWING-2</td>\n",
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              "      <td>158</td>\n",
              "      <td>52</td>\n",
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              "      <th>15709</th>\n",
              "      <td>YODACHRON-1</td>\n",
              "      <td>Yoda Chronicles Promotional Set</td>\n",
              "      <td>2013</td>\n",
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              "      <td>413</td>\n",
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              "           set_num                                 name  ...  theme_id  num_parts\n",
              "15705      wwgp1-1  Wild West Limited Edition Gift Pack  ...       476          0\n",
              "15706   XMASTREE-1                       Christmas Tree  ...       410         26\n",
              "15707      XWING-1                  Mini X-Wing Fighter  ...       158         60\n",
              "15708      XWING-2                    X-Wing Trench Run  ...       158         52\n",
              "15709  YODACHRON-1      Yoda Chronicles Promotional Set  ...       158        413\n",
              "\n",
              "[5 rows x 5 columns]"
            ]
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          "metadata": {},
          "execution_count": 16
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    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ez-UXSMUIqum"
      },
      "source": [
        "**Challenge**: In which year were the first LEGO sets released and what were these sets called?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s2aL6qrGIqum",
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      "source": [
        "sets.sort_values(\"year\").head()"
      ],
      "execution_count": 18,
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              "      <th>9521</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",
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              "      <th>9534</th>\n",
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              "      <td>365</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>9539</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>9544</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>9545</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",
              "9521  700.1-1         Extra-Large Gift Set (ABB)  1949       365        142\n",
              "9534  700.2-1               Large Gift Set (ABB)  1949       365        178\n",
              "9539  700.3-1              Medium Gift Set (ABB)  1949       365        142\n",
              "9544  700.A-1              Small Brick Set (ABB)  1949       371         24\n",
              "9545  700.B-1  Small Doors and Windows Set (ABB)  1949       371         12"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "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",
      "metadata": {
        "id": "h-Tf1w7IBBg9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "cb84cb62-9fad-43ba-9169-41e7e52002c8"
      },
      "source": [
        "sets[sets['year'] == 1949]"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "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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              "    }\n",
              "</style>\n",
              "<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>9521</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>9534</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>9539</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>9544</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>9545</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",
              "9521  700.1-1         Extra-Large Gift Set (ABB)  1949       365        142\n",
              "9534  700.2-1               Large Gift Set (ABB)  1949       365        178\n",
              "9539  700.3-1              Medium Gift Set (ABB)  1949       365        142\n",
              "9544  700.A-1              Small Brick Set (ABB)  1949       371         24\n",
              "9545  700.B-1  Small Doors and Windows Set (ABB)  1949       371         12"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RJMMYQYqIquo"
      },
      "source": [
        "**Challenge**: Find the top 5 LEGO sets with the most number of parts. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "toJvjRuQIqup",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "1d6c3865-1b77-4868-caaf-9ea6d9fb70d6"
      },
      "source": [
        "sets.sort_values(\"num_parts\", ascending=False).head()"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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>15004</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>11183</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>10551</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>295</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",
              "    <tr>\n",
              "      <th>221</th>\n",
              "      <td>10189-1</td>\n",
              "      <td>Taj Mahal</td>\n",
              "      <td>2008</td>\n",
              "      <td>673</td>\n",
              "      <td>5922</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        set_num                           name  year  theme_id  num_parts\n",
              "15004  BIGBOX-1  The Ultimate Battle for Chima  2015       571       9987\n",
              "11183   75192-1          UCS Millennium Falcon  2017       171       7541\n",
              "10551   71043-1                Hogwarts Castle  2018       246       6020\n",
              "295     10256-1                      Taj Mahal  2017       673       5923\n",
              "221     10189-1                      Taj Mahal  2008       673       5922"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "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",
      "metadata": {
        "scrolled": true,
        "id": "qjdrktZAIqus",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "923f034f-fbc2-42e3-99ed-86e5fe49fd63"
      },
      "source": [
        "sets_by_year = sets.groupby(\"year\").count()\n",
        "sets_by_year[\"set_num\"].head()"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "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"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tFInsHOkIqut",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f628c2f7-e4d8-4c38-8976-050de2e965ca"
      },
      "source": [
        "sets_by_year[\"set_num\"].tail()"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "year\n",
              "2017    786\n",
              "2018    816\n",
              "2019    840\n",
              "2020    674\n",
              "2021      3\n",
              "Name: set_num, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "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",
      "metadata": {
        "id": "Nckj4lSGIquw",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "46bf940f-0388-4c1f-e497-c1f65f53c8c6"
      },
      "source": [
        "plt.plot(sets_by_year.index, sets_by_year.set_num)"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7ffb8450e390>]"
            ]
          },
          "metadata": {},
          "execution_count": 27
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_ZqAN-5MIquz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "1901fa44-1afa-4a0c-cfcf-4f6a3c77fe67"
      },
      "source": [
        "plt.plot(sets_by_year.index[:-2], sets_by_year.set_num[:-2])"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7ffb792f8810>]"
            ]
          },
          "metadata": {},
          "execution_count": 28
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "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",
      "metadata": {
        "dc": {
          "key": "266a3f390c"
        },
        "tags": [
          "sample_code"
        ],
        "id": "qx8pTau4Iqu2"
      },
      "source": [
        "themes_by_year = sets.groupby(\"year\").agg({'theme_id': pd.Series.nunique})"
      ],
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BqF1o997Iqu3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "outputId": "ce65facf-f8cc-48ee-bb8a-6a7d62e43f0a"
      },
      "source": [
        "themes_by_year.rename(columns = {\"theme_id\":\"nr_themes\"}, inplace = True)\n",
        "themes_by_year.head()"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "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",
      "metadata": {
        "id": "r2pamQEkIqu5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "33e744f3-4888-49db-cfdf-a22999aaa228"
      },
      "source": [
        "plt.plot(themes_by_year.index[:-2], themes_by_year.nr_themes[:-2])"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7ffb78aeff90>]"
            ]
          },
          "metadata": {},
          "execution_count": 31
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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3L+3Tb3/98kTmTA3jkc3ZtLR38et3P2dRYgTXnJ0wvH8ccG5KNNllDT2TXHfXwE+L8N0aeNAEr5Siu4tm4AQPzmnmBnvY6WRdCz978wirH36fwupmvris36kg+nXbqhSWzYziwXVpnGv1mXfzs9v4/pXzyato4tYnd1Fc28KD69IG7E7qz7kp0RgD+6xqmqKaZhIifbsGHpwzMimlJjGHw1DZOHgXDTjHpNl1vBpjDM4BZJ0aWjv4178d4fWMYhwG1i+Zzl0XzWb+tPBhxxDkb+e1e1YPuP6KhfEsmxnFnvwaVs2OGfLm7emWzojC3y7sya/hkrR4q0TSt7tnQBO8UpNebUsHnQ4zdIKPCXWWSja2MTX8VNfGk5/k8/JnRdy6IplvrpnllvlNRYR/umYB9794gB9dvaDPL5jhCA6wsygxsudGa1FNS8/4OL7Mt/8+UUoNqb+p+vqTHGONX96rm8YYw6aMYs5PjeZf1i9y6+TV5yRH8+H317IoMXJU+5+XEs3BojrqWzsoa/D9GnjQBK/UpFdpPcU6VB98qjVscO8JKg4V15FX2cR1S4ff3+6Kkbbce0tPiaa9y8E7h0qtGnjfLpEETfBKTXrDbcEnRgXjZ5M+87O+vr+EALuNq0ZQ0eIt6cnOks1X9zsnG9EWvFLK5w03wfvZbdaoks4umi6H4W8HS7g4LY7IAZ6AHU+mhAYwLz6MnXnOfnh3dieNF5rglZrkKhrbCPCzER44dM2Fs1TS2YLfkVtJRUObx7pnxkJ3CaafTYgf4heaL9AEr9QkV9nQRlxY4LD6t3uPKvn6/hLCg/y4OG2qB6IcG90JPiHK92vgQRO8UpNexTBq4LulxITQ1N5FUU0LmzNLuWrRNIL87W6OcOycm+pM8ElRvt89A5rglZr0hhqHprdkq5LmiU+O09jWOaG6Z8B5o3hefBgLp0d4OxSP0AedlJrkKhrahjUoGECqNarkc7sLiY8I5PxZ/U/mMZ69ds/qYQ1S5gs0wSs1iXV2Oahubh92Cz5xSjB2m9De6eDaJdOxj2A8mPEidBg3k33F5Pg1ppTqV3VTO8YMXSLZzd9u66kfXz/Bumcmo8nzq0wpdYbynsm2h18yuGBaBMH+ds6aJP3YE5kmeKUmsVOTbfc/aXV/Hv7SYrocxqVhA5RnaIJXahLrmWw7bPgTX0yEp1aVk/bBKzWJdbfgY0fQglcThyZ4pSaxioY2wgL9CAnQP+Z9kSZ4pSYx51R92nr3VZrglZqkTlQ3sye/moRI3x82d7LSBK/UJHSsrIEv/WkHrR0OHroqzdvhKDdxKcGLSJSIvCwiWSJyVERWiki0iGwRkWPW9+E9A62U8ohDRXXc9OdPcRh46a6VLJkR5e2QlJu42oL/PfCOMSYNWAIcBR4Cthpj5gJbrfdKqXFgV14VX/nLTkID/Xj5WyuZPy3c2yEpNxp1gheRSOBC4AkAY0y7MaYWWA9stDbbCFznapBKKdeV1rWy4andTIsM4uVvrSLZGjhM+S5XWvCpQAXwlIjsF5HHRSQUiDfGnLS2KQXi+9tZRO4Ukb0isreiosKFMJRSw/HMzgLaOh08sSGdaZHDf7BJTVyuJHg/YDnwqDFmGdDEad0xxhgDmP52NsY8ZoxJN8akx8XFuRCGUmoorR1dPLe7kEvT4rXlPom4kuCLgCJjzC7r/cs4E36ZiCQAWN/LXQtRKeWqNw+epLqpndtWpXg7FOVBo07wxphS4ISIzLcWXQocAd4ANljLNgCbXIpQKeUSYwwbd+QzZ2oYq+dMvAk61Oi5+nzyd4BnRSQAyANux/lL4yURuQMoAG5y8RxKKRd8VljLoeI6fnbdIh0BcpJxKcEbYzKA9H5WXerKcZVSY+fpHfmEB/lx/TKdoGOy0SdZlfJhZfWtvH3oJDeeM2NSTVWnnDTBK+XDnt1VSJcx3Loy2duhKC/QBK+Uj2rvdPDcrkIunj+VlFgtjZyMNMEr5aPeOnSSysY2Nmhp5KSlCV4pH/XUjnxmxYayZk6st0NRXqIJXikflHGilgMnatmwKgWbTUsjJytN8Er5oI078gkL9OOGc5K8HYryIk3wSvmYioY23jxYwpfOSSJMSyMnNU3wSvmY53cX0tGlpZFKE7xSPqW908EzOwu4cF4cs+LCvB2O8jJN8Er5kHcySylvaON2LY1UaIJXyqds3JFPSkwIF83TORaUJnilfMbh4jr2FdRwy0otjVROmuCV8hFP78gnJMDOjelaGqmcNMEr5QOqGtt440AJ1y9PJCLI39vhqHFCE7xSPuCFPSdo73SwYWWKt0NR44gmeKUmuM4uZ2nkBXNimRsf7u1w1DiiCV6pCe7dI2WcrGvVUSPVGTTBKzXBPb09nxnRwVySNtXboahxRhO8UhPYkZJ6dudXc+uKFOxaGqlOowleqQls4458gv3t3JQ+w9uhqHFIE7xS49AH2eW8degknV2OAbepaWrn9Yxivrg8kcgQLY1UZ9KxRJUaZ9o7Hfzjc/tpaOskOSaEOy+cxQ3Lkwjyt/fZ7oU9J2jT0kg1CE3wSo3AB9nlBNptrJwdg4h7+rx3H6+moa2T/7Mmld35Nfz4tcP8dssxrl+eSHCvJP/inhOsnBXD/GlaGqn6pwleqWFyOAx3P7OP1g4Hi5Miufui2Vxx1rQxv7n53tEygvxtPHD5fIL8bXyaV8Wj23J57KO8Ptv524WHv7R4TM+tfIsmeKWGqbS+ldYOB1eeFU92aQN3P/sZs2JDefCqNK48a9qYnMMYw5YjZVwwJ47gAGdrfdXsWFbN1omz1cjpTValhim/sgmADStT2Prdtfzxq8ux24TvPL+f1o6uMTnH0ZMNFNe2cPlCrWlXrtMEr9Qw5Vc1A5AcG4rdJlyzOIEfXbOA9k4He/Krx+Qc7x0tQwQuSYsfk+OpyU0TvFLDVFDVRICfjYSIoJ5l56VE428XPsmpHJNzvHe0jKUzoogLDxyT46nJTRO8UsN0vLKJ5OiQPpNphAb6sWzmFLaPQYIvrWvlYFEdly3Q1rsaG5rglRqmgqpmkmNCz1i+Zk4smSX1VDe1u3T8rVllAFy+UBO8GhsuJ3gRsYvIfhF503qfKiK7RCRHRF4UkQDXw1TKuxwOQ35VE6mxIWesWz03FmNgR65rrfj3jpQxMzqEuVPDXDqOUt3GogV/L3C01/uHgd8aY+YANcAdY3AOpbyqrKGVtk5Hvy34xYmRhAf6udRN09TWyfbcKi5bEO+2B6jU5ONSgheRJOAa4HHrvQCXAC9bm2wErnPlHEqNB8etEsnU2DMTvJ/dxorZMS7daP34WCXtnQ4u0/JINYZcbcH/DvgB0D0iUgxQa4zptN4XAYn97Sgid4rIXhHZW1FR4WIYSrlXQXeJZMyZXTQAa+bGcqK6hYKqplEd/72jZUQE+XFuSvSoY1TqdKNO8CLyBaDcGLNvNPsbYx4zxqQbY9Lj4uJGG4ZSHpFf1USA3UZCZHC/61fPcT5pOppWfJfD8H5WORenTcXfrnUPauy48mlaDVwrIvnACzi7Zn4PRIlI9xAISUCxSxEqNQ7kVzYxMyZkwHFnZsWGkhAZNOJ+eIfD8G9/P0J1U/uYDXegVLdRJ3hjzA+NMUnGmBTgZuB9Y8zXgA+AL1mbbQA2uRylUl5WUNVMygDdMwAiwgVzYtmRW0WXwwzrmJ1dDh585SBPbc/ntlUprNMEr8aYO/4efBB4QERycPbJP+GGcyjlMd0lkin9VND0dsHcWGqbO8gsqRvymG2dXXzn+f38dV8R9146l5/8w8I+D1ApNRbGZDRJY8w2YJv1Og84byyOq9R4UN7QRmuHg+R+Kmh66x7x8ZOcShYnRfXav5Xy+rae98bAI5uz+PhYJf/8hYXccUGqewJXk54OF6zUELpLJAfrogGICw8kbVo423MquWftHLJK6/nzh3m8caDkjG4bm8AjX1qsc6kqt9IEr9QQuksfh+qiAbhgTiz/s7OAO57ew9asckIC7Ny2KoXzU6P7PMA0IzqYtGkRbotZKdAEr9SQ8qua8bcL06P6L5Hs7cJ5cTz+yXE+K6zhgcvncevKZKJCdLQO5R2a4JUaQn5lEzOiBy6R7G3N3FhevHMFZydFEhKgP17Ku/QTqNQQ8quaSB1G9ww4yyXPnxXj5oiUGh59bE6pQRhjBhwmWKnxThO8UoMob2ijpaOr32GClRrvNMErNYjuEkltwauJSBO8UoMYSYmkUuONJnilBnGqRDJo6I2VGmc0wSuf9MvNWdzx9B6Xj5Nf2cSMKSH46TC+agLST63ySVuPlrM1q5y8ikaXjpNf1UzKEGPQKDVeaYJXPqejy0GuldhfzygZ9XGcJZJNA87ipNR4pwle+Zy8iiY6ugwBdhubMooxZnjjs5+uoqGN5vYuvcGqJixN8MrnZJXWA3DLymQKqprJOFE74mOcrGvhN1s+B9AuGjVh6VAFyudklTbgbxfuWTub/91ZwKaMEpbNnDKsfXPKG/jzh3m8nlGMw8BN6Umsmq1DD6iJSRO88jlZJ+uZHRdGTFggl6ZN5c2DJfzTNQuGrIR5/OM8fv7WUQL9bHzt/GS+uSaVpCna/64mLk3wyudklzZwXmo0AOuXJvL24VI+yalk7fypA+7T0t7FH97PYdXsGP7z5mXEhAV6Klyl3Eb74JVPqWvuoKSulfnWZBoXp8UREeTHpiGqaTZlFFPX0sG9l87T5K58hiZ45VOyyxoASEsIByDQz87VZyewObOU5vbOfvcxxvD0jnwWJERwbsrw+uqVmgg0wSuf0l1BkzYtvGfZ+qWJNLd3seVIWb/77DpeTVZpA7etSu4zrZ5SE50meOVTskobiAz2Z1rEqbFjzk+NJiEyiNf3F/e7z8Yd+USF+LN+aaKnwlTKIzTBq3Gls8vB/S9msK+gelT7Z52sZ/608D4tcZtNuHbJdD46VklOed+hC0pqW3j3SBk3nzuTIH+7S7ErNd5oglfjyrHyRl7bX8yPXzuMwzGyJ1AdDsPnZY0s6NU90+3rK5KJCvbn5sc+JbOkrmf5MzsLMMbw9RUzXY5dqfFGE7waVw4XO5NvVmkDbxzov/Ilu7SBf3r90Bk3TYtrW2hs6+ypoOltRnQIL31rJQF2Gzc/tpN9BdW0dnTx/O5CLl8Yr/XuyidpglfjSmZJPSEBdhYmRPDrLdm0dzr6rG9u7+TuZ/fxzM5CXtlX1GddVmnfCprTzY4L4693ryI2LJCvP76bn2zKpKa5gw2rUtzyb1HK2zTBq3Els6SOhQkR/GDdfE5Ut/D87sI+63/25lGOVzaREBnExk8L+gwklnXSWUEzL77/BA+QGBXMS3etJDkmhBf3nmB+fDgrZ+lQBMo3aYJX44bDYThSUs9Z0yO4aF4cK2ZF84f3j9HU5uyK2ZxZyvO7C7nrwtl874r55JQ3sj2nqmf/rLIGZkaHEBY4+APaceGBvHjnSr64LJEfXbNASyOVz9IEr8aN/Kommtq7OCsxEhHhB+vSqGxs54lPjlNW38pDrxxkUWIED1w+jy8sSSAmNICnd+T37N9dQTMckSH+/PbLS7loXpyb/jVKeZ8meDVuHC5xdrGcNd15k3T5zClceVY8j32Ux3ee309LRxe/v3kZAX42Av3sfOW8mWzNKuNEdTOtHV0cr2zqt4JGqclKE7waNzKL6wiw25g79VSS/v6V82lu72T38Wr+3xfOYnZcWM+6r62YiU2E//k0n5zyRhwG0hLOrKBRarLS0STVuJFZ4uxiCfA71e6YMzWc+y+bR1VTO185b0af7RMig1l31jRe3HOCmdHOMsfhdtEoNRmMugUvIjNE5AMROSIimSJyr7U8WkS2iMgx67uO3qSGZIzhcEldT/dMb9+5dC4/vfasfm+G3rY6hfrWTv7wfg6BfjadXk+pXlzpoukEvmuMWQisAL4tIguBh4Ctxpi5wFbrvVKDKqlrpba5g7MSI0e0X3ryFBYmRFDe0Ma8+HDsNq2IUarbqBO8MeakMeYz63UDcBRIBNYDG63NNgLXuRqk8n3dT7Au6qcFPxgR4TbrQaU07Z5Rqo8xuckqIinAMmAXEG+MOWmtKgXiB9jnThHZKyJ7KyoqxiIMNYFlFtdhE0rpXy0AAA9xSURBVEjrZ5iBoVy7dDpLZ0Rx6YKBZ2xSajJy+SariIQBrwD3GWPqe/eTGmOMiPQ7YpQx5jHgMYD09PSRjSqlfE5mST1zpoYRHDDyER2D/O28/u3VbohKqYnNpRa8iPjjTO7PGmNetRaXiUiCtT4BKHctRDUZHC6pY9H0kfW/K6UGN+oWvDib6k8AR40xv+m16g1gA/AL6/smlyJUXnGiupk3D57E0Wusl2B/O18+dwahQwwFMFIVDW2U1bexcIT970qpwbnyk7oauAU4JCIZ1rIf4UzsL4nIHUABcJNrISpPO3qynlue2E1lY9sZ6wqqmviX9YvG9Hzd47MvGmEFjVJqcKNO8MaYT4CBatIuHe1xlXftK6jh9qd2Exrox7v3X9inrvwnb2Ty3O5C7rhgFjNjhh4/3RjDtuwK0lOmEB7kP+B2mdYQBdqCV2ps6VAFqsfHxyr4+uO7iA4N4K/fWsm8eOdTpd1f9102F5sIv9mSPazjbTlSxu1P7+G+FzL6DOt7usPFdSTHhBAxyC8BpdTIaYJXALybWcodT+8lOcY581F/MxzFRwRx++pUNh0o4YjV6h5Il8Pwy83ZBPvb2ZpVzjO7CgfcNrOkXm+wKuUGmuAVVY1t3P9iBgumR/DinSuZGh404LZ3XzSb8EA/frk5a9BjvvpZEcfKG/nVjUu4aF4c//bmEXLKG87Yrq6lg8LqZs5K1O4ZpcaaJnjFHz/IpaWji1/fuITIkMG7SSJD/Lnn4jl8kF3Brryqfrdp7ejid+8dY3FSJFefPY1f3riY0EA//vH5DNo6u/pse7CoFoCztAWv1JjTBD/JFdU088zOAm5Kn8GcqWFD7wBsWJlCfEQgD7+T1W/f+jM7CyiubeHBdWmICFPDg3jkhsUcOVnPr9/9HHCWYf7z64f55sa9hAbYWawVNEqNOR0ueJL7zZbPEYF7L5s77H2CA+zcd9k8fvjqId4+XMrVZyf0rGto7eCPH+RwwZxYVs+J7Vl+2cJ4vr5iJo99lEdueSPbPq/AJnD9siTuumgWU0IDxvTfpZTSBD+pZZc28Nr+Yu5cM4uEyOAR7XvjOUk8+clxvv3cZ1yxMJ67185h6Ywo/vJRHjXNHfxg3fwz9vnx1QvZlVfNzrwq7rgglW+sTmVa5MD9/Uop12iCnwSa2jopqmk5YzKMX27OIizQj7vXzh7xMf3sNl68ayVPbz/Oxk8L2Jy5nRWzojlYVMc1ZyewOCnqjH2CA5xjxhgYcmJspZTr9KdsEvjpG5n8dV8R6clTuOfi2Vw8fyp7C2p472g5379yPlEho+seiQ4N4IEr5nPnRbN5YXchf/k4j84uw3evmDfgPmM9zIFSamAy2AMonpKenm727t3r7TB8UmVjG6v+430WJ0Vysq6V4toW0qaFYwzUNLfz4fcvHtUIjv1p6+yitrmD+AjtdlHKE0RknzEmfaD12pzycS/sLqS9y8Evbjib5JhQ/naghEe35XKsvJGff3HRmCV3gEA/O/ERY3c8pZRrNMH7sI4uB8/sLGTN3FjmTHX2v1+/PInrliaSW9E47LJIpdTEpHXwPuzdzDJK61vZsDKlz3KbTZgbH97vJNZKKd+hCd6HbdyRz4zoYC5O06nslJqMNMH7qMySOnbnV3PrihTsNm2pKzUZaYL3AQ2tHWcs27gjn2B/Ozelz/BCREqp8UBvsk5Qxhg+PlbJo9ty+TSvivNSo7l77WzWzoujtrmDTRkl3HBO0pCDhymlfJcm+Ammy2F4+/BJHt2WS2ZJPfERgXxjdSpvHz7J7U/tYUFCBLPiQmnrdJxxc1UpNblogp9gfvpGJv+7s4BZcaE8csNi1i+bTqCfnYeuSuONAyX86cNc/n7wJCtnxZwxNIFSanLRBD+B5JQ38uyuAr56/kx+tn5Rn5unAX42vnROEtcvS2R7bqXWuCulNMFPJL9+1zkF3ncvnzdgZYzNJqyZG+fhyJRS45FW0YwzA40NlHGilrcPl/J/LpxFTFigh6NSSk1EmuDHkQ+yy0n/t/fYlFHcZ7kxhoffziImNIBvrpnlpeiUUhONJvhxoqKhje+9dIDalg7uezGDZ3cV9Kz7+Fgln+ZV8X8vmaPjqCulhk0T/DhgjOH7Lx+gsa2T1+5Zxdp5cfz4tcM8ui0Xh8Pw8DtZJE0J5qvnz/R2qEqpCUQT/DiwcUc+27Ir+PE1C1icFMWfb0nnC4sTePidLG59cjeZJfU8cPk8Av10KF6l1PDp3/vD8OyuAh7dlssNy5PYsCqF6F4TRBtj2JZdwWMf5dHU3sntq1P4wuLp+NuH97szu7SBf387i0vSpnLLimTAWfL4+5uXER7kz/O7C0mbFs76pYlu+bcppXyXzug0hP/elsMj72QzMzqEwupmgv3tfPncGXxjdSr7T9Tw6LZcskobmB4ZRGigH8fKG0mMCubOC2dxU/qMQSfUaO3o4ro/bqeysY137ruQ2NOqY4wx/HVvEctmRjE3Xh9aUkr1NdSMTprgB2CM4eF3svnTh7lcu2Q6v75pCfmVTfzpwzw2ZRTT6XBet7lTw7jrotlcu2Q6fjbhg+xy/ntbLvsKagj2txMaOHCCb+90UN/ayVO3n8vF83VIX6XUyPh0gu+ykuxYD4frcBj+edNhnt1VyNfOn8m/nvbUaHFtC699VsS8+HAuWxCPrZ/z78mv5u8HT9LR5Rj0XOckT+H65UljGr9SanLw6QT/2v4i/rA1h7sumsV1yxKHdROypqmd3IpG66uJ3PJGjlc10d55KhG3dzoob2jjWxfN5sF183XmI6XUuOTTk27HhAYSEmjnwVcO8Zstn/PNC2bxlfNnEuxvp7imhdyKRnLKG/sk9Oqm9p79A/xszIoNZX58+Bl95eelRHPzeVqWqJSauNzSgheRdcDvATvwuDHmF4Nt70ofvDGGT3Kc46LvyK0iNMBOh8P0aZHHhAYwOy6M2VNDnd+tr8QpwTrbkVJqwvJ4C15E7MAfgcuBImCPiLxhjDky1ueyzseauXGsmRtHxolaXtp7gvBAv56EPis2jCm9yhqVUmqycEcXzXlAjjEmD0BEXgDWA25J8L0tnRHF0hlR7j6NUkpNCO54kjURONHrfZG1rA8RuVNE9orI3oqKCjeEoZRSk5vXhiowxjxmjEk3xqTHxen45UopNdbckeCLgRm93idZy5RSSnmQOxL8HmCuiKSKSABwM/CGG86jlFJqEGN+k9UY0yki/xfYjLNM8kljTOZYn0cppdTg3PKgkzHmLeAtdxxbKaXU8Oh48Eop5aM0wSullI8aF4ONiUgFUDDkhsMXC1SO4fHcaSLFChMr3okUK0yseCdSrDCx4h1JrMnGmAHrzMdFgh9rIrJ3sPEZxpOJFCtMrHgnUqwwseKdSLHCxIp3LGPVLhqllPJRmuCVUspH+WqCf8zbAYzARIoVJla8EylWmFjxTqRYYWLFO2ax+mQfvFJKKd9twSul1KSnCV4ppXzUhEjwIvKkiJSLyOFey5aIyKcickhE/iYiEdbyFBFpEZEM6+tPvfY5x9o+R0T+U9w0m/ZI4rXWLbbWZVrrgzwV7wiv7dd6XdcMEXGIyFJPxTqKeP1FZKO1/KiI/LDXPutEJNuK96FxEGuAiDxlLT8gImt77eOJz8EMEflARI5Yn8N7reXRIrJFRI5Z36dYy8WKJUdEDorI8l7H2mBtf0xENox1rKOMN8267m0i8r3TjuXWz8IoYv2adU0PicgOEVky6liNMeP+C7gQWA4c7rVsD3CR9fobwM+s1ym9tzvtOLuBFYAAbwNXjYN4/YCDwBLrfQxg91S8I4n1tP3OBnLH+bX9KvCC9ToEyLc+H3YgF5gFBAAHgIVejvXbwFPW66nAPsDmwc9BArDceh0OfA4sBB4BHrKWPwQ8bL2+2opFrNh2WcujgTzr+xTr9ZRxEO9U4Fzg58D3eh3H7Z+FUcS6qvuaAVf1urYjjnXMfwDd9cVpiRuo49RN4hnAkf62O+0iZ/V6/xXgz+Mg3quBZ7wZ73BjPW2ffwd+Ps6v7VeAv+H8JRpj/WBFAyuBzb32/yHwQy/H+kfgll7bbcU5/aVHr22v82zCOa9yNpDQ6/8523r9Z+ArvbbPttb3ie/07bwVb6/tfkrfBO+xz8JIY7WWTwGKRxvrhOiiGUAmzrleAW6k7yQjqSKyX0Q+FJE11rJEnNMHdut3KkE3GijeeYARkc0i8pmI/MBa7s14B7u23b4MPG+9Hq/X9mWgCTgJFAK/MsZUM8xpJd1koFgPANeKiJ+IpALnWOs8fm1FJAVYBuwC4o0xJ61VpUC89Xqga+jxazvMeAfi0XhHEesdOP9SglHEOpET/DeAe0RkH84/e9qt5SeBmcaYZcADwHPSq7/biwaK1w+4APia9f2LInKpd0LsMVCsAIjI+UCzMeZwfzt7wUDxngd0AdOBVOC7IjLLOyH2GCjWJ3H+wO4FfgfswBm7R4lIGPAKcJ8xpr73OuNsNo6ruuqJFO9IYxWRi3Em+AdHe063jAfvCcaYLOAKABGZB1xjLW8D2qzX+0QkF2cruRjn9IHdPDqV4EDx4vyh/sgYU2mtewtnv+0z3op3kFi73cyp1juM32v7VeAdY0wHUC4i24F0nK0gr0wrOcjnthO4v3s7EdmBs0upBg9dWxHxx5mAnjXGvGotLhORBGPMSRFJAMqt5QNNzVkMrD1t+bZxEO9APDLF6EhjFZHFwOM477dUjTbWCduCF5Gp1ncb8E/An6z3cSJit17PAuYCedafQvUissKqQrgVZ1+YV+PFOfPV2SISIiJ+wEU4+2W9Fu8gsXYvuwl4oXvZOL62hcAl1rpQnDcDs/DitJKDfG5DrBgRkcuBTmOMxz4H1rGfAI4aY37Ta9UbQHclzIZe534DuFWcVgB1VqybgStEZIpVFXKFtczb8Q7E7Z+FkcYqIjOBV3Hek/ncpVjdffNjjG5KPI+z66UDZ4v3DuBenC2cz4FfcOrG1Q04+zkzgM+Af+h1nHTgMM470f/VvY8347W2/7oV82HgEU/GO4pY1wI7+znOuLu2QBjwV+vaHgG+3+s4V1vb5wI/HgexpuC86XYUeA/nMLCe/BxcgLOL4KD1s5NhXaMYnDd8j1lxRVvbC84bw7nAISC917G+AeRYX7e76dqONN5p1v9BPVBrvY7wxGdhFLE+jvMvt+5t9472c6tDFSillI+asF00SimlBqcJXimlfJQmeKWU8lGa4JVSykdpgldKKR+lCV4ppXyUJnillPJR/x85SC4qHXwdSQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uBbt9-lJIqu7"
      },
      "source": [
        "### Line Charts with Two Seperate Axes"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j7lQ_amFIqu7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "0dc0b89a-4500-461e-8c69-1ed02e654901"
      },
      "source": [
        "ax1 = plt.gca()\n",
        "ax2 = ax1.twinx()\n",
        "\n",
        "ax1.plot(sets_by_year.index[:-2], sets_by_year.set_num[:-2], color=\"g\")\n",
        "ax2.plot(themes_by_year.index[:-2], themes_by_year.nr_themes[:-2], \"b\")\n",
        "\n",
        "ax1.set_xlabel(\"Year\")\n",
        "ax1.set_ylabel(\"Number of Sets\", color=\"green\")\n",
        "ax1.set_ylabel(\"Number of Themes\", color=\"blue\")"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Number of Themes')"
            ]
          },
          "metadata": {},
          "execution_count": 37
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "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",
      "metadata": {
        "id": "W7BcH9vuIqu9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "outputId": "39d71265-de13-4328-eead-85a9655d5de6"
      },
      "source": [
        "parts_per_set.head()"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>num_parts</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>99.600000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1950</th>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1953</th>\n",
              "      <td>13.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1954</th>\n",
              "      <td>12.357143</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1955</th>\n",
              "      <td>36.607143</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      num_parts\n",
              "year           \n",
              "1949  99.600000\n",
              "1950   1.000000\n",
              "1953  13.500000\n",
              "1954  12.357143\n",
              "1955  36.607143"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fjbb3tZcIqu_"
      },
      "source": [
        "parts_per_set = sets.groupby('year').agg({'num_parts': pd.Series.mean})"
      ],
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bAeTe2XqIqvB"
      },
      "source": [
        "### Scatter Plots in Matplotlib"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "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",
      "metadata": {
        "id": "EQNZ0D7JIqvB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "db0a6f7c-0ab4-456a-de50-0a28283261ee"
      },
      "source": [
        "plt.scatter(parts_per_set.index[:-2], parts_per_set.num_parts[:-2])"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7ffb784f9290>"
            ]
          },
          "metadata": {},
          "execution_count": 40
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xK226Ip-IqvE"
      },
      "source": [
        "### Number of Sets per LEGO Theme"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "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",
      "metadata": {
        "id": "hOBcNrC9IqvE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "13aac259-8390-478d-c376-d23362416bfd"
      },
      "source": [
        "set_theme_count = sets[\"theme_id\"].value_counts()\n",
        "set_theme_count[:5]"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "158    753\n",
              "501    656\n",
              "494    398\n",
              "435    356\n",
              "503    329\n",
              "Name: theme_id, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J-i6JULGIqvG"
      },
      "source": [
        "**Challenge** Use what you know about HTML markup and tags to display the database schema:\n",
        "<img src=\"https://i.imgur.com/Sg4lcjx.png\">"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "27oDwiPHIqvH"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "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": {
        "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",
      "metadata": {
        "id": "3uN3wN5sIqvH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "c11dfc94-d227-40cb-dd02-27fea1de216a"
      },
      "source": [
        "themes = pd.read_csv(\"data/themes.csv\")\n",
        "themes.head()"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xAO2XlQGIqvJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "outputId": "2962e1ba-5ba5-4bed-d9c3-6110581a50a1"
      },
      "source": [
        "themes[themes.name == \"Star Wars\"]"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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>150</th>\n",
              "      <td>158</td>\n",
              "      <td>Star Wars</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>174</th>\n",
              "      <td>209</td>\n",
              "      <td>Star Wars</td>\n",
              "      <td>207.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>211</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",
              "150  158  Star Wars        NaN\n",
              "174  209  Star Wars      207.0\n",
              "211  261  Star Wars      258.0"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xYEDlY-_IqvL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 390
        },
        "outputId": "3ba72c7c-ae91-40f9-a9ad-efea52a68dd7"
      },
      "source": [
        "sets[sets.theme_id == 18]"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\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>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>8786</th>\n",
              "      <td>65081-1</td>\n",
              "      <td>R2-D2 / C-3PO Droid Collectors Set</td>\n",
              "      <td>2002</td>\n",
              "      <td>18</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12051</th>\n",
              "      <td>8000-1</td>\n",
              "      <td>Pit Droid</td>\n",
              "      <td>2000</td>\n",
              "      <td>18</td>\n",
              "      <td>223</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12058</th>\n",
              "      <td>8001-1</td>\n",
              "      <td>Battle Droid</td>\n",
              "      <td>2000</td>\n",
              "      <td>18</td>\n",
              "      <td>336</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12066</th>\n",
              "      <td>8002-1</td>\n",
              "      <td>Destroyer Droid</td>\n",
              "      <td>2000</td>\n",
              "      <td>18</td>\n",
              "      <td>567</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12071</th>\n",
              "      <td>8007-1</td>\n",
              "      <td>C-3PO</td>\n",
              "      <td>2001</td>\n",
              "      <td>18</td>\n",
              "      <td>339</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12072</th>\n",
              "      <td>8008-1</td>\n",
              "      <td>Stormtrooper</td>\n",
              "      <td>2001</td>\n",
              "      <td>18</td>\n",
              "      <td>360</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12073</th>\n",
              "      <td>8009-1</td>\n",
              "      <td>R2-D2</td>\n",
              "      <td>2002</td>\n",
              "      <td>18</td>\n",
              "      <td>239</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12074</th>\n",
              "      <td>8010-1</td>\n",
              "      <td>Darth Vader</td>\n",
              "      <td>2002</td>\n",
              "      <td>18</td>\n",
              "      <td>388</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12081</th>\n",
              "      <td>8011-1</td>\n",
              "      <td>Jango Fett</td>\n",
              "      <td>2002</td>\n",
              "      <td>18</td>\n",
              "      <td>425</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12083</th>\n",
              "      <td>8012-1</td>\n",
              "      <td>Super Battle Droid</td>\n",
              "      <td>2002</td>\n",
              "      <td>18</td>\n",
              "      <td>378</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15273</th>\n",
              "      <td>K8008-1</td>\n",
              "      <td>Darth Vader / Stormtrooper Kit</td>\n",
              "      <td>2002</td>\n",
              "      <td>18</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       set_num                                name  year  theme_id  num_parts\n",
              "8786   65081-1  R2-D2 / C-3PO Droid Collectors Set  2002        18          1\n",
              "12051   8000-1                           Pit Droid  2000        18        223\n",
              "12058   8001-1                        Battle Droid  2000        18        336\n",
              "12066   8002-1                     Destroyer Droid  2000        18        567\n",
              "12071   8007-1                               C-3PO  2001        18        339\n",
              "12072   8008-1                        Stormtrooper  2001        18        360\n",
              "12073   8009-1                               R2-D2  2002        18        239\n",
              "12074   8010-1                         Darth Vader  2002        18        388\n",
              "12081   8011-1                          Jango Fett  2002        18        425\n",
              "12083   8012-1                  Super Battle Droid  2002        18        378\n",
              "15273  K8008-1      Darth Vader / Stormtrooper Kit  2002        18          0"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "qBhckawNIqvN",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "outputId": "9f43ee07-4b2e-4cc9-b5f8-0d1a612d96ad"
      },
      "source": [
        "sets[sets.theme_id == 209]"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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>11013</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>11046</th>\n",
              "      <td>75056-1</td>\n",
              "      <td>Star Wars Advent Calendar 2014</td>\n",
              "      <td>2014</td>\n",
              "      <td>209</td>\n",
              "      <td>273</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11080</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>11131</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>11173</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>11206</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>11245</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>11281</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>12019</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>14352</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",
              "11013  75023-1  Star Wars Advent Calendar 2013  2013       209        254\n",
              "11046  75056-1  Star Wars Advent Calendar 2014  2014       209        273\n",
              "11080  75097-1  Star Wars Advent Calendar 2015  2015       209        291\n",
              "11131  75146-1  Star Wars Advent Calendar 2016  2016       209        282\n",
              "11173  75184-1  Star Wars Advent Calendar 2017  2017       209        309\n",
              "11206  75213-1  Star Wars Advent Calendar 2018  2018       209        307\n",
              "11245  75245-1  Star Wars Advent Calendar 2019  2019       209        280\n",
              "11281  75279-1  Star Wars Advent Calendar 2020  2020       209        312\n",
              "12019   7958-1  Star Wars Advent Calendar 2011  2011       209        267\n",
              "14352   9509-1  Star Wars Advent Calendar 2012  2012       209        235"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SmTCXWKKIqvQ"
      },
      "source": [
        "### Merging (i.e., Combining) DataFrames based on a Key\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "esKQULhcIqvR"
      },
      "source": [
        "set_theme_count = pd.DataFrame({\"id\":set_theme_count.index,\n",
        "                                \"set_count\":set_theme_count.values})"
      ],
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i0LobgIvIqvT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "outputId": "56ff93fe-6c5e-452d-fc64-52c0aeb2890b"
      },
      "source": [
        "merged_df = pd.merge(set_theme_count, themes, on='id')\n",
        "merged_df[:3]"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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>158</td>\n",
              "      <td>753</td>\n",
              "      <td>Star Wars</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>501</td>\n",
              "      <td>656</td>\n",
              "      <td>Gear</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>494</td>\n",
              "      <td>398</td>\n",
              "      <td>Friends</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    id  set_count       name  parent_id\n",
              "0  158        753  Star Wars        NaN\n",
              "1  501        656       Gear        NaN\n",
              "2  494        398    Friends        NaN"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I7UMP7VXIqvU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 609
        },
        "outputId": "11493770-1aac-44e2-f1e9-4ecf9e09f127"
      },
      "source": [
        "plt.figure(figsize=(14,8))\n",
        "plt.xticks(fontsize=14, rotation=45)\n",
        "plt.yticks(fontsize=14)\n",
        "plt.ylabel('Nr of Sets', fontsize=14)\n",
        "plt.xlabel('Theme Name', fontsize=14)\n",
        " \n",
        "plt.bar(merged_df.name[:10], merged_df.set_count[:10])"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<BarContainer object of 10 artists>"
            ]
          },
          "metadata": {},
          "execution_count": 54
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1008x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}