Lego_Analysis.ipynb

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     "context"
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   "source": [
    "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",
    "\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": [
    "# Import Statements"
   ]
  },
  {
   "cell_type": "code",
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   "metadata": {
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    "colab_type": "code",
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   "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"
   ]
  },
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   "execution_count": 16,
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   "outputs": [
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       "   id            name     rgb is_trans\n",
       "0  -1         Unknown  0033B2        f\n",
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     "execution_count": 16,
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   "source": [
    "colors = pd.read_csv('data/colors.csv')\n",
    "colors.head()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
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    "colors['name'].nunique()"
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   "execution_count": 19,
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       "f    107\n",
       "t     28\n",
       "Name: is_trans, dtype: int64"
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    "colors.is_trans.value_counts()"
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    "tags": [
     "context"
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   "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": {
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    "id": "NPNIHpvRIqug"
   },
   "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"
   ]
  },
  {
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   "execution_count": 20,
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       "      <td>Town Mini-Figures</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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     "execution_count": 20,
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   "source": [
    "sets = pd.read_csv('data/sets.csv')\n",
    "sets.head()"
   ]
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       "      <td>wwgp1-1</td>\n",
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       "      <td>Yoda Chronicles Promotional Set</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",
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     "execution_count": 21,
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   "source": [
    "sets.tail()"
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  },
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   "execution_count": 22,
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       "      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"
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     },
     "execution_count": 22,
     "metadata": {
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   ],
   "source": [
    "sets.sort_values('year').head()"
   ]
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   "execution_count": 23,
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       "      <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"
      ]
     },
     "execution_count": 23,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sets[sets['year'] == 1949]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 198
    },
    "colab_type": "code",
    "id": "toJvjRuQIqup",
    "outputId": "21c1225f-406e-4890-d991-ecabb81b2f6f"
   },
   "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>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"
      ]
     },
     "execution_count": 24,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sets.sort_values('num_parts', ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 138
    },
    "colab_type": "code",
    "id": "qjdrktZAIqus",
    "outputId": "b1347788-fbf4-4744-c800-2e16a0cd486c",
    "scrolled": true
   },
   "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": 25,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sets_by_year = sets.groupby('year').count()\n",
    "sets_by_year['set_num'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 138
    },
    "colab_type": "code",
    "id": "tFInsHOkIqut",
    "outputId": "fcb283c5-8fe9-4be0-dbbd-dec02c9b778b"
   },
   "outputs": [
    {
     "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"
      ]
     },
     "execution_count": 26,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sets_by_year['set_num'].tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 282
    },
    "colab_type": "code",
    "id": "Nckj4lSGIquw",
    "outputId": "dbc12b34-bb79-4052-914f-b7f6d1486ca8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fbe7eb1bbe0>]"
      ]
     },
     "execution_count": 27,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(sets_by_year.index, sets_by_year.set_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 282
    },
    "colab_type": "code",
    "id": "_ZqAN-5MIquz",
    "outputId": "9254139f-202c-4587-f0a8-fbd1b5a5e957"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fbe7eb0e9b0>]"
      ]
     },
     "execution_count": 28,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(sets_by_year.index[:-2], sets_by_year.set_num[:-2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "dc": {
     "key": "266a3f390c"
    },
    "id": "qx8pTau4Iqu2",
    "tags": [
     "sample_code"
    ]
   },
   "outputs": [],
   "source": [
    "themes_by_year = sets.groupby('year').agg({'theme_id': pd.Series.nunique})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 228
    },
    "colab_type": "code",
    "id": "BqF1o997Iqu3",
    "outputId": "c3a99ccf-379c-43fa-a84f-23e5061d3047"
   },
   "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": 30,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "themes_by_year.rename(columns = {'theme_id':'nr_themes'}, inplace = True) \n",
    "themes_by_year.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 228
    },
    "colab_type": "code",
    "id": "azxyYp-TrLQB",
    "outputId": "6af9d0c0-1631-40d5-c807-eb7aa8f65077"
   },
   "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>2017</th>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      nr_themes\n",
       "year           \n",
       "2017         89\n",
       "2018         93\n",
       "2019         78\n",
       "2020         82\n",
       "2021          1"
      ]
     },
     "execution_count": 31,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "themes_by_year.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 282
    },
    "colab_type": "code",
    "id": "j7lQ_amFIqu7",
    "outputId": "788f2a60-1173-4dcf-ea91-40d09fb4c86a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fbe7e435208>]"
      ]
     },
     "execution_count": 34,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(themes_by_year.index[:-2], themes_by_year.nr_themes[:-2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 282
    },
    "colab_type": "code",
    "id": "4_hcTl_cu2jN",
    "outputId": "eb2f9c34-756e-4077-fc0f-297f80657137"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fbe7e182400>]"
      ]
     },
     "execution_count": 38,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax1 = plt.gca() # get the axis\n",
    "ax2 = ax1.twinx() # create another axis that shares the same x-axis\n",
    "\n",
    "ax1.plot(sets_by_year.index[:-2], sets_by_year.set_num[:-2])\n",
    "ax2.plot(themes_by_year.index[:-2], themes_by_year.nr_themes[:-2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 296
    },
    "colab_type": "code",
    "id": "CelrQbuauqUg",
    "outputId": "90369137-7389-42c1-8b7c-2adbb02c910a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of Themes')"
      ]
     },
     "execution_count": 41,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax1 = plt.gca()\n",
    "ax2 = ax1.twinx()\n",
    "\n",
    "# Add styling\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",
    "ax2.set_ylabel('Number of Themes', color='blue')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 228
    },
    "colab_type": "code",
    "id": "W7BcH9vuIqu9",
    "outputId": "4cd6cad8-152b-43a8-ab3d-f8d8097dd44d"
   },
   "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>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"
      ]
     },
     "execution_count": 42,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parts_per_set = sets.groupby('year').agg({'num_parts': pd.Series.mean})\n",
    "parts_per_set.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 228
    },
    "colab_type": "code",
    "id": "fjbb3tZcIqu_",
    "outputId": "4a11c7e2-bfbd-4304-bcb6-f68c95f85cea"
   },
   "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>num_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>221.840967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>213.618873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>207.510714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>259.732938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       num_parts\n",
       "year            \n",
       "2017  221.840967\n",
       "2018  213.618873\n",
       "2019  207.510714\n",
       "2020  259.732938\n",
       "2021    0.000000"
      ]
     },
     "execution_count": 43,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parts_per_set.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 282
    },
    "colab_type": "code",
    "id": "EQNZ0D7JIqvB",
    "outputId": "a5b52665-29dd-4930-bbfb-8bfe08bf3176"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fbe7e60be80>"
      ]
     },
     "execution_count": 44,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(parts_per_set.index[:-2], parts_per_set.num_parts[:-2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 121
    },
    "colab_type": "code",
    "id": "hOBcNrC9IqvE",
    "outputId": "bc628417-d8a5-41ed-9c98-1ef5d9ff4d11"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "158    753\n",
       "501    656\n",
       "494    398\n",
       "435    356\n",
       "503    329\n",
       "Name: theme_id, dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set_theme_count = sets[\"theme_id\"].value_counts()\n",
    "set_theme_count[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "27oDwiPHIqvH"
   },
   "source": [
    "<img src=\"https://i.imgur.com/Sg4lcjx.png\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 198
    },
    "colab_type": "code",
    "id": "3uN3wN5sIqvH",
    "outputId": "8e4080c6-618c-48e6-b3e3-c50ea79051f6"
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <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",
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       "    <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": 46,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "themes = pd.read_csv('data/themes.csv') # has the theme names!\n",
    "themes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 168
    },
    "colab_type": "code",
    "id": "xAO2XlQGIqvJ",
    "outputId": "8b5baf31-0849-4d8c-a5bc-268fbe9826f9"
   },
   "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",
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       "      <th>parent_id</th>\n",
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       "    <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"
      ]
     },
     "execution_count": 47,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "themes[themes.name == 'Star Wars']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 378
    },
    "colab_type": "code",
    "id": "xYEDlY-_IqvL",
    "outputId": "c17eb7ce-8651-4cf6-e0c7-a6c10ece8a9a"
   },
   "outputs": [
    {
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       "      <th>set_num</th>\n",
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       "      <th>num_parts</th>\n",
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       "  <tbody>\n",
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       "      <th>8786</th>\n",
       "      <td>65081-1</td>\n",
       "      <td>R2-D2 / C-3PO Droid Collectors Set</td>\n",
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       "      <th>12066</th>\n",
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       "      <td>Destroyer Droid</td>\n",
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       "      <td>567</td>\n",
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       "      <th>12073</th>\n",
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       "      <td>18</td>\n",
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       "      <th>12074</th>\n",
       "      <td>8010-1</td>\n",
       "      <td>Darth Vader</td>\n",
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       "      <td>388</td>\n",
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       "    <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",
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      ],
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       "       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"
      ]
     },
     "execution_count": 48,
     "metadata": {
      "tags": []
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     "output_type": "execute_result"
    }
   ],
   "source": [
    "sets[sets.theme_id == 18]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 348
    },
    "colab_type": "code",
    "id": "qBhckawNIqvN",
    "outputId": "99409ffc-f588-4fec-8391-5fbacd5b778b",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <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"
      ]
     },
     "execution_count": 49,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sets[sets.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": 50,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 198
    },
    "colab_type": "code",
    "id": "esKQULhcIqvR",
    "outputId": "16b73923-da42-4285-fbca-53e150749cd6"
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "    id  set_count\n",
       "0  158        753\n",
       "1  501        656\n",
       "2  494        398\n",
       "3  435        356\n",
       "4  503        329"
      ]
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     "execution_count": 50,
     "metadata": {
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     "output_type": "execute_result"
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   ],
   "source": [
    "set_theme_count = pd.DataFrame({'id':set_theme_count.index, \n",
    "                                'set_count':set_theme_count.values})\n",
    "set_theme_count.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 138
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    "colab_type": "code",
    "id": "i0LobgIvIqvT",
    "outputId": "075be408-3b10-4834-d2e6-e974f9d2ca16"
   },
   "outputs": [
    {
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       "    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"
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     "execution_count": 51,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df = pd.merge(set_theme_count, themes, on='id')\n",
    "merged_df[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 464
    },
    "colab_type": "code",
    "id": "I7UMP7VXIqvU",
    "outputId": "ec8294b2-ef6f-4217-dc1b-3fa24824e98d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 10 artists>"
      ]
     },
     "execution_count": 53,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "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])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "1QJsV8MQLA6W"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "name": "Lego_Analysis_for_Course_(completed).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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}