lego.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Introduction\n",
    "The motivation for this project was to look at data of everyone's favorite toy, legos. \n",
    "In this project, we will analyze a fascinating dataset on every single lego block that has ever been built!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
       "    .dataframe thead th {\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>id</th>\n",
       "      <th>name</th>\n",
       "      <th>rgb</th>\n",
       "      <th>is_trans</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1</td>\n",
       "      <td>[Unknown]</td>\n",
       "      <td>0033B2</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>Black</td>\n",
       "      <td>05131D</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Blue</td>\n",
       "      <td>0055BF</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>Green</td>\n",
       "      <td>237841</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>Dark Turquoise</td>\n",
       "      <td>008F9B</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id            name     rgb is_trans\n",
       "0  -1       [Unknown]  0033B2        f\n",
       "1   0           Black  05131D        f\n",
       "2   1            Blue  0055BF        f\n",
       "3   2           Green  237841        f\n",
       "4   3  Dark Turquoise  008F9B        f"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "colors = pd.read_csv('https://raw.githubusercontent.com/indianspice/Lego/master/Data/colors.csv')\n",
    "colors.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Exploring Colors\n",
    "Examining the lego colors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "177\n"
     ]
    }
   ],
   "source": [
    "# How many distinct colors are available?\n",
    "num_colors = colors[['name']].name.nunique()\n",
    "print(num_colors)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Transparent Colors in Lego Sets\n",
    "The colors data has a column named is_trans that indicates whether a color is transparent or not. It would be interesting to explore the distribution of transparent vs. non-transparent colors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>name</th>\n",
       "      <th>rgb</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>is_trans</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>f</th>\n",
       "      <td>149</td>\n",
       "      <td>149</td>\n",
       "      <td>149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t</th>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           id  name  rgb\n",
       "is_trans                \n",
       "f         149   149  149\n",
       "t          28    28   28"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "colors_summary = colors.groupby('is_trans').count()\n",
    "colors_summary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Explore Lego Sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "sets = pd.read_csv('https://raw.githubusercontent.com/indianspice/Lego/master/Data/sets.csv')\n",
    "\n",
    "# Create a summary of average number of parts by year: `parts_by_year`\n",
    "parts_by_year = sets[['year', \n",
    "                      'num_parts']].groupby('year', as_index=False).mean().round(2)\n",
    "\n",
    "\n",
    "# Plot trends in average number of parts by year\n",
    "import matplotlib.pyplot as plt\n",
    "plt.plot('year', 'num_parts', data = parts_by_year)\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Average Number of Parts')\n",
    "plt.title('Average Lego Parts by Year')\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Lego Themes Over Years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year\n",
       "1950    2\n",
       "1953    1\n",
       "1954    2\n",
       "1955    4\n",
       "1956    3\n",
       "Name: theme_id, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# themes_by_year: Number of themes shipped by year\n",
    "themes_by_year = sets[['year', \n",
    "                       'theme_id']].groupby('year').theme_id.nunique()\n",
    "themes_by_year.head()"
   ]
  }
 ],
 "metadata": {
  "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}