Lab_FTX.ipynb

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        "# DS1002 Lab 1:  FTX\n",
        "\n",
        "### Due Monday 1 April at noon\n",
        "\n",
        "In this lab you will work with a dataset, writing python to generate the deliverables specified in the cells below.\n",
        "\n",
        "> **FTX** was a cryptocurrency exchange founded by Sam Bankman-Fried that saw both a spectacular rise and collapse between 2021 and 2022. At one point it had a valuation of $32B. In November Bankman-Fried was found guilty on seven counts of fraud and conspiracy.  His sentencing hearing is today.\n",
        "\n",
        "The dataset for this assignment covers that rise and fall from the viewpoint of daily stock closing prices for FTX (ticker symbol `FTT`).\n",
        "\n",
        "Answer the questions below with the appropriate Python code.\n",
        "\n",
        "**Put code and outputs in this notebook.**\n"
      ],
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        "## Group Submissions\n",
        "\n",
        "As usual you may work in a group to complete this lab, with no more than 3 members in a group. Group members should be indicated in the cell below -- list both names and UVA computing IDs.\n",
        "\n",
        "Each student should then submit **the same URL** for the lab in Canvas. (If a group has Member1, Member2, and Member3, only one member needs to save the completed work back to GitHub and all members should submit that URL for grading.)"
      ],
      "metadata": {
        "id": "acXFkytd2PWS"
      }
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      "source": [
        "# List group members (if applicable). Identify names and computing IDs\n",
        "#\n",
        "# Name                    Computing ID"
      ],
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        "id": "4N8_o3tG232h"
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      "source": [
        "## 1. Load Libraries & Data\n",
        "\n",
        "https://s3.amazonaws.com/ds1002-resources/data/ftx-2021-01-01--2023-12-31.csv\n",
        "\n",
        "Import any necessary libraries and load the remote CSV file below into a DataFrame. **Resolve any import issues you need to.**"
      ],
      "metadata": {
        "id": "w0VxTDNf1k2O"
      }
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      "cell_type": "code",
      "execution_count": 80,
      "metadata": {
        "id": "I0B9chFD1GR0"
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      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('https://s3.amazonaws.com/ds1002-resources/data/ftx-2021-01-01--2023-12-31.csv')\n",
        "df"
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              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 741,\n  \"fields\": [\n    {\n      \"column\": \"timeOpen;timeClose;timeHigh;timeLow;open;high;low;close;volume;marketCap;timestamp\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 727,\n        \"samples\": [\n          \"2021-08-04T00:00:00.000Z;\\\"2021-08-04T23:59:59.999Z\\\";\\\"2021-08-04T22:32:04.000Z\\\";\\\"2021-08-04T08:51:05.000Z\\\";35.95654525;38.1509959;34.44777827;37.74977039;75863474.17;3561575983.49;\\\"2021-08-04T23:59:59.999Z\\\"\",\n          \"2021-02-05T00:00:00.000Z;\\\"2021-02-05T23:59:59.999Z\\\";\\\"2021-02-05T23:59:03.000Z\\\";\\\"2021-02-05T00:00:03.000Z\\\";13.24616686;14.84458089;13.24616686;14.84458089;43477028.95;1400541042.98;\\\"2021-02-05T23:59:59.999Z\\\"\",\n          \"2021-12-18T00:00:00.000Z;\\\"2021-12-18T23:59:59.999Z\\\";\\\"2021-12-18T22:23:03.000Z\\\";\\\"2021-12-18T02:07:03.000Z\\\";37.5674349164;40.2846120528;37.3302258384;39.8977878501;110481935.78;5551079008.7;\\\"2021-12-18T23:59:59.999Z\\\"\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2. Get Summary Data"
      ],
      "metadata": {
        "id": "H389W9dkobGI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "In code, display how many rows and columns are in the raw dataset."
      ],
      "metadata": {
        "id": "KA0wbjmE-4Q9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rows = df.shape[0]\n",
        "print(\"There are\",rows, \"rows\")"
      ],
      "metadata": {
        "id": "XhULtu7qox-p",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9f27e8e8-2490-4810-ee9c-0a73aaeddb50"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "There are 741 rows\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cols = df.shape[1]\n",
        "print(\"There are\",cols, \"columns\")"
      ],
      "metadata": {
        "id": "3tOb4FIcR9k_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "aaa8145b-4b37-4b50-db19-3ae624e78fc2"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "There are 1 columns\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3. Clean and Organize the Data\n",
        "\n",
        "Check for data quality.\n",
        "\n",
        "- Resolve any empty values or duplicate rows.\n",
        "- Order the data properly if necessary.\n",
        "- Drop the following columns and save the dataframe: `timeOpen`, `timeClose`, `timeHigh`, `timeLow`, `marketCap`\n",
        "\n"
      ],
      "metadata": {
        "id": "abshX7QeooAs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "empty_values = df.isnull().sum()\n",
        "df = df.drop_duplicates()\n",
        "df = df.dropna()"
      ],
      "metadata": {
        "id": "Aq3J7yD82uwW"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#To TA: I kept getting a KeyError when trying to run this code.\n",
        "#I'll work on fixing the error and submit an updated notebook ASAP\n",
        "\n",
        "df = df.drop(['timeOpen', 'timeClose', 'timeHigh', 'timeLow', 'marketCap'], axis=1, inplace=True)\n",
        "df.to_csv('https://s3.amazonaws.com/ds1002-resources/data/ftx-2021-01-01--2023-12-31.csv')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 304
        },
        "id": "3a55SZmEWZ9i",
        "outputId": "92b9207d-2aa6-487a-8785-e81b7c7607f5"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyError",
          "evalue": "\"['timeOpen', 'timeClose', 'timeHigh', 'timeLow', 'marketCap'] not found in axis\"",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-57-3a784fcb8aba>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'timeOpen'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'timeClose'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'timeHigh'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'timeLow'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'marketCap'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'https://s3.amazonaws.com/ds1002-resources/data/ftx-2021-01-01--2023-12-31.csv'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/util/_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    329\u001b[0m                     \u001b[0mstacklevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfind_stack_level\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    330\u001b[0m                 )\n\u001b[0;32m--> 331\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    332\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    333\u001b[0m         \u001b[0;31m# error: \"Callable[[VarArg(Any), KwArg(Any)], Any]\" has no\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[1;32m   5397\u001b[0m                 \u001b[0mweight\u001b[0m  \u001b[0;36m1.0\u001b[0m     \u001b[0;36m0.8\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5398\u001b[0m         \"\"\"\n\u001b[0;32m-> 5399\u001b[0;31m         return super().drop(\n\u001b[0m\u001b[1;32m   5400\u001b[0m             \u001b[0mlabels\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5401\u001b[0m             \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/util/_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    329\u001b[0m                     \u001b[0mstacklevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfind_stack_level\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    330\u001b[0m                 )\n\u001b[0;32m--> 331\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    332\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    333\u001b[0m         \u001b[0;31m# error: \"Callable[[VarArg(Any), KwArg(Any)], Any]\" has no\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[1;32m   4503\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;32min\u001b[0m \u001b[0maxes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4504\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4505\u001b[0;31m                 \u001b[0mobj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_drop_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4506\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4507\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m_drop_axis\u001b[0;34m(self, labels, axis, level, errors, only_slice)\u001b[0m\n\u001b[1;32m   4544\u001b[0m                 \u001b[0mnew_axis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4545\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4546\u001b[0;31m                 \u001b[0mnew_axis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4547\u001b[0m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_axis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4548\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, errors)\u001b[0m\n\u001b[1;32m   6932\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mmask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0many\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6933\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0merrors\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m\"ignore\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 6934\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"{list(labels[mask])} not found in axis\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   6935\u001b[0m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m~\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6936\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdelete\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: \"['timeOpen', 'timeClose', 'timeHigh', 'timeLow', 'marketCap'] not found in axis\""
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "id": "V4XsS-itUPEd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "3c4b0f76-e28e-4cbe-b857-a3767835a66c"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    timeOpen;timeClose;timeHigh;timeLow;open;high;low;close;volume;marketCap;timestamp\n",
              "0    2022-12-31T00:00:00.000Z;\"2022-12-31T23:59:59....                                \n",
              "1    2022-12-30T00:00:00.000Z;\"2022-12-30T23:59:59....                                \n",
              "2    2022-12-29T00:00:00.000Z;\"2022-12-29T23:59:59....                                \n",
              "3    2022-12-28T00:00:00.000Z;\"2022-12-28T23:59:59....                                \n",
              "4    2022-12-27T00:00:00.000Z;\"2022-12-27T23:59:59....                                \n",
              "..                                                 ...                                \n",
              "735  2021-01-07T00:00:00.000Z;\"2021-01-07T23:59:59....                                \n",
              "736  2021-01-06T00:00:00.000Z;\"2021-01-06T23:59:59....                                \n",
              "737  2021-01-05T00:00:00.000Z;\"2021-01-05T23:59:59....                                \n",
              "738  2021-01-04T00:00:00.000Z;\"2021-01-04T23:59:59....                                \n",
              "739  2021-01-03T00:00:00.000Z;\"2021-01-03T23:59:59....                                \n",
              "\n",
              "[727 rows x 1 columns]"
            ],
            "text/html": [
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              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>timeOpen;timeClose;timeHigh;timeLow;open;high;low;close;volume;marketCap;timestamp</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2022-12-31T00:00:00.000Z;\"2022-12-31T23:59:59....</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2022-12-30T00:00:00.000Z;\"2022-12-30T23:59:59....</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2022-12-29T00:00:00.000Z;\"2022-12-29T23:59:59....</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2022-12-28T00:00:00.000Z;\"2022-12-28T23:59:59....</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2022-12-27T00:00:00.000Z;\"2022-12-27T23:59:59....</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>735</th>\n",
              "      <td>2021-01-07T00:00:00.000Z;\"2021-01-07T23:59:59....</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>736</th>\n",
              "      <td>2021-01-06T00:00:00.000Z;\"2021-01-06T23:59:59....</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>737</th>\n",
              "      <td>2021-01-05T00:00:00.000Z;\"2021-01-05T23:59:59....</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>738</th>\n",
              "      <td>2021-01-04T00:00:00.000Z;\"2021-01-04T23:59:59....</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>739</th>\n",
              "      <td>2021-01-03T00:00:00.000Z;\"2021-01-03T23:59:59....</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>727 rows × 1 columns</p>\n",
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              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "\n",
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              "        height: 32px;\n",
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              "      .colab-df-generate:hover {\n",
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              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 727,\n  \"fields\": [\n    {\n      \"column\": \"timeOpen;timeClose;timeHigh;timeLow;open;high;low;close;volume;marketCap;timestamp\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 727,\n        \"samples\": [\n          \"2021-08-04T00:00:00.000Z;\\\"2021-08-04T23:59:59.999Z\\\";\\\"2021-08-04T22:32:04.000Z\\\";\\\"2021-08-04T08:51:05.000Z\\\";35.95654525;38.1509959;34.44777827;37.74977039;75863474.17;3561575983.49;\\\"2021-08-04T23:59:59.999Z\\\"\",\n          \"2021-02-05T00:00:00.000Z;\\\"2021-02-05T23:59:59.999Z\\\";\\\"2021-02-05T23:59:03.000Z\\\";\\\"2021-02-05T00:00:03.000Z\\\";13.24616686;14.84458089;13.24616686;14.84458089;43477028.95;1400541042.98;\\\"2021-02-05T23:59:59.999Z\\\"\",\n          \"2021-12-18T00:00:00.000Z;\\\"2021-12-18T23:59:59.999Z\\\";\\\"2021-12-18T22:23:03.000Z\\\";\\\"2021-12-18T02:07:03.000Z\\\";37.5674349164;40.2846120528;37.3302258384;39.8977878501;110481935.78;5551079008.7;\\\"2021-12-18T23:59:59.999Z\\\"\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now display the first 10 rows of the cleaned dataset."
      ],
      "metadata": {
        "id": "Ccwu8bUx_Azj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.head(11)"
      ],
      "metadata": {
        "id": "1iVIskCs_Ewe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "outputId": "08f3c137-d0ff-443a-8cc9-635118dac858"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   timeOpen;timeClose;timeHigh;timeLow;open;high;low;close;volume;marketCap;timestamp\n",
              "0   2022-12-31T00:00:00.000Z;\"2022-12-31T23:59:59....                                \n",
              "1   2022-12-30T00:00:00.000Z;\"2022-12-30T23:59:59....                                \n",
              "2   2022-12-29T00:00:00.000Z;\"2022-12-29T23:59:59....                                \n",
              "3   2022-12-28T00:00:00.000Z;\"2022-12-28T23:59:59....                                \n",
              "4   2022-12-27T00:00:00.000Z;\"2022-12-27T23:59:59....                                \n",
              "5   2022-12-26T00:00:00.000Z;\"2022-12-26T23:59:59....                                \n",
              "6   2022-12-25T00:00:00.000Z;\"2022-12-25T23:59:59....                                \n",
              "7   2022-12-24T00:00:00.000Z;\"2022-12-24T23:59:59....                                \n",
              "8   2022-12-23T00:00:00.000Z;\"2022-12-23T23:59:59....                                \n",
              "9   2022-12-22T00:00:00.000Z;\"2022-12-22T23:59:59....                                \n",
              "10  2022-12-21T00:00:00.000Z;\"2022-12-21T23:59:59....                                "
            ],
            "text/html": [
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              "      <td>2022-12-29T00:00:00.000Z;\"2022-12-29T23:59:59....</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2022-12-28T00:00:00.000Z;\"2022-12-28T23:59:59....</td>\n",
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              "      <td>2022-12-26T00:00:00.000Z;\"2022-12-26T23:59:59....</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2022-12-25T00:00:00.000Z;\"2022-12-25T23:59:59....</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2022-12-24T00:00:00.000Z;\"2022-12-24T23:59:59....</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>2022-12-21T00:00:00.000Z;\"2022-12-21T23:59:59....</td>\n",
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              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
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              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
              "      let quickchartButtonEl =\n",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
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              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 727,\n  \"fields\": [\n    {\n      \"column\": \"timeOpen;timeClose;timeHigh;timeLow;open;high;low;close;volume;marketCap;timestamp\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 727,\n        \"samples\": [\n          \"2021-08-04T00:00:00.000Z;\\\"2021-08-04T23:59:59.999Z\\\";\\\"2021-08-04T22:32:04.000Z\\\";\\\"2021-08-04T08:51:05.000Z\\\";35.95654525;38.1509959;34.44777827;37.74977039;75863474.17;3561575983.49;\\\"2021-08-04T23:59:59.999Z\\\"\",\n          \"2021-02-05T00:00:00.000Z;\\\"2021-02-05T23:59:59.999Z\\\";\\\"2021-02-05T23:59:03.000Z\\\";\\\"2021-02-05T00:00:03.000Z\\\";13.24616686;14.84458089;13.24616686;14.84458089;43477028.95;1400541042.98;\\\"2021-02-05T23:59:59.999Z\\\"\",\n          \"2021-12-18T00:00:00.000Z;\\\"2021-12-18T23:59:59.999Z\\\";\\\"2021-12-18T22:23:03.000Z\\\";\\\"2021-12-18T02:07:03.000Z\\\";37.5674349164;40.2846120528;37.3302258384;39.8977878501;110481935.78;5551079008.7;\\\"2021-12-18T23:59:59.999Z\\\"\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 4. Calculate Daily Gains/Losses"
      ],
      "metadata": {
        "id": "3-4dd8nsr_zF"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Using Pandas, add a new column named `[\"close_change\"]` to the dataframe that calculates the difference between daily closing prices for FTX. This will result in positive (growth) and negative (loss) values.\n",
        "\n",
        "Note:\n",
        "- Be careful about how you calculate this. Each row should calculate that difference from the row above it, which must be the previous calendar day.\n",
        "- Hint: This is not complicated and does not require a for-loop.  See the Pandas review notebook for reference."
      ],
      "metadata": {
        "id": "HrGLlM83vH0c"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#To TA: I kept getting a KeyError when trying to run this code.\n",
        "#I'll work on fixing the error and submit an updated notebook ASAP\n",
        "\n",
        "df['close_change'] = df['close'].diff()\n",
        "df"
      ],
      "metadata": {
        "id": "7P8J0jEtvCiv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 478
        },
        "outputId": "5b28d104-a741-443c-e9d0-d3024238e7ed"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyError",
          "evalue": "'close'",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3801\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3802\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3803\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 'close'",
            "\nThe above exception was the direct cause of the following exception:\n",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-66-8ad3216ed7e8>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'close_change'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'close'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdiff\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3805\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3806\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3807\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3808\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3809\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3802\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3803\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3804\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3805\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3806\u001b[0m                 \u001b[0;31m# If we have a listlike key, _check_indexing_error will raise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 'close'"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 5. Identify Growth Days"
      ],
      "metadata": {
        "id": "KKg8d9RDyeDa"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Based on the daily change in closing prices calculated above, identify the top 10 days of positive growth for FTX.\n",
        "\n",
        "Display your results in a table showing both the rate of change and their dates."
      ],
      "metadata": {
        "id": "5kKBo3_sykGD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#To TA: I kept getting a KeyError when trying to run this code.\n",
        "#I'll work on fixing the error and submit an updated notebook ASAP\n",
        "\n",
        "df['growth'] = df['close'].diff()\n",
        "top_10_days = df.sort_values(by='growth', ascending=False).head(10)\n",
        "top_10_days = top_10_days['date', 'growth']\n",
        "print(top_10_days.to_string(index=False))"
      ],
      "metadata": {
        "id": "xRlliRMY2_-c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 478
        },
        "outputId": "41fc7ef2-f6b0-4761-9576-93aa08cd6f8f"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyError",
          "evalue": "'close'",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3801\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3802\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3803\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 'close'",
            "\nThe above exception was the direct cause of the following exception:\n",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-69-b248e669dc54>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'growth'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'close'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdiff\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mtop_10_days\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msort_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mby\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'growth'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mascending\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mtop_10_days\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtop_10_days\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'date'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'growth'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtop_10_days\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_string\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3805\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3806\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3807\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3808\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3809\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3802\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3803\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3804\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3805\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3806\u001b[0m                 \u001b[0;31m# If we have a listlike key, _check_indexing_error will raise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 'close'"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 6. Calculate the Percentage Rate of Change"
      ],
      "metadata": {
        "id": "Y3AmR3eDxgfb"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Next, create a new column named `[\"rate_of_change\"]` based on `[\"close_change\"]`.\n",
        "\n",
        "Use the Pandas Review notebook for reference. Again, this is an easy operation in Pandas and does not require a for-loop."
      ],
      "metadata": {
        "id": "XK3yweENxjdn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#To TA: I kept getting a KeyError when trying to run this code.\n",
        "#I'll work on fixing the error and submit an updated notebook ASAP\n",
        "\n",
        "df[\"rate_of_change\"] = df[\"close_change\"].pct_change()\n",
        "df"
      ],
      "metadata": {
        "id": "FqL5yyvi6w7r",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 478
        },
        "outputId": "1409ecfc-7606-4d32-90fa-8e5f34be6ca7"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyError",
          "evalue": "'close_change'",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3801\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3802\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3803\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 'close_change'",
            "\nThe above exception was the direct cause of the following exception:\n",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-70-a333f142bd56>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"rate_of_change\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"close_change\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpct_change\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3805\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3806\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3807\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3808\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3809\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3802\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3803\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3804\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3805\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3806\u001b[0m                 \u001b[0;31m# If we have a listlike key, _check_indexing_error will raise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 'close_change'"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 7. Identify Days of Greatest Percentage Change"
      ],
      "metadata": {
        "id": "2ifRW8qU3OOB"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Based on the rate change column calculated above, identify the top 5 days of greatest positive rate change, and the top 5 days of greatest negative rate change.\n",
        "\n",
        "Display your results in a table showing both the rate of change and their dates."
      ],
      "metadata": {
        "id": "ISZf2C1j3bAA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#To TA: I kept getting a KeyError when trying to run this code.\n",
        "#I'll work on fixing the error and submit an updated notebook ASAP\n",
        "\n",
        "top_5_days_of_positive_rate_change = df.sort_values(by=\"rate_of_change\", ascending=False).head(5)\n",
        "top_5_days_of_negative_rate_change = df.sort_values(by=\"rate_of_change\").head(5)\n",
        "\n",
        "top_5_days_of_positive_rate_change = top_5_days_of_positive_rate_change[[\"date\", \"rate_of_change\"]]\n",
        "top_5_days_of_negative_rate_change = top_5_days_of_negative_rate_change[[\"date\", \"rate_of_change\"]]\n",
        "\n",
        "print(\"The top 5 days of greatest positive rate change are:\")\n",
        "print(top_5_days_of_positive_rate_change.to_string(index=False))\n",
        "\n",
        "print(\"The top 5 days of greatest negative rate change are:\")\n",
        "print(top_5_days_of_negative_rate_change.to_string(index=False))"
      ],
      "metadata": {
        "id": "sGlUUWFxfS2w",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 356
        },
        "outputId": "08cfaf62-1b0b-4044-c81b-cff21ea9ca98"
      },
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyError",
          "evalue": "'rate_of_change'",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-79-b1945a81753e>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtop_5_days_of_positive_rate_change\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msort_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mby\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"rate_of_change\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mascending\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mtop_5_days_of_negative_rate_change\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msort_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mby\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"rate_of_change\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mtop_5_days_of_positive_rate_change\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtop_5_days_of_positive_rate_change\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"date\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"rate_of_change\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mtop_5_days_of_negative_rate_change\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtop_5_days_of_negative_rate_change\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"date\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"rate_of_change\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/util/_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    329\u001b[0m                     \u001b[0mstacklevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfind_stack_level\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    330\u001b[0m                 )\n\u001b[0;32m--> 331\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    332\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    333\u001b[0m         \u001b[0;31m# error: \"Callable[[VarArg(Any), KwArg(Any)], Any]\" has no\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36msort_values\u001b[0;34m(self, by, axis, ascending, inplace, kind, na_position, ignore_index, key)\u001b[0m\n\u001b[1;32m   6910\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6911\u001b[0m             \u001b[0mby\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mby\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 6912\u001b[0;31m             \u001b[0mk\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_label_or_level_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mby\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   6913\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6914\u001b[0m             \u001b[0;31m# need to rewrap column in Series to apply key function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m_get_label_or_level_values\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m   1848\u001b[0m             )\n\u001b[1;32m   1849\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1850\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1851\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1852\u001b[0m         \u001b[0;31m# Check for duplicates\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 'rate_of_change'"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 8. Plot the Data"
      ],
      "metadata": {
        "id": "2B9dxJbno0s0"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Create two separate plots of the data -- one for the daily closing price and one for the daily gains/losses. These can be histograms or line plots."
      ],
      "metadata": {
        "id": "LwMrL6-AplCH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#To TA: I kept getting a KeyError when trying to run this code.\n",
        "#I'll work on fixing the error and submit an updated notebook ASAP\n",
        "\n",
        "plt.plot(df['date'], df[\"close\"])\n",
        "plt.xlabel(\"Date\")\n",
        "plt.ylabel(\"Closing Price\")\n",
        "plt.title(\"Daily Closing Price Line Plot\")"
      ],
      "metadata": {
        "id": "R0vq1cLRB0Kl",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 478
        },
        "outputId": "51438545-8c51-41f2-81db-62ca94cad770"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyError",
          "evalue": "'close'",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3801\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3802\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3803\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 'close'",
            "\nThe above exception was the direct cause of the following exception:\n",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-73-2bedc00717fc>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"close\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Date\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mylabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Closing Price\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Daily Closing Price Line Plot\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3805\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3806\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3807\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3808\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3809\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3802\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3803\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3804\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3805\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3806\u001b[0m                 \u001b[0;31m# If we have a listlike key, _check_indexing_error will raise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 'close'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#To TA: I kept getting a KeyError when trying to run this code.\n",
        "#I'll work on fixing the error and submit an updated notebook ASAP\n",
        "\n",
        "plt.plot(df['date'], df[\"daily_gains_losses\"])\n",
        "plt.xlabel(\"Date\")\n",
        "plt.ylabel(\"Price\")\n",
        "plt.title(\"Daily gains/losses Line Plot\")"
      ],
      "metadata": {
        "id": "w0EYazKzBwnN",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 478
        },
        "outputId": "e863436f-07cb-4491-a79d-d7c6f64ea00d"
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyError",
          "evalue": "'daily_gains_losses'",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3801\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3802\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3803\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 'daily_gains_losses'",
            "\nThe above exception was the direct cause of the following exception:\n",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-74-9de428ea265e>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"daily_gains_losses\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Date\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mylabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Price\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Daily gains/losses Line Plot\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlegend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3805\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3806\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3807\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3808\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3809\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3802\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3803\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3804\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3805\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3806\u001b[0m                 \u001b[0;31m# If we have a listlike key, _check_indexing_error will raise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 'daily_gains_losses'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.show()"
      ],
      "metadata": {
        "id": "B5LykbgafVf9"
      },
      "execution_count": 75,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Combine the two plots from above into a single plot, displaying both together. Your plot should have two Y-axis scales, should differentiate the two values by color, and should include a legend."
      ],
      "metadata": {
        "id": "xRT2iFAI5s4S"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#To TA: I kept getting a KeyError when trying to run this code.\n",
        "#I'll work on fixing the error and submit an updated notebook ASAP\n",
        "\n",
        "fig, ax1 = plt.subplots(figsize=(10, 6))\n",
        "\n",
        "ax1.plot(df['date'], df[\"close\"], color=\"blue\", label=\"Daily Closing Price\")\n",
        "ax1.set_ylabel(\"Price\", color=\"blue\")\n",
        "\n",
        "ax2 = ax1.twinx()\n",
        "ax2.plot(df[\"date\"], df[\"daily_gains_losses\"], color=\"green\", label=\"Daily Gains/Losses\")\n",
        "ax2.set_ylabel(\"Daily Gains/Losses\", color=\"green\")\n",
        "\n",
        "ax1.set_xlabel(\"Date\")\n",
        "plt.legend(loc=\"upper left\")\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "epypcCLc6QxL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 989
        },
        "outputId": "7f323455-e276-4eb0-f1e5-51f3c98f41f4"
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyError",
          "evalue": "'close'",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3801\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3802\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3803\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 'close'",
            "\nThe above exception was the direct cause of the following exception:\n",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-78-788c7fa9f214>\u001b[0m in \u001b[0;36m<cell line: 3>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m6\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0max1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"close\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"blue\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"Daily Closing Price\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0max1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_ylabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Price\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"blue\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3805\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3806\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3807\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3808\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3809\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3802\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3803\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3804\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3805\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3806\u001b[0m                 \u001b[0;31m# If we have a listlike key, _check_indexing_error will raise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 'close'"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}