NasdaqDataLink.ipynb

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      "name": "NasdaqDataLink.ipynb",
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        "<a href=\"https://colab.research.google.com/github/Erickrus/leetcode/blob/master/NasdaqDataLink.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "cell_type": "markdown",
      "source": [
        "# Nasdaq Data Link\n",
        "\n",
        "## Installation\n",
        "\n",
        "install the package with pip3"
      ],
      "metadata": {
        "id": "c-n5L7xgU1vx"
      }
    },
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      "execution_count": 2,
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          "text": [
            "Collecting nasdaq-data-link\n",
            "  Downloading Nasdaq_Data_Link-1.0.0-py2.py3-none-any.whl (27 kB)\n",
            "Requirement already satisfied: numpy>=1.8 in /usr/local/lib/python3.7/dist-packages (from nasdaq-data-link) (1.19.5)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from nasdaq-data-link) (1.15.0)\n",
            "Requirement already satisfied: requests>=2.7.0 in /usr/local/lib/python3.7/dist-packages (from nasdaq-data-link) (2.23.0)\n",
            "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.7/dist-packages (from nasdaq-data-link) (2.8.2)\n",
            "Requirement already satisfied: pandas>=0.14 in /usr/local/lib/python3.7/dist-packages (from nasdaq-data-link) (1.1.5)\n",
            "Requirement already satisfied: inflection>=0.3.1 in /usr/local/lib/python3.7/dist-packages (from nasdaq-data-link) (0.5.1)\n",
            "Requirement already satisfied: more-itertools in /usr/local/lib/python3.7/dist-packages (from nasdaq-data-link) (8.12.0)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.14->nasdaq-data-link) (2018.9)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests>=2.7.0->nasdaq-data-link) (2.10)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests>=2.7.0->nasdaq-data-link) (3.0.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests>=2.7.0->nasdaq-data-link) (2021.10.8)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests>=2.7.0->nasdaq-data-link) (1.24.3)\n",
            "Installing collected packages: nasdaq-data-link\n",
            "Successfully installed nasdaq-data-link-1.0.0\n"
          ]
        }
      ],
      "source": [
        "!pip3 install nasdaq-data-link"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Imports\n",
        "import all necessary packages"
      ],
      "metadata": {
        "id": "6uK30oITU7yH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import nasdaqdatalink\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "%matplotlib inline"
      ],
      "metadata": {
        "id": "ZYnwjPMbI2xF"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Extract data\n",
        "please update the authtoken of your own account"
      ],
      "metadata": {
        "id": "nZVnQgCqVAlQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data = nasdaqdatalink.get(\"ECONOMIST/BIGMAC_USA\", authtoken=\"put your own authtoken\")"
      ],
      "metadata": {
        "id": "cE175YcQHyJy"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Explore data"
      ],
      "metadata": {
        "id": "EZ39vfsEVGVl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.head()"
      ],
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        "id": "-C3BpzPdJKIf",
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      "execution_count": 9,
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>local_price</th>\n",
              "      <th>dollar_ex</th>\n",
              "      <th>dollar_price</th>\n",
              "      <th>dollar_ppp</th>\n",
              "      <th>dollar_valuation</th>\n",
              "      <th>dollar_adj_valuation</th>\n",
              "      <th>euro_adj_valuation</th>\n",
              "      <th>sterling_adj_valuation</th>\n",
              "      <th>yen_adj_valuation</th>\n",
              "      <th>yuan_adj_valuation</th>\n",
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              "    <tr>\n",
              "      <th>Date</th>\n",
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              "      <th>2000-04-30</th>\n",
              "      <td>2.51</td>\n",
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              "      <th>2001-04-30</th>\n",
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              "      <th>2002-04-30</th>\n",
              "      <td>2.49</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.49</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <th>2003-04-30</th>\n",
              "      <td>2.71</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.71</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>2004-05-31</th>\n",
              "      <td>2.90</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.90</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>NaN</td>\n",
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            "text/plain": [
              "            local_price  dollar_ex  ...  yen_adj_valuation  yuan_adj_valuation\n",
              "Date                                ...                                       \n",
              "2000-04-30         2.51        1.0  ...                NaN                 NaN\n",
              "2001-04-30         2.54        1.0  ...                NaN                 NaN\n",
              "2002-04-30         2.49        1.0  ...                NaN                 NaN\n",
              "2003-04-30         2.71        1.0  ...                NaN                 NaN\n",
              "2004-05-31         2.90        1.0  ...                NaN                 NaN\n",
              "\n",
              "[5 rows x 10 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 9
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    },
    {
      "cell_type": "markdown",
      "source": [
        "## Plot data\n",
        "plot data and adjust format"
      ],
      "metadata": {
        "id": "nBgBJ6QJVKcI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# https://www.byteacademy.co/blog/time-series-python\n",
        "data[\"dollar_price\"].plot(color='orange')\n",
        "plt.ylim(0., 8.)\n",
        "plt.ylabel('Dollars')\n",
        "plt.xlabel('Years')\n",
        "plt.title('US: Big Mac Index')\n",
        "\n",
        "plt.show()"
      ],
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          "height": 285
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        "outputId": "c92a72cf-2e61-46c3-89ec-ef30ead09a62"
      },
      "execution_count": 73,
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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}