Example2.ipynb

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  "metadata": {
    "colab": {
      "name": "Example.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyNvOvxmMF08QZx1FIteWBGe",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/CashStory/training-datamining/blob/master/Example2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Jm4V5UZMvn8K",
        "colab_type": "text"
      },
      "source": [
        "<img src=\"https://cashstory.com/_nuxt/img/8259e1b.png\" alt=\"drawing\" width=\"200\" align='left'/>\n",
        "<br>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g-8m38fV3K3l",
        "colab_type": "text"
      },
      "source": [
        "**Example 1 : get stock data from Quandl (data agregator)**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IutcWsSg3RFr",
        "colab_type": "text"
      },
      "source": [
        "**Step 1** : Import librairies"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x_pOunb4v4-o",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import os "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xD97nhr13Uxm",
        "colab_type": "text"
      },
      "source": [
        "**Step 2** : Read CSV with pandas"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cOZm-UoFv-y6",
        "colab_type": "code",
        "outputId": "3c83d18e-4732-48a0-9f4f-96e0f7135ec7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 415
        }
      },
      "source": [
        "data = pd.read_csv('https://www.quandl.com/api/v3/datasets/BITFINEX/BTCUSD.csv?api_key=bxrSXWimkiknuCcV71uL')\n",
        "data"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Mid</th>\n",
              "      <th>Last</th>\n",
              "      <th>Bid</th>\n",
              "      <th>Ask</th>\n",
              "      <th>Volume</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2020-04-12</td>\n",
              "      <td>7192.5000</td>\n",
              "      <td>6797.600000</td>\n",
              "      <td>7016.30000</td>\n",
              "      <td>7015.786405</td>\n",
              "      <td>7015.7000</td>\n",
              "      <td>7016.90</td>\n",
              "      <td>8487.859686</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2020-04-11</td>\n",
              "      <td>6957.8000</td>\n",
              "      <td>6780.000000</td>\n",
              "      <td>6857.65000</td>\n",
              "      <td>6858.300000</td>\n",
              "      <td>6857.3000</td>\n",
              "      <td>6858.00</td>\n",
              "      <td>4034.223033</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2020-04-10</td>\n",
              "      <td>7320.8000</td>\n",
              "      <td>6756.300000</td>\n",
              "      <td>6836.85000</td>\n",
              "      <td>6836.900000</td>\n",
              "      <td>6836.8000</td>\n",
              "      <td>6836.90</td>\n",
              "      <td>15789.162262</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2020-04-09</td>\n",
              "      <td>7390.2000</td>\n",
              "      <td>7117.000000</td>\n",
              "      <td>7312.75000</td>\n",
              "      <td>7312.700000</td>\n",
              "      <td>7312.7000</td>\n",
              "      <td>7312.80</td>\n",
              "      <td>7743.279454</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2020-04-08</td>\n",
              "      <td>7420.0000</td>\n",
              "      <td>7151.644912</td>\n",
              "      <td>7354.05000</td>\n",
              "      <td>7354.100000</td>\n",
              "      <td>7354.0000</td>\n",
              "      <td>7354.10</td>\n",
              "      <td>8050.965012</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2139</th>\n",
              "      <td>2014-04-19</td>\n",
              "      <td>513.9899</td>\n",
              "      <td>473.830000</td>\n",
              "      <td>505.01065</td>\n",
              "      <td>507.499900</td>\n",
              "      <td>502.5313</td>\n",
              "      <td>507.49</td>\n",
              "      <td>8963.618369</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2140</th>\n",
              "      <td>2014-04-18</td>\n",
              "      <td>509.0000</td>\n",
              "      <td>474.250000</td>\n",
              "      <td>483.77000</td>\n",
              "      <td>482.750000</td>\n",
              "      <td>482.7500</td>\n",
              "      <td>484.79</td>\n",
              "      <td>10458.045243</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2141</th>\n",
              "      <td>2014-04-17</td>\n",
              "      <td>538.5000</td>\n",
              "      <td>486.100000</td>\n",
              "      <td>507.02000</td>\n",
              "      <td>508.000000</td>\n",
              "      <td>506.0400</td>\n",
              "      <td>508.00</td>\n",
              "      <td>20709.783819</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2142</th>\n",
              "      <td>2014-04-16</td>\n",
              "      <td>547.0000</td>\n",
              "      <td>495.000000</td>\n",
              "      <td>537.50000</td>\n",
              "      <td>538.000000</td>\n",
              "      <td>537.0000</td>\n",
              "      <td>538.00</td>\n",
              "      <td>29633.358705</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2143</th>\n",
              "      <td>2014-04-15</td>\n",
              "      <td>513.9000</td>\n",
              "      <td>452.000000</td>\n",
              "      <td>504.23500</td>\n",
              "      <td>505.000000</td>\n",
              "      <td>503.5000</td>\n",
              "      <td>504.97</td>\n",
              "      <td>21013.584774</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2144 rows × 8 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            Date       High          Low  ...        Bid      Ask        Volume\n",
              "0     2020-04-12  7192.5000  6797.600000  ...  7015.7000  7016.90   8487.859686\n",
              "1     2020-04-11  6957.8000  6780.000000  ...  6857.3000  6858.00   4034.223033\n",
              "2     2020-04-10  7320.8000  6756.300000  ...  6836.8000  6836.90  15789.162262\n",
              "3     2020-04-09  7390.2000  7117.000000  ...  7312.7000  7312.80   7743.279454\n",
              "4     2020-04-08  7420.0000  7151.644912  ...  7354.0000  7354.10   8050.965012\n",
              "...          ...        ...          ...  ...        ...      ...           ...\n",
              "2139  2014-04-19   513.9899   473.830000  ...   502.5313   507.49   8963.618369\n",
              "2140  2014-04-18   509.0000   474.250000  ...   482.7500   484.79  10458.045243\n",
              "2141  2014-04-17   538.5000   486.100000  ...   506.0400   508.00  20709.783819\n",
              "2142  2014-04-16   547.0000   495.000000  ...   537.0000   538.00  29633.358705\n",
              "2143  2014-04-15   513.9000   452.000000  ...   503.5000   504.97  21013.584774\n",
              "\n",
              "[2144 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Bqt9_5OK3Yyl",
        "colab_type": "text"
      },
      "source": [
        "**Step 3** : Import librairy to visualize & plot chart"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jolb4oNv2ucC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BGwMqP052zqE",
        "colab_type": "code",
        "outputId": "47dc8d9c-e789-4666-8d60-ab6a1c85b661",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        }
      },
      "source": [
        "data.plot()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f174dfeac18>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}