Data_Visualization_of_US_Stocks.ipynb

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      "name": "Data Visualization of US Stocks",
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        "<a href=\"https://colab.research.google.com/github/nttran8/Data-Vis/blob/master/Data_Visualization_of_US_Stocks.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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          "height": 34
        },
        "outputId": "7a08319b-16f9-4148-be14-c2176879fad8"
      },
      "source": [
        "import numpy as np #Numpy provides fast math function processing\n",
        "import pandas as pd #pandas is used for easy indexing and data collection tools\n",
        "import matplotlib as mpl #machine learning libraries\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Date', 'Open', 'High', 'Low', 'Close', 'Volume'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
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      },
      "source": [
        "Jobs=pd.read_csv(\"https://raw.githubusercontent.com/nttran8/Data-Vis/master/BAC.csv\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
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        "outputId": "10023c4e-b068-42b5-96a0-1dce42edef0e"
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      "source": [
        "Jobs.head()"
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      "execution_count": 8,
      "outputs": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Volume</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2006-01-03</td>\n",
              "      <td>46.92</td>\n",
              "      <td>47.18</td>\n",
              "      <td>46.15</td>\n",
              "      <td>47.08</td>\n",
              "      <td>16296700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2006-01-04</td>\n",
              "      <td>47.00</td>\n",
              "      <td>47.24</td>\n",
              "      <td>46.45</td>\n",
              "      <td>46.58</td>\n",
              "      <td>17757900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2006-01-05</td>\n",
              "      <td>46.58</td>\n",
              "      <td>46.83</td>\n",
              "      <td>46.32</td>\n",
              "      <td>46.64</td>\n",
              "      <td>14970900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2006-01-06</td>\n",
              "      <td>46.80</td>\n",
              "      <td>46.91</td>\n",
              "      <td>46.35</td>\n",
              "      <td>46.57</td>\n",
              "      <td>12599800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2006-01-09</td>\n",
              "      <td>46.72</td>\n",
              "      <td>46.97</td>\n",
              "      <td>46.36</td>\n",
              "      <td>46.60</td>\n",
              "      <td>15620000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            "text/plain": [
              "         Date   Open   High    Low  Close    Volume\n",
              "0  2006-01-03  46.92  47.18  46.15  47.08  16296700\n",
              "1  2006-01-04  47.00  47.24  46.45  46.58  17757900\n",
              "2  2006-01-05  46.58  46.83  46.32  46.64  14970900\n",
              "3  2006-01-06  46.80  46.91  46.35  46.57  12599800\n",
              "4  2006-01-09  46.72  46.97  46.36  46.60  15620000"
            ]
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        },
        "outputId": "9e14c1f2-43a9-4258-f14c-e63eb2606cfa"
      },
      "source": [
        "#Gives a list of columns\n",
        "Jobs.columns.values"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Date', 'Open', 'High', 'Low', 'Close', 'Volume'], dtype=object)"
            ]
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            "tags": []
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          "execution_count": 9
        }
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      "source": [
        ""
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      "outputs": []
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          "height": 34
        },
        "outputId": "dcc02f47-747c-46a0-cbce-e12815f24a95"
      },
      "source": [
        "Jobs.shape"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2517, 6)"
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      "source": [
        "Jobs"
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      "execution_count": 11,
      "outputs": [
        {
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          "data": {
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Volume</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2006-01-03</td>\n",
              "      <td>46.92</td>\n",
              "      <td>47.18</td>\n",
              "      <td>46.15</td>\n",
              "      <td>47.08</td>\n",
              "      <td>16296700</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2006-01-04</td>\n",
              "      <td>47.00</td>\n",
              "      <td>47.24</td>\n",
              "      <td>46.45</td>\n",
              "      <td>46.58</td>\n",
              "      <td>17757900</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2006-01-05</td>\n",
              "      <td>46.58</td>\n",
              "      <td>46.83</td>\n",
              "      <td>46.32</td>\n",
              "      <td>46.64</td>\n",
              "      <td>14970900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2006-01-06</td>\n",
              "      <td>46.80</td>\n",
              "      <td>46.91</td>\n",
              "      <td>46.35</td>\n",
              "      <td>46.57</td>\n",
              "      <td>12599800</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2006-01-09</td>\n",
              "      <td>46.72</td>\n",
              "      <td>46.97</td>\n",
              "      <td>46.36</td>\n",
              "      <td>46.60</td>\n",
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              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>2512</th>\n",
              "      <td>2015-12-24</td>\n",
              "      <td>17.32</td>\n",
              "      <td>17.38</td>\n",
              "      <td>17.22</td>\n",
              "      <td>17.27</td>\n",
              "      <td>29373415</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2513</th>\n",
              "      <td>2015-12-28</td>\n",
              "      <td>17.22</td>\n",
              "      <td>17.23</td>\n",
              "      <td>16.98</td>\n",
              "      <td>17.13</td>\n",
              "      <td>41777497</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2514</th>\n",
              "      <td>2015-12-29</td>\n",
              "      <td>17.25</td>\n",
              "      <td>17.35</td>\n",
              "      <td>17.16</td>\n",
              "      <td>17.28</td>\n",
              "      <td>45670376</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2515</th>\n",
              "      <td>2015-12-30</td>\n",
              "      <td>17.20</td>\n",
              "      <td>17.24</td>\n",
              "      <td>17.04</td>\n",
              "      <td>17.05</td>\n",
              "      <td>35066378</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2516</th>\n",
              "      <td>2015-12-31</td>\n",
              "      <td>17.01</td>\n",
              "      <td>17.07</td>\n",
              "      <td>16.83</td>\n",
              "      <td>16.83</td>\n",
              "      <td>47152968</td>\n",
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              "<p>2517 rows × 6 columns</p>\n",
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            "text/plain": [
              "            Date   Open   High    Low  Close    Volume\n",
              "0     2006-01-03  46.92  47.18  46.15  47.08  16296700\n",
              "1     2006-01-04  47.00  47.24  46.45  46.58  17757900\n",
              "2     2006-01-05  46.58  46.83  46.32  46.64  14970900\n",
              "3     2006-01-06  46.80  46.91  46.35  46.57  12599800\n",
              "4     2006-01-09  46.72  46.97  46.36  46.60  15620000\n",
              "...          ...    ...    ...    ...    ...       ...\n",
              "2512  2015-12-24  17.32  17.38  17.22  17.27  29373415\n",
              "2513  2015-12-28  17.22  17.23  16.98  17.13  41777497\n",
              "2514  2015-12-29  17.25  17.35  17.16  17.28  45670376\n",
              "2515  2015-12-30  17.20  17.24  17.04  17.05  35066378\n",
              "2516  2015-12-31  17.01  17.07  16.83  16.83  47152968\n",
              "\n",
              "[2517 rows x 6 columns]"
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          "execution_count": 11
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          "height": 34
        },
        "outputId": "12971eb5-53c8-4079-dc56-82cc57783552"
      },
      "source": [
        "Jobs.plot"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<pandas.plotting._core.PlotAccessor object at 0x7f5d95c2e6a0>"
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          "execution_count": 14
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      "cell_type": "code",
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        "id": "KkOeyNEGeR50",
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          "height": 312
        },
        "outputId": "082661b1-93bc-480c-f988-566b02b34aa5"
      },
      "source": [
        "#Rename Dataframe to not overwrite original\n",
        "#Execute Code\n",
        "#Pick 2 columns\n",
        "Jobs2 =Jobs.plot.scatter(x='Open',y='Volume')\n",
        "\n",
        "plt.title('Scatter Plot')\n",
        "plt.xlabel('Opening Price')\n",
        "plt.ylabel('Volume of shares')\n",
        "#Change the size of the graph\n",
        "plt.rcParams['figure.figsize']={30,4}\n",
        "plt.show"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<function matplotlib.pyplot.show>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        },
        {
          "output_type": "display_data",
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9/EF17t6vZXOma8GxU4f9evUwlNbFqEBHUuh9W9edPqRqMnYXAwAAAIDxqZbw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            "text/plain": [
              "<Figure size 1440x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vX6bzBfOfs3H",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#A large concentration of lower opening prices stocks are purchased\n",
        "#We also see about 20 stocks with low opening prices are puchased with over 7 shares"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}