StockMarketWebStatics.ipynb

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  "metadata": {
    "colab": {
      "name": "StockMarketWebStatics.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyPby93ILoJrSygFtyXHGJlP",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/PN1019/StockExchanges/blob/main/StockMarketWebStatics.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kAR3c2YZqmvr"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "%matplotlib inline\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import pandas_datareader as DataReader\n",
        "from datetime import date\n",
        "import pandas_datareader.data as web\n",
        "import matplotlib.figure as figure\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oQND9a2ar64d",
        "outputId": "4aa18b93-459a-4674-b3e6-67ed3cec5904"
      },
      "source": [
        " pip install plotly"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: plotly in /usr/local/lib/python3.7/dist-packages (4.4.1)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from plotly) (1.15.0)\n",
            "Requirement already satisfied: retrying>=1.3.3 in /usr/local/lib/python3.7/dist-packages (from plotly) (1.3.3)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RgyUlO8Sr61q",
        "outputId": "7a354527-47c9-4f25-94ce-c3a0f23bbaa1"
      },
      "source": [
        " pip install cufflinks"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 16
        },
        "id": "PRIEHkf6smt7",
        "outputId": "bc44805b-3f07-40ab-bed0-7f4c4f646e08"
      },
      "source": [
        "import cufflinks as cf\n",
        "import plotly.graph_objs as go\n",
        "import plotly as py #plotly\n",
        "from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot\n",
        "cf.go_offline()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "        <script type=\"text/javascript\">\n",
              "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
              "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
              "        if (typeof require !== 'undefined') {\n",
              "        require.undef(\"plotly\");\n",
              "        requirejs.config({\n",
              "            paths: {\n",
              "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
              "            }\n",
              "        });\n",
              "        require(['plotly'], function(Plotly) {\n",
              "            window._Plotly = Plotly;\n",
              "        });\n",
              "        }\n",
              "        </script>\n",
              "        "
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 16
        },
        "id": "LN-TciLGsmsB",
        "outputId": "e5930138-708a-4d22-acb1-15144eb563d9"
      },
      "source": [
        "    init_notebook_mode(connected=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "        <script type=\"text/javascript\">\n",
              "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
              "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
              "        if (typeof require !== 'undefined') {\n",
              "        require.undef(\"plotly\");\n",
              "        requirejs.config({\n",
              "            paths: {\n",
              "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
              "            }\n",
              "        });\n",
              "        require(['plotly'], function(Plotly) {\n",
              "            window._Plotly = Plotly;\n",
              "        });\n",
              "        }\n",
              "        </script>\n",
              "        "
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": "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",
              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 73
        },
        "id": "T8oE3bn7uFLm",
        "outputId": "d5e5cb05-3092-4348-aec9-5a65008d0c15"
      },
      "source": [
        "from google.colab import files\n",
        "uploaded = files.upload()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-b448d75f-9b8f-47c7-be9e-3e92bcf877a4\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-b448d75f-9b8f-47c7-be9e-3e92bcf877a4\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving listings.xlsx to listings.xlsx\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I8USnH7Ps7Vf"
      },
      "source": [
        "nasdaq = pd.read_excel('listings.xlsx', sheet_name='nasdaq',na_values='n/a')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EOLnP814u50L"
      },
      "source": [
        "market_cap = nasdaq['Market Capitalization'].div(10**6)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X1D3MTncu53O",
        "outputId": "352a74c8-cfab-40d5-d337-60d19e3bbe59"
      },
      "source": [
        "print('Mean of Market Capitalization: \\n' ,market_cap.mean())\n",
        "print('Median of Market Capitalization: \\n' ,market_cap.median())\n",
        "print('Mode of Market Capitalization: \\n' ,market_cap.mode())\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean of Market Capitalization: \n",
            " 3180.7126214953823\n",
            "Median of Market Capitalization: \n",
            " 225.9684285\n",
            "Mode of Market Capitalization: \n",
            " 0    0.0\n",
            "dtype: float64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E4r-_f26vMa1"
      },
      "source": [
        "## Quantiles"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a2a7PTHUvVCG"
      },
      "source": [
        "median = market_cap.quantile(.5)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v8UPRTIzvgya",
        "outputId": "90d47771-fe85-4e41-9a4c-19228cb9cef0"
      },
      "source": [
        "median == market_cap.median()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Uw83e4xSvl9a",
        "outputId": "591e1923-0629-47f4-fe7c-5577722e0a86"
      },
      "source": [
        "# quantile in  market capitalization\n",
        "quantiles = market_cap.quantile([.25,.75])\n",
        "print(quantiles)\n",
        "\n",
        "print(quantiles[.75]-quantiles[.25])\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.25     43.375930\n",
            "0.75    969.905207\n",
            "Name: Market Capitalization, dtype: float64\n",
            "926.5292771575\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VbPd2Y4Cvpo4"
      },
      "source": [
        "deciles = np.arange(start=.1,stop=.91,step=.1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xbxMzVoHv-mi",
        "outputId": "d8f43ef7-32d5-4e0c-a8e7-35cbd6380a73"
      },
      "source": [
        "deciles"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1FbN2n91v-jB",
        "outputId": "0ad50ce1-0617-4fb2-a40f-3176cc843107"
      },
      "source": [
        " market_cap.quantile(deciles)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.1       4.884565\n",
              "0.2      26.993382\n",
              "0.3      65.714547\n",
              "0.4     124.320644\n",
              "0.5     225.968428\n",
              "0.6     402.469678\n",
              "0.7     723.163197\n",
              "0.8    1441.071134\n",
              "0.9    3671.499558\n",
              "Name: Market Capitalization, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b6WCsYrMve17"
      },
      "source": [
        "\n",
        "**Visualize quantiles with bar chart**\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "26aiys4iIOM-"
      },
      "source": [
        "def enable_plotly_in_cell():\n",
        "  import IPython\n",
        "  from plotly.offline import init_notebook_mode\n",
        "  display(IPython.core.display.HTML('''<script src=\"/static/components/requirejs/require.js\"></script>'''))\n",
        "  init_notebook_mode(connected=False)\n",
        "# Alternative Way to Show each plot using this function instead of renderer=\"colab\""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_NPq6_N3INpR"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "Nq0OvTWpwJXO",
        "outputId": "b14e1e4c-5e58-42a8-b0c9-94954855044e"
      },
      "source": [
        "#from plotly.offline import iplot\n",
        "#import plotly.graph_objs as go\n",
        "\n",
        "#enable_plotly_in_cell()\n",
        "\n",
        "data=[go.Bar(x=deciles,y=market_cap.quantile(deciles),name=\"NASDAQ Market Capitalization (million USD)\")]\n",
        "layout=go.Layout(title=\"NASDAQ Market Capitalization (million USD)\")\n",
        "fig=go.Figure(data=data,layout=layout)\n",
        "#fig.show()\n",
        "fig.show(renderer=\"colab\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<html>\n",
              "<head><meta charset=\"utf-8\" /></head>\n",
              "<body>\n",
              "    <div>\n",
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          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5gcEWJd5z_Dp"
      },
      "source": [
        "**EDA Stats**:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UnFKYX0mzfgN",
        "outputId": "857a30a2-08d0-4f22-951b-56ba91f435c3"
      },
      "source": [
        "market_cap.describe()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count      3167.000000\n",
              "mean       3180.712621\n",
              "std       25471.038707\n",
              "min           0.000000\n",
              "25%          43.375930\n",
              "50%         225.968428\n",
              "75%         969.905207\n",
              "max      740024.467000\n",
              "Name: Market Capitalization, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gZg8aqlc0P6h",
        "outputId": "35f0ac4a-b56d-4ed4-db66-d94a124b82a0"
      },
      "source": [
        "market_cap.describe(percentiles=np.arange(.1,.91,.1))\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count      3167.000000\n",
              "mean       3180.712621\n",
              "std       25471.038707\n",
              "min           0.000000\n",
              "10%           4.884565\n",
              "20%          26.993382\n",
              "30%          65.714547\n",
              "40%         124.320644\n",
              "50%         225.968428\n",
              "60%         402.469678\n",
              "70%         723.163197\n",
              "80%        1441.071134\n",
              "90%        3671.499558\n",
              "max      740024.467000\n",
              "Name: Market Capitalization, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Lbhhk9VSz_Aq"
      },
      "source": [
        "### Decade treasury trend distribution"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7M7PkrtM0knL"
      },
      "source": [
        "ty10 = web.DataReader('DGS10','fred',date(1962,1,1))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R7mL6f27MAMV"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WzZzPUJj0vHl",
        "outputId": "a12b3a59-05f6-4714-fb07-a4b222edf179"
      },
      "source": [
        "ty10.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "DatetimeIndex: 15478 entries, 1962-01-02 to 2021-04-29\n",
            "Data columns (total 1 columns):\n",
            " #   Column  Non-Null Count  Dtype  \n",
            "---  ------  --------------  -----  \n",
            " 0   DGS10   14817 non-null  float64\n",
            "dtypes: float64(1)\n",
            "memory usage: 241.8 KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_yhUBxjY0uwq"
      },
      "source": [
        "**Aggregating data by Category**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yKo07TMn1DCG"
      },
      "source": [
        "**Group data by sector**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x-iIKp6b0vCw",
        "outputId": "f0d107c2-5fce-4e45-f5b1-33f059ab890d"
      },
      "source": [
        "nasdaq.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 3167 entries, 0 to 3166\n",
            "Data columns (total 7 columns):\n",
            " #   Column                 Non-Null Count  Dtype  \n",
            "---  ------                 --------------  -----  \n",
            " 0   Stock Symbol           3167 non-null   object \n",
            " 1   Company Name           3167 non-null   object \n",
            " 2   Last Sale              3165 non-null   float64\n",
            " 3   Market Capitalization  3167 non-null   float64\n",
            " 4   IPO Year               1386 non-null   float64\n",
            " 5   Sector                 2767 non-null   object \n",
            " 6   Industry               2767 non-null   object \n",
            "dtypes: float64(3), object(4)\n",
            "memory usage: 173.3+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d4qheqo11NXd"
      },
      "source": [
        "nasdaq['market_cap_m'] = nasdaq['Market Capitalization'].div(1e6)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bnq8mnyN1iA2"
      },
      "source": [
        "nasdaq = nasdaq.drop('Market Capitalization', axis=1) # Drop column\n",
        "\n",
        "nasdaq_by_sector = nasdaq.groupby('Sector') # create groupby onject\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MdjIOafl2FYn",
        "outputId": "eff9e6be-9435-4026-85ed-9c97154b478e"
      },
      "source": [
        "for sector, data in nasdaq_by_sector:\n",
        "    print(sector, data.market_cap_m.mean())\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Basic Industries 724.8999338580088\n",
            "Capital Goods 1511.2373727809975\n",
            "Consumer Durables 839.8026066270018\n",
            "Consumer Non-Durables 3104.0512055241334\n",
            "Consumer Services 5582.344174819412\n",
            "Energy 826.607607854727\n",
            "Finance 1044.0902047996321\n",
            "Health Care 1758.709196756265\n",
            "Miscellaneous 3445.655934557965\n",
            "Public Utilities 2357.8653150733353\n",
            "Technology 10883.43421354678\n",
            "Transportation 2869.660006726413\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KNwZL5792ISY",
        "outputId": "ccdd698e-a458-4159-ee1e-267e3fa8a419"
      },
      "source": [
        "# easy way\n",
        "\n",
        "mcap_by_sector = nasdaq_by_sector.market_cap_m.mean()\n",
        "print(mcap_by_sector)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Sector\n",
            "Basic Industries           724.899934\n",
            "Capital Goods             1511.237373\n",
            "Consumer Durables          839.802607\n",
            "Consumer Non-Durables     3104.051206\n",
            "Consumer Services         5582.344175\n",
            "Energy                     826.607608\n",
            "Finance                   1044.090205\n",
            "Health Care               1758.709197\n",
            "Miscellaneous             3445.655935\n",
            "Public Utilities          2357.865315\n",
            "Technology               10883.434214\n",
            "Transportation            2869.660007\n",
            "Name: market_cap_m, dtype: float64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "nmeWG_672P0v",
        "outputId": "b59f546e-3426-4470-978e-e211aa4d450e"
      },
      "source": [
        "data=[go.Bar(x=nasdaq_by_sector.market_cap_m.mean(),y=nasdaq['Sector'].unique(),name=\"NASDAQ = Avg. Market Cap by Sector\",orientation='h',marker=dict(\n",
        "        color='rgba(246, 78, 139, 0.6)',\n",
        "        line=dict(color='rgba(246, 78, 139, 1.0)', width=0)\n",
        "))]\n",
        "layout=go.Layout(title=\"NASDAQ Avg. Market Cap by Sector\",xaxis_title=\"USD (In Millions)\",\n",
        "    yaxis=dict(title=\"Sectors\"))\n",
        "\n",
        "fig=go.Figure(data=data,layout=layout)\n",
        "fig.show(renderer=\"colab\")\n",
        "#all sector are listed now but data is not showing the avg. mcap by sector\n",
        " "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "      <th></th>\n",
              "      <th>Last Sale</th>\n",
              "      <th>IPO Year</th>\n",
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              "      <td>21.597679</td>\n",
              "      <td>2000.766667</td>\n",
              "      <td>724.899934</td>\n",
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              "      <th>Capital Goods</th>\n",
              "      <td>26.188681</td>\n",
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              "      <td>34.917318</td>\n",
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              "      <td>15.496834</td>\n",
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              "      <td>29.644242</td>\n",
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              "      <td>28.096758</td>\n",
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              "                       Last Sale     IPO Year  market_cap_m\n",
              "Sector                                                     \n",
              "Basic Industries       21.597679  2000.766667    724.899934\n",
              "Capital Goods          26.188681  2001.324675   1511.237373\n",
              "Consumer Durables      24.363391  2003.222222    839.802607\n",
              "Consumer Non-Durables  25.749565  2000.609756   3104.051206\n",
              "Consumer Services      34.917318  2004.104575   5582.344175\n",
              "Energy                 15.496834  2008.034483    826.607608\n",
              "Finance                29.644242  2010.321101   1044.090205\n",
              "Health Care            19.462531  2009.240409   1758.709197\n",
              "Miscellaneous          46.094369  2004.333333   3445.655935\n",
              "Public Utilities       18.643705  2006.040000   2357.865315\n",
              "Technology             31.100317  2002.653285  10883.434214\n",
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              "                       size          mean\n",
              "Sector                                   \n",
              "Transportation           52   2869.660007\n",
              "Energy                   66    826.607608\n",
              "Public Utilities         66   2357.865315\n",
              "Basic Industries         78    724.899934\n",
              "Consumer Durables        88    839.802607\n",
              "Miscellaneous            89   3445.655935\n",
              "Consumer Non-Durables   103   3104.051206\n",
              "Capital Goods           172   1511.237373\n",
              "Consumer Services       348   5582.344175\n",
              "Technology              433  10883.434214\n",
              "Finance                 627   1044.090205\n",
              "Health Care             645   1758.709197"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qnRNARnU4X1Q"
      },
      "source": [
        "**Different Statics by Column**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 437
        },
        "id": "8ewrxvEH2r5w",
        "outputId": "fe2854e8-429a-4df8-f9dd-29a64f020f12"
      },
      "source": [
        "nasdaq_by_sector.agg({'market_cap_m':'size','IPO Year':'median'})\n"
      ],
      "execution_count": null,
      "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>market_cap_m</th>\n",
              "      <th>IPO Year</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sector</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Basic Industries</th>\n",
              "      <td>78</td>\n",
              "      <td>1996.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Capital Goods</th>\n",
              "      <td>172</td>\n",
              "      <td>2000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Consumer Durables</th>\n",
              "      <td>88</td>\n",
              "      <td>2002.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Consumer Non-Durables</th>\n",
              "      <td>103</td>\n",
              "      <td>1998.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Consumer Services</th>\n",
              "      <td>348</td>\n",
              "      <td>2005.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Energy</th>\n",
              "      <td>66</td>\n",
              "      <td>2011.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Finance</th>\n",
              "      <td>627</td>\n",
              "      <td>2014.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Health Care</th>\n",
              "      <td>645</td>\n",
              "      <td>2014.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Miscellaneous</th>\n",
              "      <td>89</td>\n",
              "      <td>2005.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Public Utilities</th>\n",
              "      <td>66</td>\n",
              "      <td>2007.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Technology</th>\n",
              "      <td>433</td>\n",
              "      <td>2001.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Transportation</th>\n",
              "      <td>52</td>\n",
              "      <td>1997.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                       market_cap_m  IPO Year\n",
              "Sector                                       \n",
              "Basic Industries                 78    1996.5\n",
              "Capital Goods                   172    2000.0\n",
              "Consumer Durables                88    2002.5\n",
              "Consumer Non-Durables           103    1998.0\n",
              "Consumer Services               348    2005.0\n",
              "Energy                           66    2011.0\n",
              "Finance                         627    2014.0\n",
              "Health Care                     645    2014.0\n",
              "Miscellaneous                    89    2005.0\n",
              "Public Utilities                 66    2007.0\n",
              "Technology                      433    2001.5\n",
              "Transportation                   52    1997.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8e5y8InT5Em2",
        "outputId": "3ba7f3e8-3577-4a06-bae5-755ffef9e7ad"
      },
      "source": [
        "nasdaq_by_sector = nasdaq.groupby(['Sector','IPO Year'])\n",
        "\n",
        "nasdaq_by_sector.market_cap_m.mean()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Sector            IPO Year\n",
              "Basic Industries  1972.0       877.240005\n",
              "                  1973.0      1445.697371\n",
              "                  1986.0      1396.817381\n",
              "                  1988.0        24.847526\n",
              "                  1990.0       434.808483\n",
              "                                 ...     \n",
              "Transportation    2006.0      1397.129447\n",
              "                  2007.0       413.900806\n",
              "                  2009.0       552.445919\n",
              "                  2011.0      3711.638317\n",
              "                  2013.0       125.740421\n",
              "Name: market_cap_m, Length: 280, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZLZPNORk53BI",
        "outputId": "b79930b7-8984-49fe-c0e4-67a7df0284f8"
      },
      "source": [
        "# In the Sorted order\n",
        "sector_size = nasdaq.groupby('Sector').size()\n",
        "order = sector_size.sort_values(ascending=False)\n",
        "order.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Sector\n",
              "Health Care          645\n",
              "Finance              627\n",
              "Technology           433\n",
              "Consumer Services    348\n",
              "Capital Goods        172\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qvHLHQAlTSZG"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sKZFn7G0TSCQ"
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        ""
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        },
        "id": "StMjGpzm5EkR",
        "outputId": "45d12ef7-697c-4432-a335-8551b8e02201"
      },
      "source": [
        "data=[go.Bar(x=nasdaq['Sector'].unique(),y=sector_size,textangle=45)]\n",
        "layout=go.Layout(title=\"Observations Per Sector\",yaxis_title=\"Sector Size\",xaxis_title=\"Sectors\")\n",
        "fig=go.Figure(data=data,layout=layout)\n",
        "#fig.show()\n",
        "fig.show(renderer=\"colab\") "
      ],
      "execution_count": null,
      "outputs": [
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          "metadata": {
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    {
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        "id": "pNGAQUqaTScX",
        "outputId": "5d152bf9-6b64-48ff-f4db-dbb28680f028"
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      "source": [
        "trace=[]\n",
        "recent_ipos = nasdaq[nasdaq['IPO Year'] > 2014]\n",
        "recent_ipos['IPO Year'] = recent_ipos['IPO Year'].astype(int)\n",
        "yyyy = recent_ipos['IPO Year'].unique().tolist()[-3:] #slice function\n",
        "for y in yyyy:\n",
        "    tmp_df = recent_ipos[recent_ipos['IPO Year'] == y].groupby('Sector')['market_cap_m'].sum()\n",
        "    trace.append(go.Bar(x=tmp_df.index, y=tmp_df, name=y))\n",
        "    \n",
        "# Change the bar mode\n",
        "fig = go.Figure(trace)\n",
        "fig.update_layout(barmode='group',title=\"NASDAQ Market Capitalization Sectorwise in Last Three IPO Year (million USD)\",yaxis_title=\"Market Capital(In Millions USD)\", xaxis_title=\"Sectors\")\n",
        "fig.show(renderer=\"colab\")\n",
        "# need to show data tips with modified text"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          "metadata": {
            "tags": []
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    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c6DYM7QALEPz"
      },
      "source": [
        "# Active companies only\n",
        "nasdaq = nasdaq[nasdaq.market_cap_m > 0]\n",
        "\n",
        "#Outlier threshold\n",
        "outliers = nasdaq.market_cap_m.quantile(.9)\n",
        "\n",
        "# Removing Outliers\n",
        "nasdaq = nasdaq[nasdaq.market_cap_m < outliers]\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "QMQ4OWmULEJs",
        "outputId": "06dcfed3-4865-4145-9a32-2a448bca4ee8"
      },
      "source": [
        "traces=[]\n",
        "traces.append(go.Box(\n",
        "            y=nasdaq['market_cap_m'],\n",
        "            x=nasdaq['Sector'],\n",
        "            notched=True, # used notched shape\n",
        "            boxpoints='all',\n",
        "            jitter=0.5,\n",
        "            whiskerwidth=0.2,\n",
        "            marker_color='rgb(7,40,89)',\n",
        "    line_color='rgb(7,40,89)',\n",
        "    marker=dict(\n",
        "        color='rgb(8,81,156)',\n",
        "        outliercolor='rgba(219, 64, 82, 0.6)',\n",
        "        line=dict(\n",
        "            outliercolor='rgba(219, 64, 82, 0.6)',\n",
        "            outlierwidth=2))\n",
        "    \n",
        "        ))\n",
        "layout = go.Layout(\n",
        "    title='Boxplot Showing Quartiles and Outliers in Different Sectors',\n",
        "    yaxis=dict(\n",
        "        range=[0,10.50],\n",
        "        autorange=True,\n",
        "        showgrid=True,\n",
        "        zeroline=True,\n",
        "        dtick=5,\n",
        "        gridcolor='rgb(255, 255, 255)',\n",
        "        gridwidth=1,\n",
        "        zerolinecolor='rgb(255, 255, 255)',\n",
        "        zerolinewidth=2,\n",
        "    ),\n",
        "    margin=dict(\n",
        "        l=40,\n",
        "        r=30,\n",
        "        b=80,\n",
        "        t=100,\n",
        "    ),\n",
        "    paper_bgcolor='rgb(243, 243, 243)',\n",
        "    plot_bgcolor='rgb(243, 243, 243)',\n",
        "    showlegend=False\n",
        ")\n",
        "fig = go.Figure(data=traces, layout=layout)\n",
        "fig.show(renderer=\"colab\")\n",
        "# issue with y axis here why the scale is not showing here\n",
        "#issue is reported on https://github.com/plotly/plotly.py/issues/3264"
      ],
      "execution_count": null,
      "outputs": [
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