Plotly_2A_LinePlots_Ex.ipynb

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      "name": "Plotly_2A_LinePlots_Ex.ipynb",
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      "authorship_tag": "ABX9TyMTQQsD6aWuWdXPq9LjKi4X",
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      "name": "python3",
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    }
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Divyanshu-ISM/Oil-and-Gas-data-analysis/blob/master/Plotly_2A_LinePlots_Ex.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qBFs4A_cjait",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import plotly.offline as pyo\n",
        "from plotly.offline import iplot\n",
        "\n",
        "import plotly.graph_objects as go"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ean7PEl8kO0b",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "skoyzc9cjj8B",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "url = 'https://github.com/Divyanshu-ISM/Plotly-Dashboards-with-Dash/blob/master/Data/2010YumaAZ.csv?raw=true'\n",
        "\n",
        "df = pd.read_csv(url)"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GWrg8VSkkSrL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "5f66fe08-1024-4533-ea4d-df3f630adf68"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 4,
      "outputs": [
        {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>LST_DATE</th>\n",
              "      <th>DAY</th>\n",
              "      <th>LST_TIME</th>\n",
              "      <th>T_HR_AVG</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>20100601</td>\n",
              "      <td>TUESDAY</td>\n",
              "      <td>0:00</td>\n",
              "      <td>25.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>20100601</td>\n",
              "      <td>TUESDAY</td>\n",
              "      <td>1:00</td>\n",
              "      <td>24.1</td>\n",
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              "      <th>2</th>\n",
              "      <td>20100601</td>\n",
              "      <td>TUESDAY</td>\n",
              "      <td>2:00</td>\n",
              "      <td>24.4</td>\n",
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              "      <td>20100601</td>\n",
              "      <td>TUESDAY</td>\n",
              "      <td>3:00</td>\n",
              "      <td>24.9</td>\n",
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              "      <th>4</th>\n",
              "      <td>20100601</td>\n",
              "      <td>TUESDAY</td>\n",
              "      <td>4:00</td>\n",
              "      <td>22.8</td>\n",
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            "text/plain": [
              "   LST_DATE      DAY LST_TIME  T_HR_AVG\n",
              "0  20100601  TUESDAY     0:00      25.2\n",
              "1  20100601  TUESDAY     1:00      24.1\n",
              "2  20100601  TUESDAY     2:00      24.4\n",
              "3  20100601  TUESDAY     3:00      24.9\n",
              "4  20100601  TUESDAY     4:00      22.8"
            ]
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          "execution_count": 4
        }
      ]
    },
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      "cell_type": "code",
      "metadata": {
        "id": "MSocF5q3kURh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "days = ['TUESDAY','WEDNESDAY','THURSDAY','FRIDAY','SATURDAY','SUNDAY','MONDAY']"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t7yvIGPOkxZu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# data =[ go.Scatter(x=df['LST_TIME'], y=df['T_HR_AVG'], mode = 'lines', name=day) for day in days]\n",
        "\n",
        "data = [ ]\n",
        "\n",
        "for day in days:\n",
        "\n",
        "  df_temp = df[df['DAY'] == day]\n",
        "\n",
        "  trace = go.Scatter(x=df_temp['LST_TIME'] , y = df_temp['T_HR_AVG'], mode='lines', name = day)\n",
        "\n",
        "  data.append(trace)\n",
        "\n"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bZF-t4oNlYTd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "layo = go.Layout(title='linePlotExcercise')\n",
        "\n",
        "fig = go.Figure(data=data, layout = layo)"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rfAffUzAmJLI",
        "colab_type": "code",
        "colab": {
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          "height": 542
        },
        "outputId": "93a055d6-f2e6-4e27-be5e-b8b67ce5a838"
      },
      "source": [
        "iplot(fig)"
      ],
      "execution_count": 9,
      "outputs": [
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            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Oxfz9IEImLmT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}