Data_Visualization_with_Plotly.ipynb

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        "<a href=\"https://colab.research.google.com/github/rajeshmore1/DataScience_Mentorship/blob/main/Data_Visualization_with_Plotly.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "Python Plotly Library is an open-source library that can be used for data visualization and understanding data simply and easily. Plotly supports various types of plots like line charts, scatter plots, histograms, cox plots, etc. So you all must be wondering why Plotly over other visualization tools or libraries? Here’s the answer –\n",
        "\n",
        "* Plotly has hover tool capabilities that allow us to detect any outliers or anomalies in a large number of data points.\n",
        "* It is visually attractive that can be accepted by a wide range of audiences.\n",
        "* It allows us for the endless customization of our graphs that makes our plot more meaningful and understandable for others."
      ],
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    {
      "cell_type": "code",
      "source": [
        "pip install plotly\n"
      ],
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          "text": [
            "Requirement already satisfied: plotly in /usr/local/lib/python3.10/dist-packages (5.15.0)\n",
            "Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from plotly) (8.2.3)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from plotly) (23.1)\n"
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      "source": [
        "#Package Structure of Plotly\n",
        "There are three main modules in Plotly. They are:\n",
        "\n",
        "plotly.plotly\n",
        "\n",
        "plotly.graph.objects\n",
        "\n",
        "plotly.tools\n",
        "\n",
        "1. plotly.plotly acts as the interface between the local machine and Plotly. It contains functions that require a response from Plotly’s server.\n",
        "\n",
        "2. plotly.graph_objects module contains the objects (Figure, layout, data, and the definition of the plots like scatter plot, line chart) that are responsible for creating the plots.  The Figure can be represented either as dict or instances of plotly.graph_objects.Figure and these are serialized as JSON before it gets passed to plotly.js. Consider the below example for better understanding.\n",
        "\n",
        "Note: plotly.express module can create the entire Figure at once. It uses the graph_objects internally and returns the graph_objects.Figure instance."
      ],
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      "source": [
        "import plotly.express as px\n",
        "\n",
        "\n",
        "# Creating the Figure instance\n",
        "fig = px.line(x=[1,2, 3], y=[1, 2, 3])\n",
        "\n",
        "# printing the figure instance\n",
        "print(fig)\n"
      ],
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            "Figure({\n",
            "    'data': [{'hovertemplate': 'x=%{x}<br>y=%{y}<extra></extra>',\n",
            "              'legendgroup': '',\n",
            "              'line': {'color': '#636efa', 'dash': 'solid'},\n",
            "              'marker': {'symbol': 'circle'},\n",
            "              'mode': 'lines',\n",
            "              'name': '',\n",
            "              'orientation': 'v',\n",
            "              'showlegend': False,\n",
            "              'type': 'scatter',\n",
            "              'x': array([1, 2, 3]),\n",
            "              'xaxis': 'x',\n",
            "              'y': array([1, 2, 3]),\n",
            "              'yaxis': 'y'}],\n",
            "    'layout': {'legend': {'tracegroupgap': 0},\n",
            "               'margin': {'t': 60},\n",
            "               'template': '...',\n",
            "               'xaxis': {'anchor': 'y', 'domain': [0.0, 1.0], 'title': {'text': 'x'}},\n",
            "               'yaxis': {'anchor': 'x', 'domain': [0.0, 1.0], 'title': {'text': 'y'}}}\n",
            "})\n"
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      "source": [
        "Figures are represented as trees where the root node has three top layer attributes – data, layout, and frames and the named nodes called ‘attributes’.\n",
        "\n",
        " Consider the above example, layout.legend is a nested dictionary where the legend is the key inside the dictionary whose value is also a dictionary.  \n",
        "\n",
        "plotly.tools module contains various tools in the forms of the functions that can enhance the Plotly experience."
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        "import plotly.express as px\n",
        "\n",
        "\n",
        "# Creating the Figure instance\n",
        "fig = px.line(x=[1, 2, 3], y=[1, 2, 3])\n",
        "\n",
        "# showing the plot\n",
        "fig.show()\n"
      ],
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      "source": [
        "In the above example, the plotly.express module is imported which returns the Figure instance. We have created a simple line chart by passing the x, y coordinates of the points to be plotted."
      ],
      "metadata": {
        "id": "_y25SKPCmBVm"
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      "cell_type": "markdown",
      "source": [
        "#Line plot\n",
        "Line plot in Plotly is much accessible and illustrious annexation to plotly which manage a variety of types of data and assemble easy-to-style statistic. With px.line each data position is represented as a vertex  (which location is given by the x and y columns) of a polyline mark in 2D space."
      ],
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      "source": [
        "import plotly.express as px\n",
        "\n",
        "# using the iris dataset\n",
        "df = px.data.iris()\n",
        "\n",
        "# plotting the line chart\n",
        "fig = px.line(df, x=\"species\", y=\"petal_width\")\n",
        "\n",
        "# showing the plot\n",
        "fig.show()\n"
      ],
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    },
    {
      "cell_type": "markdown",
      "source": [
        "#Bar Chart\n",
        "A bar chart is a pictorial representation of data that presents categorical data with rectangular bars with heights or lengths proportional to the values that they represent. In other words, it is the pictorial representation of dataset. These data sets contain the numerical values of variables that represent the length or height.\n",
        "\n"
      ],
      "metadata": {
        "id": "D03_CJd6mc1x"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import plotly.express as px\n",
        "\n",
        "# using the iris dataset\n",
        "df = px.data.iris()\n",
        "\n",
        "# plotting the bar chart\n",
        "fig = px.bar(df, x=\"sepal_width\", y=\"sepal_length\")\n",
        "\n",
        "# showing the plot\n",
        "fig.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "H2_J87DUmf2o",
        "outputId": "19d4d1fd-558e-4e7c-c2d9-253914591b73"
      },
      "execution_count": 5,
      "outputs": [
        {
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          "metadata": {}
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    },
    {
      "cell_type": "markdown",
      "source": [
        "#Histograms\n",
        "A histogram contains a rectangular area to display the statistical information which is proportional to the frequency of a variable and its width in successive numerical intervals. A graphical representation that manages a group of data points into different specified ranges. It has a special feature that shows no gaps between the bars and similar to a vertical bar graph.\n",
        "\n",
        "Example:"
      ],
      "metadata": {
        "id": "ihayiuVGmoOl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import plotly.express as px\n",
        "\n",
        "# using the iris dataset\n",
        "df = px.data.iris()\n",
        "\n",
        "# plotting the histogram\n",
        "fig = px.histogram(df, x=\"sepal_length\", y=\"petal_width\")\n",
        "\n",
        "# showing the plot\n",
        "fig.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "o5HrwIdWmhxw",
        "outputId": "3ab01ee8-c064-4412-d528-e7ae14e21b4b"
      },
      "execution_count": 6,
      "outputs": [
        {
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            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#scatter plot\n",
        "import plotly.express as px\n",
        "\n",
        "# using the iris dataset\n",
        "df = px.data.iris()\n",
        "\n",
        "# plotting the scatter chart\n",
        "fig = px.scatter(df, x=\"species\", y=\"petal_width\")\n",
        "\n",
        "# showing the plot\n",
        "fig.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "y32RwcWGmuTq",
        "outputId": "c251eb4c-42a7-461a-f518-0d311a1caad2"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          },
          "metadata": {}
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Bubble plot\n",
        "import plotly.express as px\n",
        "\n",
        "# using the iris dataset\n",
        "df = px.data.iris()\n",
        "\n",
        "# plotting the bubble chart\n",
        "fig = px.scatter(df, x=\"species\", y=\"petal_width\",\n",
        "\t\t\t\tsize=\"petal_length\", color=\"species\")\n",
        "\n",
        "# showing the plot\n",
        "fig.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "INaIYWjKm08N",
        "outputId": "73cd0022-7f67-4cb6-f410-f9f48911de93"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          "metadata": {}
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#A pie chart\n",
        "It is a circular statistical graphic, which is divided into slices to illustrate numerical proportions. It depicts a special chart that uses “pie slices”, where each sector shows the relative sizes of data. A circular chart cuts in a form of radii into segments describing relative frequencies or magnitude also known as circle graph."
      ],
      "metadata": {
        "id": "l2AOLvdknA3s"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import plotly.express as px\n",
        "\n",
        "# using the tips dataset\n",
        "df = px.data.tips()\n",
        "\n",
        "# plotting the pie chart\n",
        "fig = px.pie(df, values=\"total_bill\", names=\"day\")\n",
        "\n",
        "# showing the plot\n",
        "fig.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "xSueJbO9m80T",
        "outputId": "e88aa55e-3499-4b1e-cad2-d82366cd2ca5"
      },
      "execution_count": 9,
      "outputs": [
        {
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    },
    {
      "cell_type": "code",
      "source": [
        "#box plot\n",
        "import plotly.express as px\n",
        "\n",
        "# using the tips dataset\n",
        "df = px.data.tips()\n",
        "\n",
        "# plotting the box chart\n",
        "fig = px.box(df, x=\"day\", y=\"total_bill\")\n",
        "\n",
        "# showing the plot\n",
        "fig.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "voJA8O9enFxA",
        "outputId": "dd1e9718-b012-48dc-dc3a-ed9978de7fcf"
      },
      "execution_count": 10,
      "outputs": [
        {
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    },
    {
      "cell_type": "code",
      "source": [
        "#violin plot\n",
        "import plotly.express as px\n",
        "\n",
        "# using the tips dataset\n",
        "df = px.data.tips()\n",
        "\n",
        "# plotting the violin chart\n",
        "fig = px.violin(df, x=\"day\", y=\"total_bill\")\n",
        "\n",
        "# showing the plot\n",
        "fig.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "QhxeR5u4nKt3",
        "outputId": "3474dffb-4206-47de-efc6-19415d785990"
      },
      "execution_count": 11,
      "outputs": [
        {
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          "metadata": {}
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    },
    {
      "cell_type": "markdown",
      "source": [
        "#Gantt Charts\n",
        "Generalized Activity Normalization Time Table (GANTT) chart is type of chart in which series of horizontal lines are present that show the amount of work done or production completed in given period of time in relation to amount planned for those projects."
      ],
      "metadata": {
        "id": "P-1ILCDCnYEK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import plotly.figure_factory as ff\n",
        "\n",
        "# Data to be plotted\n",
        "df = [dict(Task=\"A\", Start='2020-01-01', Finish='2009-02-02'),\n",
        "\tdict(Task=\"Job B\", Start='2020-03-01', Finish='2020-11-11'),\n",
        "\tdict(Task=\"Job C\", Start='2020-08-06', Finish='2020-09-21')]\n",
        "\n",
        "# Creating the plot\n",
        "fig = ff.create_gantt(df)\n",
        "fig.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 617
        },
        "id": "YLLRVbYHnQCI",
        "outputId": "7c22ab5e-1177-434a-9699-a6a879cabef3"
      },
      "execution_count": 12,
      "outputs": [
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    {
      "cell_type": "markdown",
      "source": [
        "#Heatmaps\n",
        "Heatmap is defined as a graphical representation of data using colors to visualize the value of the matrix. In this, to represent more common values or higher activities brighter colors basically reddish colors are used and to represent less common or activity values, darker colors are preferred. Heatmap is also defined by the name of the shading matrix.\n",
        "\n",
        "Example:"
      ],
      "metadata": {
        "id": "V8pVfgMCn4Hh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import plotly.graph_objects as go\n",
        "import numpy as np\n",
        "\n",
        "feature_x = np.arange(0, 50, 2)\n",
        "feature_y = np.arange(0, 50, 3)\n",
        "\n",
        "# Creating 2-D grid of features\n",
        "[X, Y] = np.meshgrid(feature_x, feature_y)\n",
        "\n",
        "Z = np.cos(X / 2) + np.sin(Y / 4)\n",
        "\n",
        "# plotting the figure\n",
        "fig = go.Figure(data =\n",
        "\tgo.Heatmap(x = feature_x, y = feature_y, z = Z,))\n",
        "\n",
        "fig.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "EmqkjDDOnnsQ",
        "outputId": "76c51c06-8c4d-470d-a897-3529785737ce"
      },
      "execution_count": 14,
      "outputs": [
        {
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              "}}\n",
              "\n",
              "                        })                };                            </script>        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#3D Scatter Plot Plotly\n",
        "3D Scatter Plot can plot two-dimensional graphics that can be enhanced by mapping up to three additional variables while using the semantics of hue, size, and style parameters. All the parameter control visual semantic which are used to identify the different subsets. Using redundant semantics can be helpful for making graphics more accessible. It can be created using the scatter_3d function of plotly.express class.\n",
        "\n",
        "Example:"
      ],
      "metadata": {
        "id": "We_QUT20oHww"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import plotly.express as px\n",
        "\n",
        "# Data to be plotted\n",
        "df = px.data.iris()\n",
        "\n",
        "# Plotting the figure\n",
        "fig = px.scatter_3d(df, x = 'sepal_width',\n",
        "\t\t\t\t\ty = 'sepal_length',\n",
        "\t\t\t\t\tz = 'petal_width',\n",
        "\t\t\t\t\tcolor = 'species')\n",
        "\n",
        "fig.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "8RLbe-97n87M",
        "outputId": "8ac1546f-43e8-44b7-baf1-79a93437e220"
      },
      "execution_count": 15,
      "outputs": [
        {
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          "metadata": {}
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import plotly.graph_objects as px\n",
        "import numpy as np\n",
        "\n",
        "\n",
        "# creating random data through randomint\n",
        "# function of numpy.random\n",
        "np.random.seed(42)\n",
        "\n",
        "# Data to be Plotted\n",
        "random_x = np.random.randint(1, 101, 100)\n",
        "random_y = np.random.randint(1, 101, 100)\n",
        "\n",
        "plot = px.Figure(data=[px.Scatter(\n",
        "\tx=random_x,\n",
        "\ty=random_y,\n",
        "\tmode='markers',)\n",
        "])\n",
        "\n",
        "# Add dropdown\n",
        "plot.update_layout(\n",
        "\tupdatemenus=[\n",
        "\t\tdict(\n",
        "\t\t\tbuttons=list([\n",
        "\t\t\t\tdict(\n",
        "\t\t\t\t\targs=[\"type\", \"scatter\"],\n",
        "\t\t\t\t\tlabel=\"Scatter Plot\",\n",
        "\t\t\t\t\tmethod=\"restyle\"\n",
        "\t\t\t\t),\n",
        "\t\t\t\tdict(\n",
        "\t\t\t\t\targs=[\"type\", \"bar\"],\n",
        "\t\t\t\t\tlabel=\"Bar Chart\",\n",
        "\t\t\t\t\tmethod=\"restyle\"\n",
        "\t\t\t\t)\n",
        "\t\t\t]),\n",
        "\t\t\tdirection=\"down\",\n",
        "\t\t),\n",
        "\t]\n",
        ")\n",
        "\n",
        "plot.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "9bTGXSmGoLLq",
        "outputId": "eae48eab-7b47-4660-d51e-4454b8ac2fbb"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          "metadata": {}
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import plotly.express as px\n",
        "\n",
        "\n",
        "df = px.data.gapminder().query(\"year == 2007\")\n",
        "print(df.head())\n",
        "plot = px.scatter_geo(df, locations=\"iso_alpha\",\n",
        "\t\t\t\t\tsize=\"gdpPercap\",\n",
        "\t\t\t\t\tcolor = \"country\")\n",
        "plot.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 774
        },
        "id": "KhuaXl5loUax",
        "outputId": "95bd39df-b10f-4421-9023-88e847de87e4"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "        country continent  year  lifeExp       pop     gdpPercap iso_alpha  \\\n",
            "11  Afghanistan      Asia  2007   43.828  31889923    974.580338       AFG   \n",
            "23      Albania    Europe  2007   76.423   3600523   5937.029526       ALB   \n",
            "35      Algeria    Africa  2007   72.301  33333216   6223.367465       DZA   \n",
            "47       Angola    Africa  2007   42.731  12420476   4797.231267       AGO   \n",
            "59    Argentina  Americas  2007   75.320  40301927  12779.379640       ARG   \n",
            "\n",
            "    iso_num  \n",
            "11        4  \n",
            "23        8  \n",
            "35       12  \n",
            "47       24  \n",
            "59       32  \n"
          ]
        },
        {
          "output_type": "display_data",
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    },
    {
      "cell_type": "markdown",
      "source": [
        "#sunburst plots\n",
        "\n",
        "Sunburst plot visualizes stratified data gradually from roots to leaves. The root starts from the center and squirt are added to the outer rings. Each level of the hierarchy is represented by one ring or circle with the innermost circle, further rings are divided into slices that represent data points and the size of the slice represents data values."
      ],
      "metadata": {
        "id": "e6DhdSlypQSW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import plotly.express as px\n",
        "\n",
        "df = px.data.tips()\n",
        "\n",
        "fig = px.sunburst(df, path=['day', 'sex'],\n",
        "\t\t\t\tvalues='total_bill')\n",
        "fig.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "W7P47jr8o3W7",
        "outputId": "45be7f55-f21c-40dc-d1ff-b7a8f9c125c1"
      },
      "execution_count": 19,
      "outputs": [
        {
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          },
          "metadata": {}
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import plotly.express as px\n",
        "\n",
        "df = px.data.tips()\n",
        "\n",
        "fig = px.sunburst(df, path=['day', 'sex'],\n",
        "\t\t\t\tvalues='total_bill', color='total_bill')\n",
        "fig.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "XUZYMc4ppTHA",
        "outputId": "bf6ef9ff-c9f4-492f-b596-93c24789aabf"
      },
      "execution_count": 20,
      "outputs": [
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