Table of content

1. Virtual env setup
2. Install required packages from requirements.txt
3. Project content
4. Chart types
   1. Line Chart
   2. Scatter Plot
   3. Bar Chart
   4. Pie Chart
   5. Box Plot
   6. Heatmap
   7. Histogram
   8. Bubble Chart
   9. Area Chart
   10. Contour Plot
   11. 3D Surface Plot
   12. Candlestick Chart
   13. OHLC Chart
   14. Violin Plot
   15. Choropleth Map
   16. Scatter Map
   17. Sunburst Chart
   18. Treemap Chart
   19. Parallel Coordinates Plot

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1. Virtual environment setup:

• Create virtual environment.
  Script below is creating environment with name 'env'.
  If you want to create environment with unique name, please replace the env name using your env name in script
  python -m {here_is_your_venv_name} ../env

  Working script for creating venv with name 'venv' is below:

  python -m venv ../env
  

  then activate it

  • for unix-based

  source ../venv/bin/activate
  

  • for windows

  .\.venv\Scripts\Activate
  

  If you like to have different name for the environment

  python -m venv {venv_for_project}   
  

  and then

  source {venv_for_project}/Scripts/activate
  

For deactivating created env use command

• deactivate

2. Install required packages from requirements.txt

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pip install -r requirements.txt

Project content

Plotly is a powerful Python library for creating interactive and visually appealing charts.
It supports a variety of chart types, catering to diverse data visualization needs.
This guide showcases some of the most common chart types available in Plotly, along with implementation examples.

All examples presented below were created using references from Plotly tutorial page

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Line Chart

A line chart is ideal for visualizing data trends over time, with data points connected by straight lines.

    import plotly.express as px  

    def line_chart():
        """A line chart is ideal for visualizing data trends over time, with data points connected by straight lines."""
        data = {'Year': [2015, 2016, 2017, 2018, 2019], 'Value': [10, 15, 13, 17, 14]}
        fig = px.line(data, x='Year', y='Value', title='Line Chart Example')
        fig.show()

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Scatter plot

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def scatter_plot():
    """A scatter plot displays the relationship between two numerical variables using dots."""
    data = {'Variable1': [10, 20, 30, 40, 50], 'Variable2': [15, 25, 35, 45, 55]}
    fig = px.scatter(data, x='Variable1', y='Variable2', title='Scatter Plot Example')
    fig.show()

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Bar Chart

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def bar_chart():
    """Bar charts are used to compare categorical data, with rectangular bars representing values."""
    data = {'Category': ['A', 'B', 'C', 'D'], 'Value': [10, 20, 30, 40]}
    fig = px.bar(data, x='Category', y='Value', title='Bar Chart Example')
    fig.show()

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Pie chart

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def pie_chart():
    """A pie chart divides data into slices to illustrate proportions."""
    data = {'Category': ['A', 'B', 'C', 'D'], 'Value': [10, 20, 30, 40]}
    fig = px.pie(data, names='Category', values='Value', title='Pie Chart Example')
    fig.show()

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Box Plot

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def box_plot():
    """Box plots summarize data distribution through their quartiles, highlighting outliers."""
    data = {'Category': ['A', 'A', 'A', 'B', 'B', 'B'], 'Value': [10, 15, 14, 22, 24, 23]}
    fig = px.box(data, x='Category', y='Value', title='Box Plot Example')
    fig.show()

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Heatmap

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def heatmap():
    """Heatmaps display data intensity using varying colors in a grid layout."""
    data = np.random.rand(5, 5)
    fig = px.imshow(data, color_continuous_scale='Viridis', title='Heatmap Example')
    fig.show()

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Histogram

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def histogram():
    """Histograms display the distribution of a dataset by grouping data into bins."""
    data = {'Values': [1, 2, 2, 3, 3, 3, 4, 4, 4, 4]}
    fig = px.histogram(data, x='Values', title='Histogram Example')
    fig.show()

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Bubble Chart

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def bubble_chart():
    """Bubble charts extend scatter plots with a third dimension using bubble size."""
    data = {'X': [10, 20, 30, 40, 50], 'Y': [15, 25, 35, 45, 55], 'Size': [100, 200, 300, 400, 500]}
    fig = px.scatter(data, x='X', y='Y', size='Size', title='Bubble Chart Example')
    fig.show()

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Area chart

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def area_chart():
    """Area charts show quantitative data over time, emphasizing magnitude."""
    data = {'Year': [2015, 2016, 2017, 2018, 2019], 'Value': [10, 15, 13, 17, 14]}
    fig = px.area(data, x='Year', y='Value', title='Area Chart Example')
    fig.show()

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Contour Plot

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def contour_plot():
    """Contour plots display three-dimensional data in two dimensions with contour lines."""
    x = np.linspace(0, 10, 100)
    y = np.linspace(0, 10, 100)
    z = np.sin(x[:, None] * y[None, :])

    # Create a contour plot using go.Contour
    fig = go.Figure(go.Contour(
        z=z,
        x=x,
        y=y,
        colorscale='Viridis',
        colorbar=dict(title="Value"),
    ))

    fig.update_layout(title='Contour Plot Example', xaxis_title="X", yaxis_title="Y")
    fig.show()

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3D surface plot

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def three_d_surface_plot():
    """3D surface plots provide a three-dimensional representation of data, showing relationships in three dimensions."""
    x = np.linspace(-5, 5, 100)
    y = np.linspace(-5, 5, 100)
    x, y = np.meshgrid(x, y)
    z = np.sin(np.sqrt(x ** 2 + y ** 2))

    fig = go.Figure(data=[go.Surface(z=z, x=x, y=y)])
    fig.update_layout(title='3D Surface Plot Example',
                      scene={'xaxis_title': 'X', 'yaxis_title': 'Y', 'zaxis_title': 'Z'})
    fig.show()

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Candlestick Chart

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def candlestick_chart():
    """Candlestick charts are used to visualize stock price movements over time, displaying the opening, closing, high, and low prices."""
    data = {
        'Date': ['2021-01-01', '2021-01-02', '2021-01-03', '2021-01-04'],
        'Open': [100, 102, 103, 107],
        'High': [105, 106, 108, 110],
        'Low': [98, 100, 101, 104],
        'Close': [104, 105, 106, 109]
    }

    fig = go.Figure(
        data=[
            go.Candlestick(x=data['Date'], open=data['Open'], high=data['High'], low=data['Low'], close=data['Close'])])
    fig.update_layout(title='Candlestick Chart Example')
    fig.show()

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OHLC

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def ohlc_chart():
    """OHLC (Open, High, Low, Close) charts are used for visualizing financial data with four key values for each period."""

    data = {
        'Date': ['2021-01-01', '2021-01-02', '2021-01-03', '2021-01-04'],
        'Open': [100, 102, 103, 107],
        'High': [105, 106, 108, 110],
        'Low': [98, 100, 101, 104],
        'Close': [104, 105, 106, 109]
    }

    fig = go.Figure(
        data=[go.Ohlc(x=data['Date'], open=data['Open'], high=data['High'], low=data['Low'], close=data['Close'])])
    fig.update_layout(title='OHLC (Open, High, Low, Close)) Chart Example ')
    fig.show()

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Violin plot

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def violin_plot():
    """Violin plots combine aspects of box plots and density plots to display data distribution."""
    data = {'Category': ['A', 'A', 'A', 'B', 'B', 'B'], 'Value': [10, 15, 14, 22, 24, 23]}
    fig = px.violin(data, x='Category', y='Value', title='Violin Plot Example')
    fig.show()

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Choropleth Map

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def choropleth_map():
    """Choropleth maps visualize data geographically, with color shading to represent values."""
    data = {'Country': ['USA', 'Canada', 'Mexico'], 'Value': [10, 20, 30]}
    fig = px.choropleth(data, locations='Country', color='Value', title='Choropleth Map Example')
    fig.show()

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Scatter Map

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def scatter_map():
    """Scatter maps visualize geographical data with points plotted on a map."""
    data = {'Latitude': [34.0522, 40.7128, 41.8781], 'Longitude': [-118.2437, -74.0060, -87.6298],
            'City': ['Los Angeles', 'New York', 'Chicago']}
    fig = px.scatter_geo(data, lat='Latitude', lon='Longitude', text='City', title='Scatter Map Example')
    fig.show()

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Sunburst chart

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def sunburst_chart():
    """Sunburst charts visualize hierarchical data with a circular layout."""
    data = {'Labels': ['A', 'B', 'C'], 'Parents': ['', 'A', 'A'], 'Values': [10, 20, 30]}
    fig = px.sunburst(data
                      , path=['Parents', 'Labels'], values='Values', title='Sunburst Chart Example')
    fig.show()

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Treemap Chart

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def treemap_chart():
    """Treemaps display hierarchical data as nested rectangles, with size and color indicating values."""
    data = {'Labels': ['A', 'B', 'C'], 'Parents': ['', 'A', 'A'], 'Values': [10, 20, 30]}
    fig = px.treemap(data, path=['Parents', 'Labels'], values='Values', title='Treemap Chart Example')
    fig.show()

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Parallel Coordinates Plot

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def parallel_coordinates_plot():
    """Parallel coordinates plots are used to visualize multivariate data by plotting variables on parallel axes."""
    data = {'Feature1': [1, 2, 3], 'Feature2': [4, 5, 6], 'Feature3': [7, 8, 9]}
    fig = px.parallel_coordinates(data, color='Feature1', title='Parallel Coordinates Plot Example')
    fig.show()

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