Plotly

Plotly is an open-source library for creating interactive and visually appealing data visualizations in Python. It provides a wide range of chart types, including scatter plots, line charts, bar charts, histograms, box plots, pie charts, and more.

Some key features of Plotly include:

1. Interactive Plots: Plotly generates interactive plots that allow users to zoom, pan, hover over data points, and even export data. This makes it easy to explore and understand complex datasets.

2. Customization: Plotly offers extensive customization options, allowing you to change the colors, labels, legends, and other aspects of your plots to suit your needs.

3. Animations: Plotly supports creating animated plots, which can be useful for visualizing changes over time or highlighting specific data points.

4. Offline Plotting: Plotly plots can be saved as HTML files, allowing you to share them with others without requiring an internet connection.

Here’s a simple example of how to create a scatter plot using Plotly:

import plotly.graph_objects as go

x = [1, 2, 3, 4, 5]
y = [1, 4, 9, 16, 25]

fig = go.Figure(data=go.Scatter(x=x, y=y))
fig.show()

In this example, we first import the go module from plotly.graph_objects. We then define the x and y coordinates for our scatter plot. Next, we create a Figure object using go.Figure() and pass our data to go.Scatter(). Finally, we call the show() method to display the plot.

Plotly also provides a higher-level interface called Plotly Express, which allows you to create entire figures with a single function call. Here’s an example using Plotly Express:

import plotly.express as px

df = px.data.tips()
fig = px.scatter(df, x="total_bill", y="tip", color="sex")
fig.show()

In this example, we import the px module from plotly.express. We then load a built-in dataset called tips using px.data.tips(). We create a scatter plot using px.scatter(), specifying the x and y variables and coloring the points by the “sex” variable. Finally, we call show() to display the plot.

Plotly is widely used in various fields, such as data science, machine learning, and business analytics. It is a powerful tool for creating interactive and informative visualizations that can help you gain insights from your data.

First, let’s import the necessary modules:

import plotly.graph_objects as go
import plotly.express as px
import pandas as pd
import numpy as np

1. Basic Line Plot

   fig = px.line(x=[0, 1, 2, 3, 4], y=[0, 1, 4, 9, 16])
   fig.show()
   

2. Scatter Plot

   fig = px.scatter(x=[0, 1, 2, 3, 4], y=[0, 1, 4, 9, 16])
   fig.show()
   

3. Bar Chart

   fig = px.bar(x=['A', 'B', 'C'], y=[1, 3, 2])
   fig.show()
   

4. Histogram

   fig = px.histogram(x=np.random.randn(1000))
   fig.show()
   

5. Box Plot

   fig = px.box(y=np.random.randn(100))
   fig.show()
   

6. Violin Plot

   fig = px.violin(y=np.random.randn(100))
   fig.show()
   

7. Heatmap

   fig = px.imshow(np.random.randn(20, 20))
   fig.show()
   

8. 3D Scatter Plot

   fig = px.scatter_3d(x=np.random.randn(100), y=np.random.randn(100), z=np.random.randn(100))
   fig.show()
   

9. Bubble Chart

   fig = px.scatter(x=[1, 2, 3, 4], y=[1, 2, 3, 4], size=[10, 20, 30, 40])
   fig.show()
   

10. Pie Chart

    fig = px.pie(values=[1, 2, 3, 4], names=['A', 'B', 'C', 'D'])
    fig.show()
    

11. Sunburst Chart

    fig = px.sunburst(
        names=['A', 'B', 'C', 'D', 'E', 'F'],
        parents=['', 'A', 'A', 'B', 'B', 'C']
    )
    fig.show()
    

12. Treemap

    fig = px.treemap(
        names=['A', 'B', 'C', 'D', 'E', 'F'],
        parents=['', 'A', 'A', 'B', 'B', 'C']
    )
    fig.show()
    

13. Contour Plot

    fig = px.density_contour(x=np.random.randn(1000), y=np.random.randn(1000))
    fig.show()
    

14. Polar Chart

    fig = px.line_polar(r=[0, 1, 2, 3, 4], theta=[0, 45, 90, 135, 180])
    fig.show()
    

15. Radar Chart

    fig = px.line_polar(r=[1, 2, 3, 4, 1], theta=['A', 'B', 'C', 'D', 'E'], line_close=True)
    fig.show()
    

16. Candlestick Chart

    fig = go.Figure(data=[go.Candlestick(x=['2021-01-01', '2021-01-02', '2021-01-03'],
                open=[33.0, 33.3, 33.5],
                high=[33.1, 33.8, 34.0],
                low=[32.7, 32.9, 33.2],
                close=[33.3, 33.5, 33.7])])
    fig.show()
    

17. OHLC Chart

    fig = go.Figure(data=[go.Ohlc(x=['2021-01-01', '2021-01-02', '2021-01-03'],
                open=[33.0, 33.3, 33.5],
                high=[33.1, 33.8, 34.0],
                low=[32.7, 32.9, 33.2],
                close=[33.3, 33.5, 33.7])])
    fig.show()
    

18. Funnel Chart

    fig = px.funnel(y=['Website visit', 'Downloads', 'Potential customers', 'Requested price', 'invoice sent'],
                    x=[39, 27.4, 20.6, 11, 2])
    fig.show()
    

19. Waterfall Chart

    fig = go.Figure(go.Waterfall(
        name = "20", orientation = "v",
        measure = ["relative", "relative", "total", "relative", "total"],
        x = ["Sales", "Consulting", "Net revenue", "Purchases", "Profit"],
        textposition = "outside",
        text = ["+60", "+80", "", "-40", "Total"],
        y = [60, 80, 0, -40, 0],
        connector = {"line":{"color":"rgb(63, 63, 63)"}},
    ))
    fig.show()
    

20. Gantt Chart

    df = pd.DataFrame([
        dict(Task="Job A", Start='2009-01-01', Finish='2009-02-28'),
        dict(Task="Job B", Start='2009-03-05', Finish='2009-04-15'),
        dict(Task="Job C", Start='2009-02-20', Finish='2009-05-30')
    ])
    fig = px.timeline(df, x_start="Start", x_end="Finish", y="Task")
    fig.show()
    

21. Choropleth Map

    fig = px.choropleth(locations=["USA", "CAN", "MEX"], color=[1, 2, 3], scope="north america")
    fig.show()
    

22. Animated Scatter Plot

    df = px.data.gapminder()
    fig = px.scatter(df, x="gdpPercap", y="lifeExp", animation_frame="year", size="pop", color="continent", 
                     hover_name="country", log_x=True, size_max=55, range_x=[100,100000], range_y=[25,90])
    fig.show()
    

23. Density Heatmap

    fig = px.density_heatmap(x=np.random.randn(1000), y=np.random.randn(1000))
    fig.show()
    

24. Parallel Coordinates Plot

    df = px.data.iris()
    fig = px.parallel_coordinates(df, color="species_id", labels={"species_id": "Species",
                  "sepal_width": "Sepal Width", "sepal_length": "Sepal Length",
                  "petal_width": "Petal Width", "petal_length": "Petal Length", },
                    color_continuous_scale=px.colors.diverging.Tealrose)
    fig.show()
    

25. Parallel Categories Diagram

    df = px.data.tips()
    fig = px.parallel_categories(df, dimensions=['day', 'sex', 'smoker', 'time'])
    fig.show()
    

26. Ternary Plot

    fig = px.scatter_ternary(a=[1, 2, 3, 4], b=[4, 3, 2, 1], c=[2, 2, 2, 2])
    fig.show()
    

27. Indicator

    fig = go.Figure(go.Indicator(
        mode = "gauge+number",
        value = 270,
        domain = {'x': [0, 1], 'y': [0, 1]},
        title = {'text': "Speed"}))
    fig.show()
    

28. Carpet Plot

    fig = go.Figure(go.Carpet(
        a = [4, 4, 4, 4.5, 4.5, 4.5, 5, 5, 5, 6, 6, 6],
        b = [1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3],
        y = [2, 3.5, 4, 3, 4.5, 5, 5.5, 6.5, 7.5, 8, 8.5, 10]
    ))
    fig.show()
    

29. Facet Plot

    df = px.data.tips()
    fig = px.scatter(df, x="total_bill", y="tip", color="smoker", facet_col="sex")
    fig.show()
    

30. Custom Layout

    fig = go.Figure()
    fig.add_trace(go.Scatter(x=[1, 2, 3], y=[4, 5, 6], mode='lines+markers', name='Data'))
    fig.update_layout(title='Custom Plot', xaxis_title='X Axis', yaxis_title='Y Axis')
    fig.show()
    

Citations:

[1] https://www.geeksforgeeks.org/python-plotly-tutorial/
[2] https://github.com/plotly/plotly.py
[3] Open AI
[4] https://www.geeksforgeeks.org/getting-started-with-plotly-python/
[5] https://plotly.com/python/plotly-express/