Plotting Multiple Dataframes in a Single Output Using Python Bokeh: A Step-by-Step Guide to Creating Legends for Each Plot in a Loop

Are you tired of cluttered and confusing plots? Do you want to visualize multiple dataframes in a single output, with each plot having its own legend? Look no further! In this article, we’ll show you how to use Python’s Bokeh library to plot multiple dataframes in a single output, with a unique legend for each plot, all within a loop. Buckle up, because we’re about to dives into the world of Bokeh!

What is Bokeh?

Bokeh is an interactive visualization library in Python that provides elegant, concise construction of versatile graphics, and also delivers this capability with high-performance interactivity over large or streaming datasets.

What are Dataframes?

Dataframes are two-dimensional labeled data structures with columns of potentially different types. They are a fundamental concept in data analysis and data science, and are used to store and manipulate data in Python using the popular Pandas library.

Why Plot Multiple Dataframes in a Single Output?

Plotting multiple dataframes in a single output can be useful in various scenarios, such as:

• Comparing similar data across different categories or groups
• Visualizing relationships between different variables or datasets
• Showing trends or patterns across multiple datasets
• Creating interactive and dynamic dashboards for data exploration

The Problem: Creating Legends for Each Plot in a Loop

When plotting multiple dataframes in a single output, one of the biggest challenges is creating unique legends for each plot. This can be especially tricky when using a loop to generate the plots, as the legends can quickly become cluttered and confusing.

The Solution: Using Bokeh’s `figures` and `legend_items`

Bokeh provides a solution to this problem through its `figures` and `legend_items` functions. By creating a separate figure for each plot, and then adding legend items to each figure, we can generate unique legends for each plot, even when using a loop.

Step-by-Step Guide to Plotting Multiple Dataframes in a Single Output with Unique Legends

Now that we’ve set the stage, let’s dive into the step-by-step guide to plotting multiple dataframes in a single output with unique legends for each plot, using Bokeh’s `figures` and `legend_items` functions.

Step 1: Import Required Libraries and Load Data

import pandas as pd
import numpy as np
from bokeh.plotting import figure, show, ColumnDataSource
from bokeh.layouts import gridplot
from bokeh.models import Legend

# Load data
df1 = pd.DataFrame({'x': [1, 2, 3, 4, 5], 'y': [2, 4, 6, 8, 10]})
df2 = pd.DataFrame({'x': [1, 2, 3, 4, 5], 'y': [10, 8, 6, 4, 2]})
df3 = pd.DataFrame({'x': [1, 2, 3, 4, 5], 'y': [5, 5, 5, 5, 5]})

Step 2: Create a List of Dataframes and Plotting Parameters

# Create a list of dataframes
dataframes = [df1, df2, df3]

# Create a list of plotting parameters
plot_params = [{'color': 'red', 'label': 'Plot 1'}, 
               {'color': 'green', 'label': 'Plot 2'}, 
               {'color': 'blue', 'label': 'Plot 3'}]

Step 3: Create a Loop to Generate Plots and Legends

# Create a list to store figures
figures = []

# Create a loop to generate plots and legends
for i, (df, params) in enumerate(zip(dataframes, plot_params)):
    # Create a figure
    fig = figure(title=params['label'], width=300, height=300)

    # Add a line plot to the figure
    fig.line('x', 'y', source=ColumnDataSource(df), color=params['color'], legend_label=params['label'])

    # Add a legend item to the figure
    legend = Legend(items=[(params['label'], [fig.line[0]])], location='top_right')
    fig.add_layout(legend, 'right')

    # Add the figure to the list
    figures.append(fig)

Step 4: Create a Gridplot and Show the Output

# Create a gridplot
grid = gridplot(figures, ncols=2)

# Show the output
show(grid)

Output

The output of the above code will be a gridplot with three plots, each with its own unique legend. The legends will be displayed on the top right of each plot, and will only show the label for the corresponding plot.

Conclusion

In this article, we’ve shown you how to plot multiple dataframes in a single output using Python’s Bokeh library, with unique legends for each plot generated within a loop. By using Bokeh’s `figures` and `legend_items` functions, we can create interactive and dynamic plots that are easy to read and understand. Whether you’re a data scientist, data analyst, or simply a data enthusiast, Bokeh’s `figures` and `legend_items` functions are an essential tool in your data visualization toolkit.

Additional Tips and Tricks

• Use Bokeh’s ` ColumnDataSource` to create dynamic and interactive plots
• Customize your plots using Bokeh’s various plotting functions, such as `line`, `circle`, and `scatter`
• Use Bokeh’s `hovertool` and `tooltip` to add interactive tooltips to your plots
• Experiment with different legend locations and orientations to find the one that works best for your plot
• Use Bokeh’s `gridplot` to create complex layouts with multiple plots

Happy plotting!

Frequently Asked Question

Get ready to unleash the power of Bokeh and create stunning plots with legends in no time!