**💡 Problem Formulation:** When working with data visualization in Python, a common challenge is combining multiple graphs into a single figure for comparative analysis or comprehensive presentations. Whether you’re looking to overlay line charts, juxtapose bar graphs, or create multi-panel plots, finding the right method to unite them cohesively is key. Users seek a solution for inputting several individual plots and obtaining a unified graphical representation as their output.

## Method 1: Using Matplotlib’s Subplots

Matplotlib’s `subplots()`

function provides a flexible way to create a figure with a grid of subplots. You can define the number of rows and columns to organize multiple graphs in a structured manner. Each subplot can contain a different graph, and they share a common axis label to maintain cohesion. This method is highly customizable and great for comparing similar datasets.

Here’s an example:

import matplotlib.pyplot as plt # Create a figure with 2x1 grid of subplots fig, axes = plt.subplots(2, 1) # Plot data on the first subplot axes[0].plot([1,2,3], [4,5,6]) # Plot data on the second subplot axes[1].plot([4,5,6], [1,2,3]) # Display the figure plt.show()

The output is a figure with two vertically aligned graphs.

This code snippet sets up a figure with two subplots stacked vertically. The line `plt.subplots(2, 1)`

creates a figure and a 2×1 grid of subplots. The first subplot at the top is accessed with `axes[0]`

and the second subplot below it with `axes[1]`

. Each plot function draws a simple line graph on its respective subplot, and `plt.show()`

visually renders the combined figure.

## Method 2: Using Seaborn’s FacetGrid

Seaborn is a statistical plotting library built on Matplotlib. `FacetGrid`

is one of Seaborn’s powerful features for generating a grid of plots based on the values of one or more categorical variables. It’s effective for creating complex multi-plot grids when you want to condition on different dataset features.

Here’s an example:

import seaborn as sns import matplotlib.pyplot as plt # Load an example dataset tips = sns.load_dataset('tips') # Create a grid of plots conditioned on 'time' variable g = sns.FacetGrid(tips, col='time') g.map(plt.hist, 'total_bill') # Show the plots plt.show()

The output is a figure with separate histograms for ‘Lunch’ and ‘Dinner’.

After importing Seaborn and loading the ‘tips’ dataset, we use `FacetGrid()`

to create a grid of histograms. The `col='time'`

argument specifies the variable for conditioning, creating multiple subplots. `g.map()`

applies the plotting function to each subset of the data, effectively separating the total bill amounts into lunch and dinner time slots, depicted in individual histograms.

## Method 3: Using Pandas Plotting

Pandas, Python’s premier data manipulation library, integrates with Matplotlib to offer direct plotting from DataFrames. Using the `plot()`

method with the `subplots=True`

argument can quickly generate multiple graphs for different DataFrame columns, each in their own subplot, making it superbly convenient for quick analysis.

Here’s an example:

import pandas as pd import numpy as np # Construct a DataFrame with random data df = pd.DataFrame(np.random.randn(10, 4), columns=list('ABCD')) # Plot each DataFrame column as a separate subplot axes = df.plot(subplots=True) # Show the plots plt.show()

The output is four separate line graphs, one for each column of the DataFrame.

This snippet starts with the creation of a simple DataFrame `df`

containing random numbers. Invoking the `plot()`

method with `subplots=True`

instructs pandas to draw each column in the DataFrame as a distinct line graph in separate subplots. These are automatically arranged in a grid and displayed with `plt.show()`

.

## Method 4: Using Plotly’s Subplots

Plotly is an interactive graphing library, and its subplots feature allows creating customizable and interactive multi-plot layouts. Plotly subplots enable not only the combination of multiple plots but also the incorporation of different chart types within the same figure.

Here’s an example:

from plotly.subplots import make_subplots import plotly.graph_objs as go # Initialize a figure with a 1x2 subplot layout fig = make_subplots(rows=1, cols=2) # Add a scatter plot to the first column fig.add_trace(go.Scatter(x=[1, 2, 3], y=[4, 5, 6]), row=1, col=1) # Add a bar chart to the second column fig.add_trace(go.Bar(x=[1, 2, 3], y=[6, 5, 4]), row=1, col=2) # Show the figure fig.show()

The output is an interactive figure with a scatter plot on the left and a bar chart on the right.

By using Plotly’s `make_subplots()`

function, we establish a 1×2 subplot grid. Two traces are then added to the figure: a `Scatter()`

trace and a `Bar()`

trace, which are positioned into the first row, first column, and first row, second column, respectively. `fig.show()`

displays the interactive figure in a web browser.

## Bonus One-Liner Method 5: Stacked Plots with `pyplot`

In situations where simplicity is needed, Matplotlib’s `pyplot`

interface allows stacking of multiple plots with a one-liner using the `plot()`

method multiple times before calling `show()`

.

Here’s an example:

import matplotlib.pyplot as plt # Plot multiple lines on the same axes plt.plot([1, 2, 3], [4, 5, 6]) plt.plot([1, 2, 3], [6, 5, 4]) # Show the combined plot plt.show()

The output is a single graph with two overlapping line plots.

This is the simplest method to combine multiple plots. We call `plt.plot()`

twice, each time passing a different dataset. Matplotlib overlays the second plot on top of the first on the same set of axes. Once all desired plots are added, `plt.show()`

is used to display the combined plot.

## Summary/Discussion

**Method 1:**Using Matplotlib’s Subplots. Offers granular control over the layout of multiple graphs. Strengths: highly customizable, efficient for similar datasets. Weaknesses: may become complex with a large number of subplots.**Method 2:**Using Seaborn’s FacetGrid. Best for creating a cohesive set of plots based on categorical data. Strengths: streamlined for statistical comparison. Weaknesses: less flexibility compared to Matplotlib for non-statistical tasks.**Method 3:**Using Pandas Plotting. Convenient for quick exploratory data analysis directly from DataFrames. Strengths: easy to use and quick to implement. Weaknesses: depends on Matplotlib’s backend, which may limit interactivity.**Method 4:**Using Plotly’s Subplots. Ideal for creating interactive, web-friendly visualizations. Strengths: interactive graphs, integration of different types of plots. Weaknesses: bit steeper learning curve and requires internet connection for full features.**Bonus Method 5:**Stacked Plots with`pyplot`

. Quick and easy stacking of graphs in one line. Strengths: simplest method for combining plots. Weaknesses: limited control over individual plot aesthetics and arrangement.

Emily Rosemary Collins is a tech enthusiast with a strong background in computer science, always staying up-to-date with the latest trends and innovations. Apart from her love for technology, Emily enjoys exploring the great outdoors, participating in local community events, and dedicating her free time to painting and photography. Her interests and passion for personal growth make her an engaging conversationalist and a reliable source of knowledge in the ever-evolving world of technology.