**💡 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.