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Four Plotting Tips in Python

TECHNOLOGY

When visualizing data, it’s often necessary to plot multiple charts in a single figure. For example, if you want to visualize the same variable from different perspectives (e.g., side-by-side histograms and boxplots of numerical variables), multiple plots are useful. In this article, I share 4 simple but practical tips for plotting multiple charts.

Dataset 📦

Let’s import packages and update default settings to add some personal style to the charts. We’ll use Seaborn’s built-in dataset ’tips':

import seaborn as sns  # v0.11.2  
import matplotlib.pyplot as plt  # v3.4.2  
sns.set(style='darkgrid', context='talk', palette='rainbow')
df = sns.load_dataset('tips')  
df.head()

Tip 1: plt.subplots()

An easy way to plot multiple subplots is to use plt.subplots(). Here’s an example syntax for plotting 2 side-by-side subplots:

fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(10,4))  
sns.histplot(data=df, x='tip', ax=ax[0])  
sns.boxplot(data=df, x='tip', ax=ax[1]);

You can further customize each subplot, for example by adding titles:

fig, ax = plt.subplots(1, 2, figsize=(10,4))  
sns.histplot(data=df, x='tip', ax=ax[0])  
ax[0].set_title("Histogram")  
sns.boxplot(data=df, x='tip', ax=ax[1])  
ax[1].set_title("Boxplot");

To plot the same pair of charts for all numerical variables in a loop:

numerical = df.select_dtypes('number').columns
for col in numerical:  
    fig, ax = plt.subplots(1, 2, figsize=(10,4))  
    sns.histplot(data=df, x=col, ax=ax[0])  
    sns.boxplot(data=df, x=col, ax=ax[1]);

Tip 2: plt.subplot()

Another way to visualize multiple plots is using plt.subplot() (note: no “s” at the end). The syntax differs slightly:

plt.figure(figsize=(10,4))  
ax1 = plt.subplot(1, 2, 1)  
sns.histplot(data=df, x='tip', ax=ax1)  
ax2 = plt.subplot(1, 2, 2)  
sns.boxplot(data=df, x='tip', ax=ax2);

This method is especially useful when plotting the same type of chart for multiple variables side-by-side in a single figure:

plt.figure(figsize=(14,4))  
for i, col in enumerate(numerical):  
    ax = plt.subplot(1, len(numerical), i+1)  
    sns.boxplot(data=df, x=col, ax=ax)

You can also add titles to each subplot: 

plt.figure(figsize=(14,4))  
for i, col in enumerate(numerical):  
    ax = plt.subplot(1, len(numerical), i+1)  
    sns.boxplot(data=df, x=col, ax=ax)   
    ax.set_title(f"Boxplot of {col}")

Tip 3: plt.tight_layout()

When plotting multiple charts, labels sometimes overlap neighboring subplots, like this:

categorical = df.select_dtypes('category').columns
plt.figure(figsize=(8, 8))  
for i, col in enumerate(categorical):  
    ax = plt.subplot(2, 2, i+1)  
    sns.countplot(data=df, x=col, ax=ax)

The x-axis labels of the top plots get cut off, and y-axis labels overlap. Using plt.tight_layout() fixes this nicely:

plt.figure(figsize=(8, 8))  
for i, col in enumerate(categorical):  
    ax = plt.subplot(2, 2, i+1)  
    sns.countplot(data=df, x=col, ax=ax)   
plt.tight_layout()

Tip 4: plt.suptitle()

Add an overall title to the whole figure:

plt.figure(figsize=(8, 8))  
for i, col in enumerate(categorical):  
    ax = plt.subplot(2, 2, i+1)  
    sns.countplot(data=df, x=col, ax=ax)   
plt.suptitle('Category counts for all categorical variables')  
plt.tight_layout()

You can also customize individual subplots by adding titles, legends, or other features as needed.

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