Decluttering Your Matplotlib Plots, A Simple Guide

When visualizing data with Matplotlib in Python, it’s common to run into the issue of cluttered x-axis or y-axis ticks. Especially for dense time series data, the axis can become a confusing jumble of overlapping labels. The good news? Matplotlib offers easy-to-use tools to help you make your plots clearer and more professional-looking. Today, we’re diving into a simple approach to declutter your x-axis ticks, focusing on time series data.

Problem Statement

Consider you have time series data, say, stock closing prices sampled every 5 minutes. If you try to plot the closing prices against time directly using Matplotlib, you’ll probably get an x-axis swamped with tick labels, especially for long trading sessions. This makes the plot less readable and, at times, counter-productive.

import matplotlib.pyplot as plt

# Extract the time series data
time_series = data['Time Series (5min)']

# Extract time and closing prices
times = sorted(list(time_series.keys()))
prices = [float(time_series[t]['4. close']) for t in times]

# Plot
plt.figure(figsize=(10, 6))
plt.plot(times, prices, '-o', marker="*")
plt.xticks(rotation=45)
plt.xlabel('Time')
plt.ylabel('Close Price')
plt.title('Closing Price vs Time')
plt.tight_layout()
plt.show()

If you’ve done something similar, you know that the result can be less than ideal: overlapping x-tick labels.

The Solution: Adjusting X-Ticks

Luckily, Matplotlib provides the tools we need to make our x-axis more comprehensible.

Step 1: First, import the necessary libraries:

from matplotlib.ticker import MultipleLocator

Step 2: Determine the number of ticks you want on your x-axis. A general guideline might be to aim for 5-10 ticks for clarity, but this can vary based on the length of your dataset and the specific context.

n = len(times) // 10  # Display approximately 10 x-ticks

Step 3: Set the tick locator for the x-axis using the MultipleLocator:

ax = plt.gca()  # Get the current axes
ax.xaxis.set_major_locator(MultipleLocator(n))

After these steps, the modified code will look something like:

import matplotlib.pyplot as plt
from matplotlib.ticker import MultipleLocator

# Extract the time series data
time_series = data['Time Series (5min)']

# Extract time and closing prices
times = sorted(list(time_series.keys()))
prices = [float(time_series[t]['4. close']) for t in times]

# Plot
plt.figure(figsize=(10, 6))
plt.plot(times, prices, '-o', marker="*")
plt.xticks(rotation=45)
plt.xlabel('Time')
plt.ylabel('Close Price')
plt.title('Closing Price vs Time')

# Adjust the x-ticks to reduce clutter
ax = plt.gca()
n = len(times) // 10
ax.xaxis.set_major_locator(MultipleLocator(n))

plt.tight_layout()
plt.show()

Now the plot will looks something like this, much better: