Understanding Area Charts and X-Axis Label Display Issues with Matplotlib

In this article, we will delve into the world of area charts using matplotlib. We’ll explore how to create an area chart and why the x-axis labels are not displaying.

Introduction to Area Charts

An area chart is a type of chart that displays the cumulative total or accumulation of data points over a specific period. It’s commonly used in finance, economics, and other fields where trends need to be visualized.

Matplotlib provides an efficient way to create area charts using its built-in plotting functions. In this section, we’ll discuss how to create a simple area chart.

Creating an Area Chart with Matplotlib

To create an area chart with matplotlib, you can use the plot.area() function. This function plots the area under the curve of the data points.

Here’s a basic example:

import pandas as pd
import matplotlib.pyplot as plt

# Create some sample data
data = [(12.20, 14.50, 15.60, 24.80),
        (11.30, 8.70, 23.20, 10.0),
        (6.40, 10.70, 22.60, 11.60),
        (15.0, 16.0, 23.40, 10.8)]

# Create a pandas DataFrame from the data
df = pd.DataFrame(data=data, index=['2019Q3', '2019Q2', '2019Q1', '2018Q4'], columns=['Expense Management', 'Guidance', 'Interest Rate and Deposit Costs', 'Loan Growth'])

# Plot the area chart
plt.figure(figsize=(10,6))
df.plot.area(stacked=True)

# Display the plot
plt.show()

In this example, we create a pandas DataFrame from some sample data. We then use the plot.area() function to create an area chart.

X-Axis Label Issues

Now that we’ve created an area chart, let’s discuss why the x-axis labels are not displaying.

When you create an area chart with matplotlib, the default behavior is to hide the x-axis labels. This can be done using the get_xaxis().set_visible() function.

However, in your example code, you’re trying to display the index labels on the x-axis by calling graph2.axes.get_xaxis().set_visible(True). But why isn’t this working for you?

The Issue with X-Axis Label Display

The issue is that when you create an area chart using the plot.area() function, the resulting plot object doesn’t have any axes. That’s right - there are no axes!

When we call graph2.axes.get_xaxis().set_visible(True), it’s trying to set the visibility of an axis that doesn’t exist.

The Solution

To fix this issue, you need to explicitly create a set of axes using the plt.subplots() function.

Here’s how you can modify your code:

import pandas as pd
import matplotlib.pyplot as plt

# Create some sample data
data = [(12.20, 14.50, 15.60, 24.80),
        (11.30, 8.70, 23.20, 10.0),
        (6.40, 10.70, 22.60, 11.60),
        (15.0, 16.0, 23.40, 10.8)]

# Create a pandas DataFrame from the data
df = pd.DataFrame(data=data, index=['2019Q3', '2019Q2', '2019Q1', '2018Q4'], columns=['Expense Management', 'Guidance', 'Interest Rate and Deposit Costs', 'Loan Growth'])

# Plot the area chart
fig, ax = plt.subplots(figsize=(10,6))
df.plot.area(ax=ax)

ax.get_yaxis().set_visible(False)
ax.get_xaxis().set_visible(True)

# Display the plot
plt.show()

In this modified code, we’re explicitly creating a set of axes using plt.subplots(). We then pass the resulting axis object to the plot.area() function.

By doing so, we ensure that the x-axis labels are displayed correctly on the area chart.

Additional Tips and Tricks

Here are some additional tips and tricks for working with area charts in matplotlib:

To customize the appearance of your area chart, you can use various options available in the plot.area() function. For example, you can specify the color scheme, line width, and marker style.
If you want to create an overlay plot that sits on top of another plot, you can use the plt.plot() function along with the alpha parameter to set the transparency level.

import pandas as pd
import matplotlib.pyplot as plt

# Create some sample data
data = [(12.20, 14.50, 15.60, 24.80),
        (11.30, 8.70, 23.20, 10.0),
        (6.40, 10.70, 22.60, 11.60),
        (15.0, 16.0, 23.40, 10.8)]

# Create a pandas DataFrame from the data
df = pd.DataFrame(data=data, index=['2019Q3', '2019Q2', '2019Q1', '2018Q4'], columns=['Expense Management', 'Guidance', 'Interest Rate and Deposit Costs', 'Loan Growth'])

# Plot the area chart
fig, ax = plt.subplots(figsize=(10,6))
df.plot.area(ax=ax)

ax.get_yaxis().set_visible(False)
ax.get_xaxis().set_visible(True)

# Create an overlay plot with a different color scheme
ax2 = ax.twinx()
ax2.plot(df['Expense Management'], color='red', alpha=0.5)

# Display the plot
plt.show()

In this example, we create an overlay plot using ax.twinx() and plot it on top of the original area chart.

By following these tips and tricks, you can create stunning area charts that showcase your data in a visually appealing way.

Conclusion

Creating area charts with matplotlib is a breeze. By understanding how to use various options available in the plot.area() function, you can customize the appearance of your chart to suit your needs.

Remember to explicitly create a set of axes using plt.subplots(), and use the get_xaxis().set_visible() function to display the x-axis labels correctly.

With these tips and tricks under your belt, you’re ready to create stunning area charts that impress your audience. Happy plotting!

Last modified on 2024-03-22