When it comes to data visualization, Matplotlib has been a go-to tool for many programmers and data scientists. With its extensive library of customizable plots and charts, it allows for the creation of visually appealing and informative visuals. One common challenge when working with Matplotlib is creating a plot with a shared X-axis, where two or more plots share the same horizontal axis. In this article, we will explore how to enable shared X-axis in Matplotlib without displaying tick labels for both axes.
To begin with, let's consider a scenario where we have two plots, one representing the sales data for the first quarter of the year and the other representing the sales data for the second quarter. We want to compare the sales data for both quarters on a single plot, with a shared X-axis. This can be achieved using the `twinx()` function in Matplotlib, which creates a twin Axes sharing the X-axis with the original axes. However, by default, this function also displays the tick labels for both axes, which can make the plot look cluttered and confusing.
To overcome this issue, we can use the `get_shared_x_axes()` function in Matplotlib. This function allows us to specify the axes that we want to share and the direction in which the axes should be shared. In our case, we want to share the X-axis in the vertical direction. So, we will use `get_shared_x_axes()` to create a shared axes object and then use that object to create our plots. This will ensure that the tick labels are only displayed for the bottom plot, which represents the second quarter sales data.
Let's see how this can be implemented in code:
```python
import matplotlib.pyplot as plt
# create a figure and axes object for the first plot
fig, ax1 = plt.subplots()
# plot the sales data for the first quarter
ax1.plot([1, 2, 3, 4], [500, 600, 700, 800], label='Q1 Sales')
# create a shared axes object for the X-axis
ax2 = ax1.get_shared_x_axes().inverted()
# create a twin axes object for the second plot
ax3 = ax2.twinx()
# plot the sales data for the second quarter
ax3.plot([1, 2, 3, 4], [600, 700, 800, 900], label='Q2 Sales')
# hide the tick labels for the top plot
ax3.set_xticks([])
# set labels and legend
ax1.set_xlabel('Month')
ax1.set_ylabel('Sales (in thousands)')
ax3.set_ylabel('Sales (in thousands)')
ax1.legend(loc='upper left')
ax3.legend(loc='upper right')
# display the plot
plt.show()
```
In the above code, we first create a figure and axes object for the first plot. Then, we plot the sales data for the first quarter on this axes object. Next, we use `get_shared_x_axes()` to create a shared axes object for the X-axis. We then use this shared axes object to create a twin axes object for the second plot. Finally, we plot the sales data for the second quarter on this twin axes object.
To hide the tick labels for the top plot, we use the `set_xticks([])` function, which sets the X-axis tick locations to an empty list. This ensures that no tick labels are displayed for the top plot.
In the end, we set the labels and legend for both plots and display the final plot using `plt.show()`. The resulting plot will have a shared X-axis without displaying tick labels for both axes, making it easier to compare the sales data for both quarters.
In conclusion, the `get_shared_x_axes()` function in Matplotlib allows us to create a shared X-axis without displaying tick labels for both axes. This can be useful in situations where we want to compare data on a common horizontal axis but do not want to clutter the plot with tick labels. With this knowledge, you can now create more organized and informative plots using Matplotlib.
