Python Tutorial - Small multiples
# Enhancing Data Visualization: Using Small Multiples in Matplotlib
## The Problem of Overcrowded Plots
When working with data visualization, it's easy to fall into the trap of overloading a single plot with too much information. Adding more data to a plot can often make it harder to discern patterns rather than making them clearer. For instance, consider plotting the average precipitation in Seattle throughout the year. If we also include the 25th and 75th percentiles of precipitation as dashed lines above and below the average, the plot becomes informative. However, this simplicity changes when comparing data from another city like Austin.
By adding Austin's data to the same plot, the visualization quickly becomes cluttered. The result is a messy display that obscures the patterns we're trying to observe. This highlights the need for a more organized approach to presenting multiple datasets side by side.
## Introducing Small Multiples
A effective solution to this problem is the use of "small multiples," which are multiple small plots that display similar data across different conditions or categories. For example, instead of combining Seattle and Austin's precipitation data into one plot, we can create separate subplots for each city. This approach makes it easier to compare data while keeping the visualization clean and organized.
In Matplotlib, these small multiples are referred to as "subplots." The function that creates these subplots is also named `subplots`, reflecting its purpose. Previously, this function was used without any inputs, generating a single subplot. However, by providing inputs, we can create multiple subplots arranged in a grid format with rows and columns.
## Creating Subplots in Matplotlib
To create small multiples using Matplotlib, you first need to initialize a figure object. This figure will contain the subplots, which are typically arranged as a grid. For example, if we want three rows and two columns of subplots, we can set this up by specifying these dimensions when creating the figure.
Before any data is added, the figure consists of an array of axes objects. Instead of having just one axis object (`ax`), you now have an array of axis objects with a shape corresponding to the number of rows and columns specified (e.g., 3x2 for three rows and two columns). To add data to each subplot, you must index into this array and call the `plot()` method on the specific element of the array.
There's also a special case when dealing with only one row or column of plots. In such scenarios, the resulting array will be one-dimensional, meaning you only need to provide one index to access the elements within the array.
## Adding Data and Labels to Subplots
Let's take an example where we want to compare rainfall data between two cities: Seattle and Austin. We'll create a figure with two rows and one column of subplots. The first subplot will display Seattle's data, while the second will show Austin's.
1. **Creating the Figure and Axes Array**:
```python
fig, ax = plt.subplots(2, 1)
```
Here, `ax` is a one-dimensional array containing two elements, corresponding to the two subplots.
2. **Adding Data to Subplots**:
- For the first subplot (Seattle):
```python
ax[0].plot(seattle_data)
ax[0].set_title('Seattle Precipitation')
```
- For the second subplot (Austin):
```python
ax[1].plot(austin_data)
ax[1].set_title('Austin Precipitation')
```
3. **Adding Labels**:
Since the subplots are stacked vertically, you should add y-axis labels to both plots for clarity. However, x-axis labels should only be added to the bottom subplot to avoid repetition. This ensures that the visualization remains uncluttered.
## Ensuring Consistent Y-Axis Ranges
One potential issue when creating multiple subplots is that their y-axis ranges might differ due to variations in data scales. To address this, you can initialize the figure and its subplots with the `sharey` parameter set to `True`. This ensures that all subplots share the same y-axis range, making comparisons between datasets more straightforward.
```python
fig, ax = plt.subplots(2, 1, sharey=True)
```
By implementing these steps, you can create clean and organized visualizations that facilitate direct comparisons between different datasets.
## Conclusion
Adding too much data to a single plot can obscure patterns rather than reveal them. Using small multiples in the form of subplots is an effective way to present multiple datasets while maintaining clarity. By arranging plots in a grid format and ensuring consistent axis ranges, you can create visualizations that are both informative and easy to interpret.
Next, practice creating visualizations with small multiples to enhance your data storytelling capabilities.