Binning is a process used to group a set of data points in somewhat equal intervals (bins). It gives us the ability to reduce the oscillation, or noise, in our data, and make it easier to observe trends, gaps, and clusters.
We can typically arrange our data into any number of bins, depending on the detail and precision we’d like. In this tutorial, we’ll tackle how you can modify the number of bins in Python.
Step 1: Importing the Necessary Libraries
Before we can start, we need to invoke some important Python libraries. Here, we will be using two namely: NumPy and Matplotlib. These libraries are a staple in data visualization and computation in Python. You might want to check out how to install them here and here, respectively, if you have not done so yet.
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# Importing libraries import numpy as np import matplotlib.pyplot as plt |
Step 2: Creating Data
For this tutorial, let’s create a random set of data using NumPy’s “random” module. This module lets us generate random numbers, which we can set up to follow a seamless distribution.
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# Setting up random data data = np.random.randn(1000) |
Step 3: Visualizing the Data With Different Number of Bins
We will use Matplotlib’s “hist” method to create a histogram. The number of bins provided will rationalize how the data points are grouped. In the following example, we will start with 10 bins, but you can adjust this to any number of your preference.
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plt.hist(data, bins=10) plt.title('Histogram with 10 bins') plt.show() |
Full Code
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# Importing libraries import numpy as np import matplotlib.pyplot as plt # Setting up random data data = np.random.randn(1000) # Displaying the histogram plt.hist(data, bins=10) plt.title('Histogram with 10 bins') plt.show() |
Output
Conclusion
And there you have it! In just three easy steps, we have learned to adjust the number of bins in our histograms in Python.
This handy tool will give you more flexibility when visualizing your data, allowing you to refine your analysis and deliver high-quality research. Stay tuned for more of these practical guides for efficient Python coding!