Adjusting the number of bins in a histogram

The histogram you just made had ten bins. This is the default of matplotlib. The "square root rule" is a commonly-used rule of thumb for choosing number of bins: choose the number of bins to be the square root of the number of samples. Plot the histogram of Iris versicolor petal lengths again, this time using the square root rule for the number of bins. You specify the number of bins using the bins keyword argument of plt.hist().

The plotting utilities are already imported and the seaborn defaults already set. The variable versicolor_petal_length contains an array of petal lengths and is already in your namespace.

This is a part of the course

“Statistical Thinking in Python (Part 1)”

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Exercise instructions

Import numpy as np. This gives access to the square root function, np.sqrt().
Determine how many data points you have using len().
Compute the number of bins using the square root rule.
Convert the number of bins to an integer using the built in int() function.
Generate the histogram and make sure to use the bins keyword argument.
Hit submit to plot the figure and see the fruit of your labors!

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Import numpy


# Compute number of data points: n_data


# Number of bins is the square root of number of data points: n_bins


# Convert number of bins to integer: n_bins


# Plot the histogram


# Label axes
_ = plt.xlabel('petal length (cm)')
_ = plt.ylabel('count')

# Show histogram
plt.show()

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