A normalized histogram is a histogram in which the area under the histogram sums to one. This is achieved by dividing the count of each bin by the total number of data points and the width of the bin. Normalizing a histogram allows for comparison between different datasets or different bin sizes, as it represents the probability density function of the variable.
In Python, you can create a normalized histogram using Matplotlib by setting the density parameter to True in the hist function. Here's an example:
import matplotlib.pyplot as plt
import numpy as np
# Sample data
data = np.random.randn(1000)
# Create a normalized histogram
plt.hist(data, bins=30, density=True)
plt.title('Normalized Histogram')
plt.xlabel('Value')
plt.ylabel('Density')
plt.show()
This code generates a normalized histogram of the sample data.
