5 Best Ways to Normalize a Histogram in Python

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πŸ’‘ Problem Formulation: When dealing with histograms in Python, normalization is often required to compare the shape of distributions or to apply statistical methods that assume normality. Specifically, normalizing a histogram entails adjusting the data such that the area under the histogram sums to one, making it a probability density. For example, if your input is a NumPy array of values, the desired output is a normalized histogram array and corresponding bin edges.

Method 1: Using NumPy for Manual Histogram Normalization

The NumPy library offers tools for histogram computation and manipulation. To normalize a histogram manually, divide the count in each bin by the total number of observations and the bin width. This results in a density-based histogram, where the integral over the range is 1.

Here’s an example:

import numpy as np

# Sample data
data = np.random.randn(1000)

# Compute histogram
hist, bins = np.histogram(data, bins=50)

# Normalize histogram
hist_normalized = hist / (np.sum(hist) * np.diff(bins))

# Display normalized histogram
print(hist_normalized)

The output will be an array containing the normalized values of the histogram, which when plotted, will yield a normalized histogram.

This method involves direct computation with NumPy arrays, making it a more explicit and instructive approach. The division by the sum of histogram counts and the bin width converts raw frequencies into probabilities.

Method 2: Using Matplotlib’s Normalization Feature

Matplotlib’s hist() function can directly normalize a histogram if the density parameter is set to True. This method allows you to visualize the normalized histogram and also get the values for further analysis without manual calculations.

Here’s an example:

import matplotlib.pyplot as plt
import numpy as np

data = np.random.randn(1000)

# Create a normalized histogram with Matplotlib's hist() function
n, bins, patches = plt.hist(data, bins=50, density=True)

plt.show()

The output is a normalized histogram plot with the bin heights representing the probability density.

By using Matplotlib’s density parameter, the library internally computes the normalization rendering the histogram as a probability density. This is beneficial for both the visualization and understanding of the data’s distribution.

Method 3: Using the Scipy Library

Scipy’s gaussian_kde() function can be used to estimate the probability density function of a dataset, effectively normalizing the histogram. Scipy is particularly useful for larger datasets and more complex analyses.

Here’s an example:

from scipy.stats import gaussian_kde
import numpy as np

# Generate some data
data = np.random.randn(1000)

# Calculate the kernel density estimate
kde = gaussian_kde(data)

# Evaluate the estimate on a grid
grid = np.linspace(min(data), max(data), 100)
kde_values = kde(grid)

# Normalize the histogram using the estimated density
hist_normalized = kde_values / np.sum(kde_values)

print(hist_normalized)

The output will be an array of the normalized probabilities of the histogram estimated through a kernel density function.

This snippet demonstrates how to employ Scipy’s gaussian_kde() to estimate and normalize a histogram. The key advantage here is getting a smooth estimate of the probability density function, which is particularly useful for continuous data.

Method 4: Utilizing Pandas for Quick Normalization

Pandas library with its high-level data manipulation tools also supports straightforward histogram normalization through the plot.hist() function by exploiting the underlying Matplotlib library for plotting.

Here’s an example:

import pandas as pd
import numpy as np

# Creating a Pandas Series from numpy array
data = pd.Series(np.random.randn(1000))

# Plotting the normalized histogram
data.plot.hist(bins=50, density=True)

When run, this code block results in a normalized histogram plot drawn directly from a pandas Series object.

In this code, Pandas simplifies the data structure management, providing a rapid plotting interface to achieve normalization with zero manual calculations.

Bonus One-Liner Method 5: Using Seaborn for Elegant Normalized Plots

Seaborn is a statistical plotting library that works on top of Matplotlib, offering an even higher level of abstraction and ease for normalization with aesthetically pleasing results by default.

Here’s an example:

import seaborn as sns
import numpy as np

# Sample data
data = np.random.randn(1000)

# One-liner to plot a normalized histogram using seaborn
sns.histplot(data, kde=False, stat="density")

This will generate a polished normalized histogram visual which easily translates the distribution character of the dataset.

The Seaborn library’s histplot() function, with its defaults, is capable of returning a normalized histogram which is ideal for quick exploratory data analysis and presentations.

Summary/Discussion

  • Method 1: Manual Normalization with NumPy. Offers full control and is highly instructive. However, it requires a solid understanding of histogram normalization mechanics.
  • Method 2: Matplotlib’s Density Parameter. Excellent for immediate visualization. Can be less transparent for beginners trying to understand the underlying normalization process.
  • Method 3: Scipy’s Gaussian KDE. Provides a smooth density estimate which is great for analysis but may obscure individual data properties due to smoothing.
  • Method 4: Pandas Plot Histogram. Quick and user-friendly, leveraging both Pandas and Matplotlib advantages, but offers less flexibility in terms of plot customization.
  • Bonus Method 5: Seaborn’s Histplot. Combines elegance and simplicity. Ideal for presentations but less suitable for learning the foundational aspects of data normalization.