numpy.histogram() Method in Python

Syntax for NumPy histogram()

numpy.histogram(data, bins=10, range=None, weights=None, density=None)

Parameters for NumPy histogram()

The parameters for the NumPy histogram are:

Examples of NumPy histogram()

We'll examine different examples and ways to use the NumPy histogram() function.

Basic Histogram and Graphical Representation

Now, we will discuss the NumPy Histogram method using an example.

Code

# Import Libraries
import numpy as np
import matplotlib.pyplot as plt

# Data related to Coding Ninjas
ninjas_data = np.array([15, 18, 20, 22, 25, 30, 28, 24, 27, 29, 32])

# Basic histogram and graphical representation
hist_ninjas, bins_ninjas = np.histogram(ninjas_data, bins=5)
print("Histogram values:", hist_ninjas)
print("Bin edges:", bins_ninjas)
plt.hist(ninjas_data, bins=bins_ninjas, edgecolor='black')
plt.title("Coding Ninjas Histogram")
plt.xlabel("Value")
plt.ylabel("Frequency")
plt.show()

Output

Explanation

This code uses two valuable tools in Python, NumPy and Matplotlib. They function together to create a bar graph showing how often different data points about Coding Ninjas appear in this dataset. It first calculates histogram values and bin edges using the numpy.histogram method. Then, it plots the histogram using Matplotlib, customizing plot attributes like title, labels, and edge color before displaying the plot. We divide the data into five bins for the histogram.

Print Histogram with Custom Bin Arrays

Now, we will discuss an example that prints histograms with custom Bin Arrays.

Code

# Import Libraries
import numpy as np

# coding ninjas data
ninjas_data = np.array([15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65])

# Print histogram with custom bin arrays
custom_bins = np.array([10, 30, 50, 70])
hist_custom, bins_custom = np.histogram(ninjas_data, bins=custom_bins)
print("Custom Histogram values:", hist_custom)
print("Custom Bin edges:", bins_custom)

Output

Explanation

This code uses Python's NumPy library to construct a histogram for a dataset defining the ages of "coding ninjas." Initially, we reserve the ages in an array and define a custom set of bins to group these ages. The np.histogram() function, a part of the NumPy library, calculates how many ages fall into each of these custom bins. The code prints the resulting histogram values and the bin edges.

Print Histogram Based on Density Argument

Now, we will discuss an example that prints a histogram based on the density argument.

Code

# Import Libraries
import numpy as np

# Data
cn = np.array([15, 20, 18, 25, 30, 22, 17, 28, 32, 35, 40])

# Print histogram based on the density argument (probability density function)
hist_density, bins_density = np.histogram(cn, bins=5, density=True)
print("Density Histogram values:", hist_density)
print("Density Bin edges:", bins_density)

Output

Explanation

This code uses NumPy to create a density histogram for "coding ninjas" age data. The 'cn' array holds ages. We use ‘np.histogram()' with density set to 'True' for the probability density function. We split the data into five bins, placing histogram values in 'hist_density' and bin edges in 'bins_density.' We will further discuss numpy.histogram() Method in Python use cases.

Use case and Applications of NumPy histogram

Some of the use cases and applications of NumPy histogram are:

• Data Binning: To create histograms, Numpy.histogram() divides data into intervals (bins) and counts the occurrences in each bin.
• Visualization: Creates effective visual representations of data distributions when used with plotting tools (such as Matplotlib).
• Financial Analysis: Histograms can illustrate patterns in economic data, helping with risk assessment, portfolio analysis, and trading strategies.
• Image Processing: Histogram equalization, a technique used in image processing, can enhance contrast and improve visual quality.

Frequently Asked Questions

What role does the density parameter play?

You can normalize the histogram so that the area beneath it equals one by using the density option in the numpy.histogram() function. 

Can numpy.histogram() work with arrays of multi-dimensions?

You can use multi-dimensional arrays with numpy.histogram(). However, the array is treated as if it's flat. To make histograms for specific dimensions, you must change the shape or cut the array accordingly before using the function.

What factors should you consider when determining the number of bins for your histogram?

Choosing the correct number of bins is crucial. Oversimplifying the distribution could result from using too few or too many bins.

Can you use numpy.histogram() for categorical or qualitative data?

People mostly use the numpy.histogram() function for numbers. If you have words or categories, you can use numpy.unique() to count how often each appears.

What are the use cases of the numpy.histogram() method in Python?

We can use numpy.histogram() to analyze data distributions, imagine frequencies, and create histograms in Python. It helps you learn how data is structured and create decisions based on it.

Conclusion

In this article, we have learned about the Numpy histogram method in Python. We also know how to create Numpy Histograms. We have even explored the syntax of the Numpy histogram method in Python. Further, we learned about the attributes and parameters associated with the Numpy histogram. We also learned about this in more detail through different examples. We even explored areas where the developer can use the Numpy histogram method.

Check out the link to learn more about such a topic.

• NumPy.Reshape() in Python
• NumPy.Transpose() in Python
• NumPy Basics
• Python packages
   

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