Mastering Histograms: Plotting and Styling with Python’s Matplotlib

Plotting a Histogram in Python using Matplotlib

Data analysis is one of the most critical aspects of drawing insights from data. Histograms represent one of the most powerful tools in the field of data analysis.

Histograms are used to visualize the distribution of a dataset. A histogram is a graph that shows an estimation of the probability distribution of a dataset.

Python has several libraries for data analysis, but we shall use Matplotlib library to learn how to plot histograms in Python. This article will discuss how to plot histograms and derive bins for histograms using Python’s Matplotlib library.

Before plotting the histogram, collect the data. In Python, data can be in the form of a list, a tuple, or an array.

Determine the number of bins to use in the histogram. In Matplotlib library, the number of bins is an optional parameter.

By default, the algorithm of Matplotlib will create ten equal-width bins in the range of the data. A bin is a range of data that is divided into intervals or ranges for the purpose of analysing distributions.

Plotting a histogram requires three primary keywords: data, histogram, and plot. Given the data, we can plot a histogram by calling the histogram() function in Matplotlib.

Deriving Bins for Histogram

1. Introduction to Bins

Bins are used to divide the entire range of data into a series of intervals or ranges. Each interval represents a certain class, while the height of the interval represents the frequency or number of data points that fall within that interval.

2. Deriving Bins

In deriving formulas for bins, statisticians look for certain properties, which aim to optimize the information that can be visualized in the histogram. Given data points, let us see how we can derive bins for a histogram.

We can start by creating a frequency table, which helps us understand the frequency of the data points based on their individual values. The frequency table breaks the data into smaller groups, after which we create ranges or bins that can capture the frequency of the various groups.

These bins can then be used to plot the histogram.

Plotting Histogram with Derived Bins in Python

Having derived the bins, we can now proceed to plot the histogram. The number of bins is an important aspect that affects the amount of detail the histogram shows.

The height of each interval represents the frequency of the data in that specific interval. The bins are arranged sequentially, with the intervals to the left representing smaller data points, and those to the right representing larger data points.

In Matplotlib, we can customize the plot to include a title, x and y-axis labels, and a legend that explains the plot’s meaning.

Conclusion

Histograms are versatile and powerful tools that allow us to visualize the distribution of data. They are commonly used in data analysis, statistical research, and scientific fields such as medical and weather analysis.

In this article, we have learned how to plot histograms and derive bins for histograms using Python’s Matplotlib library. Whether one is in the software engineering, data analysis or scientific research fields, understanding how to plot histograms and derive bins for histograms is a crucial skill, which will prove valuable in their career growth.

Histograms are an essential tool for data analysis because they allow us to visualize the distribution of a dataset. However, plotting the histogram is just one step in our data analysis, and we can take our analysis further by exploring other aspects of the data, such as its skew and styling.

In this addition to our article, we will explore how to derive skew in Python using Scipy library and how to style histograms.

Deriving Skew in Python using Scipy Library

1. Introduction to Skewness

Skewness is a statistical measure that determines the degree of asymmetry in a dataset. A symmetrical dataset has a skewness value of zero, while a dataset with positive skewness has more data points to the right of the center and fewer points to the left.

A dataset with a negative skewness value has more data points to the left of the center and fewer points to the right. Python’s Scipy library has a skew() function that calculates the skewness of a dataset.

2. Deriving Skewness

To derive the skew in Python using Scipy library, we first import the necessary libraries – Numpy and Scipy. We then create an array with our data points.

Suppose we have data points in a list: [10, 20, 15, 30, 40, 25, 35, 50]. We can convert the list to an array using Numpy’s array() function.

Once we have the array, we can call Scipy’s skew() function to derive the skewness of the data. In our example, we would write:

``````import numpy as np
from scipy.stats import skew
data = np.array([10, 20, 15, 30, 40, 25, 35, 50])
print("Skew:", skew(data))
``````

This code will output the skewness of our dataset as 0.23444750337424107. Since the skewness is positive, we know our dataset is slightly skewed to the right.

Styling Histograms in Python

1. Introduction to Styling

Aesthetics play a crucial role in data visualization because they make the charts more appealing and easier to understand. Python provides many options for customizing the style of a histogram using Matplotlib’s pyplot library.

2. Styling Histograms

We can adjust the color, line style, and marker style of the histogram to make it more visually appealing. To style our histogram, we first import Matplotlib’s pyplot library:

``````import matplotlib.pyplot as plt
``````

We then plot our histogram with customized styling.

For example, we can change the color of the histogram. The default color is blue, but we can change it to any color using the color parameter.

Suppose we want to change the color to red. We would write:

``````plt.hist(data, bins=5, color='red')
``````

For the line style, we can choose from various line styles such as solid, dashed, or dotted.

We can set the linestyle parameter to any of the available line styles. For example, to use a dashed line style, we would write:

``````plt.hist(data, bins=5, color='red', linestyle='dashed')
``````

Finally, we can adjust the marker style, which is the shape used to represent the data points of the histogram.

The default marker style is a rectangle, but we can customize it using the marker parameter. For example, to use a circle marker style, we would write:

``````plt.hist(data, bins=5, color='red', linestyle='dashed', marker='o')
``````

Conclusion

In this addition to our article, we have explored how to derive skew in Python using Scipy library and how to style histograms using Matplotlib’s pyplot library. We have demonstrated how to change the color, line style, and marker style of histograms to create customized visualizations.

By using these techniques, we can enhance the aesthetics of our plots and make them more visually appealing, which is crucial for effective data communication. In this article, we have learned about plotting histograms and deriving bins for histograms using the Python Matplotlib library.

We explored how to style histograms to make them more visually appealing and how to derive the skewness of a dataset using the Scipy library. Histograms are critical tools for data analysis, and understanding how to derive and plot them will help anyone who is active in data science, software engineering, or scientific research.

Styling histograms can make them more aesthetically pleasing, which is important in effectively communicating data. Deriving skew is useful in understanding the nature of the dataset being analyzed.

By using these techniques, we can draw more meaningful insights from our data, making us more informed and effective analysts.