To visualize a dataset, we generally plot a graph. There are many types of graphs that enable us to have a good insight into our data. But what if there is a ton of data that needs to be studied and plotted accordingly? So, today we are going to learn how to plot histograms, binning, and density in Python.
What is a histogram?
A histogram is a type of graph which is presented in the form of rectangles. It has both an x-axis and a y-axis. The area of the rectangle is always proportional to the frequency of the variable present in the statistics, whereas the width denotes the class interval.
What is binning?
Binning is a technique that is used to preprocess the data and reduce small errors. The errors that are observed are thrown into the bin and some valuable data is placed over them.
What is kernel density estimation?
The non-parametric way of representing the probability density function of a variable in statistics is known as kernel density estimation. All its concepts are based on statistics.
How to plot histogram, binning, and density in Python
Step 1: Import All the Libraries
First, we are going to import the required Python libraries. The libraries that we are using are NumPy and Matplotlib. For easy execution of code, we are using declaring abbreviations of each and every library.
NumPy: It is used for scientific computation and enables all kinds of mathematical functions, such as linear algebra, algebraic routines, statistics, etc.
Matplotlib: It is used to represent data in the form of graphs and figures.
Plotting Histogram
Step 2: Plot the graph of the histogram from the data.
Step 3: Customizing the histogram
Step 4: Compute the Histogram
2 Dimensions Histogram and Binning
We are going to make 2 dimension histogram by dividing points into two bins. We will start by taking data in the x and y array.
Step 5: Array data in X and Y
Step 6: Two-Dimensional Histogram
Hexagonal Binning
Step 7: Binning
Kernel density estimation
Read More: How to implement Naive Bayes Classifier in Python
