How does pandas qcut decide the bin edges?

Python, being the most commonly used programming language, contains multiple libraries supporting a multitude of functionalities. One of them is the well-renowned library, pandas. pandas is equipped to achieve many data manipulation tasks in Python, making working with data more intuitive and hassle-free. Some of the functionalities which are commonly known are stated below:

Data manipulation
Data exploration
Data visualization
Data cleaning

What are bin edges?

In data analysis, the data can be categoricallyData divided into distinct and non-ordered categories . or ordinallyArrangement or ranking of items in a sequential or relative order. divided into intervals and groups which are also known as bins. Further diving into this, we can see that continuous data can be divided into some set categories to break it into chunks. This results in many useful use cases such as:

Data preprocessing
Data mining
Identifying patterns
Reducing noise
Handling outliers

By structuring our data into distinct categories, we can analyze and interpret the data to extract some meaning from it. This helps in clearing up the data. As we can represent our continuous data and organize it into graphs, the shape of the distribution is highly dependent on the size of the bins. The size is referring to the width or intervals or edges of the bins that are created.

These bins that are created have some edges on which the data is compared and then distributed. These edges are, in simple terms, the boundaries of the specific interval. We can see this in this example:

What is the `qcut()` method

As we looked at previously, pandas is used for applying data analysis methods to a set of data. One of the methods we are focusing on is the qcut() method. It is a quantile-based discretization function. Let's break down that definition to understand it clearly. Discretizing means breaking a continuous stream of data into equal-sized categories or ordinal bins or buckets. There are multiple ways to achieve this such as:

Equal-width binning
Equal-frequency binning
Quantile based binning
Custom binning

Therefore, when we divide the data into categories, we assign each data pointSingle unit of information in a dataset. to a quantile. Quantile is a statistical concept where the data is assigned on the basis of set percentages. Some of the common quantiles are quartiles, percentiles, and deciles. Summarizing this, we can see that the qcut() method divides the data into distinct equal intervals, categorically or with respect to a rank.

Let’s look at the code and how we can use this method.

Parameters

# We are going to use the iris dataset for this example
# We have selected one column from the dataset
# 'Sepal length (cm)' is the column header 
df['sepal length (cm)'], bins=pandas.qcut(df['sepal length (cm)'],
q=3,# the number of quantiles we need
labels = ['Short','Medium','Long'],# quantile labels
duplicates='drop',# dropping any duplicates found
precision = 2,# setting the bin edges to two deecimal places
retbins=True)# returns bin edges + bin labels
print(df)
print(df['sepal length (cm)'].value_counts())# displaying count of elements in each bin
print(df['sepal length (cm)'].cat.codes)# displaying corresponding bin values

Limitations

The possible constraints for the qcut() method only occurs with problems in the dataset. These limitations are stated below:

When dealing with a relatively small dataset
When duplicate values are found in the dataset
When we want to set the binning criteria manually

How does `qcut()` method determine the bin edges?

As we have seen, the qcut() method is used to divide the data into distinct intervals. However, we have not looked at how this method determines the bins or how it chooses its edges. The way it defines the bins is through quantiles based on the distribution of the data while overlooking the actual numeric edges of the bins. The qcut() function focuses on allocating the same number of elements to each bin or bucket. While forcing the bins into equal counts by adjusting the edges in such a way.

Summary

In this Answer, we looked at what binning is and how pandas determine the bin edges for the qcut() method. Moreover, we went over the qcut() method, explaining the parameters, and their effect on the resulting bins. This was then demonstrated through a program showcasing this method.