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
In data analysis, the data can be
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:
qcut()
methodAs 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 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.
pandas.qcut(x, q, labels, retbins, precision, duplicates)
x
: This is for data array or series which you have to manipulate
q
: This is for specifying the number of quantiles
labels
: This parameter gives headings to the resulting bins
retbinds
: This parameter decides whether the function returns an output containing bins and labels or not
precision
: This parameter specifies the precision at which the bins are stored and displayed
duplicates
: This parameter deals with duplicates values in the targeted data set
# 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 headerdf['sepal length (cm)'], bins=pandas.qcut(df['sepal length (cm)'],q=3,# the number of quantiles we needlabels = ['Short','Medium','Long'],# quantile labelsduplicates='drop',# dropping any duplicates foundprecision = 2,# setting the bin edges to two deecimal placesretbins=True)# returns bin edges + bin labelsprint(df)print(df['sepal length (cm)'].value_counts())# displaying count of elements in each binprint(df['sepal length (cm)'].cat.codes)# displaying corresponding bin values
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
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.
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.
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