KLIB LIBRARY IN PYTHON
Automated EDA libraries: Klib
Library in Python
Introduction
klib is a library for exploratory data analysis (EDA).
It includes several functions that help with data exploration, visualisation,
and preprocessing, all of which are essential components of EDA.
Exploratory data analysis
Exploratory data analysis (EDA) is used by data scientists to analyze
and investigate data sets and summarize their main characteristics, often
employing data visualization methods.
What is Klib library?
Klib is a Python library that provides amazing
functionality for exploring your data in just a few lines of code. If we find
that data exploration takes a lot of time, we can use this library as it gives
you all the functions that will help you to explore, clean and prepare our
data.
Here are some functions of klib library:
1.
Data
Cleaning: The klib library offers functions to assist with data cleaning tasks,
such as handling missing values and outliers. The klib. missingval_plot () function generates a
matrix plot to visualize missing values in a dataset, helping identify patterns
and inform imputation strategies. The klib.data_cleaning ()
function provides an overview of missing values, duplicated rows, and data
types in a DataFrame, making it easier to identify and address data quality
issues.
2.
Data
Preprocessing: klib provides functions for preprocessing data before analysis
or modeling. The klib. convert_datatypes ()
function helps automatically convert data types in a DataFrame to optimize
memory usage. The klib. drop missing ()
function allows you to drop columns or rows with missing values. The klib.cat_convert () function enables converting
categorical columns to numerical representations using label encoding or
one-hot encoding.
3.
Data
Visualization: klib includes various visualization functions to quickly
generate informative plots. The klib. corr_plot ()
function creates a correlation matrix plot, which helps identify relationships
between variables in a dataset. The klib. dist_plot ()
function generates histograms and density plots for multiple columns, making it
easy to compare distributions. The klib.cat_plot ()
function creates bar plots and counts the frequency of categorical variables.
4.
Feature
Selection: The klib library offers functions to assist with feature selection
tasks. The klib. feature_selection_pipe ()
function combines several feature selection techniques into a single pipeline,
making it easier to apply and compare different methods. It supports techniques
such as filtering based on variance, correlation, and mutual information.
5.
Dataframe
Summary: The klib. describe ()
function provides a comprehensive summary of a DataFrame, including descriptive
statistics, data types, missing values, unique values, and the number of rows
and columns. It provides a quick overview of the dataset, making it easier to
understand and explore the data.
EXAMPLE:
1.
klib.cat_convert
()
USAGE: This function enables converting categorical columns to numerical
representations using label encoding or one-hot encoding (as mentioned in point
2).
INPUT:
OUTPUT:
2.
The klib. corr_plot ()
USAGE: This function creates a correlation matrix plot, which helps identify
relationships between variables in a dataset.
INPUT:
OUTPUT:
Conclusion
In Python, the klib
module provides a simple set of tools for simplifying typical data cleaning,
preprocessing, and visualisation chores. It is especially beneficial for
performing rapid exploratory data analysis and data preparation prior to
applying machine learning algorithms.
References
https://thecleverprogrammer.com/2021/08/26/klib-tutorial-in-python/
Narsima Ahmed
@INTERNATIONAL SCHOOL OF MANAGEMENT EXCELLENCE
Intern @Hunnarvi Technologies under
guidance of Nanobi data and analytics pvt ltd.
Views are personal.
#EDA #klib#automatedlibraries#python
#nanobi #hunnarvi #ISME
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