It is essential to consider the concept of Data Analytics before entering into Python libraries that assist the operation of Data Analytics.
What is Data Analytics?
Data Analysis is a sub-domain of the larger topic of Data Science and Machine Learning. Yes, it is essential to evaluate and clean the data before modeling it against various methods.
By analyzing the data, we imply that the data must be understood in terms of distribution, statistical analysis of measurement, and data visualization in order to provide a clear image of the data.
Data analysis comprises the following:
- Data cleansing
- Recognizing and understanding the distribution of data values
- Data statistical analysis with relation to the mean, standard deviation, and so on.
- Visualization of data values in relation to statistical measurements.
- The data is being formatted for processing into the model.
Data Analytics Libraries in Python
Python provides a large number of libraries that support the concept of data analytics. Yes, Python has a number of modules for pre-processing and analyzing data values.
Among them, some of the top most used libraries are:
This is a free software library for Machine Learning development in Python. David Cournapeau created it as a Google Summer of Code project in 2007, and it was first released in June 2007. Scikit-learn is built on top of other Python libraries such as NumPy, SciPy, Matplotlib, Pandas, and others, therefore it has full interoperability with them.
While Scikit-learn is mostly built-in Python, some fundamental algorithms have been developed in Cython to increase efficiency. Scikit-learn allows you to implement numerous Supervised and Unsupervised Machine Learning models such as
- Support Vector Machines
- Random Forests
- Nearest Neighbors
- Naive Bayes
- Decision Trees
- Clustering, and so on.
This is a free Python data analysis and manipulation software library. It was developed as a community library project and was first made available in 2008. Pandas offer a variety of high-performance and user-friendly data structures and operations for manipulating data in the form of numerical tables and time series. Pandas also include a number of tools for reading and writing data between in-memory data structures and various file formats.
In a nutshell, it is ideal for quick and easy data manipulation, data aggregation, reading and writing data, and data visualization. Pandas can also read data from files such as CSV, Excel, and others, or from a SQL database, and generate a Python object known as a data frame. A data frame is made up of rows and columns and can be used to manipulate data using operations like join, merge, groupby, concatenate, and so on.
OpenCV is a massive open-source computer vision, machine learning, and image processing library. OpenCV is compatible with a wide range of programming languages, including Python, C++, and Java. It can analyse photos and videos to recognise items, faces, and even human handwriting. When it is paired with other libraries, such as Numpy, a highly optimised library for numerical operations, the number of weapons in your arsenal grows, as any operation that can be done in Numpy can be merged with OpenCV.
This OpenCV tutorial will teach you the fundamentals of image processing, such as operations on images and videos, through the use of a large collection of OpenCV programs and projects.
As a result, OpenCV supports the following–
- Facial recognition
- object identification
- tracking motion and mobility, and so on.
Pybrain is an open-source machine learning library in python. The library includes some simple training methods for networks, datasets, and trainers for training and testing the network.
Pybrain is defined as follows in its official documentation:
PyBrain is a Python Machine Learning Library that is modular. Its purpose is to provide flexible, user-friendly, yet strong algorithms for Machine Learning Tasks, as well as a range of predefined environments in which to test and compare your algorithms.
Python-Based Reinforcement Learning, Artificial Intelligence, and Neural Network Library is abbreviated as PyBrain. In fact, we invented the term first and then reverse-engineered this highly descriptive “Backronym.”
It supports a number of data analysis algorithms that may be used to improve data analysis and evaluate the results in a number of contexts.