Home News Python and Data Analysis: A closer look

Python and Data Analysis: A closer look

You may be thinking: How is Python used for data analysis, and what topics should I look into to learn data analysis? Python & data analytics work closely together. Python is a high-level language used for general-purpose programming. It is dynamic, supporting both structured programming and object oriented programming. Data analytics, on the other hand, is the science of analyzing raw data, and its ultimate goal is to make conclusions about the gathered information. To help you get started with both concepts, here is a list of 9 Python data analytics libraries gathered:

  • Statsmodels

Statsmodels is a Python module which enables users to estimate statistical models, examine data, as well as perform statistical tests. There is a long list of plotting functions, descriptive stats, statistical tests, and result statistics made available for users, made according to the different types of data and its estimators.

  • scikit-learn

This database is an open source library specifically for Python. It is designed to operate with the Python numerical and scientific libraries called NumPy and SciPy, featuring various algorithms of classification, regression, and clustering. It also includes support vector machines, naive Bayes, logistic regression, gradient boosting, random forests, k-means, and DBSCAN.

  • Pandas

This library was specifically written for data manipulation and analysis. It features data structures and operations, which manipulates numerical tables and time series. Pandas is a free software under the three-clause BSD license.

  • Mlpy

Built on top of NumPy and SciPy, mlpy features a range of machine learning methods for both supervised and unsupervised problems. This is also multiplatform, which works with Python 2 and 3.

  • SciPy

Similar to data libraries you use when you look for “help with my thesis”, SciPy is well-known and widely used in the scientific and technical computing field. It has modules on linear algerbra, optimization, integration, special functions, FFT, interpolation, and other tasks common and necessary in science and engineering.

  • NumPy

This one is an open source extension module, which provides precompiled functions for numerical routines. It also adds support to Python for multidimensional arrays and matrices. Moreover, it supplies a huge database of high-level mathematical functions, specifically to operate on the arrays.

  • matplotlib

This one is a plotting library for NumPy, which provides an object-oriented API for embedding plots into applications. It makes use of general-purpose GUI toolkits such as Gt, GTK+, and wxPython.

  • Theano

This Python library allows you to optimize, define, and also evaluate mathematical expressions effectively, which involve multidimensional array.

  • NLTK

The NLTK, or the Natural Language Toolkit, is a database of libraries and programs statistical natural language processing (NLP). It utilizes graphical demonstrations and sample data. It has also been used as a platform for building and prototyping research systems.

Previous articlePath of Exile: A beginner’s guide to PoE currency
Next articleBest fitness gadgets that you can buy in 2019
Nitin started PC-Tablet because of his keen interest in space research, technology, and gadgets. He is an avid reader, technology enthusiast, and like to explore new places. His passion for knowledge keeps him running all the time.