Overview
Python has been built with extraordinary Python libraries that are used by programmers every day to solve problems. Let’s learn about Python libraries for Data Science in this tutorial.
Data science involves a set of tasks like data collection, cleaning, visualization, statistical analysis, and machine learning. Python is one of the most popular data science languages, offering rich libraries that cater to these various aspects.
Python Libraries for Data Science
Some of the Python libraries for data science are as follows:
- NumPy
- SciPy
- Matplotlib
- Pandas
NumPy
NumPy stands for Numerical Python. It is Python’s fundamental package for numerical computation; it contains a powerful N-dimensional array object. It’s a general-purpose array-processing package that provides high-performance multidimensional objects called arrays and tools for working with them.
NumPy provides precompiled functions for numerical routines, array-oriented computing for better efficiency, and faster computations with vectorization. NumPy is highly useful for data analysis, creating powerful N-dimensional arrays, forming the base of other libraries, such as SciPy and scikitlearn, and replacing MATLAB when used with SciPy and matplotlib.
SciPy
SciPy stands for Scientific Python. It is a free and open-source Python library extensively used in data science for high-level computations. It’s widely used for scientific and technical computations because it extends NumPy and provides many user-friendly and efficient routines for scientific calculations.
SciPy is a collection of algorithms and functions built on the NumPy extension of Python, high-level commands for data manipulation and visualization, and multidimensional image processing. It includes built-in functions for solving differential equations. SciPy is particularly useful for Multidimensional image operations, solving differential equations and the Fourier transform, optimization algorithms, and Linear algebra.
Matplotlib
Matplotlib is a plotting library that provides a MATLAB-like interface. It’s extensively used for data visualization because of the graphs and plots. It also provides an object-oriented API, which can be used to embed those plots into software applications.
Matplotlib is free and open-source and supports dozens of backends and output types, which means you can use it regardless of which operating system you’re using or which output format you wish to use. Matplotlib is particularly useful for Correlation analysis of variables, visualization of 95 percent confidence intervals of the models, Outlier detection using a scatter plot, and visualization of the distribution of data to gain instant insights.
Pandas
Pandas library is widely used for data science. It is heavily used for data analysis and cleaning. Pandas provide fast, flexible data structures, such as DataFrames and Series, which are designed to work with structured data very quickly and intuitively.
Pandas give you the freedom to deal with missing data, enable you to create your functions and run them across a series of data, and high-level abstraction and manipulation tools. It is useful for general data wrangling and cleaning, ETL (extract, transform, load) jobs for data transformation and data storage, as it has excellent support for loading CSV files into its data frame format, used in a variety of academic and commercial areas, including statistics, finance, moving window, linear regression and data shifting.
Data scientists should familiarize themselves with these libraries to excel in their field.
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Python Tutorials
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