Top libraries in Python used for data science
Posted by j vimala on July 10th, 2019
Top libraries in Python used for data science
Data science and Python are the two trends that are most talked about now. When we combine both of them, then there is a unique advantage for tech enthusiasts. Python's application and significance have been growing with time, specifically with the data science community.
Data Science Python Libraries
There are various data science libraries now. The programmers have already adopted some of these libraries. Now let's see some of the popular data science libraries in Python.
NumPy is the short form of Numerical Python. It is the basic package for numerical computation in Python. It consists of a robust N-dimensional array object. It chiefly supports multidimensional arrays and vectors for complicated arithmetic operations.
NumPy is one of the chief applications for scientific purposes. Its role is to process substantial multidimensional arrays and matrices. A massive collection of high-level mathematical functions and execution methods paves the way for it to carry several operations on these functions. It also takes care of the slowness issue through the multidimensional arrays.
This is another essential library for scientific computing. Being based on NumPy, SciPy adds to the capabilities of the former. It provides advanced operations, including integration, regression, etc. You have to install NumPy initially to use SciPy.
SciPy consists of high-level commands for data manipulation and visualization. It comprises built-in functions for fixing differential equations.
Python Data Analysis Library (Pandas) is essential in the life cycle of data science. It is the most famous and widely applied Python library for data science. There are a plethora of built-in types, including series and frames that contribute to the appeal of Pandas.
Pandas also offer a three-dimensional panel data structure that assists in good visualization of the data types. Pandas comprise various built-in methods for grouping, filtering, and joining data into one or two commands.
Matplotlib is a part of the SciPy core package. It is applied for the graphical representation of the processed data according to the user's requirements. Due to the graphs and plots that it generates, Matplotlib is widely applied for data visualization. It can also be applied as an alternative to MATLAB.
Matplotlib supports lots of backends and output types. It consumes low memory and has better runtime behavior.
If you have complex data, then Scikit-Learn can be a great choice. It is developed on top of the NumPy, Matplotlib and SciPy libraries. This Python library meant for Machine Learning has several simple yet powerful tools for achieving data assessment and mining.
Sciklit-Learn comprises commonly used ML algorithms to preprocess, regression, classification, and clustering. There are well-written APIs for this library, and hence the beginners can apply it easily.
TensorFlow chiefly concentrates on neural networks. It is a Deep Learning library and is highly extensible. TensorFlow boasts of flexible architecture. Another remarkable aspect is that it lets training multiple neural networks and GPUs.
With TensorFlow, you can use the parts that you require and ignore the ones that you don't. Its great API support makes it an excellent option for training neural networks and speech recognition applying natural language processing.
There are several Python libraries besides these libraries that are helpful for data science. I believe this piece of writing helps the data science enthusiasts know the significance of some critical data science libraries in Python. If you want to learn data science with Python, then enroll in the best Data science with Python training in Chennai from Softlogic.
About the Authorj vimala
Joined: June 24th, 2019
Articles Posted: 9
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