Data Science has complicated and repetitive hard work that makes the task excruciating, here comes python and its libraries.
Python is a great tool for a Data Scientist because of its lack of strict syntax, requiring only indentation.
Python is also vastly supported by developers and is the leading language in terms of global use, from developers to college students to even small kids learning about programming languages, all prefer Python.
Top Python libraries for Data Science are:
 |
| Top Python libraries for Data Science |
The ones listed above earned their place because they are crucial for data scientists to work effectively and meticulously in today's industry. There are many python libraries such as these e.g. PyTorch, Scrapy, BeautifulSoup.
NumPy:
NumPy is a mathematical Python package. NumPy provides numerical computing tools that are needed in Data Science when working with complex & advance mathematical operations specifically scientific manipulations, Generally used for its N-dimensional Array, linear algebra, and Fourier transform.
NumPy is also used to work with "unrefined" data. It is also applicable to big data as large amounts of data can be handled, re-arranged, and re-shaped.
NumPy provides ndarray which is 50 times faster than a traditional python list.
To get NumPy, use the code:
pip install numpy
Pandas:
Pandas is a BSD-3 Licensed python library that provides fast, flexible data frames that make it great for working with "labeled" data.
To get Pandas, use the code:
pip install pandas
Amongst the many features, this library provides, some of them are column size flexibility, handling missing data, merging and reshaping data sets, and it has a great connection with other formats like .csv files, excel files, and databases.
Matplotlib:
Matplotlib library is a very important library for any data science and analytics work requiring graphical I/O.
Matplotlib generated interactive and comprehensive 2-d data visualizations in the form of graphs, and provisions for embedding plots are given as well. Layouts and Visual styling are up for customization.
Matplotlib plots are also provided with exporting interface for many file formats.
SciPy:
Based on another python library NumPy, SciPy is an open-source BSD licensed library that provides optimization, integration, and other easy-to-use fundamental algorithms for scientific computations used to solve complex mathematical problems.
High-level syntactical arrangements provide accessibility to anyone in need of this library regardless of background and skill level.
Although its application and execution are generally seen in the field of data science as cross-domain numerical computing algorithms are required.
Scikit-Learn:
Scikit-Learn provides efficient codes for predictive analysis. It is an open-sourced, BSD-licensed library that is accessible and commercially viable.
From algorithms like Classification, Regression to pre-processing, dimensionality reduction, and model selection, Scikit-Learn is a great source for anyone.
To install:
pip install -U scikit-learn
Keras:
Fast experimentation-based deep learning API that works great with TensorFlow appropriately summarizes this simple yet powerful python library.
Seaborn:
Seaborn is a python statistical data visualization based on matplotlib, that is generally used to develop statistical graphs.
TensorFlow:
TensorFlow designed by the google brain team for its internal use, it is an open-source python library that is generally used in data science for the work of machine learning and artificial intelligence.
It is great for building and mending models.
It has great synchronization with keras
To get TensorFlow, use the code:
pip install tensorflow
Tags:
Data Base