Data in recent years has sprouted as an omnipotent substance in the field of computer science that has ability to determine accomplishment of an endeavor. If you are not yet familiar with data science, we suggest you to go through our beginner’s guide to data science and Python
However, the enormous amount of data is of no use if valuable and decision impacting ones are sorted out meaningfully. Because of this reason, data scientists are responsible to support and contribute enterprises and firms in strategic decision making.
Data scientists widely prefer Python over any other language like R, Scala, JavaScript, C++, etc. available as their tool to get the most of every datum. Why do they love Python so much? You can read on why Data Scientists prefer Python on our blog: Data Science and Python: Why Data Scientists Love Python
Python is preferred as the best for Data Science projects because:
- Broad Ecosystem
- Huge reservoir of data-centric packages
- RAD (Rapid Application Development)
- Consistently evolving
- Scalable
Python is language that feels like its hand knitted for data enthusiasts and aspirants. Being high level and easy to understand with the simpler and few lines of codes, the language is favorite among people that recent to the scene as well.
Moreover, Python is perfect in order get into Machine learning as well that helps in proper utilization and implement of data and its outcome.
With things being cleared out on why Python is the great one for Data Science, one thing that needs to be discussed in depth is the availability of various Data Science libraries and packages.
These are the Python Libraries for Data Science you should know about:
Scrapy
Forte: Data Gathering
Scrapy as its name suggests is a free and open-source Python framework that helps developing crawler like programs called “Spiders” that revolve around the web scraping and extracting data as per specified by a small set of instructions.
These data taken out from numerous websites are then stored well-structured and organized in data structure such as tabular and JSON.
One the of the top advantage of using Scrapy is it handles request asynchronously i.e. if a request while in process stops working because of an error or fail, it does not wait, rather allows other request keep being executed.
Top features-
- Extracts data from APIs as well
- Structured storage of scraped data
- Extracting of data from HTML/XML via XPath expressions and extended CSS selectors
- Set of built-in extensions for working with cookies and session, HTTP features, robots.txt and further
- Interactive shell for testing and debugging codes without implementing it on the spiders.
NumPy
Forte: Data Cleansing and Transformation
NumPy is one of the most powerful libraries used to perform scientific computing, calculations and array operations. This library is widely used in order to perform operations on n-array and matrices in Python.
NumPy is an abbreviation to Numerical Python and has a wide range of functions for linear algebra, indexing, math functions, statistics, trigonometry, sorting and searching, financial calculations, sampling, etc.
Top features-
- Fast and efficient computation
- Array-oriented computing
- Uses less memory to store data
- Wide range of functions available
- Supports object-oriented approach
- Matrix calculations
Pandas
Forte: Data Cleansing and Transformation
Pandas is a free to use and open-source framework that offers data structures like series (one-dimensional), dataframe (two-dimensional) and panels and operations for maneuvering numerical tables and time series. It is library created to help data enthusiasts to work with “labeled” and “relational” data instinctually.
It has a very efficient way of handling data even the missing ones so that contamination in our data case data is taken care of.
Pandas, extended is known as Python Data Analysis also helps in performing various functions like filtering data according to a certain given condition, or even segmenting, fragmenting and segregating huge data according to the project’s orientation.
Top features-
- Input and output tools for stable I/O process and data reading from CSV, TSV, XLSX files and many more
- Excellent data filtering
- High level abstraction
- Includes high-level data structures and manipulation tools
Matplotlib
Forte: Data Visualization
Matplotlib is the Python library available out there in order to achieve your visualization idea. It helps to generate data visualizations such as 2D diagrams and graphs like histograms, scatterplots, coordinate graphs, bar charts, line plots, spectrogram, etc.
Being free and open source, Matplotlib is used as a replacement to MATLAB. Its flexible and adaptive library with NumPy offers similar features to MATLAB. It has cross-platform support which means it will operate fine on every operating system you are in and every output type you wish your result to be.
Although of its great data visualization features, Matplotlib has a fairly old and outdated style interface.
Top features:
- Advance data visualization
- Free and open source
- Cross-platform support
- Low memory consumption
- Better runtime behavior
- Helps to make static, animated and interactive plots and visualization
Scikit-Learn
Forte: Data Modeling
Scikit-Learn is the best and most popular library for data modeling and machine learning purpose. It has a large set of algorithms for supervised (classification, regression) and unsupervised (clustering, dimensionality reduction, anomaly decision) learning approaches.
Scikit-Learn is an industry standard for data science projects and is broadly used for both scientific researches and industrial systems. It also has detailed documentation and vast community for support.
Top features-
- Wide range of supervised and unsupervised learning algorithms
- Large community support
- Detailed documentation for learning
- Specializes exclusively in machine learning
- Helps to create robust machine learning programs
PyTorch
Forte: Data Modeling
Developed by Facebook, PyTorch is an open source deep learning library for Python. It competes with TensorFlow but is easier to learn and start to use. It allows tensor computations with GPU acceleration.
PyTorch consists of wealthy and rich API and built-in functions to assist data scientists, data enthusiasts, researcher or machine learning enthusiasts to quickly guide using deep learning models.
PyTorch supports asynchronous computation execution and also has a very useful feature called data parallelism that helps distributing computational work among multiple CPU and GPU cores.
Top features-
- More Pythonic i.e. easy to learn and use
- High quality optimization
- Data parallelism
- Dynamic computational graph support
- Asynchronous computation execution
- Rich API and built-in functions.
Concluding Thoughts:
These data science libraries discussed above cannot envelope the overwhelming number of libraries that are developed consistently in every evolving Python ecosystem. However, there are the must and essentials for anyone to get into data science, machine learning and deep learning spectrum of Python.
I hope this article guided you to know about Python data science libraries and their features. The thriving interest in data sector is always exciting to see and we hope we quenched your thirst of curiosity.
Thanks for the read!