Python is an open-source, object-oriented, high-level programming language. This programming language is one of the best tools used by data scientists for various data science projects.
This multifunctional programming language can be used for multiple problems in various fields, such as mathematics, statistics, and other scientific problems. Python is designed with various powerful libraries to handle data science.
One of the reasons why this programming language is widely used by various groups, because it’s simple syntax, making it easier for users, even users who don’t have basic IT, to use Python.
Research and Data
According to academics and professionals in various fields of industry, building frameworks using the python API is more accessible, especially for deep learning and machine learning projects.
Besides, Python provides many connection libraries to solve complex problems such as natural language processing, sentiment analysis, and detecting fraud.
The combination of python libraries can produce powerful tools. It’s no wonder that data scientist combines several Python libraries to complete various projects in their daily work.
Python has many powerful libraries for dealing with data science. Curious about what are these libraries? Let’s see this article to the end!
1. Tensor Flow
TensorFlow is an open-source end-to-end platform for building fast numerical computing or machine learning applications built and released by Google.
This base library can create deep learning models directly or use wrapper libraries to simplify processes built on top of TensorFlow.
- The main features of TensorFlow range from working efficiently with:
- mathematical expressions involving multi-dimensional arrays
- good support from deep neural networks and machine learning concepts
- to GPU/CPU computing,
- The same code can be executed on both architectures.
Key Features of TensorFlow
- It is a Google-developed open-source framework.
- Deep learning networks and machine learning principles are supported.
- It’s simple to use and provides for rapid debugging.
NumPy (Numerical Python) is a python library used for working with arrays and also has functions that work in the domain of linear algebra, Fourier transforms, and matrices. This library, created in 2005 by Travis Oliphant, is an open-source project, so you can use it freely.
While python has a list of how to serve the purpose of arrays, the processing is so slow that it requires NumPy to provide array objects up to 50 times faster than traditional python lists.
Below are some of the features provided by NumPy-
1. Integration with legacy languages.
2. Mathematical Operations: It provides all the standard functions required to perform operations on large data sets swiftly and efficiently, which otherwise have to be achieved through looping constructs.
3. ndarray: It is a fast and efficient multidimensional array that can perform vector-based arithmetic operations and has powerful broadcasting capabilities.
4. I/O Operations: It provides various tools which can be used to write/read huge data sets from disk. It also supports I/O operations on memory-based file mappings.
5. Fourier transform capabilities, Linear Algebra, and Random Number Generation.
SciPy (Scientific Python) is an open-source library for high-level scientific computations.
These libraries build on top of NumPy extensions and work together to handle complex calculations. NumPy allows the sorting and indexing of array data, while numeric data codes are stored in SciPy.
Pros of using SciPy
- Visualizing and manipulating data with high-level commands and classes.
- Python sessions that are both robust and interactive.
- For parallel programming, there are classes and web and database procedures.
Cons of using SciPy
- SciPy does not provide any plotting function because its focus is on numerical objects and algorithms.
Pandas are an essential library for data scientists. This open-source library for machine learning provides flexible high-level data structures and various analysis tools. Its use facilitates data analysis, data manipulation, and data cleaning.
Pandas support multiple operations such as sorting, reindexing, iteration, merging, data conversion, visualization, aggregation, and so on.
This type of library is responsible for plotting numeric data. That is the reason Matplotlib is used in data analysis. This open-source python library can plot high-definition figures such as pie charts, histograms, scatterplots, graphs, and more.
Keras is a deep learning API written in python and running on top of the TensorFlow machine learning platform.
With over one million individual users by the end of 2021, Keras is currently massively used in the industry and research community.
Together with TensorFlow, Keras is more widely used than any other deep learning solution. It is trendy among startups that place deep learning at the core of their product offerings.
Without realizing it, you are constantly interacting with features made with Keras (parts that are used in Netflix, among other things).
Keras & TensorFlow are also a favourite among researchers, even being adopted by researchers at large scientific organizations, such as CERN and NASA.
Scikit-learn is a well-known python library used for complex data. This open-source library supports machine learning by supporting various supervised and unsupervised algorithms such as linear regression, classification, clustering, etc. This library works with Numpy and SciPy.
PyTorch is the most extensive machine-learning library that optimizes tensor computations. It has a rich API for performing tensor computations with strong GPU acceleration, making it capable of helping solve application problems related to neural networks.
This optimized tensor library is mainly used for deep learning applications using GPUs and CPUs. The Python library, mainly developed by Facebook’s AI Research team, is one of the most widely used besides TensorFlow and Keras.
Tired of writing endless lines of code to build data-friendly machine-learning models?
PyCaret is a Python open-source machine learning library that helps data friends from data preparation to model deployment. This allows data pals to save time by being a low-code library.
It is an easy-to-use machine learning library that will help data perform end-to-end experiments by inputting missing values, encoding categorical data, feature engineering, tuning hyperparameters, or building ensemble models.
SQLAlchemy is the database toolkit in Python that helps access data warehouses efficiently. It features the most widely implemented patterns for high-performance database access.
SQLAlchemy ORM and SQLAlchemy Core are the two main components of SQLAlchemy. Covering Python database APIs and characteristics, SQLAlchemy core adds a level of abstraction.
It also delivers SQL statements and schema to users. SQLAlchemy ORM is a self-contained object-relational mapper. SQLAlchemy allows developers to control their databases while also automating redundant activities.
Key Features of SQLAlchemy
- The Core and the ORM are the two separate elements of SQLAlchemy. The Core is a complete SQL abstraction toolkit, while the Object Relational Mapper is an optional package that extends the Core.
- SQLAlchemy is a high-performance and accurate library that has been deployed in millions of environments and has been thoroughly tested.
- SQLAlchemy’s components can be used independently of one another. Connection pooling, SQL statement compilation, and transactional services are separate components extended through multiple plugin points.