Source: Deep Learning on Medium
Why Use Python for DS and ML?
Python is ranked at number 1 for the most popular programming language used to implement machine learning and data science. Let’s see how
- Ease of learning: Python uses a very simple syntax that can be used to implement complex ML models.
- Less Code: Implementing data science and machine learning involves tons and tons of algorithms. Thanks to Python’s support for pre-defined packages, we don’t have to code algorithms.
- Prebuilt Libraries: Python has 100s of pre-built libraries to implement various ML and Deep Learning algorithms. So every time you want to run an algorithm on a dataset, all you have to do is install and load the necessary packages with a single command.
- Platform Independent: Python can run on multiple platforms including Windows, macOS, Linux, Unix, and so on.
- Massive Community Support: Apart from a huge fan following, Python has multiple communities, groups, and forums where programmers post their errors and help each other.
Python Libraries for Data Science and Machine Learning
The single most important reason for the popularity of Python in the field of AI and ML is the fact that Python provides 1000s of inbuilt libraries that have in-built functions and methods to easily carry out data analysis, processing, wrangling, modeling and so on.
Libraries for Statistical Analysis
Statistics is one of the most basic fundamentals of data science and machine learning. All ML and DL algorithms, techniques, etc. are built on the basic principles and concepts of statistics. Python comes with tons of libraries for the sole purpose of statistical analysis. Some important once are
NumPy or Numerical Python is one of the most commonly used Python libraries. The main feature of this library is its support for multi-dimensional arrays for mathematical and logical operations. Functions provided by NumPy can be used for indexing, sorting, reshaping and conveying images and sound waves as an array of real numbers in multi-dimension.
Pandas is another important statistical library mainly used in a wide range of fields including, statistics, finance, economics, data analysis and so on. The library relies on the NumPy array for the purpose of processing pandas data objects. NumPy, Pandas, and SciPy are heavily dependent on each other for performing scientific computations, data manipulation and so on.
Built on top of NumPy and SciPy, the StatsModels Python package is the best for creating statistical models, data handling and model evaluation. Along with using NumPy arrays and scientific models from SciPy library, it also integrates with Pandas for effective data handling. This library is famously known for statistical computations, statistical testing, and data exploration.