Statistical Modeling with Python: How-to & Top Libraries

Table of Contents

Introduction: Why Python for data science

One of the most important factors driving Python’s popularity as a statistical modeling language is its widespread use as the language of choice in data science and machine learning. 

Today, there’s a huge demand for data science expertise as more and more businesses apply it within their operations. Python offers the right mix of power, versatility, and support from its community to lead the way.

There are a number of reasons for data scientists to adopt Python as their preferred programming language, including:

  • Open-source nature and active community
  • Shorter learning curve and intuitive syntax
  • Large collection of powerful and standardized libraries
  • Powerful integration with fast, compiled languages (e.g. C/C++) for numerical computation primitives (as used in NumPy and pandas)
  • Ease of integrating the core modeling process with database access, wrangling post-processing, such as visualization and web-serving
  • Availability and continued development of Pythonic interfaces to Big Data frameworks such as Apache Spark or MongoDB
  • Support and development of Python libraries by large and influential organizations such as Google or Facebook (e.g. TensorFlow and PyTorch)

It’s worth noting, however, that sound statistical modeling occupies a central role in a data science stack, but some statistical modeling fundamentals often get overlooked, leading to poor analysis and bad decisions.

This article covers some of the essential statistical modeling frameworks and methods for Python, which can help us do statistical modeling and probabilistic computation.

Why these frameworks are necessary

While Python is most popular for data wrangling, visualization, general machine learning, deep learning and associated linear algebra (tensor and matrix operations), and web integration, its statistical modeling abilities are far less advertised. A large percentage of data scientists still use other special statistical languages such as R, MATLAB, or SAS over Python for their modeling and analysis.

While each of these alternatives offer their own unique blend of features and power for statistical analyses, it’s useful for an up-and-coming data scientist to know more about various Python frameworks and methods that can be used for routine operations of descriptive and inferential statistics.

The biggest motivation for learning about these frameworks is that statistical inference and probabilistic modeling represent the bread and butter of a data scientists’ daily work. However, only by using such Python-based tools can a powerful end-to-end data science pipeline (a complete flow extending from data acquisition to final business decision generation) be built using a single programming language.

If using different statistical languages for various tasks, you may face some problems. For example:

  • Conducting any web scraping and database access using SQL commands and Python libraries such as BeautifulSoup and SQLalchemy
  • Cleaning up and preparing your data tables using Pandas, but then switching to R or SPSS for performing statistical tests and computing confidence intervals
  • Using ggplot2 for creating visualization, and then using a standalone LaTeX editor to type up the final analytics report

Switching between multiple programmatic frameworks makes the process cumbersome and error-prone.

What if you could do statistical modeling, analysis, and visualization all inside a core Python platform? 

Let’s see what frameworks and methods exist for accomplishing such tasks.

Start with NumPy

NumPy is the de-facto standard for numerical computation in Python, used as the base for building more advanced libraries for data science and machine learning applications such as TensorFlow or Scikit-learn. For numeric processing, NumPy is much faster than native Python code due to the vectorized implementation of its methods and the fact that many of its core routines are written in C (based on the CPython framework).

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