With its ease of use and robust collection of data libraries, it’s no wonder that Python continues to be one of the fastest growing and most popular programming languages. Data analytics and machine learning have become more important than ever to enterprise businesses in every field, leading many non-programmers to start learning to code solutions to meet the demands of their business. The growth of Python goes hand in hand with the growth of big data, and here are a few of the most effective uses of Python that are helping drive this trend.
NumPy has a long history as one of the most reliable tools for mathematical calculations in Python. While not explicitly intended for use in the sciences, Python has gained popularity in the scientific community thanks to numerical libraries such as NumPy. NumPy’s main feature is the power of its array manipulation, defining lists in fundamentally more efficient ways than the generic Python list. Many forms of data, such as sound or images, can be conceptually reduced to lists of sound intensity levels or pixel brightness, therefore allowing NumPy to work its magic on a wide range of use cases. As the name suggests, NumPy also has strong functionality in performing calculations with linear algebra or advanced mathematical formulas.
Pandas is a library built on top of NumPy to provide more robust data management features. For example, duplicate or missing records are easier to handle, as well as inconsistencies that can creep into formatting over large datasets. Pandas can very easily organize your data with a concise, readable API to perform SQL-esque operations. The end result is typically an easily understood data frame complete with labels. Ultimately, Pandas helps better organize the loose collections of data found in more real-world situations.
SciKit Learn features solid implementations of the most commonly used machine learning algorithms. It can accept data in the form of NumPy arrays or Pandas data frames, and analyze that data with a wide range of machine learning techniques. Regardless of which machine learning algorithm you want to apply, SciKit Learn provides a consistent and straightforward Estimator API that helps ease the transition between different uses of machine learning. With easy to read model methods such as fit() and predict(), SciKit Learn makes the power of machine learning algorithms easily accessible to novice programmers.
While Python has the capacity to compete with other programming languages in other areas, its easily accessible data analytics are driving the language’s explosive growth in popularity. These libraries show that you don’t have to be a scientist to use data science tools effectively. Python’s focus on readable and clean code has made it the language of choice for anyone looking to use programming tools as quickly and painlessly as possible, and these features have guaranteed its place in the data toolset for 2018 and beyond.