Python for Data Analysis
What is Python for Data Analysis?
Python is a popular multi-purpose programming language widely used for its flexibility, as well as its extensive collection of libraries, which are valuable for analytics and complex calculations.
Python’s extensibility means that it has thousands of libraries dedicated to analytics, including the widely used Python Data Analysis Library (also known as Pandas).
For the most part, data analytics libraries in Python are at least somewhat derived from the NumPy library, which includes hundreds of mathematical calculations, operations, and functions.
Python analytics tools have become popular due to the computer language’s widespread adoption and its versatility when it comes to developing multifaceted solutions.
The fact that it’s a truly general-purpose language means that it can also add deeper functionality to data analytics software than domain-specific languages that have a narrow scope and functionality.
Additionally, Python’s performance capability is much higher than other popular languages used in data analytics, and its compatibility with a greater array of other languages means that it is simply more convenient in most cases.
Python’s relatively light usage of memory and other processing resources means that it can quickly outstrip languages like MatLab or R, which are built specifically for statistical analysis.
How can I use Python for Data Analytics?
There are several ways you can integrate python data analytics into your existing business intelligence and analytics tools.
One of the most common uses for Python is in its ability to create and manage data structures quickly — Pandas, for instance, offers a plethora of tools to manipulate, analyze, and even represent data structures and complex datasets.
This includes time series and more complex data structures such as merging, pivoting, and slicing tables to create new views and perspectives on existing sets.
Elsewhere, tools like Scikit-Learn (also known as Sklearn) provides advanced analytics tools combined with complex machine learning capabilities.
This allows you to build more sophisticated models, performing more complex and multivariate regressions, as well as data preprocessing.
Combined with libraries such as iPython and NumPy itself, these tools can form the foundation of a powerful data analytics suite.
Additionally, you can use Python to write your own data analysis algorithms that can be directly integrated into your business intelligence tools via API.
