Packages for Data Analysis

This article introduces the fundamental packages for data analysis in Python: NumPy, Pandas, SciPy, and Matplotlib.

NumPy: the cornerstone of data analysis

As data analysts, we work with NumPy almost every day. It is the fundamental package for scientific computing in Python. Generally speaking, a 2‑D or 3‑D array is sufficient to handle most of the problems we encounter. Therefore, after importing raw data, I typically work with it as an ndarray.

The key advantage of using ndarray in Python is computational speed. In base Python, built‑in data structures fall into four categories:

• three mutable types: List, Dictionary, and Set
• one immutable type: Tuple

Compared to List, the following are the main reasons behind the speed of NumPy (based on a helpful StackOverflow answer by Ferferite):[Reference]

• An ndarray stores elements of the same data type in a densely packed memory layout. A Python list may contain mixed data types, imposing additional constraints during computation.
• NumPy can split a task into multiple subtasks and process them in parallel.
• NumPy functions are implemented in C, which makes them faster than operations on Python lists.

If you encounter speed issues in Python‑based data analysis, consider leveraging NumPy.

In this library, the core element is always a multidimensional array object (or derived objects such as masked arrays and matrices). NumPy also provides a wide range of routines for fast array operations, including mathematical, logical, shape manipulation, sorting, selecting, I/O, discrete Fourier transforms, basic linear algebra, statistical operations, random simulation, and more.

Believe it or not, once you have solved a data problem with NumPy, the only remaining challenge next time will be how to translate your analysis into ndarray operations. Please refer to the official documentation for detailed guidance. [Official Reference]

Pandas: specialist for data analysis and manipulation

Pandas is a fast, powerful, flexible, and easy‑to‑use open‑source data analysis and manipulation tool, built on top of the Python programming language.

You can perform all the same data analysis operations as in R and Stata. When dealing with moderately sized datasets and not requiring extreme speed, I often use Pandas as a Pythonic version of Excel—it is convenient and even allows you to write SQL‑style queries via the pandasql package. [pandasql]

For further details, consult the official documentation. [Official Reference]