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How to learn NumPy and pandas (Complete Guide for Beginners)

NumPy for Scientific Computation with Python - How to learn NumPy and pandas (Complete Guide for Beginners)
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How to learn NumPy and pandas (Complete Guide for Beginners)

How to learn NumPy and pandas (Complete Guide for Beginners)How to learn NumPy and pandas (Complete Guide for Beginners)

NumPy and Pandas are two of the most used libraries in Python and their demand in the data science market is ever-growing.

Most data professionals need to use either one of the two Python libraries due to the sheer need to efficiently performing data science-related processes such as data cleaning or data algorithm development.

So, how to learn NumPy and Pandas? Our suggestion is to take about an hour-long online course of each of the two libraries since these libraries are easy to learn by yourself. A well-made course will give you enough knowledge to become a NumPy programmer or a Pandas programmer in no substantial time.

In this article, we will break down everything you need to know as a beginner to start off with learning NumPy and Pandas. Plus, we will provide you two courses that are the best in the market for teaching the two libraries.

What is NumPy?

NumPy, or Numerical Python, is an open-source Python library that helps you perform simple as well as complex computations on numerical data. It is the go-to scientific computation library for beginners as well as advanced Python programmers and it is used mostly by statisticians, data scientists, and engineers.

NumPy Logo - How to learn NumPy and pandas (Complete Guide for Beginners)

The popularity behind NumPy is credited to its in-built capability of working with arrays and matrix-like data structures. On top of that, the library provides a large set of functions that are optimized to work on multi-dimensional arrays of data, also known as, n-dimensional arrays.

The first stable version of NumPy was released by Travis Oliphant in 2005 as an effort to unify the Python community around a single package to work with arrays.

Benefits of NumPy: Fast Numerical Computations in Python

Traditionally, Python programmers wrote explicit for-loops in a nested format to work on nested arrays. This was slow as well as inefficient and thus, NumPy addressed this problem by working on making these operations much faster.

As a result, NumPy started using vectorized forms of arrays (termed as, ‘vectorization’) and over the years, the library has been further improved and optimized to perform numerical operations on vectors. The benefits of vectorization in NumPy are as follows:

  • Vectorized code is clear, concise, and easy to read.
  • It removes the need for explicit for-loops to work on arrays. This makes the code feel more ‘Pythonic’.
  • The code resembles standard mathematical notation.
  • The number of potential bug encounters decreases as only a few lines of code are needed to perform numerical computation.

What is pandas?

According to the official documentation, pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with “relational” or “labeled” data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real-world data analysis in Python.

pandas logo - How to learn NumPy and pandas (Complete Guide for Beginners)

The pandas library is built on top of Numpy and it provides flexible data structures for manipulating numerical tables and time series. Additionally, it has the broader goal of becoming the most powerful and flexible open-source data analysis/manipulation tool available in any language and is working towards that goal.

Using only two kinds of data structures, pandas Series and pandas DataFrame, the library can handle the majority of data used in finance, statistics, and various other fields alike. You will be learning about these data structures in upcoming lessons.

Benefits of pandas

Here is a list of some of the benefits that pandas provides:

  • It provides tools for reading and writing data between in-memory data structures and different formats: CSV and text files, Microsoft Excel, SQL databases, and the fast HDF5 format.
  • It provides high performance merging and joining of data sets.
  • It provides time series-functionality: date range generation and frequency conversion, moving window statistics, date shifting and lagging. You can even create domain-specific time offsets and join time series without losing data.
  • It is highly optimized for performance, with critical code paths written in Cython or C.

Best courses to learn NumPy and pandas

The best courses we recommend to learn NumPy and pandas are our own courses! The courses have been made using our years of experience working with NumPy and pandas are taught by industry experts.

To enroll in the NumPy for Scientific Computation with Python course, please click here: Enroll in NumPy for Scientific Computation with Python.

NumPy for Scientific Computation with Python - How to learn NumPy and pandas (Complete Guide for Beginners)

To enroll in the pandas for Data Manipulation with Python course, please click here: Enroll in pandas for Data Manipulation with Python.

Pandas for Data Manipulation with Python - How to learn NumPy and pandas (Complete Guide for Beginners)

Are you interested in working with data? Get a free trial month of LinkedIn Learning and learn Data Science and Machine Learning courses using Python, R, and SQL from top instructors.

Enroll in any one of the courses from the following fields today on LinkedIn Learning and start your free month of learning:

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How to learn NumPy and pandas (Complete Guide for Beginners)How to learn NumPy and pandas (Complete Guide for Beginners)

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