Numpy vs. Pandas: A brief Comparison Of Python Libraries

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Apr 08, 2022

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Python is one of the most versatile programming languages which finds its use across a broad spectrum. Python has a very intrinsic simple learning curve, and it is easy for both programmers and those who don't know the program to learn its programming language structure. 

Today's programming and software development world is filled with programming tools and languages. Python has been accepted worldwide and is the fastest-growing programming language.

Thus, Python has numerous benefits from its usage, discussed below. Before that, it is essential to understand Python and its uses. A Data Science Course will help you understand the concepts in depth with practical experience. 

Difference Between NumPy and Pandas

Python is a famous and commonly used coding and analysis language used by data scientists, analysts, and software developers. 

Nowadays, Python is being used in various fields like:

  • Data science
  • Software engineering
  • Artificial Intelligence
  • Machine learning
  • Application development

If you are also a python developer, you must know about the several libraries and in-built functions present in Python. 

Two viral libraries among these are Pandas and NumPy. The libraries-Pandas and NumPy, are a part of the SciPy tech stack of the Python coding language. This tech stack is used in various applications, software, and Machine learning coding purposes. You can learn this tech stack through a Data Science Certification through a data science online course. This article will give you an insight into the significant differences between Pandas and NumPy.

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What is the Meaning of NumPy?

  • Developers use NumPy when they code in C language.
  • NumPy is a library or a module extension of the Python coding language.
  • It is used for several mathematical and algorithmic calculations algorithms and defining various types of arrays like a single array or a multidimensional array.
  • NumPy can also perform multiplication operations on the matrices and handle many mathematical data.
  • NumPy can perform calculations and computations at a faster rate as compared to a standard array declared in Python.

What is the Meaning of Pandas?

Pandas is defined as an open-source python library. It is being used in high-level development, especially among data scientists. 

Pandas is an efficient framework that enables users to extract data from various sources like a zipped file, Excel file, JSON format, XML format, etc. Pandas is built on top of another popular library of Python named NumPy and has some enhanced features compared to NumPy.

The main functions of Pandas include:

  • Data Modelling
  • Data Analysis
  • Manipulation of data
  • Preparation of data

The two types of Panda data objects are as follows:

  • Pandas Series
  • Pandas Dataframe

Comparison in a Tabular Form- NumPy vs. Pandas

If you are looking into the main differences between Numpy vs. Pandas, below is a brief comparison of the two python libraries- NumPy vs. Pandas on different parameters:

The Basis for NumPy and Pandas DifferencePandas Python LibraryNumPy Python Library
Working MethodsPandas library work on the data modulation in a tabular form.NumPy library work on the data modulation in a Numeric form
Tools incorporated in the libraries 

The powerful tools of Pandas include:

  • Series
  • DataFrame
Arrays are the most widely used tool of NumPy.
Usage at the organizational level

Various organizations using Pandas are:

  • Instacart
  • SendGrid
  • Tighten

An organization using Numpy is

  • SweepSouth
Performance Metrics of the LibrariesPandas are known for their working methods on even 500K rows or more.NumPy can perform best for 50K rows or less.
The usage of Memory Pandas comparatively use more memory than NumPyNumPy is known to consume less memory
Coverage at the industry levelPandas are presently being used in 70 company-level and 46 developer tech stacks.NumPy is presently being used in 62 company-level and 32 developer tech stacks.
Objects provided by the libraryPandas allow us to use a two-dimensional table known as DataFrame.

NumPy allows us to use multidimensional arrays.


Highlighting Features of Pandas

Key features of Pandas are as follows:

  1. You can reshape as well as pivot the datasets and data.
  2. You can merge and join various data sets.
  3. With the data frame object of Pandas, you can perform operations to manipulate the datasets.
  4. You can also perform functions for integration handling of data.
  5. Many other Pandas tools help write and read data from a database or different file formats.
  6. Support for data filtration is also provided.
  7. Other features include:
  • Fancy indexing
  • Label based slicing
  • Subsetting data sets
  • Hierarchical axis indexing
  • MultiIndexing
  • performing algorithmic data queries

Highlighting Features of Numpy

Key features of NumPy are as follows:

  • One of the widely being used objects of NumPy is "ndarray," used for handling Multidimensional arrays.
  • Multidimensional arrays operate very fast by using NumPy.
  • You can perform computations of linear algebra.
  • It is being used in the OpenCV software.
  • It allows an easy way to append data sets and entries into the arrays.
  • You can also perform the operations of Fourier transform and random numbers.
  • It also comprises broadcasting functions.

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Overall, it is quite evident that the programming language - Python is one of the most advanced and reputed languages in coding. 

The features of Python which make it popular among beginners also are:

  1. Easy syntax
  2. Portability
  3. High versatility 

These features make it one of the first choices for developers. 

In today's digital world, a specialization in Python can help a person earn high and explore themselves in various fields. An effective and detailed Data science online training is the best option if you are also interested in learning Python and its libraries. In this article, we saw that NumPy and Pandas are the two libraries that offer many user-friendly syntax and built-in functions and codes. These intrinsic features are appreciated and accepted worldwide. 

And together with low maintenance cost and easy to debug features, they have achieved global acceptance in diversified fields of application. Thus, with its benefits and usages, it would be easy for anyone to start with Python language with basic knowledge on the ground. We hope that the above difference between Pandas and NumPy will be helpful for you.

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