Pandas vs. NumPy

What is Pandas?

Pandas is defined as an open-source library that provides high-performance data manipulation in Python. It is built on top of the NumPy package, which means?Numpy?is required for operating the Pandas. The name of Pandas is derived from the word?Panel Data, which means?an Econometrics from Multidimensional data. It is used for data analysis in Python and developed by?Wes McKinney in 2008.

Before Pandas, Python was capable for data preparation, but it only provided limited support for data analysis. So, Pandas came into the picture and enhanced the capabilities of data analysis. It can perform five significant steps required for processing and analysis of data irrespective of the origin of the data, i.e.,?load, manipulate, prepare, model, and analyze.

What is NumPy?

NumPy is mostly written in C language, and it is an extension module of Python. It is defined as a Python package used for performing the various numerical computations and processing of the multidimensional and single-dimensional array elements. The calculations using Numpy arrays are faster than the normal Python array.

The NumPy package is created by the?Travis Oliphant?in 2005 by adding the functionalities of the ancestor module Numeric into another module?Numarray. It is also capable of handling a vast amount of data and convenient with Matrix multiplication and data reshaping.

Difference between Pandas and NumPy:

There are some differences between Pandas and NumPy that is listed below:

• The?Pandas?module mainly works with the tabular data, whereas the?NumPy?module works with the numerical data.
• The Pandas provides some sets of powerful tools like?DataFrame?and?Series?that mainly used for analyzing the data, whereas in?NumPy?module offers a powerful object called?Array.
• Instacart, SendGrid,?and?Sighten?are some of the famous companies that work on the?Pandas?module, whereas?NumPy?is used by?SweepSouth.
• The Pandas covered the broader application because it is mentioned in?73?company stacks and?46?developer stacks, whereas in NumPy,?62?company stacks and?32?developer stacks are being mentioned.
• The performance of NumPy is better than the NumPy for 50K rows or less.
• The performance of Pandas is better than the NumPy for 500K rows or more. Between 50K to 500K rows, performance depends on the kind of operation.
• NumPy library provides objects for multi-dimensional arrays, whereas Pandas is capable of offering an in-memory 2d table object called Data Frame.
• NumPy?consumes less memory as compared to?Pandas.
• Indexing of the Series objects is quite slow as compared to NumPy arrays.