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.The Pandas module mainly works with the tabular data, whereas the NumPy module works with the numerical data.
There are some differences between Pandas and NumPy that is listed below: These two libraries are also best suited for data science applications. It is also capable of handling a vast amount of data and convenient with Matrix multiplication and data reshaping.īoth the Pandas and NumPy can be seen as an essential library for any scientific computation, including machine learning due to their intuitive syntax and high-performance matrix computation capabilities. The NumPy package is created by the Travis Oliphant in 2005 by adding the functionalities of the ancestor module Numeric into another module Numarray. The calculations using Numpy arrays are faster than the normal Python array. It is defined as a Python package used for performing the various numerical computations and processing of the multidimensional and single-dimensional array elements. NumPy is mostly written in C language, and it is an extension module of Python. 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. So, Pandas came into the picture and enhanced the capabilities of data analysis. It is used for data analysis in Python and developed by Wes McKinney in 2008.īefore Pandas, Python was capable for data preparation, but it only provided limited support for data analysis. The name of Pandas is derived from the word Panel Data, which means an Econometrics from Multidimensional data. It is built on top of the NumPy package, which means Numpy is required for operating the Pandas. Pandas is defined as an open-source library that provides high-performance data manipulation in Python.
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