Panda is an open-source library of Python which is highly dependent on NumPy. It gives high-performance and its data structures & data analysis tools are easy to use. Principally it is a software library that is specially written for Python for the purpose of data analysis & data manipulation. In order to manipulate the numerical tables &time-series, pandas in python offer data structures & operations.


NumPy stands for Numerical Python which is a fundamental package of array processing that has a very general-purpose used for scientific computing with Python. NumPy is capable of performing different operations. Arrays in NumPy are guided in sequence order, starting with zero. NumPy has multi-dimensional containers of generic data.
Difference between NumPy and Pandas

  • The data structure level of Pandas is very high whereas NumPy has a low-level data structure.
  • Pandas in Python handle tabular data & its functionality has rich-time series. NumPy supports multi-dimensional arrays & matrices largely.
  • For its working, Pandas use various methods like data, alignment, groupby, missing-data friendly statistics and join methods. On the other hand, NumPy has a wide range of mathematical array operations.

*Pandas data structures are used which are freely drawn on the functions of NumPy & SciPy in order to manipulate them.
Dependencies: Stack of Python

Pandas is built on NumPy which means that NumPy is a dependency for Pandas. In order to install Pandas, NumPy needs to install first. Not only Pandas is dependent on NumPy but SciPy & Matplotlib are also dependent on NumPy which makes it the central part of Python library.
Advantages of using Pandas library

  • The data representation of Pandas is excellent as compared to other Python libraries.
  • In Pandas library there is very less involvement of writing that results in the more work done.
  • It is specially designed for Python.
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Characteristics of NumPy

  • NumPy has statistical models and has a filtering feature.
  • It helps in reshaping the arrays as well for better performance.
  • NumPy has array air thematic and does aggregation.
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Above mentioned are some of the reasons that reflect the popularity of both the libraries and which gives you a reason to learn it and make a career in it.
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