NumPy and Pandas serve as essential libraries for any type of scientific computation. Since they have very intuitive syntax and their matrix computation capabilities are high-performance, they are also very helpful in machine learning. But there is a fundamental difference between Pandas and NumPy. Although these two libraries work the best for applications in data sciences, they differ a lot in terms of their performance, memory utilisation, and many more.
In this article, we will discover the differences between Pandas and Numpy. But before we do that, let us know a bit more about each of these in detail.
What is Pandas?
It’s an open-source library providing Python users with a high-performance capability of data manipulation. Pandas is basically developed on the NumPy package’s top. Meaning, operating Pandas would always require NumPy.
The term Pandas was originally derived from Panel Data. It refers to the Econometrics out of Multidimensional data. Pandas was developed back in 2008 by Wes McKinney, and it is useful for the Python language in terms of data analysis. Before Pandas came into the picture, Python was already capable of data preparation, but the overall support that Python provided for data analysis was very little.
Thus, Pandas was introduced for enhancing the data analysis capabilities of multi-folds. It performs five major steps to process and analyse the available data, irrespective of its origin. These five steps are loading, manipulation, preparation, modelling, and analysis.
What is NumPy?
NumPy is mainly Python’s extension module. C language is mostly used to write NumPy. It acts as a Python package that performs the processing and numerical computations of single-dimensional and multi-dimensional array elements. When we use NumPy arrays, the calculations become much faster than that of the normal Python arrays.
Travis Oliphant created the NumPy package way back in 2005. It was developed by the addition of the Numeric module’s functionalities (ancestor module) into another module named Numarray. It can handle a huge amount of data and information, and it is also very much convenient with data reshaping and Matrix multiplication.
Difference between Pandas and NumPy
Let us talk about the differences between Pandas and NumPy.
| Parameters | Pandas | NumPy |
| Working with Data | This module works along with the tabular data. | This module works along with the numerical data. |
| Usage among Organisations | Popular organisations such as Sighten, SendGrid, and Instacart make use of the Pandas module. | Popular organisations such as SweepSouth make use of the NumPy module. |
| Powerful Tools | Various tools like DataFrame, Series, etc., are available with Pandas. | NumPy also comes with some powerful tools, such as Arrays. |
| Performance | The performance of Pandas is much better for about 500k rows or even more. | The performance of NumPy is better for about 50k rows or less. |
| Utilisation of Memory | This module consumes comparatively much larger memory than the NumPy module. | This module consumes much less memory than the Pandas module. |
| Industrial Coverage | This module is mentioned in about 46 developer stacks and 73 company stacks. | This module is mentioned in about 32 developer stacks and 62 company stacks. |
| Type of Objects | The Pandas module provides us with DataFrame, a two-dimensional table object. | The NumPy module provides us with a multidimensional array. |
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