Difference between Pandas and Numpy
Let’s look at the side-by-side comparison of Pandas and Numpy in this table:
|
Pandas vs NumPy |
|
|---|---|
|
Pandas |
NumPy |
| When we have to work on Tabular data, we prefer the pandas module. | When we have to work on Numerical data, we prefer the NumPy module. |
| The powerful tools of pandas are DataFrame and Series. | Whereas the powerful tool of NumPy is Arrays. |
| Pandas consume more memory. | Numpy is memory efficient. |
| Pandas have a better performance when the number of rows is 500K or more. | Numpy has a better performance when number of rows is 50K or less. |
| Indexing of the Pandas series is very slow as compared to Numpy arrays. | Indexing of Numpy arrays is very fast. |
| Pandas have a 2D table object called DataFrame. | Numpy is capable of providing multi-dimensional arrays. |
| It was developed by Wes McKinney and was released in 2008. | It was developed by Travis Oliphant and was released in |
| It is used in a lot of organizations like Kaidee, Trivago, Abeja Inc., and a lot more. | It is being used in organizations like Walmart Tokopedia, Instacart, and many more. |
| It has a higher industry application. | It has a lower industry application. |
Read More: Python Libraries
Difference between Pandas VS NumPy
Python is one of the most popular languages for Machine Learning, Data Analysis, and Deep learning tasks. It is powerful because of its libraries that provide the user full command over the data.
Today, we will look into the most popular libraries i.e. NumPy and Pandas in Python, and then we will compare them.