Pandas & Numpy 101

The two most crucial tools of the pythonic data-science ecosystem!

Before we get started, here is the link to my article with extensive cheat sheets on all Python libraries (including Numpy and Pandas). Feel free to keep it handy for a quick future reference.

What is Numpy?

Numpy is a popular python library used for mathematical operations.

• It has support for multidimensional arrays and mathematical functions that can operate on these arrays.
• NumPy is homogeneously typed — it can hold elements of the same type.
• NumPy is designed specifically to speed up operations — it is faster than Python lists and consumes much less memory. It supports matrix and vector operations as well.

What is Pandas ?

Pandas is built on top of numpy and is the world’s most popular python library used extensively for data analysis and manipulation.

• It is comparatively slower than numpy and usually takes more memory.
• It is more suitable for handling tabular data than numpy and also support SQL like operations.
• It can contain dissimilar data types and supports two dimensional data only.

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Pandas

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Here are some of my one of my top favorite Pandas Tutorials (basic to advanced) :

1. Dataschool
2. Kaggle-advanced pandas
3. Pandas_advanced
4. Data flair
5. Towards AI

Here is my github repo with extensive Pandas resources and notebooks.

Interesting medium article on advanced pandas.

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Numpy

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1. Learn NUMPY in 5 minutes — BEST Python Library!
2. Complete Python NumPy Tutorial
3. 101 NumPy Exercises for Data Analysis
4. Python Numpy Tutorial

Here is my github repo with extensive Numpy resources and notebooks.

Excellent medium article on Numpy — special focus on beginners

Final Thoughts

That covers almost everything one can expect out of the two most important and widely used Python Libraries when it comes to Data Science. In upcoming blogs I shall cover rest of the Python libraries .

Till then…

Keep practising and stay curious :)

Happy Learning!!!

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