Difference between NumPy and Pandas

What is NumPy? What is Pandas? What is the difference between NumPy and Pandas?
Have you started trading using Python and wondering what these terms mean?
Well, one must have a sound knowledge of which Python library would benefit them.

Python has a huge collection of libraries that can be used for various functionalities like computing, machine learning, visualizations, etc. This video discusses the difference between NumPy and Pandas, their functionalities, and where we can use them.

When people start working with python, especially in trading, they tend to import NumPy sometimes and Pandas sometimes. They get confused if they are the same or if there is any difference.

Pandas is built on top of NumPy so earlier uh python used to have only NumPy but when data analysts used to analyze the data they created efficient ways to import the data and manipulate the data.

This led to the creation of Pandas and the power of numbers is unleashed. So both Pandas and NumPy go hand in hand having specific functionality where they shine.
For example: If you are working with the tabular data, it is preferable to use Pandas.

The power tools of pandas are series and data frames where you can store one-dimensional data in series and two-dimensional data and data frames. So if you have close prices of a stock like Apple (brand) you can just save it in series or if you have multiple columns with OHLCV data (Open, High, Low, Close and Volume), you can create and store the data in the tabular data frame.
NumPy is used for numerical calculations and manipulating numerical data. NumPy means number python. It is created to do numerical calculations and we have arrays in NumPy which is multi-dimensional.

So, when you're doing the pandas you will be limited to two-dimensional data structures like a dataframe. But when you have NumPy, you can create multi-dimensional data using arrays.

Now let's see the differences!
1. Generally, the number is more memory efficient than Pandas. That means pandas consumes more memory and more ram than NumPy.
2. The reason we use Pandas, is because we are working with tabular data. Specifically, we have methods in Pandas to load, manipulate and model the data. But when you are calculating something when you are doing numerical calculations on the data you can use number in this way you can efficiently use python and you can use both as well python and NumPy.
3. There is a thumb rule - when you have data with rows less than 50k, generally, the number is faster and when you have data with rows more than half a million like then you can use python. Python is faster so this is a thumb rule.

So, you can pick any of these. You can either convert your data into arrays and use NumPy or you can convert your data into data frames and use python. This thumb rule can save your time and make the code more efficient.

If you have any queries feel free to mention them in the comment section and keep learning!

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Important Reads

📙 Python Numpy Tutorial: Installation, Arrays And Random Sampling

📗 Guide to Python Pandas Tutorial: Installation, Series and DataFrame

💻 Popular Python Libraries For Algorithmic Trading

💡 A good place to start your adventure with quantitative finance and Python

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