What are the differences between Pandas and NumPy+SciPy in Python?

They both seem exceedingly similar and I'm curious as to which package would be more beneficial for financial data analysis.


Indeed, pandas provides high level data manipulation tools built on top of NumPy. NumPy by itself is a fairly low-level tool, and will be very much similar to using MATLAB. pandas on the other hand provides rich time series functionality, data alignment, NA-friendly statistics, groupby, merge and join methods, and lots of other conveniences. It has become very popular in recent years in financial applications. I will have a chapter dedicated to financial data analysis using pandas in my upcoming book.

Numpy is required by pandas (and by virtually all numerical tools for Python). Scipy is not strictly required for pandas but is listed as an "optional dependency". I wouldn't say that pandas is an alternative to Numpy and/or Scipy. Rather, it's an extra tool that provides a more streamlined way of working with numerical and tabular data in Python. You can use pandas data structures but freely draw on Numpy and Scipy functions to manipulate them.

Pandas offer a great way to manipulate tables, as you can make binning easy (binning a dataframe in pandas in Python) and calculate statistics. Other thing that is great in pandas is the Panel class that you can join series of layers with different properties and combine it using groupby function.

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