pandas first and foremost excels in data manipulation. All of the needs itemized earlier will be covered in this book using pandas. This is the core of pandas and is most of what we will focus on in this book.

It is worth noting that that pandas has a specific design goal: emphasizing data

But pandas does provide several features for performing data analysis. These capabilities typically revolve around descriptive statistics and functions required for finance such as correlations.

Therefore, pandas itself is not a data science toolkit. It is more of a manipulation tool with some analysis capabilities. pandas explicitly leaves complex statistical, financial, and other types of analyses to other Python libraries, such as SciPy, NumPy, scikit-learn, and leans upon graphics libraries such as matplotlib and ggvis for data visualization.

This focus is actually a strength of pandas over other languages such as R as pandas applications are able to leverage an extensive network of robust Python frameworks already built and tested elsewhere by the Python community.