Visualizing static market data with pandas
pandas is “a fast, powerful, flexible, and easy to use open source data analysis and manipulation tool, built on top of the Python programming language”, as declared on its official web page at https://pandas.pydata.org. It was originally developed exactly for the purpose of manipulating time series data, especially market prices.
Instead of native Python lists or NumPy arrays, pandas uses DataFrames as a core data object. You can think of a DataFrame as a table, where columns represent various named time series (or any other series) and rows contain actual data, with the first row always containing the names of the series. Pretty much the same as with the historical market data file that we’ve used so far? Yes, and this makes the learning curve with pandas really steep.
pandas offers methods to add, delete, and rearrange columns, create and modify indices, slice and create subsets, merge and reshape DataFrames,...
