Pandas Series and DataFrames for Financial Data Handling
Summary
This introductory tutorial explains pandas Series and DataFrames as tools for handling financial datasets containing mixed data types, such as stock identifiers, prices, and volume. It describes Series as indexed one-dimensional data and DataFrames as collections of columns, with each column represented as a Series. Examples show creating these structures from arrays, dictionaries, and other Series or DataFrames, including how indexes align and missing values are represented.
The tutorial also covers accessing and combining data by labels, positions, slices, and Boolean filters. It distinguishes row and column selection, and introduces methods for accessing individual cells. The material is a practical orientation to data structures rather than a discussion of trading methods or statistical analysis. Its examples refer to an older pandas release and include the then-current mixed indexer, so readers should check modern pandas behavior and APIs before applying the examples directly.
Key ideas
- A Series is one-dimensional data paired with an index, which can use labels as well as positions.
- A DataFrame organizes columns of potentially different data types, with each column behaving as a Series.
- Creating pandas objects from dictionaries or combining indexed objects aligns data by index and can introduce missing values.
- Rows and columns can be selected using labels, integer positions, slices, and Boolean conditions.
- The examples use an old pandas version, so API details may need updating for current environments.
Tags
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.