The Index is an immutable sequence that’s used for indexing and alignment that serves as the label or key for rows in the DataFrame or elements in a series. It allows for fast lookup and relational operations and as of pandas version 2, it can contain values of any type, including integers, strings, and even tuples. Indexes in pandas are immutable, which makes them safe to share across multiple DataFrames or Series. They also have several built-in methods for common operations, such as sorting, grouping, and set operations such as union and intersection. pandas supports multiple indexes, allowing for complex, hierarchical data organization. This feature is particularly useful when dealing with high-dimensional data such as option chains. We’ll see examples of MultiIndexes later in this chapter.

There are seven types of pandas indexes. The differences are dependent on the type of data used to create the index. For example, an Int64Index...

The pandas library was invented by Wes McKinney while at the investment management firm AQR Capital Management, where he researched macro and credit trading strategies. He built pandas to provide flexible, easy-to-use data structures for data analysis. Since it was open sourced in 2009, pandas has become the standard tool to analyze and transform data using Python.

pandas is well-suited for working with tabular data, like that stored in spreadsheets or databases, and it integrates well with many other data analysis libraries in the Python ecosystem. Its capabilities extend to handling missing data, reshaping datasets, and merging and joining datasets, and it also provides robust tools for loading data from flat files, Excel files, databases, and HDF5 file formats. It’s widely used in academia, finance, and many areas of business due to its rich features and ease of use.

This chapter will begin by covering recipes...

The Index is an immutable sequence that’s used for indexing and alignment that serves as the label or key for rows in the DataFrame or elements in a series. It allows for fast lookup and relational operations and as of pandas version 2, it can contain values of any type, including integers, strings, and even tuples. Indexes in pandas are immutable, which makes them safe to share across multiple DataFrames or Series. They also have several built-in methods for common operations, such as sorting, grouping, and set operations such as union and intersection. pandas supports multiple indexes, allowing for complex, hierarchical data organization. This feature is particularly useful when dealing with high-dimensional data such as option chains. We’ll see examples of MultiIndexes later in this chapter.

There are seven types of pandas indexes. The differences are dependent on the type of data used to create the index. For example, an Int64Index...

A Series is a one-dimensional labeled array that can hold any data type, including integers, floats, strings, and objects. The axis labels of a Series are collectively referred to as the index, which allows for easy data manipulation and access. A key feature of the pandas Series is its ability to handle missing data, represented as a NumPy nan (Not a Number).

Important

NumPy’s nan is a special floating-point value. It is commonly used as a marker for missing data in numerical datasets. The nan value being a float is useful because it can be used in numerical computations and included in arrays of numbers without changing their data type, which aids in maintaining consistent data types in numeric datasets. Unlike other values, nan doesn’t equal anything, which is why we need to use functions such as numpy.isnan() to check for nan.

Furthermore, the Series object provides a host of methods for operations such as statistical...

Before moving on to more advanced recipes, it’s important to understand the fundamentals of working with data. DataFrames are the most common pandas data structures you’ll work with. Despite the existence of hundreds of methods for DataFrame manipulation, only a subset of these are regularly used.

In this recipe, we will show you how to manipulate a DataFrame using the following common methods:

• Creating new columns using aggregates, Booleans, and strings
• Concatenating two DataFrames together
• Pivoting a DataFrame such as Excel
• Grouping data on a key or index and applying an aggregate
• Joining options data together to create straddle prices

Getting ready…

We’ll start by importing the necessary libraries and downloading market price data:

1. Start by importing NumPy, pandas, and the OpenBB Platform:

   import numpy as np
   import pandas as pd
   from openbb import obb
   obb.user.preferences...

Once you’ve loaded, manipulated, and transformed data in DataFrames, the next step is retrieving the data from DataFrames. This is where indexing and selecting data come into play. This functionality allows you to access data using methods such as iloc and loc and techniques such as Boolean indexing or query functions. These methods can target data based on its position, labels, or condition based on whether you’re after a specific row, column, or combination. Inspection enables potential issues to be identified, such as missing values, outliers, or inconsistencies, that can affect analysis and modeling. Additionally, an initial inspection provides insights into the nature of data, helping determine appropriate preprocessing steps and analysis methods.

How to do it…

Let’s start by downloading stock price data:

1. Start by importing pandas and the OpenBB Platform:

   import pandas as pd
   from openbb import...

Returns are integral to understanding the performance of a portfolio. There are two types: simple returns and compound (or log) returns.

Simple returns, which are calculated as the difference in price from one period to the next divided by the price at the beginning of the period, are beneficial in certain circumstances. They aggregate across assets, meaning the simple return of a portfolio is the aggregate of the returns of the individual assets, weighted according to their proportions. This trait makes simple returns practical for comparing assets and evaluating portfolio performance over short-term intervals.

Simple returns are defined as follows:

R t =  P t − P t−1 _ P t−1  =  P t _ P t−1  − 1

On the other hand, compound returns, which are calculated using the natural logarithm of the price-relative change, are additive over...

Volatility plays an integral role in finance, serving as a key indicator of risk linked to a particular asset. A higher degree of volatility suggests a greater risk associated with the asset as it indicates more significant price changes and, therefore, a less predictable investment outcome.

Standard deviation is widely used as the measure of asset return volatility. It statistically quantifies the dispersion of asset returns from their mean, thus providing an effective metric for risk. When asset returns exhibit a larger standard deviation, it signifies more pronounced volatility, pointing to a higher risk level. Conversely, a lower standard deviation implies that the asset returns are more stable and less likely to deviate significantly from their average, indicating a lower risk.

The standard deviation’s value as a risk measure extends beyond its ability to quantify risk alone. It is a common component in calculating risk...

Cumulative returns quantify the total change in the value of an investment over a specific period. To compute the cumulative return of a series of simple single-period returns, you need to add 1 to each return, then multiply these results together, and finally subtract 1 from the product. Recall the formula for the simple return:

R t =  P t − P t−1 _ P t−1  =  P t _ P t−1  − 1

Upon writing ( P t _ P t−2) = ( P t _ P t−1)( P t−1 _ P t−2), the two-period return can be expressed as follows:

R(2) = ( P t _ P t−1 )( P t−1 _ P t−2 ) − 1

= (1 + R t)(1 + R t−1) − 1

To compute the cumulative return of a series of continuously...