In this step, we visualize the data we extracted from the AAC database with the additional features that were added to the data in the previous notebook. The visualization of the outcomes and variables of which we have an interest will help us better understand the data and how the variables relate to each other. This knowledge will be crucial when selecting which variables we should focus on and include in our prediction model during the model building phase.

## Articles in the Python category

## Extraction and Feature Engineering of Animal Austin Center's Shelter Outcomes Dataset using Requests and Pandas

The Austin Animal Center is the largest no-kill animal shelter and shelters and protects over 18,000 animals each year. As part of the City of Austin's Open Data Initiative, the Center makes available their data detailing shelter pet intake and outcomes. According to the data portal, over 90% of animal outcomes are adoptions, transfers to other shelter partners or returning lost pets to owners.

## Measuring Sensitivity to Derivatives Pricing Changes with the "Greeks" and Python

The Greeks are used as risk measures that represent how sensitive the price of derivatives are to change. This is useful as risks can be treated in isolation and thus allows for tuning in a portfolio to reach a desired level of risk. The values are called 'the Greeks' as they are denoted by Greek letters. Each will be presented in turn as an introduction:

## Black-Scholes Formula and Python Implementation

The Black-Scholes model was first introduced by Fischer Black and Myron Scholes in 1973 in the paper "The Pricing of Options and Corporate Liabilities". Since being published, the model has become a widely used tool by investors and is still regarded as one of the best ways to determine fair prices of options.

## Implied Volatility Calculations with Python

Implied volatility $\sigma_{imp}$ is the volatility value $\sigma$ that makes the Black-Scholes value of the option equal to the traded price of the option.

Recall that in the Black-Scholes model, the volatility parameter $\sigma$ is the only parameter that can't be directly observed. All other parameters can be determined through market data (in the case of the risk-free rate $r$ and dividend yield $q$ and when the option is quoted. This being the case, the volatility parameter is the result of a numerical optimization technique given the Black-Scholes model.

## Put-Call Parity of Vanilla European Options and Python Implementation

In the paper, it is stated the premium of a call option implies a certain fair price for the corresponding put option (same asset, strike price and expiration date). The Put-Call Parity is used to validate option pricing models as any pricing model that produces option prices which violate the parity should be considered flawed.

## Download 45,000 Adoptable Cat Images in 6.5 Minutes with petpy and multiprocessing

Combining the multiprocessing package for concurrent use of multiple CPUs and the petpy package for interacting with the Petfinder API allows one to find and download a vast amount of animal images for use in other tasks, such as image classification.

This post will introduce how to use the multiprocessing and petpy packages to quickly and easily download a large set of cat images of all the different breeds available in the Petfinder database. We will end up with a collection of just under 45,000 of cat images sorted by user-defined breed classifications.

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