Implicit differentiation can also be employed to find the derivatives of logarithmic functions, which are of the form \(y = \log_a{x}\). In this post, we explore several derivatives of logarithmic functions and also prove some commonly used derivatives. The symbolic computation library SymPy is also employed to verify our answers.

## Product, Quotient and Power Rules of Differentiation

Several rules exist for finding the derivatives of functions with several components such as \(x \space sin \space x\). With these rules and the chain rule, which will be explored later, any derivative of a function can be found (assuming they exist). There are five rules that help simplify the computation of derivatives, of which each will be explored in turn. We will also take advantage of SymPy to perform symbolic computations to confirm our results.

## Continuous Functions

In this post, we explore the definition of a continuous function and introduce several examples with Python code.

## Building a Poetry Database in PostgreSQL with Python, poetpy, pandas and Sqlalchemy

In this example, we walk through a sample use case of extracting data from a database using an API and then structuring that data in a cohesive manner that allows us to create a relational database that we can then query with SQL statements. The database we will create with the extracted data will use Postgresql. The Python libraries that will be used in this example are poetpy, a Python wrapper for the PoetryDB API written by yours truly, pandas for transforming and cleansing the data as needed, and sqlalchemy for handling the SQL side of things.

## Introduction to poetpy

The poetpy library is a Python wrapper for the PoetryDB API. The library provides a Pythonic interface for interacting with and extracting information from the PoetryDB database. In this introductory example, we will explore some of the basic functionality of the poetpy library for interacting with the PoetryDB database.

## From Intake to Outcome: Analyzing the Austin Animal Center's Intake and Outcomes Datasets

The Austin Animal Center provides its animal intake and outcome datasets on Socrata. When an animal is taken into the shelter, it is given a unique identifier that is also used in the outcomes dataset. We have already investigated and performed exploratory data analysis on the Austin Animal Center's intakes and animal outcomes individually and found several interesting facets of information. In this analysis, we merge the intakes and outcomes dataset using pandas to enable us to perform exploratory data analysis on the merged data. With the data merged, we will be able to explore in more depth the transition from intake to outcome.

## Austin Animal Center Intakes Exploratory Data Analysis with Python, Pandas and Seaborn

The Austin Animal Center, the largest no-kill municipal shelter in the United States, makes available its collected data on Austin's Open Data Portal. This data includes both animals incoming into the shelter and the animals' outcome. In this post, we perform some exploratory data analysis on the intakes dataset to see if we can find any noticeable trends or interesting pieces of information of the data. First, we will extract the data from Austin's Data Portal, which is supported by Socrata. We will then perform some data transformation and cleaning steps to get the data ready for analysis.

## Extract and Analyze the Seattle Pet Licenses Dataset

The city of Seattle makes available its database of pet licenses issued from 2005 to the beginning of 2017 as part of the city's ongoing Open Data Initiative. This post will explore extracting the data from Seattle's Open Data portal using requests, then transform the extracted JSON data into a workable dataset with pandas to analyze and investigate the pet license database.

## Predicting Shelter Cat Adoptions and Transfers with Scikit-learn and Machine Learning

Following from the previous analyses of the Austin Animal Center's shelter outcomes dataset, we now take what we learned from the exploratory data analysis component of the investigation and build and train a machine learning model for predicting if a cat entering the shelter will be adopted or transferred to a partner facility. Adoptions and transfers make up about 90% of all the outcomes.

## Exploratory Data Analysis of Shelter Cat Outcomes with Pandas and Seaborn

The following post walked through how to extract and transform the shelter outcome data to make it tidy and suitable for data analysis. In this post, we perform exploratory data analysis using pandas and seaborn to investigate and visualize the shelter outcomes of cats. The findings that are garnered from the exploratory data analysis step can help tremendously in the model building phase when we need to select the important features of the data.

## Analyzing the Consumer Complaints Database with Python, SQL and Plotly

The consumer complaints database is a collection of complaints received by the Bureau of Consumer Financial Protection related to financial products and services. This post explores creating a database file using SQLite and analyzing the data with Pandas and Plotly.

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

The Austin Animal Center, the largest no-kill municipal animal shelter in the United States, makes available its shelter animal outcomes dataset as patrt of the City of Austin's Open Data program. This post demonstrates how to extract the data from the City of Austin's Open Data portal using the requests library and convert the resulting JSON to a tabular pandas DataFrame. We will then enrich the data by applying feature engineering to the data to add more information, which should help improve the outcome prediction model.

## 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.

## Black-Scholes Formula and Python Implementation

Introduces the call and put option pricing using the Black-Scholes formula and Python implementations.

## Implied Volatility Calculations with Python

Discusses calculations of the implied volatility measure in pricing security options with the Black-Scholes model.

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

Introduces the put-call parity as identified by Hans Stoll in 1969 as well as Python code for computing the put-call parity both numerically and symbolically.

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

Using petpy and multiprocessing to download 45,000 cat images in 6.5 minutes

## Introduction to petpy

Introduction to using the petpy Python library for interacting with the Petfinder API.

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