Integration by parts is another technique for simplifying integrands. As we saw in previous posts, each differentiation rule has a corresponding integration rule. In the case of integration by parts, the corresponding differentiation rule is the Product Rule. The technique of integration by parts allows us to simplify integrands of the form: $$ \int f(x) g(x) dx $$

## L'Hospital's Rule for Calculating Limits and Indeterminate Forms

L'Hospital's Rule allows us to simplify the evaluation of limits that involve indeterminate forms. An indeterminate form is defined as a limit that does not give enough information to determine the original limit. The most common indeterminate forms that occur in calculus and other areas of mathematics include:

$$ \frac{0}{0}, \qquad \frac{\infty}{\infty}, \qquad 0 \times \infty, \qquad 1^\infty, \qquad \infty - \infty, \qquad 0^0, \qquad \infty^0 $$

## The Fundamental Theorem of Calculus

The Fundamental Theorem of Calculus is a theorem that connects the two branches of calculus, differential and integral, into a single framework. We saw the computation of antiderivatives previously is the same process as integration; thus we know that differentiation and integration are inverse processes. The Fundamental Theorem of Calculus formalizes this connection. The theorem is given in two parts.

## Indefinite Integrals

As we noted in the previous sections on the Fundamental Theorem of Calculus and Antiderivatives, indefinite integrals are also called antiderivatives and are the same process. Indefinite integrals are expressed without upper and lower limits on the integrand, the notation $\int f(x)$ is used to denote the function as an antiderivative of $F$. Therefore, $\int f(x) \space dx = F^\prime(x)$.

## Substitution Rule

The Substitution Rule is another technique for integrating complex functions and is the corresponding process of integration as the chain rule is to differentiation.

The Substitution Rule is applicable to a wide variety of integrals, but is most performant when the integral in question is of the form:

$$ \int F\big(g(x)\big) g^\prime (x) \space dx $$

## Antiderivatives

Antiderivatives, which are also referred to as indefinite integrals or primitive functions, is essentially the opposite of a derivative (hence the name). More formally, an antiderivative $F$ is a function whose derivative is equivalent to the original function $f$, or stated more concisely: $F^\prime(x) = f(x)$.

The Fundamental Theorem of Calculus defines the relationship between differential and integral calculus. We will see later that an antiderivative can be thought of as a restatement of an indefinite integral. Therefore, the discussion of antiderivatives provides a nice segue from the differential to integral calculus.

## Newton's Method for Finding Equation Roots

Newton's method, also known as Newton-Raphson, is an approach for finding the roots of nonlinear equations and is one of the most common root-finding algorithms due to its relative simplicity and speed. The root of a function is the point at which $f(x) = 0$. Many equations have more than one root. Every real polynomial of odd degree has an odd number of real roots ("Zero of a function," 2016). Newton-Raphson is an iterative method that begins with an initial guess of the root. The method uses the derivative of the function $f'(x)$ as well as the original function $f(x)$, and thus only works when the derivative can be determined.

## Implicit Differentiation

# Implicit Differentiation¶

An explicit function is of the form that should be the most familiar, such as:

$$ f(x) = x^2 + 3 $$ $$ y = \sin{x} $$

Whereas an

*implicit function*defines an algebraic relationship between variables. These functions have a form similar to the following:$$ x^2 + y^2 = 25 $$ $$ y^5 + xy = 3 $$

## The Chain Rule of Differentiation

The chain rule is a powerful and useful derivation technique that allows the derivation of functions that would not be straightforward or possible with the only the previously discussed rules at our disposal. The rule takes advantage of the "compositeness" of a function. For example, consider the function:

## Limit of a Function

A function limit, roughly speaking, describes the behavior of a function around a specific value. Limits play a role in the definition of the derivative and function continuity and are also used in the convergent sequences.

Before getting to the precise definition of a limit, we can investigate limit of a function by plotting it and examining the area around the limit value.

## Derivatives of Logarithmic Functions

Implicit differentiation, which we explored in the last section, can also be employed to find the derivatives of logarithmic functions, which are of the form $y = \log_a{x}$. This also includes the natural logarithmic function $y = \ln{x}$.

## Proving $\frac{d}{dx} (\log_a{x}) = \frac{1}{x \ln{a}}$¶

Taking advantage of the fact that $y = \log_a{x}$ can be rewritten as an exponential equation, $a^y = x$, we can state the derivative of $\log_a{x}$ as:

## 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.

## Continuous Functions

# Continuous Functions¶

A function is said to be continuous at a point $a$ if the following statements hold:

- the function $f$ is defined at $a$
- the limit $\lim_{x \to a} \space f(x)$ exists
- the limit is equal to $f(a)$, $\lim_{x \to a} \space f(x) = f(a)$

Continuity of a function can also be expressed more compactly by the statement: $f(x) \to f(a) \space \text{as} \space f \to a$

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

The PoetryDB API stores its data in MongoDB, a popular NoSQL database. Indeed, a NoSQL database is a solid choice for the type of data that is stored in PoetryDB (unstructured text, for example). However, what if we wanted to create a more traditional SQL database with the PoetryDB API data for use in other projects where a relational database would be preferred? By extracting the data from the PoetryDB API using a combination of a few Python libraries, we can recreate the NoSQL PoetryDB database as a SQL database which will allow us more freedom to create additional data features and avoid the need to hit the PoetryDB database more than necessary.

## 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 to explore nearly 130 poets and more than 3,200 poems. In this introductory notebook, we will explore some of the basic functionality 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

## 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

## 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

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

In the previous notebook analysis, we identified several likely candidate features and variables that could be significant in predicting a cat's outcome as it enters the shelter. Using that information and scikit-learn, we can train a machine learning model to predict if a cat will be adopted or transferred to a partner facility. For this first task, we are only interested in the adoption and transfer outcomes to see if our assumptions based on experience and the information we learned from the previous analysis align with predicted results. Adoptions and transfers represent over 90% of all the outcomes in the Austin Animal Center shelter system, therefore focusing on these outcomes and their more specific subtype outcomes and building a model to predict these outcomes is still quite valuable.

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

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.

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

The consumer complaints database provided by the Bureau of Consumer Financial Protection, can be downloaded as a 190mb csv file.

Although the csv file is not large relative to other available datasets that can exceed many gigabytes in size, it still provides good motivation for aggregating the data using SQL and outputting into a Pandas DataFrame. This can all be done conveniently with Pandas's iotools

## 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.

## Introduction to petpy

The following post introduces the

`petpy`

package and its methods for interacting with the Petfinder API. The goal of the`petpy`

library is to enable other users to interact with the rich data available in the Petfinder database with an easy-to-use and straightforward Python interface. Methods for coercing the often messy JSON and XML API outputs into pandas DataFrame

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