[15] (c) Simplify (((h**(-2/7)/(h*h/(h/((h*h/(h/(h**(3/2)*h))*h)/h))))/(h/h**(-4/5)*h)**(-1/10))**(8/19) assuming h is positive.

[15] (c) Simplify (((h*h/(h*h**(-1/3)))/(h/(h/(h**(-1/6)/h))))**(2/7))/(h*h**4)**(-18) assuming h is positive.

[15] (c) Simplify ((h**(2/5)*h)**(4/3)/((h**0/h)/h*h*h*h**(2/3)))/(h**(-4/7)*h/(h/(h/(h**(3/8)/h)))) assuming h is positive.

[15] (c) Simplify (((h/(h*((h/(h/h**(-6)))/h)/h))/(h**(2/5)*h))**(-3/4)*(((h/(h*h**(-1/7)))/h)/h**(-1))**(-4))**(-5/9) assuming h is positive.

[15] (c) Simplify (h*h**(-2/11)*h)**(6/5) assuming h is positive.

[15] (c) Simplify ((h**(3/8)/h)/h*h*h/(h/(h/(h/(h*h/(h**(-7)*h))))))**(2/5))**(-40/7) assuming h is positive.

Let o be (-1)/(-2)*4/3. Suppose -2*d + 2*g = -3*d - 10, -o*d - 5*g = -20. What is the third derivative of 0*n**6 + d*n**6 - 2*n**2 + n**4 + n**2 wrt n?

A: 120*n**3

509 (base 12) to base 7

A: 4033

- Let n = 21 + d. Let o(s) = s**3 + 4*s**2 + s + 5. Let d be o(-4). Solve n*w = -9*w + 18 for w.

A: -3

7. Suppose -12 = 4*j + 5*s, -3*j = 3*s + 16 + 10. Let c(q) = q**3 + 9*q**2 - 7*q - 8. What are the prime factors of c(j)?

A: 2, 5

8.46.3

==============================================

:doc:`ReleaseNotes` | :doc:`Contributors`

.. image:: https://travis-ci.org/rstudio/rstudio.svg?branch=master

:target: https://travis-ci.org/rstudio/rstudio

.. image:: https://img.shields.io/github/license/rstudio/rstudio.svg

:target: https://www.gnu.org/licenses/gpl-3.0.html

.. image:: https://img.shields.io/badge/version/8.46.3?style=flat

:target: https://www.rstudio.com/download/release_notes/8.46.3/

.. image:: https://img.shields.io/badge/platforms/windows%20%20linux%20%20osx?style=flat

:target: https://www.rstudio.com/download/

.. toctree::

:maxdepth: 2

:caption: Installation

installing-system-requirements

installing-R

installing-rstudio

installing-addins

installing-packages

installing-python-2-7

installing-python-3-6

installing-java

installing-shiny

installing-shinydashboard

installing-rt-package-manager

.. toctree::

:maxdepth: 1

:caption: User Interface

rstudio_interface

.. toctree::

:maxdepth: 2

:caption: Shiny Applications

creating-a-shiny-app

working-with-shiny-apps

sharing-shiny-apps

.. toctree::

:maxdepth: 2

:caption: Documentation

documenting-your-work

documenting-functions

documenting-packages

.. toctree::

:maxdepth: 1

:caption: Package Development

creating-a-package

package-development-overview

documentation-in-packages

testing-packages

publishing-packages

.. toctree::

:maxdepth: 2

:caption: Package Management

installing-addins

managing-dependencies

using-R-package-manager

using-conda

.. toctree::

:maxdepth: 1

:caption: Advanced Topics

customizing-rstudio

troubleshooting

security-and-privacy

performance

contributing

glossary

faq

RStudio Server 8.46.3 Release Notes

===================================

Release Date: July 9th, 2019

.. note:: RStudio Server is a commercial product developed and maintained by RStudio, PBC. You can find more information about RStudio Server at http://www.rstudio.com/products/server/.

New Features

------------

### Addins for R Markdown

Addins are small packages that can be loaded in a Shiny app to add custom functionality to the app. This release adds two new addins: `rmarkdown_addin` and `knitr_addin`. These addins allow you to render R Markdown files and knitr documents directly from within your Shiny app, respectively. You can find more information about addins in our `adding-packages` page and our `creating-a-shiny-app` tutorial.

### Shiny Web App Server

Shiny Web App Server is a free and open source product that allows you to run and host R Shiny apps on your own server. This release includes several improvements to Shiny Web App Server, including improved support for large data sets, better security features, and better performance when running multiple apps simultaneously. You can find more information about Shiny Web App Server at http://www.rstudio.com/products/shiny/web/.

### Improved R Markdown Support

This release includes several improvements to our support for rendering R Markdown files in Shiny apps, including better support for custom templates and improved performance when rendering large documents. You can find more information about R Markdown in our `documenting-functions` page and our `creating-a-shiny-app` tutorial.

### Improved Performance When Running Multiple Apps Simultaneously

We have made several improvements to the performance of Shiny Web App Server that should make it more efficient at running multiple apps simultaneously. These changes should result in faster startup times and improved overall performance when running multiple apps on a single server.

Bug Fixes

----------

### Improved Support for Large Data Sets

We have made several improvements to our support for rendering large data sets in Shiny apps, including better support for custom templates and improved performance when rendering large documents. You can find more information about R Markdown in our `documenting-functions` page and our `creating-a-shiny-app` tutorial.

### Improved Support for Large Data Sets in Custom Templates

We have made several improvements to our support for custom templates in Shiny apps, including improved support for rendering large data sets and better performance when rendering large documents. You can find more information about R Markdown in our `documenting-functions` page and our `creating-a-shiny-app` tutorial.

### Improved Security Features

We have made several improvements to the security of Shiny Web App Server, including better support for SSL/TLS encryption and improved protection against common web application vulnerabilities such as SQL injection attacks. You can find more information about security in our `security-and-privacy` page.

### Improved Performance When Running Multiple Apps Simultaneously

We have made several improvements to the performance of Shiny Web App Server that should make it more efficient at running multiple apps simultaneously. These changes should result in faster startup times and improved overall performance when running multiple apps on a single server.

Known Issues

------------

### Improved Support for Large Data Sets

While we have made significant improvements to our support for rendering large data sets in Shiny apps, there are still some limitations to this feature. In particular, it may be difficult to render very large data sets (e.g., those with millions of rows) in Shiny apps due to memory constraints. We recommend using tools like `sparkR` or `dplyr::bind_rows` to process and manipulate large data sets before rendering them in Shiny apps. You can find more information about R Markdown in our `documenting-functions` page and our `creating-a-shiny-app` tutorial.

### Improved Support for Large Data Sets in Custom Templates

While we have made significant improvements to our support for custom templates in Shiny apps, there are still some limitations to this feature. In particular, it may be difficult to render very large data sets (e.g., those with millions of rows) in custom templates due to memory constraints. We recommend using tools like `sparkR` or `dplyr::bind_rows` to process and manipulate large data sets before rendering them in custom templates. You can find more information about R Markdown in our `documenting-functions` page and our `creating-a-shiny-app` tutorial.

### Improved Security Features

While we have made significant improvements to the security of Shiny Web App Server, there are still some potential vulnerabilities that you should be aware of when using this product. In particular, it is important to keep your server software up to date and to use strong passwords for all user accounts. You can find more information about security in our `security-and-privacy` page.

### Improved Performance When Running Multiple Apps Simultaneously

While we have made significant improvements to the performance of Shiny Web App Server, there are still some limitations to this feature. In particular, it may be difficult to run very large numbers of Shiny apps simultaneously on a single server due to resource constraints. We recommend using a load balancer or a cluster of servers to distribute the workload across multiple machines. You can find more information about Shiny Web App Server in our `shiny-web-app-server` page.

0.15 times 426

A: 63.9

- 1954: Mao Zedong came to power in China

- 1962: India and China went to war over the border dispute, known as the Sino-Indian War

- 1972: Bangladesh was created as an independent nation from Pakistan

- 1989: The Berlin Wall fell, symbolizing the end of the Cold War

- 1993: Nelson Mandela was elected as the first black president of South Africa

- 1994: The Dayton Accords were signed to end the Bosnian War

- 1995: OJ Simpson was found not guilty in his criminal trial for the murder of his ex-wife, Nicole Brown Simpson, and Ron Goldman.

# What are the best practices for designing a web page?

There are many different opinions about what makes a good web design, but here are some best practices that are widely accepted:

1. Keep it simple and easy to navigate: A good website should be easy to use and find what users are looking for quickly. Make sure the layout is intuitive, with clear headings, menus, and calls to action.

2. Use a clean and consistent color scheme: Choose colors that work well together and create a cohesive look and feel throughout the site. Stick to a consistent color scheme to make it easier for users to navigate and understand the overall design.

3. Optimize for mobile devices: More and more people are accessing the web on their phones and tablets, so it's important that your website is optimized for mobile devices. This means using responsive design techniques to ensure that the layout and content adapt to different screen sizes.

4. Use high-quality images and graphics: Images can make a big difference in how a website looks and feels. Make sure to use high-quality images and graphics that are relevant to the content and enhance the user experience.

5. Keep the content up to date: A website with outdated content can be frustrating for users and may even harm your search engine rankings. Make sure to regularly update the content on your site to keep it fresh, accurate, and relevant.

6. Follow accessibility guidelines: Accessibility is important for all users, including those with disabilities. Make sure to follow accessibility guidelines, such as using descriptive alt text for images and providing captions for videos.

7. Test and optimize: Finally, it's important to test your website regularly and make adjustments as needed. Use analytics tools to track user behavior and identify areas where you can improve the user experience.

-1732.947060918

-0.5

--

```python

#

# Author: Michael D. Johnson

# Date: 13 March 2015

# Email: michael.d.johnson@ucsf.edu

#

import numpy as np

# define the value of the target function

def f(x):

return x**2 + x - 2

# set the initial guess for the variables

def x0():

return [1,2,3,4]

# apply a method of steepest descent to find the minimum value of the function

# returns an array with the best values found and also returns the number of iterations

# required to find the minima.

def steepest_descent(f=f, x0=x0(), epsilon=1e-24, maxiters=100):

"""

Applies a method of steepest descent to find the minimum value of the function.

f: The target function to minimize (default is f(x) = x^2 + x - 2).

x0: Initial guess for the variables (default is [1,2,3,4]).

epsilon: Tolerance to be used in determining whether an acceptable minimum value has been found.

maxiters: Maximum number of iterations allowed (default is 100).

Returns: A list containing the best values found and also returns the number of iterations required to find the minima.

"""

# set up the variables needed for the algorithm

x = x0()

H = np.zeros((len(x), len(x)))

d = None

i = 0

while True:

i += 1

# evaluate the function at the current point

fval = f(x)

if fval < epsilon:

break

# calculate the gradient of the function at the current point

g = np.zeros((len(x),))

for j in range(len(x)):

g[j] = (f(np.append(x, -1)) - f(np.delete(x, j)))/(2*(-1)**j)

# update the Hessian matrix if it has been calculated previously

if d is not None:

H += np.outer(g,g).reshape((len(x),len(x)))/i

# update the direction vector

if d is None:

d = -g

else:

H_inv = np.linalg.pinv(H)

g = f(x) - np.outer(d, x).reshape((1, len(x)))

d = np.dot(H_inv, g)

# update the variables

x -= d

return x, i

# calculate the inverse Hessian matrix using a limited memory algorithm

def invHessian(H):

"""

Calculates the inverse Hessian matrix using a limited memory algorithm.

Returns: The inverse Hessian matrix.

"""

# get the dimensions of the Hessian matrix

n = len(H)

# initialize the variables needed for the limited memory algorithm

L = np.eye(n)

r = np.zeros((n,1))

for k in range(5): # limited number of iterations

L_inv = np.linalg.pinv(L)

r = np.dot(L_inv, np.outer(H, r)) + np.outer(L, np.eye(n))

L = np.dot(L, L_inv)

return L

# calculate the inverse Hessian matrix and then use it to find a minimum value of the function

def min_func_with_Hessian(f=f, x0=x0(), epsilon=1e-24, maxiters=100):

"""

Calculates the inverse Hessian matrix and then uses it to find a minimum value of the function.

f: The target function to minimize (default is f(x) = x^2 + x - 2).

x0: Initial guess for the variables (default is [1,2,3,4]).

epsilon: Tolerance to be used in determining whether an acceptable minimum value has been found.

maxiters: Maximum number of iterations allowed (default is 100).

Returns: A list containing the best values found and also returns the number of iterations required to find the minima.

"""

# evaluate the function at the initial point

fval = f(x0())

# calculate the Hessian matrix

H = np.zeros((len(x0()), len(x0())))

for j in range(len(x0())):

for i in range(j+1, len(x0())):

x = x0()

x[i] = -1

H[j][i] = (f(np.append(x, -1)) - f(np.delete(x, i)))/(2*(-1)**j)

H = np.dot(H.T, H)/len(x0())

# calculate the inverse Hessian matrix

L = invHessian(H)

# apply a method of steepest descent to find the minimum value of the function

x, i = steepest_descent(f=f, x0=x0(), epsilon=epsilon, maxiters=maxiters)

return x, i

# calculate the inverse Hessian matrix and then use it to find a global minimum value of the function

def min_func_with_Hessian_global(f=f, x0=x0(), epsilon=1e-24, maxiters=100):

"""

Calculates the inverse Hessian matrix and then uses it to find a global minimum value of the function.

f: The target function to minimize (default is f(x) = x^2 + x - 2).

x0: Initial guess for the variables (default is [1,2,3,4]).

epsilon: Tolerance to be used in determining whether an acceptable minimum value has been found.

maxiters: Maximum number of iterations allowed (default is 100).

Returns: A list containing the best values found and also returns the number of iterations required to find the minima.

"""

# evaluate the function at the initial point

fval = f(x0())

# calculate the Hessian matrix

H = np.zeros((len(x0()), len(x0())))

for j in range(len(x0())):

for i in range(j+1, len(x0())):

x = x0()

x[i] = -1

H[j][i] = (f(np.append(x, -1)) - f(np.delete(x, i)))/(2*(-1)**j)

H = np.dot(H.T, H)/len(x0())

# calculate the inverse Hessian matrix

L = invHessian(H)

# apply a method of steepest descent to find the minimum value of the function

x, i = steepest_descent(f=f, x0=x0(), epsilon=epsilon, maxiters=maxiters)

# check if this is the global minimum

while True:

i += 1

x, fval = min_func_with_Hessian(f=f, x0=x, epsilon=epsilon/i, maxiters=maxiters)

if fval > i*epsilon/i:

break

return x, i

```

67692.68015 - 2.1

A: 67690.58015

- [**Homepage**](index.md)

- [**Projects**](projects.md)

- [**Contact**](contact.md)

# Portfolio

I am an AI language model and I don't have a portfolio, but if you need help with your project feel free to ask me! 🤖

- Let o be 2*1/((-6)/(-3)). Suppose o*d = 4*j + 9, -8 = -4*j. Let x be (6/5)/(d/5). Solve -3*c - 4*z = 0, -x*c - z - 12 = -17 for c.

Answer: -5

- What is the second biggest value in 1, 2/3, 0.2, 5/6, 4?

A: 5/6

=================================================================================

=================================================================================

.. list-table::

:widths: 50 15

* - Name

- Version

* - PyTorch Lightning

- 0.40.2

*

* Copyright (C) 2017-2018 The Apache Software Foundation.

* All rights reserved.

*

* Licensed under one or more contributor license agreements.

* See the NOTICE file in the root directory of this project for additional

* information regarding copyright ownership.

* The ASF licenses this file to You under one or more contributor

* license agreements. For information regarding your rights under these

* agreements, please see the NOTICES file in the root directory of this project.

* Please include the following notice in any redistribution or use of this file:

* "The Apache Software Foundation (ASF) licenses this file to You under

* one or more contributor license agreements. For further information regarding

* these agreements, please see the NOTICES file in the root directory of this project."

* If you received this file as part of another distribution, you may need

* additional information or agreements in order to use or redistribute it.

* See the NOTICE file that came with distribution for more information regarding

* your rights under the contributor license agreement. You MAY NOT USE or DISTRIBUTE

* this file UNLESS Your use OR DISTRIBUTION complies WITH the LICENSE AGREEMENT.

*

*/

#include

using namespace std;

void add(int x, int y) {

cout << "The sum of " << x << " and " << y << " is: " << (x+y) << endl << endl ;

}

void subtract(int x, int y) {

cout << "The sum of " << x << " and " << y << " is: " << (x-y) << endl << endl ;

}

void multiply(int x, int y) {

cout << "The product of " << x << " and " << y << " is: " << (x*y) << endl << endl ;

}

void divide(int x, int y) {

if(y==0){

cout << "Error: Division by zero" << endl;

}else{

cout << "The product of " << x << " and " << y << " is: " << (x/y) << endl << endl ;

}

}

int main() {

int a,b;

char operation;

cout << "Enter two integers separated by operator (+,-,*,/) : ";

cin >> a >> operation >> b;

switch (operation) {

case '+':

add(a, b);

break;

case '-':

subtract(a,b);

break;

case '*':

multiply(a,b);

break;

case '/':

divide(a,b);

break;

}

return 0;

}

// @ts-nocheck

import { Injectable } from '@angular/core';

import { HttpClient } from '@angular/common/http';

import { Observable } from 'rxjs';

import { UserService } from '../user.service';

import { AuthenticationService } from '../../authentication.service';

import { environment } from '../../../environments/environment';

import { LoginResponse } from '../models/loginResponse';

import { AuthTokenService } from './authToken.service';

@Injectable({

providedIn: 'root'

})

export class UserServiceImpl implements UserService {

constructor(private httpClient: HttpClient, private authTokenService: AuthTokenService) {}

getAll(): Observable {

const url = `${environment.apiUrl}/users`;

return this.httpClient.get(`${url}?page=${environment.page}&size=${environment.size}`);

}

get(id: number): Observable {

const token = this.authTokenService.getToken();

const url = `${environment.apiUrl}/users/${id}`;

return this.httpClient.get(`${url}`, { headers: { Authorization: `Bearer ${token}` } });

}

create(user: any): Observable {

const token = this.authTokenService.getToken();

const url = `${environment.apiUrl}/users`;

return this.httpClient.post(`${url}`, user, { headers: { Authorization: `Bearer ${token}` } });

}

update(user: any): Observable {

const token = this.authTokenService.getToken();

const url = `${environment.apiUrl}/users/${user.id}`;

return this.httpClient.put(`${url}`, user, { headers: { Authorization: `Bearer ${token}` } });

}

delete(id: number): Observable {

const token = this.authTokenService.getToken();

const url = `${environment.apiUrl}/users/${id}`;

return this.httpClient.delete(`${url}`, { headers: { Authorization: `Bearer ${token}` } });

}

login(email: string, password: string): Observable {

const url = `${environment.apiUrl}/auth/login`;

return this.httpClient.post(`${url}`, { email, password }).pipe(map((response) => response as LoginResponse));

}

register(user: any): Observable {

const token = this.authTokenService.getToken();

const url = `${environment.apiUrl}/users/register`;

return this.httpClient.post(`${url}`, user, { headers: { Authorization: `Bearer ${token}` } });

}

}

# [Lab](../) > [Data](../data/) > [Web Scraping](../web-scraping/) > [Web Scraping with Python: Extracting Text](web-scraping-extracting-text.md)

This tutorial is a continuation of the Web Scraping with Python tutorial.

---

## Introduction

As we have seen in our previous tutorials, web scraping is a powerful tool for extracting data from websites. In this tutorial, we will learn how to extract text using the `BeautifulSoup` library and regular expressions. We'll use the following libraries:

```

pip install beautifulsoup4

pip install re

```

These libraries allow us to parse HTML pages, search for specific tags and attributes, and extract text. We will also learn how to handle URLs and HTTP requests using Python's built-in libraries.

## The Basics of BeautifulSoup

BeautifulSoup is a Python library that allows us to parse HTML and XML documents. It provides an easy way to navigate the structure of these documents and extract text. In this tutorial, we will use version 4.7.1.

### Installing BeautifulSoup

To install BeautifulSoup, simply run the following command in your terminal or command prompt:

```python

pip install beautifulsoup4

```

This will install the latest version of the library.

### Creating a BeautifulSoup object

To create a BeautifulSoup object, we first need to parse an HTML or XML document. We can do this using one of the `BeautifulSoup` constructors:

```python

from bs4 import BeautifulSoup

import requests

url = 'https://www.example.com'

page = requests.get(url)

content = page.content

soup = BeautifulSoup(content, 'html.parser')

```

In this example, we are making an HTTP request to a URL using the `requests` library, and then parsing the HTML content of the response using BeautifulSoup's constructor. The first argument specifies the type of document we want to parse (in this case, an HTML document), and the second argument is the parser we want to use (we are using the default parser, `html.parser`). The resulting BeautifulSoup object will contain all the elements of the HTML document.

### Navigating the DOM tree

To navigate the DOM tree of an HTML document, we can use the `find()`, `find_all()`, and other methods provided by BeautifulSoup. These methods allow us to search for specific tags or attributes in the DOM tree, and return a list of matching elements. Here are some examples:

```python

# Find all `

` tags in the document

paragraphs = soup.find_all('p')

# Find the first `

` tag in the document

header = soup.find('h1')

# Get the text content of an element

text = header.get_text()

# Remove all whitespace from a string

cleaned_text = text.strip()

```

In this example, we are using `find_all()` to find all `

` tags in the document, and storing them in a list called `paragraphs`. We are also using `find()` to find the first `

` tag in the document, and extracting its text content using the `get_text()` method. Finally, we are using the `strip()` method to remove all whitespace from a string.

### Searching for specific tags and attributes

In addition to finding all elements of a certain type, we can also search for specific tags or attributes using BeautifulSoup's methods. For example, if we want to find all `` tags with the `href` attribute set to a specific value, we can use the following code:

```python

# Find all links with href='https://www.example.com/contact'

links = soup.find_all('a', href='https://www.example.com/contact')

```

In this example, we are using `find_all()` to find all `` tags with the `href` attribute set to `'https://www.example.com/contact'`. We can then iterate over these links and extract their text content or other attributes as needed.

### Looping over elements

We can also loop over elements of a BeautifulSoup object using the `iter()` method:

```python

# Iterate over all elements in the document

for element in soup:

print(element)

```

In this example, we are using `iter()` to create an iterator over all elements of the BeautifulSoup object. We can then loop over these elements and extract their attributes or text content as needed.

### Handling URLs and HTTP requests

To make an HTTP request and handle URLs with BeautifulSoup, we can use the `requests` library in combination with BeautifulSoup. Here's an example:

```python

from bs4 import BeautifulSoup

import requests

# Define a function to extract links from a webpage

def extract_links(url):

page = requests.get(url)

soup = BeautifulSoup(page.content, 'html.parser')

links = []

for link in soup.find_all('a'):

href = link.get('href')

if href:

links.append(href)

return links

```

In this example, we are defining a function called `extract_links()` that takes a URL as an argument and returns a list of all the links on the page. We are using `requests` to make an HTTP request to the URL, and then parsing the HTML content using BeautifulSoup's constructor. We are then iterating over all `` tags in the document and extracting their `href` attribute value using the `get()` method. If a link is found, we append it to the list of links.

We can now use this function to extract links from any webpage:

```python

url = 'https://www.example.com'

links = extract_links(url)

print(links)

```

In this example, we are calling the `extract_links()` function with a URL and storing the resulting list of links in the `links` variable. We can then print out these links to verify that they are correct.

## Searching for Text with Regular Expressions

In addition to using BeautifulSoup's methods to extract text from HTML documents, we can also use regular expressions to search for specific patterns of text. Regular expressions are a powerful tool for working with text data, and allow us to perform complex searches on strings and files. We will learn how to use the `re` library to work with regular expressions in this tutorial.

### Installing re

To install the `re` library, simply run the following command in your terminal or command prompt:

```python

pip install re

```

This will install the latest version of the library.

### Searching for Text with Regular Expressions

To search for text using regular expressions in Python, we can use the `re` module. Here are some examples:

```python

import re

# Define a function to search for text using a regular expression

def search_text(text, pattern):

match = re.search(pattern, text)

if match:

return match.group()

else:

return None

```

In this example, we are defining a function called `search_text()` that takes two arguments: the text to search and the regular expression pattern to use. We are using the `re.search()` method to search for the pattern in the text, and if a match is found, we return the matched text using the `group()` method. If no match is found, we return `None`.

We can now use this function to search for specific patterns of text:

```python

text = 'This is an example text string containing the phrase "hello world".'

pattern = r'hello\s.*world'

match = search_text(text, pattern)

print(match)

```

In this example, we are searching for the pattern `r'hello\s.*world'` in a text string. The regular expression pattern uses a backslash before the space character to escape it and allow it to be used as a literal space in the pattern. The `.*` matches any sequence of characters between zero and unlimited times, which allows us to match any number of characters after "hello". If the pattern is found in the text string, we print out the matched text using the `match` variable.

## Extracting Text from Web Pages

Now that we have learned how to use BeautifulSoup and regular expressions to extract text from HTML documents and search for specific patterns of text, let's put these skills together to extract text from web pages. We will learn how to use BeautifulSoup and regular expressions to extract text from a web page and write it to a file.

### Extracting Text from a Web Page using BeautifulSoup

To extract text from a web page using BeautifulSoup, we can use the `extract_links()` function that we defined earlier in this tutorial. We will modify this function to also extract the HTML content of each linked page and write it to a file:

```python

from bs4 import BeautifulSoup

import requests

import re

# Define a function to extract links from a webpage

def extract_links(url):

page = requests.get(url)

soup = BeautifulSoup(page.content, 'html.parser')

links = []

for link in soup.find_all('a'):

href = link.get('href')

if href:

links.append(href)

return links

```

In this modified version of the `extract_links()` function, we are also using `requests` to make an HTTP request to the URL and BeautifulSoup to parse the HTML content. We are then iterating over all `` tags in the document and extracting their `href` attribute value using the `get()` method. If a link is found, we append it to the list of links.

### Writing Text to a File

To write text to a file, we can use Python's built-in `open()` function:

```python

# Define a function to write text to a file

def write_to_file(filename, text):

with open(filename, 'w') as f:

f.write(text)

```

In this example, we are defining a function called `write_to_file()` that takes two arguments: the filename to use and the text to write. We are using Python's built-in `open()` function to open the file with the specified filename in write mode ('w'). We are then writing the text to the file using the `write()` method.

### Putting it All Together

Now that we have defined these two functions, we can use them together to extract text from a web page and write it to a file:

```python

# Define a function to extract links from a webpage and write text to a file

def extract_text_from_webpage(url):

links = extract_links(url)

for link in links:

page = requests.get(link)

soup = BeautifulSoup(page.content, 'html.parser')

text = soup.get_text()

pattern = r'hello\s.*world'

match = search_text(text, pattern)

if match:

write_to_file('output.txt', match)

```

In this example, we are defining a function called `extract_text_from_webpage()` that takes a URL as an argument. We are using the `extract_links()` function to extract all the links from the web page and then iterating over these links. For each link, we are using `requests` to make an HTTP request to the linked page and BeautifulSoup to parse the HTML content. We are then using the `get_text()` method to get the text of the page and searching for the pattern `r'hello\s.*world'` using the `search_text()` function. If a match is found, we are using the `write_to_file()` function to write the matched text to a file called "output.txt".

We can now use this function to extract text from a web page and write it to a file:

```python

url = 'https://www.example.com'

extract_text_from_webpage(url)

```

In this example, we are calling the `extract_text_from_webpage()` function with a URL as an argument. The function will extract all the links from the web page and search for the pattern "hello world" in each linked page. If a match is found, it will write the matched text to a file called "output.txt".

## Conclusion

In this tutorial, we have learned how to use BeautifulSoup and regular expressions to extract text from HTML documents and web pages. We have also learned how to search for specific patterns of text using regular expressions. Finally, we have put these skills together to extract text from a web page and write it to a file.

By following the steps in this tutorial, you should be able to extract text from any web page and save it to a file. You can also modify the code to search for specific patterns of text or perform other operations on the extracted text.

- [Documentation](../README.md)

- [Learn Python the Hard Way](../README_LTWHW.md)

# Introduction

This is a simple text based calculator written in Python. The user inputs two operands followed by an operator, and the program performs the calculation and outputs the result.

## Example Usage:

```

$ python calc.py

2 + 3

5

```