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Chapter 6: Problem 29

For each of a collection of weather forecast sites, say $$ \begin{aligned} &\text { http://weather.yahoo.com } \\ &\text { http://www.weather.com } \\ &\text { http://www.weatherchannel.com } \\ &\text { http://weather.cnn.com } \\ &\text { http://yr.no } \end{aligned} $$ find the pages corresponding to your favorite location. Study the HTML sources and write a function for each HTML page that downloads the web page and extracts basic forecast information: date, weather type (name of symbol), and temperature. Write out a comparison of different forecasts on the screen. Name of program file: weather_forecast_comparison1.py.

Short Answer

Expert verified
Extract forecast data from HTML and compare results using a Python script.

Step by step solution

01

Identify the Target URL for Forecast Information

Determine the specific URL within each weather forecast site that contains the weather information for your desired location. For this step, navigate to each of the sites listed in the exercise and use their search functionality to find the URL that contains the forecast for your chosen location.
02

Investigate the HTML Structure

Access each URL identified in Step 1 and inspect their respective HTML pages to understand where the pertinent forecast data (date, weather type, and temperature) is located. This can be achieved using your web browser's 'Inspect Element' or 'View Page Source' tools to locate the required data within the HTML tags.
03

Install Necessary Python Packages

To download and parse the HTML pages, ensure that you have the necessary Python libraries installed. Use pip to install requests (to handle downloading web pages) and BeautifulSoup from bs4 (to handle HTML parsing). Execute the command: `pip install requests beautifulsoup4`.
04

Write a Function to Download and Parse a Web Page

For each URL, write a Python function that downloads the page using the requests library and parses the HTML using BeautifulSoup. The function should locate the HTML elements identified in Step 2 and extract the date, weather type, and temperature information.
05

Extract Forecast Information

Within each function from Step 4, use BeautifulSoup to navigate through tags like
, , or to find and extract the date, weather type, and temperature. You can do so by using methods such as `soup.find()` or `soup.select()` to target the specific elements or classes. Store these values in a structured format such as a dictionary.
06

Compare and Display the Forecasts

Create a main function in `weather_forecast_comparison1.py` that calls each of your individual functions from Step 4. Aggregate the results and print a comparison showing the forecasted details (dates, weather type, temperature) for each source. Format this output for clarity, possibly using tabular formatting techniques.

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Key Concepts

These are the key concepts you need to understand to accurately answer the question.

HTML parsing
HTML parsing is essential for extracting specific data from web pages. It involves analyzing the HTML source code of a website to find the needed information. When we parse HTML, we're essentially reading and making sense of the document's structure.

The HTML code is made up of tags, such as `
`, ``, and others, which contain the data we're interested in, like weather details. For example, a `
` tag might store a temperature value or a forecast date.

Using tools like your browser's 'Inspect Element' option allows you to see exactly where these pieces of information are stored on the page. This is the first step towards scraping, as it helps us gather the path to access these elements programmatically.
Python programming
Python programming is at the heart of web scraping due to its simplicity and rich set of libraries. Python is a perfect choice because it allows easy handling of data and text through its concise syntax and powerful libraries like `requests` and `BeautifulSoup`.

In this particular task, Python enables the downloading of web pages and parsing of their HTML content. With Python, we can automate the repetitive process of fetching and analyzing data from multiple online sources, such as weather sites. This automation saves time and minimizes manual effort.

To get started with Python for web scraping, make sure you have Python installed on your system, and use pip to install necessary libraries. Coding in Python for tasks like these not only helps improve programming skills but also enhances problem-solving abilities.
BeautifulSoup
BeautifulSoup is an incredible library in Python designed for parsing HTML and XML documents. It creates a structured representation of incoming documents, which lets you navigate and search through the HTML tree.

Using BeautifulSoup, you can easily search for HTML elements and extract data. This is done through functions like `soup.find()` and `soup.find_all()`, which let you target specific tags, attributes, or classes from the HTML code.

BeautifulSoup simplifies the task of handling poorly formed HTML, which is common on many websites. With BeautifulSoup, you get clean and navigable tree structures of HTML, making data extraction straightforward, efficient, and reliable.
Weather Forecasting
Weather forecasting is the process of predicting atmospheric conditions and determining future weather events. This involves the collection and processing of data from multiple sources, often including various weather websites and sensors.

Each website might present its own forecast for temperature, precipitation, and weather types, and these predictions can vary. Analyzing forecasts from multiple sites helps compare and understand different weather predictions for more informed decision-making.

For students engaged in scraping and analyzing weather data, understanding the basics of weather forecasting is invaluable. Knowing what information you're collecting, like dates and temperature forecasts, is crucial for interpreting the gathered data correctly and effectively.
Data Extraction
Data extraction in web scraping refers to the process of automatically retrieving specific information from websites. This involves a series of steps to ensure accurate data capture, from identifying the target data to its final collection and storage.

In the context of weather data, this means pulling out forecasts, temperatures, and dates from our targeted weather websites. Each piece of data needs to be captured in a consistent format, often using dictionaries or JSON, to allow for easy comparison and analysis later on.

The key to effective data extraction is the precision with which you identify and target the needed data within HTML using tools like BeautifulSoup. Proper data extraction methods ensure that scraped content is accurate and ready for further analysis and presentation.

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Most popular questions from this chapter

Files with data in a tabular fashion are very common and so is the operation of the reading the data into arrays. Therefore, the scitools.filetable module offers easy-to-use functions for loading data files with columns of numbers into NumPy arrays. First read about scitools.filetable using pydoc in a terminal window (cf. page 80). Then solve Exercise \(6.1\) using appropriate functions from the scitools.filetable module. Name of program file: read_2columns_filetable.py.

The purpose of this exercise is to tell you how hard it may be to write Python programs in the standard programs that most people use for writing text. Type the following one-line program in either Microsoft Word or OpenOffice: $$ \text { print "Hello, World!" } $$ Both Word and OpenOffice are so "smart" that they automatically edit "print" to "Print" since a sentence should always start with a capital. This is just an example that word processors are made for writing documents, not computer programs. Save the program as a .doc (Word) or .odt (OpenOffice) file. Now try to run this file as a Python program. You will get a message SyntaxError: Non-ASCII character Explain why you get this error. Then save the program as a .txt file. Run this file as a Python program. It may work well if you wrote the program text in Microsoft Word, but with OpenOffice there may still be strange characters in the file. Use a text editor to view the exact contents of the file. Name of program file: office.py.

The file src/files/xy.dat contains two columns of numbers, corresponding to \(x\) and \(y\) coordinates on a curve. The start of the file looks as this: \(\begin{array}{cc}-1.0000 & -0.0000 \\ -0.9933 & -0.0087 \\ -0.9867 & -0.0179 \\ -0.9800 & -0.0274 \\ -0.9733 & -0.0374\end{array}\) Make a program that reads the first column into a list \(\mathrm{x}\) and the second column into a list y. Then convert the lists to arrays, and plot the curve. Print out the maximum and minimum \(y\) coordinates. (Hint: Read the file line by line, split each line into words, convert to float, and append to \(\mathrm{x}\) and y.) Name of program file: read_2columns.py

Imagine that a GPS device measures your position at every \(s\) seconds. The positions are stored as \((x, y)\) coordinates in a file src/files/pos.dat with the an \(x\) and \(y\) number on each line, except for the first line which contains the value of \(s\). First, load \(s\) into a float variable and the \(x\) and \(y\) numbers into two arrays and draw a straight line between the points (i.e., plot the \(y\) coordinates versus the \(x\) coordinates). The next task is to compute and plot the velocity of the movements. If \(x(t)\) and \(y(t)\) are the coordinates of the positions as a function of time, we have that the velocity in \(x\) direction is \(v_{x}(t)=d x / d t\), and the velocity in \(y\) direction is \(v_{y}=d y / d t .\) Since \(x\) and \(y\) are only known for some discrete times, \(t_{k}=k s, k=0, \ldots, n-1\), we must use numerical differentation. A simple (forward) formula is \(v_{x}\left(t_{k}\right) \approx \frac{x\left(t_{k+1}\right)-x\left(t_{k}\right)}{s}, \quad v_{y}\left(t_{k}\right) \approx \frac{y\left(t_{k+1}\right)-y\left(t_{k}\right)}{s}, \quad k=0, \ldots, n-2\) Compute arrays vx and vy with velocities based on the formulas above for \(v_{x}\left(t_{k}\right)\) and \(v_{y}\left(t_{k}\right), k=0, \ldots, n-2 .\) Plot vx versus time and vy versus time. Name of program file: position2velocity.py.

The program src/basic/lnsum.py produces, among other things, this output: $$ \begin{aligned} &\text { epsilon: } 1 e-04, \text { exact error: } 8.18 \mathrm{e}-04, \mathrm{n}=55 \\ &\text { epsilon: } 1 \mathrm{e}-06, \text { exact error: } 9.02 \mathrm{e}-06, \mathrm{n}=97 \\ &\text { epsilon: } 1 \mathrm{e}-08, \text { exact error: } 8.70 \mathrm{e}-08, \mathrm{n}=142 \\ &\text { epsilon: } 1 \mathrm{e}-10, \text { exact error: } 9.20 \mathrm{e}-10, \mathrm{n}=187 \\ &\text { epsilon: } 1 \mathrm{e}-12, \text { exact error: } 9.31 \mathrm{e}-12, \mathrm{n}=233 \end{aligned} $$ Redirect the output to a file. Write a Python program that reads the file and extracts the numbers corresponding to epsilon, exact error, and \(\mathrm{n}\). Store the numbers in three arrays and plot epsilon and the exact error versus \(\mathrm{n}\). Use a logarithmic scale on the \(y\) axis, which is enabled by the \(\log =\) ' \(\mathrm{y}\) ' keyword argument to the plot function. Name of program file: read_error.py.
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