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Our printed output looks like this in our terminal:ДЄs you can see, the logic to load and parse the HTML is simple thanks to Requests and Beautiful Soup. We can print those out, but letвЂ™s first do some aggregation by looping over the city and races values. WeвЂ™ve ended up with a list, inmates_links, that contains all of the values. Each inmate gets a dictionary and all the dictionaries get appended to an inmates list. Once the inmate details page is parsed, we extract the age, race, sex, name, booking time and city values to a dictionary.Д«eautifulSoupвЂ™s select and findAll methods did the hard work for us - we just told it where to look in our HTML (using our browser inspection tools above). We now loop through each inmate detail link in our inmates_links list, and for each one we load its HTML and parse it using the same Requests and BeautifulSoup methods we used previously. We donвЂ™t do anything with our link yet, we just add it to a list, inmates_links. Once the HTML is parsed, we loop through each row of the inmatesList table and extract the link to the inmate details page. Our first chunk of logic loads the HTML of the inmate listing page using requests.get(url_to_scrape)В and then parses it using BeautifulSoup(r.text). WeвЂ™ll set ourselves up for success by importing requests and BeautifulSoup at the top of our script.

#ANACONDA JAIL ROSTER INSTALL#

Both of these packages are so popular that you might already have them installed if not, install them before you run the code below.ДЄt a high level, our web scraping script does three things: (1) Load the inmate listing page and extract the links to the inmate detail pages (2) Load each inmate detail page and extract inmate data (3) Print extracted inmate data and aggregate on race and city of residence. WeвЂ™ll rely on two common Python packages to do the heavy lifting, Requests and Beautiful Soup. Now that we have a rough idea of how our values are arranged in the HTML, letвЂ™s write a script that will extract them. In our example we see that links to inmate details,, are neatly listed in table rows. You want your values to predictably appear in rows in a or in a set of, , or other common elements. When viewing a pageвЂ™s HTML source, youвЂ™re looking for patterns in the arrangement of your desired data. (Page source of the Polk County Current Inmate Listing page) Access to these tools varies by browser, but the View Page Source option is a mainstay and is usually available when you right click directly on a page. Most browsers provide a set of HTML inspection tools that help you lift the engine-bay hatch and get a feel for how the page is structured. The code has lots of commentary to help you.)ДЄ web browser is the first tool you should reach for when scraping a website. (The entire script weвЂ™ll walk through is open and stored here at GitHub, the most popular online platform for sharing computer code. From this site, using a Python script, weвЂ™ll extract a list of inmates, and for each inmate weвЂ™ll get some data like race and city of residence. In this tip sheet weвЂ™ll be using the Polk County Current Inmate Listing site as an example. If you have no familiarity whatsoever, Codecademy can get you started.

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We recommend that you download the Anaconda Python distributionВ and take a tutorial in the basics of the language. It will challenge you a bit to think about how data is structured. This will give you a strong sense of the basics and insights into how web pages work.

#ANACONDA JAIL ROSTER HOW TO#

(See the Data Journalism Handbookfor more.) But here weвЂ™ll go through how to use the language Python to perform this task. There are many ways to scrape, many programming languages in which to do it and many tools that can aid with it. In cases like these, you might want to leverage a technique called web scraping to programmatically gather the data for you. ItвЂ™s often on the web, but it isnвЂ™t always packaged up and available for download. Unfortunately, the data you want isnвЂ™t always readily available. It can be the backbone of an investigation, and it can lead to new insights and new ways of thinking.

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