Nearly all useful programs rely to some extent on pre-existing code in various forms. The existing code that your code relies on is known as a dependency. You have already come across some dependencies in previous tutorials: you used the
math module to calculate quadratic equations in the first tutorial and you used the
requests module to fetch weather data in the second tutorial.
In the first tutorial, you also wrote the
solver.py module, and imported this into the
We can think of dependencies as falling into three broad categories:
In this tutorial, you'll gain more experience with all three categories of dependencies. Specifically, you'll write an NLP (natural language processing) program to analyse sentences, using spaCy, a third-party dependency.
Dependency management is a hugely complicated area, and there is a large ecosystem of related tools to help manage packaging and installing Python programs. We won't be covering all of the options and background, but you can read an overview of the different tools here.
In nearly all programming environments, you have to explicitly install third-party dependencies. Let's say you wanted to use the
requests library (which is not included in Python by default) on your local machine. If you try to import it, you would get a
ModuleNotFound error, as shown below.
In order to use this library, you would first have to install it using a command similar to
pip install requests, and only then would the import statement run correctly.
Replit, by contrast, can often do the installation for you completely automatically, using the Universal Package Manager. The moment you run the
import requests line of code, the package manager will go find the correct package and install it, or in some cases Replit will even have pre-installed the package. Either way, your code will "just work".
This is super convenient, but sometimes you need more control. For example, you might need a specific version of a package, or the universal package manager might not be able to automatically install all of your dependencies. In these cases, you can use more advanced ways to install packages.
If you're not sure exactly which package you need, you can use Replit's built-in package manager GUI to search for packages. In the example below, we are looking for a package called
To use this, you need to
This will take you to a page showing an overview and summary of the selected package. You can install it to your repl by using the
+ button, as shown below.
Once the package is installed, we can use it in our code. Run the example shown below to extract the "Google Search" text from the main button on the homepage.
import requests from bs4 import BeautifulSoup r = requests.get("https://google.com").text soup = BeautifulSoup(r, "html.parser") print([x.get("title") for x in soup.findAll("input") if x.get("title")])
This code uses the
requests library to scrape the HTML from google.com and then uses the
beautifulsoup4 library to get the title of the button off the page and print it to the console.
requests is one of the most commonly used Python libraries, Replit probably installed it in a slightly different way from most packages. However,
beautifulsoup4 is less common and this will have been installed in the standard way using poetry.
If you go back to the files tab, you'll see two new files
pyproject.toml which were created automatically by the installer. Take a look inside the
In this case, line 9 says that our project relies on the
beautifulsoup4 package and needs at least version 4.9.1. If we look at the
beautifulsoup page on PyPi, we'll see that the latest stable version is 4.9.1, so if this project is run in the future and there is a new version available, it will automatically use the updated package.
So far, we have installed packages that are easy for the Replit universal dependency manager to install automatically, behind the scenes. Some packages are more complicated though.
spaCy, for example, is an NLP library that relies on a large external data file. When installing this library, you usually have to install this data file as a separate step.
To get this to work on Replit, we'll have to manually modify the
Create a new repl,
SpacyExample, then click on the
Packages icon and search for "spacy".
Select the version at the top and hit the
+ button to add this package to your application. Once this is complete, head across to your
main.py and enter the following code:
import spacy print(spacy.__version__)
This should output the version of
spaCy that we are using, which means that
spaCy has been added as a dependency correctly.
If you take a look at your
pyproject.toml file now, you should see that it has specified
spaCy as a dependency.
[tool.poetry] name = "spacy-example" version = "0.1.0" description = "" authors = ["Your Name <[email protected]>"] [tool.poetry.dependencies] python = "^3.8" spacy = "^2.3.2" [tool.poetry.dev-dependencies] [build-system] requires = ["poetry>=0.12"] build-backend = "poetry.masonry.api"
An important component of
spaCy is a set of pretrained statistical models that support NLP. These do not come with
spaCy by default, nor are they indexed on PyPi. One of these models is
main.py file, replace your current code with the following:
import spacy nlp = spacy.load("en_core_web_sm") doc = nlp("The quick brown fox jumps over the lazy dog.") for token in doc: print(token.text)
This code should simply break our short sentence into tokens (words), and print each one out.
However, at this point, if you run your code you will get an error, as Python cannot find the
OSError: [E050] Can't find model 'en_core_web_sm'. It doesn't seem to be a shortcut link, a Python package or a valid path to a data directory.
We will now explicitly tell our application how to access this dependency. To do this, we need to find where the model is stored online.
First, we need to find the
spaCy documentation for this model. This can be accessed here.
RELEASE DETAILS button will guide us to where the model is stored online, on GitHub. GitHub is a very common place to store code and related components online.
The GitHub page also lets us know what version of
spaCy is needed to make sure the model runs correctly.
Here we see that
spaCy version should be greater than or equal to 2.3.0, but less than 2.4.0. We should make a note of this for later, so we can check that we have pinned an appropriate
If we scroll right to the bottom of the page, you will see an "Assets" section, and under this you will see the same
Package icon we used in Replit with "en_core_web_sm-2.3.1.tar.gz" next to it. This is what we have been looking for: the file containing the model.
Right-click on this file and select
copy link address. We will need this shortly, as this is the URL of the file.
We now need to modify our
pyproject.toml file in Replit. Open this file and add the following section to it
[tool.poetry.dependencies.en_core_web_sm] url = "https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-2.3.1/en_core_web_sm-2.3.1.tar.gz"
url should be the one that you copied from GitHub in the previous step. Your whole
pyproject.toml file should now look like the one below.
At this point that we should also check that we are using an appropriate version of
spaCy. We are using version 2.3.2, which is in the allowed range for the model release (>=2.3.0, <2.4.0) , so we do not need to modify this.
Finally, hit the
run button. This will cause your configuration files to be updated and then will run your application. If everything has gone correctly, you should see the following in the output pane once it completes.
We've now seen how to install common packages like
requests simply by importing them, how to find and install slightly more complicated packages like
beautifulsoup using the GUI package manager, and how to manually install even more complicated packages like
spaCy (which have their own dependencies) by manually writing sections of the
Let's put everything together and use all three packages to extract people's names from today's headlines. We'll use the plaintext version of CNN at lite.cnn.com as it's easier to extract text from.
Replace the code in your
main.py with the following.
import spacy import requests from bs4 import BeautifulSoup from collections import Counter nlp = spacy.load("en_core_web_sm") response = requests.get("http://lite.cnn.com/en") soup = BeautifulSoup(response.text, "html.parser") # https://stackoverflow.com/questions/1936466/beautifulsoup-grab-visible-webpage-text [s.extract() for s in soup(['style', 'script', '[document]', 'head', 'title'])] text = soup.getText() doc = nlp(text) names =  for ent in doc.ents: if ent.label_ == "PERSON": names.append(ent.lemma_) print("These people are in the headlines today") print(Counter(names).most_common(10))
Then we loop through all of the named entities that
spaCy detects as part of its standard parse, and print out any that look like people.
If you run this code, you should see a list of people making headlines today. At the time of writing, John Lewis is mentioned in the most headlines. (Note that named entity recognition is a difficult task and here
spaCy considers the possessive form
John Lewis' to be a separate entity. We can see that John Lewis was mentioned a total of 7 times though.)
If you followed along, you'll already have your own version of the repl to extend. If not, start from ours. Fork it from the embed below.
spaCy is a very powerful NLP library and it can do far more than simply extract people's names. See what other interesting insights you can automatically extract from today's news.
Now you can use the Replit IDE, write programs that use files, and install third-party dependencies. Next up, we'll be taking a look at doing data science with Replit by visualising data using