Join Lillian Pierson, P.E. for an in-depth discussion in this video Cleaning and stemming textual data, part of Python for Data Science Essential Training Part 1.
- [Instructor] Now let's look at removing stop words. … When you remove stop words, … you're basically removing a noise from your text. … To remove stop words, we actually need to access … the corpus module from within nltk, … so we're going to say from nltk.corpus, … we want to import stop words. … Now let's create a variable called SW, … and it's going to be English stop words, … so we'll call this set function, … and then we will call in the words function, … and we're going to say StopWords.Words, … and then within that function, … we're going to pass the perimeter English … because we are actually looking … to access the English stop words … that are built into the nltk library, … and then what we need to do is we want to print out … a small label, so we're going to call the print function, … and we're going to say, okay, … these are our stop words in English language, … and then we'll go ahead and print out our SW object. … We have a little syntax error, … and we actually have a lookup error. …
AuthorLillian Pierson, P.E.
- Why use Python for working with data
- Filtering and selecting data
- Concatenating and transforming data
- Data visualization best practices
- Visualizing data
- Creating a plot
- Creating statistical data graphics
- Performing basic math and linear algebra
- Correlation analysis
- Multivariate analysis
- Data sourcing via web scraping
- Introduction to natural language processing
- Collaborative analytics with Plotly
Skill Level Intermediate
Python Statistics Essential Trainingwith Michele Vallisneri2h 58m Intermediate
1. Introduction to the Data Professions
High-level course road map1m 28s
2. Data Preparation Basics
3. Data Visualization 101
4. Practical Data Visualization
5. Basic Math and Statistics
6. Data Sourcing via Web Scraping
7. Collaborative Analytics with Plotly
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