Join Lillian Pierson, P.E. for an in-depth discussion in this video Lemmatizing and analyzing textual data, part of Python for Data Science Essential Training Part 1.
- [Instructor] Now let's look at how to Lemmatize words. … Let's just go ahead and throw some caution to the wind … by downloading WordNet in case you need to do that. … I prefer just to have you download it now … rather than wait and see for sure if you need it, … because better safe than sorry. … So we'll just access nltk … and then call the download method … and we'll pass in our module that we need which is WordNet. … Then run that and you will basically be sure … that you have the WordNet package … available to you in your Jupyter environment. … And then what we need to do is we need to import … the WordNet Lemmatizer. … So from nltk.stem.wordnet, … we want to import WordNet Lemmatizer. … And then lets instantiate that. … Just call it lem and then we'll set Lem equal … to WordNet Lemmatizer so let's go ahead … and instantiate a PorterStemmer object. … So from nltk.stem.porter we want to import … PorterStemmer and we'll just instantiate that … and call it stem, so stem is equal to PorterStemmer …
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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