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Python Data Analysis

You're reading from   Python Data Analysis Perform data collection, data processing, wrangling, visualization, and model building using Python

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Product type Paperback
Published in Feb 2021
Publisher Packt
ISBN-13 9781789955248
Length 478 pages
Edition 3rd Edition
Languages
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Authors (2):
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Ivan Idris Ivan Idris
Author Profile Icon Ivan Idris
Ivan Idris
Avinash Navlani Avinash Navlani
Author Profile Icon Avinash Navlani
Avinash Navlani
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Table of Contents (20) Chapters Close

Preface 1. Section 1: Foundation for Data Analysis
2. Getting Started with Python Libraries FREE CHAPTER 3. NumPy and pandas 4. Statistics 5. Linear Algebra 6. Section 2: Exploratory Data Analysis and Data Cleaning
7. Data Visualization 8. Retrieving, Processing, and Storing Data 9. Cleaning Messy Data 10. Signal Processing and Time Series 11. Section 3: Deep Dive into Machine Learning
12. Supervised Learning - Regression Analysis 13. Supervised Learning - Classification Techniques 14. Unsupervised Learning - PCA and Clustering 15. Section 4: NLP, Image Analytics, and Parallel Computing
16. Analyzing Textual Data 17. Analyzing Image Data 18. Parallel Computing Using Dask 19. Other Books You May Enjoy

Removing stopwords

Stopwords are counted as noise in text analysis. Any text paragraph has to have verbs, articles, and propositions. These are all considered stop words. Stop words are necessary for human conversation but they don't make many contributions in text analysis. Removing stopwords from text is called noise elimination.

Let's see how to remove stopwords using NLTK:

# import the nltk stopwords
from nltk.corpus import stopwords

# Load english stopwords list
stopwords_set=set(stopwords.words("english"))

# Removing stopwords from text
filtered_word_list=[]
for word in tokenized_words:
# filter stopwords
if word not in stopwords_set:
filtered_word_list.append(word)

# print tokenized words
print("Tokenized Word List:", tokenized_words)

# print filtered words
print("Filtered Word List:", filtered_word_list)

This results in the following output:

Tokenized Word List: ['Taj', 'Mahal', 'is', 'one', &apos...
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