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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

Reading and writing data from HTML tables

HTML tables store rows in the <tr>...</tr> tag and each row has corresponding <td>...</td> cells for holding values. In pandas, we can also read the HTML tables from a file or URL. The read_html() function reads an HTML table from a file or URL and returns HTML tables into a list of pandas DataFrames:

# Reading HTML table from given URL
table_url = 'https://en.wikipedia.org/wiki/List_of_sovereign_states_and_dependent_territories_in_North_America'
df_list = pd.read_html(table_url)

print("Number of DataFrames:",len(df_list))

This results in the following output:

Number of DataFrames: 7

In the preceding code example, we have read the HTML table from a given web page using the read_html() method. read_html() will return all the tables as a list of DataFrames. Let's check one of the DataFrames from the list:

# Check first DataFrame
df_list[0].head()

This results in the following output:

In the preceding...

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