You're reading from Haskell Data Analysis cookbook Explore intuitive data analysis techniques and powerful machine learning methods using over 130 practical recipes

Product type Paperback

Published in Jun 2014

Publisher

ISBN-13 9781783286331

Length 334 pages

Edition 1st Edition

Languages

Haskell

Concepts

Data Analysis

Author (1):

Nishant Shukla

View More author details

Table of Contents (14) Chapters

Preface

1. The Hunt for Data FREE CHAPTER

2. Integrity and Inspection

3. The Science of Words

4. Data Hashing

5. The Dance with Trees

6. Graph Fundamentals

7. Statistics and Analysis

8. Clustering and Classification

9. Parallel and Concurrent Design

10. Real-time Data

11. Visualizing Data

12. Exporting and Presenting

Index

Capturing table rows from an HTML page

Mining Hypertext Markup Language (HTML) is often a feat of identifying and parsing only its structured segments. Not all text in an HTML file may be useful, so we find ourselves only focusing on a specific subset. For instance, HTML tables and lists provide a strong and commonly used structure to extract data whereas a paragraph in an article may be too unstructured and complicated to process.

In this recipe, we will find a table on a web page and gather all rows to be used in the program.

Getting ready

We will be extracting the values from an HTML table, so start by creating an input.html file containing a table as shown in the following figure:

The HTML behind this table is as follows:

$ cat input.html

<!DOCTYPE html>
<html>
    <body>
        <h1>Course Listing</h1>
        <table>
            <tr>
                <th>Course</th>
                <th>Time</th>
                <th>Capacity</th>
            </tr>
            <tr>
                <td>CS 1501</td>
                <td>17:00</td>
                <td>60</td>
            </tr>
            <tr>
                <td>MATH 7600</td>
                <td>14:00</td>
                <td>25</td>
            </tr>
            <tr>
                <td>PHIL 1000</td>
                <td>9:30</td>
                <td>120</td>
            </tr>
        </table>
    </body>
</html>

If not already installed, use Cabal to set up the HXT library and the split library, as shown in the following command lines:

$ cabal install hxt
$ cabal install split

How to do it...

We will need the htx package for XML manipulations and the chunksOf function from the split package, as presented in the following code snippet:
```
import Text.XML.HXT.Core
import Data.List.Split (chunksOf)
```

Define and implement main to read the input.html file.

main :: IO ()
main = do
  input <- readFile "input.html"

Feed the HTML data into readString, thereby setting withParseHTML to yes and optionally turning off warnings. Extract all the td tags and obtain the remaining text, as shown in the following code:
```
  texts <- runX $ readString 
           [withParseHTML yes, withWarnings no] input 
    //> hasName "td"
    //> getText
```
The data is now usable as a list of strings. It can be converted into a list of lists similar to how CSV was presented in the previous CSV recipe, as shown in the following code:
```
  let rows = chunksOf 3 texts
  print $ findBiggest rows
```

By folding through the data, identify the course with the largest capacity using the following code snippet:

findBiggest :: [[String]] -> [String]
findBiggest [] = []
findBiggest items = foldl1 
                    (\a x -> if capacity x > capacity a 
                             then x else a) items

capacity [a,b,c] = toInt c
capacity _ = -1

toInt :: String -> Int
toInt = read

Running the code will display the class with the largest capacity as follows:
```
$ runhaskell Main.hs

{"PHIL 1000", "9:30", "120"}
```

How it works...

This is very similar to XML parsing, except we adjust the options of readString to [withParseHTML yes, withWarnings no].