Tuesday, November 29, 2016

A Reason for Avoiding Programming

From someone in the process of becoming a data scientist. They had a question on regular expressions, which made almost no sense. It appears that the core concepts of ETL -- Extracting source data, Transforming it into a useful form and the Loading into some persistent storage for long-term analysis -- had not been embraced. It appears the design pattern was unknown. All I could gather from the sketchy email chain was that something involving regular expressions had become difficult.

I wrote this in response: Handling Irregular File Formats.

Here's part of the follow-up.

"I have been focusing on the math associated w/ math optimization. I have been using spreadsheets to perform the computations."

Really.

Spreadsheets.

The ETL pipeline question/rant/complaint was part of loading a spreadsheet?

That seems somehow wrong. There are real tools available that really do real data science work. The word "optimization" hints that scipy.optimize might be a more useful exercise than hacking around with spreadsheets.

Perhaps some advice from a real data scientist might help: http://www.becomingadatascientist.com

Tuesday, November 22, 2016

The Modern Python Cookbook

See https://www.packtpub.com/application-development/modern-python-cookbook

This is a large (!) collection of recipes, focused on Python 3, exclusively.

It's much easier to write about the version of Python I actually use each day, and leave the old, quirky, slow Python 2 behind. This book doesn't have any "this will be different in Python 2" warnings. Those days seem to have passed. Finally.

The clock is counting down. https://pythonclock.org


Wednesday, November 16, 2016

Tuesday, November 1, 2016

Handling Irregular File Formats

This is a common issue. We have a file which was printed for human consumption. Consequently, it has many different kinds of lines.

These are the two kinds of lines of interest:

900296268 4/9/16 Mobility, Data Mining and Privacy Expired

900295204 4/1/16 Pro .NET Best Practices
Expired

The first is a single physical line.  It has four data elements. The second is two physical lines. The first has three data elements.

There are a number of other noise lines in the file which must be filtered out.

The first "solution" pitched to me could be summarized with this:

Move "Expired" on a line by itself to the previous line

That was part of the email subject line. The body of the email was some whining about regular expressions. Which I mostly ignored. Multiline regular expressions are their own kind of challenge.

We (should) all know this: https://blog.codinghorror.com/regular-expressions-now-you-have-two-problems/

Let's do this without regular expressions. There are two things we need to know. One is buffering, and the other is the best way to split each line. It turns out that there are spaces as well as tabs, and can can, by splitting on tabs, make a lot of progress.

Instead of the good approach, I'll pick the other approach that doesn't involve splitting on tabs.

Here's the simulated file, with data lightly redacted.

sample_text = '''
"Your eBooks"

Show 200



Page: 1



Order # Date Title Formats Status Download
-------
xxx315605 9/30/16 R for Cloud Computing Available



xxx304790 6/21/16 Java XML and JSON Available
xxx304790 6/21/16 Accelerated DOM Scripting with Ajax, APIs, and Libraries Available

xxx291633 2/28/16 Practical Google Analytics and Google Tag Manager for Developers
Expired
'''

It's not perfectly obvious (because of line wrapping) but there are three examples of the "all-complete-in-one-line" records. There's one example of the "two-lines" record.

Rather than mess with the file, we'll build a file-like object with our sample data.

import io
file_like_object = io.StringIO(sample_text)

I like this because it lets me write proper unit test cases.

The file has four kinds of lines:

  • Complete Records
  • Record Headers (without Available/Expired)
  • Record Trailers (only Available/Expired)
  • Noise

We'll create some decision rules for the two obvious kinds of file lines: complete records and trailers. We can deduce the headers based on a simple adjacency rule: they precede a trailer. The fourth kind of lines are those which are possible headers but are not immediately prior to a trailer.

def complete(words):
    return len(words) > 3 and words[-1] in ('Available', 'Expired')

def trailer(words):
    return len(words) == 1 and words[0] in ('Available', 'Expired')    

We can spot these two kinds of lines easily. The other kinds require a Buffered Generator.

def emit_clean(source):
    header = None
    for line in (line.strip() for line in source):
        words = [w.strip() for w in line.split()]
        if len(words) == 0: continue
        if complete(words):
            yield(line)
            header = None
        elif trailer(words) and header:
            yield(header + '\t\t' + line)
            header = None
        else:
            # Possible header
            # print('??', line)
            header = line

The Buffered Generator is a way to implement a "look ahead one item" (LA1) algorithm. We do this by buffering rows. When we get to the next row we can use the buffered row and the current row to implement the look-ahead logic.

The actual implementation uses a look-behind buffer, header.

The (line.strip() for line in source) generator expression strips away leading and trailing spaces. This gets rid of the newline characters at the end of each input line.

The default behavior of split() is to split on whitespace. In this case, it will create a number of words for complete records or header records, and a single word for a trailer record. If we had split on tab characters, some of this logic would be simplified.

That's left as an exercise for the reader.

If the len(words) is zero, the line is blank.

If the line matches the complete() function, we can yield it as one of the iterable results of the generator function. We also clear out the look-behind buffer, header.

If the line is a trailer and we have a buffered look-behind line, this is the two-physical-line case. We can assemble a complete record and emit it.

Otherwise, we don't know what the line is. It's a possible header line, so we'll save it for later examination.

This algorithm involves no regular expressions.

With Regular Expressions


An alternative would use three regular expressions to match the three kinds of lines.

import re
all_one_pat = re.compile("(.*)\t(.*)\t(.*)\t\t((?:Available)|(?:Expired))")
header_pat = re.compile("(.*)\t(.*)\t(.*)")
trailer_pat = re.compile("((?:Available)|(?:Expired))")

This has the advantage that we can then use the groups() method of each successful match to emit useful data instead of text which needs subsequent parsing. This leads to a slightly more robust process.

def emit_clean2(source):
    header = None
    for line in (line.strip() for line in source):
        if len(line) == 0: continue
        all_one_match = all_one_pat.match(line)
        header_match = header_pat.match(line)
        trailer_match = trailer_pat.match(line)
        if all_one_match:
            yield(all_one_match.groups())
            header = None
        elif header_match and not header:
            header = header_match.groups()
        elif trailer_match and header:
            yield header + trailer_match.groups()
            header = None
        else:
            pass # noise

The essential processing involves seeing which of the regular expressions match the line at hand. If it's all-in-one, this is good. We can yield the groups of meaningful data. If it's a header, we can save the groups. If it's a trailer, we can combine header and trailer groups and yield the composite.

This has the advantage of explicitly rejecting noise lines instead of treating each noise line as a possible header.

Handling Irregular File Formats

This is a common issue. We have a file which was printed for human consumption. Consequently, it has many different kinds of lines.

These are the two kinds of lines of interest:

900296268 4/9/16 Mobility, Data Mining and Privacy Expired

900295204 4/1/16 Pro .NET Best Practices
Expired

The first is a single physical line.  It has four data elements. The second is two physical lines. The first has three data elements.

There are a number of other noise lines in the file which must be filtered out.

The first "solution" pitched to me could be summarized with this:

Move "Expired" on a line by itself to the previous line

That was part of the email subject line. The body of the email was some whining about regular expressions. Which I mostly ignored. Multiline regular expressions are their own kind of challenge.

We (should) all know this: https://blog.codinghorror.com/regular-expressions-now-you-have-two-problems/

Let's do this without regular expressions. There are two things we need to know. One is buffering, and the other is the best way to split each line. It turns out that there are spaces as well as tabs, and can can, by splitting on tabs, make a lot of progress.

Instead of the good approach, I'll pick the other approach that doesn't involve splitting on tabs.

Here's the simulated file, with data lightly redacted.

sample_text = '''
"Your eBooks"

Show 200



Page: 1



Order # Date Title Formats Status Download
-------
xxx315605 9/30/16 R for Cloud Computing Available



xxx304790 6/21/16 Java XML and JSON Available
xxx304790 6/21/16 Accelerated DOM Scripting with Ajax, APIs, and Libraries Available

xxx291633 2/28/16 Practical Google Analytics and Google Tag Manager for Developers
Expired
'''

It's not perfectly obvious (because of line wrapping) but there are three examples of the "all-complete-in-one-line" records. There's one example of the "two-lines" record.

Rather than mess with the file, we'll build a file-like object with our sample data.

import io
file_like_object = io.StringIO(sample_text)

I like this because it lets me write proper unit test cases.

The file has four kinds of lines:

  • Complete Records
  • Record Headers (without Available/Expired)
  • Record Trailers (only Available/Expired)
  • Noise

We'll create some decision rules for the two obvious kinds of file lines: complete records and trailers. We can deduce the headers based on a simple adjacency rule: they precede a trailer. The fourth kind of lines are those which are possible headers but are not immediately prior to a trailer.

def complete(words):
    return len(words) > 3 and words[-1] in ('Available', 'Expired')

def trailer(words):
    return len(words) == 1 and words[0] in ('Available', 'Expired')    

We can spot these two kinds of lines easily. The other kinds require a Buffered Generator.

def emit_clean(source):
    header = None
    for line in (line.strip() for line in source):
        words = [w.strip() for w in line.split()]
        if len(words) == 0: continue
        if complete(words):
            yield(line)
            header = None
        elif trailer(words) and header:
            yield(header + '\t\t' + line)
            header = None
        else:
            # Possible header
            # print('??', line)
            header = line

The Buffered Generator is a way to implement a "look ahead one item" (LA1) algorithm. We do this by buffering rows. When we get to the next row we can use the buffered row and the current row to implement the look-ahead logic.

The actual implementation uses a look-behind buffer, header.

The (line.strip() for line in source) generator expression strips away leading and trailing spaces. This gets rid of the newline characters at the end of each input line.

The default behavior of split() is to split on whitespace. In this case, it will create a number of words for complete records or header records, and a single word for a trailer record. If we had split on tab characters, some of this logic would be simplified.

That's left as an exercise for the reader.

If the len(words) is zero, the line is blank.

If the line matches the complete() function, we can yield it as one of the iterable results of the generator function. We also clear out the look-behind buffer, header.

If the line is a trailer and we have a buffered look-behind line, this is the two-physical-line case. We can assemble a complete record and emit it.

Otherwise, we don't know what the line is. It's a possible header line, so we'll save it for later examination.

This algorithm involves no regular expressions.

With Regular Expressions


An alternative would use three regular expressions to match the three kinds of lines.

import re
all_one_pat = re.compile("(.*)\t(.*)\t(.*)\t\t((?:Available)|(?:Expired))")
header_pat = re.compile("(.*)\t(.*)\t(.*)")
trailer_pat = re.compile("((?:Available)|(?:Expired))")

This has the advantage that we can then use the groups() method of each successful match to emit useful data instead of text which needs subsequent parsing. This leads to a slightly more robust process.

def emit_clean2(source):
    header = None
    for line in (line.strip() for line in source):
        if len(line) == 0: continue
        all_one_match = all_one_pat.match(line)
        header_match = header_pat.match(line)
        trailer_match = trailer_pat.match(line)
        if all_one_match:
            yield(all_one_match.groups())
            header = None
        elif header_match and not header:
            header = header_match.groups()
        elif trailer_match and header:
            yield header + trailer_match.groups()
            header = None
        else:
            pass # noise

The essential processing involves seeing which of the regular expressions match the line at hand. If it's all-in-one, this is good. We can yield the groups of meaningful data. If it's a header, we can save the groups. If it's a trailer, we can combine header and trailer groups and yield the composite.

This has the advantage of explicitly rejecting noise lines instead of treating each noise line as a possible header.