RDBMS Cleaning Techniques - Megan Squire (2015)

Megan Squire (2015)

Chapter 7. RDBMS Cleaning Techniques

Home refrigerators all come with shelves and most have one or two drawers for vegetables. But if you ever visit a home organization store or talk to a professional organizer, you will learn that there are also numerous additional storage options, including egg containers, cheese boxes, soda can dispensers, wine bottle holders, labeling systems for leftovers, and stackable, color-coded bins in a variety of sizes. But do we really need all these extras? To answer this, ask yourself these questions, are my frequently used foods easy to find? Are food items taking up more space than they should? Are leftovers clearly labeled so I remember what they are and when I made them? If our answers are no, organization experts say that containers and labels can help us optimize storage, reduce waste, and make our lives easier.

The same is true in our Relational Database Management System (RDBMS). As the classic long-term data storage solution, RDBMS is a standard part of the modern data science toolkit. But all too often, we are guilty of just depositing data in the database, with little thought about the details. In this chapter, we will learn how to design an RDBMS that goes beyond two shelves and a drawer. We will learn a few techniques that will ensure our RDBMS is optimizing storage, reducing waste, and making our lives easier. Specifically, we will:

· Learn how to find anomalies in our RDBMS data

· Learn several strategies to clean different types of problematic data

· Learn when and how to create new tables for your cleaned data, including creating both child tables and lookup tables

· Learn how to document the rules governing the changes you made

Getting ready

To set up the examples in this chapter, we will be working with a popular dataset called Sentiment140. This dataset was created to help learn about the positive and negative sentiments in messages on Twitter. We are not really concerned with sentiment analysis in this book, but we are going to use this dataset to practice cleaning data after it has already been imported into a relational database.

To get started with the Sentiment140 dataset, you will need a MySQL server set up and ready to go, just like in the earlier Enron examples.

Step one – download and examine Sentiment140

The version of the Sentiment140 data that we want to use is the original set of two files available directly from the Sentiment140 project at http://help.sentiment140.com/for-students. This ZIP file of tweets and their positive and negative polarity (or sentiment, on a scale of 0, 2, or 4) was created by some graduate students at Stanford University. Since this file was made publicly available, the original Sentiment140 files have been added by other websites and made available as part of many larger collections of tweets. For this chapter, we will use the original Sentiment140 text file, which is either available as a link from the preceding site or by following the precise path to http://cs.stanford.edu/people/alecmgo/trainingandtestdata.zip.

Download the ZIP file, extract it, and take a look at the two CSV files inside using your text editor. Right away, you will notice that one file has many more lines than the other, but both these files have the same number of columns in them. The data is comma-delimited, and each column has been enclosed in double quotes. The description of each column can be found on the for-students page linked in the preceding section.

Step two – clean for database import

For our purposes—learning how to clean data—it will be sufficient to load the smaller of these files into a single MySQL database table. Everything we need to do to learn, we can accomplish with the smaller file, the one called testdata.manual.2009.06.14.csv.

As we are looking at the data, we may notice a few areas that will trip us up if we try to import this file directly into MySQL. One of the trouble spots is located at line 28 in the file:

"4","46","Thu May 14 02:58:07 UTC 2009","""booz allen""",

Do you see the triple quotation marks """ right before the booz keyword and after the word allen? The same issue comes up later on line 41 with double quotation marks around the song title P.Y.T:

"4","131","Sun May 17 15:05:03 UTC 2009","Danny Gokey","VickyTigger","I'm listening to ""P.Y.T"" by Danny Gokey…"

The problem with these extra quotation marks is that the MySQL import routine will use the quotation marks to delimit the column text. This will produce an error, as MySQL will think that the line has more columns than there really are.

To fix this, in our text editor, we can use Find and Replace to replace all instances of """ with " (double quote) and all instances of "" with ' (single quote).


These "" could also probably be removed entirely with very little negative effect on this cleaning exercise. To do this, we would simply search for "" and replace it with nothing. But if you want to stick close to the original intent of the tweet, a single quote (or even an escaped double quote like this \") is a safe choice for a replacement character.

Save this cleaned file to a new filename, something like cleanedTestData.csv. We are now ready to import it into MySQL.

Step three – import the data into MySQL in a single table

To load our somewhat cleaner data file into MySQL, we will need to revisit the CSV-to-SQL techniques from the Importing spreadsheet data into MySQL section in Chapter 3, Workhorses of Clean Data – Spreadsheets and Text Editors:

1. From the command line, navigate to the directory where you have saved the file you created in step two. This is the file we are going to import into MySQL.

2. Then, launch your MySQL client, and connect to your database server:

3. user@machine:~/sentiment140$ mysql -hlocalhost -umsquire -p

Enter password:

4. Enter your password, and after you are logged in, create a database within MySQL to hold the table, as follows:

5. mysql> CREATE DATABASE sentiment140;

mysql> USE sentiment140;

6. Next, we need to create a table to hold the data. The data type and lengths for each column should represent our best attempt to match the data we have. Some of the columns will be varchars, and each of them will need a length. As we might not know what those lengths should be, we can use our cleaning tools to discern an appropriate range.

7. If we open our CSV file in Excel (Google Spreadsheets will work just fine for this as well), we can run some simple functions to find the maximum lengths of some of our text fields. The len() function, for example, gives the length of a text string in characters, and the max() function can tell us the highest number in a range. With our CSV file open, we can apply these functions to see how long our varchar columns in MySQL should be.

The following screenshot shows a method to use functions to solve this problem. It shows the length() function applied to column G, and the max() function used in column H but applied to column G.

Step three – import the data into MySQL in a single table

Columns G and H show how to get the length of a text column in Excel and then get the maximum value.

8. To calculate these maximum lengths more quickly, we can also take an Excel shortcut. The following array formula can work to quickly combine the maximum value with the length of a text column in a single cell—just make sure you press Ctrl + Shift + Enter after typing this nested function rather than just Enter:


This nested function can be applied to any text column to get the maximum length of the text in that column, and it only uses a single cell to do this without requiring any intermediate length calculations.

After we run these functions, it turns out that the maximum length for any of our tweets is 144 characters.

Detecting and cleaning abnormalities

You might be wondering how a tweet in this dataset could possibly be 144 characters long as Twitter limits all tweets to a maximum length of 140 characters. It turns out that in the sentiment140 dataset, the & character was sometimes translated to the HTML equivalent code, &amp, but not always. Some other HTML code was used too, for instance, sometimes, the < character became < and > became >. So, for a few very long tweets, this addition of just a few more characters can easily push this tweet over the length limit of 140. As we know that these HTML-coded characters were not what the original person tweeted, and because we see that they happen sometimes but not all the time, we call these data abnormalities.

To clean these, we have two choices. We can either go ahead and import the messy data into the database and try to clean it there, or we can attempt to clean it first in Excel or a text editor. To show the difference in these two techniques, we will do both here. First, we will use find and replace in our spreadsheet or text editor to try to convert the characters shown in the following table. We can import the CSV file into Excel and see how much cleaning we can do there:

HTML code

Replace with

The count of instances

The Excel function used to find count













The first two character swaps work fine with Find and Replace in Excel. The &lt; and &gt; HTML-encoded characters are changed. Take a look at the text like this:

I'm listening to 'P.Y.T' by Danny Gokey <3 <3 <3 Aww, he's so amazing. I <3 him so much :)

The preceding becomes text like this:

I'm listening to 'P.Y.T' by Danny Gokey <3 <3 <3 Aww, he's so amazing. I <3 him so much :)

However, when we attempt to use Excel to find & and replace it with &, you may run into an error, as shown:

Detecting and cleaning abnormalities

Some operating systems and versions of Excel have a problem with our selection of the & character as a replacement. At this point, if we run into this error, we could take a few different approaches:

· We could use our search engine of choice to attempt to find an Excel solution to this error

· We could move our CSV text data into a text editor and perform the find and replace function in there

· We could forge ahead and throw the data into the database despite it having the weird &amp; characters in it and then attempt to clean it inside the database

Normally, I would be in favor of not moving dirty data into the database if it is even remotely possible to clean it outside of the database. However, as this is a chapter about cleaning inside a database, let's go ahead and import the half-cleaned data into the database, and we will work on cleaning the &amp; issue once the data is inside the table.

Creating our table

To move our half-cleaned data into the database, we will first write our CREATE statement and then run it on our MySQL database. The CREATE statement is shown as follows:

mysql> CREATE TABLE sentiment140 (

-> polarity enum('0','2','4') DEFAULT NULL,

-> id int(11) PRIMARY KEY,

-> date_of_tweet varchar(28) DEFAULT NULL,

-> query_phrase varchar(10) DEFAULT NULL,

-> user varchar(10) DEFAULT NULL,

-> tweet_text varchar(144) DEFAULT NULL



This statement uses the simple and fast MyISAM engine as we do not anticipate needing any InnoDB features such as row-level locking or transactions. For more on the difference between MyISAM and InnoDB, there is a handy discussion of when to use each storage engine located here:http://stackoverflow.com/questions/20148/myisam-versus-innodb.

You might notice that the code still requires 144 for the length of the tweet_text column. This is because we were unable to clean these columns with the &amp; code in them. However, this does not bother me too much because I know that varchar columns will not use their extra space unless they need it. After all, this is why they are called varchar, or variable character, columns. But if this extra length really bothers you, you can alter the table later to only have 140 characters for that column.

Next, we will use the MySQL command line to run the following import statement from the location:

mysql> LOAD DATA LOCAL INFILE 'cleanedTestData.csv'

-> INTO TABLE sentiment140


-> (polarity, id, date_of_tweet, query_phrase, user, tweet_text);

This command loads the data from our cleaned CSV file into the new table we created. A success message will look like this, indicating that all 498 rows were loaded into the table:

Query OK, 498 rows affected (0.00 sec)

Records: 498 Deleted: 0 Skipped: 0 Warnings: 0


If you have access to a browser-based interface such as phpMyAdmin (or a desktop application such as MySQL Workbench or Toad for MySQL), all of these SQL commands can be completed inside these tools very easily and without having to type on the command line, for example, in phpMyAdmin, you can use the Import tab and upload the CSV file there. Just make sure that the data file is cleaned following the procedures in Step two – clean for database import, or you may get errors about having too many columns in your file. This error is caused by quotation mark problems.

Step four – clean the & character

In the last step, we decided to postpone cleaning the &amp; character because Excel was giving a weird error about it. Now that we have finished Step three – import the data into MySQL in a single table and our data is imported into MySQL, we can very easily clean the data using an UPDATEstatement and the replace()string function. Here is the SQL query needed to take all instances of &amp; and replace them with &:

UPDATE sentiment140 SET tweet_text = replace(tweet_text,'&', '&');

The replace()function works just like find and replace in Excel or in a text editor. We can see that tweet ID 594, which used to say #at&t is complete fail, now reads #at&t is complete fail.

Step five – clean other mystery characters

As we are perusing the tweet_text column, we may have noticed a few odd tweets, such as tweet IDs 613 and 2086:

613, Talk is Cheap: Bing that, I?ll stick with Google

2086, Stanford University?s Facebook Profile

The ? character is what we should be concerned about. As with the HTML-encoded characters we saw earlier, this character issue is also very likely an artifact of a prior conversion between character sets. In this case, there was probably some kind of high-ASCII or Unicode apostrophe (sometimes called a smart quote) in the original tweet, but when the data was converted into a lower-order character set, such as plain ASCII, that particular flavor of apostrophe was simply changed to a ?.

Depending on what we plan to do with the data, we might not want to leave out the ? character, for example, if we are performing word counting or text mining, it may be very important that we convert I?ll to I'll and University?s to University's. If we decide that this is important, then our job is to detect the tweets, where this error happened, and then devise a strategy to convert the question mark back to a single quote. The trick, of course, is that we cannot just replace every question mark in the tweet_text column with a single quote character, because some tweets have question marks in them that should be left alone.

To locate the problem characters, we can run some SQL queries that attempts to locate the problems using a regular expression. We are interested in question marks that appear in odd places, such as with an alphabetic character immediately following them. Here is an initial pass at a regular SQL expression using the MySQL REGEXP feature. Running this will give us a rough idea of where the problem question marks might reside:

SELECT id, tweet_text

FROM sentiment140

WHERE tweet_text

REGEXP '\\?[[:alpha:]]+';

This SQL regular expression asks for any question mark characters that are immediately followed by one or more alphabetic characters. The SQL query yields six rows, four of which turn out to have odd question marks and two of which are false positives. False positives are tweets that matched our pattern but that should not actually be changed. The two false positives are tweets with IDs 234 and 2204. These two included question marks as part of a legitimate URL. Tweets 139, 224, 613, and 2086 are true positives, which means tweets that were correctly detected as anomalous and should be changed. All the results are shown in the following phpMyAdmin screenshot:

Step five – clean other mystery characters

Tweet 139 is strange, though. It has a question mark before the word Obama, as if the name of a news article was being quoted, but there is no matching quote (or missing quote) at the end of the string. Was this supposed to be some other character? This might actually be a false positive too, or at least not enough of a true positive to actually make us fix it. While we are looking at the tweets closely, 224 also has an extra strange question mark in a place where it does not seem to belong.

If we are going to write a replace() function to change problematic question marks to single quotes, we will somehow need to write a regular expression that matches only the true positives and does not match any of the false positives. However, as this dataset is small, and there are only four true positives—or three if we decide 139 does not need to be cleaned—then we could just clean the true positives by hand. This is especially a good idea as we have a few questions about other possible issues such as the extra question mark in tweet 224.

In this case, as we only have three problem rows, it will be faster to simply run three small UPDATE commands on the data rather than attempting to craft the perfect regular expression. Here is the SQL query to take care of tweets 224 (first issue only), 613, and 2086:

UPDATE sentiment140 SET tweet_text = 'Life''s a bitch? and so is Dick Cheney. #p2 #bipart #tlot #tcot #hhrs #GOP #DNC http://is.gd/DjyQ' WHERE id = 224;

UPDATE sentiment140 SET tweet_text = 'Talk is Cheap: Bing that, I''ll stick with Google. http://bit.ly/XC3C8' WHERE id = 613;

UPDATE sentiment140 SET tweet_text = 'Stanford University''s Facebook Profile is One of the Most Popular Official University Pages - http://tinyurl.com/p5b3fl' WHERE id = 2086;


Note that we had to escape our single quotes in these update statements. In MySQL, the escape character is either the backslash or single quote itself. These examples show the single quote as the escape character.

Step six – clean the dates

If we take a look at the date_of_tweet column, we see that we created it as a simple variable character field, varchar(30). What is so wrong with that? Well, suppose we want to put the tweets in order from earliest to latest. Right now, we cannot use a simple SQL ORDER BY clause and get the proper date order, because we will get an alphabetical order instead. All Fridays will come before any Mondays, and May will always come after June. We can test this with the following SQL query:

SELECT id, date_of_tweet

FROM sentiment140

ORDER BY date_of_tweet;

The first few rows are in order but down near row 28, we start to see a problem:

2018 Fri May 15 06:45:54 UTC 2009

2544 Mon Jun 08 00:01:27 UTC 2009

3 Mon May 11 03:17:40 UTC 2009

May 11 does not come after May 15 or June 8. To fix this, we will need to create a new column that cleans these date strings and turns them into proper MySQL datetime data types. We learned in the Converting between data types section in Chapter 2, Fundamentals – Formats, Types, and Encodings, that MySQL works best when dates and time are stored as native date, time, or datetime types. The format to insert a datetime type looks like this: YYYY-MM-DD HH:MM:SS. But this is not what our data looks like in the date_of_tweet column.

There are numerous built-in MySQL functions that can help us format our messy date string into the preferred format. By doing this, we will be able to take advantage of MySQL's ability to perform math on the dates and time, for example, finding the difference between two dates or times or sorting items properly in the order of their dates or times.

To get our string into a MySQL-friendly datetime type, we will perform the following procedure:

1. Alter the table to include a new column, the purpose of which is to hold the new datetime information. We can call this new column date_of_tweet_new or date_clean or some other name that clearly differentiates it from the original date_of_tweet column. The SQL query to perform this task is as follows:

2. ALTER TABLE sentiment140

3. ADD date_clean DATETIME NULL

AFTER date_of_tweet;

4. Perform an update on each row, during which we format the old date string into a properly formatted datetime type instead of a string and add the new value into the newly created date_clean column. The SQL to perform this task is as follows:

5. UPDATE sentiment140

SET date_clean = str_to_date(date_of_tweet, '%a %b %d %H:%i:%s UTC %Y');

At this point, we have a new column that has been populated with the clean datetime. Recall that the old date_of_tweet column was flawed in that it was not sorting dates properly. To test whether the dates are being sorted correctly now, we can select our data in the order of the new column:

SELECT id, date_of_tweet

FROM sentiment140

ORDER BY date_clean;

We see that the rows are now perfectly sorted, with the May 11 date coming first, and no dates are out of order.

Should we remove the old date column? This is up to you. If you are worried that you may have made a mistake or that you might need to have the original data for some reason, then by all means, keep it. But if you feel like removing it, simply drop the column, as shown:

ALTER TABLE sentiment140

DROP date_of_tweet;

You could also create a copy of the Sentiment140 table that has the original columns in it as a backup.

Step seven – separate user mentions, hashtags, and URLs

Another problem with this data right now is that there are lots of interesting pieces of information hidden inside the tweet_text column, for example, consider all the times that a person directs a tweet to the attention of another person using the @ symbol before their username. This is called amention on Twitter. It might be interesting to count how many times a particular person is mentioned or how many times they are mentioned in conjunction with a particular keyword. Another interesting piece of data hidden in some of the tweets is hashtags; for example, the tweet with ID 2165 discusses the concepts of jobs and babysitting using the #jobs and #sittercity hashtags.

This same tweet also includes an external, non-Twitter URL. We can extract each of these mentions, hashtags, and URLs and save them separately in the database.

This task will be similar to how we cleaned the dates, but with one important difference. In the case of the dates, we only had one possible corrected version of the date, so it was sufficient to add a single new column to hold the new, cleaned version. With mentions, hashtags, and URLs, however, we may have zero or more in a single tweet_text value, for example, the tweet we looked at earlier (ID 2165) had two hashtags in it, as does this tweet (ID 2223):

HTML 5 Demos! Lots of great stuff to come! Yes, I'm excited. :) http://htmlfive.appspot.com #io2009 #googleio

This tweet has zero mentions, one URL, and two hashtags. Tweet 13078 includes three mentions but no hashtags or URLs:

Monday already. Iran may implode. Kitchen is a disaster. @annagoss seems happy. @sebulous had a nice weekend and @goldpanda is great. whoop.

We will need to change our database structure to hold these new pieces of information—hashtags, URLs, and mentions—all the while keeping in mind that a given tweet can have a lot of these in it.

Create some new tables

Following relational database theory, we should avoid creating columns that will store multivalue attributes, for example, if a tweet has three hashtags, we should not just deposit all three hashtags into a single column. The impact of this rule for us is that we cannot just copy the ALTERprocedure we used for the date cleaning problem earlier.

Instead, we need to create three new tables: sentiment140_mentions, sentiment140_urls, and sentiment140_hashtags. The primary key for each new table will be a synthetic ID column, and each table will include just two other columns: tweet_id, which ties this new table back to the originalsentiment140 table, and the actual extracted text of the hashtag, mention, or URL. Here are three CREATE statements to create the tables we need:

CREATE TABLE IF NOT EXISTS sentiment140_mentions (


tweet_id int(11) NOT NULL,

mention varchar(144) NOT NULL,



CREATE TABLE IF NOT EXISTS sentiment140_hashtags (


tweet_id int(11) NOT NULL,

hashtag varchar(144) NOT NULL,



CREATE TABLE IF NOT EXISTS sentiment140_urls (


tweet_id int(11) NOT NULL,

url varchar(144) NOT NULL,




These tables do not use foreign keys back to the original sentiment140 tweet table. If you would like to add these, that is certainly possible. For the purposes of learning how to clean this dataset, however, it is not necessary.

Now that our tables are created, it is time to fill them with the data that we have carefully extracted from tweet_text column. We will work on each extraction case separately, starting with user mentions.

Extract user mentions

To design a procedure that can handle the extraction of the user mentions, let's first review what we know about mentions in tweets:

· The user mention always starts with the @ sign

· The user mention is the word that immediately follows the @ sign

· If there is a space after @, it is not a user mention

· There are no spaces inside the user mention itself

· As e-mail addresses also use @, we should be mindful of them

Using these rules, we can construct some valid user mentions:

· @foo

· @foobar1

· @_1foobar_

We can construct some examples of invalid user mentions:

· @ foo (the space following the @ invalidates it)

· foo@bar.com (bar.com is not recognized)

· @foo bar (only @foo will be recognized)

· @foo.bar (only @foo will be recognized)


In this example, we assume that we do not care about the difference between a regular @mention and.@mention, sometimes called a dot-mention. These are tweets with a period in front of the @ sign. They are designed to push a tweet to all of the user's followers.

As this rule set is more complicated than what we can execute efficiently in SQL, it is preferable to write a simple little script to clean these tweets using some regular expressions. We can write this type of script in any language that can connect to our database, such as Python or PHP. As we used PHP to connect to the database in Chapter 2, Fundamentals – Formats, Types, and Encodings, let's use a quick PHP script here as well. This script connects to the database, searches for user mentions in the tweet_text column, and moves any found mentions into the newsentiment140_mentions table:


// connect to db

$dbc = mysqli_connect('localhost', 'username', 'password', 'sentiment140')

or die('Error connecting to database!' . mysqli_error());


// pull out the tweets

$select_query = "SELECT id, tweet_text FROM sentiment140";

$select_result = mysqli_query($dbc, $select_query);

// die if the query failed

if (!$select_result)

die ("SELECT failed! [$select_query]" . mysqli_error());

// pull out the mentions, if any

$mentions = array();

while($row = mysqli_fetch_array($select_result))


if (preg_match_all(






foreach ($mentions[0] as $name)


$insert_query = "INSERT into sentiment140_mentions (id, tweet_id, mention) VALUES (NULL," . $row["id"] . ",'$name')";

echo "<br />$insert_query";

$insert_result = mysqli_query($dbc, $insert_query);

// die if the query failed

if (!$insert_result)

die ("INSERT failed! [$insert_query]" . mysqli_error());





After running this little script on the sentiment140 table, we see that 124 unique user mentions have been extracted out of the 498 original tweets. A few interesting things about this script include that it will handle Unicode characters in usernames, even though this dataset does not happen to have any. We can test this by quickly inserting a test row at the end of the sentiment140 table, for example:

INSERT INTO sentiment140 (id, tweet_text) VALUES(99999, "This is a @тест");

Then, run the script again; you will see that a row has been added to the sentiment140_mentions table, with the @тест Unicode user mention successfully extracted. In the next section, we will build a similar script to extract hashtags.

Extract hashtags

Hashtags have their own rules, which are slightly different to user mentions. Here is a list of some of the rules we can use to determine whether something is a hashtag:

· Hashtags start with the # sign

· The hashtag is the word that immediately follows the # sign

· Hashtags can have underscores in them but no spaces and no other punctuation

The PHP code to extract hashtags into their own table is mostly identical to the user mentions code, with the exception of the regular expression in the middle of the code. We can simply change the $mentions variable to $hashtags, and then adjust the regular expression to look like this:

if (preg_match_all(





This regular expression says that we are interested in matching case-insensitive Unicode letter characters. Then, we need to change our INSERT line to use the correct table and column names like this:

$insert_query = "INSERT INTO sentiment140_hashtags (id, tweet_id, hashtag) VALUES (NULL," . $row["id"] . ",'$name')";

When we successfully run this script, we see that 54 hashtags have been added to the sentiment140_hashtags table. Many more of the tweets have multiple hashtags, even more than the tweets that had multiple user mentions, for example, we can see right away that tweets 174 and 224 both have several embedded hashtags.

Next, we will use this same skeleton script and modify it again to extract URLs.

Extract URLs

Pulling out the URLs from the text can be as simple as looking for any string that starts with http:// or https://, or it could get a lot more complex depending on what types of URLs the text string includes, for example, some strings might include file:// URLs or torrent links, such as magnet URLs, or other types of unusual links. In the case of our Twitter data, we have it somewhat easier, as the URLs that were included in our dataset all start with HTTP. So, we could be lazy and just design a simple regular expression to extract any string that follows http:// or https://. This regular expression would just look like this:

if (preg_match_all(





However, if we do a bit of hunting on our favorite search engine, it turns out that we can easily find some pretty impressive and useful generic URL matching patterns that will handle more sophisticated link patterns. The reason that this is useful is that if we write our URL extraction to handle more sophisticated cases, then it will still work if our data changes in the future.

A very well-documented URL pattern matching routine is given on the http://daringfireball.net/2010/07/improved_regex_for_matching_urls website. The following code shows how to modify our PHP code to use this pattern for URL extraction in the Sentiment140 dataset:


// connect to db

$dbc = mysqli_connect('localhost', 'username', 'password', 'sentiment140')

or die('Error connecting to database!' . mysqli_error());


// pull out the tweets

$select_query = "SELECT id, tweet_text FROM sentiment140";

$select_result = mysqli_query($dbc, $select_query);

// die if the query failed

if (!$select_result)

die ("SELECT failed! [$select_query]" . mysqli_error());

// pull out the URLS, if any

$urls = array();

$pattern = '#\b(([\w-]+://?|www[.])[^\s()<>]+(?:\([\w\d]+\)|([^[:punct:]\s]|/)))#';

while($row = mysqli_fetch_array($select_result))


echo "<br/>working on tweet id: " . $row["id"];

if (preg_match_all(






foreach ($urls[0] as $name)


echo "<br/>----url: ".$name;

$insert_query = "INSERT into sentiment140_urls (id, tweet_id, url)

VALUES (NULL," . $row["id"] . ",'$name')";

echo "<br />$insert_query";

$insert_result = mysqli_query($dbc, $insert_query);

// die if the query failed

if (!$insert_result)

die ("INSERT failed! [$insert_query]" .mysqli_error());





This program is nearly identical to the mention extracting program we wrote earlier, with two exceptions. First, we stored the regular expression pattern in a variable called $pattern, as it was long and complicated. Second, we made small changes to our database INSERT command, just as we did for the hasthtag extraction.

A full line-by-line explanation of the regular expression pattern is available on its original website, but the short explanation is that the pattern shown will match any URL protocol, such as http:// or file://, and it also attempts to match valid domain name patterns as well and directory/file patterns a few levels deep. The source website provides its own test dataset too if you want to see the variety of patterns that it will match and a few known patterns that will definitely not match.

Step eight – cleaning for lookup tables

In the Step seven – Separate user mentions, hashtags, and URLs section, we created new tables to hold the extracted hashtags, user mentions, and URLs, and then provided a way to link each row back to the original table via the id column. We followed the rules of database normalization by creating new tables that represent the one-to-many relationship between a tweet and user mentions, between a tweet and hashtags, or between a tweet and URLs. In this step, we will continue optimizing this table for performance and efficiency.

The column we are concerned with now is the query_phrase column. Looking at the column data, we can see that it contains the same phrases repeated over and over. These were apparently the search phrases that were originally used to locate and select the tweets that now exist in this dataset. Of the 498 tweets in the sentiment140 table, how many of the query phrases are repeated over and over? We can use the following SQL to detect this:

SELECT count(DISTINCT query_phrase)

FROM sentiment140;

The query result shows that there are only 80 distinct query phrases, but these are used over and over in the 498 rows.

This may not seem like a problem in a table of 498 rows, but if we had an extremely large table, such as with hundreds of millions of rows, we should be concerned with two things about this column. First, duplicating these strings over and over takes up unnecessary space in the database, and second, searching for distinct string values is very slow.

To solve this problem, we will create a lookup table of query values. Each query string will exist only once in this new table, and we will also create an ID number for each one. Then, we will change the original table to use these new numeric values rather than the string values that it is using now. Our procedure to accomplish this is as follows:

1. Create a new lookup table:

2. CREATE TABLE sentiment140_queries (

3. query_id int(11) NOT NULL AUTO_INCREMENT,

4. query_phrase varchar(25) NOT NULL,

5. PRIMARY KEY (query_id)


6. Populate the lookup table with the distinct query phrases and automatically give each one a query_id number:

7. INSERT INTO sentiment140_queries (query_phrase)

SELECT DISTINCT query_phrase FROM sentiment140;

8. Create a new column in the original table to hold the query phrase number:

9. ALTER TABLE sentiment140

ADD query_id INT NOT NULL AFTER query_phrase;

10.Make a backup of the sentiment140 table in case the next step goes wrong. Any time we perform UPDATE on a table, it is a good idea to make a backup. To create a copy of the sentiment140 table, we can use a tool like phpMyAdmin to copy the table easily (use the Operations tab). Alternately, we can recreate a copy of the table and then import into it the rows from the original table, as shown in the following SQL:

11.CREATE TABLE sentiment140_backup(

12. polarity int(1) DEFAULT NULL,

13. id int(5)NOT NULL,

14. date_of_tweet varchar(30) CHARACTER SET utf8 DEFAULT NULL ,

15. date_clean datetime DEFAULT NULL COMMENT 'holds clean, formatted date_of_tweet',

16. query_id int(11) NOT NULL,

17. user varchar(25) CHARACTER SET utf8 DEFAULT NULL,

18. tweet_text varchar(144) CHARACTER SET utf8 DEFAULT NULL ,




INSERT INTO sentiment140_backup SELECT * FROM sentiment140;

22.Populate the new column with the correct number. To do this, we join the two tables together on their text column, then look up to the correct number value from the lookup table, and insert it into the sentiment140 table. In the following query, each table has been given an alias, s andsq:

23.UPDATE sentiment140 s

24.INNER JOIN sentiment140_queries sq

25.ON s.query_phrase = sq.query_phrase

SET s.query_id = sq.query_id;

26.Remove the old query_phrase column in the sentiment140 table:

27.ALTER TABLE sentiment140

DROP query_phrase;

At this point, we have an effective way to create a list of phrases, as follows. These are shown in the alphabetical order:

SELECT query_phrase

FROM sentiment140_queries


We can also find out the tweets with a given phrase (baseball) by performing a join between the two tables:

SELECT s.id, s.tweet_text, sq.query_phrase

FROM sentiment140 s

INNER JOIN sentiment140_queries sq

ON s.query_id = sq.query_id

WHERE sq.query_phrase = 'baseball';

At this point, we have a cleaned sentiment140 table and four new tables to hold various extracted and cleaned values, including hashtags, user mentions, URLs, and query phrases. Our tweet_text and date_clean columns are clean, and we have a lookup table for query phrases.

Step nine – document what you did

With nine steps of cleaning and multiple languages and tools in use, there is no doubt there'll be a point at which we will make a mistake and have to repeat a step. If we had to describe to someone else what we did, we will almost certainly have trouble remembering the exact steps and all the reasons why we did each thing.

To save ourselves mistakes along the way, it is essential that we keep a log of our cleaning steps. At a minimum, the log should contain these in the order in which they were performed:

· Every SQL statement

· Every Excel function or text editor routine, including screenshots if necessary

· Every script

· Notes and comments about why you did each thing

Another excellent idea is to create a backup of the tables at each stage, for example, we created a backup just before we performed UPDATE on the sentiment140 table, and we discussed performing backups after we created the new date_clean column. Backups are easy to do and you can always drop the backed-up table later if you decide you do not need it.


In this chapter, we used a sample dataset, a collection of tweets called Sentiment140, to learn how to clean and manipulate data in a relational database management system. We performed a few basic cleaning procedures in Excel, and then we reviewed how to get the data out of a CSV file and into the database. At this point, the rest of the cleaning procedures were performed inside the RDBMS itself. We learned how to manipulate strings into proper dates, and then we worked on extracting three kinds of data from within the tweet text, ultimately moving these extracted values to new, clean tables. Next, we learned how to create a lookup table of values that are currently stored inefficiently, thus allowing us to update the original table with efficient, numeric lookup values. Finally, because we performed a lot of steps and because there is always the potential for mistakes or miscommunication about what we did, we reviewed some strategies to document our cleaning procedures.

In the next chapter, we will switch our perspective away from cleaning what has been given to us toward preparing cleaned data for others to use. We will learn some best practices to create datasets that require the least amount of cleaning by others.