Dealing with CSV challenges in Wrangler

This page describes various options available in Wrangler to deal with complex scenarios of processing CSV / TSV or any delimited files.

Before you begin

Some of the instructions here require you to use power-mode in Wrangler. Power-mode in Wrangler exposes the full capabilities of Wrangler directives. Power-mode is available at the bottom of the Wrangler screen.

Non-Printable and CTRL-M Characters

• Sometimes a file looks fine, but it could contain non-printable ASCII characters that usually don’t belong in CSV files. It can be hard to track these down. Use the find-and-replace directive. `find-and-replace :col 's/\000-\007\013-\037//g'.

• To remove CTRL-M from the end of each line, again use the find-and-replace directive. find-and-replace :col 's/\r$//g' if you want to remove CTRL-M at end of the line. Make sure you apply this directive before applying parse-as-csv.

Removing the Header Row

It is not recommended to use the auto-detect header option because it is error prone. That said, how do you go about removing the header from the data rows that need to be processed and more importantly how you set the header? Let’s take the following CSV file as input:

school_id, student_id, last_name, first_name
14J456,33445566,Potter,Harry
14J456,44333433,Weasley,Ron
14J456,65765566,Granger,Hermione
14J456,13233121,Diggory,Cedric
14J456,98786868,Weasley,George
14J456,78977876,Weasley,Fred

To parse the CSV file without the header, use the following directive:

• parse-as-csv :body ',' false

false at the end specifies that no auto-header detection is enabled. The columns would be named as body_1, body_2, … body_n. The name of the columns are based on the input column name, in this case it’s body.

Now that we have all rows of the CSV file parsed, we can use the filter-rows-on directive to filter and remove the header row. For this, you will have to identify a column name in the header that will never occur in the data. In this example, we will use school_id as the column name:

• filter-rows-on regex-match body_1 ^school_id$

The directive specifies that we are filtering on regex-match on the column body_1 (this is the name of the column that was automatically generated from the previous step).

Once this directive is issued, the header row is removed. Now, you can go ahead and set the actual names of the column using the set columns directive as follows:

• set columns school_id,student_id,first_name,last_name

Here’s the full recipe:

parse-as-csv :body ',' false
filter-rows-on regex-match body_1 ^school_id$
drop body
set columns school_id,student_id,first_name,last_name

Incorrect Width

• Filtering records that do not have a specified number of columns in a record can be achieved with send-to-error or filter-row. For example, send-to-error exp : { this.width() < 4 } will send all records that have less than 4 (0..3) to error. filter-row exp: { this.width() < 4 } true will filter records that are less than 4 from the main dataset.

• To find rows that have issues, you can use filter-row with a minor change as follows: filter-row exp: { this.width() < 4 } false. This will show you only rows that are problematic.

Trailing Commas

Saving an Excel file as a CSV file can create extra commas at the end of each row. Trailing commas can result when columns are deleted or column headers are removed.

school_id, student_id, last_name, first_name,,,
14J456,33445566,Potter,Harry,,,
14J456,44333433,Weasley,Ron,,,
14J456,65765566,Granger,Hermione,,,
14J456,13233121,Diggory,Cedric,,,
14J456,98786868,Weasley,George,,,
14J456,78977876,Weasley,Fred,,,

Take the following steps to resolve the issue using the Wrangler interface:

• Open the file in Wrangler.

• Parse CSV.

• Use the directive keep to keep the columns that you need, for example, keep school_id, student_id, last_name, first_name.

In Wrangler, type in the directive:

• keep school_id, student_id, last_name, first_name

Here’s the full recipe:

parse-as-csv body , false
filter-rows-on regex-match body_1 ^school_id$
drop body
set columns school_id, student_id, last_name, first_name
keep school_id,student_id,last_name,first_name

Extra Comma in field values

Your data might sometimes include a comma within a field value (for example, if a student’s last name is “Potter, Jr.”). The comma-separated values file will treat the comma as a field separator unless the entire field value is enclosed in double quotation marks.

school_id, student_id, last_name, first_name
14J456,33445566,Potter,Jr,Harry
14J456,44333433,Weasley,Ron

The three commas in the data row (1) separate that into five fields instead of 4. So you would have to merge field (3) and (4) only when the record size is five. Following is the recipe for fixing extra column issues:

parse-as-csv :body ',' false
filter-rows-on regex-match body_1 ^school_id$
drop body
set columns school_id, student_id, last_name, first_name, body_5
set-column :last_name exp:{ this.width() == 5 ? (last_name + ',' + first_name) : last_name}
set-column :first_name exp:{ this.width() == 5 ? body_5 : first_name}
drop body_5

• Line 1 parses the record as CSV with no headers.

• Line 2 filters the header from the data rows.

• Line 3 drops the body column since we don’t store the original data.

• Line 5 If the number of columns is 5, then it merges salute (in wrong place first name) and last name into a single column last name.

• Line 6 copies the data value from body_5 into first_name when column width is 5.

The result of the recipe is as follows:

school_id, student_id, last_name, first_name
14J456,33445566,"Potter,Jr",Harry
14J456,44333433,Weasley,Ron

Leading Zeros

Numerical values (in fields like ‘school_id’, ‘student_id’, and ‘teacher_id’) often begin with “0”, but Excel removes these leading zeros, which changes the value listed for that field. For example, student_id “009844” becomes “9844”.

In the case of Wrangler, when parsed, the leading zeros are maintained until you either remove them explicitly or convert it to int, float, or double type.

UTF-8 encoded files

At times, you might receive CSV files that are UTF-8 encoded. When a file is exported from Excel, you can configure it to be exported as UTF-8. In order to better handle such files, Wrangler provides a set-character directive to set the right encoding. This directive has to be applied before any other operations are performed as follows.

• set-charset :body 'utf-8'

Here’s the full recipe:

set-charset :body 'utf-8'
parse-as-csv :body ',' false
filter-rows-on regex-match body_1 ^school_id$
drop body
set columns school_id,student_id,first_name,last_name