Techniques for Scrubbing or Cleaning Data in Data Science
As we know the obtained data has inconsistencies, errors, weird characters, missing values or different problems. In this situation, you have to scrub or clean the data before to use this data.
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So for scrubbing the data in Data Science, some techniques are used which are as follows:-
- Filter lines
- Extract certain columns or words
- Replace values
- Handle missing values
- Convert data from one format to another
Filtering Lines
The first scrubbing operation is to filter lines. It means that from the input data every line will be calculated to determine whether it may be passed on as output.
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Based on their location is the simplest way to filter lines. It is useful when you want to inspect, say, the top 5 lines of a file, or when you want to extract a particular row from the output of another command-line tool.
If you want to extract or remove lines based on their contents then use grep which is the canonical command-line tool for filtering lines. We can print every line that matches a certain pattern or regular expression.
When you’re in the process of formulating your data pipeline and have a bulk of data, then debugging your pipeline can be cumbersome. In that case, sampling from the data might be useful. The core reason of the command-line tool sample is to get a subset of the data by outputting only a particular percentage of the input on a line-by-line basis.
Replacing and Deleting Values
Command-line tool tr, which stands for translate that can be used to replace the individual characters. For example, spaces can be replaced by a comma as follows:
$ echo 'hello world!' | tr ' ' ','
Hello,world!
If more than one character needs to be replaced, then
$ echo 'hello world!' | tr ' !' ',?'
Hello,world?
tr can also be used to delete individual characters by specifying the -d option:
$ echo 'hello world!' | tr -d -c '[a-z]'
helloworld
Working with CSV
The command-line tools which are used to scrub plain text, like grep and tr, cannot always be applied to CSV. The reason is that these command-line tools have no notion of headers, bodies, and columns. In order to leverage ordinary command-line tools for CSV: body, header, and cols.
The first command-line tool is the body. With this command-line tool, you can apply any command-line tool to the body of a CSV file i.e., everything excluding the header.
For example:
$ echo -e "value\n7\n2\n5" | body sort -n
value
2
5
7
The second command-line tool header is used to permit us to operate the header of a CSV file. The third command-line tool is cols, which are similar to the header and body. It permits you to apply a certain command to only a subset of the columns.
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