Once you understand the input data, the next step is to determine what would be a more usable format. Well, this depends entirely on how you plan on using the data. For further data analysis, I highly recommend reading the data into a pandas DataFrame.
If you a Python data analyst then you are most likely familiar with pandas. It is a Python package that provides the DataFrame class and other functions to do insanely powerful data analysis with minimal effort. It is an abstraction on top of Numpy which provides multi-dimensional arrays, similar to Matlab.
The DataFrame is a 2D array, but it can have multiple row and column indices, which pandas calls MultiIndex , that essentially allows it to store multi-dimensional data. Although, we would want to read the data into a feature-rich data structure like a pandas DataFrame , it would be very inefficient to create an empty DataFrame and directly write data to it. A DataFrame is a complex data structure, and writing something to a DataFrame item by item is computationally expensive.
Once the list or dict is created, pandas allows us to easily convert it to a DataFrame as you will see later on. The image below shows the standard process when it comes to parsing any file.
If your data is in a standard format or close enough, then there is probably an existing package that you can use to read your data with minimal effort. You can handle this easily with pandas. Python is incredible when it comes to dealing with strings. It is worth internalising all the common string operations. We can use these methods to extract data from a string as you can see in the simple example below.
As you saw in the previous two sections, if the parsing problem is simple we might get away with just using an existing parser or some string methods. How do we go about parsing a complex text file? The data it contains is pretty simple though as you can see below:. The sample text looks similar to a CSV in that it uses commas to separate out some information. There is a title and some metadata at the top of the file.
School, Grade and Student number are keys. Name and Score are fields. In other words, School, Grade, and Student Number together form a compound key. The data is given in a hierarchical format. First, a School is declared, then a Grade. This is followed by two tables providing Name and Score for each Student number. Then Grade is incremented.
This is followed by another set of tables. Then the pattern repeats for another School. Note that the number of students in a Grade or the number of classes in a school are not constant, which adds a bit of complexity to the file.
This is just a small dataset. You can easily imagine this being a massive file with lots of schools, grades and students.
It goes without saying that the data format is exceptionally poor. I have done this on purpose. If you understand how to handle this, then it will be a lot easier for you to master simpler formats.
In the past when those systems were being designed, it may not have been a requirement for the data output to be machine readable. However, nowadays everything needs to be machine-readable! We will need the Regular expressions module and the pandas package.
Privacy policy. This article applies to mapping data flows. If you are new to transformations, please refer to the introductory article Transform data using a mapping data flow. Use the Parse transformation to parse columns in your data that are in document form. In the parse transformation configuration panel, you will first pick the type of data contained in the columns that you wish to parse inline.
The parse transformation also contains the following configuration settings. Similar to derived columns and aggregates, this is where you will either modify an exiting column by selecting it from the drop-down picker. Or you can type in the name of a new column here.
However, the features are limited. For this, automation is the only solution. Classic Software : Building software to automate stuff is a simple solution. This software has all the basic operations and instructions to get the job done. But, these are not scalable and extensible. The features will be limited and will be using local storage to save all the data. Therefore, a parser-based out of simple software can be used for small files that need to be regularly cleaned up.
We could use languages like Python or Java to read this file and perform specific operations and iterate it onto multiple files. Therefore, users prefer the cloud when it comes to parsing data from images. Web Applications: Web applications are utilised for UI to automate the file parsing process. All the communication between the UI, backend, and database happens mainly through the databases.
If the website is served on a powerful cloud solution, OCRs can also be integrated to perform all kinds of data parsing operations. However, this solution might be time-consuming, as it involves many steps and requests to consume all across the web. Sometimes, due to confidentiality requirements, businesses opt for on-prem solutions in which the software will be a third party application, but the database can be hosted internally.
In RPA, robots take care of automating all the manual tasks instead of humans doing manual stuff. These robots can be connected with different data sources, APIs, and third-party integrations; this gives us an advantage in collecting and processing data for parsing differently. In this section, we'll look at a couple of use-cases on how file data parsers can help automate manual entry for your business. We'll also be studying a rough outline of all the techniques involved for these specific use-cases.
Collecting data from invoices and receipts : Invoicing is everywhere, from small businesses, startups to giant industries and corporations.
Most of these organisations use excel sheets, save these in cloud drives and manually enter them into different data formats. Also, many invoices contain tabular data in which details of products or services are in line items. Therefore, copy-pasting these would mess up the data format. But building a generic parser is challenging.
Below are the essential aspects that we'll need to take care of:. Digitising your invoices saves a lot of time and consumes less human work, eventually reducing the expenses spent maintaining the invoices. With expense minimisation and expedited payments, you'll have more resources to invest in innovation, hire and improve your offerings, and conduct core business operations. This drives up the overhead and lacks quality control. In such cases, introducing automation can streamline, improve, and drive efficiency.
Fortunately, with a file data parser, we can make automating KYC documents much more effortless. The job of the file data parser is to parse through all the customer's documents such as government-provided IDs, Professional IDs, Financial documents, etc. This helps the business to quickly review the customer's documents and proceed with further processing. Building workflows allows us to connect with different solutions and automate processes. For example, consider the following scenario:.
Usually, doing this with a data parser takes a lot of time, and tasks like downloading invoices, uploading them to cloud storage, and renaming them with parsers can be annoying.
Therefore, to automate the boring stuff, we could build workflows using different integrations. These workflows are mostly built on the cloud, which can talk to different services using APIs and Webhooks. If you're not a developer, there are products like Zapier that can connect with your data parser and perform particular tasks.
Now let's see how these webhooks and APIs work. Therefore, we can leverage these APIs to build powerful workflows and build data parsing solutions.
Let's discuss a few fundamentals. First, we build data parsers for working with massive data. Therefore, the starting point is to use cloud storage for all of your data, and for this, we need not build a data centre. Instead, we can subscribe to an online cloud service and leverage APIs for your data. We can't download every file from the cloud and run OCRs to extract data, but building and maintaining one online is a complicated process.
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