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What Is Data Processing in Python?

We live in the era of Big Data. There is a tremendous amount of data flowing around us constantly. It seems like this flow of data will keep increasing. In order not to drown in this stream, you should know how to properly process data, analyze it, and draw correct conclusions from it. One of the best tools for this is Python!

It’s become very easy to collect, store, and transfer data. Furthermore, an increasing number of businesses are realizing the value of data. Raw data can be converted to business value by way of improved processes, better forecasting, predictive maintenance, customer churn prediction, and so on. Furthermore, big data solutions enable real-time data processing, thereby enhancing customer experiences and reducing operational costs.

However, the process of creating value out of raw data has many challenges. We cannot just collect data and use it as-is. Data usually requires lots of processing before it can be used as a valuable asset.

In this article, we will explain why data processing is a fundamental part of data science and how Python makes data processing easier.

Why Is Data Processing Important?

Before starting our discussion on the importance of data processing, let’s define three terms:

  • Data processing refers to the entire process of collecting, transforming (i.e. cleaning, or putting the data into a usable state), and classifying data.
  • Raw data is the data collected from various sources, in its original state. It is usually not in the most proper format for data analysis or modeling.
  • Clean data is the data obtained after processing the raw data – i.e. it’s data that’s ready to be analyzed. It has been transformed into a usable format; incorrect, inconsistent, or missing data has (as much as possible) been corrected or removed.

There are several reasons why we need to apply data processing operations to raw data. For instance, there might be missing values in the dataset. Suppose we have a dataset that contains personal information for bank customers and one of the attributes is customer age. If we are doing an analysis that involves customer age, then not knowing the age of some customers will have a negative impact on our results. So this data needs to be processed to remove the missing values.

The following dataset contains raw data that needs some processing. Let’s try to determine what kind of processing is required.

10134Houston, TX2018-08-11$65,000Accounting
10227San Jose, CA2017-08-24$70,000Field Quality
103<NA>Dallas, TX2020/04/16$58,500human resources
10441Miami, FL2021-02-11$49,500accounting
10525Santa Clara, CA2020/09/01$62,000field quality
10629Atlanta, GA2021-10-20$54,500engineering
  • The customer_age column has a missing value represented by <NA>.
  • The dates in the start_date column have different formats; the format needs to be standardized.
  • Some of the text in the profession column is capitalized and some is not. In this case, the computer thinks “Accounting” and “accounting” are different. Any data analysis based on this column might be inaccurate.
  • The estimated_salary column is not in a numerical format. It is stored as text, meaning $65,000 does not represent any quantity.
  • The city column includes both the city and state information. It is better to represent city and state data in separate columns.

These are only some of the issues that we are likely to encounter in raw data. As the size of data and the number of attributes (i.e. columns) increases, the amount of data processing needed usually increases as well.

You might be asking why raw data is not stored in a usable format so that we do not have to deal with data processing. It would be very nice to be able to use raw data as-is. However, it is usually not the case with real-life datasets. The main reasons for this are:

  • User error / incorrect input: Whoever entered the values might have made a mistake.
  • Missing input: In some cases, customers do not provide the information.
  • Software-related issues: Problems in the processing of extracting, transforming, loading, and transferring of raw data can create “dirty” data.

Whatever the cause of the problem, we need to clean the data before making use of it.

Going back to our raw customer dataset, the following is a “cleaned” version of the raw dataset:

10227San JoseCA2017-08-2470000field quality
10525Santa ClaraCA2020-09-0162000field quality

It is important to note that how we choose to handle missing values depends on the task and situation. If age is of vital importance for our analysis, dropping rows that do not have an age value is a viable option. In some cases, we may instead choose to replace the missing age values with an average value.

Who Should Learn Data Processing?

Data processing is a highly valuable skill for data engineers, data analysts, and data scientists. If you are working with data, sooner or later you will encounter some data that needs to be processed and cleaned.

In an ideal world, data scientists work on clean and processed data. Their job is to explore the data and come up with accurate models. However, usable data is not always served on a silver platter to data scientists. They might have to process and clean the raw data before doing any analysis and modeling work.

This is the reason why data processing is specified as an expected skill in most job openings. Whether you are a data engineer or data scientist, data processing is worth learning.

Data Processing in Python

I think we all agree that data processing is a must-have operation in the data science ecosystem. In fact, a substantial amount of time in a typical workflow is spent on data processing.

Python has very powerful libraries that ease and expedite data processing. For instance, the library I used to process the raw customer dataset above is pandas, one of Python’s most popular data analysis and manipulation libraries.

Since it is a Python library, pandas has a highly intuitive syntax and is very easy to learn. For instance, the code that I used for standardizing the profession column is:

customer["profession"] = customer["profession"].str.lower()

This simply transforms all the text data in the profession column to lowercase, regardless of how it was originally stored. The other operations I did are also quite simple.

Another important part of data processing is dealing with different file formats. Raw data might be stored in various formats like Excel, CSV, or JSON. We need to be able to read the data stored in these files and also write data in these formats.

The file format selected depends on the application. Even if the data is the same, the way to read it and save it changes according to the file format. We should be familiar with the commonly used file formats.

Python has several other libraries for data cleaning.Check out the most helpful Python data cleaning modules and our top 15 libraries for data science for more information.

Learn More About Data Processing with Python

Considering that real-life datasets almost always come in a format that needs to be processed and cleaned, data processing is a must-have skill in data science. The best way to acquire this skill is an online interactive Python course, such as our Data Processing with Python track. It covers everything from working with strings to managing different file types and directories using Python. This interactive track will not only give you the necessary knowledge, but also the opportunity to test it in practice.

What Is Data Processing in Python?

This track is for those who understand the basics of Python. If you are an absolute beginner, I suggest starting with the Python Basics track. It will help you get into programming and learn foundational Python.

Are you excited about learning how to use Python to make data processing more efficient? Try our Data Processing with Python track. Master data processing and you'll get even more out of your analyses!