CSV vs JSON for your Data Science Projects

Modern industries are swamped with data and so, there is immense value in processing and analyzing the data to generate insights from it. Uncovering actionable insights can provide enormous value to any business by stimulating creative ideas.

 

When you are delving into Data Science, most of your time will be utilized in deriving value from data and using it to build Machine Learning and Deep Learning models. The accuracy and efficiency of these models will highly depend on the data you feed to these models.

To build a successful Data Science project, you need to have a clear understanding of what you are tasked to build and how can you utilize the data at hand in order to design a solid solution.

In this blog we will cover the following sections:

• Why do we need to choose between different formats?
• What is a CSV format?
• What is a JSON format?
• When to use which format?
• CSV vs JSON Comparison table

Why do you need to choose between different formats?

The data being used in your model can be collected from various (external or internal) sources and stored in various file formats for processing. Your choice of data format can greatly impact the space requirements, cost, and performance of your project. There are several considerations that need to be taken into account when trying to determine which data format you should use. In this blog, we will mainly focus on two most popular text-based file formats: CSV and JSON.

To learn more about data science, read our blog – What is data science?

What is a CSV Format?

A CSV (comma separated values) file is literally a matrix of data. Each row is an array that represents one record, and each column represents a specific field within that record. Each field (or element) is separated by a comma.

CSV files store data in a simple and easy-to-read manner. These can be opened using pretty much any piece of software, even plain text editors. This is how a CSV file would look when opened through a Notepad:

 

 

This is how the above CSV file would look like in Excel:

 

 

Also Read – How to become a data scientist?

What is a JSON Format?

JSON (JavaScript Object Notation) files to store data based on JavaScript object index. Each object can have multiple keys/value pairs or other objects within it. Data can be stored in many data types including strings, arrays, integers, etc.

Unlike CSV, JSON allows you to create a hierarchical structure of your data.

It is basically used for transmitting data in web/mobile applications projects as it is easy to integrate with APIs.

This is how a JSON file looks like:

{   “guest”: { “guestId”: 001, “firstName”: “Chris”, “lastName”: “Evans”, “contactNumber”: “555-555-5555”, “email”: “captain_usa@info.com” }, { “guestId”: 002, “firstName”: “Chris”, “lastName”: “Hemsworth”, “contactNumber”: “222-222-2222”, “email”: “loki_brother@info.com” } }

JSON Data Types

Strings	“Hello World”   “James”    “P”
Numbers	11       3.4       -13       1.2e10
Booleans	True       False
Null	null
Arrays	[ 1  2  3]       [“Hello”, “World”]
Objects	{ “key” : ”value” }       { “age” : 25 }

When to use which format?

When deciding upon when to use which format, consider asking yourself the following questions:

Your use case:

Who is going to use the data? Or how is it going to be used?  Some file formats are meant for general use, while others are apt for more specific use cases.

Your system specifications:

• What ETL processes are you working with?
• What tools are you going to use for analyzing your data?
• Are you constrained on storage or memory?

Your data characteristics:

• What is the type of your data? How large or small is it?
• How complex is your data? Is it structured or unstructured?
• How many columns are relevant to your use case, if not all?
• Is your data evolving over time?

Now let’s compare CSV and JSON formats on the considerations mentioned above.

CSV vs JSON

Project Type

JSON is a highly efficient data exchange format. In application projects, JSON is used in the interaction between server and client (HTTP requests). It is lightweight and hence, does not burden the network. Due to its low memory demand, it can transfer huge amounts of data pretty quickly.

CSV files on the other hand are convenient for storing and analyzing problems involving small datasets but are nowhere near a good option when your data is huge and complex.

Format Type

Both CSV and JSON are text-based formats, hence are easier to use. They can be read by the end-user who can also modify the file content.

These formats are usually compressed to reduce their storage footprints. But their performance is subpar to binary file formats.

Storage

CSV format is so widely used because of its ability to store data in a simple tabular manner. Who doesn’t like their data readable and easy to troubleshoot right? But CSVs are also slow to query and difficult to store efficiently. However, they also have an excellent compression ratio.

JSON files are like a mini database for textual data. JSON is a partially structured format and can be used to efficiently store tons of data that you may need to use within your project.

Explore Statistics for Data Science Online Courses

Parsing

CSVs are simple in nature, hence simple to parse and split (just break on comma to convert to column). Although there is a significant potential for data loss or data corruption if the application receiving CSV input isn’t the same application that created it.

An important point to remember is that CSV is not a standardized format. This means that a file may be in the CSV format, but not necessarily will be read properly by the CSV parser used in your project.

A JSON file by default is larger and more flexible. So, it is more complicated to parse and split. JSON is loaded and parsed into memory and hence depending upon the size, can take up a lot of disk space.

CSV is easier to parse than JSON and is therefore potentially faster to write.

Format Orientation

Both CSV and JSON formats are row-oriented, meaning they are used when all fields in a row need to be accessed.

Columnar formats are typically used when several columns are needed to work with but not all. Parquet and ORC are examples of such formats.

Schema Evolution

Schema evolution deals with the need to retain current data when your database structure changes with time. Unless your data is guaranteed to never change or is immutable in nature, your database design must involve handling schema updates.

You can choose CSV to create your tables if your schema evolution requires only renaming of the columns but not reordering them. CSV does not support removing columns. Nor does it allow adding columns at the beginning or in the middle of the table. For such operations, you can use other formats, preferably columnar.

JSON allows you to do all schema manipulations except the renaming of columns.

The following table explains it more clearly:

Expected Schema Update	CSV	JSON
Rename columns	Y	N
Add columns at the beginning or in the middle of the table	N	Y
Add columns at the end of the table	Y	Y
Remove columns	N	Y
Reorder columns	N	Y
Change column data type	Y	Y

So, let us summarize the comparison in a table:

Properties	CSV	JSON
Data Type	Doesn’t allow multiple types of data	Supports different data types
Compressible	Yes	Yes
Columnar	No	No
Readable	Yes	Yes
Scalability	Difficult to integrate and not easily scalable	Integrates with APIs easily and allows scalability
Ease of Parsing	Easier to parse but inefficient	More difficult to parse than CSV
Data Splitability	Easier to split (not always splittable though)	Difficult to split (not splittable in many cases)
Flexibility	Less flexible than JSON	More Flexible than CSV
Supports Complex Data Structures	No	Yes
Supports Schema Evolution	No	No

Interesting read – Statistical Methods Every Data Scientist Should Know 

Takeaways

If you want to retrieve simple data as lists or a table with rows and some columns, CSVs are a good option. However, you need to remember that you lose type when exporting data to CSV.

Use JSON format for communication. Although JSON is referred to as comparatively better than CSV when dealing with massive data sets and in terms of scalability of files or applications, you should avoid this format when working with big data. There are more efficient alternatives.

More optimized data formats can be utilized to meet the needs of your data science project in terms of splitability, compression support, and the ability to support complex data structures. But for easy readability and faster read/write time, JSON and CSV are hands down the preferable choices.

The biggest of business achievements in recent times have had a data-driven approach in some way or the other. Hence, it is important that you choose the right data storage format for your business needs.

About the Author

 

 

Prerna is a Tech enthusiast and former Research analyst. She is currently exploring Machine Learning & Data Science with previous experience in Blockchain & Big Data Analytics.

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