Why Does Network Automation Use Python?

Because Python is simple to learn, easy to read, and capable of handling actual network operations, it is utilized in network automation. It can interact with JSON and CSV files, connect to devices, communicate with APIs, parse raw CLI output, and assist engineers in creating repeatable workflows with less human labor. Additionally, Python boasts a sizable standard library and an established network automation ecosystem centered on programs like Netmiko, NAPALM, Jinja, TextFSM, and pyATS/Genie.

Python is important because of this.

It is more than just a language for scripting. It serves as the link between controllers, devices, logs, reports, and your own judgment.
Python Cuts Down on Repetitive Tasks

Once completed, many network jobs appear insignificant.

Verify the status of the interface.
Gather device information.
Apply the same modification to numerous devices.
Compare the standard configuration and the running configuration.
Make a report about your health.
Verify if a change was effective.

By themselves, none of these chores are difficult. The challenge arises when you have to execute them on tens, hundreds, or even thousands of devices. At this point, manual labor becomes tedious, slow, and dangerous. Engineers need automation as networks grow since manual CLI labor is more error-prone and not scalable.

Additionally, Python works well for this since it lets you write simple scripts before expanding them into larger routines.

For those who are just starting, it is crucial. Building a big platform on the first day is not necessary. To begin, log in to one device and run a single command. Fifty separate gadgets will run the same script tomorrow. The results might be saved in a CSV file, compared, and a clean report sent to you within the next week.
Network engineers can easily learn and read Python.

Python's understandable syntax is a major factor in its widespread use in networking. Python seems less daunting than many other languages, even if you have a CLI experience rather than a background in software programming. One of the reasons Python continues to be the leading choice for network automation on both training sites and comparison blogs is this learning curve.

People don't realize how vital this is. The cleverest code is not always the best in network automation. After six months, your staff will be able to comprehend the code. Three benefits come from readable code.

You can learn more quickly, to start.
Second, it can be reviewed by your teammates.
Third, because the reasoning is obvious, you are less inclined to break items.

In production networks, where a single poor script can result in a costly error, that is a major concern.
Python Operates on Both Conventional and Contemporary Networks

Python is particularly helpful in this situation.

Contemporary networks are intricate. Certain gadgets still use SSH and CLI extensively. Modern APIs are available on some systems. JSON is returned by certain tools. Others give back unprocessed text. You will have all of them at once in many situations. Python can be positioned in the center of this.

Python may communicate via SSH with CLI-driven devices using modules such as Netmiko. The library is built on connection handling for network devices, and Netmiko's documentation displays a substantial collection of platform-specific drivers.

Python can communicate with several network operating systems via a single API thanks to NAPALM. This implies that you can manage setup across several vendors or access data in a more uniform manner.

Python may communicate with controllers and cloud platforms via APIs by using HTTP libraries such as Requests. Python is a perfect fit for contemporary network platforms since Requests describes itself as a straightforward way to submit HTTP requests.

One of Python's greatest advantages is this. You are not compelled to select between "modern API networking" and "old school networking." It is capable of handling both.
Python Is Excellent at Managing Data

Many novices believe that network automation is limited to config pushing. This is just a portion of the narrative. There is a lot of benefit both before and after the shift in real work.

You must gather information.
Make it clean.
Analyze it.
Examine it.
Determine what is important.
Produce useful product for individuals.

Python excels at this intermediate layer.

Tools for processing IP addresses, JSON, and CSV are included in its standard library. You can load and manipulate structured API data with the aid of the json module. You can read and create tabular reports with the aid of the CSV module. You may create and manage IPv4 and IPv6 addresses and networks with the aid of the IP address module.

This is important since networks generate data in a variety of ways.

JSON may be returned by a controller.
CSV may be used for an inventory report.
It can be necessary for a validation script to compute subnets or compare addresses.
It can be necessary to rewrite a change report in a file.

All of that is handled by Python without requiring you to jump between languages. This is one of the reasons it continues to be successful in network automation.

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