Top 10 Python Libraries Every Network Engineer Should Know

If you've spent years typing commands into network devices one at a time, you know the problems: a simple configuration change across 50 routers means 50 separate SSH sessions, 50 opportunities for typos, and hours of your day gone. When something breaks at 2 AM, you're left manually checking each device to isolate the issue.

Python libraries for network engineers solve this by handling repetitive, error-prone work. They allow you to programmatically connect to devices, parse command outputs, validate configurations, and execute changes consistently and at scale.

This guide covers ten essential Python libraries that will save you and your team countless hours, cut down on errors, and make your work life way easier. We'll explain what each library does, why it's useful, where it fits in your workflow, and give you practical examples, focusing on Cisco environments but noting multi-vendor support where applicable.

Table of contents

• Before You Start: Which Python Library Should You Learn First?
• Nornir – The Orchestrator for Pythonic Network Workflows
• NAPALM – Your Multi-vendor Abstraction Layer
• Netmiko – Reliable SSH CLI Automation
• Scrapli & Scrapli-NETCONF – Modern, High-Performance Drivers (Sync + Async)
• pyATS / Genie – Parsing and Test Automation
• TextFSM – Turn CLI Text into Structured Data
• Jinja2 – Rendering Your Network Configurations Predictably
• Pydantic – Intent Models and Data Validation
• HTTPX (or Requests) – Modern HTTP(S) Client for Controllers & APIs
• PyYAML – The Standard for Handling YAML Data
• Putting the Python Libraries Stack Together
• Start Building Production-Ready Network Automation Today

Before You Start: Which Python Library Should You Learn First?

You don't need to learn all ten libraries at once. Here's a sensible learning path:

Beginner Path (Get the Fundamentals):

• Netmiko: Master the basics of SSH CLI automation. This is the best place to start.
• PyYAML: Learn how to organize your device data and variables in clean, human-readable files.
• Jinja2: Get good at generating consistent and dynamic device configurations from templates.

Intermediate Path (Expand and Scale):

• NAPALM: Add a layer of multi-vendor abstraction to your automation.
• Nornir: Learn to orchestrate tasks concurrently across many devices.
• TextFSM: Turn messy CLI output into structured data you can actually use.

Advanced Path (Build Production-Ready Workflows):

• Scrapli: Level up to high-performance, asynchronous operations.
• pyATS/Genie: Implement comprehensive, Cisco-grade testing and validation.
• Pydantic: Define and enforce data validation and model your network intent.
• HTTPX: Embrace API-based automation (for controllers, RESTCONF, etc.).

This progression makes sense because it builds on itself. Netmiko teaches you SSH basics, NAPALM adds vendor abstraction, Nornir gives you orchestration, and the advanced libraries let you build enterprise-scale workflows.

1. Nornir – The Orchestrator for Pythonic Network Workflows

Nornir is a pure-Python automation framework. It helps manage your inventory, connect to devices, and run tasks in parallel. It keeps you in control of your Python code.

How it works:

• Inventory plugins: SimpleInventory (YAML files), AnsibleInventory, NetBox, etc.
• Runners: threaded or process-based parallelism with per-host results and exception capture.
• Task plugins: wrappers around Netmiko, Scrapli, NAPALM, HTTP, Jinja2, etc.
• Result objects: structured, per-host outputs that can be post-processed, saved, or reported.

Why it's great:

• Lets you build composable tasks with clear inputs and outputs.
• Keeps your inventory data separate from your execution logic.
• Has a mature ecosystem of plugins (nornir-netmiko, nornir-scrapli, nornir-napalm, nornir-jinja2, nornir-utils).

Where to use it: Perfect for tasks like backing up configs, running compliance checks, deploying standard configs ("golden configs"), and building pre-/post-change validation pipelines. Understanding common network automation challenges and mistakes helps you design better Nornir workflows from the start.

Example – Collect CDP neighbors from Cisco devices and save to a CSV:

# pip install nornir nornir-netmiko
from nornir import InitNornir
from nornir_netmiko.tasks import netmiko_send_command
from nornir_utils.plugins.functions import print_result
import csv

nr = InitNornir(config_file="config.yaml") # uses inventory/{hosts,groups}.yaml

def cdp(task):
   r = task.run(netmiko_send_command, command_string="show cdp neighbors detail", use_textfsm=True)
   task.host["cdp"] = r.result # list[dict]

result = nr.run(task=cdp)
print_result(result)

with open("cdp_neighbors.csv", "w", newline="") as f:
   writer = csv.DictWriter(f, fieldnames=["host","local_port","neighbor","neighbor_port","platform"])
   writer.writeheader()
   for host, tasks in result.items():
       for row in tasks[0].result:
           writer.writerow({
               "host": host,
               "local_port": row.get("local_port"),
               "neighbor": row.get("destination_host"),
               "neighbor_port": row.get("remote_port"),
               "platform": row.get("platform"),
           })  

Multi-vendor Support: Nornir works with any platform supported by its task plugins like Netmiko, Scrapli, NAPALM, or HTTPX, covering Cisco IOS-XE, NX-OS, IOS-XR, Arista EOS, Juniper Junos, FortiOS, PAN-OS, and many more. Inventory from NetBox or Ansible enables a single orchestrator over heterogeneous fleets.

Tip: Nornir vs. Ansible: Which Framework is Right for You?

Both are powerful tools that orchestrate tasks across multiple devices, but they have different approaches.

Use Nornir when: You want full Python control, need to integrate with other Python libraries, need complex post-processing of results, and your team is comfortable with Python.

Use Ansible when: Your team prefers declarative YAML, you need to automate a broader range of IT tasks, you want to leverage a vast library of ready-made modules, or you need enterprise features like a web UI and RBAC (available in AWX/AAP).

Can You Use Both? Yes. Many teams use Ansible for server/cloud automation and Nornir for network-specific workflows. You can even call Nornir scripts from Ansible playbooks.

2. NAPALM – Your Multi-vendor Abstraction Layer

NAPALM (Network Automation and Programmability Abstraction Layer with Multivendor support) is a Python library that gives you a standard, unified way to get device state and manage configurations across different network OSes.

How it works:

• Uses Driver Model: Has specific drivers for each network OS (like ios, nxos, junos, eos).
• Getters: Offers standard methods to grab structured data (facts, interfaces, ARP tables, LLDP/BGP neighbors).
• Configuration Subsystem: Lets you make changes safely using merge, replace, diff, and commit/rollback.

Why it's great:

• Gives you a stable, consistent API across different vendors. No more endless if vendor == 'cisco' logic.
• Has built-in safety nets like showing config differences before committing and rolling back on errors.
• Powerful for use cases like firewall automation with NAPALM scripts and Ansible.

Where to use it: Perfect for getting baseline configurations, managing standard changes reliably across various platforms.

Example – Get facts from a Cisco IOS-XE device and push a configuration change:

# pip install napalm
from napalm import get_driver

driver = get_driver("ios")
dev = driver(hostname="10.0.0.10", username="admin", password="admin", optional_args={"secret": "admin"})
dev.open()

facts = dev.get_facts()
print(facts["hostname"], facts["os_version"])

candidate_cfg = """
interface Loopback100
description added-by-napalm
ip address 192.0.2.100 255.255.255.255
"""
dev.load_merge_candidate(config=candidate_cfg)
print(dev.compare_config()) # show diff
dev.commit_config()
dev.close()  

Multi-vendor Support: Has solid drivers for Cisco (IOS, IOS-XE, NX-OS, IOS-XR), Juniper (Junos), and Arista EOS. Community drivers exist for others too.

3. Netmiko – Reliable SSH CLI Automation

Netmiko is a device-aware layer built on Paramiko that simplifies CLI automation by understanding network device prompts, handling command paging, and managing privilege levels. Learning Netmiko is the best place to start with Python network automation. If you're new to Python-based network automation, learning how to leverage Netmiko for network automation is the best place to start.

How it works:

• Device types (e.g., cisco_ios, cisco_xr, cisco_nxos, cisco_asa, arista_eos, juniper_junos).
• Channel helpers for send_command(), send_config_set(), and file transfers.
• Optional TextFSM integration for structured parsing.

Why it's great:

• “Just works” for interactive CLI workflows.
• Large platform coverage and community templates.

Where to use it: Quick scripts, brownfield devices without API/NETCONF, or when you want deterministic CLI transactions.

Example – Configure a description on IOS-XE and save

# pip install netmiko
from netmiko import ConnectHandler

dev = {
   "device_type": "cisco_ios",
   "host": "10.0.0.10",
   "username": "admin",
   "password": "admin",
   "secret": "admin",
}

with ConnectHandler(**dev) as conn:
   conn.enable()
   output = conn.send_config_set([
       "interface GigabitEthernet1",
       "description Uplink-to-ISP",
   ])
   print(output)
   print(conn.save_config()) # write memory on supported platforms  

Multi-vendor Support: Built-in drivers for Cisco, Arista, and Juniper. NETCONF support works on any vendor implementing the standard and relevant YANG models.

4. Scrapli & Scrapli-NETCONF – Modern, High-Performance Drivers (Sync + Async)

Scrapli is a modern, high-performance Python library for talking to network devices via CLI, NETCONF, or RESTCONF. It offers both synchronous and asynchronous (asyncio) support, making it super fast for interacting with many devices at once.

How it works:

• Core Drivers: Has specific drivers for different network OSes (like IOSXEDriver, NXOSDriver, EOSDriver, JunosDriver).
• Transports: Supports sync (Paramiko, ssh2-python) and async (AsyncSSH).
• scrapli-netconf: A separate extension specifically for NETCONF operations.

Why it's great:

• Offers excellent speed, especially with concurrent asyncio tasks.
• Has predictable parsing patterns and is easy to extend.

Where to use it: Perfect for grabbing lots of data quickly, doing NETCONF/YANG operations, and any automation that benefits from asyncio's speed.

Example – Async CLI Get (show ip interface brief) on Cisco devices:

import asyncio
from scrapli.driver.core import AsyncIOSXEDriver, AsyncNXOSDriver

devices = [
   (AsyncIOSXEDriver, {"host": "10.0.0.11", "auth_username": "admin", "auth_password": "admin", "auth_strict_key": False}),
   (AsyncNXOSDriver, {"host": "10.0.0.12", "auth_username": "admin", "auth_password": "admin", "auth_strict_key": False}),
]

async def run(cls, params):
   async with cls(**params) as conn:
       r = await conn.send_command("show ip interface brief")
       return params["host"], r.result.splitlines()[0]

async def main():
   results = await asyncio.gather(*(run(cls, p) for cls, p in devices))
   for host, first_line in results:
       print(host, first_line)

asyncio.run(main()) 

Example – NETCONF get-config on Cisco IOS-XE:

# pip install scrapli scrapli-netconf
from scrapli_netconf.driver import NetconfDriver

with NetconfDriver(host="10.0.0.11", auth_username="admin", auth_password="admin", auth_strict_key=False) as nc:
   reply = nc.get(filter_='<interfaces xmlns="urn:ietf:params:xml:ns:yang:ietf-interfaces"/>')
   print(reply.result)   # NETCONF XML payload 

Multi-vendor Support: Has built-in drivers for Cisco, Arista, and Juniper. NETCONF support works on any vendor that implements the standard and relevant YANG models.

Tip: When Should You Use Netmiko vs NAPALM vs Scrapli?

Network engineers often ask which Python library to use for CLI automation. Here's a practical comparison:

• Use Netmiko when: You need quick, straightforward CLI automation, are just starting, or its built-in TextFSM parsing is a key requirement.
• Use NAPALM when: You are managing configurations across multiple vendors (Cisco, Juniper, Arista) and want to use the same code, or need built-in safety features like config diffs and rollbacks.
• Use Scrapli when: Performance is critical and you need to interact with many devices concurrently (its async support is a game-changer), you are working with modern Python codebases using async/await, or you need to automate devices via NETCONF.

5. pyATS / Genie – Parsing and Test Automation

pyATS is Cisco’s extensive network testing framework, which includes Genie, a library with a vast collection of accurate, structured parsers for Cisco CLI outputs.

How it works:

• Testbed (YAML or Python) describing devices.
• AEtest harness for composing tests/pre-/post-checks.
• Genie parsers: OS-specific parsers turn CLI output into Python dicts.
• Diff/learn: capture baseline ("golden") state and compare after changes.

Why it's great:

• Very high parser accuracy on Cisco OS families.
• Built-in diffing for intent validation.

Where to use it: Pre-/post-change validation, continuous compliance, and "learn and compare" workflows. For advanced use cases, pyATS can power network digital twins for testing and validation before production changes.

Example – Parse BGP summary on IOS-XE

# pip install pyats[full] genie
from genie.conf.base import Device

dev = Device(name="edge1", os="iosxe")
dev.custom.abstraction = {"order": ["os"]}

sample = """
BGP router identifier 192.0.2.1, local AS number 65001
Neighbor       V         AS MsgRcvd MsgSent   TblVer InQ OutQ Up/Down State/PfxRcd
203.0.113.2     4     65002   1200   1188       9   0   0 1d02h         42
"""
parsed = dev.parse("show ip bgp summary", output=sample)
neighbor = list(parsed["vrf"]["default"]["neighbor"].keys())[0]
print(neighbor, parsed["vrf"]["default"]["neighbor"][neighbor]["address_family"]["ipv4 unicast"]["prefix_received"])  

Multi-vendor Support: Best and most comprehensive coverage for Cisco IOS-XE, NX-OS, IOS-XR, and ASA. Parsers also exist for Arista and Juniper but are smaller and community-supported. For unsupported commands or vendors, you can combine pyATS with TextFSM.

6. TextFSM – Turn CLI Text into Structured Data

TextFSM is a powerful state machine and regex-based templating engine for extracting specific fields from unstructured CLI output.

How it works:

• Templates: Text files defining Value captures (variables) and state transitions.
• Records: As the parser matches lines, it emits records. Headers from the template become keys for Python dictionaries.

Why it's great:

• Lightweight, transparent, and fast.
• Perfect for filling gaps where a pre-built API or Genie parser doesn't exist.

Where to use it: Writing custom parsers for rare or proprietary commands, handling interim parsing needs during migrations, and parsing vendor-specific outputs other libraries don't cover.

Example – Parse show cdp neighbors detail

# pip install textfsm
import textfsm, io

template = io.StringIO(r"""
Value LocalPort (\S+)
Value Neighbor (\S+)
Value RemotePort (.+?)
Value Platform (.+?)

Start
^Device ID:\s+${Neighbor}
^Interface:\s+${LocalPort},\s+Port ID \(outgoing port\):\s+${RemotePort}
^Platform:\s+${Platform} -> Record
""")

sample = """
Device ID: dist1
Interface: GigabitEthernet1, Port ID (outgoing port): Ethernet1/1
Platform: cisco WS-C3850-48P, Capabilities: Switch IGMP
"""

parser = textfsm.TextFSM(template)
rows = parser.ParseText(sample)
records = [dict(zip(parser.header, r)) for r in rows]
print(records[0]["Neighbor"], records[0]["RemotePort"])  

Multi-vendor Support: Templates are device-agnostic, though different variants may be needed for different OS output formats. Netmiko ships with a large library of community-contributed TextFSM templates..

Tip: TextFSM vs. pyATS/Genie Parsers – Which Parsing Strategy is Best?

Both Python libraries convert unstructured CLI output into structured data, but they take different approaches.

Many production workflows use a hybrid approach: pyATS/Genie for reliable Cisco parsers and TextFSM for gaps or non-Cisco devices.

Use TextFSM when:

• Parsing simple, predictable CLI outputs
• Need a custom parser for unsupported commands
• Want full control over parsing logic
• Working with non-Cisco devices
• Building lightweight scripts

Use pyATS/Genie when:

• Working primarily with Cisco devices
• Need highly accurate, maintained parsers
• Want baseline/diff capabilities
• Building comprehensive test automation
• Need parser reliability at scale

Comparison:

7. Jinja2 – Rendering Your Network Configurations Predictably

Jinja2 is a powerful Python templating engine. It's perfect for creating device configurations from structured data (like YAML or JSON).

How it works:

• Environment: Manages loading templates, global variables, and custom filters.
• Templates: Text files with variables, loops, conditionals, and macros.
• Custom Filters: Write your own for network-specific formatting needs.

Why it's great:

• Generates idempotent, reproducible configurations.
• Keeps your "intent" (the data describing the desired state) separate from the rendering logic (the template).

Where to use it: Creating configs for L3 interfaces, BGP/OSPF/IS-IS peers, QoS policies, ACLs, and those essential "golden configurations."

Example – Render a Cisco IOS-XE BGP config from YAML data:

# pip install jinja2 pyyaml
from jinja2 import Environment, BaseLoader
import yaml

data = yaml.safe_load("""
hostname: edge1
asn: 65001
router_id: 192.0.2.1
neighbors:
- ip: 203.0.113.2
   remote_as: 65002
   desc: "upstream-1"
   address_family: ["ipv4", "ipv6"]
""")

tpl = """
hostname 
router bgp 
bgp router-id 

neighbor  remote-as 
neighbor  description 

address-family ipv4
neighbor  activate
exit-address-family


address-family ipv6
neighbor  activate
exit-address-family


"""

print(Environment(loader=BaseLoader).from_string(tpl).render(**data))  

Multi-vendor Support: Jinja2 itself is vendor-neutral. You can create different templates (e.g., cisco_iosxe.j2, juniper_junos.j2) that take the same YAML data and output vendor-specific configurations.

8. Pydantic – Intent Models and Data Validation

Pydantic is a Python library for defining data models and validating them using standard Python type hints.

How it works:

• BaseModel Classes: You create classes inheriting from BaseModel to define your data structure. Fields have types, defaults, and you can add constraints.
• Validators: It has built-in validation and lets you add your own custom ones.
• Custom Types: Supports useful types like IPvAnyInterface for IP addresses.

Why it's great:

• Helps you "fail fast" if input data (from YAML, an API call, etc.) is wrong.
• Pydantic models act as self-documenting code for your data.

Where to use it: Validating inventory data, site details, BGP policies, VLAN/IPAM info, and API payloads.

Example – Validating a Layer 3 interface intent:

# pip install pydantic
from pydantic import BaseModel, IPvAnyInterface, Field
from typing import Literal

class L3Interface(BaseModel):
   name: str
   address: IPvAnyInterface
   vrf: str = "default"
   enabled: bool = True
   platform: Literal["iosxe","nxos","iosxr"]

intf = L3Interface(name="Loopback0", address="192.0.2.1/32", platform="iosxe")
print(intf.model_dump()) # safe to feed into Jinja2 or NAPALM  

Multi-vendor Support: You can create a single Pydantic model with a platform field and then use custom validators to enforce rules specific to each vendor.

9. HTTPX (or Requests) – Modern HTTP(S) Client for Controllers & APIs

HTTPX is a modern, full-featured Python library for making HTTP requests (sync and async). It handles HTTP/1.1 and HTTP/2, connection pooling, timeouts, and retries. It's perfect for interacting with network controllers and device APIs. (The older Requests library is also great but lacks async support).

How it works:

• Clients: Has Client (sync) and AsyncClient (asyncio) with built-in session management and connection pooling.
• Core: Built on httpcore with pluggable transports.
• Key Features: First-class support for timeouts, retries, proxies.

Why it's great:

• Async support makes sending requests to many controllers or devices at once super efficient.
• Provides a consistent way to talk to RESTCONF, controller REST APIs (like Cisco DNA Center), and various SaaS APIs.

Where to use it: Automating Cisco Catalyst Center, Cisco vManage (SD-WAN), Meraki Dashboard, ACI APIC, device-level RESTCONF/NETCONF gateways, and integrating with third-party ITSM/CMDB tools.

Example – List interfaces from Cisco IOS-XE via RESTCONF:

# pip install httpx
import httpx, urllib3
urllib3.disable_warnings() # demo only

host = "10.0.0.11"
auth = ("admin","admin")
headers = {"Accept":"application/yang-data+json"}
url = f"https://{host}/restconf/data/ietf-interfaces:interfaces"

with httpx.Client(verify=False, timeout=20) as c:
   r = c.get(url, headers=headers, auth=auth)
   r.raise_for_status()
   for iface in r.json()["ietf-interfaces:interfaces"]["interface"]:
       print(iface["name"], iface.get("enabled", True)) 

Catalyst Center (DNA Center) – get token + list devices

import httpx, urllib3
urllib3.disable_warnings()

BASE = "https://dnac.example.com"
cred = ("admin","password")

with httpx.Client(base_url=BASE, verify=False, timeout=30) as c:
   t = c.post("/dna/system/api/v1/auth/token", auth=cred)
   t.raise_for_status()
   token = t.json()["Token"]
   headers = {"X-Auth-Token": token}
   r = c.get("/dna/intent/api/v1/network-device", headers=headers)
   r.raise_for_status()
   devices = [(d["hostname"], d["managementIpAddress"]) for d in r.json()["response"]]
   print(devices) 

Meraki Dashboard – simple org list

API_KEY = "xxxxxxxx"
with httpx.Client(base_url="https://api.meraki.com/api/v1", headers={"X-Cisco-Meraki-API-Key": API_KEY}) as c:
   r = c.get("/organizations")
   r.raise_for_status()
   print([o["name"] for o in r.json()]) 

Multi-vendor Support: Use the same HTTPX client patterns for REST APIs from Arista CloudVision, Juniper Apstra, FortiManager, and other controllers.

10. PyYAML – The Standard for Handling YAML Data

PyYAML is the most popular Python library for working with YAML files – that human-readable data format that's the backbone of Ansible and many network automation data models.

How it works:

• Loaders: safe_load() (best for data you don't fully trust) and full_load() (for more complex Python objects, use carefully).
• Dumpers: Lets you turn Python objects (like dictionaries) back into YAML files.

Why it's great:

• Makes it easy to map human-readable YAML data into Python dictionaries and lists.
• Keeps your "intent" (the data describing what you want) separate from your code (the logic), which is fantastic for code reviews and CI/CD.

Where to use it: Defining device inventories, site-specific variables, policy lists, and feeding data into Jinja2 templates.

Example – Merging inventory data from different YAML files:

groups.yml:

# groups.yaml
iosxe:
 data:
   platform: iosxe
   snmp_community: "netops-ro"
   ntp: ["time.google.com","time.cloudflare.com"]
nxos:
 data:
   platform: nxos
   ntp: ["time.cloudflare.com","time.google.com"] 

hosts.yml:

# hosts.yaml
edge1:
 hostname: 10.0.0.11
 groups: [iosxe]
spine1:
 hostname: 10.0.0.12
 groups: [nxos]
 data:
   snmp_community: "dc-ro" 

Python script to merge and use this data:

# Python merge (PyYAML)
import yaml
groups = yaml.safe_load(open("groups.yaml"))
hosts = yaml.safe_load(open("hosts.yaml"))
print(hosts["spine1"]["data"]["snmp_community"]) # dc-ro (override) 

Multi-vendor Support: Organize your YAML files logically (by platform, device role, etc.). The format itself is vendor-neutral.

Putting the Python Libraries Stack Together

1. Data & Intent: Define what you want your network to look like in YAML files. Use Pydantic models to make sure that data is structured correctly and validated.
2. Rendering: Use Jinja2templates to turn that structured data into vendor-specific configurations.
3. Delivery: Use Nornir to manage the process. Nornir will use the right tool for the job: Netmiko or Scrapli for CLI tasks; HTTPX, Scrapli-NETCONF, or NAPALM for API interactions.
4. Verification: After changes are made, use pyATS/Genie or TextFSM to parse the device's actual state. Compare this actual state against your original intent (from Pydantic or YAML) to confirm everything worked as expected. Save the results for auditing.

Python Library Interoperability for Network Engineers

• Nornir → Orchestrator, with plugins like nornir-netmiko, nornir-scrapli, nornir-napalm, nornir-jinja2. Can use NetBox as inventory source.
• NAPALM→ Standardized config/getter interface for IOS-XE, NX-OS, IOS-XR, EOS, Junos.
• Scrapli-NETCONF→ Your go-to for NETCONF on IOS-XE, IOS-XR, NX-OS.
• HTTPX→ Your tool for REST APIs on Catalyst Center, Meraki, vManage, ACI APIC, RESTCONF.
• pyATS/Genie→ Extensive, accurate parsers for Cisco devices, plus diffing and "learn" capabilities.
• Netmiko→ Can leverage TextFSM templates and handles secure file transfers (SCP).

Operational Tips for Using Python Libraries in Production

• Timeouts & retries: set them explicitly (HTTPX, Scrapli, Netmiko).
• Secrets: do not store credentials in YAML; prefer environment variables, Vault, or keychains.
• Idempotence: prefer merges/diffs over ad-hoc send_config_set.
• Pre-flight checks: always parse and validate "pre" state before changing anything.
• CI for templates: unit-test Jinja2 renders with small fixtures; lint YAML; validate with Pydantic.

Common Automation Scenarios with Python Libraries

Here are common scenarios network engineers face and which Python libraries solve them:

Scenario 1: Automated Configuration Backup

• Challenge: Nightly backups of 50+ network device configs.
• Solution Stack: Nornir(orchestration), Netmiko(CLI interaction), PyYAML(inventory).
• Workflow: Define inventory in YAML. Write a Nornir task using Netmiko to get show running-config. Nornir runs this concurrently across all devices, reducing runtime. Save configs with timestamps and commit to Git for version control. Nornir's result object captures per-device failures without stopping the job. Schedule with cron or Task Scheduler.

Scenario 2: Compliance Auditing

• Challenge: Verify "golden configurations" (NTP, SNMP, AAA) are deployed correctly across all devices.
• Solution Stack: Jinja2(define golden configs), pyATS/Genie(parse current state and compare), Nornir(scale audit across devices). Alternative:NAPALM's config diff.

Scenario 3: Network Health Dashboard

• Challenge: Build a real-time monitoring dashboard (interface status, BGP neighbors, CPU/memory).
• Solution Stack: Scrapli(fast, async data collection), TextFSM(for any commands not covered by other parsers), PyYAML(store collected data/dashboard config). Alternative: HTTPXfor API-capable devices.

Scenario 4: A Modern Change Management Pipeline

• Challenge: Test, validate, and deploy network changes safely and repeatably.
• Solution Stack: Pydantic(validate change request), Jinja2(render new config), pyATS(pre/post-change state validation), NAPALM(push config with diff/rollback).

Start Building Production-Ready Network Automation Today

Python libraries are essential tools for modern network engineers. They help you move beyond manual CLI tasks, enabling you to build automation that is consistent, scalable, and reliable. Whether you're just starting out with Netmiko or building complex orchestration with Nornir, these libraries will significantly boost your efficiency and reduce errors.

The best way to learn? Practice. Use tools like GNS3, EVE-NG, or Cisco Modeling Labs (especially hosted solutions like CloudMyLab) to build realistic network scenarios.

Practice until automation becomes second nature. When you're ready to test, we're ready to provide the lab environment.

Explore CloudMyLab's Network Automation Labs →

Frequently Asked Questions

Which Python library is best for Cisco device automation?

It depends on the task: Netmiko for CLI tasks, NAPALM for config management and multi-vendor consistency, Nornir for orchestrating tasks across many devices, Scrapli for high-performance and async operations, pyATS/Genie for deep Cisco testing and parsing, Jinja2 for rendering configs, and HTTPX for APIs.

Do I need to learn all 10 Python libraries for network engineers?

Nope. Start with the basics like Netmiko, PyYAML, and Jinja2. Then, add others as your needs grow. Many engineers become very effective using just a handful of these.

Netmiko vs NAPALM: Which Python library should I learn first?

Start with Netmiko. It’s simpler and teaches the core concepts of SSH automation. Then move to NAPALM for its multi-vendor config management features.

How long does it take to learn Python libraries for network engineers?

With existing network knowledge: Basic scripting (Netmiko) might take 2-4 weeks of consistent practice. Intermediate workflows (Nornir, Jinja2) could take 2-3 months to become proficient. Building production-ready pipelines will take longer, perhaps 4-6 months or more.

The key is practice. Automate one real task each week rather than trying to learn everything at once. Understanding network engineer certifications and career paths can help you plan your automation learning journey alongside traditional networking skills.

Can I use these Python libraries with Ansible?

Yes! Many teams use Ansible for broader IT automation and these Python libraries for specific network tasks or when they need more control/performance. You can call Python scripts from Ansible playbooks or use Ansible's Python API.

What if my device isn't supported by these Python libraries?

FFor CLI automation, Netmiko supports over 100 device types. If yours isn't listed, try the "generic" device type, create a custom Scrapli driver, use Paramiko directly, or look for community support to add your device type.

Are there alternatives to these Python modules for network engineers?

Yes, there are alternatives like Fabric (another async SSH library), the Ansible Python API, netdev, and various vendor-specific SDKs. However, these ten libraries are the most widely adopted and powerful for general network automation.

Should I use Python libraries or learn Ansible instead?

You should learn both. Use Python for custom logic, heavy data processing, high performance needs, or network-specific workflows if your team is comfortable with Python. Use Ansible when your team prefers declarative YAML, needs to automate across a broader IT scope, wants to leverage ready-made modules, or requires enterprise features like a GUI and RBAC. Many successful teams use both.

Where can I practice using these Python libraries safely?

Practice environments are essential for learning Python automation without risking production networks. Options include:

Cloud-based labs: Cloud-based training labs provide on-demand access to realistic network topologies without hardware investment

Network emulators: Boost your network simulation skills using GNS3, EVE-NG, or CML 2.0 with virtual routers and switches

Cisco DevNet sandboxes: Free always-on and reservable lab environments

Vendor lab environments: Trial accounts from network equipment manufacturers

For comprehensive practice, network simulators for CCNA, CCNP, and CCIE exam prep offer structured learning paths that include automation components.

Always test automation scripts in a lab environment before running against production devices. This protects your network while you learn and validates your code logic.

What security and best practices should I follow when using Python libraries for network automation?

• Never hardcode credentials. Use environment variables, secure vaults (Ansible Vault, HashiCorp Vault), or OS credential managers.
• Least privilege: Grant automation accounts only the permissions they absolutely need.
• Validate inputs: Sanitize any data your scripts receive.
• Dry runs: Use Ansible's --check mode or Python logic to simulate changes without applying them.
• Error handling: Build robust try/except blocks in Python and block/rescue in Ansible.
• Logging: Log everything—what changed, when, and any errors encountered.
• Version Control: Store all code in Git and enforce peer reviews.