We’ve collected our state data in Part 2.
Now let’s do something interesting with our data. I’ve switched out the role I will be using. So this will be a long blog post on how to set up and finally make something pretty with your data!
The Setup
What you’ll need –
- Python
- Ansible
- PyATS and Genie Modules
- Ansible PyATS – You can git clone this into your roles directory
- Pandas
- Jupyter Notebook
The reason I’m using a different module for this, is that it will automatically grab a snapshot and format it into JSON. It has other features, like compare that I won’t get into. But for now, this will be good enough to start manipulating data.
The Play Book
---
- hosts: localhost
connection: local
gather_facts: no
- name: Interface Snapshot
hosts: switches
gather_facts: no
connection: network_cli
roles:
- ansible-pyats
tasks:
- name: Gather Snapshot
include_role:
name: ansible-pyats
tasks_from: snapshot_command
vars:
command: show interfaces
file: "snapshots/{{ inventory_hostname }}_interface_snapshot.json"
This play book is assigned to the switches in the inventory. It calls the role that we downloaded from GitHub with the task snapshot_command. I’m telling it to snapshot the show interfaces command.
Step 1 – Grab the state of the interfaces using your playbook and output to JSON.
Step 2 – Import the data into Jupyter Notebook and convert it into useable information using Pandas.
Now you may be asking why Pandas and why Jupyter? I have a blog post here on why it’s easier to work with. On the note about Pandas, it’s a great tool for quickly putting information into a structure that easier to analyze. Need to do math based on Date time? Need to do some quick counting on cell values? Or maybe convert row data to columns? Very quick an easy to do in Pandas. So let’s get started.
Now this may seem a little denser than normal. But this is a direct export from Jupyter. This is using Python and the two modules to make the magic happen. If you want to try it out, you can copy the code below into Jupyter and use this JSON document.
import pandas as pd #import pandas for data manipulation
import plotly.express as px #For a quick pretty graph at the end
df = pd.read_json('/Users/**/Automation/ansible/snapshots/SW1_interface_snapshot.json') #Import the JSON document
df.loc['arp_timeout':'bandwidth' , 'FastEthernet0/1':'FastEthernet0/13'] #grab the first 5 columns and top 3 rows| FastEthernet0/1 | FastEthernet0/10 | FastEthernet0/11 | FastEthernet0/12 | FastEthernet0/13 | |
|---|---|---|---|---|---|
| arp_timeout | 04:00:00 | 04:00:00 | 04:00:00 | 04:00:00 | 04:00:00 |
| arp_type | arpa | arpa | arpa | arpa | arpa |
| bandwidth | 100000 | 10000 | 10000 | 10000 | 10000 |
df2 = df.loc[ ['line_protocol', 'last_input'] , : ] #export the desired values
df2.loc['line_protocol':'last_input', 'FastEthernet0/1':'FastEthernet0/13']| FastEthernet0/1 | FastEthernet0/10 | FastEthernet0/11 | FastEthernet0/12 | FastEthernet0/13 | |
|---|---|---|---|---|---|
| line_protocol | up | down | down | down | down |
| last_input | 00:00:01 | never | never | never | never |
df2 = df2.transpose() #flip the columns and rows; by default the interfaces are the columns
df2.head(3)| line_protocol | last_input | |
|---|---|---|
| FastEthernet0/1 | up | 00:00:01 |
| FastEthernet0/10 | down | never |
| FastEthernet0/11 | down | never |
df2.loc[(df2.last_input == "never"), 'last_input']='23:59:59' #convert never to time value
df2.head(3)| line_protocol | last_input | |
|---|---|---|
| FastEthernet0/1 | up | 00:00:01 |
| FastEthernet0/10 | down | 23:59:59 |
| FastEthernet0/11 | down | 23:59:59 |
df2["last_input"]= pd.to_datetime(df2["last_input"]) #convert time values to datetime; panda adds todays date
df2.head(3)| line_protocol | last_input | |
|---|---|---|
| FastEthernet0/1 | up | 2020-06-18 00:00:01 |
| FastEthernet0/10 | down | 2020-06-18 23:59:59 |
| FastEthernet0/11 | down | 2020-06-18 23:59:59 |
df_value_counts = df2['line_protocol'].value_counts() #grab value counts and put into new dataframe
df_value_counts = df_value_counts.reset_index() #reset the index
df_value_counts.columns = ['State', 'Count'] #set column values
df_value_counts #display values
| State | Count | |
|---|---|---|
| 0 | down | 26 |
| 1 | up | 6 |
fig = px.bar(df_value_counts, # dataframe
x="State", # x will be the 'State' column of the dataframe
y="Count", # y will be the 'Count' column of the dataframe
color="State", # color gets assigned to the State axis
title=f"Interface State",
labels={"State": "Up/Down","Count": "Count"}, # the axis names
color_discrete_sequence=["red", "green"], # the colors used
height=500,
width=800) fig.show()
