I am new to Python and am struggling to find the right method for the following:
I have 2 API responses, one is a list of devices, the other one is a list of organizations. Each device is linked to an organization with an Organization ID.
organizations = [
{
'name': 'Aperture Science Inc.',
'description': 'Just a corporation!',
'id': 1
},
{
'name': 'Software Development Inc',
'description': "Making the world's next best app!",
'id': 2
}
]
devices = [
{
'id': 1,
'organizationId': 2,
'nodeClass': 'WINDOWS_WORKSTATION',
'displayName': 'DESKTOP_01'
},{
'id': 2,
'organizationId': 2,
'nodeClass': 'WINDOWS_SERVER',
'displayName': 'SERVER_01'
},{
'id': 3,
'organizationId': 1,
'nodeClass': 'WINDOWS_WORSTATION',
'displayName': 'DESKTOP_0123'
}
]
The OrganizationID in devices = the id in organizations. I want to get a result with the number of Servers and workstations respectively for each organizations, like this:
results = [
{
'Organization Name' : 'Aperture Science Inc.',
'Number of Workstations': 1,
'Number of Servers': 0,
'Total devices': 1
},
{
'Organization Name' : 'Software Development Inc',
'Number of Workstations': 1,
'Number of Servers': 1,
'Total devices': 2
}
I started with this
wks_sum = sum(d.nodeClass == "WINDOWS_WORKSTATION" for d in devices)
print(wks_sum)
but I get this error:
AttributeError: 'dict' object has no attribute 'nodeClass'
and at the very end I convert and save in a csv file:
df = pd.DataFrame(results)
df.to_csv('results.csv', index=False)
I am struggling doing the count of each device types and also to map devices to the right organization name and would really appreciate some help :)
EDIT:
Thanks to @Vincent, I could come up with:
for device in devices:
for organization in organizations:
organization["workstations"] = organization.get("workstations", [])
organization["servers"] = organization.get("servers", [])
if device["organizationId"] != organization["id"]:
continue
if device["nodeClass"].__eq__("WINDOWS_SERVER"):
organization["servers"].append(device["nodeClass"])
elif device["nodeClass"].__eq__("WINDOWS_WORKSTATION"):
organization["workstations"].append(device["nodeClass"])
break
results = [
{
"Organization Name": organization["name"],
"Number of Workstations": len(organization["workstations"]),
"Number of Servers": len(organization["servers"]),
"Total devices": len(organization["workstations"] organization["servers"]),
} for organization in organizations
]
# print(f"{results = }")
print(results)
# convert and save in a csv file
df = pd.DataFrame(results)
df.to_csv('results.csv', index=False)
CodePudding user response:
This code will achieve you goal:
organizations = [
{
'name': 'Aperture Science Inc.',
'description': 'Just a corporation!',
'id': 1
},
{
'name': 'Software Development Inc',
'description': "Making the world's next best app!",
'id': 2
}
]
devices = [
{
'id': 1,
'organizationId': 2,
'nodeClass': 'WINDOWS_WORKSTATION',
'displayName': 'DESKTOP_01'
},{
'id': 2,
'organizationId': 2,
'nodeClass': 'WINDOWS_SERVER',
'displayName': 'SERVER_01'
},{
'id': 3,
'organizationId': 1,
'nodeClass': 'WINDOWS_WORSTATION',
'displayName': 'DESKTOP_0123'
}
]
for device in devices:
for organization in organizations:
organization["workstations"] = organization.get("workstations", [])
organization["servers"] = organization.get("servers", [])
if device["organizationId"] != organization["id"]:
continue
if device["displayName"].startswith("SERVER_"):
organization["servers"].append(device["nodeClass"])
elif device["displayName"].startswith("DESKTOP_"):
organization["workstations"].append(device["nodeClass"])
break
results = [
{
"Organization Name": organization["name"],
"Number of Workstations": len(organization["workstations"]),
"Number of Servers": len(organization["servers"]),
"Total devices": len(organization["workstations"] organization["servers"]),
} for organization in organizations
]
print(f"{results = }")
Result:
[{'Organization Name': 'Aperture Science Inc.', 'Number of Workstations': 1, 'Number of Servers': 0, 'Total devices': 1}, {'Organization Name': 'Software Development Inc', 'Number of Workstations': 1, 'Number of Servers': 1, 'Total devices': 2}]
Indeed you can do it using obscure lib such as pandas, but I think a good slow code like this is better to know what is done and easier to modify if needed.
To deal with a huge amount of data, you should dump into two sql tables using sqlite3 for example and deal with SQL.