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Apply function to specific element's value of a list of dictionaries [duplicate]

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Getting a map() to return a list in Python 3.x
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tbl_headers = db_admin.execute("SELECT name, type FROM PRAGMA_TABLE_INFO(?);", table_name)
tbl_headers is same below:
[{'name': 'id', 'type': 'INTEGER'}, {'name': 'abasdfasd', 'type': 'TEXT'}, {'name': 'sx', 'type': 'TEXT'}, {'name': 'password', 'type': 'NULL'}, {'name': 'asdf', 'type': 'TEXT'}]
I need apply hash_in() function on the 'name' values are in dictionary elements of above list.
Have tried these:
tbl_headers = [hash_in(i['name']) for i in tbl_headers]
suppresses dictionaries and return only a list of 'name' values:
['sxtw001c001h', 'sxtw001c001r001Z001e001c001r001Z001a001Z', 'sxtw001w001r', 'sxtw001c001q001n001v001r001r001Z001o', 'sxtw001e001c001r001Z']
OR
tbl_headers = map(hash_in, tbl_headers)
Returns error.
Update
The Output result I have seek is same:
[{'name': hash_in('id'), 'type': 'INTEGER'}, {'name': hash_in('abasdfasd'), 'type': 'TEXT'}, {'name': hash_in('sx'), 'type': 'TEXT'}, {'name': ('password'), 'type': 'NULL'}, {'name': ('asdf'), 'type': 'TEXT'}]
Appreciate you.

Try this list comprehension:
tbl_headers = [{'name': hash_in(i['name']), 'type': i['type']} for i in tbl_headers]

Changing schema of avro file when writing to it in append mode

I'm looking for a way to modify the schema of an avro file in python. Taking the following example, using the fastavro package, first write out some initial records, with corresponding schema:
from fastavro import writer, parse_schema
schema = {
'name': 'test',
'type': 'record',
'fields': [
{'name': 'id', 'type': 'int'},
{'name': 'val', 'type': 'long'},
],
}
records = [
{u'id': 1, u'val': 0.2},
{u'id': 2, u'val': 3.1},
]
with open('test.avro', 'wb') as f:
writer(f, parse_schema(schema), records)
Uhoh, I've got some more records, but they contain None values. I'd like to append these records to the avro file, and modify my schema accordingly:
more_records = [
{u'id': 3, u'val': 1.5},
{u'id': 2, u'val': None},
]
schema['fields'][1]['type'] = ['long', 'null']
with open('test.avro', 'a+b') as f:
writer(f, parse_schema(schema), more_records)
Instead of overwriting the schema, this results in an error:
ValueError: Provided schema {'type': 'record', 'name': 'test', 'fields': [{'name': 'id', 'type': 'int'}, {'name': 'val', 'type': ['long', 'null']}], '__fastavro_parsed': True, '__named_schemas': {'test': {'type': 'record', 'name': 'test', 'fields': [{'name': 'id', 'type': 'int'}, {'name': 'val', 'type': ['long', 'null']}]}}} does not match file writer_schema {'type': 'record', 'name': 'test', 'fields': [{'name': 'id', 'type': 'int'}, {'name': 'val', 'type': 'long'}], '__fastavro_parsed': True, '__named_schemas': {'test': {'type': 'record', 'name': 'test', 'fields': [{'name': 'id', 'type': 'int'}, {'name': 'val', 'type': 'long'}]}}}
Is there a workaround for this? The fastavro docs for this suggest it's not possible, but I'm hoping someone knows of a way!
Cheers

The append API in fastavro does not currently support this. You could open an issue in that repository and discuss if something like this makes sense.

How to Match two APIs to update one API dataset using Python

I want to be able to GET information from API 1 and match it with API 2 and be able to update API 2's information with API 1. I am trying to figure out the most efficient/automated way to accomplish this as it also needs to be updated at a interval of every 10 minutes
I can query and get the results from API 1 this is my code and what my code looks like.
import json
import requests
myToken = '52c32f6588004cb3ab33b0ff320b8e4f'
myUrl = 'https://api1.com/api/v1/devices.json'
head = {'Authorization': 'Token {}'.format(myToken)}
response = requests.get(myUrl, headers=head)
r = json.loads(response.content)
r
The payload looks like this from API 1
{ "device" : {
"id": 153,
"battery_status" : 61,
"serial_no": "5QBYGKUI05",
"location_lat": "-45.948917",
"location_lng": "29.832179",
"location_address": "800 Laurel Rd, Lansdale, PA 192522,USA"}
}
I want to be able to take this information and match by "serial_no" and update all the other pieces of information for the corresponding device in API 2
I query the data for API 2 and this is what my code looks like
params = {
"location":'cf6707e3-f0ae-4040-a184-737b21a4bbd1',
"dateAdded":'ge:11/23/2020'}
url = requests.get('https://api2.com/api/assets',auth=('api2', '123456'), params=params)
r = json.loads(url.content)
r['items']
The JSON payload looks like this
[{'id': '064ca857-3783-460e-a7a2-245e054dcbe3',
'name': 'Apple Laptop 1',
'model': {'id': '50f5993e-2abf-49c8-86e0-8743dd58db6f',
'name': 'MacBook Pro'},
'manufacturer': {'id': 'f56244e2-76e3-46da-97dd-f72f92ca0779',
'name': 'APPLE'},
'room': {'id': '700ff2dc-0118-46c6-936a-01f0fa88c620',
'name': 'Storage Room 1',
'thirdPartyId': ''},
'location': {'id': 'cf6707e3-f0ae-4040-a184-737b21a4bbd1',
'name': 'Iron Mountain',
'thirdPartyId': ''},
'position': 'NonMounted',
'containerAsset': {'id': '00000000-0000-0000-0000-000000000000',
'name': None},
'baseAsset': {'id': '064ca857-3783-460e-a7a2-245e054dcbe3',
'name': 'Apple Laptop 1'},
'description': None,
'status': {'id': 'df9906d8-2856-45e3-9cba-bd7a1ac4971f',
'name': 'Production'},
'serialNumber': '5QBYGKUI06',
'tagNumber': None,
'alternateTagNumber': None,
'verificationStatus': {'id': 'cb3560a9-eef5-47b9-b033-394d3a09db18',
'name': 'Verified'},
'requiresRFID': False,
'requiresHangTag': False,
'bottomPosition': 0.0,
'leftPosition': 0.0,
'rackPosition': 'Front',
'labelX': None,
'labelY': None,
'verifyNameInRear': False,
'verifySerialNumberInRear': False,
'verifyBarcodeInRear': False,
'isNonDataCenter': False,
'rotate': False,
'customer': {'id': '00000000-0000-0000-0000-000000000000', 'name': None},
'thirdPartyId': '',
'temperature': None,
'dateLastScanned': None,
'placement': 'Floor',
'lastScannedLabelX': None,
'lastScannedLabelY': None,
'userDefinedValues': [{'userDefinedKeyId': '79e77a1e-4030-4308-a8ff-9caf40c04fbd',
'userDefinedKeyName': 'Longitude ',
'value': '-75.208917'},
{'userDefinedKeyId': '72c8056e-9b7d-40ac-9270-9f5929097e82',
'userDefinedKeyName': 'Address',
'value': '800 Laurel Rd, New York ,NY 19050, USA'},
{'userDefinedKeyId': '31aeeb91-daef-4364-8dd6-b0e3436d6a51',
'userDefinedKeyName': 'Battery Level',
'value': '67'},
{'userDefinedKeyId': '22b7ce4f-7d3d-4282-9ecb-e8ec2238acf2',
'userDefinedKeyName': 'Latitude',
'value': '35.932179'}]}
The documentation provided by API 2 tells me they only support PUT for updates as of right now but I would also want to know how I would do this using PATCH as it will be available in the future. So the data payload that I need to successful PUT is this
payload = {'id': '064ca857-3783-460e-a7a2-245e054dcbe3',
'name': 'Apple Laptop 1',
'model': {'id': '50f5993e-2abf-49c8-86e0-8743dd58db6f',
'name': 'MacBook Pro'},
'manufacturer': {'id': 'f56244e2-76e3-46da-97dd-f72f92ca0779',
'name': 'APPLE'},
'room': {'id': '700ff2dc-0118-46c6-936a-01f0fa88c620',
'name': 'Storage Room 1',
'thirdPartyId': ''},
'status': {'id': 'df9906d8-2856-45e3-9cba-bd7a1ac4971f',
'name': 'Production'},
'serialNumber': '5QBYGKUI06',
'verificationStatus': {'id': 'cb3560a9-eef5-47b9-b033-394d3a09db18',
'name': 'Verified'},
'requiresRFID': 'False',
'requiresHangTag': 'False',
'userDefinedValues': [{'userDefinedKeyId': '79e77a1e-4030-4308-a8ff-9caf40c04fbd',
'userDefinedKeyName': 'Longitude ',
'value': '-75.248920'},
{'userDefinedKeyId': '72c8056e-9b7d-40ac-9270-9f5929097e82',
'userDefinedKeyName': 'Address',
'value': '801 Laurel Rd, New York, Ny 192250, USA'},
{'userDefinedKeyId': '31aeeb91-daef-4364-8dd6-b0e3436d6a51',
'userDefinedKeyName': 'Battery Level',
'value': '67'},
{'userDefinedKeyId': '22b7ce4f-7d3d-4282-9ecb-e8ec2238acf2',
'userDefinedKeyName': 'Latitude',
'value': '29.782177'}]}
So apart of this is figuring out how I can query the json data portions that I need for the update
I am able to update the information using this line
requests.put('https://api2.com/api/assets/064ca857-3783-460e-a7a2-245e054dcbe3',auth=('API2', '123456'), data=json.dumps(payload))
but I need for it to dynamically update so I don't think the hard coded id parameter in the line will be efficient in a automation/efficiency standpoint. If anybody has any ideas, resources to point me in the right direction to know more about this process (I don't really know what it is even called) would be greatly appreciated.

Not entirely sure what you are trying to do here, but if you want to pull information nested in the responses you can do this.
Serial number from API 1
r['device']['serial_no']
Serial number for API 2
either r[0]['serialNumber'] or r['items'][0]['serialNumber'] depending on what you are showing
To modify the payload serial number, for example
payload['serialNumber'] = '123456abcdef'

How to convert json into a pandas dataframe?

I'm trying to covert an api response from json to a dataframe in pandas. the problem I am having is that de data is nested in the json format and I am not getting the right columns in my dataframe.
The data is collect from a api with the following format:
{'tickets': [{'url': 'https...',
'id': 1,
'external_id': None,
'via': {'channel': 'web',
'source': {'from': {}, 'to': {}, 'rel': None}},
'created_at': '2020-05-01T04:16:33Z',
'updated_at': '2020-05-23T03:02:49Z',
'type': 'incident',
'subject': 'Subject',
'raw_subject': 'Raw subject',
'description': 'Hi, this is the description',
'priority': 'normal',
'status': 'closed',
'recipient': None,
'requester_id': 409467360874,
'submitter_id': 409126461453,
'assignee_id': 409126461453,
'organization_id': None,
'group_id': 360009916453,
'collaborator_ids': [],
'follower_ids': [],
'email_cc_ids': [],
'forum_topic_id': None,
'problem_id': None,
'has_incidents': False,
'is_public': True,
'due_at': None,
'tags': ['tag_1',
'tag_2',
'tag_3',
'tag_4'],
'custom_fields': [{'id': 360042034433, 'value': 'value of the first custom field'},
{'id': 360041487874, 'value': 'value of the second custom field'},
{'id': 360041489414, 'value': 'value of the third custom field'},
{'id': 360040980053, 'value': 'correo_electrónico'},
{'id': 360040980373, 'value': 'suscribe_newsletter'},
{'id': 360042046173, 'value': None},
{'id': 360041028574, 'value': 'product'},
{'id': 360042103034, 'value': None}],
'satisfaction_rating': {'score': 'unoffered'},
'sharing_agreement_ids': [],
'comment_count': 2,
'fields': [{'id': 360042034433, 'value': 'value of the first custom field'},
{'id': 360041487874, 'value': 'value of the second custom field'},
{'id': 360041489414, 'value': 'value of the third custom field'},
{'id': 360040980053, 'value': 'correo_electrónico'},
{'id': 360040980373, 'value': 'suscribe_newsletter'},
{'id': 360042046173, 'value': None},
{'id': 360041028574, 'value': 'product'},
{'id': 360042103034, 'value': None}],
'followup_ids': [],
'ticket_form_id': 360003608013,
'deleted_ticket_form_id': 360003608013,
'brand_id': 360004571673,
'satisfaction_probability': None,
'allow_channelback': False,
'allow_attachments': True},
What I already tried is the following: I have converted the JSON format into a dict as following:
x = response.json()
df = pd.DataFrame(x['tickets'])
But I'm struggling with the output. I don't know how to get a correct, ordered, normalized dataframe.
(I'm new in this :) )

Let's supose you get your request data by this code r = requests.get(url, auth)
Your data ins't clear yet, so let's get a dataframe of it data = pd.read_json(json.dumps(r.json, ensure_ascii = False))
But, probably you will get a dataframe with one single row.
When I faced a problem like this, I wrote this function to get the full data:
listParam = []
def listDict(entry):
if type(entry) is dict:
listParam.append(entry)
elif type(entry) is list:
for ent in entry:
listDict(ent)
Because your data looks like a dict because of {'tickets': ...} you will need to get the information like that:
listDict(data.iloc[0][0])
And then,
pd.DataFrame(listParam)
I can't show the results because you didn't post the complete data nor told where I can find the data to test, but this will probably work.

You have to convert the json to dictionary first and then convert the dictionary value for key 'tickets' into dataframe.
file = open('file.json').read()
ticketDictionary = json.loads(file)
df = pd.DataFrame(ticketDictionary['tickets'])
'file.json' contains your data here.
df now contains your dataFrame in this format.
For the lists within the response you can have separate dataframes if required:
for field in df['fields']:
df = pd.DataFrame(field)
It will give you this for lengths:
id value
0 360042034433 value of the first custom field
1 360041487874 value of the second custom field
2 360041489414 value of the third custom field
3 360040980053 correo_electrónico
4 360040980373 suscribe_newsletter
5 360042046173 None
6 360041028574 product
7 360042103034 None
This can be one way to structure as you haven't mentioned the exact expected format.

Accessing YAML data in Python

I have a YAML file that parses into an object, e.g.:
{'name': [{'proj_directory': '/directory/'},
{'categories': [{'quick': [{'directory': 'quick'},
{'description': None},
{'table_name': 'quick'}]},
{'intermediate': [{'directory': 'intermediate'},
{'description': None},
{'table_name': 'intermediate'}]},
{'research': [{'directory': 'research'},
{'description': None},
{'table_name': 'research'}]}]},
{'nomenclature': [{'extension': 'nc'}
{'handler': 'script'},
{'filename': [{'id': [{'type': 'VARCHAR'}]},
{'date': [{'type': 'DATE'}]},
{'v': [{'type': 'INT'}]}]},
{'data': [{'time': [{'variable_name': 'time'},
{'units': 'minutes since 1-1-1980 00:00 UTC'},
{'latitude': [{'variable_n...
I'm having trouble accessing the data in python and regularly see the error TypeError: list indices must be integers, not str
I want to be able to access all elements corresponding to 'name' so to retrieve each data field I imagine it would look something like:
import yaml
settings_stream = open('file.yaml', 'r')
settingsMap = yaml.safe_load(settings_stream)
yaml_stream = True
print 'loaded settings for: ',
for project in settingsMap:
print project + ', ' + settingsMap[project]['project_directory']
and I would expect each element would be accessible via something like ['name']['categories']['quick']['directory']
and something a little deeper would just be:
['name']['nomenclature']['data']['latitude']['variable_name']
or am I completely wrong here?

The brackets, [], indicate that you have lists of dicts, not just a dict.
For example, settingsMap['name'] is a list of dicts.
Therefore, you need to select the correct dict in the list using an integer index, before you can select the key in the dict.
So, giving your current data structure, you'd need to use:
settingsMap['name'][1]['categories'][0]['quick'][0]['directory']
Or, revise the underlying YAML data structure.
For example, if the data structure looked like this:
settingsMap = {
'name':
{'proj_directory': '/directory/',
'categories': {'quick': {'directory': 'quick',
'description': None,
'table_name': 'quick'}},
'intermediate': {'directory': 'intermediate',
'description': None,
'table_name': 'intermediate'},
'research': {'directory': 'research',
'description': None,
'table_name': 'research'},
'nomenclature': {'extension': 'nc',
'handler': 'script',
'filename': {'id': {'type': 'VARCHAR'},
'date': {'type': 'DATE'},
'v': {'type': 'INT'}},
'data': {'time': {'variable_name': 'time',
'units': 'minutes since 1-1-1980 00:00 UTC'}}}}}
then you could access the same value as above with
settingsMap['name']['categories']['quick']['directory']
# quick