Develop Reference

How to read and convert this json into a DF?

I want to convert this nested json into a df.
Tried different functions but none works correctly.
The encoding that worked for my was -
encoding = "utf-8-sig"
[{'replayableActionOperationState': 'SKIPPED',
'replayableActionOperationGuid': 'RAO_1037351',
'failedMessage': 'Cannot replay action: RAO_1037351: com.ebay.sd.catedor.core.model.DTOEntityPropertyChange; local class incompatible: stream classdesc serialVersionUID = 7777212484705611612, local class serialVersionUID = -1785129380151507142',
'userMessage': 'Skip all mode',
'username': 'gfannon',
'sourceAuditData': [{'guid': '24696601-b73e-43e4-bce9-28bc741ac117',
'operationName': 'UPDATE_CATEGORY_ATTRIBUTE_PROPERTY',
'creationTimestamp': 1563439725240,
'auditCanvasInfo': {'id': '165059', 'name': '165059'},
'auditUserInfo': {'id': 1, 'name': 'gfannon'},
'externalId': None,
'comment': None,
'transactionId': '0f135909-66a7-46b1-98f6-baf1608ffd6a',
'data': {'entity': {'guid': 'CA_2511202',
'tagType': 'BOTH',
'description': None,
'name': 'Number of Shelves'},
'propertyChanges': [{'propertyName': 'EntityProperty',
'oldEntity': {'guid': 'CAP_35',
'name': 'DisableAsVariant',
'group': None,
'action': 'SET',
'value': 'true',
'tagType': 'SELLER'},
'newEntity': {'guid': 'CAP_35',
'name': 'DisableAsVariant',
'group': None,
'action': 'SET',
'value': 'false',
'tagType': 'SELLER'}}],
'entityChanges': None,
'primary': True}}],
'targetAuditData': None,
'conflictedGuids': None,
'fatal': False}]
This is what i tried so far, there are more tries but that got me as close as i can.
with open(r"Desktop\Ann's json parsing\report.tsv", encoding='utf-8-sig') as data_file:
data = json.load(data_file)
df = json_normalize(data)
print (df)
pd.DataFrame(df) ## The nested lists are shown as a whole column, im trying to parse those colums - 'failedMessage' and 'sourceAuditData'`I also tried json.loads/json(df) but the output isnt correct.
pd.DataFrame.from_dict(a['sourceAuditData'][0]['data']['propertyChanges'][0]) ##This line will retrive one of the outputs i need but i dont know how to perform it on the whole file.
The expected result should be a csv/xlsx file with a column and value for each row.

For your particular example:
def unroll_dict(d):
data = []
for k, v in d.items():
if isinstance(v, list):
data.append((k, ''))
data.extend(unroll_dict(v[0]))
elif isinstance(v, dict):
data.append((k, ''))
data.extend(unroll_dict(v))
else:
data.append((k,v))
return data
And given the data in your question is stored in the variable example:
df = pd.DataFrame(unroll_dict(example[0])).set_index(0).transpose()

Extracting value for one dictionary key in Pandas based on another in the same dictionary

This is from an R guy.
I have this mess in a Pandas column: data['crew'].
array(["[{'credit_id': '54d5356ec3a3683ba0000039', 'department': 'Production', 'gender': 1, 'id': 494, 'job': 'Casting', 'name': 'Terri Taylor', 'profile_path': None}, {'credit_id': '56407fa89251417055000b58', 'department': 'Sound', 'gender': 0, 'id': 6745, 'job': 'Music Editor', 'name': 'Richard Henderson', 'profile_path': None}, {'credit_id': '5789212392514135d60025fd', 'department': 'Production', 'gender': 2, 'id': 9250, 'job': 'Executive In Charge Of Production', 'name': 'Jeffrey Stott', 'profile_path': None}, {'credit_id': '57892074c3a36835fa002886', 'department': 'Costume & Make-Up', 'gender': 0, 'id': 23783, 'job': 'Makeup Artist', 'name': 'Heather Plott', 'profile_path': None}
It goes on for quite some time. Each new dict starts with a credit_id field. One sell can hold several dicts in an array.
Assume I want the names of all Casting directors, as shown in the first entry. I need to check check the job entry in every dict and, if it's Casting, grab what's in the name field and store it in my data frame in data['crew'].
I tried several strategies, then backed off and went for something simple.
Running the following shut me down, so I can't even access a simple field. How can I get this done in Pandas.
for row in data.head().iterrows():
if row['crew'].job == 'Casting':
print(row['crew'])
EDIT: Error Message
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-138-aa6183fdf7ac> in <module>()
1 for row in data.head().iterrows():
----> 2 if row['crew'].job == 'Casting':
3 print(row['crew'])
TypeError: tuple indices must be integers or slices, not str
EDIT: Code used to get the array of dict (strings?) in the first place.
def convert_JSON(data_as_string):
try:
dict_representation = ast.literal_eval(data_as_string)
return dict_representation
except ValueError:
return []
data["crew"] = data["crew"].map(lambda x: sorted([d['name'] if d['job'] == 'Casting' else '' for d in convert_JSON(x)])).map(lambda x: ','.join(map(str, x))

To create a DataFrame from your sample data, write:
df = pd.DataFrame(data=[
{ 'credit_id': '54d5356ec3a3683ba0000039', 'department': 'Production',
'gender': 1, 'id': 494, 'job': 'Casting', 'name': 'Terri Taylor',
'profile_path': None},
{ 'credit_id': '56407fa89251417055000b58', 'department': 'Sound',
'gender': 0, 'id': 6745, 'job': 'Music Editor',
'name': 'Richard Henderson', 'profile_path': None},
{ 'credit_id': '5789212392514135d60025fd', 'department': 'Production',
'gender': 2, 'id': 9250, 'job': 'Executive In Charge Of Production',
'name': 'Jeffrey Stott', 'profile_path': None},
{ 'credit_id': '57892074c3a36835fa002886', 'department': 'Costume & Make-Up',
'gender': 0, 'id': 23783, 'job': 'Makeup Artist',
'name': 'Heather Plott', 'profile_path': None}])
Then you can get your data with a single instruction:
df[df.job == 'Casting'].name
The result is:
0 Terri Taylor
Name: name, dtype: object
The above result is Pandas Series object with names found.
In this case, 0 is the index value for the record found and
Terri Taylor is the name of (the only in your data) Casting Director.
Edit
If you want just a list (not Series), write:
df[df.job == 'Casting'].name.tolist()
The result is ['Terri Taylor'] - just a list.
I think, both my solutions should be quicker than "ordinary" loop
based on iterrows().
Checking the execution time, you may try also yet another solution:
df.query("job == 'Casting'").name.tolist()
==========
And as far as your code is concerned:
iterrows() returns each time a pair containing:
the key of the current row,
a named tuple - the content of this row.
So your loop should look something like:
for row in df.iterrows():
if row[1].job == 'Casting':
print(row[1]['name'])
You can not write row[1].name because it refers to the index value
(here we have a collision with default attributes of the named tuple).

Extract specific keys from list of dict in python. Sentinelhub

I seem to be stuck on very simple task. I'm still dipping my toes into Python.
I'm trying to download Sentinel 2 Images with SentinelHub API:SentinelHub
The result of data that my code returns is like this:
{'geometry': {'coordinates': [[[[35.895906644, 31.602691754],
[36.264307655, 31.593801516],
[36.230618703, 30.604681346],
[35.642363693, 30.617971909],
[35.678587829, 30.757888786],
[35.715700562, 30.905919341],
[35.754290061, 31.053632806],
[35.793289298, 31.206946419],
[35.895906644, 31.602691754]]]],
'type': 'MultiPolygon'},
'id': 'ee923fac-0097-58a8-b861-b07d89b99310',
'properties': {'**productType**': '**S2MSI1C**',
'centroid': {'coordinates': [18.1321538275, 31.10368655], 'type': 'Point'},
'cloudCover': 10.68,
'collection': 'Sentinel2',
'completionDate': '2017-06-07T08:15:54Z',
'description': None,
'instrument': 'MSI',
'keywords': [],
'license': {'description': {'shortName': 'No license'},
'grantedCountries': None,
'grantedFlags': None,
'grantedOrganizationCountries': None,
'hasToBeSigned': 'never',
'licenseId': 'unlicensed',
'signatureQuota': -1,
'viewService': 'public'},
'links': [{'href': 'http://opensearch.sentinel-hub.com/resto/collections/Sentinel2/ee923fac-0097-58a8-b861-b07d89b99310.json?&lang=en',
'rel': 'self',
'title': 'GeoJSON link for ee923fac-0097-58a8-b861-b07d89b99310',
'type': 'application/json'}],
'orbitNumber': 10228,
'organisationName': None,
'parentIdentifier': None,
'platform': 'Sentinel-2',
'processingLevel': '1C',
'productIdentifier': 'S2A_OPER_MSI_L1C_TL_SGS__20170607T120016_A010228_T36RYV_N02.05',
'published': '2017-07-26T13:09:17.405352Z',
'quicklook': None,
'resolution': 10,
's3Path': 'tiles/36/R/YV/2017/6/7/0',
's3URI': 's3://sentinel-s2-l1c/tiles/36/R/YV/2017/6/7/0/',
'sensorMode': None,
'services': {'download': {'mimeType': 'text/html',
'url': 'http://sentinel-s2-l1c.s3-website.eu-central-1.amazonaws.com#tiles/36/R/YV/2017/6/7/0/'}},
'sgsId': 2168915,
'snowCover': 0,
'spacecraft': 'S2A',
'startDate': '2017-06-07T08:15:54Z',
'thumbnail': None,
'title': 'S2A_OPER_MSI_L1C_TL_SGS__20170607T120016_A010228_T36RYV_N02.05',
'updated': '2017-07-26T13:09:17.405352Z'},
'type': 'Feature'}
Can you explain how can I iterate through this set of data and extract only 'productType'? For example, if there are several similar data sets it would return only different product types.
My code is :
import matplotlib.pyplot as plt
import numpy as np
from sentinelhub import AwsProductRequest, AwsTileRequest, AwsTile, BBox, CRS
betsiboka_coords_wgs84 = [31.245117,33.897777,34.936523,36.129002]
bbox = BBox(bbox=betsiboka_coords_wgs84, crs=CRS.WGS84)
date= '2017-06-05',('2017-06-08')
data=sentinelhub.opensearch.get_area_info(bbox, date_interval=date, maxcc=None)
for i in data:
print(i)

Based on what you have provided, replace your bottom for loop:
for i in data:
print(i)
with the following:
for i in data:
print(i['properties']['**productType**'])

If you want to access only the propertyType you can use i['properties']['productType'] in your for loop. If you want to access it any time you want without writing each time those keys, you can define a generator like this:
def property_types(data_array):
for data in data_array
yield data['properties']['propertyType']
So you can use it like this in a loop (your data_array is data, as returned by sentinelhub api):
for property_type in property_types(data):
# do stuff with property_type

keys = []
for key in d.keys():
if key == 'properties':
for k in d[key].keys():
if k == '**productType**' and k not in keys:
keys.append(d[key][k])
print(keys)

Getting only specific (nested) values: Since your request key is nested, and resides inside the parent "properties" object, you need to access it first, preferably using the get method. This can be done as follows (note the '{}' parameter in the first get, this returns an empty dictionary if the first key is not present)
data_dictionary = json.loads(data_string)
product_type = data_dictionary.get('properties', {}).get('**productType**')
You can then aggregate the different product_type objects in a set, which will automatically guarantee that no 2 objects are the same
product_type_set = set()
product_type.add(product_type)

Merge a specific value from one array of dicts into another if a value matches

How do I merge a specific value from one array of dicts into another array of dicts if a single specific value matches between them?
I have an array of dicts that represent books
books = [{'writer_id': '123-456-789', 'index': None, 'title': 'Yellow Snow'}, {'writer_id': '888-888-777', 'index': None, 'title': 'Python for Dummies'}, {'writer_id': '999-121-223', 'index': 'Foo', 'title': 'Something Else'}]
and I have an array of dicts that represents authors
authors = [{'roles': ['author'], 'profile_picture': None, 'author_id': '123-456-789', 'name': 'Pat'}, {'roles': ['author'], 'profile_picture': None, 'author_id': '999-121-223', 'name': 'May'}]
I want to take the name from authors and add it to the dict in books where the books writer_id matches the authors author_id.
My end result would ideally change the book array of dicts to be (notice the first dict now has the value of 'name': 'Pat' and the second book has 'name': 'May'):
books = [{'writer_id': '123-456-789', 'index': None, 'title': 'Yellow Snow', 'name': 'Pat'}, {'writer_id': '888-888-777', 'index': None, 'title': 'Python for Dummies'}, {'writer_id': '999-121-223', 'index': 'Foo', 'title': 'Something Else', 'name': 'May'}]
My current solution is:
for book in books:
for author in authors:
if book['writer_id'] == author['author_id']:
book['author_name'] = author['name']
And this works. However, the nested statements bother me and feel unwieldy. I also have a number of other such structures so I end up with a function that has a bunch of code resembling this in it:
for book in books:
for author in authors:
if book['writer_id'] == author['author_id']:
book['author_name'] = author['name']
books_with_foo = []
for book in books:
for thing in things:
if something:
// do something
for blah in books_with_foo:
for book_foo in otherthing:
if blah['bar'] == stuff['baz']:
// etc, etc.
Alternatively, how would you aggregate data from multiple database tables into one thing... some of the data comes back as dicts, some as arrays of dicts?

Pandas is almost definitely going to help you here. Convert your dicts to DataFrames for easier manipulation, then merge them:
import pandas as pd
authors = [{'roles': ['author'], 'profile_picture': None, 'author_id': '123-456-789', 'name': 'Pat'}, {'roles': ['author'], 'profile_picture': None, 'author_id': '999-121-223', 'name': 'May'}]
books = [{'writer_id': '123-456-789', 'index': None, 'title': 'Yellow Snow'}, {'writer_id': '888-888-777', 'index': None, 'title': 'Python for Dummies'}, {'writer_id': '999-121-223', 'index': 'Foo', 'title': 'Something Else'}]
df1 = pd.DataFrame.from_dict(books)
df2 = pd.DataFrame.from_dict(authors)
df1['author_id'] = df1.writer_id
df1 = df1.set_index('author_id')
df2 = df2.set_index('author_id')
result = pd.concat([df1, df2], axis=1)
you may find this page helpful for different ways of combining (merging, concatenating, etc) separate DataFrames.

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