Hi,
I have a parent-child (one to many) dataset. I...
# gooddata-cnk
Kshirod Mohanty
03/23/2023, 1:39 AMHi,
I have a parent-child (one to many) dataset. I am using Python SDK to query parent and child fields, the sample code is pasted below. The for_items function returns the result as flattened rows, it means I have to rebuild the parent-child relationship in my code. Is there way to get the result in unflatten parent-child format?
from gooddata_sdk import sdk, Attributesdk = gooddata_sdk.GoodDataSdk.create(host, token)items = [Attribute(local_id="parent_field_P", label="parent.field_P"),Attribute(local_id="child_field_C", label="child.field_C"),]table = sdk.tables.for_items(workspace_id, items=items)for result in table.read_all():print(result)j
Jan Kadlec
03/23/2023, 7:07 AMHi,
I think that gooddata-pandas is more suitable for your use case.
Copy code
from gooddata_pandas import *
from gooddata_sdk import Attribute
good_pandas = GoodPandas(host, token)
df_factory = good_pandas.data_frames(workspace_id)
df_factory.for_items({"category":Attribute(local_id="abc", label="campaign_channels.category"), "name": Attribute(local_id="xyz", label="campaign_name" )})
# or you do not need Attribute object at all
df_factory.for_items({"category": "label/campaign_channels.category", "name": "label/campaign_name"}) Copy code
category name
0 Advertising 2015 Bamity 1AZ713
1 Advertising 2015 Bitchip 5FY971
2 Advertising 2015 Fintone 2PG648
3 Advertising 2015 Gembucket 6NG829
4 Advertising 2015 Kanlam 5YM478
.. ... ...
138 Web 2017 It 9XF277
139 Web 2017 Lotstring 4RF728
140 Web 2017 Tempsoft 4AZ168
141 Web 2018 Quo Lux 2CJ961
142 Web 2018 Ronstring 9LY887k
Kshirod Mohanty
03/23/2023, 6:15 PMI am getting the circular import error for good data_pandas.
ImportError: cannot import name 'GoodPandas' from partially initialized module 'gooddata_pandas' (most likely due to a circular import)
j
Jan Kadlec
03/23/2023, 6:15 PMWhat version do you use?
k
Kshirod Mohanty
03/23/2023, 6:18 PMgooddata-pandas 1.3.0
gooddata-sdk 1.3.0
Kshirod Mohanty
03/23/2023, 6:18 PMI just ran
pip install gooddata-pandasj
Jan Kadlec
03/23/2023, 6:22 PMUnfortunately, I am not able to simulate the error. I created a new virtualenv and
pip install gooddata-pandas Copy code
>>> from gooddata_pandas import *
>>> pandas = GoodPandas("<http://localhost:3000>","YWRtaW46Ym9vdHN0cmFwOmFkbWluMTIz")
>>> df_factory = pandas.data_frames("demo")Jan Kadlec
03/23/2023, 6:28 PMCould you please send me the code that raises the error?
k
Kshirod Mohanty
03/23/2023, 6:45 PMfrom gooddata_pandas import GoodPandasgood_pandas = GoodPandas("host", "token")Kshirod Mohanty
03/23/2023, 6:45 PMAre not you getting "NameError: name 'GoodPandas' is not defined" for the code you shared above?
j
Jan Kadlec
03/23/2023, 6:46 PMNo.
Copy code
.venv in ~/Downloads python
>>> from gooddata_pandas import GoodPandas
>>> good_pandas = GoodPandas("host", "token")
>>>k
Kshirod Mohanty
03/23/2023, 6:49 PMI also tried creating a new virtual environment. No luck. What's your gooddata_pandas and good data-sdk version?
j
Jan Kadlec
03/23/2023, 6:49 PM Copy code
gooddata-afm-client 1.3.0
gooddata-api-client 1.3.0
gooddata-metadata-client 1.3.0
gooddata-pandas 1.3.0
gooddata-scan-client 1.3.0
gooddata-sdk 1.3.0Jan Kadlec
03/23/2023, 6:49 PMAll gooddata packages
k
Kshirod Mohanty
03/23/2023, 9:21 PM@Jan Kadlec the issue was related to python version. Its working now. Coming back to original question I still see the data flattened in pandas dataframe too.. I also tried .to_dict('records') on result dataframe. I am expecting data in this format
Copy code
[{"category: "Advertising", "name: ["value1", "value2"] , {"category: "Web", "name: ["value3", "value4"] }j
Jan Kadlec
03/23/2023, 9:38 PMI am happy that it works for you now. I see what is your goal. Unfortunately, you cannot get your expected data out of the box using GoodPandas, but you need to do some post processing.
Try the following:
x.set_index("name").groupby("category").groups Copy code
{'Advertising': ['2015 Bamity 1AZ713', '2015 Bitchip 5FY971', ...], 'Content': ['2015 Bitchip 5FY971', '2015 Cookley 7NR903', ...], ...}