John Yaist, if you can reproduce the issue, why does this code work on my machine?
>>> import pandas as pd
>>> from arcgis.features import FeatureSet
>>>
>>> esri_json = {
... "geometryType":"esriGeometryPolygon",
... "spatialReference":{"wkid":102100, "latestWkid":3857},
... "fields":[
... {"name":"Field1", "type":"esriFieldTypeString"}
... ],
... "features":[
... {
... "attributes":{"Field1":"Hello"},
... "geometry":{"rings":[[[0,0],[0,10],[10,10],[10,0],[0,0]]]}
... },{
... "attributes":{"Field1":"World"},
... "geometry":{"rings":[[[11,11],[11,21],[21,21],[21,11],[11,11]]]}
... }
... ]
... }
>>>
>>> sdf = FeatureSet.from_dict(esri_json).sdf
>>> print(sdf.to_string())
Field1 OBJECTID SHAPE
0 Hello 1 {"rings": [[[0, 0], [0, 10], [10, 10], [10, 0]...
1 World 2 {"rings": [[[11, 11], [11, 21], [21, 21], [21,...
>>>
>>> rec = sdf.loc[0].copy(deep=True)
>>> rec.OBJECTID = None
>>> rec.Field1 = "Bye"
>>> rec.SHAPE = {"rings":[[[0,11],[5,11],[5,16],[0,16],[0,11]]]}
>>>
>>> sdf = sdf.append(rec, ignore_index=True)
>>> print(sdf.to_string())
Field1 OBJECTID SHAPE
0 Hello 1 {'rings': [[[0, 0], [0, 10], [10, 10], [10, 0]...
1 World 2 {'rings': [[[11, 11], [11, 21], [21, 21], [21,...
2 Bye None {'rings': [[[0, 11], [5, 11], [5, 16], [0, 16]...
>>>
UPDATE: If I change line #30 to
rec.SHAPE["rings"] = [[[0,11],[5,11],[5,16],[0,16],[0,11]]]
the outcome becomes:
>>> print(sdf.to_string())
Field1 OBJECTID SHAPE
0 Hello 1 {'rings': [[[0, 11], [5, 11], [5, 16], [0, 16]...
1 World 2 {'rings': [[[11, 11], [11, 21], [21, 21], [21,...
2 Bye None {'rings': [[[0, 11], [5, 11], [5, 16], [0, 16]...
>>>
Oisin Slevin, regarding:
but as I append a row it overwrites the SHAPE column of all previous rows.
All previous rows or just the previous row, or maybe the first row?
In terms of using pandas.DataFrame.copy(deep=True), the documentation notes are key to understanding what is happening: pandas.DataFrame.copy — pandas 0.23.4 documentation :
Notes
When deep=True, data is copied but actual Python objects will not be copied recursively, only the reference to the object. This is in contrast to copy.deepcopy in the Standard Library, which recursively copies object data (see examples below).
In short, Pandas deepcopy isn't quite a deep copy.
UPDATE 2: It appears deepcopy used to behave differently: pd.DataFrame.__deepcopy__ is broken · Issue #17406 · pandas-dev/pandas · GitHub
UPDATE 3: I think a different approach is warranted overall. That said, I think the most straightforward workaround is to use copy.deepcopy on a dictionary of the template record since pandas.DataFrame.append — pandas 0.23.4 documentation works with "dict-like" objects. Using the following
>>> rec = copy.deepcopy(sdf.loc[0].to_dict())
>>> rec["OBJECTID"] = None
>>> rec["Field1"] = "Bye"
>>> rec["SHAPE"]["rings"] = [[[0,11],[5,11],[5,16],[0,16],[0,11]]]
results in:
Field1 OBJECTID SHAPE
0 Hello 1 {'rings': [[[0, 0], [0, 10], [10, 10], [10, 0]...
1 World 2 {'rings': [[[11, 11], [11, 21], [21, 21], [21,...
2 Bye None {'rings': [[[0, 11], [5, 11], [5, 16], [0, 16]...
