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gpdvega is a bridge between GeoPandas and Altair that allows to seamlessly chart geospatial data

Home Page: https://iliatimofeev.github.io/gpdvega/

License: BSD 3-Clause "New" or "Revised" License

Python 97.33% Makefile 2.67%

altair geopandas

gpdvega's Issues

Unexpected position of 'Oceania'

import altair as alt
import geopandas as gpd
import gpdvega 

alt.renderers.enable('notebook') # render for Jupyter Notebook

world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))

alt.Chart(world [world.continent=='Oceania']).mark_geoshape(
    stroke='#1f77b4',  
    fill='#7f7f7f',
    fillOpacity=0.3

).project(
).encode( 
).properties( 
    width=500,
    height=300
)

saving to png gives error: TypeError: Object of type 'Polygon' is not JSON serializable

using

chart.save('something.png')

Gives the below error:

TypeError: Object of type 'Polygon' is not JSON serializable

What does work:

When you to .to_json() there is not an error and it produces it fine.

combined_chart.to_json()

In jupyterlab at the top right of plots you have the (...) menu where you can tell it to save to a png - and that does work fine.

What does not work:

combined_chart.save('something.png')

Here is a reproducible example:

import altair as alt
import geopandas as gpd
import gpdvega
import pandas as pd
from shapely.geometry import Point
from gpdvega import gpd_to_values


alt.data_transformers.register(
    'gpd_to_values',
    lambda data: alt.pipe(data, gpd_to_values)
)
alt.data_transformers.enable('gpd_to_values')


world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))

# GeoDataFrame could be passed as usual pd.DataFrame
chart_one = alt.Chart(world[world.continent!='Antarctica']).mark_geoshape(
).project(
).encode(
    color='pop_est', # shorthand infer types as for regular pd.DataFrame
    tooltip='id:Q' # GeoDataFrame.index is accessible as id
).properties(
    width=500,
    height=300
)


# generate some points to push us over the max rows
some = [[-70.05179, 25.10815] for x in range(6000)]

some = pd.DataFrame(some, columns=['x', 'y'])

some['Coordinates'] = list(zip(some.x, some.y))
some['Coordinates'] = some['Coordinates'].apply(Point)
gdfo = gpd.GeoDataFrame(some, geometry='Coordinates')
chart_two = alt.Chart(gdfo).mark_point(color='red').encode(#.mark_point(size=550, color='orange').encode(
    longitude='x:Q',
    latitude='y:Q'
)

combined_chart = chart_one + chart_two

To get the error - now run:

combined_chart.save('something.png')

Looking for ideas as it seems to serialize to json fine with .to_json() but runs into trouble with saving to a png.

Also of note - is in jupyterlab at the top right of plots you have the (...) menu where you can tell it to save to a png - and that does work fine.

Here is the full error:

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-5-fe96c4e38374> in <module>()
----> 1 combined_chart.save('something.png')

~\AppData\Local\Continuum\anaconda3\envs\data_analysis\lib\site-packages\altair\vegalite\v2\api.py in save(self, fp, format, override_data_transformer, scale_factor, vegalite_version, vega_version, vegaembed_version, **kwargs)
    500         if override_data_transformer:
    501             with data_transformers.enable('default', max_rows=None):
--> 502                 result = save(**kwds)
    503         else:
    504             result = save(**kwds)

~\AppData\Local\Continuum\anaconda3\envs\data_analysis\lib\site-packages\altair\utils\save.py in save(chart, fp, vega_version, vegaembed_version, format, mode, vegalite_version, embed_options, json_kwds, webdriver, scale_factor)
     58                              "['png', 'svg', 'html', 'json']")
     59 
---> 60     spec = chart.to_dict()
     61 
     62     if mode is None:

~\AppData\Local\Continuum\anaconda3\envs\data_analysis\lib\site-packages\altair\vegalite\v2\api.py in to_dict(self, *args, **kwargs)
    406 
    407         try:
--> 408             dct = super(TopLevelMixin, copy).to_dict(*args, **kwargs)
    409         except jsonschema.ValidationError:
    410             dct = None

~\AppData\Local\Continuum\anaconda3\envs\data_analysis\lib\site-packages\altair\utils\schemapi.py in to_dict(self, validate, ignore, context)
    243             result = _todict(self._args[0])
    244         elif not self._args:
--> 245             result = _todict({k: v for k, v in self._kwds.items()
    246                               if k not in ignore})
    247         else:

~\AppData\Local\Continuum\anaconda3\envs\data_analysis\lib\site-packages\altair\utils\schemapi.py in _todict(val)
    235                 return [_todict(v) for v in val]
    236             elif isinstance(val, dict):
--> 237                 return {k: _todict(v) for k, v in val.items()
    238                         if v is not Undefined}
    239             else:

~\AppData\Local\Continuum\anaconda3\envs\data_analysis\lib\site-packages\altair\utils\schemapi.py in <dictcomp>(.0)
    236             elif isinstance(val, dict):
    237                 return {k: _todict(v) for k, v in val.items()
--> 238                         if v is not Undefined}
    239             else:
    240                 return val

~\AppData\Local\Continuum\anaconda3\envs\data_analysis\lib\site-packages\altair\utils\schemapi.py in _todict(val)
    233                 return val.to_dict(validate=sub_validate, context=context)
    234             elif isinstance(val, (list, tuple)):
--> 235                 return [_todict(v) for v in val]
    236             elif isinstance(val, dict):
    237                 return {k: _todict(v) for k, v in val.items()

~\AppData\Local\Continuum\anaconda3\envs\data_analysis\lib\site-packages\altair\utils\schemapi.py in <listcomp>(.0)
    233                 return val.to_dict(validate=sub_validate, context=context)
    234             elif isinstance(val, (list, tuple)):
--> 235                 return [_todict(v) for v in val]
    236             elif isinstance(val, dict):
    237                 return {k: _todict(v) for k, v in val.items()

~\AppData\Local\Continuum\anaconda3\envs\data_analysis\lib\site-packages\altair\utils\schemapi.py in _todict(val)
    231         def _todict(val):
    232             if isinstance(val, SchemaBase):
--> 233                 return val.to_dict(validate=sub_validate, context=context)
    234             elif isinstance(val, (list, tuple)):
    235                 return [_todict(v) for v in val]

~\AppData\Local\Continuum\anaconda3\envs\data_analysis\lib\site-packages\altair\vegalite\v2\api.py in to_dict(self, *args, **kwargs)
    396         copy = self.copy()
    397         original_data = getattr(copy, 'data', Undefined)
--> 398         copy.data = _prepare_data(original_data, context)
    399 
    400         if original_data is not Undefined:

~\AppData\Local\Continuum\anaconda3\envs\data_analysis\lib\site-packages\altair\vegalite\v2\api.py in _prepare_data(data, context)
     90     # consolidate inline data to top-level datasets
     91     if data_transformers.consolidate_datasets:
---> 92         data = _consolidate_data(data, context)
     93 
     94     # if data is still not a recognized type, then return

~\AppData\Local\Continuum\anaconda3\envs\data_analysis\lib\site-packages\altair\vegalite\v2\api.py in _consolidate_data(data, context)
     57 
     58     if values is not Undefined:
---> 59         name = _dataset_name(values)
     60         data = core.NamedData(name=name, **kwds)
     61         context.setdefault('datasets', {})[name] = values

~\AppData\Local\Continuum\anaconda3\envs\data_analysis\lib\site-packages\altair\vegalite\v2\api.py in _dataset_name(values)
     33     if isinstance(values, core.InlineDataset):
     34         values = values.to_dict()
---> 35     values_json = json.dumps(values, sort_keys=True)
     36     hsh = hashlib.md5(values_json.encode()).hexdigest()
     37     return 'data-' + hsh

~\AppData\Local\Continuum\anaconda3\envs\data_analysis\lib\json\__init__.py in dumps(obj, skipkeys, ensure_ascii, check_circular, allow_nan, cls, indent, separators, default, sort_keys, **kw)
    236         check_circular=check_circular, allow_nan=allow_nan, indent=indent,
    237         separators=separators, default=default, sort_keys=sort_keys,
--> 238         **kw).encode(obj)
    239 
    240 

~\AppData\Local\Continuum\anaconda3\envs\data_analysis\lib\json\encoder.py in encode(self, o)
    197         # exceptions aren't as detailed.  The list call should be roughly
    198         # equivalent to the PySequence_Fast that ''.join() would do.
--> 199         chunks = self.iterencode(o, _one_shot=True)
    200         if not isinstance(chunks, (list, tuple)):
    201             chunks = list(chunks)

~\AppData\Local\Continuum\anaconda3\envs\data_analysis\lib\json\encoder.py in iterencode(self, o, _one_shot)
    255                 self.key_separator, self.item_separator, self.sort_keys,
    256                 self.skipkeys, _one_shot)
--> 257         return _iterencode(o, 0)
    258 
    259 def _make_iterencode(markers, _default, _encoder, _indent, _floatstr,

~\AppData\Local\Continuum\anaconda3\envs\data_analysis\lib\json\encoder.py in default(self, o)
    178         """
    179         raise TypeError("Object of type '%s' is not JSON serializable" %
--> 180                         o.__class__.__name__)
    181 
    182     def encode(self, o):

TypeError: Object of type 'Polygon' is not JSON serializable

`max_rows=None` doesn't work

Hello when I try out my own example, I get this error:

TypeError: Object of type 'Polygon' is not JSON serializable

I first tested the example on your site;

import altair as alt
import geopandas as gpd
import gpdvega


world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))

# GeoDataFrame could be passed as usual pd.DataFrame
alt.Chart(world[world.continent!='Antarctica']).mark_geoshape(
).project(
).encode(
    color='pop_est', # shorthand infer types as for regular pd.DataFrame
    tooltip='id:Q' # GeoDataFrame.index is accessible as id
).properties(
    width=500,
    height=300
)

That worked great, and the plot showed up in jupyterlab.

I then tried my own example and got the above error.

alt.data_transformers.enable('default', max_rows=None)
ga = gpd.read_file('GA.json')
alt.Chart(ga).mark_geoshape(
).project(
).encode(
    color='STATE:O', # shorthand infer types as for regular pd.DataFrame
    tooltip='TRACT:Q' # GeoDataFrame.index is accessible as id
).properties(
    width=500,
    height=300
)

Since this did not work, and the error sayd type 'Polygon', I checked the dtypes on your example and on mine.

On yours:

world.dtypes

shows:

field	dtype
pop_est	float64
continent	object
name	object
iso_a3	object
gdp_md_est	float64
geometry	object

dtype: object

and on mine:

ga.dtypes

field	dtype
GEO_ID	object
STATE	object
COUNTY	object
TRACT	object
BLKGRP	object
NAME	object
LSAD	object
CENSUSAREA	float64
geometry	object

dtype: object

So my geometry is of type object just like in your example - any ideas on why/how the error is being produced and how to resolve it?

Note:
If I do:

%matplotlib inline 
ga.plot()

Which uses matplot lib - the plot shows up fine.

The file ga.json is just a state shapefile downloaded from here (2010 Georgia):
https://www.census.gov/geo/maps-data/data/cbf/cbf_blkgrp.html
and converted go geojson from here:
http://mapshaper.org/

Altair producing a deprication warning

fyi,
Altair is now producing a deprecation warning:

AltairDeprecationWarning: alt.pipe() is deprecated, and will be removed in a future release. Use toolz.curried.pipe() instead.

wrong 'Antarctica'

import altair as alt
import geopandas as gpd
import gpdvega 

alt.renderers.enable('notebook') # render for Jupyter Notebook

world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))

# GeoDataFrame could be passed as usual pd.DataFrame 
alt.Chart(world[world.continent=='Antarctica']).mark_geoshape(
    stroke='#1f77b4',  
    fill='#7f7f7f',
    fillOpacity=0.3
).project(
).encode( 
).properties( 
    width=500,
    height=300
)

but

world[world.continent=='Antarctica'].plot()

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