def pandas_hook(dct):
if not isinstance(dct, dict):
return dct
if '__pandas_dataframe__' not in dct and '__pandas_series__' not in dct:
return dct
if '__pandas_dataframe__' in dct:
try:
from pandas import DataFrame
except ImportError:
raise NoPandasException('Trying to decode a map which appears to repr esent a pandas data structure, but pandas appears not to be installed.')
from numpy import dtype, array
meta = dct.pop('__pandas_dataframe__')
indx = dct.pop('index') if 'index' in dct else None
dtypes = dict((colname, dtype(tp)) for colname, tp in zip(meta['column_order'], meta['types']))
data = OrderedDict()
for name, col in dct.items():
data[name] = array(col, dtype=dtypes[name])
return DataFrame(
data=data,
index=indx,
columns=meta['column_order'],
# mixed `dtypes` argument not supported, so use duct of numpy arrays
)
elif '__pandas_series__' in dct:
from pandas import Series
from numpy import dtype, array
meta = dct.pop('__pandas_series__')
indx = dct.pop('index') if 'index' in dct else None
return Series(
data=dct['data'],
index=indx,
name=meta['name'],
dtype=dtype(meta['type']),
)
return dct # impossible
def nopandas_hook(dct):
if isinstance(dct, dict) and ('__pandas_dataframe__' in dct
or '__pandas_series__' in dct):
raise NoPandasException((
'Trying to decode a map which appears to represent a pandas '
'data structure, but pandas support is not enabled, perhaps it is not installed.'
))
return dct
def pandas_hook(dct):
if '__pandas_dataframe__' in dct or '__pandas_series__' in dct:
# todo: this is experimental
if not getattr(pandas_hook, '_warned', False):
pandas_hook._warned = True
warning(
'Pandas loading support in json-tricks is experimental and may change in future versions.'
)
if '__pandas_dataframe__' in dct:
try:
from pandas import DataFrame
except ImportError:
raise NoPandasException(
'Trying to decode a map which appears to represent a pandas data structure, but pandas appears not to be installed.'
)
from numpy import dtype, array
meta = dct.pop('__pandas_dataframe__')
indx = dct.pop('index') if 'index' in dct else None
dtypes = dict(
(colname, dtype(tp))
for colname, tp in zip(meta['column_order'], meta['types']))
data = OrderedDict()
for name, col in dct.items():
data[name] = array(col, dtype=dtypes[name])
return DataFrame(
data=data,
index=indx,
columns=meta['column_order'],
# mixed `dtypes` argument not supported, so use duct of numpy arrays
)
elif '__pandas_series__' in dct:
from pandas import Series
from numpy import dtype, array
meta = dct.pop('__pandas_series__')
indx = dct.pop('index') if 'index' in dct else None
return Series(
data=dct['data'],
index=indx,
name=meta['name'],
dtype=dtype(meta['type']),
)
return dct
