def _value_with_fmt(self, val):
"""Convert numpy types to Python types for the Excel writers.
Parameters
----------
val : object
Value to be written into cells
Returns
-------
Tuple with the first element being the converted value and the second
being an optional format
"""
fmt = None
if is_integer(val):
val = int(val)
elif is_float(val):
val = float(val)
elif is_bool(val):
val = bool(val)
elif isinstance(val, datetime):
fmt = self.datetime_format
elif isinstance(val, date):
fmt = self.date_format
elif isinstance(val, timedelta):
val = val.total_seconds() / float(86400)
fmt = '0'
else:
val = compat.to_str(val)
return val, fmt
def _value_with_fmt(self, val):
"""Convert numpy types to Python types for the Excel writers.
Parameters
----------
val : object
Value to be written into cells
Returns
-------
Tuple with the first element being the converted value and the second
being an optional format
"""
fmt = None
if is_integer(val):
val = int(val)
elif is_float(val):
val = float(val)
elif is_bool(val):
val = bool(val)
elif isinstance(val, datetime):
fmt = self.datetime_format
elif isinstance(val, date):
fmt = self.date_format
elif isinstance(val, timedelta):
val = val.total_seconds() / float(86400)
fmt = '0'
else:
val = compat.to_str(val)
return val, fmt
def read(self):
"""Read the whole JSON input into a pandas object"""
if self.lines and self.chunksize:
obj = concat(self)
elif self.lines:
data = to_str(self.data)
obj = self._get_object_parser(self._combine_lines(
data.split('\n')))
else:
obj = self._get_object_parser(self.data)
self.close()
return obj
def read(self):
"""Read the whole JSON input into a pandas object"""
if self.lines and self.chunksize:
obj = concat(self)
elif self.lines:
data = to_str(self.data)
obj = self._get_object_parser(
self._combine_lines(data.split('\n'))
)
else:
obj = self._get_object_parser(self.data)
self.close()
return obj
def construct_1d_arraylike_from_scalar(value, length, dtype):
"""
create a np.ndarray / pandas type of specified shape and dtype
filled with values
Parameters
----------
value : scalar value
length : int
dtype : pandas_dtype / np.dtype
Returns
-------
np.ndarray / pandas type of length, filled with value
"""
if is_datetime64tz_dtype(dtype):
from pandas import DatetimeIndex
subarr = DatetimeIndex([value] * length, dtype=dtype)
elif is_categorical_dtype(dtype):
from pandas import Categorical
subarr = Categorical([value] * length, dtype=dtype)
else:
if not isinstance(dtype, (np.dtype, type(np.dtype))):
dtype = dtype.dtype
if length and is_integer_dtype(dtype) and isna(value):
# coerce if we have nan for an integer dtype
dtype = np.dtype('float64')
elif isinstance(dtype, np.dtype) and dtype.kind in ("U", "S"):
# we need to coerce to object dtype to avoid
# to allow numpy to take our string as a scalar value
dtype = object
if not isna(value):
value = to_str(value)
subarr = np.empty(length, dtype=dtype)
subarr.fill(value)
return subarr
