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