def _format_label(x, precision=3, dtype=None): fmt_str = "%%.%dg" % precision if is_datetime64_dtype(dtype): return to_datetime(x, unit="ns") if is_timedelta64_dtype(dtype): return to_timedelta(x, unit="ns") if np.isinf(x): return str(x) elif is_float(x): frac, whole = np.modf(x) sgn = "-" if x < 0 else "" whole = abs(whole) if frac != 0.0: val = fmt_str % frac # rounded up or down if "." not in val: if x < 0: return "%d" % (-whole - 1) else: return "%d" % (whole + 1) if "e" in val: return _trim_zeros(fmt_str % x) else: val = _trim_zeros(val) if "." in val: return sgn + ".".join(("%d" % whole, val.split(".")[1])) else: # pragma: no cover return sgn + ".".join(("%d" % whole, val)) else: return sgn + "%0.f" % whole else: return str(x)
def __new__(cls, data=None, ordinal=None, freq=None, start=None, end=None, periods=None, copy=False, name=None, tz=None, dtype=None, **kwargs): if periods is not None: if is_float(periods): periods = int(periods) elif not is_integer(periods): raise ValueError('Periods must be a number, got %s' % str(periods)) if name is None and hasattr(data, 'name'): name = data.name if dtype is not None: dtype = pandas_dtype(dtype) if not is_period_dtype(dtype): raise ValueError('dtype must be PeriodDtype') if freq is None: freq = dtype.freq elif freq != dtype.freq: msg = 'specified freq and dtype are different' raise IncompatibleFrequency(msg) if data is None: if ordinal is not None: data = np.asarray(ordinal, dtype=np.int64) else: data, freq = cls._generate_range(start, end, periods, freq, kwargs) else: ordinal, freq = cls._from_arraylike(data, freq, tz) data = np.array(ordinal, dtype=np.int64, copy=copy) return cls._simple_new(data, name=name, freq=freq)
def _maybe_cast_slice_bound(self, label, side, kind): """ If label is a string or a datetime, cast it to Period.ordinal according to resolution. Parameters ---------- label : object side : {'left', 'right'} kind : {'ix', 'loc', 'getitem'} Returns ------- bound : Period or object Notes ----- Value of `side` parameter should be validated in caller. """ assert kind in ['ix', 'loc', 'getitem'] if isinstance(label, datetime): return Period(label, freq=self.freq) elif isinstance(label, compat.string_types): try: _, parsed, reso = parse_time_string(label, self.freq) bounds = self._parsed_string_to_bounds(reso, parsed) return bounds[0 if side == 'left' else 1] except Exception: raise KeyError(label) elif is_integer(label) or is_float(label): self._invalid_indexer('slice', label) return label
def _simple_new(cls, data, sp_index, fill_value): if not isinstance(sp_index, SparseIndex): # caller must pass SparseIndex raise ValueError('sp_index must be a SparseIndex') if fill_value is None: if sp_index.ngaps > 0: # has missing hole fill_value = np.nan else: fill_value = na_value_for_dtype(data.dtype) if (is_integer_dtype(data) and is_float(fill_value) and sp_index.ngaps > 0): # if float fill_value is being included in dense repr, # convert values to float data = data.astype(float) result = data.view(cls) if not isinstance(sp_index, SparseIndex): # caller must pass SparseIndex raise ValueError('sp_index must be a SparseIndex') result.sp_index = sp_index result._fill_value = fill_value return result
def __new__(cls, data=None, ordinal=None, freq=None, start=None, end=None, periods=None, copy=False, name=None, tz=None, **kwargs): if periods is not None: if is_float(periods): periods = int(periods) elif not is_integer(periods): raise ValueError('Periods must be a number, got %s' % str(periods)) if name is None and hasattr(data, 'name'): name = data.name if data is None: if ordinal is not None: data = np.asarray(ordinal, dtype=np.int64) else: data, freq = cls._generate_range(start, end, periods, freq, kwargs) else: ordinal, freq = cls._from_arraylike(data, freq, tz) data = np.array(ordinal, dtype=np.int64, copy=copy) return cls._simple_new(data, name=name, freq=freq)
def _maybe_cast_slice_bound(self, label, side, kind): """ If label is a string, cast it to timedelta according to resolution. Parameters ---------- label : object side : {'left', 'right'} kind : {'ix', 'loc', 'getitem'} Returns ------- label : object """ assert kind in ['ix', 'loc', 'getitem', None] if isinstance(label, compat.string_types): parsed = _coerce_scalar_to_timedelta_type(label, box=True) lbound = parsed.round(parsed.resolution) if side == 'left': return lbound else: return (lbound + to_offset(parsed.resolution) - Timedelta(1, 'ns')) elif is_integer(label) or is_float(label): self._invalid_indexer('slice', label) return label
def _convert_scalar_indexer(self, key, kind=None): """ we don't allow integer or float indexing on datetime-like when using loc Parameters ---------- key : label of the slice bound kind : {'ix', 'loc', 'getitem', 'iloc'} or None """ assert kind in ['ix', 'loc', 'getitem', 'iloc', None] # we don't allow integer/float indexing for loc # we don't allow float indexing for ix/getitem if is_scalar(key): is_int = is_integer(key) is_flt = is_float(key) if kind in ['loc'] and (is_int or is_flt): self._invalid_indexer('index', key) elif kind in ['ix', 'getitem'] and is_flt: self._invalid_indexer('index', key) return (super(DatetimeIndexOpsMixin, self) ._convert_scalar_indexer(key, kind=kind))
def _format_label(x, precision=3, dtype=None): fmt_str = '%%.%dg' % precision if is_datetime64_dtype(dtype): return to_datetime(x, unit='ns') if is_timedelta64_dtype(dtype): return to_timedelta(x, unit='ns') if np.isinf(x): return str(x) elif is_float(x): frac, whole = np.modf(x) sgn = '-' if x < 0 else '' whole = abs(whole) if frac != 0.0: val = fmt_str % frac # rounded up or down if '.' not in val: if x < 0: return '%d' % (-whole - 1) else: return '%d' % (whole + 1) if 'e' in val: return _trim_zeros(fmt_str % x) else: val = _trim_zeros(val) if '.' in val: return sgn + '.'.join(('%d' % whole, val.split('.')[1])) else: # pragma: no cover return sgn + '.'.join(('%d' % whole, val)) else: return sgn + '%0.f' % whole else: return str(x)
def _convert_scalar_indexer(self, key, kind=None): """ we don't allow integer or float indexing on datetime-like when using loc Parameters ---------- key : label of the slice bound kind : {'ix', 'loc', 'getitem', 'iloc'} or None """ assert kind in ['ix', 'loc', 'getitem', 'iloc', None] # we don't allow integer/float indexing for loc # we don't allow float indexing for ix/getitem if is_scalar(key): is_int = is_integer(key) is_flt = is_float(key) if kind in ['loc'] and (is_int or is_flt): self._invalid_indexer('index', key) elif kind in ['ix', 'getitem'] and is_flt: self._invalid_indexer('index', key) return (super(DatetimeIndexOpsMixin, self)._convert_scalar_indexer(key, kind=kind))
def _format_label(x, precision=3): fmt_str = '%%.%dg' % precision if np.isinf(x): return str(x) elif is_float(x): frac, whole = np.modf(x) sgn = '-' if x < 0 else '' whole = abs(whole) if frac != 0.0: val = fmt_str % frac # rounded up or down if '.' not in val: if x < 0: return '%d' % (-whole - 1) else: return '%d' % (whole + 1) if 'e' in val: return _trim_zeros(fmt_str % x) else: val = _trim_zeros(val) if '.' in val: return sgn + '.'.join(('%d' % whole, val.split('.')[1])) else: # pragma: no cover return sgn + '.'.join(('%d' % whole, val)) else: return sgn + '%0.f' % whole else: return str(x)
def _get_string_slice(self, key, use_lhs=True, use_rhs=True): freq = getattr(self, 'freqstr', getattr(self, 'inferred_freq', None)) if is_integer(key) or is_float(key) or key is tslib.NaT: self._invalid_indexer('slice', key) loc = self._partial_td_slice(key, freq, use_lhs=use_lhs, use_rhs=use_rhs) return loc
def convert(value, unit, axis): valid_types = (str, pydt.time) if (isinstance(value, valid_types) or is_integer(value) or is_float(value)): return time2num(value) if isinstance(value, Index): return value.map(time2num) if isinstance(value, (list, tuple, np.ndarray, Index)): return [time2num(x) for x in value] return value
def get_datevalue(date, freq): if isinstance(date, Period): return date.asfreq(freq).ordinal elif isinstance(date, (compat.string_types, datetime, pydt.date, pydt.time)): return Period(date, freq).ordinal elif (is_integer(date) or is_float(date) or (isinstance(date, (np.ndarray, Index)) and (date.size == 1))): return date elif date is None: return None raise ValueError("Unrecognizable date '%s'" % date)
def _simple_new(cls, data, sp_index, fill_value): if is_integer_dtype(data) and is_float(fill_value) and sp_index.ngaps > 0: # if float fill_value is being included in dense repr, # convert values to float data = data.astype(float) result = data.view(cls) if not isinstance(sp_index, SparseIndex): # caller must pass SparseIndex raise ValueError("sp_index must be a SparseIndex") result.sp_index = sp_index result.fill_value = fill_value return result
def test_from_to_scipy(spmatrix, index, columns, fill_value, dtype): # GH 4343 tm.skip_if_no_package('scipy') # Make one ndarray and from it one sparse matrix, both to be used for # constructing frames and comparing results arr = np.eye(2, dtype=dtype) try: spm = spmatrix(arr) assert spm.dtype == arr.dtype except (TypeError, AssertionError): # If conversion to sparse fails for this spmatrix type and arr.dtype, # then the combination is not currently supported in NumPy, so we # can just skip testing it thoroughly return sdf = pd.SparseDataFrame(spm, index=index, columns=columns, default_fill_value=fill_value) # Expected result construction is kind of tricky for all # dtype-fill_value combinations; easiest to cast to something generic # and except later on rarr = arr.astype(object) rarr[arr == 0] = np.nan expected = pd.SparseDataFrame(rarr, index=index, columns=columns).fillna( fill_value if fill_value is not None else np.nan) # Assert frame is as expected sdf_obj = sdf.astype(object) tm.assert_sp_frame_equal(sdf_obj, expected) tm.assert_frame_equal(sdf_obj.to_dense(), expected.to_dense()) # Assert spmatrices equal tm.assert_equal(dict(sdf.to_coo().todok()), dict(spm.todok())) # Ensure dtype is preserved if possible was_upcast = ((fill_value is None or is_float(fill_value)) and not is_object_dtype(dtype) and not is_float_dtype(dtype)) res_dtype = (bool if is_bool_dtype(dtype) else float if was_upcast else dtype) tm.assert_contains_all(sdf.dtypes, {np.dtype(res_dtype)}) tm.assert_equal(sdf.to_coo().dtype, res_dtype) # However, adding a str column results in an upcast to object sdf['strings'] = np.arange(len(sdf)).astype(str) tm.assert_equal(sdf.to_coo().dtype, np.object_)
def convert(values, units, axis): if not hasattr(axis, 'freq'): raise TypeError('Axis must have `freq` set to convert to Periods') valid_types = (compat.string_types, datetime, Period, pydt.date, pydt.time) if (isinstance(values, valid_types) or is_integer(values) or is_float(values)): return get_datevalue(values, axis.freq) if isinstance(values, PeriodIndex): return values.asfreq(axis.freq)._values if isinstance(values, Index): return values.map(lambda x: get_datevalue(x, axis.freq)) if is_period_arraylike(values): return PeriodIndex(values, freq=axis.freq)._values if isinstance(values, (list, tuple, np.ndarray, Index)): return [get_datevalue(x, axis.freq) for x in values] return values
def _ensure_numeric(x): if isinstance(x, np.ndarray): if is_integer_dtype(x) or is_bool_dtype(x): x = x.astype(np.float64) elif is_object_dtype(x): try: x = x.astype(np.complex128) except: x = x.astype(np.float64) else: if not np.any(x.imag): x = x.real elif not (is_float(x) or is_integer(x) or is_complex(x)): try: x = float(x) except Exception: try: x = complex(x) except Exception: raise TypeError('Could not convert %s to numeric' % str(x)) return x
def convert(values, unit, axis): def try_parse(values): try: return _dt_to_float_ordinal(tools.to_datetime(values)) except Exception: return values if isinstance(values, (datetime, pydt.date)): return _dt_to_float_ordinal(values) elif isinstance(values, np.datetime64): return _dt_to_float_ordinal(lib.Timestamp(values)) elif isinstance(values, pydt.time): return dates.date2num(values) elif (is_integer(values) or is_float(values)): return values elif isinstance(values, compat.string_types): return try_parse(values) elif isinstance(values, (list, tuple, np.ndarray, Index)): if isinstance(values, Index): values = values.values if not isinstance(values, np.ndarray): values = com._asarray_tuplesafe(values) if is_integer_dtype(values) or is_float_dtype(values): return values try: values = tools.to_datetime(values) if isinstance(values, Index): values = _dt_to_float_ordinal(values) else: values = [_dt_to_float_ordinal(x) for x in values] except Exception: values = _dt_to_float_ordinal(values) return values
def convert(values, unit, axis): def try_parse(values): try: return _dt_to_float_ordinal(tools.to_datetime(values)) except Exception: return values if isinstance(values, (datetime, pydt.date)): return _dt_to_float_ordinal(values) elif isinstance(values, np.datetime64): return _dt_to_float_ordinal(lib.Timestamp(values)) elif isinstance(values, pydt.time): return dates.date2num(values) elif (is_integer(values) or is_float(values)): return values elif isinstance(values, compat.string_types): return try_parse(values) elif isinstance(values, (list, tuple, np.ndarray, Index)): if isinstance(values, Index): values = values.values if not isinstance(values, np.ndarray): values = com._asarray_tuplesafe(values) if is_integer_dtype(values) or is_float_dtype(values): return values try: values = tools.to_datetime(values) if isinstance(values, Index): values = values.map(_dt_to_float_ordinal) else: values = [_dt_to_float_ordinal(x) for x in values] except Exception: values = _dt_to_float_ordinal(values) return values
def default_display_func(x): if is_float(x): return '{:>.{precision}g}'.format(x, precision=self.precision) else: return x
def test_is_float(self): self.assertTrue(is_float(1.1)) self.assertTrue(is_float(np.float64(1.1))) self.assertTrue(is_float(np.nan)) self.assertFalse(is_float(True)) self.assertFalse(is_float(1)) self.assertFalse(is_float(1 + 3j)) self.assertFalse(is_float(np.bool(False))) self.assertFalse(is_float(np.bool_(False))) self.assertFalse(is_float(np.int64(1))) self.assertFalse(is_float(np.complex128(1 + 3j))) self.assertFalse(is_float(None)) self.assertFalse(is_float('x')) self.assertFalse(is_float(datetime(2011, 1, 1))) self.assertFalse(is_float(np.datetime64('2011-01-01'))) self.assertFalse(is_float(Timestamp('2011-01-01'))) self.assertFalse(is_float(Timestamp('2011-01-01', tz='US/Eastern'))) self.assertFalse(is_float(timedelta(1000))) self.assertFalse(is_float(np.timedelta64(1, 'D'))) self.assertFalse(is_float(Timedelta('1 days')))
def to_datetime(arg, errors='raise', dayfirst=False, yearfirst=False, utc=None, box=True, format=None, exact=True, unit=None, infer_datetime_format=False, origin='unix'): """ Convert argument to datetime. Parameters ---------- arg : integer, float, string, datetime, list, tuple, 1-d array, Series .. versionadded: 0.18.1 or DataFrame/dict-like errors : {'ignore', 'raise', 'coerce'}, default 'raise' - If 'raise', then invalid parsing will raise an exception - If 'coerce', then invalid parsing will be set as NaT - If 'ignore', then invalid parsing will return the input dayfirst : boolean, default False Specify a date parse order if `arg` is str or its list-likes. If True, parses dates with the day first, eg 10/11/12 is parsed as 2012-11-10. Warning: dayfirst=True is not strict, but will prefer to parse with day first (this is a known bug, based on dateutil behavior). yearfirst : boolean, default False Specify a date parse order if `arg` is str or its list-likes. - If True parses dates with the year first, eg 10/11/12 is parsed as 2010-11-12. - If both dayfirst and yearfirst are True, yearfirst is preceded (same as dateutil). Warning: yearfirst=True is not strict, but will prefer to parse with year first (this is a known bug, based on dateutil beahavior). .. versionadded: 0.16.1 utc : boolean, default None Return UTC DatetimeIndex if True (converting any tz-aware datetime.datetime objects as well). box : boolean, default True - If True returns a DatetimeIndex - If False returns ndarray of values. format : string, default None strftime to parse time, eg "%d/%m/%Y", note that "%f" will parse all the way up to nanoseconds. exact : boolean, True by default - If True, require an exact format match. - If False, allow the format to match anywhere in the target string. unit : string, default 'ns' unit of the arg (D,s,ms,us,ns) denote the unit, which is an integer or float number. This will be based off the origin. Example, with unit='ms' and origin='unix' (the default), this would calculate the number of milliseconds to the unix epoch start. infer_datetime_format : boolean, default False If True and no `format` is given, attempt to infer the format of the datetime strings, and if it can be inferred, switch to a faster method of parsing them. In some cases this can increase the parsing speed by ~5-10x. origin : scalar, default is 'unix' Define the reference date. The numeric values would be parsed as number of units (defined by `unit`) since this reference date. - If 'unix' (or POSIX) time; origin is set to 1970-01-01. - If 'julian', unit must be 'D', and origin is set to beginning of Julian Calendar. Julian day number 0 is assigned to the day starting at noon on January 1, 4713 BC. - If Timestamp convertible, origin is set to Timestamp identified by origin. .. versionadded: 0.20.0 Returns ------- ret : datetime if parsing succeeded. Return type depends on input: - list-like: DatetimeIndex - Series: Series of datetime64 dtype - scalar: Timestamp In case when it is not possible to return designated types (e.g. when any element of input is before Timestamp.min or after Timestamp.max) return will have datetime.datetime type (or correspoding array/Series). Examples -------- Assembling a datetime from multiple columns of a DataFrame. The keys can be common abbreviations like ['year', 'month', 'day', 'minute', 'second', 'ms', 'us', 'ns']) or plurals of the same >>> df = pd.DataFrame({'year': [2015, 2016], 'month': [2, 3], 'day': [4, 5]}) >>> pd.to_datetime(df) 0 2015-02-04 1 2016-03-05 dtype: datetime64[ns] If a date does not meet the `timestamp limitations <http://pandas.pydata.org/pandas-docs/stable/timeseries.html #timeseries-timestamp-limits>`_, passing errors='ignore' will return the original input instead of raising any exception. Passing errors='coerce' will force an out-of-bounds date to NaT, in addition to forcing non-dates (or non-parseable dates) to NaT. >>> pd.to_datetime('13000101', format='%Y%m%d', errors='ignore') datetime.datetime(1300, 1, 1, 0, 0) >>> pd.to_datetime('13000101', format='%Y%m%d', errors='coerce') NaT Passing infer_datetime_format=True can often-times speedup a parsing if its not an ISO8601 format exactly, but in a regular format. >>> s = pd.Series(['3/11/2000', '3/12/2000', '3/13/2000']*1000) >>> s.head() 0 3/11/2000 1 3/12/2000 2 3/13/2000 3 3/11/2000 4 3/12/2000 dtype: object >>> %timeit pd.to_datetime(s,infer_datetime_format=True) 100 loops, best of 3: 10.4 ms per loop >>> %timeit pd.to_datetime(s,infer_datetime_format=False) 1 loop, best of 3: 471 ms per loop Using a non-unix epoch origin >>> pd.to_datetime([1, 2, 3], unit='D', origin=pd.Timestamp('1960-01-01')) 0 1960-01-02 1 1960-01-03 2 1960-01-04 """ from pandas.tseries.index import DatetimeIndex tz = 'utc' if utc else None def _convert_listlike(arg, box, format, name=None, tz=tz): if isinstance(arg, (list, tuple)): arg = np.array(arg, dtype='O') # these are shortcutable if is_datetime64tz_dtype(arg): if not isinstance(arg, DatetimeIndex): return DatetimeIndex(arg, tz=tz, name=name) if utc: arg = arg.tz_convert(None).tz_localize('UTC') return arg elif is_datetime64_ns_dtype(arg): if box and not isinstance(arg, DatetimeIndex): try: return DatetimeIndex(arg, tz=tz, name=name) except ValueError: pass return arg elif unit is not None: if format is not None: raise ValueError("cannot specify both format and unit") arg = getattr(arg, 'values', arg) result = tslib.array_with_unit_to_datetime(arg, unit, errors=errors) if box: if errors == 'ignore': from pandas import Index return Index(result) return DatetimeIndex(result, tz=tz, name=name) return result elif getattr(arg, 'ndim', 1) > 1: raise TypeError('arg must be a string, datetime, list, tuple, ' '1-d array, or Series') arg = _ensure_object(arg) require_iso8601 = False if infer_datetime_format and format is None: format = _guess_datetime_format_for_array(arg, dayfirst=dayfirst) if format is not None: # There is a special fast-path for iso8601 formatted # datetime strings, so in those cases don't use the inferred # format because this path makes process slower in this # special case format_is_iso8601 = _format_is_iso(format) if format_is_iso8601: require_iso8601 = not infer_datetime_format format = None try: result = None if format is not None: # shortcut formatting here if format == '%Y%m%d': try: result = _attempt_YYYYMMDD(arg, errors=errors) except: raise ValueError("cannot convert the input to " "'%Y%m%d' date format") # fallback if result is None: try: result = tslib.array_strptime(arg, format, exact=exact, errors=errors) except tslib.OutOfBoundsDatetime: if errors == 'raise': raise result = arg except ValueError: # if format was inferred, try falling back # to array_to_datetime - terminate here # for specified formats if not infer_datetime_format: if errors == 'raise': raise result = arg if result is None and (format is None or infer_datetime_format): result = tslib.array_to_datetime( arg, errors=errors, utc=utc, dayfirst=dayfirst, yearfirst=yearfirst, require_iso8601=require_iso8601) if is_datetime64_dtype(result) and box: result = DatetimeIndex(result, tz=tz, name=name) return result except ValueError as e: try: values, tz = tslib.datetime_to_datetime64(arg) return DatetimeIndex._simple_new(values, name=name, tz=tz) except (ValueError, TypeError): raise e if arg is None: return None # handle origin if origin == 'julian': original = arg j0 = tslib.Timestamp(0).to_julian_date() if unit != 'D': raise ValueError("unit must be 'D' for origin='julian'") try: arg = arg - j0 except: raise ValueError("incompatible 'arg' type for given " "'origin'='julian'") # premptively check this for a nice range j_max = tslib.Timestamp.max.to_julian_date() - j0 j_min = tslib.Timestamp.min.to_julian_date() - j0 if np.any(arg > j_max) or np.any(arg < j_min): raise tslib.OutOfBoundsDatetime( "{original} is Out of Bounds for " "origin='julian'".format(original=original)) elif origin not in ['unix', 'julian']: # arg must be a numeric original = arg if not ((is_scalar(arg) and (is_integer(arg) or is_float(arg))) or is_numeric_dtype(np.asarray(arg))): raise ValueError( "'{arg}' is not compatible with origin='{origin}'; " "it must be numeric with a unit specified ".format( arg=arg, origin=origin)) # we are going to offset back to unix / epoch time try: offset = tslib.Timestamp(origin) - tslib.Timestamp(0) except tslib.OutOfBoundsDatetime: raise tslib.OutOfBoundsDatetime( "origin {} is Out of Bounds".format(origin)) except ValueError: raise ValueError("origin {} cannot be converted " "to a Timestamp".format(origin)) # convert the offset to the unit of the arg # this should be lossless in terms of precision offset = offset // tslib.Timedelta(1, unit=unit) # scalars & ndarray-like can handle the addition if is_list_like(arg) and not isinstance( arg, (ABCSeries, ABCIndexClass, np.ndarray)): arg = np.asarray(arg) arg = arg + offset if isinstance(arg, tslib.Timestamp): result = arg elif isinstance(arg, ABCSeries): from pandas import Series values = _convert_listlike(arg._values, False, format) result = Series(values, index=arg.index, name=arg.name) elif isinstance(arg, (ABCDataFrame, MutableMapping)): result = _assemble_from_unit_mappings(arg, errors=errors) elif isinstance(arg, ABCIndexClass): result = _convert_listlike(arg, box, format, name=arg.name) elif is_list_like(arg): result = _convert_listlike(arg, box, format) else: result = _convert_listlike(np.array([arg]), box, format)[0] return result
def __new__(cls, data=None, unit=None, freq=None, start=None, end=None, periods=None, copy=False, name=None, closed=None, verify_integrity=True, **kwargs): if isinstance(data, TimedeltaIndex) and freq is None and name is None: if copy: return data.copy() else: return data._shallow_copy() freq_infer = False if not isinstance(freq, DateOffset): # if a passed freq is None, don't infer automatically if freq != 'infer': freq = to_offset(freq) else: freq_infer = True freq = None if periods is not None: if is_float(periods): periods = int(periods) elif not is_integer(periods): raise ValueError('Periods must be a number, got %s' % str(periods)) if data is None and freq is None: raise ValueError("Must provide freq argument if no data is " "supplied") if data is None: return cls._generate(start, end, periods, name, freq, closed=closed) if unit is not None: data = to_timedelta(data, unit=unit, box=False) if not isinstance(data, (np.ndarray, Index, ABCSeries)): if is_scalar(data): raise ValueError('TimedeltaIndex() must be called with a ' 'collection of some kind, %s was passed' % repr(data)) # convert if not already if getattr(data, 'dtype', None) != _TD_DTYPE: data = to_timedelta(data, unit=unit, box=False) elif copy: data = np.array(data, copy=True) # check that we are matching freqs if verify_integrity and len(data) > 0: if freq is not None and not freq_infer: index = cls._simple_new(data, name=name) inferred = index.inferred_freq if inferred != freq.freqstr: on_freq = cls._generate( index[0], None, len(index), name, freq) if not np.array_equal(index.asi8, on_freq.asi8): raise ValueError('Inferred frequency {0} from passed ' 'timedeltas does not conform to ' 'passed frequency {1}' .format(inferred, freq.freqstr)) index.freq = freq return index if freq_infer: index = cls._simple_new(data, name=name) inferred = index.inferred_freq if inferred: index.freq = to_offset(inferred) return index return cls._simple_new(data, name=name, freq=freq)
def __new__(cls, data=None, ordinal=None, freq=None, start=None, end=None, periods=None, copy=False, name=None, tz=None, dtype=None, **kwargs): if periods is not None: if is_float(periods): periods = int(periods) elif not is_integer(periods): raise ValueError('Periods must be a number, got %s' % str(periods)) if name is None and hasattr(data, 'name'): name = data.name if dtype is not None: dtype = pandas_dtype(dtype) if not is_period_dtype(dtype): raise ValueError('dtype must be PeriodDtype') if freq is None: freq = dtype.freq elif freq != dtype.freq: msg = 'specified freq and dtype are different' raise IncompatibleFrequency(msg) # coerce freq to freq object, otherwise it can be coerced elementwise # which is slow if freq: freq = Period._maybe_convert_freq(freq) if data is None: if ordinal is not None: data = np.asarray(ordinal, dtype=np.int64) else: data, freq = cls._generate_range(start, end, periods, freq, kwargs) return cls._from_ordinals(data, name=name, freq=freq) if isinstance(data, PeriodIndex): if freq is None or freq == data.freq: # no freq change freq = data.freq data = data._values else: base1, _ = _gfc(data.freq) base2, _ = _gfc(freq) data = period.period_asfreq_arr(data._values, base1, base2, 1) return cls._simple_new(data, name=name, freq=freq) # not array / index if not isinstance( data, (np.ndarray, PeriodIndex, DatetimeIndex, Int64Index)): if is_scalar(data) or isinstance(data, Period): cls._scalar_data_error(data) # other iterable of some kind if not isinstance(data, (list, tuple)): data = list(data) data = np.asarray(data) # datetime other than period if is_datetime64_dtype(data.dtype): data = dt64arr_to_periodarr(data, freq, tz) return cls._from_ordinals(data, name=name, freq=freq) # check not floats if infer_dtype(data) == 'floating' and len(data) > 0: raise TypeError("PeriodIndex does not allow " "floating point in construction") # anything else, likely an array of strings or periods data = _ensure_object(data) freq = freq or period.extract_freq(data) data = period.extract_ordinals(data, freq) return cls._from_ordinals(data, name=name, freq=freq)
def to_datetime(arg, errors='raise', dayfirst=False, yearfirst=False, utc=None, box=True, format=None, exact=True, unit=None, infer_datetime_format=False, origin='unix'): """ Convert argument to datetime. Parameters ---------- arg : integer, float, string, datetime, list, tuple, 1-d array, Series .. versionadded: 0.18.1 or DataFrame/dict-like errors : {'ignore', 'raise', 'coerce'}, default 'raise' - If 'raise', then invalid parsing will raise an exception - If 'coerce', then invalid parsing will be set as NaT - If 'ignore', then invalid parsing will return the input dayfirst : boolean, default False Specify a date parse order if `arg` is str or its list-likes. If True, parses dates with the day first, eg 10/11/12 is parsed as 2012-11-10. Warning: dayfirst=True is not strict, but will prefer to parse with day first (this is a known bug, based on dateutil behavior). yearfirst : boolean, default False Specify a date parse order if `arg` is str or its list-likes. - If True parses dates with the year first, eg 10/11/12 is parsed as 2010-11-12. - If both dayfirst and yearfirst are True, yearfirst is preceded (same as dateutil). Warning: yearfirst=True is not strict, but will prefer to parse with year first (this is a known bug, based on dateutil beahavior). .. versionadded: 0.16.1 utc : boolean, default None Return UTC DatetimeIndex if True (converting any tz-aware datetime.datetime objects as well). box : boolean, default True - If True returns a DatetimeIndex - If False returns ndarray of values. format : string, default None strftime to parse time, eg "%d/%m/%Y", note that "%f" will parse all the way up to nanoseconds. exact : boolean, True by default - If True, require an exact format match. - If False, allow the format to match anywhere in the target string. unit : string, default 'ns' unit of the arg (D,s,ms,us,ns) denote the unit, which is an integer or float number. This will be based off the origin. Example, with unit='ms' and origin='unix' (the default), this would calculate the number of milliseconds to the unix epoch start. infer_datetime_format : boolean, default False If True and no `format` is given, attempt to infer the format of the datetime strings, and if it can be inferred, switch to a faster method of parsing them. In some cases this can increase the parsing speed by ~5-10x. origin : scalar, default is 'unix' Define the reference date. The numeric values would be parsed as number of units (defined by `unit`) since this reference date. - If 'unix' (or POSIX) time; origin is set to 1970-01-01. - If 'julian', unit must be 'D', and origin is set to beginning of Julian Calendar. Julian day number 0 is assigned to the day starting at noon on January 1, 4713 BC. - If Timestamp convertible, origin is set to Timestamp identified by origin. .. versionadded: 0.20.0 Returns ------- ret : datetime if parsing succeeded. Return type depends on input: - list-like: DatetimeIndex - Series: Series of datetime64 dtype - scalar: Timestamp In case when it is not possible to return designated types (e.g. when any element of input is before Timestamp.min or after Timestamp.max) return will have datetime.datetime type (or correspoding array/Series). Examples -------- Assembling a datetime from multiple columns of a DataFrame. The keys can be common abbreviations like ['year', 'month', 'day', 'minute', 'second', 'ms', 'us', 'ns']) or plurals of the same >>> df = pd.DataFrame({'year': [2015, 2016], 'month': [2, 3], 'day': [4, 5]}) >>> pd.to_datetime(df) 0 2015-02-04 1 2016-03-05 dtype: datetime64[ns] If a date does not meet the `timestamp limitations <http://pandas.pydata.org/pandas-docs/stable/timeseries.html #timeseries-timestamp-limits>`_, passing errors='ignore' will return the original input instead of raising any exception. Passing errors='coerce' will force an out-of-bounds date to NaT, in addition to forcing non-dates (or non-parseable dates) to NaT. >>> pd.to_datetime('13000101', format='%Y%m%d', errors='ignore') datetime.datetime(1300, 1, 1, 0, 0) >>> pd.to_datetime('13000101', format='%Y%m%d', errors='coerce') NaT Passing infer_datetime_format=True can often-times speedup a parsing if its not an ISO8601 format exactly, but in a regular format. >>> s = pd.Series(['3/11/2000', '3/12/2000', '3/13/2000']*1000) >>> s.head() 0 3/11/2000 1 3/12/2000 2 3/13/2000 3 3/11/2000 4 3/12/2000 dtype: object >>> %timeit pd.to_datetime(s,infer_datetime_format=True) 100 loops, best of 3: 10.4 ms per loop >>> %timeit pd.to_datetime(s,infer_datetime_format=False) 1 loop, best of 3: 471 ms per loop Using a non-unix epoch origin >>> pd.to_datetime([1, 2, 3], unit='D', origin=pd.Timestamp('1960-01-01')) 0 1960-01-02 1 1960-01-03 2 1960-01-04 """ from pandas.tseries.index import DatetimeIndex tz = 'utc' if utc else None def _convert_listlike(arg, box, format, name=None, tz=tz): if isinstance(arg, (list, tuple)): arg = np.array(arg, dtype='O') # these are shortcutable if is_datetime64tz_dtype(arg): if not isinstance(arg, DatetimeIndex): return DatetimeIndex(arg, tz=tz, name=name) if utc: arg = arg.tz_convert(None).tz_localize('UTC') return arg elif is_datetime64_ns_dtype(arg): if box and not isinstance(arg, DatetimeIndex): try: return DatetimeIndex(arg, tz=tz, name=name) except ValueError: pass return arg elif unit is not None: if format is not None: raise ValueError("cannot specify both format and unit") arg = getattr(arg, 'values', arg) result = tslib.array_with_unit_to_datetime(arg, unit, errors=errors) if box: if errors == 'ignore': from pandas import Index return Index(result) return DatetimeIndex(result, tz=tz, name=name) return result elif getattr(arg, 'ndim', 1) > 1: raise TypeError('arg must be a string, datetime, list, tuple, ' '1-d array, or Series') arg = _ensure_object(arg) require_iso8601 = False if infer_datetime_format and format is None: format = _guess_datetime_format_for_array(arg, dayfirst=dayfirst) if format is not None: # There is a special fast-path for iso8601 formatted # datetime strings, so in those cases don't use the inferred # format because this path makes process slower in this # special case format_is_iso8601 = _format_is_iso(format) if format_is_iso8601: require_iso8601 = not infer_datetime_format format = None try: result = None if format is not None: # shortcut formatting here if format == '%Y%m%d': try: result = _attempt_YYYYMMDD(arg, errors=errors) except: raise ValueError("cannot convert the input to " "'%Y%m%d' date format") # fallback if result is None: try: result = tslib.array_strptime(arg, format, exact=exact, errors=errors) except tslib.OutOfBoundsDatetime: if errors == 'raise': raise result = arg except ValueError: # if format was inferred, try falling back # to array_to_datetime - terminate here # for specified formats if not infer_datetime_format: if errors == 'raise': raise result = arg if result is None and (format is None or infer_datetime_format): result = tslib.array_to_datetime( arg, errors=errors, utc=utc, dayfirst=dayfirst, yearfirst=yearfirst, require_iso8601=require_iso8601 ) if is_datetime64_dtype(result) and box: result = DatetimeIndex(result, tz=tz, name=name) return result except ValueError as e: try: values, tz = tslib.datetime_to_datetime64(arg) return DatetimeIndex._simple_new(values, name=name, tz=tz) except (ValueError, TypeError): raise e if arg is None: return None # handle origin if origin == 'julian': original = arg j0 = tslib.Timestamp(0).to_julian_date() if unit != 'D': raise ValueError("unit must be 'D' for origin='julian'") try: arg = arg - j0 except: raise ValueError("incompatible 'arg' type for given " "'origin'='julian'") # premptively check this for a nice range j_max = tslib.Timestamp.max.to_julian_date() - j0 j_min = tslib.Timestamp.min.to_julian_date() - j0 if np.any(arg > j_max) or np.any(arg < j_min): raise tslib.OutOfBoundsDatetime( "{original} is Out of Bounds for " "origin='julian'".format(original=original)) elif origin not in ['unix', 'julian']: # arg must be a numeric original = arg if not ((is_scalar(arg) and (is_integer(arg) or is_float(arg))) or is_numeric_dtype(np.asarray(arg))): raise ValueError( "'{arg}' is not compatible with origin='{origin}'; " "it must be numeric with a unit specified ".format( arg=arg, origin=origin)) # we are going to offset back to unix / epoch time try: offset = tslib.Timestamp(origin) - tslib.Timestamp(0) except tslib.OutOfBoundsDatetime: raise tslib.OutOfBoundsDatetime( "origin {} is Out of Bounds".format(origin)) except ValueError: raise ValueError("origin {} cannot be converted " "to a Timestamp".format(origin)) # convert the offset to the unit of the arg # this should be lossless in terms of precision offset = offset // tslib.Timedelta(1, unit=unit) # scalars & ndarray-like can handle the addition if is_list_like(arg) and not isinstance( arg, (ABCSeries, ABCIndexClass, np.ndarray)): arg = np.asarray(arg) arg = arg + offset if isinstance(arg, tslib.Timestamp): result = arg elif isinstance(arg, ABCSeries): from pandas import Series values = _convert_listlike(arg._values, False, format) result = Series(values, index=arg.index, name=arg.name) elif isinstance(arg, (ABCDataFrame, MutableMapping)): result = _assemble_from_unit_mappings(arg, errors=errors) elif isinstance(arg, ABCIndexClass): result = _convert_listlike(arg, box, format, name=arg.name) elif is_list_like(arg): result = _convert_listlike(arg, box, format) else: result = _convert_listlike(np.array([arg]), box, format)[0] return result