我们从Python开源项目中，提取了以下6个代码示例，用于说明如何使用pandas.infer_freq()。
def get_approximate_frequency(trace): if trace.data is None: logger.warn( "Could not determine frequency:" " {} is placeholder instance." .format(trace) ) return None def _log_success(freq): logger.debug( "Determined frequency of '{}' for {}." .format(freq, trace) ) try: freq = pd.infer_freq(trace.data.index) except ValueError: # too few data points logger.error("Could not determine frequency - too few points.") return None else: if freq is not None: _log_success(freq) return freq # freq is None - maybe because of a DST change (23/25 hours)? # strategy: try two groups of 5 dates for i in range(0, 9, 5): try: freq = pd.infer_freq(trace.data.index[i:i + 5]) except ValueError: pass else: if freq is not None: _log_success(freq) return freq logger.warning("Could not determine frequency - no dominant frequency.") return None
def indexed_temperatures(self, index, unit, allow_mixed_frequency=False): ''' Return average temperatures over the given index. Parameters ---------- index : pandas.DatetimeIndex Index over which to supply average temperatures. The :code:`index` should be given as either an hourly ('H') or daily ('D') frequency. unit : str, {"degF", "degC"} Target temperature unit for returned temperature series. Returns ------- temperatures : pandas.Series with DatetimeIndex Average temperatures over series indexed by :code:`index`. ''' if index.shape == (0,): return pd.Series([], index=index, dtype=float) self._verify_index_presence(index) # fetches weather data if needed if index.freq is not None: freq = index.freq else: try: freq = pd.infer_freq(index) except ValueError: freq = None if freq == 'D': return self._daily_indexed_temperatures(index, unit) elif freq == 'H': return self._hourly_indexed_temperatures(index, unit) elif allow_mixed_frequency: return self._mixed_frequency_indexed_temperatures(index, unit) else: message = 'DatetimeIndex with unknown frequency not supported.' raise ValueError(message)
def test_constructor_from_series(self): expected = DatetimeIndex([Timestamp('20110101'), Timestamp('20120101'), Timestamp('20130101')]) s = Series([Timestamp('20110101'), Timestamp('20120101'), Timestamp( '20130101')]) result = Index(s) self.assertTrue(result.equals(expected)) result = DatetimeIndex(s) self.assertTrue(result.equals(expected)) # GH 6273 # create from a series, passing a freq s = Series(pd.to_datetime(['1-1-1990', '2-1-1990', '3-1-1990', '4-1-1990', '5-1-1990'])) result = DatetimeIndex(s, freq='MS') expected = DatetimeIndex( ['1-1-1990', '2-1-1990', '3-1-1990', '4-1-1990', '5-1-1990' ], freq='MS') self.assertTrue(result.equals(expected)) df = pd.DataFrame(np.random.rand(5, 3)) df['date'] = ['1-1-1990', '2-1-1990', '3-1-1990', '4-1-1990', '5-1-1990'] result = DatetimeIndex(df['date'], freq='MS') self.assertTrue(result.equals(expected)) self.assertEqual(df['date'].dtype, object) exp = pd.Series( ['1-1-1990', '2-1-1990', '3-1-1990', '4-1-1990', '5-1-1990' ], name='date') self.assert_series_equal(df['date'], exp) # GH 6274 # infer freq of same result = pd.infer_freq(df['date']) self.assertEqual(result, 'MS')
def data_freq(time_series): """ Determine frequency of given time series Args: time_series (Series): Series with datetime index Returns: string: frequency specifier """ try: freq = time_series.index.freq return freq.freqstr or pd.infer_freq(time_series.index) except AttributeError: return pd.infer_freq(time_series.index)
def shift_dates(self,h): """ Auxiliary function for creating dates for forecasts Parameters ---------- h : int How many steps to forecast Returns ---------- A transformed date_index object """ date_index = copy.deepcopy(self.index) date_index = date_index[self.max_lag:len(date_index)] if self.is_pandas is True: if isinstance(date_index, pd.core.indexes.datetimes.DatetimeIndex): if pd.infer_freq(date_index) in ['H', 'M', 'S']: for t in range(h): date_index += pd.DateOffset((date_index[len(date_index)-1] - date_index[len(date_index)-2]).seconds) else: # Assume higher frequency (configured for days) for t in range(h): date_index += pd.DateOffset((date_index[len(date_index)-1] - date_index[len(date_index)-2]).days) elif isinstance(date_index, pd.core.indexes.numeric.Int64Index): for i in range(h): new_value = date_index.values[len(date_index.values)-1] + (date_index.values[len(date_index.values)-1] - date_index.values[len(date_index.values)-2]) date_index = pd.Int64Index(np.append(date_index.values,new_value)) else: for t in range(h): date_index.append(date_index[len(date_index)-1]+1) return date_index
def read_knmi(fname, variables='RD'): """This method can be used to import KNMI data. Parameters ---------- fname: str Filename and path to a Dino file. Returns ------- ts: Pandas Series returns a standard Pastas TimeSeries object or a list of it. """ knmi = KnmiStation.fromfile(fname) if variables is None: variables = knmi.variables.keys() if type(variables) == str: variables = [variables] stn_codes = knmi.data['STN'].unique() ts = [] for code in stn_codes: for variable in variables: if variable not in knmi.data.keys(): raise (ValueError( "variable %s is not in this dataset. Please use one of " "the following keys: %s" % (variable, knmi.data.keys()))) series = knmi.data.loc[knmi.data['STN'] == code, variable] # get rid of the hours when data is daily if pd.infer_freq(series.index) == 'D': series.index = series.index.normalize() metadata = {} if knmi.stations is not None and not knmi.stations.empty: station = knmi.stations.loc[str(code), :] metadata['x'] = station.LON_east metadata['y'] = station.LAT_north metadata['z'] = station.ALT_m metadata['projection'] = 'epsg:4326' stationname = station.NAME else: stationname = str(code) metadata['description'] = knmi.variables[variable] if variable == 'RD' or variable == 'RH': kind = 'prec' elif variable == 'EV24': kind = 'evap' else: kind = None ts.append(TimeSeries(series, name=variable + stationname, metadata=metadata, kind=kind)) if len(ts) == 1: ts = ts[0] return ts
