Python pandas.RangeIndex() Examples
The following are code examples for showing how to use pandas.RangeIndex(). They are extracted from open source Python projects. You can vote up the examples you like or vote down the ones you don't like. You can also save this page to your account.
Example 1
Project: psst Author: power-system-simulation-toolbox File: descriptors.py (license) View Source Project | 6 votes |
def setattributeindex(self, instance, value): bus_name = instance.bus.index instance.branch['F_BUS'] = instance.branch['F_BUS'].apply(lambda x: value[bus_name.get_loc(x)]) instance.branch['T_BUS'] = instance.branch['T_BUS'].apply(lambda x: value[bus_name.get_loc(x)]) instance.gen['GEN_BUS'] = instance.gen['GEN_BUS'].apply(lambda x: value[bus_name.get_loc(x)]) try: instance.load.columns = [v for b, v in zip(instance.bus_name.isin(instance.load.columns), value) if b == True] except ValueError: instance.load.columns = value except AttributeError: instance.load = pd.DataFrame(0, index=range(0, 1), columns=value, dtype='float') instance.bus.index = value if isinstance(instance.bus_name, pd.RangeIndex) or isinstance(instance.bus_name, pd.Int64Index): logger.debug('Forcing string types for all bus names') instance.bus_name = ['Bus{}'.format(b) for b in instance.bus_name]
Example 2
Project: pecos Author: sandialabs File: test_monitoring.py (license) View Source Project | 5 votes |
def test_check_exact_times_true(self): self.pm.check_timestamp(3600, exact_times=True) expected = pd.DataFrame( array([['', '', Timestamp('2016-10-17 02:05:00'), Timestamp('2016-10-17 03:05:00'), 2, 'Missing timestamp']], dtype=object), columns=['System Name', 'Variable Name', 'Start Date', 'End Date', 'Timesteps', 'Error Flag'], index=RangeIndex(start=0, stop=1, step=1) ) assert_frame_equal(expected, self.pm.test_results)
Example 3
Project: pecos Author: sandialabs File: test_monitoring.py (license) View Source Project | 5 votes |
def test_check_exact_times_false(self): self.pm.check_timestamp(3600, exact_times=False) expected = pd.DataFrame( array([['', '', Timestamp('2016-10-17 02:00:00'), Timestamp('2016-10-17 02:00:00'), 1, 'Missing timestamp']], dtype=object), columns=['System Name', 'Variable Name', 'Start Date', 'End Date', 'Timesteps', 'Error Flag'], index=RangeIndex(start=0, stop=1, step=1) ) assert_frame_equal(expected, self.pm.test_results)
Example 4
Project: pecos Author: sandialabs File: test_monitoring.py (license) View Source Project | 5 votes |
def test_check_exact_times_true_with_start_time(self): self.pm.check_timestamp(3600, expected_start_time=Timestamp('2016-10-17 01:00:00'), exact_times=True) expected = pd.DataFrame( array([['', '', Timestamp('2016-10-17 01:00:00'), Timestamp('2016-10-17 03:00:00'), 3, 'Missing timestamp']], dtype=object), columns=['System Name', 'Variable Name', 'Start Date', 'End Date', 'Timesteps', 'Error Flag'], index=RangeIndex(start=0, stop=1, step=1) ) assert_frame_equal(expected, self.pm.test_results)
Example 5
Project: pecos Author: sandialabs File: test_monitoring.py (license) View Source Project | 5 votes |
def test_deadsensor(self): # dead sensor = < 1 in 5 hours self.pm.check_delta([1, None], window=5*3600+1, absolute_value=True) expected = pd.DataFrame( array([['Test', 'A', Timestamp('2017-01-01 00:00:00'), Timestamp('2017-01-01 05:00:00'), 6, '|Delta| < lower bound, 1'], ['Test', 'A', Timestamp('2017-01-01 16:00:00'), Timestamp('2017-01-01 23:00:00'), 8, '|Delta| < lower bound, 1']], dtype=object), columns=['System Name', 'Variable Name', 'Start Date', 'End Date', 'Timesteps', 'Error Flag'], index=RangeIndex(start=0, stop=2, step=1) ) assert_frame_equal(expected, self.pm.test_results)
Example 6
Project: pecos Author: sandialabs File: test_monitoring.py (license) View Source Project | 5 votes |
def test_abrupt_change(self): # abrupt change = > 7 in 3 hours self.pm.check_delta([None, 7], window=3*3600+1, absolute_value=True) expected = pd.DataFrame( array([['Test', 'A', Timestamp('2017-01-01 13:00:00'), Timestamp('2017-01-01 16:00:00'), 4, '|Delta| > upper bound, 7'], ['Test', 'B', Timestamp('2017-01-01 10:00:00'), Timestamp('2017-01-01 12:00:00'), 3, '|Delta| > upper bound, 7'], ['Test', 'B', Timestamp('2017-01-01 16:00:00'), Timestamp('2017-01-01 19:00:00'), 4, '|Delta| > upper bound, 7']], dtype=object), columns=['System Name', 'Variable Name', 'Start Date', 'End Date', 'Timesteps', 'Error Flag'], index=RangeIndex(start=0, stop=3, step=1) ) assert_frame_equal(expected, self.pm.test_results)
Example 7
Project: pecos Author: sandialabs File: test_monitoring.py (license) View Source Project | 5 votes |
def test_abrupt_negative_change(self): # abrupt negative change = < -7 in 3 hours self.pm.check_delta([-7, None], window=3*3600+1, absolute_value=False) expected = pd.DataFrame( array([['Test', 'B', Timestamp('2017-01-01 10:00:00'), Timestamp('2017-01-01 12:00:00'), 3, 'Delta < lower bound, -7']], dtype=object), columns=['System Name', 'Variable Name', 'Start Date', 'End Date', 'Timesteps', 'Error Flag'], index=RangeIndex(start=0, stop=1, step=1) ) assert_frame_equal(expected, self.pm.test_results)
Example 8
Project: pecos Author: sandialabs File: test_monitoring.py (license) View Source Project | 5 votes |
def test_outlier(self): # outlier if stdev > 1.9 self.pm.check_outlier([-1.9, 1.9], window=None, absolute_value=False) expected = pd.DataFrame( array([['Test', 'A', Timestamp('2017-01-01 19:00:00'), Timestamp('2017-01-01 19:00:00'), 1, 'Outlier < lower bound, -1.9'], ['Test', 'A', Timestamp('2017-01-01 06:00:00'), Timestamp('2017-01-01 06:00:00'), 1, 'Outlier > upper bound, 1.9']], dtype=object), columns=['System Name', 'Variable Name', 'Start Date', 'End Date', 'Timesteps', 'Error Flag'], index=RangeIndex(start=0, stop=2, step=1) ) assert_frame_equal(expected, self.pm.test_results)
Example 9
Project: fireant Author: kayak File: datatables.py (license) View Source Project | 5 votes |
def transform(self, dataframe, display_schema): csv_df = self._format_columns(dataframe, display_schema['metrics'], display_schema['dimensions']) if isinstance(dataframe.index, pd.RangeIndex): # If there are no dimensions, just serialize to csv without the index return csv_df.to_csv(index=False) csv_df = self._format_index(csv_df, display_schema['dimensions']) row_dimension_labels = self._format_row_dimension_labels(display_schema['dimensions']) return csv_df.to_csv(index_label=row_dimension_labels)
Example 10
Project: PyData-PandasFromTheInside Author: stevesimmons File: pfi.py (license) View Source Project | 5 votes |
def calc_ladder(scores_df, year=2016): ''' DataFrame with championship ladder with round-robin games for the given year. Wins, draws and losses are worth 4, 2 and 0 points respectively. ''' # Select a subset of the rows # df.loc[] matches dates as strings like '20160506' or '2016'. # Note here rounds are simple strings so sort with R1 < R10 < R2 < .. < R9 # (we could change this with a CategoricalIndex) # Note also that pandas 0.18.0 has a bug with .loc on MultiIndexes # if dates are the first level. It works as expected if we # move the dates to the end before slicing scores2 = scores_df.reorder_levels([1, 2, 3, 0]).sort_index() x = scores2.loc(axis=0)[:, 'R1':'R9', :, str(year):str(year)] # Don't need to put levels back in order as we are about to drop 3 of them # x = x.reorder_levels([3, 0, 1, 2]).sort_index() # Just keep Team. This does a copy too, avoiding SettingWithCopy warning y = x.reset_index(['Date', 'Venue', 'Round'], drop=True) # Add cols with 0/1 for number of games played, won, drawn and lost y['P'] = 1 y['W'] = (y['F'] > y['A']).astype(int) y['D'] = 0 y.loc[y['F'] == y['A'], 'D'] = 1 y.eval('L = 1*(A>F)', inplace=True) print(y) # Subtotal by team and then sort by Points/Percentage t = y.groupby(level='Team').sum() t['PCT'] = 100.0 * t.F / t.A t['PTS'] = 4 * t['W'] + 2 * t['D'] ladder = t.sort_values(['PTS', 'PCT'], ascending=False) # Add ladder position (note: assumes no ties!) ladder['Pos'] = pd.RangeIndex(1, len(ladder) + 1) print(ladder) return ladder
Example 11
Project: psst Author: power-system-simulation-toolbox File: descriptors.py (license) View Source Project | 5 votes |
def setattributeindex(self, instance, value): instance.gen.index = value instance.gencost.index = value if isinstance(instance.gen_name, pd.RangeIndex) or isinstance(instance.bus_name, pd.Int64Index): instance.gen_name = ['GenCo{}'.format(g) for g in instance.gen_name]
Example 12
Project: dask_gdf Author: gpuopenanalytics File: utils.py (Apache License 2.0) View Source Project | 4 votes |
def make_meta(x): """Create an empty pygdf object containing the desired metadata. Parameters ---------- x : dict, tuple, list, pd.Series, pd.DataFrame, pd.Index, dtype, scalar To create a DataFrame, provide a `dict` mapping of `{name: dtype}`, or an iterable of `(name, dtype)` tuples. To create a `Series`, provide a tuple of `(name, dtype)`. If a pygdf object, names, dtypes, and index should match the desired output. If a dtype or scalar, a scalar of the same dtype is returned. Examples -------- >>> make_meta([('a', 'i8'), ('b', 'O')]) Empty DataFrame Columns: [a, b] Index: [] >>> make_meta(('a', 'f8')) Series([], Name: a, dtype: float64) >>> make_meta('i8') 1 """ if hasattr(x, '_meta'): return x._meta if isinstance(x, (gd.Series, gd.DataFrame, gd.index.Index)): out = x[:2] return out.copy() if hasattr(out, 'copy') else out meta = dd.utils.make_meta(x) if isinstance(meta, (pd.DataFrame, pd.Series, pd.Index)): meta2 = dd.utils.meta_nonempty(meta) if isinstance(meta2, pd.DataFrame): return gd.DataFrame.from_pandas(meta2) elif isinstance(meta2, pd.Series): return gd.Series.from_any(meta2) else: if isinstance(meta2, pd.RangeIndex): return gd.index.RangeIndex(meta2.start, meta2.stop) return gd.index.GenericIndex(meta2) return meta