demo + utils venv

2019-02-03 13:40:10 +01:00
parent 5fa112490b
commit cfa9c8ea23
5994 changed files with 1353819 additions and 0 deletions
@@ -0,0 +1,141 @@
+import numpy as np
+
+from pandas.util._decorators import cache_readonly
+
+import pandas as pd
+from pandas import compat
+import pandas.util.testing as tm
+
+_seriesd = tm.getSeriesData()
+_tsd = tm.getTimeSeriesData()
+
+_frame = pd.DataFrame(_seriesd)
+_frame2 = pd.DataFrame(_seriesd, columns=['D', 'C', 'B', 'A'])
+_intframe = pd.DataFrame({k: v.astype(int)
+                          for k, v in compat.iteritems(_seriesd)})
+
+_tsframe = pd.DataFrame(_tsd)
+
+_mixed_frame = _frame.copy()
+_mixed_frame['foo'] = 'bar'
+
+
+class TestData(object):
+
+    @cache_readonly
+    def frame(self):
+        return _frame.copy()
+
+    @cache_readonly
+    def frame2(self):
+        return _frame2.copy()
+
+    @cache_readonly
+    def intframe(self):
+        # force these all to int64 to avoid platform testing issues
+        return pd.DataFrame({c: s for c, s in compat.iteritems(_intframe)},
+                            dtype=np.int64)
+
+    @cache_readonly
+    def tsframe(self):
+        return _tsframe.copy()
+
+    @cache_readonly
+    def mixed_frame(self):
+        return _mixed_frame.copy()
+
+    @cache_readonly
+    def mixed_float(self):
+        return pd.DataFrame({'A': _frame['A'].copy().astype('float32'),
+                             'B': _frame['B'].copy().astype('float32'),
+                             'C': _frame['C'].copy().astype('float16'),
+                             'D': _frame['D'].copy().astype('float64')})
+
+    @cache_readonly
+    def mixed_float2(self):
+        return pd.DataFrame({'A': _frame2['A'].copy().astype('float32'),
+                             'B': _frame2['B'].copy().astype('float32'),
+                             'C': _frame2['C'].copy().astype('float16'),
+                             'D': _frame2['D'].copy().astype('float64')})
+
+    @cache_readonly
+    def mixed_int(self):
+        return pd.DataFrame({'A': _intframe['A'].copy().astype('int32'),
+                             'B': np.ones(len(_intframe['B']), dtype='uint64'),
+                             'C': _intframe['C'].copy().astype('uint8'),
+                             'D': _intframe['D'].copy().astype('int64')})
+
+    @cache_readonly
+    def all_mixed(self):
+        return pd.DataFrame({'a': 1., 'b': 2, 'c': 'foo',
+                             'float32': np.array([1.] * 10, dtype='float32'),
+                             'int32': np.array([1] * 10, dtype='int32')},
+                            index=np.arange(10))
+
+    @cache_readonly
+    def tzframe(self):
+        result = pd.DataFrame({'A': pd.date_range('20130101', periods=3),
+                               'B': pd.date_range('20130101', periods=3,
+                                                  tz='US/Eastern'),
+                               'C': pd.date_range('20130101', periods=3,
+                                                  tz='CET')})
+        result.iloc[1, 1] = pd.NaT
+        result.iloc[1, 2] = pd.NaT
+        return result
+
+    @cache_readonly
+    def empty(self):
+        return pd.DataFrame({})
+
+    @cache_readonly
+    def ts1(self):
+        return tm.makeTimeSeries(nper=30)
+
+    @cache_readonly
+    def ts2(self):
+        return tm.makeTimeSeries(nper=30)[5:]
+
+    @cache_readonly
+    def simple(self):
+        arr = np.array([[1., 2., 3.],
+                        [4., 5., 6.],
+                        [7., 8., 9.]])
+
+        return pd.DataFrame(arr, columns=['one', 'two', 'three'],
+                            index=['a', 'b', 'c'])
+
+# self.ts3 = tm.makeTimeSeries()[-5:]
+# self.ts4 = tm.makeTimeSeries()[1:-1]
+
+
+def _check_mixed_float(df, dtype=None):
+    # float16 are most likely to be upcasted to float32
+    dtypes = dict(A='float32', B='float32', C='float16', D='float64')
+    if isinstance(dtype, compat.string_types):
+        dtypes = {k: dtype for k, v in dtypes.items()}
+    elif isinstance(dtype, dict):
+        dtypes.update(dtype)
+    if dtypes.get('A'):
+        assert(df.dtypes['A'] == dtypes['A'])
+    if dtypes.get('B'):
+        assert(df.dtypes['B'] == dtypes['B'])
+    if dtypes.get('C'):
+        assert(df.dtypes['C'] == dtypes['C'])
+    if dtypes.get('D'):
+        assert(df.dtypes['D'] == dtypes['D'])
+
+
+def _check_mixed_int(df, dtype=None):
+    dtypes = dict(A='int32', B='uint64', C='uint8', D='int64')
+    if isinstance(dtype, compat.string_types):
+        dtypes = {k: dtype for k, v in dtypes.items()}
+    elif isinstance(dtype, dict):
+        dtypes.update(dtype)
+    if dtypes.get('A'):
+        assert(df.dtypes['A'] == dtypes['A'])
+    if dtypes.get('B'):
+        assert(df.dtypes['B'] == dtypes['B'])
+    if dtypes.get('C'):
+        assert(df.dtypes['C'] == dtypes['C'])
+    if dtypes.get('D'):
+        assert(df.dtypes['D'] == dtypes['D'])