Removed TestData in pandas/tests/frame/common.py

Author: SaturnFromTitan

pandas/tests/frame/common.py

@@ -1,127 +1,3 @@
-import numpy as np
-
-from pandas.util._decorators import cache_readonly
-
-import pandas as pd
-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 _seriesd.items()})
-
-_tsframe = pd.DataFrame(_tsd)
-
-_mixed_frame = _frame.copy()
-_mixed_frame["foo"] = "bar"
-
-
-class TestData:
-    @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 _intframe.items()}, 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": 1.0,
-                "b": 2,
-                "c": "foo",
-                "float32": np.array([1.0] * 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([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0]])
-
-        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")