def frame_eq(ans, sol, digits=0):
if digits == 0:
pd_test.assert_frame_equal(ans, sol)
else:
nose_eq(ans.index.name, sol.index.name)
nose_eq(ans.columns.name, sol.columns.name)
np_test.assert_array_almost_equal(ans.values, sol.values, digits)
def frame_eq(ans, sol, digits=0):
if digits == 0:
pd_test.assert_frame_equal(ans, sol)
else:
nose_eq(ans.index.name, sol.index.name)
nose_eq(ans.columns.name, sol.columns.name)
np_test.assert_array_almost_equal(ans.values, sol.values, digits)
def eq_(ans, sol, digits=0):
if type(ans) == np.ndarray and type(sol) == np.ndarray:
array_eq(ans, sol, digits)
elif type(ans) == pd.Series and type(sol) == pd.Series:
series_eq(ans, sol)
elif type(ans) == pd.TimeSeries and type(sol) == pd.TimeSeries:
series_eq(ans, sol, digits)
elif type(ans) == pd.DataFrame and type(sol) == pd.DataFrame:
frame_eq(ans, sol, digits)
elif isinstance(ans, pd.Index) and isinstance(sol, pd.Index):
array_eq(ans.values, sol.values, digits)
elif digits != 0:
nose_eq(round(ans-sol, digits), 0)
else:
nose_eq(ans, sol, digits)
def eq_(ans, sol, digits=0):
if type(ans) == np.ndarray and type(sol) == np.ndarray:
array_eq(ans, sol, digits)
elif type(ans) == pd.Series and type(sol) == pd.Series:
series_eq(ans, sol)
elif type(ans) == pd.TimeSeries and type(sol) == pd.TimeSeries:
series_eq(ans, sol, digits)
elif type(ans) == pd.DataFrame and type(sol) == pd.DataFrame:
frame_eq(ans, sol, digits)
elif isinstance(ans, pd.Index) and isinstance(sol, pd.Index):
array_eq(ans.values, sol.values, digits)
elif digits != 0:
nose_eq(round(ans - sol, digits), 0)
else:
nose_eq(ans, sol, digits)
def series_eq(ans, sol, digits=0):
if digits == 0:
pd_test.assert_series_equal(ans, sol, digits)
else:
nose_eq(ans.name, sol.name)
np_test.assert_array_almost_equal(ans.values, sol.values, digits)
def series_eq(ans, sol, digits=0):
if digits == 0:
pd_test.assert_series_equal(ans, sol, digits)
else:
nose_eq(ans.name, sol.name)
np_test.assert_array_almost_equal(ans.values, sol.values, digits)
