def test_inplace_dropping_single_row_in_column_distributed_dataframe(self):
df = ParallelDataFrame(self.dict2, dist_data=False)
self.assertEqual(df.globalShape, (3, 4))
df.drop(2, axis=0, inplace=True)
self.assertEqual(set(list(df.globalColumns)),
set(['key1', 'key2', 'key3', 'key4']))
self.assertEqual(list(df.globalIndex), [0, 1])
def test_div_constant_distributed_df(self):
df1 = ParallelDataFrame(self.pd_df1, dist_data=False)
pd_df2 = self.pd_df1.div(10)
df2 = ParallelDataFrame(pd_df2, dist_data=False)
result = df1.div(10)
self.assertTrue(result.equals(df2))
def test_inplace_dropping_multiple_columns_in_column_distributed_dataframe(
self):
df = ParallelDataFrame(self.dict3, dist_data=False)
self.assertEqual(df.globalShape, (3, 8))
df.drop(['key4', 'key8'], axis=1, inplace=True)
self.assertEqual(set(list(df.globalColumns)),
set(['key1', 'key2', 'key3', 'key5', 'key6', 'key7']))
self.assertEqual(list(df.globalIndex), [0, 1, 2])
def test_div_by_multiIndex_by_level_replicated_df(self):
df = ParallelDataFrame(self.pd_df2, dist='replicated')
rep_multindex = ParallelDataFrame(self.df_multindex, dist='replicated')
result = df.div(rep_multindex, level=1, fill_value=0)
pd_result = self.pd_df2.div(self.df_multindex, level=1, fill_value=0)
self.assertTrue(result.equals(pd_result))
def test_replicated_df_apply_function_sqrt_returns_replicated_df(self):
df1 = ParallelDataFrame(self.pd_df1, dist='replicated')
pd_result = self.pd_df1.apply(np.sqrt)
result = df1.apply(np.sqrt)
self.assertTrue(result.equals(pd_result))
self.assertEqual(result.dist, 'replicated')
def test_non_inplace_dropping_multiple_columns_and_row_in_same_call_replicated_dataframe(
self):
df = ParallelDataFrame(self.dict3, dist='replicated')
new_df = df.drop(columns=['key4', 'key7'], index=1, inplace=False)
self.assertEqual(
set(list(new_df.globalColumns)),
set(['key1', 'key2', 'key3', 'key5', 'key6', 'key8']))
self.assertEqual(list(new_df.globalIndex), [0, 2])
def test_non_inplace_dropping_single_column_in_column_distributed_dataframe(
self):
df = ParallelDataFrame(self.dict2, dist_data=False)
self.assertEqual(df.globalShape, (3, 4))
new_df = df.drop('key4', axis=1, inplace=False)
self.assertEqual(set(list(new_df.globalColumns)),
set(['key1', 'key2', 'key3']))
self.assertEqual(list(new_df.globalIndex), [0, 1, 2])
def test_corr_with_replicated_dataframe(self):
pd_df = pd.DataFrame([(.2, .3, .4), (.0, .6, .9), (.6, .0, .6),
(.2, .1, .1)],
columns=['dogs', 'cats', 'rats'])
rep_df = ParallelDataFrame(pd_df, dist='replicated')
rep_corr = rep_df.corr()
pd_corr = pd_df.corr()
self.assertTrue(rep_corr.equals(pd_corr))
def test_replicated_df_apply_function_list_like_result_returns_replicated_series(
self):
df = ParallelDataFrame(self.pd_df1, dist='replicated')
pd_result = self.pd_df1.apply(lambda x: [1, 2], axis=1)
result = df.apply(lambda x: [1, 2], axis=1)
self.assertTrue(isinstance(result, ParallelSeries))
self.assertEqual(result.dist, 'replicated')
self.assertTrue(result.equals(pd_result))
def test_replicated_df_apply_function_sum_axis1_returns_replicated_series(
self):
df1 = ParallelDataFrame(self.pd_df1, dist='replicated')
pd_result = self.pd_df1.apply(np.sum, axis=1)
result = df1.apply(np.sum, axis=1)
self.assertTrue(isinstance(result, ParallelSeries))
self.assertEqual(result.dist, 'replicated')
self.assertTrue(result.equals(pd_result))
def test_column_distributed_df_apply_function_sqrt_returns_distributed_df(
self):
df1 = ParallelDataFrame(self.pd_df1, dist_data=False)
result = df1.apply(np.sqrt)
df3 = result.apply(np.square)
self.assertTrue(isinstance(result, ParallelDataFrame))
self.assertEqual(result.dist, 'distributed')
self.assertFalse(result.equals(df1))
self.assertTrue(df1.equals(df3))
def test_slicing_with_slice_object_getting_dist_df_in_column_distributed_df(
self):
d = {'col1': [1, 2], 'col2': [3, 4], 'col3': [5, 6]}
pd_df = pd.DataFrame(data=d)
dist_df = ParallelDataFrame(data=d, dist_data=False)
dist_slice = dist_df.loc[0:1]
pd_slice = pd_df.loc[0:1]
pd_slice_dist = ParallelDataFrame(data=pd_slice, dist_data=False)
self.assertTrue(isinstance(dist_slice, ParallelDataFrame))
self.assertEqual(dist_slice.dist, 'distributed')
self.assertTrue(dist_slice.equals(pd_slice_dist))
def test_column_distributed_df_apply_function_sum_returns_distributed_series_raw_False(
self):
df1 = ParallelDataFrame(self.pd_df1, dist_data=False)
pd_result = self.pd_df1.apply(np.sum, axis=0, raw=False)
result = df1.apply(np.sum, axis=0, raw=False)
self.assertTrue(isinstance(result, ParallelSeries))
self.assertEqual(result.dist, 'distributed')
self.assertEqual(set(list(result.globalIndex)),
set(list(pd_result.index)))
self.assertTrue(result.collect().sort_index().equals(
pd_result.sort_index()))
def test_corr_with_col_distributed_dataframe(self):
pd_df = pd.DataFrame([(.2, .3, .4), (.0, .6, .9), (.6, .0, .6),
(.2, .1, .1)],
columns=['dogs', 'cats', 'rats'])
dist_df = ParallelDataFrame(pd_df, dist_data=False)
dist_corr = dist_df.corr()
pd_corr = pd_df.corr()
#compare values of each row (rounded to 6 digits)
for row in dist_corr.globalIndex:
self.assertEqual(
list(dist_corr.loc[row].collect().sort_index().round(6)),
list(pd_corr.loc[row].sort_index().round(6)))
def test_distributed_df_creation_with_from_dict_function_orient_index(
self):
df = ParallelDataFrame.from_dict(self.dict2, orient='index')
self.assertEqual(df.globalShape, (4, 3))
self.assertEqual(set(list(df.globalIndex)),
set(['key1', 'key2', 'key3', 'key4']))
self.assertEqual(list(df.globalColumns), [0, 1, 2])
def test_inplace_dropping_single_row_in_index_distributed_dataframe(self):
df = ParallelDataFrame.from_dict(self.dict2, orient='index')
self.assertEqual(df.globalShape, (4, 3))
df.drop('key4', axis=0, inplace=True)
self.assertEqual(set(list(df.globalIndex)),
set(['key1', 'key2', 'key3']))
self.assertEqual(list(df.globalColumns), [0, 1, 2])
def test_replicated_df_creation_with_from_dict_function_orient_index(self):
pd_df = pd.DataFrame.from_dict(self.dict2, orient='index')
df = ParallelDataFrame.from_dict(self.dict2,
orient='index',
dist='replicated')
self.assertTrue(df.equals(pd_df))
def test_replicated_df_creation_with_constructor_input_dictionary(self):
df = pd.DataFrame(self.dict1)
rep_df = ParallelDataFrame(self.dict1, dist='replicated')
self.assertEqual(df.shape, rep_df.shape)
self.assertTrue(isinstance(rep_df, ParallelDataFrame))
self.assertEqual(rep_df.dist, 'replicated')
def test_distributed_df_creation_with_constructor_input_dataframe(self):
df = pd.DataFrame(np.array([[1, 2, 3], [4, 5, 6]]))
dist_df = ParallelDataFrame(df, dist_data=False)
self.assertTrue(isinstance(dist_df, ParallelDataFrame))
self.assertEqual(dist_df.dist, 'distributed')
self.assertEqual(dist_df.globalShape, df.shape)
self.assertNotEqual(dist_df.shape, dist_df.globalShape)
def test_globalIndex_replicated_series(self): pd_df = pd.DataFrame(self.dict3) pd_series = pd_df.loc[1] dist_df = ParallelDataFrame(data= self.dict3, dist = 'replicated') dist_series = dist_df.loc[1] self.assertEqual(set(dist_series.globalIndex), set(pd_series.index))
def test_global_to_local_distributed_series(self): pd_df = pd.DataFrame(self.dict3) pd_series = pd_df.loc[1] dist_df = ParallelDataFrame(data= self.dict3, dist_data = False) dist_series = dist_df.loc[1] self.assertEqual(set(dist_series.global_to_local.keys()), set(pd_series.index))
def test_slicing_getting_cell_value_in_replicated_df(self):
d = {'col1': [1, 2], 'col2': [3, 4], 'col3': [5, 6]}
pd_df = pd.DataFrame(data=d)
rep_df = ParallelDataFrame(data=d, dist='replicated')
rep_series = rep_df.loc[1, 'col2']
pd_series = pd_df.loc[1, 'col2']
self.assertEqual(rep_series, pd_series)
def test_slicing_with_boolean_array_getting_rep_df_from_replicated_df(
self):
d = {'col1': [1, 2, 4, 5], 'col2': [3, 4, 6, 7], 'col3': [5, 6, 1, 3]}
pd_df = pd.DataFrame(data=d)
rep_df = ParallelDataFrame(data=d, dist='replicated')
rep_series = rep_df.loc[[True, False, False, True]]
pd_series = pd_df.loc[[True, False, False, True]]
self.assertTrue(rep_series.sort_index().equals(pd_series.sort_index()))
def test_creating_distributed_series_by_gettting_row_from_column_distributed_dataframe(self): dist_df = ParallelDataFrame(data=self.d, dist_data = False) parallel_series = dist_df.loc[1] pd_df = pd.DataFrame(self.d) pd_series = pd_df.loc[1] self.assertTrue(isinstance(parallel_series, ParallelSeries)) self.assertEqual(parallel_series.dist, "distributed") self.assertTrue(parallel_series.collect().sort_index().equals(pd_series.sort_index()))
def test_value_count_with_distributed_series_and_string_data(self): pd_df = pd.DataFrame(self.np_array2) pd_series = pd_df.loc[0] dist_df = ParallelDataFrame(pd_df, dist_data = False) dist_series = dist_df.loc[0] pd_series_vc = pd_series.value_counts() dist_series_vc = dist_series.value_counts() self.assertTrue(dist_series_vc.dist, 'replicated') self.assertTrue(dist_series_vc.sort_index().equals(pd_series_vc.sort_index()))
def test_slicing_with_list_of_labels_getting_rep_df_from_replicated_df(
self):
d = {'col1': [1, 2, 4, 5], 'col2': [3, 4, 6, 7], 'col3': [5, 6, 1, 3]}
pd_df = pd.DataFrame(data=d)
rep_df = ParallelDataFrame(data=d, dist='replicated')
rep_slice = rep_df.loc[[0, 3]]
pd_slice = pd_df.loc[[0, 3]]
self.assertTrue(isinstance(rep_slice, ParallelDataFrame))
self.assertEqual(rep_slice.dist, 'replicated')
self.assertTrue(rep_slice.sort_index().equals(pd_slice.sort_index()))
def test_slicing_with_single_label_getting_rep_series_from_replicated_df(
self):
d = {'col1': [1, 2], 'col2': [3, 4], 'col3': [5, 6]}
pd_df = pd.DataFrame(data=d)
rep_df = ParallelDataFrame(data=d, dist='replicated')
rep_series = rep_df.loc[1]
pd_series = pd_df.loc[1]
self.assertTrue(isinstance(rep_series, ParallelSeries))
self.assertEqual(rep_series.dist, 'replicated')
self.assertTrue(rep_series.sort_index().equals(pd_series.sort_index()))
def test_slicing_with_single_label_getting_dist_series_from_column_distributed_df(
self):
d = {'col1': [1, 2], 'col2': [3, 4], 'col3': [5, 6]}
pd_df = pd.DataFrame(data=d)
dist_df = ParallelDataFrame(data=d, dist_data=False)
dist_series = dist_df.loc[1]
pd_series = pd_df.loc[1]
self.assertTrue(isinstance(dist_series, ParallelSeries))
self.assertEqual(dist_series.dist, 'distributed')
self.assertTrue(dist_series.collect().sort_index().equals(
pd_series.sort_index()))
def test_value_count_with_distributed_series_and_float_data(self): pd_df = pd.DataFrame(self.dict3) pd_series = pd_df.loc[1] dist_df = ParallelDataFrame(data= self.dict3, dist_data = False) dist_series = dist_df.loc[1] pd_series_vc = pd_series.value_counts() dist_series_vc = dist_series.value_counts() #convert the indices to string (that is what the parallelPandas returns) pd_series_vc.index = pd_series_vc.index.map(str) self.assertTrue(dist_series_vc.dist, 'replicated') self.assertTrue(dist_series_vc.sort_index().equals(pd_series_vc.sort_index()))
def test_global_to_local_replicated_series(self): dist_df = ParallelDataFrame(data= self.dict3, dist = 'replicated') dist_series = dist_df.loc[1] self.assertRaises(ValueError, dist_series.find_global_to_local, )