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Categorical polars update #1135

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Categorical polars update #1135

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18772d2
Categorical polars update
abajpai15 Apr 23, 2024
95679a5
Merge branch 'feature/polars' into polars_feature_rebase
taylorfturner Apr 29, 2024
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23 changes: 15 additions & 8 deletions dataprofiler/profilers/categorical_column_profile.py
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Original file line number Diff line number Diff line change
Expand Up @@ -476,7 +476,7 @@ def _check_stop_condition_is_met(self, sample_size: int, unqiue_ratio: float):
return True
return False

def _update_stop_condition(self, data: DataFrame):
def _update_stop_condition(self, data: DataFrame | pl.DataFrame):
"""Return value stop_condition_is_met given stop conditions.

:param data: Dataframe currently being processed by categorical profiler
Expand All @@ -499,8 +499,8 @@ def _get_categories_cms(self, df_series, len_df):
"""Return count min sketch and heavy hitters for both the batch and stream case.

:param df_series: Series currently being processed by categorical profiler
:type df_series: Series
:param len_df: the total number of samples iin df_series
:type df_series: polars.Series
:param len_df: the total number of samples in df_series
:type len_df: int
:return: cms, heavy_hitter_dict, missing_heavy_hitter_dict
"""
Expand Down Expand Up @@ -603,13 +603,13 @@ def _get_categories_full(self, df_series) -> dict:
:return: dict of counts for each unique value
:rtype: dict
"""
category_count: dict = dict(df_series.value_counts(sort=True).iter_rows())
category_count: dict = Series(df_series).value_counts(dropna=False).to_dict()
return category_count

@BaseColumnProfiler._timeit(name="categories")
def _update_categories(
self,
df_series: DataFrame,
df_series: DataFrame | pl.DataFrame,
prev_dependent_properties: dict = None,
subset_properties: dict = None,
) -> None:
Expand Down Expand Up @@ -659,7 +659,9 @@ def _update_categories(
if self._stop_condition_is_met:
self._categories = {}

def _update_helper(self, df_series_clean: Series, profile: dict) -> None:
def _update_helper(
self, df_series_clean: Series | pl.Series, profile: dict
) -> None:
"""
Update col profile properties with clean dataset and its known profile.

Expand All @@ -671,7 +673,7 @@ def _update_helper(self, df_series_clean: Series, profile: dict) -> None:
"""
self._update_column_base_properties(profile)

def update(self, df_series: Series) -> CategoricalColumn:
def update(self, df_series: pl.Series | Series) -> CategoricalColumn:
"""
Update the column profile.

Expand All @@ -687,12 +689,17 @@ def update(self, df_series: Series) -> CategoricalColumn:
if len(df_series) == 0 or self._stop_condition_is_met:
return self

if isinstance(df_series, pl.Series):
pandas_df = df_series.to_pandas()
else:
pandas_df = df_series

profile = dict(sample_size=len(df_series))
CategoricalColumn._update_categories(self, df_series)
BaseColumnProfiler._perform_property_calcs(
self,
self.__calculations,
df_series=df_series,
df_series=pandas_df,
prev_dependent_properties={},
subset_properties=profile,
)
Expand Down
10 changes: 5 additions & 5 deletions dataprofiler/tests/profilers/test_categorical_column_profile.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1181,7 +1181,7 @@ def test_fewer_than_MAXIMUM_UNIQUE_VALUES_TO_CLASSIFY_AS_CATEGORICAL(self):
]

len_unique = len(set(cat_sentence_list))
cat_sentence_df = pd.Series(cat_sentence_list)
cat_sentence_df = pl.Series(cat_sentence_list)
column_profile = StructuredColProfiler(cat_sentence_df)
cat_profiler = column_profile.profiles["data_stats_profile"]._profiles[
"category"
Expand All @@ -1206,7 +1206,7 @@ def test_greater_than_CATEGORICAL_THRESHOLD_DEFAULT_identify_as_text(self):
)
cat_sentence_list = list_unique_values * num_sentences

cat_sentence_df = pd.Series(cat_sentence_list)
cat_sentence_df = pl.Series(cat_sentence_list)
column_profile = StructuredColProfiler(cat_sentence_df)
cat_profiler = column_profile.profiles["data_stats_profile"]._profiles[
"category"
Expand All @@ -1233,7 +1233,7 @@ def test_less_than_CATEGORICAL_THRESHOLD_DEFAULT(self):
cat_sentence_list = list_unique_values * num_sentences

len_unique = len(set(cat_sentence_list))
cat_sentence_df = pd.Series(cat_sentence_list)
cat_sentence_df = pl.Series(cat_sentence_list)
column_profile = StructuredColProfiler(cat_sentence_df)
cat_profiler = column_profile.profiles["data_stats_profile"]._profiles[
"category"
Expand Down Expand Up @@ -1263,7 +1263,7 @@ def test_uppercase_less_than_CATEGORICAL_THRESHOLD_DEFAULT(self):
cat_sentence_list[-3] = self.test_sentence_upper3 + str(num_sentences - 2)

len_unique = len(set(cat_sentence_list))
cat_sentence_df = pd.Series(cat_sentence_list)
cat_sentence_df = pl.Series(cat_sentence_list)
column_profile = StructuredColProfiler(cat_sentence_df)
cat_profiler = column_profile.profiles["data_stats_profile"]._profiles[
"category"
Expand All @@ -1288,7 +1288,7 @@ def test_long_sentences_fewer_than_MAXIMUM_UNIQUE_VALUES_TO_CLASSIFY_AS_CATEGORI
]

len_unique = len(set(cat_sentence_list))
cat_sentence_df = pd.Series(cat_sentence_list)
cat_sentence_df = pl.Series(cat_sentence_list)
column_profile = StructuredColProfiler(cat_sentence_df)
cat_profiler = column_profile.profiles["data_stats_profile"]._profiles[
"category"
Expand Down
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