feat: add support for python 3.8

Author: vorel99

src/ydata_profiling/model/alerts.py

@@ -698,7 +698,7 @@ def check_correlation_alerts(config: Settings, correlations: dict) -> List[Alert
 def get_alerts(
     config: Settings,
     table_stats: dict,
-    series_description: dict[str, VarDescription],
+    series_description: Dict[str, VarDescription],
     correlations: dict,
 ) -> List[Alert]:
     alerts: List[Alert] = check_table_alerts(table_stats)

src/ydata_profiling/model/describe.py

@@ -73,7 +73,7 @@ def describe(
 
         # Variable-specific
         pbar.total += len(df.columns)
-        series_description: dict[str, VarDescription] = get_series_descriptions(
+        series_description: Dict[str, VarDescription] = get_series_descriptions(
             config, df, summarizer, typeset, pbar
         )
 

src/ydata_profiling/model/pandas/correlations_pandas.py

@@ -2,7 +2,7 @@
 
 import itertools
 import warnings
-from typing import Callable, Optional
+from typing import Callable, Dict, Optional
 
 import numpy as np
 import pandas as pd
@@ -86,9 +86,9 @@ def _pairwise_cramers(col_1: pd.Series, col_2: pd.Series) -> float:
     return _cramers_corrected_stat(pd.crosstab(col_1, col_2), correction=True)
 
 
-@Cramers.compute.register(Settings, pd.DataFrame, dict[str, VarDescription])
+@Cramers.compute.register(Settings, pd.DataFrame, Dict[str, VarDescription])
 def pandas_cramers_compute(
-    config: Settings, df: pd.DataFrame, summary: dict[str, VarDescription]
+    config: Settings, df: pd.DataFrame, summary: Dict[str, VarDescription]
 ) -> Optional[pd.DataFrame]:
     threshold = config.categorical_maximum_correlation_distinct
 
@@ -127,9 +127,9 @@ def pandas_cramers_compute(
     return correlation_matrix
 
 
-@PhiK.compute.register(Settings, pd.DataFrame, dict[str, VarDescription])
+@PhiK.compute.register(Settings, pd.DataFrame, Dict[str, VarDescription])
 def pandas_phik_compute(
-    config: Settings, df: pd.DataFrame, summary: dict[str, VarDescription]
+    config: Settings, df: pd.DataFrame, summary: Dict[str, VarDescription]
 ) -> Optional[pd.DataFrame]:
     df_cols_dict = {i: list(df.columns).index(i) for i in df.columns}
 
@@ -163,9 +163,9 @@ def pandas_phik_compute(
     return correlation
 
 
-@Auto.compute.register(Settings, pd.DataFrame, dict[str, VarDescription])
+@Auto.compute.register(Settings, pd.DataFrame, Dict[str, VarDescription])
 def pandas_auto_compute(
-    config: Settings, df: pd.DataFrame, summary: dict[str, VarDescription]
+    config: Settings, df: pd.DataFrame, summary: Dict[str, VarDescription]
 ) -> Optional[pd.DataFrame]:
     threshold = config.categorical_maximum_correlation_distinct
     numerical_columns = [

src/ydata_profiling/model/pandas/summary_pandas.py

@@ -2,7 +2,7 @@
 
 import multiprocessing
 import multiprocessing.pool
-from typing import Tuple
+from typing import Dict, Tuple
 
 import numpy as np
 import pandas as pd
@@ -65,7 +65,7 @@ def pandas_get_series_descriptions(
     summarizer: BaseSummarizer,
     typeset: VisionsTypeset,
     pbar: tqdm,
-) -> dict[str, VarDescription]:
+) -> Dict[str, VarDescription]:
     def multiprocess_1d(args: tuple) -> Tuple[str, VarDescription]:
         """Wrapper to process series in parallel.
 

src/ydata_profiling/model/pandas/table_pandas.py

@@ -1,4 +1,5 @@
 from collections import Counter
+from typing import Dict
 
 import pandas as pd
 
@@ -9,7 +10,7 @@
 
 @get_table_stats.register
 def pandas_get_table_stats(
-    config: Settings, df: pd.DataFrame, variable_stats: dict[str, VarDescription]
+    config: Settings, df: pd.DataFrame, variable_stats: Dict[str, VarDescription]
 ) -> dict:
     """General statistics for the DataFrame.
 

src/ydata_profiling/model/summary.py

@@ -1,6 +1,6 @@
 """Compute statistical description of datasets."""
 
-from typing import Any
+from typing import Any, Dict
 
 from multimethod import multimethod
 from tqdm import tqdm
@@ -28,5 +28,5 @@ def get_series_descriptions(
     summarizer: BaseSummarizer,
     typeset: VisionsTypeset,
     pbar: tqdm,
-) -> dict[str, VarDescription]:
+) -> Dict[str, VarDescription]:
     raise NotImplementedError()