diff --git a/ehrdata.py b/ehrdata.py
index 534839c..98c6725 100644
--- a/ehrdata.py
+++ b/ehrdata.py
@@ -1,26 +1,17 @@
-import awkward as ak
-import numpy as np
-import pandas as pd
 import csv
-import pandas as pd
-import matplotlib.pyplot as plt
-import seaborn as sns
-import ehrapy as ep
-import scanpy as sc
-from anndata import AnnData
-import mudata as md
-from mudata import MuData
-from typing import List, Union, Literal
-import os
 import glob
-import dask.dataframe as dd
-from thefuzz import process
-import sys
-from rich import print as rprint
-import missingno as msno
-import warnings
 import numbers
+import os
+import warnings
+from typing import Literal, Union
 
+import awkward as ak
+import dask.dataframe as dd
+import ehrapy as ep
+import matplotlib.pyplot as plt
+import pandas as pd
+import seaborn as sns
+from rich import print as rprint
 
 clinical_tables_columns = {
     "person": ["person_id", "year_of_birth", "gender_source_value"],
@@ -102,7 +93,6 @@ def get_close_matches_using_dict(word, possibilities, n=2, cutoff=0.6):
     Optional arg cutoff (default 0.6) is a float in [0, 1].  Possibilities
     that don't score at least that similar to word are ignored.
     """
-
     if not n > 0:
         raise ValueError("n must be > 0: %r" % (n,))
     if not 0.0 <= cutoff <= 1.0:
@@ -133,7 +123,7 @@ def get_column_types(csv_path=None, columns=None):
     column_types = {}
     parse_dates = []
     if csv_path:
-        with open(csv_path, "r") as f:
+        with open(csv_path) as f:
             dict_reader = csv.DictReader(f)
             columns = dict_reader.fieldnames
             columns_lowercase = [column.lower() for column in columns]
@@ -311,14 +301,14 @@ def load(self, level="stay_level", tables=["visit_occurrence", "person", "death"
                     for visit_occurrence_id in adata.obs.index:
                         obs_list.append(list(self.measurement[self.measurement['visit_occurrence_id'] == int(visit_occurrence_id)][column]))
                     adata.obsm[column]= ak.Array(obs_list)
-            
+
             for column in self.drug_exposure.columns:
                 if column != 'visit_occurrence_id':
                     obs_list = []
                     for visit_occurrence_id in adata.obs.index:
                         obs_list.append(list(self.drug_exposure[self.drug_exposure['visit_occurrence_id'] == int(visit_occurrence_id)][column]))
                     adata.obsm[column]= ak.Array(obs_list)
-            
+
             for column in self.observation.columns:
                 if column != 'visit_occurrence_id':
                     obs_list = []
@@ -368,7 +358,7 @@ def feature_statistics(
         plt.tight_layout()
         return feature_counts
 
-    def map_concept_id(self, concept_id: Union[str, List], verbose=True):
+    def map_concept_id(self, concept_id: Union[str, list], verbose=True):
         column_types, parse_dates = get_column_types(self.filepath["concept_relationship"])
         df_concept_relationship = dd.read_csv(
             self.filepath["concept_relationship"], dtype=column_types, parse_dates=parse_dates
@@ -413,7 +403,7 @@ def map_concept_id(self, concept_id: Union[str, List], verbose=True):
         else:
             return concept_id_1, concept_id_2
 
-    def get_concept_name(self, concept_id: Union[str, List], raise_error=False, verbose=True):
+    def get_concept_name(self, concept_id: Union[str, list], raise_error=False, verbose=True):
         if isinstance(concept_id, numbers.Integral):
             concept_id = [concept_id]
 
@@ -474,9 +464,9 @@ def extract_features(
             "drug_exposure",
             "condition_occurrence",
         ],
-        features: str or int or List[Union[str, int]] = None,
+        features: str or int or list[Union[str, int]] = None,
         key: str = None,
-        columns_in_source_table: str or List[str] = None,
+        columns_in_source_table: str or list[str] = None,
         map_concept=True,
         add_aggregation_to_X: bool = True,
         aggregation_methods=None,