diff --git a/lottery_predictor/analyze.py b/lottery_predictor/analyze.py
index a871a83..76d1b42 100644
--- a/lottery_predictor/analyze.py
+++ b/lottery_predictor/analyze.py
@@ -11,7 +11,13 @@ import util.drawing as drawutil
 def load_dataframe(
         table_name: str, start_date: datetime | None = None, end_date: datetime | None = None
     ) -> pd.DataFrame:
-    """connect to the database and load game data into a pandas dataframe"""
+    """
+        :param table_name: the name of the table to load data from
+        :param start_date: the start date for the data
+        :param end_date: the end date for the data
+
+        :returns: a pandas DataFrame containing the data
+    """
 
     dal.connect()
     session = dal.Session()
@@ -36,10 +42,15 @@ def load_dataframe(
 
 
 def get_most_common_number(
-        data_frame: pd.DataFrame,
-        columns: list | None = None,
-        top: int = 1
+        data_frame: pd.DataFrame, columns: list | None = None, top: int = 1
     ) -> list[int]:
+    """
+    :param data_frame: a pandas DataFrame containing the data
+    :param columns: a list of column names to use
+    :param top: the number of top (most seen) numbers to return
+
+    :returns: a list of the most common numbers
+    """
     if columns is None:
         columns = ['main_ball1', 'main_ball2', 'main_ball3', 'main_ball4', 'main_ball5']
     flat_numbers = data_frame[columns].values.flatten()
@@ -48,10 +59,15 @@ def get_most_common_number(
 
 
 def get_least_common_number(
-        data_frame: pd.DataFrame,
-        columns: list | None = None,
-        bottom: int = 1
+        data_frame: pd.DataFrame, columns: list | None = None, bottom: int = 1
 ) -> list[int]:
+    """
+    :param data_frame: a pandas DataFrame containing the data
+    :param columns: a list of column names to use
+    :param bottom: the number of bottom (least seen) numbers to return
+
+    :returns: a list of the least common numbers
+    """
     if columns is None:
         columns = ['main_ball1', 'main_ball2', 'main_ball3', 'main_ball4', 'main_ball5']
     flat_numbers = data_frame[columns].values.flatten()