Correct some comments

Author: php1ic

nuclearmasses/ame_mass_parse.py

@@ -78,7 +78,7 @@ def read_file(self) -> pd.DataFrame:
         """Read the file using it's known format
 
         The AMEMassFile and other functions in this class have hopefully sanitized the
-        column names, data types and locations of the date so we can not make the generic
+        column names, data types and locations of the date so we can now make the generic
         call to parse the file.
         """
         try:
@@ -104,7 +104,6 @@ def read_file(self) -> pd.DataFrame:
                 # Isomeric states are sometimes included in this version of the file
                 # For each row in the dataframe, if the previous row has equal A and Z, drop the current row
                 df = df[~((df['A'] == df['A'].shift()) & (df['Z'] == df['Z'].shift()))]
-                # Total binding energy is recorded in this years file so convert to per A to match the other years
 
             if self.year == 1983 or self.year == 1993 or self.year == 1995:
                 df["BindingEnergyPerA"] = df["BindingEnergyPerA"].astype(float) / df['A'].astype(float)

nuclearmasses/nubase_parse.py

@@ -142,21 +142,22 @@ def read_file(self) -> pd.DataFrame:
                     )
             # We use the NUBASE data to define whether or not an isotope is experimentally measured,
             df['Experimental'] = ~df["NUBASEMassExcess"].astype("string").str.contains('#', na=False)
+            # Once we have used the '#' to determine if it's experimental or not, we can remove all instances of it
             df.replace("#", "", regex=True, inplace=True)
 
             df["TableYear"] = self.year
             df["N"] = pd.to_numeric(df["A"]) - pd.to_numeric(df["Z"])
             df["Symbol"] = pd.to_numeric(df["Z"]).map(self.z_to_symbol)
             # For the moment, we will ignore anything this is not the ground state
             df = df[df["State"] == 0]
-            # As 'State' is now necessarily 0 and the Isomer columns are empty. Drop them.
+            # As 'State' is now necessarily 0 and the Isomer columns are empty, drop them.
             df = df.drop(columns=['State', 'IsomerEnergy', 'IsomerEnergyError'])
 
             # Convert stable isotopes into ones with enormous lifetimes with zero error so we can cast
             df.loc[df['HalfLifeValue'] == 'stbl', ['HalfLifeValue', 'HalfLifeUnit', 'HalfLifeError']] = [99.99, 'Zy', 0.0]
 
             df['HalfLifeValue'] = df['HalfLifeValue'].astype("string").str.replace(r'[<>?~]','', regex=True)
-            # We'll be lazy here an remove any characters in this column. Future us will parse this properly
+            # We'll be lazy here and remove any characters in this column. Future us will parse this properly
             df['HalfLifeError'] = df['HalfLifeError'].astype("string").str.replace(r'[<>?~a-z]','', regex=True)
 
             return df.astype(self._data_types())