diff --git a/features/communication.py b/features/communication.py
index 0c44259..35de332 100644
--- a/features/communication.py
+++ b/features/communication.py
@@ -151,10 +151,8 @@ def count_comms(comm_df: pd.DataFrame) -> pd.DataFrame:
         comm_features = comm_counts.join(comm_duration_total)
         comm_features = comm_features.join(comm_duration_max)
         try:
-            comm_features.drop(columns="duration_total_" +
-                               call_types[3], inplace=True)
-            comm_features.drop(columns="duration_max_" +
-                               call_types[3], inplace=True)
+            comm_features.drop(columns="duration_total_" + call_types[3], inplace=True)
+            comm_features.drop(columns="duration_max_" + call_types[3], inplace=True)
         # The missed calls are always of 0 duration.
         except KeyError:
             pass
@@ -175,8 +173,7 @@ def count_comms(comm_df: pd.DataFrame) -> pd.DataFrame:
         )
         # Ratio of incoming and outgoing messages to all messages.
     else:
-        raise KeyError(
-            "The dataframe contains neither call_type or message_type")
+        raise KeyError("The dataframe contains neither call_type or message_type")
     return comm_features
 
 
@@ -203,11 +200,10 @@ def contact_features(df_enumerated: pd.DataFrame) -> pd.DataFrame:
     if "call_duration" in df_enumerated:
         # Add a column with the total duration of calls between two people
         duration_count = (
-            df_enumerated.groupby(
-                ["participant_id", "contact_id"]
-            )
+            df_enumerated.groupby(["participant_id", "contact_id"])
             # For each participant and for each caller, sum durations of their calls
-            ["call_duration"].sum()
+            ["call_duration"]
+            .sum()
             .reset_index()  # Make index (which is actually the participant id) a normal column
             .rename(columns={"call_duration": "total_call_duration"})
         )
@@ -215,17 +211,18 @@ def contact_features(df_enumerated: pd.DataFrame) -> pd.DataFrame:
         # participants, callers and the total duration of their calls. All that
         # is now left to do is to merge the original df with the new one.
         df_enumerated = df_enumerated.merge(
-            duration_count,
-            on=["participant_id", "contact_id"]
-            )
+            duration_count, on=["participant_id", "contact_id"]
+        )
 
     contact_count = (
         df_enumerated.groupby(["participant_id"])
-        .nunique()["contact_id"] # For each participant, count the number of distinct contacts
+        .nunique()[
+            "contact_id"
+        ]  # For each participant, count the number of distinct contacts
         .reset_index()  # Make index (which is actually the participant id) a normal column
         .rename(columns={"contact_id": "no_contacts"})
     )
-    
+
     df_enumerated = (
         # Merge df with the newely created df containing info about number of contacts
         df_enumerated.merge(contact_count, on="participant_id")
@@ -258,7 +255,7 @@ def calls_sms_features(df_calls: pd.DataFrame, df_sms: pd.DataFrame) -> pd.DataF
         * proportion_calls:
             proportion of calls in total number of communications
         * proportion_calls_incoming:
-            proportion of incoming calls in total number of incoming/recieved communications
+            proportion of incoming calls in total number of incoming/received communications
         * proportion_calls_outgoing:
             proportion of outgoing calls in total number of outgoing/sent communications
         * proportion_calls_missed_sms_received:
@@ -290,12 +287,14 @@ def calls_sms_features(df_calls: pd.DataFrame, df_sms: pd.DataFrame) -> pd.DataF
             )
             # Calculate new features and create additional columns
         )[
-            ["participant_id",
-             "proportion_calls",
-             "proportion_calls_incoming",
-             "proportion_calls_outgoing",
-             "proportion_calls_missed_sms_received"]
-        ]  # Filter out only the relevant feautres
+            [
+                "participant_id",
+                "proportion_calls",
+                "proportion_calls_incoming",
+                "proportion_calls_outgoing",
+                "proportion_calls_missed_sms_received",
+            ]
+        ]  # Filter out only the relevant features
     )
 
     features_calls = contact_features(enumerate_contacts(df_calls))
@@ -305,22 +304,21 @@ def calls_sms_features(df_calls: pd.DataFrame, df_sms: pd.DataFrame) -> pd.DataF
         features_calls.merge(
             features_sms, on="participant_id", suffixes=("_calls", "_sms")
         )  # Merge calls and sms features
-        .reset_index()  # Make participand_id a regular column
+        .reset_index()  # Make participant_id a regular column
         .assign(
             proportion_calls_contacts=(
-                lambda x: x.no_contacts_calls /
-                    (x.no_contacts_calls + x.no_contacts_sms)
+                lambda x: x.no_contacts_calls
+                / (x.no_contacts_calls + x.no_contacts_sms)
             )  # Calculate new features and create additional columns
         )[
-            ["participant_id",
-             "proportion_calls_contacts"]
-        ]  # Filter out only the relevant feautres
+            ["participant_id", "proportion_calls_contacts"]
+        ]  # Filter out only the relevant features
         # Since we are interested only in some features and ignored
         # others, a lot of duplicate rows were created. Remove them.
         .drop_duplicates()
     )
 
-    # Join the newely created dataframes
+    # Join the newly created dataframes
     df_calls_sms = count_joined.merge(features_joined, on="participant_id")
 
     return df_calls_sms