Fill in 0.5 for undefined ratio.

When there are no calls and no smses (of a particular type), the ratio is undefined. But since their number is the same, I argue that the ratio can represent that with a 0.5, similarly to the case where no_calls_all = no_sms_all != 0.

exploration/expl_communication.py

@@ -13,14 +13,15 @@
 #     name: straw2analysis
 # ---
 
+# %%
+import importlib
+
 # %%
 # %matplotlib inline
 import os
 import sys
 
 import matplotlib.pyplot as plt
-
-# %%
 import seaborn as sns
 
 nb_dir = os.path.split(os.getcwd())[0]
@@ -28,21 +29,29 @@ if nb_dir not in sys.path:
     sys.path.append(nb_dir)
 
 # %%
-from features.communication import *
+from features import communication, helper
+
+# %%
+importlib.reload(communication)
 
 # %% [markdown]
 # # Example of communication data and feature calculation
 
 # %%
-df_calls = get_call_data(["nokia_0000003"])
+df_calls = communication.get_call_data(["nokia_0000003"])
 print(df_calls)
 
 # %%
-count_comms(df_calls)
+df_calls = helper.get_date_from_timestamp(df_calls)
+communication.count_comms(df_calls, ["date_lj"])
 
 # %%
-df_sms = get_sms_data(["nokia_0000003"])
-count_comms(df_sms)
+df_sms = communication.get_sms_data(["nokia_0000003"])
+df_sms = helper.get_date_from_timestamp(df_sms)
+communication.count_comms(df_sms, ["date_lj"])
+
+# %%
+communication.calls_sms_features(df_calls, df_sms, ["date_lj"])
 
 # %% [markdown]
 # # Call data

features/communication.py

@@ -135,7 +135,7 @@ def enumerate_contacts(comm_df: pd.DataFrame) -> pd.DataFrame:
     return comm_df
 
 
-def count_comms(comm_df: pd.DataFrame) -> pd.DataFrame:
+def count_comms(comm_df: pd.DataFrame, group_by=None) -> pd.DataFrame:
     """
     Calculate frequencies (and duration) of messages (or calls), grouped by their types.
 
@@ -143,6 +143,9 @@ def count_comms(comm_df: pd.DataFrame) -> pd.DataFrame:
     ----------
     comm_df: pd.DataFrame
         A dataframe of calls or SMSes.
+    group_by: list
+        A list of strings, specifying by which parameters to group.
+        By default, the features are calculated per participant, but could be "date_lj" etc.
 
     Returns
     -------
@@ -155,11 +158,13 @@ def count_comms(comm_df: pd.DataFrame) -> pd.DataFrame:
         * the number of messages by type (received, sent), and
         * the number of communication contacts by type. 
     """
+    if group_by is None:
+        group_by = []
     if "call_type" in comm_df:
         data_type = "calls"
         comm_counts = (
-            comm_df.value_counts(subset=["participant_id", "call_type"])
-            .unstack()
+            comm_df.value_counts(subset=group_by + ["participant_id", "call_type"])
+            .unstack(level="call_type", fill_value=0)
             .rename(columns=call_types)
             .add_prefix("no_")
         )
@@ -172,17 +177,17 @@ def count_comms(comm_df: pd.DataFrame) -> pd.DataFrame:
         )
         # Ratio of incoming and outgoing calls to all calls.
         comm_duration_total = (
-            comm_df.groupby(["participant_id", "call_type"])
+            comm_df.groupby(group_by + ["participant_id", "call_type"])
             .sum()["call_duration"]
-            .unstack()
+            .unstack(level="call_type", fill_value=0)
             .rename(columns=call_types)
             .add_prefix("duration_total_")
         )
         # Total call duration by type.
         comm_duration_max = (
-            comm_df.groupby(["participant_id", "call_type"])
+            comm_df.groupby(group_by + ["participant_id", "call_type"])
             .max()["call_duration"]
-            .unstack()
+            .unstack(level="call_type", fill_value=0)
             .rename(columns=call_types)
             .add_prefix("duration_max_")
         )
@@ -200,8 +205,8 @@ def count_comms(comm_df: pd.DataFrame) -> pd.DataFrame:
     elif "message_type" in comm_df:
         data_type = "sms"
         comm_counts = (
-            comm_df.value_counts(subset=["participant_id", "message_type"])
-            .unstack()
+            comm_df.value_counts(subset=group_by + ["participant_id", "message_type"])
+            .unstack(level="message_type", fill_value=0)
             .rename(columns=sms_types)
             .add_prefix("no_")
         )
@@ -216,7 +221,7 @@ def count_comms(comm_df: pd.DataFrame) -> pd.DataFrame:
         raise KeyError("The dataframe contains neither call_type or message_type")
     comm_contacts_counts = (
         enumerate_contacts(comm_df)
-        .groupby(["participant_id"])
+        .groupby(group_by + ["participant_id"])
         .nunique()["contact_id"]
         .rename("no_contacts_" + data_type)
     )
@@ -268,7 +273,9 @@ def contact_features(comm_df: pd.DataFrame) -> pd.DataFrame:
     return contacts_count
 
 
-def calls_sms_features(df_calls: pd.DataFrame, df_sms: pd.DataFrame) -> pd.DataFrame:
+def calls_sms_features(
+    df_calls: pd.DataFrame, df_sms: pd.DataFrame, group_by=None
+) -> pd.DataFrame:
     """
     Calculates additional features relating calls and sms data.
 
@@ -278,6 +285,9 @@ def calls_sms_features(df_calls: pd.DataFrame, df_sms: pd.DataFrame) -> pd.DataF
         A dataframe of calls (return of get_call_data).
     df_sms: pd.DataFrame
         A dataframe of SMSes (return of get_sms_data).
+    group_by: list
+        A list of strings, specifying by which parameters to group.
+        By default, the features are calculated per participant, but could be "date_lj" etc.
 
     Returns
     -------
@@ -295,24 +305,38 @@ def calls_sms_features(df_calls: pd.DataFrame, df_sms: pd.DataFrame) -> pd.DataF
         * proportion_calls_contacts:
             proportion of calls contacts in total number of communication contacts
     """
-    count_calls = count_comms(df_calls)
-    count_sms = count_comms(df_sms)
-    count_joined = count_calls.join(count_sms).assign(
-        proportion_calls_all=(
-            lambda x: x.no_calls_all / (x.no_calls_all + x.no_sms_all)
-        ),
-        proportion_calls_incoming=(
-            lambda x: x.no_incoming / (x.no_incoming + x.no_received)
-        ),
-        proportion_calls_missed_sms_received=(
-            lambda x: x.no_missed / (x.no_missed + x.no_received)
-        ),
-        proportion_calls_outgoing=(
-            lambda x: x.no_outgoing / (x.no_outgoing + x.no_sent)
-        ),
-        proportion_calls_contacts=(
-            lambda x: x.no_contacts_calls / (x.no_contacts_calls + x.no_contacts_sms)
+    if group_by is None:
+        group_by = []
+    count_calls = count_comms(df_calls, group_by)
+    count_sms = count_comms(df_sms, group_by)
+    count_joined = (
+        count_calls.merge(
+            count_sms,
+            how="outer",
+            left_index=True,
+            right_index=True,
+            validate="one_to_one",
         )
-        # Calculate new features and create additional columns
+        .fillna(0, downcast="infer")
+        .assign(
+            proportion_calls_all=(
+                lambda x: x.no_calls_all / (x.no_calls_all + x.no_sms_all)
+            ),
+            proportion_calls_incoming=(
+                lambda x: x.no_incoming / (x.no_incoming + x.no_received)
+            ),
+            proportion_calls_missed_sms_received=(
+                lambda x: x.no_missed / (x.no_missed + x.no_received)
+            ),
+            proportion_calls_outgoing=(
+                lambda x: x.no_outgoing / (x.no_outgoing + x.no_sent)
+            ),
+            proportion_calls_contacts=(
+                lambda x: x.no_contacts_calls
+                / (x.no_contacts_calls + x.no_contacts_sms)
+            )
+            # Calculate new features and create additional columns
+        )
+        .fillna(0.5, downcast="infer")
     )
     return count_joined