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stress_at_work_analysis
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Simplify merge into join.

communication
junos 2021-08-17 13:49:53 +02:00
parent cf28aa547a
commit e7fe4e8398
1 changed files with 13 additions and 22 deletions
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31
features/communication.py
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@ -152,17 +152,13 @@ def count_comms(comm_df: pd.DataFrame) -> pd.DataFrame:
comm_contacts_counts = ( comm_contacts_counts = (
enumerate_contacts(comm_df) enumerate_contacts(comm_df)
.groupby(["participant_id"]) .groupby(["participant_id"])
.nunique()["contact_id"] .nunique()
.reset_index()
.rename(columns={"contact_id": "no_contacts"}) .rename(columns={"contact_id": "no_contacts"})
) )
# Number of communication contacts # Number of communication contacts
comm_features = comm_counts.join(comm_duration_total) comm_features = comm_counts.join(comm_duration_total)
comm_features = comm_features.join(comm_duration_max) comm_features = comm_features.join(comm_duration_max)
comm_features = comm_features.merge( comm_features = comm_features.join(comm_contacts_counts)
comm_contacts_counts,
on="participant_id"
).set_index("participant_id")
try: try:
comm_features.drop(columns="duration_total_" + 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) comm_features.drop(columns="duration_max_" + call_types[3], inplace=True)
@ -188,15 +184,11 @@ def count_comms(comm_df: pd.DataFrame) -> pd.DataFrame:
comm_contacts_counts = ( comm_contacts_counts = (
enumerate_contacts(comm_df) enumerate_contacts(comm_df)
.groupby(["participant_id"]) .groupby(["participant_id"])
.nunique()["contact_id"] .nunique()
.reset_index()
.rename(columns={"contact_id": "no_contacts"}) .rename(columns={"contact_id": "no_contacts"})
) )
# Number of communication contacts # Number of communication contacts
comm_features = comm_features.merge( comm_features = comm_features.join(comm_contacts_counts)
comm_contacts_counts,
on="participant_id"
).set_index("participant_id")
else: 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 return comm_features
@ -221,25 +213,23 @@ def contact_features(comm_df: pd.DataFrame) -> pd.DataFrame:
""" """
df_enumerated = enumerate_contacts(comm_df) df_enumerated = enumerate_contacts(comm_df)
contacts_count = ( contacts_count = (
df_enumerated df_enumerated.groupby(["participant_id", "contact_id"]).size().reset_index()
.groupby(["participant_id","contact_id"])
.size()
.reset_index()
) )
# Check whether df contains calls or SMS data since some # Check whether df contains calls or SMS data since some
# features we want to calculate are type-specyfic # features we want to calculate are type-specyfic
if "call_duration" in df_enumerated: if "call_duration" in df_enumerated:
# Add a column with the total duration of calls between two people # Add a column with the total duration of calls between two people
duration_count = ( duration_count = (
df_enumerated df_enumerated.groupby(["participant_id", "contact_id"])
.groupby(["participant_id", "contact_id"])
# For each participant and for each caller, sum durations of their calls # For each participant and for each caller, sum durations of their calls
["call_duration"] ["call_duration"]
.sum() .sum()
.reset_index() # Make index (which is actually the participant id) a normal column .reset_index() # Make index (which is actually the participant id) a normal column
.rename(columns={"call_duration": "total_call_duration"}) .rename(columns={"call_duration": "total_call_duration"})
) )
contacts_count = contacts_count.merge(duration_count, on=["participant_id", "contact_id"]) contacts_count = contacts_count.merge(
duration_count, on=["participant_id", "contact_id"]
)
contacts_count.rename(columns={0: "no_calls"}, inplace=True) contacts_count.rename(columns={0: "no_calls"}, inplace=True)
else: else:
contacts_count.rename(columns={0: "no_sms"}, inplace=True) contacts_count.rename(columns={0: "no_sms"}, inplace=True)
@ -295,7 +285,8 @@ def calls_sms_features(df_calls: pd.DataFrame, df_sms: pd.DataFrame) -> pd.DataF
lambda x: x.no_outgoing / (x.no_outgoing + x.no_sent) lambda x: x.no_outgoing / (x.no_outgoing + x.no_sent)
), ),
proportion_calls_contacts=( 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 # Calculate new features and create additional columns
)[ )[