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stress_at_work_analysis
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Add date part of datetime when preprocessing.

communication
junos 2021-07-03 16:39:32 +02:00
parent e0da6757ec
commit a6d8f70e7f
1 changed files with 7 additions and 1 deletions
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8
features/esm.py
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@ -54,7 +54,8 @@ def get_esm_data(usernames: Collection) -> pd.DataFrame:
def preprocess_esm(df_esm: pd.DataFrame) -> pd.DataFrame: def preprocess_esm(df_esm: pd.DataFrame) -> pd.DataFrame:
""" """
Convert timestamps into human-readable datetimes and expand the JSON column into several Pandas DF columns. Convert timestamps into human-readable datetimes and dates
and expand the JSON column into several Pandas DF columns.
Parameters Parameters
---------- ----------
@ -69,6 +70,11 @@ def preprocess_esm(df_esm: pd.DataFrame) -> pd.DataFrame:
df_esm["datetime_lj"] = df_esm["double_esm_user_answer_timestamp"].apply( df_esm["datetime_lj"] = df_esm["double_esm_user_answer_timestamp"].apply(
lambda x: datetime.datetime.fromtimestamp(x / 1000.0, tz=TZ_LJ) lambda x: datetime.datetime.fromtimestamp(x / 1000.0, tz=TZ_LJ)
) )
df_esm = df_esm.assign(
date_lj=lambda x: (x.datetime_lj - datetime.timedelta(hours=4)).dt.date
)
# Since daytime EMAs could *theoretically* last beyond midnight, but never after 4 AM,
# the datetime is first translated to 4 h earlier.
df_esm_json = pd.json_normalize(df_esm["esm_json"]).drop( df_esm_json = pd.json_normalize(df_esm["esm_json"]).drop(
columns=["esm_trigger"] columns=["esm_trigger"]
) # The esm_trigger column is already present in the main df. ) # The esm_trigger column is already present in the main df.