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import sys |
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import os |
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import pandas as pd |
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from lib.experiment_specs import study_config |
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from lib.utilities import serialize |
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""" |
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Class that contains functions to anonomize all PII info or de-anonymize PII columns |
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- all PII columns are replace with the appcode value |
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""" |
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class Confidential: |
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id_file = os.path.join("data","external", "dropbox_confidential","ContactLists","Generator","PII") |
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"""populate the PII dataframe with column values for the given survey""" |
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@staticmethod |
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def build_id_map(df, survey_name, id_file = id_file): |
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for survey, id_cols in study_config.id_cols.items(): |
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if survey in survey_name: |
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id_dict = serialize.soft_df_open(id_file).to_dict(orient = 'index') |
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id_cols = ["AppCode"] + [x for x in df.columns if x in study_config.id_cols[survey]] |
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new_pii = df.loc[df["AppCode"].notnull(), id_cols] |
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new_pii.index = new_pii["AppCode"] |
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new_pii_dict = new_pii.drop(columns = "AppCode").to_dict("index") |
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if len(id_dict) ==0: |
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"""if id dict is empty, replace with with the new data""" |
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id_dict = new_pii_dict.copy() |
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else: |
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"""update pii dict""" |
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for appcode in new_pii_dict.keys(): |
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"""if appcode not in the id_dict, add it""" |
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if appcode not in id_dict: |
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id_dict[appcode] = new_pii_dict[appcode] |
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else: |
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"""if appcode is in the id_dict, add or update the columns""" |
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for col,val in new_pii_dict[appcode].items(): |
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"""if col is not in the id_dict, add it (UNCLEAR HOW THIS WILL WORK WITH THE DELAYED SURVEY""" |
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id_dict[appcode][col] = val |
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id_df = pd.DataFrame.from_dict(id_dict, orient= 'index') |
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serialize.save_pickle(id_df,id_file,test_override=True) |
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break |
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"""anonymize pii columns in df """ |
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@staticmethod |
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def anonymize_cols(df): |
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all_pii_cols = sum(list(study_config.id_cols.values()),[]) |
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for col in df.columns: |
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if col in all_pii_cols: |
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df[col] = df["AppCode"] |
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return df |
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""" Adds PII back to data frame by replacing the values of all anonymized columns with the pii""" |
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@staticmethod |
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def add_pii(df, id_file = id_file,only_main_cols = False): |
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id_df = serialize.soft_df_open(id_file) |
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all_pii_cols = list(id_df.columns) |
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relevant_pii = [x for x in df.columns if x in all_pii_cols] |
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df = df.drop(columns = relevant_pii) |
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id_df = id_df.reset_index().rename(columns = {"index":"AppCode"}) |
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if only_main_cols==True: |
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id_main_cols = [x for x in study_config.main_cols if x in id_df.columns] |
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id_df = id_df[id_main_cols] |
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df_pii = id_df.merge(df, on = "AppCode", how = 'right') |
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return df_pii |
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