| import pandas as pd |
| import numpy as np |
| import os |
| import sys |
|
|
| from lib.experiment_specs import study_config |
| from lib.utilities import serialize |
|
|
|
|
| main_codebook_path = os.path.join("data","external","intermediate","CompressedCodebook.csv") |
| final_codebook_path = os.path.join("data","external","final","CodebookFinal.csv") |
| manual_specs_path = os.path.join("lib","experiment_specs","ManualCodebookSpecs.xlsx") |
| value_label_path = os.path.join("lib","experiment_specs","value_labels.yaml") |
|
|
| phase_encode_options = ["Survey","OldPhase","NewPhase"] |
| codebook_vars = ["VariableName","VariableLabel","RawVariableName","DataType","PrefixEncoding","ValueLabels"] |
|
|
|
|
| """ |
| Function which saves over previous codebook and creates new codebook starting with master_codebook. It only reads the codebook sheet you input. |
| """ |
| def initialize_main_codebook(): |
| cb = pd.read_excel(manual_specs_path,sheet_name = "User") |
| new_cols = [x for x in codebook_vars if x not in cb.columns] |
| for new_col in new_cols: |
| cb[new_col] = "" |
| cb.to_csv(main_codebook_path, index = False) |
|
|
| """ |
| Function which adds all non-empty values in the manual_specs to the codebook on disk. This is a substitute for initialize codebook, when we don't |
| want to reread all the survey names |
| """ |
| def update_master_specs(): |
| |
| cb = pd.read_csv(main_codebook_path, index_col = "VariableName").to_dict(orient = 'index') |
| ms = pd.read_excel(manual_specs_path, index_col = "VariableName", sheet_name= "User").to_dict(orient = 'index') |
| for col,chars in ms.items(): |
| |
| try: |
| if col not in cb: |
| cb[col] = ms[col] |
| cb[col]["ValueLabels"] = None |
|
|
| |
| else: |
| for char, val in chars.items(): |
| |
| cb[col][char] = ms[col][char] |
| except: |
| print(f"error updating {col} in codebook") |
|
|
| pd.DataFrame.from_dict(cb, orient='index').to_csv(main_codebook_path, index_label="VariableName") |
|
|
| """ |
| changes variable names in the dataframe according to all vars in the master codebook for which the raw name is different from the regular name |
| (this function should only be applied to surveys) |
| """ |
| def rename_vars(df): |
| specs = pd.read_excel(manual_specs_path) |
| new_vars = specs.loc[(specs["VariableName"] != specs["RawVariableName"]) & |
| (specs["VariableName"].notnull()) & |
| (specs["RawVariableName"].notnull())] |
|
|
| var_dic = dict(zip(new_vars["RawVariableName"], new_vars["VariableName"])) |
| for var in df.columns.values: |
| if var in var_dic.keys(): |
| df = df.rename(columns = {var : var_dic[var]}) |
| return df |
|
|
| """ |
| Adds raw variable names and labels form surveys to codebook |
| """ |
| def add_vardic_to_codebook(vardic): |
| cb = pd.read_csv(main_codebook_path,index_col = "VariableName").to_dict(orient = 'index') |
| for col, chars in vardic.items(): |
|
|
| |
| if col in cb: |
|
|
| |
| for cb_var in ["VariableLabel","DataType","PrefixEncoding"]: |
| val = cb[col][cb_var] |
| if type(val) == float and np.isnan(val): |
| cb[col][cb_var] = vardic[col][cb_var] |
|
|
| |
| else: |
| cb[col] = { |
| "VariableLabel": chars["VariableLabel"], |
| "RawVariableName": None, |
| "DataType": chars["DataType"], |
| "PrefixEncoding": chars["PrefixEncoding"], |
| "ValueLabels": None |
| } |
|
|
| pd.DataFrame.from_dict(cb, orient = 'index').to_csv(main_codebook_path, index_label = "VariableName" ) |
|
|
| """ |
| Reads in dictionary from value_labels.yaml and populates the ValueLabels column in the master codebook |
| """ |
| def add_labels_to_codebook(): |
| cb = pd.read_csv(main_codebook_path, index_col="VariableName").to_dict(orient='index') |
| label_dic = serialize.open_yaml(value_label_path) |
| for label, chars in label_dic.items(): |
| for variable in chars["VariableList"]: |
| if variable in cb: |
| cb[variable]["ValueLabels"] = chars["ValueLabels"] |
| pd.DataFrame.from_dict(cb, orient = 'index').to_csv(main_codebook_path, index_label = "VariableName") |
|
|
|
|
|
|
| def create_expanded_codebook(df): |
| cb_e = df.iloc[0,:].transpose().reset_index() |
| cb_e.columns = ["VariableName","VariableLabel"] |
| cb_e.index = cb_e["VariableName"] |
| cb_e["ValueLabels"] = "" |
| cb_e = cb_e.drop(columns = "VariableName").to_dict(orient='index') |
|
|
| label_dic = serialize.open_yaml(value_label_path) |
| for label, chars in label_dic.items(): |
| for variable in chars["VariableList"]: |
| for code in [study_config.surveys[x]["Code"] for x in study_config.surveys.keys()]: |
| full_var = code+"_"+variable |
| if full_var in cb_e: |
| cb_e[full_var]["ValueLabels"] = chars["ValueLabels"] |
|
|
| pd.DataFrame.from_dict(cb_e, orient = 'index').to_csv(final_codebook_path, index_label = "VariableName") |
|
|
| """given the treatment phase and the codebook dic, this function inputs a varname and outputs |
| the varname with a prefix""" |
| def add_prefix_var(var,phase,codebook_dic,): |
| start_code = study_config.phases[phase]["StartSurvey"]["Code"] |
| end_code = study_config.phases[phase]["EndSurvey"]["Code"] |
|
|
| if var in study_config.main_cols+study_config.embedded_main_cols: |
| return var |
|
|
| elif var not in codebook_dic: |
| print(f"{var} not in codebook!!") |
| return start_code + "_" + var |
|
|
| elif codebook_dic[var]["PrefixEncoding"] == "Survey": |
| "if it's a survey variable, assume that it's phrased as 'in the past three weeks' " |
|
|
| if phase == "Phase1": |
| "for phase1 the survey vars focus on the 3 weeks BEFORE the baseline(start) survey" |
| return start_code + "_" + var |
|
|
| else: |
| "For phase 2 and 3, the survey vars focus on the 3 weeks AFTER the start survey" |
| return end_code + "_" + var |
|
|
| elif codebook_dic[var]["PrefixEncoding"] == "NewPhase": |
| "If it's a variable on use in the future, always use the start survey" |
| return start_code + "_" + var |
|
|
| elif codebook_dic[var]["PrefixEncoding"] == "OldPhase": |
| """These variables collect use data that are reported in the end survey (i.e. Web Data) """ |
| return end_code + "_" + var |
