from Main import get_outcome_lvl1 import pandas as pd # get group2 mean data group2mean = get_outcome_lvl1("Data4") group2mean_df = pd.DataFrame(group2mean) # name each column (number depends on outcome number) group2mean_df.columns = [ "out_g2_mean_" + '{}'.format(column + 1) for column in group2mean_df.columns ] # fill blanks with NA group2mean_df.fillna("NA", inplace=True) # remove problematic text group2mean_df = group2mean_df.replace(r'^\s*$', "NA", regex=True) # get outcometypeId data outcomeid = get_outcome_lvl1("OutcomeTypeId") outcometypeid_df = pd.DataFrame(outcomeid) # name each column (number depends on outcome number) outcometypeid_df.columns = [ "outcometype_df_" + '{}'.format(column + 1) for column in outcometypeid_df.columns ] mask = pd.DataFrame(outcometypeid_df["outcometype_df_1"] == 0) mask.columns = ["mask"]
from Main import load_json, get_outcome_lvl1 import pandas as pd # load json file load_json() # get outcome ID data outcome_ID = get_outcome_lvl1("OutcomeId") outcome_ID_df = pd.DataFrame(outcome_ID) # name each column (number depends on outcome number) outcome_ID_df.columns = [ "Outcome_ID_" + '{}'.format(column + 1) for column in outcome_ID_df.columns ] # fill blanks with NA outcome_ID_df.fillna("NA", inplace=True) # replace problematic text outcome_ID_df = outcome_ID_df.replace(r'^\s*$', "NA", regex=True) # save to disk """ outcome_ID_df.to_csv("outcomeID.csv", index=False) """
from Main import get_outcome_lvl1 import pandas as pd # extract confidence interval lower data cilowersmd = get_outcome_lvl1("CILowerSMD") cilowersmd_df = pd.DataFrame(cilowersmd) # round data to 4 decimal places cilowersmd_df = cilowersmd_df.applymap( lambda x: round(x, 4) if isinstance(x, (int, float)) else x) # name each column (number depends on outcome number) cilowersmd_df.columns=[ "ci_lower_"+'{}'.format(column+1) for column in cilowersmd_df.columns] # fill blanks with NA cilowersmd_df.fillna("NA", inplace=True) # replace problematic text cilowersmd_df = cilowersmd_df.replace(r'^\s*$', "NA", regex=True) # save to disk cilowersmd_df.to_csv("cilowersmd.csv", index=False)
from Main import load_json, get_outcome_lvl1 import pandas as pd # load json file load_json() # get outcome comparison data out_comp = get_outcome_lvl1("ControlText") out_comp_df = pd.DataFrame(out_comp) # name each column (number depends on outcome number) out_comp_df.columns = [ "out_comp_" + '{}'.format(column + 1) for column in out_comp_df.columns ] # fill blanks with NA out_comp_df.fillna("NA", inplace=True) # save to disk """ out_comp_df.to_csv("out_compe.csv", index=False) """
from Main import load_json, get_outcome_lvl1 import pandas as pd # load json file load_json() # get outcome measure data outcome_measure = get_outcome_lvl1("InterventionText") outcome_measure_df = pd.DataFrame(outcome_measure) # name each column (number depends on outcome number) outcome_measure_df.columns = [ "out_measure_" + '{}'.format(column + 1) for column in outcome_measure_df.columns ] # fill blanks with NA outcome_measure_df.fillna("NA", inplace=True) # remove problematic text outcome_measure_df = outcome_measure_df.replace(r'^\s*$', "NA", regex=True) # save to disk """ outcome_measure_df.to_csv("outcome_measure.csv", index=False) """
from Main import get_outcome_lvl1 import pandas as pd # get group 1 N data group2n = get_outcome_lvl1("Data2") group2N_df = pd.DataFrame(group2n) # name each column (number depends on outcome number) group2N_df.columns = [ "out_g2_n_" + '{}'.format(column + 1) for column in group2N_df.columns ] # fill blanks with NA group2N_df.fillna("NA", inplace=True) # remove problematic text group2N_df = group2N_df.replace(r'^\s*$', "NA", regex=True) # get outcometypeId data (to check) outcomeid = get_outcome_lvl1("OutcomeTypeId") outcometypeid_df = pd.DataFrame(outcomeid) # name each column (number depends on outcome number) outcometypeid_df.columns = [ "outcometype_df_" + '{}'.format(column + 1) for column in outcometypeid_df.columns ] mask = pd.DataFrame(outcometypeid_df["outcometype_df_1"] == 0) mask.columns = ["mask"] mask = mask.iloc[:, 0]
from Main import get_outcome_lvl1 import pandas as pd # get group1 sd data group1sd = get_outcome_lvl1("Data5") group1sd_df = pd.DataFrame(group1sd) # name each column (number depends on outcome number) group1sd_df.columns = [ "out_g1_sd_"+'{}'.format(column+1) for column in group1sd_df.columns] # fill blanks with NA group1sd_df.fillna("NA", inplace=True) # remove problematic text group1sd_df = group1sd_df.replace(r'^\s*$', "NA", regex=True) # get outcometypeId data (to check) getoucomeid = get_outcome_lvl1("OutcomeTypeId") outcometypeid_df = pd.DataFrame(getoucomeid) # name each column (number depends on outcome number) outcometypeid_df.columns = [ "outcometype_df_"+'{}'.format(column+1) for column in outcometypeid_df.columns] mask = pd.DataFrame(outcometypeid_df["outcometype_df_1"] == 0) mask.columns=["mask"] mask = mask.iloc[:, 0] # replace all 0 instances (null data) with "NA" for col in group1sd_df.columns:
from Main import load_json, get_outcome_lvl1 import pandas as pd # load json file load_json() # get sesmd data sesmd = get_outcome_lvl1("SESMD") sesmd_df = pd.DataFrame(sesmd) # round data to 4 decimal places sesmd_df = sesmd_df.applymap(lambda x: round(x, 4) if isinstance(x, (int, float)) else x) # name each column (number depends on outcome number) sesmd_df.columns = [ "se_" + '{}'.format(column + 1) for column in sesmd_df.columns ] # fill blanks with NA sesmd_df.fillna("NA", inplace=True) # replace problematic text sesmd_df = sesmd_df.replace(r'^\s*$', "NA", regex=True) # save to disk """ sesmd_df.to_csv("sesmd.csv", index=False) """
from Main import load_json, get_outcome_lvl1 import pandas as pd # load json file load_json() # get outcome title data outcome_title = get_outcome_lvl1("Title") outcome_title_df = pd.DataFrame(outcome_title) # name each column (number depends on outcome number) outcome_title_df.columns = [ "out_tit_" + '{}'.format(column + 1) for column in outcome_title_df.columns ] """ outcome_label_text_df.fillna("NA", inplace=True) """ # replace problematic text outcome_title_df = outcome_title_df.replace(r'^\s*$', "NA", regex=True) # save to disk """ outcome_title_df.to_csv("out_tit.csv", index=False) """
from Main import load_json, get_outcome_lvl1 import pandas as pd # load json file load_json() # get outcome description data outcome_description = get_outcome_lvl1("OutcomeDescription") outcome_description_df = pd.DataFrame(outcome_description) # name each column (number depends on outcome number) outcome_description_df.columns = [ "out_desc_" + '{}'.format(column + 1) for column in outcome_description_df.columns ] """ outcome_description_df = outcome_description_df.fillna("NA") """ outcome_description_df = outcome_description_df.replace(r'^\s*$', "NA", regex=True) # replace problematic text outcome_description_df.replace('\r', ' ', regex=True, inplace=True) outcome_description_df.replace('\n', ' ', regex=True, inplace=True) outcome_description_df.replace(':', ' ', regex=True, inplace=True) outcome_description_df.replace(';', ' ', regex=True, inplace=True) # save to disk """ outcome_description_df.to_csv("outcomedescription.csv", index=False) """
from Main import load_json, get_outcome_lvl1 import pandas as pd # load json file load_json() # get outcome data outcome = get_outcome_lvl1("OutcomeText") outcome_df = pd.DataFrame(outcome) # name each column (number depends on outcome number) outcome_df.columns = [ "out_label_" + '{}'.format(column + 1) for column in outcome_df.columns ] # fill blanks with NA outcome_df.fillna("NA", inplace=True) # save to disk """ outcome_df.to_csv("outcome.csv", index=False) """