Description:
edcanalytics 0.1.2
edc-analytics
Build analytic tables from EDC data
Read your data into a dataframe, for example an EDC screening table:
qs_screening = SubjectScreening.objects.all()
df = read_frame(qs_screening)
Convert all numerics to pandas numerics:
cols = [
"age_in_years",
"dia_blood_pressure_avg",
"fbg_value",
"hba1c_value",
"ogtt_value",
"sys_blood_pressure_avg",
]
df[cols] = df[cols].apply(pd.to_numeric)
Pass the dataframe to each Table class
gender_tbl = GenderTable(main_df=df)
age_tbl = AgeTable(main_df=df)
bp_table = BpTable(main_df=df)
In the Table instance,
data_df is the supporting dataframe
table_df is the dataframe to display. The table_df displays formatted data in the first 5 columns (“Characteristic”, “Statistic”, “F”, “M”, “All”). The table_df has additional columns that contain the statistics used for the statistics displayed in columns [“F”, “M”, “All”].
From above, gender_tbl.table_df is just a dataframe and can be combined with other table_df dataframes using pd.concat() to make a single table_df.
table_df = pd.concat(
[gender_tbl.table_df, age_tbl.table_df, bp_table.table_df]
)
Show just the first 5 columns:
table_df.iloc[:, :5]
Like any dataframe, you can export to csv:
path = "my/path/to/csv/folder/table_df.csv"
table_df.to_csv(path_or_buf=path, encoding="utf-8", index=0, sep="|")
License
For personal and professional use. You cannot resell or redistribute these repositories in their original state.
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