cncd-package 0.1.3

Description:

cncdpackage 0.1.3

Download
pip install cncd-package
Loading the QC checks class
from cncd_package.QC import QC_Check

Reading data
sharing paths of different datasets:

\cncd-dc1\DATA SCIENCE DEPARTMENT\1_phenotype_data\3_master_files_entry_1_entry_2_raw\april_2023\entry_1\updated_master_cad_2nd_entry_april_2023.csv
\cncd-dc1\DATA SCIENCE DEPARTMENT\1_phenotype_data\3_master_files_entry_1_entry_2_raw\april_2023\entry_1\updated_master_dm_2nd_entry_april_2023.csv
\cncd-dc1\DATA SCIENCE DEPARTMENT\1_phenotype_data\3_master_files_entry_1_entry_2_raw\april_2023\entry_1\updated_master_hf_1st_entry_april_2023.csv
\cncd-dc1\DATA SCIENCE DEPARTMENT\1_phenotype_data\3_master_files_entry_1_entry_2_raw\april_2023\entry_1\updated_master_nafld_1st_entry_april_2023.csv
\cncd-dc1\DATA SCIENCE DEPARTMENT\1_phenotype_data\3_master_files_entry_1_entry_2_raw\april_2023\entry_1\updated_master_pgr_1st_entry_april_2023.csv
\cncd-dc1\DATA SCIENCE DEPARTMENT\1_phenotype_data\3_master_files_entry_1_entry_2_raw\april_2023\entry_1\updated_master_promis_1st_entry_april_2023.csv
\cncd-dc1\DATA SCIENCE DEPARTMENT\1_phenotype_data\3_master_files_entry_1_entry_2_raw\april_2023\entry_1\updated_master_stroke_1st_entry_april_2023.csv

import pandas as pd
import warnings

warnings.simplefilter('ignore')

promis_data = pd.read_csv(r"\\cncd-dc1\DATA SCIENCE DEPARTMENT\1_phenotype_data\3_master_files_entry_1_entry_2_raw\april_2023\entry_1\updated_master_promis_1st_entry_april_2023.csv")

Doing QC
qc = QC_Check(dataframe=promis_data, project_id="PROMIS")

Age Check
qc.age_check().head()






study_id
age




0
D969
106.0


1
LY6648
5.0


2
LY6753
99.0


3
LZ6394
96.0


4
V3629
0.0




Gender Checks
qc.gender_check(gender='male')






study_id
gender
result




0
LY7080
2.0
MR


1
LY7094
2.0
MR


2
LY7470
2.0
MR


3
V2125
2.0
MR


4
V2134
2.0
MR


...
...
...
...
...


65
R891
2.0
MR


66
R1052
2.0
MR


67
R1560
2.0
MR


68
R1577
2.0
MR


69
R1658
2.0
MR



70 rows × 4 columns

qc.gender_check(gender='female')






study_id
gender
result




0
K1884
1.0
BIBI


1
LY2898
1.0
BIBI


2
LY2903
1.0
BIBI


3
LY2910
1.0
BANO


4
LY3855
1.0
BIBI


...
...
...
...
...


85
HZ93
1.0
BIBI


86
PZ6
1.0
BIBI


87
PY27
1.0
BIBI


88
SM191
1.0
BB


89
RZ9
1.0
BIBI



90 rows × 4 columns

Check QC status
qc.check_qc_status()






study_id
status
mi
pre_mi
st_elevation
troponin_positive




0
D1
1.0
0.0
0.0
1.0
0.0


1
D2
1.0
0.0
0.0
1.0
NaN


2
D4
1.0
0.0
0.0
1.0
0.0


3
D5
1.0
0.0
0.0
1.0
2.0


4
D6
1.0
1.0
1.0
1.0
0.0


...
...
...
...
...
...
...


82820
RZ111
1.0
0.0
NaN
1.0
NaN


82827
RZ118
1.0
0.0
NaN
0.0
1.0


82828
RZ119
1.0
0.0
NaN
0.0
1.0


82830
RZ121
1.0
0.0
NaN
1.0
NaN


82831
RZ122
1.0
0.0
NaN
0.0
1.0



31966 rows × 6 columns