What association analysis would you like me to perform on this Excel file?

cd /home/daytona/workspace/attachments/451a809a-a430-4e9e-a771-af6016fd2494/ && python3 -c " import openpyxl wb = openpyxl.load_workbook('dr smita today.xlsx') print('Sheets:', wb.sheetnames) ws = wb.active print('Dimensions:', ws.dimensions) print('Max row:', ws.max_row, 'Max col:', ws.max_column) # Print headers headers = [cell.value for cell in ws[1]] print('Headers:', headers) # Print first 3 data rows for row in ws.iter_rows(min_row=2, max_row=4, values_only=True): print(row) "

python3 -c " import openpyxl wb = openpyxl.load_workbook('/home/daytona/workspace/attachments/451a809a-a430-4e9e-a771-af6016fd2494/dr smita today.xlsx') ws = wb.active # Print all rows to understand structure for i, row in enumerate(ws.iter_rows(values_only=True), 1): print(f'Row {i}:', row) "

python3 << 'EOF' import openpyxl import pandas as pd import numpy as np from scipy import stats wb = openpyxl.load_workbook('/home/daytona/workspace/attachments/451a809a-a430-4e9e-a771-af6016fd2494/dr smita today.xlsx') ws = wb.active # Extract data rows 6-55 (50 patients), columns: # B=PTS NAME, C=Sex, D=AGE, E=UREA, F=CREATININE, G=CHOL, H=TG, I=HDL, J=LDL(formula), K=VLDL(formula) # L=TC/HDL(formula), M=LDL/HDL(formula), O=CYS-C, T=GFR-Creat, U=GFR-CYS-C rows = [] for row in ws.iter_rows(min_row=6, max_row=55, values_only=True): rows.append(row) # Columns (0-indexed from A): # A=0, B=1(name), C=2(sex), D=3(age), E=4(urea), F=5(creat), G=6(chol), H=7(TG), I=8(HDL), J=9(LDL-formula), K=10(VLDL-formula), L=11(TC/HDL), M=12(LDL/HDL), O=14(CYS-C), T=19(GFR-Creat), U=20(GFR-CysC) # Note: LDL, VLDL, TC/HDL, LDL/HDL are stored as formula strings - need to compute them data = [] for r in rows: urea = r[4] creat = r[5] chol = r[6] tg = r[7] hdl = r[8] cysc = r[14] gfr_creat = r[19] gfr_cysc = r[20] # Compute derived lipid values try: ldl = float(chol) - float(hdl) - float(tg)/5 except: ldl = None try: vldl = float(tg)/5 except: vldl = None try: tc_hdl = float(chol)/float(hdl) except: tc_hdl = None try: ldl_hdl = float(ldl)/float(hdl) if ldl is not None else None except: ldl_hdl = None data.append({ 'UREA': urea, 'CREATININE': creat, 'CHOL': chol, 'TG': tg, 'HDL': hdl, 'LDL': ldl, 'VLDL': vldl, 'TC_HDL': tc_hdl, 'LDL_HDL': ldl_hdl, 'CYS_C': cysc, 'GFR_CREAT': gfr_creat, 'GFR_CYSC': gfr_cysc }) df = pd.DataFrame(data) # Convert to numeric for col in df.columns: df[col] = pd.to_numeric(df[col], errors='coerce') print("Dataset shape:", df.shape) print("\nDescriptive Statistics:") print(df.describe().round(2)) print("\nMissing values per column:") print(df.isnull().sum()) EOF

python3 << 'EOF' import openpyxl import pandas as pd import numpy as np from scipy import stats wb = openpyxl.load_workbook('/home/daytona/workspace/attachments/451a809a-a430-4e9e-a771-af6016fd2494/dr smita today.xlsx') ws = wb.active rows = [] for row in ws.iter_rows(min_row=6, max_row=55, values_only=True): rows.append(row) data = [] for r in rows: urea = r[4]; creat = r[5]; chol = r[6]; tg = r[7]; hdl = r[8] cysc = r[14]; gfr_creat = r[19]; gfr_cysc = r[20] try: ldl = float(chol) - float(hdl) - float(tg)/5 except: ldl = None try: vldl = float(tg)/5 except: vldl = None try: tc_hdl = float(chol)/float(hdl) except: tc_hdl = None try: ldl_hdl = float(ldl)/float(hdl) if ldl is not None else None except: ldl_hdl = None data.append({'UREA': urea, 'CREATININE': creat, 'CHOL': chol, 'TG': tg, 'HDL': hdl, 'LDL': ldl, 'VLDL': vldl, 'TC_HDL': tc_hdl, 'LDL_HDL': ldl_hdl, 'CYS_C': cysc, 'GFR_CREAT': gfr_creat, 'GFR_CYSC': gfr_cysc}) df = pd.DataFrame(data) for col in df.columns: df[col] = pd.to_numeric(df[col], errors='coerce') lipid_vars = ['CHOL', 'TG', 'HDL', 'LDL', 'VLDL', 'TC_HDL', 'LDL_HDL'] renal_vars = ['CYS_C', 'GFR_CYSC', 'GFR_CREAT', 'CREATININE', 'UREA'] print("=" * 90) print("PEARSON CORRELATION: Lipid Markers vs Renal/GFR Markers") print("=" * 90) print(f"{'Variable Pair':<35} {'r':>8} {'p-value':>12} {'Significance':>14}") print("-" * 90) pearson_results = [] for renal in renal_vars: for lipid in lipid_vars: x = df[lipid].dropna() y = df[renal].dropna() common = df[[lipid, renal]].dropna() r, p = stats.pearsonr(common[lipid], common[renal]) sig = '***' if p < 0.001 else ('**' if p < 0.01 else ('*' if p < 0.05 else 'NS')) pearson_results.append((renal, lipid, r, p, sig)) pair = f"{lipid} vs {renal}" print(f" {pair:<33} {r:>8.4f} {p:>12.4f} {sig:>14}") print() print("\n" + "=" * 90) print("SPEARMAN CORRELATION: Lipid Markers vs Renal/GFR Markers") print("=" * 90) print(f"{'Variable Pair':<35} {'rho':>8} {'p-value':>12} {'Significance':>14}") print("-" * 90) spearman_results = [] for renal in renal_vars: for lipid in lipid_vars: common = df[[lipid, renal]].dropna() rho, p = stats.spearmanr(common[lipid], common[renal]) sig = '***' if p < 0.001 else ('**' if p < 0.01 else ('*' if p < 0.05 else 'NS')) spearman_results.append((renal, lipid, rho, p, sig)) pair = f"{lipid} vs {renal}" print(f" {pair:<33} {rho:>8.4f} {p:>12.4f} {sig:>14}") print() print("Significance: *** p<0.001 | ** p<0.01 | * p<0.05 | NS = not significant") EOF