88 lines
3.7 KiB
Python
88 lines
3.7 KiB
Python
import pandas as pd
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import os
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import numpy as np
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import matplotlib.pyplot as plt
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curdir = os.getcwd()
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path_delim = '/'
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df1 = pd.read_excel(curdir + path_delim + "data/users_09_11_2023_18_35.xlsx", sheet_name="Sheet1")
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df2 = pd.read_excel(curdir + path_delim + "data/2023-11-09_to_2023-11-10.xlsx", sheet_name="Sheet2")
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df2 = df2.sort_values('testTime')
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variance_column = df2["led2Buffer"].var(ddof=0)
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print(variance_column)
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# df2.plot(kind = 'scatter', x = 'testTime', y = 'calculatedRatio')
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# plt.show()
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# df2.plot(kind = 'scatter', x = 'testTime', y = 'led1Buffer')
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# plt.show()
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# df2.plot(kind = 'scatter', x = 'testTime', y = 'led2Buffer')
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# plt.show()
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# df2.plot(kind = 'scatter', x = 'testTime', y = 'led1Sample')
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# plt.show()
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# df2.plot(kind = 'scatter', x = 'testTime', y = 'led2Sample')
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# plt.show()
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df = df1.merge(df2, on="_id", how="outer")
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print(df.columns)
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# # df = pd.concat([df1, df3], ignore_index=True)
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df['Age'] = 2023 - df['birthYear']
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df['deviceId'].hist()
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plt.show()
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# df['led2Sample'].plot.box()
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# plt.show()
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# df['led2Sample'].plot.kde()
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# plt.show()
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# df['led2Sample'].plot.density()
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# plt.show()
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# df['category'].plot.pie()
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# plt.show()
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df = df[["_id", "name_x", "abhaId", "aadharId", "Age", "gender", "category", "maritalStatus", "house", "district", "state", "pinCode", "phoneNumber", "classificationResult", "bloodGroup", "testTime", "caste", "registrationCenterName"]]
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print(df)
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df.rename(columns={'_id': "Sample ID", "name_x": "Name", "abhaId": "ABHA ID", "aadharId": "Aadhaar ID", "gender": "Gender", "category": "Category", "maritalStatus": "Marital Status", "house": "Address", "district": "District", "state": "State", "pinCode": "Pincode", "phoneNumber": "Mobile Number", "testTime": "Date", "classificationResult": "Test Result", "bloodGroup": "Blood Group", "Age": "Age", "caste": "Caste", "registrationCenterName": "Center"}, inplace = True)
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df = df.reindex(["Sample ID", "Name", "ABHA ID", "Aadhaar ID", "Age", "Gender", "Caste", "Category", "Marital Status", "Address", "District", "State", "Pincode", "Mobile Number", "Date", "Test Result", "Blood Group", "Center"], axis=1)
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# df['Date'] = pd.to_datetime(df["Date"].dt.strftime('%d-%m-%Y'))
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# df = df.sort_values(by=['Date'], ascending=True)
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df["Test Result"].fillna("NOTEST", inplace = True)
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print("NOTEST: ", len(df[df["Test Result"] == "NOTEST"]))
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df = df[df["Test Result"] != "NOTEST"]
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df["Blood Group"].fillna("NOBLOODGROUP", inplace = True)
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print("NO BLOOD GROUP: ", len(df[df["Blood Group"] == "NOBLOODGROUP"]))
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df_final = df.sort_values('Date').drop_duplicates('Sample ID', keep='last')
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df_final.loc[df_final['Test Result'] == 'Normal', 'Test Result'] = 'Normal (HbA)'
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df_final.loc[df_final['Test Result'] == 'Sickle Cell Trait', 'Test Result'] = 'Sickle Cell Trait (HbAS)'
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df_final.loc[df_final['Test Result'] == 'Sickle Cell Disease', 'Test Result'] = 'Sickle Cell Disease (HbAS)'
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df_final.loc[df_final['Test Result'] == 'SCT', 'Test Result'] = 'Sickle Cell Trait (HbAS)'
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df_final.loc[df_final['Test Result'] == 'SCD', 'Test Result'] = 'Sickle cell Disease (HbSS)'
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df_final.loc[df_final['Test Result'] == 'PBL', 'Test Result'] = 'Positive Borderline'
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df_final.loc[df_final['Test Result'] == 'NBL', 'Test Result'] = 'Negative Borderline'
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df_final.loc[df_final['Gender'] == 'Male', 'Gender'] = 'MALE'
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df_final.loc[df_final['Gender'] == 'Female', 'Gender'] = 'FEMALE'
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print(df_final.groupby(["Test Result"]).describe())
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df_count = df.groupby(["Test Result"]).describe()["ABHA ID"]["count"]
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print(df.groupby(["Test Result"]).describe()["ABHA ID"]["count"])
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writer = pd.ExcelWriter(curdir + path_delim + "data/Nov09.xlsx", engine = 'openpyxl')
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df_final.to_excel(writer, sheet_name = 'op', index=False)
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df_count.to_excel(writer, sheet_name = "count")
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writer.close() |