completed

app.py

 import smtplib
 import ssl
-from readxl import df1
+
+# importing from readxl
+from readxl import avg, maximum, minimum, final_time
 
 port = 465  # For SSL
 smtp_server = "smtp.gmail.com"
 sender_email = "logesh.n@knowledgelens.com"  # Enter your address
-receiver_email = ["rakeshpvofficial@gmail.com", "rakeshpvuokl@gmail.com"]  # Enter receiver address
-print("heloo")
-print(df1)
-print(type(df1))
-df2 = str(df1)
-print("df2", df2)
-print(type(df2))
+receiver_email = ["rakeshpvofficial@gmail.com", "rakeshpvuokl@gmail.com", "logeshnandhakumar12@gmail.com"]
+# Enter receiver address
+
 password = input("Type your password and press enter: ")
-message = df2
+message1 = avg + maximum + minimum
+message2 = final_time
+message3 = str(final_time)
+
 context = ssl.create_default_context()
 with smtplib.SMTP_SSL(smtp_server, port, context=context) as server:
     server.login(sender_email, password)
 
     for i in range(len(receiver_email)):
-        server.sendmail(sender_email, receiver_email[i], message)
+        server.sendmail(sender_email, receiver_email[i], message1)

readxl.py

 # importing pandas to read the xlsx file
-# df1 stores avg of all columns except timestamp, df2 stores max of all columns
+# df1 stores avg of all columns except timestamp, df2 stores max of all columns, df3 stores min of all columns
 # Pandas dataframe.idxmax() method returns the index of the first occurrence of maximum over the requested axis.
 import pandas as pd
 
@@ -8,23 +8,24 @@ df = pd.read_excel('D:/sources/task3.xlsx')
 
 # rounding of the columns to two decimal points and getting the average of each of the columns
 df1 = round(df[["kWh", "kVAh", "kW", "kVA", "current"]].mean(), 2)
-print("The average of kWh, kVAh, kW, kVA, current as respectively >>\n\n", df1)
+avg = str(df1)
 
 # rounding of the columns to two decimal points and getting the max of each of the columns
 df2 = round(df[["kWh", "kVAh", "kW", "kVA", "current"]].max(), 2)
-print("\nThe Maximum of kWh, kVAh, kW, kVA, current as respectively >>\n\n", df2)
+maximum = str(df2)
 
 # rounding of the columns to two decimal points and getting the min of each of the columns
 df3 = round(df[["kWh", "kVAh", "kW", "kVA", "current"]].min(), 2)
-print("\nThe Minimum of kWh, kVAh, kW, kVA, current as respectively >>\n\n", df3)
+minimum = str(df3)
 
-dff = round(df[["kWh", "kVAh", "kW", "kVA", "current"]].idxmax(), 0)
-dfff = df["current"].idxmax()
-print(dff)
-# col = "current"
-# max_x = df.loc[df[col].idxmax()]
-# print("DS")
-# print(max_x)
+# finding the timestamp of max value of each column
+df_time = round(df[["kWh", "kVAh", "kW", "kVA", "current"]].idxmax(), 0)
+max_time = df["Timestamp"][df_time]
+timestamps_max = str(max_time)
 
-new = df["Timestamp"][dff]
-print(new)
+# finding the timestamp of max value of each column
+df_time = round(df[["kWh", "kVAh", "kW", "kVA", "current"]].idxmin(), 0)
\ No newline at end of file
+min_time = df["Timestamp"][df_time]
+timestamps_min = str(min_time)
+
+final_time = timestamps_max + timestamps_min