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on February 27, 2015

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GROUP BY()

What's GROUP BY(): The Group By feature in Power BI summarises data based on one or more aggregate functions. You can use the Group By feature to find the average, min, max etc. It's same as the SQL Server Power BI GroupBy function. However Aggregation is optional it also can be used to to make set/group of unique. Let's see with below data. image: Sales_Tbl (click enlarge to read clearly) GroupBy() function gives result as Table output, so you'll take New Table, same as the below snapshot. image: take as 'New Table' Syntax: after New Table, GroupBy_Tbl=GroupBy(Table, [GroupBy_columnName],..,[New_Name], [Expression]) GroupBy_Total_Qty_City = GROUPBY ( Sales_Tbl , Sales_Tbl [City] , "Total_Quantity" , SUMX ( Sales_Tbl , Sales_Tbl [Qty] ) ) Note: Group By Function need a Helper Function CURRENTGROUP() which make it differ from SUMMERIZE function(explained in next chapter, SUMMARIZE function does not need any helper function). GroupBy_Total_Q

Gmail configuration for 2 step verification

Open the page using link: https://myaccount.google.com/u/2/?tab=kk Step-1). Now this below page will come up, and click on highlighted "Security" Step-2). Now click on 2-Step Verification as per below highlighted. Step-3). Now it'll again ask for your Gmail id confirmation so confirm it with your normal Gmail login password(which you use always normally). Step-4). Now you will find the below screen (scroll downward and you'll see below highlighted area). Step-5). Now, here you'll give any name like (Test_Email) and click to create. Step-5). Now, final password has been generated and copy this password and click on Done. as below image. Notice: for example password is b g b x t n g o y f h c n k t s but you'll take without space so the real password is like b g b x t n g o y f h c n k t s Now its done. It takes lot's of efforts to make content like this so please share with more person to grow our page and YouTube Channel www.youtube.

Sending Email to multiple user (using file records)

import pandas as pd df=pd.read_csv( r"C:\Users\Home\Downloads\apple_products2.csv" , usecols =[ 'ID' , 'msg' , 'sub' ], low_memory = True ) df=pd.DataFrame(df) import smtplib from email.mime.text import MIMEText subject = "Test Email Subject" sender = "sk9775589@gmail.com" #recipients = ["sabhajeetkumar1@gmail.com"] password = "qwsnxvnheoqyauij" def send_email (subject, body, sender, recipients, password): msg = MIMEText(body) msg[ 'Subject' ] = subject msg[ 'From' ] = sender msg[ 'To' ] = ', ' .join(recipients) with smtplib.SMTP_SSL( 'smtp.gmail.com' , 465 ) as smtp_server: smtp_server.login(sender, password) smtp_server.sendmail(sender, recipients, msg.as_string()) print ( "Message sent!" ) for x in range ( len (df)): send_email(df.loc[x, 'sub' ], df.loc[x, 'msg' ], sender, df.loc[x, 'ID' ], pas

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