Create a function in Python that will read CSV data $($without using import csv$),$ modify fields, and return an array in CSV format with the columns Email, Age, and Order At transformed $($note the space between rows in the output$)$:

from datetime import datetime

def age$\_$range$($age$)$:

$$ age $=$ int$($age$)$

$$ if $1<=$ age $<=20$:

$$ return $"1->20"$

$$ elif $21<=$ age $<=40$:

$$ return $"21->40"$

$$ elif $41<=$ age $<=65$:

$$ return $"41->65"$

$$ elif $66<=$ age $<=99$:

$$ return $"66->99"$

$$ else:

$$ return "NOAGE"

def time$\_$of$\_$day$($datetime$\_$str$)$:

$$ t $=$ datetime.strptime$($datetime$\_$str$,\text{'}\%$Y$-\%$m$-\%$d $\%$H:$\%$M:$\%$S$\text{'})$

$$ th $=$ t$.$hour

$$ if $6<=$ th $<12$:

$$ return "morning"

$$ elif $12<=$ th $<18$:

$$ return "afternoon"

$$ elif $18<=$ th $<24$:

$$ return "evening"

$$ else:

$$ return "NONE"

$"""$

:type $$csv$\_$content: $\{$String$\}$

:rtype: string$[]$

$"""$

def my$\_$data$\_$transform$($csv$\_$content$)$:

$$ # Split rows into an array

$$ rows $=$ csv$\_$content.split$("$

$")$

$$

$$ # Extract headers and data rows

$$ headers $=$ rows.pop$(0)$# Remove and capture the first row $($headers$)$

$$ transformed$\_$rows $=[$headers$]$# Initialize transformed rows with headers

$$

$$ # Process each row

$$ for row in rows:

$$ fields $=$ row.split$(",")$

$$ # Apply transformations

$$ fields$[4]=$ fields$[4].$split$("$@$")$ # $[1]$# Remove username from email

$$ fields$[5]=$ age$\_$range$($fields$[5])$# Transform age to range

$$ fields$[9]=$ time$\_$of$\_$day$($fields$[9])$# Transform order time to time of day

$$ # Rejoin fields and add to transformed rows

$$ transformed$\_$rows.append$(",".$join$($fields$))$

$$ return transformed$\_$rows

print$($my$\_$data$\_$transform$("$Gender$,$FirstName,LastName,UserName,Email,Age,City,Device,Coffee Quantity,Order At

Male,Carl,Wilderman,carl,wilderman$\_$carl@yahoo.com,$29,$Seattle,Safari iPhone,$2,2020-03-0616$:$37$:$56$

Male,Marvin,Lind,marvin,marvin$\_$lind@hotmail.com,$77,$Detroit,Chrome Android,$2,2020-03-0213$:$55$:$51$

Female,Shanelle,Marquardt,shanelle,marquardt.shanelle@hotmail.com,$21,$Las Vegas,Chrome,$1,2020-03-0517$:$53$:$05$

Female,Lavonne,Romaguera,lavonne,romaguera.lavonne@yahoo.com,$81,$Seattle,Chrome,$2,2020-03-0410$:$33$:$53$

Male,Derick,McLaughlin,derick,mclaughlin.derick@hotmail.com,$47,$Chicago,Chrome Android,$1,2020-03-0515$:$19$:$48$

$"))$

# input:

# Gender,FirstName,LastName,UserName,Email,Age,City,Device,Coffee Quantity,Order At

# Male,Carl,Wilderman,carl,wilderman$\_$carl@yahoo.com,$29,$Seattle,Safari iPhone,$2,2020-03-0616$:$37$:$56$

# Male,Marvin,Lind,marvin,marvin$\_$lind@hotmail.com,$77,$Detroit,Chrome Android,$2,2020-03-0213$:$55$:$51$

# Female,Shanelle,Marquardt,shanelle,marquardt.shanelle@hotmail.com,$21,$Las Vegas,Chrome,$1,2020-03-0517$:$53$:$05$

# Female,Lavonne,Romaguera,lavonne,romaguera.lavonne@yahoo.com,$81,$Seattle,Chrome,$2,2020-03-0410$:$33$:$53$

# Male,Derick,McLaughlin,derick,mclaughlin.derick@hotmail.com,$47,$Chicago,Chrome Android,$1,2020-03-0515$:$19$:$48$

$")$

# expected output $($return$,$ with spaces between rows$)$:

# $["$Gender$,$FirstName,LastName,UserName,Email,Age,City,Device,Coffee Quantity,Order At$",$ "Male,Carl,Wilderman,carl,yahoo.com,$21->40,$Seattle,Safari iPhone,$2,$afternoon", "Male,Marvin,Lind,marvin,hotmail.com,$66->99,$Detroit,Chrome Android,$2,$afternoon", "Female,Shanelle,Marquardt,shanelle,hotmail.com,$21->40,$Las Vegas,Chrome,$1,$afternoon", "Female,Lavonne,Romaguera,lavonne,yahoo.com,$66->99,$Seattle,Chrome,$2,$morning", "Male,Derick,McLaughlin,derick,hotmail.com,$41->65,$Chicago,Chrome Android,$1,$afternoon"$]$