Sample of my existing script:
# import python libraries
import pandas as pd
# declare file name as 'infileName' & 'outfileName'
infileName="FFH069.3-W02_dielist.csv"
outfileName="Import_FFH069.3-W02_dielist.csv"
# load the data
data = pd.read_csv(infileName)
# convert to a DataFrame
df = pd.DataFrame(data)
# remove unneeded columns
df.drop(columns=["x_center","y_center","x_extent","y_extent"], axis=1, inplace=True)
# rename the infilename columns to match Object Attributes
df.rename(columns={'layout_id':'Design'}, inplace = True)
# insert new columns to match the Object Attributes
df.insert(0,"Wafer + Die ID", "FFH069.3-W02-")
df.insert(1,"Chip Preference", "Primary")
# print the infileName head to validate changes
print(df.head())
Wafer + Die ID Chip Preference Design x_die y_die
0 FFH069.3-W02- Primary DS-CHR-0009-01-01 2 0
1 FFH069.3-W02- Primary DS-CHR-0008-01-02 3 0
2 FFH069.3-W02- Primary DS-CHR-0009-01-01 4 0
3 FFH069.3-W02- Primary DS-TLS-001-D6 5 0
4 FFH069.3-W02- Primary DS-CHR-0008-01-02 1 1
I have made attempts using the concat function to combine
'Wafer + Die ID' + 'x_die' + '.' + 'y_die'
and read through other options, but am not sure which is best served, nor how to implement.
Desired Output:
Wafer + Die ID Chip Preference Design x_die y_die
0 FFH069.3-W02-2.0 Primary DS-CHR-0009-01-01 2 0
1 FFH069.3-W02-3.0 Primary DS-CHR-0008-01-02 3 0
2 FFH069.3-W02-4.0 Primary DS-CHR-0009-01-01 4 0
3 FFH069.3-W02-5.0 Primary DS-TLS-001-D6 5 0
4 FFH069.3-W02-1.1 Primary DS-CHR-0008-01-02 1 1