Reputation: 147
I have a massive data-set that is entirely repetitive and i was thinking the best way to automate the task was with python. This is for an engineering project that uses motors of only 2 types, Follower, or Leader.
For Follower it will have a structure exactly like this(where IFDXXXX is will be the individual drive number):
For a Leader it will have a structure exactly like this(where IFDXXXX is will be the individual drive number):
My idea is that im going to import an excel sheet with the following format and store it as a dataframe with pandas to manipulate later for auto-generation:
Whats the easiest approach to do this, or is there another easier method? I would like to stick with python if possible as im trying to expand my knowledge with this language.
EDIT:
The ultimate end goal is to end up with a sheet that looks something like this:
Update1:
Upvotes: 1
Views: 340
Reputation: 528
suppose you have a table like you wanted to save with headlines drive_id and role and they are saved in dataframe df1 to iterate over your lines and create a new table, I prefer doing it with a list of dicts
out_table = []
for index, row in df1.iterrows()
drive_id = row["drive id"]
if row["role"]=="follower":
out_row ={}
out_row["drive_id"]= drive_id
out_row["task name"] = "rotate rev"
out_row["description"] = "check rotate hmi"
out_row["comment"] = "NA"
out_table.append(out_row)
#this is the end of the first row, so on you add all the rows for this role
out_row ={}
out_row["drive_id"] = driver_id
out_row["task name"] = "rotate fwd"
out_row["description"] = "check hdmi for footlock"
out_row["comment"] = ""
out_table.append(out_row)
if row["role"] == "FOLLOWER"
# here you add all the rows for this role
df2 = pd.DataFrame(out_table) # this makes a dataframe where the dict keys are the table headers
df2.to_excel(r"D:\drive_table.xlsx")
Upvotes: 2
Reputation: 134
import pandas as pd
df1=pd.read_excel('insert file path here',sheet_name = 0)
this allows pandas to store the excel sheet as a dataframe.
if you want to push a dataframe that is produced after your code you can use
df.to_excel(x)
Upvotes: 1