I am pulling in data from an excel spreadsheet and creating a list of dictionary objects from the rows in that spreadsheet. I converted that to a pandas dataframe. In this data frame I have a column with unique names and then two more columns success and failed in these column I was keeping a count per so either 1 0.
Name success failed
Name1 1 0
Name1 0 1
Name1 1 0
Name1 0 1
Name1 1 0
Name2 1 0
Name2 1 0
Name2 0 1
Name3 1 0
Name3 0 1
Name3 0 1
Name3 1 0
I found using df.groupby("name").to_values() displays the name and success and failed column and the columns are grouped with the totals per name of success and failed, but I do not know how to continue working with this as a new dataframe object. If I output this to excel I just have one row with totals. Is there a way to create a new object from this with all my columns? My intention was to then show a percentage of a total from the two success and failed per name counts after that.
Name Success
Name1 90%
Name2 94%
Name3 89%
Code
out = df.groupby('Name')['success'].mean().map('{:.1%}'.format).reset_index()
out:
Name success
0 Name1 60.0%
1 Name2 66.7%
2 Name3 50.0%