Before I ask my question, I want it to be known that I looked at the following page but it did not return what I need specifically:
Count unique values using pandas groupby
Let's say I have the following df of four individuals trying to guess a code. Each individual has two guesses:
df = pd.DataFrame({'name':['Sally', 'John', 'Lucy', 'Mark','Sally', 'John', 'Lucy', 'Mark'],
'guess':['123', '321', '213', '312', '321', '231', '123', '312']})
df
name guess
0 Sally 123
1 John 321
2 Lucy 213
3 Mark 312
4 Sally 321
5 John 231
6 Lucy 123
7 Mark 312
I want to know how many completely unique guesses each individual has. That is, I don't want to know how many unique guesses each individual has out of their own guesses, rather, I want to know how many unique guesses they have out of all guesses. Let me elaborate.
Using the code from the post linked above, this is what I get:
df.groupby('name')[['guess']].nunique()
guess
name
John 2
Lucy 2
Mark 1
Sally 2
This returns how many unique guesses each individual has when compared to their own guesses. Again, what I am looking for is how many unique guesses each individual has out of all total guesses (aka the entire coulmn). This is the output I am looking for:
guess count
name
John 1 2
Lucy 1 2
Mark 0 2
Sally 0 2
Because one of John's guesses (231) and one of Lucy's guesses (213) are unique out of all guesses. It would also be nice to have a column showing each individuals total guess count.
Thank you in advance!
You can first find out which guesses were unique by grouping by guess, then just doing a grouped count and sum on name afterwards gives you the final output:
In [64]: df['unique'] = df['guess'].map(df.groupby("guess").count()['name'] == 1).astype(int)
In [65]: df.groupby("name")['unique'].agg(['sum', 'count']).rename(columns={'sum': 'guess'})
Out[65]:
guess count
name
John 1 2
Lucy 1 2
Mark 0 2
Sally 0 2