Selecting the higher of two data - pandas

Question

I'm working with Python Pandas trying to sort some student testing data. On occasion, students will test twice during the same testing window, and I want to save only the highest of the two tests. Here's an example of my dataset.
Name Score
Alice 32
Alice 75
John 89
Mark 40
Mark 70
Amy 60
Any ideas of how can I save only the higher score for each student?

Answer 1

If your data is in the dataframe df, you can sort by the score in descencing order and drop duplicate names, keeping the first:
df.sort_values(by='Score', ascending=False).drop_duplicates(subset='Name', keep='first')

Answer 2

You can do this with groupby. It works like this:
df.groupby('Name').agg({'Score': 'max'})
It results in:
Score
Name
Alice 75
Amy 60
John 89
Mark 70
Btw. in that special setup, you could also use drop_duplicates to make the name unique after sorting on the score. This would yield the same result, but would not be extensible (e.g. if you later would like to add the average score etc). It would look like this:
df.sort_values(['Name', 'Score']).drop_duplicates(['Name'], keep='last')
From the test data you posted:
import pandas as pd
from io import StringIO
sio= StringIO("""Name Score
Alice 32
Alice 75
John 89
Mark 40
Mark 70
Amy 60 """)
df= pd.read_csv(sio, sep='\s+')

Answer 3

There are multiple ways to do that, two of them are:
In [8]: df = pd.DataFrame({"Score" : [32, 75, 89, 40, 70, 60],
...: "Name" : ["Alice", "Alice", "John", "Mark", "Mark", "Amy"]})
...: df
Out[8]:
Score Name
0 32 Alice
1 75 Alice
2 89 John
3 40 Mark
4 70 Mark
5 60 Amy
In [13]: %time df.groupby("Name").max()
CPU times: user 2.26 ms, sys: 286 µs, total: 2.54 ms
Wall time: 2.11 ms
Out[13]:
Score
Name
Alice 75
Amy 60
John 89
Mark 70
In [14]: %time df.sort_values("Name").drop_duplicates(subset="Name", keep="last")
CPU times: user 2.25 ms, sys: 0 ns, total: 2.25 ms
Wall time: 1.89 ms
Out[14]:
Score Name
1 75 Alice
5 60 Amy
2 89 John
4 70 Mark

Answer 4

This question has already been answered here on StackOverflow.
You can merge two pandas data frames and after that calculate the maximum number in each row. df1 and df2 are the pandas of students score:
import pandas as pd
df1 = pd.DataFrame({'Alice': 3,
'John': 8,
'Mark': 7.5,
'Amy': 0},
index=[0])
df2 = pd.DataFrame({'Alice': 7,
'Mark': 7},
index=[0])
result = pd.concat([df1, df2], sort=True)
result = result.T
result["maxvalue"] = result.max(axis=1)