Pandas - understanding output of pivot table - pandas

Question

Here is my example:
import pandas as pd
df = pd.DataFrame({
'Student': ['A', 'B', 'B'],
'Assessor': ['C', 'D', 'D'],
'Score': [72, 19, 92]})
df = df.pivot_table(
index='Student',
columns='Assessor',
values='Score',
aggfunc=lambda x: x)
print(df)
The output looks like:
Assessor C D
Student
A 72 NaN
B NaN [1, 2]
I am not sure why I get '[1,2]' as output. I would expect something like:
Assessor C D
Student
A 72 NaN
B NaN 19
B NaN 92
Here is related question:
if I replace my dataframe with
df = pd.DataFrame({
'Student': ['A', 'B', 'B'],
'Assessor': ['C', 'D', 'D'],
'Score': ['foo', 'bar', 'foo']})
The output of the same pivot is going to be
Process finished with exit code 255
Any thoughts.

Answer 1

pivot_table finds the unique values of the index/columns and aggregates if there are multiple rows in the original DataFrame in a particular cell.
Indexes/columns are generally meant to be unique, so if you want to get the data in that form, you have do something a little ugly like this, although you probably don't want to.
In [21]: pivoted = pd.DataFrame(columns=df['Assessor'], index=df['Student'])
In [22]: for (assessor, score, student) in df.itertuples(index=False):
...: pivoted.loc[student, assessor] = score
For your second question, the reason that groupby generally fails if that there are no numeric columns to aggregate, although it seems to be a bug that it completely crashes like that. I added a note to the issue here.