Pandas pivot table value counts - pandas

Question

I have a dataframe in format:
Name Score Bin
John 90 80-100
Marc 30 20-40
John 10 0-20
David 20 0-20
...
I want to create a pivot table that looks like this:
Name 0-20 20-40 40-60 60-80 80-100 Total count Avg score
John 1 2 nan nan 2 5 60.53
Marc nan 2 nan nan nan 2 32.13
David 3 2 nan nan nan 5 21.80
So I want to have columns that show count of values for each bucket, as well as total count of values and average score.
I have tried
table = pd.pivot_table(df, values=['Score', "Bin"], index=["nAME"],
aggfunc={"Score" : np.average, "Bin" : "count"},
dropna=True, margins = True)
however I just get overall count and not broken down per bucket

Answer 1

Do your task in 3 steps:
Generate a pivot_table:
df2 = pd.pivot_table(df, index='Name', columns='Bin', values='Score', aggfunc='count')\
.reindex(columns=['0-20', '20-40', '40-60', '60-80', '80-100'])\
.rename_axis(columns='')
The result, for your source data extended to give roughly your expected
result, is:
0-20 20-40 40-60 60-80 80-100
Name
David 3.0 2.0 NaN NaN NaN
John 1.0 2.0 NaN NaN 2.0
Marc NaN 2.0 NaN NaN NaN
Note: As NaN is a special case of float, other values are also of
float type.
Generate Total_count and Avg_score:
df3 = df.groupby('Name')\
.agg(Total_count=('Score', 'count'), Avg_score=('Score', 'mean'))\
.rename(columns={'Total_count': 'Total count', 'Avg_score': 'Avg score'})
The result is:
Total count Avg score
Name
David 5 21.8
John 5 61.0
Marc 2 32.0
Join both above tables:
result = df2.join(df3)
The result is:
0-20 20-40 40-60 60-80 80-100 Total count Avg score
Name
David 3.0 2.0 NaN NaN NaN 5 21.8
John 1.0 2.0 NaN NaN 2.0 5 61.0
Marc NaN 2.0 NaN NaN NaN 2 32.0