Get counts by group using pandas [duplicate]

Question

I have a pandas dataframe which contains data as shown below:

ID  year_month_id   Class
1   201612          A
2   201612          D
3   201612          B
4   201612          Other
5   201612          Other
6   201612          Other
7   201612          A
8   201612          Other
9   201612          A
1   201701          B

So an ID can be under any class in a particular month and next month his class might change. Now what I want to do is for each ID get the number of months it has been under a particular class and also the latest class under which it falls. Something like below:

ID  Class_A Class_B Class_D Other Latest_Class
1   2        3       4         0    B
2   12       0       0         0    D

How do I achieve this in python. Can someone please help me with this? Also , since the real dataset is huge and manually verifying is not possible, how can I get a list of ID's which fall under more than 1 class?

Answer 1

We can use pivot table and concat i.e

ndf = df.pivot_table(index=['ID'],columns=['Class'],aggfunc='count',fill_value=0)\
    .xs('year_month_id', axis=1, drop_level=True)

ndf['latest'] = df.sort_values('ID').groupby('ID')['Class'].tail(1).values

Class  A  B  D  Other latest
ID                          
1      1  1  0      0      B
2      0  0  1      0      D
3      0  1  0      0      B
4      0  0  0      1  Other
5      0  0  0      1  Other
6      0  0  0      1  Other
7      1  0  0      0      A
8      0  0  0      1  Other
9      1  0  0      0      A

Answer 2

You can get counts by groupby with aggregate count, reshape by unstack. Last add new column with drop_duplicates:

df1 = df.groupby(['ID','Class'])['year_month_id'].count().unstack(fill_value=0)
df1['Latest_Class'] = df.drop_duplicates('ID', keep='last').set_index('ID')['Class']
print (df1)
Class  A  B  D  Other Latest_Class
ID                                
1      1  1  0      0            B
2      0  0  1      0            D
3      0  1  0      0            B
4      0  0  0      1        Other
5      0  0  0      1        Other
6      0  0  0      1        Other
7      1  0  0      0            A
8      0  0  0      1        Other
9      1  0  0      0            A

Answer 3

You can get a count of classes attended with groupby + value_counts + unstack -

g = df.groupby('ID')
i = g.Class.value_counts().unstack(fill_value=0)

To get the last Class, use groupby + last -

j = g.Class.last()

Concatenate to get your result -

pd.concat([i, j], 1).rename(columns={'Class': 'LastClass'})

    A  B  D  Other LastClass
ID                          
1   1  1  0      0         B
2   0  0  1      0         D
3   0  1  0      0         B
4   0  0  0      1     Other
5   0  0  0      1     Other
6   0  0  0      1     Other
7   1  0  0      0         A
8   0  0  0      1     Other
9   1  0  0      0         A

---

To get a list of IDs which have more than 1 per row, use sum + a mask -

k = i.sum(axis=1)
k[k > 1]

ID
1    2
dtype: int64