How to calculate the number of months during which a particular person has been in the service

Question

I have a pandas framework that 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

Thus, the identifier can be under any class for a certain month, and in the next month its class can change. Now I want each identifier to receive the number of months that it was under a particular class, as well as the last 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 to achieve this in python. Can someone please help me with this? In addition, since the real data set is huge and manual verification is not possible, how can I get a list of identifiers that fall under more than 1 class?

Answer 1

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

groupby count, unstack. 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

, groupby + value_counts + unstack -

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

, groupby + last -

j = g.Class.last()

-

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

---

, 1 , sum + -

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

ID
1    2
dtype: int64

Answer 4

2 count aggfunc, ( ), pd.crosstab:

 >> new_df = pd.crosstab(df.ID, df.Class)
 >> new_df
Class  A  B  D  Other
ID
1      1  1  0      0
2      0  0  1      0
3      0  1  0      0
4      0  0  0      1
5      0  0  0      1
6      0  0  0      1
7      1  0  0      0
8      0  0  0      1
9      1  0  0      0

---

:

>> df.groupby('ID').Class.last()
ID
1        B
2        D
3        B
4    Other
5    Other
6    Other
7        A
8    Other
9        A

---

Then you can combine them with concatenation:

>> new_df = pd.concat([new_df, df.groupby('ID').Class.last()], 1)
    A  B  D  Other  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

---

And to get the result exactly the way you wanted it:

>> new_df = new_df.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

---

Putting it all together as an oneliner:

>> new_df = pd.concat([pd.crosstab(df.ID, df.Class),df.groupby('ID').Class.last()],1).rename(columns={'Class':'LastClass'})

>> new_df
    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