Python: max occurence of consecutive days - python-2.7

Question

I have an Input file:
ID,ROLL_NO,ADM_DATE,FEES
1,12345,01/12/2016,500
2,12345,02/12/2016,200
3,987654,01/12/2016,1000
4,12345,03/12/2016,0
5,12345,04/12/2016,0
6,12345,05/12/2016,100
7,12345,06/12/2016,0
8,12345,07/12/2016,0
9,12345,08/12/2016,0
10,987654,02/12/2016,150
11,987654,03/12/2016,300
I'm trying to find maximum count of consecutive days where FEES is 0 for a particular ROLL_NO. If FEES is not equal to zero for consecutive days, max count will be zero for that particular ROLL_NO.
Expected Output:
ID,ROLL_NO,MAX_CNT -- First occurrence of ID for a particular ROLL_NO should come as ID in output
1,12345,3
3,987654,0
This is what I've come up with so far,
import pandas as pd
df = pd.read_csv('I5.txt')
df['COUNT'] = df.groupby(['ROLLNO','ADM_DATE'])['ROLLNO'].transform(pd.Series.value_counts)
print df
But I don't believe this is the right way to approach this.
Could someone help out a python newbie out here?

Answer 1

You can use:
#consecutive groups
r = df['ROLL_NO'] * df['FEES'].eq(0)
a = r.ne(r.shift()).cumsum()
print (a)
ID
1 1
2 1
3 1
4 2
5 2
6 3
7 4
8 4
9 4
10 5
11 5
dtype: int32
#filter 0 FEES, count, get max per first level and last add missing roll no by reindex
mask = df['FEES'].eq(0)
df = (df[mask].groupby(['ROLL_NO',a[mask]])
.size()
.max(level=0)
.reindex(df['ROLL_NO'].unique(), fill_value=0)
.reset_index(name='MAX_CNT'))
print (df)
ROLL_NO MAX_CNT
0 12345 3
1 987654 0
Explanation:
First compare FEES column with 0, eq is same as == and multiple mask by column ROLL_NO:
mask = df['FEES'].eq(0)
r = df['ROLL_NO'] * mask
print (r)
0 0
1 0
2 0
3 12345
4 12345
5 0
6 12345
7 12345
8 12345
9 0
10 0
dtype: int64
Get consecutive groups by compare shifted Series r and cumsum:
a = r.ne(r.shift()).cumsum()
print (a)
0 1
1 1
2 1
3 2
4 2
5 3
6 4
7 4
8 4
9 5
10 5
dtype: int32
Filter only 0 in FEES and groupby with size, also filter a for same indexes:
print (df[mask].groupby(['ROLL_NO',a[mask]]).size())
ROLL_NO
12345 2 2
4 3
dtype: int64
Get max values per first level of MultiIndex:
print (df[mask].groupby(['ROLL_NO',a[mask]]).size().max(level=0))
ROLL_NO
12345 3
dtype: int64
Last add missing ROLL_NO without 0 by reindex:
print (df[mask].groupby(['ROLL_NO',a[mask]])
.size()
.max(level=0)
.reindex(df['ROLL_NO'].unique(), fill_value=0))
ROLL_NO
12345 3
987654 0
dtype: int64
and for columns from index use reset_index.
EDIT:
For first ID use drop_duplicates with insert and map:
r = df['ROLL_NO'] * df['FEES'].eq(0)
a = r.ne(r.shift()).cumsum()
s = df.drop_duplicates('ROLL_NO').set_index('ROLL_NO')['ID']
mask = df['FEES'].eq(0)
df1 = (df[mask].groupby(['ROLL_NO',a[mask]])
.size()
.max(level=0)
.reindex(df['ROLL_NO'].unique(), fill_value=0)
.reset_index(name='MAX_CNT'))
df1.insert(0, 'ID', df1['ROLL_NO'].map(s))
print (df1)
ID ROLL_NO MAX_CNT
0 1 12345 3
1 3 987654 0