pandas element wise conditional return index - pandas

Question

I want to find abnormal values and replace them with corresponding day of next week.
year week day v1 v2
2001 1 1 46 9999
2001 1 2 60 9335
2001 1 3 9999 9318
2001 1 4 47 9999
2001 1 5 57 9373
2001 1 6 9999 9384
2001 1 7 72 9444
2001 2 1 75 73
2001 2 2 74 63
2001 2 3 79 377
2001 2 4 70 361
2001 2 5 75 73
2001 2 6 77 64
2001 2 7 76 57
I could carry out column by column,code as follows:
index_row=df[df['v1']==9999].index
for i in index_row:
df['v1'][i]=df['v1'][i+7] # i+7 is the index of next week
How to element-wise the whole dataframe? Such as pd.applymap.
How get the columns number(name) and row number base on conditional seiving values?
The target df I want as follows:
( * indicated modified values and the next week values)
year week day v1 v2
2001 1 1 46 *73
2001 1 2 60 9335
2001 1 3 *79 9318
2001 1 4 47 *361
2001 1 5 57 9373
2001 1 6 *77 9384
2001 1 7 72 9444
2001 2 1 75 *73
2001 2 2 74 63
2001 2 3 *79 377
2001 2 4 70 *361
2001 2 5 75 73
2001 2 6 *77 64
2001 2 7 76 57

Answer 1

create d1 with set_index on columns ['year', 'week', 'day']
create d2 with same index as d1 except, subtract 1 from week
mask with other
cols = ['year', 'week', 'day']
d1 = df.set_index(cols)
d2 = df.assign(week=df.week - 1).set_index(cols)
d1.mask(d1.eq(9999), d2).reset_index()
year week day v1 v2
0 2001 1 1 46 73
1 2001 1 2 60 9335
2 2001 1 3 79 9318
3 2001 1 4 47 361
4 2001 1 5 57 9373
5 2001 1 6 77 9384
6 2001 1 7 72 9444
7 2001 2 1 75 73
8 2001 2 2 74 63
9 2001 2 3 79 377
10 2001 2 4 70 361
11 2001 2 5 75 73
12 2001 2 6 77 64
13 2001 2 7 76 57
old answer
One approach is to setup d1 with index of ['year', 'week', 'day'] and manipulate that to shift a week. Then mask it for equal to 9999 and fillna
d1 = df.set_index(['year', 'week', 'day'])
s1 = d1.unstack(['year', 'day']).shift(-1).stack(['year', 'day']).swaplevel(0, 1)
d1.mask(d1==9999).fillna(s1).reset_index()
year week day v1 v2
0 2001 1 1 46.0 73.0
1 2001 1 2 60.0 9335.0
2 2001 1 3 79.0 9318.0
3 2001 1 4 47.0 361.0
4 2001 1 5 57.0 9373.0
5 2001 1 6 77.0 9384.0
6 2001 1 7 72.0 9444.0
7 2001 2 1 75.0 73.0
8 2001 2 2 74.0 63.0
9 2001 2 3 79.0 377.0
10 2001 2 4 70.0 361.0
11 2001 2 5 75.0 73.0
12 2001 2 6 77.0 64.0
13 2001 2 7 76.0 57.0

Answer 2

You can working with DatetimeIndex, set value by mask with shifted rows:
a = df['year'].astype(str).add('-').add(df['week'].astype(str))
.add('-').add(df['day'].sub(1).astype(str))
#http://strftime.org/
df.index = pd.to_datetime(a, format='%Y-%U-%w')
df2 = df.shift(-1,freq='7D')
df = df.mask(df.eq(9999), df2).reset_index(drop=True)
print (df)
year week day v1 v2
0 2001 1 1 46 73
1 2001 1 2 60 9335
2 2001 1 3 79 9318
3 2001 1 4 47 361
4 2001 1 5 57 9373
5 2001 1 6 77 9384
6 2001 1 7 72 9444
7 2001 2 1 75 73
8 2001 2 2 74 63
9 2001 2 3 79 377
10 2001 2 4 70 361
11 2001 2 5 75 73
12 2001 2 6 77 64
13 2001 2 7 76 57