Fill Pandas dataframe based on conditions from same row values (based on Excel COUNTIFS formula)

Question

I have a table like the following, and I want to fill a new column based on conditions from other columns. In this case:

• Having the same value in Cond1
• Amount being different than zero
• Being from previous months

Column "CountPreviousMonth" is what I need to fill out

Cond1	Amount	Month	CountPreviousMonth
a	10	1	0
a	20	2	1
a	15	3	2
b	10	1	0
b	0	2	1
b	15	3	1
c	5	1	0
c	25	2	1
c	15	3	2

When month is 1 then the count is zero because is the first one. In Cond1=b it stays at count = 1 because in Month 2 the Amount was zero

In Excel I used COUNTIFS but would like to do it in Python, where I could do it in a for loop but the real table has many rows and it wouldn't be efficient. Is there a better way to calculate it?

Answer 1

First replace Month to missing values if Amount=0 ant hen use custom lambda function with Series.shift and forward filling missing values:

f = lambda x: x.shift(fill_value=0).ffill().astype(int)
df['count'] = df['Month'].mask(df['Amount'].eq(0)).groupby(df['Cond1']).transform(f)
print (df)
  Cond1  Amount  Month  CountPreviousMonth  count
0     a      10      1                   0      0
1     a      20      2                   1      1
2     a      15      3                   2      2
3     b      10      1                   0      0
4     b       0      2                   1      1
5     b      15      3                   1      1
6     c       5      1                   0      0
7     c      25      2                   1      1
8     c      15      3                   2      2

Answer 2

Move down by 1, and check which ones are not equal to 0:

arr = df.Amount.shift().ne(0)

Get boolean where month is 1:

repl = df.Month.eq(1)

index arr with repl, to keep track of first month per group:

arr[repl] = True

Groupby, run a cumulative sum, and finally deduct 1, to ensure every group starts at 0:

df.assign(CountPreviousMonth = arr.groupby(df.Cond1).cumsum().sub(1))
  Cond1  Amount  Month  CountPreviousMonth
0     a      10      1                   0
1     a      20      2                   1
2     a      15      3                   2
3     b      10      1                   0
4     b       0      2                   1
5     b      15      3                   1
6     c       5      1                   0
7     c      25      2                   1
8     c      15      3                   2