CODEWITHSUNDEEP
pythonarrayspandasnumpydataframe
patronlargibi
patronlargibi

Reputation: 115

Count how many consecutive TRUEs on each row in a dataframe

I am trying to count how many consecutive TRUEs on each row and I solved that part myself but I need to find a solution for this part: If a row starts with FALSE then result must be 0. There is a sample dataset below. Can you recommend me your tips to how to solve this.

PS. my original question is at the link below.

how to find number of consecutive decreases(increases)

Sample data, .csv file
idx,Expected Results,M_1,M_2,M_3,M_4,M_5,M_6,M_7,M_8,M_9,M_10,M_11,M_12
1001,0,FALSE,FALSE,FALSE,TRUE,TRUE,TRUE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE
1002,3,TRUE,TRUE,TRUE,FALSE,FALSE,FALSE,TRUE,TRUE,TRUE,FALSE,FALSE,FALSE
1003,1,TRUE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE
1004,4,TRUE,TRUE,TRUE,TRUE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE
1005,0,FALSE,FALSE,FALSE,TRUE,TRUE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE
1006,0,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE
1007,0,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE
1008,1,TRUE,FALSE,FALSE,TRUE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE
1009,0,FALSE,TRUE,FALSE,TRUE,FALSE,TRUE,FALSE,TRUE,FALSE,TRUE,FALSE,FALSE
1010,1,TRUE,FALSE,TRUE,FALSE,TRUE,FALSE,TRUE,FALSE,TRUE,FALSE,TRUE,FALSE
1011,0,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,FALSE,FALSE,FALSE,FALSE
1013,0,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE
1014,1,TRUE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE
1015,1,TRUE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE
1016,0,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE
1017,2,TRUE,TRUE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE
1018,0,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE

After John Solution; How can I count the Trues till I see the "False"

result = df.where(df[0], 0)

idx,M_1,M_2,M_3,M_4,M_5,M_6,M_7,M_8,M_9,M_10,M_11,M_12
1001,0,0,0,0,0,0,0,0,0,0,0,0
1002,TRUE,TRUE,TRUE,FALSE,FALSE,FALSE,TRUE,TRUE,TRUE,FALSE,FALSE,FALSE
1003,TRUE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE
1004,TRUE,TRUE,TRUE,TRUE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE
1005,0,0,0,0,0,0,0,0,0,0,0,0
1006,0,0,0,0,0,0,0,0,0,0,0,0
1007,0,0,0,0,0,0,0,0,0,0,0,0
1008,TRUE,FALSE,FALSE,TRUE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE
1009,0,0,0,0,0,0,0,0,0,0,0,0
1010,TRUE,FALSE,TRUE,FALSE,TRUE,FALSE,TRUE,FALSE,TRUE,FALSE,TRUE,FALSE
1011,0,0,0,0,0,0,0,0,0,0,0,0
1013,0,0,0,0,0,0,0,0,0,0,0,0
1014,TRUE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE
1015,TRUE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE
1016,0,0,0,0,0,0,0,0,0,0,0,0
1017,TRUE,TRUE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE
1018,0,0,0,0,0,0,0,0,0,0,0,0

Upvotes: 1

Views: 91

Answers (3)

Andy L.
Andy L.

Reputation: 25259

reverse values of columns M-1 - M-12 using negation '~'. I.e, True to False and vice-versa. Doing cummax to separate first group of consecutive True (note: at this point True represent False-value and 'False' represent True-value). Doing another negation on the result of cummax and finally sum

(~(~df.drop(['idx'], 1)).cummax(1)).sum(1)


Out[503]:
0     0
1     3
2     1
3     4
4     0
5     0
6     0
7     1
8     0
9     1
10    0
11    0
12    1
13    1
14    0
15    2
16    0
dtype: int64

Upvotes: 0

Paul Panzer
Paul Panzer

Reputation: 53079

You can use np.argmin. You needn't prefilter your df, it will handle rows starting with False correctly.

df.loc[:, 'M_1':'M_12'].values.argmin(1)
#array([0, 3, 1, 4, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 2, 0])

Note that this assumes there is at least one False in every row.

Upvotes: 3

GZ0
GZ0

Reputation: 4273

df.loc[:, 'M_1':'M_12'].apply(np.logical_and.accumulate, axis=1).sum(axis=1)

Upvotes: 2

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