In Pandas how can I reduce the rows so that I only accept the row with the first true in every sub group of...
0
As an example, consider the following:
Input
a b c bool
1 243 0 true
1 253 1 false
1 267 0 true
1 245 0 true
1 234 0 false
1 255 0 true
1 275 0 true
1 295 0 true
I want the output to be the following:
a b c bool
1 243 0 true
1 253 1 false
1 267 0 true
1 234 0 false
1 255 0 true
python pandas
asked Nov 14 '18 at 23:09
NothingNothing
5211
add a comment |
0
As an example, consider the following:
Input
a b c bool
1 243 0 true
1 253 1 false
1 267 0 true
1 245 0 true
1 234 0 false
1 255 0 true
1 275 0 true
1 295 0 true
I want the output to be the following:
a b c bool
1 243 0 true
1 253 1 false
1 267 0 true
1 234 0 false
1 255 0 true
python pandas
asked Nov 14 '18 at 23:09
NothingNothing
5211
add a comment |
0
0
0
As an example, consider the following:
Input
a b c bool
1 243 0 true
1 253 1 false
1 267 0 true
1 245 0 true
1 234 0 false
1 255 0 true
1 275 0 true
1 295 0 true
I want the output to be the following:
a b c bool
1 243 0 true
1 253 1 false
1 267 0 true
1 234 0 false
1 255 0 true
python pandas
asked Nov 14 '18 at 23:09
NothingNothing
5211
As an example, consider the following:
Input
a b c bool
1 243 0 true
1 253 1 false
1 267 0 true
1 245 0 true
1 234 0 false
1 255 0 true
1 275 0 true
1 295 0 true
I want the output to be the following:
a b c bool
1 243 0 true
1 253 1 false
1 267 0 true
1 234 0 false
1 255 0 true
python pandas
python pandas
asked Nov 14 '18 at 23:09
NothingNothing
5211
asked Nov 14 '18 at 23:09
NothingNothing
5211
asked Nov 14 '18 at 23:09
NothingNothing
5211
asked Nov 14 '18 at 23:09
NothingNothing
5211
asked Nov 14 '18 at 23:09
NothingNothing
5211
5211
add a comment |
add a comment |
2 Answers
2
active
oldest
votes
1
Use some boolean masking for this and do it in one line:
df.loc[(df['bool'] != df['bool'].shift(1))]
out:
>>> df.loc[(df['bool'] != df['bool'].shift(1))]
a b c bool
0 1 243 0 True
1 1 253 1 False
2 1 267 0 True
4 1 234 0 False
5 1 255 0 True
answered Nov 14 '18 at 23:18
d_kennetzd_kennetz
2,2803724
add a comment |
1
IIUC
df.groupby(df['bool'].ne(True).cumsum()).head(2)
Out[201]:
a b c bool
0 1 243 0 True
1 1 253 1 False
2 1 267 0 True
4 1 234 0 False
5 1 255 0 True
answered Nov 14 '18 at 23:20
Wen-BenWen-Ben
112k83367
add a comment |
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2 Answers
2
active
oldest
votes
2 Answers
2
active
oldest
votes
active
oldest
votes
active
oldest
votes
1
Use some boolean masking for this and do it in one line:
df.loc[(df['bool'] != df['bool'].shift(1))]
out:
>>> df.loc[(df['bool'] != df['bool'].shift(1))]
a b c bool
0 1 243 0 True
1 1 253 1 False
2 1 267 0 True
4 1 234 0 False
5 1 255 0 True
answered Nov 14 '18 at 23:18
d_kennetzd_kennetz
2,2803724
add a comment |
1
Use some boolean masking for this and do it in one line:
df.loc[(df['bool'] != df['bool'].shift(1))]
out:
>>> df.loc[(df['bool'] != df['bool'].shift(1))]
a b c bool
0 1 243 0 True
1 1 253 1 False
2 1 267 0 True
4 1 234 0 False
5 1 255 0 True
answered Nov 14 '18 at 23:18
d_kennetzd_kennetz
2,2803724
add a comment |
1
1
1
Use some boolean masking for this and do it in one line:
df.loc[(df['bool'] != df['bool'].shift(1))]
out:
>>> df.loc[(df['bool'] != df['bool'].shift(1))]
a b c bool
0 1 243 0 True
1 1 253 1 False
2 1 267 0 True
4 1 234 0 False
5 1 255 0 True
answered Nov 14 '18 at 23:18
d_kennetzd_kennetz
2,2803724
Use some boolean masking for this and do it in one line:
df.loc[(df['bool'] != df['bool'].shift(1))]
out:
>>> df.loc[(df['bool'] != df['bool'].shift(1))]
a b c bool
0 1 243 0 True
1 1 253 1 False
2 1 267 0 True
4 1 234 0 False
5 1 255 0 True
answered Nov 14 '18 at 23:18
d_kennetzd_kennetz
2,2803724
answered Nov 14 '18 at 23:18
d_kennetzd_kennetz
2,2803724
answered Nov 14 '18 at 23:18
d_kennetzd_kennetz
2,2803724
answered Nov 14 '18 at 23:18
d_kennetzd_kennetz
2,2803724
2,2803724
add a comment |
add a comment |
1
IIUC
df.groupby(df['bool'].ne(True).cumsum()).head(2)
Out[201]:
a b c bool
0 1 243 0 True
1 1 253 1 False
2 1 267 0 True
4 1 234 0 False
5 1 255 0 True
answered Nov 14 '18 at 23:20
Wen-BenWen-Ben
112k83367
add a comment |
1
IIUC
df.groupby(df['bool'].ne(True).cumsum()).head(2)
Out[201]:
a b c bool
0 1 243 0 True
1 1 253 1 False
2 1 267 0 True
4 1 234 0 False
5 1 255 0 True
answered Nov 14 '18 at 23:20
Wen-BenWen-Ben
112k83367
add a comment |
1
1
1
IIUC
df.groupby(df['bool'].ne(True).cumsum()).head(2)
Out[201]:
a b c bool
0 1 243 0 True
1 1 253 1 False
2 1 267 0 True
4 1 234 0 False
5 1 255 0 True
answered Nov 14 '18 at 23:20
Wen-BenWen-Ben
112k83367
IIUC
df.groupby(df['bool'].ne(True).cumsum()).head(2)
Out[201]:
a b c bool
0 1 243 0 True
1 1 253 1 False
2 1 267 0 True
4 1 234 0 False
5 1 255 0 True
answered Nov 14 '18 at 23:20
Wen-BenWen-Ben
112k83367
answered Nov 14 '18 at 23:20
Wen-BenWen-Ben
112k83367
answered Nov 14 '18 at 23:20
Wen-BenWen-Ben
112k83367
answered Nov 14 '18 at 23:20
Wen-BenWen-Ben
112k83367
112k83367
add a comment |
add a comment |
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