pandas DataFrame isin and following row
For a given DataFrame, sorted by b and index reset:
df = pd.DataFrame('a': list('abcdef'),
'b': [0, 2, 7, 3, 9, 15]
).sort_values('b').reset_index(drop=True)
a b
0 a 0
1 b 2
2 d 3
3 c 7
4 e 9
5 f 15
and a list, v
v = list('adf')
I would like to pull out just the rows in v and the following row (if there is one), similar to grep -A1:
a b
0 a 0
1 b 2
2 d 3
3 c 7
5 f 15
I can do this by concatenating the index from isin and the index from isin plus one, like so:
df[df.index.isin(
np.concatenate(
(df[df['a'].isin(v)].index,
df[df['a'].isin(v)].index + 1)))]
But this is long and not too easy to understand. Is there a better way?
python pandas
asked Nov 15 '18 at 15:31
AlexAlex
1,063822
add a comment |
For a given DataFrame, sorted by b and index reset:
df = pd.DataFrame('a': list('abcdef'),
'b': [0, 2, 7, 3, 9, 15]
).sort_values('b').reset_index(drop=True)
a b
0 a 0
1 b 2
2 d 3
3 c 7
4 e 9
5 f 15
and a list, v
v = list('adf')
I would like to pull out just the rows in v and the following row (if there is one), similar to grep -A1:
a b
0 a 0
1 b 2
2 d 3
3 c 7
5 f 15
I can do this by concatenating the index from isin and the index from isin plus one, like so:
df[df.index.isin(
np.concatenate(
(df[df['a'].isin(v)].index,
df[df['a'].isin(v)].index + 1)))]
But this is long and not too easy to understand. Is there a better way?
python pandas
asked Nov 15 '18 at 15:31
AlexAlex
1,063822
add a comment |
For a given DataFrame, sorted by b and index reset:
df = pd.DataFrame('a': list('abcdef'),
'b': [0, 2, 7, 3, 9, 15]
).sort_values('b').reset_index(drop=True)
a b
0 a 0
1 b 2
2 d 3
3 c 7
4 e 9
5 f 15
and a list, v
v = list('adf')
I would like to pull out just the rows in v and the following row (if there is one), similar to grep -A1:
a b
0 a 0
1 b 2
2 d 3
3 c 7
5 f 15
I can do this by concatenating the index from isin and the index from isin plus one, like so:
df[df.index.isin(
np.concatenate(
(df[df['a'].isin(v)].index,
df[df['a'].isin(v)].index + 1)))]
But this is long and not too easy to understand. Is there a better way?
python pandas
asked Nov 15 '18 at 15:31
AlexAlex
1,063822
For a given DataFrame, sorted by b and index reset:
df = pd.DataFrame('a': list('abcdef'),
'b': [0, 2, 7, 3, 9, 15]
).sort_values('b').reset_index(drop=True)
a b
0 a 0
1 b 2
2 d 3
3 c 7
4 e 9
5 f 15
and a list, v
v = list('adf')
I would like to pull out just the rows in v and the following row (if there is one), similar to grep -A1:
a b
0 a 0
1 b 2
2 d 3
3 c 7
5 f 15
I can do this by concatenating the index from isin and the index from isin plus one, like so:
df[df.index.isin(
np.concatenate(
(df[df['a'].isin(v)].index,
df[df['a'].isin(v)].index + 1)))]
But this is long and not too easy to understand. Is there a better way?
python pandas
python pandas
asked Nov 15 '18 at 15:31
AlexAlex
1,063822
asked Nov 15 '18 at 15:31
AlexAlex
1,063822
asked Nov 15 '18 at 15:31
AlexAlex
1,063822
asked Nov 15 '18 at 15:31
AlexAlex
1,063822
asked Nov 15 '18 at 15:31
AlexAlex
1,063822
1,063822
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
You can combine the isin condition and the shift (next row) to create the boolean you needed:
df[df.a.isin(v).pipe(lambda x: x | x.shift())]
# a b
#0 a 0
#1 b 2
#2 d 3
#3 c 7
#5 f 15
answered Nov 15 '18 at 15:36
PsidomPsidom
127k1293138
2
Nice answer: to note, this is functionally equivalent tox = df.a.isin(v); x | x.shift().pipeis here just for convenience.
– jpp
Nov 15 '18 at 15:40
So what isx | x.shift()doing here? Is thisx OR x.shift()? @jpp
– Alex
Nov 15 '18 at 15:47
1
Yes.xis a Boolean series,|means vectorised "or".
– jpp
Nov 15 '18 at 15:48
add a comment |
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1 Answer
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active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
You can combine the isin condition and the shift (next row) to create the boolean you needed:
df[df.a.isin(v).pipe(lambda x: x | x.shift())]
# a b
#0 a 0
#1 b 2
#2 d 3
#3 c 7
#5 f 15
answered Nov 15 '18 at 15:36
PsidomPsidom
127k1293138
2
Nice answer: to note, this is functionally equivalent tox = df.a.isin(v); x | x.shift().pipeis here just for convenience.
– jpp
Nov 15 '18 at 15:40
So what isx | x.shift()doing here? Is thisx OR x.shift()? @jpp
– Alex
Nov 15 '18 at 15:47
1
Yes.xis a Boolean series,|means vectorised "or".
– jpp
Nov 15 '18 at 15:48
add a comment |
You can combine the isin condition and the shift (next row) to create the boolean you needed:
df[df.a.isin(v).pipe(lambda x: x | x.shift())]
# a b
#0 a 0
#1 b 2
#2 d 3
#3 c 7
#5 f 15
answered Nov 15 '18 at 15:36
PsidomPsidom
127k1293138
2
Nice answer: to note, this is functionally equivalent tox = df.a.isin(v); x | x.shift().pipeis here just for convenience.
– jpp
Nov 15 '18 at 15:40
So what isx | x.shift()doing here? Is thisx OR x.shift()? @jpp
– Alex
Nov 15 '18 at 15:47
1
Yes.xis a Boolean series,|means vectorised "or".
– jpp
Nov 15 '18 at 15:48
add a comment |
You can combine the isin condition and the shift (next row) to create the boolean you needed:
df[df.a.isin(v).pipe(lambda x: x | x.shift())]
# a b
#0 a 0
#1 b 2
#2 d 3
#3 c 7
#5 f 15
answered Nov 15 '18 at 15:36
PsidomPsidom
127k1293138
You can combine the isin condition and the shift (next row) to create the boolean you needed:
df[df.a.isin(v).pipe(lambda x: x | x.shift())]
# a b
#0 a 0
#1 b 2
#2 d 3
#3 c 7
#5 f 15
answered Nov 15 '18 at 15:36
PsidomPsidom
127k1293138
edited Nov 15 '18 at 15:40
answered Nov 15 '18 at 15:36
PsidomPsidom
127k1293138
answered Nov 15 '18 at 15:36
PsidomPsidom
127k1293138
answered Nov 15 '18 at 15:36
PsidomPsidom
127k1293138
127k1293138
2
Nice answer: to note, this is functionally equivalent tox = df.a.isin(v); x | x.shift().pipeis here just for convenience.
– jpp
Nov 15 '18 at 15:40
So what isx | x.shift()doing here? Is thisx OR x.shift()? @jpp
– Alex
Nov 15 '18 at 15:47
1
Yes.xis a Boolean series,|means vectorised "or".
– jpp
Nov 15 '18 at 15:48
add a comment |
2
Nice answer: to note, this is functionally equivalent tox = df.a.isin(v); x | x.shift().pipeis here just for convenience.
– jpp
Nov 15 '18 at 15:40
So what isx | x.shift()doing here? Is thisx OR x.shift()? @jpp
– Alex
Nov 15 '18 at 15:47
1
Yes.xis a Boolean series,|means vectorised "or".
– jpp
Nov 15 '18 at 15:48
2
2
Nice answer: to note, this is functionally equivalent to
x = df.a.isin(v); x | x.shift(). pipe is here just for convenience.– jpp
Nov 15 '18 at 15:40
Nice answer: to note, this is functionally equivalent to
x = df.a.isin(v); x | x.shift(). pipe is here just for convenience.– jpp
Nov 15 '18 at 15:40
So what is
x | x.shift() doing here? Is this x OR x.shift()? @jpp– Alex
Nov 15 '18 at 15:47
So what is
x | x.shift() doing here? Is this x OR x.shift()? @jpp– Alex
Nov 15 '18 at 15:47
1
1
Yes.
x is a Boolean series, | means vectorised "or".– jpp
Nov 15 '18 at 15:48
Yes.
x is a Boolean series, | means vectorised "or".– jpp
Nov 15 '18 at 15:48
add a comment |
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