Checking whether value in current row of pandas Series is in lagging window
I have a pandas DataFrame similar to the one generated by this code:
names = ['steve', 'bob', 'harry', 'jeff'] * 5
df = pd.DataFrame(
index=pd.DatetimeIndex(start='2018-10-10', end='2018-10-29', freq='D'),
data={'value': [x for x in range(20)],
'names': names,
}
)
df['roll'] = np.random.randint(1, 6, df.shape[0])
Which produces data that looks like this:
value names roll
2018-10-10 0 steve 2
2018-10-11 1 bob 5
2018-10-12 2 harry 4
2018-10-13 3 jeff 2
2018-10-14 4 steve 2
2018-10-15 5 bob 4
2018-10-16 6 harry 1
2018-10-17 7 jeff 2
2018-10-18 8 steve 3
2018-10-19 9 bob 3
...
I'd like to add another column result that is boolean, grouped by names and true when the value of roll in the current row appears within a 10-day lagging window. I.e. I want this:
value names roll result
2018-10-10 0 steve 2 False
2018-10-11 1 bob 5 False
2018-10-12 2 harry 4 False
2018-10-13 3 jeff 2 False
2018-10-14 4 steve 2 True
2018-10-15 5 bob 4 False
2018-10-16 6 harry 1 False
2018-10-17 7 jeff 2 True
2018-10-18 8 steve 3 True
2018-10-19 9 bob 3 False
...
I've tried this:
df['result'] = (
df.groupby('names').apply(lambda x: x['roll'].isin(x.shift().rolling('10D')['roll']))
)
which feels logical to me, but I get a NotImplementedError that points me here: https://github.com/pandas-dev/pandas/issues/11704.
Is there a pandas-native way to get where I want to be?
python pandas dataframe
asked Dec 29 '18 at 2:41
bigsimbigsim
826
add a comment |
I have a pandas DataFrame similar to the one generated by this code:
names = ['steve', 'bob', 'harry', 'jeff'] * 5
df = pd.DataFrame(
index=pd.DatetimeIndex(start='2018-10-10', end='2018-10-29', freq='D'),
data={'value': [x for x in range(20)],
'names': names,
}
)
df['roll'] = np.random.randint(1, 6, df.shape[0])
Which produces data that looks like this:
value names roll
2018-10-10 0 steve 2
2018-10-11 1 bob 5
2018-10-12 2 harry 4
2018-10-13 3 jeff 2
2018-10-14 4 steve 2
2018-10-15 5 bob 4
2018-10-16 6 harry 1
2018-10-17 7 jeff 2
2018-10-18 8 steve 3
2018-10-19 9 bob 3
...
I'd like to add another column result that is boolean, grouped by names and true when the value of roll in the current row appears within a 10-day lagging window. I.e. I want this:
value names roll result
2018-10-10 0 steve 2 False
2018-10-11 1 bob 5 False
2018-10-12 2 harry 4 False
2018-10-13 3 jeff 2 False
2018-10-14 4 steve 2 True
2018-10-15 5 bob 4 False
2018-10-16 6 harry 1 False
2018-10-17 7 jeff 2 True
2018-10-18 8 steve 3 True
2018-10-19 9 bob 3 False
...
I've tried this:
df['result'] = (
df.groupby('names').apply(lambda x: x['roll'].isin(x.shift().rolling('10D')['roll']))
)
which feels logical to me, but I get a NotImplementedError that points me here: https://github.com/pandas-dev/pandas/issues/11704.
Is there a pandas-native way to get where I want to be?
python pandas dataframe
asked Dec 29 '18 at 2:41
bigsimbigsim
826
add a comment |
I have a pandas DataFrame similar to the one generated by this code:
names = ['steve', 'bob', 'harry', 'jeff'] * 5
df = pd.DataFrame(
index=pd.DatetimeIndex(start='2018-10-10', end='2018-10-29', freq='D'),
data={'value': [x for x in range(20)],
'names': names,
}
)
df['roll'] = np.random.randint(1, 6, df.shape[0])
Which produces data that looks like this:
value names roll
2018-10-10 0 steve 2
2018-10-11 1 bob 5
2018-10-12 2 harry 4
2018-10-13 3 jeff 2
2018-10-14 4 steve 2
2018-10-15 5 bob 4
2018-10-16 6 harry 1
2018-10-17 7 jeff 2
2018-10-18 8 steve 3
2018-10-19 9 bob 3
...
I'd like to add another column result that is boolean, grouped by names and true when the value of roll in the current row appears within a 10-day lagging window. I.e. I want this:
value names roll result
2018-10-10 0 steve 2 False
2018-10-11 1 bob 5 False
2018-10-12 2 harry 4 False
2018-10-13 3 jeff 2 False
2018-10-14 4 steve 2 True
2018-10-15 5 bob 4 False
2018-10-16 6 harry 1 False
2018-10-17 7 jeff 2 True
2018-10-18 8 steve 3 True
2018-10-19 9 bob 3 False
...
I've tried this:
df['result'] = (
df.groupby('names').apply(lambda x: x['roll'].isin(x.shift().rolling('10D')['roll']))
)
which feels logical to me, but I get a NotImplementedError that points me here: https://github.com/pandas-dev/pandas/issues/11704.
Is there a pandas-native way to get where I want to be?
python pandas dataframe
asked Dec 29 '18 at 2:41
bigsimbigsim
826
I have a pandas DataFrame similar to the one generated by this code:
names = ['steve', 'bob', 'harry', 'jeff'] * 5
df = pd.DataFrame(
index=pd.DatetimeIndex(start='2018-10-10', end='2018-10-29', freq='D'),
data={'value': [x for x in range(20)],
'names': names,
}
)
df['roll'] = np.random.randint(1, 6, df.shape[0])
Which produces data that looks like this:
value names roll
2018-10-10 0 steve 2
2018-10-11 1 bob 5
2018-10-12 2 harry 4
2018-10-13 3 jeff 2
2018-10-14 4 steve 2
2018-10-15 5 bob 4
2018-10-16 6 harry 1
2018-10-17 7 jeff 2
2018-10-18 8 steve 3
2018-10-19 9 bob 3
...
I'd like to add another column result that is boolean, grouped by names and true when the value of roll in the current row appears within a 10-day lagging window. I.e. I want this:
value names roll result
2018-10-10 0 steve 2 False
2018-10-11 1 bob 5 False
2018-10-12 2 harry 4 False
2018-10-13 3 jeff 2 False
2018-10-14 4 steve 2 True
2018-10-15 5 bob 4 False
2018-10-16 6 harry 1 False
2018-10-17 7 jeff 2 True
2018-10-18 8 steve 3 True
2018-10-19 9 bob 3 False
...
I've tried this:
df['result'] = (
df.groupby('names').apply(lambda x: x['roll'].isin(x.shift().rolling('10D')['roll']))
)
which feels logical to me, but I get a NotImplementedError that points me here: https://github.com/pandas-dev/pandas/issues/11704.
Is there a pandas-native way to get where I want to be?
python pandas dataframe
python pandas dataframe
asked Dec 29 '18 at 2:41
bigsimbigsim
826
asked Dec 29 '18 at 2:41
bigsimbigsim
826
asked Dec 29 '18 at 2:41
bigsimbigsim
826
asked Dec 29 '18 at 2:41
bigsimbigsim
826
asked Dec 29 '18 at 2:41
bigsimbigsim
826
826
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
I think rolling here is no needed
df.reset_index().groupby(['names','roll'])['index'].diff().dt.days<10
Out[49]:
0 False
1 False
2 False
3 False
4 True
5 False
6 False
7 True
8 False
9 False
Name: index, dtype: bool
answered Dec 29 '18 at 3:16
W-BW-B
105k73165
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
I think rolling here is no needed
df.reset_index().groupby(['names','roll'])['index'].diff().dt.days<10
Out[49]:
0 False
1 False
2 False
3 False
4 True
5 False
6 False
7 True
8 False
9 False
Name: index, dtype: bool
answered Dec 29 '18 at 3:16
W-BW-B
105k73165
add a comment |
I think rolling here is no needed
df.reset_index().groupby(['names','roll'])['index'].diff().dt.days<10
Out[49]:
0 False
1 False
2 False
3 False
4 True
5 False
6 False
7 True
8 False
9 False
Name: index, dtype: bool
answered Dec 29 '18 at 3:16
W-BW-B
105k73165
add a comment |
I think rolling here is no needed
df.reset_index().groupby(['names','roll'])['index'].diff().dt.days<10
Out[49]:
0 False
1 False
2 False
3 False
4 True
5 False
6 False
7 True
8 False
9 False
Name: index, dtype: bool
answered Dec 29 '18 at 3:16
W-BW-B
105k73165
I think rolling here is no needed
df.reset_index().groupby(['names','roll'])['index'].diff().dt.days<10
Out[49]:
0 False
1 False
2 False
3 False
4 True
5 False
6 False
7 True
8 False
9 False
Name: index, dtype: bool
answered Dec 29 '18 at 3:16
W-BW-B
105k73165
answered Dec 29 '18 at 3:16
W-BW-B
105k73165
answered Dec 29 '18 at 3:16
W-BW-B
105k73165
answered Dec 29 '18 at 3:16
W-BW-B
105k73165
105k73165
add a comment |
add a comment |
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