Comments (1)
Hello there, sorry for the belated reply, I've been away for awhile.
I just recently pushed out a new version and I don't remember if masking was broken before or not, but here's and example of one way to do what you want using the mask function:
In [2]: d = diamonds >> group_by(X.cut) >> mutate(price_lag=lag(X.price)) >> head(2) >> select(X.cut, X.price_lag)
In [3]: d
Out[3]:
cut price_lag
8 Fair NaN
91 Fair 337.0
2 Good NaN
4 Good 327.0
0 Ideal NaN
11 Ideal 326.0
1 Premium NaN
3 Premium 326.0
5 Very Good NaN
6 Very Good 336.0
In [4]: d >> mask(X.price_lag.isnull())
Out[4]:
cut price_lag
8 Fair NaN
2 Good NaN
0 Ideal NaN
1 Premium NaN
5 Very Good NaN
In [5]: d >> mask(~X.price_lag.isnull())
Out[5]:
cut price_lag
91 Fair 337.0
4 Good 327.0
11 Ideal 326.0
3 Premium 326.0
6 Very Good 336.0
You're right that it would probably be a good addition to the readme. The mask
function takes boolean arrays corresponding to rows to filter by, so anything that evaluates to that should work.
from dfply.
Related Issues (20)
- A way to outer_join() by index?
- rename(CUT=X.cut, COLOR='color') - counterintuitive IMO HOT 1
- Liveness of this project HOT 1
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- unable to aggregate and summarize counts for a categorical variable HOT 1
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- Issues to convert to pandas dataframe
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- Issue arranging data after summarising with a new variable HOT 1
- Not taking into account None values in group_by HOT 1
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- #group_by #mode #iter()
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