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Replacing NAs with latest non-NA value
...m the help page:
library(zoo)
az <- zoo(1:6)
bz <- zoo(c(2,NA,1,4,5,2))
na.locf(bz)
1 2 3 4 5 6
2 2 1 4 5 2
na.locf(bz, fromLast = TRUE)
1 2 3 4 5 6
2 1 1 4 5 2
cz <- zoo(c(NA,9,3,2,3,2))
na.locf(cz)
2 3 4 5 6
9 3 2 3 2
...
Does Python support short-circuiting?
...
answered Apr 5 '10 at 18:20
Alex MartelliAlex Martelli
724k148148 gold badges11251125 silver badges13241324 bronze badges
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What is the fastest integer division supporting division by zero no matter what the result is?
...anch?
– Haatschii
May 27 '13 at 17:15
1
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Java: how can I split an ArrayList in multiple small ArrayLists?
...t<Integer> numbers = new ArrayList<Integer>(
Arrays.asList(5,3,1,2,9,5,0,7)
);
List<Integer> head = numbers.subList(0, 4);
List<Integer> tail = numbers.subList(4, 8);
System.out.println(head); // prints "[5, 3, 1, 2]"
System.out.println(tail); // prints "[9, 5, 0, 7]"
C...
How do I replace NA values with zeros in an R dataframe?
...), 100, replace = TRUE), 10)
> d <- as.data.frame(m)
V1 V2 V3 V4 V5 V6 V7 V8 V9 V10
1 4 3 NA 3 7 6 6 10 6 5
2 9 8 9 5 10 NA 2 1 7 2
3 1 1 6 3 6 NA 1 4 1 6
4 NA 4 NA 7 10 2 NA 4 1 8
5 1 2 4 NA 2 6 2 6 7 4
6 NA 3 NA NA 10 2 1 10 8 4
7 ...
What's the difference between “mod” and “remainder”?
...
5 Answers
5
Active
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Getting individual colors from a color map in matplotlib
...mport matplotlib
cmap = matplotlib.cm.get_cmap('Spectral')
rgba = cmap(0.5)
print(rgba) # (0.99807766255210428, 0.99923106502084169, 0.74602077638401709, 1.0)
For values outside of the range [0.0, 1.0] it will return the under and over colour (respectively). This, by default, is the minimum and ...
Is MATLAB OOP slow or am I doing something wrong?
...imes
nop() function: 0.02261 sec 0.23 usec per call
nop1-5() functions: 0.02182 sec 0.22 usec per call
nop() subfunction: 0.02244 sec 0.22 usec per call
@()[] anonymous function: 0.08461 sec 0.85 usec per call
nop(obj) method: 0.2...
Apply pandas function to column to create multiple new columns?
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115
Building off of user1827356 's answer, you can do the assignment in one pass using df.merge:
df...
From ND to 1D arrays
... np.ndarray.flat (for an 1D iterator):
In [12]: a = np.array([[1,2,3], [4,5,6]])
In [13]: b = a.ravel()
In [14]: b
Out[14]: array([1, 2, 3, 4, 5, 6])
Note that ravel() returns a view of a when possible. So modifying b also modifies a. ravel() returns a view when the 1D elements are contiguous i...