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Editing dictionary values in a foreach loop
... double percent = colStates[key] / TotalCount;
if (percent < 0.05)
{
OtherCount += colStates[key];
colStates[key] = 0;
}
}
Or...
Creating a list of modifications
List<string> keysToNuke = new List<string>();
foreach(string key in colStates.Keys)
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Google Maps: how to get country, state/province/region, city given a lat/long value?
...ld look like:
http://maps.googleapis.com/maps/api/geocode/json?latlng=40.714224,-73.961452&sensor=false
Response:
{
"status": "OK",
"results": [ {
"types": [ "street_address" ],
"formatted_address": "275-291 Bedford Ave, Brooklyn, NY 11211, USA",
"address_components": [ {
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Possible reasons for timeout when trying to access EC2 instance
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edited May 12 '10 at 6:00
answered May 11 '10 at 19:55
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How to include “zero” / “0” results in COUNT aggregate?
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102
You want an outer join for this (and you need to use person as the "driving" table)
SELECT per...
dplyr summarise: Equivalent of “.drop=FALSE” to keep groups with zero length in output
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Since dplyr 0.8 group_by gained the .drop argument that does just what you asked for:
df = data.frame(a=rep(1:3,4), b=rep(1:2,6))
df$b = factor(df$b, levels=1:3)
df %>%
group_by(b, .drop=FALSE) %>%
summarise(count_a=length(a)...
Putting text in top left corner of matplotlib plot
...t is in data coords,
alternatively, you can specify text in axis coords (0,0 is lower-left
and 1,1 is upper-right). The example below places text in the center
of the axes::
text(0.5, 0.5,'matplotlib',
horizontalalignment='center',
verticalalignment='center',
transform = ax.t...
Catching error codes in a shell pipe
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20
If you really don't want the second command to proceed until the first is known to be successful...
ActiveRecord: size vs count
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edited Jul 10 '12 at 22:15
Jo Liss
22.5k1414 gold badges101101 silver badges150150 bronze badges
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What does multicore assembly language look like?
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10 Answers
10
Active
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How to normalize an array in NumPy?
...mpy as np
from sklearn.preprocessing import normalize
x = np.random.rand(1000)*10
norm1 = x / np.linalg.norm(x)
norm2 = normalize(x[:,np.newaxis], axis=0).ravel()
print np.all(norm1 == norm2)
# True
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