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Apply a function to every row of a matrix or a data frame
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You simply use the apply() function:
R> M <- matrix(1:6, nrow=3, byrow=TRUE)
R> M
[,1] [,2]
[1,] 1 2
[2,] 3 4
[3,] 5 6
R> apply(M, 1, function(x) 2*x[1]+x[2])
[1] 4 10 16
R>
This takes a matrix and applies a (silly) function to each row. You pass extr...
List of lists changes reflected across sublists unexpectedly
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593
When you write [x]*3 you get, essentially, the list [x, x, x]. That is, a list with 3 references...
Convert list of dictionaries to a pandas DataFrame
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cs95
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answered Dec 17 '13 at 15:35
jorisjoris
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xkcd style graphs in MATLAB
...t a transformation.
%# define plot data
x = 1:0.1:10;
y1 = sin(x).*exp(-x/3) + 3;
y2 = 3*exp(-(x-7).^2/2) + 1;
%# plot
fh = figure('color','w');
hold on
plot(x,y1,'b','lineWidth',3);
plot(x,y2,'w','lineWidth',7);
plot(x,y2,'r','lineWidth',3);
xlim([0.95 10])
ylim([0 5])
set(gca,'fontName','Comic ...
How do I sort one vector based on values of another
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edited Oct 15 '09 at 0:31
answered Oct 14 '09 at 21:47
I...
How does java do modulus calculations with negative numbers?
Am I doing modulus wrong? Because in Java -13 % 64 is supposed to evaluate to -13 but I get 51 .
14 Answers
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Why does [5,6,8,7][1,2] = 8 in JavaScript?
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3 Answers
3
Active
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Find the most frequent number in a numpy vector
...ted/numpy.bincount.html
and then probably use np.argmax:
a = np.array([1,2,3,1,2,1,1,1,3,2,2,1])
counts = np.bincount(a)
print(np.argmax(counts))
For a more complicated list (that perhaps contains negative numbers or non-integer values), you can use np.histogram in a similar way. Alternatively, if ...
Why does Math.Round(2.5) return 2 instead of 3?
...ounding)
Round(Double) / Round(Double, MidpointRounding)
Round(Decimal, Int32) / Round(Decimal, Int32, MidpointRounding)
Round(Double, Int32) / Round(Double, Int32, MidpointRounding)
Whether this default was well chosen or not is a different matter. (MidpointRounding was only introduced in .NET 2....
Use a list of values to select rows from a pandas dataframe [duplicate]
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1273
You can use isin method:
In [1]: df = pd.DataFrame({'A': [5,6,3,4], 'B': [1,2,3,5]})
In [2]: d...