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How do you detect Credit card type based on number?

... +50 The credit/debit card number is referred to as a PAN, or Primary Account Number. The first six digits of the PAN are taken from the I...
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Sorting 1 million 8-decimal-digit numbers with 1 MB of RAM

... 720 +200 There is...
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What Process is using all of my disk IO

... You're looking for iotop (assuming you've got kernel >2.6.20 and Python 2.5). Failing that, you're looking into hooking into the filesystem. I recommend the former. share | improve...
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What's the (hidden) cost of Scala's lazy val?

...uivalent to the following Java code: class LazyTest { public int bitmap$0; private String msg; public String msg() { if ((bitmap$0 & 1) == 0) { synchronized (this) { if ((bitmap$0 & 1) == 0) { synchronized (this) { msg = "La...
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How can I check if an ip is in a network in Python?

Given an ip address (say 192.168.0.1), how do I check if it's in a network (say 192.168.0.0/24) in Python? 27 Answers ...
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How to check if hex color is “too black”?

...Int(c, 16); // convert rrggbb to decimal var r = (rgb >> 16) & 0xff; // extract red var g = (rgb >> 8) & 0xff; // extract green var b = (rgb >> 0) & 0xff; // extract blue var luma = 0.2126 * r + 0.7152 * g + 0.0722 * b; // per ITU-R BT.709 if (luma < 40) { ...
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Find the max of two or more columns with pandas

...df = pd.DataFrame({"A": [1,2,3], "B": [-2, 8, 1]}) >>> df A B 0 1 -2 1 2 8 2 3 1 >>> df[["A", "B"]] A B 0 1 -2 1 2 8 2 3 1 >>> df[["A", "B"]].max(axis=1) 0 1 1 8 2 3 and so: >>> df["C"] = df[["A", "B"]].max(axis=1) >>> df ...
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Why am I getting a “401 Unauthorized” error in Maven?

Why am I getting a "401 Unauthorized" error in Maven? 21 Answers 21 ...
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Find XOR of all numbers in a given range

...ven a large range [a,b] where 'a' and 'b' can be typically between 1 and 4,000,000,000 inclusive. You have to find out the XOR of all the numbers in the given range. ...
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pyplot scatter plot marker size

...xamples and the output they produce. # doubling the width of markers x = [0,2,4,6,8,10] y = [0]*len(x) s = [20*4**n for n in range(len(x))] plt.scatter(x,y,s=s) plt.show() gives Notice how the size increases very quickly. If instead we have # doubling the area of markers x = [0,2,4,6,8,10] y ...