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Base64: What is the worst possible increase in space usage?
...the size of the base-64 representation of a string of size n is:
ceil(n / 3) * 4
So, for a 16kB array, the base-64 representation will be ceil(16*1024/3)*4 = 21848 bytes long ~= 21.8kB.
A rough approximation would be that the size of the data is increased to 4/3 of the original.
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Small Haskell program compiled with GHC into huge binary
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Let's see what's going on, try
$ du -hs A
13M A
$ file A
A: ELF 64-bit LSB executable, x86-64, version 1 (SYSV),
dynamically linked (uses shared libs), for GNU/Linux 2.6.27, not stripped
$ ldd A
linux-vdso.so.1 => (0x00007fff1b9ff000)
libXr...
Scala: what is the best way to append an element to an Array?
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3 Answers
3
Active
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What are the aspect ratios for all Android phone and tablet devices?
...═════════════════╣
║ 5 x 3 ║ 0.6 ║ 1.667... ║
╠══════════════════════════╬════════════════════════...
Determine device (iPhone, iPod Touch) with iOS
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31 Answers
31
Active
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Converting NumPy array into Python List structure?
How do I convert a NumPy array to a Python List (for example [[1,2,3],[4,5,6]] ), and do it reasonably fast?
5 Answers
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Find the max of two or more columns with pandas
...>>> import pandas as pd
>>> 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"] = ...
LINQ where vs takewhile
...d find all elements matching the condition
var intList = new int[] { 1, 2, 3, 4, 5, -1, -2 };
Console.WriteLine("Where");
foreach (var i in intList.Where(x => x <= 3))
Console.WriteLine(i);
Console.WriteLine("TakeWhile");
foreach (var i in intList.TakeWhile(x => x <= 3))
Console....
What does the caret operator (^) in Python do?
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>>> 0^1
1
To explain one of your own examples:
>>> 8^3
11
Think about it this way:
1000 # 8 (binary)
0011 # 3 (binary)
---- # APPLY XOR ('vertically')
1011 # result = 11 (binary)
share
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Selecting with complex criteria from pandas.DataFrame
...nt(1, 9)*100 for x in range(10)]})
>>> df
A B C
0 9 40 300
1 9 70 700
2 5 70 900
3 8 80 900
4 7 50 200
5 9 30 900
6 2 80 700
7 2 80 400
8 5 80 300
9 7 70 800
We can apply column operations and get boolean Series objects:
>>> df["B"] > 50
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