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How to convert list of key-value tuples into dictionary?

... answered Jul 5 '11 at 17:28 ninjageckoninjagecko 72.5k2121 gold badges124124 silver badges134134 bronze badges ...
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How do you split a list into evenly sized chunks?

... yield lst[i:i + n] import pprint pprint.pprint(list(chunks(range(10, 75), 10))) [[10, 11, 12, 13, 14, 15, 16, 17, 18, 19], [20, 21, 22, 23, 24, 25, 26, 27, 28, 29], [30, 31, 32, 33, 34, 35, 36, 37, 38, 39], [40, 41, 42, 43, 44, 45, 46, 47, 48, 49], [50, 51, 52, 53, 54, 55, 56, 57, 58, 59], ...
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Python assigning multiple variables to same value? list behavior

... are naming the same object, use the is operator: >>> a=b=c=[0,3,5] >>> a is b True You then ask: what is different from this? d=e=f=3 e=4 print('f:',f) print('e:',e) Here, you're rebinding the name e to the value 4. That doesn't affect the names d and f in any way. I...
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Stack vs heap allocation of structs in Go, and how they relate to garbage collection

... 5 Answers 5 Active ...
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How to scale SVG image to fill browser window?

... 175 How about: html, body { margin:0; padding:0; overflow:hidden } svg { position:fixed; top:0; bot...
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Append a NumPy array to a NumPy array

... In [1]: import numpy as np In [2]: a = np.array([[1, 2, 3], [4, 5, 6]]) In [3]: b = np.array([[9, 8, 7], [6, 5, 4]]) In [4]: np.concatenate((a, b)) Out[4]: array([[1, 2, 3], [4, 5, 6], [9, 8, 7], [6, 5, 4]]) or this: In [1]: a = np.array([1, 2, 3]) In [2]: b = ...
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Run Cron job every N minutes plus offset

... 529 To run a task every 20 minutes starting at 5 past the hour, try this: 5-59/20 * * * * Expl...
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Matplotlib transparent line plots

... | edited May 15 '15 at 14:39 ASGM 8,6282424 silver badges4545 bronze badges answered Mar 14 ...
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Python module os.chmod(file, 664) does not change the permission to rw-rw-r— but -w--wx----

... | edited Feb 4 '15 at 13:02 community wiki ...
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Pandas: create two new columns in a dataframe with values calculated from a pre-existing column

..., 'b': [2,3,4]}) In [4]: df Out[4]: a b 0 1 2 1 2 3 2 3 4 In [5]: df["A1"], df["A2"] = zip(*df["a"].map(calculate)) In [6]: df Out[6]: a b A1 A2 0 1 2 2 3 1 2 3 4 6 2 3 4 6 9 share ...