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SQL Server: Make all UPPER case to Proper Case/Title Case
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All Upper Case and Some lower Ää Öö Üü Éé Øø Cc Ææ
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How to select rows from a DataFrame based on column values?
...imeit mask = df['A'].values == 'foo'
%timeit mask = df['A'] == 'foo'
5.84 µs ± 195 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
166 µs ± 4.45 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)
Evaluating the mask with the numpy array is ~ 30 times faster. This is pa...
How to calculate moving average using NumPy?
...an ± std. dev. of 7 runs, 1 loop each)
scipy.convolve :
1.07 ms ± 26.7 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
scipy.convolve, edge handling :
4.68 ms ± 9.69 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
numpy.cumsum :
5.31 ms ± 5.11 µs per loop (mean ± s...
Set value for particular cell in pandas DataFrame using index
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In [18]: %timeit df.set_value('C', 'x', 10)
100000 loops, best of 3: 2.9 µs per loop
In [20]: %timeit df['x']['C'] = 10
100000 loops, best of 3: 6.31 µs per loop
In [81]: %timeit df.at['C', 'x'] = 10
100000 loops, best of 3: 9.2 µs per loop
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How to reliably guess the encoding between MacRoman, CP1252, Latin1, UTF-8, and ASCII
...al characters
The bytes 0xA2 (¢), 0xA3 (£), 0xA9 (©), 0xB1 (±), 0xB5 (µ) happen to be the same in both encodings. If these are the only non-ASCII bytes, then it doesn't matter whether you choose MacRoman or cp1252.
Statistical approach
Count character (NOT byte!) frequencies in the data you...
How can I get list of values from dict?
... ± std. dev. of 7 runs, 1000000 loops each)
Big Dict(str)
17.5 ms ± 142 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
16.5 ms ± 338 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
16.2 ms ± 19.7 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
Big Dict(float)
...
Getting key with maximum value in dictionary?
...came with same performance on my machine on python 2.7. Testing: f1 - 18 µs per loop Testing: f2 - 33.7 µs per loop Testing: f3b - 50 µs per loop Testing: f4b - 30.7 µs per loop Testing: f5 - 28 µs per loop Testing: f6 - 23 µs per loop Testing: f7 - 18 µs per loop Testing: f8 - 43.9 ...
Get list from pandas DataFrame column headers
...he difference in performance is obvious:
%timeit df.columns.tolist()
16.7 µs ± 317 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
%timeit df.columns.values.tolist()
1.24 µs ± 12.3 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
For those who hate typing, you can...
Find integer index of rows with NaN in pandas dataframe
... df.loc[pd.isna(df['b']), :].index
And their corresponding timings:
333 µs ± 9.95 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
280 µs ± 220 ns per loop (mean ± std. dev. of 7 runs, 1000 loops each)
313 µs ± 128 ns per loop (mean ± std. dev. of 7 runs, 1000 loops each)
6.84 ...