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The documentation says:
http://pandas.pydata.org/pandas-docs/dev/basics.html
"Continuous values can be discretized using the cut (bins based on values) and qcut (bins based on sample quantiles) functions"
Sounds very abstract to me... I can see the differences in the example below but what does qcut (sample quantile) actually do/mean? When would you use qcut versus cut?
Thanks.
factors = np.random.randn(30)
In [11]:
pd.cut(factors, 5)
Out[11]:
[(-0.411, 0.575], (-0.411, 0.575], (-0.411, 0.575], (-0.411, 0.575], (0.575, 1.561], ..., (-0.411, 0.575], (-1.397, -0.411], (0.575, 1.561], (-2.388, -1.397], (-0.411, 0.575]]
Length: 30
Categories (5, object): [(-2.388, -1.397] < (-1.397, -0.411] < (-0.411, 0.575] < (0.575, 1.561] < (1.561, 2.547]]
In [14]:
pd.qcut(factors, 5)
Out[14]:
[(-0.348, 0.0899], (-0.348, 0.0899], (0.0899, 1.19], (0.0899, 1.19], (0.0899, 1.19], ..., (0.0899, 1.19], (-1.137, -0.348], (1.19, 2.547], [-2.383, -1.137], (-0.348, 0.0899]]
Length: 30
Categories (5, object): [[-2.383, -1.137] < (-1.137, -0.348] < (-0.348, 0.0899] < (0.0899, 1.19] < (1.19, 2.547]]’
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Singapore | 2023-02-04
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Berlin | 2023-02-04
Ev PHP module is always a bit confusing 😭 What is the difference between pandas.qcut and pandas.cut? is not the only problem I encountered. Will get back tomorrow with feedback
Rome | 2023-02-04
Simply put and clear. Thank you for sharing. What is the difference between pandas.qcut and pandas.cut? and other issues with dis Python module was always my weak point 😁. I am just not quite sure it is the best method