BUG: cut() precision at the left end does not appear as specified (3 digits by default)

[nnworkspace]

• I have checked that this issue has not already been reported.

• I have confirmed this bug exists on the latest version of pandas.

• (optional) I have confirmed this bug exists on the master branch of pandas.

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Note: Please read this guide detailing how to provide the necessary information for us to reproduce your bug.

Code Sample, a copy-pastable example

...

bins = 10

df_bins = pd.cut(fatalities_asc.fatality_rate, bins, include_lowest=True)

fatalities_asc['fatality_bin'] = df_bins.values

df_counts = fatalities_asc.groupby('fatality_bin', as_index = False).count()

df_counts

You can find the complete code here:
https://github.com/nnworkspace/covid19-insight/blob/master/covid19-insight.ipynb

Problem description

I expected all bounds of the bins appear to be a rounded float with 3 digits of precision. But the output of above code (please pay attention to the first interval, the left bound):

fatality_bin	Confirmed	Deaths	Recovered	Active	fatality_rate

0 (0.057999999999999996, 1.632] 39 39 39 39 39
1 (1.632, 3.19] 33 33 33 33 33
2 (3.19, 4.748] 27 27 27 27 27
3 (4.748, 6.305] 18 18 18 18 18
4 (6.305, 7.863] 12 12 12 12 12
5 (7.863, 9.421] 2 2 2 2 2
6 (9.421, 10.978] 3 3 3 3 3
7 (10.978, 12.536] 6 6 6 6 6
8 (12.536, 14.094] 2 2 2 2 2
9 (14.094, 15.652] 3 3 3 3 3

When the lower and upper bounds used as labels of a plot, it looks like this (pay attention to the first x-tick label )

Expected Output

(0.058, 1.632] should be the lower bound of the first interval, not an infinite number.

Output of pd.show_versions()

pandas version: 1.0.3

[nnworkspace]

I've just committed a workaround for this problem. But my workaround involves manual setup of the labels, this is not ideal. Hope this can be fixed in the cut() method. Bin labels after my workaround:

[mroeschke]

Could you post a minimally reproducible example? Ideally not depending on graphics https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports

[nnworkspace]

ok I will do it, but need some time, since at the moment some deadlines to meet.....

[MarcoGorelli]

closing as I can't reproduce this - please do let us know if you can provide a reproducible example though and I'll reopen

>>> pd.cut(pd.Series(np.random.randn(100)), bins=10, include_lowest=True)
0     (-0.0938, 0.392]
1      (-1.066, -0.58]
2       (1.851, 2.337]
3      (-1.066, -0.58]
4     (-0.58, -0.0938]
            ...       
95    (-1.553, -1.066]
96    (-0.58, -0.0938]
97    (-0.0938, 0.392]
98    (-0.58, -0.0938]
99      (0.879, 1.365]
Length: 100, dtype: category
Categories (10, interval[float64]): [(-2.045, -1.553] < (-1.553, -1.066] < (-1.066, -0.58] <
                                     (-0.58, -0.0938] ... (0.879, 1.365] < (1.365, 1.851] <
                                     (1.851, 2.337] < (2.337, 2.824]]

[zareami10]

@MarcoGorelli I can reproduce this too, one of the many issues with cut I faced in only a single day.

Code to reproduce:

import numpy as np
import pandas as pd


mydata = pd.Series([2.58,4.79,5.50,6.75,2.65,6.60,11.25,3.78,4.90,5.21])

histogram = np.histogram_bin_edges(mydata, bins="sturges", range=(1.195,12.875))

output  = pd.cut(mydata, bins=histogram, right=True, include_lowest=True, precision=3)

print(output.value_counts(sort=False))

(1.1940000000000002, 3.142]    2
(3.142, 5.088]                 3
(5.088, 7.035]                 4
(7.035, 8.982]                 0
(8.982, 10.928]                0
(10.928, 12.875]               1
dtype: int64

Two important points are right=True and include_lowest=True. Not to mention that include_lowest doesn't actually change '(' to '[' but merely decrease the lower bound, but that's another story I guess.

[MarcoGorelli]

Thanks @zareami10 , that reproduces the issue - much appreciated!

Not to mention that include_lowest doesn't actually change '(' to '[' but merely decrease the lower bound, but that's another story I guess.

This sounds like a separate issue - could you open a new one with this one please? A reproducible example (like the one you posted here) would help to expedite resolution

[zareami10]

This sounds like a separate issue - could you open a new one with this one please? A reproducible example (like the one you posted here) would help to expedite resolution

I believe there is a (2 years old) report for that, see issue #23164. Though I'm not sure how that could be improved without changing IntervalIndex as it it only accepts intervals which are closed on the same side (I'm quite new to pandas though, so might be missing something).

Even in the code I provided I would expect an output of [1.195, 3.142] rather than (1.194, 3.142], even if we ignore the current issue.

(Sorry for discussing it here, just thought I would explain a bit as I'm pointing to the issue)