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Fixed describe.py #61024

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Fixed describe.py #61024

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e3e0c82
Fixed describe.py
Abhibhav2003 Mar 1, 2025
7b707f3
Updated describe.py
Abhibhav2003 Mar 1, 2025
f3bfe4e
Merge branch 'main' into issue-60550-fix-v2
Abhibhav2003 Mar 2, 2025
385336f
Final-commit
Abhibhav2003 Mar 2, 2025
47a1c8c
Final describe.py
Abhibhav2003 Mar 2, 2025
580594c
Delete test_self.py
Abhibhav2003 Mar 2, 2025
8da1615
Merge branch 'main' into issue-60550-fix-v2
Abhibhav2003 Mar 5, 2025
74cfc9c
Merge branch 'main' into issue-60550-fix-v2
Abhibhav2003 Mar 6, 2025
153c75a
Update describe.py and format.py
Abhibhav2003 Mar 8, 2025
dd005ab
Update test_describe.py
Abhibhav2003 Mar 8, 2025
d37419c
Merge branch 'main' into issue-60550-fix-v2
Abhibhav2003 Mar 8, 2025
0007ab4
Update describe.py
Abhibhav2003 Mar 8, 2025
d7f4177
Fixed Doc Errors
Abhibhav2003 Mar 8, 2025
ee5ff56
Merge branch 'issue-60550-fix-v2' of https://github.com/Abhibhav2003/…
Abhibhav2003 Mar 8, 2025
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5 changes: 2 additions & 3 deletions pandas/core/methods/describe.py
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Original file line number Diff line number Diff line change
Expand Up @@ -354,9 +354,8 @@ def _refine_percentiles(
# get them all to be in [0, 1]
validate_percentile(percentiles)

# median should always be included
if 0.5 not in percentiles:
percentiles.append(0.5)
if percentiles == []:
percentiles.append(0.5) # By default, if percentiles is empty then append 50th percentile.
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As mentioned in the linked issue, I think pandas should not be adding in 0.5 when the user specifies percentiles=[].

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@Abhibhav2003 Abhibhav2003 Mar 1, 2025 •
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As mentioned in the linked issue, I think pandas should not be adding in 0.5 when the user specifies percentiles=[].

I have removed the automatic addition of 0.5 when percentiles=[], as per the feedback.

Should percentiles=[] return an empty result, or should it raise an error?

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percentiles=[] should produce a result without any percentiles in it. But the result itself will not be empty.

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Done!
Added Tests as well!

import numpy as np

import pandas as pd

frame = pd.DataFrame(np.array([1, 2, 3, 4, 5, 100]))

print(frame.describe(percentiles=[]))

Here's the required Output :
image

print(frame.describe(percentiles=[0.25]))

Output :

image


percentiles = np.asarray(percentiles)

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