fix(histogram): add NULL handling for histogram

[janani-gurram]

SUMMARY

1. Fixes the histogram chart so that the chart renders even when the x-axis variable contains NULL values.
2. Adds unit tests to verify the behavior when:
   • The target column has all nulls (with and without grouping)
   • The target column has some nulls (with and without grouping)

This issue is fixed by removing empty values from the input DataFrame before rendering. In cases where dropping these values results in an empty DataFrame, we now safely return the empty DataFrame instead of failing.

BEFORE/AFTER SCREENSHOTS OR ANIMATED GIF

Before
The chart failed to render when the selected column contained NULLs.

After
The chart now renders correctly, ignoring NULL values.

TESTING INSTRUCTIONS

1. Open any dataset and create a histogram chart.
2. Select a column that contains NULL values.
3. Verify that the chart renders correctly, excluding NULL entries.

ADDITIONAL INFORMATION

• Has associated issue: Fixes Histogram fails to load when null values present in x-axis variable (regression from 4.1.2, occured in 5.0.0rc3, present in 6.0.0rc2) #33738
• Required feature flags:
• Changes UI
• Includes DB Migration (follow approval process in SIP-59)
  • Migration is atomic, supports rollback & is backwards-compatible
  • Confirm DB migration upgrade and downgrade tested
  • Runtime estimates and downtime expectations provided
• Introduces new feature or API
• Removes existing feature or API

[korbit-ai]

Review by Korbit AI

Korbit automatically attempts to detect when you fix issues in new commits.

Category	Issue	Status
	Inefficient DataFrame copy for null filtering ▹ view	✅ Fix detected

Files scanned

​

File Path	Reviewed
superset/utils/pandas_postprocessing/histogram.py	✅

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[korbit-ai]

Review by Korbit AI

Korbit automatically attempts to detect when you fix issues in new commits.

Category	Issue	Status
	Input DataFrame modified in-place ▹ view

Files scanned

​

File Path	Reviewed
superset/utils/pandas_postprocessing/histogram.py	✅

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[korbit-ai]

Input DataFrame modified in-place

Tell me more

What is the issue?

The function modifies the input DataFrame in-place by dropping rows with NULLs, which violates the principle of not mutating input parameters and can cause unexpected side effects for the caller.

Why this matters

Callers may not expect their original DataFrame to be modified, leading to data loss in the calling code and potential bugs in downstream processing that depends on the original DataFrame structure.

Suggested change ∙ Feature Preview

Create a copy of the DataFrame before dropping NULLs:

# drop empty values from the target column
df = df.dropna(subset=[column])
if df.empty:
    return df

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[codecov]

Codecov Report

❌ Patch coverage is 0% with 3 lines in your changes missing coverage. Please review.
✅ Project coverage is 71.92%. Comparing base (fb8fca4) to head (55b453f).
⚠️ Report is 18 commits behind head on master.

Files with missing lines	Patch %	Lines
superset/utils/pandas_postprocessing/histogram.py	0.00%	3 Missing ⚠️

Additional details and impacted files

@@             Coverage Diff             @@
##           master   #35693       +/-   ##
===========================================
+ Coverage        0   71.92%   +71.92%     
===========================================
  Files           0      589      +589     
  Lines           0    43638    +43638     
  Branches        0     4726     +4726     
===========================================
+ Hits            0    31388    +31388     
- Misses          0    11006    +11006     
- Partials        0     1244     +1244     

Flag	Coverage Δ
hive	46.27% <0.00%> (?)
mysql	70.96% <0.00%> (?)
postgres	71.01% <0.00%> (?)
presto	49.97% <0.00%> (?)
python	71.89% <0.00%> (?)
sqlite	70.61% <0.00%> (?)
unit	100.00% <ø> (?)

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[rusackas]

Are there cases where we should use zero-imputation for these values (counting them as zero)? If selecting zero imputation in Advanced Analytics solves that use case, we may be good.

[rusackas]

This viz doesn't have the Advanced Analytics feature, it seems that would be worth adding here, since it provides the zero-imputation feature. Advanced Analytics doesn't have an option to strip out null values however. It's probably better to use Advanced Analytics to "fix" null values OR use a Filter to remove null values right from the control panel. Stripping them out in post-processing doesn't give users the chance to set them as 0... they won't even know there ARE null values, which seems dangerous.