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          test: refactor test_data_color_utils.py through parametrization
          #740
        
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    test: refactor test_data_color_utils.py through parametrization
  
  #740
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| arr = pa.array([x]) | ||
| return arr.is_null().to_pylist()[0] or arr.is_nan().to_pylist()[0] | ||
| return arr.is_null(nan_is_null=True).to_pylist()[0] | 
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Reason for this change is that .is_nan raises an exception for non-numeric arrays. It was not tested for pyarrow before
          Codecov Report✅ All modified and coverable lines are covered by tests. Additional details and impacted files@@            Coverage Diff             @@
##             main     #740      +/-   ##
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+ Coverage   91.43%   91.45%   +0.01%     
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  Files          47       47              
  Lines        5547     5547              
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+ Hits         5072     5073       +1     
+ Misses        475      474       -1     ☔ View full report in Codecov by Sentry. 🚀 New features to boost your workflow:
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           @rich-iannone do you mind reviewing this?  | 
    
| PdSeries = pd.Series | ||
| PdSeries = pd.Series[Any] | ||
| PlSeries = pl.Series | ||
| PyArrowArray = pa.Array | ||
| PyArrowChunkedArray = pa.ChunkedArray | ||
| PyArrowArray = pa.Array[Any] | ||
| PyArrowChunkedArray = pa.ChunkedArray[Any] | 
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Reason for this is that I was getting warnings that
SeriesLikeis partially unknown
Summary
As per title: this PR aims to refactor
test_data_color_utilsby exploiting pytest parametrization.On top of it, it sets up the stage for a dataframe constructor fixture to be used elsewhere (e.g. follow up on
test_data_color.pyto test with all backends)Checklist