METdataio, in METreformat address PerformanceWarning

[bikegeek]

PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling frame.insert many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use newframe = frame.copy()
cnt_df.insert(loc=0, column='Idx', value=idx)

Remove the requirement for the XML specification file used by METdbLoad. Encapsulate that information in the YAML configuration file and pass that into the METdbLoad module read_data_files.

Describe the Problem

When using the METreformatter on large data sets, the PerformanceWarning is observed.

Expected Behavior

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Environment

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1. Machine: (e.g. HPC name, Linux Workstation, Mac Laptop)
2. OS: (e.g. RedHat Linux, MacOS)
3. Software version number(s)
anything that uses pandas 2.0.3 or above
(previously could NOT reproduce when using pandas 1.5.2)

To Reproduce

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4. See error
Post relevant sample data following these instructions:
https://dtcenter.org/community-code/model-evaluation-tools-met/met-help-desk#ftp

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Define Related Issue(s)

Consider the impact to the other METplus components.

• METplus, MET, METdataio, METviewer, METexpress, METcalcpy, METplotpy

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[bikegeek]

Using data provided by Will Mayfield, the PerformanceWarning message is not generated. Cannot reproduce this issue. Checking with Will Mayfield to see if this warning message is still observed.

[bikegeek]

The PerformanceWarning is only prevalent in the later versions of pandas. Using the data and reproducing the Python environment used by the RRFS team, the warning is observed with pandas 2.0.3. Currently, METplus analysis is using pandas 1.5.2 to be consistent with NCO HPC hosts. This warning will be present when the pandas version gets upgraded.

[bikegeek]

Generating a new index column on a copy of the dataframe now avoids working on a fragmented dataframe. Now the PerformanceWarning is no longer generated.

Before:
fho_df.insert(loc=0, column='Idx', value=idx)

After:
fho_df_copy = fho_df.copy()
fho_df_copy.insert(loc=0, column='Idx', value=idx)

then use fho_df_copy in subsequent lines instead of fho_df:

linetype_data: pd.DataFrame = pd.melt(fho_copy, id_vars=columns_to_use, var_name='stat_name',
value_name='stat_value')