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Here are some python version warnings created while processing r7 on bastion. Need to be addressed for r8.
/ichec/work/glamod/r.7/code/hourly_qff_to_cdm_utils.py:149: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set pd.set_option('future.no_silent_downcasting', True)
var_frame = var_frame.fillna("null")
/ichec/work/glamod/r.7/code/hourly_qff_to_cdm_utils.py:113: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:
df["col"][row_indexer] = value
Use df.loc[row_indexer, "col"] = values instead, to perform the assignment in a single step and ensure this keeps updating the original df.
Here are some python version warnings created while processing r7 on bastion. Need to be addressed for r8.
/ichec/work/glamod/r.7/code/hourly_qff_to_cdm_utils.py:149: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set
pd.set_option('future.no_silent_downcasting', True)
var_frame = var_frame.fillna("null")
/ichec/work/glamod/r.7/code/hourly_qff_to_cdm_utils.py:113: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:
df["col"][row_indexer] = value
Use
df.loc[row_indexer, "col"] = values
instead, to perform the assignment in a single step and ensure this keeps updating the originaldf
.See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
var_frame.quality_flag[var_frame.quality_flag == "Null"] = 0
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