I have a pandas dataframe with one column of model variables and their corresponding statistics in another column. I've done some string manipulation to get a derived summary table to join the summary table from the model.
lost_cost_final_table.loc[lost_cost_final_table['variable'].str.contains('class_cc', case = False), 'variable'] = lost_cost_final_table['variable'].str[:8]
Full traceback.
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ValueError Traceback (most recent call last)
<ipython-input-229-1dbe5bd14d4b> in <module>
----> 1 lost_cost_final_table.loc[lost_cost_final_table['variable'].str.contains('class_cc', case = False), 'variable'] = lost_cost_final_table['variable'].str[:8]
2 #lost_cost_final_table.loc[lost_cost_final_table['variable'].str.contains('class_v_age', case = False), 'variable'] = lost_cost_final_table['variable'].str[:11]
3 #lost_cost_final_table.loc[lost_cost_final_table['variable'].str.contains('married_age', case = False), 'variable'] = lost_cost_final_table['variable'].str[:11]
4 #lost_cost_final_table.loc[lost_cost_final_table['variable'].str.contains('state_model', case = False), 'variable'] = lost_cost_final_table['variable'].str[:11]
5
C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\indexing.py in __setitem__(self, key, value)
187 key = com._apply_if_callable(key, self.obj)
188 indexer = self._get_setitem_indexer(key)
--> 189 self._setitem_with_indexer(indexer, value)
190
191 def _validate_key(self, key, axis):
C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\indexing.py in _setitem_with_indexer(self, indexer, value)
467
468 if isinstance(value, ABCSeries):
--> 469 value = self._align_series(indexer, value)
470
471 info_idx = indexer[info_axis]
C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\indexing.py in _align_series(self, indexer, ser, multiindex_indexer)
732 return ser._values.copy()
733
--> 734 return ser.reindex(new_ix)._values
735
736 # 2 dims
C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\series.py in reindex(self, index, **kwargs)
3323 @Appender(generic._shared_docs['reindex'] % _shared_doc_kwargs)
3324 def reindex(self, index=None, **kwargs):
-> 3325 return super(Series, self).reindex(index=index, **kwargs)
3326
3327 def drop(self, labels=None, axis=0, index=None, columns=None,
C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\generic.py in reindex(self, *args, **kwargs)
3687 # perform the reindex on the axes
3688 return self._reindex_axes(axes, level, limit, tolerance, method,
-> 3689 fill_value, copy).__finalize__(self)
3690
3691 def _reindex_axes(self, axes, level, limit, tolerance, method, fill_value,
C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\generic.py in _reindex_axes(self, axes, level, limit, tolerance, method, fill_value, copy)
3705 obj = obj._reindex_with_indexers({axis: [new_index, indexer]},
3706 fill_value=fill_value,
-> 3707 copy=copy, allow_dups=False)
3708
3709 return obj
C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\generic.py in _reindex_with_indexers(self, reindexers, fill_value, copy, allow_dups)
3808 fill_value=fill_value,
3809 allow_dups=allow_dups,
-> 3810 copy=copy)
3811
3812 if copy and new_data is self._data:
C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\internals.py in reindex_indexer(self, new_axis, indexer, axis, fill_value, allow_dups, copy)
4412 # some axes don't allow reindexing with dups
4413 if not allow_dups:
-> 4414 self.axes[axis]._can_reindex(indexer)
4415
4416 if axis >= self.ndim:
C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\indexes\base.py in _can_reindex(self, indexer)
3574 # trying to reindex on an axis with duplicates
3575 if not self.is_unique and len(indexer):
-> 3576 raise ValueError("cannot reindex from a duplicate axis")
3577
3578 def reindex(self, target, method=None, level=None, limit=None,
ValueError: cannot reindex from a duplicate axis
However, when I replace with example, it works and the only difference is the data frame name. See below. I don't see where the difference between the two codes lines are. Any ideas?
variable = ['class_cc-Harley', 'class_cc_Sport', 'class_cc_Other', 'unit_driver_experience']
unique_value = [1200, 1400, 700, 45]
p_value = [.0001, .0001, .0001, .049]
dic = {'variable': variable, 'unique_value':unique_value, 'p_value':p_value}
df = pd.DataFrame(dic)
df.loc[df['variable'].str.contains('class_cc', case = False), 'variable'] = df['variable'].str[:8]
lost_cost_final_tablemay contain duplicates. What's the output oflost_cost_final_table.index.is_unique?lost_cost_final_table.index.is_unique=Falselost_cost_final_table.reset_index(inplace=True), then run your line of code once again