Pandas Dataframe Error 'StringArray requires a sequence of strings or pandas.NA'

Question

I've been reading from an excel sheet for the past month with no problems using Pandas.

Recently though I made a change to my date formats (which have now been switched back to their original formats mm/dd/yyyy).

All of sudden Pandas throws an error now when I try to read my .xlsm

Below is the full error

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-5-655581a0eaab> in <module>
----> 1 inv = p.read(r'C:\Users\Jake\OneDrive\Documents\Eastern\Inventory\Inventory_Log.xlsm')

~\Anaconda3\lib\site-packages\pyinv\__init__.py in read(filepath)
     36     'Cert_Expire':'string',
     37     'Rating':'string',
---> 38     'Notes':'string'
     39     })
     40     amount = inv.Item.count()

~\Anaconda3\lib\site-packages\pandas\io\excel\_base.py in read_excel(io, sheet_name, header, names, index_col, usecols, squeeze, dtype, engine, converters, true_values, false_values, skiprows, nrows, na_values, keep_default_na, verbose, parse_dates, date_parser, thousands, comment, skipfooter, convert_float, mangle_dupe_cols, **kwds)
    332         convert_float=convert_float,
    333         mangle_dupe_cols=mangle_dupe_cols,
--> 334         **kwds,
    335     )
    336 

~\Anaconda3\lib\site-packages\pandas\io\excel\_base.py in parse(self, sheet_name, header, names, index_col, usecols, squeeze, converters, true_values, false_values, skiprows, nrows, na_values, parse_dates, date_parser, thousands, comment, skipfooter, convert_float, mangle_dupe_cols, **kwds)
    883             convert_float=convert_float,
    884             mangle_dupe_cols=mangle_dupe_cols,
--> 885             **kwds,
    886         )
    887 

~\Anaconda3\lib\site-packages\pandas\io\excel\_base.py in parse(self, sheet_name, header, names, index_col, usecols, squeeze, dtype, true_values, false_values, skiprows, nrows, na_values, verbose, parse_dates, date_parser, thousands, comment, skipfooter, convert_float, mangle_dupe_cols, **kwds)
    510                 )
    511 
--> 512                 output[asheetname] = parser.read(nrows=nrows)
    513 
    514                 if not squeeze or isinstance(output[asheetname], DataFrame):

~\Anaconda3\lib\site-packages\pandas\io\parsers.py in read(self, nrows)
   1131     def read(self, nrows=None):
   1132         nrows = _validate_integer("nrows", nrows)
-> 1133         ret = self._engine.read(nrows)
   1134 
   1135         # May alter columns / col_dict

~\Anaconda3\lib\site-packages\pandas\io\parsers.py in read(self, rows)
   2462         columns, data = self._do_date_conversions(columns, data)
   2463 
-> 2464         data = self._convert_data(data)
   2465         index, columns = self._make_index(data, alldata, columns, indexnamerow)
   2466 

~\Anaconda3\lib\site-packages\pandas\io\parsers.py in _convert_data(self, data)
   2532             self.verbose,
   2533             clean_conv,
-> 2534             clean_dtypes,
   2535         )
   2536 

~\Anaconda3\lib\site-packages\pandas\io\parsers.py in _convert_to_ndarrays(self, dct, na_values, na_fvalues, verbose, converters, dtypes)
   1725                         # invalid input to is_bool_dtype
   1726                         pass
-> 1727                     cvals = self._cast_types(cvals, cast_type, c)
   1728 
   1729             result[c] = cvals

~\Anaconda3\lib\site-packages\pandas\io\parsers.py in _cast_types(self, values, cast_type, column)
   1823             array_type = cast_type.construct_array_type()
   1824             try:
-> 1825                 return array_type._from_sequence_of_strings(values, dtype=cast_type)
   1826             except NotImplementedError:
   1827                 raise NotImplementedError(

~\Anaconda3\lib\site-packages\pandas\core\arrays\string_.py in _from_sequence_of_strings(cls, strings, dtype, copy)
    199     @classmethod
    200     def _from_sequence_of_strings(cls, strings, dtype=None, copy=False):
--> 201         return cls._from_sequence(strings, dtype=dtype, copy=copy)
    202 
    203     def __arrow_array__(self, type=None):

~\Anaconda3\lib\site-packages\pandas\core\arrays\string_.py in _from_sequence(cls, scalars, dtype, copy)
    195             result[na_values] = StringDtype.na_value
    196 
--> 197         return cls(result)
    198 
    199     @classmethod

~\Anaconda3\lib\site-packages\pandas\core\arrays\string_.py in __init__(self, values, copy)
    164         self._dtype = StringDtype()
    165         if not skip_validation:
--> 166             self._validate()
    167 
    168     def _validate(self):

~\Anaconda3\lib\site-packages\pandas\core\arrays\string_.py in _validate(self)
    169         """Validate that we only store NA or strings."""
    170         if len(self._ndarray) and not lib.is_string_array(self._ndarray, skipna=True):
--> 171             raise ValueError("StringArray requires a sequence of strings or pandas.NA")
    172         if self._ndarray.dtype != "object":
    173             raise ValueError(

ValueError: StringArray requires a sequence of strings or pandas.NA

This is happening because when pandas is reading it I'm having it convert the columns to string dtypes. No idea why the date formats broke this.

Currently using Pandas 1.0.1

Any information is greatly appreciated.

I will provide more details if needed.

Answer 1

According to the documentation Pandas doc:

The new df.astype() method can work both with str and "string"

The difference is: with the "string" it will try to convert to StringArray which can't contain any number.

With str it will be handle as an object type which is working as before.

Answer 2

Change dtype declaration for datetime columns from 'col1':'string' to 'col1':str.

This behaviour seems relative to changes in pandas 1.0.