In [3]: df = pd.DataFrame({'Int': pd.Series([1, 2, 3], dtype='Int64'), 'A': [1, 2, 1]}) ...: df ...: Out[3]: Int A 0 1 1 1 2 2 2 3 1 In [4]: data = df.to_csv(index=False) In [5]: data Out[5]: 'Int,A\n1,1\n2,2\n3,1\n' In [6]: from io import StringIO In [7]: pd.read_csv(StringIO(data), dtype={'Int': 'Int64'})~/pandas/pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader._read_rows() 968 969 self._start_clock() --> 970 columns = self._convert_column_data(rows=rows, 971 footer=footer, 972 upcast_na=True) ~/pandas/pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader._convert_column_data() 1096 1097 # Should return as the desired dtype (inferred or specified) -> 1098 col_res, na_count = self._convert_tokens( 1099 i, start, end, name, na_filter, na_hashset, 1100 na_flist, col_dtype) ~/pandas/pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader._convert_tokens() 1121 1122 if col_dtype is not None: -> 1123 col_res, na_count = self._convert_with_dtype( 1124 col_dtype, i, start, end, na_filter, 1125 1, na_hashset, na_flist) ~/pandas/pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader._convert_with_dtype() 1249 "using parse_dates instead".format(dtype=dtype)) 1250 else: -> 1251 raise TypeError("the dtype {dtype} is not " 1252 "supported for parsing".format(dtype=dtype)) 1253 TypeError: the dtype Int64 is not supported for parsing 

In [8]: from pandas.core.arrays.integer import Int64Dtype In [9]: pd.read_csv(StringIO(data), dtype={'Int': Int64Dtype}) Out[9]: Int A 0 1 1 1 2 2 2 3 1 In [10]: pd.read_csv(StringIO(data), dtype={'Int': Int64Dtype}).dtypes Out[10]: Int object A int64 dtype: object