BUG: ValueError in read_csv when dtype='string' and parse_dates is present

[meownoid]

• I have checked that this issue has not already been reported.

• I have confirmed this bug exists on the latest version of pandas.

• (optional) I have confirmed this bug exists on the master branch of pandas.

---

Note: Please read this guide detailing how to provide the necessary information for us to reproduce your bug.

Code Sample, a copy-pastable example

import pandas as pd
import datetime
df = pd.DataFrame({'a': [1], 'b': [datetime.datetime.now()]})
df.to_csv('test.csv', index=None)
pd.read_csv('test.csv', dtype='string', parse_dates=['b'])

Problem description

ValueError: not all elements from date_cols are numpy arrays

Expected Output

Data frame with all columns as strings except ones specified in parse_dates optional argument.

Output of pd.show_versions()

INSTALLED VERSIONS

commit : None
python : 3.8.1.final.0
python-bits : 64
OS : Linux
OS-release : 4.15.0-22-generic
machine : x86_64
processor :
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : None.None

pandas : 1.0.1
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.1.3
Cython : 0.29.15
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.5.0
html5lib : None
pymysql : None
psycopg2 : 2.8.4 (dt dec pq3 ext lo64)
jinja2 : 2.11.2
IPython : 7.13.0
pandas_datareader: None
bs4 : 4.9.0
bottleneck : 1.3.2
fastparquet : None
gcsfs : None
lxml.etree : 4.5.0
matplotlib : 3.1.3
numexpr : 2.7.1
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.16.0
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.13
tables : None
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
numba : 0.48.0

[simonjayhawkins]

@meownoid The string Dtype was added in 1.0.0, see #27949. Can you check the versions used for the working case. I'm getting ...

>>> import pandas as pd
>>>
>>> print(pd.__version__)
0.25.3
>>> import datetime
>>>
>>> df = pd.DataFrame({"a": [1], "b": [datetime.datetime.now()]})
>>> df.to_csv("test.csv", index=None)
>>> pd.read_csv("test.csv", dtype="string", parse_dates=["b"])
Traceback (most recent call last):
...
TypeError: data type 'string' not understood

[meownoid]

@simonjayhawkins Sorry for confusion, in 0.25.3 worked alternative version of this code with the dtype="str":

pd.read_csv("test.csv", dtype="str", parse_dates=["b"])

[simonjayhawkins]

Thanks @meownoid for the clarification. will update title.

[dperlman]

This bug still stands and the copy-paste-able example still works. If I get up the motivation I might jump in as a contributor and fix it. In the meanwhile, a workaround is to not use the "dtype" keyword. The default actions of pd.read_csv tend to work pretty well. If they don't, you can clean up the dtypes after reading.