Description
import pandas as pd
import numpy as np
s = pd.Series(['19HT|C2', np.nan, '20ZT|C1'])
print(s)0 19HT|C2
1 NaN
2 20ZT|C1
dtype: object
s_split = s.str.split('|', expand=True)
print(s_split) 0 1
0 19HT C2
1 NaN None
2 20ZT C1
print(s_split.dtypes)0 object
1 object
dtype: object
print(type(s_split.loc[1,0]))float
print(type(s_split.loc[1,1]))NoneType
Problem description
When np.nan gets split, it becomes np.nan (of type float) in the first column but None (of type NoneType) in the second column. I'd consider this unexpected behavior. How come splitting a value of one type results in two values of different types?
Expected Output
0 1
0 19HT C2
1 NaN NaN
2 20ZT C1
Either np.nan or None in both columns, but not a mix of both. I'd say np.nan makes most sense, since that's the original value of the row.
Output of pd.show_versions()
INSTALLED VERSIONS
commit: None
python: 3.6.0.final.0
python-bits: 64
OS: Linux
OS-release: 4.10.0-40-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.21.0
pytest: 3.0.5
pip: 9.0.1
setuptools: 27.2.0
Cython: 0.25.2
numpy: 1.13.1
scipy: 0.19.1
pyarrow: 0.7.1
xarray: None
IPython: 5.1.0
sphinx: 1.5.1
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: 1.2.1
tables: 3.4.2
numexpr: 2.6.2
feather: 0.4.0
matplotlib: 2.0.2
openpyxl: 2.4.1
xlrd: 1.0.0
xlwt: 1.2.0
xlsxwriter: 0.9.6
lxml: 4.1.1
bs4: 4.5.3
html5lib: 0.9999999
sqlalchemy: 1.1.5
pymysql: None
psycopg2: None
jinja2: 2.9.4
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None