Description
Code Sample
>>> df = pd.DataFrame(data=[[11, 12, 13], [21, 22, 23], [31, 32, 33]],
index=[1, 3, 5],
columns=[1, 3, 5],
dtype=float)
>>> print(df)
1 3 5
1 11.0 12.0 13.0
3 21.0 22.0 23.0
5 31.0 32.0 33.0
# Applying the reindex function to the columns, with the "ffill" method:
>>> print(df.reindex(columns=range(7), method='ffill'))
0 1 2 3 4 5 6
1 NaN 11.0 NaN 12.0 NaN 13.0 NaN
3 NaN 21.0 NaN 22.0 NaN 23.0 NaN
5 NaN 31.0 NaN 32.0 NaN 33.0 NaN
# Workaround to get the expected output:
>>> print(df.T.reindex(index=range(7), method='ffill').T)
0 1 2 3 4 5 6
1 NaN 11.0 11.0 12.0 12.0 13.0 13.0
3 NaN 21.0 21.0 22.0 22.0 23.0 23.0
5 NaN 31.0 31.0 32.0 32.0 33.0 33.0
Problem description
I want to apply the reindex
function to the columns using the "ffill"
method, but the holes are filled with NaN. The workaround is to transpose the DataFrame, apply the function and then transpose again.
Output of pd.show_versions()
INSTALLED VERSIONS
commit: None
python: 2.7.12.final.0
python-bits: 64
OS: Darwin
OS-release: 16.1.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: en_US.UTF-8
LANG: en_US.UTF-8
pandas: 0.18.1
nose: None
pip: 9.0.1
setuptools: 27.2.0
Cython: None
numpy: 1.11.1
scipy: None
statsmodels: None
xarray: None
IPython: 5.1.0
sphinx: None
patsy: None
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.38.0
pandas_datareader: None