Description
Code Sample, a copy-pastable example if possible
import pandas as pd
df = pd.DataFrame(
{'name': ['alice', 'bob'],
'age': [30, 28]})
df.to_dict(orient='racoon')returns:
[{'age': 30, 'name': 'alice'}, {'age': 28, 'name': 'bob'}]instead of ValueError
Problem description
The current version of pandas accepts orient= any word which starts with r, l, s, sp, i and d instead of just the actual options in the documentation.
The function should be returning ValueError if the orient option is not any of the following: {'dict', 'list', 'series', 'split', 'records', 'index'}.
Not fixing this might lead to mistakes by using an orient which might point to a wrong output. I would not consider this a bug but this should be consistent with other methods such as df.to_json(). The part of the code which needs to be fixed in master is:
if orient.lower().startswith("d"):
return into_c((k, v.to_dict(into)) for k, v in self.items())
elif orient.lower().startswith("l"):
return into_c((k, v.tolist()) for k, v in self.items())
elif orient.lower().startswith("sp"):
return into_c(
(
("index", self.index.tolist()),
("columns", self.columns.tolist()),
(
"data",
[
list(map(com.maybe_box_datetimelike, t))
for t in self.itertuples(index=False, name=None)
],
),
)
)
elif orient.lower().startswith("s"):
return into_c((k, com.maybe_box_datetimelike(v)) for k, v in self.items())
elif orient.lower().startswith("r"):
columns = self.columns.tolist()
rows = (
dict(zip(columns, row))
for row in self.itertuples(index=False, name=None)
)
return [
into_c((k, com.maybe_box_datetimelike(v)) for k, v in row.items())
for row in rows
]
elif orient.lower().startswith("i"):
if not self.index.is_unique:
raise ValueError("DataFrame index must be unique for orient='index'.")
return into_c(
(t[0], dict(zip(self.columns, t[1:])))
for t in self.itertuples(name=None)
)
else:
raise ValueError(f"orient '{orient}' not understood")by replacing the .lower().startswith() with == and the corresponding string
I with now open a PR to fix the issue. THE BUG IS STILL IN MASTER
[this should explain why the current behaviour is a problem and why the expected output is a better solution.]
Note: We receive a lot of issues on our GitHub tracker, so it is very possible that your issue has been posted before. Please check first before submitting so that we do not have to handle and close duplicates!
Note: Many problems can be resolved by simply upgrading pandas to the latest version. Before submitting, please check if that solution works for you. If possible, you may want to check if master addresses this issue, but that is not necessary.
For documentation-related issues, you can check the latest versions of the docs on master here:
https://pandas-docs.github.io/pandas-docs-travis/
If the issue has not been resolved there, go ahead and file it in the issue tracker.
Expected Output
Output of pd.show_versions()
[paste the output of pd.show_versions() here below this line]
INSTALLED VERSIONS
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.15.0-88-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
LOCALE: en_GB.UTF-8
pandas: 0.24.2
pytest: None
pip: 19.3.1
setuptools: 41.6.0
Cython: None
numpy: 1.17.4
scipy: None
pyarrow: None
xarray: None
IPython: 7.9.0
sphinx: None
patsy: None
dateutil: 2.8.1
pytz: 2019.3
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: None
sqlalchemy: 1.3.11
pymysql: 0.9.3
psycopg2: None
jinja2: 2.10.3
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None