Inconsistent type casting between DataFrame and Series

[dwyatte]

There is an inconsistency between how Series and DataFrames export types to dicts. The issue only manifests if the dataframe has multiple columns with different types. I think the issue is probably pretty far under the surface with differences between Series and DataFrame dtypes and just happened to be uncovered by export to dict.

Code Sample, a copy-pastable example if possible

import pandas as pd

orig_dict = [{0: 'a', 1: 1}]

# does not happen with
# orig_dict =[{0: 0, 1: 1}]

print 'Original dict'
for k in orig_dict[0]:
    print type(orig_dict[0][k])
print

df = pd.DataFrame(orig_dict)
dict_exported_from_dataframe = [df.loc[[x]].to_dict(orient='records')[0] for x in df.index]
dict_exported_from_series = [df.loc[x].to_dict() for x in df.index]

print 'Dict exported from DataFrame'
for k in dict_exported_from_dataframe[0]:
    print type(dict_exported_from_dataframe[0][k])
print

print 'Dict exported from Series'
for k in dict_exported_from_series[0]:
    print type(dict_exported_from_series[0][k])
print

Expected Output

Original dict
<type 'str'>
<type 'int'>

Dict exported from DataFrame
<type 'str'>
<type 'int'>

Dict exported from Series
<type 'str'>
<type 'int'>

OR

Original dict
<type 'str'>
<type 'int'>

Dict exported from DataFrame
<type 'str'>
<type 'numpy.int64'>

Dict exported from Series
<type 'str'>
<type 'numpy.int64'>

output of pd.show_versions()

INSTALLED VERSIONS

commit: None
python: 2.7.10.final.0
python-bits: 64
OS: Darwin
OS-release: 14.5.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8

pandas: 0.18.1
nose: None
pip: 8.1.2
setuptools: 25.2.0
Cython: None
numpy: 1.11.1
scipy: 0.13.0b1
statsmodels: None
xarray: None
IPython: 5.1.0
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2016.6.1
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: None
boto: None
pandas_datareader: None

[chris-b1]

Slightly simpler repro.

d = {'a':[1], 'b':['b']}

pd.DataFrame(d).to_dict()
Out[92]: {'a': {0: 1}, 'b': {0: 'b'}}

pd.DataFrame(d).to_dict(orient='records')
Out[93]: [{'a': 1L, 'b': 'b'}]

[jreback]

this is a duplicate of #13236

so this could be a test for that issue

[jreback]

which in turn is xref to http://pandas-docs.github.io/pandas-docs-travis/whatsnew.html#series-tolist-will-now-return-python-types

[jorisvandenbossche]

@jreback I don't think this is a duplicate of #13236. You repurposed that issue to document the behaviour of map (mapping on python/pandas types and not numpy scalars). While here is shown that to_dict still does return numpy scalars in certain cases.

[jreback]

right I didn't repurpose that issue. The underlying, which is slated for 0.20 is #13258

[jorisvandenbossche]

Ah yes, so it is a duplicate of that one (#13258). Will add it there as case to test

[dwyatte]

I checked out #17491 and the bug in my original code sample where to_dict() returns different types for DataFrame and Series is still reproducible.

@chris-b1's code snippet is used as a test case and correctly works now, so I think there is still something in Series.to_dict() that needs to be changed to return python types, assuming that is the expected behavior.