# Your code here
df = pd.util.testing.makeTimeDataFrame(5)
df2 = df.reset_index()
ff = lambda row: {'some key': row['A']}

# this is what I want, but my dataframe is unfortunately like df2
df.apply(ff, axis = 1)
2000-01-03      {u'some key': 1.70177486613}
2000-01-04    {u'some key': -0.833481157829}
2000-01-05    {u'some key': -0.117445006362}
2000-01-06     {u'some key': -1.12638444617}
2000-01-07    {u'some key': -0.981868953754}
Freq: B, dtype: object

# not correct
df2.apply(ff, axis = 1)
   index   A   B   C   D
0    NaN NaN NaN NaN NaN
1    NaN NaN NaN NaN NaN
2    NaN NaN NaN NaN NaN
3    NaN NaN NaN NaN NaN
4    NaN NaN NaN NaN NaN
>>>

Expected Output

returns me a pandas Series with dictionary as value

Output of pd.show_versions()

INSTALLED VERSIONS

commit: None python: 2.7.12.final.0 python-bits: 64 OS: Windows OS-release: 7 machine: AMD64 processor: Intel64 Family 6 Model 44 Stepping 2, GenuineIntel byteorder: little LC_ALL: None LANG: en_GB

pandas: 0.18.1 nose: 1.3.7 pip: 8.1.0 setuptools: 0.6c11 Cython: 0.21 numpy: 1.10.1 scipy: 0.16.0 statsmodels: 0.6.1 xarray: None IPython: 5.1.0 sphinx: 1.3.1 patsy: 0.4.0 dateutil: 2.4.2 pytz: 2015.7 blosc: None bottleneck: None tables: 3.1.1 numexpr: 2.4.4 matplotlib: 1.4.3 openpyxl: 1.8.5 xlrd: 0.9.3 xlwt: 0.7.5 xlsxwriter: 0.5.7 lxml: 3.5.0 bs4: 4.4.1 html5lib: None httplib2: None apiclient: None sqlalchemy: 0.9.7 pymysql: None psycopg2: None jinja2: 2.8 boto: 2.32.1 pandas_datareader: None

# Paste the output here

Comment From: jreback

IIRC this was fixed n 0.18.0 are you sure the pd.show_versions() is in the same session?

In [1]: pd.__version__
Out[1]: u'0.18.1'

In [2]: In [11]: df = pd.util.testing.makeTimeDataFrame(5)
   ...:     ...: df2 = df.reset_index()
   ...:     ...: ff = lambda row: {'some key': row['A']}
   ...:     ...: 
   ...: 

In [3]: df.apply(ff, axis = 1)
   ...: 
Out[3]: 
2000-01-03    {u'some key': -0.890972464634}
2000-01-04    {u'some key': -0.888101404353}
2000-01-05      {u'some key': 1.29259890055}
2000-01-06     {u'some key': -1.54193140739}
2000-01-07     {u'some key': -1.33524721342}
Freq: B, dtype: object

Comment From: httassadar

As said in the OP, it works on a DF with time index.

Does it work for you on df2.apply where it is normal index?

Comment From: jreback

So apply tries to coerce to the original dtype. This is not very pandonic in any event. use .to_dict or .to_json are WAY faster. Further you could use .iterrows/.itertuples if you really really want to do this.

In [21]: df.reset_index().astype(object).apply(ff, axis=1)
Out[21]:
0    {'some key': -0.7199673839410661}
1      {'some key': 0.277297347761761}
2    {'some key': -1.4338707486999223}
3     {'some key': 0.8916998617825874}
4    {'some key': 0.26514706847054426}
dtype: object