Pandas df.itertuples renaming dataframe columns when printing

Question

I know that normally pandas' itertuples() will return the values of each including the column names as follows:

ab=pd.DataFrame(np.random.random([3,3]),columns=['hi','low','med'])
for i in ab.itertuples():
    print(i)

and the output is as follows:

Pandas(Index=0, hi=0.05421443, low=0.2456833, med=0.491185)
Pandas(Index=1, hi=0.28670429, low=0.5828551, med=0.279305)
Pandas(Index=2, hi=0.53869406, low=0.3427290, med=0.750075)

However, I have no idea why it doesn't shows the columns as I expected for my another set of code as below:

            us qqq equity  us spy equity
date                                    
2017-06-19            0.0            1.0
2017-06-20            0.0           -1.0
2017-06-21            0.0            0.0
2017-06-22            0.0            0.0
2017-06-23            1.0            0.0
2017-06-26            0.0            0.0
2017-06-27           -1.0            0.0
2017-06-28            1.0            0.0
2017-06-29           -1.0            0.0
2017-06-30            0.0            0.0

the above is a Pandas Dataframe with Timestamp as index, float64 as the values in the list, and a list of string ['us qqq equity','us spy equity'] as the columns.

When I do this:

for row in data.itertuples():
    print (row)

It shows the columns as _1 and _2 as follows:

Pandas(Index=Timestamp('2017-06-19 00:00:00'), _1=0.0, _2=1.0)
Pandas(Index=Timestamp('2017-06-20 00:00:00'), _1=0.0, _2=-1.0)
Pandas(Index=Timestamp('2017-06-21 00:00:00'), _1=0.0, _2=0.0)
Pandas(Index=Timestamp('2017-06-22 00:00:00'), _1=0.0, _2=0.0)
Pandas(Index=Timestamp('2017-06-23 00:00:00'), _1=1.0, _2=0.0)
Pandas(Index=Timestamp('2017-06-26 00:00:00'), _1=0.0, _2=0.0)
Pandas(Index=Timestamp('2017-06-27 00:00:00'), _1=-1.0, _2=0.0)
Pandas(Index=Timestamp('2017-06-28 00:00:00'), _1=1.0, _2=0.0)
Pandas(Index=Timestamp('2017-06-29 00:00:00'), _1=-1.0, _2=0.0)
Pandas(Index=Timestamp('2017-06-30 00:00:00'), _1=0.0, _2=0.0)

Does anyone has any clue about what have I done wrong? Does it have to do with some variable referencing issue when creating the original dataframe? (Also, as a side question, I learnt from the community that the type of data generated from itertuples() should be tuples, but it seems (as shown above), the return type is as I verified from the type statement?)

Thank you for all your patience as I am still trying to master the application of DataFrame.

Answer 1

This seems to be an issue with handling column names having spaces in them. If you replace the column names with different ones without spaces, it will work:

df.columns = ['us_qqq_equity', 'us_spy_equity'] 
# df.columns = df.columns.str.replace(r'\s+', '_')  # Courtesy @MaxU  
for r in df.head().itertuples():
    print(r)

# Pandas(Index='2017-06-19', us_qqq_equity=0.0, us_spy_equity=1.0)
# Pandas(Index='2017-06-20', us_qqq_equity=0.0, us_spy_equity=-1.0)
# ...

Column names with spaces cannot effectively be represented in named tuples, so they are renamed automatically when printing.

Answer 2

Interesting observation: out of DataFrame.iterrows(), DataFrame.iteritems(), DataFrame.itertuples() only the last one renames the columns, containing spaces:

In [140]: df = df.head(3)

In [141]: list(df.iterrows())
Out[141]:
[(Timestamp('2017-06-19 00:00:00'), us qqq equity    0.0
  us spy equity    1.0
  Name: 2017-06-19 00:00:00, dtype: float64),
 (Timestamp('2017-06-20 00:00:00'), us qqq equity    0.0
  us spy equity   -1.0
  Name: 2017-06-20 00:00:00, dtype: float64),
 (Timestamp('2017-06-21 00:00:00'), us qqq equity    0.0
  us spy equity    0.0
  Name: 2017-06-21 00:00:00, dtype: float64)]

In [142]: list(df.iteritems())
Out[142]:
[('us qqq equity', date
  2017-06-19    0.0
  2017-06-20    0.0
  2017-06-21    0.0
  Name: us qqq equity, dtype: float64), ('us spy equity', date
  2017-06-19    1.0
  2017-06-20   -1.0
  2017-06-21    0.0
  Name: us spy equity, dtype: float64)]

In [143]: list(df.itertuples())
Out[143]:
[Pandas(Index=Timestamp('2017-06-19 00:00:00'), _1=0.0, _2=1.0),
 Pandas(Index=Timestamp('2017-06-20 00:00:00'), _1=0.0, _2=-1.0),
 Pandas(Index=Timestamp('2017-06-21 00:00:00'), _1=0.0, _2=0.0)]