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.