I have a Python dictionary like the following:
{
U'2012-06-08': 388, u'2012-06-09': 388, u'2012-06-10': 388, u'2012-06-11': 389, u'2012-06-12': 389, u'2012-06-13': 389, u'2012-06-14': 389, u'2012-06-15': 389, u'2012-06-16': 389, u'2012-06-17': 389, u'2012-06-18': 390, u'2012-06-19': 390, u'2012-06-20': 390, u'2012-06-21': 390, u'2012-06-22': 390, u'2012-06-23': 390, u'2012-06-24': 390, u'2012-06-25': 391, u'2012-06-26': 391, u'2012-06-27': 391, u'2012-06-28': 391, u'2012-06-29': 391, u'2012-06-30': 391, u'2012-07-01': 391, u'2012-07-02': 392, u'2012-07-03': 392, u'2012-07-04': 392, u'2012-07-05': 392, u'2012-07-06': 392
}
The keys are Unicode dates and the values are integers. I would like to convert this into a pandas dataframe by having the dates and their corresponding values as two separate columns. Example: col1: Dates col2: DateValue (the dates are still Unicode and data values are still integers)
Date DateValue
0 2012-07-01 391
1 2012-07-02 392
2 2012-07-03 392
. 2012-07-04 392
. ... ...
. ... ...
Any help in this direction would be much appreciated. I am unable to find resources on the pandas docs to help me with this.
I know one solution might be to convert each key-value pair in this dict, into a dict so the entire structure becomes a dict of dicts, and then we can add each row individually to the dataframe. But I want to know if there is an easier way and a more direct way to do this.
So far I have tried converting the dict into a series object but this doesn't seem to maintain the relationship between the columns:
s = Series(my_dict,index=my_dict.keys())