Convert a Pandas Dataframe in Python list

Question

I have this data frame in pandas.

course = Courses.objects.filter(date__date__gte=dateFrom.date(),date__date__lte=dateTo.date()).values('course','price','date')
df_course = pd.DataFrame(course)
df_course['day_of_year'] = df_course.formatted_date.apply(lambda x: x.dayofyear)
dailyMinPrices = df_course.groupby(['hotel', 'day_of_year'])['price'].min()

It return:

 Course    day_of_year     Price
 Course 1  154             70.0
           155             74.0
           156             85.0
           157             90.0
           158             83.0
           159             79.0
           160             81.0
           161             75.0
           162            113.0
           163             85.0
           164             83.0
 Course 2  154             96.0
           155            112.0
           156             73.0
           157             78.0
 Course 3  154             99.0
           155             74.0
           156             78.0
           157             70.0
           158             94.0
           159             82.0

I am trying to convert it to django list or django dict, but It always return:

  "dailyMinPrices": [70.0,74.0,85.0,90.0,83.0, 79.0,81.0,75.0,113.0,85.0]

I am trying to get this estructure:

  "dailyMinPrices": {'course 1':{['day_of_year':154,'price':70.0], ['day_of_year':154,'price':70.0],['day_of_year':154,'price':70.0]}, 'course 2':{...},'course 3':{...}}

I am trying with:

 dailyMinPrices.values.tolist() #Not working
 dailyMinPrices.to_dict(), Return errors

I would like to do it without for, because it is a lot of registers.

Answer 1

to_dict(orient='records') is close to the expected result. But as you want a hierarchical dict, you should first use a groupby to split the dataframe on the first level of index. With your sample data,

{i[0]: i[1].reset_index(level=1).to_dict(orient='records')
    for i in df.groupby(level=0)}

gives as expected (using pprint ):

{'Course 1': [{'Price': 70.0, 'day_of_year': 154},
              {'Price': 74.0, 'day_of_year': 155},
              {'Price': 85.0, 'day_of_year': 156},
              {'Price': 90.0, 'day_of_year': 157},
              {'Price': 83.0, 'day_of_year': 158},
              {'Price': 79.0, 'day_of_year': 159},
              {'Price': 81.0, 'day_of_year': 160},
              {'Price': 75.0, 'day_of_year': 161},
              {'Price': 113.0, 'day_of_year': 162},
              {'Price': 85.0, 'day_of_year': 163},
              {'Price': 83.0, 'day_of_year': 164}],
 'Course 2': [{'Price': 96.0, 'day_of_year': 154},
              {'Price': 112.0, 'day_of_year': 155},
              {'Price': 73.0, 'day_of_year': 156},
              {'Price': 78.0, 'day_of_year': 157}],
 'Course 3': [{'Price': 99.0, 'day_of_year': 154},
              {'Price': 74.0, 'day_of_year': 155},
              {'Price': 78.0, 'day_of_year': 156},
              {'Price': 70.0, 'day_of_year': 157},
              {'Price': 94.0, 'day_of_year': 158},
              {'Price': 82.0, 'day_of_year': 159}]}