Как разбить столбец со словарем на 2 столбца

Question

Каков наилучший способ разбить следующий столбец на фрейм данных со столбцом с названием каждой страны и двумя другими столбцами с данными из первого столбца (истории)?

Из этого фрейма данных :

+-----------------------------------------+----------------------------------+----------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+----------+------------------------------+
| coordinates                             | country                          | country_code   | history                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     |   latest | province                     |
|-----------------------------------------+----------------------------------+----------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+----------+------------------------------|
| {'lat': '15', 'long': '101'}            | Thailand                         | TH             | {'1/22/20': 0, '1/23/20': 0, '1/24/20': 0, '1/25/20': 0, '1/26/20': 0, '1/27/20': 0, '1/28/20': 0, '1/29/20': 0, '1/30/20': 0, '1/31/20': 0, '2/1/20': 0, '2/10/20': 0, '2/11/20': 0, '2/12/20': 0, '2/13/20': 0, '2/14/20': 0, '2/15/20': 0, '2/16/20': 0, '2/17/20': 0, '2/18/20': 0, '2/19/20': 0, '2/2/20': 0, '2/20/20': 0, '2/21/20': 0, '2/22/20': 0, '2/23/20': 0, '2/24/20': 0, '2/25/20': 0, '2/26/20': 0, '2/27/20': 0, '2/28/20': 0, '2/29/20': 0, '2/3/20': 0, '2/4/20': 0, '2/5/20': 0, '2/6/20': 0, '2/7/20': 0, '2/8/20': 0, '2/9/20': 0, '3/1/20': 1, '3/10/20': 1, '3/11/20': 1, '3/12/20': 1, '3/13/20': 1, '3/14/20': 1, '3/15/20': 1, '3/16/20': 1, '3/2/20': 1, '3/3/20': 1, '3/4/20': 1, '3/5/20': 1, '3/6/20': 1, '3/7/20': 1, '3/8/20': 1, '3/9/20': 1}                                                                                                                                            |        1 |                              |
| {'lat': '36', 'long': '138'}            | Japan                            | JP             | {'1/22/20': 0, '1/23/20': 0, '1/24/20': 0, '1/25/20': 0, '1/26/20': 0, '1/27/20': 0, '1/28/20': 0, '1/29/20': 0, '1/30/20': 0, '1/31/20': 0, '2/1/20': 0, '2/10/20': 0, '2/11/20': 0, '2/12/20': 0, '2/13/20': 1, '2/14/20': 1, '2/15/20': 1, '2/16/20': 1, '2/17/20': 1, '2/18/20': 1, '2/19/20': 1, '2/2/20': 0, '2/20/20': 1, '2/21/20': 1, '2/22/20': 1, '2/23/20': 1, '2/24/20': 1, '2/25/20': 1, '2/26/20': 2, '2/27/20': 4, '2/28/20': 4, '2/29/20': 5, '2/3/20': 0, '2/4/20': 0, '2/5/20': 0, '2/6/20': 0, '2/7/20': 0, '2/8/20': 0, '2/9/20': 0, '3/1/20': 6, '3/10/20': 10, '3/11/20': 15, '3/12/20': 16, '3/13/20': 19, '3/14/20': 22, '3/15/20': 22, '3/16/20': 27, '3/2/20': 6, '3/3/20': 6, '3/4/20': 6, '3/5/20': 6, '3/6/20': 6, '3/7/20': 6, '3/8/20': 6, '3/9/20': 10}                                                                                                                                    |       27 |                              

в это:

 country  days    values
Thailand  1/2/22     0
Thailand  2/2/22     0
Thailand  2/2/22     0
....
Sweden    3/4/55     0
Sweden    3/4/55     0

Answer 1

IIU C,

new_df = (pd.DataFrame(df['history'].tolist(),
                       index = df['country'])
             .reset_index()
             .melt('country',var_name = 'days')
             .sort_values('country'))

или просто:

#import numpy as np
pd.DataFrame(data = np.concatenate([[(k, v) for k, v in d.items()] 
                                    for d in df['history']]),
             columns = ['days','values'],
            index = df['country'].repeat(df['history'].str.len())).reset_index()

Пример

print(df)
  country  country_code       history
0       A             0  {1: 0, 2: 0}
1       B             1  {1: 0, 2: 0}
2       C             2  {1: 0, 2: 0}

---

new_df = (pd.DataFrame(df['history'].tolist(),
                       index = df['country'])
             .reset_index()
             .melt('country',var_name = 'days',value_name='values')
             .sort_values('country'))
print(new_df)
  country days  values
0       A    1       0
3       A    2       0
1       B    1       0
4       B    2       0
2       C    1       0
5       C    2       0

может быть, второй метод лучше

%%timeit
pd.DataFrame(data = np.concatenate([[(k,v) for k,v in d.items()] 
                                    for d in df['history']]),
             columns = ['days','values'],
            index = df['country'].repeat(df['history'].str.len())).reset_index()
1.71 ms ± 137 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

---

%%timeit
new_df = (pd.DataFrame(df['history'].tolist(),
                       index = df['country'])
             .reset_index()
             .melt('country',var_name = 'days')
             .sort_values('country'))
new_df
5.01 ms ± 272 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)