字典的结构系列（python）

Question

I currently have the following dictionary:

  "2019-09-30": {
    "date": "2019-09-30",
    "period": "+1y",
    "growth": "0.1540",
    "earningsEstimateAvg": "13.2600",
    "earningsEstimateLow": "11.5200",
  },
  "2018-09-30": {
    "date": "2018-09-30",
    "period": "+1q",
    "growth": "0.2800",
    "earningsEstimateAvg": "2.6500",
    "earningsEstimateLow": "2.4300",
  },
  "2018-06-30": {
    "date": "2018-06-30",
    "period": "+1q",
    "growth": "0.2930",
    "earningsEstimateAvg": "2.1600",
    "earningsEstimateLow": "1.8000",
  },

I have defined the dictionary as a variable ('market_data') and I would like to extract the respective series (with each time the date).

For instance, the growth variable would include: ("2019-09-30" .1540, "2018-09-30" .2800, "2018-06-30 .2930)

while earningsEstimateAvg would include: ("2019-09-30" 13.2600, "2018-09-30" 2.6500, "2018-06-30 2.1600)

I have tried to play with DataFrame but do not manage to get the proper format.

On a second step, I would like to be able to call that list (which will include more than 3 dates) and return all growth rate between two user-defined dates.

Thanks a lot for your help guys, really much appreciated! :)

Max

Answer 1

Try this:

df = pd.DataFrame(list(market_data.values()))

Output:

         date earningsEstimateAvg earningsEstimateLow  growth period
0  2019-09-30             13.2600             11.5200  0.1540    +1y
1  2018-09-30              2.6500              2.4300  0.2800    +1q
2  2018-06-30              2.1600              1.8000  0.2930    +1q

To get data in the format you want, you can use this:

df[['date', 'growth']].set_index('date').to_dict()
df[['date', 'earningsEstimateAvg']].set_index('date').to_dict()

Output:

{'growth': {'2018-06-30': '0.2930',
            '2018-09-30': '0.2800',
            '2019-09-30': '0.1540'}}

{'earningsEstimateAvg': {'2018-06-30': '2.1600',
                         '2018-09-30': '2.6500',
                         '2019-09-30': '13.2600'}}

To get data between two dates:

mask = (df['date'] > start_date) & (df['date'] <= end_date)
df.loc[mask]

Answer 2

First, I would go over your dictionary, that holds all data and flatten it:

my_array = [data for key, data in my_dictionary.items()]

Then I would create the dictionary you need, eg:

growth = {x['date']: x['growth'] for x in my_array}

If you need a list of dictionaries:

growth = [{"date": x['date'], "growth": x['growth']} for x in my_array]