Turning my dictionary into a pandas dataframe

Question

I have a function which create several dicts of dicts, based on some conditions.

However, I would really like to turn the dict into a dataframe after collecting it. But I can't find an easy way to do so... Right now I'm thinking the solution is to multiply each key in the dict by the number of keys in the most inner dict, but hopefully there is a better way

Since my function creates the dict I can alter it in any way, if there is a better way to do this.

Here is my dict right now

{'TSLA': {2011: {'negative': {'lowPrice': 185.16,
    'lowDate': '05/27/19',
    'highPrice': 365.71,
    'highDate': '12/10/18',
    'change': -0.49}},
  2012: {'negative': {'lowPrice': 185.16,
    'lowDate': '05/27/19',
    'highPrice': 365.71,
    'highDate': '12/10/18',
    'change': -0.49}},
  2013: {'negative': {'lowPrice': 32.91,
    'lowDate': '01/07/13',
    'highPrice': 37.24,
    'highDate': '03/26/12',
    'change': -0.12},
   'positive': {'lowPrice': 32.91,
    'lowDate': '01/07/13',
    'highPrice': 190.9,
    'highDate': '09/23/13',
    'change': 4.8}}}}

My desired output would be something like this, of course with the values:

                    lowPrice lowDate highPrice highDate change
ATVI  2012 Negative      NaN     NaN       NaN      NaN  NaN
           Positive      NaN     NaN       NaN      NaN  NaN
      2013 Negative      NaN     NaN       NaN      NaN  NaN
TSLA  2014 Positive      NaN     NaN       NaN      NaN  NaN
      2012 Negative      NaN     NaN       NaN      NaN  NaN
      2013 Positive      NaN     NaN       NaN      NaN  NaN
      2014 Positive      NaN     NaN       NaN      NaN  NaN

Answer 1

You can flatten nested dictionaries 2 times for tuples for keys and pass to DataFrame.from_dict:

d1 = {(k1, k2, k3): v3 
      for k1, v1 in d.items() 
      for k2, v2 in v1.items()
      for k3, v3 in v2.items()}

df = pd.DataFrame.from_dict(d1, orient='index')
#alternative
#df = pd.DataFrame(d1).T

---

print (df)
                   lowPrice   lowDate highPrice  highDate change
TSLA 2011 negative   185.16  05/27/19    365.71  12/10/18  -0.49
     2012 negative   185.16  05/27/19    365.71  12/10/18  -0.49
     2013 negative    32.91  01/07/13     37.24  03/26/12  -0.12
          positive    32.91  01/07/13     190.9  09/23/13    4.8