Turning my dictionary into a pandas dataframe

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一生所求
一生所求 2021-01-19 23:01

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.

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  • 2021-01-19 23:16
    x = {'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}}}}
    
    y = []
    z = []
    for k0 in x:
        for k1 in x[k0]:
            for k2 in x[k0][k1]:
                y .append((k0, k1, k2))     
                col = x[k0][k1][k2].keys()
                for c in col:
                    z.append(x[k0][k1][k2][c])
    
    
    index = pd.MultiIndex.from_tuples(y)
    df = pd.DataFrame(columns=col, index=index)
    z  = np.array(z).reshape(df.shape)
    df = pd.DataFrame(columns=col, index=index, data=z)
    
    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
    
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  • 2021-01-19 23:28

    refer: Construct pandas DataFrame from items in nested dictionary

    df = pd.DataFrame.from_dict({(i,j): dict_[i][j][z] 
                                   for i in dict_.keys() 
                                   for j in dict_[i].keys()
                                   for z in dict_[i][j].keys()},
                               orient='index')
    df
    
    
               lowPrice   lowDate  highPrice  highDate  change
    TSLA 2011    185.16  05/27/19     365.71  12/10/18   -0.49
         2012    185.16  05/27/19     365.71  12/10/18   -0.49
         2013     32.91  01/07/13     190.90  09/23/13    4.80
    
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  • 2021-01-19 23:31

    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
    
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  • 2021-01-19 23:31

    Similar but you can also use from_dict:

    df=pd.DataFrame.from_dict({(i, j, x) : y
                               for i in d.keys()
                               for j in d[i].keys()
                               for x, y in d[i][j].items()},
                               orient='index')
    
    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     190.90  09/23/13    4.80
    
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