pandas explode fails with KeyError: 0

Question

How does a pandas.DataFrame.explode work?

In the documentation:

https://pandas.pydata.org/pandas-docs/version/0.25/reference/api/pandas.DataFrame.explode.html

df = pd.DataFrame({'A': [[1, 2, 3], 'foo', [], [3, 4]], 'B': 1})
display(df)
print(df.columns)
print(df.dtypes)
df.explode('A')

works just fine. But for my data it fails with a key exception. My data originally looks like this:

with the following types:

print(foo.columns)
print(foo.dtypes)
Index(['model', 'id_min_days_cutoff'], dtype='object')
model                 object
id_min_days_cutoff     int64
dtype: object

where model is obtained using a statsmodels regression using:

model.summary2().tables[1]

When calling: df.explode('model')

it fails with:

KeyError: 0

Trying to reproduce this:

df_json = df.to_json()

# now load it again for SF purposes
df_json = '{"model":{"0":{"Coef.":{"ALQ_15PLUS_perc":95489.7866599741,"AST_perc":-272.9213162565,"BEV_UNTER15_perc":6781.448845533,"BEV_UEBER65_perc":-46908.2889142205},"Std.Err.":{"ALQ_15PLUS_perc":1399665.9788843254,"AST_perc":1558.1286516172,"BEV_UNTER15_perc":2027111.8764156068,"BEV_UEBER65_perc":1230965.9812726702},"z":{"ALQ_15PLUS_perc":0.0682232676,"AST_perc":-0.1751596802,"BEV_UNTER15_perc":0.0033453747,"BEV_UEBER65_perc":-0.038106893},"P>|z|":{"ALQ_15PLUS_perc":0.9456079052,"AST_perc":0.8609541651,"BEV_UNTER15_perc":0.9973307821,"BEV_UEBER65_perc":0.9696024555},"[0.025":{"ALQ_15PLUS_perc":-2647805.1223393031,"AST_perc":-3326.7973567063,"BEV_UNTER15_perc":-3966284.8215624653,"BEV_UEBER65_perc":-2459557.2784026605},"0.975]":{"ALQ_15PLUS_perc":2838784.6956592514,"AST_perc":2780.9547241933,"BEV_UNTER15_perc":3979847.7192535317,"BEV_UEBER65_perc":2365740.7005742197}},"1":{"Coef.":{"ALQ_15PLUS_perc":-140539.5196612777,"AST_perc":142.579413527,"BEV_UNTER15_perc":-45288.5612893498,"BEV_UEBER65_perc":-152106.9841374909},"Std.Err.":{"ALQ_15PLUS_perc":299852250.9155113101,"AST_perc":24013.7007484301,"BEV_UNTER15_perc":417010365.7919532657,"BEV_UEBER65_perc":171876588.9403209388},"z":{"ALQ_15PLUS_perc":-0.0004686959,"AST_perc":0.0059374194,"BEV_UNTER15_perc":-0.000108603,"BEV_UEBER65_perc":-0.0008849779},"P>|z|":{"ALQ_15PLUS_perc":0.9996260348,"AST_perc":0.9952626525,"BEV_UNTER15_perc":0.9999133474,"BEV_UEBER65_perc":0.9992938899},"[0.025":{"ALQ_15PLUS_perc":-587840151.997330904,"AST_perc":-46923.4091889186,"BEV_UNTER15_perc":-817370586.6933914423,"BEV_UEBER65_perc":-337024031.0927618742},"0.975]":{"ALQ_15PLUS_perc":587559072.9580082893,"AST_perc":47208.5680159725,"BEV_UNTER15_perc":817280009.5708128214,"BEV_UEBER65_perc":336719817.1244869232}}},"id_min_days_cutoff":{"0":2,"1":3}}'
pd.read_json(df_json).explode('model')

fails with:

KeyError: 0

edit

trying to find an alternative using one of: How to unnest (explode) a column in a pandas DataFrame? choosing 2.1

pd.DataFrame({'model':np.concatenate(df_json.model.values)},
               index=df_json.index.repeat(ddf_jsonf.model.str.len()))

but this fails with:

ValueError: zero-dimensional arrays cannot be concatenated

When instead applying it to the original df, not read from JSON:

Exception: Data must be 1-dimensional

How can I get the unnest/explode to work?

Answer 1

If you do have the results of your statsmodels regression in json / dictionary form, you could try to explode the dataframe "by hand". I tried below using list comprehensions. Does the result you are trying to achieve look something like this:

df_json = '{"model":{"0":{"Coef.":{"ALQ_15PLUS_perc":95489.7866599741,"AST_perc":-272.9213162565,"BEV_UNTER15_perc":6781.448845533,"BEV_UEBER65_perc":-46908.2889142205},"Std.Err.":{"ALQ_15PLUS_perc":1399665.9788843254,"AST_perc":1558.1286516172,"BEV_UNTER15_perc":2027111.8764156068,"BEV_UEBER65_perc":1230965.9812726702},"z":{"ALQ_15PLUS_perc":0.0682232676,"AST_perc":-0.1751596802,"BEV_UNTER15_perc":0.0033453747,"BEV_UEBER65_perc":-0.038106893},"P>|z|":{"ALQ_15PLUS_perc":0.9456079052,"AST_perc":0.8609541651,"BEV_UNTER15_perc":0.9973307821,"BEV_UEBER65_perc":0.9696024555},"[0.025":{"ALQ_15PLUS_perc":-2647805.1223393031,"AST_perc":-3326.7973567063,"BEV_UNTER15_perc":-3966284.8215624653,"BEV_UEBER65_perc":-2459557.2784026605},"0.975]":{"ALQ_15PLUS_perc":2838784.6956592514,"AST_perc":2780.9547241933,"BEV_UNTER15_perc":3979847.7192535317,"BEV_UEBER65_perc":2365740.7005742197}},"1":{"Coef.":{"ALQ_15PLUS_perc":-140539.5196612777,"AST_perc":142.579413527,"BEV_UNTER15_perc":-45288.5612893498,"BEV_UEBER65_perc":-152106.9841374909},"Std.Err.":{"ALQ_15PLUS_perc":299852250.9155113101,"AST_perc":24013.7007484301,"BEV_UNTER15_perc":417010365.7919532657,"BEV_UEBER65_perc":171876588.9403209388},"z":{"ALQ_15PLUS_perc":-0.0004686959,"AST_perc":0.0059374194,"BEV_UNTER15_perc":-0.000108603,"BEV_UEBER65_perc":-0.0008849779},"P>|z|":{"ALQ_15PLUS_perc":0.9996260348,"AST_perc":0.9952626525,"BEV_UNTER15_perc":0.9999133474,"BEV_UEBER65_perc":0.9992938899},"[0.025":{"ALQ_15PLUS_perc":-587840151.997330904,"AST_perc":-46923.4091889186,"BEV_UNTER15_perc":-817370586.6933914423,"BEV_UEBER65_perc":-337024031.0927618742},"0.975]":{"ALQ_15PLUS_perc":587559072.9580082893,"AST_perc":47208.5680159725,"BEV_UNTER15_perc":817280009.5708128214,"BEV_UEBER65_perc":336719817.1244869232}}},"id_min_days_cutoff":{"0":2,"1":3}}'

df = pd.read_json(df_json)

# "Explode" the model column (containing a dict of dicts) using list comprehension:
model_col = [k+':'+kk+':'+str(vv) for i in range(0,len(df.model)) for k,v in df.model.iloc[i].items() for kk,vv in v.items()]

# Generate the second column (assuming each row of the original df "explodes" into the same number of rows):
cutoff_col = np.repeat([df['id_min_days_cutoff'].iloc[i] for i in range(0,len(df.model))], len(model_col)/2)

# Get everything into one dataframe
    exploded_df = pd.DataFrame({'model':model_col, 'id_min_days_cutoff': cutoff_col})
    
    exploded_df
                                               model  id_min_days_cutoff
    0         Coef.:ALQ_15PLUS_perc:95489.7866599741                   2
    1                 Coef.:AST_perc:-272.9213162565                   2
    2          Coef.:BEV_UNTER15_perc:6781.448845533                   2
    3       Coef.:BEV_UEBER65_perc:-46908.2889142205                   2
    4    Std.Err.:ALQ_15PLUS_perc:1399665.9788843254                   2
    5              Std.Err.:AST_perc:1558.1286516172                   2
    6   Std.Err.:BEV_UNTER15_perc:2027111.8764156068                   2
    7   Std.Err.:BEV_UEBER65_perc:1230965.9812726702                   2
    8                 z:ALQ_15PLUS_perc:0.0682232676                   2
    9                       z:AST_perc:-0.1751596802                   2
    10               z:BEV_UNTER15_perc:0.0033453747                   2
    11               z:BEV_UEBER65_perc:-0.038106893                   2
    12            P>|z|:ALQ_15PLUS_perc:0.9456079052                   2
    13                   P>|z|:AST_perc:0.8609541651                   2
    14           P>|z|:BEV_UNTER15_perc:0.9973307821                   2
    15           P>|z|:BEV_UEBER65_perc:0.9696024555                   2
    16     [0.025:ALQ_15PLUS_perc:-2647805.122339303                   2
    17              [0.025:AST_perc:-3326.7973567063                   2
    18   [0.025:BEV_UNTER15_perc:-3966284.8215624653                   2
    19   [0.025:BEV_UEBER65_perc:-2459557.2784026605                   2
    20     0.975]:ALQ_15PLUS_perc:2838784.6956592514                   2
    21               0.975]:AST_perc:2780.9547241933                   2
    22    0.975]:BEV_UNTER15_perc:3979847.7192535317                   2
    23    0.975]:BEV_UEBER65_perc:2365740.7005742197                   2
    24      Coef.:ALQ_15PLUS_perc:-140539.5196612777                   3
    25                  Coef.:AST_perc:142.579413527                   3
    26      Coef.:BEV_UNTER15_perc:-45288.5612893498                   3
    27     Coef.:BEV_UEBER65_perc:-152106.9841374909                   3
    28    Std.Err.:ALQ_15PLUS_perc:299852250.9155113                   3
    29            Std.Err.:AST_perc:24013.7007484301                   3
    30  Std.Err.:BEV_UNTER15_perc:417010365.79195327                   3
    31  Std.Err.:BEV_UEBER65_perc:171876588.94032094                   3
    32               z:ALQ_15PLUS_perc:-0.0004686959                   3
    33                       z:AST_perc:0.0059374194                   3
    34               z:BEV_UNTER15_perc:-0.000108603                   3
    35              z:BEV_UEBER65_perc:-0.0008849779                   3
    36            P>|z|:ALQ_15PLUS_perc:0.9996260348                   3
    37                   P>|z|:AST_perc:0.9952626525                   3
    38           P>|z|:BEV_UNTER15_perc:0.9999133474                   3
    39           P>|z|:BEV_UEBER65_perc:0.9992938899                   3
    40     [0.025:ALQ_15PLUS_perc:-587840151.9973309                   3
    41             [0.025:AST_perc:-46923.4091889186                   3
    42    [0.025:BEV_UNTER15_perc:-817370586.6933914                   3
    43    [0.025:BEV_UEBER65_perc:-337024031.0927619                   3
    44      0.975]:ALQ_15PLUS_perc:587559072.9580083                   3
    45              0.975]:AST_perc:47208.5680159725                   3
    46     0.975]:BEV_UNTER15_perc:817280009.5708128                   3
    47     0.975]:BEV_UEBER65_perc:336719817.1244869                   3