Q:

Saving a pandas dataframe to separate jsons without NaNs

I have a dataframe with some NaN values.  
Here is a sample dataframe:
sample_df = pd. DataFrame([[1,np.nan,1],[2,2,np.nan], [np.nan, 3, 3], [4,4,4],[np.nan,np.nan,5], [6,np.nan,np.nan]])

It looks like:

What I did after to get a json:
sample_df.to_json(orient = 'records')

Which gives:
'[{"0":1.0,"1":null,"2":1.0},{"0":2.0,"1":2.0,"2":null},{"0":null,"1":3.0,"2":3.0},{"0":4.0,"1":4.0,"2":4.0},{"0":null,"1":null,"2":5.0},{"0":6.0,"1":null,"2":null}]'

I want to save this dataframe to a json with 2 rows in each json, but with none of the Nan values.  Here is how I tried to do it:
df_dict = dict((n, sample_df.iloc[n:n+2, :]) for n in range(0, len(sample_df), 2))

for k, v in df_dict.items():
    print(k)
    print(v)
    for d in (v.to_dict('record')):
        for k,v in list(d.items()):
            if type(v)==float:
                if math.isnan(v):
                    del d[k]

json.dumps(df_dict)

Output I want:
'[{"0":1.0,"2":1.0},{"0":2.0,"1":2.0}]' -> in one .json file
'[{"1":3.0,"2":3.0},{"0":4.0,"1":4.0,"2":4.0}]' -> in second .json file
'[{"2":5.0},{"0":6.0}]' -> in third .json file

A:

Use apply to drop NaNs, groupby to group and dfGroupBy.apply to JSONify. 
s = sample_df.apply(lambda x: x.dropna().to_dict(), 1)\
        .groupby(sample_df.index // 2)\
        .apply(lambda x: x.to_json(orient='records'))
s    

0            [{"0":1.0,"2":1.0},{"0":2.0,"1":2.0}]
1    [{"1":3.0,"2":3.0},{"0":4.0,"1":4.0,"2":4.0}]
2                            [{"2":5.0},{"0":6.0}]
dtype: object

Finally, iterate over .values and save to separate JSON files. 
import json
for i, j_data in enumerate(s.values):
    json.dump(j_data, open('File{}.json'.format(i + 1), 'w'))

