python - Saving a pandas dataframe to separate jsons without NaNs -


i have dataframe nan values.

here 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:

enter image description here

what did after 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 save dataframe json 2 rows in each json, none of nan values. here how tried it:

df_dict = dict((n, sample_df.iloc[n:n+2, :]) n in range(0, len(sample_df), 2))  k, v in df_dict.items():     print(k)     print(v)     d in (v.to_dict('record')):         k,v in list(d.items()):             if type(v)==float:                 if math.isnan(v):                     del d[k]  json.dumps(df_dict)

output want:

'[{"0":1.0,"2":1.0},{"0":2.0,"1":2.0}]' -> in 1 .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

use apply drop nans, groupby group , dfgroupby.apply 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 on .values , save separate json files.

import json i, j_data in enumerate(s.values):     json.dump(j_data, open('file{}.json'.format(i + 1), 'w'))

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