i have actually a pandas dataframe and i want to save it to json format. From the pandas docs it says:
Note NaN‘s, NaT‘s and None will be converted to null and datetime objects will be converted based on the date_format and date_unit parameters
Then using the orient option records i have something like this
[{"A":1,"B":4,"C":7},{"A":null,"B":5,"C":null},{"A":3,"B":null,"C":null}]
Is it possible to have this instead:
[{"A":1,"B":4,"C":7},{"B":5},{"A":3}]'
Thank you
The following gets close to what you want, essentially we create a list of the non-NaN values and then call to_json on this:
In [136]:
df.apply(lambda x: [x.dropna()], axis=1).to_json()
Out[136]:
'{"0":[{"a":1.0,"b":4.0,"c":7.0}],"1":[{"b":5.0}],"2":[{"a":3.0}]}'
creating a list is necessary here otherwise it will try to align the result with your original df shape and this will reintroduce the NaN values which is what you want to avoid:
In [138]:
df.apply(lambda x: pd.Series(x.dropna()), axis=1).to_json()
Out[138]:
'{"a":{"0":1.0,"1":null,"2":3.0},"b":{"0":4.0,"1":5.0,"2":null},"c":{"0":7.0,"1":null,"2":null}}'
also calling list on the result of dropna will broadcast the result with the shape, like filling:
In [137]:
df.apply(lambda x: list(x.dropna()), axis=1).to_json()
Out[137]:
'{"a":{"0":1.0,"1":5.0,"2":3.0},"b":{"0":4.0,"1":5.0,"2":3.0},"c":{"0":7.0,"1":5.0,"2":3.0}}'
The solution above doesn't actually produce results in the 'records' format. This solution also uses the json package, but produces exactly the result asked for in the original question.
import pandas as pd
import json
json.dumps([row.dropna().to_dict() for index,row in df.iterrows()])
Additionally, if you want to include the index (and you are on Python 3.5+) you can do:
json.dumps([{'index':index, **row.dropna().to_dict()} for index,row in df.iterrows()])
I got the same problem and my solution is use json module instead of pd.DataFrame.to_json()
My solution is
Here is the code:
import pandas as pd
import json
from pandas import compat
def to_dict_dropna(df):
return {int(k): v.dropna().astype(int).to_dict() for k, v in compat.iteritems(df)}
json.dumps(to_dict_dropna(df))
