I'd like to replace null values with empty dicts when using pandas json_normalize. here is the sample:
{
"id": {
"0": "x0123455",
"1": "x0123456"
},
"team": {
"0": null,
"1": [
{
"name": "Jenny",
"email": "[email protected]"
}
]
},
I read somewhere that I need to replace null values with empty dicts to avoid getting errors. How can I achieve this?
CodePudding user response:
I think you may do something simple, like
for item in items:
for email in item['team'].values():
if email == null:
email = {}
CodePudding user response:
When I ran JSON normalize function on this dictionary (although it was not a JSON object), it provided an output that included the None value.
import pandas as pd
test_dict ={
"id": {"0": "x0123455", "1": "x0123456"},
"team": {"0": None, "1": [{"name": "Jenny", "email": "[email protected]"}]},
}
df = pd.io.json.json_normalize(test_dict)
print(df)
This code produces the following output when I did df.to_csv:
,id.0,id.1,team.0,team.1 0,x0123455,x0123456,,"[{'name': 'Jenny', 'email': '[email protected]'}]"
*Note this does not work if the value is null because there is no such thing as null in Python.
I suggest just running with json normalize if it works for you, if not this question on stack overflow should answer your question with recursive code to remove all null values.