Change row values with certain condition, using row values from json dataset Pandas

user_2347307 0
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I have this dataset. 

{
    "date": "2018-01-01", 
    "body": "some txt", 
    "id": 111, 
    "sentiment": null
}, 
{
    "date": "2018-01-02", 
    "body": "some txt", 
    "id": 112, 
    "sentiment": {
        "basic": "Bearish"
    }
}

I want to read this with pandas, and change the column sentiment foreach rows differents from null.

When I do this:

pd.read_json(path)

this is the result that I get: 

body           ...    sentiment
0                      None
1                      {u'basic': u'Bullish'}

I don't want to have {u'basic': u'Bullish'} but only the value of basic. So to find the correct rows I use

df.loc[self.df['sentiment'].isnull() != True, 'sentiment'] = (?)

and it work, but I don't know what I have to put instead of (?)

I've tried this but doens't work

df.loc[self.df['sentiment'].isnull() != True, 'sentiment'] = df['sentiment']['basic]

Any ideas? Thanks

pandas dataset python
3 Answers

You can try:

mask = df['sentiment'].notnull()
df.loc[mask, 'sentiment'] = df.loc[mask, 'sentiment'].apply(lambda x: x['basic'])
user_27000720

You can do this:

df = pd.read_json(path)  # creates the dataframe with dict objects in sentiment column 
pd.concat([df.drop(['sentiment'], axis=1), df['sentiment'].apply(pd.Series)], axis=1)  # create new columns for each sentiment type

So for example, if your json is :

[{
    "date": "2018-01-01", 
    "body": "some txt", 
    "id": 111, 
    "sentiment": null
}, 
{
    "date": "2018-01-02", 
    "body": "some txt", 
    "id": 112, 
    "sentiment": {
        "basic": "Bearish"
    }
},
{
    "date": "2018-01-03", 
    "body": "some other txt", 
    "id": 113, 
    "sentiment": {
        "basic" : "Bullish",
        "non_basic" : "Bearish"
    }
}]

df after line 1:

             body       date   id                                     sentiment
0        some txt 2018-01-01  111                                          None
1        some txt 2018-01-02  112                          {'basic': 'Bearish'}
2  some other txt 2018-01-03  113  {'basic': 'Bullish', 'non_basic': 'Bearish'}

df after line 2:

             body       date   id    basic non_basic
0        some txt 2018-01-01  111      NaN       NaN
1        some txt 2018-01-02  112  Bearish       NaN
2  some other txt 2018-01-03  113  Bullish   Bearish

HTH.

user_39245660

fillna + pop + join

Here's an extendible solution which avoids row-wise apply and converts an arbitrary number of keys to series:

df = pd.DataFrame({'body': [0, 1],
                   'sentiment': [None, {u'basic': u'Bullish'}]})

df['sentiment'] = df['sentiment'].fillna(pd.Series([{}]*len(df.index), index=df.index))

df = df.join(pd.DataFrame(df.pop('sentiment').values.tolist()))

print(df)

   body    basic
0     0      NaN
1     1  Bullish
user_92095460
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