Pandas add values from dictionary iteratively-CodePudding

We have the following dummy dataframe that scrapes many messages:

temp = pd.DataFrame(np.array([['I am feeling very well',],['It is hard to believe this happened',],
                              ['What is love?',], ['Amazing day today',]]),
                            columns = ['message',])

Output:

     message            
0    I hate the weather today
1    It is hard to believe this happened
2    What is love
3    Amazing day today

I iterate through each individual message in order to extract the sentiment from them

for i in temp.message:
    x = model.predict(i, 'roberta')

where x is a dictionary of the form:

x = {
    "Love" : 0.0931,
    "Hate" : 0.9169,
    }

How can I add all of the values in the dictionary to the data frame while iterating through each?

for i in temp.message:
    x = model.predict(i, 'roberta')
    y = pd.DataFrame.from_dict(x,orient='index')
    y = y.T
    # what would the next step be?

Maybe creating the columns with null values and then creating a left join on every iteration on the message column would be a plausible solution? What would be most optimal?

Expected output:

     message                                   Love           Hate
0    I hate the weather today                  0.0931         0.9169
1    It is hard to believe this happened       0.444          0.556
...

CodePudding user response：

Don't try to assign while looping, collect in a list and assign/join in the end:

df = temp.join(pd.json_normalize([model.predict(i, 'roberta')
                                  for i in temp.message]))

# OR
df = temp.join(pd.DataFrame([model.predict(i, 'roberta')
                             for i in temp.message]))

Example:

                               message    Love    Hate
0               I am feeling very well  0.0931  0.9169
1  It is hard to believe this happened  0.0931  0.9169
2                        What is love?  0.0931  0.9169
3                    Amazing day today  0.0931  0.9169

CodePudding user response：

You can create columns with np.NaN values initially and update when necessary

temp = pd.DataFrame(np.array([['I am feeling very well',],['It is hard to believe this happened',],
                              ['What is love?',], ['Amazing day today',]]),
                            columns = ['message',])
temp['Love'] = np.nan
temp['Hate'] = np.nan

Then update the values in the loop -

for i, message in enumerate(temp.message):
    x = model. Predictt(message, 'Roberta')
    temp.loc[index].Love = x["Love"]
    temp.loc[index].Hate= x["Hate"]