我們有以下虛擬資料幀，可以抓取許多訊息：
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',])
輸出：
message
0 I hate the weather today
1 It is hard to believe this happened
2 What is love
3 Amazing day today
我遍歷每個單獨的訊息，以便從中提取情緒
for i in temp.message:
x = model.predict(i, 'roberta')
其中x是以下形式的字典：
x = {
"Love" : 0.0931,
"Hate" : 0.9169,
}
如何在迭代每個值時將字典中的所有值添加到資料框中？
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?
也許創建具有空值的列，然后在訊息列的每次迭代上創建左連接將是一個合理的解決方案？什么是最優化的？
預期輸出：
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
...
uj5u.com熱心網友回復：
不要嘗試在回圈時分配，在串列中收集并在最后分配/加入：
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]))
例子：
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
uj5u.com熱心網友回復：
您可以np.NaN最初創建具有值的列，并在必要時更新
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
然后更新回圈中的值 -
for i, message in enumerate(temp.message):
x = model. Predictt(message, 'Roberta')
temp.loc[index].Love = x["Love"]
temp.loc[index].Hate= x["Hate"]
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