Python-'str'和'int'的实例之间不支持'TypeError:'< =' [英] Python - 'TypeError: '<=' not supported between instances of 'str' and 'int''

问题描述

我有一个df列，其值的范围是-5到10.我想将值<== -1更改为negative，将所有0值更改为neutral，并将所有值> = 1更改为.但是，下面的代码为负"产生以下错误.

I have a df column that has values ranging from -5 to 10. I want to change values <= -1 to negative, all 0 values to neutral, and all values >= 1 to positive. The code below, however, produces the following error for 'negative'.

# Function to change values to labels

test.loc[test['sentiment_score'] > 0, 'sentiment_score'] = 'positive'
test.loc[test['sentiment_score'] == 0, 'sentiment_score'] = 'neutral'
test.loc[test['sentiment_score'] < 0, 'sentiment_score'] = 'negative'

Data:                                  Data After Code:
Index     Sentiment                    Index     Sentiment
 0         2                            0         positive
 1         0                            1         neutral
 2        -3                            2         -3
 3         4                            3         positive
 4        -1                            4         -1
 ...                                    ...
 k         5                            k         positive

pandas._libs.ops.scalar_compare中的文件"pandas_libs \ ops.pyx"，第98行 TypeError:"str"和"int

File "pandas_libs\ops.pyx", line 98, in pandas._libs.ops.scalar_compare TypeError: '<=' not supported between instances of 'str' and 'int

我认为这与将负数视为字符串而不是float/int的函数有关，但是我尝试了以下代码来更正此错误，并且它什么都不会改变.任何帮助将不胜感激.

I assume that this has something to do with the function seeing negative numbers as string rather than float/int, however I've tried the following code to correct this error and it changes nothing. Any help would be appreciated.

test['sentiment_score'] = test['sentiment_score'].astype(float)
test['sentiment_score'] = test['sentiment_score'].apply(pd.as_numeric)

推荐答案

正如roganjosh所指出的，您要分3步进行替换-这引起了问题，因为在第1步之后，您会遇到混合的列dtypes，因此后续的相等性检查开始失败.

As roganjosh pointed out, you're doing your replacement in 3 steps - this is causing a problem because after step 1, you end up with a column of mixed dtypes, so subsequent equality checks start to fail.

您可以分配给新列，也可以使用 numpy.select .

You can either assign to a new column, or use numpy.select.

condlist = [
   test['sentiment_score'] > 0,
   test['sentiment_score'] < 0
]
choicelist = ['pos', 'neg']

test['sentiment_score'] = np.select(
   condlist, choicelist, default='neutral')

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