pandas Python - 'TypeError: '<=' 在 'str' 和 'int' 的实例之间不受支持
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Python - 'TypeError: '<=' not supported between instances of 'str' and 'int''
提问by Laurie
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'.
我有一个 df 列，其值范围从 -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positive。但是，下面的代码会为“否定”产生以下错误。
# 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
File "pandas_libs\ops.pyx", line 98, in pandas._libs.ops.scalar_compare TypeError: '<=' not supported between instances of 'str' and 'int
文件“pandas_libs\ops.pyx”，第 98 行，在 pandas._libs.ops.scalar_compare 类型错误：“str”和“int”的实例之间不支持“<=”
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.
我认为这与将负数视为字符串而不是浮点/整数的函数有关，但是我尝试了以下代码来纠正此错误，但它没有任何改变。任何帮助，将不胜感激。
test['sentiment_score'] = test['sentiment_score'].astype(float)
test['sentiment_score'] = test['sentiment_score'].apply(pd.as_numeric)
回答by cs95
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.
正如 roganjosh 指出的那样，您要分 3 个步骤进行替换 - 这会导致问题，因为在第 1 步之后，您最终会得到一列混合 dtype，因此后续的相等性检查开始失败。
You can either assign to a new column, or use numpy.select.
您可以分配给新列，也可以使用numpy.select.
condlist = [
test['sentiment_score'] > 0,
test['sentiment_score'] < 0
]
choicelist = ['pos', 'neg']
test['sentiment_score'] = np.select(
condlist, choicelist, default='neutral')
回答by melihozbek
Another alternative is to define a custom function:
另一种选择是定义一个自定义函数：
def transform_sentiment(x):
if x < 0:
return 'Negative'
elif x == 0:
return 'Neutral'
else:
return 'Positive'
df['Sentiment_new'] = df['Sentiment'].apply(lambda x: transform_sentiment(x))
