[英]ValueError: invalid literal for int() with base 10: '' in Python/ IndexError: list index out of range
I'm trying to learn Python and I'm working on a project.我正在尝试学习 Python 并且我正在做一个项目。 I want to split a column.
我想拆分一列。 Column like this;
像这样的列; income 60-80 120- 0-40 Here is my code: For def["min_income] line, I get invalid literal for int() with base error , for the other line (max_income) I receive list index out of range error.
收入 60-80 120- 0-40 这是我的代码:对于 def["min_income] 行,我得到int() 的无效文字和基本错误,对于另一行 (max_income) 我收到列表索引超出范围错误。
income = df["Income"]
income = income.replace({"Unknown": ""})
df["min_income"] = income.apply(lambda x: int(x.split("-")[0]))
df["max_income"] = income.apply(lambda x: x.split("-")[1])
But the outcome give an error like this:但结果给出了这样的错误:
df["min_income"] = income.apply(lambda x: int(x.split("-")[0]))
Traceback (most recent call last):
File "<ipython-input-69-9be6a45724ad>", line 1, in <module>
df["min_income"] = income.apply(lambda x: int(x.split("-")[0]))
File "C:\Users\memin\anaconda3\lib\site-packages\pandas\core\series.py", line 4138, in apply
mapped = lib.map_infer(values, f, convert=convert_dtype)
File "pandas\_libs\lib.pyx", line 2467, in pandas._libs.lib.map_infer
File "<ipython-input-69-9be6a45724ad>", line 1, in <lambda>
df["min_income"] = income.apply(lambda x: int(x.split("-")[0]))
ValueError: invalid literal for int() with base 10: ''
I want to split the income column into two different parts(columns)-min_income and max_income- as integer form.我想将收入列分成两个不同的部分(列)-min_income 和 max_income- 作为 integer 形式。 I check the error in the internet but I could not fix the problem.
我检查了互联网上的错误,但我无法解决问题。 How can I solve this problem?
我怎么解决这个问题? Also I tired.astype(int) func.
我也累了.astype(int) func。
If you have this dataframe:如果你有这个 dataframe:
income
0 60-80
1 0-40
2 120-
3 80-120
4 -255
Then:然后:
df[["min_income", "max_income"]] = df["income"].str.split("-", expand=True)
print(df)
Will create two columns "min_income"
and "max_income"
:将创建两列
"min_income"
和"max_income"
:
income min_income max_income
0 60-80 60 80
1 0-40 0 40
2 120- 120
3 80-120 80 120
4 -255 255
You then can fill the blank values as you wish (and then convert to numeric format).然后,您可以根据需要填充空白值(然后转换为数字格式)。
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