将包含负字符串的 Pandas DataFrame 列转换为浮点数

本文介绍了将包含负字符串的 Pandas DataFrame 列转换为浮点数的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！ 问题描述

我有一个包含负字符串的 Pandas Dataframe df，我想将它们转换为浮点:

NY_resitor1 NY_resitor2 SF_type SF_resitor245"-36" 抵抗 4047 36"当前 34....49 39"当前 3945"-11" 1212-200"抵抗45

这是我写的代码

df["NY_resitor2 "]=df["NY_resitor2 "].astype(float)

但我有错误:

ValueError: 无法将字符串转换为浮点数:-32"

有什么问题?

解决方案

我认为这可能是在您的字符串数据中某处有一个奇怪的 "-" Unicode 版本的情况.例如，这应该有效:

>>>将熊猫导入为 pd>>>ser = pd.Series(['-36', '36'])>>>ser.astype(浮动)0 -361 36数据类型:float64

但这不是，因为我已经用 U+2212 减号:

>>>ser2 = pd.Series(['−32', '36'])>>>ser2.astype(浮动)...ValueError: 无法将字符串转换为浮点数:'−32'

你可以通过使用 str.replace() 专门去除有问题的字符来解决这个问题:

>>>ser2.str.replace('−', '-').astype(float)0 -321 36数据类型:float64

如果这不是问题，那么我不知道是什么！

另一种可能性是您的字符串中可能有引号.例如

>>>ser3 = pd.Series(['"-36"', '"36"'])>>>ser3.astype(浮动)...ValueError: 无法将字符串转换为浮点数:'"-36"'

在这种情况下，您需要先删除这些:

>>>ser3.str.replace('"', '').astype(float)0 -361 36数据类型:float64

I have a pandas Dataframe df that contains negative strings and i would like to convert them to float:

NY_resitor1 NY_resitor2 SF_type SF_resitor2 45 "-36" Resis 40 47 "36" curr 34 . . . . 49 "39" curr 39 45 "-11" curr 12 12 "-200" Resis 45

This is the code I wrote

df["NY_resitor2 "]=df["NY_resitor2 "].astype(float)

but I have the error:

ValueError: could not convert string to float: "-32"

what is the problem?

解决方案

I think this might be a case of having a strange unicode version of "-" somewhere in your string data. For example, this should work:

>>> import pandas as pd >>> ser = pd.Series(['-36', '36']) >>> ser.astype(float) 0 -36 1 36 dtype: float64

But this doesn't, because I've replaced the standard minus sign with a U+2212 minus sign:

>>> ser2 = pd.Series(['−32', '36']) >>> ser2.astype(float) ... ValueError: could not convert string to float: '−32'

you could address this by specifically getting rid of the offending characters, using str.replace():

>>> ser2.str.replace('−', '-').astype(float) 0 -32 1 36 dtype: float64

If that's not the issue, then I don't know what is!

Edit: another possibility is that your strings could have quotes within them. e.g.

>>> ser3 = pd.Series(['"-36"', '"36"']) >>> ser3.astype(float) ... ValueError: could not convert string to float: '"-36"'

In this case, you need to strip these out first:

>>> ser3.str.replace('"', '').astype(float) 0 -36 1 36 dtype: float64