将pandas DataFrame中带逗号的数字字符串转换为float [英] Convert number strings with commas in pandas DataFrame to float

问题描述

我有一个DataFrame，其中包含数字作为字符串，并带有千位标记的逗号.我需要将它们转换为浮点数.

I have a DataFrame that contains numbers as strings with commas for the thousands marker. I need to convert them to floats.

a = [['1,200', '4,200'], ['7,000', '-0.03'], [ '5', '0']]
df=pandas.DataFrame(a)

我猜我需要使用locale.atof.确实

I am guessing I need to use locale.atof. Indeed

df[0].apply(locale.atof)

按预期工作.我有一系列的花车.

works as expected. I get a Series of floats.

但是当我将其应用于DataFrame时，会出现错误.

But when I apply it to the DataFrame, I get an error.

df.apply(locale.atof)

TypeError :(无法将系列转换为，您在索引0处发生了")

TypeError: ("cannot convert the series to ", u'occurred at index 0')

和

df[0:1].apply(locale.atof)

给出另一个错误:

ValueError:('float()的无效文字:1,200'，u'发生在索引0')

ValueError: ('invalid literal for float(): 1,200', u'occurred at index 0')

那么，如何将字符串的DataFrame转换为浮点数的DataFrame?

So, how do I convert this DataFrame of strings to a DataFrame of floats?

推荐答案

如果您数千arg :

If you're reading in from csv then you can use the thousands arg:

df.read_csv('foo.tsv', sep='\t', thousands=',')

与单独执行该操作相比，此方法可能更有效.

This method is likely to be more efficient than performing the operation as a separate step.

您需要先设置语言环境:

In [ 9]: import locale

In [10]: from locale import atof

In [11]: locale.setlocale(locale.LC_NUMERIC, '')
Out[11]: 'en_GB.UTF-8'

In [12]: df.applymap(atof)
Out[12]:
      0        1
0  1200  4200.00
1  7000    -0.03
2     5     0.00

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