pd.read_csv给了我str但需要浮点数 [英] pd.read_csv gives me str but need float

问题描述

我有一个看起来像这样的CSV:

I have a CSV which looks like this:

Date,Open,High,Low,Close,Adj Close,Volume
2007-07-25,4.929000,4.946000,4.896000,4.904000,4.904000,0
2007-07-26,4.863000,4.867000,4.759000,4.777000,4.777000,0
2007-07-27,4.741000,4.818000,4.741000,4.788000,4.788000,0
2007-07-30,4.763000,4.810000,4.763000,4.804000,4.804000,0

之后

data = pd.read_csv(file, index_col='Date').drop(['Open','Close','Adj Close','Volume'], axis=1)

我最终得到一个看起来像这样的df:

i end up with a df which looks like this:

                High       Low
Date                          
2007-07-25  4.946000  4.896000
2007-07-26  4.867000  4.759000
2007-07-27  4.818000  4.741000
2007-07-30  4.810000  4.763000
2007-07-31  4.843000  4.769000

现在我想获得高-低.尝试过:

Now i want to get High - Low. Tried:

np.diff(data.values, axis=1)

但出现错误:-:'str'和'str'不支持的操作数类型

but getting an error: unsupported operand type(s) for -: 'str' and 'str'

，但是请确定为什么df中的值首先是str.感谢您提出任何解决方案.

but sure why the values in the df are str in the first place. Grateful for any solution.

推荐答案

我认为您需要 to_numeric 和errors='coerce'，因为似乎有一些不良数据:

I think you need to_numeric with errors='coerce' because it seems there are some bad data:

data = pd.read_csv(file, index_col='Date', usecols=['High','Low'])

data = data.apply(pd.to_numeric, errors='coerce')

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