pandas pd.read_csv 给了我 str 但需要浮动
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pd.read_csv gives me str but need float
提问by steff
I have a CSV which looks like this:
我有一个 CSV 看起来像这样：
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
after
后
data = pd.read_csv(file, index_col='Date').drop(['Open','Close','Adj Close','Volume'], axis=1)
i end up with a df which looks like this:
我最终得到一个 df ，它看起来像这样：
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
Now i want to get High - Low. Tried:
现在我想获得高 - 低。尝试过：
np.diff(data.values, axis=1)
but getting an error: unsupported operand type(s) for -: 'str' and 'str'
但是得到一个错误：不支持的操作数类型-：'str'和'str'
but sure why the values in the df are str in the first place. Grateful for any solution.
但肯定为什么 df 中的值首先是 str 。感谢任何解决方案。
回答by jezrael
I think you need to_numericwith errors='coerce'because it seems there are some bad data:
我认为您需要to_numeric使用，errors='coerce'因为似乎有一些不好的数据：
data = pd.read_csv(file, index_col='Date', usecols=['High','Low'])
data = data.apply(pd.to_numeric, errors='coerce')
回答by Sébastien S.
The read_csv dtype option doesn't work ?
read_csv dtype 选项不起作用？
from the documentationdtype : Type name or dict of column -> type, default None Data type for data or columns. E.g. {‘a': np.float64, ‘b': np.int32} Use str or object to preserve and not interpret dtype. If converters are specified, they will be applied INSTEAD of dtype conversion.
来自文档dtype ：类型名称或列的 dict -> 类型，默认为无数据或列的数据类型。例如 {'a': np.float64, 'b': np.int32} 使用 str 或 object 来保留而不是解释 dtype。如果指定了转换器，它们将被应用于 dtype 转换的 INSTEAD。
data = pd.read_csv(file,
index_col='Date',
usecols=['High','Low'],
dtype={'High': np.float64, 'Low': np.float64})
