pandas 将熊猫数据框列转换为数字的更好方法
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Better way to convert pandas dataframe columns to numeric
提问by Sveinn
I have a dataframe with some columns containing data of type object because of some funky data entries (aka a . or whatnot).
由于一些时髦的数据条目（又名 . 或诸如此类），我有一个数据框，其中一些列包含 object 类型的数据。
I have been able to correct this by identifying the object columns and then doing this:
我已经能够通过识别对象列然后执行以下操作来纠正此问题：
obj_cols = df.loc[:, df.dtypes == object]
conv_cols = obj_cols.convert_objects(convert_numeric='force')
This works fine and allows me to run the regression I need, but generates this error:
这工作正常，并允许我运行我需要的回归，但会产生此错误：
FutureWarning: convert_objects is deprecated.
Is there a better way to do this so as to avoid the error? I also tried constructing a lambda function but that didn't work.
有没有更好的方法来做到这一点以避免错误？我也尝试构建一个 lambda 函数，但没有奏效。
回答by Vaishali
Convert_objects is deprecated. Use this instead. You can add parameter errors='coerce' to convert bad non numeric values to NaN.
Convert_objects 已弃用。改用这个。您可以添加参数 errors='coerce' 将错误的非数值转换为 NaN。
conv_cols = obj_cols.apply(pd.to_numeric, errors = 'coerce')
The function will be applied to the whole DataFrame. Columns that can be converted to a numeric type will be converted, while columns that cannot (e.g. they contain non-digit strings or dates) will be left alone.
该函数将应用于整个 DataFrame。可以转换为数字类型的列将被转换，而不能转换的列（例如它们包含非数字字符串或日期）将被保留。
回答by MissBleu
If you have a sample data frame:
如果您有示例数据框：
sales = [{'account': 'Jones LLC', 'Jan': 150, 'Feb': 'f', 'Mar': 140},
{'account': 'Alpha Co', 'Jan': 'e', 'Feb': 210, 'Mar': 215},
{'account': 'Blue Inc', 'Jan': 50, 'Feb': 90, 'Mar': 'g' }]
df = pd.DataFrame(sales)
and you want to get rid of the strings in the columns that should be numeric, you can do this with pd.to_numeric
并且你想去掉列中应该是数字的字符串，你可以用 pd.to_numeric 做到这一点
cols = ['Jan', 'Feb', 'Mar']
df[cols] = df[cols].apply(pd.to_numeric, errors='coerce', axis=1)
your new data frame will have NaN in place of the 'wacky' data
您的新数据框将用 NaN 代替“古怪”数据
