将pandas.Series从dtype对象转换为float，将错误转换为nans [英] Convert pandas.Series from dtype object to float, and errors to nans

问题描述

请考虑以下情况:

In [2]: a = pd.Series([1,2,3,4,'.'])

In [3]: a
Out[3]: 
0    1
1    2
2    3
3    4
4    .
dtype: object

In [8]: a.astype('float64', raise_on_error = False)
Out[8]: 
0    1
1    2
2    3
3    4
4    .
dtype: object

我本来希望有一个选项，该选项允许在将错误值(例如.)转换为NaN s时进行转换.有没有办法做到这一点?

I would have expected an option that allows conversion while turning erroneous values (such as that .) to NaNs. Is there a way to achieve this?

推荐答案

使用

pd.to_numeric(s, errors='coerce')

0    1.0
1    2.0
2    3.0
3    4.0
4    NaN
dtype: float64

如果需要填写NaN，请使用

If you need the NaNs filled in, use Series.fillna.

pd.to_numeric(s, errors='coerce').fillna(0, downcast='infer')

0    1
1    2
2    3
3    4
4    0
dtype: float64

请注意，downcast='infer'将尝试在可能的情况下将浮点型转换为整数.如果不想，请删除该参数.

Note, downcast='infer' will attempt to downcast floats to integers where possible. Remove the argument if you don't want that.

从v0.24 +开始，pandas引入了可空整数类型，允许 与NaN共存的整数.如果您的栏中有整数， 您可以使用

From v0.24+, pandas introduces a Nullable Integer type, which allows integers to coexist with NaNs. If you have integers in your column, you can use

pd.__version__
# '0.24.1'

pd.to_numeric(s, errors='coerce').astype('Int32')

0      1
1      2
2      3
3      4
4    NaN
dtype: Int32

还有其他选项可供选择，请阅读文档以了解更多信息.

There are other options to choose from as well, read the docs for more.

---

DataFrames

的扩展名

如果需要将此扩展到DataFrames，则需要将其 apply 应用于每一行.您可以使用 DataFrame.apply 进行此操作.

---

Extension for DataFrames

If you need to extend this to DataFrames, you will need to apply it to each row. You can do this using DataFrame.apply.

# Setup.
np.random.seed(0)
df = pd.DataFrame({
    'A' : np.random.choice(10, 5), 
    'C' : np.random.choice(10, 5), 
    'B' : ['1', '###', '...', 50, '234'], 
    'D' : ['23', '1', '...', '268', '$$']}
)[list('ABCD')]
df

   A    B  C    D
0  5    1  9   23
1  0  ###  3    1
2  3  ...  5  ...
3  3   50  2  268
4  7  234  4   $$

df.dtypes

A     int64
B    object
C     int64
D    object
dtype: object

df2 = df.apply(pd.to_numeric, errors='coerce')
df2

   A      B  C      D
0  5    1.0  9   23.0
1  0    NaN  3    1.0
2  3    NaN  5    NaN
3  3   50.0  2  268.0
4  7  234.0  4    NaN

df2.dtypes

A      int64
B    float64
C      int64
D    float64
dtype: object

您也可以使用

You can also do this with DataFrame.transform; although my tests indicate this is marginally slower:

df.transform(pd.to_numeric, errors='coerce')

   A      B  C      D
0  5    1.0  9   23.0
1  0    NaN  3    1.0
2  3    NaN  5    NaN
3  3   50.0  2  268.0
4  7  234.0  4    NaN

如果您有许多列(数字；非数字)，则可以通过仅在非数字列上应用pd.to_numeric来提高性能.

If you have many columns (numeric; non-numeric), you can make this a little more performant by applying pd.to_numeric on the non-numeric columns only.

df.dtypes.eq(object)

A    False
B     True
C    False
D     True
dtype: bool

cols = df.columns[df.dtypes.eq(object)]
# Actually, `cols` can be any list of columns you need to convert.
cols
# Index(['B', 'D'], dtype='object')

df[cols] = df[cols].apply(pd.to_numeric, errors='coerce')
# Alternatively,
# for c in cols:
#     df[c] = pd.to_numeric(df[c], errors='coerce')

df

   A      B  C      D
0  5    1.0  9   23.0
1  0    NaN  3    1.0
2  3    NaN  5    NaN
3  3   50.0  2  268.0
4  7  234.0  4    NaN

对于长的DataFrame，沿列

应用pd.to_numeric(即默认为axis=0)应稍快一些.

Applying pd.to_numeric along the columns (i.e., axis=0, the default) should be slightly faster for long DataFrames.

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