如何在 Pandas Dataframe、Python3.x 中将“字节”对象转换为文字字符串？
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How to translate "bytes" objects into literal strings in pandas Dataframe, Python3.x?
提问by ShanZhengYang
I have a Python3.x pandas DataFrame whereby certain columns are strings which as expressed as bytes (like in Python2.x)
我有一个 Python3.x pandas DataFrame，其中某些列是表示为字节的字符串（如在 Python2.x 中）
import pandas as pd
df = pd.DataFrame(...)
df
COLUMN1 ....
0 b'abcde' ....
1 b'dog' ....
2 b'cat1' ....
3 b'bird1' ....
4 b'elephant1' ....
When I access by column with df.COLUMN1, I see Name: COLUMN1, dtype: object
当我按列访问时df.COLUMN1，我看到Name: COLUMN1, dtype: object
However, if I access by element, it is a "bytes" object
但是，如果我按元素访问，它是一个“字节”对象
df.COLUMN1.ix[0].dtype
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
AttributeError: 'bytes' object has no attribute 'dtype'
How do I convert these into "regular" strings? That is, how can I get rid of this b''prefix?
如何将这些转换为“常规”字符串？也就是说，我怎样才能摆脱这个b''前缀？
回答by EdChum
You can use vectorised str.decodeto decode byte strings into ordinary strings:
您可以使用 vectorisedstr.decode将字节字符串解码为普通字符串：
df['COLUMN1'].str.decode("utf-8")
To do this for multiple columns you can select just the str columns:
要对多列执行此操作，您可以仅选择 str 列：
str_df = df.select_dtypes([np.object])
convert all of them:
转换所有这些：
str_df = str_df.stack().str.decode('utf-8').unstack()
You can then swap out converted cols with the original df cols:
然后，您可以用原始 df cols 换出转换后的 cols：
for col in str_df:
df[col] = str_df[col]
回答by Yu Zhou
df['COLUMN1'].apply(lambda x: x.decode("utf-8"))
回答by Dinesh.hmn
df.columns = [x.decode("utf-8") for x in df.columns]
This will make it faster and easier.
这将使它更快更容易。
