在 Pandas 中断言列数据类型
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Asserting column(s) data type in Pandas
提问by nfmcclure
I'm trying to find a better way to assert the column data type in Python/Pandas of a given dataframe.
我试图找到一种更好的方法来断言给定数据帧的 Python/Pandas 中的列数据类型。
For example:
例如：
import pandas as pd
t = pd.DataFrame({'a':[1,2,3], 'b':[2,6,0.75], 'c':['foo','bar','beer']})
I would like to assert that specific columns in the data frame are numeric. Here's what I have:
我想断言数据框中的特定列是数字。这是我所拥有的：
numeric_cols = ['a', 'b'] # These will be given
assert [x in ['int64','float'] for x in [t[y].dtype for y in numeric_cols]]
This last assert line doesn't feel very pythonic. Maybe it is and I'm just cramming it all in one hard to read line. Is there a better way? I would like to write something like:
最后一个断言行感觉不是很pythonic。也许是这样，我只是把它全部塞进了一条难以阅读的行中。有没有更好的办法？我想写一些类似的东西：
assert t[numeric_cols].dtype.isnumeric()
I can't seem to find something like that though.
我似乎无法找到类似的东西。
回答by unutbu
You could use ptypes.is_numeric_dtypeto identify numeric columns, ptypes.is_string_dtypeto identify string-like columns, and ptypes.is_datetime64_any_dtypeto identify datetime64 columns:
您可以使用ptypes.is_numeric_dtype来标识数字列、ptypes.is_string_dtype标识类似字符串的列以及ptypes.is_datetime64_any_dtype标识 datetime64 列：
import pandas as pd
import pandas.api.types as ptypes
t = pd.DataFrame({'a':[1,2,3], 'b':[2,6,0.75], 'c':['foo','bar','beer'],
'd':pd.date_range('2000-1-1', periods=3)})
cols_to_check = ['a', 'b']
assert all(ptypes.is_numeric_dtype(t[col]) for col in cols_to_check)
# True
assert ptypes.is_string_dtype(t['c'])
# True
assert ptypes.is_datetime64_any_dtype(t['d'])
# True
The pandas.api.typesmodule (which I aliased to ptypes) has both a is_datetime64_any_dtypeand a is_datetime64_dtypefunction. The difference is in how they treat timezone-aware array-likes:
该pandas.api.types模块（我将其别名为ptypes）同时具有 ais_datetime64_any_dtype和is_datetime64_dtype函数。不同之处在于他们如何处理时区感知类数组：
In [239]: ptypes.is_datetime64_any_dtype(pd.DatetimeIndex([1, 2, 3], tz="US/Eastern"))
Out[239]: True
In [240]: ptypes.is_datetime64_dtype(pd.DatetimeIndex([1, 2, 3], tz="US/Eastern"))
Out[240]: False
回答by ely
You can do this
你可以这样做
import numpy as np
numeric_dtypes = [np.dtype('int64'), np.dtype('float64')]
# or whatever types you want
assert t[numeric_cols].apply(lambda c: c.dtype).isin(numeric_dtypes).all()
