pandas 确认两个熊猫数据框的相等性？
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Confirming equality of two pandas dataframes?
提问by Mehdi Jafarnia Jahromi
How to assert that the following two dataframes df1and df2are equal?
如何断言以下两个数据帧df1和df2是相等的？
import pandas as pd
df1 = pd.DataFrame([1, 2, 3])
df2 = pd.DataFrame([1.0, 2, 3])
The output of df1.equals(df2)is False.
As of now, I know two ways:
的输出df1.equals(df2)是False。到目前为止，我知道两种方法：
print (df1 == df2).all()[0]
or
或者
df1 = df1.astype(float)
print df1.equals(df2)
It seems a little bit messy. Is there a better way to do this comparison?
好像有点乱。有没有更好的方法来进行这种比较？
回答by Alexander
You can use assert_frame_equaland not check the dtype of the columns.
您可以使用assert_frame_equal而不是检查列的 dtype。
# Pre v. 0.20.3
# from pandas.util.testing import assert_frame_equal
from pandas.testing import assert_frame_equal
assert_frame_equal(df1, df2, check_dtype=False)
回答by MaxU
Using elegant @Divakar's idea- numpy's allclose()will do the main trick for numbers:
使用优雅的@Divakar的想法- numpy 的allclose()将执行数字的主要技巧：
In [128]: df1
Out[128]:
0 s n
0 1 aaa 1
1 2 aaa 2
2 3 aaa 3
In [129]: df2
Out[129]:
0 s n
0 1.0 aaa 1.0
1 2.0 aaa 2.0
2 3.0 aaa 3.0
In [130]: (np.allclose(df1.select_dtypes(exclude=[object]), df2.select_dtypes(exclude=[object]))
.....: &
.....: df1.select_dtypes(include=[object]).equals(df2.select_dtypes(include=[object]))
.....: )
Out[130]: True
select_dtypes()will help you to separate strings and all other numeric dtypes
select_dtypes()将帮助您分隔字符串和所有其他数字类型
