pandas 比较熊猫列中的浮点数
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Comparing floats in a pandas column
提问by darkpool
I have the following dataframe:
我有以下数据框：
actual_credit min_required_credit
0 0.3 0.4
1 0.5 0.2
2 0.4 0.4
3 0.2 0.3
I need to add a column indicating where actual_credit >= min_required_credit. The result would be:
我需要添加一列指示实际信用 >= min_required_credit 的位置。结果将是：
actual_credit min_required_credit result
0 0.3 0.4 False
1 0.5 0.2 True
2 0.4 0.4 True
3 0.1 0.3 False
I am doing the following:
我正在做以下事情：
df['result'] = abs(df['actual_credit']) >= abs(df['min_required_credit'])
However the 3rd row (0.4 and 0.4) is constantly resulting in False. After researching this issue at various places including: What is the best way to compare floats for almost-equality in Python?I still can't get this to work. Whenever the two columns have an identical value, the result is False which is not correct.
然而，第三行（0.4 和 0.4）不断导致 False。在多个地方研究了这个问题之后，包括：在 Python 中比较浮点数以实现几乎相等的最佳方法是什么？我仍然无法让这个工作。每当两列具有相同的值时，结果为 False，这是不正确的。
I am using python 3.3
我正在使用 python 3.3
回答by EdChum
Due to imprecise float comparison you can oryour comparison with np.isclose, isclosetakes a relative and absolute tolerance param so the following should work:
由于不精确的浮点比较，您可以or与np.isclose,isclose进行比较，采用相对和绝对容差参数，因此以下内容应该有效：
df['result'] = df['actual_credit'].ge(df['min_required_credit']) | np.isclose(df['actual_credit'], df['min_required_credit'])
回答by Tomasz Bartkowiak
In general numpyComparisonfunctions work well with pd.Seriesand allow for element-wise comparisons:
isclose, allclose, greater, greater_equal, less, less_equaletc.
一般来说numpy比较功能与正常工作pd.Series，并允许逐元素的比较：
isclose，allclose，greater，greater_equal，less，less_equal等。
In your case greater_equalwould do:
在你的情况下greater_equal会这样做：
df['result'] = np.greater_equal(df['actual_credit'], df['min_required_credit'])
or alternatively, as proposed, using pandas.ge(alternatively le, gtetc.):
或替代地，所建议，使用pandas.ge（可替换地le，gt等）：
df['result'] = df['actual_credit'].ge(df['min_required_credit'])
The risk with oring with ge(as mentioned above) is that e.g. comparing 3.999999999999and 4.0might return Truewhich might not necessarily be what you want.
使用oring with ge（如上所述）的风险在于，例如比较3.999999999999并且4.0可能返回True这可能不一定是您想要的。
回答by NPE
Use pandas.DataFrame.abs()instead of the built-in abs():
使用pandas.DataFrame.abs()而不是内置的abs()：
df['result'] = df['actual_credit'].abs() >= df['min_required_credit'].abs()
