在 Python 中处理 Pandas DataFrames 列分区中的零
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handling zeros in pandas DataFrames column divisions in Python
提问by Jeff
What's the best way to handle zero denominators when dividing pandas DataFrame columns by each other in Python? for example:
在 Python 中将 Pandas DataFrame 列彼此分开时，处理零分母的最佳方法是什么？例如：
df = pandas.DataFrame({"a": [1, 2, 0, 1, 5], "b": [0, 10, 20, 30, 50]})
df.a / df.b # yields error
I'd like the ratios where the denominator is zero to be registered as NA (numpy.nan). How can this be done efficiently in pandas?
我希望将分母为零的比率注册为 NA ( numpy.nan)。如何在Pandas中有效地做到这一点？
Casting to float64does not work at level of columns:
强制转换float64在列级别不起作用：
In [29]: df
Out[29]:
a b
0 1 0
1 2 10
2 0 20
3 1 30
4 5 50
In [30]: df["a"].astype("float64") / df["b"].astype("float64")
...
FloatingPointError: divide by zero encountered in divide
How can I do it just for particular columns and not entire df?
我怎么能只针对特定的列而不是整个 df？
回答by Jeff
You need to work in floats, otherwise you will have integer division, prob not what you want
您需要在浮点数中工作，否则您将进行整数除法，概率不是您想要的
In [12]: df = pandas.DataFrame({"a": [1, 2, 0, 1, 5],
"b": [0, 10, 20, 30, 50]}).astype('float64')
In [13]: df
Out[13]:
a b
0 1 0
1 2 10
2 0 20
3 1 30
4 5 50
In [14]: df.dtypes
Out[14]:
a float64
b float64
dtype: object
Here's one way
这是一种方法
In [15]: x = df.a/df.b
In [16]: x
Out[16]:
0 inf
1 0.200000
2 0.000000
3 0.033333
4 0.100000
dtype: float64
In [17]: x[np.isinf(x)] = np.nan
In [18]: x
Out[18]:
0 NaN
1 0.200000
2 0.000000
3 0.033333
4 0.100000
dtype: float64
Here's another way
这是另一种方式
In [20]: df.a/df.b.replace({ 0 : np.nan })
Out[20]:
0 NaN
1 0.200000
2 0.000000
3 0.033333
4 0.100000
dtype: float64
