处理在Python中的熊猫DataFrames列分区中的零。

What's the best way to handle zero denominators when dividing pandas DataFrame columns by each other in Python? for example:

在Python中，当将熊猫DataFrame列相互分隔时，处理零分母的最佳方式是什么?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?

我希望分母为0的比率被注册为NA (numpy.nan)。如何在熊猫身上有效地做到这一点?

Casting to float64 does not work at level of columns:

对float64的转换在列的层次上不起作用:

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?

如何只针对特定的列而不是整个df?

1 个解决方案

#1

You need to work in floats, otherwise you will have integer division, prob not what you want

你需要使用浮点数，否则你会得到整数除法，而不是你想要的

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

这是方法之一

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

#1