Python 如何在数据帧的每一行上应用一个函数？
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How to apply a function on every row on a dataframe?
提问by Koen
I am new to Python and I am not sure how to solve the following problem.
我是 Python 新手，不知道如何解决以下问题。
I have a function:
我有一个功能：
def EOQ(D,p,ck,ch):
Q = math.sqrt((2*D*ck)/(ch*p))
return Q
Say I have the dataframe
说我有数据框
df = pd.DataFrame({"D": [10,20,30], "p": [20, 30, 10]})
D p
0 10 20
1 20 30
2 30 10
ch=0.2
ck=5
And chand ckare float types. Now I want to apply the formula to every row on the dataframe and return it as an extra row 'Q'. An example (that does not work) would be:
而ch和ck是浮点类型。现在我想将公式应用于数据帧上的每一行，并将其作为额外的行“Q”返回。一个例子（不起作用）是：
df['Q']= map(lambda p, D: EOQ(D,p,ck,ch),df['p'], df['D'])
(returns only 'map' types)
（仅返回“地图”类型）
I will need this type of processing more in my project and I hope to find something that works.
我将在我的项目中更多地需要这种类型的处理，我希望找到一些有用的东西。
采纳答案by EdChum
The following should work:
以下应该工作：
def EOQ(D,p,ck,ch):
Q = math.sqrt((2*D*ck)/(ch*p))
return Q
ch=0.2
ck=5
df['Q'] = df.apply(lambda row: EOQ(row['D'], row['p'], ck, ch), axis=1)
df
If all you're doing is calculating the square root of some result then use the np.sqrtmethod this is vectorised and will be significantly faster:
如果您所做的只是计算某个结果的平方根，那么使用np.sqrt矢量化的方法会明显更快：
In [80]:
df['Q'] = np.sqrt((2*df['D']*ck)/(ch*df['p']))
df
Out[80]:
D p Q
0 10 20 5.000000
1 20 30 5.773503
2 30 10 12.247449
Timings
时间安排
For a 30k row df:
对于 30k 行 df：
In [92]:
import math
ch=0.2
ck=5
def EOQ(D,p,ck,ch):
Q = math.sqrt((2*D*ck)/(ch*p))
return Q
%timeit np.sqrt((2*df['D']*ck)/(ch*df['p']))
%timeit df.apply(lambda row: EOQ(row['D'], row['p'], ck, ch), axis=1)
1000 loops, best of 3: 622 μs per loop
1 loops, best of 3: 1.19 s per loop
You can see that the np method is ~1900 X faster
你可以看到 np 方法快了 ~1900 X
