Pandas:如何更快地应用数据帧? [英] Pandas: How to make apply on dataframe faster?
问题描述
考虑这个 Pandas 示例，我通过将 A 与 B 和一个 float 相乘来计算列 C如果使用带有 lambda 函数的 apply 满足某个条件:
Consider this pandas example where I'm calculating column C by multiplying A with B and a float if a certain condition is fulfilled using apply with a lambda function:
import pandas as pd
df = pd.DataFrame({'A':[1,2,3,4,5,6,7,8,9],'B':[9,8,7,6,5,4,3,2,1]})
df['C'] = df.apply(lambda x: x.A if x.B > 5 else 0.1*x.A*x.B, axis=1)
预期的结果是:
A B C
0 1 9 1.0
1 2 8 2.0
2 3 7 3.0
3 4 6 4.0
4 5 5 2.5
5 6 4 2.4
6 7 3 2.1
7 8 2 1.6
8 9 1 0.9
问题是这段代码很慢，我需要在一个大约有 5600 万行的数据帧上执行这个操作.
The problem is that this code is slow and I need to do this operation on a dataframe with around 56 million rows.
上述 lambda 操作的 %timeit-结果是:
The %timeit-result of the above lambda operation is:
1000 loops, best of 3: 1.63 ms per loop
从计算时间以及在我的大型数据帧上执行此操作时的内存使用情况来看，我认为此操作在进行计算时使用了中间系列.
Going from the calculation time and also the memory usage when doing this on my large dataframe I presume this operation uses intermediary series while doing the calculations.
我尝试以不同的方式来制定它，包括使用临时列，但我想出的每个替代解决方案都更慢.
I tried to formulate it in different ways including using temporary columns, but every alternative solution I came up with is even slower.
有没有办法以不同且更快的方式获得我需要的结果，例如通过使用 numpy?
Is there a way to get the result I need in a different and faster way, e.g. by using numpy?
推荐答案
为了提高性能，最好使用 NumPy 数组并使用 np.where -
For performance, you might be better off working with NumPy array and using np.where -
a = df.values # Assuming you have two columns A and B
df['C'] = np.where(a[:,1]>5,a[:,0],0.1*a[:,0]*a[:,1])
<小时>
运行时测试
def numpy_based(df):
a = df.values # Assuming you have two columns A and B
df['C'] = np.where(a[:,1]>5,a[:,0],0.1*a[:,0]*a[:,1])
时间 -
In [271]: df = pd.DataFrame(np.random.randint(0,9,(10000,2)),columns=[['A','B']])
In [272]: %timeit numpy_based(df)
1000 loops, best of 3: 380 µs per loop
In [273]: df = pd.DataFrame(np.random.randint(0,9,(10000,2)),columns=[['A','B']])
In [274]: %timeit df['C'] = df.A.where(df.B.gt(5), df[['A', 'B']].prod(1).mul(.1))
100 loops, best of 3: 3.39 ms per loop
In [275]: df = pd.DataFrame(np.random.randint(0,9,(10000,2)),columns=[['A','B']])
In [276]: %timeit df['C'] = np.where(df['B'] > 5, df['A'], 0.1 * df['A'] * df['B'])
1000 loops, best of 3: 1.12 ms per loop
In [277]: df = pd.DataFrame(np.random.randint(0,9,(10000,2)),columns=[['A','B']])
In [278]: %timeit df['C'] = np.where(df.B > 5, df.A, df.A.mul(df.B).mul(.1))
1000 loops, best of 3: 1.19 ms per loop
<小时>
仔细观察
让我们仔细看看 NumPy 的数字运算能力，并与 Pandas 进行比较 -
Let's take a closer look at NumPy's number crunching capability and compare with pandas into the mix -
# Extract out as array (its a view, so not really expensive
# .. as compared to the later computations themselves)
In [291]: a = df.values
In [296]: %timeit df.values
10000 loops, best of 3: 107 µs per loop
案例 #1:使用 NumPy 数组并使用 numpy.where :
Case #1 : Work with NumPy array and use numpy.where :
In [292]: %timeit np.where(a[:,1]>5,a[:,0],0.1*a[:,0]*a[:,1])
10000 loops, best of 3: 86.5 µs per loop
同样，分配到一个新列:df['C'] 也不会很昂贵 -
Again, assigning into a new column : df['C'] would not be very expensive either -
In [300]: %timeit df['C'] = np.where(a[:,1]>5,a[:,0],0.1*a[:,0]*a[:,1])
1000 loops, best of 3: 323 µs per loop
案例#2:使用pandas 数据框并使用其.where 方法(无NumPy)
Case #2 : Work with pandas dataframe and use its .where method (no NumPy)
In [293]: %timeit df.A.where(df.B.gt(5), df[['A', 'B']].prod(1).mul(.1))
100 loops, best of 3: 3.4 ms per loop
案例 #3:使用 Pandas 数据框(没有 NumPy 数组)，但使用 numpy.where -
Case #3 : Work with pandas dataframe (no NumPy array), but use numpy.where -
In [294]: %timeit np.where(df['B'] > 5, df['A'], 0.1 * df['A'] * df['B'])
1000 loops, best of 3: 764 µs per loop
案例 #4:再次使用 Pandas 数据框(没有 NumPy 数组)，但使用 numpy.where -
Case #4 : Work with pandas dataframe again (no NumPy array), but use numpy.where -
In [295]: %timeit np.where(df.B > 5, df.A, df.A.mul(df.B).mul(.1))
1000 loops, best of 3: 830 µs per loop
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