我什么时候应该(不希望)在代码中使用pandas apply()? [英] When should I (not) want to use pandas apply() in my code?

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问题描述

我已经看到许多有关使用Pandas方法 apply 的堆栈溢出问题的答案.我还看到用户在他们的下面发表评论，说" apply 很慢，应避免使用".

我已经阅读了许多有关性能的文章，这些文章解释了 apply 的运行速度很慢.我还在文档中看到了关于免于应用 apply 仅仅是传递UDF的便捷功能的免责声明(现在似乎找不到).因此，普遍的共识是，应尽可能避免使用 apply .但是，这引发了以下问题:

  1. 如果 apply 太糟糕了，那为什么在API中会如此?
  2. 如何以及何时使我的代码免费应用?
  3. 在任何情况下 apply 都好(比其他可能的解决方案要好)吗?

解决方案

应用，您不需要的便捷功能

我们首先在OP中逐一解决问题.

"如果 apply 太糟糕了，那为什么在API中呢?"

该图是使用 perfplot 库绘制的.

 导入性能perfplot.show(setup = lambda n:pd.Series(np.random.randint(0，n，n))，内核= [lambda s:s.astype(str)，lambda s:s.apply(str)]，labels = ['astype'，'apply']，n_range = [对于范围(1，20)中的k为2 ** k]，xlabel ='N'，logx = True，logy = True，equal_check = lambda x，y:(x == y).all())

使用浮点数时，我看到 astype 始终与 apply 一样快，或稍快.因此，这与测试中的数据是整数类型有关.

具有链接转换的

GroupBy 操作

到目前为止，尚未讨论

GroupBy.apply ，但是 GroupBy.apply 还是一个迭代便利函数，用于处理现有 GroupBy 函数没有.

一个常见的要求是先执行GroupBy，然后执行两个主要操作，例如滞后的累积量":

  df = pd.DataFrame({"A":list('aabcccddee')，"B":[12、7、5、4、5、4、3、2、1、10]})df甲乙0至121一72 b 53 c 44例55 c 46天37天28 19和10

<！-->

您需要在此处进行两个连续的groupby呼叫:

  df.groupby('A').B.cumsum().groupby(df.A).shift()0 NaN1 12.02 NaN3 NaN4 4.05 9.06 NaN7 3.08 NaN9 1.0名称:B，dtype:float64

使用应用，您可以将其缩短为一个呼叫.

  df.groupby('A').B.apply(lambda x:x.cumsum().shift())0 NaN1 12.02 NaN3 NaN4 4.05 9.06 NaN7 3.08 NaN9 1.0名称:B，dtype:float64

很难量化性能，因为它取决于数据.但总的来说，如果目标是减少 groupby 调用(因为 groupby 也是相当昂贵的)，则 apply 是可以接受的解决方案.

其他警告

除了上述注意事项外，还值得一提的是 apply 在第一行(或列)上操作两次.这样做是为了确定该功能是否有任何副作用.如果没有，则 apply 可能能够使用快速路径来评估结果，否则将退回到缓慢的实施方式.

  df = pd.DataFrame({'A':[1、2]，'B':['x'，'y']})def func(x):打印(x ['A'])返回xdf.apply(func，axis = 1)#1#1#2甲乙0 1 x1 2年

在熊猫版本< 0.25的 GroupBy.apply 中也可以看到此行为(已针对0.25进行了修复，apply is slow, and should be avoided".

I have read many articles on the topic of performance that explain apply is slow. I have also seen a disclaimer in the docs about how apply is simply a convenience function for passing UDFs (can't seem to find that now). So, the general consensus is that apply should be avoided if possible. However, this raises the following questions:

  1. If apply is so bad, then why is it in the API?
  2. How and when should I make my code apply-free?
  3. Are there ever any situations where apply is good (better than other possible solutions)?

解决方案

apply, the Convenience Function you Never Needed

We start by addressing the questions in the OP, one by one.

"If apply is so bad, then why is it in the API?"

DataFrame.apply and Series.apply are convenience functions defined on DataFrame and Series object respectively. apply accepts any user defined function that applies a transformation/aggregation on a DataFrame. apply is effectively a silver bullet that does whatever any existing pandas function cannot do.

Some of the things apply can do:

  • Run any user-defined function on a DataFrame or Series
  • Apply a function either row-wise (axis=1) or column-wise (axis=0) on a DataFrame
  • Perform index alignment while applying the function
  • Perform aggregation with user-defined functions (however, we usually prefer agg or transform in these cases)
  • Perform element-wise transformations
  • Broadcast aggregated results to original rows (see the result_type argument).
  • Accept positional/keyword arguments to pass to the user-defined functions.

...Among others. For more information, see Row or Column-wise Function Application in the documentation.

So, with all these features, why is apply bad? It is because apply is slow. Pandas makes no assumptions about the nature of your function, and so iteratively applies your function to each row/column as necessary. Additionally, handling all of the situations above means apply incurs some major overhead at each iteration. Further, apply consumes a lot more memory, which is a challenge for memory bounded applications.

There are very few situations where apply is appropriate to use (more on that below). If you're not sure whether you should be using apply, you probably shouldn't.

Let's address the next question.

"How and when should I make my code apply-free?"

To rephrase, here are some common situations where you will want to get rid of any calls to apply.

Numeric Data

If you're working with numeric data, there is likely already a vectorized cython function that does exactly what you're trying to do (if not, please either ask a question on Stack Overflow or open a feature request on GitHub).

Contrast the performance of apply for a simple addition operation.

df = pd.DataFrame({"A": [9, 4, 2, 1], "B": [12, 7, 5, 4]})
df

   A   B
0  9  12
1  4   7
2  2   5
3  1   4

<!- ->

df.apply(np.sum)

A    16
B    28
dtype: int64

df.sum()

A    16
B    28
dtype: int64

Performance wise, there's no comparison, the cythonized equivalent is much faster. There's no need for a graph, because the difference is obvious even for toy data.

%timeit df.apply(np.sum)
%timeit df.sum()
2.22 ms ± 41.2 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
471 µs ± 8.16 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)

Even if you enable passing raw arrays with the raw argument, it's still twice as slow.

%timeit df.apply(np.sum, raw=True)
840 µs ± 691 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

Another example:

df.apply(lambda x: x.max() - x.min())

A    8
B    8
dtype: int64

df.max() - df.min()

A    8
B    8
dtype: int64

%timeit df.apply(lambda x: x.max() - x.min())
%timeit df.max() - df.min()

2.43 ms ± 450 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
1.23 ms ± 14.7 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)

In general, seek out vectorized alternatives if possible.

String/Regex

Pandas provides "vectorized" string functions in most situations, but there are rare cases where those functions do not... "apply", so to speak.

A common problem is to check whether a value in a column is present in another column of the same row.

df = pd.DataFrame({
    'Name': ['mickey', 'donald', 'minnie'],
    'Title': ['wonderland', "welcome to donald's castle", 'Minnie mouse clubhouse'],
    'Value': [20, 10, 86]})
df

     Name  Value                       Title
0  mickey     20                  wonderland
1  donald     10  welcome to donald's castle
2  minnie     86      Minnie mouse clubhouse

This should return the row second and third row, since "donald" and "minnie" are present in their respective "Title" columns.

Using apply, this would be done using

df.apply(lambda x: x['Name'].lower() in x['Title'].lower(), axis=1)

0    False
1     True
2     True
dtype: bool
 
df[df.apply(lambda x: x['Name'].lower() in x['Title'].lower(), axis=1)]

     Name                       Title  Value
1  donald  welcome to donald's castle     10
2  minnie      Minnie mouse clubhouse     86

However, a better solution exists using list comprehensions.

df[[y.lower() in x.lower() for x, y in zip(df['Title'], df['Name'])]]

     Name                       Title  Value
1  donald  welcome to donald's castle     10
2  minnie      Minnie mouse clubhouse     86

<!- ->

%timeit df[df.apply(lambda x: x['Name'].lower() in x['Title'].lower(), axis=1)]
%timeit df[[y.lower() in x.lower() for x, y in zip(df['Title'], df['Name'])]]

2.85 ms ± 38.4 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
788 µs ± 16.4 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)

The thing to note here is that iterative routines happen to be faster than apply, because of the lower overhead. If you need to handle NaNs and invalid dtypes, you can build on this using a custom function you can then call with arguments inside the list comprehension.

For more information on when list comprehensions should be considered a good option, see my writeup: Are for-loops in pandas really bad? When should I care?.

Note
Date and datetime operations also have vectorized versions. So, for example, you should prefer pd.to_datetime(df['date']), over, say, df['date'].apply(pd.to_datetime).

Read more at the docs.

A Common Pitfall: Exploding Columns of Lists

s = pd.Series([[1, 2]] * 3)
s

0    [1, 2]
1    [1, 2]
2    [1, 2]
dtype: object

People are tempted to use apply(pd.Series). This is horrible in terms of performance.

s.apply(pd.Series)

   0  1
0  1  2
1  1  2
2  1  2

A better option is to listify the column and pass it to pd.DataFrame.

pd.DataFrame(s.tolist())

   0  1
0  1  2
1  1  2
2  1  2

<!- ->

%timeit s.apply(pd.Series)
%timeit pd.DataFrame(s.tolist())

2.65 ms ± 294 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
816 µs ± 40.5 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)

Lastly,

"Are there any situations where apply is good?"

Apply is a convenience function, so there are situations where the overhead is negligible enough to forgive. It really depends on how many times the function is called.

Functions that are Vectorized for Series, but not DataFrames
What if you want to apply a string operation on multiple columns? What if you want to convert multiple columns to datetime? These functions are vectorized for Series only, so they must be applied over each column that you want to convert/operate on.

df = pd.DataFrame(
         pd.date_range('2018-12-31','2019-01-31', freq='2D').date.astype(str).reshape(-1, 2), 
         columns=['date1', 'date2'])
df

       date1      date2
0 2018-12-31 2019-01-02
1 2019-01-04 2019-01-06
2 2019-01-08 2019-01-10
3 2019-01-12 2019-01-14
4 2019-01-16 2019-01-18
5 2019-01-20 2019-01-22
6 2019-01-24 2019-01-26
7 2019-01-28 2019-01-30

df.dtypes

date1    object
date2    object
dtype: object

This is an admissible case for apply:

df.apply(pd.to_datetime, errors='coerce').dtypes

date1    datetime64[ns]
date2    datetime64[ns]
dtype: object

Note that it would also make sense to stack, or just use an explicit loop. All these options are slightly faster than using apply, but the difference is small enough to forgive.

%timeit df.apply(pd.to_datetime, errors='coerce')
%timeit pd.to_datetime(df.stack(), errors='coerce').unstack()
%timeit pd.concat([pd.to_datetime(df[c], errors='coerce') for c in df], axis=1)
%timeit for c in df.columns: df[c] = pd.to_datetime(df[c], errors='coerce')

5.49 ms ± 247 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
3.94 ms ± 48.1 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
3.16 ms ± 216 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
2.41 ms ± 1.71 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

You can make a similar case for other operations such as string operations, or conversion to category.

u = df.apply(lambda x: x.str.contains(...))
v = df.apply(lambda x: x.astype(category))

v/s

u = pd.concat([df[c].str.contains(...) for c in df], axis=1)
v = df.copy()
for c in df:
    v[c] = df[c].astype(category)

And so on...

Converting Series to str: astype versus apply

This seems like an idiosyncrasy of the API. Using apply to convert integers in a Series to string is comparable (and sometimes faster) than using astype.

The graph was plotted using the perfplot library.

import perfplot

perfplot.show(
    setup=lambda n: pd.Series(np.random.randint(0, n, n)),
    kernels=[
        lambda s: s.astype(str),
        lambda s: s.apply(str)
    ],
    labels=['astype', 'apply'],
    n_range=[2**k for k in range(1, 20)],
    xlabel='N',
    logx=True,
    logy=True,
    equality_check=lambda x, y: (x == y).all())

With floats, I see the astype is consistently as fast as, or slightly faster than apply. So this has to do with the fact that the data in the test is integer type.

GroupBy operations with chained transformations

GroupBy.apply has not been discussed until now, but GroupBy.apply is also an iterative convenience function to handle anything that the existing GroupBy functions do not.

One common requirement is to perform a GroupBy and then two prime operations such as a "lagged cumsum":

df = pd.DataFrame({"A": list('aabcccddee'), "B": [12, 7, 5, 4, 5, 4, 3, 2, 1, 10]})
df

   A   B
0  a  12
1  a   7
2  b   5
3  c   4
4  c   5
5  c   4
6  d   3
7  d   2
8  e   1
9  e  10

<!- ->

You'd need two successive groupby calls here:

df.groupby('A').B.cumsum().groupby(df.A).shift()
 
0     NaN
1    12.0
2     NaN
3     NaN
4     4.0
5     9.0
6     NaN
7     3.0
8     NaN
9     1.0
Name: B, dtype: float64

Using apply, you can shorten this to a a single call.

df.groupby('A').B.apply(lambda x: x.cumsum().shift())

0     NaN
1    12.0
2     NaN
3     NaN
4     4.0
5     9.0
6     NaN
7     3.0
8     NaN
9     1.0
Name: B, dtype: float64

It is very hard to quantify the performance because it depends on the data. But in general, apply is an acceptable solution if the goal is to reduce a groupby call (because groupby is also quite expensive).

Other Caveats

Aside from the caveats mentioned above, it is also worth mentioning that apply operates on the first row (or column) twice. This is done to determine whether the function has any side effects. If not, apply may be able to use a fast-path for evaluating the result, else it falls back to a slow implementation.

df = pd.DataFrame({
    'A': [1, 2],
    'B': ['x', 'y']
})

def func(x):
    print(x['A'])
    return x

df.apply(func, axis=1)

# 1
# 1
# 2
   A  B
0  1  x
1  2  y

This behaviour is also seen in GroupBy.apply on pandas versions <0.25 (it was fixed for 0.25, see here for more information.)

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