python - 链接分组、过滤和聚合

Question

DataFrameGroupby.filter 方法过滤组，并返回包含通过过滤器的行的DataFrame。

但是过滤后如何获取新的DataFrameGroupBy对象而不是DataFrame？

例如，假设我有一个 DataFrame df，其中包含两列 A 和 B。我想为 A 列的每个值获取 B 列的平均值，只要该组中至少有 5 行:

# pandas 0.18.0
# doesn't work because `filter` returns a DF not a GroupBy object
df.groupby('A').filter(lambda x: len(x)>=5).mean()
# works but slower and awkward to write because needs to groupby('A') twice
df.groupby('A').filter(lambda x: len(x)>=5).reset_index().groupby('A').mean()
# works but more verbose than chaining
groups = df.groupby('A')
groups.mean()[groups.size() >= 5]

Answer 1

你可以这样做:

In [310]: df
Out[310]:
    a  b
0   1  4
1   7  3
2   6  9
3   4  4
4   0  2
5   8  4
6   7  7
7   0  5
8   8  5
9   8  7
10  6  1
11  3  8
12  7  4
13  8  0
14  5  3
15  5  3
16  8  1
17  7  2
18  9  9
19  3  2
20  9  1
21  1  2
22  0  3
23  8  9
24  7  7
25  8  1
26  5  8
27  9  6
28  2  8
29  9  0

In [314]: r = df.groupby('a').apply(lambda x: x.b.mean() if len(x)>=5 else -1)

In [315]: r
Out[315]:
a
0   -1.000000
1   -1.000000
2   -1.000000
3   -1.000000
4   -1.000000
5   -1.000000
6   -1.000000
7    4.600000
8    3.857143
9   -1.000000
dtype: float64

In [316]: r[r>0]
Out[316]:
a
7    4.600000
8    3.857143
dtype: float64

单行代码，返回数据框而不是系列:

df.groupby('a') \
  .apply(lambda x: x.b.mean() if len(x)>=5 else -1) \
  .to_frame() \
  .rename(columns={0:'mean'}) \
  .query('mean > 0')

与具有 100.000 行的 DF 的时间比较:

def maxu():
    r = df.groupby('a').apply(lambda x: x.b.mean() if len(x)>=5 else -1)
    return r[r>0]

def maxu2():
    return df.groupby('a') \
             .apply(lambda x: x.b.mean() if len(x)>=5 else -1) \
             .to_frame() \
             .rename(columns={0:'mean'}) \
             .query('mean > 0')

def alexander():
    return df.groupby('a', as_index=False).filter(lambda group: group.a.count() >= 5).groupby('a').mean()

def alexander2():
    vc = df.a.value_counts()
    return df.loc[df.a.isin(vc[vc >= 5].index)].groupby('a').mean()

结果:

In [419]: %timeit maxu()
1 loop, best of 3: 1.12 s per loop

In [420]: %timeit maxu2()
1 loop, best of 3: 1.12 s per loop

In [421]: %timeit alexander()
1 loop, best of 3: 34.9 s per loop

In [422]: %timeit alexander2()
10 loops, best of 3: 66.6 ms per loop

检查:

In [423]: alexander2().sum()
Out[423]:
b   19220943.162
dtype: float64

In [424]: maxu2().sum()
Out[424]:
mean   19220943.162
dtype: float64

结论:

明显的赢家是 alexander2() 函数

@Alexander，恭喜!