数据聚合与分组运算学习笔记2

2 数据聚合

从数组产生标量值的数据转化过程。

In [5]: df
Out[5]:
      data1     data2 key1 key2
0 -1.809227  2.231607    a  one
1  1.663738 -0.113191    a  two
2  0.411805 -0.842909    b  one
3  1.244803 -1.147212    b  two
4  1.014822  0.122223    a  one

In [6]: grouped = df.groupby('key1')

In [7]: grouped['data1'].quantile(0.9)   # 0.9分位数,
Out[7]:
key1
a    1.533955
b    1.161503
Name: data1, dtype: float64

# 还有之前的sum,mean等。

# 也能传入自定义函数

In [5]: def peak_to_peak(arr):
   ...:     return arr.max()-arr.min()

In [9]: grouped.agg(peak_to_peak)
Out[9]:
         data1     data2
key1                    
a     3.472965  2.344798
b     0.832998  0.304303


In [10]: grouped.describe()
Out[10]:
               data1     data2
key1                          
a    count  3.000000  3.000000
     mean   0.289777  0.746880
     std    1.846521  1.291188
     min   -1.809227 -0.113191
     25%   -0.397203  0.004516
     50%    1.014822  0.122223
     75%    1.339280  1.176915
     max    1.663738  2.231607
b    count  2.000000  2.000000
     mean   0.828304 -0.995061
     std    0.589018  0.215175
     min    0.411805 -1.147212
     25%    0.620055 -1.071137
     50%    0.828304 -0.995061
     75%    1.036554 -0.918985
     max    1.244803 -0.842909



经过groupby优化过的方法

2.1 面向列的多函数应用

针对列的聚合运算其实就是agg或者使用mean、sum等方法。如果需要多个聚合函数同时作用:

# 以列表的形式传入agg,元素为函数名称或函数对象。
In [9]: df.groupby('key1').agg(['mean', peak_to_peak])
Out[9]:
         data1                  data2
          mean peak_to_peak      mean peak_to_peak
key1
a     0.143435     2.047937  0.934671     1.279921
b     0.037396     1.884494 -1.087873     1.018864

# 还可以定义列名 (列名,函数名)
In [10]: df.groupby('key1').agg([('Mean','mean'),('bar',np.std)])
Out[10]:
         data1               data2
          Mean       bar      Mean       bar
key1
a     0.143435  1.125854  0.934671  0.673188
b     0.037396  1.332538 -1.087873  0.720446

# 对不同的列,应用不同的函数,使用字典

In [11]: func_dict = {'data1':'mean','data2':np.std}

In [12]: df.groupby('key1').agg(func_dict)
Out[12]:
         data1     data2
key1
a     0.143435  0.673188
b     0.037396  0.720446



2.2 以无索引的形式返回聚合数据

在groupby中默认会以唯一的分组键作为索引,如果参数中传入“as_index=False”即可禁止索引。

In [13]: df.groupby('key1',as_index=False).mean()
Out[13]:
  key1     data1     data2
0    a  0.143435  0.934671
1    b  0.037396 -1.087873

# 相同的结果
In [17]: df.groupby('key1').mean().reset_index()
Out[17]:
  key1     data1     data2
0    a  0.143435  0.934671
1    b  0.037396 -1.087873

3 分组级运算和转换

聚合只是分组运算和转换的一个特例,他能够接受的是将一维数组简化为标量的函数。
本节的transform和apply函数能够执行更多的分组运算

In [19]: df
Out[19]:
      data1     data2 key1 key2
0  0.897195  0.693473    a  one
1  0.683851  1.695230    a  two
2 -0.904851 -0.578441    b  one
3  0.979643 -1.597305    b  two
4 -1.150742  0.415309    a  one

In [22]: k1_mean = df.groupby('key1').mean().add_prefix('mean_')

In [25]: df.merge(k1_mean,left_on='key1',right_index=True)
Out[25]:
      data1     data2 key1 key2  mean_data1  mean_data2
0  0.897195  0.693473    a  one    0.143435    0.934671
1  0.683851  1.695230    a  two    0.143435    0.934671
4 -1.150742  0.415309    a  one    0.143435    0.934671
2 -0.904851 -0.578441    b  one    0.037396   -1.087873
3  0.979643 -1.597305    b  two    0.037396   -1.087873

# 这样稍显麻烦

我们使用 transform 再来试一试。

In [12]: key = ['one','two','one','two','one']

In [13]: people
Out[13]:
               a         b         c         d         e
Joe    -0.632221  0.074549 -0.964887 -2.144542  1.787703
Steve  -0.442427  0.925263  0.646603  0.719694 -0.208777
Wes    -0.019415  0.032652 -0.475912  0.490444 -0.305742
Jim     0.214406 -0.263113 -1.867597  1.762480  1.024320
Travis  0.348362 -0.650411 -1.660043 -0.332139 -0.669246

In [14]: people.groupby(key).mean()  # 求用key分类后的平均值
Out[14]:
            a         b         c         d         e
one -0.101091 -0.181070 -1.033614 -0.662079  0.270905
two -0.114010  0.331075 -0.610497  1.241087  0.407772

In [15]: people.groupby(key).transform(np.mean)  # 将通过key计算出来的平均值,赋值回去
Out[15]:
               a         b         c         d         e
Joe    -0.101091 -0.181070 -1.033614 -0.662079  0.270905
Steve  -0.114010  0.331075 -0.610497  1.241087  0.407772
Wes    -0.101091 -0.181070 -1.033614 -0.662079  0.270905
Jim    -0.114010  0.331075 -0.610497  1.241087  0.407772
Travis -0.101091 -0.181070 -1.033614 -0.662079  0.270905

In [16]: def demean(arr):
    ...:     return arr-arr.mean()
    ...:

# transform 会将传入的函数应用到各个分组中,并将结果放到适当的位置上去。
#
In [17]: demeaned = people.groupby(key).transform(demean)

In [18]: demeaned
Out[18]:
               a         b         c         d         e
Joe    -0.531130  0.255619  0.068727 -1.482463  1.516798
Steve  -0.328417  0.594188  1.257100 -0.521393 -0.616549
Wes     0.081676  0.213722  0.557702  1.152523 -0.576647
Jim     0.328417 -0.594188 -1.257100  0.521393  0.616549
Travis  0.449453 -0.469341 -0.626429  0.329940 -0.940151

In [19]: demeaned.groupby(key).mean()
Out[19]:
                a             b             c             d    e
one  1.850372e-17  1.850372e-17 -1.110223e-16 -5.551115e-17  0.0
two  0.000000e+00  0.000000e+00 -1.110223e-16  0.000000e+00  0.0

3.1 apply:一般性的“拆分-应用-合并”

transform与agg是一样的,要么产生一个可以广播的标量,要么产生一个相同大小的数组。
apply会将对象拆分成多个片段,然后对各个片段传入函数,最后尝试组合。
即“拆分-应用-合并”。

In [14]: tips = pd.read_csv('tips.csv')

In [15]: tips['tip_pct']=tips['tip']/tips['total_bill']

In [16]: tips
Out[16]:
     total_bill   tip     sex smoker   day    time  size   tip_pct
0         16.99  1.01  Female     No   Sun  Dinner     2  0.059447
1         10.34  1.66    Male     No   Sun  Dinner     3  0.160542
2         21.01  3.50    Male     No   Sun  Dinner     3  0.166587

In [17]: def top(df, n=5, column = 'tip_pct'):
    ...:     return df.sort_index(by=column)[-n:]
    ...:

In [18]: top(tips, n=6)
C:\Users\yangfl\Anaconda3\Scripts\ipython-script.py:2: FutureWarning: by argumen
t to sort_index is deprecated, pls use .sort_values(by=...)
  import sys
Out[18]:
     total_bill   tip     sex smoker  day    time  size   tip_pct
109       14.31  4.00  Female    Yes  Sat  Dinner     2  0.279525
183       23.17  6.50    Male    Yes  Sun  Dinner     4  0.280535
232       11.61  3.39    Male     No  Sat  Dinner     2  0.291990
67         3.07  1.00  Female    Yes  Sat  Dinner     1  0.325733
178        9.60  4.00  Female    Yes  Sun  Dinner     2  0.416667
172        7.25  5.15    Male    Yes  Sun  Dinner     2  0.710345

In [17]: def top(df, n=5, column = 'tip_pct'):
    ...:     return df.sort_index(by=column)[-n:]
    ...:

In [18]: top(tips, n=6)
C:\Users\yangfl\Anaconda3\Scripts\ipython-script.py:2: FutureWarning: by argumen
t to sort_index is deprecated, pls use .sort_values(by=...)
  import sys
Out[18]:
     total_bill   tip     sex smoker  day    time  size   tip_pct
109       14.31  4.00  Female    Yes  Sat  Dinner     2  0.279525
183       23.17  6.50    Male    Yes  Sun  Dinner     4  0.280535
232       11.61  3.39    Male     No  Sat  Dinner     2  0.291990
67         3.07  1.00  Female    Yes  Sat  Dinner     1  0.325733
178        9.60  4.00  Female    Yes  Sun  Dinner     2  0.416667
172        7.25  5.15    Male    Yes  Sun  Dinner     2  0.710345

In [19]: tips.groupby('smoker').apply(top)
C:\Users\yangfl\Anaconda3\Scripts\ipython-script.py:2: FutureWarning: by argumen
t to sort_index is deprecated, pls use .sort_values(by=...)
  import sys
Out[19]:
            total_bill   tip     sex smoker   day    time  size   tip_pct
smoker
No     88        24.71  5.85    Male     No  Thur   Lunch     2  0.236746
       185       20.69  5.00    Male     No   Sun  Dinner     5  0.241663
       51        10.29  2.60  Female     No   Sun  Dinner     2  0.252672
       149        7.51  2.00    Male     No  Thur   Lunch     2  0.266312
       232       11.61  3.39    Male     No   Sat  Dinner     2  0.291990
Yes    109       14.31  4.00  Female    Yes   Sat  Dinner     2  0.279525
       183       23.17  6.50    Male    Yes   Sun  Dinner     4  0.280535
       67         3.07  1.00  Female    Yes   Sat  Dinner     1  0.325733
       178        9.60  4.00  Female    Yes   Sun  Dinner     2  0.416667
       172        7.25  5.15    Male    Yes   Sun  Dinner     2  0.710345

In [20]: result = tips.groupby('smoker')['tip_pct'].describe()

In [21]: result
Out[21]:
smoker
No      count    151.000000
       mean       0.159328
       std        0.039910
       min        0.056797
       25%        0.136906
       50%        0.155625
       75%        0.185014
       max        0.291990
Yes     count     93.000000
       mean       0.163196
       std        0.085119
       min        0.035638
       25%        0.106771
       50%        0.153846
       75%        0.195059
       max        0.710345
Name: tip_pct, dtype: float64

禁止分组键

In [22]: tips.groupby('smoker', group_keys=False).apply(top)
C:\Users\yangfl\Anaconda3\Scripts\ipython-script.py:2: FutureWarning: by argumen
t to sort_index is deprecated, pls use .sort_values(by=...)
  import sys
Out[22]:
     total_bill   tip     sex smoker   day    time  size   tip_pct
88        24.71  5.85    Male     No  Thur   Lunch     2  0.236746
185       20.69  5.00    Male     No   Sun  Dinner     5  0.241663
51        10.29  2.60  Female     No   Sun  Dinner     2  0.252672
149        7.51  2.00    Male     No  Thur   Lunch     2  0.266312
232       11.61  3.39    Male     No   Sat  Dinner     2  0.291990
109       14.31  4.00  Female    Yes   Sat  Dinner     2  0.279525
183       23.17  6.50    Male    Yes   Sun  Dinner     4  0.280535
67         3.07  1.00  Female    Yes   Sat  Dinner     1  0.325733
178        9.60  4.00  Female    Yes   Sun  Dinner     2  0.416667
172        7.25  5.15    Male    Yes   Sun  Dinner     2  0.710345

3.2 分位数和桶分析

分位数

In [23]: frame = DataFrame({'data1':np.random.randn(1000),'data2':np.random.ran
    ...: dn(1000)})

In [24]: factor = pd.cut(frame.data1, 4)  #分成四份

In [25]: factor[:5]
Out[25]:
0    (-0.122, 1.486]
1    (-1.73, -0.122]
2    (1.486, 3.0932]
3    (-1.73, -0.122]
4    (-0.122, 1.486]
Name: data1, dtype: category
Categories (4, object): [(-3.344, -1.73] < (-1.73, -0.122] < (-0.122, 1.486] < (
1.486, 3.0932]]

In [26]: def get_stats(group):
    ...:     return {"min":group.min(),
    ...:     'max':group.max(),
    ...:     'count':group.count(),
    ...:     'mean':group.mean()}
    ...:

In [27]: grouped = frame.data2.groupby(factor)

In [28]: grouped.apply(get_stats).unstack()
Out[28]:
                 count       max      mean       min
data1
(-3.344, -1.73]   38.0  2.219138  0.068483 -2.613319
(-1.73, -0.122]  421.0  3.024953 -0.043523 -2.733123
(-0.122, 1.486]  472.0  3.882440 -0.023289 -4.073087
(1.486, 3.0932]   69.0  2.520630  0.067271 -2.593762

桶分析

In [29]: grouping = pd.qcut(frame.data1, 10, labels=False)

In [30]: grouped= frame.data2.groupby(grouping)

In [31]: grouped.apply(get_stats).unstack()
Out[31]:
       count       max      mean       min
data1
0      100.0  2.219138 -0.140476 -2.613319
1      100.0  2.877679  0.047305 -2.483696
2      100.0  2.716929 -0.043014 -2.446612
3      100.0  2.387355 -0.080337 -2.733123
4      100.0  3.024953  0.031980 -2.157749
5      100.0  2.617329  0.011424 -2.048438
6      100.0  2.362811 -0.074931 -2.942770
7      100.0  3.882440  0.024096 -3.038111
8      100.0  2.587888 -0.014792 -4.073087
9      100.0  2.520630  0.018031 -2.613203

3.3 示例:用特定于分组的值填充缺失值

In [32]: s = Series(np.random.randn(6))

In [33]: s[::2] = np.nan

In [34]: s
Out[34]:
0         NaN
1    0.818362
2         NaN
3    0.624027
4         NaN
5   -1.260203
dtype: float64

In [35]: s.fillna(s.mean())  # 使用平均值填充空值
Out[35]:
0    0.060729
1    0.818362
2    0.060729
3    0.624027
4    0.060729
5   -1.260203
dtype: float64

3.4 示例:随机采样和排列

一副扑克牌:

In [36]: suits = ['H','S','C','D']  # 四个花色

In [42]: card_val = (list(range(1,11))+[10]*3) * 4  # 21点

# A ~ K
In [44]: base_names = ['A'] + list(range(2,11)) + ['J','Q','K']

In [45]: cards =[]

In [46]: for suit in suits:
    ...:     cards.extend(str(num)+suit for num in base_names)
    ...:
# 做一个Series
In [47]: deck = Series(card_val, index=cards)

In [48]: deck
Out[48]:
AH      1
2H      2
3H      3
4H      4
5H      5
6H      6
...

随机抽出5张牌

In [49]: def draw(deck,n=5):
    ...:     return deck.take(np.random.permutation(len(deck))[:n])
    ...:
# np.random.permutation 随机排序
In [50]: draw(deck)  # 随机抽出5张牌
Out[50]:
7C      7
2D      2
10D    10
2H      2
AD      1
dtype: int64

随机每种花色抽取两张牌

In [51]: get_suit = lambda card:card[-1]

In [52]: deck.groupby(get_suit).apply(draw, 2)
Out[52]:
C  2C      2
   9C      9
D  8D      8
   6D      6
H  AH      1
   10H    10
S  3S      3
   6S      6
dtype: int64

3.5 示例:分组加权平均数和相关系数

In [54]: df = DataFrame({'category':list('aaaabbbb'),
    ...: 'data':np.random.randn(8),
    ...: 'weights':np.random.rand(8)})

In [55]: df
Out[55]:
  category      data   weights
0        a -0.239087  0.172904
1        a -0.615509  0.511186
2        a  1.015113  0.778079
3        a -0.726262  0.284090
4        b  0.443518  0.189707
5        b  1.025131  0.764808
6        b -1.725867  0.108514
7        b  0.613947  0.696840

In [56]: grouped = df.groupby('category')  # 分组

# 加权平均值
In [57]: get_wavg = lambda g: np.average(g['data'],weights=g['weights'])

In [58]: grouped.apply(get_wavg)
Out[58]:
category
a    0.130299
b    0.629996
dtype: float64
posted @ 2017-02-16 21:04  felo  阅读(814)  评论(0编辑  收藏  举报