切片一個數據集：

In [122]: df = pd.DataFrame(data={'Case' : ['A','A','A','B','A','A','B','A','A'],
   .....:                         'Data' : np.random.randn(9)})
   .....: 

In [123]: dfs = list(zip(*df.groupby((1*(df['Case']=='B')).cumsum().rolling(window=3,min_periods=1).median())))[-1]

In [124]
 
: dfs[0]
Out[124]: 
  Case      Data
0    A  0.174068
1    A -0.439461
2    A -0.741343
3    B -0.079673

In [125]: dfs[1]
Out[125]: 
  Case      Data
4    A -0.922875
5    A  0.303638
6    B -0.917368

In [126]: dfs[2]
Out[126]: 
  Case      Data
7    A -1.624062
8    A -0.758514

資料透視表：

In [127]: df = pd.DataFrame(
 
data={'Province' : ['ON','QC','BC','AL','AL','MN','ON'],
   .....:                          'City' : ['Toronto','Montreal','Vancouver','Calgary','Edmonton','Winnipeg','Windsor'],
   .....:                          'Sales' : [13,6,16,8,4,3,1]})
   .....: 

In [128]: table = pd.pivot_table(df,values=[
 
'Sales'],index=['Province'],columns=['City'],aggfunc=np.sum,margins=True)

In [129]: table.stack('City')
Out[129]: 
                    Sales
Province City            
AL       All         12.0
         Calgary      8.0
         Edmonton     4.0
BC       All         16.0
         Vancouver   16.0
MN       All          3.0
         Winnipeg     3.0
...                   ...
All      Calgary      8.0
         Edmonton     4.0
         Montreal     6.0
         Toronto     13.0
         Vancouver   16.0
         Windsor      1.0
         Winnipeg     3.0

[20 rows x 1 columns]

In [130]: grades = [48,99,75,80,42,80,72,68,36,78]

In [131]: df = pd.DataFrame( {'ID': ["x%d" % r for r in range(10)],
   .....:                     'Gender' : ['F', 'M', 'F', 'M', 'F', 'M', 'F', 'M', 'M', 'M'],
   .....:                     'ExamYear': ['2007','2007','2007','2008','2008','2008','2008','2009','2009','2009'],
   .....:                     'Class': ['algebra', 'stats', 'bio', 'algebra', 'algebra', 'stats', 'stats', 'algebra', 'bio', 'bio'],
   .....:                     'Participated': ['yes','yes','yes','yes','no','yes','yes','yes','yes','yes'],
   .....:                     'Passed': ['yes' if x > 50 else 'no' for x in grades],
   .....:                     'Employed': [True,True,True,False,False,False,False,True,True,False],
   .....:                     'Grade': grades})
   .....: 

In [132]: df.groupby('ExamYear').agg({'Participated': lambda x: x.value_counts()['yes'],
   .....:                     'Passed': lambda x: sum(x == 'yes'),
   .....:                     'Employed' : lambda x : sum(x),
   .....:                     'Grade' : lambda x : sum(x) / len(x)})
   .....: 
Out[132]: 
          Participated  Passed  Employed      Grade
ExamYear                                           
2007                 3       2         3  74.000000
2008                 3       3         0  68.500000
2009                 3       2         2  60.666667

以年度資料形式展現：

In [133]: df = pd.DataFrame({'value': np.random.randn(36)},
   .....:                   index=pd.date_range('2011-01-01', freq='M', periods=36))
   .....: 

In [134]: pd.pivot_table(df, index=df.index.month, columns=df.index.year,
   .....:                values='value', aggfunc='sum')
   .....: 
Out[134]: 
        2011      2012      2013
1  -0.560859  0.120930  0.516870
2  -0.589005 -0.210518  0.343125
3  -1.070678 -0.931184  2.137827
4  -1.681101  0.240647  0.452429
5   0.403776 -0.027462  0.483103
6   0.609862  0.033113  0.061495
7   0.387936 -0.658418  0.240767
8   1.815066  0.324102  0.782413
9   0.705200 -1.403048  0.628462
10 -0.668049 -0.581967 -0.880627
11  0.242501 -1.233862  0.777575
12  0.313421 -3.520876 -0.779367