7. 矩阵转置

 In[62]: college = pd.read_csv('data/college.csv', index_col='INSTNM')
         college_ugds_ = college.filter(like='UGDS_')
         college_ugds_.head()
Out[62]:

#  count()返回非缺失值的个数
 In[63]: college_ugds_.count()
Out[63]: UGDS_WHITE    6874
         UGDS_BLACK    6874
         UGDS_HISP     6874
         UGDS_ASIAN    6874
                       ... 
         UGDS_NHPI     6874
         UGDS_2MOR     6874
         UGDS_NRA      6874
         UGDS_UNKN     6874
         Length: 9, dtype: int64

#  axis默认设为0
 In[64]: college_ugds_.count(axis=0)
Out[64]: UGDS_WHITE    6874
         UGDS_BLACK    6874
         UGDS_HISP     6874
         UGDS_ASIAN    6874
                       ... 
         UGDS_NHPI     6874
         UGDS_2MOR     6874
         UGDS_NRA      6874
         UGDS_UNKN     6874
         Length: 9, dtype: int64

#  等价于axis='index'
 In[65]: college_ugds_.count(axis='index')
Out[65]: UGDS_WHITE    6874
         UGDS_BLACK    6874
         UGDS_HISP     6874
         UGDS_ASIAN    6874
                       ... 
         UGDS_NHPI     6874
         UGDS_2MOR     6874
         UGDS_NRA      6874
         UGDS_UNKN     6874
         Length: 9, dtype: int64

#  统计每行的非缺失值个数
 In[66]: college_ugds_.count(axis='columns').head()
Out[66]: INSTNM
         Alabama A & M University               9
         University of Alabama at Birmingham    9
         Amridge University                     9
         University of Alabama in Huntsville    9
         Alabama State University               9
         dtype: int64

#  除了统计每行的非缺失值个数，也可以求和加以确认
 In[67]: college_ugds_.sum(axis='columns').head()
Out[67]: INSTNM
         Alabama A & M University               1.0000
         University of Alabama at Birmingham    0.9999
         Amridge University                     1.0000
         University of Alabama in Huntsville    1.0000
         Alabama State University               1.0000
         dtype: float64

#  用中位数了解每列的分布
 In[68]: college_ugds_.median(axis='index')
Out[68]: UGDS_WHITE    0.55570
         UGDS_BLACK    0.10005
         UGDS_HISP     0.07140
         UGDS_ASIAN    0.01290
                        ...   
         UGDS_NHPI     0.00000
         UGDS_2MOR     0.01750
         UGDS_NRA      0.00000
         UGDS_UNKN     0.01430
         Length: 9, dtype: float64

更多

#  使用累积求和cumsum()可以很容易看到白人、黑人、西班牙裔的比例
 In[69]: college_ugds_cumsum = college_ugds_.cumsum(axis=1)
         college_ugds_cumsum.head()
Out[69]:

#  UGDS_HISP一列降序排列
 In[70]: college_ugds_cumsum.sort_values('UGDS_HISP', ascending=False)
Out[70]: