二、 查看数据

详情请参阅：基础。

1、 查看DataFrame中头部和尾部的行：

  1. In [14]: df.head()
  2. Out[14]: 
  3.                    A         B         C         D
  4. 2013-01-01  0.469112 -0.282863 -1.509059 -1.135632
  5. 2013-01-02  1.212112 -0.173215  0.119209 -1.044236
  6. 2013-01-03 -0.861849 -2.104569 -0.494929  1.071804
  7. 2013-01-04  0.721555 -0.706771 -1.039575  0.271860
  8. 2013-01-05 -0.424972  0.567020  0.276232 -1.087401
  10. In [15]: df.tail(3)
  11. Out[15]: 
  12.                    A         B         C         D
  13. 2013-01-04  0.721555 -0.706771 -1.039575  0.271860
  14. 2013-01-05 -0.424972  0.567020  0.276232 -1.087401
  15. 2013-01-06 -0.673690  0.113648 -1.478427  0.524988

2、 显示索引、列和底层的 numpy 数据：

  1. In [16]: df.index
  2. Out[16]: 
  3. DatetimeIndex(['2013-01-01', '2013-01-02', '2013-01-03', '2013-01-04',
  4.                '2013-01-05', '2013-01-06'],
  5.               dtype='datetime64[ns]', freq='D')
  7. In [17]: df.columns
  8. Out[17]: Index([u'A', u'B', u'C', u'D'], dtype='object')
  10. In [18]: df.values
  11. Out[18]: 
  12. array([[ 0.4691, -0.2829, -1.5091, -1.1356],
  13.        [ 1.2121, -0.1732,  0.1192, -1.0442],
  14.        [-0.8618, -2.1046, -0.4949,  1.0718],
  15.        [ 0.7216, -0.7068, -1.0396,  0.2719],
  16.        [-0.425 ,  0.567 ,  0.2762, -1.0874],
  17.        [-0.6737,  0.1136, -1.4784,  0.525 ]])

3、 describe()函数对于数据的快速统计汇总：

  1. In [19]: df.describe()
  2. Out[19]: 
  3.               A         B         C         D
  4. count  6.000000  6.000000  6.000000  6.000000
  5. mean   0.073711 -0.431125 -0.687758 -0.233103
  6. std    0.843157  0.922818  0.779887  0.973118
  7. min   -0.861849 -2.104569 -1.509059 -1.135632
  8. 25%   -0.611510 -0.600794 -1.368714 -1.076610
  9. 50%    0.022070 -0.228039 -0.767252 -0.386188
  10. 75%    0.658444  0.041933 -0.034326  0.461706
  11. max    1.212112  0.567020  0.276232  1.071804

4、 对数据的转置：

  1. In [20]: df.T
  2. Out[20]: 
  3.    2013-01-01  2013-01-02  2013-01-03  2013-01-04  2013-01-05  2013-01-06
  4. A    0.469112    1.212112   -0.861849    0.721555   -0.424972   -0.673690
  5. B   -0.282863   -0.173215   -2.104569   -0.706771    0.567020    0.113648
  6. C   -1.509059    0.119209   -0.494929   -1.039575    0.276232   -1.478427
  7. D   -1.135632   -1.044236    1.071804    0.271860   -1.087401    0.524988

5、 按轴进行排序

  1. In [21]: df.sort_index(axis=1, ascending=False)
  2. Out[21]: 
  3.                    D         C         B         A
  4. 2013-01-01 -1.135632 -1.509059 -0.282863  0.469112
  5. 2013-01-02 -1.044236  0.119209 -0.173215  1.212112
  6. 2013-01-03  1.071804 -0.494929 -2.104569 -0.861849
  7. 2013-01-04  0.271860 -1.039575 -0.706771  0.721555
  8. 2013-01-05 -1.087401  0.276232  0.567020 -0.424972
  9. 2013-01-06  0.524988 -1.478427  0.113648 -0.673690

6、 按值进行排序

  1. In [22]: df.sort_values(by='B')
  2. Out[22]: 
  3.                    A         B         C         D
  4. 2013-01-03 -0.861849 -2.104569 -0.494929  1.071804
  5. 2013-01-04  0.721555 -0.706771 -1.039575  0.271860
  6. 2013-01-01  0.469112 -0.282863 -1.509059 -1.135632
  7. 2013-01-02  1.212112 -0.173215  0.119209 -1.044236
  8. 2013-01-06 -0.673690  0.113648 -1.478427  0.524988
  9. 2013-01-05 -0.424972  0.567020  0.276232 -1.087401