Pandas

是一种构建于Numpy的高级数据结构和精巧工具，快速简单的处理数据。

支持自动或明确的数据对齐的带有标签轴的数据结构

整合的时间序列功能

以相同的数据结构来处理时间序列和非时间序列

支持传递元数据(坐标轴标签)的算术运算

>>> import pandas as pd
>>> a=pd.Series([1,3,5,np.nan,6,8])#生成一个序列,np.nan是生成一个空的字符
>>> a
0    1.0
1    3.0
2    5.0
3    NaN
4    6.0
5    8.0
dtype: float64

>>> dates =pd.date_range('20160102',periods=6)##日期，周期是6，periods
>>> dates
DatetimeIndex(['2016-01-02', '2016-01-03', '2016-01-04', '2016-01-05',
               '2016-01-06', '2016-01-07'],
              dtype='datetime64[ns]', freq='D')

>>> df =pd.DataFrame(np.random.randn(6,4),index=dates,columns=list('ABCD'))
>>> df
                   A         B         C         D
2016-01-02  0.461499 -0.935497 -1.008590 -0.438713
2016-01-03 -0.566233 -1.614755  1.207207 -1.286580
2016-01-04  2.002371  1.333078  0.264322  1.215232
2016-01-05  0.242900 -1.508960  1.651483  0.229316
2016-01-06 -0.365214 -0.518801 -0.141358 -0.051713
2016-01-07  0.539730 -0.235725  1.101934 -1.360333

>>> df.head()
                   A         B         C         D
2016-01-02  0.461499 -0.935497 -1.008590 -0.438713
2016-01-03 -0.566233 -1.614755  1.207207 -1.286580
2016-01-04  2.002371  1.333078  0.264322  1.215232
2016-01-05  0.242900 -1.508960  1.651483  0.229316
2016-01-06 -0.365214 -0.518801 -0.141358 -0.051713
>>> df.tail()
                   A         B         C         D
2016-01-03 -0.566233 -1.614755  1.207207 -1.286580
2016-01-04  2.002371  1.333078  0.264322  1.215232
2016-01-05  0.242900 -1.508960  1.651483  0.229316
2016-01-06 -0.365214 -0.518801 -0.141358 -0.051713
2016-01-07  0.539730 -0.235725  1.101934 -1.360333
>>> df.T##行列的转置
   2016-01-02  2016-01-03  2016-01-04  2016-01-05  2016-01-06  2016-01-07
A    0.461499   -0.566233    2.002371    0.242900   -0.365214    0.539730
B   -0.935497   -1.614755    1.333078   -1.508960   -0.518801   -0.235725
C   -1.008590    1.207207    0.264322    1.651483   -0.141358    1.101934
D   -0.438713   -1.286580    1.215232    0.229316   -0.051713   -1.360333

>>> df.sort_values(by='B')##以B这列进行排列
                   A         B         C         D
2016-01-03 -0.566233 -1.614755  1.207207 -1.286580
2016-01-05  0.242900 -1.508960  1.651483  0.229316
2016-01-02  0.461499 -0.935497 -1.008590 -0.438713
2016-01-06 -0.365214 -0.518801 -0.141358 -0.051713
2016-01-07  0.539730 -0.235725  1.101934 -1.360333
2016-01-04  2.002371  1.333078  0.264322  1.215232