import pandas as pd

import numpy as np

import matplotlib.pyplot as plt

对象创建
创建一个series通过传递值得列表，让pandas创建一个默认得整数索引
s=pd.Series([1,3,5,np.nan,6,8])

s

0 1.0
1 3.0
2 5.0
3 NaN
4 6.0
5 8.0
dtype: float64

#DataFrame通过传递带有日期时间索引和标记列得numpy数组创建

dates=pd.date_range('20130101',periods=6)

dates

DatetimeIndex(['2013-01-01', '2013-01-02', '2013-01-03', '2013-01-04',
'2013-01-05', '2013-01-06'],
dtype='datetime64[ns]', freq='D')

df=pd.DataFrame(np.random.rand(6,4),index=dates,columns=list('ABCD'))

df

2013-01-010.0025090.3058060.7494110.015479

2013-01-020.6428160.1269930.3798500.099668

2013-01-030.8174320.6920700.7730930.401504

2013-01-040.3149290.6678720.3187770.975938

2013-01-050.8721480.6668280.5162990.046083

2013-01-060.4801560.3089530.0441420.480998

colunms=['A','B','C','D']

df=pd.DataFrame(np.random.rand(6,4),index=dates,columns=colunms)

df

当然，DataFrame通过传递可以转换为类似系列的对象的dict来创建。

In [10]: df2 = pd.DataFrame({ 'A' : 1.,

....: 'B' : pd.Timestamp('20130102'),

....: 'C' : pd.Series(1,index=list(range(4)),dtype='float32'),

....: 'D' : np.array([3] * 4,dtype='int32'),

....: 'E' : pd.Categorical(["test","train","test","train"]),

....: 'F' : 'foo' })

....:

In [11]: df2

Out[11]:

A B C D E F

0 1.0 2013-01-02 1.0 3 test foo

1 1.0 2013-01-02 1.0 3 train foo

2 1.0 2013-01-02 1.0 3 test foo

3 1.0 2013-01-02 1.0 3 train foo

​