# 创建数据

```python
import pandas as pd
import numpy as np
```

## 创建一个Series数据

```python
s = pd.Series([1,2,3,np.nan,5])
print(s)
'''
0    1.0
1    2.0
2    3.0
3    NaN
4    5.0
dtype: float64
'''
```

## 时间类型

```python
date = pd.date_range('20170101',periods=7)
print(date)
'''
DatetimeIndex(['2017-01-01', '2017-01-02', '2017-01-03', '2017-01-04',
               '2017-01-05', '2017-01-06', '2017-01-07'],
              dtype='datetime64[ns]', freq='D')
'''
```

## 表格类型，index是行的索引

```python
s=pd.DataFrame(np.random.rand(7,4),index=date,columns=['A','B','C','D'])
s

'''
    A    B    C    D
2017-01-01    0.246755    0.886655    0.024619    0.078501
2017-01-02    0.095470    0.012911    0.723686    0.217888
2017-01-03    0.059372    0.635223    0.073233    0.531136
2017-01-04    0.437067    0.454805    0.909538    0.603861
2017-01-05    0.501499    0.651960    0.022764    0.519166
2017-01-06    0.148038    0.564203    0.051323    0.001642
2017-01-07    0.375003    0.318459    0.376165    0.712314

'''
```

## 多类型表格

```python
# 要么一个长度，数据会被扩展到全部；要么等于最大长度（4）
df2 = pd.DataFrame({ 'A' : 1.,
                     'B' : pd.Timestamp('20170102'),
                     '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' })

print(df2)
'''
 A          B    C  D      E    F
0  1.0 2017-01-02  1.0  3   test  foo
1  1.0 2017-01-02  1.0  3  train  foo
2  1.0 2017-01-02  1.0  3   test  foo
3  1.0 2017-01-02  1.0  3  train  foo
'''
```


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