Pandas 数据结构

first:

import numpy as np

import pandas as pd

Series

1.支持任意类型的一维标签数据，分为数据部分和轴标签部分（索引）
2.可以从list,dict,ndarray,scalar value等数据类型来创建
3.Series的取值和向量运算

From ndarray

python s = pd.Series(np.random.randn(5),index = ‘a b c d e‘.split(‘ ‘))

python s

a    0.299422
b    0.593082
c   -2.120001
d   -2.062322
e   -1.493702
dtype: float64

python s.index

Index([‘a‘, ‘b‘, ‘c‘, ‘d‘, ‘e‘], dtype=‘object‘)

python s.name = ‘test‘

python s.name

‘test‘

From dict

```python d = {} index = ‘abcde‘ for i in range(5): d[index[i]] = i

pd.Series(d) ```

a    0
b    1
c    2
d    3
e    4
dtype: int64

python pd.Series(d,index = ‘e d c b f‘.split(‘ ‘))

e    4.0
d    3.0
c    2.0
b    1.0
f    NaN
dtype: float64

From scalar value

python pd.Series(5.,index = [‘a‘,‘b‘,‘c‘])

a    5.0
b    5.0
c    5.0
dtype: float64

ndarray-like operation

python s[0]

0.29942203654066651

python s[:3]

a    0.299422
b    0.593082
c   -2.120001
Name: test, dtype: float64

python s[s>0]

a    0.299422
b    0.593082
Name: test, dtype: float64

python s[[1,3]]

b    0.593082
d   -2.062322
Name: test, dtype: float64


python np.exp(s)

a    1.349079
b    1.809557
c    0.120032
d    0.127158
e    0.224540
Name: test, dtype: float64

dict-like operation

python s[‘a‘]

0.29942203654066651

python s[‘a‘] = 1.

python s

a    1.000000
b    0.593082
c   -2.120001
d   -2.062322
e   -1.493702
Name: test, dtype: float64

python ‘a‘ in s

True

python s.get(‘a‘)

1.0

python s.get(‘f‘,1)

1

Vectorized operations and label alignment

python s + s

a    2.000000
b    1.186164
c   -4.240002
d   -4.124643
e   -2.987405
Name: test, dtype: float64

python s*2

a    2.000000
b    1.186164
c   -4.240002
d   -4.124643
e   -2.987405
Name: test, dtype: float64

python s[1:] + s[:-1]

a         NaN
b    1.186164
c   -4.240002
d   -4.124643
e         NaN
Name: test, dtype: float64