Pandas 数据结构（Series，DataFrame）

In [1]: import numpy as np

In [2]: import pandas as pd

1、Series

In [3]: s = pd.Series(np.random.randn(5), index=['a', 'b', 'c', 'd', 'e'])

In [4]: s
Out[4]: 
a   -2.7828
b    0.4264
c   -0.6505
d    1.1465
e   -0.6631
dtype: float64

In [6]: pd.Series(np.random.randn(5))
Out[6]: 
0    0.2939
1   -0.4049
2    1.1665
3    0.8420
4    0.5398

数据也可以为字典dict数据

In [7]: d = {'a' : 0., 'b' : 1., 'c' : 2.}

需要注意的是，如果指定index，则将会替代字典中的key作为index

以下为series的基本操作

In [11]: s[0]
Out[11]: -2.7827595933769937

In [12]: s[:3]
Out[12]: 
a   -2.7828
b    0.4264
c   -0.6505
dtype: float64

In [13]: s[s > s.median()]
Out[13]: 
b    0.4264
d    1.1465
dtype: float64

In [14]: s[[4, 3, 1]]
Out[14]: 
e   -0.6631
d    1.1465
b    0.4264
dtype: float64

In [15]: np.exp(s)
Out[15]: 
a    0.0619
b    1.5318
c    0.5218
d    3.1472
e    0.5153
dtype: float64

In [16]: s['a']
Out[16]: -2.7827595933769937

In [19]: 'e' in s
Out[19]: True

In [20]: 'f' in s
Out[20]: False

Series和ndarray的一个主要区别是Series可以基于标签进行操作，可以自动匹配标签，如下所示：

In [26]: s[1:] + s[:-1]
Out[26]: 
a       NaN
b    0.8529
c   -1.3010
d    2.2930
e       NaN
dtype: float64

（其中s[1:]为bcde行， s[:-1]为abcd行）

2、DataFrame

是pandas中最常用的数据结构

声明dataframe：

In [32]: d = {'one' : pd.Series([1., 2., 3.], index=['a', 'b', 'c']),
   ....:      'two' : pd.Series([1., 2., 3., 4.], index=['a', 'b', 'c', 'd'])}
   ....: 

In [33]: df = pd.DataFrame(d)

In [34]: df
Out[34]: 
one  two
a  1.0  1.0
b  2.0  2.0
c  3.0  3.0
d  NaN  4.0

In [39]: d = {'one' : [1., 2., 3., 4.],
 ....: 'two' : [4., 3., 2., 1.]}
   ....: 

In [40]: pd.DataFrame(d)

In [41]: pd.DataFrame(d, index=['a', 'b', 'c', 'd'])
结果同上

In [47]: data2 = [{'a': 1, 'b': 2}, {'a': 5, 'b': 10, 'c': 20}]

In [48]: pd.DataFrame(data2)
Out[48]: 
   a   b     c
0  1   2   NaN
1  5  10  20.0

In [81]: index = pd.date_range('1/1/2000', periods=8)

In [82]: df = pd.DataFrame(np.random.randn(8, 3), index=index, columns=list('ABC'))
In [83]: df
Out[83]: 
                 A       B       C
2000-01-01  0.0627 -0.0284  0.4436
2000-01-02 -0.2688 -1.5776  1.8502
2000-01-03  0.6381 -0.5566 -0.0712
2000-01-04 -0.5114  0.1563 -1.0756
2000-01-05  1.6636 -0.4377 -0.0773
2000-01-06  0.0292  0.1790  1.7401
2000-01-07 -0.7290 -0.8980 -0.3142
2000-01-08 -0.0481 -0.8756  0.1691

以下为dataframe的基本操作

1、选取／新增列操作

In [56]: df['one']
Out[56]: 
a    1.0
b    2.0
c    3.0
d    NaN
Name: one, dtype: float64

In [57]: df['three'] = df['one'] * df['two']

新增flag列
In [58]: df['flag'] = df['one'] > 2

In [59]: df
Out[59]: 
   one  two  three   flag
a  1.0  1.0    1.0  False
b  2.0  2.0    4.0  False
c  3.0  3.0    9.0   True
d  NaN  4.0    NaN  False

2、删除／pop操作

In [60]: del df['two']

In [61]: three = df.pop('three')

3、 插入操作

insert插入到指定位置

In [67]: df.insert(1, 'bar', df['one'])

In [68]: df
Out[68]: 
   one  bar   flag  foo  one_trunc
a  1.0  1.0  False  bar        1.0
b  2.0  2.0  False  bar        2.0
c  3.0  3.0   True  bar        NaN
d  NaN  NaN  False  bar        NaN

assign得到的是一个新的dataframe，而不是在原dataframe上操作

In [71]: (iris.assign(sepal_ratio = iris['SepalWidth'] / iris['SepalLength']))

In [72]: iris.assign(sepal_ratio = lambda x: (x['SepalWidth'] /
   ....:                                      x['SepalLength'])).head()
   ....: 

In [73]: (iris.query('SepalLength > 5')
   ....:      .assign(SepalRatio = lambda x: x.SepalWidth / x.SepalLength,
   ....:              PetalRatio = lambda x: x.PetalWidth / x.PetalLength)
   ....:      .plot(kind='scatter', x='SepalRatio', y='PetalRatio'))

4、计算

In [86]: df * 5 + 2

In [87]: 1 / df

In [88]: df ** 4

In [95]: df[:5].T  ＃转置

与numpy结合进行计算
In [96]: np.exp(df)

In [97]: np.asarray(df)
Out[97]: 
array([[ 0.0627, -0.0284, 0.4436],
 [-0.2688, -1.5776, 1.8502],
 [ 0.6381, -0.5566, -0.0712],
 [-0.5114, 0.1563, -1.0756],
 [ 1.6636, -0.4377, -0.0773],
 [ 0.0292, 0.179 , 1.7401],
 [-0.729 , -0.898 , -0.3142],
 [-0.0481, -0.8756, 0.1691]])