python数据分析之pandas库的DataFrame应用二

　　本节介绍Series和DataFrame中的数据的基本手段

1. 重新索引

　　pandas对象的一个重要方法就是reindex,作用是创建一个适应新索引的新对象

'''
Created on 2016-8-10
@author: xuzhengzhu
'''
'''
Created on 2016-8-10
@author: xuzhengzhu
'''
from pandas import  *

print "--------------obj result:-----------------"
obj=Series([4.5,7.2,-5.3,3.6],index=['d','b','a','c'])
print obj

print "--------------obj2 result:-----------------"
obj2=obj.reindex(['a','b','c','d','e'])
print obj2

print "--------------obj3 result:-----------------"
obj3=obj.reindex(['a','b','c','d','e'],fill_value=0)
print obj3

reindex

#reindex对索引值进行重排，如果当前索引值不存在，就引入缺失值
 #可以指定fill_value=0来进行缺失值的替换

--------------obj result:-----------------
d    4.5
b    7.2
a   -5.3
c    3.6
dtype: float64
--------------obj2 result:-----------------
a   -5.3
b    7.2
c    3.6
d    4.5
e    NaN
dtype: float64
--------------obj3 result:-----------------
a   -5.3
b    7.2
c    3.6
d    4.5
e    0.0
dtype: float64

reindex_index

　　2.插值

　　对于时间序列这样的有序数据，重新索引时可能需要做一些插值处理，method选项即可达到此目的：

对于时间序列这样的有序数据，重新索引时可能需要做一些插值处理，method选项即可达到此目的：

method参数介绍
参数	说明
ffill或pad	前向填充
bfill或backfill	后向填充

'''
Created on 2016-8-10
@author: xuzhengzhu
'''
from pandas import  *

print "--------------obj3 result:-----------------"
obj3=Series(['blue','red','yellow'],index=[0,2,4])
print obj3

print "--------------obj4 result:-----------------"
obj4=obj3.reindex(range(6),method='ffill')

print obj4

ffill前向填充

--------------obj3 result:-----------------
0      blue
2       red
4    yellow
dtype: object
--------------obj4 result:-----------------
0      blue
1      blue
2       red
3       red
4    yellow
5    yellow
dtype: object

ffill结果：

　　对于DataFrame数据类型，reindex可以修改行与列索引，但如果仅传入一个序列，则优先重新索引行：

'''
Created on 2016-8-10
@author: xuzhengzhu
'''
from pandas import  *

print "--------------frame result:-----------------"
frame=DataFrame(np.arange(9).reshape((3,3)),index=['a','c','d'],columns=['ohio','texas','california'])
print frame

print "--------------frame2 result:-----------------"
frame2=frame.reindex(['a','b','c','d'])
print frame2

print "--------------frame3 result:-----------------"
frame3=frame.reindex(columns=['texas','utah','california'])
print frame3

print "--------------frame3 result:-----------------"
frame4=frame.ix[['a','b','c','d'],['texas','utah','california']]
print frame4

reindex_dataframe

--------------frame result:-----------------
   ohio  texas  california
a     0      1           2
c     3      4           5
d     6      7           8
--------------frame2 result:-----------------
   ohio  texas  california
a   0.0    1.0         2.0
b   NaN    NaN         NaN
c   3.0    4.0         5.0
d   6.0    7.0         8.0
--------------frame3 result:-----------------
   texas  utah  california
a      1   NaN           2
c      4   NaN           5
d      7   NaN           8
--------------frame3 result:-----------------
   texas  utah  california
a    1.0   NaN         2.0
b    NaN   NaN         NaN
c    4.0   NaN         5.0
d    7.0   NaN         8.0

reindex结果：

　　3.指定轴上的项

'''
Created on 2016-8-10
@author: xuzhengzhu
'''
from pandas import  *

print "--------------Series drop item by index:-----------------"
obj=Series(np.arange(3,8),index=['a','b','c','d','e'])
print obj

obj1=obj.drop('c')
print obj1

print "--------------DataFrame drop item by index :-----------------"
frame=DataFrame(np.arange(9).reshape((3,3)),index=['a','c','d'],columns=['ohio','texas','california'])
print frame

frame1=frame.drop(['ohio'],axis=1)
print frame1

指定轴上的项

--------------Series drop item by index:-----------------
a    3
b    4
c    5
d    6
e    7
dtype: int32
a    3
b    4
d    6
e    7
dtype: int32
--------------DataFrame drop item by index :-----------------
   ohio  texas  california
a     0      1           2
c     3      4           5
d     6      7           8
   texas  california
a      1           2
c      4           5
d      7           8

drop_item

#对于DataFrame，可以删除任意轴上的索引值

　　4.索引，选取和过滤

　　Series利用标签的切片运算与普通的python切片运算不同，其末端是包含的，

　　DataFrame进行索引就是获取一个或多个列

'''
Created on 2016-8-10
@author: xuzhengzhu
'''
from pandas import  *

print "--------------DataFrame drop item by index :-----------------"
frame=DataFrame(np.arange(9).reshape((3,3)),index=['a','c','d'],columns=['ohio','texas','california'])
print frame

frame1=frame.drop(['ohio'],axis=1)
print frame1

print "--------------DataFrame filter item by index :-----------------"
#也可通过切片和布尔型来选取
print frame['ohio']
print frame[:2]
print frame[frame['ohio']>=3]

print "--------------DataFrame filter item by index :-----------------"
#在DateFrame上进行标签索引，引入ix： 注意行标签在前，列标签在后
print frame.ix['a',['ohio','texas']]

索引选取和过滤

--------------DataFrame drop item by index :-----------------
   ohio  texas  california
a     0      1           2
c     3      4           5
d     6      7           8
   texas  california
a      1           2
c      4           5
d      7           8
--------------DataFrame filter item by index :-----------------
a    0
c    3
d    6
Name: ohio, dtype: int32
   ohio  texas  california
a     0      1           2
c     3      4           5
   ohio  texas  california
c     3      4           5
d     6      7           8
--------------DataFrame filter item by index :-----------------
ohio     0
texas    1
Name: a, dtype: int32

结果：

 

　　5.算术运算和数据对齐

'''
Created on 2016-8-10
@author: xuzhengzhu
'''
from pandas import  *

print "--------------DataFrame drop item by index :-----------------"
s1=Series([7.3,-2.5,3.4,1.5],index=['a','c','d','e'])
s2=Series([-2.1,3.6,-1.5,4,3.1],index=['a','c','e','f','g'])
print s1+s2

算术运算和数据对齐

--------------DataFrame drop item by index :-----------------
a    5.2
c    1.1
d    NaN
e    0.0
f    NaN
g    NaN
dtype: float64

结果：

'''
Created on 2016-8-10
@author: xuzhengzhu
'''
from pandas import  *

print "--------------DataFrame drop item by index :-----------------"
df1=DataFrame(np.arange(9).reshape((3,3)),columns=list('bcd'),index=['ohio','texas','colorado'])
df2=DataFrame(np.arange(12).reshape((4,3)),columns=list('bde'),index=['utah','ohio','texas','oregon'])

print df1
print "--------------------"

print df2

#只返回行列均匹配的数值
print "-------df1+df2-------------"
print df1+df2

#在对不同的索引对象进行算术运算时，当一个对象中某个轴标签在另一个对象中找不到时填充一个特殊值
print "-------df3-------------"
df3=df1.add(df2,fill_value=0)
print df3

对齐操作

--------------DataFrame drop item by index :-----------------
          b  c  d
ohio      0  1  2
texas     3  4  5
colorado  6  7  8
--------------------
        b   d   e
utah    0   1   2
ohio    3   4   5
texas   6   7   8
oregon  9  10  11
-------df1+df2-------------
            b   c     d   e
colorado  NaN NaN   NaN NaN
ohio      3.0 NaN   6.0 NaN
oregon    NaN NaN   NaN NaN
texas     9.0 NaN  12.0 NaN
utah      NaN NaN   NaN NaN
-------df3-------------
            b    c     d     e
colorado  6.0  7.0   8.0   NaN
ohio      3.0  1.0   6.0   5.0
oregon    9.0  NaN  10.0  11.0
texas     9.0  4.0  12.0   8.0
utah      0.0  NaN   1.0   2.0

结果：