本节介绍Series和DataFrame中的数据的基本手段
- 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：