pandas（一）操作Series和DataFrame的基本功能

reindex：重新索引

pandas对象有一个重要的方法reindex，作用：创建一个适应新索引的新对象

以Series为例

 >>> series_obj = Series([4.5,1.3,5,-5.5],index=('a','b','c','d'))
 >>> series_obj
 a    4.5
 b    1.3
 c    5.0
 d   -5.5
 dtype: float64
 >>> obj2 = series_obj.reindex(['a','b','c','e','f'])
 >>> obj2
 a    4.5
 b    1.3
 c    5.0
 e    NaN
 f    NaN
 dtype: float64

重新索引的时候可以自动填充Nan值

 >>> obj3 = series_obj.reindex(['a','b','c','e','f'],fill_value='')
 >>> obj3
 a    4.5
 b    1.3
 c      5
 e      0
 f      0

对于时间序列这样的有序数据，重新索引可能需要做一些插值操作，reindex的method参数提供此功能。

method的可选选项有：

ffill或pad ：前向填充或搬运值

bfill或backfill：后向填充或搬运值

不存在前向或后项的行自动填充Nan

 >>> obj4 = Series(['red','blue','green'],index=[0,2,4])
 >>> obj4
 0      red
 2     blue
 4    green
 dtype: object
 >>> obj4.reindex(range(6),method='ffill')
 0      red
 1      red
 2     blue
 3     blue
 4    green
 5    green
 dtype: object

DataFrame的重新索引

只传入一个序列的时候，默认是重新索引“行”，可以用关键字参数来定义行索引（index）和列索引（columns）。

 >>> frame = DataFrame(np.arange(9).reshape((3,3)),index = ['a','b','c'],columns = ['Ohio','Texas',"Cali"])
 >>> frame2 = frame.reindex(['a','b','c','d'])
 >>> frame2
    Ohio  Texas  Cali
 a   0.0    1.0   2.0
 b   3.0    4.0   5.0
 c   6.0    7.0   8.0
 d   NaN    NaN   NaN

 >>> frame3 = frame.reindex(columns = ['Ohio','Texas','Cali','Wile'],index=['a','b','c','d'],fill_value=4)
 >>> frame3
    Ohio  Texas  Cali  Wile
 a     0      1     2     4
 b     3      4     5     4
 c     6      7     8     4
 d     4      4     4     4
 >>>

如果对DataFrame的行和列重新索引的时候，插值只能按行应用

如果利用ix的标签索功能，重新索引会变得更简洁

 >>> frame5 = frame.ix[['a','b','c','d'], ['Ohio','Texas','Cali','Wile']]
 >>> frame5
    Ohio  Texas  Cali  Wile
 a   0.0    1.0   2.0   NaN
 b   3.0    4.0   5.0   NaN
 c   6.0    7.0   8.0   NaN
 d   NaN    NaN   NaN   NaN

drop：丢弃指定轴上的项

>>> obj = Series(np.arange(5),index=['a','b','c','d','e'])
>>> obj
a    0
b    1
c    2
d    3
e    4
dtype: int32
>>> new_obj = obj.drop('b')
>>> new_obj
a    0
c    2
d    3
e    4

>>> new_obj2 = obj.drop(['b','c'])
>>> new_obj2
a    0
d    3
e    4
dtype: int32

#dataframe
>>> frame = DataFrame(np.arange(16).reshape((4,4)),index=['a','b','c','d'],columns=['one','two','three','four'])
>>> frame
   one  two  three  four
a    0    1      2     3
b    4    5      6     7
c    8    9     10    11
d   12   13     14    15
>>> new_frame = frame.drop('a')
>>> new_frame
   one  two  three  four
b    4    5      6     7
c    8    9     10    11
d   12   13     14    15
>>> new_frame2 = frame.drop(['two','four'],axis = 1)
>>> new_frame2
   one  three
a    0      2
b    4      6
c    8     10
d   12     14

索引、选取和过滤

Series的索引，既可以是类似NumPy数组的索引，也可以是自定义的index

>>> obj
a    0
b    1
c    2
d    3
e    4
dtype: int32
>>> obj['a']
0
>>> obj[1]
1
注意：利用标签的切片运算，标签的右侧是封闭区间的，即包含末端。
>>> obj['a':'c']
a    0
b    1
c    2
dtype: int32
>>> obj[3:4]
d    3
dtype: int32
>>> obj[2:3]
c    2
dtype: int32
>>> obj[[3,1]]
d    3
b    1
dtype: int32
>>> obj[['a','c']]
a    0
c    2
dtype: int32
>>>

通过索引修改值

>>> obj[['b','d']] *=2
>>> obj
a    0
b    2
c    2
d    6
e    4
dtype: int32

dataframe的索引：

通过直接索引只能获取列

>>> frame
   one  two  three  four
a    0    1      2     3
b    4    5      6     7
c    8    9     10    11
d   12   13     14    15
>>> frame['a']
KeyError: 'a'
>>> frame['one']
a     0
b     4
c     8
d    12
Name: one, dtype: int32
>>> frame[['one','four']]
   one  four
a    0     3
b    4     7
c    8    11
d   12    15

通过切片或布尔型数组，选取的是行

>>> frame[1:3] #不闭合区间
   one  two  three  four
b    4    5      6     7
c    8    9     10    11
>>> frame[frame['three'] > 8]
   one  two  three  four
c    8    9     10    11
d   12   13     14    15
>>>

DataFrame的索引字段ix

>>> frame.ix['a'] #按照行索引
one      0
two      1
three    2
four     3
Name: a, dtype: int32
>>> frame.ix[['b','d']]
   one  two  three  four
b    4    5      6     7
d   12   13     14    15

>>> frame.ix[1]#同样是按照行索引
one      4
two      5
three    6
four     7
Name: b, dtype: int32
>>> frame.ix[1:3]
   one  two  three  four
b    4    5      6     7
c    8    9     10    11

>>> frame.ix[1:2,[2,3,1]]
   three  four  two
b      6     7    5
>>> frame.ix[1:3,[2,3,1]]
   three  four  two
b      6     7    5
c     10    11    9
>>> frame.ix[['b','d'],['one','three']]
   one  three
b    4      6
d   12     14
>>> frame.ix[['b','d'],[3,1,2]]
   four  two  three
b     7    5      6
d    15   13     14
>>> frame.ix[:,[2,3,1]]# 选取所有行
   three  four  two
a      2     3    1
b      6     7    5
c     10    11    9
d     14    15   13

>>> frame.ix[frame.three >5,:3]
one two three
b 4 5 6
c 8 9 10
d 12 13 14

算术运算和数据对齐

>>> s1 = Series([1.3,4.5,6.6,3.4],index=['a','b','c','d'])
>>> s2 = Series([1,2,3,4,5,6,7],index=['a','b','c','d','e','f','g'])
>>> s1+s2
a    2.3
b    6.5
c    9.6
d    7.4
e    NaN
f    NaN
g    NaN
dtype: float64
#不重叠的索引处引入缺失值
#DataFrame也是同理

再算术方法中填充缺失值

>>> df1 = DataFrame(np.arange(12).reshape((3,4)),columns=list('abcd'))
>>> df2 = DataFrame(np.arange(20).reshape((4,5)),columns=list('abcde'))
>>> df1+df2#普通的算术运算会产生缺失值
      a     b     c     d   e
0   0.0   2.0   4.0   6.0 NaN
1   9.0  11.0  13.0  15.0 NaN
2  18.0  20.0  22.0  24.0 NaN
3   NaN   NaN   NaN   NaN NaN
#用算术运算方法，可以填充缺失值
>>> df1.add(df2,fill_value=0)
      a     b     c     d     e
0   0.0   2.0   4.0   6.0   4.0
1   9.0  11.0  13.0  15.0   9.0
2  18.0  20.0  22.0  24.0  14.0
3  15.0  16.0  17.0  18.0  19.0
>>>

算术运算方法有

add 加法

sub 减法

div 除法

mul 乘法

DataFrame和Series之间的运算

>>> frame
   one  two  three  four
a    0    1      2     3
b    4    5      6     7
c    8    9     10    11
d   12   13     14    15
>>> series = frame.ix[0]
>>> series
one      0
two      1
three    2
four     3
Name: a, dtype: int32
>>> frame - series
   one  two  three  four
a    0    0      0     0
b    4    4      4     4
c    8    8      8     8
d   12   12     12    12
>>>

两者之间的运算会将Series的索引匹配到DataFrame的列，然后沿着行一直向下广播。

如果某个索引值在DataFrame的列或Series的索引中找不到，则参与运算的连个对象就会被重新索引以形成并集。

>>> series2 = Series(range(3),index = ['two','four','five'])
>>> frame +series2
   five  four  one  three   two
a   NaN   4.0  NaN    NaN   1.0
b   NaN   8.0  NaN    NaN   5.0
c   NaN  12.0  NaN    NaN   9.0
d   NaN  16.0  NaN    NaN  13.0

如果希望匹配行，且在列上传播，则必须使用算术方法

>>> series3 = frame['two']
>>> frame.sub(series3,axis = 0)
   one  two  three  four
a   -1    0      1     2
b   -1    0      1     2
c   -1    0      1     2
d   -1    0      1     2
>>>