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

>>>

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