重建索引（改变索引顺序）

重要方法，创建一个符合新索引的新对象。

In [1]: import pandas as pd

In [2]: obj = pd.Series([4.5, 7.2, -5.3, 3.6], index=['d', 'b', 'a', 'c'])

In [3]: obj
Out[3]:
d    4.5
b    7.2
a   -5.3
c    3.6
dtype: float64

In [4]: obj2 = obj.reindex(['a', 'b', 'c', 'd', 'e'])

In [5]: obj2
Out[5]:
a   -5.3
b    7.2
c    3.6
d    4.5
e    NaN
dtype: float64

method可选参数允许使用ffill 方法将值前向填充。

In [6]: obj3 = pd.Series(['blue', 'purple', 'yellow'], index=[0, 2, 4])

In [7]: obj3
Out[7]:
0      blue
2    purple
4    yellow
dtype: object

In [8]: obj3.reindex(range(6), method='ffill')
Out[8]:
0      blue
1      blue
2    purple
3    purple
4    yellow
5    yellow
dtype: object

在 DataFrame 中，reindex 可以改变行索引，列索引，也可以同时改变两者。只传入一个序列时，默认改变行索引。

In [9]: import numpy as np
In [16]: frame = pd.DataFrame(np.arange(9).reshape((3, 3)), index=['a', 'b', 'c'], columns=['Ohio', 'Texas', 'Californi
    ...: a'])

In [17]: frame
Out[17]:
   Ohio  Texas  California
a     0      1           2
b     3      4           5
c     6      7           8

In [18]: frame2 = frame.reindex(['a', 'b', 'c', 'd'])

In [20]: frame2
Out[20]:
   Ohio  Texas  California
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

使用 columns 关键字重建索引

In [21]: states = ['Texas', 'Utah', 'California']

In [22]: frame.reindex(columns=states)
Out[22]:
   Texas  Utah  California
a      1   NaN           2
b      4   NaN           5
c      7   NaN           8

更多人使用 loc 进行更简洁的索引

In [23]: frame.loc[['a', 'b', 'c', 'd'], states]
c:\users\a\appdata\local\programs\python\python36\lib\site-packages\pandas\core\indexing.py:1494: FutureWarning:
Passing list-likes to .loc or [] with any missing label will raise
KeyError in the future, you can use .reindex() as an alternative.

See the documentation here:
https://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecate-loc-reindex-listlike
  return self._getitem_tuple(key)
Out[23]:
   Texas  Utah  California
a    1.0   NaN         2.0
b    4.0   NaN         5.0
c    7.0   NaN         8.0
d    NaN   NaN         NaN

轴向上删除条目

如果已经拥有索引数组，drop 方法会返回一个含有指示值或轴向上删除值的新对象。

In [24]: obj = pd.Series(np.arange(5), index=['a', 'b', 'c', 'd', 'e'])

In [25]: obj
Out[25]:
a    0
b    1
c    2
d    3
e    4
dtype: int32

IIn [26]: new_obj = obj.drop('c')

In [27]: new_obj
Out[27]:
a    0
b    1
d    3
e    4
dtype: int32

In [28]: obj
Out[28]:
a    0
b    1
c    2
d    3
e    4
dtype: int32

In [29]: obj.drop(['d', 'c'])
Out[29]:
a    0
b    1
e    4
dtype: int32

在 DataFrame 中，索引值可以从轴向上删除

删除行

In [30]: data = pd.DataFrame(np.arange(16).reshape((4, 4)),
    ...:                     index=['Ohio', 'Colorado', 'Utah', 'New York'],
    ...:                     columns=['one', 'two', 'three', 'four'])

In [31]: data
Out[31]:
          one  two  three  four
Ohio        0    1      2     3
Colorado    4    5      6     7
Utah        8    9     10    11
New York   12   13     14    15

In [32]: data.drop(['Colorado', 'Ohio'])
Out[32]:
          one  two  three  four
Utah        8    9     10    11
New York   12   13     14    15

删除列

In [33]: data.drop('two', axis=1)
Out[33]:
          one  three  four
Ohio        0      2     3
Colorado    4      6     7
Utah        8     10    11
New York   12     14    15

In [34]: data.drop(['two', 'four'], axis='columns')
Out[34]:
          one  three
Ohio        0      2
Colorado    4      6
Utah        8     10
New York   12     14

drop 会修改 Series 或 DataFrame 的尺寸或形状，直接操作原对象而不返回新对象。

In [35]: obj.drop('c')
Out[35]:
a    0
b    1
d    3
e    4
dtype: int32

In [36]: obj
Out[36]:
a    0
b    1
c    2
d    3
e    4
dtype: int32

In [37]: obj.drop('c', inplace=True)

In [38]: obj
Out[38]:
a    0
b    1
d    3
e    4
dtype: int32