官方函式

DataFrame.loc
Access a group of rows and columns by label(s) or a boolean array.
.loc[] is primarily label based,but may also be used with a boolean array.
# 可以使用label值，但是也可以使用布林值

• Allowed inputs are: # 可以接受單個的label，多個label的列表，多個label的切片
• A single label,e.g. 5 or ‘a',(note that 5 is interpreted as a label of the index,and never as an integer position along the index). #這裡的5不是數值指定的位置，而是label值
• A list or array of labels,e.g. [‘a',‘b',‘c'].
  

slice object with labels,e.g. ‘a':'f'.

Warning: #如果使用多個label的切片，那麼切片的起始位置都是包含的

Note that contrary to usual python slices,both the start and the stop are included

• A boolean array of the same length as the axis being sliced,e.g. [True,False,True].

例項詳解

一、選擇數值

1、生成df

df = pd.DataFrame([[1,2],[4,5],[7,8]],...   index=['cobra','viper','sidewinder'],...   columns=['max_speed','shield'])

df
Out[15]: 
      max_speed shield
cobra        1    2
viper        4    5
sidewinder     7    8

2、Single label. 單個 row_label 返回的Series

df.loc['viper']
Out[17]: 
max_speed  4
shield    5
Name: viper,dtype: int64

2、List of labels. 列表 row_label 返回的DataFrame

df.loc[['cobra','viper']]
Out[20]: 
    max_speed shield
cobra     1    2
viper     4    5

3、Single label for row and column 同時選定行和列

df.loc['cobra','shield']
Out[24]: 2

4、Slice with labels for row and single label for column. As mentioned above,note that both the start and stop of the slice are included. 同時選定多個行和單個列，注意的是通過列表選定多個row label 時，首位均是選定的。

df.loc['cobra':'viper','max_speed']
Out[25]: 
cobra  1
viper  4
Name: max_speed,dtype: int64

5、Boolean list with the same length as the row axis 布林列表選擇row label
布林值列表是根據某個位置的True or False 來選定，如果某個位置的布林值是True，則選定該row

df
Out[30]: 
      max_speed shield
cobra        1    2
viper        4    5
sidewinder     7    8

df.loc[[True]]
Out[31]: 
    max_speed shield
cobra     1    2

df.loc[[True,False]]
Out[32]: 
    max_speed shield
cobra     1    2

df.loc[[True,True]]
Out[33]: 
      max_speed shield
cobra        1    2
sidewinder     7    8

6、Conditional that returns a boolean Series 條件布林值

df.loc[df['shield'] > 6]
Out[34]: 
      max_speed shield
sidewinder     7    8

7、Conditional that returns a boolean Series with column labels specified 條件布林值和具體某列的資料

df.loc[df['shield'] > 6,['max_speed']]
Out[35]: 
      max_speed
sidewinder     7

8、Callable that returns a boolean Series 通過函式得到布林結果選定資料

df
Out[37]: 
      max_speed shield
cobra        1    2
viper        4    5
sidewinder     7    8

df.loc[lambda df: df['shield'] == 8]
Out[38]: 
      max_speed shield
sidewinder     7    8

二、賦值

1、Set value for all items matching the list of labels 根據某列表選定的row 及某列 column 賦值

df.loc[['viper',['shield']] = 50

df
Out[43]: 
      max_speed shield
cobra        1    2
viper        4   50
sidewinder     7   50

2、Set value for an entire row 將某行row的資料全部賦值

df.loc['cobra'] =10

df
Out[48]: 
      max_speed shield
cobra       10   10
viper        4   50
sidewinder     7   50

3、Set value for an entire column 將某列的資料完全賦值

df.loc[:,'max_speed'] = 30

df
Out[50]: 
      max_speed shield
cobra       30   10
viper       30   50
sidewinder     30   50

4、Set value for rows matching callable condition 條件選定rows賦值

df.loc[df['shield'] > 35] = 0

df
Out[52]: 
      max_speed shield
cobra       30   10
viper        0    0
sidewinder     0    0

三、行索引是數值

df = pd.DataFrame([[1,...   index=[7,8,9],columns=['max_speed','shield'])

df
Out[54]: 
  max_speed shield
7     1    2
8     4    5
9     7    8

通過 行 rows的切片的方式取多個：

df.loc[7:9]
Out[55]: 
  max_speed shield
7     1    2
8     4    5
9     7    8

四、多維索引

1、生成多維索引

tuples = [
...  ('cobra','mark i'),('cobra','mark ii'),...  ('sidewinder',('sidewinder',...  ('viper',('viper','mark iii')
... ]
index = pd.MultiIndex.from_tuples(tuples)
values = [[12,[0,4],[10,20],...     [1,1],[16,36]]
df = pd.DataFrame(values,'shield'],index=index)


df
Out[57]: 
           max_speed shield
cobra   mark i      12    2
      mark ii      0    4
sidewinder mark i      10   20
      mark ii      1    4
viper   mark ii      7    1
      mark iii     16   36

2、Single label. 傳入的就是最外層的row label，返回DataFrame

df.loc['cobra']
Out[58]: 
     max_speed shield
mark i     12    2
mark ii     0    4

3、Single index tuple.傳入的是索引元組，返回Series

df.loc[('cobra','mark ii')]
Out[59]: 
max_speed  0
shield    4
Name: (cobra,mark ii),dtype: int64

4、Single label for row and column.如果傳入的是row和column，和傳入tuple是類似的，返回Series

df.loc['cobra','mark i']
Out[60]: 
max_speed  12
shield    2
Name: (cobra,mark i),dtype: int64

5、Single tuple. Note using [[ ]] returns a DataFrame.傳入一個數組，返回一個DataFrame

df.loc[[('cobra','mark ii')]]
Out[61]: 
        max_speed shield
cobra mark ii     0    4

6、Single tuple for the index with a single label for the column 獲取某個colum的某row的資料，需要左邊傳入多維索引的tuple，然後再傳入column

df.loc[('cobra','shield']
Out[62]: 2

7、傳入多維索引和單個索引的切片：

df.loc[('cobra','mark i'):'viper']
Out[63]: 
           max_speed shield
cobra   mark i      12    2
      mark ii      0    4
sidewinder mark i      10   20
      mark ii      1    4
viper   mark ii      7    1
      mark iii     16   36

df.loc[('cobra','mark i'):'sidewinder']
Out[64]: 
          max_speed shield
cobra   mark i     12    2
      mark ii     0    4
sidewinder mark i     10   20
      mark ii     1    4

df.loc[('cobra','mark i'):('sidewinder','mark i')]
Out[65]: 
          max_speed shield
cobra   mark i     12    2
      mark ii     0    4
sidewinder mark i     10   20

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