pandas.DataFrame.loc函数 python pandas.DataFrame.loc函数使用详解

时间:2021-04-21 brucewong0516 人气:0

官方函数

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，但是也可以使用布尔值

Allowed inputs are: # 可以接受单个的label，多个label的列表，多个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). #这里的5不是数值指定的位置，而是label值
A list or array of labels, e.g. [‘a', ‘b', ‘c'].

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

Warning: #如果使用多个label的切片，那么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].

实例详解

一、选择数值

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. 单个 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 同时选定行和列

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. 同时选定多个行和单个列，注意的是通过列表选定多个row label 时，首位均是选定的。

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 布尔列表选择row label
布尔值列表是根据某个位置的True or False 来选定，如果某个位置的布尔值是True，则选定该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,False,True]]
Out[33]: 
      max_speed shield
cobra        1    2
sidewinder     7    8

6、Conditional that returns a boolean Series 条件布尔值

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

7、Conditional that returns a boolean Series with column labels specified 条件布尔值和具体某列的数据

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

8、Callable that returns a boolean Series 通过函数得到布尔结果选定数据

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、赋值

1、Set value for all items matching the list of labels 根据某列表选定的row 及某列 column 赋值

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

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

2、Set value for an entire row 将某行row的数据全部赋值

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 将某列的数据完全赋值

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 条件选定rows赋值

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

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

三、行索引是数值

df = pd.DataFrame([[1, 2], [4, 5], [7, 8]],
...   index=[7, 8, 9], columns=['max_speed', 'shield'])

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

通过行 rows的切片的方式取多个：

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

四、多维索引

1、生成多维索引

tuples = [
...  ('cobra', 'mark i'), ('cobra', 'mark ii'),
...  ('sidewinder', 'mark i'), ('sidewinder', 'mark ii'),
...  ('viper', 'mark ii'), ('viper', 'mark iii')
... ]
index = pd.MultiIndex.from_tuples(tuples)
values = [[12, 2], [0, 4], [10, 20],
...     [1, 4], [7, 1], [16, 36]]
df = pd.DataFrame(values, columns=['max_speed', '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. 传入的就是最外层的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.传入的是索引元组，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.如果传入的是row和column，和传入tuple是类似的，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.传入一个数组，返回一个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 获取某个colum的某row的数据，需要左边传入多维索引的tuple，然后再传入column

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

7、传入多维索引和单个索引的切片：

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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