pythonpandas.DataFrame.loc函数使⽤详解
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
rows函数的使用方法及实例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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