官方函數
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，但是也可以使用布爾值
- Allowed inputs are: # 可以接受單個的label，多個label的清單，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].
執行個體詳解
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 複制
2、Single label. 單個 row_label 傳回的Series
df.loc['viper']
Out[17]:
max_speed 4
shield 5
Name: viper, dtype: int64 複制
2、List of labels. 清單 row_label 傳回的DataFrame
df.loc[['cobra','viper']]
Out[20]:
max_speed shield
cobra 1 2
viper 4 5 複制
3、Single label for row and column 同時標明行和列
df.loc['cobra', 'shield']
Out[24]: 2 複制
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. 同時標明多個行和單個列，注意的是通過清單標明多個row label 時，首位均是標明的。
df.loc['cobra':'viper', 'max_speed']
Out[25]:
cobra 1
viper 4
Name: max_speed, dtype: int64 複制
5、Boolean list with the same length as the row axis 布爾清單選擇row label
布爾值清單是根據某個位置的True or False 來標明，如果某個位置的布爾值是True，則標明該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,False,True]]
Out[33]:
max_speed shield
cobra 1 2
sidewinder 7 8 複制
6、Conditional that returns a boolean Series 條件布爾值
df.loc[df['shield'] 6]
Out[34]:
max_speed shield
sidewinder 7 8 複制
7、Conditional that returns a boolean Series with column labels specified 條件布爾值和具體某列的資料
df.loc[df['shield'] 6, ['max_speed']]
Out[35]:
max_speed
sidewinder 7 複制
8、Callable that returns a boolean Series 通過函數得到布爾結果標明資料
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 複制
二、指派
1、Set value for all items matching the list of labels 根據某清單標明的row 及某列 column 指派
df.loc[['viper', 'sidewinder'], ['shield']] = 50
df
Out[43]:
max_speed shield
cobra 1 2
viper 4 50
sidewinder 7 50 複制
2、Set value for an entire row 将某行row的資料全部指派
df.loc['cobra'] =10
df
Out[48]:
max_speed shield
cobra 10 10
viper 4 50
sidewinder 7 50 複制
3、Set value for an entire column 将某列的資料完全指派
df.loc[:, 'max_speed'] = 30
df
Out[50]:
max_speed shield
cobra 30 10
viper 30 50
sidewinder 30 50 複制
4、Set value for rows matching callable condition 條件標明rows指派
df.loc[df['shield'] 35] = 0
df
Out[52]:
max_speed shield
cobra 30 10
viper 0 0
sidewinder 0 0 複制
三、行索引是數值
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 複制
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 複制
四、多元索引
1、生成多元索引
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 複制
2、Single label. 傳入的就是最外層的row label，傳回DataFrame
df.loc['cobra']
Out[58]:
max_speed shield
mark i 12 2
mark ii 0 4 複制
3、Single index tuple.傳入的是索引元組，傳回Series
df.loc[('cobra', 'mark ii')]
Out[59]:
max_speed 0
shield 4
Name: (cobra, mark ii), dtype: int64 複制
4、Single label for row and column.如果傳入的是row和column，和傳入tuple是類似的，傳回Series
df.loc['cobra', 'mark i']
Out[60]:
max_speed 12
shield 2
Name: (cobra, mark i), dtype: int64 複制
5、Single tuple. Note using [[ ]] returns a DataFrame.傳入一個數組，傳回一個DataFrame
df.loc[[('cobra', 'mark ii')]]
Out[61]:
max_speed shield
cobra mark ii 0 4 複制
6、Single tuple for the index with a single label for the column 擷取某個colum的某row的資料，需要左邊傳入多元索引的tuple，然後再傳入column
df.loc[('cobra', 'mark i'), 'shield']
Out[62]: 2 複制
7、傳入多元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 複制
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