删除
drop（...）
def drop(
self,
labels=None, # 需要删除的标签，一个或者是列表形式的多个
axis=0, # 指定给出的labels是在哪个轴上，axis=0表示行，axis=1表示列
index=None, # 某一索引或者多个索引
columns=None, # 某一列或者多列
level=None, # 等级，针对多重索引的情况
inplace=False, # 是否替换原来的dataframe
errors="raise",
):
"""
Examples
--------
>>> df = pd.DataFrame(np.arange(12).reshape(3, 4),
... columns=['A', 'B', 'C', 'D'])
>>> df
A B C D
0 0 1 2 3
1 4 5 6 7
2 8 9 10 11
Drop columns
>>> df.drop(['B', 'C'], axis=1)
A D
0 0 3
1 4 7
2 8 11
>>> df.drop(columns=['B', 'C'])
A D
0 0 3
1 4 7
2 8 11
Drop a row by index
>>> df.drop([0, 1])
A B C D
2 8 9 10 11
Drop columns and/or rows of MultiIndex DataFrame
>>> midx = pd.MultiIndex(levels=[['lama', 'cow', 'falcon'],
... ['speed', 'weight', 'length']],
... codes=[[0, 0, 0, 1, 1, 1, 2, 2, 2],
... [0, 1, 2, 0, 1, 2, 0, 1, 2]])
>>> df = pd.DataFrame(index=midx, columns=['big', 'small'],
... data=[[45, 30], [200, 100], [1.5, 1], [30, 20],
... [250, 150], [1.5, 0.8], [320, 250],
... [1, 0.8], [0.3, 0.2]])
>>> df
big small
lama speed 45.0 30.0
weight 200.0 100.0
length 1.5 1.0
cow speed 30.0 20.0
weight 250.0 150.0
length 1.5 0.8
falcon speed 320.0 250.0
weight 1.0 0.8
length 0.3 0.2
>>> df.drop(index='cow', columns='small')
big
lama speed 45.0
weight 200.0
length 1.5
falcon speed 320.0
weight 1.0
length 0.3
>>> df.drop(index='length', level=1)
big small
lama speed 45.0 30.0
weight 200.0 100.0
cow speed 30.0 20.0
weight 250.0 150.0
falcon speed 320.0 250.0
weight 1.0 0.8
"""
return super().drop(
labels=labels,
axis=axis,
index=index,
columns=columns,
level=level,
inplace=inplace,
errors=errors,
)
拓展
- drop函数删除特定条件的行
"""
>>> df = pd.DataFrame(np.arange(12).reshape(3, 4),
... columns=['A', 'B', 'C', 'D'])
>>> df
A B C D
0 0 1 2 3
1 4 5 6 7
2 8 9 10 11
"""
IN [2]: data.drop(index=0) #删除index=0的行
Out[2]:
A B C D
1 4 5 6 7
2 8 9 10 11
IN [3]: data.drop(labels=0, axis=0) #删除 "行号为0" 的行
Out[3]:
A B C D
1 4 5 6 7
2 8 9 10 11
In [4]: data.drop(index=data[data['A'].isin([4])].index) #删除包含4的行
Out[4]:
A B C D
0 0 1 2 3
2 8 9 10 11
In [5]: data.drop(index=data[data['A']==4].index) #删除包含4的行
Out[5]:
A B C D
0 0 1 2 3
2 8 9 10 11
- 删除列（ 基于Index和columns删除列 ）
IN [6]: data.drop(columns='A') #删除columns为A的列
Out[6]:
B C D
0 1 2 3
1 5 6 7
2 9 10 11
IN [7]: data.drop(labels='A', axis=1) #删除 "列名为A" 的列
Out[7]:
B C D
0 1 2 3
1 5 6 7
2 9 10 11
delete(...)
pop(...)
pop(self, item: Label) -> Series:
"""
>>> df
name class max_speed
0 falcon bird 389.0
1 parrot bird 24.0
2 lion mammal 80.5
3 monkey mammal NaN
>>> df.pop('class')
0 bird
1 bird
2 mammal
3 mammal
Name: class, dtype: object
>>> df
name max_speed
0 falcon 389.0
1 parrot 24.0
2 lion 80.5
3 monkey NaN
"""
return super().pop(item=item)
def pop(self, item: Label) -> Union["Series", Any]:
result = self[item]
del self[item]
if self.ndim == 2:
result._reset_cacher()
return result
