Python格式和熊猫

发布时间：2020-12-20 11:55:47 所属栏目：Python 来源：网络整理

导读：我想使用格式删除一些列.(想要删除列：new_cost0,new_0_quantity,new_2_cost和new_2_quantity)但不是每个列都被删除.以下是数据框和代码. 数据帧 |new_0_cost|new_0_quantity|new_2_cost|new_2_quantity|quality|weights| 0| 10 | 20 | 10 | 20 | good | 40

我想使用格式删除一些列.(想要删除列：new_cost0,new_0_quantity,new_2_cost和new_2_quantity)但不是每个列都被删除.以下是数据框和代码.

数据帧

|new_0_cost|new_0_quantity|new_2_cost|new_2_quantity|quality|weights|     
   0| 10       | 20           |  10      | 20           | good  | 40    |

功能

def drop_cost_and_quan(data):
    # data is a dataframe described above
    # try to drop new_cost0,new_2_cost,and new_2_quantity
    data3 = data.copy()
    for i,item in enumerate(data3.columns):
        if item == 'new_{0}_cost'.format(i):
            data3 = data3.drop(item,axis=1)
        print('cost:',item == 'new_{0}_cost'.format(i))

    for i,item in enumerate(data3.columns):
        if item == 'new_{0}_quantity'.format(i):
            data3 = data3.drop(item,axis=1)
        print(item == 'item_{0}_quantity'.format(i))

    return data3

Outptut：

data3 = drop_cost_and_quan(data):

cost: True
cost: False
cost: True
cost: False
cost: False
cost: False
quntity: True
quntity: False
quntity: False
quntity: False

data3 
 |new_2_quantity|quality| weights|
0| 20           |good   |40

解决方法

或者@ vinod的方法你也可以这样做：

In [148]: df
Out[148]:
   new_0_cost  new_0_quantity  new_2_cost  new_2_quantity  new_0_total_cost  new_2_total_cost quality  weights
0          10              20          10              20              1111              2222    good       40

In [151]: df.drop(df.columns[df.columns.str.contains(r'^new_d+_(?:quantity|cost)')],1,inplace=True)

In [152]: df
Out[152]:
   new_0_total_cost  new_2_total_cost quality  weights
0              1111              2222    good       40

说明：

In [148]: df
Out[148]:
   new_0_cost  new_0_quantity  new_2_cost  new_2_quantity  new_0_total_cost  new_2_total_cost quality  weights
0          10              20          10              20              1111              2222    good       40

In [149]: df.columns.str.contains(r'^new_d+_(?:quantity|cost)')
Out[149]: array([ True,True,False,False],dtype=bool)

In [150]: df.columns[df.columns.str.contains(r'^new_d+_(?:quantity|cost)')]
Out[150]: Index(['new_0_cost','new_0_quantity','new_2_cost','new_2_quantity'],dtype='object')

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