Python格式和熊猫

概述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 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: Truecost: Falsecost: Truecost: Falsecost: Falsecost: Falsequntity: Truequntity: Falsequntity: Falsequntity: Falsedata3  |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]: dfOut[148]:   new_0_cost  new_0_quantity  new_2_cost  new_2_quantity  new_0_total_cost  new_2_total_cost quality  weights0          10              20          10              20              1111              2222    good       40In [151]: df.drop(df.columns[df.columns.str.contains(r'^new_\d+_(?:quantity|cost)')],1,inplace=True)In [152]: dfOut[152]:   new_0_total_cost  new_2_total_cost quality  weights0              1111              2222    good       40

说明：

In [148]: dfOut[148]:   new_0_cost  new_0_quantity  new_2_cost  new_2_quantity  new_0_total_cost  new_2_total_cost quality  weights0          10              20          10              20              1111              2222    good       40In [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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