python中列名的字符串替换

Question

我有一个包含 15 列的数据框 df（见下文）

A_phase_1,A_phase_2,A_phase_3,A_phase_4,A_phase_5,B_phase_1,B_phase_2,B_phase_3,C_phase_233@,B_phase_5,B_phase_5,B_phase_5 ,C_phase_3,C_phase_4,C_phase_5.

同时使用phase_1 保留列，例如=A_phase_1, B_phase_1 and C_phase_1，

我想以编程方式执行以下操作：

1. 删除包含phase_2即=A_phase_2,B_phase_2,C_phase_2的列

2. 将剩余的列名phase_n 替换为phase_n-1。例如，

所有phase_3变成phase_2；

phase_4 --> phase_3;

phase_5 --> phase_4

所以想要的输出应该是这样的：

A_phase_1,A_phase_2,A_phase_3,A_phase_4,

B_phase_1,B_phase_2,B_phase_3,B_phase_4,

C_phase_1,C_phase_2,C_phase_3,C_phase_4.

提前致谢！

@seaBean 我收到了这个错误

Answer 1

您可以使用.filter() 过滤要删除的列。然后，使用 df.columns.map() 和 lambda 函数重命名列，如下：

df = df.drop(df.filter(like='phase_2').columns, axis=1)

df.columns = df.columns.map(lambda x: (x[:-1] + str(int(x[-1]) -1)) if (x[-1].isdigit() and (int(x[-1]) > 1)) else x)

输入：

print(df)

  A_phase_1 A_phase_2 A_phase_3 A_phase_4 A_phase_5 B_phase_1 B_phase_2 B_phase_3 B_phase_4 B_phase_5 C_phase_1 C_phase_2 C_phase_3 C_phase_4 C_phase_5 X_phase_t
0        A1        A2        A3        A4        A5        B1        B2        B3        B4        B5        C1        C2        C3        C4        C5        Xt

输出：

print(df)

  A_phase_1 A_phase_2 A_phase_3 A_phase_4 B_phase_1 B_phase_2 B_phase_3 B_phase_4 C_phase_1 C_phase_2 C_phase_3 C_phase_4 X_phase_t
0        A1        A3        A4        A5        B1        B3        B4        B5        C1        C3        C4        C5        Xt

Answer 2

# 1.) delete phase 2 cols 
#    more precisely, keep only the other columns
df = df[[col for col in df.columns if col[-1]!='2']]
# This next could be done inline but keeping it separate here
rename_dict = {col:col[:-1]+str(int(col[-1])-1) for col in df.columns if col[-1] in ['3','4','5']}
df.rename(columns=rename_dict,inplace=True)

Answer 3

from pandas import DataFrame
from itertools import product

def ren(col):
    prefix, sufix = col.rsplit('_', maxsplit=1)
    sufix = int(sufix)
    return f'{prefix}_{max(sufix-1, 1)}'

# some dummy df
df = DataFrame({f'{letter}_phase_{n}':[i, i, i] 
                for i, (letter, n) in 
                enumerate(product('ABC', range(1, 6)), start=1)})

df = df.drop(df.filter(like='phase_2').columns, axis=1)
# df = df.drop([f'{letter}_phase_2' for letter in 'ABC'], axis=1) # alternative
df = df.rename(mapper=ren, axis=1)
print(df)