我有一个大型数据框（＆gt; 1m行，10 + cols），我需要执行以下操作：
A＆amp; B）C）进行排序C的排序值的顺序递增（例如E）D）我有以下代码正常工作，它给出了正确的结果。但是，它很慢。任何人都可以建议一些熊猫魔法来提高性能吗？
import pandas as pd
import numpy as np
np.random.seed(0)
A = list()
B = list()
C = list()
D = list()
E = list()
np_alphabet = np.array(list('ABCEEFGHIJKLMNOPQRSTUVWXYZ'), dtype="|S1")
np_codes = np.random.choice(np_alphabet, [5, 10])
for a in np_codes:
for b in range(2):
for i in range(5):
A.append(''.join(a))
B.append('{}_{}'.format(b, A[-1]))
C.append(np.random.rand())
D.append(i)
E.append(B[-1])
df = pd.DataFrame({
'A': A,
'B': B,
'C': C,
'D': D,
'E': E
})
df.set_index(['A', 'B'], drop=False, inplace=True)
df.sort_index(inplace=True)
print(df)
grouped_sizes = df.groupby(level=[0, 1]).size()
num_indices = grouped_sizes.shape[0]
print_num = max(1, num_indices // 20)
for idx in grouped_sizes.index:
if grouped_sizes[idx] > 1:
tmp_df = df.loc[idx].sort_values('C', inplace=False)
tmp_df['E'] = map(lambda x: '{}_{}'.format(*x), zip(range(1, tmp_df.size + 1), tmp_df['E']))
df.loc[idx] = tmp_df
else:
df.loc[idx, 'E'] = '1_{}'.format(df.loc[idx, 'E'])
print(df)
，它提供以下输出
# before
A B C D E
A B
AXEJWVIZMZ 0_AXEJWVIZMZ AXEJWVIZMZ 0_AXEJWVIZMZ 0.954914 0 0_AXEJWVIZMZ
0_AXEJWVIZMZ AXEJWVIZMZ 0_AXEJWVIZMZ 0.758615 1 0_AXEJWVIZMZ
0_AXEJWVIZMZ AXEJWVIZMZ 0_AXEJWVIZMZ 0.952573 2 0_AXEJWVIZMZ
0_AXEJWVIZMZ AXEJWVIZMZ 0_AXEJWVIZMZ 0.903142 3 0_AXEJWVIZMZ
0_AXEJWVIZMZ AXEJWVIZMZ 0_AXEJWVIZMZ 0.154262 4 0_AXEJWVIZMZ
1_AXEJWVIZMZ AXEJWVIZMZ 1_AXEJWVIZMZ 0.560586 0 1_AXEJWVIZMZ
1_AXEJWVIZMZ AXEJWVIZMZ 1_AXEJWVIZMZ 0.528869 1 1_AXEJWVIZMZ
1_AXEJWVIZMZ AXEJWVIZMZ 1_AXEJWVIZMZ 0.115331 2 1_AXEJWVIZMZ
1_AXEJWVIZMZ AXEJWVIZMZ 1_AXEJWVIZMZ 0.380718 3 1_AXEJWVIZMZ
1_AXEJWVIZMZ AXEJWVIZMZ 1_AXEJWVIZMZ 0.147092 4 1_AXEJWVIZMZ
BVBSPSACVA 0_BVBSPSACVA BVBSPSACVA 0_BVBSPSACVA 0.824997 0 0_BVBSPSACVA
0_BVBSPSACVA BVBSPSACVA 0_BVBSPSACVA 0.264456 1 0_BVBSPSACVA
0_BVBSPSACVA BVBSPSACVA 0_BVBSPSACVA 0.282663 2 0_BVBSPSACVA
0_BVBSPSACVA BVBSPSACVA 0_BVBSPSACVA 0.678287 3 0_BVBSPSACVA
0_BVBSPSACVA BVBSPSACVA 0_BVBSPSACVA 0.409996 4 0_BVBSPSACVA
1_BVBSPSACVA BVBSPSACVA 1_BVBSPSACVA 0.149984 0 1_BVBSPSACVA
1_BVBSPSACVA BVBSPSACVA 1_BVBSPSACVA 0.711210 1 1_BVBSPSACVA
1_BVBSPSACVA BVBSPSACVA 1_BVBSPSACVA 0.840399 2 1_BVBSPSACVA
1_BVBSPSACVA BVBSPSACVA 1_BVBSPSACVA 0.804939 3 1_BVBSPSACVA
1_BVBSPSACVA BVBSPSACVA 1_BVBSPSACVA 0.290150 4 1_BVBSPSACVA
FHNMIRQRSP 0_FHNMIRQRSP FHNMIRQRSP 0_FHNMIRQRSP 0.119058 0 0_FHNMIRQRSP
0_FHNMIRQRSP FHNMIRQRSP 0_FHNMIRQRSP 0.021955 1 0_FHNMIRQRSP
0_FHNMIRQRSP FHNMIRQRSP 0_FHNMIRQRSP 0.299527 2 0_FHNMIRQRSP
0_FHNMIRQRSP FHNMIRQRSP 0_FHNMIRQRSP 0.449371 3 0_FHNMIRQRSP
0_FHNMIRQRSP FHNMIRQRSP 0_FHNMIRQRSP 0.179845 4 0_FHNMIRQRSP
1_FHNMIRQRSP FHNMIRQRSP 1_FHNMIRQRSP 0.075765 0 1_FHNMIRQRSP
1_FHNMIRQRSP FHNMIRQRSP 1_FHNMIRQRSP 0.413373 1 1_FHNMIRQRSP
1_FHNMIRQRSP FHNMIRQRSP 1_FHNMIRQRSP 0.835250 2 1_FHNMIRQRSP
1_FHNMIRQRSP FHNMIRQRSP 1_FHNMIRQRSP 0.371984 3 1_FHNMIRQRSP
1_FHNMIRQRSP FHNMIRQRSP 1_FHNMIRQRSP 0.265494 4 1_FHNMIRQRSP
TJSECSLWFT 0_TJSECSLWFT TJSECSLWFT 0_TJSECSLWFT 0.804553 0 0_TJSECSLWFT
0_TJSECSLWFT TJSECSLWFT 0_TJSECSLWFT 0.376646 1 0_TJSECSLWFT
0_TJSECSLWFT TJSECSLWFT 0_TJSECSLWFT 0.904908 2 0_TJSECSLWFT
0_TJSECSLWFT TJSECSLWFT 0_TJSECSLWFT 0.274501 3 0_TJSECSLWFT
0_TJSECSLWFT TJSECSLWFT 0_TJSECSLWFT 0.820866 4 0_TJSECSLWFT
1_TJSECSLWFT TJSECSLWFT 1_TJSECSLWFT 0.886687 0 1_TJSECSLWFT
1_TJSECSLWFT TJSECSLWFT 1_TJSECSLWFT 0.198887 1 1_TJSECSLWFT
1_TJSECSLWFT TJSECSLWFT 1_TJSECSLWFT 0.857795 2 1_TJSECSLWFT
1_TJSECSLWFT TJSECSLWFT 1_TJSECSLWFT 0.326926 3 1_TJSECSLWFT
1_TJSECSLWFT TJSECSLWFT 1_TJSECSLWFT 0.116743 4 1_TJSECSLWFT
WXEKPQSLQK 0_WXEKPQSLQK WXEKPQSLQK 0_WXEKPQSLQK 0.249891 0 0_WXEKPQSLQK
0_WXEKPQSLQK WXEKPQSLQK 0_WXEKPQSLQK 0.945414 1 0_WXEKPQSLQK
0_WXEKPQSLQK WXEKPQSLQK 0_WXEKPQSLQK 0.235062 2 0_WXEKPQSLQK
0_WXEKPQSLQK WXEKPQSLQK 0_WXEKPQSLQK 0.082703 3 0_WXEKPQSLQK
0_WXEKPQSLQK WXEKPQSLQK 0_WXEKPQSLQK 0.894169 4 0_WXEKPQSLQK
1_WXEKPQSLQK WXEKPQSLQK 1_WXEKPQSLQK 0.595575 0 1_WXEKPQSLQK
1_WXEKPQSLQK WXEKPQSLQK 1_WXEKPQSLQK 0.769144 1 1_WXEKPQSLQK
1_WXEKPQSLQK WXEKPQSLQK 1_WXEKPQSLQK 0.917691 2 1_WXEKPQSLQK
1_WXEKPQSLQK WXEKPQSLQK 1_WXEKPQSLQK 0.567448 3 1_WXEKPQSLQK
1_WXEKPQSLQK WXEKPQSLQK 1_WXEKPQSLQK 0.832299 4 1_WXEKPQSLQK
和
# after
A B C D E
A B
AXEJWVIZMZ 0_AXEJWVIZMZ AXEJWVIZMZ 0_AXEJWVIZMZ 0.154262 4 1_0_AXEJWVIZMZ
0_AXEJWVIZMZ AXEJWVIZMZ 0_AXEJWVIZMZ 0.758615 1 2_0_AXEJWVIZMZ
0_AXEJWVIZMZ AXEJWVIZMZ 0_AXEJWVIZMZ 0.903142 3 3_0_AXEJWVIZMZ
0_AXEJWVIZMZ AXEJWVIZMZ 0_AXEJWVIZMZ 0.952573 2 4_0_AXEJWVIZMZ
0_AXEJWVIZMZ AXEJWVIZMZ 0_AXEJWVIZMZ 0.954914 0 5_0_AXEJWVIZMZ
1_AXEJWVIZMZ AXEJWVIZMZ 1_AXEJWVIZMZ 0.115331 2 1_1_AXEJWVIZMZ
1_AXEJWVIZMZ AXEJWVIZMZ 1_AXEJWVIZMZ 0.147092 4 2_1_AXEJWVIZMZ
1_AXEJWVIZMZ AXEJWVIZMZ 1_AXEJWVIZMZ 0.380718 3 3_1_AXEJWVIZMZ
1_AXEJWVIZMZ AXEJWVIZMZ 1_AXEJWVIZMZ 0.528869 1 4_1_AXEJWVIZMZ
1_AXEJWVIZMZ AXEJWVIZMZ 1_AXEJWVIZMZ 0.560586 0 5_1_AXEJWVIZMZ
BVBSPSACVA 0_BVBSPSACVA BVBSPSACVA 0_BVBSPSACVA 0.264456 1 1_0_BVBSPSACVA
0_BVBSPSACVA BVBSPSACVA 0_BVBSPSACVA 0.282663 2 2_0_BVBSPSACVA
0_BVBSPSACVA BVBSPSACVA 0_BVBSPSACVA 0.409996 4 3_0_BVBSPSACVA
0_BVBSPSACVA BVBSPSACVA 0_BVBSPSACVA 0.678287 3 4_0_BVBSPSACVA
0_BVBSPSACVA BVBSPSACVA 0_BVBSPSACVA 0.824997 0 5_0_BVBSPSACVA
1_BVBSPSACVA BVBSPSACVA 1_BVBSPSACVA 0.149984 0 1_1_BVBSPSACVA
1_BVBSPSACVA BVBSPSACVA 1_BVBSPSACVA 0.290150 4 2_1_BVBSPSACVA
1_BVBSPSACVA BVBSPSACVA 1_BVBSPSACVA 0.711210 1 3_1_BVBSPSACVA
1_BVBSPSACVA BVBSPSACVA 1_BVBSPSACVA 0.804939 3 4_1_BVBSPSACVA
1_BVBSPSACVA BVBSPSACVA 1_BVBSPSACVA 0.840399 2 5_1_BVBSPSACVA
FHNMIRQRSP 0_FHNMIRQRSP FHNMIRQRSP 0_FHNMIRQRSP 0.021955 1 1_0_FHNMIRQRSP
0_FHNMIRQRSP FHNMIRQRSP 0_FHNMIRQRSP 0.119058 0 2_0_FHNMIRQRSP
0_FHNMIRQRSP FHNMIRQRSP 0_FHNMIRQRSP 0.179845 4 3_0_FHNMIRQRSP
0_FHNMIRQRSP FHNMIRQRSP 0_FHNMIRQRSP 0.299527 2 4_0_FHNMIRQRSP
0_FHNMIRQRSP FHNMIRQRSP 0_FHNMIRQRSP 0.449371 3 5_0_FHNMIRQRSP
1_FHNMIRQRSP FHNMIRQRSP 1_FHNMIRQRSP 0.075765 0 1_1_FHNMIRQRSP
1_FHNMIRQRSP FHNMIRQRSP 1_FHNMIRQRSP 0.265494 4 2_1_FHNMIRQRSP
1_FHNMIRQRSP FHNMIRQRSP 1_FHNMIRQRSP 0.371984 3 3_1_FHNMIRQRSP
1_FHNMIRQRSP FHNMIRQRSP 1_FHNMIRQRSP 0.413373 1 4_1_FHNMIRQRSP
1_FHNMIRQRSP FHNMIRQRSP 1_FHNMIRQRSP 0.835250 2 5_1_FHNMIRQRSP
TJSECSLWFT 0_TJSECSLWFT TJSECSLWFT 0_TJSECSLWFT 0.274501 3 1_0_TJSECSLWFT
0_TJSECSLWFT TJSECSLWFT 0_TJSECSLWFT 0.376646 1 2_0_TJSECSLWFT
0_TJSECSLWFT TJSECSLWFT 0_TJSECSLWFT 0.804553 0 3_0_TJSECSLWFT
0_TJSECSLWFT TJSECSLWFT 0_TJSECSLWFT 0.820866 4 4_0_TJSECSLWFT
0_TJSECSLWFT TJSECSLWFT 0_TJSECSLWFT 0.904908 2 5_0_TJSECSLWFT
1_TJSECSLWFT TJSECSLWFT 1_TJSECSLWFT 0.116743 4 1_1_TJSECSLWFT
1_TJSECSLWFT TJSECSLWFT 1_TJSECSLWFT 0.198887 1 2_1_TJSECSLWFT
1_TJSECSLWFT TJSECSLWFT 1_TJSECSLWFT 0.326926 3 3_1_TJSECSLWFT
1_TJSECSLWFT TJSECSLWFT 1_TJSECSLWFT 0.857795 2 4_1_TJSECSLWFT
1_TJSECSLWFT TJSECSLWFT 1_TJSECSLWFT 0.886687 0 5_1_TJSECSLWFT
WXEKPQSLQK 0_WXEKPQSLQK WXEKPQSLQK 0_WXEKPQSLQK 0.082703 3 1_0_WXEKPQSLQK
0_WXEKPQSLQK WXEKPQSLQK 0_WXEKPQSLQK 0.235062 2 2_0_WXEKPQSLQK
0_WXEKPQSLQK WXEKPQSLQK 0_WXEKPQSLQK 0.249891 0 3_0_WXEKPQSLQK
0_WXEKPQSLQK WXEKPQSLQK 0_WXEKPQSLQK 0.894169 4 4_0_WXEKPQSLQK
0_WXEKPQSLQK WXEKPQSLQK 0_WXEKPQSLQK 0.945414 1 5_0_WXEKPQSLQK
1_WXEKPQSLQK WXEKPQSLQK 1_WXEKPQSLQK 0.567448 3 1_1_WXEKPQSLQK
1_WXEKPQSLQK WXEKPQSLQK 1_WXEKPQSLQK 0.595575 0 2_1_WXEKPQSLQK
1_WXEKPQSLQK WXEKPQSLQK 1_WXEKPQSLQK 0.769144 1 3_1_WXEKPQSLQK
1_WXEKPQSLQK WXEKPQSLQK 1_WXEKPQSLQK 0.832299 4 4_1_WXEKPQSLQK
1_WXEKPQSLQK WXEKPQSLQK 1_WXEKPQSLQK 0.917691 2 5_1_WXEKPQSLQK
from __future__ import print_function, division
from timeit import Timer
import pandas as pd
import numpy as np
def create_df():
np.random.seed(0)
A = list()
B = list()
C = list()
D = list()
E = list()
np_alphabet = np.array(list('ABCEEFGHIJKLMNOPQRSTUVWXYZ'), dtype="|S1")
np_codes = np.random.choice(np_alphabet, [100, 10])
for a in np_codes:
for b in range(2):
for i in range(5):
A.append(''.join(a))
B.append('{}_{}'.format(b, A[-1]))
C.append(np.random.rand())
D.append(i)
E.append(B[-1])
df = pd.DataFrame({
'A': A,
'B': B,
'C': C,
'D': D,
'E': E
})
return df.copy()
def method1(df):
df.set_index(['A', 'B'], drop=False, inplace=True)
df.sort_index(inplace=True)
grouped_sizes = df.groupby(level=[0, 1]).size()
for idx in grouped_sizes.index:
if grouped_sizes[idx] > 1:
tmp_df = df.loc[idx].sort_values('C', inplace=False)
tmp_df['E'] = map(lambda x: '{}_{}'.format(*x), zip(range(1, tmp_df.size + 1), tmp_df['E']))
df.loc[idx] = tmp_df
else:
df.loc[idx, 'E'] = '1_{}'.format(df.loc[idx, 'E'])
return df
def method1a(df):
df.set_index(['A', 'B'], drop=False, inplace=True)
df.sort_values(['A', 'B', 'C'], inplace=True)
grouped_sizes = df.groupby(level=[0, 1]).size()
for idx in grouped_sizes.index:
if grouped_sizes[idx] > 1:
df.loc[idx, 'E'] = map(lambda x: '{}_{}'.format(*x), zip(range(1, grouped_sizes[idx] + 1), df.loc[idx, 'E']))
else:
df.loc[idx, 'E'] = '1_{}'.format(df.loc[idx, 'E'])
return df
def method2(df):
df.set_index(['A', 'B'], drop=False, inplace=True)
df['F'] = 0
df.sort_values(['A', 'B', 'C'], inplace=True)
grouped_sizes = df.groupby(level=[0, 1]).size()
for idx in grouped_sizes.index:
if grouped_sizes[idx] > 1:
df.loc[idx, 'F'] = range(1, grouped_sizes[idx] + 1)
else:
df.loc[idx, 'F'] = 1
df['E'] = df['F'].map(str) + '_' + df['E']
df.drop('F', axis=1, inplace=True)
return df
def method3(df):
df.set_index(['A', 'B'], drop=False, inplace=True)
df['F'] = 0
df.sort_values(['A', 'B', 'C'], inplace=True)
grouped_sizes = df.groupby(level=[0, 1]).size()
for idx in grouped_sizes.index:
if grouped_sizes[idx] > 1:
df.loc[idx, 'F'] = map(str, range(1, grouped_sizes[idx] + 1))
else:
df.loc[idx, 'F'] = '1'
df['E'] = df['F'] + '_' + df['E']
df.drop('F', axis=1, inplace=True)
return df
def method4(df):
prefixes = df.groupby(['A', 'B']).C.apply(pd.Series.argsort).add(1).astype(str)
df['E'] = prefixes + '_' + df.E
return df
def method5(df):
df.set_index(['A', 'B'], drop=False, inplace=True)
df.sort_values(['A', 'B', 'C'], inplace=True)
df['E'] = df.groupby(level=[0, 1]).cumcount().add(1).astype(str) + '_' + df['E']
return df
def assert_success(df):
row = df[(df['A'] == 'AEVFGIURPE') & (df['B'] == '0_AEVFGIURPE') & (df['D'] == 2)].iloc[0]
if not np.allclose(row['C'], 0.381397) or row['E'] != '3_0_AEVFGIURPE':
print('A: method{}() failed: {} != 0.871083 or {} != 5_1_XOYRFZNIJU'.format(func, row['C'], row['E']))
return
row = df[(df['A'] == 'XOYRFZNIJU') & (df['B'] == '1_XOYRFZNIJU') & (df['D'] == 1)].iloc[0]
if not np.allclose(row['C'], 0.871083) or row['E'] != '5_1_XOYRFZNIJU':
print('B: method{}() failed: {} != 0.871083 or {} != 5_1_XOYRFZNIJU'.format(func, row['C'], row['E']))
return
functions = list()
functions.append('1')
functions.append('1a')
functions.append('2')
functions.append('3')
functions.append('4')
functions.append('5')
for func in functions:
print('method{}'.format(func),
Timer(setup='from __main__ import create_df, assert_success, method{} as func'.format(func),
stmt='df = create_df(); df = func(df); assert_success(df)').repeat(number=10))
其中给出了以下结果：
method1 [6.581194877624512, 6.625822067260742, 6.722187042236328]
method1a [1.9003210067749023, 1.9387969970703125, 1.9142169952392578]
method2 [0.9547598361968994, 0.9532740116119385, 0.9760739803314209]
method3 [1.0121638774871826, 1.0000989437103271, 0.9709858894348145]
B: method4() failed: 0.871082572438 != 0.871083 or 1_1_XOYRFZNIJU != 5_1_XOYRFZNIJU
B: method4() failed: 0.871082572438 != 0.871083 or 1_1_XOYRFZNIJU != 5_1_XOYRFZNIJU
B: method4() failed: 0.871082572438 != 0.871083 or 1_1_XOYRFZNIJU != 5_1_XOYRFZNIJU
B: method4() failed: 0.871082572438 != 0.871083 or 1_1_XOYRFZNIJU != 5_1_XOYRFZNIJU
B: method4() failed: 0.871082572438 != 0.871083 or 1_1_XOYRFZNIJU != 5_1_XOYRFZNIJU
B: method4() failed: 0.871082572438 != 0.871083 or 1_1_XOYRFZNIJU != 5_1_XOYRFZNIJU
B: method4() failed: 0.871082572438 != 0.871083 or 1_1_XOYRFZNIJU != 5_1_XOYRFZNIJU
B: method4() failed: 0.871082572438 != 0.871083 or 1_1_XOYRFZNIJU != 5_1_XOYRFZNIJU
B: method4() failed: 0.871082572438 != 0.871083 or 1_1_XOYRFZNIJU != 5_1_XOYRFZNIJU
B: method4() failed: 0.871082572438 != 0.871083 or 1_1_XOYRFZNIJU != 5_1_XOYRFZNIJU
B: method4() failed: 0.871082572438 != 0.871083 or 1_1_XOYRFZNIJU != 5_1_XOYRFZNIJU
B: method4() failed: 0.871082572438 != 0.871083 or 1_1_XOYRFZNIJU != 5_1_XOYRFZNIJU
B: method4() failed: 0.871082572438 != 0.871083 or 1_1_XOYRFZNIJU != 5_1_XOYRFZNIJU
B: method4() failed: 0.871082572438 != 0.871083 or 1_1_XOYRFZNIJU != 5_1_XOYRFZNIJU
B: method4() failed: 0.871082572438 != 0.871083 or 1_1_XOYRFZNIJU != 5_1_XOYRFZNIJU
B: method4() failed: 0.871082572438 != 0.871083 or 1_1_XOYRFZNIJU != 5_1_XOYRFZNIJU
B: method4() failed: 0.871082572438 != 0.871083 or 1_1_XOYRFZNIJU != 5_1_XOYRFZNIJU
B: method4() failed: 0.871082572438 != 0.871083 or 1_1_XOYRFZNIJU != 5_1_XOYRFZNIJU
B: method4() failed: 0.871082572438 != 0.871083 or 1_1_XOYRFZNIJU != 5_1_XOYRFZNIJU
B: method4() failed: 0.871082572438 != 0.871083 or 1_1_XOYRFZNIJU != 5_1_XOYRFZNIJU
B: method4() failed: 0.871082572438 != 0.871083 or 1_1_XOYRFZNIJU != 5_1_XOYRFZNIJU
B: method4() failed: 0.871082572438 != 0.871083 or 1_1_XOYRFZNIJU != 5_1_XOYRFZNIJU
B: method4() failed: 0.871082572438 != 0.871083 or 1_1_XOYRFZNIJU != 5_1_XOYRFZNIJU
B: method4() failed: 0.871082572438 != 0.871083 or 1_1_XOYRFZNIJU != 5_1_XOYRFZNIJU
B: method4() failed: 0.871082572438 != 0.871083 or 1_1_XOYRFZNIJU != 5_1_XOYRFZNIJU
B: method4() failed: 0.871082572438 != 0.871083 or 1_1_XOYRFZNIJU != 5_1_XOYRFZNIJU
B: method4() failed: 0.871082572438 != 0.871083 or 1_1_XOYRFZNIJU != 5_1_XOYRFZNIJU
B: method4() failed: 0.871082572438 != 0.871083 or 1_1_XOYRFZNIJU != 5_1_XOYRFZNIJU
B: method4() failed: 0.871082572438 != 0.871083 or 1_1_XOYRFZNIJU != 5_1_XOYRFZNIJU
B: method4() failed: 0.871082572438 != 0.871083 or 1_1_XOYRFZNIJU != 5_1_XOYRFZNIJU
method4 [0.3202958106994629, 0.3348369598388672, 0.33800482749938965]
method5 [0.11518096923828125, 0.10490703582763672, 0.09626197814941406]
答案 0 :(得分：1)
我认为您首先需要sort_values，然后需要groupby + cumcount作为计数器，然后添加1并转换为str：
df.sort_values(['A', 'B', 'C'], inplace=True)
df['E'] = df.groupby(level=['A','B']).cumcount().add(1).astype(str) + '_' + df['E']
print(df)
A B C D E
A B
MPVAEEHJTV 0_MPVAEEHJTV MPVAEEHJTV 0_MPVAEEHJTV 0.264556 3 1_0_MPVAEEHJTV
0_MPVAEEHJTV MPVAEEHJTV 0_MPVAEEHJTV 0.414662 2 2_0_MPVAEEHJTV
0_MPVAEEHJTV MPVAEEHJTV 0_MPVAEEHJTV 0.521848 1 3_0_MPVAEEHJTV
0_MPVAEEHJTV MPVAEEHJTV 0_MPVAEEHJTV 0.774234 4 4_0_MPVAEEHJTV
0_MPVAEEHJTV MPVAEEHJTV 0_MPVAEEHJTV 0.944669 0 5_0_MPVAEEHJTV
1_MPVAEEHJTV MPVAEEHJTV 1_MPVAEEHJTV 0.018790 2 1_1_MPVAEEHJTV
1_MPVAEEHJTV MPVAEEHJTV 1_MPVAEEHJTV 0.456150 0 2_1_MPVAEEHJTV
1_MPVAEEHJTV MPVAEEHJTV 1_MPVAEEHJTV 0.568434 1 3_1_MPVAEEHJTV
1_MPVAEEHJTV MPVAEEHJTV 1_MPVAEEHJTV 0.612096 4 4_1_MPVAEEHJTV
1_MPVAEEHJTV MPVAEEHJTV 1_MPVAEEHJTV 0.617635 3 5_1_MPVAEEHJTV
RTTTOHABJZ 0_RTTTOHABJZ RTTTOHABJZ 0_RTTTOHABJZ 0.096098 2 1_0_RTTTOHABJZ
0_RTTTOHABJZ RTTTOHABJZ 0_RTTTOHABJZ 0.097101 0 2_0_RTTTOHABJZ
0_RTTTOHABJZ RTTTOHABJZ 0_RTTTOHABJZ 0.468651 4 3_0_RTTTOHABJZ
0_RTTTOHABJZ RTTTOHABJZ 0_RTTTOHABJZ 0.837945 1 4_0_RTTTOHABJZ
0_RTTTOHABJZ RTTTOHABJZ 0_RTTTOHABJZ 0.976459 3 5_0_RTTTOHABJZ
1_RTTTOHABJZ RTTTOHABJZ 1_RTTTOHABJZ 0.039188 3 1_1_RTTTOHABJZ
...
...
编辑：
只有当列名与索引名相同时，如果不按列定义levels，似乎是错误：
df['E'] = df.groupby(['A','B']).cumcount().add(1).astype(str) + '_' + df['E']
FutureWarning：'A'既是列名又是索引级 默认为列但这将在未来版本中引发歧义错误 df ['E'] = df.groupby（column = ['A'，'B']）。cumcount（）。add（1）.astype（str）+'_'+ df ['E']
FutureWarning：'B'既是列名又是索引级。 默认为列但这将在未来版本中引发歧义错误 df ['E'] = df.groupby（column = ['A'，'B']）。cumcount（）。add（1）.astype（str）+'_'+ df ['E']
一个可能的解决方案是rename_axis，如果需要groupby by columns names（或重命名与索引名称相同的列）：
df.sort_values(['A', 'B', 'C'], inplace=True)
df = df.rename_axis(('A_lev','B_lev'))
df['E'] = df.groupby(['A','B']).cumcount().add(1).astype(str) + '_' + df['E']
print(df)
A B C D E
A_lev B_lev
MPVAEEHJTV 0_MPVAEEHJTV MPVAEEHJTV 0_MPVAEEHJTV 0.264556 3 1_0_MPVAEEHJTV
0_MPVAEEHJTV MPVAEEHJTV 0_MPVAEEHJTV 0.414662 2 2_0_MPVAEEHJTV
0_MPVAEEHJTV MPVAEEHJTV 0_MPVAEEHJTV 0.521848 1 3_0_MPVAEEHJTV
0_MPVAEEHJTV MPVAEEHJTV 0_MPVAEEHJTV 0.774234 4 4_0_MPVAEEHJTV
0_MPVAEEHJTV MPVAEEHJTV 0_MPVAEEHJTV 0.944669 0 5_0_MPVAEEHJTV
1_MPVAEEHJTV MPVAEEHJTV 1_MPVAEEHJTV 0.018790 2 1_1_MPVAEEHJTV
1_MPVAEEHJTV MPVAEEHJTV 1_MPVAEEHJTV 0.456150 0 2_1_MPVAEEHJTV
1_MPVAEEHJTV MPVAEEHJTV 1_MPVAEEHJTV 0.568434 1 3_1_MPVAEEHJTV
1_MPVAEEHJTV MPVAEEHJTV 1_MPVAEEHJTV 0.612096 4 4_1_MPVAEEHJTV
1_MPVAEEHJTV MPVAEEHJTV 1_MPVAEEHJTV 0.617635 3 5_1_MPVAEEHJTV
RTTTOHABJZ 0_RTTTOHABJZ RTTTOHABJZ 0_RTTTOHABJZ 0.096098 2 1_0_RTTTOHABJZ
0_RTTTOHABJZ RTTTOHABJZ 0_RTTTOHABJZ 0.097101 0 2_0_RTTTOHABJZ
0_RTTTOHABJZ RTTTOHABJZ 0_RTTTOHABJZ 0.468651 4 3_0_RTTTOHABJZ
0_RTTTOHABJZ RTTTOHABJZ 0_RTTTOHABJZ 0.837945 1 4_0_RTTTOHABJZ
0_RTTTOHABJZ RTTTOHABJZ 0_RTTTOHABJZ 0.976459 3 5_0_RTTTOHABJZ
1_RTTTOHABJZ RTTTOHABJZ 1_RTTTOHABJZ 0.039188 3 1_1_RTTTOHABJZ
1_RTTTOHABJZ RTTTOHABJZ 1_RTTTOHABJZ 0.282807 4 2_1_RTTTOHABJZ
1_RTTTOHABJZ RTTTOHABJZ 1_RTTTOHABJZ 0.604846 1 3_1_RTTTOHABJZ
1_RTTTOHABJZ RTTTOHABJZ 1_RTTTOHABJZ 0.739264 2 4_1_RTTTOHABJZ
1_RTTTOHABJZ RTTTOHABJZ 1_RTTTOHABJZ 0.976761 0 5_1_RTTTOHABJZ
...
...
答案 1 :(得分：0)
首先对数据框进行排序，然后将.groupby()方法与.cumcount()一起使用：
df.sort_values(['A','B','C'], inplace = True)
df['D'] = df.groupby(['A','B']).cumcount()
如果您想以另一种方式对其进行排序，请向ascending提供.sort_values()参数：ascending = [True,True,False]。如果你想保留数据的原始排序（它们仍然可以'正确排列'），你实际上也不需要按A和B排序。
一般性评论：如果你发现自己在pandas中使用for循环来迭代数据，那么你很可能通过使用pandas的矢量化来大大加速你的代码。