按列排序并使用pandas追加计数器问题的回答

按列排序并使用pandas追加计数器

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我有一个大的数据帧（超过1百万行，10+列），我需要执行以下操作： <ul> <li>按两列分组（示例中为<code>A</code>&amp；<code>B</code>）</li> <li>在分组中按另一列排序（示例中为<code>C</code>）</li> <li>向另一列追加一个递增计数器，按<code>C</code>（示例中为<code>E</code>）的排序顺序递增</li> <li>不编辑其他列（示例中为<code>D</code>）</li> </ul> 我已经得到了下面的代码工作，它给出了正确的结果。然而，这是非常缓慢的。有人能推荐一些熊猫魔术来提高表演水平吗？在 <pre><code>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.<a href="https://www.cnpython.com/list/append" class="inner-link">append</a>(''.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) </code></pre> 它给出了以下输出 ^{pr2}$ 以及 <pre><code># 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 </code></pre> <h2>新的答案，建议的测试时间</h2> <pre><code>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)) </code></pre> 结果如下： <pre><code>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] </code></pre>

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分类：Python问答

默认排序时间排序

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匿名 1天前

　擅长：python、mysql、java

我想您首先需要<a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.sort_values.html" rel="nofollow noreferrer">^{<cd1>}</a>，然后<a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.groupby.html" rel="nofollow noreferrer">^{<cd2>}</a>+<a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.core.groupby.GroupBy.cumcount.html" rel="nofollow noreferrer">^{<cd3>}</a>作为计数器，然后添加<code>1</code>并转换为{<cd5>}： <pre><code>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 ... ... </code></pre> 编辑： 如果不按列定义<code>levels</code>只在列名与索引名相同时才会出现bug： ^{pr2}$ <blockquote> FutureWarning: 'A' is both a column name and an index level. Defaulting to column but this will raise an ambiguity error in a future version df['E'] = df.groupby(column=['A','B']).cumcount().add(1).astype(str) + '_' + df['E'] FutureWarning: 'B' is both a column name and an index level. Defaulting to column but this will raise an ambiguity error in a future version df['E'] = df.groupby(column=['A','B']).cumcount().add(1).astype(str) + '_' + df['E'] </blockquote> 一种可能的解决方案是<a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.rename_axis.html" rel="nofollow noreferrer">^{<cd7>}</a>如果需要，按列名称分组（或重命名与索引名称相同的列）： <pre><code>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 ... ... </code></pre>

按列排序并使用pandas追加计数器

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