如何根据多个条件对熊猫数据框的列进行重新排序？答案

【问题标题】：How to reorder columns of pandas dataframe based on multiple conditions?如何根据多个条件对熊猫数据框的列进行重新排序？
【发布时间】：2022-07-25 22:06:23
【问题描述】：

我想根据每个患者 ID 的列名按升序对 hsp_treatment_df 数据框重新排序，其中：

如果在第一个下划线之后的数值最小，则列在前，与时间无关
如果第一个下划线之前的数值最小（即时间），则列在前；同样，如果值最大，则排在最后

在列名中，第一个下划线之前的子字符串是时间，第一个下划线之后的子字符串是患者ID。例如，在12h_P1_T4_TimeC2_PIDC4_Non-Survivor 中：

12h代表时间（t）
P1 代表患者 ID (pid)

代码：

import pandas as pd
import numpy as np

# Analyse time points for each patient
## Get the patient ID from each column 

for cols in hsp_treatment_df.columns:
  pid = cols.split('_', 2)[1]
  pid = pid.lstrip("P") # get patient ID without the "P" substring

  time = cols.split('_', 2)[0]
  time = time.rstrip(time[-1]) # get time without the "h" substring

  # Rearrange the columns so that for each "pid", the "t" is in ascending order
  if (min(pid)) and (min(time)):
    c = []
    c.append(cols)
    hsp_treatment_df.columns = c

追溯：

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-128-f48ee0e3d3de> in <module>()
     13     c = []
     14     c.append(cols)
---> 15     hsp_treatment_df.columns = c

4 frames
/usr/local/lib/python3.7/dist-packages/pandas/core/internals/base.py in _validate_set_axis(self, axis, new_labels)
     56         elif new_len != old_len:
     57             raise ValueError(
---> 58                 f"Length mismatch: Expected axis has {old_len} elements, new "
     59                 f"values have {new_len} elements"
     60             )

ValueError: Length mismatch: Expected axis has 29 elements, new values have 1 elements

数据框：

`hsp_treatment_df.iloc[:,0:12].head().to_dict()`

{'0h_P1_T1_TimeC1_PIDC4_Non-Survivor': {'DNAJB6 /// TMEM135': '9.25',
  'DNAJC14': '8.44',
  'DNAJC15': '8.66',
  'DNAJC30': '7.34',
  'DNAJC9': '7.62'},
 '0h_P2_T1_TimeC2_PIDC2_Survivor': {'DNAJB6 /// TMEM135': '9.26',
  'DNAJC14': '8.34',
  'DNAJC15': '8.63',
  'DNAJC30': '7.42',
  'DNAJC9': '7.19'},
 '12h_P1_T4_TimeC2_PIDC4_Non-Survivor': {'DNAJB6 /// TMEM135': '8.58',
  'DNAJC14': '8.64',
  'DNAJC15': '8.98',
  'DNAJC30': '7.2',
  'DNAJC9': '7.23'},
 '12h_P2_T4_TimeC3_PIDC2_Survivor': {'DNAJB6 /// TMEM135': '8.94',
  'DNAJC14': '8.38',
  'DNAJC15': '8.03',
  'DNAJC30': '7.32',
  'DNAJC9': '7.26'},
 '24h_P1_T5_TimeC4_PIDC4_Non-Survivor': {'DNAJB6 /// TMEM135': '8.13',
  'DNAJC14': '8.43',
  'DNAJC15': '8.33',
  'DNAJC30': '7.41',
  'DNAJC9': '6.68'},
 '24h_P2_T5_TimeC3_PIDC2_Survivor': {'DNAJB6 /// TMEM135': '8.85',
  'DNAJC14': '8.54',
  'DNAJC15': '8.73',
  'DNAJC30': '7.26',
  'DNAJC9': '7.49'},
 '48h_P1_T6_TimeC3_PIDC1_Non-Survivor': {'DNAJB6 /// TMEM135': '9.05',
  'DNAJC14': '8.27',
  'DNAJC15': '7.63',
  'DNAJC30': '7.46',
  'DNAJC9': '7.42'},
 '48h_P2_T6_TimeC3_PIDC3_Survivor': {'DNAJB6 /// TMEM135': '8.57',
  'DNAJC14': '8.23',
  'DNAJC15': '8.6',
  'DNAJC30': '7.45',
  'DNAJC9': '7.62'},
 '4h_P1_T2_TimeC1_PIDC4_Non-Survivor': {'DNAJB6 /// TMEM135': '8.25',
  'DNAJC14': '8.69',
  'DNAJC15': '8.78',
  'DNAJC30': '7.53',
  'DNAJC9': '7.29'},
 '4h_P2_T2_TimeC2_PIDC1_Survivor': {'DNAJB6 /// TMEM135': '9.49',
  'DNAJC14': '8.47',
  'DNAJC15': '8.7',
  'DNAJC30': '7.41',
  'DNAJC9': '7.23'},
 '8h_P1_T3_TimeC4_PIDC4_Non-Survivor': {'DNAJB6 /// TMEM135': '8.49',
  'DNAJC14': '8.58',
  'DNAJC15': '8.22',
  'DNAJC30': '7.29',
  'DNAJC9': '7.13'},
 '8h_P2_T3_TimeC2_PIDC2_Survivor': {'DNAJB6 /// TMEM135': '9.16',
  'DNAJC14': '8.13',
  'DNAJC15': '7.88',
  'DNAJC30': '7.58',
  'DNAJC9': '7.18'}}

预期输出：

Gene Symbol	0h_P1_T1_TimeC1_PIDC4_Non-Survivor	4h_P1_T2_TimeC1_PIDC4_Non-Survivor	8h_P1_T3_TimeC4_PIDC4_Non-Survivor	12h_P1_T4_TimeC2_PIDC4_Non-Survivor	24h_P1_T5_TimeC4_PIDC4_Non-Survivor	48h_P1_T6_TimeC3_PIDC1_Non-Survivor	0h_P2_T1_TimeC2_PIDC2_Survivor	4h_P2_T2_TimeC2_PIDC1_Survivor	8h_P2_T3_TimeC2_PIDC2_Survivor	12h_P2_T4_TimeC3_PIDC2_Survivor	24h_P2_T5_TimeC3_PIDC2_Survivor	48h_P2_T6_TimeC3_PIDC3_Survivor
DNAJC9	7.62	7.29	7.23	7.13	6.68	7.42	7.19	7.23	7.18	7.26	7.49	7.62
DNAJC30	7.34	7.53	7.29	7.2	7.41	7.46	7.42	7.41	7.58	7.32	7.26	7.45
DNAJC14	8.44	8.69	8.58	8.64	8.43	8.27	8.34	8.47	8.13	8.38	8.54	8.23
DNAJC15	8.66	8.78	8.22	8.98	8.33	7.63	8.63	8.7	7.88	8.03	8.73	8.6
DNAJB6 /// TMEM135	9.25	8.25	8.49	8.58	8.13	9.05	9.26	9.49	9.16	8.94	8.85	8.57

【问题讨论】：

标签： python pandas numpy

【解决方案1】：

您需要使用key 对列进行排序，请参阅sorted。 key 函数提取患者 ID 和时间，将患者 ID 放在首位，然后添加 zero filled 时间。

import re

def key_fun(s):
    m = re.search(r'(\d+)h_P(\d)', s)
    return m.group(2) + m.group(1).zfill(2)

df = df[sorted(df.columns, key=key_fun)]

【讨论】：