有点复杂的熊猫数据框，行到列

时间：2020-07-25 03:15:14

标签： python pandas

我有一个数据框，很复杂。数据框有很多按日期时间和项目划分的块。起源excel：

name    sex age ID  start   end main    data    testtime    item    subitem result  unit    mark    reference   testman comfirmman
LSF female  60  12345678    2018-12-18 08:58    2018-12-29 08:30    knee    11                                  
2018-12-28 13:59    metabolism II               comfirm 12345678                                        
subitem         result  unit    mark    reference                                       
Na          142 mmol/L      135 - 145                                       
K           3.98    mmol/L      3.50 - 5.30                                     
Cl          105 mmol/L      96 - 110                                        
PHOS            1.25    mmol/L      0.97 - 1.62                                     
testman：YYY             comfirmman：AAA                                              
2018-12-28 9:57 routine blood               comfirm 12345678                                        
subitem         result  unit    mark    reference                                       
CRP         14.72   mg/L    ↑   0.00 - 10.00                                        
WBC         6.73    x10^9/L     4.00 - 10.00                                        
NEUT%           0.524           0.460 - 0.750                                       
testman：BBB             comfirmman：EEE

我想将有关列索引的行更改为列。我想要什么：

name    sex age ID  start   end main    data    testtime    item    subitem result  unit    mark    reference   testman comfirmman
LSF female  60  12345678    2018-12-18 08:58    2018-12-29 08:30    knee    11  2018-12-28 13:59    metabolism II   Na  142 mmol/L      135 - 145   YYY AAA
LSF female  60  12345678    2018-12-18 08:58    2018-12-29 08:30    knee    11  2018-12-28 13:59    metabolism II   K   3.98    g/L     3.50 - 5.30 YYY AAA
LSF female  60  12345678    2018-12-18 08:58    2018-12-29 08:30    knee    11  2018-12-28 13:59    metabolism II   Cl  105 mmol/L      96 - 110    YYY AAA
LSF female  60  12345678    2018-12-18 08:58    2018-12-29 08:30    knee    11  2018-12-28 13:59    metabolism II   PHOS    1.25    u/L     0.97 - 1.62 YYY AAA
LSF female  60  12345678    2018-12-18 08:58    2018-12-29 08:30    knee    11  2018-12-28 9:57 routine blood   CRP 14.72   mg/L    ↑   0.00 - 10.00    BBB EEE
LSF female  60  12345678    2018-12-18 08:58    2018-12-29 08:30    knee    11  2018-12-28 9:57 routine blood   WBC 6.73    x10^9/L     4.00 - 10.00    BBB EEE
LSF female  60  12345678    2018-12-18 08:58    2018-12-29 08:30    knee    11  2018-12-28 9:57 routine blood   NEUT%   0.524           0.460 - 0.750   BBB EEE

enter image description here

谢谢前进！

2 个答案:

答案 0 :(得分：0)

您可以通过使用Transpose方法来做到这一点

transposed_dataframe = your_dataframe.T

示例： 

import numpy as np
import pandas as pd

# Just random value
a = np.random.random(10)
b = np.random.random(10)
c = np.random.random(10)

df = pd.DataFrame({'a':a,'b':b,'c':c})

print('Original Dataframe')
print(df)

transposed_dataframe = df.T
print('Transposed Dataframe')
print(df.T)

输出： 

Original Dataframe
          a         b         c
0  0.254146  0.017214  0.024618
1  0.958870  0.297118  0.935739
2  0.492764  0.626654  0.259336
3  0.979305  0.811364  0.321847
4  0.723043  0.570478  0.222365
5  0.717678  0.833348  0.188363
6  0.695006  0.712678  0.313900
7  0.071923  0.529029  0.018965
8  0.868739  0.152821  0.349268
9  0.766499  0.651031  0.109461

Transposed Dataframe
          0         1         2         3         4         5         6         7         8         9
a  0.254146  0.958870  0.492764  0.979305  0.723043  0.717678  0.695006  0.071923  0.868739  0.766499
b  0.017214  0.297118  0.626654  0.811364  0.570478  0.833348  0.712678  0.529029  0.152821  0.651031
c  0.024618  0.935739  0.259336  0.321847  0.222365  0.188363  0.313900  0.018965  0.349268  0.109461

答案 1 :(得分：0)

从半结构化Excel中提取数据总是很丑

  1. 您有两个标题行，这些标题行构成了 master 数据集
  2. 您有多个 detail 记录插入主数据库的主键
  3. 详细数据的进一步准备
  4. 加入主人以获取详细信息，您就可以拥有数据
data = '''name    sex  age  ID  start   end  main    data    testtime    item    subitem result  unit    mark    reference   testman comfirmman
LSF  female  60  12345678    2018-12-18 08:58    2018-12-29 08:30    knee    11                                  
2018-12-28 13:59    metabolism II               comfirm 12345678                                        
subitem         result  unit    mark    reference                                       
Na          142 mmol/L      135 - 145                                       
K           3.98    mmol/L      3.50 - 5.30                                     
Cl          105 mmol/L      96 - 110                                        
PHOS            1.25    mmol/L      0.97 - 1.62                                     
testman：YYY             comfirmman：AAA                                              
2018-12-28 9:57 routine blood               comfirm 12345678                                        
subitem         result  unit    mark    reference                                       
CRP         14.72   mg/L    ↑   0.00 - 10.00                                        
WBC         6.73    x10^9/L     4.00 - 10.00                                        
NEUT%           0.524           0.460 - 0.750                                       
testman：BBB             comfirmman：EEE                                              '''
# first two rows are master data
h = [[t.strip() for t in re.split("  ", l) if t!=""] for l in data.split("\n")[:2] ]
h[0][:len(h[1])] # strip columns down to number of data items found
hf = pd.DataFrame(h[1:], columns=h[0][:len(h[1])])
# insert ID into detail data
d = [[hf.loc[0:,"ID"].values[0]]+[t.strip() for t in re.split("  ", l) if t.strip()!=""] for l in data.split("\n")[3:] ]
d[0][0] = "ID" # modify column header
df = pd.DataFrame(d[1:], columns=d[0])
# find the rows that have testman and confirmman
rows = df[df["subitem"].str.contains("testman")].index.values
# update each row with testman and confirmman
for i, r in enumerate(rows):
    rs = 0 if i==0 else rows[i-1]+1
    df.loc[rs:r-1, "testman"] = df.loc[r:r,"subitem"].values[0].replace("testman：", "")
    df.loc[rs:r-1, "confirmman"] = df.loc[r:r,"result"].values[0].replace("comfirmman：", "")

df.loc[df["unit"].isna(),"testman"] = np.nan  # a bit more cleanup
# join it all together excluding detail rows that are not test results
hf.merge(df[~df["testman"].isna()], on="ID")

输出 

name    sex age ID  start   end main    data    subitem result  unit    mark    reference   testman confirmman
0   LSF female  60  12345678    2018-12-18 08:58    2018-12-29 08:30    knee    11  Na  142 mmol/L  135 - 145   None    None    YYY AAA
1   LSF female  60  12345678    2018-12-18 08:58    2018-12-29 08:30    knee    11  K   3.98    mmol/L  3.50 - 5.30 None    YYY AAA
2   LSF female  60  12345678    2018-12-18 08:58    2018-12-29 08:30    knee    11  Cl  105 mmol/L  96 - 110    None    None    YYY AAA
3   LSF female  60  12345678    2018-12-18 08:58    2018-12-29 08:30    knee    11  PHOS    1.25    mmol/L  0.97 - 1.62 None    YYY AAA
4   LSF female  60  12345678    2018-12-18 08:58    2018-12-29 08:30    knee    11  subitem result  unit    mark    reference   BBB EEE
5   LSF female  60  12345678    2018-12-18 08:58    2018-12-29 08:30    knee    11  CRP 14.72   mg/L    ↑   0.00 - 10.00    BBB EEE
6   LSF female  60  12345678    2018-12-18 08:58    2018-12-29 08:30    knee    11  WBC 6.73    x10^9/L 4.00 - 10.00    None    BBB EEE
7   LSF female  60  12345678    2018-12-18 08:58    2018-12-29 08:30    knee    11  NEUT%   0.524   0.460 - 0.750   None    None    BBB EEE
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