从pandas,python中的列表生成多个csv文件

时间:2018-06-11 10:27:02

标签: python pandas dataframe itertools

我正在尝试为“组合”中的每个可能组合创建一个新数据框,从数据框中读取一些值,这是数据帧的一个示例:

+-------------------------------+-----+----------+---------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+---------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
|            Species            | OGT |  Domain  |       A       |      C       |      D       |      E       |      F       |      G       |      H       |      I       |      K       |       L       |      M       |      N       |      P       |      Q       |      R       |      S       |      T       |      V       |      W       |      Y       |
+-------------------------------+-----+----------+---------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+---------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+
| Aeropyrum pernix              |  95 | Archaea  |  9.7659115711 | 0.6720465616 | 4.3895390781 | 7.6501943794 | 2.9344881615 | 8.8666657183 | 1.5011817208 | 5.6901432494 | 4.1428307243 | 11.0604191603 |   2.21143353 | 1.9387130928 | 5.1038552753 | 1.6855017182 | 7.7664358772 |  6.266067034 | 4.2052190807 | 9.2692433532 |  1.318690698 | 3.5614200159 |
| Argobacterium fabrum          |  26 | Bacteria | 11.5698896021 | 0.7985475923 | 5.5884500155 | 5.8165463343 | 4.0512504104 | 8.2643271309 | 2.0116736244 | 5.7962804605 | 3.8931525401 |  9.9250463349 | 2.5980609708 | 2.9846761128 | 4.7828063605 | 3.1262365491 | 6.5684282943 | 5.9454781844 | 5.3740045968 | 7.3382308193 | 1.2519739683 | 2.3149400984 |
| Anaeromyxobacter dehalogenans |  27 | Bacteria | 16.0337898849 | 0.8860252895 | 5.1368827707 | 6.1864992608 | 2.9730203513 | 9.3167603253 | 1.9360386851 |  2.940143349 | 2.3473650439 |  10.898494736 | 1.6343905351 | 1.5247123262 | 6.3580285706 | 2.4715303021 | 9.2639057482 | 4.1890063803 | 4.3992339725 | 8.3885969061 | 1.2890166336 | 1.8265589289 |
| Aquifex aeolicus              |  85 | Bacteria |  5.8730327277 |  0.795341216 | 4.3287799008 | 9.6746388172 | 5.1386954322 | 6.7148035486 | 1.5438364179 | 7.3358775924 | 9.4641440609 | 10.5736658776 | 1.9263080969 | 3.6183861236 | 4.0518679067 | 2.0493569604 | 4.9229955632 | 4.7976564501 | 4.2005259246 | 7.9169763709 | 0.9292167138 | 4.1438942987 |
| Archaeoglobus fulgidus        |  83 | Archaea  |  7.8742687687 | 1.1695110027 | 4.9165979364 | 8.9548767369 |  4.568636662 | 7.2640358917 | 1.4998752909 | 7.2472039919 | 6.8957233203 |  9.4826333048 | 2.6014466253 |  3.206476915 | 3.8419576418 | 1.7789787933 | 5.7572748236 | 5.4763351139 | 4.1490633048 | 8.6330814159 | 1.0325605451 | 3.6494619148 |
+-------------------------------+-----+----------+---------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+---------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+--------------+

这是我目前的代码。

import itertools
import pandas as pd

letters = ['G','A','L','M','F','W','K','Q','E','S','P','V','I','C','Y','H','R','N','D','T']

combinations = [''.join(i) for j in range(1,len(letters) + 1) for i in itertools.combinations(letters,r=j)]

df = pd.read_csv('COMPLETECOPYFORR.csv')

for combination in combinations:
    new_df = df[['Species', 'OGT']]
    new_df['Sum of percentage'] = df[list(combination)]
    new_df.to_csv(combination + '.csv')

所需的输出类似于1000万个CSV文件,每个文件都有不同组合的名称,所以

G.csv,A.csv,到GALMFWKQESPVICYHRNDT.csv

             Species              OGT   Sum of percentage  
 ------------------------------- ----- ------------------- 
  Aeropyrum pernix                 95             23.4353  
  Anaeromyxobacter dehalogenans    26             20.3232  
  Argobacterium fabrum             27             14.2312  
  Aquifex aeolicus                 85             15.0403  
  Archaeoglobus fulgidus           83             34.0532  

1 个答案:

答案 0 :(得分:1)

看起来需要:

new_df['Sum of percentage'] = df[list(combination)].sum(axis=1)