입력 파일에서 특정 행 값 가져 오기 .csv 새로운 .csv 파일로 작성

Question

.csv 파일에서 특정 값을 순서대로 추출하려고합니다. 내 .csv 파일이 다음과 같이 표시됩니다

t,px,py,pz,vx,vy 
0.00040489,14.12025084,-0.002009773,0.000840943,0.195026080,1.355758297 
0.00110044,12.83635303,-0.000548573,-0.00014433,0.130413970,-2.43868875 
1.68165647,0.255617890,-0.001845726,-0.00084086,0.023879232,-8.39354209 
1.68610220,0.255606449,-0.001963713,-0.00059876,0.023861204,-8.31501485 
1.68900455,0.255587527,-0.002043956,-0.00044366,0.023849428,-8.26700131 
4.99693312,0.229725068,0.0003837980,0.003745918,0.019379076,-3.09994830 
5.00358073,0.229685715,0.0003664936,0.003709321,0.019376030,-3.11744550 
5.00677838,0.229666449,0.0003580808,0.003691646,0.019374567,-3.12616105 
5.01067399,0.229642709,0.0003477605,0.003670047,0.019372785,-3.13703933 
9.99574307,0.237937739,-0.104983657,-0.08335162,0.018869346,-0.00283944 
9.99935327,0.237987378,-0.105109574,-0.08342908,0.018871016,-0.00284325 
10.0027278,0.238033837,-0.105227240,-0.08350144,0.018872581,-0.00284681 
10.0060302,0.238079357,-0.105342358,-0.08357221,0.018874116,-0.00285029 
10.0099914,0.238134027,-0.105480397,-0.08365702,0.018875962,-0.00285447 
10.0142878,0.238193410,-0.105630066,-0.08374895,0.018877970,-0.00285901 
......... 
14.9980889,0.237862082,-0.054564047,-0.062497959,0.020898590,-0.0023804 
15.0013937,0.237843713,-0.054438091,-0.062480914,0.020897489,-0.0023755 
15.0056053,0.237820487,-0.054278111,-0.062459537,0.020896085,-0.0023693 
15.0087124,0.237803480,-0.054160455,-0.062444016,0.020895048,-0.0023648 
................. 

을이 위의 파일에서, 나는 (5, 10, 등등의 열)의 행을 싶어. 5로 나눌 수있는 값과 하나의 값으로 충분합니다. 예를 들어, 5가 찍히면 10, 15 등을 찾습니다.

나는 내 출력 파일은 다음과 같이해야합니다 :

t,px,vx 
5.00358073,0.229685715,0.019376030 
10.0027278,0.238033837,0.018872581 
15.0013937,0.237843713,0.020897489 

Answer 1

IIUC, 우리는 당신이 더 정교한 코드를 게시하시기 바랍니다 수 있습니다! : 여기

df['G']=df.t//5 

df.drop_duplicates('G',keep='first').loc[lambda x : x.G!=0] 
Out[877]: 
      t  px  py  pz  vx  vy G 
6 5.003581 0.229686 0.000366 0.003709 0.019376 -3.117446 1.0 
11 10.002728 0.238034 -0.105227 -0.083501 0.018873 -0.002847 2.0 
16 15.001394 0.237844 -0.054438 -0.062481 0.020897 -0.002376 3.0 

Adding `to_csv` 

df.drop_duplicates('G',keep='first').loc[lambda x : x.G!=0].to_csv('your.csv')