Code: Select all
# t= 400000
1601 -0.207587E-02 0.454623E-03 0.193855E-02 0.996661E+00
1602 -0.202550E-02 0.447789E-03 0.191164E-02 0.996661E+00
1603 -0.197461E-02 0.440763E-03 0.188373E-02 0.996660E+00
1604 -0.192326E-02 0.433553E-03 0.185483E-02 0.996660E+00
1605 -0.187150E-02 0.426169E-03 0.182494E-02 0.996659E+00
# t= 410000
1601 -0.207587E-02 0.454623E-03 0.193855E-02 0.996661E+00
1602 -0.202550E-02 0.447789E-03 0.191164E-02 0.996661E+00
1603 -0.197461E-02 0.440763E-03 0.188373E-02 0.996660E+00
1604 -0.192326E-02 0.433553E-03 0.185483E-02 0.996660E+00
1605 -0.187150E-02 0.426169E-03 0.182494E-02 0.996659E+00
Code: Select all
import pandas as pd
from io import StringIO
df = pd.read_csv(StringIO(filename),
sep="\t",
skiprows=1,
usecols=[0,1,2,3,4],
names=['position','ux', 'uy', 'uz', 'rho'])
print(df)
Code: Select all
t pos ux uy uz rho
400000 1601 -0.207587E-02 0.454623E-03 0.193855E-02
0.996661E+00
400000 1602 -0.202550E-02 0.447789E-03 0.191164E-02
0.996661E+00 ....
410000 1603 -0.197461E-02 0.440763E-03 0.188373E-02
0.996660E+00
410000 1604 -0.192326E-02 0.433553E-03 0.185483E-02
0.996660E+00
410000 1605 -0.187150E-02 0.426169E-03 0.182494E-02
0.996659E+00