如何使用numpy或pandas仅在python中的两行之间读取数据? [英] How to read data only between two lines in python using numpy or pandas?
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问题描述
我有一个这样的数据文件:
I have a datafile like this:
# column 1 is the angle of incidence (degrees)
# column 2 is the wavelength (microns)
# column 3 is the transmission probability
# column 4 is the reflection probability
14.2000 0.300000 0.01 0.999920
14.2000 0.301000 0.02 0.999960
14.2000 0.302000 0.03 0.999980
14.2000 0.303000 0.04 0.999980
14.2000 0.304000 0.06 0.999980
14.2000 0.305000 0.08 0.999970
14.2000 0.306000 0.2 0.999950
14.2000 0.307000 0.4 0.999910
14.2000 0.308000 0.8 0.999860
14.2000 0.309000 0.9 0.999960
14.2000 0.310000 0.8 0.999990
14.2000 0.311000 0.4 0.999980
14.2000 0.312000 0.2 0.999960
14.2000 0.313000 0.06 0.999940
14.2000 0.314000 0.03 0.999930
14.2000 0.315000 0.02 1.00000
14.2000 0.316000 0.01 1.00000
所需的输出文件output.csv是这样的:
Required output file output.csv is this:
# column 1 is the angle of incidence (degrees)
# column 2 is the wavelength (microns)
# column 3 is the transmission probability
# column 4 is the reflection probability
14.2000 0.304000 0.06 0.999980
14.2000 0.305000 0.08 0.999970
14.2000 0.306000 0.2 0.999950
14.2000 0.307000 0.4 0.999910
14.2000 0.308000 0.8 0.999860
14.2000 0.309000 0.9 0.999960
14.2000 0.310000 0.8 0.999990
14.2000 0.311000 0.4 0.999980
14.2000 0.312000 0.2 0.999960
14.2000 0.313000 0.06 0.999940
14.2000 0.314000 0.03 0.999930
# conditions are:
# output first element of column3 >= 0.05 i.e. 0.06
# output last element of column3 < 0.05 i.e. 0.03
# for the second may be we need to get the index of second 0.06 and
# get the value of next index.
我们如何在 python pandas 或 numpy 中这样做?
How can we do so in python pandas or numpy?
我最初的尝试是这样的:
My initial attempt is this:
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Author : Bhishan Poudel
# Date : June 16, 2016
# Imports
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
#==============================================================================
# read in a file
infile = 'filter_2.txt'
colnames = ['angle', 'wave','trans', 'refl']
print('{} {} {} {}'.format('\nreading file : ', infile, '','' ))
df = pd.read_csv(infile,sep='\s+', header = None,skiprows = 0,
comment='#',names=colnames,usecols=(0,1,2,3))
print(df)
# find value of wavelength just above 0.05
print("\n")
df = df[(df['trans'] >= 0.05) ]
print(df)
一些类似的链接如下:
如何在python中的2个特定行之间读取
推荐答案
我会完全跳过 pandas 或 numpy
I'd skip pandas or numpy altogether
fo = open('filter_3.txt', 'w')
with open('filter_2.txt', 'r') as fi:
line = fi.readline()
while line:
split = line.split()
if (split[0] == '#') or (float(split[2]) >= 0.027):
print line,
fo.write(line)
line = fi.readline()
fo.close()
# column 1 is the angle of incidence (degrees)
# column 2 is the wavelength (microns)
# column 3 is the transmission probability
# column 4 is the reflection probability
14.2000 0.302000 0.028 0.999980
14.2000 0.303000 0.030 0.999980
14.2000 0.304000 0.032 0.999980
14.2000 0.305000 0.030 0.999970
14.2000 0.306000 0.028 0.999950
<小时>
添加一行的新代码
fo = open('filter_3.txt', 'w')
with open('filter_2.txt', 'r') as fi:
new_line = fi.readline()
old_line = None
while new_line:
split_new = new_line.split()
if old_line is not None:
split_old = old_line.split()
cond0 = False if old_line is None else (split_old[0] == '#')
cond1 = split_new[0] == '#'
cond2 = float(split_new[2]) >= 0.05
cond3 = False if old_line is None else (float(split_old[2]) >= 0.05)
if (cond1 or cond2) or (cond3 and not cond0):
print new_line,
fo.write(new_line)
printed_old = True
old_line = new_line
new_line = fi.readline()
fo.close()
# column 1 is the angle of incidence (degrees)
# column 2 is the wavelength (microns)
# column 3 is the transmission probability
# column 4 is the reflection probability
14.2000 0.304000 0.06 0.999980
14.2000 0.305000 0.08 0.999970
14.2000 0.306000 0.2 0.999950
14.2000 0.307000 0.4 0.999910
14.2000 0.308000 0.8 0.999860
14.2000 0.309000 0.9 0.999960
14.2000 0.310000 0.8 0.999990
14.2000 0.311000 0.4 0.999980
14.2000 0.312000 0.2 0.999960
14.2000 0.313000 0.06 0.999940
14.2000 0.314000 0.03 0.999930
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