如果某个值在某个范围内，如何从一个文件读入，然后再写到另一个文件？

Question

这是我的一个样本_ef.xpk公司文件，我正在读。你知道吗

label dataset sw sf
1H 1H_2
NOESY_F1eF2f.nv
4807.69238281 4803.07373047
600.402832031 600.402832031
1H.L 1H.P 1H.W 1H.B 1H.E 1H.J 1H.U 1H_2.L 1H_2.P 1H_2.W 1H_2.B 1H_2.E 1H_2.J 1H_2.U vol int stat comment flag0 flag8 flag9
0 {1.H2'} 4.93607 0.05000 0.10000 ++ {0.0} {} {1.H1'} 5.82020 0.05000 0.10000 ++ {0.0} {} 0.0 100.0000 0 {} 0 0 0
1 {1.H2'} 4.93607 0.05000 0.10000 ++ {0.0} {} {1.H1'} 5.82020 0.05000 0.10000 ++ {0.0} {} 0.0 100.0000 0 {} 0 0 0
2 {1.H3'} 4.70891 0.05000 0.10000 ++ {0.0} {} {1.H8} 8.13712 0.05000 0.10000 ++ {0.0} {} 0.0 100.0000 0 {} 0 0 0
3 {1.H2'} 4.93607 0.05000 0.10000 ++ {0.0} {} {1.H8} 8.13712 0.05000 0.10000 ++ {0.0} {} 0.0 100.0000 0 {} 0 0 0
4 {2.H2'} 4.55388 0.05000 0.10000 ++ {0.0} {} {2.H1'} 5.90291 0.05000 0.10000 ++ {0.0} {} 0.0 100.0000 0 {} 0 0 0
5 {2.H2'} 4.55388 0.05000 0.10000 ++ {0.0} {} {2.H1'} 5.90291 0.05000 0.10000 ++ {0.0} {} 0.0 100.0000 0 {} 0 0 0
6 {2.H3'} 4.60420 0.05000 0.10000 ++ {0.0} {} {2.H8} 7.61004 0.05000 0.10000 ++ {0.0} {} 0.0 100.0000 0 {} 0 0 0
7 {2.H2'} 4.55388 0.05000 0.10000 ++ {0.0} {} {2.H8} 7.61004 0.05000 0.10000 ++ {0.0} {} 0.0 100.0000 0 {} 0 0 0
8 {1.H3'} 4.70891 0.05000 0.10000 ++ {0.0} {} {2.H8} 7.61004 0.05000 0.10000 ++ {0.0} {} 0.0 100.0000 0 {} 0 0 0
9 {1.H2'} 4.93607 0.05000 0.10000 ++ {0.0} {} {2.H8} 7.61004 0.05000 0.10000 ++ {0.0} {} 0.0 100.0000 0 {} 0 0 0
10 {3.H5} 5.20481 0.05000 0.10000 ++ {0.0} {} {2.H8} 7.61004 0.05000 0.10000 ++ {0.0} {} 0.0 100.0000 0 {} 0 0 0

我想获取1H.p和1H琰u 2.p列中的值，并将它们写入另一个文件，但我只想包含某个范围内的值。我以为我这么做是为了我的代码。mask变量应该“过滤”值，对吗？你知道吗

这是我的密码：

import pandas as pd
import os
import sys
import re

i=0;
contents_peak=[]
peak_lines=[]
with open ("ee_pinkH1.xpk","r") as peakPPM:
    for PPM in peakPPM.readlines():
        float_num = re.findall("[\s][1-9]{1}\.[0-9]+",PPM)
        if (len(float_num)>1):
            i=i+1
            value = ('Peak '+ str(i) + ' ' + str(float_num[0]) + ' 0.05 ' + str(float_num[1]) + ' 0.05' + '\n')
            peak_lines.append(value)
tclust_peak = open("tclust.txt","w+")
tclust_peak.write("rbclust \n")
for value in peak_lines:
    tclust_peak.write(value)
tclust_peak.close()

result={}
text = 'ee'
filename= 'ee_pinkH1.xpk'

if text == 'ee':
    df=pd.read_csv("peaks_ee.xpk",sep=" ", skiprows=5)

    shift1 = df["1H.P"]
    shift2 = df["1H_2.P"]

    if filename=='ee_pinkH1.xpk':
        mask = ((shift1>5.1) & (shift1<6)) & ((shift2>7) & (shift2<8.25))
    elif filename == 'ee_pinkH2.xpk':
        mask = ((shift1>3.25)&(shift1<5))&((shift2>7)&(shift2<8.5))

if text == 'ef':
    df = pd.read_csv('peaks_ef.xpk',sep = " ",skiprows=5)
    shift1=df["1H.P"]
    shift2=df["1H_2.P"]

    if filename == 'ef_blue.xpk':
        mask = ((shift1>5) & (shift1<6)) & ((shift2>7.25) & (shift2<8.25))
    elif filename == 'ef_green.xpk':
        mask = ((shift1>7) & (shift1<9)) & ((shift2>5.25) & (shift2<6.2))
    elif filename == 'ef_orange.xpk':
        mask = ((shift1>3) & (shift1<5)) & ((shift2>5.2) & (shift2<6.25))

if text == 'fe':
    df = pd.read_csv('peaks_fe.xpk', sep=" ",skiprows=5)

    shift1= df["1H.P"]
    shift2= df["1H_2.P"]

    if filename == 'fe_yellow.xpk':
        mask = ((shift1>3) & (shift1<5)) & ((shift2>5) & (shift2<6))
    elif filename == 'fe_green.xpk':
        mask = ((shift1>5.1) & (shift1<6)) & ((shift2>7) & (shift2<8.25))

result = df[mask]
result = result[["1H.L","1H_2.L"]]

for col in result.columns:
    result[col] = result[col].str.strip("{} ")
result.drop_duplicates(keep='first', inplace=True)
result = result.set_index([['Atom '+str(i) for i in range(1,len(result)+1)]])
tclust_atom=open("tclust.txt","a")
result.to_string(tclust_atom, header = False)
df1 = df.copy()[['1H.L','1H.P']]
df2 = df.copy()[['1H_2.L','1H_2.P']]

df2.rename(columns={'1H_2.L': '1H.L', '1H_2.P': '1H.P'}, inplace=True)
df = pd.concat([df1,df2])
df['1H.L']=df['1H.L'].apply(lambda row: row.strip('{}'))
df['new']=0.3
df.drop_duplicates(keep='first',inplace=True)

tclust_atom=open("tclust_ppm.txt","w+")
df.to_csv("tclust_ppm.txt",sep=" ", index=False, header=False)

我的输出示例如下：

5.H3' 4.43488 0.3
6.H2' 4.49744 0.3
7.H1' 5.95115 0.3
6.H3' 4.51612 0.3
8.H5 5.39709 0.3
7.H3' 4.62099 0.3
7.H2 7.67414 0.3
8.H2' 4.31783 0.3
9.H1' 5.91813 0.3
8.H3' 4.45577 0.3
10.H5 5.17157 0.3
9.H3' 4.66179 0.3

根据我的代码，filter或“mask”变量位于if语句中：

if text == 'ef':
df = pd.read_csv('peaks_ef.xpk',sep = " ",skiprows=5)
shift1=df["1H.P"]
shift2=df["1H_2.P"]

if filename == 'ef_blue.xpk':
    mask = ((shift1>5) & (shift1<6)) & ((shift2>7.25) & (shift2<8.25))
elif filename == 'ef_green.xpk':
    mask = ((shift1>7) & (shift1<9)) & ((shift2>5.25) & (shift2<6.2))
elif filename == 'ef_orange':
    mask = ((shift1>3) & (shift1<5)) & ((shift2>5.2) & (shift2<6.25))

它应该来自elif filename =='ef_orange':，shift1和shift2都不应该大于6.25，但是在我的输出中，我得到的答案是7.67414。为什么我的过滤不起作用，我如何修复它？你知道吗

Answer 1

通过使用

shift1=df["1H.P"]
shift2=df["1H_2.P"]

你将你的过滤器压缩为一个序列，那就是你的列，当你想要在整个数据帧上填充时，为了你的缘故，它将更容易被看作是它自己的函数。你知道吗

def fil(df,oneLow,oneHigh,twoLow,twoHigh):
    df = df[((df['1H.P'] > oneLow) & (df['1H.P'] < oneHigh)) & ((df['1H_2.P'] > twoLow) & (df['1H_2.P'] < twoHigh))]
    return df


if text == 'ef':
    df = pd.read_csv('peaks_ef.xpk',sep = " ",skiprows=5)
    #shift1=df["1H.P"] remove
    #shift2=df["1H_2.P"] remove

    if filename == 'ef_blue.xpk':
        #mask = ((shift1>5) & (shift1<6)) & ((shift2>7.25) & (shift2<8.25))
        df = fil(df,5,6,7.25,8.25)
    elif filename == 'ef_green.xpk':
        #mask = ((shift1>7) & (shift1<9)) & ((shift2>5.25) & (shift2<6.2))
        df = fil(df,7,9,5.25,6.2)
    elif filename == 'ef_orange':
        #mask = ((shift1>3) & (shift1<5)) & ((shift2>5.2) & (shift2<6.25))
        df = fil(df,3,5,5.2,6.25)

用完整代码编辑

import pandas as pd
import os
import sys
import re

def fil(df,oneLow,oneHigh,twoLow,twoHigh):
    df = df[((df['1H.P'] > oneLow) & (df['1H.P'] < oneHigh)) & ((df['1H_2.P'] > twoLow) & (df['1H_2.P'] < twoHigh))]
    return df



i=0;
contents_peak=[]
peak_lines=[]
with open ("ee_pinkH1.xpk","r") as peakPPM:
    for PPM in peakPPM.readlines():
        float_num = re.findall("[\s][1-9]{1}\.[0-9]+",PPM)
        if (len(float_num)>1):
            i=i+1
            value = ('Peak '+ str(i) + ' ' + str(float_num[0]) + ' 0.05 ' + str(float_num[1]) + ' 0.05' + '\n')
            peak_lines.append(value)
tclust_peak = open("tclust.txt","w+")
tclust_peak.write("rbclust \n")
for value in peak_lines:
    tclust_peak.write(value)
tclust_peak.close()

result={}
text = 'ee'
filename= 'ee_pinkH1.xpk'

if text == 'ee':
    df=pd.read_csv("peaks_ee.xpk",sep=" ", skiprows=5)

    if filename=='ee_pinkH1.xpk':
        result = fil(df,5.1,6,7,8.25)
    elif filename == 'ee_pinkH2.xpk':
        result = fil(df,3.25,5,7,8.5)
if text == 'ef':
    df = pd.read_csv('peaks_ef.xpk',sep = " ",skiprows=5)

    if filename == 'ef_blue.xpk':
        result = fil(df,5,6,7.25,8.25)
    elif filename == 'ef_green.xpk':
        result = fil(df,7,9,5.25,6.2)
    elif filename == 'ef_orange.xpk':
        result = fil(df,3,5,5.2,6.25)
if text == 'fe':
    df = pd.read_csv('peaks_fe.xpk', sep=" ",skiprows=5)

    if filename == 'fe_yellow.xpk':
        result= fil(df,3,5,5,6)
    elif filename == 'fe_green.xpk':
        result= fil(df,5.1,6,7,8.25)

for col in result.columns:
    result[col] = result[col].str.strip("{} ")
result.drop_duplicates(keep='first', inplace=True)
result = result.set_index([['Atom '+str(i) for i in range(1,len(result)+1)]])
tclust_atom=open("tclust.txt","a")
result.to_string(tclust_atom, header = False)
df1 = df.copy()[['1H.L','1H.P']]
df2 = df.copy()[['1H_2.L','1H_2.P']]

df2.rename(columns={'1H_2.L': '1H.L', '1H_2.P': '1H.P'}, inplace=True)
df = pd.concat([df1,df2])
df['1H.L']=df['1H.L'].apply(lambda row: row.strip('{}'))
df['new']=0.3
df.drop_duplicates(keep='first',inplace=True)

tclust_atom=open("tclust_ppm.txt","w+")
df.to_csv("tclust_ppm.txt",sep=" ", index=False, header=False)