Basic code
1.split [‘a;b’] to [‘a’],[‘b’]
split_target = open("Processing_target_nan_split.txt",'w')
with open('Processing_target_nan_withHeader.txt') as Processing_target:
count = 0
for line in Processing_target:
count += 1
try:
line = line.replace('\n','')#去掉末尾换行符
Target = line.split('\t')[7].split(";")[:]#split
Index_drug = line.split("\t")[0]
Index_cellLine = line.split("\t")[1]
AUC = line.split("\t")[2]
cellLine_name= line.split("\t")[5]
DrugName = line.split("\t")[6]
for t in Target:
split_target.write(('\t').join([str(i) for i in [Index_drug,Index_cellLine,AUC,cellLine_name,DrugName,t]]) + '\n')#最后一定要加换行符
except:
with open('./Error.txt','a') as h:
h.write('%s :%s have error! \n' % (str(count),line))
split_target.close()
2.Window和Linux文件中粘贴时末尾的换行符
os.system('paste {} {} | tr -d "\r" > {}'.format('a.txt','b.txt','a_b.txt'))
3.Python3和Python2
(1). 中文字符
# -*- coding: UTF-8 -*-
- Python2
import sys
reload(sys)
sys.setdefaultencoding('utf-8')
- Python3
import sys
import importlib
importlib.reload(sys)
(2).写文件
- Python2
with open('file.txt','w') as g:
for i in dict.keys():
print >>g, ('\t').join([str(i) for i in dict[key])
- Python3
with open('file.txt','w') as g:
for i in dict.keys():
print(('\t').join([str(i) for i in [key] +dict[key]),file = g)
(3).字典
<1>.测试字典是否包含指定的键item
- python 2
if dict.has_key(item): #中间
- python 3
if item in dict: #最快
if item in dict.keys()： #最慢
<2>.查看字典中键和值
- python 2
print(dict.keys()[0:5])#键
print(dict.values()[0:5])#值
- python 3
print(list(dict.keys())[0:5])#键
print(list(dict.values())[0:5])#值
4.list和str转
<1>. list to str
list = ['www', 'google', 'com']
list_to_str = ";"join(list)
- 输出为：‘www;google;com’
<2>. str to list
str = 'www.google.com'
str_to_list = str.split('.')
- 输出为：[‘www’, ‘google’, ‘com’]
5.Pandas读取文件
header=None:不把第一行作为列属性
ata1=pd.read_csv=("test.csv")#自动把第一行作列属性，第一行不能用
data2pd.read_csv("test.csv",header=None)#不把第一行作列属性
6.DataFrame操作
1. 创建空的dataframe
df = pd.DataFrame(columns = list())
2.Series合并
df=pd.concat([series1,series2],axis=1),默认情况按照axis = 0进行行连接，axis = 1为按照列连接，示例可参考这里
3.合并列数据为新列
- 如果某一列是非
str类型的数据，需要用map(str)做转换
dataframe["newColumn"] = dataframe["age"].map(str) + dataframe["phone"] + dataframe["address"]
4.Dataframe中按照某一列对dataframe重新排列
- 降序排列
df.reindex(df['col_1'].abs().sort_values(ascending=False).index)
- 升序排列
df.reindex(df['col_1'].abs().sort_values(ascending=True).index)
5.列名
- 重命名所有的列：
df.columns = ['col_1','col_2'] - 重命名某几列：
df = df.rename(columns = {'old_col','new_col'})
7. 循环存储图片
- 画图结束后要添加
plt.close(),否则会在同一张图上反复画 - 不要
plt.show()后再储存，因为plt.show()后会默认清空，储存的图片回事空白
8.选择列表中最长的字符串
max(list,key = len)
mylist = ['123','123456','1234']
print (max(mylist, key=len))
out:123456
9.字符串操作
1). string.strip(): 去除头尾字符和空白符（包括\n、\r、\t、' ‘，即：换行、回车、制表符、空格）
2). string.lstrip(): 去除开头字符和空白符（包括\n、\r、\t、’ ‘，即：换行、回车、制表符、空格）
3). string.rstrip(): 去除结尾字符和空白符（包括\n、\r、\t、’ ‘，即：换行、回车、制表符、空格）
- 这些函数只删除头尾的字符，中间的不会删除
- 返回的是去除字符的string副本，string本身不会发生改变
10.清空文件并替换内容
def modify_text():
with open('test.txt', "r+") as f:
read_data = f.read()
f.seek(0)
f.truncate() #清空文件
f.write(read_data.replace('apple', 'android'))
f.seek(0):将文件定位到postion 0r+:读写
Important Code
1.将文件分成10部分
import numpy as np
import pandas as pd
import sys
import os
import time
part_number = 10
reload(sys)
sys.setdefaultencoding('utf-8')
print('{}\t{}\n'.format(time.ctime(), 'start to analyze!'))
SL_ReDisease_score_withHeader = pd.read_csv('SL_ReDisease_score_withHeader.txt',sep='\t').values
os.system('head -n 1 {} > {}'.format('SL_ReDisease_score_withHeader.txt','SL_ReDisease_score_withHeader_header.txt'))
print(len(SL_ReDisease_score_withHeader))
a = len(SL_ReDisease_score_withHeader)
for i in range(part_number):
if i < part_number - 1:
SL_ReDisease_score_withHeader_part = SL_ReDisease_score_withHeader[int(i*(a/10)):int((i+1)*(a/10))].tolist()
else:
SL_ReDisease_score_withHeader_part = SL_ReDisease_score_withHeader[int(i*(a/10)):].tolist()
filename = "SL_ReDisease_score_withHeader_{}_temp.txt".format(i+1)
with open(filename,'w') as m:
for j in SL_ReDisease_score_withHeader_part:
print >>m, ('\t').join([str(x) for x in j])
os.system('cat {} {} > {}'.format('SL_ReDisease_score_withHeader_header.txt', filename, "SL_ReDisease_score_withHeader_{}.txt".format(i+1)))
os.system('rm -r *temp.txt')
2.添加Index和search以及多进程
#!/usr/bin/env python
import numpy as np
import pandas as pd
import sys
import os
import time
import re
# from re_module import re_module_search
reload(sys)
sys.setdefaultencoding('utf-8')
print('{}\t{}\n'.format(time.ctime(), 'start to analyze!'))
'''
add arguement
'''
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("-i", "--input", help="input file for analysis", required=True)
#parser.add_argument("-o", "--output", help="output file for analysis", required=True)
args = parser.parse_args()
SL_ReDisease_score_withHeader = pd.read_csv('{}'.format(args.input),sep='\t').values
# os.system('head -n 1 {} > {}'.format('SL_ReDisease_score_withHeader.txt','SL_ReDisease_score_withHeader_header.txt'))
ReDisease_MutationGene_Score = pd.read_csv('ReDisease_MutationGene_Score.txt',sep='\t').values
print(len(SL_ReDisease_score_withHeader))
print(len(ReDisease_MutationGene_Score))
# print('Step1 has been completed!')
SL_ReDisease_score = {}
ReDisease_MutationGene = {}
for i in range(len(SL_ReDisease_score_withHeader)):
_key = str(SL_ReDisease_score_withHeader[i][0]).lower()+"#"+str(SL_ReDisease_score_withHeader[i][1]).lower()+"#"+str(SL_ReDisease_score_withHeader[i][2]).lower()+"#"+str(SL_ReDisease_score_withHeader[i][3]).lower()+"#"+str(SL_ReDisease_score_withHeader[i][4]).lower()+"#"+str(SL_ReDisease_score_withHeader[i][5]).lower()
_value = np.array(SL_ReDisease_score_withHeader[i][0:],dtype = str).tolist()
SL_ReDisease_score[_key] = _value
print(len(SL_ReDisease_score))
# print('Step2 has been completed!')
for i in range(len(ReDisease_MutationGene_Score)):
_key = str(ReDisease_MutationGene_Score[i][0]).lower()+"#"+str(ReDisease_MutationGene_Score[i][4]).lower()+"#"+str(ReDisease_MutationGene_Score[i][1]).lower()
_value = [str (i) for i in [str(ReDisease_MutationGene_Score[i][1])]+[str(ReDisease_MutationGene_Score[i][4])]+[str(ReDisease_MutationGene_Score[i][5])]]
ReDisease_MutationGene[_key] = _value
print(len(ReDisease_MutationGene))
# print('Step3 has been completed!')
# print('Dictionary has been made! Start to search!')
#print(SL_ReDisease_score['10018#lymphoma;large-cell;follicular#bcl2'])
#print(ReDisease_MutationGene['10018#lymphoma;large-cell;follicular#bcl2'])
ReDisease_MutationGene_Score={}
ReDisease_MutationGene_index = {}
for _key in ReDisease_MutationGene.keys():
id_3 = _key.split('#')[1]
id_4 = _key.split('#')[2]
new_key = id_3 + '#' + id_4
_index = ReDisease_MutationGene.keys().index(_key)
if ReDisease_MutationGene_index.has_key(new_key):
ReDisease_MutationGene_index[new_key].append(_index)
else:
ReDisease_MutationGene_index[new_key] = [_index]
# print(ReDisease_MutationGene_index['lymphoma, follicular#mtor'])
def re_module_search(input1, input2):
char = ''
# for key1 in input1.keys():
# char += key1 + '\n'
# n = re.findall('.*' + 'ki-1+ anaplastic large cell lymphoma' + '.*', char, re.MULTILINE)
# if len(n) > 0:
# print(n)
# sys.exit(0)
# else:
# pass
# sys.exit(0)
for key2 in input2.keys():
id_1 = key2.split('#')[2]
id_2 = key2.split('#')[5]
key3 = id_1 + '#' + id_2
if ReDisease_MutationGene_index.has_key(key3):
key3_index = ReDisease_MutationGene_index[key3]
choosed_keys = np.array(input1.keys())[key3_index].tolist()
for item in choosed_keys:
char += str(item) + '\n'
m = re.findall('.*' + id_1 + '#' + id_2 + '.*', char, re.MULTILINE)
# m = re.findall('.*' + id_1 + '.*' + '#' + id_2 + '.*', char, re.MULTILINE)
# if len(re.findall('.*' + id_1 + '#' + id_2 + '.*', char, re.MULTILINE)) > 0:
# m = re.findall('.*' + id_1 + '#' + id_2 + '.*', char, re.MULTILINE)
# elif len(re.findall('.*' + id_1 + '#' + id_2 + '.*', char, re.MULTILINE)) == 0 and len(re.findall('.*' + id_1 + '.*' + '#' + id_2 + '.*', char, re.MULTILINE)) > 0:
# m = re.findall('.*' + id_1 + '.*' + '#' + id_2 + '.*', char, re.MULTILINE)
# else:
# m = []
# n = re.findall('.*anaplastic large cell lymphoma.*', key2)
# if len(n) > 0:
# #print([key2]+[m[0]]+ReDisease_MutationGene[m[0]])
# print([n, id_1 + "#" + id_2, m])
# # print(m)
# #print(n)
# sys.exit(0)
# else:
# pass
#sys.exit(0)
if len(m) > 0:
for i in range(len(m)):
id_3 = m[i].split('#')[1]
id_4 = m[i].split('#')[2]
if id_1 == id_3 and id_2 == id_4:
#print([key2,m[i]])
if ReDisease_MutationGene_Score.has_key(key2):
ReDisease_MutationGene_Score[key2].append(SL_ReDisease_score[key2] +ReDisease_MutationGene[m[i]])
else:
ReDisease_MutationGene_Score[key2] = [SL_ReDisease_score[key2] + ReDisease_MutationGene[m[i]]]
else:
ReDisease_MutationGene_Score[key2]= [SL_ReDisease_score[key2] +['&&']]
else:
ReDisease_MutationGene_Score[key2]= [SL_ReDisease_score[key2] +['%']]
#print([key2,])
else:
#pass
print([key2, key3])
# print(ReDisease_MutationGene_Score.values()[0:5])
return ReDisease_MutationGene_Score
re_module_search(ReDisease_MutationGene, SL_ReDisease_score)
print(len(ReDisease_MutationGene_Score))
with open('./ReDiseaseScore.txt','w') as f:
f.write('MutationGene\tRepositionDisease\tReDiseaseScore\n')
f.close()
'''
SL_ReDisease_score_withHeader.txt
SL_ReDisease_MutationGene_score.txt
'''
prefix = args.input.split('.')[0]
with open('{}_MutationGene_score.txt'.format(prefix),'w') as g:
for key in ReDisease_MutationGene_Score.keys():
for j in range(len(ReDisease_MutationGene_Score[key])):
print >>g, ('\t').join([str(i) for i in ReDisease_MutationGene_Score[key][j]])
os.system('paste {} {} | tr -d "\r" > {}'.format('SL_ReDisease_score_withHeader_header.txt','ReDiseaseScore.txt','add_ReDiseasescore_header.txt'))
# 'SL_ReDisease_MutationGene_score_withHeader.txt'
os.system('sort {} | uniq > {}'.format('{}_MutationGene_score.txt'.format(prefix), '{}_MutationGene_score.temp.txt'.format(prefix)))
os.system('cat {} {} > {}'.format('add_ReDiseasescore_header.txt','{}_MutationGene_score.temp.txt'.format(prefix),'{}_MutationGene_score_withHeader.txt'.format(prefix)))
os.system('rm {}'.format('{}_MutationGene_score.temp.txt'.format(prefix)))
print('{}\t{}\n'.format(time.ctime(), 'end to analyze!'))
3.主函数
from multiprocessing import Pool
import os
os.system('python2.7 divide_ten.py')
def sh(*args):
os.system('python2.7 search.py -i {}'.format(*args))
pool = Pool(processes=10)
for i in range(1,11):
infile = '{}_{}.txt'.format('SL_ReDisease_score_withHeader',i)
pool.apply_async(sh, (infile,))
#sh(infile)
pool.close()
pool.join()
4. defaultdict
- 一键多值
from collections import defaultdict
# mutation mapping A
multi_mutGene_A = defaultdict(list)
for key_sl in sl_pair:
mutGene_sl = key_sl.split("+")[0]
new_key_A = ('\t').join(sl_pair[key_sl])
for key_mutGene in select_TSG:
mutGene_TSG = key_mutGene.split("-")[0]
if mutGene_sl == mutGene_TSG:
multi_mutGene_A[new_key_A].append(select_TSG[key_mutGene])
else:
pass
对象的魔力
对象的优点
1.多态（Polymorphism）
- 多态意味着即便不知道变量所引用的对象类型，依旧能对对象进行操作并根据对象类型的不同表现出不同的行为。
- 绑定到对象特性上的函数即为方法
（method）。
2.封装（Encapsulation）
- 向程序中其他部分隐藏对象的具体实现细节。
- 调用方法时将其作为特性
（attribute）存储。
3.继承（Inheritance）
类
- 鸟类： 通用（抽象）的类，百灵鸟类的超类
（superclass） - 鸟：类的实例
（instance） - 百灵鸟类： 鸟类的子类
（subclass）
1.创建自定义的类
class Person:
def setName(self.name):
self.name = name
def getName(self):
return self.name
def greet(self):
print ("Hello, world. I'm %s." % self.name)
foo = Person()
foo.setName("Lucky")
foo.greet()
# Hello, world. I'm Lucky.
- 自定义类：
Person - 方法：
setName,getName,greet - 实例：
foo self：调用实例foo的setName和greet方法时，foo自动将自己作为第一个参数传入方法- 调用方法：一般为实例.方法
（instance.method）
2.指定超类
- 将其他类名写在
class语句后的圆括号内
class Filter:
def init(self):
self.blocked = []
def filter(self.sequence):
return [x for x in self.sequence if x not in self.blocked]
class SPAMFilter(Filter): #SPAMFilter是Filter的子类
def init(self):
self.blocked = ['SPAM'] #重写Filter超类中的init方法
s=SPAMFilter()
s.filter(['SPAM', 'greet', 'SPAM', 'eggs'])
# ['greet', 'eggs']
3.多个超类
1). 查找已知类的基类们
SPAMFilter.__bases__
# (class __main__.Filter at 0x171e40)
Filter.__bases__
# ()
2). 多个超类
class TalkingCalculator(Calculator, Talking):
pass
- 多重继承：
class语句中先继承的类中的方法会重写后继承的类中的方法
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