list 列表相关
list 中最小值、最大值
import operator
values = [1, 2, 3, 4, 5]
min_index, min_value = min(enumerate(values), key=operator.itemgetter(1))
max_index, max_value = max(enumerate(values), key=operator.itemgetter(1))
print('min_index:', min_index, 'min_value:', min_value)
print('max_index:', max_index, 'max_value:', max_value)
# Out
min_index: 0 min_value: 1
max_index: 4 max_value: 5
list 中连续元素之间的差
from itertools import islice
ls = [1,2,3,5,8]
diff = [j-i for i,j in zip(ls, islice(ls, 1, None))]
print(diff)
# Out
[1, 1, 2, 3]
删除列表中的重复元素
下面这种方法不能维持顺序：
x = [1, 8, 4, 5, 5, 5, 8, 1, 8]
list(set(x))
# Out
[8, 1, 4, 5]
下面的方法，可以维持顺序：
from collections import OrderedDict
x = [1, 8, 4, 5, 5, 5, 8, 1, 8]
list(OrderedDict.fromkeys(x))
# Out
[1,8,4,5]
并行遍历2个列表
a = [1, 2, 3]
b = [4, 5, 6]
for (a_val, b_val) in zip(a, b):
print(a_val, b_val)
# Out
1 4
2 5
3 6
合并列表值
输入的两个数组，输出一个是数组&值相加或者相乘：
# input
first = [1,2,3,4,5]
second = [6,7,8,9,10]
#output
three = [7,9,11,13,15]
# The zip function is useful here, used with a list comprehension.
# add
[x + y for x, y in zip(first, second)]
# other
[x*y for x, y in zip(first, second)]
[max(x,y) for x, y in zip(first, second)]
参考：
字典处理
字典做交、差、并
a={'name':'michael','age':"27",'sex':'male'}
b={'name':'hqh','age':'27'}
{k:a[k] for k in a.keys()-b.keys()}
out: {'sex': 'male'}
dict(a.items()-b.items())
out: {'name': 'michael', 'sex': 'male'}
需要注意的是，当字典的值有字典时，a.items()-b.items() 这种方式会报错 TypeError: unhashable type: 'dict'；
参考：
字典的Key与Value对调
m = {'A': 1, 'B': 2, 'C': 3}
invert_map_key_value = lambda m: dict(zip(m.values(), m.keys()))
invert_map_key_value(m)
# output: {1: 'A', 2: 'B', 3: 'C'}
参考：
合并字典值
>>> from collections import Counter
>>> A = Counter({'a':1, 'b':2, 'c':3})
>>> B = Counter({'b':3, 'c':4, 'd':5})
>>> A + B
Counter({'c': 7, 'b': 5, 'd': 5, 'a': 1})
字典的增加
用 update 方法往已有字典中增加键值对：
deploy_info=dict()
for idx, row in raw_data.iterrows():
temp=dict()
version = row['version']
app_comp_name = row['app_comp_name']
pkg_name = "{}_{}.tar.gz".format(app_comp_name, version)
time.sleep(2)
data = get_verify_value(api_url,pkg_name)
temp = {
deploy_history_id:{
'app_comp_name':app_comp_name,
'version':version,
'pkg_name':pkg_name,
'data':data
}
}
deploy_info.update(temp)
字符串相关
索引
tag='hx/mitaka_compute/12.0.0'
[m.start() for m in re.finditer('/',tag)]
参考：
将百分号的百分比字符串转为数字
p="75%"
float(p.strip('%'))/100
参考：
剔除分隔符
通常做法：
''.join('A|B|C|D|E|F|G'.split('|'))
# output: 'ABCDEFG'
用 itertools.islice，因为可以节选字符串：
import itertools
''.join(itertools.islice('A|B|C|D|E|F|G', 6, None, 2))
# output: 'DEFG'
''.join(itertools.islice('A|B|C|D|E|F|G', 0, None, 2))
# output: ''ABCDEFG'
美观打印
import pprint as pp
animals = [{'animal': 'dog', 'legs': 4, 'breeds': ['Border Collie', 'Pit Bull', 'Huskie']}, {'animal': 'cat', 'legs': 4, 'breeds': ['Siamese', 'Persian', 'Sphynx']}]
pp.pprint(animals, width=1)
# Out
[{'animal': 'dog',
'breeds': ['Border '
'Collie',
'Pit '
'Bull',
'Huskie'],
'legs': 4},
{'animal': 'cat',
'breeds': ['Siamese',
'Persian',
'Sphynx'],
'legs': 4}]
width参数指定一行上最大的字符数。设置width为1确保字典打印在单独的行
文件读写
基本文件读 txt
# Note: rb opens file in binary mode to avoid issues with Windows systems
# where 'rn' is used instead of 'n' as newline character(s).
# A) Reading in Byte chunks
reader_a = open("file.txt", "rb")
chunks = []
data = reader_a.read(64) # reads first 64 bytes
while data != "":
chunks.append(data)
data = reader_a.read(64)
if data:
chunks.append(data)
print(len(chunks))
reader_a.close()
# B) Reading whole file at once into a list of lines
with open("file.txt", "rb") as reader_b: # recommended syntax, auto closes
data = reader_b.readlines() # data is assigned a list of lines
print(len(data))
# C) Reading whole file at once into a string
with open("file.txt", "rb") as reader_c:
data = reader_c.read() # data is assigned a list of lines
print(len(data))
# D) Reading line by line into a list
data = []
with open("file.txt", "rb") as reader_d:
for line in reader_d:
data.append(line)
print(len(data))
json 读写json文件
- json.loads()是将str转化成dict格式，json.dumps()是将dict转化成str格式。
- json.load()和json.dump()也是类似的功能，只是与文件操作结合起来了。
# 解码
import json
with open('build_info.json','r') as f:
array = json.load(f)
print(array)
在编码JSON的时候，还有一些选项很有用。 如果你想获得漂亮的格式化字符串后输出，可以使用 json.dumps() 的indent参数。 它会使得输出和pprint() 函数效果类似：
>>> print(json.dumps(data))
{"price": 542.23, "name": "ACME", "shares": 100}
>>> print(json.dumps(data, indent=4))
{
"price": 542.23,
"name": "ACME",
"shares": 100
}
>>>
保存为 json 文件：
# 编码
import json
a = {"name":"michael"}
with open("demo.json","w") as f:
json.dump(a, f, indent=4)
- CookBook-6.2 读写JSON数据
- 简书-Python: json模块实例详解
- 官宣-json — JSON encoder and decoder
- Reading JSON file with Python 3
- What is the difference between json.load() and json.loads() functions in Python?
时间日期
基本时间（time）和日期（date）
import time
# print time HOURS:MINUTES:SECONDS
# e.g., '10:50:58'
print(time.strftime("%H:%M:%S"))
# print current date DAY:MONTH:YEAR
# e.g., '05/01/2019'
print(time.strftime("%d/%m/%Y"))
# Out
15:18:03
05/01/2019
字符串和日期的相互转换
strptime 是将字符串转换为 datetime，其实这个方法的全称是 “string parse time”，叫做字符串解析成时间，重点在解析（parse）:
from datetime import datetime
date_obj = datetime.strptime('2018-10-15 20:59:29', '%Y-%m-%d %H:%M:%S')
print(type(date_obj),date_obj)
# Out
<class 'datetime.datetime'> 2018-10-15 20:59:29
strftime 是将 datetime 转换为字符串，全称是 “string format time”，翻译过来就是将字符串的形式来格式化时间，重点在格式化（format），使之以一种可读的字符串形式返回：
from datetime import datetime
date_obj = datetime.now()
date_string = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
print(type(date_string),date_string)
# Out
<class 'str'> 2019-01-05 18:41:04
参考：
编码相关
Python Requests 编码问题
下载
Python下载文件
Python根据url下载目录或者文件
def download_package(self, package_url):
print("start download_build_result")
if not package_url.endswith("/"):
package_url += '/'
cmd = "wget -c -r -nd -np -P %s %s" % ("output", package_url)
print(cmd)
os.system(cmd)
print(os.getcwd())
print("finish download_build_result")
数据处理
Python Pandas处理Excel数据
逐行处理数据 iterrows
for idx, row in data.iterrows():
project_name=row['projectName']
tag_name=row['tagName']
Pandas追加模式写入csv文件
data = pd.DataFrame([[1,2,3]])
csv_headers=['A','B','C']
data.to_csv('./Marvel3_yingpping.csv', header=csv_headers, index=False, mode='a+', encoding='utf-8')
data = pd.DataFrame([[4,5,6]])
data.to_csv('./Marvel3_yingpping.csv', header=False, index=False, mode='a+', encoding='utf-8')
data = pd.DataFrame([[7,8,9]])
data.to_csv('./Marvel3_yingpping.csv', header=False, index=False, mode='a+', encoding='utf-8')
Python-CSV-Excel
for idx, row in data.iterrows():
project_name=row['projectName']
tag_name=row['tagName']
to_csv表格中文乱码
ipython中直接打印df，中文没有乱码，但是to_csv方法存储时，中文有乱码。
df.to_csv('file.csv',encoding='utf-8-sig')
参考：
- Pandas df.to_csv(“file.csv” encode=“utf-8”) still gives trash characters for minus sign
- 关于pandas中,to_csv函数输出的utf8数据用Excel打开是乱码
itero
看题目：
- python数据处理，字典生成的一个问题答案中有位前辈用这个用的炉火纯青啊！
Shell/Linux 操作相关
Python运行shell命令的函数：
def run(cmd_str, fatal=True):
# this is not a good implement
log.command(log.term.cmd(cmd_str))
ret = os.system(cmd_str)
if ret is not 0:
if fatal:
log.error('[ERROR] run cmd: %s failed', cmd_str)
os._exit(1)
else:
log.info('[INFO] %s is not fatal' % cmd_str)
调用外部的命令
#
import subprocess
subprocess.call(['mkdir', 'empty_folder'])
# 运行一条命令并输出得到的结果
output = subprocess.check_output(['ls', '-l'])
# 上面的调用是阻塞的
# 如果运行shell中内置的命令，如cd或者dir，需要指定标记shell=True
output = subprocess.call(['cd', '/'], shell=True)
# 对于更高级的用例，可以使用 Popen constructor。
Python 3.5引进了一个新的run函数，它的行为与call和check_output很相似。如果你使用的是3.5版本或更高版本，看一看run的文档，里面有一些有用的例子。否则，如果你使用的是Python 3.5以前的版本或者你想保持向后兼容性，上面的call和check_output代码片段是你最安全和最简单的选择
参考：
计算文件的校验值
可以计算文件的 md5、sha256 等值
# https://pymotw.com/3/hashlib/index.html#module-hashlib
def get_verify_value(file_path, verify_type):
"""
计算指定文件的校验值
:param file_path: 文件路径
:param verify_type: 校验值类型，md5 sha256 等等
:return:
"""
h = hashlib.new(verify_type)
if not file_path:
return None
with open(file_path, 'rb') as f:
for block in iter(lambda: f.read(4096), b""):
h.update(block)
return h.hexdigest()
性能相关
脚本的运行时间
import time
start_time = time.clock()
for i in range(10000000):
pass
elapsed_time = time.clock() - start_time
print("Time elapsed: {} seconds".format(elapsed_time))
# Out
Time elapsed: 0.30121700000000007 seconds
import timeit
elapsed_time = timeit.timeit('for i in range(10000000): pass', number=1)
print("Time elapsed: {} seconds".format(elapsed_time))
# Out
Time elapsed: 0.2051873060000844 seconds
计算运行时间
class Timer(object):
def __enter__(self):
self.error = None
self.start = time.time()
return self
def __exit__(self, type, value, tb):
self.finish = time.time()
if type:
self.error = (type, value, tb)
def duration(self):
return self.finish - self.start
with Timer() as timer:
func()
timer.duration()
# Out
0.29994797706604004
参考：
目录、路径相关
基本目录文件操作
import os
import shutil
import glob
# working directory
c_dir = os.getcwd() # show current working directory
os.listdir(c_dir) # shows all files in the working directory
os.chdir('~/Data') # change working directory
# get all files in a directory
glob.glob('/Users/sebastian/Desktop/*')
# e.g., ['/Users/sebastian/Desktop/untitled folder', '/Users/sebastian/Desktop/Untitled.txt']
# walk
tree = os.walk(c_dir)
# moves through sub directories and creates a 'generator' object of tuples
# ('dir', [file1, file2, ...] [subdirectory1, subdirectory2, ...]),
# (...), ...
#check files: returns either True or False
os.exists('../rel_path')
os.exists('/home/abs_path')
os.isfile('./file.txt')
os.isdir('./subdir')
# file permission (True or False
os.access('./some_file', os.F_OK) # File exists? Python 2.7
os.access('./some_file', os.R_OK) # Ok to read? Python 2.7
os.access('./some_file', os.W_OK) # Ok to write? Python 2.7
os.access('./some_file', os.X_OK) # Ok to execute? Python 2.7
os.access('./some_file', os.X_OK | os.W_OK) # Ok to execute or write? Python 2.7
# join (creates operating system dependent paths)
os.path.join('a', 'b', 'c')
# 'a/b/c' on Unix/Linux
# 'a\b\c' on Windows
os.path.normpath('a/b/c') # converts file separators
# os.path: direcory and file names
os.path.samefile('./some_file', '/home/some_file') # True if those are the same
os.path.dirname('./some_file') # returns '.' (everythin but last component)
os.path.basename('./some_file') # returns 'some_file' (only last component
os.path.split('./some_file') # returns (dirname, basename) or ('.', 'some_file)
os.path.splitext('./some_file.txt') # returns ('./some_file', '.txt')
os.path.splitdrive('./some_file.txt') # returns ('', './some_file.txt')
os.path.isabs('./some_file.txt') # returns False (not an absolute path)
os.path.abspath('./some_file.txt')
# create and delete files and directories
os.mkdir('./test') # create a new direcotory
os.rmdir('./test') # removes an empty direcotory
os.removedirs('./test') # removes nested empty directories
os.remove('file.txt') # removes an individual file
shutil.rmtree('./test') # removes directory (empty or not empty)
os.rename('./dir_before', './renamed') # renames directory if destination doesn't exist
shutil.move('./dir_before', './renamed') # renames directory always
shutil.copytree('./orig', './copy') # copies a directory recursively
shutil.copyfile('file', 'copy') # copies a file
# Getting files of particular type from directory
files = [f for f in os.listdir(s_pdb_dir) if f.endswith(".txt")]
# Copy and move
shutil.copyfile("/path/to/file", "/path/to/new/file")
shutil.copy("/path/to/file", "/path/to/directory")
shutil.move("/path/to/file","/path/to/directory")
# Check if file or directory exists
os.path.exists("file or directory")
os.path.isfile("file")
os.path.isdir("directory")
# Working directory and absolute path to files
os.getcwd()
os.path.abspath("file")
参考：
Python 删除文件夹
def onerror(func, path, exc_info):
"""
Error handler for ``shutil.rmtree``.
If the error is due to an access error (read only file)
it attempts to add write permission and then retries.
If the error is for another reason it re-raises the error.
Usage : ``shutil.rmtree(path, onerror=onerror)``
"""
import stat
if not os.access(path, os.W_OK):
# Is the error an access error ?
os.chmod(path, stat.S_IWUSR)
func(path)
else:
raise
参考：
Python 切换目录
执行完，返回之前目录
import contextlib
@contextlib.contextmanager
def cdir(path):
prev_cwd = os.getcwd()
os.chdir(path)
try:
yield
finally:
os.chdir(prev_cwd)
用法：
with cdir(path):
func()
搜索指定目录下的文件
将指定目录及其子目录下的文件搜索出来：
def find_file(start_path, name):
"""
search the files of name from the dir start_path，存放的是搜索文件的路径
:param start_path: the search scope of dir
:param name: the name of search file
:return: set of files path
"""
files_path = set()
for rel_path, dirs, files in os.walk(start_path):
# if name in files:
for f in files:
if name in f:
full_path = os.path.join(start_path, rel_path, f)
path = os.path.normpath(os.path.abspath(full_path))
files_path.add(path)
return files_path
只列出文件夹下的文件夹
[ name for name in os.listdir(thedir) if os.path.isdir(os.path.join(thedir, name)) ]
filter(os.path.isdir, os.listdir(os.getcwd()))
Python Path相关问题
os.path.split(r"C:\foo\bar\file_name.txt")
数据库
MySQL 数据库
db = MySQLdb.connect("localhost","your_username","your_password","your_dbname")
cursor = db.cursor()
sql = "select Column1,Column2 from Table1"
cursor.execute(sql)
results = cursor.fetchall()
for row in results:
print row[0]+row[1]
db.close()
参考：
MongoDB
uri="mongodb://admin:admin@xxx.xxx.xxx.xxx:27017,xxx.xxx.xxx.xxx:27018,xxx.xxx.xxx.xxx:27019/test"
client=pymongo.MongoClient(uri,replicaSet='noah-cluster',readPreference='primaryPreferred')
db=client.get_default_database()
decouple_history=db.rpm_decouple_release_history_info
pprint(decouple_history.find_one({'service_name':'test'}))