PyQT5环境配置

1. Conda环境

建议安装conda环境,可以方便的维护和切换多个不用的Python环境

创建新的conda虚拟python环境:

conda create -n env_name python=3.x

启动conda环境:

conda activate env_name

2. PyQt5安装

pip install pyqt5

3. QtDesigner

对于开发稍微复杂一些的界面,建议安装QtDesigner

https://build-system.fman.io/qt-designer-download

4. UI资源编译

编译.ui文件

例如,ui文件为w_main.ui, 编译命令如下

pyuic5 -o w_main.py w_main.ui

编译资源文件

例如,资源文件为apprcc.qrc, 编译命令如下:

pyrcc5 -o apprcc_rc.py apprcc.qrc

Python 坏习惯

1、拼接字符串用 + 号

坏的做法

def manual_str_formatting(name, subscribers):
    if subscribers > 100000:
        print("Wow " + name + "! you have " + str(subscribers) + " subscribers!")
    else:
        print("Lol " + name + " that's not many subs")

调整后的做法是使用 f-string,而且效率会更高

def manual_str_formatting(name, subscribers):
    # better
    if subscribers > 100000:
        print(f"Wow {name}! you have {subscribers} subscribers!")
    else:
        print(f"Lol {name} that's not many subs")

2、使用 finaly 而不是上下文管理器

坏的做法:

def finally_instead_of_context_manager(host, port):
    s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
    try:
        s.connect((host, port))
        s.sendall(b'Hello, world')
    finally:
        s.close()

调整后的做法是使用上下文管理器,即使发生异常,也会关闭 socket

def finally_instead_of_context_manager(host, port):
    # close even if exception
    with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
        s.connect((host, port))
        s.sendall(b'Hello, world')

3、尝试手动关闭文件

坏的做法:

def manually_calling_close_on_a_file(filename):
    f = open(filename, "w")
    f.write("hello!\n")
    f.close()

调整后的做法是使用上下文管理器,即使发生异常,也会自动关闭文件,凡是有上下文管理器的,都应该首先采用:

def manually_calling_close_on_a_file(filename):
    with open(filename) as f:
        f.write("hello!\n")
    # close automatic, even if exception

4、except 后面什么也不写

坏的做法

def bare_except():
    while True:
        try:
            s = input("Input a number: ")
            x = int(s)
            break
        except:  # oops! can't CTRL-C to exit
            print("Not a number, try again")

这样会捕捉所有异常,导致按下 CTRL-C 程序都不会终止,调整后的做法是

def bare_except():
    while True:
        try:
            s = input("Input a number: ")
            x = int(s)
            break
        except Exception:  # 比这更好的是用 ValueError
            print("Not a number, try again")

5、函数参数使用可变对象

如果函数参数使用可变对象,那么下次调用时可能会产生非预期结果,坏的做法

def mutable_default_arguments():
    def append(n, l=[]):
        l.append(n)
        return l

    l1 = append(0)  # [0]
    l2 = append(1)  # [0, 1]

调整后的做法,如下

def mutable_default_arguments():

    def append(n, l=None):
        if l is None:
            l = []
        l.append(n)
        return l

    l1 = append(0)  # [0]
    l2 = append(1)  # [1]

6、从不用推导式

坏的做法

squares = {}
for i in range(10):
    squares[i] = i * i

调整后的做法

odd_squares = {i: i * i for i in range(10)}

7、推导式用的上瘾

推导式虽然好用,但是不可以牺牲可读性,坏的做法

c = [
    sum(a[n * i + k] * b[n * k + j] for k in range(n))
    for i in range(n)
    for j in range(n)
]

调整后的做法,如下:

c = []
for i in range(n):
    for j in range(n):
        ij_entry = sum(a[n * i + k] * b[n * k + j] for k in range(n))
        c.append(ij_entry)

8、用 == 判断是否单例

坏的做法

def equality_for_singletons(x):
    if x == None:
        pass

    if x == True:
        pass

    if x == False:
        pass

调整后的做法,如下

def equality_for_singletons(x):
    # better
    if x is None:
        pass

    if x is True:
        pass

    if x is False:
        pass

9、使用类 C 风格的 for 循环

坏的做法

def range_len_pattern():
    a = [1, 2, 3]
    for i in range(len(a)):
        v = a[i]
        ...
    b = [4, 5, 6]
    for i in range(len(b)):
        av = a[i]
        bv = b[i]
        ...

调整后的做法,如下:

def range_len_pattern():
    a = [1, 2, 3]
    # instead
    for v in a:
        ...

    # or if you wanted the index
    for i, v in enumerate(a):
        ...

    # instead use zip
    for av, bv in zip(a, b):
        ...

10、不使用 dict.items

坏的做法

def not_using_dict_items():
    d = {"a": 1, "b": 2, "c": 3}
    for key in d:
        val = d[key]
        ...

调整后的做法,如下

def not_using_dict_items():
    d = {"a": 1, "b": 2, "c": 3}
    for key, val in d.items():
        ...

11、记录日志使用 print 而不是 logging

坏的做法

def print_vs_logging():
    print("debug info")
    print("just some info")
    print("bad error")

调整后的做法,如下

def print_vs_logging():
    # versus
    # in main
    level = logging.DEBUG
    fmt = '[%(levelname)s] %(asctime)s - %(message)s'
    logging.basicConfig(level=level, format=fmt)

    # wherever
    logging.debug("debug info")
    logging.info("just some info")
    logging.error("uh oh :(")

12、调用外部命令时使用 shell=True

坏的做法

subprocess.run(["ls -l"], capture_output=True, shell=True)

如果 shell=True,则将 ls -l 传递给/bin/sh(shell) 而不是 Unix 上的 ls 程序,会导致 subprocess 产生一个中间 shell 进程, 换句话说,使用中间 shell 意味着在命令运行之前,命令字符串中的变量、glob 模式和其他特殊的 shell 功能都会被预处理。比如,$HOME 会在在执行 echo 命令之前被处理处理。

调整后的做法是拒绝从 shell 执行,如下:

subprocess.run(["ls", "-l"], capture_output=True)

13、从不尝试使用 numpy

坏的做法

def not_using_numpy_pandas():
    x = list(range(100))
    y = list(range(100))
    s = [a + b for a, b in zip(x, y)]

调整后的的做法,如下:

import numpy as np
def not_using_numpy_pandas():
    # 性能更快
    x = np.arange(100)
    y = np.arange(100)
    s = x + y

14、喜欢 import *

调整后的做法,如下:

from itertools import *

count()

这样的话,没有人知道这个脚本到底有多数变量, 比较好的做法:

from mypackage.nearby_module import awesome_function

def main():
    awesome_function()

if __name__ == '__main__':
    main()