# 参考





python的re模块说明：

[https://docs.python.org/zh-cn/3/library/re.html](https://docs.python.org/zh-cn/3/library/re.html)

一些常见的正则表达式写法（邮箱、手机号、密码、身份证）：

[https://zhuanlan.zhihu.com/p/338826624](https://zhuanlan.zhihu.com/p/338826624)

# 使用

## re.findall

非常有用！

![在这里插入图片描述](/static/img/images/00334.png)

```powershell
in_str = """
Name: 10.10.90.86:9866 (hadoop001)
DFS Used: 149719834624 dgf
Name: 10.10.90.87:9866 (hadoop002)
DFS Used: 1497198231234624 dgfg
Name: 10.10.90.88:9866 (hadoop003)
DFS Used: 14971981323134624 gfdg
"""
import re
pattern1 = re.compile(r"Name: [0-9]+.[0-9]+.[0-9]+.[0-9]+")
dalllist1 = re.findall(pattern1, in_str)
dalllist1 = list(map(lambda x: x[6:], dalllist1))
pattern2 = re.compile(r"DFS Used: [0-9]+")
dalllist2 = re.findall(pattern2, in_str)
dalllist2 = list(map(lambda x: x[14:], dalllist2))
for i in zip(dalllist1, dalllist2):
    print(i)
```

## re.search

感觉基本没啥用。

![在这里插入图片描述](/static/img/images/00335.png)

```powershell
in_str = """
Name: 10.10.90.86:9866 (hadoop001)
DFS Used: 149719834624 dgf
Name: 10.10.90.87:9866 (hadoop002)
DFS Used: 1497198231234624 dgfg
Name: 10.10.90.88:9866 (hadoop003)
DFS Used: 14971981323134624 gfdg
"""
import re
pattern1 = re.compile(r"Name: [0-9]+.[0-9]+.[0-9]+.[0-9]+")
dalllist1 = re.search(pattern1, in_str)
print(dalllist1.group())
```

## re.match

用这个来匹配密码很好，用户设置密码的时候必须有数字和大小写之类的。

![在这里插入图片描述](/static/img/images/00336.png)

```powershell
import re
while True:
    pwd = input("input your password\r\n")
    if re.match(re.compile("^[a-z]{8,16}$"), pwd) == None:
        print("no pass")
    else:
        print("password is ok")
```

密码强度：[https://www.huaweicloud.com/articles/e43ea3c29dfbebc830af0e0b69b71ce3.html](https://www.huaweicloud.com/articles/e43ea3c29dfbebc830af0e0b69b71ce3.html)

## re.split

```powershell
import re
text = """Ross McFluff: 834.345.1254 155 Elm Street
...
... Ronald Heathmore: 892.345.3428 436 Finley Avenue
... Frank Burger: 925.541.7625 662 South Dogwood Way
...
...
... Heather Albrecht: 548.326.4584 919 Park Place"""
entries = re.split("\n+", text)
print([re.split(":? ", entry, 3) for entry in entries])
```


# 一些别的

> []

匹配方括号里的所有字符的集合。集合set的意味在里面。

比如`[34]`就是匹配3或者匹配4。

比如`[A-Za-z]` 就是匹配所有的大写字母或者小写字母。

比如`[0-9]` 就是匹配所有的数字。

  

> ^

如果放到 [  ]  里面，就表示这个集合里能匹配的东西都不是想匹配的。

比如`[^heo]`就表示匹配除了这三个字母集合外的所有字符集合。

如果放到 [  ]  外面，就表示匹配一个行开始。

比如`^[heo]`就表示匹配某一个新行，新行以三个字母的某一个字母开头。

要匹配 ^ 字符本身，请使用 `\^`。

> $

匹配行结尾。这符号很特殊。是一个零长度字符的匹配。

> .

匹配除换行符 \n 之外的任何单字符。要匹配 . ，请使用 `\.` 。

python 正则表达式 教程 使用 查找字符串 搜索是否含有 匹配密码强度 分割出合适数据

身在Docker容器里身不由己，wget能很好解决：





下载：
```powershell
wget https://repo.anaconda.com/miniconda/Miniconda3-py38_4.9.2-Linux-x86_64.sh
```

安装：

-b是静默安装，无需交互。
```powershell
sh Miniconda3-py38_4.9.2-Linux-x86_64.sh -b
```

所以记住这2个仓库：

[https://repo.anaconda.com/miniconda/](https://repo.anaconda.com/miniconda/)

[https://repo.anaconda.com/archive/](https://repo.anaconda.com/archive/)

Linux wget miniconda 快速安装miniconda环境

参考：[https://zhuanlan.zhihu.com/p/108418193](https://zhuanlan.zhihu.com/p/108418193)





![在这里插入图片描述](/static/img/images/00360.png)

什么时候需要定义类 而不是函数？

python安装opencv：





> pip install opencv-python

提取视频帧 视频转图片：
```python
import os
mp4file = 'input.mp4'
savefile = "./output"
if not os.path.exists(savefile):
    os.mkdir(savefile)
import cv2
cap = cv2.VideoCapture(mp4file)
fourcc = cv2.VideoWriter_fourcc(*'XVID')
fps = cap.get(cv2.CAP_PROP_FPS)
size = (int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)), int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)))
print(fps)
print(size)
i = 0
while (cap.isOpened()):
    i = i + 1
    ret, frame = cap.read()
    if ret == True:
        cv2.imwrite(savefile + '/' + str(i) + '.jpg', frame)
        if cv2.waitKey(1) & 0xFF == ord('q'):
            break
    else:
        break
print(i)
cap.release()
cv2.destroyAllWindows()
```

图片转视频：

```python
import os
mp4file = 'out.mp4'
savefile = "./output"
if not os.path.exists(savefile):
    os.mkdir(savefile)
import cv2
fourcc = cv2.VideoWriter_fourcc(*'XVID')
fps = 30
size =(720, 1280)
videoWriter = cv2.VideoWriter(mp4file, fourcc, fps, size)  # 最后一个是保存图片的尺寸
for i in range(1, 1402):
    frame = cv2.imread(savefile + '/' + str(i) + '.jpg')
    videoWriter.write(frame)
videoWriter.release()
```


合成，一定得注意尺寸的大小。

```python
# coding=utf-8
import os
import cv2
from PIL import Image
def makevideo(path, fps):
    """ 将图片合成视频. path: 视频路径，fps: 帧率 """
    fourcc = cv2.VideoWriter_fourcc(*"mp4v")
    path1 = r'D:\yifei111\PycharmProjects\python_test\jigan_res4'
    im = Image.open(r'D:\yifei111\PycharmProjects\python_test\jigan1\251.jpg')
    print(im.size)
    vw = cv2.VideoWriter(path, fourcc, fps, im.size)
    for i in os.listdir(path1):
        frame = cv2.imread(path1 + '\\' + i)
        frame = frame[200:649, :]
        frame = cv2.resize(frame, im.size)
        vw.write(frame)
if __name__ == '__main__':
    video_path = r'D:\yifei111\PycharmProjects\python_test\DST_test_new4.mp4'
    makevideo(video_path, 30)  # 图片转视频
```

python opencv 提取视频帧 视频转图片 图片转视频

借助pd.date_range快速实现





## 生成 Timestamp对象 列表

生成一系列Timestamp对象，Timestamp对象是对python datetime再封装得到的，一般情况直接用起来就好了。
```python
import pandas as pd
a = list(pd.date_range(start='2021-06-21 00:00:00', end='2021-06-22 00:00:00', freq='H'))
print(a)
```

得到：

[Timestamp('2021-06-21 00:00:00', freq='H'), Timestamp('2021-06-21 01:00:00', freq='H'), Timestamp('2021-06-21 02:00:00', freq='H'), Timestamp('2021-06-21 03:00:00', freq='H'), Timestamp('2021-06-21 04:00:00', freq='H'), Timestamp('2021-06-21 05:00:00', freq='H'), Timestamp('2021-06-21 06:00:00', freq='H'), Timestamp('2021-06-21 07:00:00', freq='H'), Timestamp('2021-06-21 08:00:00', freq='H'), Timestamp('2021-06-21 09:00:00', freq='H'), Timestamp('2021-06-21 10:00:00', freq='H'), Timestamp('2021-06-21 11:00:00', freq='H'), Timestamp('2021-06-21 12:00:00', freq='H'), Timestamp('2021-06-21 13:00:00', freq='H'), Timestamp('2021-06-21 14:00:00', freq='H'), Timestamp('2021-06-21 15:00:00', freq='H'), Timestamp('2021-06-21 16:00:00', freq='H'), Timestamp('2021-06-21 17:00:00', freq='H'), Timestamp('2021-06-21 18:00:00', freq='H'), Timestamp('2021-06-21 19:00:00', freq='H'), Timestamp('2021-06-21 20:00:00', freq='H'), Timestamp('2021-06-21 21:00:00', freq='H'), Timestamp('2021-06-21 22:00:00', freq='H'), Timestamp('2021-06-21 23:00:00', freq='H'), Timestamp('2021-06-22 00:00:00', freq='H')]

## 生成 str对象 列表

```python
import pandas as pd
a = list(pd.date_range(start='2021-06-21 00:00:00', end='2021-06-22 00:00:00', freq='H'))
a=list(map(str,a))
print(a)
```

得到：

['2021-06-21 00:00:00', '2021-06-21 01:00:00', '2021-06-21 02:00:00', '2021-06-21 03:00:00', '2021-06-21 04:00:00', '2021-06-21 05:00:00', '2021-06-21 06:00:00', '2021-06-21 07:00:00', '2021-06-21 08:00:00', '2021-06-21 09:00:00', '2021-06-21 10:00:00', '2021-06-21 11:00:00', '2021-06-21 12:00:00', '2021-06-21 13:00:00', '2021-06-21 14:00:00', '2021-06-21 15:00:00', '2021-06-21 16:00:00', '2021-06-21 17:00:00', '2021-06-21 18:00:00', '2021-06-21 19:00:00', '2021-06-21 20:00:00', '2021-06-21 21:00:00', '2021-06-21 22:00:00', '2021-06-21 23:00:00', '2021-06-22 00:00:00']

## 生成 python datetime对象 列表

```python
import pandas as pd
a = list(pd.date_range(start='2021-06-21 00:00:00', end='2021-06-22 00:00:00', freq='H'))
a=list(map(lambda x:x.to_pydatetime(),a))
print(a)
```

得到：

```python
[datetime.datetime(2021, 6, 21, 0, 0), datetime.datetime(2021, 6, 21, 1, 0), datetime.datetime(2021, 6, 21, 2, 0), datetime.datetime(2021, 6, 21, 3, 0), datetime.datetime(2021, 6, 21, 4, 0), datetime.datetime(2021, 6, 21, 5, 0), datetime.datetime(2021, 6, 21, 6, 0), datetime.datetime(2021, 6, 21, 7, 0), datetime.datetime(2021, 6, 21, 8, 0), datetime.datetime(2021, 6, 21, 9, 0), datetime.datetime(2021, 6, 21, 10, 0), datetime.datetime(2021, 6, 21, 11, 0), datetime.datetime(2021, 6, 21, 12, 0), datetime.datetime(2021, 6, 21, 13, 0), datetime.datetime(2021, 6, 21, 14, 0), datetime.datetime(2021, 6, 21, 15, 0), datetime.datetime(2021, 6, 21, 16, 0), datetime.datetime(2021, 6, 21, 17, 0), datetime.datetime(2021, 6, 21, 18, 0), datetime.datetime(2021, 6, 21, 19, 0), datetime.datetime(2021, 6, 21, 20, 0), datetime.datetime(2021, 6, 21, 21, 0), datetime.datetime(2021, 6, 21, 22, 0), datetime.datetime(2021, 6, 21, 23, 0), datetime.datetime(2021, 6, 22, 0, 0)]
```

# 转换

 pd.Timestamp 转 datetime.datetime  ：
```python
pd.Timestamp("2021-06-06").to_pydatetime()
```

datetime.datetime  转 pd.Timestamp：

```python
pd.to_datetime(datetime.datetime.now())
```

python 生成一段日期列表 pd.date_range Timestamp 转 datetime datetime 转 Timestamp

我是一个专注技术分享的博主,尤其爱搞深度学习和单片机电子。你现在看到的是我的个人博客网站。