Conversion of character case in string
1. str.lower() //Lowercase
>>> ; 'SkatE'.lower()
'skate'
2. str.upper() //Capital
>>> 'SkatE'.upper ()
'SKATE'
3. str.swapcase() //Case swap
>>> 'SkatE'.swapcase()
'sKATe'
4. str.title() //The first letter is capitalized and the rest are lowercase
>>> 'SkatE'.title()
'Skate'
String alignment during output
1. str.ljust(width,[fillchar]) // Output width characters, str is left-aligned, the missing part is filled with fillchar, the default is spaces
>>> 'skate'.ljust(10)
'skate '
>>> 'skate'.ljust(10,'0')
'skate00000'
2. str.rjust(width,[fillchar]) //Output width characters, str is right-aligned, and the missing part is filled with fillchar. The default is spaces
>>> 'skate'.rjust(10,'0')
'00000skate'
>>> 'skate'.rjust(10)
' skate'
3. str.center(width,[fillchar]) //Center alignment
>>> 'skate'.center(10)
' skate '
>>> 'skate'.center(10,'0')
'00skate000'
4. str.zfill(width) //Change str to width length, align it on the right, and fill in the missing part with 0
>>> 'skate'. zfill(10)
'00000skate'
String search related
str.find('t',start) //Specify the starting position to search
str.find('t',start,end) //Specify the start and end position search:
str.rfind('t') //Search from the right
str.count('t') //How many specified strings are searched:
eg:##>>> 'skate'.find('t')
3>>> 'skate'.find('t ',2)
3
>>> 'skate'.find('t',2,4)
3
>>> 'skate'.rfind( 't')
3
>>> 'skate'.count('t')
1
str.replace('old','new') //Replace old with new
str.replace('old','new',maxReplaceTimes) //Replace specified number of times old with new
>>> 'skateskate'.replace('s','S')
> >> 'skateskate'.replace('s','S',1)
'Skateskate'
>>>
str.strip([chars]) //Remove the chars on both sides, the default is spaces
str.lstrip([chars]) //Remove the chars on the left, the default is spacesstr.rstrip([chars]) //Remove the chars on the right, the default is space
str.split([sep, [maxsplit]]) //Use sep as the separator to divide str into a list. maxsplit represents the number of splits. The default separator is a blank character
str.rsplit([sep, [maxsplit]])str.splitlines([keepends]) //Divide str into a list according to the line separator, keepends is a bool Value, if true will retain line separators after each line.
#>>> 'skateskate'.split()['skateskate']
['skat', 'skat', '']
>>> 'skate skate' .rsplit(' ')
['skate', 'skate']
>>> 'skate\n skate1'.splitlines()
['skate', ' skate1 ']
['skate\n', ' skate1']
>>>
String connection
str.join(seq) //Connect the sequence (string sequence) represented by seq with str
eg:
>>> 'skate'.join('111') String judgment str.startwith(prefix [,start[,end]]) //Whether it starts with prefix >>> 'skate'.startswith('s',1,2)False>>> 'skate'.endswith('s',1,2) >>> 'skate'.endswith('e',1,2) True
'1skate1skate1
str.endwith(suffix[,start[,end]]) //Whether it ends with suffix
str.isalnum() //Whether it is complete It is the letter and number, and at least one character
Str.isalpha () // Whether it is all letter, and at least one character
Str.isdigit () // Whether it is all numbers, and at least one character
str.isSpace () // Whether it is all blank characters, and at least one character
Str.islower () // Whether the letters in the sTR are all lowercase
str.isuper () // str. Whether the letter in the middle is a capitalized
Str.istition () // Whether the STR is the first letter? 'skate'.startswith('s')
False
> >> 'skate'.endswith('e',1)
True
>>>
True
>>> 'skate'.isalnum ()
True
>>> '222'.isalnum()
True
> ;>> 'skate 222'.isalnum()
False
>>>
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Key applications of Python in web development include the use of Django and Flask frameworks, API development, data analysis and visualization, machine learning and AI, and performance optimization. 1. Django and Flask framework: Django is suitable for rapid development of complex applications, and Flask is suitable for small or highly customized projects. 2. API development: Use Flask or DjangoRESTFramework to build RESTfulAPI. 3. Data analysis and visualization: Use Python to process data and display it through the web interface. 4. Machine Learning and AI: Python is used to build intelligent web applications. 5. Performance optimization: optimized through asynchronous programming, caching and code

Python is better than C in development efficiency, but C is higher in execution performance. 1. Python's concise syntax and rich libraries improve development efficiency. 2.C's compilation-type characteristics and hardware control improve execution performance. When making a choice, you need to weigh the development speed and execution efficiency based on project needs.

Python's real-world applications include data analytics, web development, artificial intelligence and automation. 1) In data analysis, Python uses Pandas and Matplotlib to process and visualize data. 2) In web development, Django and Flask frameworks simplify the creation of web applications. 3) In the field of artificial intelligence, TensorFlow and PyTorch are used to build and train models. 4) In terms of automation, Python scripts can be used for tasks such as copying files.

Python is widely used in data science, web development and automation scripting fields. 1) In data science, Python simplifies data processing and analysis through libraries such as NumPy and Pandas. 2) In web development, the Django and Flask frameworks enable developers to quickly build applications. 3) In automated scripts, Python's simplicity and standard library make it ideal.

Python's flexibility is reflected in multi-paradigm support and dynamic type systems, while ease of use comes from a simple syntax and rich standard library. 1. Flexibility: Supports object-oriented, functional and procedural programming, and dynamic type systems improve development efficiency. 2. Ease of use: The grammar is close to natural language, the standard library covers a wide range of functions, and simplifies the development process.

Python is highly favored for its simplicity and power, suitable for all needs from beginners to advanced developers. Its versatility is reflected in: 1) Easy to learn and use, simple syntax; 2) Rich libraries and frameworks, such as NumPy, Pandas, etc.; 3) Cross-platform support, which can be run on a variety of operating systems; 4) Suitable for scripting and automation tasks to improve work efficiency.

Yes, learn Python in two hours a day. 1. Develop a reasonable study plan, 2. Select the right learning resources, 3. Consolidate the knowledge learned through practice. These steps can help you master Python in a short time.


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