Def foo(n): for x in range(n): yield ***3 for x in foo(5): print x
I shall assume that you are familiar with some programming languages such as C/C++/Java. This article is NOT meant to be an introduction to programming. Show
I personally recommend that you learn a traditional general-purpose programming language (such as C/C++/Java) before learning scripting language like Python/JavaScript/Perl/PHP because they are less structure than the traditional languages with many fancy features. Python By ExamplesThis section is for experienced programmers to look at Python's syntaxes and those who need to refresh their memory. For novices, go to the next section. Syntax Summary and Comparison
Example grade_statistics.py - Basic Syntaxes and ConstructsThis example repeatably prompts user for grade (between 0 and 100 with input validation). It then compute the sum, average, minimum, and print the horizontal histogram. This example illustrates the basic Python syntaxes and constructs, such as comment, statement, block indentation, conditional if-else, for-loop, while-loop, input/output, string, list and function.
To run the Python script: $ cd /path/to/project_directory $ python3 grade_statistics.py $ cd /path/to/project_directory $ chmod u+x grade_statistics.py $ ./grade_statistics.pyThe expected output is: $ Python3 grade_statistics.py Enter a grade between 0 and 100 (or -1 to end): 9 Enter a grade between 0 and 100 (or -1 to end): 999 invalid grade, try again... Enter a grade between 0 and 100 (or -1 to end): 101 invalid grade, try again... Enter a grade between 0 and 100 (or -1 to end): 8 Enter a grade between 0 and 100 (or -1 to end): 7 Enter a grade between 0 and 100 (or -1 to end): 45 Enter a grade between 0 and 100 (or -1 to end): 90 Enter a grade between 0 and 100 (or -1 to end): 100 Enter a grade between 0 and 100 (or -1 to end): 98 Enter a grade between 0 and 100 (or -1 to end): -1 --------------- The list is: [9, 8, 7, 45, 90, 100, 98] The minimum is: 7 The minimum using built-in function is: 7 The sum is: 357 The sum using built-in function is: 357 The average is: 51.00 --------------- 0-9 : *** 10-19 : 20-29 : 30-39 : 40-49 : * 50-59 : 60-69 : 70-79 : 80-89 : 90-100: ***How it Works
Example number_guess.py - Guess a NumberThis is a number guessing game. It illustrates nested-if (if-elif-else), while-loop with bool flag, and random module. For example, Enter your guess (between 0 and 100): 50 Try lower... Enter your guess (between 0 and 100): 25 Try higher... Enter your guess (between 0 and 100): 37 Try higher... Enter your guess (between 0 and 100): 44 Try lower... Enter your guess (between 0 and 100): 40 Try lower... Enter your guess (between 0 and 100): 38 Try higher... Enter your guess (between 0 and 100): 39 Congratulation! You got it in 7 trials.
How it Works
Exmaple magic_number.py - Check if Number Contains a Magic DigitThis example prompts user for a number, and check if the number contains a magic digit. This example illustrate function, int and str operations. For example, Enter a number: 123456789 123456789 is a magic number 123456789 is a magic number
How it Works
Example hex2dec.py - Hexadecimal To Decimal ConversionThis example prompts user for a hexadecimal (hex) string, and print its decimal equivalent. It illustrates for-loop with index, nested-if, string operation and dictionary (associative array). For example, Enter a hex string: 1abcd The decimal equivalent for hex "1abcd" is: 109517 The decimal equivalent for hex "1abcd" using built-in function is: 109517
How it Works
Example bin2dec.py - Binary to Decimal ConversionThis example prompts user for a binary string (with input validation), and print its decimal equivalent. For example, Enter a binary string: 1011001110 The decimal equivalent for binary "1011001110" is: 718 The decimal equivalent for binary "1011001110" using built-in function is: 718
How it Works
Example dec2hex.py - Decimal to Hexadecimal ConversionThis program prompts user for a decimal number, and print its hexadecimal equivalent. For example, Enter a decimal number: 45678 The hex for decimal 45678 is: B26E The hex for decimal 45678 using built-in function is: 0xb26e
How it Works
Example wc.py - Word CountThis example reads a filename from command-line and prints the line, word and character counts (similar to wc utility in Unix). It illustrates the text file input and text string processing.
How it works
Example htmlescape.py - Escape Reserved HTML CharactersThis example reads the input and output filenames from the command-line and replaces the reserved HTML characters by their corresponding HTML entities. It illustrates file input/output and string substitution.
How it works
Example files_rename.py - Rename FilesThis example renames all the files in the given directory using regular expression (regex). It illustrates directory/file processing (using module os) and regular expression (using module re).
How it works
IntroductionPython is created by Dutch Guido van Rossum around 1991. Python is an open-source project. The mother site is www.python.org. The main features of Python are:
Python has 3 versions:
Python 2 or Python 3?Currently, two versions of Python are supported in parallel, version 2.7 and version 3.5. There are unfortunately incompatible. This situation arises because when Guido Van Rossum (the creator of Python) decided to bring significant changes to Python 2, he found that the new changes would be incompatible with the existing codes. He decided to start a new version called Python 3, but continue maintaining Python 2 without introducing new features. Python 3.0 was released in 2008, while Python 2.7 in 2010. AGAIN, TAKE NOTE THAT PYTHON 2 AND PYTHON 3 ARE NOT COMPATIBLE!!! You need to decide whether to use Python 2 or Python 3. Start your new projects using Python 3. Use Python 2 only for maintaining legacy projects. To check the version of your Python, issue this command: $ Python --versionInstallation and Getting StartedInstallationFor Newcomers to Python (Windows, Mac OSX, Ubuntu)I suggest you install "Anaconda distribution" of Python 3, which includes a Command Prompt, IDEs (Jupyter Notebook and Spyder), and bundled with commonly-used packages (such as NumPy, Matplotlib and Pandas that are used for data analytics). Goto Anaconda mother site (@ https://www.anaconda.com/) ⇒ Choose "Anaconda Distribution" Download ⇒ Choose "Python 3.x" ⇒ Follow the instructions to install. Check If Python Already Installed and its VersionTo check if Python is already installed and its the version, issue the following command:, $ python3 --version Python 3.5.2 $ python2 --version Python 2.7.12Ubuntu (16.04LTS)Both the Python 3 and Python 2 should have already installed by default. Otherwise, you can install Python via: $ sudo apt-get install python3 $ sudo apt-get install python2To verify the Python installation: $ which python2 /usr/bin/python2 $ which python3 /usr/bin/python3 $ ll /usr/bin/python* lrwxrwxrwx 1 root root 9 xxx xx xxxx python -> python2.7* lrwxrwxrwx 1 root root 9 xxx xx xxxx python2 -> python2.7* -rwxr-xr-x 1 root root 3345416 xxx xx xxxx python2.7* lrwxrwxrwx 1 root root 9 xxx xx xxxx python3 -> python3.5* -rwxr-xr-x 2 root root 3709944 xxx xx xxxx python3.5* -rwxr-xr-x 2 root root 3709944 xxx xx xxxx python3.5m* lrwxrwxrwx 1 root root 10 xxx xx xxxx python3m -> python3.5m*WindowsYou could install either:
Mac OS X[TODO] DocumentationPython documentation and language reference are provided online @ https://docs.python.org. Getting Started with Python InterpreterStart the Interactive Python InterpreterYou can run the "Python Interpreter" in interactive mode under a "Command-Line Shell" (such as Anaconda Prompt, Windows' CMD, Mac OS X's Terminal, Ubuntu's Bash Shell): $ python Python 3.7.0 ...... Type "help", "copyright", "credits" or "license" for more information. >>>The Python's command prompt is denoted as >>>. You can enter Python statement at the Python's command prompt, e.g., >>> print('hello, world') hello, world >>> print(2 ** 88) 309485009821345068724781056 >>> print(8.01234567890123456789) 8.012345678901234 >>> print((1+2j) * (3+4j)) (-5+10j) >>> x = 123 >>> x 123 >>> msg = 'hi!' >>> msg 'hi!' >>> exit()To exit Python Interpreter:
Writing and Running Python ScriptsFirst Python Script - hello.pyUse a programming text editor to write the following Python script and save as "hello.py" in a directory of your choice:
How it Works
Expected OutputThe expected outputs are: Hello, world 309485009821345068724781056 8.012345678901234 (-0.2+0.4j) 22Running Python ScriptsYou can develop/run a Python script in many ways - explained in the following sections. Running Python Scripts in Command-Line Shell (Anaconda Prompt, CMD, Terminal, Bash)You can run a python script via the Python Interpreter under the Command-Line Shell: $ cdUnix's Executable Shell ScriptIn Linux/Mac OS X, you can turn a Python script into an executable program (called Shell Script or Executable Script) by:
The drawback is that you have to hard code the path to the Python Interpreter, which may prevent the program from being portable across different machines. Alternatively, you can use the following to pick up the Python Interpreter from the environment: ......The env utility will locate the Python Interpreter (from the PATH entries). This approach is recommended as it does not hard code the Python's path. Windows' Exeutable ProgramIn Windows, you can associate ".py" file extension with the Python Interpretable, to make the Python script executable. Running Python Scripts inside Python's InterpreterTo run a script "hello.py" inside Python's Interpreter: $ python3 ...... >>> exec(open('/path/to/hello.py').read()) $ python2 ...... >>> execfile('/path/to/hello.py') >>> exec(open('/path/to/hello.py'))
Interactive Development Environment (IDE)Using an IDE with graphic debugging can greatly improve on your productivity. For beginners, I recommend:
For Webapp developers, I recommend:
See "Python IDE and Debuggers" for details. Python Basic SyntaxesCommentsA Python comment begins with a hash sign (#) and last till the end of the current line. Comments are ignored by the Python Interpreter, but they are critical in providing explanation and documentation for others (and yourself three days later) to read your program. Use comments liberally. There is NO multi-line comment in Python?! (C/C++/Java supports multi-line comments via /* ... */.) StatementsA Python statement is delimited by a newline. A statement cannot cross line boundaries, except:
Unlike C/C++/C#/Java, you don't place a semicolon (;) at the end of a Python statement. But you can place multiple statements on a single line, separated by semicolon (;). For examples, >>> x = 1 >>> print(x) 1 >>> x + 1 2 >>> y = x / 2 >>> y 0.5 >>> print(x); print(x+1); print(x+2) 1 2 3 >>> x = [1, 22, 333] >>> x [1, 22, 333] >>> x = {'name':'Peter', 'gender':'male', 'age':21 } >>> x {'name': 'Peter', 'gender': 'male', 'age': 21} >>> x =(1 + 2 + 3 - 4) >>> x 2 >>> s = ('testing ' 'hello, ' 'world!') >>> s 'testing hello, world!'Block, Indentation and Compound StatementsA block is a group of statements executing as a unit. Unlike C/C++/C#/Java, which use braces {} to group statements in a body block, Python uses indentation for body block. In other words, indentation is syntactically significant in Python - the body block must be properly indented. This is a good syntax to force you to indent the blocks correctly for ease of understanding!!! A compound statement, such as conditional (if-else), loop (while, for) and function definition (def), begins with a header line terminated with a colon (:); followed by the indented body block, as follows: header_1: statement_1_1 statement_1_2 ...... header_2: statement_2_1 statement_2_2 ...... header_1: statement_1_1 header_2: statement_2_1; statement_2_2; ......For examples, x = 0 if x == 0: print('x is zero') else: print('x is not zero') if x == 0: print('x is zero') else: print('x is not zero') sum = 0 number = 1 while number <= 100: sum += number number += 1 print(sum) while number <= 100: sum += number; number += 1 def sum_1_to_n(n): sum = 0; i = 0; while (i <= n): sum += i i += 1 return sum print(sum_1_to_n(100))Python does not specify how much indentation to use, but all statements of the SAME body block must start at the SAME distance from the right margin. You can use either space or tab for indentation but you cannot mix them in the SAME body block. It is recommended to use 4 spaces for each indentation level. The trailing colon (:) and body indentation is probably the most strange feature in Python, if you come from C/C++/C#/Java. Python imposes strict indentation rules to force programmers to write readable codes! Variables, Identifiers and ConstantsLike all programming languages, a variable is a named storage location. A variable has a name (or identifier) and holds a value. Like most of the scripting interpreted languages (such as JavaScript/Perl), Python is dynamically typed. You do NOT need to declare a variable before using it. A variables is created via the initial assignment. (Unlike traditional general-purpose static typed languages like C/C++/Java/C#, where you need to declare the name and type of the variable before using the variable.) For example, >>> sum = 1 >>> sum 1 >>> type(sum)As mentioned, Python is dynamic typed. Python associates types with the objects, not the variables, i.e., a variable can hold object of any types, as shown in the above examples. Rules of Identifier (Names)An identifier starts with a letter (A-Z, a-z) or an underscore (_), followed by zero or more letters, underscores and digits (0-9). Python does not allow special characters such as $ and @. KeywordsPython 3 has 35 reserved words, or keywords, which cannot be used as identifiers.
Variable Naming ConventionA variable name is a noun, or a noun phrase made up of several words. There are two convenctions:
Recommendations
ConstantsPython does not support constants, where its contents cannot be modified. (C supports constants via keyword const, Java via final.) It is a convention to name a variable in uppercase (joined with underscore), e.g., MAX_ROWS, SCREEN_X_MAX, to indicate that it should not be modified in the program. Nevertheless, nothing prevents it from being modified. Data Types: Number, String and ListPython supports various number type such as int (for integers such as 123, -456), float (for floating-point number such as 3.1416, 1.2e3, -4.5E-6), and bool (for boolean of either True and False). Python supports text string (a sequence of characters). In Python, strings can be delimited with single-quotes or double-quotes, e.g., 'hello', "world", '' or "" (empty string). Python supports a dynamic-array structure called list, denoted as lst = [v1, v2, ..., vn]. You can reference the i-th element as lst[i]. Python's list is similar to C/C++/Java's array, but it is NOT fixed size, and can be expanded dynamically during runtime. I will describe these data types in details in the later section. Console Input/Output: input() and print() Built-in FunctionsYou can use built-in function input() to read input from the console (as a string) and print() to print output to the console. For example, >>> x = input('Enter a number: ') Enter a number: 5 >>> x '5' >>> type(x)print()The built-in function print() has the following signature: print(*objects, sep=' ', end='\n', file=sys.stdout, flush=False)For examples, >>> print('apple') apple >>> print('apple', 'orange') apple orange >>> print('apple', 'orange', 'banana') apple orange bananaprint()'s separator (sep) and ending (end)You can use the optional keyword-argument sep='x' to set the separator string (default is space), and end='x' for ending string (default is newline). For examples, >>> for item in [1, 2, 3, 4]: print(item) 1 2 3 4 >>> for item in [1, 2, 3, 4]: print(item, end='') 1234 >>> for item in [1, 2, 3, 4]: print(item, end='--') 1--2--3--4-->>> print('apple', 'orange', 'banana')apple orange banana >>> print('apple', 'orange', 'banana', sep=',') apple,orange,banana >>> print('apple', 'orange', 'banana', sep=':') apple:orange:banana >>> print('apple', 'orange', 'banana', sep='|') apple|orange|banana >>> print('apple', 'orange', 'banana', sep='\n') apple orange banana print in Python 2 vs Python 3Recall that Python 2 and Python 3 are NOT compatible. In Python 2, you can use "print item", without the parentheses (because print is a keyword in Python 2). In Python 3, parentheses are required as print() is a function. For example, >>> print('hello') hello >>> print 'hello' File "Important: Always use print() function with parentheses, for portability! Data Types and Dynamic TypingPython has a large number of built-in data types, such as Numbers (Integer, Float, Boolean, Complex Number), String, List, Tuple, Set, Dictionary and File. More high-level data types, such as Decimal and Fraction, are supported by external modules. You can use the built-in function type(varName) to check the type of a variable or literal. Number TypesPython supports these built-in number types:
Dynamic Typing and Assignment OperatorRecall that Python is dynamic typed (instead of static typed). Python associates types with objects, instead of variables. That is, a variable does not have a fixed type and can be assigned an object of any type. A variable simply provides a reference to an object. You do not need to declare a variable before using a variable. A variable is created automatically when a value is first assigned, which links the assigned object to the variable. You can use built-in function type(var_name) to get the object type referenced by a variable. >>> x = 1 >>> x 1 >>> type(x)Type Casting: int(x), float(x), str(x)You can perform type conversion (or type casting) via built-in functions int(x), float(x), str(x), bool(x), etc. For example, >>> x = '123' >>> type(x)In summary, a variable does not associate with a type. Instead, a type is associated with an object. A variable provides a reference to an object (of a certain type). Check Instance's Type: isinstance(instance, type)You can also use the built-in function isinstance(instance, type) to check if the instance belong to the type. For example, >>> isinstance(123, int) True >>> isinstance('a', int) False >>> isinstance('a', str) TrueThe Assignment Operator (=)In Python, you do not need to declare variables before using the variables. The initial assignment creates a variable and links the assigned value to the variable. For example, >>> x = 8 >>> x = 'Hello' >>> y NameError: name 'y' is not definedPair-wise Assignment and Chain AssignmentFor example, >>> a = 1 >>> a 1 >>> b, c, d = 123, 4.5, 'Hello' >>> b 123 >>> c 4.5 >>> d 'Hello' >>> e = f = g = 123 >>> e 123 >>> f 123 >>> g 123Assignment operator is right-associative, i.e., a = b = 123 is interpreted as (a = (b = 123)). del OperatorYou can use del operator to delete a variable. For example, >>> x = 8 >>> x 8 >>> del x >>> x NameError: name 'x' is not definedNumber OperationsArithmetic Operators (+, -, *, /, //, **, %)Python supports these arithmetic operators:
Compound Assignment Operators (+=, -=, *=, /=, //=, **=, %=)Each of the arithmetic operators has a corresponding shorthand assignment counterpart, i.e., +=, -=, *=, /=, //=, **= and %=. For example i += 1 is the same as i = i + 1. Increment/Decrement (++, --)?Python does not support increment (++) and decrement (--) operators (as in C/C++/Java). You need to use i = i + 1 or i += 1 for increment. Python accepts ++i ⇒ +(+i) ⇒ i, and --i. Don't get trap into this. But Python flags a syntax error for i++ and i--. Mixed-Type OperationsFor mixed-type operations, e.g., 1 + 2.3 (int + float), the value of the "smaller" type is first promoted to the "bigger" type. It then performs the operation in the "bigger" type and returns the result in the "bigger" type. In Python, int is "smaller" than float, which is "smaller" than complex. Relational (Comparison) Operators (==, !=, <, <=, >, >=, in, not in, is, is not)Python supports these relational (comparison) operators that return a bool value of either True or False.
Example: [TODO] Logical Operators (and, or, not)Python supports these logical (boolean) operators, that operate on boolean values.
Notes:
Example: [TODO] Built-in FunctionsPython provides many built-in functions for numbers, including:
For examples, >>> x = 1.23456 >>> type(x)Bitwise Operators (Advanced)Python supports these bitwise operators:
StringIn Python, strings can be delimited by a pair of single-quotes ('...') or double-quotes ("..."). Python also supports multi-line strings via triple-single-quotes ('''...''') or triple-double-quotes ("""..."""). To place a single-quote (') inside a single-quoted string, you need to use escape sequence \'. Similarly, to place a double-quote (") inside a double-quoted string, use \". There is no need for escape sequence to place a single-quote inside a double-quoted string; or a double-quote inside a single-quoted string. A triple-single-quoted or triple-double-quoted string can span multiple lines. There is no need for escape sequence to place a single/double quote inside a triple-quoted string. Triple-quoted strings are useful for multi-line documentation, HTML and other codes. Python 3 uses Unicode character set to support internationalization (i18n). >>> s1 = 'apple' >>> s1 'apple' >>> s2 = "orange" >>> s2 'orange' >>> s3 = "'orange'" >>> s3 "'orange'" >>> s3 ="\"orange\"" >>> s3 '"orange"' >>> s4 = """testing 12345""" >>> s4 'testing\n12345'Escape Sequences for Characters (\code)Like C/C++/Java, you need to use escape sequences (a back-slash + a code) for:
Raw Strings (r'...' or r"...")You can prefix a string by r to disable the interpretation of escape sequences (i.e., \code), i.e., r'\n' is '\'+'n' (two characters) instead of newline (one character). Raw strings are used extensively in regex (to be discussed in module re section). Strings are ImmutableStrings are immutable, i.e., their contents cannot be modified. String functions such as upper(), replace() returns a new string object instead of modifying the string under operation. Built-in Functions and Operators for StringsYou can operate on strings using:
Note: These functions and operators are applicable to all sequence data types including string, list, and tuple (to be discussed later).
For examples, >>> s = "Hello, world" >>> type(s)Character Type?Python does not have a dedicated character data type. A character is simply a string of length 1. You can use the indexing operator to extract individual character from a string, as shown in the above example; or process individual character using for-in loop (to be discussed later). The built-in functions ord() and chr() operate on character, e.g., >>> ord('A') 65 >>> ord('水') 27700 >>> chr(65) 'A' >>> chr(27700) '水'Unicode vs ASCIIIn Python 3, strings are defaulted to be Unicode. ASCII strings are represented as byte strings, prefixed with b, e.g., b'ABC'. In Python 2, strings are defaulted to be ASCII strings (byte strings). Unicode strings are prefixed with u. You should always use Unicode for internationalization (i18n)! String-Specific Member FunctionsPython supports strings via a built-in class called str (We will describe class in the Object-Oriented Programming chapter). The str class provides many member functions. Since string is immutable, most of these functions return a new string. The commonly-used member functions are as follows, supposing that s is a str object:
String Formatting 1 (New Style): Using str.format() functionThere are a few ways to produce a formatted string for output. Python 3 introduces a new style in the str's format() member function with {} as place-holders (called format fields). For examples, >>> '|{}|{}|more|'.format('Hello', 'world') '|Hello|world|more|' >>> '|{0}|{1}|more|'.format('Hello', 'world') '|Hello|world|more|' >>> '|{1}|{0}|more|'.format('Hello', 'world') '|world|Hello|more|' >>> '|{greeting}|{name}|'.format(greeting='Hello', name='Peter') '|Hello|Peter|' >>> '|{0}|{name}|more|'.format('Hello', name='Peter') '|Hello|Peter|more|' >>> '|{}|{name}|more|'.format('Hello', name='Peter') '|Hello|Peter|more|' >>> '|{1:8}|{0:7}|'.format('Hello', 'Peter') '|Peter |Hello |' >>> '|{1:8}|{0:>7}|{2:-<10}|'.format('Hello', 'Peter', 'again') '|Peter | Hello|again-----|' >>> '|{greeting:8}|{name:7}|'.format(name='Peter', greeting='Hi') '|Hi |Peter |' >>> '|{0:.3f}|{1:6.2f}|{2:4d}|'.format(1.2, 3.456, 78) '|1.200| 3.46| 78|' >>> '|{a:.3f}|{b:6.2f}|{c:4d}|'.format(a=1.2, b=3.456, c=78) '|1.200| 3.46| 78|'When you pass lists, tuples, or dictionaries (to be discussed later) as arguments into the format() function, you can reference the sequence's elements in the format fields with [index]. For examples, >>> tup = ('a', 11, 22.22) >>> tup = ('a', 11, 11.11) >>> lst = ['b', 22, 22.22] >>> '|{0[2]}|{0[1]}|{0[0]}|'.format(tup) '|11.11|11|a|' >>> '|{0[2]}|{0[1]}|{0[0]}|{1[2]}|{1[1]}|{1[0]}|'.format(tup, lst) '|11.11|11|a|22.22|22|b|' >>> dict = {'c': 33, 'cc': 33.33} >>> '|{0[cc]}|{0[c]}|'.format(dict) '|33.33|33|' >>> '|{cc}|{c}|'.format(**dict) '|33.33|33|'String Formatting 2: Using str.rjust(n), str.ljust(n), str.center(n), str.zfill(n)You can also use str's member functions like str.rjust(n) (where n is the field-width), str.ljust(n), str.center(n), str.zfill(n) to format a string. For example, >>> '123'.rjust(5) ' 123' >>> '123'.ljust(5) '123 ' >>> '123'.center(5) ' 123 ' >>> '123'.zfill(5) '00123' >>> '1.2'.rjust(5) ' 1.2' >>> '-1.2'.zfill(6) '-001.2'String Formatting 3 (Old Style): Using % operatorThe old style (in Python 2) is to use the % operator, with C-like printf() format specifiers. For examples, >>> '|%s|%8s|%-8s|more|' % ('Hello', 'world', 'again') '|Hello| world|again |more|' >>> '|%d|%4d|%6.2f|' % (11, 222, 33.333) '|11| 222| 33.33|'Avoid using old style for formatting. Conversion between String and Number: int(), float() and str()You can use built-in functions int() and float() to parse a "numeric" string to an integer or a float; and str() to convert a number to a string. For example, >>> s = '12345' >>> s '12345' >>> type(s)12345 >>> type(i) 55.66 >>> type(f) '123' >>> type(s) Concatenate a String and a Number?You CANNOT concatenate a string and a number (which results in TypeError). Instead, you need to use the str() function to convert the number to a string. For example, >>> 'Hello' + 123 TypeError: cannot concatenate 'str' and 'int' objects >>> 'Hello' + str(123) 'Hello123'The None ValuePython provides a special value called None (take note of the spelling in initial-capitalized), which can be used to initialize an object (to be discussed in OOP later). For example, >>> x = None >>> type(x)List, Tuple, Dictionary and SetList [v1, v2,...]Python has a powerful built-in dynamic array called list.
Built-in Functions and Operators for listA list, like string, is a sequence. Hence, you can operate lists using:
Notes:
list, unlike string, is mutable. You can insert, remove and modify its items. For examples, >>> lst = [123, 4.5, 'hello', True, 6+7j] >>> lst [123, 4.5, 'hello', True, (6+7j)] >>> len(lst) 5 >>> type(lst)Appending Items to a list>>> lst = [123, 'world'] >>> lst[2] IndexError: list index out of range >>> lst[len(lst)] = 4.5 IndexError: list assignment index out of range >>> lst[len(lst):] = [4.5] >>> lst [123, 'world', 4.5] >>> lst[len(lst):] = [6, 7, 8] >>> lst [123, 'world', 4.5, 6, 7, 8] >>> lst.append('nine') >>> lst [123, 'world', 4.5, 6, 7, 8, 'nine'] >>> lst.extend(['a', 'b']) >>> lst [123, 'world', 4.5, 6, 7, 8, 'nine', 'a', 'b'] >>> lst + ['c'] [123, 'world', 4.5, 6, 7, 8, 'nine', 'a', 'b', 'c'] >>> lst [123, 'world', 4.5, 6, 7, 8, 'nine', 'a', 'b']Copying a list>>> l1 = [123, 4.5, 'hello'] >>> l2 = l1[:] >>> l2 [123, 4.5, 'hello'] >>> l2[0] = 8 >>> l2 [8, 4.5, 'hello'] >>> l1 [123, 4.5, 'hello'] >>> l3 = l1.copy() >>> l4 = l1 >>> l4 [123, 4.5, 'hello'] >>> l4[0] = 8 >>> l4 [8, 4.5, 'hello'] >>> l1 [8, 4.5, 'hello']list-Specific Member FunctionsThe list class provides many member functions. Suppose lst is a list object:
Recall that list is mutable (unlike string which is immutable). These functions modify the list directly. For examples, >>> lst = [123, 4.5, 'hello', [6, 7, 8]] >>> lst [123, 4.5, 'hello', [6, 7, 8]] >>> type(lst)Using list as a last-in-first-out StackTo use a list as a last-in-first-out (LIFO) stack, use append(item) to add an item to the top-of-stack (TOS) and pop() to remove the item from the TOS. Using list as a first-in-first-out QueueTo use a list as a first-in-first-out (FIFO) queue, use append(item) to add an item to the end of the queue and pop(0) to remove the first item of the queue. However, pop(0) is slow! The standard library provide a class collections.deque to efficiently implement deque with fast appends and pops from both ends. Tuple (v1, v2,...)Tuple is similar to list except that it is immutable (just like string). Hence, tuple is more efficient than list. A tuple consists of items separated by commas, enclosed in parentheses (). >>> tup = (123, 4.5, 'hello') >>> tup (123, 4.5, 'hello') >>> tup[1] 4.5 >>> tup[1:3] (4.5, 'hello') >>> tup[1] = 9 TypeError: 'tuple' object does not support item assignment >>> type(tup)An one-item tuple needs a comma to differentiate from parentheses: >>> tup = (5,) >>> tup (5,) >>> x = (5) >>> x 5The parentheses are actually optional, but recommended for readability. Nevertheless, the commas are mandatory. For example, >>> tup = 123, 4.5, 'hello' >>> tup (123, 4.5, 'hello') >>> tup2 = 88, >>> tup2 (88,) >>> tup3 = () >>> tup3 () >>> len(tup3) 0You can operate on tuples using (supposing that tup is a tuple):
Conversion between List and TupleYou can covert a list to a tuple using built-in function tuple(); and a tuple to a list using list(). For examples, >>> tuple([1, 2, 3, 1]) (1, 2, 3, 1) >>> list((1, 2, 3, 1)) [1, 2, 3, 1]Dictionary {k1:v1, k2:v2,...}Python's built-in dictionary type supports key-value pairs (also known as name-value pairs, associative array, or mappings).
Dictionary-Specific Member FunctionsThe dict class has many member methods. The commonly-used are follows (suppose that dct is a dict object):
For Examples, >>> dct = {'name':'Peter', 'age':22, 'gender':'male'} >>> dct {'gender': 'male', 'name': 'Peter', 'age': 22} >>> type(dct)Set {k1, k2,...}A set is an unordered, non-duplicate collection of objects. A set is delimited by curly braces {}, just like dictionary. You can think of a set as a collection of dictionary keys without associated values. Sets are mutable. For example, >>> st = {123, 4.5, 'hello', 123, 'Hello'} >>> st {'Hello', 'hello', 123, 4.5} >>> 123 in st True >>> 88 in st False >>> st2 = set([2, 1, 3, 1, 3, 2]) >>> st2 {1, 2, 3} >>> st3 = set('hellllo') >>> st3 {'o', 'h', 'e', 'l'}Set-Specific Operators (&, !, -, ^)Python supports set operators & (intersection), | (union), - (difference) and ^ (exclusive-or). For example, >>> st1 = {'a', 'e', 'i', 'o', 'u'} >>> st1 {'e', 'o', 'u', 'a', 'i'} >>> st2 = set('hello') >>> st2 {'o', 'l', 'e', 'h'} >>> st1 & st2 {'o', 'e'} >>> st1 | st2 {'o', 'l', 'h', 'i', 'e', 'a', 'u'} >>> st1 - st2 {'i', 'u', 'a'} >>> st1 ^ st2 {'h', 'i', 'u', 'a', 'l'}Sequence Types: list, tuple, strlist, tuple, and str are parts of the sequence types. list is mutable, while tuple and str are immutable. They share the common sequence's built-in operators and built-in functions, as follows:
For mutable sequences (list), the following built-in operators and built-in functions (func(seq)) and member functions (seq.func(*args)) are supported:
OthersDeque[TODO] Heap[TODO] Flow Control ConstructsConditional if-elif-elseThe syntax is as follows. The elif (else-if) and else blocks are optional. if test: true_block else: false_block if test_1: block_1 elif test_2: block_2 elif test_3: block_3 ...... ...... elif test_n: block_n else: else_blockFor example: if x == 0: print('x is zero') elif x > 0: print('x is more than zero') print('xxxx') else: print('x is less than zero') print('yyyy')There is no switch-case statement in Python (as in C/C++/Java). Comparison and Logical OperatorsPython supports these comparison (relational) operators, which return a bool of either True or False.
Python supports these logical (boolean) operators: and, or, not. (C/C++/Java uses &&, ||, !.) Chain Comparison v1 < x < v2Python supports chain comparison in the form of v1 < x < v2, e.g., >>> x = 8 >>> 1 < x < 10 True >>> 1 < x and x < 10 True >>> 10 < x < 20 False >>> 10 > x > 1 True >>> not (10 < x < 20) TrueComparing SequencesThe comparison operators (such as ==, <=) are overloaded to support sequences (such as string, list and tuple). In comparing sequences, the first items from both sequences are compared. If they differ the outcome is decided. Otherwise, the next items are compared, and so on. >>> 'a' < 'b' True >>> 'ab' < 'aa' False >>> 'a' < 'b' < 'c' True >>> (1, 2, 3) < (1, 2, 4) True >>> [1, 2, 3] <= [1, 2, 3] True >>> [1, 2, 3] < [1, 2, 3]False Shorthand if-else (or Conditional Expression)The syntax is: true_expr if test else false_exprFor example, >>> x = 0 >>> print('zero' if x == 0 else 'not zero') zero >>> x = -8 >>> abs_x = x if x > 0 else -x >>> abs_x 8Note: Python does not use "? :" for shorthand if-else, as in C/C++/Java. The while loopThe syntax is as follows: while test: true_block while test: true_block else: else_blockThe else block is optional, which will be executed if the loop exits normally without encountering a break statement. For example, upperbound = int(input('Enter the upperbound: ')) sum = 0 number = 1 while number <= upperbound: sum += number number += 1 print(sum)break, continue, pass and loop-elseThe break statement breaks out from the innermost loop; the continue statement skips the remaining statements of the loop and continues the next iteration. This is the same as C/C++/Java. The pass statement does nothing. It serves as a placeholder for an empty statement or empty block. The loop-else block is executed if the loop is exited normally, without encountering the break statement. Examples: [TODO] Using Assignment in while-loop's Test?In many programming languages, assignment can be part of an expression, which return a value. It can be used in while-loop's test, e.g., while data = func(): do_something_on_dataPython issues a syntax error at the assignment operator. In Python, you cannot use assignment operator in an expression. You could do either of the followings: while True: data = func() if not data: break do_something_on_data data = func() while data: do_something_on_data data = func()The for-in loopThe for-in loop has the following syntax: for item in sequence: true_block for item in sequence: true_block else: else_blockYou shall read it as "for each item in the sequence...". Again, the else block is executed only if the loop exits normally, without encountering the break statement. Iterating through SequencesIterating through a Sequence (String, List, Tuple, Dictionary, Set) using for-in LoopThe for-in loop is primarily used to iterate through all the items of a sequence. For example, >>> for char in 'hello': print(char) h e l l o >>> for item in [123, 4.5, 'hello']: print(item) 123 4.5 hello >>> for item in (123, 4.5, 'hello'): print(item) 123 4.5 hello >>> dct = {'a': 1, 2: 'b', 'c': 'cc'} >>> for key in dct: print(key, ':', dct[key]) a : 1 c : cc 2 : b >>> for item in {'apple', 1, 2, 'apple'}: print(item) 1 2 apple >>> infile = open('test.txt', 'r') >>> for line in infile: print(line) ...Each line of the file... >>> infile.close()for(;;) LoopPython does NOT support the C/C++/Java-like for(int i; i < n; ++i) loop, which uses a varying index for the iterations. Take note that you cannot use the "for item in lst" loop to modify a list. To modify the list, you need to get the list indexes by creating an index list. For example, >>> lst = [11, 22, 33] >>> for item in lst: item += 1 >>> print(lst) [11, 22, 33] >>> idx_lst = [0, 1, 2] >>> for idx in idx_lst: lst[idx] += 1 >>> print(lst) [12, 23, 34]Manually creating the index list is not practical. You can use the range() function to create the index list (described below). The range() Built-in FunctionThe range() function produces a series of running integers, which can be used as index list for the for-in loop.
For example, upperbound = int(input('Enter the upperbound: ')) sum = 0 for number in range(1, upperbound+1): sum += number print('The sum is:', sum) lst = [9, 8, 4, 5] sum = 0 for idx in range(len(lst)): sum += lst[idx] print('The sum is:', sum) lst = [9, 8, 4, 5] sum = 0 for item in lst: sum += item print('The sum is:', sum) del sum print('The sum is:', sum(lst)) for idx in range(len(lst)): lst[idx] += 1 print(lst) idx = 0 while idx < len(lst): lst[idx] += 1 idx += 1 print(lst) lst = [11, 22, 33] lst1 = [item + 1 for item in lst] print(lst1)Using else-clause in LoopRecall that the else-clause will be executed only if the loop exits without encountering a break. for number in range(2, 101): for factor in range(2, number//2+1): if number % factor == 0: print('{} is NOT a prime'.format(number)) break else: print('{} is a prime'.format(number))Iterating through a Sequence of SequencesA sequence (such as list, tuple) can contain sequences. For example, >>> lst = [(1,'a'), (2,'b'), (3,'c')] >>> for v1, v2 in lst: print(v1, v2) 1 a 2 b 3 c >>> lst = [[1, 2, 3], ['a', 'b', 'c']] >>> for v1, v2, v3 in lst: print(v1, v2, v3) 1 2 3 a b cIterating through a DictionaryThere are a few ways to iterate through an dictionary: >>> dct = {'name':'Peter', 'gender':'male', 'age':21} >>> for key in dct: print(key, ':', dct[key]) age : 21 name : Peter gender : male >>> for key, value in dct.items(): print(key, ':', value) age : 21 name : Peter gender : male >>> dct.items() [('gender', 'male'), ('age', 21), ('name', 'Peter')]The iter() and next() Built-in FunctionsThe built-in function iter(iterable) takes a iterable (such as sequence) and returns an iterator object. You can then use next(iterator) to iterate through the items. For example, >>> lst = [11, 22, 33] >>> iterator = iter(lst) >>> next(iterator) 11 >>> next(iterator) 22 >>> next(iterator) 33 >>> next(iterator) StopIteration >>> type(iterator)The reversed() Built-in FunctionTo iterate a sequence in the reverse order, apply the reversed() function which reverses the iterator over values of the sequence. For example, >>> lst = [11, 22, 33] >>> for item in reversed(lst): print(item, end=' ') 33 22 11 >>> reversed(lst)The enumerate() Built-in FunctionYou can use the built-in function enumerate() to obtain the positional indexes, when looping through a sequence. For example, >>> lst = ['a', 'b', 'c'] >>> for idx, value in enumerate(lst): print(idx, value) 0 a 1 b 2 c >>> enumerate(lst)Multiple Sequences and the zip() Built-in FunctionTo loop over two or more sequences concurrently, you can pair the entries with the zip() built-in function. For examples, >>> lst1 = ['a', 'b', 'c'] >>> lst2 = [11, 22, 33] >>> for i1, i2 in zip(lst1, lst2): print(i1, i2) a 11 b 22 c 33 >>> zip(lst1, lst2) [('a', 11), ('b', 22), ('c', 33)] >>> tuple3 = (44, 55) >>> zip(lst1, lst2, tuple3) [('a', 11, 44), ('b', 22, 55)]Comprehension for Generating Mutable List, Dictionary and SetList comprehension provides concise way to generate a new list. The syntax is: result_list = [expression_with_item for item in list] result_list = [expression_with_item for item in list if test] result_list = [] for item in list: if test: result_list.append(item)For examples, >>> sq_lst = [item * item for item in range(1, 11)] >>> sq_lst [1, 4, 9, 16, 25, 36, 49, 64, 81, 100] >>> sq_lst = [] >>> for item in range(1, 11): sq_lst.append(item * item) >>> lst = [3, 4, 1, 5] >>> sq_lst = [item * item for item in lst] >>> sq_lst [9, 16, 1, 25] >>> sq_lst_odd = [item * item for item in lst if item % 2 != 0] >>> sq_lst_odd [9, 1, 25] >>> sq_lst_odd = [] >>> for item in lst: if item % 2 != 0: sq_lst_odd.append(item * item) >>> lst = [(x, y) for x in range(1, 3) for y in range(1, 4) if x != y] >>> lst [(1, 2), (1, 3), (2, 1), (2, 3)] >>> lst = [] >>> for x in range(1,3): for y in range(1,4): if x != y: lst.append((x, y)) >>> lst [(1, 2), (1, 3), (2, 1), (2, 3)]Similarly, you can create dictionary and set (mutable sequences) via comprehension. For example, >>> dct = {x:x**2 for x in range(1, 5)} >>> dct {1: 1, 2: 4, 3: 9, 4: 16} >>> set = {ch for ch in 'hello' if ch not in 'aeiou'} >>> set {'h', 'l'}Comprehension cannot be used to generate string and tuple, as they are immutable and append() cannot be applied. Naming Conventions and Coding Styles (PEP 8 & PEP 257)Naming ConventionsThese are the recommended naming conventions in Python:
Coding StylesRead:
The recommended styles are:
FunctionsSyntaxIn Python, you define a function via the keyword def followed by the function name, the parameter list, the doc-string and the function body. Inside the function body, you can use a return statement to return a value to the caller. There is no need for type declaration like C/C++/Java. The syntax is: def function_name(arg1, arg2, ...): """Function doc-string""" body_block return return-valueExample 1>>> def my_square(x): """Return the square of the given number""" return x * x >>> my_square(8) 64 >>> my_square(1.8) 3.24 >>> my_square('hello') TypeError: can't multiply sequence by non-int of type 'str' >>> my_squareTake note that you need to define the function before using it, because Python is interpretative. Example 2def fibon(n): """Print the first n Fibonacci numbers, where f(n)=f(n-1)+f(n-2) and f(1)=f(2)=1""" a, b = 1, 1 for count in range(n): print(a, end=' ') a, b = b, a+b print() fibon(20)Example 3: Function doc-stringdef my_cube(x): return x*x*x print(my_cube(8)) print(my_cube(-8)) print(my_cube(0))This example elaborates on the function's doc-string:
The pass statementThe pass statement does nothing. It is sometimes needed as a dummy statement placeholder to ensure correct syntax, e.g., def my_fun(): passFunction Parameters and ArgumentsPassing Arguments by Value vs. by ReferenceIn Python:
For examples, >>> def increment_int(number): number += 1 >>> number = 5 >>> increment_int(number) >>> number 5 >>> def increment_list(lst): for i in range(len(lst)): lst[i] += lst[i] >>> lst = [1, 2, 3, 4, 5] >>> increment_list(lst) >>> lst [2, 4, 6, 8, 10]Function Parameters with Default ValuesYou can assign a default value to the "trailing" function parameters. These trailing parameters having default values are optional during invocation. For example, >>> def my_sum(n1, n2 = 4, n3 = 5): """Return the sum of all the arguments""" return n1 + n2 + n3 >>> print(my_sum(1, 2, 3)) 6 >>> print(my_sum(1, 2)) 8 >>> print(my_sum(1)) 10 >>> print(my_sum()) TypeError: my_sum() takes at least 1 argument (0 given) >>> print(my_sum(1, 2, 3, 4)) TypeError: my_sum() takes at most 3 arguments (4 given)Another example, def greet(name): return 'hello, ' + name greet('Peter')In stead of hard-coding the 'hello, ', it is more flexible to use a parameter with a default value, as follows: def greet(name, prefix='hello'): return prefix + ', ' + name greet('Peter') greet('Peter', 'hi') greet('Peter', prefix='hi') greet(name='Peter', prefix='hi')Positional and Keyword ArgumentsPython functions support both positional and keyword (or named) arguments. Normally, Python passes the arguments by position from left to right, i.e., positional, just like C/C++/Java. Python also allows you to pass arguments by keyword (or name) in the form of kwarg=value. For example, def my_sum(n1, n2 = 4, n3 = 5): """Return the sum of all the arguments""" return n1 + n2 + n3 print(my_sum(n2 = 2, n1 = 1, n3 = 3)) print(my_sum(n2 = 2, n1 = 1)) print(my_sum(n1 = 1)) print(my_sum(1, n3 = 3)) print(my_sum(n2 = 2)) # TypeError, n1 missingYou can also mix the positional arguments and keyword arguments, but you need to place the positional arguments first, as shown in the above examples. Variable Number of Positional Parameters (*args)Python supports variable (arbitrary) number of arguments. In the function definition, you can use * to pack all the remaining positional arguments into a tuple. For example, def my_sum(a, *args): """Return the sum of all the arguments (one or more)""" sum = a print('args is:', args) for item in args: sum += item return sum print(my_sum(1)) print(my_sum(1, 2)) print(my_sum(1, 2, 3)) print(my_sum(1, 2, 3, 4))Python supports placing *args in the middle of the parameter list. However, all the arguments after *args must be passed by keyword to avoid ambiguity. For example def my_sum(a, *args, b): sum = a print('args is:', args) for item in args: sum += item sum += b return sum print(my_sum(1, 2, 3, 4)) # TypeError: my_sum() missing 1 required keyword-only argument: 'b' print(my_sum(1, 2, 3, 4, b=5))Unpacking List/Tuple into Positional Arguments (*lst, *tuple)In the reverse situation when the arguments are already in a list/tuple, you can also use * to unpack the list/tuple as separate positional arguments. For example, >>> def my_sum(a, b, c): return a+b+c >>> lst = [11, 22, 33] >>> my_sum(lst) TypeError: my_sum() missing 2 required positional arguments: 'b' and 'c' >>> my_sum(*lst) 66 >>> lst = [44, 55] >>> my_sum(*lst) TypeError: my_sum() missing 1 required positional argument: 'c' >>> def my_sum(*args): sum = 0 for item in args: sum += item return sum >>> my_sum(11, 22, 33) 66 >>> lst = [44, 55, 66] >>> my_sum(*lst) 165 >>> tup = (7, 8, 9, 10) >>> my_sum(*tup) 34Variable Number of Keyword Parameters (**kwargs)For keyword parameters, you can use ** to pack them into a dictionary. For example, >>> def my_print_kwargs(msg, **kwargs): print(msg) for key, value in kwargs.items(): print('{}: {}'.format(key, value)) >>> my_print_kwargs('hello', name='Peter', age=24) hello name: Peter age: 24Unpacking Dictionary into Keyword Arguments (**dict)Similarly, you can also use ** to unpack a dictionary into individual keyword arguments >>> def my_print_kwargs(msg, **kwargs): print(msg) for key, value in kwargs.items(): print('{}: {}'.format(key, value)) >>> dict = {'k1':'v1', 'k2':'v2', 'k3':'v3'} >>> my_print_kwargs('hello', **dict) hello k1: v1 k2: v2 k3: v3Using both *args and **kwargsYou can use both *args and **kwargs in your function definition. Place *args before **kwargs. For example, >>> def my_print_all_args(*args, **kwargs): for item in args: print(item) for key, value in kwargs.items(): print('%s: %s' % (key, value)) >>> my_print_all_args('a', 'b', 'c', name='Peter', age=24) a b c name: Peter age: 24 >>> lst = [1, 2, 3] >>> dict = {'name': 'peter'} >>> my_print_all_args(*lst, **dict) 1 2 3 name: peterFunction OverloadingPython does NOT support Function Overloading like Java/C++ (where the same function name can have different versions differentiated by their parameters). Function Return ValuesYou can return multiple values from a Python function, e.g., >>> def my_fun(): return 1, 'a', 'hello' >>> x, y, z = my_fun() >>> z 'hello' >>> my_fun() (1, 'a', 'hello')It seems that Python function can return multiple values. In fact, a tuple that packs all the return values is returned. Recall that a tuple is actually formed through the commas, not the parentheses, e.g., >>> x = 1, 'a' >>> x (1, 'a')Types Hints via Function AnnotationsFrom Python 3.5, you can provide type hints via function annotations in the form of: def say_hello(name:str) -> str: return 'hello, ' + name say_hello('Peter')The type hints annotations are usually ignored, and merely serves as documentation. But there are external library that can perform the type check. Read: "PEP 484 -- Type Hints". Modules, Import-Statement and PackagesModulesA Python module is a file containing Python codes - including statements, variables, functions and classes. It shall be saved with file extension of ".py". The module name is the filename, i.e., a module shall be saved
as " By convention, modules names shall be short and all-lowercase (optionally joined with underscores if it improves readability). A module typically begins with a triple-double-quoted documentation string (doc-string) (available in Example: The greet ModuleCreate a module called greet and save as "greet.py" as follows: msg = 'Hello' def greet(name): print('{}, {}'.format(msg, name))This greet module defines a variable msg and a function greet(). The import statementTo use an external module in your script, use the import statement: importOnce imported, you can reference the module's attributes as For example, to use the greet module created earlier: $ cd /path/to/target-module $ python3 >>> import greet >>> greet.greet('Peter') Hello, Peter >>> print(greet.msg) Hello >>> greet.__doc__ 'greet.py: the greet module with attributes msg and greet()' >>> greet.__name__ 'greet' >>> dir(greet) ['__built-ins__', '__cached__', '__doc__', '__file__', '__loader__', '__name__', '__package__', '__spec__', 'greet', 'msg'] >>> help(greet) Help on module greet: NAME greet DESCRIPTION ...doc-string... FUNCTIONS greet(name) DATA msg = 'Hello' FILE /path/to/greet.py >>> import greet as grt >>> grt.greet('Paul') Hello, PaulThe import statements should be grouped in this order:
The from-import StatementThe syntax is: fromWith the from-import statement, you can reference the imported attributes using For example, >>> from greet import greet, msg as message >>> greet('Peter') Hello, Peter >>> message 'Hello' >>> msg NameError: name 'msg' is not definedimport vs. from-importThe from-import statement actually loads the entire module (like import statement); and NOT just the imported attributes. But it exposes ONLY the imported attributes to the namespace. Furthermore, you can reference them directly without qualifying with the module name. For example, let create the following module called imtest.py for testing import vs. from-import: x = 1 y = 2 print('x is: {}'.format(x)) def foo(): print('y is: {}'.format(y)) def bar(): foo()Let's try out import: $ python3 >>> import imtest x is: 1 >>> imtest.y 2 >>> imtest.bar() y is: 2Now, try the from-import and note that the entire module is loaded, just like the import statement. $ python3 >>> from imtest import x, bar x is: 1 >>> x 1 >>> bar() y is: 2 >>> foo() NameError: name 'foo' is not definedConditional ImportPython supports conditional import too. For example, if ....: import xxx else: import yyysys.path and PYTHONPATH/PATH environment variablesThe environment variable PATH shall include the path to Python Interpreter "python3". The Python module search path is maintained in a Python variable path of the sys module, i.e. sys.path. The sys.path is initialized from the environment variable PYTHONPATH, plus an installation-dependent default. The environment variable PYTHONPATH is empty by default. For example, >>> import sys >>> sys.path ['', '/usr/lib/python3.5', '/usr/local/lib/python3.5/dist-packages', '/usr/lib/python3.5/dist-packages', ...]sys.path default includes the current working directory (denoted by an empty string), the standard Python directories, plus the extension directories in dist-packages. The imported modules must be available in one of the sys.path entries. >>> import some_mod ImportError: No module named 'some_mod' >>> some_mod.var NameError: name 'some_mod' is not definedTo show the PATH and PYTHONPATH environment variables, use one of these commands: > echo %PATH% > set PATH > PATH > echo %PYTHONPATH% > set PYTHONPATH $ echo $PATH $ printenv PATH $ echo $PYTHONPATH $ printenv PYTHONPATHReloading Module using imp.reload() or importlib.reload()If you modify a module, you can use reload() function of the imp (for import) module to reload the module, for example, >>> import greet >>> import imp >>> imp.reload(greet)NOTE: Since Python 3.4, the imp package is pending deprecation in favor of importlib. >>> import greet >>> import importlib >>> importlib.reload(greet)Template for Python Standalone ModuleThe following is a template of standalone module for performing a specific task: importWhen you execute a Python module (via the Python Interpreter), the __name__ is set to '__main__'. On the other hand, when a module is imported, its __name__ is set to the module name. Hence, the above module will be executed if it is loaded by the Python interpreter, but not imported by another module. Example: [TODO] PackagesA module contains attributes (such as variables, functions and classes). Relevant modules (kept in the same directory) can be grouped into a package. Python also supports sub-packages (in sub-directories). Packages and sub-packages are a way of organizing Python's module namespace by using "dotted names" notation, in the form of ' To create a Python package:
The '__init__.py' marks the directory as a package. For example, suppose that you have this directory/file structure: myapp/ | + mypack1/ | | | + __init__.py | + mymod1_1.py | + mymod1_2.py | + mypack2/ | + __init__.py + mymod2_1.py + mymod2_2.pyIf 'myapp' is in your 'sys.path', you can import 'mymod1_1' as: import mypack1.mymod1_1 from mypack1 import mymod1_1Without the '__init__.py', Python will NOT search the 'mypack1' directory for 'mymod1_1'. Moreover, you cannot reference modules in the 'mypack1' directory directly (e.g., 'import mymod1_1') as it is not in the 'sys.path'. Attributes in '__init__.py'The '__init__.py' file is usually empty, but it can be used to initialize the package such as exporting selected portions of the package under more convenient name, hold convenience functions, etc. The attributes of the '__init__.py' module can be accessed via the package name directly (i.e., ' Sub-PackagesA package can contain sub-packages too. For example, myapp/ | + mypack1/ | + __init__.py + mymod1_1.py | + mysubpack1_1/ | | | + __init__.py | + mymod1_1_1.py | + mymod1_1_2.py | + mysubpack1_2/ | + __init__.py + mymod1_2_1.pyClearly, the package's dot structure corresponds to the directory structure. Relative from-importIn the from-import statement, you can use . to refer to the current package and .. to refer to the parent package. For example, inside 'mymod1_1_1.py', you can write: from . import mymod1_1_2 from .. import mymod1_1 from .mymod1_1_2 import attr from ..mysubpack1_2 import mymod1_2_1Take note that in Python, you write '.mymod1_1_2', '..mysubpack1_2' by omitting the separating dot (instead of '..mymod1_1_2', '...mysubpack1_2'). Circular Import Problem[TODO] Advanced Functions and NamespacesLocal Variables vs. Global VariablesNames created inside a function (i.e. within def statement) are local to the function and are available inside the function only. Names created outside all functions are global to that particular module (or file), but not available to the other modules. Global variables are available inside all the functions defined in the module. Global-scope in Python is equivalent to module-scope or file-scope. There is NO all-module-scope in Python. For example, x = 'global' def myfun(arg): y = 'local' print(x) print(y) print(arg) myfun('abc') print(x) #print(y) # locals are not visible outside the function #print(arg)Function Variables (Variables of Function Object)In Python, a variable takes a value or object (such as int, str). It can also take a function. For example, >>> def square(n): return n * n >>> square(5) 25 >>> sq = square >>> sq(5) 25 >>> type(square)A variable in Python can hold anything, a value, a function or an object. In Python, you can also assign a specific invocation of a function to a variable. For example, >>> def square(n): return n * n >>> sq5 = square(5) >>> sq5 25 >>> type(sq5)Nested FunctionsPython supports nested functions, i.e., defining a function inside a function. For example, def outer(a): print('outer() begins with arg =', a) x = 1 def inner(b): print('inner() begins with arg =', b) y = 2 print('a = {}, x = {}, y = {}'.format(a, x, y)) print('inner() ends') inner('bbb') print('outer() ends') outer('aaa')The expected output is: outer begins with arg = aaa inner begins with arg = bbb a = aaa, x = 1, y = 2 inner ends outer endsTake note that the inner function has read-access to all the attributes of the enclosing outer function, and the global variable of this module. Lambda Function (Anonymous Function)Lambda functions are anonymous function or un-named function. They are used to inline a function definition, or to defer execution of certain codes. The syntax is: lambda arg1, arg2, ...: return_expressionFor example, >>> def f1(a, b, c): return a + b + c >>> f1(1, 2, 3) 6 >>> type(f1)f1 and f2 do the same thing. Take note that return keyword is NOT needed inside the lambda function. Instead, it is similar to evaluating an expression to obtain a value. Lambda function, like ordinary function, can have default values for its parameters. >>> f3 = lambda a, b=2, c=3: a + b + c >>> f3(1, 2, 3) 6 >>> f3(8) 13More usages for lambda function will be shown later. Multiple Statements?Take note that the body of a lambda function is an one-liner return_expression. In other words, you cannot place multiple statements inside the body of a lambda function. You need to use a regular function for multiple statements. Functions are ObjectsIn Python, functions are objects (like instances of a class). Like any object,
Example: Passing a Function Object as a Function ArgumentA function name is a variable name that can be passed into another function as argument. def my_add(x, y): return x + y def my_sub(x, y): return x - y def my_apply(func, x, y): return func(x, y) print(my_apply(my_add, 3, 2)) print(my_apply(my_sub, 3, 2)) print(my_apply(lambda x, y: x * y, 3, 2))Example: Returning an Inner Function object from an Outer Functiondef my_outer(): def my_inner(): print('hello from inner') return my_inner result = my_outer() result() print(result)Example: Returning a Lambda Functiondef increase_by(n): return lambda x: x + n plus_8 = increase_by(8) print(plus_8(1)) plus_88 = increase_by(88) print(plus_88(1)) print(increase_by(8)(1)) print(increase_by(88)(1))Function ClosureIn the above example, n is not local to the lambda function. Instead, n is obtained from the outer function. When we assign increase_by(8) to plus_8, n takes on the value of 8 during the invocation. But we expect n to go out of scope after the outer function terminates. If this is the case, calling plus_8(1) would encounter an non-existent n? This problem is resolved via so called Function Closure. A closure is an inner function that is passed outside the enclosing function, to be used elsewhere. In brief, the inner function creates a closure (enclosure) for its enclosing namespaces at definition time. Hence, in plus_8, an enclosure with n=8 is created; while in plus_88, an enclosure with n=88 is created. Take note that Python only allows the read access to the outer scope, but not write access. You can inspect the enclosure via function_name.func_closure, e.g., print(plus_8.func_closure) print(plus_88.func_closure)Functional Programming: Using Lambda Function in filter(), map(), reduce() and ComprehensionInstead of using a for-in loop to iterate through all the items in an iterable (sequence), you can use the following functions to apply an operation to all the items. This is known as functional programming or expression-oriented programming. Filter-map-reduce is popular in big data analysis (or data science).
These mechanism replace the traditional for-loop, and express their functionality in simple function calls. It is called functional programming, i.e., applying a series of functions (filter-map-reduce) over a collection. DecoratorsIn Python, a decorator is a callable (function) that takes a function as an argument and returns a replacement function. Recall that functions are objects in Python, i.e., you can pass a function as argument, and a function can return an inner function. A decorator is a transformation of a function. It can be used to pre-process the function arguments before passing them into the actual function; or extending the behavior of functions that you don't want to modify, such as ascertain that the user has login and has the necessary permissions. Example: Decorating an 1-argument Functiondef clamp_range(func): def _wrapper(x): if x < 0: x = 0 elif x > 100: x = 100 return func(x) return _wrapper def square(x): return x*x print(clamp_range(square)(5)) print(clamp_range(square)(111))print(clamp_range(square)(-5)) square = clamp_range(square) print(square(50)) print(square(-1)) print(square(101)) Notes:
Example: Using the @ symbolUsing 'square=clamp_range(square)' to decorate a function is messy?! Instead, Python uses the @ symbol to denote the replacement. For example, def clamp_range(func): def _wrapper(x): if x < 0: x = 0 elif x > 100: x = 100 return func(x) return _wrapper @clamp_range def cube(x): return x**3 print(cube(50)) print(cube(-1)) print(cube(101))For Java programmers, do not confuse the Python decorator @ with Java's annotation like @Override. Example: Decorator with an Arbitrary Number of Function ArgumentsThe above example only work for one-argument function. You can use *args and/or **kwargs to handle variable number of arguments. For example, the following decorator log all the arguments before the actual processing. def logger(func): def _wrapper(*args, **kwargs): print('The arguments are: {}, {}'.format(args, kwargs)) return func(*args, **kwargs) return _wrapper @logger def myfun(a, b, c=3, d=4): pass myfun(1, 2, c=33, d=44) myfun(1, 2, c=33)We can also modify our earlier clamp_range() to handle an arbitrary number of arguments: def clamp_range(func): def _wrapper(*args): newargs = [] for item in args: if item < 0: newargs.append(0) elif item > 100: newargs.append(100) else: newargs.append(item) return func(*newargs) return _wrapper @clamp_range def my_add(x, y, z): return x + y + z print(my_add(1, 2, 3)) print(my_add(-1, 5, 109))The @wraps DecoratorDecorator can be hard to debug. This is because it wraps around and replaces the original function and hides variables like __name__ and __doc__. This can be solved by using the @wraps of functools, which modifies the signature of the replacement functions so they look more like the decorated function. For example, from functools import wraps def without_wraps(func): def _wrapper(*args, **kwargs): return func(*args, **kwargs) return _wrapper def with_wraps(func): @wraps(func) def _wrapper(*args, **kwargs): return func(*args, **kwargs) return _wrapper @without_wraps def fun_without_wraps(): pass @with_wraps def fun_with_wraps(): pass print(fun_without_wraps.__name__) print(fun_without_wraps.__doc__) print(fun_with_wraps.__name__) print(fun_with_wraps.__doc__)Example: Passing Arguments into DecoratorsLet's modify the earlier clamp_range decorator to take two arguments - min and max of the range. from functools import wraps def clamp_range(min, max): def _decorator(func): @wraps(func) def _wrapper(*args): newargs = [] for item in args: if item < min: newargs.append(min) elif item > max: newargs.append(max) else: newargs.append(item) return func(*newargs) return _wrapper return _decorator @clamp_range(1, 10) def my_add(x, y, z): return x + y + z print(my_add(1, 2, 3)) print(my_add(-1, 5, 109)) print(my_add.__name__) print(my_add.__doc__)The decorator clamp_range takes the desired arguments and returns a wrapper function which takes a function argument (for the function to be decorated). Confused?! Python has many fancy features that is not available in traditional languages like C/C++/Java! NamespaceNames, Namespaces and ScopeIn Python, a name is roughly analogous to a variable in other languages but with some extras. Because of the dynamic nature of Python, a name is applicable to almost everything, including variable, function, class/instance, module/package. Names defined inside a function are local. Names defined outside all functions are global for that module, and are accessible by all functions inside the module (i.e., module-global scope). There is no all-module-global scope in Python. A namespace is a collection of names (i.e., a space of names). A scope refers to the portion of a program from where a names can be accessed without a qualifying prefix. For example, a local variable defined inside a function has local scope (i.e., it is available within the function, and NOT available outside the function). Each Module has a Global NamespaceA module is a file containing attributes (such as variables, functions and classes). Each module has its own global namespace. Hence, you cannot define two functions or classes of the same name within a module. But you can define functions of the same name in different modules, as the namespaces are isolated. When you launch the interactive shell, Python creates a module called __main__, with its associated global namespace. All subsequent names are added into __main__'s namespace. When you import a module via 'import However, if you import an attribute via 'from On the other hand, when you import a module inside another module (instead of interactive shell), the imported The built-in functions are kept in a module called __built-in__, which is imported into __main__ automatically. The globals(), locals() and dir() Built-in FunctionsYou can list the names of the current scope via these built-in functions:
For example, $ python3 >>> globals() {'__name__': '__main__', '__built-ins__':To show the difference between locals and globals, we need to define a function to create a local scope. For example, $ python3 >>> x = 88 >>> def myfun(arg): y = 99 print(x) print(globals()) print(locals()) print(dir()) >>> myfun(11) 88 {'__built-ins__':More on Module's Global NamespaceLet's create two modules: mod1 and mod2, where mod1 imports mod2, as follows: import mod2 mod1_var = 'mod1 global variable' print('Inside mod1, __name__ = ', __name__) if __name__ == '__main__': print('Run module 1')mod2_var = 'mod2 global variable' print('Inside mod2, __name__ = ', __name__) if __name__ == '__main__': print('Run module 2')Let's import mod1 (which in turn import mod2) under the interpreter shell, and check the namespaces: >>> import mod1 Inside mod2, __name__ = mod2 Inside mod1, __name__ = mod1 >>> dir() ['__built-ins__', '__doc__', '__loader__', '__name__', '__package__', '__spec__', 'mod1'] >>> dir(mod1) ['__built-ins__', '__cached__', '__doc__', '__file__', '__loader__', '__name__', '__package__', '__spec__', 'mod1_var', 'mod2'] >>> dir(mod2) NameError: name 'mod2' is not defined >>> dir(mod1.mod2) ['__built-ins__', '__cached__', '__doc__', '__file__', '__loader__', '__name__', '__package__', '__spec__', 'mod2_var']Take note that the interpreter's current scope __name__ is __main__. It's namespace contains mod1 (imported). The mod1's namespace contains mod2 (imported) and mod1_var. To refer to mod2, you need to go thru mod1, in the form of mod1.mod2. The mod1.mod2's namespace contains mod2_var. Now, let run mod1 instead, under IDLE3, and check the namespaces: Inside mod2, __name__ = mod2 Inside mod1, __name__ = __main__ Run module 1 >>> dir() ['__built-ins__', '__doc__', '__file__', '__loader__', '__name__', '__package__', '__spec__', 'mod1_var', 'mod2'] >>> dir(mod2) ['__built-ins__', '__cached__', '__doc__', '__file__', '__loader__', '__name__', '__package__', '__spec__', 'mod2_var']Take note that the current scope's name is again __main__, which is the executing module mod1. Its namespace contains mod2 (imported) and mod1_var. Name ResolutionWhen you ask for a name (variable), says x, Python searches the LEGB namespaces, in this order, of the current scope:
If x cannot be found, Python raises a NameError. Modifying Global Variables inside a FunctionRecall that names created inside a function are local, while names created outside all functions are global for that module. You can "read" the global variables inside all functions defined in that module. For example, x = 'global' def myfun(): y = 'local' print(y) print(x) myfun() print(x) #print(y)If you assign a value to a name inside a function, a local name is created, which hides the global name. For example, x = 'global' def myfun(): x = 'change' print(x) myfun() print(x)To modify a global variable inside a function, you need to use a global statement to declare the name global; otherwise, the modification (assignment) will create a local variable (see above). For example, x = 'global' def myfun(): global x x = 'change' print(x) myfun() print(x)For nested functions, you need to use the nonlocal statement in the inner function to modify names in the enclosing outer function. For example, def outer(): count = 0 def inner(): nonlocal count count += 1 print(count) inner() print(count) outer()To modify a global variable inside a nested function, declare it via global statement too. For example, count = 100 def outer(): count = 0 def inner(): global count count += 1 print(count) inner() print(count) outer() print(count)In summary,
More on global StatementThe global statement is necessary if you are changing the reference to an object (e.g. with an assignment). It is not needed if you are just mutating or modifying the object. For example, >>> a = [] >>> def myfun(): a.append('hello') >>> myfun() >>> a ['hello']In the above example, we modify the contents of the array. The global statement is not needed. >>> a = 1 >>> def myfun(): global a a = 8 >>> myfun() >>> a 8In the above example, we are modifying the reference to the variable. global is needed, otherwise, a local variable will be created inside the function. Built-in NamespaceThe built-in namespace is defined in the __built-ins__ module, which contains built-in functions such as len(), min(), max(), int(), float(), str(), list(), tuple() and etc. You can use help(__built-ins__) or dir(__built-ins__) to list the attributes of the __built-ins__ module. [TODO] del StatementYou can use del statement to remove names from the namespace, for example, >>> del x, pi >>> globals() ...... x and pi removed ...... >>> del random >>> globals() ...... random module removed ......If you override a built-in function, you could also use del to remove it from the namespace to recover the function from the built-in space. >>> len = 8 >>> len('abc') TypeError: 'int' object is not callable >>> del len >>> len('abc') 3Assertion and Exception Handlingassert StatementYou can use assert statement to test a certain assertion (or constraint). For example, if x is supposed to be 0 in a certain part of the program, you can use the assert statement to test this constraint. An AssertionError will be raised if x is not zero. For example, >>> x = 0 >>> assert x == 0, 'x is not zero?!' >>> x = 1 >>> assert x == 0, 'x is not zero?!' ...... AssertionError: x is not zero?!The assertions are always executed in Python. SyntaxThe syntax for assert is: assert test, error-messageIf the test if True, nothing happens; otherwise, an AssertionError will be raised with the error-message. ExceptionsIn Python, errors detected during execution are called exceptions. For example, >>> 1/0 ZeroDivisionError: division by zero >>> zzz NameError: name 'zzz' is not defined >>> '1' + 1 TypeError: Can't convert 'int' object to str implicitly >>> lst = [0, 1, 2] >>> lst[3] IndexError: list index out of range >>> lst.index(8) ValueError: 8 is not in list >>> int('abc') ValueError: invalid literal for int() with base 10: 'abc' >>> tup = (1, 2, 3) >>> tup[0] = 11 TypeError: 'tuple' object does not support item assignmentWhenever an exception is raised, the program terminates abruptly. try-except-else-finallyYou can use try-except-else-finally exception handling facility to prevent the program from terminating abruptly. Example 1: Handling Index out-of-range for List Accessdef get_item(seq, index): try: result = seq[index] print('try succeed') except IndexError: result = 0 print('Index out of range') except: result = 0 print('other exception') else: print('no exception raised') finally: print('run finally') print('continue after try-except') return result print(get_item([0, 1, 2, 3], 1)) print('-----------') print(get_item([0, 1, 2, 3], 4))The expected outputs are: try succeed no exception raised run finally continue after try-except 1 ----------- Index out of range run finally continue after try-except 0The exception handling process for try-except-else-finally is:
SyntaxThe syntax for try-except-else-finally is: try: statements except exception_1: statements except (exception_2, exception_3): statements except exception_4 as var_name: statements except: statements else: statements finally: statementsThe try-block (mandatory) must follow by at least one except or finally block. The rests are optional. CAUTION: Python 2 uses older syntax of "except exception-4, var_name:", which should be re-written as "except exception-4 as var_name:" for portability. Example 2: Input Validation>>> while True: try: x = int(input('Enter an integer: ')) break except ValueError: print('Invalid input! Try again...') Enter an integer: abc Wrong input! Try again... Enter an integer: 11.22 Wrong input! Try again... Enter an integer: 123raise StatementYou can manually raise an exception via the raise statement, for example, >>> raise IndexError('out-of-range') IndexError: out-of-rangeThe syntax is: raise exception_class_name raise exception_instance_name raiseA raise without argument in the except block re-raise the exception to the outer block, e.g., try: ...... except: raiseBuilt-in Exceptions
User-defined ExceptionYou can defined your own exception by sub-classing the Exception class. Exampleclass MyCustomError(Exception): def __init__(self, value): self.value = value def __str__(self): return repr(self.value) try: raise MyCustomError('an error occurs') print('after exception') except MyCustomError as e: print('MyCustomError: ', e.value) else: print('running the else block') finally: print('always run the finally block')with-as Statement and Context ManagersThe syntax of the with-as statement is as follows: with ... as ...: statements with ... as ..., ... as ..., ...: statementsPython’s with statement supports the concept of a runtime context defined by a context manager. In programming, context can be seen as a bucket to pass information around, i.e., the state at a point in time. Context Managers are a way of allocating and releasing resources in the context. Example 1with open('test.log', 'r') as infile: for line in infile: print(line)This is equivalent to: infile = open('test.log', 'r') try: for line in infile: print(line) finally: infile.close()The with-statement's context manager acquires, uses, and releases the context (of the file) cleanly, and eliminate a bit of boilerplate. However, the with-as statement is applicable to certain objects only, such as file; while try-finally can be applied to all. Example 2:with open('in.txt', 'r') as infile, open('out.txt', 'w') as outfile: for line in infile: outfile.write(line)Frequently-Used Python Standard Library ModulesPython provides a set of standard library. (Many non-standard libraries are provided by third party!) To use a module, use 'import math and cmath ModulesThe math module provides access to the mathematical functions defined by the C language standard. The commonly-used attributes are:
For examples, >>> import math >>> dir(math) ...... >>> help(math) ...... >>> help(math.trunc) ...... >>> x = 1.5 >>> type(x)In addition, the cmath module provides mathematical functions for complex numbers. See Python documentation for details. statistics ModuleThe statistics module computes the basic statistical properties such as mean, median, variance, and etc. (Many third-party vendors provide advanced statistics packages!) For examples, >>> import statistics >>> dir(statistics) ['mean', 'median', 'median_grouped', 'median_high', 'median_low', 'mode', 'pstdev', 'pvariance', 'stdev', 'variance', ...] >>> help(statistics) ...... >>> help(statistics.pstdev) ...... >>> data = [5, 7, 8, 3, 5, 6, 1, 3] >>> statistics.mean(data) 4.75 >>> statistics.median(data) 5.0 >>> statistics.stdev(data) 2.3145502494313788 >>> statistics.variance(data) 5.357142857142857 >>> statistics.mode(data) statistics.StatisticsError: no unique mode; found 2 equally common valuesrandom ModuleThe module random can be used to generate various pseudo-random numbers. For examples, >>> import random >>> dir(random) ...... >>> help(random) ...... >>> help(random.random) ...... >>> random.random() 0.7259532743815786 >>> random.random() 0.9282534690123855 >>> random.randint(1, 6) 3 >>> random.randrange(6) 0 >>> random.choice(['apple', 'orange', 'banana']) 'apple'sys ModuleThe module sys (for system) provides system-specific parameters and functions. The commonly-used are:
Example: Command-Line ArgumentsThe command-line arguments are kept in sys.argv as a list. For example, create the following script called "test_argv.py": import sys print(sys.argv) print(len(sys.argv))Run the script: $ python3 test_argv.py ['test_argv.py'] 1 $ python3 test_argv.py hello 1 2 3 apple orange ['test_argv.py', 'hello', '1', '2', '3', 'apple', 'orange'] 7logging ModuleThe logging moduleThe logging module supports a flexible event logging system for your applications and libraries. The logging supports five levels:
The logging functions are:
Basic Logging via logging.basicConfig()For example, import logging logging.basicConfig(filename='myapp.log', level=logging.DEBUG) logging.debug('A debug message') logging.info('An info message {}, {}'.format('apple', 'orange')) logging.error('error {}, some error messages'.format(1234))The logging functions support printf-like format specifiers such as %s, %d, with values as function arguments (instead of via % operator in Python). Run the script. A log file myapp.log would be created, with these records: DEBUG:root:A debug message INFO:root:An info message apple, orange ERROR:root:error 1234, some error messagesBy default, the log records include the log-level and logger-name (default of root) before the message. Getting the Log Level from a Configuration FileLog levels, such as logging.DEBUG and logging.INFO, are stored as certain integers in the logging module. For example, >>> import logging >>> logging.DEBUG 10 >>> logging.INFO 20The log level is typically read from a configuration file, in the form of a descriptive string. The following example shows how to convert a string log-level (e.g., 'debug') to the numeric log-level (e.g., 10) used by logging module: import logging str_level = 'info' numeric_level = getattr(logging, str_level.upper(), None) if not isinstance(numeric_level, int): raise ValueError('Invalid log level: {}'.format(str_level)) logging.basicConfig(level=numeric_level) logging.debug('a debug message') logging.info('an info message') logging.error('an error message')Log Record FormatTo set the log message format, use the format keyword: import logging logging.basicConfig( format='%(asctime)s|%(levelname)s|%(name)s|%(pathname)s:%(lineno)d|%(message)s', level=logging.DEBUG)where asctime for date/time, levelname for log level, name for logger name, pathname for full-path filename (filename for filename only), lineno (int) for the line number, and message for the log message. Advanced Logging: Logger, Handler, Filter and FormatterSo far, we presented the basic logging facilities. The logging library is extensive and organized into these components:
LoggersTo create a Logger instance, invoke the logging.getLogger(logger-name), where the optional logger-name specifies the logger name (default of root). The Logger's methods falls into two categories: configuration and logging. The commonly-used logging methods are: debug(), info(), warning(), error(), critical() and the general log(). The commonly-used configuration methods are:
HandlersThe logging library provides handlers like StreamHandler (sys.stderr, sys.stdout), FileHandler, RotatingFileHandler, and SMTPHandler (emails). The commonly-used methods are:
You can add more than one handlers to a logger, possibly handling different log levels. For example, you can add a SMTPHandler to receive emails for ERROR level; and a RotatingFileHandler for INFO level. FormattersAttach to a handler (via Example: Using Logger with Console Handler and a Formatterimport logging logger = logging.getLogger('MyApp') logger.setLevel(logging.INFO) ch = logging.StreamHandler() ch.setLevel(logging.INFO) formatter = logging.Formatter('%(asctime)s|%(name)s|%(levelname)s|%(message)s') ch.setFormatter(formatter) logger.addHandler(ch) logger.debug('a debug message') logger.info('an info message') logger.warn('a warn message') logger.error('error %d, an error message', 1234) logger.critical('a critical message')
The expected outputs are: 2015-12-09 00:32:33,521|MyApp|INFO|an info message 2015-12-09 00:32:33,521|MyApp|WARNING|a warn message 2015-12-09 00:32:33,521|MyApp|ERROR|error 1234: an error message 2015-12-09 00:32:33,521|MyApp|CRITICAL|a critical messageExample: Using Rotating Log Files with RotatingFileHandlerimport logging from logging.handlers import RotatingFileHandler config = { 'loggername' : 'myapp', 'logLevel' : logging.INFO, 'logFilename' : 'test.log', 'logFileBytes': 300, 'logFileCount': 3} logger = logging.getLogger(config['loggername']) logger.setLevel(config['logLevel']) handler = RotatingFileHandler( config['logFilename'], maxBytes=config['logFileBytes'], backupCount=config['logFileCount']) handler.setLevel(config['logLevel']) handler.setFormatter(logging.Formatter( "%(asctime)s|%(levelname)s|%(message)s|%(filename)s:%(lineno)d")) logger.addHandler(handler) logger.info('An info message') logger.debug('A debug message') for i in range(1, 10): logger.error('Error message %d', i)
Example: Using an Email Log for CRITICAL Level and Rotating Log Files for INFO Levelimport logging from logging.handlers import RotatingFileHandler, SMTPHandler config = { 'loggername' : 'myapp', 'fileLogLevel' : logging.INFO, 'logFilename' : 'test.log', 'logFileBytes' : 300, 'logFileCount' : 5, 'emailLogLevel': logging.CRITICAL, 'smtpServer' : 'your_smtp_server', 'email' : '', 'emailAdmin' : ''} logger = logging.getLogger(config['loggername']) logger.setLevel(config['fileLogLevel']) fileHandler = RotatingFileHandler( config['logFilename'], maxBytes=config['logFileBytes'], backupCount=config['logFileCount']) fileHandler.setLevel(config['fileLogLevel']) fileHandler.setFormatter(logging.Formatter( "%(asctime)s|%(levelname)s|%(message)s|%(filename)s:%(lineno)d")) emailHandler = SMTPHandler( config['smtpServer'], config['email'], config['emailAdmin'], '%s - CRITICAL ERROR' % config['loggername']) emailHandler.setLevel(config['emailLogLevel']) logger.addHandler(fileHandler) logger.addHandler(emailHandler) logger.debug('A debug message') logger.info('An info message') logger.warning('A warning message') logger.error('An error message') logger.critical('A critical message')Example: Separating ERROR Log and INFO Log with Different Formatimport logging, sys from logging.handlers import RotatingFileHandler class MaxLevelFilter(logging.Filter): def __init__(self, maxlevel): self.maxlevel = maxlevel def filter(self, record): return (record.levelno <= self.maxlevel) file_handler = RotatingFileHandler('test.log', maxBytes=500, backupCount=3) file_handler.addFilter(MaxLevelFilter(logging.INFO)) file_handler.setFormatter(logging.Formatter( "%(asctime)s|%(levelname)s|%(message)s")) err_handler = logging.StreamHandler(sys.stderr) err_handler.setLevel(logging.WARNING) err_handler.setFormatter(logging.Formatter( "%(asctime)s|%(levelname)s|%(message)s|%(pathname)s:%(lineno)d")) logger = logging.getLogger("myapp") logger.setLevel(logging.DEBUG) logger.addHandler(file_handler) logger.addHandler(err_handler) logger.debug("A DEBUG message") logger.info("An INFO message") logger.warning("A WARNING message") logger.error("An ERROR message") logger.critical("A CRITICAL message")ConfigParser (Python 2) or configparser (Python 3) ModuleThe ConfigParser module implements a basic configuration file parser for .ini. A .ini file contains key-value pairs organized in sections and looks like: [app] name = my application version = 0.9.1 authors = ["Peter", "Paul"] debug = False [db] host = localhost port = 3306 [DEFAULT] message = hello
You can use ConfigParser to parse the .ini file, e.g., import ConfigParser cp = ConfigParser.SafeConfigParser() cp.read('test1.ini') config = {} for section in cp.sections(): print("Section [%s]" % section) for option in cp.options(section): print("|%s|%s|" % (option, cp.get(section, option))) config[option] = cp.get(section, option) print(config) cp.get('app', 'debug') cp.getboolean('app', 'debug') cp.getint('app', 'version')
Interpolation with SafeConfigParserA value may contain formatting string in the form of %(name)s, which refers to another name in the SAME section, or a special DEFAULT (in uppercase) section. This interpolation feature is, however, supported only in SafeConfigParser. For example, suppose we have the following configuration file called myapp.ini: [My Section] msg: %(head)s + %(body)s body = bbb [DEFAULT] head = aaaThe msg will be interpolated as aaa + bbb, interpolated from the SAME section and DEFAULT section. datetime ModuleThe datetime module supplies classes for manipulating dates and time in both simple and complex ways.
smtplib and email ModulesThe SMTP (Simple Mail Transfer Protocol) is a protocol, which handles sending email and routing email between mail servers. Python provides a smtplib module, which defines an SMTP client session object that can be used to send email to any Internet machine with an SMTP listener daemon. To use smtplib: import smtplib smtpobj = smtplib.SMTP([host [,port [, local_hostname [, timeout]]]]) ...... smtpobj.sendmail(form_addr, to_addrs, msg) smtpobj.quit()The email module can be used to construct an email message. [TODO] more json ModuleJSON (JavaScript Object Notation) is a lightweight data interchange format inspired by JavaScript object literal syntax. The json module provides implementation for JSON encoder and decoder.
For example, >>> import json >>> lst = [123, 4.5, 'hello', True] >>> json_lst = json.dumps(lst) >>> json_lst '[123, 4.5, "hello", true]' >>> dct = {'a': 11, 2: 'b', 'c': 'cc'} >>> json_dct = json.dumps(dct) >>> json_dct '{"a": 11, "c": "cc", "2": "b"}' >>> lst_decoded = json.loads(json_lst) >>> lst_decoded [123, 4.5, 'hello', True] >>> dct_decoded = json.loads(json_dct) >>> dct_decoded {'a': 11, 'c': 'cc', '2': 'b'} >>> f = open('json.txt', 'w') >>> json.dump(dct, f) >>> f.close() >>> f = open('json.txt', 'r') >>> dct_decoded_from_file = json.load(f) >>> dct_decoded_from_file {'a': 11, 'c': 'cc', '2': 'b'} >>> f.seek(0) 0 >>> f.read() '{"a": 11, "c": "cc", "2": "b"}' >>> f.close()pickle and cPickle ModulesThe json module (described earlier) handles lists and dictionaries, but serializing arbitrary class instances requires a bit of extra effort. On the other hand, the pickle module implements serialization and de-serialization of any Python object. Pickle is a protocol which allows the serialization of arbitrarily complex Python objects. It is specific to the Python languages and not applicable to other languages. The pickle module provides the same functions as the json module:
The module cPickle is an improved version of pickle. signal moduleSignals (software interrupt) are a limited form of asynchronous inter-process communication, analogous to hardware interrupts. It is generally used by the operating system to notify processes about certain issues/states/errors, like division by zero, etc. The signal module provides mechanisms to use signal handlers in Python. signal.signal()The signal.signal() method takes two arguments: the signal number to handle, and the handling function. For example, import sys, signal, time def my_signal_handler(signalnum, handler): print('Signal received %d: %s' % (signalnum, handler)); signal.signal(signal.SIGINT, my_signal_handler); signal.signal(signal.SIGUSR1, my_signal_handler); while(1): print("Wait...") time.sleep(10)Run the program in the background (with &) and send signals to the process: $ ./test_signal.py & [1] 24078 $ Wait... $ kill -INT 24078 Signal received 2: $ kill -USR1 24078 Signal received 10: $ kill -9 24078REFERENCES & RESOURCES
Latest version tested: Python (Ubuntu, Windows, Cygwin, Jupyter Notebook) 3.7.1 and
2.7.14 |