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Python for absolute beginners, next episode, variables and assignment.

Overview Teaching: 15 min Exercises: 15 min Questions How can I store data in programs? Objectives Write scripts that assign values to variables and perform calculations with those values. Correctly trace value changes in scripts that use assignment.

Use variables to store values

Variables are one of the fundamental building blocks of Python. A variable is like a tiny container where you store values and data, such as filenames, words, numbers, collections of words and numbers, and more.

The variable name will point to a value that you “assign” it. You might think about variable assignment like putting a value “into” the variable, as if the variable is a little box 🎁

(In fact, a variable is not a container as such but more like an adress label that points to a container with a given value. This difference will become relevant once we start talking about lists and mutable data types.)

You assign variables with an equals sign ( = ). In Python, a single equals sign = is the “assignment operator.” (A double equals sign == is the “real” equals sign.)

• Variables are names for values.
• In Python the = symbol assigns the value on the right to the name on the left.
• The variable is created when a value is assigned to it.
• Here, Python assigns an age to a variable age and a name in quotation marks to a variable first_name :

Variable names

Variable names can be as long or as short as you want, but there are certain rules you must follow.

• Cannot start with a digit.
• Cannot contain spaces, quotation marks, or other punctuation.
• May contain an underscore (typically used to separate words in long variable names).
• Having an underscore at the beginning of a variable name like _alistairs_real_age has a special meaning. So we won’t do that until we understand the convention.
• The standard naming convention for variable names in Python is the so-called “snake case”, where each word is separated by an underscore. For example my_first_variable . You can read more about naming conventions in Python here .

Use meaningful variable names

Python doesn’t care what you call variables as long as they obey the rules (alphanumeric characters and the underscore). As you start to code, you will almost certainly be tempted to use extremely short variables names like f . Your fingers will get tired. Your coffee will wear off. You will see other people using variables like f . You’ll promise yourself that you’ll definitely remember what f means. But you probably won’t.

So, resist the temptation of bad variable names! Clear and precisely-named variables will:

• Make your code more readable (both to yourself and others).
• Reinforce your understanding of Python and what’s happening in the code.
• Clarify and strengthen your thinking.

Use meaningful variable names to help other people understand what the program does. The most important “other person” is your future self!

Python is case-sensitive

Python thinks that upper- and lower-case letters are different, so Name and name are different variables. There are conventions for using upper-case letters at the start of variable names so we will use lower-case letters for now.

Off-Limits Names

The only variable names that are off-limits are names that are reserved by, or built into, the Python programming language itself — such as print , True , and list . Some of these you can overwrite into variable names (not ideal!), but Jupyter Lab (and many other environments and editors) will catch this by colour coding your variable. If your would-be variable is colour-coded green, rethink your name choice. This is not something to worry too much about. You can get the object back by resetting your kernel.

Use print() to display values

We can check to see what’s “inside” variables by running a cell with the variable’s name. This is one of the handiest features of a Jupyter notebook. Outside the Jupyter environment, you would need to use the print() function to display the variable.

You can run the print() function inside the Jupyter environment, too. This is sometimes useful because Jupyter will only display the last variable in a cell, while print() can display multiple variables. Additionally, Jupyter will display text with \n characters (which means “new line”), while print() will display the text appropriately formatted with new lines.

• Python has a built-in function called print() that prints things as text.
• Provide values to the function (i.e., the things to print) in parentheses.
• To add a string to the printout, wrap the string in single or double quotations.
• The values passed to the function are called ‘arguments’ and are separated by commas.
• When using the print() function, we can also separate with a ‘+’ sign. However, when using ‘+’ we have to add spaces in between manually.
• print() automatically puts a single space between items to separate them.
• And wraps around to a new line at the end.

Variables must be created before they are used

If a variable doesn’t exist yet, or if the name has been misspelled, Python reports an error (unlike some languages, which “guess” a default value).

The last line of an error message is usually the most informative. This message lets us know that there is no variable called eye_color in the script.

Variables Persist Between Cells Variables defined in one cell exist in all other cells once executed, so the relative location of cells in the notebook do not matter (i.e., cells lower down can still affect those above). Notice the number in the square brackets [ ] to the left of the cell. These numbers indicate the order, in which the cells have been executed. Cells with lower numbers will affect cells with higher numbers as Python runs the cells chronologically. As a best practice, we recommend you keep your notebook in chronological order so that it is easier for the human eye to read and make sense of, as well as to avoid any errors if you close and reopen your project, and then rerun what you have done. Remember: Notebook cells are just a way to organize a program! As far as Python is concerned, all of the source code is one long set of instructions.

Variables can be used in calculations

• We can use variables in calculations just as if they were values. Remember, we assigned 42 to age a few lines ago.

This code works in the following way. We are reassigning the value of the variable age by taking its previous value (42) and adding 3, thus getting our new value of 45.

Use an index to get a single character from a string

• The characters (individual letters, numbers, and so on) in a string are ordered. For example, the string ‘AB’ is not the same as ‘BA’. Because of this ordering, we can treat the string as a list of characters.
• Each position in the string (first, second, etc.) is given a number. This number is called an index or sometimes a subscript.
• Indices are numbered from 0 rather than 1.
• Use the position’s index in square brackets to get the character at that position.

Use a slice to get a substring

A part of a string is called a substring. A substring can be as short as a single character. A slice is a part of a string (or, more generally, any list-like thing). We take a slice by using [start:stop] , where start is replaced with the index of the first element we want and stop is replaced with the index of the element just after the last element we want. Mathematically, you might say that a slice selects [start:stop] . The difference between stop and start is the slice’s length. Taking a slice does not change the contents of the original string. Instead, the slice is a copy of part of the original string.

Use the built-in function len() to find the length of a string

The built-in function len() is used to find the length of a string (and later, of other data types, too).

Note that the result is 6 and not 7. This is because it is the length of the value of the variable (i.e. 'helium' ) that is being counted and not the name of the variable (i.e. element )

Also note that nested functions are evaluated from the inside out, just like in mathematics. Thus, Python first reads the len() function, then the print() function.

Choosing a Name Which is a better variable name, m , min , or minutes ? Why? Hint: think about which code you would rather inherit from someone who is leaving the library: ts = m * 60 + s tot_sec = min * 60 + sec total_seconds = minutes * 60 + seconds Solution minutes is better because min might mean something like “minimum” (and actually does in Python, but we haven’t seen that yet).

Swapping Values Draw a table showing the values of the variables in this program after each statement is executed. In simple terms, what do the last three lines of this program do? x = 1.0 y = 3.0 swap = x x = y y = swap Solution swap = x # x->1.0 y->3.0 swap->1.0 x = y # x->3.0 y->3.0 swap->1.0 y = swap # x->3.0 y->1.0 swap->1.0 These three lines exchange the values in x and y using the swap variable for temporary storage. This is a fairly common programming idiom.

Predicting Values What is the final value of position in the program below? (Try to predict the value without running the program, then check your prediction.) initial = "left" position = initial initial = "right" Solution initial = "left" # Initial is assigned the string "left" position = initial # Position is assigned the variable initial, currently "left" initial = "right" # Initial is assigned the string "right" print(position) left The last assignment to position was “left”

Can you slice integers? If you assign a = 123 , what happens if you try to get the second digit of a ? Solution Numbers are not stored in the written representation, so they can’t be treated like strings. a = 123 print(a[1]) TypeError: 'int' object is not subscriptable

Slicing What does the following program print? library_name = 'social sciences' print('library_name[1:3] is:', library_name[1:3]) If thing is a variable name, low is a low number, and high is a high number: What does thing[low:high] do? What does thing[low:] (without a value after the colon) do? What does thing[:high] (without a value before the colon) do? What does thing[:] (just a colon) do? What does thing[number:negative-number] do? Solution library_name[1:3] is: oc It will slice the string, starting at the low index and ending an element before the high index It will slice the string, starting at the low index and stopping at the end of the string It will slice the string, starting at the beginning on the string, and ending an element before the high index It will print the entire string It will slice the string, starting the number index, and ending a distance of the absolute value of negative-number elements from the end of the string

Key Points Use variables to store values. Use meaningful variable names. Python is case-sensitive. Use print() to display values. Variables must be created before they are used. Variables persist between cells. Variables can be used in calculations. Use an index to get a single character from a string. Use a slice to get a substring. Use the built-in function len to find the length of a string.

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Assignment operators are used to assign values to variables:

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Assignment Statements

Learn about assignment statements in Python.

• Assignment shortcuts
• Walrus operator

Assignment statements consist of a variable , an equal sign, and an expression .

Here’s an example:

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Assignment Operators in Python

The Python Operators are used to perform operations on values and variables. These are the special symbols that carry out arithmetic, logical, and bitwise computations. The value the operator operates on is known as the Operand. Here, we will cover Different Assignment operators in Python .

Here are the Assignment Operators in Python with examples.

Assignment Operator

Assignment Operators are used to assign values to variables. This operator is used to assign the value of the right side of the expression to the left side operand.

Addition Assignment Operator

The Addition Assignment Operator is used to add the right-hand side operand with the left-hand side operand and then assigning the result to the left operand.

Example: In this code we have two variables ‘a’ and ‘b’ and assigned them with some integer value. Then we have used the addition assignment operator which will first perform the addition operation and then assign the result to the variable on the left-hand side.

S ubtraction Assignment Operator

The Subtraction Assignment Operator is used to subtract the right-hand side operand from the left-hand side operand and then assigning the result to the left-hand side operand.

Example: In this code we have two variables ‘a’ and ‘b’ and assigned them with some integer value. Then we have used the subtraction assignment operator which will first perform the subtraction operation and then assign the result to the variable on the left-hand side.

M ultiplication Assignment Operator

The Multiplication Assignment Operator is used to multiply the right-hand side operand with the left-hand side operand and then assigning the result to the left-hand side operand.

Example: In this code we have two variables ‘a’ and ‘b’ and assigned them with some integer value. Then we have used the multiplication assignment operator which will first perform the multiplication operation and then assign the result to the variable on the left-hand side.

D ivision Assignment Operator

The Division Assignment Operator is used to divide the left-hand side operand with the right-hand side operand and then assigning the result to the left operand.

Example: In this code we have two variables ‘a’ and ‘b’ and assigned them with some integer value. Then we have used the division assignment operator which will first perform the division operation and then assign the result to the variable on the left-hand side.

M odulus Assignment Operator

The Modulus Assignment Operator is used to take the modulus, that is, it first divides the operands and then takes the remainder and assigns it to the left operand.

Example: In this code we have two variables ‘a’ and ‘b’ and assigned them with some integer value. Then we have used the modulus assignment operator which will first perform the modulus operation and then assign the result to the variable on the left-hand side.

F loor Division Assignment Operator

The Floor Division Assignment Operator is used to divide the left operand with the right operand and then assigs the result(floor value) to the left operand.

Example: In this code we have two variables ‘a’ and ‘b’ and assigned them with some integer value. Then we have used the floor division assignment operator which will first perform the floor division operation and then assign the result to the variable on the left-hand side.

Exponentiation Assignment Operator

The Exponentiation Assignment Operator is used to calculate the exponent(raise power) value using operands and then assigning the result to the left operand.

Example: In this code we have two variables ‘a’ and ‘b’ and assigned them with some integer value. Then we have used the exponentiation assignment operator which will first perform exponent operation and then assign the result to the variable on the left-hand side.

Bitwise AND Assignment Operator

The Bitwise AND Assignment Operator is used to perform Bitwise AND operation on both operands and then assigning the result to the left operand.

Example: In this code we have two variables ‘a’ and ‘b’ and assigned them with some integer value. Then we have used the bitwise AND assignment operator which will first perform Bitwise AND operation and then assign the result to the variable on the left-hand side.

Bitwise OR Assignment Operator

The Bitwise OR Assignment Operator is used to perform Bitwise OR operation on the operands and then assigning result to the left operand.

Example: In this code we have two variables ‘a’ and ‘b’ and assigned them with some integer value. Then we have used the bitwise OR assignment operator which will first perform bitwise OR operation and then assign the result to the variable on the left-hand side.

Bitwise XOR Assignment Operator 

The Bitwise XOR Assignment Operator is used to perform Bitwise XOR operation on the operands and then assigning result to the left operand.

Example: In this code we have two variables ‘a’ and ‘b’ and assigned them with some integer value. Then we have used the bitwise XOR assignment operator which will first perform bitwise XOR operation and then assign the result to the variable on the left-hand side.

Bitwise Right Shift Assignment Operator

The Bitwise Right Shift Assignment Operator is used to perform Bitwise Right Shift Operation on the operands and then assign result to the left operand.

Example: In this code we have two variables ‘a’ and ‘b’ and assigned them with some integer value. Then we have used the bitwise right shift assignment operator which will first perform bitwise right shift operation and then assign the result to the variable on the left-hand side.

Bitwise Left Shift Assignment Operator

The Bitwise Left Shift Assignment Operator is used to perform Bitwise Left Shift Opertator on the operands and then assign result to the left operand.

Example: In this code we have two variables ‘a’ and ‘b’ and assigned them with some integer value. Then we have used the bitwise left shift assignment operator which will first perform bitwise left shift operation and then assign the result to the variable on the left-hand side.

Walrus Operator

The Walrus Operator in Python is a new assignment operator which is introduced in Python version 3.8 and higher. This operator is used to assign a value to a variable within an expression.

Example: In this code, we have a Python list of integers. We have used Python Walrus assignment operator within the Python while loop . The operator will solve the expression on the right-hand side and assign the value to the left-hand side operand ‘x’ and then execute the remaining code.

Assignment Operators in Python – FAQs

What are assignment operators in python.

Assignment operators in Python are used to assign values to variables. These operators can also perform additional operations during the assignment. The basic assignment operator is = , which simply assigns the value of the right-hand operand to the left-hand operand. Other common assignment operators include += , -= , *= , /= , %= , and more, which perform an operation on the variable and then assign the result back to the variable.

What is the := Operator in Python?

The := operator, introduced in Python 3.8, is known as the “walrus operator”. It is an assignment expression, which means that it assigns values to variables as part of a larger expression. Its main benefit is that it allows you to assign values to variables within expressions, including within conditions of loops and if statements, thereby reducing the need for additional lines of code. Here’s an example: # Example of using the walrus operator in a while loop while (n := int(input("Enter a number (0 to stop): "))) != 0: print(f"You entered: {n}") This loop continues to prompt the user for input and immediately uses that input in both the condition check and the loop body.

What is the Assignment Operator in Structure?

In programming languages that use structures (like C or C++), the assignment operator = is used to copy values from one structure variable to another. Each member of the structure is copied from the source structure to the destination structure. Python, however, does not have a built-in concept of ‘structures’ as in C or C++; instead, similar functionality is achieved through classes or dictionaries.

What is the Assignment Operator in Python Dictionary?

In Python dictionaries, the assignment operator = is used to assign a new key-value pair to the dictionary or update the value of an existing key. Here’s how you might use it: my_dict = {} # Create an empty dictionary my_dict['key1'] = 'value1' # Assign a new key-value pair my_dict['key1'] = 'updated value' # Update the value of an existing key print(my_dict) # Output: {'key1': 'updated value'}

What is += and -= in Python?

The += and -= operators in Python are compound assignment operators. += adds the right-hand operand to the left-hand operand and assigns the result to the left-hand operand. Conversely, -= subtracts the right-hand operand from the left-hand operand and assigns the result to the left-hand operand. Here are examples of both: # Example of using += a = 5 a += 3 # Equivalent to a = a + 3 print(a) # Output: 8 # Example of using -= b = 10 b -= 4 # Equivalent to b = b - 4 print(b) # Output: 6 These operators make code more concise and are commonly used in loops and iterative data processing.

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Variables, Expressions, and Assignments

Variables, expressions, and assignments 1 #, introduction #.

In this chapter, we introduce some of the main building blocks needed to create programs–that is, variables, expressions, and assignments. Programming related variables can be intepret in the same way that we interpret mathematical variables, as elements that store values that can later be changed. Usually, variables and values are used within the so-called expressions. Once again, just as in mathematics, an expression is a construct of values and variables connected with operators that result in a new value. Lastly, an assignment is a language construct know as an statement that assign a value (either as a constant or expression) to a variable. The rest of this notebook will dive into the main concepts that we need to fully understand these three language constructs.

Values and Types #

A value is the basic unit used in a program. It may be, for instance, a number respresenting temperature. It may be a string representing a word. Some values are 42, 42.0, and ‘Hello, Data Scientists!’.

Each value has its own type : 42 is an integer ( int in Python), 42.0 is a floating-point number ( float in Python), and ‘Hello, Data Scientists!’ is a string ( str in Python).

The Python interpreter can tell you the type of a value: the function type takes a value as argument and returns its corresponding type.

Observe the difference between type(42) and type('42') !

Expressions and Statements #

On the one hand, an expression is a combination of values, variables, and operators.

A value all by itself is considered an expression, and so is a variable.

When you type an expression at the prompt, the interpreter evaluates it, which means that it calculates the value of the expression and displays it.

In boxes above, m has the value 27 and m + 25 has the value 52 . m + 25 is said to be an expression.

On the other hand, a statement is an instruction that has an effect, like creating a variable or displaying a value.

The first statement initializes the variable n with the value 17 , this is a so-called assignment statement .

The second statement is a print statement that prints the value of the variable n .

The effect is not always visible. Assigning a value to a variable is not visible, but printing the value of a variable is.

Assignment Statements #

We have already seen that Python allows you to evaluate expressions, for instance 40 + 2 . It is very convenient if we are able to store the calculated value in some variable for future use. The latter can be done via an assignment statement. An assignment statement creates a new variable with a given name and assigns it a value.

The example in the previous code contains three assignments. The first one assigns the value of the expression 40 + 2 to a new variable called magicnumber ; the second one assigns the value of π to the variable pi , and; the last assignment assigns the string value 'Data is eatig the world' to the variable message .

Programmers generally choose names for their variables that are meaningful. In this way, they document what the variable is used for.

Do It Yourself!

Let’s compute the volume of a cube with side \(s = 5\) . Remember that the volume of a cube is defined as \(v = s^3\) . Assign the value to a variable called volume .

Well done! Now, why don’t you print the result in a message? It can say something like “The volume of the cube with side 5 is \(volume\) ”.

Beware that there is no checking of types ( type checking ) in Python, so a variable to which you have assigned an integer may be re-used as a float, even if we provide type-hints .

Names and Keywords #

Names of variable and other language constructs such as functions (we will cover this topic later), should be meaningful and reflect the purpose of the construct.

In general, Python names should adhere to the following rules:

It should start with a letter or underscore.

It cannot start with a number.

It must only contain alpha-numeric (i.e., letters a-z A-Z and digits 0-9) characters and underscores.

They cannot share the name of a Python keyword.

If you use illegal variable names you will get a syntax error.

By choosing the right variables names you make the code self-documenting, what is better the variable v or velocity ?

The following are examples of invalid variable names.

These basic development principles are sometimes called architectural rules . By defining and agreeing upon architectural rules you make it easier for you and your fellow developers to understand and modify your code.

If you want to read more on this, please have a look at Code complete a book by Steven McConnell [ McC04 ] .

Every programming language has a collection of reserved keywords . They are used in predefined language constructs, such as loops and conditionals . These language concepts and their usage will be explained later.

The interpreter uses keywords to recognize these language constructs in a program. Python 3 has the following keywords:

False class finally is return

None continue for lambda try

True def from nonlocal while

and del global not with

as elif if or yield

assert else import pass break

except in raise

Reassignments #

It is allowed to assign a new value to an existing variable. This process is called reassignment . As soon as you assign a value to a variable, the old value is lost.

The assignment of a variable to another variable, for instance b = a does not imply that if a is reassigned then b changes as well.

You have a variable salary that shows the weekly salary of an employee. However, you want to compute the monthly salary. Can you reassign the value to the salary variable according to the instruction?

Updating Variables #

A frequently used reassignment is for updating puposes: the value of a variable depends on the previous value of the variable.

This statement expresses “get the current value of x , add one, and then update x with the new value.”

Beware, that the variable should be initialized first, usually with a simple assignment.

Do you remember the salary excercise of the previous section (cf. 13. Reassignments)? Well, if you have not done it yet, update the salary variable by using its previous value.

Updating a variable by adding 1 is called an increment ; subtracting 1 is called a decrement . A shorthand way of doing is using += and -= , which stands for x = x + ... and x = x - ... respectively.

Order of Operations #

Expressions may contain multiple operators. The order of evaluation depends on the priorities of the operators also known as rules of precedence .

For mathematical operators, Python follows mathematical convention. The acronym PEMDAS is a useful way to remember the rules:

Parentheses have the highest precedence and can be used to force an expression to evaluate in the order you want. Since expressions in parentheses are evaluated first, 2 * (3 - 1) is 4 , and (1 + 1)**(5 - 2) is 8 . You can also use parentheses to make an expression easier to read, even if it does not change the result.

Exponentiation has the next highest precedence, so 1 + 2**3 is 9 , not 27 , and 2 * 3**2 is 18 , not 36 .

Multiplication and division have higher precedence than addition and subtraction . So 2 * 3 - 1 is 5 , not 4 , and 6 + 4 / 2 is 8 , not 5 .

Operators with the same precedence are evaluated from left to right (except exponentiation). So in the expression degrees / 2 * pi , the division happens first and the result is multiplied by pi . To divide by 2π, you can use parentheses or write: degrees / 2 / pi .

In case of doubt, use parentheses!

Let’s see what happens when we evaluate the following expressions. Just run the cell to check the resulting value.

Floor Division and Modulus Operators #

The floor division operator // divides two numbers and rounds down to an integer.

For example, suppose that driving to the south of France takes 555 minutes. You might want to know how long that is in hours.

Conventional division returns a floating-point number.

Hours are normally not represented with decimal points. Floor division returns the integer number of hours, dropping the fraction part.

You spend around 225 minutes every week on programming activities. You want to know around how many hours you invest to this activity during a month. Use the \(//\) operator to give the answer.

The modulus operator % works on integer values. It computes the remainder when dividing the first integer by the second one.

The modulus operator is more useful than it seems.

For example, you can check whether one number is divisible by another—if x % y is zero, then x is divisible by y .

String Operations #

In general, you cannot perform mathematical operations on strings, even if the strings look like numbers, so the following operations are illegal: '2'-'1' 'eggs'/'easy' 'third'*'a charm'

But there are two exceptions, + and * .

The + operator performs string concatenation, which means it joins the strings by linking them end-to-end.

The * operator also works on strings; it performs repetition.

Speedy Gonzales is a cartoon known to be the fastest mouse in all Mexico . He is also famous for saying “Arriba Arriba Andale Arriba Arriba Yepa”. Can you use the following variables, namely arriba , andale and yepa to print the mentioned expression? Don’t forget to use the string operators.

Asking the User for Input #

The programs we have written so far accept no input from the user.

To get data from the user through the Python prompt, we can use the built-in function input .

When input is called your whole program stops and waits for the user to enter the required data. Once the user types the value and presses Return or Enter , the function returns the input value as a string and the program continues with its execution.

Try it out!

You can also print a message to clarify the purpose of the required input as follows.

The resulting string can later be translated to a different type, like an integer or a float. To do so, you use the functions int and float , respectively. But be careful, the user might introduce a value that cannot be converted to the type you required.

We want to know the name of a user so we can display a welcome message in our program. The message should say something like “Hello \(name\) , welcome to our hello world program!”.

Script Mode #

So far we have run Python in interactive mode in these Jupyter notebooks, which means that you interact directly with the interpreter in the code cells . The interactive mode is a good way to get started, but if you are working with more than a few lines of code, it can be clumsy. The alternative is to save code in a file called a script and then run the interpreter in script mode to execute the script. By convention, Python scripts have names that end with .py .

Use the PyCharm icon in Anaconda Navigator to create and execute stand-alone Python scripts. Later in the course, you will have to work with Python projects for the assignments, in order to get acquainted with another way of interacing with Python code.

This Jupyter Notebook is based on Chapter 2 of the books Python for Everybody [ Sev16 ] and Think Python (Sections 5.1, 7.1, 7.2, and 5.12) [ Dow15 ] .

Python Variables – The Complete Beginner's Guide

Variables are an essential part of Python. They allow us to easily store, manipulate, and reference data throughout our projects.

This article will give you all the understanding of Python variables you need to use them effectively in your projects.

If you want the most convenient way to review all the topics covered here, I've put together a helpful cheatsheet for you right here:

Download the Python variables cheatsheet (it takes 5 seconds).

What is a Variable in Python?

So what are variables and why do we need them?

Variables are essential for holding onto and referencing values throughout our application. By storing a value into a variable, you can reuse it as many times and in whatever way you like throughout your project.

You can think of variables as boxes with labels, where the label represents the variable name and the content of the box is the value that the variable holds.

In Python, variables are created the moment you give or assign a value to them.

How Do I Assign a Value to a Variable?

Assigning a value to a variable in Python is an easy process.

You simply use the equal sign = as an assignment operator, followed by the value you want to assign to the variable. Here's an example:

In this example, we've created two variables: country and year_founded. We've assigned the string value "United States" to the country variable and integer value 1776 to the year_founded variable.

There are two things to note in this example:

• Variables in Python are case-sensitive . In other words, watch your casing when creating variables, because Year_Founded will be a different variable than year_founded even though they include the same letters
• Variable names that use multiple words in Python should be separated with an underscore _ . For example, a variable named "site name" should be written as "site name" ._ This convention is called snake case (very fitting for the "Python" language).

How Should I Name My Python Variables?

There are some rules to follow when naming Python variables.

Some of these are hard rules that must be followed, otherwise your program will not work, while others are known as conventions . This means, they are more like suggestions.

Variable naming rules

• Variable names must start with a letter or an underscore _ character.
• Variable names can only contain letters, numbers, and underscores.
• Variable names cannot contain spaces or special characters.

Variable naming conventions

• Variable names should be descriptive and not too short or too long.
• Use lowercase letters and underscores to separate words in variable names (known as "snake_case").

What Data Types Can Python Variables Hold?

One of the best features of Python is its flexibility when it comes to handling various data types.

Python variables can hold various data types, including integers, floats, strings, booleans, tuples and lists:

Integers are whole numbers, both positive and negative.

Floats are real numbers or numbers with a decimal point.

Strings are sequences of characters, namely words or sentences.

Booleans are True or False values.

Lists are ordered, mutable collections of values.

Tuples are ordered, immutable collections of values.

There are more data types in Python, but these are the most common ones you will encounter while working with Python variables.

Python is Dynamically Typed

Python is what is known as a dynamically-typed language. This means that the type of a variable can change during the execution of a program.

Another feature of dynamic typing is that it is not necessary to manually declare the type of each variable, unlike other programming languages such as Java.

You can use the type() function to determine the type of a variable. For instance:

What Operations Can Be Performed?

Variables can be used in various operations, which allows us to transform them mathematically (if they are numbers), change their string values through operations like concatenation, and compare values using equality operators.

Mathematic Operations

It's possible to perform basic mathematic operations with variables, such as addition, subtraction, multiplication, and division:

It's also possible to find the remainder of a division operation by using the modulus % operator as well as create exponents using the ** syntax:

String operators

Strings can be added to one another or concatenated using the + operator.

Equality comparisons

Values can also be compared in Python using the < , > , == , and != operators.

These operators, respectively, compare whether values are less than, greater than, equal to, or not equal to each other.

Finally, note that when performing operations with variables, you need to ensure that the types of the variables are compatible with each other.

For example, you cannot directly add a string and an integer. You would need to convert one of the variables to a compatible type using a function like str() or [int()](https://www.freecodecamp.org/news/python-string-to-int-convert-a-string-example/) .

Variable Scope

The scope of a variable refers to the parts of a program where the variable can be accessed and modified. In Python, there are two main types of variable scope:

Global scope : Variables defined outside of any function or class have a global scope. They can be accessed and modified throughout the program, including within functions and classes.

Local scope : Variables defined within a function or class have a local scope. They can only be accessed and modified within that function or class.

In this example, attempting to access local_var outside of the function_with_local_var function results in a NameError , as the variable is not defined in the global scope.

Don't be afraid to experiment with different types of variables, operations, and scopes to truly grasp their importance and functionality. The more you work with Python variables, the more confident you'll become in applying these concepts.

Finally, if you want to fully learn all of these concepts, I've put together for you a super helpful cheatsheet that summarizes everything we've covered here.

Just click the link below to grab it for free. Enjoy!

Download the Python variables cheatsheet

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Assignment in Python

00:00 Since Python’s argument passing mechanism relies so much on how Python deals with assignment, the next couple of lessons will go into a bit more depth about how assignment works in Python.

00:12 Recall some things I’ve already mentioned: Assignment is the process of binding a name to an object. Parameter names are also bound to objects on function entry in Python. And again, this is how Python’s argument passing mechanism gets its name.

00:29 But how exactly does that work?

00:33 Let’s take a closer look.

00:36 In other languages like C++, assignment is quite simple, especially for basic types. In the first statement, a space of memory which has been designated for the variable x has the value 5 stored in it.

00:51 Then, in that second statement, that same piece of memory is overwritten with the value of 10 , and the value 5 is lost completely.

01:01 Python works much differently. In the first statement, an object representing the number 5 is created, if one doesn’t already exist, and then x is made to refer— or we sometimes say “point”—to that object, basically by storing the memory address to where the object 5 is stored.

01:21 In the second statement, x is reassigned to refer to an object representing the value 10 . Again, if one didn’t already exist, one is created.

01:31 In other words, x is rebound to a new object. The object representing 5 may still exist because there could be other variables or names referring to that object.

01:42 Let’s look at this process in more detail. There’s a particular structure used to implement objects in Python. A reference counter keeps track of how many references a specific object has. So again, in the statement x = 5 , an object representing the value 5 is found or created, the reference counter for that object is incremented, and an entry is made binding that variable name to the object.

02:14 This is basically done in a dictionary, and depending on the namespace of the variable, it can be found using either the locals() or globals() function.

02:26 Then, when x is reassigned to the value 10 , the reference count for the object representing 5 is reduced by one, the reference counter for the object representing 10 is increased by one, and finally, the appropriate dictionary is updated to indicate that x is now bound to that new object 10 . To see this happen, you can write a small program which uses the sys.getrefcount() function.

02:54 This function takes an object as an argument and returns the number of references to it. Here is an example. It will track the number of references to two objects, cleverly named "value_1" and "value_2" . First, it displays the number of references to each object before either are assigned to a variable.

03:20 Then it assigns x to be bound to the "value_1" . It repeats calls to getrefcount() to see how the counts have been changed as a result of that assignment statement.

03:33 Then it reassigns x from "value_1" to "value_2" and displays the ref counts again. Let’s try this out.

03:48 Well, that’s interesting! Before either object is bound to x , they each already have three references. Why is that? Well, one is an internal reference to the object when it was created.

04:01 Python has to know where it is so it can be bound to something else as the program runs. Second, it’s being used as an argument to this function call, so there’s a reference. And it’s used in the parameter variable inside getrefcount() .

04:17 So you likely always get a count of at least 3 for any object you try. The point to notice is that once x was assigned to be "value_1" , the ref count for "value_1" was incremented.

04:32 Then when x was reassigned to "value_2" , the ref count for "value_1" went back down to 3 and the ref count for "value_2" increased to 4 .

04:44 So, you can see the ref counts are going up and down as objects are bound and unbound to the variable x .

04:54 Next, you’ll see how this notion of assignments and bindings works with function arguments.

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Python Conditional Assignment

When you want to assign a value to a variable based on some condition, like if the condition is true then assign a value to the variable, else assign some other value to the variable, then you can use the conditional assignment operator.

In this tutorial, we will look at different ways to assign values to a variable based on some condition.

1. Using Ternary Operator

The ternary operator is very special operator in Python, it is used to assign a value to a variable based on some condition.

It goes like this:

Here, the value of variable will be value_if_true if the condition is true, else it will be value_if_false .

Let's see a code snippet to understand it better.

You can see we have conditionally assigned a value to variable c based on the condition a > b .

2. Using if-else statement

if-else statements are the core part of any programming language, they are used to execute a block of code based on some condition.

Using an if-else statement, we can assign a value to a variable based on the condition we provide.

Here is an example of replacing the above code snippet with the if-else statement.

3. Using Logical Short Circuit Evaluation

Logical short circuit evaluation is another way using which you can assign a value to a variable conditionally.

The format of logical short circuit evaluation is:

It looks similar to ternary operator, but it is not. Here the condition and value_if_true performs logical AND operation, if both are true then the value of variable will be value_if_true , or else it will be value_if_false .

Let's see an example:

But if we make condition True but value_if_true False (or 0 or None), then the value of variable will be value_if_false .

So, you can see that the value of c is 20 even though the condition a < b is True .

So, you should be careful while using logical short circuit evaluation.

While working with lists , we often need to check if a list is empty or not, and if it is empty then we need to assign some default value to it.

Let's see how we can do it using conditional assignment.

Here, we have assigned a default value to my_list if it is empty.

Assign a value to a variable conditionally based on the presence of an element in a list.

Now you know 3 different ways to assign a value to a variable conditionally. Any of these methods can be used to assign a value when there is a condition.

The cleanest and fastest way to conditional value assignment is the ternary operator .

if-else statement is recommended to use when you have to execute a block of code based on some condition.

Happy coding! 😊

Python Programming

Practice Python Exercises and Challenges with Solutions

Free Coding Exercises for Python Developers. Exercises cover Python Basics , Data structure , to Data analytics . As of now, this page contains 18 Exercises.

What included in these Python Exercises?

Each exercise contains specific Python topic questions you need to practice and solve. These free exercises are nothing but Python assignments for the practice where you need to solve different programs and challenges.

• All exercises are tested on Python 3.
• Each exercise has 10-20 Questions.
• The solution is provided for every question.
• Practice each Exercise in Online Code Editor

These Python programming exercises are suitable for all Python developers. If you are a beginner, you will have a better understanding of Python after solving these exercises. Below is the list of exercises.

Select the exercise you want to solve .

Basic Exercise for Beginners

Practice and Quickly learn Python’s necessary skills by solving simple questions and problems.

Topics : Variables, Operators, Loops, String, Numbers, List

Python Input and Output Exercise

Solve input and output operations in Python. Also, we practice file handling.

Topics : print() and input() , File I/O

Python Loop Exercise

This Python loop exercise aims to help developers to practice branching and Looping techniques in Python.

Topics : If-else statements, loop, and while loop.

Python Functions Exercise

Practice how to create a function, nested functions, and use the function arguments effectively in Python by solving different questions.

Topics : Functions arguments, built-in functions.

Python String Exercise

Solve Python String exercise to learn and practice String operations and manipulations.

Python Data Structure Exercise

Practice widely used Python types such as List, Set, Dictionary, and Tuple operations in Python

Python List Exercise

This Python list exercise aims to help Python developers to learn and practice list operations.

Python Dictionary Exercise

This Python dictionary exercise aims to help Python developers to learn and practice dictionary operations.

Python Set Exercise

This exercise aims to help Python developers to learn and practice set operations.

Python Tuple Exercise

This exercise aims to help Python developers to learn and practice tuple operations.

Python Date and Time Exercise

This exercise aims to help Python developers to learn and practice DateTime and timestamp questions and problems.

Topics : Date, time, DateTime, Calendar.

Python OOP Exercise

This Python Object-oriented programming (OOP) exercise aims to help Python developers to learn and practice OOP concepts.

Topics : Object, Classes, Inheritance

Python JSON Exercise

Practice and Learn JSON creation, manipulation, Encoding, Decoding, and parsing using Python

Python NumPy Exercise

Practice NumPy questions such as Array manipulations, numeric ranges, Slicing, indexing, Searching, Sorting, and splitting, and more.

Python Pandas Exercise

Practice Data Analysis using Python Pandas. Practice Data-frame, Data selection, group-by, Series, sorting, searching, and statistics.

Python Matplotlib Exercise

Practice Data visualization using Python Matplotlib. Line plot, Style properties, multi-line plot, scatter plot, bar chart, histogram, Pie chart, Subplot, stack plot.

Random Data Generation Exercise

Practice and Learn the various techniques to generate random data in Python.

Topics : random module, secrets module, UUID module

Python Database Exercise

Practice Python database programming skills by solving the questions step by step.

Use any of the MySQL, PostgreSQL, SQLite to solve the exercise

Exercises for Intermediate developers

The following practice questions are for intermediate Python developers.

If you have not solved the above exercises, please complete them to understand and practice each topic in detail. After that, you can solve the below questions quickly.

Exercise 1: Reverse each word of a string

Expected Output

• Use the split() method to split a string into a list of words.
• Reverse each word from a list
• finally, use the join() function to convert a list into a string

Steps to solve this question :

• Split the given string into a list of words using the split() method
• Use a list comprehension to create a new list by reversing each word from a list.
• Use the join() function to convert the new list into a string
• Display the resultant string

Exercise 2: Read text file into a variable and replace all newlines with space

Given : Assume you have a following text file (sample.txt).

Expected Output :

• First, read a text file.
• Next, use string replace() function to replace all newlines ( \n ) with space ( ' ' ).

Steps to solve this question : -

• First, open the file in a read mode
• Next, read all content from a file using the read() function and assign it to a variable.
• Display final string

Exercise 3: Remove items from a list while iterating

Description :

In this question, You need to remove items from a list while iterating but without creating a different copy of a list.

Remove numbers greater than 50

Expected Output : -

• Get the list's size
• Iterate list using while loop
• Check if the number is greater than 50
• If yes, delete the item using a del keyword
• Reduce the list size

Solution 1: Using while loop

Solution 2: Using for loop and range()

Exercise 4: Reverse Dictionary mapping

Exercise 5: display all duplicate items from a list.

• Use the counter() method of the collection module.
• Create a dictionary that will maintain the count of each item of a list. Next, Fetch all keys whose value is greater than 2

Solution 1 : - Using collections.Counter()

Solution 2 : -

Exercise 6: Filter dictionary to contain keys present in the given list

Exercise 7: print the following number pattern.

Refer to Print patterns in Python to solve this question.

• Use two for loops
• The outer loop is reverse for loop from 5 to 0
• Increment value of x by 1 in each iteration of an outer loop
• The inner loop will iterate from 0 to the value of i of the outer loop
• Print value of x in each iteration of an inner loop
• Print newline at the end of each outer loop

Exercise 8: Create an inner function

Question description : -

• Create an outer function that will accept two strings, x and y . ( x= 'Emma' and y = 'Kelly' .
• Create an inner function inside an outer function that will concatenate x and y.
• At last, an outer function will join the word 'developer' to it.

Exercise 9: Modify the element of a nested list inside the following list

Change the element 35 to 3500

Exercise 10: Access the nested key increment from the following dictionary

Under Exercises: -

Python Object-Oriented Programming (OOP) Exercise: Classes and Objects Exercises

Updated on:  December 8, 2021 | 52 Comments

Python Date and Time Exercise with Solutions

Updated on:  December 8, 2021 | 10 Comments

Python Dictionary Exercise with Solutions

Updated on:  May 6, 2023 | 56 Comments

Python Tuple Exercise with Solutions

Updated on:  December 8, 2021 | 96 Comments

Python Set Exercise with Solutions

Updated on:  October 20, 2022 | 27 Comments

Python if else, for loop, and range() Exercises with Solutions

Updated on:  September 3, 2024 | 298 Comments

Updated on:  August 2, 2022 | 155 Comments

Updated on:  September 6, 2021 | 109 Comments

Python List Exercise with Solutions

Updated on:  December 8, 2021 | 201 Comments

Updated on:  December 8, 2021 | 7 Comments

Python Data Structure Exercise for Beginners

Updated on:  December 8, 2021 | 116 Comments

Python String Exercise with Solutions

Updated on:  October 6, 2021 | 221 Comments

Updated on:  March 9, 2021 | 23 Comments

Updated on:  March 9, 2021 | 51 Comments

Updated on:  July 20, 2021 | 29 Comments

Python Basic Exercise for Beginners

Updated on:  August 29, 2024 | 498 Comments

Useful Python Tips and Tricks Every Programmer Should Know

Updated on:  May 17, 2021 | 23 Comments

Python random Data generation Exercise

Updated on:  December 8, 2021 | 13 Comments

Python Database Programming Exercise

Updated on:  March 9, 2021 | 17 Comments

• Online Python Code Editor

Updated on:  June 1, 2022 |

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Parallel Processing in Python – A Practical Guide with Examples

• October 31, 2018
• Selva Prabhakaran

Parallel processing is a mode of operation where the task is executed simultaneously in multiple processors in the same computer. It is meant to reduce the overall processing time. In this tutorial, you’ll understand the procedure to parallelize any typical logic using python’s multiprocessing module.

1. Introduction

Parallel processing is a mode of operation where the task is executed simultaneously in multiple processors in the same computer. It is meant to reduce the overall processing time.

However, there is usually a bit of overhead when communicating between processes which can actually increase the overall time taken for small tasks instead of decreasing it.

In python, the multiprocessing module is used to run independent parallel processes by using subprocesses (instead of threads).

It allows you to leverage multiple processors on a machine (both Windows and Unix), which means, the processes can be run in completely separate memory locations. By the end of this tutorial you would know:

• How to structure the code and understand the syntax to enable parallel processing using multiprocessing ?
• How to implement synchronous and asynchronous parallel processing?
• How to parallelize a Pandas DataFrame?
• Solve 3 different usecases with the multiprocessing.Pool() interface.

2. How many maximum parallel processes can you run?

The maximum number of processes you can run at a time is limited by the number of processors in your computer. If you don’t know how many processors are present in the machine, the cpu_count() function in multiprocessing will show it.

3. What is Synchronous and Asynchronous execution?

In parallel processing, there are two types of execution: Synchronous and Asynchronous.

A synchronous execution is one the processes are completed in the same order in which it was started. This is achieved by locking the main program until the respective processes are finished.

Asynchronous, on the other hand, doesn’t involve locking. As a result, the order of results can get mixed up but usually gets done quicker.

There are 2 main objects in multiprocessing to implement parallel execution of a function: The Pool Class and the Process Class.

• Pool.map() and Pool.starmap()
• Pool.apply()
• Pool.map_async() and Pool.starmap_async()
• Pool.apply_async() )
• Process Class

Let’s take up a typical problem and implement parallelization using the above techniques.

In this tutorial, we stick to the Pool class, because it is most convenient to use and serves most common practical applications.

4. Problem Statement: Count how many numbers exist between a given range in each row

The first problem is: Given a 2D matrix (or list of lists), count how many numbers are present between a given range in each row. We will work on the list prepared below.

Solution without parallelization

Let’s see how long it takes to compute it without parallelization.

For this, we iterate the function howmany_within_range() (written below) to check how many numbers lie within range and returns the count.

<heborder=”0″ scrolling=”auto” allowfullscreen=”allowfullscreen”> <!– /wp:parag4>    

5. How to parallelize any function?

The general way to parallelize any operation is to take a particular function that should be run multiple times and make it run parallelly in different processors.

To do this, you initialize a Pool with n number of processors and pass the function you want to parallelize to one of Pool s parallization methods.

multiprocessing.Pool() provides the apply() , map() and starmap() methods to make any function run in parallel.

So what’s the difference between apply() and map() ?

Both apply and map take the function to be parallelized as the main argument.

But the difference is, apply() takes an args argument that accepts the parameters passed to the ‘function-to-be-parallelized’ as an argument, whereas, map can take only one iterable as an argument.

So, map() is really more suitable for simpler iterable operations but does the job faster.

We will get to starmap() once we see how to parallelize howmany_within_range() function with apply() and map() .

5.1. Parallelizing using Pool.apply()

Let’s parallelize the howmany_within_range() function using multiprocessing.Pool() .

5.2. Parallelizing using Pool.map()

Pool.map() accepts only one iterable as argument.

So as a workaround, I modify the howmany_within_range function by setting a default to the minimum and maximum parameters to create a new howmany_within_range_rowonly() function so it accetps only an iterable list of rows as input.

I know this is not a nice usecase of map() , but it clearly shows how it differs from apply() .

5.3. Parallelizing using Pool.starmap()

In previous example, we have to redefine howmany_within_range function to make couple of parameters to take default values.

Using starmap() , you can avoid doing this.

How you ask?

Like Pool.map() , Pool.starmap() also accepts only one iterable as argument, but in starmap() , each element in that iterable is also a iterable.

You can to provide the arguments to the ‘function-to-be-parallelized’ in the same order in this inner iterable element, will in turn be unpacked during execution.

So effectively, Pool.starmap() is like a version of Pool.map() that accepts arguments.

6. Asynchronous Parallel Processing

The asynchronous equivalents apply_async() , map_async() and starmap_async() lets you do execute the processes in parallel asynchronously, that is the next process can start as soon as previous one gets over without regard for the starting order.

As a result, there is no guarantee that the result will be in the same order as the input.

6.1 Parallelizing with Pool.apply_async()

apply_async() is very similar to apply() except that you need to provide a callback function that tells how the computed results should be stored.

However, a caveat with apply_async() is, the order of numbers in the result gets jumbled up indicating the processes did not complete in the order it was started.

A workaround for this is, we redefine a new howmany_within_range2() to accept and return the iteration number ( i ) as well and then sort the final results.

It is possible to use apply_async() without providing a callback function.

Only that, if you don’t provide a callback, then you get a list of pool.ApplyResult objects which contains the computed output values from each process.

From this, you need to use the pool.ApplyResult.get() method to retrieve the desired final result.

6.2 Parallelizing with Pool.starmap_async()

You saw how apply_async() works.

Can you imagine and write up an equivalent version for starmap_async and map_async ?

The implementation is below anyways.

7. How to Parallelize a Pandas DataFrame?

So far you’ve seen how to parallelize a function by making it work on lists.

But when working in data analysis or machine learning projects, you might want to parallelize Pandas Dataframes, which are the most commonly used objects (besides numpy arrays) to store tabular data.

When it comes to parallelizing a DataFrame , you can make the function-to-be-parallelized to take as an input parameter:

• one row of the dataframe
• one column of the dataframe
• the entire dataframe itself

The first 2 can be done using multiprocessing module itself.

But for the last one, that is parallelizing on an entire dataframe, we will use the pathos package that uses dill for serialization internally.

First, lets create a sample dataframe and see how to do row-wise and column-wise paralleization.

Something like using pd.apply() on a user defined function but in parallel.

We have a dataframe. Let’s apply the hypotenuse function on each row, but running 4 processes at a time.

To do this, we exploit the df.itertuples(name=False) .

By setting name=False , you are passing each row of the dataframe as a simple tuple to the hypotenuse function.

That was an example of row-wise parallelization.

Let’s also do a column-wise parallelization.

For this, I use df.iteritems() to pass an entire column as a series to the sum_of_squares function.

Now comes the third part – Parallelizing a function that accepts a Pandas Dataframe, NumPy Array, etc. Pathos follows the multiprocessing style of: Pool > Map > Close > Join > Clear.

Check out the pathos docs for more info.

Thanks to notsoprocoder for this contribution based on pathos.

If you are familiar with pandas dataframes but want to get hands-on and master it, check out these pandas exercises .

8. Exercises

Problem 1: Use Pool.apply() to get the row wise common items in list_a and list_b .

9. Conclusion

Hope you were able to solve the above exercises, congratulations if you did! In this post, we saw the overall procedure and various ways to implement parallel processing using the multiprocessing module. The procedure described above is pretty much the same even if you work on larger machines with many more number of processors, where you may reap the real speed benefits of parallel processing. Happy coding and I’ll see you in the next one !

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Optimizing Job Assignments with Python: A Greedy Approach

In this article, we will learn the skill of job assignment which is a very important topic in the field of Operations Research. For this, we will utilize Python programming language and the Numpy library for the same. We will also solve a small case on a job assignment.

Job assignment involves allocating tasks to workers while minimizing overall completion time or cost. Python’s greedy algorithm, combined with NumPy, can solve such problems by iteratively assigning jobs based on worker skills and constraints, enabling efficient resource management in various industries.

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What is a Job Assignment?

Let us understand what a job assignment is with an example. In our example, three tasks have to be completed. Three workers have different sets of skills and take different amounts of time to complete the above-mentioned tasks. Now our goal is to assign the jobs to the workers to minimize the period of completing the three tasks.

Now, we solve the above problem using the concepts of Linear programming. Now there are certain constraints as well, each worker can be assigned only a single job at a time. Our objective function is the sum of all the time taken by the workers and minimize it. Let us now solve this problem using the power of the Numpy library of Python programming language.

Let us now look at the output of the problem.

From the output, we can see that The assignment is complete and optimized. Let us now look at a small case and understand the job assignment further.

A Real-World Job Assignment Scenario

Continuing with the example of assigning workers some jobs, in this case, a company is looking to get some work done with the help of some freelancers. There are 15 jobs and we have 10 freelancers. We have to assign jobs to workers in such a way, that we minimize the time as well as the cost of the whole operation. Let us now model this in the Python programming language.

This problem is solved using the greedy algorithm. In short, the greedy algorithm selects the most optimal choice available and does not consider what will happen in the future while making this choice. In the above code, we have randomly generated data on freelancer details. Let us now look at the output of the code.

Thus, we complete our agenda of job assignment while minimizing costs as evidenced by the output.

Assigning jobs optimally is crucial for maximizing productivity and minimizing costs in today’s competitive landscape. Python’s powerful libraries like NumPy make it easy to implement greedy algorithms and solve complex job assignment problems, even with larger sets of jobs and workers. How could you adapt this approach to accommodate dynamic changes in job requirements or worker availability?

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multiprocessing — Process-based parallelism ¶

Source code: Lib/multiprocessing/

Availability : not Emscripten, not WASI.

This module does not work or is not available on WebAssembly platforms wasm32-emscripten and wasm32-wasi . See WebAssembly platforms for more information.

Introduction ¶

multiprocessing is a package that supports spawning processes using an API similar to the threading module. The multiprocessing package offers both local and remote concurrency, effectively side-stepping the Global Interpreter Lock by using subprocesses instead of threads. Due to this, the multiprocessing module allows the programmer to fully leverage multiple processors on a given machine. It runs on both POSIX and Windows.

The multiprocessing module also introduces APIs which do not have analogs in the threading module. A prime example of this is the Pool object which offers a convenient means of parallelizing the execution of a function across multiple input values, distributing the input data across processes (data parallelism). The following example demonstrates the common practice of defining such functions in a module so that child processes can successfully import that module. This basic example of data parallelism using Pool ,

will print to standard output

concurrent.futures.ProcessPoolExecutor offers a higher level interface to push tasks to a background process without blocking execution of the calling process. Compared to using the Pool interface directly, the concurrent.futures API more readily allows the submission of work to the underlying process pool to be separated from waiting for the results.

The Process class ¶

In multiprocessing , processes are spawned by creating a Process object and then calling its start() method. Process follows the API of threading.Thread . A trivial example of a multiprocess program is

To show the individual process IDs involved, here is an expanded example:

For an explanation of why the if __name__ == '__main__' part is necessary, see Programming guidelines .

Contexts and start methods ¶

Depending on the platform, multiprocessing supports three ways to start a process. These start methods are

spawn The parent process starts a fresh Python interpreter process. The child process will only inherit those resources necessary to run the process object’s run() method. In particular, unnecessary file descriptors and handles from the parent process will not be inherited. Starting a process using this method is rather slow compared to using fork or forkserver . Available on POSIX and Windows platforms. The default on Windows and macOS. fork The parent process uses os.fork() to fork the Python interpreter. The child process, when it begins, is effectively identical to the parent process. All resources of the parent are inherited by the child process. Note that safely forking a multithreaded process is problematic. Available on POSIX systems. Currently the default on POSIX except macOS. Note The default start method will change away from fork in Python 3.14. Code that requires fork should explicitly specify that via get_context() or set_start_method() . Changed in version 3.12: If Python is able to detect that your process has multiple threads, the os.fork() function that this start method calls internally will raise a DeprecationWarning . Use a different start method. See the os.fork() documentation for further explanation. forkserver When the program starts and selects the forkserver start method, a server process is spawned. From then on, whenever a new process is needed, the parent process connects to the server and requests that it fork a new process. The fork server process is single threaded unless system libraries or preloaded imports spawn threads as a side-effect so it is generally safe for it to use os.fork() . No unnecessary resources are inherited. Available on POSIX platforms which support passing file descriptors over Unix pipes such as Linux.

Changed in version 3.4: spawn added on all POSIX platforms, and forkserver added for some POSIX platforms. Child processes no longer inherit all of the parents inheritable handles on Windows.

Changed in version 3.8: On macOS, the spawn start method is now the default. The fork start method should be considered unsafe as it can lead to crashes of the subprocess as macOS system libraries may start threads. See bpo-33725 .

On POSIX using the spawn or forkserver start methods will also start a resource tracker process which tracks the unlinked named system resources (such as named semaphores or SharedMemory objects) created by processes of the program. When all processes have exited the resource tracker unlinks any remaining tracked object. Usually there should be none, but if a process was killed by a signal there may be some “leaked” resources. (Neither leaked semaphores nor shared memory segments will be automatically unlinked until the next reboot. This is problematic for both objects because the system allows only a limited number of named semaphores, and shared memory segments occupy some space in the main memory.)

To select a start method you use the set_start_method() in the if __name__ == '__main__' clause of the main module. For example:

set_start_method() should not be used more than once in the program.

Alternatively, you can use get_context() to obtain a context object. Context objects have the same API as the multiprocessing module, and allow one to use multiple start methods in the same program.

Note that objects related to one context may not be compatible with processes for a different context. In particular, locks created using the fork context cannot be passed to processes started using the spawn or forkserver start methods.

A library which wants to use a particular start method should probably use get_context() to avoid interfering with the choice of the library user.

The 'spawn' and 'forkserver' start methods generally cannot be used with “frozen” executables (i.e., binaries produced by packages like PyInstaller and cx_Freeze ) on POSIX systems. The 'fork' start method may work if code does not use threads.

Exchanging objects between processes ¶

multiprocessing supports two types of communication channel between processes:

The Queue class is a near clone of queue.Queue . For example: from multiprocessing import Process , Queue def f ( q ): q . put ([ 42 , None , 'hello' ]) if __name__ == '__main__' : q = Queue () p = Process ( target = f , args = ( q ,)) p . start () print ( q . get ()) # prints "[42, None, 'hello']" p . join () Queues are thread and process safe. Any object put into a multiprocessing queue will be serialized.

The Pipe() function returns a pair of connection objects connected by a pipe which by default is duplex (two-way). For example: from multiprocessing import Process , Pipe def f ( conn ): conn . send ([ 42 , None , 'hello' ]) conn . close () if __name__ == '__main__' : parent_conn , child_conn = Pipe () p = Process ( target = f , args = ( child_conn ,)) p . start () print ( parent_conn . recv ()) # prints "[42, None, 'hello']" p . join () The two connection objects returned by Pipe() represent the two ends of the pipe. Each connection object has send() and recv() methods (among others). Note that data in a pipe may become corrupted if two processes (or threads) try to read from or write to the same end of the pipe at the same time. Of course there is no risk of corruption from processes using different ends of the pipe at the same time. The send() method serializes the the object and recv() re-creates the object.

Synchronization between processes ¶

multiprocessing contains equivalents of all the synchronization primitives from threading . For instance one can use a lock to ensure that only one process prints to standard output at a time:

Without using the lock output from the different processes is liable to get all mixed up.

Sharing state between processes ¶

As mentioned above, when doing concurrent programming it is usually best to avoid using shared state as far as possible. This is particularly true when using multiple processes.

However, if you really do need to use some shared data then multiprocessing provides a couple of ways of doing so.

Shared memory

Data can be stored in a shared memory map using Value or Array . For example, the following code from multiprocessing import Process , Value , Array def f ( n , a ): n . value = 3.1415927 for i in range ( len ( a )): a [ i ] = - a [ i ] if __name__ == '__main__' : num = Value ( 'd' , 0.0 ) arr = Array ( 'i' , range ( 10 )) p = Process ( target = f , args = ( num , arr )) p . start () p . join () print ( num . value ) print ( arr [:]) will print 3.1415927 [ 0 , - 1 , - 2 , - 3 , - 4 , - 5 , - 6 , - 7 , - 8 , - 9 ] The 'd' and 'i' arguments used when creating num and arr are typecodes of the kind used by the array module: 'd' indicates a double precision float and 'i' indicates a signed integer. These shared objects will be process and thread-safe. For more flexibility in using shared memory one can use the multiprocessing.sharedctypes module which supports the creation of arbitrary ctypes objects allocated from shared memory.

Server process

A manager object returned by Manager() controls a server process which holds Python objects and allows other processes to manipulate them using proxies. A manager returned by Manager() will support types list , dict , Namespace , Lock , RLock , Semaphore , BoundedSemaphore , Condition , Event , Barrier , Queue , Value and Array . For example, from multiprocessing import Process , Manager def f ( d , l ): d [ 1 ] = '1' d [ '2' ] = 2 d [ 0.25 ] = None l . reverse () if __name__ == '__main__' : with Manager () as manager : d = manager . dict () l = manager . list ( range ( 10 )) p = Process ( target = f , args = ( d , l )) p . start () p . join () print ( d ) print ( l ) will print { 0.25 : None , 1 : '1' , '2' : 2 } [ 9 , 8 , 7 , 6 , 5 , 4 , 3 , 2 , 1 , 0 ] Server process managers are more flexible than using shared memory objects because they can be made to support arbitrary object types. Also, a single manager can be shared by processes on different computers over a network. They are, however, slower than using shared memory.

Using a pool of workers ¶

The Pool class represents a pool of worker processes. It has methods which allows tasks to be offloaded to the worker processes in a few different ways.

For example:

Note that the methods of a pool should only ever be used by the process which created it.

Functionality within this package requires that the __main__ module be importable by the children. This is covered in Programming guidelines however it is worth pointing out here. This means that some examples, such as the multiprocessing.pool.Pool examples will not work in the interactive interpreter. For example:

(If you try this it will actually output three full tracebacks interleaved in a semi-random fashion, and then you may have to stop the parent process somehow.)

Reference ¶

The multiprocessing package mostly replicates the API of the threading module.

Process and exceptions ¶

Process objects represent activity that is run in a separate process. The Process class has equivalents of all the methods of threading.Thread .

The constructor should always be called with keyword arguments. group should always be None ; it exists solely for compatibility with threading.Thread . target is the callable object to be invoked by the run() method. It defaults to None , meaning nothing is called. name is the process name (see name for more details). args is the argument tuple for the target invocation. kwargs is a dictionary of keyword arguments for the target invocation. If provided, the keyword-only daemon argument sets the process daemon flag to True or False . If None (the default), this flag will be inherited from the creating process.

By default, no arguments are passed to target . The args argument, which defaults to () , can be used to specify a list or tuple of the arguments to pass to target .

If a subclass overrides the constructor, it must make sure it invokes the base class constructor ( Process.__init__() ) before doing anything else to the process.

Changed in version 3.3: Added the daemon parameter.

Method representing the process’s activity.

You may override this method in a subclass. The standard run() method invokes the callable object passed to the object’s constructor as the target argument, if any, with sequential and keyword arguments taken from the args and kwargs arguments, respectively.

Using a list or tuple as the args argument passed to Process achieves the same effect.

Start the process’s activity.

This must be called at most once per process object. It arranges for the object’s run() method to be invoked in a separate process.

If the optional argument timeout is None (the default), the method blocks until the process whose join() method is called terminates. If timeout is a positive number, it blocks at most timeout seconds. Note that the method returns None if its process terminates or if the method times out. Check the process’s exitcode to determine if it terminated.

A process can be joined many times.

A process cannot join itself because this would cause a deadlock. It is an error to attempt to join a process before it has been started.

The process’s name. The name is a string used for identification purposes only. It has no semantics. Multiple processes may be given the same name.

The initial name is set by the constructor. If no explicit name is provided to the constructor, a name of the form ‘Process-N 1 :N 2 :…:N k ’ is constructed, where each N k is the N-th child of its parent.

Return whether the process is alive.

Roughly, a process object is alive from the moment the start() method returns until the child process terminates.

The process’s daemon flag, a Boolean value. This must be set before start() is called.

The initial value is inherited from the creating process.

When a process exits, it attempts to terminate all of its daemonic child processes.

Note that a daemonic process is not allowed to create child processes. Otherwise a daemonic process would leave its children orphaned if it gets terminated when its parent process exits. Additionally, these are not Unix daemons or services, they are normal processes that will be terminated (and not joined) if non-daemonic processes have exited.

In addition to the threading.Thread API, Process objects also support the following attributes and methods:

Return the process ID. Before the process is spawned, this will be None .

The child’s exit code. This will be None if the process has not yet terminated.

If the child’s run() method returned normally, the exit code will be 0. If it terminated via sys.exit() with an integer argument N , the exit code will be N .

If the child terminated due to an exception not caught within run() , the exit code will be 1. If it was terminated by signal N , the exit code will be the negative value -N .

The process’s authentication key (a byte string).

When multiprocessing is initialized the main process is assigned a random string using os.urandom() .

When a Process object is created, it will inherit the authentication key of its parent process, although this may be changed by setting authkey to another byte string.

See Authentication keys .

A numeric handle of a system object which will become “ready” when the process ends.

You can use this value if you want to wait on several events at once using multiprocessing.connection.wait() . Otherwise calling join() is simpler.

On Windows, this is an OS handle usable with the WaitForSingleObject and WaitForMultipleObjects family of API calls. On POSIX, this is a file descriptor usable with primitives from the select module.

Added in version 3.3.

Terminate the process. On POSIX this is done using the SIGTERM signal; on Windows TerminateProcess() is used. Note that exit handlers and finally clauses, etc., will not be executed.

Note that descendant processes of the process will not be terminated – they will simply become orphaned.

If this method is used when the associated process is using a pipe or queue then the pipe or queue is liable to become corrupted and may become unusable by other process. Similarly, if the process has acquired a lock or semaphore etc. then terminating it is liable to cause other processes to deadlock.

Same as terminate() but using the SIGKILL signal on POSIX.

Added in version 3.7.

Close the Process object, releasing all resources associated with it. ValueError is raised if the underlying process is still running. Once close() returns successfully, most other methods and attributes of the Process object will raise ValueError .

Note that the start() , join() , is_alive() , terminate() and exitcode methods should only be called by the process that created the process object.

Example usage of some of the methods of Process :

The base class of all multiprocessing exceptions.

Exception raised by Connection.recv_bytes_into() when the supplied buffer object is too small for the message read.

If e is an instance of BufferTooShort then e.args[0] will give the message as a byte string.

Raised when there is an authentication error.

Raised by methods with a timeout when the timeout expires.

Pipes and Queues ¶

When using multiple processes, one generally uses message passing for communication between processes and avoids having to use any synchronization primitives like locks.

For passing messages one can use Pipe() (for a connection between two processes) or a queue (which allows multiple producers and consumers).

The Queue , SimpleQueue and JoinableQueue types are multi-producer, multi-consumer FIFO queues modelled on the queue.Queue class in the standard library. They differ in that Queue lacks the task_done() and join() methods introduced into Python 2.5’s queue.Queue class.

If you use JoinableQueue then you must call JoinableQueue.task_done() for each task removed from the queue or else the semaphore used to count the number of unfinished tasks may eventually overflow, raising an exception.

One difference from other Python queue implementations, is that multiprocessing queues serializes all objects that are put into them using pickle . The object return by the get method is a re-created object that does not share memory with the original object.

Note that one can also create a shared queue by using a manager object – see Managers .

multiprocessing uses the usual queue.Empty and queue.Full exceptions to signal a timeout. They are not available in the multiprocessing namespace so you need to import them from queue .

When an object is put on a queue, the object is pickled and a background thread later flushes the pickled data to an underlying pipe. This has some consequences which are a little surprising, but should not cause any practical difficulties – if they really bother you then you can instead use a queue created with a manager .

After putting an object on an empty queue there may be an infinitesimal delay before the queue’s empty() method returns False and get_nowait() can return without raising queue.Empty .

If multiple processes are enqueuing objects, it is possible for the objects to be received at the other end out-of-order. However, objects enqueued by the same process will always be in the expected order with respect to each other.

If a process is killed using Process.terminate() or os.kill() while it is trying to use a Queue , then the data in the queue is likely to become corrupted. This may cause any other process to get an exception when it tries to use the queue later on.

As mentioned above, if a child process has put items on a queue (and it has not used JoinableQueue.cancel_join_thread ), then that process will not terminate until all buffered items have been flushed to the pipe.

This means that if you try joining that process you may get a deadlock unless you are sure that all items which have been put on the queue have been consumed. Similarly, if the child process is non-daemonic then the parent process may hang on exit when it tries to join all its non-daemonic children.

Note that a queue created using a manager does not have this issue. See Programming guidelines .

For an example of the usage of queues for interprocess communication see Examples .

Returns a pair (conn1, conn2) of Connection objects representing the ends of a pipe.

If duplex is True (the default) then the pipe is bidirectional. If duplex is False then the pipe is unidirectional: conn1 can only be used for receiving messages and conn2 can only be used for sending messages.

The send() method serializes the the object using pickle and the recv() re-creates the object.

Returns a process shared queue implemented using a pipe and a few locks/semaphores. When a process first puts an item on the queue a feeder thread is started which transfers objects from a buffer into the pipe.

The usual queue.Empty and queue.Full exceptions from the standard library’s queue module are raised to signal timeouts.

Queue implements all the methods of queue.Queue except for task_done() and join() .

Return the approximate size of the queue. Because of multithreading/multiprocessing semantics, this number is not reliable.

Note that this may raise NotImplementedError on platforms like macOS where sem_getvalue() is not implemented.

Return True if the queue is empty, False otherwise. Because of multithreading/multiprocessing semantics, this is not reliable.

May raise an OSError on closed queues. (not guaranteed)

Return True if the queue is full, False otherwise. Because of multithreading/multiprocessing semantics, this is not reliable.

Put obj into the queue. If the optional argument block is True (the default) and timeout is None (the default), block if necessary until a free slot is available. If timeout is a positive number, it blocks at most timeout seconds and raises the queue.Full exception if no free slot was available within that time. Otherwise ( block is False ), put an item on the queue if a free slot is immediately available, else raise the queue.Full exception ( timeout is ignored in that case).

Changed in version 3.8: If the queue is closed, ValueError is raised instead of AssertionError .

Equivalent to put(obj, False) .

Remove and return an item from the queue. If optional args block is True (the default) and timeout is None (the default), block if necessary until an item is available. If timeout is a positive number, it blocks at most timeout seconds and raises the queue.Empty exception if no item was available within that time. Otherwise (block is False ), return an item if one is immediately available, else raise the queue.Empty exception ( timeout is ignored in that case).

Changed in version 3.8: If the queue is closed, ValueError is raised instead of OSError .

Equivalent to get(False) .

multiprocessing.Queue has a few additional methods not found in queue.Queue . These methods are usually unnecessary for most code:

Indicate that no more data will be put on this queue by the current process. The background thread will quit once it has flushed all buffered data to the pipe. This is called automatically when the queue is garbage collected.

Join the background thread. This can only be used after close() has been called. It blocks until the background thread exits, ensuring that all data in the buffer has been flushed to the pipe.

By default if a process is not the creator of the queue then on exit it will attempt to join the queue’s background thread. The process can call cancel_join_thread() to make join_thread() do nothing.

Prevent join_thread() from blocking. In particular, this prevents the background thread from being joined automatically when the process exits – see join_thread() .

A better name for this method might be allow_exit_without_flush() . It is likely to cause enqueued data to be lost, and you almost certainly will not need to use it. It is really only there if you need the current process to exit immediately without waiting to flush enqueued data to the underlying pipe, and you don’t care about lost data.

This class’s functionality requires a functioning shared semaphore implementation on the host operating system. Without one, the functionality in this class will be disabled, and attempts to instantiate a Queue will result in an ImportError . See bpo-3770 for additional information. The same holds true for any of the specialized queue types listed below.

It is a simplified Queue type, very close to a locked Pipe .

Close the queue: release internal resources.

A queue must not be used anymore after it is closed. For example, get() , put() and empty() methods must no longer be called.

Added in version 3.9.

Return True if the queue is empty, False otherwise.

Always raises an OSError if the SimpleQueue is closed.

Remove and return an item from the queue.

Put item into the queue.

JoinableQueue , a Queue subclass, is a queue which additionally has task_done() and join() methods.

Indicate that a formerly enqueued task is complete. Used by queue consumers. For each get() used to fetch a task, a subsequent call to task_done() tells the queue that the processing on the task is complete.

If a join() is currently blocking, it will resume when all items have been processed (meaning that a task_done() call was received for every item that had been put() into the queue).

Raises a ValueError if called more times than there were items placed in the queue.

Block until all items in the queue have been gotten and processed.

The count of unfinished tasks goes up whenever an item is added to the queue. The count goes down whenever a consumer calls task_done() to indicate that the item was retrieved and all work on it is complete. When the count of unfinished tasks drops to zero, join() unblocks.

Miscellaneous ¶

Return list of all live children of the current process.

Calling this has the side effect of “joining” any processes which have already finished.

Return the number of CPUs in the system.

This number is not equivalent to the number of CPUs the current process can use. The number of usable CPUs can be obtained with len(os.sched_getaffinity(0))

When the number of CPUs cannot be determined a NotImplementedError is raised.

os.cpu_count()

Return the Process object corresponding to the current process.

An analogue of threading.current_thread() .

Return the Process object corresponding to the parent process of the current_process() . For the main process, parent_process will be None .

Added in version 3.8.

Add support for when a program which uses multiprocessing has been frozen to produce a Windows executable. (Has been tested with py2exe , PyInstaller and cx_Freeze .)

One needs to call this function straight after the if __name__ == '__main__' line of the main module. For example:

If the freeze_support() line is omitted then trying to run the frozen executable will raise RuntimeError .

Calling freeze_support() has no effect when invoked on any operating system other than Windows. In addition, if the module is being run normally by the Python interpreter on Windows (the program has not been frozen), then freeze_support() has no effect.

Returns a list of the supported start methods, the first of which is the default. The possible start methods are 'fork' , 'spawn' and 'forkserver' . Not all platforms support all methods. See Contexts and start methods .

Added in version 3.4.

Return a context object which has the same attributes as the multiprocessing module.

If method is None then the default context is returned. Otherwise method should be 'fork' , 'spawn' , 'forkserver' . ValueError is raised if the specified start method is not available. See Contexts and start methods .

Return the name of start method used for starting processes.

If the start method has not been fixed and allow_none is false, then the start method is fixed to the default and the name is returned. If the start method has not been fixed and allow_none is true then None is returned.

The return value can be 'fork' , 'spawn' , 'forkserver' or None . See Contexts and start methods .

Changed in version 3.8: On macOS, the spawn start method is now the default. The fork start method should be considered unsafe as it can lead to crashes of the subprocess. See bpo-33725 .

Set the path of the Python interpreter to use when starting a child process. (By default sys.executable is used). Embedders will probably need to do some thing like

before they can create child processes.

Changed in version 3.4: Now supported on POSIX when the 'spawn' start method is used.

Changed in version 3.11: Accepts a path-like object .

Set a list of module names for the forkserver main process to attempt to import so that their already imported state is inherited by forked processes. Any ImportError when doing so is silently ignored. This can be used as a performance enhancement to avoid repeated work in every process.

For this to work, it must be called before the forkserver process has been launched (before creating a Pool or starting a Process ).

Only meaningful when using the 'forkserver' start method. See Contexts and start methods .

Set the method which should be used to start child processes. The method argument can be 'fork' , 'spawn' or 'forkserver' . Raises RuntimeError if the start method has already been set and force is not True . If method is None and force is True then the start method is set to None . If method is None and force is False then the context is set to the default context.

Note that this should be called at most once, and it should be protected inside the if __name__ == '__main__' clause of the main module.

See Contexts and start methods .

multiprocessing contains no analogues of threading.active_count() , threading.enumerate() , threading.settrace() , threading.setprofile() , threading.Timer , or threading.local .

Connection Objects ¶

Connection objects allow the sending and receiving of picklable objects or strings. They can be thought of as message oriented connected sockets.

Connection objects are usually created using Pipe – see also Listeners and Clients .

Send an object to the other end of the connection which should be read using recv() .

The object must be picklable. Very large pickles (approximately 32 MiB+, though it depends on the OS) may raise a ValueError exception.

Return an object sent from the other end of the connection using send() . Blocks until there is something to receive. Raises EOFError if there is nothing left to receive and the other end was closed.

Return the file descriptor or handle used by the connection.

Close the connection.

This is called automatically when the connection is garbage collected.

Return whether there is any data available to be read.

If timeout is not specified then it will return immediately. If timeout is a number then this specifies the maximum time in seconds to block. If timeout is None then an infinite timeout is used.

Note that multiple connection objects may be polled at once by using multiprocessing.connection.wait() .

Send byte data from a bytes-like object as a complete message.

If offset is given then data is read from that position in buffer . If size is given then that many bytes will be read from buffer. Very large buffers (approximately 32 MiB+, though it depends on the OS) may raise a ValueError exception

Return a complete message of byte data sent from the other end of the connection as a string. Blocks until there is something to receive. Raises EOFError if there is nothing left to receive and the other end has closed.

If maxlength is specified and the message is longer than maxlength then OSError is raised and the connection will no longer be readable.

Changed in version 3.3: This function used to raise IOError , which is now an alias of OSError .

Read into buffer a complete message of byte data sent from the other end of the connection and return the number of bytes in the message. Blocks until there is something to receive. Raises EOFError if there is nothing left to receive and the other end was closed.

buffer must be a writable bytes-like object . If offset is given then the message will be written into the buffer from that position. Offset must be a non-negative integer less than the length of buffer (in bytes).

If the buffer is too short then a BufferTooShort exception is raised and the complete message is available as e.args[0] where e is the exception instance.

Changed in version 3.3: Connection objects themselves can now be transferred between processes using Connection.send() and Connection.recv() .

Connection objects also now support the context management protocol – see Context Manager Types . __enter__() returns the connection object, and __exit__() calls close() .

The Connection.recv() method automatically unpickles the data it receives, which can be a security risk unless you can trust the process which sent the message.

Therefore, unless the connection object was produced using Pipe() you should only use the recv() and send() methods after performing some sort of authentication. See Authentication keys .

If a process is killed while it is trying to read or write to a pipe then the data in the pipe is likely to become corrupted, because it may become impossible to be sure where the message boundaries lie.

Synchronization primitives ¶

Generally synchronization primitives are not as necessary in a multiprocess program as they are in a multithreaded program. See the documentation for threading module.

Note that one can also create synchronization primitives by using a manager object – see Managers .

A barrier object: a clone of threading.Barrier .

A bounded semaphore object: a close analog of threading.BoundedSemaphore .

A solitary difference from its close analog exists: its acquire method’s first argument is named block , as is consistent with Lock.acquire() .

On macOS, this is indistinguishable from Semaphore because sem_getvalue() is not implemented on that platform.

A condition variable: an alias for threading.Condition .

If lock is specified then it should be a Lock or RLock object from multiprocessing .

Changed in version 3.3: The wait_for() method was added.

A clone of threading.Event .

A non-recursive lock object: a close analog of threading.Lock . Once a process or thread has acquired a lock, subsequent attempts to acquire it from any process or thread will block until it is released; any process or thread may release it. The concepts and behaviors of threading.Lock as it applies to threads are replicated here in multiprocessing.Lock as it applies to either processes or threads, except as noted.

Note that Lock is actually a factory function which returns an instance of multiprocessing.synchronize.Lock initialized with a default context.

Lock supports the context manager protocol and thus may be used in with statements.

Acquire a lock, blocking or non-blocking.

With the block argument set to True (the default), the method call will block until the lock is in an unlocked state, then set it to locked and return True . Note that the name of this first argument differs from that in threading.Lock.acquire() .

With the block argument set to False , the method call does not block. If the lock is currently in a locked state, return False ; otherwise set the lock to a locked state and return True .

When invoked with a positive, floating-point value for timeout , block for at most the number of seconds specified by timeout as long as the lock can not be acquired. Invocations with a negative value for timeout are equivalent to a timeout of zero. Invocations with a timeout value of None (the default) set the timeout period to infinite. Note that the treatment of negative or None values for timeout differs from the implemented behavior in threading.Lock.acquire() . The timeout argument has no practical implications if the block argument is set to False and is thus ignored. Returns True if the lock has been acquired or False if the timeout period has elapsed.

Release a lock. This can be called from any process or thread, not only the process or thread which originally acquired the lock.

Behavior is the same as in threading.Lock.release() except that when invoked on an unlocked lock, a ValueError is raised.

A recursive lock object: a close analog of threading.RLock . A recursive lock must be released by the process or thread that acquired it. Once a process or thread has acquired a recursive lock, the same process or thread may acquire it again without blocking; that process or thread must release it once for each time it has been acquired.

Note that RLock is actually a factory function which returns an instance of multiprocessing.synchronize.RLock initialized with a default context.

RLock supports the context manager protocol and thus may be used in with statements.

When invoked with the block argument set to True , block until the lock is in an unlocked state (not owned by any process or thread) unless the lock is already owned by the current process or thread. The current process or thread then takes ownership of the lock (if it does not already have ownership) and the recursion level inside the lock increments by one, resulting in a return value of True . Note that there are several differences in this first argument’s behavior compared to the implementation of threading.RLock.acquire() , starting with the name of the argument itself.

When invoked with the block argument set to False , do not block. If the lock has already been acquired (and thus is owned) by another process or thread, the current process or thread does not take ownership and the recursion level within the lock is not changed, resulting in a return value of False . If the lock is in an unlocked state, the current process or thread takes ownership and the recursion level is incremented, resulting in a return value of True .

Use and behaviors of the timeout argument are the same as in Lock.acquire() . Note that some of these behaviors of timeout differ from the implemented behaviors in threading.RLock.acquire() .

Release a lock, decrementing the recursion level. If after the decrement the recursion level is zero, reset the lock to unlocked (not owned by any process or thread) and if any other processes or threads are blocked waiting for the lock to become unlocked, allow exactly one of them to proceed. If after the decrement the recursion level is still nonzero, the lock remains locked and owned by the calling process or thread.

Only call this method when the calling process or thread owns the lock. An AssertionError is raised if this method is called by a process or thread other than the owner or if the lock is in an unlocked (unowned) state. Note that the type of exception raised in this situation differs from the implemented behavior in threading.RLock.release() .

A semaphore object: a close analog of threading.Semaphore .

On macOS, sem_timedwait is unsupported, so calling acquire() with a timeout will emulate that function’s behavior using a sleeping loop.

Some of this package’s functionality requires a functioning shared semaphore implementation on the host operating system. Without one, the multiprocessing.synchronize module will be disabled, and attempts to import it will result in an ImportError . See bpo-3770 for additional information.

Shared ctypes Objects ¶

It is possible to create shared objects using shared memory which can be inherited by child processes.

Return a ctypes object allocated from shared memory. By default the return value is actually a synchronized wrapper for the object. The object itself can be accessed via the value attribute of a Value .

typecode_or_type determines the type of the returned object: it is either a ctypes type or a one character typecode of the kind used by the array module. *args is passed on to the constructor for the type.

If lock is True (the default) then a new recursive lock object is created to synchronize access to the value. If lock is a Lock or RLock object then that will be used to synchronize access to the value. If lock is False then access to the returned object will not be automatically protected by a lock, so it will not necessarily be “process-safe”.

Operations like += which involve a read and write are not atomic. So if, for instance, you want to atomically increment a shared value it is insufficient to just do

Assuming the associated lock is recursive (which it is by default) you can instead do

Note that lock is a keyword-only argument.

Return a ctypes array allocated from shared memory. By default the return value is actually a synchronized wrapper for the array.

typecode_or_type determines the type of the elements of the returned array: it is either a ctypes type or a one character typecode of the kind used by the array module. If size_or_initializer is an integer, then it determines the length of the array, and the array will be initially zeroed. Otherwise, size_or_initializer is a sequence which is used to initialize the array and whose length determines the length of the array.

If lock is True (the default) then a new lock object is created to synchronize access to the value. If lock is a Lock or RLock object then that will be used to synchronize access to the value. If lock is False then access to the returned object will not be automatically protected by a lock, so it will not necessarily be “process-safe”.

Note that lock is a keyword only argument.

Note that an array of ctypes.c_char has value and raw attributes which allow one to use it to store and retrieve strings.

The multiprocessing.sharedctypes module ¶

The multiprocessing.sharedctypes module provides functions for allocating ctypes objects from shared memory which can be inherited by child processes.

Although it is possible to store a pointer in shared memory remember that this will refer to a location in the address space of a specific process. However, the pointer is quite likely to be invalid in the context of a second process and trying to dereference the pointer from the second process may cause a crash.

Return a ctypes array allocated from shared memory.

typecode_or_type determines the type of the elements of the returned array: it is either a ctypes type or a one character typecode of the kind used by the array module. If size_or_initializer is an integer then it determines the length of the array, and the array will be initially zeroed. Otherwise size_or_initializer is a sequence which is used to initialize the array and whose length determines the length of the array.

Note that setting and getting an element is potentially non-atomic – use Array() instead to make sure that access is automatically synchronized using a lock.

Return a ctypes object allocated from shared memory.

Note that setting and getting the value is potentially non-atomic – use Value() instead to make sure that access is automatically synchronized using a lock.

Note that an array of ctypes.c_char has value and raw attributes which allow one to use it to store and retrieve strings – see documentation for ctypes .

The same as RawArray() except that depending on the value of lock a process-safe synchronization wrapper may be returned instead of a raw ctypes array.

The same as RawValue() except that depending on the value of lock a process-safe synchronization wrapper may be returned instead of a raw ctypes object.

Return a ctypes object allocated from shared memory which is a copy of the ctypes object obj .

Return a process-safe wrapper object for a ctypes object which uses lock to synchronize access. If lock is None (the default) then a multiprocessing.RLock object is created automatically.

A synchronized wrapper will have two methods in addition to those of the object it wraps: get_obj() returns the wrapped object and get_lock() returns the lock object used for synchronization.

Note that accessing the ctypes object through the wrapper can be a lot slower than accessing the raw ctypes object.

Changed in version 3.5: Synchronized objects support the context manager protocol.

The table below compares the syntax for creating shared ctypes objects from shared memory with the normal ctypes syntax. (In the table MyStruct is some subclass of ctypes.Structure .)

Below is an example where a number of ctypes objects are modified by a child process:

The results printed are

Managers provide a way to create data which can be shared between different processes, including sharing over a network between processes running on different machines. A manager object controls a server process which manages shared objects . Other processes can access the shared objects by using proxies.

Returns a started SyncManager object which can be used for sharing objects between processes. The returned manager object corresponds to a spawned child process and has methods which will create shared objects and return corresponding proxies.

Manager processes will be shutdown as soon as they are garbage collected or their parent process exits. The manager classes are defined in the multiprocessing.managers module:

Create a BaseManager object.

Once created one should call start() or get_server().serve_forever() to ensure that the manager object refers to a started manager process.

address is the address on which the manager process listens for new connections. If address is None then an arbitrary one is chosen.

authkey is the authentication key which will be used to check the validity of incoming connections to the server process. If authkey is None then current_process().authkey is used. Otherwise authkey is used and it must be a byte string.

serializer must be 'pickle' (use pickle serialization) or 'xmlrpclib' (use xmlrpc.client serialization).

ctx is a context object, or None (use the current context). See the get_context() function.

shutdown_timeout is a timeout in seconds used to wait until the process used by the manager completes in the shutdown() method. If the shutdown times out, the process is terminated. If terminating the process also times out, the process is killed.

Changed in version 3.11: Added the shutdown_timeout parameter.

Start a subprocess to start the manager. If initializer is not None then the subprocess will call initializer(*initargs) when it starts.

Returns a Server object which represents the actual server under the control of the Manager. The Server object supports the serve_forever() method:

Server additionally has an address attribute.

Connect a local manager object to a remote manager process:

Stop the process used by the manager. This is only available if start() has been used to start the server process.

This can be called multiple times.

A classmethod which can be used for registering a type or callable with the manager class.

typeid is a “type identifier” which is used to identify a particular type of shared object. This must be a string.

callable is a callable used for creating objects for this type identifier. If a manager instance will be connected to the server using the connect() method, or if the create_method argument is False then this can be left as None .

proxytype is a subclass of BaseProxy which is used to create proxies for shared objects with this typeid . If None then a proxy class is created automatically.

exposed is used to specify a sequence of method names which proxies for this typeid should be allowed to access using BaseProxy._callmethod() . (If exposed is None then proxytype._exposed_ is used instead if it exists.) In the case where no exposed list is specified, all “public methods” of the shared object will be accessible. (Here a “public method” means any attribute which has a __call__() method and whose name does not begin with '_' .)

method_to_typeid is a mapping used to specify the return type of those exposed methods which should return a proxy. It maps method names to typeid strings. (If method_to_typeid is None then proxytype._method_to_typeid_ is used instead if it exists.) If a method’s name is not a key of this mapping or if the mapping is None then the object returned by the method will be copied by value.

create_method determines whether a method should be created with name typeid which can be used to tell the server process to create a new shared object and return a proxy for it. By default it is True .

BaseManager instances also have one read-only property:

The address used by the manager.

Changed in version 3.3: Manager objects support the context management protocol – see Context Manager Types . __enter__() starts the server process (if it has not already started) and then returns the manager object. __exit__() calls shutdown() .

In previous versions __enter__() did not start the manager’s server process if it was not already started.

A subclass of BaseManager which can be used for the synchronization of processes. Objects of this type are returned by multiprocessing.Manager() .

Its methods create and return Proxy Objects for a number of commonly used data types to be synchronized across processes. This notably includes shared lists and dictionaries.

Create a shared threading.Barrier object and return a proxy for it.

Create a shared threading.BoundedSemaphore object and return a proxy for it.

Create a shared threading.Condition object and return a proxy for it.

If lock is supplied then it should be a proxy for a threading.Lock or threading.RLock object.

Create a shared threading.Event object and return a proxy for it.

Create a shared threading.Lock object and return a proxy for it.

Create a shared Namespace object and return a proxy for it.

Create a shared queue.Queue object and return a proxy for it.

Create a shared threading.RLock object and return a proxy for it.

Create a shared threading.Semaphore object and return a proxy for it.

Create an array and return a proxy for it.

Create an object with a writable value attribute and return a proxy for it.

Create a shared dict object and return a proxy for it.

Create a shared list object and return a proxy for it.

Changed in version 3.6: Shared objects are capable of being nested. For example, a shared container object such as a shared list can contain other shared objects which will all be managed and synchronized by the SyncManager .

A type that can register with SyncManager .

A namespace object has no public methods, but does have writable attributes. Its representation shows the values of its attributes.

However, when using a proxy for a namespace object, an attribute beginning with '_' will be an attribute of the proxy and not an attribute of the referent:

Customized managers ¶

To create one’s own manager, one creates a subclass of BaseManager and uses the register() classmethod to register new types or callables with the manager class. For example:

Using a remote manager ¶

It is possible to run a manager server on one machine and have clients use it from other machines (assuming that the firewalls involved allow it).

Running the following commands creates a server for a single shared queue which remote clients can access:

One client can access the server as follows:

Another client can also use it:

Local processes can also access that queue, using the code from above on the client to access it remotely:

Proxy Objects ¶

A proxy is an object which refers to a shared object which lives (presumably) in a different process. The shared object is said to be the referent of the proxy. Multiple proxy objects may have the same referent.

A proxy object has methods which invoke corresponding methods of its referent (although not every method of the referent will necessarily be available through the proxy). In this way, a proxy can be used just like its referent can:

Notice that applying str() to a proxy will return the representation of the referent, whereas applying repr() will return the representation of the proxy.

An important feature of proxy objects is that they are picklable so they can be passed between processes. As such, a referent can contain Proxy Objects . This permits nesting of these managed lists, dicts, and other Proxy Objects :

Similarly, dict and list proxies may be nested inside one another:

If standard (non-proxy) list or dict objects are contained in a referent, modifications to those mutable values will not be propagated through the manager because the proxy has no way of knowing when the values contained within are modified. However, storing a value in a container proxy (which triggers a __setitem__ on the proxy object) does propagate through the manager and so to effectively modify such an item, one could re-assign the modified value to the container proxy:

This approach is perhaps less convenient than employing nested Proxy Objects for most use cases but also demonstrates a level of control over the synchronization.

The proxy types in multiprocessing do nothing to support comparisons by value. So, for instance, we have:

One should just use a copy of the referent instead when making comparisons.

Proxy objects are instances of subclasses of BaseProxy .

Call and return the result of a method of the proxy’s referent.

If proxy is a proxy whose referent is obj then the expression

will evaluate the expression

in the manager’s process.

The returned value will be a copy of the result of the call or a proxy to a new shared object – see documentation for the method_to_typeid argument of BaseManager.register() .

If an exception is raised by the call, then is re-raised by _callmethod() . If some other exception is raised in the manager’s process then this is converted into a RemoteError exception and is raised by _callmethod() .

Note in particular that an exception will be raised if methodname has not been exposed .

An example of the usage of _callmethod() :

Return a copy of the referent.

If the referent is unpicklable then this will raise an exception.

Return a representation of the proxy object.

Return the representation of the referent.

A proxy object uses a weakref callback so that when it gets garbage collected it deregisters itself from the manager which owns its referent.

A shared object gets deleted from the manager process when there are no longer any proxies referring to it.

Process Pools ¶

One can create a pool of processes which will carry out tasks submitted to it with the Pool class.

A process pool object which controls a pool of worker processes to which jobs can be submitted. It supports asynchronous results with timeouts and callbacks and has a parallel map implementation.

processes is the number of worker processes to use. If processes is None then the number returned by os.cpu_count() is used.

If initializer is not None then each worker process will call initializer(*initargs) when it starts.

maxtasksperchild is the number of tasks a worker process can complete before it will exit and be replaced with a fresh worker process, to enable unused resources to be freed. The default maxtasksperchild is None , which means worker processes will live as long as the pool.

context can be used to specify the context used for starting the worker processes. Usually a pool is created using the function multiprocessing.Pool() or the Pool() method of a context object. In both cases context is set appropriately.

Note that the methods of the pool object should only be called by the process which created the pool.

multiprocessing.pool objects have internal resources that need to be properly managed (like any other resource) by using the pool as a context manager or by calling close() and terminate() manually. Failure to do this can lead to the process hanging on finalization.

Note that it is not correct to rely on the garbage collector to destroy the pool as CPython does not assure that the finalizer of the pool will be called (see object.__del__() for more information).

Changed in version 3.2: Added the maxtasksperchild parameter.

Changed in version 3.4: Added the context parameter.

Worker processes within a Pool typically live for the complete duration of the Pool’s work queue. A frequent pattern found in other systems (such as Apache, mod_wsgi, etc) to free resources held by workers is to allow a worker within a pool to complete only a set amount of work before being exiting, being cleaned up and a new process spawned to replace the old one. The maxtasksperchild argument to the Pool exposes this ability to the end user.

Call func with arguments args and keyword arguments kwds . It blocks until the result is ready. Given this blocks, apply_async() is better suited for performing work in parallel. Additionally, func is only executed in one of the workers of the pool.

A variant of the apply() method which returns a AsyncResult object.

If callback is specified then it should be a callable which accepts a single argument. When the result becomes ready callback is applied to it, that is unless the call failed, in which case the error_callback is applied instead.

If error_callback is specified then it should be a callable which accepts a single argument. If the target function fails, then the error_callback is called with the exception instance.

Callbacks should complete immediately since otherwise the thread which handles the results will get blocked.

A parallel equivalent of the map() built-in function (it supports only one iterable argument though, for multiple iterables see starmap() ). It blocks until the result is ready.

This method chops the iterable into a number of chunks which it submits to the process pool as separate tasks. The (approximate) size of these chunks can be specified by setting chunksize to a positive integer.

Note that it may cause high memory usage for very long iterables. Consider using imap() or imap_unordered() with explicit chunksize option for better efficiency.

A variant of the map() method which returns a AsyncResult object.

A lazier version of map() .

The chunksize argument is the same as the one used by the map() method. For very long iterables using a large value for chunksize can make the job complete much faster than using the default value of 1 .

Also if chunksize is 1 then the next() method of the iterator returned by the imap() method has an optional timeout parameter: next(timeout) will raise multiprocessing.TimeoutError if the result cannot be returned within timeout seconds.

The same as imap() except that the ordering of the results from the returned iterator should be considered arbitrary. (Only when there is only one worker process is the order guaranteed to be “correct”.)

Like map() except that the elements of the iterable are expected to be iterables that are unpacked as arguments.

Hence an iterable of [(1,2), (3, 4)] results in [func(1,2), func(3,4)] .

A combination of starmap() and map_async() that iterates over iterable of iterables and calls func with the iterables unpacked. Returns a result object.

Prevents any more tasks from being submitted to the pool. Once all the tasks have been completed the worker processes will exit.

Stops the worker processes immediately without completing outstanding work. When the pool object is garbage collected terminate() will be called immediately.

Wait for the worker processes to exit. One must call close() or terminate() before using join() .

Changed in version 3.3: Pool objects now support the context management protocol – see Context Manager Types . __enter__() returns the pool object, and __exit__() calls terminate() .

The class of the result returned by Pool.apply_async() and Pool.map_async() .

Return the result when it arrives. If timeout is not None and the result does not arrive within timeout seconds then multiprocessing.TimeoutError is raised. If the remote call raised an exception then that exception will be reraised by get() .

Wait until the result is available or until timeout seconds pass.

Return whether the call has completed.

Return whether the call completed without raising an exception. Will raise ValueError if the result is not ready.

Changed in version 3.7: If the result is not ready, ValueError is raised instead of AssertionError .

The following example demonstrates the use of a pool:

Listeners and Clients ¶

Usually message passing between processes is done using queues or by using Connection objects returned by Pipe() .

However, the multiprocessing.connection module allows some extra flexibility. It basically gives a high level message oriented API for dealing with sockets or Windows named pipes. It also has support for digest authentication using the hmac module, and for polling multiple connections at the same time.

Send a randomly generated message to the other end of the connection and wait for a reply.

If the reply matches the digest of the message using authkey as the key then a welcome message is sent to the other end of the connection. Otherwise AuthenticationError is raised.

Receive a message, calculate the digest of the message using authkey as the key, and then send the digest back.

If a welcome message is not received, then AuthenticationError is raised.

Attempt to set up a connection to the listener which is using address address , returning a Connection .

The type of the connection is determined by family argument, but this can generally be omitted since it can usually be inferred from the format of address . (See Address Formats )

If authkey is given and not None , it should be a byte string and will be used as the secret key for an HMAC-based authentication challenge. No authentication is done if authkey is None . AuthenticationError is raised if authentication fails. See Authentication keys .

A wrapper for a bound socket or Windows named pipe which is ‘listening’ for connections.

address is the address to be used by the bound socket or named pipe of the listener object.

If an address of ‘0.0.0.0’ is used, the address will not be a connectable end point on Windows. If you require a connectable end-point, you should use ‘127.0.0.1’.

family is the type of socket (or named pipe) to use. This can be one of the strings 'AF_INET' (for a TCP socket), 'AF_UNIX' (for a Unix domain socket) or 'AF_PIPE' (for a Windows named pipe). Of these only the first is guaranteed to be available. If family is None then the family is inferred from the format of address . If address is also None then a default is chosen. This default is the family which is assumed to be the fastest available. See Address Formats . Note that if family is 'AF_UNIX' and address is None then the socket will be created in a private temporary directory created using tempfile.mkstemp() .

If the listener object uses a socket then backlog (1 by default) is passed to the listen() method of the socket once it has been bound.

Accept a connection on the bound socket or named pipe of the listener object and return a Connection object. If authentication is attempted and fails, then AuthenticationError is raised.

Close the bound socket or named pipe of the listener object. This is called automatically when the listener is garbage collected. However it is advisable to call it explicitly.

Listener objects have the following read-only properties:

The address which is being used by the Listener object.

The address from which the last accepted connection came. If this is unavailable then it is None .

Changed in version 3.3: Listener objects now support the context management protocol – see Context Manager Types . __enter__() returns the listener object, and __exit__() calls close() .

Wait till an object in object_list is ready. Returns the list of those objects in object_list which are ready. If timeout is a float then the call blocks for at most that many seconds. If timeout is None then it will block for an unlimited period. A negative timeout is equivalent to a zero timeout.

For both POSIX and Windows, an object can appear in object_list if it is

a readable Connection object;

a connected and readable socket.socket object; or

the sentinel attribute of a Process object.

A connection or socket object is ready when there is data available to be read from it, or the other end has been closed.

POSIX : wait(object_list, timeout) almost equivalent select.select(object_list, [], [], timeout) . The difference is that, if select.select() is interrupted by a signal, it can raise OSError with an error number of EINTR , whereas wait() will not.

Windows : An item in object_list must either be an integer handle which is waitable (according to the definition used by the documentation of the Win32 function WaitForMultipleObjects() ) or it can be an object with a fileno() method which returns a socket handle or pipe handle. (Note that pipe handles and socket handles are not waitable handles.)

The following server code creates a listener which uses 'secret password' as an authentication key. It then waits for a connection and sends some data to the client:

The following code connects to the server and receives some data from the server:

The following code uses wait() to wait for messages from multiple processes at once:

Address Formats ¶

An 'AF_INET' address is a tuple of the form (hostname, port) where hostname is a string and port is an integer.

An 'AF_UNIX' address is a string representing a filename on the filesystem.

An 'AF_PIPE' address is a string of the form r'\\.\pipe\ PipeName ' . To use Client() to connect to a named pipe on a remote computer called ServerName one should use an address of the form r'\\ ServerName \pipe\ PipeName ' instead.

Note that any string beginning with two backslashes is assumed by default to be an 'AF_PIPE' address rather than an 'AF_UNIX' address.

Authentication keys ¶

When one uses Connection.recv , the data received is automatically unpickled. Unfortunately unpickling data from an untrusted source is a security risk. Therefore Listener and Client() use the hmac module to provide digest authentication.

An authentication key is a byte string which can be thought of as a password: once a connection is established both ends will demand proof that the other knows the authentication key. (Demonstrating that both ends are using the same key does not involve sending the key over the connection.)

If authentication is requested but no authentication key is specified then the return value of current_process().authkey is used (see Process ). This value will be automatically inherited by any Process object that the current process creates. This means that (by default) all processes of a multi-process program will share a single authentication key which can be used when setting up connections between themselves.

Suitable authentication keys can also be generated by using os.urandom() .

Some support for logging is available. Note, however, that the logging package does not use process shared locks so it is possible (depending on the handler type) for messages from different processes to get mixed up.

Returns the logger used by multiprocessing . If necessary, a new one will be created.

When first created the logger has level logging.NOTSET and no default handler. Messages sent to this logger will not by default propagate to the root logger.

Note that on Windows child processes will only inherit the level of the parent process’s logger – any other customization of the logger will not be inherited.

This function performs a call to get_logger() but in addition to returning the logger created by get_logger, it adds a handler which sends output to sys.stderr using format '[%(levelname)s/%(processName)s] %(message)s' . You can modify levelname of the logger by passing a level argument.

Below is an example session with logging turned on:

For a full table of logging levels, see the logging module.

The multiprocessing.dummy module ¶

multiprocessing.dummy replicates the API of multiprocessing but is no more than a wrapper around the threading module.

In particular, the Pool function provided by multiprocessing.dummy returns an instance of ThreadPool , which is a subclass of Pool that supports all the same method calls but uses a pool of worker threads rather than worker processes.

A thread pool object which controls a pool of worker threads to which jobs can be submitted. ThreadPool instances are fully interface compatible with Pool instances, and their resources must also be properly managed, either by using the pool as a context manager or by calling close() and terminate() manually.

processes is the number of worker threads to use. If processes is None then the number returned by os.cpu_count() is used.

Unlike Pool , maxtasksperchild and context cannot be provided.

A ThreadPool shares the same interface as Pool , which is designed around a pool of processes and predates the introduction of the concurrent.futures module. As such, it inherits some operations that don’t make sense for a pool backed by threads, and it has its own type for representing the status of asynchronous jobs, AsyncResult , that is not understood by any other libraries.

Users should generally prefer to use concurrent.futures.ThreadPoolExecutor , which has a simpler interface that was designed around threads from the start, and which returns concurrent.futures.Future instances that are compatible with many other libraries, including asyncio .

Programming guidelines ¶

There are certain guidelines and idioms which should be adhered to when using multiprocessing .

All start methods ¶

The following applies to all start methods.

Avoid shared state

As far as possible one should try to avoid shifting large amounts of data between processes. It is probably best to stick to using queues or pipes for communication between processes rather than using the lower level synchronization primitives.

Picklability

Ensure that the arguments to the methods of proxies are picklable.

Thread safety of proxies

Do not use a proxy object from more than one thread unless you protect it with a lock. (There is never a problem with different processes using the same proxy.)

Joining zombie processes

On POSIX when a process finishes but has not been joined it becomes a zombie. There should never be very many because each time a new process starts (or active_children() is called) all completed processes which have not yet been joined will be joined. Also calling a finished process’s Process.is_alive will join the process. Even so it is probably good practice to explicitly join all the processes that you start.

Better to inherit than pickle/unpickle

When using the spawn or forkserver start methods many types from multiprocessing need to be picklable so that child processes can use them. However, one should generally avoid sending shared objects to other processes using pipes or queues. Instead you should arrange the program so that a process which needs access to a shared resource created elsewhere can inherit it from an ancestor process.

Avoid terminating processes

Using the Process.terminate method to stop a process is liable to cause any shared resources (such as locks, semaphores, pipes and queues) currently being used by the process to become broken or unavailable to other processes. Therefore it is probably best to only consider using Process.terminate on processes which never use any shared resources.

Joining processes that use queues

Bear in mind that a process that has put items in a queue will wait before terminating until all the buffered items are fed by the “feeder” thread to the underlying pipe. (The child process can call the Queue.cancel_join_thread method of the queue to avoid this behaviour.) This means that whenever you use a queue you need to make sure that all items which have been put on the queue will eventually be removed before the process is joined. Otherwise you cannot be sure that processes which have put items on the queue will terminate. Remember also that non-daemonic processes will be joined automatically. An example which will deadlock is the following: from multiprocessing import Process , Queue def f ( q ): q . put ( 'X' * 1000000 ) if __name__ == '__main__' : queue = Queue () p = Process ( target = f , args = ( queue ,)) p . start () p . join () # this deadlocks obj = queue . get () A fix here would be to swap the last two lines (or simply remove the p.join() line).

Explicitly pass resources to child processes

On POSIX using the fork start method, a child process can make use of a shared resource created in a parent process using a global resource. However, it is better to pass the object as an argument to the constructor for the child process. Apart from making the code (potentially) compatible with Windows and the other start methods this also ensures that as long as the child process is still alive the object will not be garbage collected in the parent process. This might be important if some resource is freed when the object is garbage collected in the parent process. So for instance from multiprocessing import Process , Lock def f (): ... do something using "lock" ... if __name__ == '__main__' : lock = Lock () for i in range ( 10 ): Process ( target = f ) . start () should be rewritten as from multiprocessing import Process , Lock def f ( l ): ... do something using "l" ... if __name__ == '__main__' : lock = Lock () for i in range ( 10 ): Process ( target = f , args = ( lock ,)) . start ()

Beware of replacing sys.stdin with a “file like object”

multiprocessing originally unconditionally called: os . close ( sys . stdin . fileno ()) in the multiprocessing.Process._bootstrap() method — this resulted in issues with processes-in-processes. This has been changed to: sys . stdin . close () sys . stdin = open ( os . open ( os . devnull , os . O_RDONLY ), closefd = False ) Which solves the fundamental issue of processes colliding with each other resulting in a bad file descriptor error, but introduces a potential danger to applications which replace sys.stdin() with a “file-like object” with output buffering. This danger is that if multiple processes call close() on this file-like object, it could result in the same data being flushed to the object multiple times, resulting in corruption. If you write a file-like object and implement your own caching, you can make it fork-safe by storing the pid whenever you append to the cache, and discarding the cache when the pid changes. For example: @property def cache ( self ): pid = os . getpid () if pid != self . _pid : self . _pid = pid self . _cache = [] return self . _cache For more information, see bpo-5155 , bpo-5313 and bpo-5331

The spawn and forkserver start methods ¶

There are a few extra restrictions which don’t apply to the fork start method.

More picklability

Ensure that all arguments to Process.__init__() are picklable. Also, if you subclass Process then make sure that instances will be picklable when the Process.start method is called.

Global variables

Bear in mind that if code run in a child process tries to access a global variable, then the value it sees (if any) may not be the same as the value in the parent process at the time that Process.start was called. However, global variables which are just module level constants cause no problems.

Safe importing of main module

Make sure that the main module can be safely imported by a new Python interpreter without causing unintended side effects (such as starting a new process). For example, using the spawn or forkserver start method running the following module would fail with a RuntimeError : from multiprocessing import Process def foo (): print ( 'hello' ) p = Process ( target = foo ) p . start () Instead one should protect the “entry point” of the program by using if __name__ == '__main__': as follows: from multiprocessing import Process , freeze_support , set_start_method def foo (): print ( 'hello' ) if __name__ == '__main__' : freeze_support () set_start_method ( 'spawn' ) p = Process ( target = foo ) p . start () (The freeze_support() line can be omitted if the program will be run normally instead of frozen.) This allows the newly spawned Python interpreter to safely import the module and then run the module’s foo() function. Similar restrictions apply if a pool or manager is created in the main module.

Demonstration of how to create and use customized managers and proxies:

Using Pool :

An example showing how to use queues to feed tasks to a collection of worker processes and collect the results:

Table of Contents

• The Process class
• Contexts and start methods
• Exchanging objects between processes
• Synchronization between processes
• Sharing state between processes
• Using a pool of workers
• Process and exceptions
• Pipes and Queues
• Miscellaneous
• Connection Objects
• Synchronization primitives
• The multiprocessing.sharedctypes module
• Customized managers
• Using a remote manager
• Process Pools
• Address Formats
• Authentication keys
• The multiprocessing.dummy module
• All start methods
• The spawn and forkserver start methods

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Solving Assignment Problem using Linear Programming in Python

Learn how to use Python PuLP to solve Assignment problems using Linear Programming.

In earlier articles, we have seen various applications of Linear programming such as transportation, transshipment problem, Cargo Loading problem, and shift-scheduling problem. Now In this tutorial, we will focus on another model that comes under the class of linear programming model known as the Assignment problem. Its objective function is similar to transportation problems. Here we minimize the objective function time or cost of manufacturing the products by allocating one job to one machine.

If we want to solve the maximization problem assignment problem then we subtract all the elements of the matrix from the highest element in the matrix or multiply the entire matrix by –1 and continue with the procedure. For solving the assignment problem, we use the Assignment technique or Hungarian method, or Flood’s technique.

The transportation problem is a special case of the linear programming model and the assignment problem is a special case of transportation problem, therefore it is also a special case of the linear programming problem.

In this tutorial, we are going to cover the following topics:

Assignment Problem

A problem that requires pairing two sets of items given a set of paired costs or profit in such a way that the total cost of the pairings is minimized or maximized. The assignment problem is a special case of linear programming.

For example, an operation manager needs to assign four jobs to four machines. The project manager needs to assign four projects to four staff members. Similarly, the marketing manager needs to assign the 4 salespersons to 4 territories. The manager’s goal is to minimize the total time or cost.

Problem Formulation

A manager has prepared a table that shows the cost of performing each of four jobs by each of four employees. The manager has stated his goal is to develop a set of job assignments that will minimize the total cost of getting all 4 jobs.  

Initialize LP Model

In this step, we will import all the classes and functions of pulp module and create a Minimization LP problem using LpProblem class.

Define Decision Variable

In this step, we will define the decision variables. In our problem, we have two variable lists: workers and jobs. Let’s create them using  LpVariable.dicts()  class.  LpVariable.dicts()  used with Python’s list comprehension.  LpVariable.dicts()  will take the following four values:

• First, prefix name of what this variable represents.
• Second is the list of all the variables.
• Third is the lower bound on this variable.
• Fourth variable is the upper bound.
• Fourth is essentially the type of data (discrete or continuous). The options for the fourth parameter are  LpContinuous  or  LpInteger .

Let’s first create a list route for the route between warehouse and project site and create the decision variables using LpVariable.dicts() the method.

Define Objective Function

In this step, we will define the minimum objective function by adding it to the LpProblem  object. lpSum(vector)is used here to define multiple linear expressions. It also used list comprehension to add multiple variables.

Define the Constraints

Here, we are adding two types of constraints: Each job can be assigned to only one employee constraint and Each employee can be assigned to only one job. We have added the 2 constraints defined in the problem by adding them to the LpProblem  object.

Solve Model

In this step, we will solve the LP problem by calling solve() method. We can print the final value by using the following for loop.

From the above results, we can infer that Worker-1 will be assigned to Job-1, Worker-2 will be assigned to job-3, Worker-3 will be assigned to Job-2, and Worker-4 will assign with job-4.

In this article, we have learned about Assignment problems, Problem Formulation, and implementation using the python PuLp library. We have solved the Assignment problem using a Linear programming problem in Python. Of course, this is just a simple case study, we can add more constraints to it and make it more complicated. You can also run other case studies on Cargo Loading problems , Staff scheduling problems . In upcoming articles, we will write more on different optimization problems such as transshipment problem, balanced diet problem. You can revise the basics of mathematical concepts in  this article  and learn about Linear Programming  in this article .

• Solving Blending Problem in Python using Gurobi
• Transshipment Problem in Python Using PuLP

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How can I process command line arguments in Python? [duplicate]

What would be an easy expression to process command line arguments if I'm expecting anything like 001 or 999 (let's limit expectations to 001...999 range for this time), and few other arguments passed, and would like to ignore any unexpected?

I understand if for example I need to find out if "debug" was passed among parameters it'll be something like that:

How to find out if 009 or 575 was passed?

All those are expected calls:

At this point I don't care about calls like that:

...first one - because of more than one "numeric" argument; second - because of... well, unexpected arguments; third and fourth - because of non-3-digits arguments.

• command-line
• command-line-arguments

• check out this one stackoverflow.com/questions/25605380/… –  geekyjazzy Commented Sep 1, 2014 at 13:41

5 Answers 5

As others answered, optparse is the best option, but if you just want quick code try something like this:

EDIT : Here's an optparse example because so many people are answering optparse without really explaining why, or explaining what you have to change to make it work.

The primary reason to use optparse is it gives you more flexibility for expansion later, and gives you more flexibility on the command line. In other words, your options can appear in any order and usage messages are generated automatically. However to make it work with optparse you need to change your specifications to put '-' or '--' in front of the optional arguments and you need to allow all the arguments to be in any order.

So here's an example using optparse:

The differences here with optparse and your spec is that now you can have command lines like:

and you can easily add new options by calling parser.add_option()

• 4 but note: optparse has been "deprecated since [Python] version 2.7: The optparse module is deprecated and will not be developed further; development will continue with the argparse module." See: docs.python.org/2/library/optparse.html –  kkurian Commented Feb 11, 2014 at 6:38

Have a look at the optparse module. Dealing with sys.argv yourself is fine for really simple stuff, but it gets out of hand quickly.

Note that you may find optparse easier to use if you can change your argument format a little; e.g. replace debug with --debug and xls with --xls or --output=xls .

• I'm not sure this is worth it when the OP is just using arguments, not options. –  Nikhil Commented Feb 20, 2009 at 1:18
• 6 Note that optparse has been replaced with argparse –  Skip Huffman Commented Oct 17, 2011 at 11:46

optparse is your best friend for parsing the command line. Also look into argparse ; it's not in the standard library, though.

• 3 Just as a note argparse is now standard in Python 2.7 and even seems to have been ported to 2.6.8. According to the optparse documentation , optparse is now deprecated. –  Trebor Rude Commented Apr 3, 2013 at 15:31

If you want to implement actual command line switches, give getopt a look. It's incredibly simple to use, too.

• I wouldn't say getopt is all that simple - sure, it's better than doing the parsing manually, but it's just a port of some C code that doesn't really take advantage of Python's strengths. I'd almost always recommend optparse over getopt. –  David Z Commented Feb 20, 2009 at 2:26

Van Gale is largely correct in using the regular expression against the argument. However, it is NOT absolutely necessary to make everything an option when using optparse, which splits sys.argv into options and arguments, based on whether a "-" or "--" is in front or not. Some example code to go through just the arguments:

Yes, the args array is parsed much the same way as sys.argv would be, but the ability to easily add options if needed has been added. For more about optparse, check out the relevant Python doc .

Not the answer you're looking for? Browse other questions tagged python command-line command-line-arguments or ask your own question .

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