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Remember, the most efficient way to learn a programming language is to practice!
Google is a great resource for any programming language.
Just like any other language, variables can store data of different types in Python, such as int , float , or str . We can use the type() function to find the type of an expression.
What are the correct types for variables c and d ?
Consider the following example:
In this case, a and b are not separate entities, even though they are different variables. The list [1, 2, 3] is an object, and both variable names a and b are bounded to the same list.
Now let's consider another example:
b.append(4) modifies the object list, such that the list is now [1, 2, 3, 4] . We know that b is now [1, 2, 3, 4] , but what happens to the variable name a?
Let's see how we can use a built-in Python function to see whether a and b point to the same object or not.
To review the way Python manages memory, let's look at the following examples:
In Python, objects are divided into mutable and immutable . Mutable objects can be modified after they are first created, including lists, dictionaries, NumPy arrays, etc. Immutable objects cannot be modified once they are created, including tuples, strings, floats, etc.
For example,
A list is an example of a mutable object. Let's take a look at how it behaves in different situations:
Which of the following code snippets result in print(a==c) -> True :
A is incorrect because c ends up having two "hey" elements in its list. B is incorrect because a += b adds each character of "hey" as its own element. Hence, C must be the correct choice.
Advanced naming.
Let's make some objects and see what happens in this next snippet of code:
What is the correct output for the following code snippet?
A: computer_science, math ['electrical', 'physics']
B: computer_science, math ['electrical']
C: computer_science ['electrical', 'physics']
D: computer_science ['electrical']
In the above code snippet, John and Tim refer to separate objects since strings are immutable in Python, and so the variable John is equal to 'computer_science' . Lists are mutable, so Julie and Anna are equal to the same list: ['electrical', 'physics'] . Hence, C is the correct choice.
Indexing is important to iterate through for loops in a variety of ways in Python. Given a list a , the formatting for indexing follows this standard: a[i:j:k] . Here, i is the starting index of the iteration, j is the stopping index (exclusive) of the iteration, and k is the step size.
What is the output for the command line above?
Control flow includes iterating over list-like objects with for/while loops, using if/else blocks to perform logic for certain conditions, and using break and continue statements to control when you exit a for loop or move onto the next iteration of a loop, respectively. Let's see a simple example of this:
Sometimes it's useful to define a function. This is done by using the keyword "def" followed by a list of parameters. Here is a basic example of a function definition:
Here is the official documentation for function definition.
It's important to understand the idea of scope. A variable created inside a function can only be used inside that function, because it has local scope . A variable created in the main body of the Python code is a global variable and hence has global scope . This variable has no restrictions and can be used/accessed anywhere. Again, the best way to learn this is by looking at an example:
Which code snippet does not modify the variables?
B is the correct answer because ints in Python are immutable. This means that incrementing a by 1 and b by 2 inside the function just creates new objects for both a and b. These new objects will be restricted to the scope of this function, and as a result the variables will not be modified. However, lists are mutable objects, and appending values to them does not create new objects. The function simply updates the variables that already have global scope, and so these variables are modified.
A dictionary is an important and useful structure in Python. A dictionary is made up of key-value pairs, where the value is extracted using the key. You will be using dictionaries a lot in this class for later chapters. Here are some examples on how to use a dictionary:
Here is the official documentation for dictionaries and some other useful data structures.
Python is a dynamically typed language, meaning that the variable type is determined at runtime. As you've likely noticed, this is why we have the luxury to declare and initialize variables without assigning a type to the variable. This wouldn't be allowed in languages like C++ or Java.
Type Annotations , however, provide a way for developers to specify the type of a variable, function parameter, or function result. While it doesn't really improve performance, it helps developers get a better understanding of the type of data they're dealing with, whether that be in the form of variables or inputs and outputs of a function call. Here's an example of what a type-annotated variable/function definition might look like:
NumPy is a Python library used for numerical computing. Developed almost entirely in C and C++, it is highly performant and can easily support operations on large amounts of data. This section will serve as a brief intro to some common NumPy tools you'll be expected to use throughout the class. While this section will explain some of these common tools, the NumPy documentation goes into much greater detail and is always the go-to resource whenever unsure how to use a specific function.
NumPy arrays are generally preferred over lists because you can conduct highly performant operations on them to accomplish any task. This is specifically important in a class like Numerical Methods. The following are ways you can initialize different types of NumPy arrays:
We can find out additional information about the NumPy array and do more with them with the following functions:
We can use indexing and slicing on NumPy arrays in order to extract specific information from arrays/matrices. This will be a constant part of the class, so it's better to get used to indexing/slicing now! Note that this section is similar to the above indexing section. A NumPy array a will follow the standard: a[i:j:k] , where i is the starting index of the iteration, j is the stopping index (exclusive) of the iteration, and k is the step size.
Array manipulation is particularly useful when certain formulas in later chapters require building matrices or perform certain operations on matrices (for instance, transposing a matrix). Below are some examples:
NumPy provides several math functions that can be performed on each element in the array. Rather than iterating through each element, these functions will do the operation over the entire array and are hence extremely convenient. Which math functions might be relevant in Numerical Methods? Let's take a look:
This class will often ask you to perform linear algebra operations on vectors/matrices. This is when the numpy.linalg library and the following functions it provides will be useful:
Random numbers are always an integral part of Numerical Methods. NumPy has provided several functions that makes it super easy to use random numbers, and this will be key during chapters like Monte Carlo. Let's dive into what NumPy has to offer for random numbers:
Python also has a random module that is separate from NumPy but can be used to do a lot of similar operations as the ones introduced above.
Broadcasting is a powerful technique in Python that allows us to perform arithmetic between two differently-shaped arrays.
Say we have a smaller array A (with a shape of 1 x 5) and a larger array B (with a shape of 4 x 5), and we want to add these arrays together.
Without broadcasting, only the first row in B would be modified by A . With broadcasting, however, A 's values are added to each row of B . Hence, A is broadcasted onto B . The dimension sizes need to cooperate as they did in this example, or we'll receive some error when performing this arithmetic.
Here is an example illustrating the concept:
This result demonstrates how the values from array A were added to each row of array B, thanks to broadcasting. The dimension of A (1 x 5) was compatible with B (4 x 5), allowing A to be "stretched" across B to perform the element-wise addition.
Here is some documentation for other packages we will be using in CS 357.
SciPy, a scientific computing package used to solve mathematical problems.
MatPlotLib, a visualizaion and graphing package.
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hi guys i am new to data structures and i have been trying to solve a maze using left hand rule algorithm. However, i have been facing difficulty in trying to print out the number of steps to take to solve the maze as i keep facing this error :
i tried to initialize a step variable to no avail.
the error in the code happened in this few lines
Insert global steps as the first line of every function that assigns a new value to steps . This lets Python know that you mean to modify the global variable, and not set a local variable that just happens to have the same name.
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COMMENTS
File "weird.py", line 5, in main. print f(3) UnboundLocalError: local variable 'f' referenced before assignment. Python sees the f is used as a local variable in [f for f in [1, 2, 3]], and decides that it is also a local variable in f(3). You could add a global f statement: def f(x): return x. def main():
x = "Hello "def say_hello (name): x = x + name print (x) say_hello("Billy") # UnboundLocalError: local variable 'x' referenced before assignment But wait, why does this also produce the error? Isn't x assigned before it's used in the say_hello function?
Output. Hangup (SIGHUP) Traceback (most recent call last): File "Solution.py", line 7, in <module> example_function() File "Solution.py", line 4, in example_function x += 1 # Trying to modify global variable 'x' without declaring it as global UnboundLocalError: local variable 'x' referenced before assignment Solution for Local variable Referenced Before Assignment in Python
Therefore, we have examined the local variable referenced before the assignment Exception in Python. The differences between a local and global variable declaration have been explained, and multiple solutions regarding the issue have been provided.
The Python "UnboundLocalError: Local variable referenced before assignment" occurs when we reference a local variable before assigning a value to it in a function. To solve the error, mark the variable as global in the function definition, e.g. global my_var .
If you run this code, you'll get. BASH. UnboundLocalError: local variable 'value' referenced before assignment. The issue is that in this line: PYTHON. value = value + 1. We are defining a local variable called value and then trying to use it before it has been assigned a value, instead of using the variable that we defined in the first line.
The UnboundLocalError: local variable 'x' referenced before assignment occurs when you reference a variable inside a function before declaring that variable. To resolve this error, you need to use a different variable name when referencing the existing variable, or you can also specify a parameter for the function. I hope this tutorial is useful.
Avoid Reassignment of Global Variables. Below, code calculates a new value (local_var) based on the global variable and then prints both the local and global variables separately.It demonstrates that the global variable is accessed directly without being reassigned within the function.
Trying to assign a value to a variable that does not have local scope can result in this error: UnboundLocalError: local variable referenced before assignment. Python has a simple rule to determine the scope of a variable. If a variable is assigned in a function, that variable is local. This is because it is assumed that when you define a ...
Strategy 2: Using the Global Keyword. In Python, variables declared inside a function are considered local variables. Thus, they are separate from other variables declared outside of the function.
UnboundLocalError: local variable referenced before assignment. Example #1: Accessing a Local Variable. Solution #1: Passing Parameters to the Function. Solution #2: Use Global Keyword. Example #2: Function with if-elif statements. Solution #1: Include else statement. Solution #2: Use global keyword. Summary.
This tutorial explains the reason and solution of the python error local variable referenced before assignment
@HoKy22: Are you asking why dct[key] = val does not raise a "local variable referenced before assignment" error? The reason is that this is not a bare name assignment. Instead, it causes Python to make the function call dct.__setitem__ ... As the Python interpreter reads the definition of a function (or, I think, ...
Using nonlocal keyword. The nonlocal keyword is used to work with variables inside nested functions, where the variable should not belong to the inner function. It allows you to modify the value of a non-local variable in the outer scope. For example, if you have a function outer that defines a variable x, and another function inner inside outer that tries to change the value of x, you need to ...
1 UnboundLocalError: local variable referenced before assignment. Python has a simple rule to determine the scope of a variable. To clarify, a variable is assigned in a function, that variable is local. Because it is assumed that when you define a variable inside a function, you only need to access it inside that function.
What is a Local Variable? A local variable is a variable that is declared inside a function or block and is only accessible within that function or block. It cannot be accessed from outside its local scope. Python's Variable Scope Hierarchy. Python follows the LEGB rule for scopes: Local (L): Defined inside a function.
To fix this, you can either move the assignment of the variable x before the print statement, or give it an initial value before the print statement. def example (): x = 5 print (x) example()
6. When Python sees that you are assigning to x it forces it to be a local variable name. Now it becomes impossible to see the global x in that function (unless you use the global keyword) So. Case 1) Since there is no local x, you get the global. Case 2) You are assigning to a local x so all references to x in the function will be the local one.
The official dedicated python forum. This is topic with lot of subtlety. Some of the scenarios are discussed in this thread: Namespace and scope difference I think that this boils down to this: Quote:You should also understand difference between reference and assignment: When you reference a variable in an expression, the Python interpreter will traverse the scope to resolve the reference in ...
Here is the official documentation for function definition. Function Scope. It's important to understand the idea of scope. A variable created inside a function can only be used inside that function, because it has local scope. A variable created in the main body of the Python code is a global variable and hence has global scope. This variable ...
Notice the two spaces before print in the snippet with the if, elif, else statements. These spaces are called indentation and they are important as they tell python when the condition starts and stops. ... < and > are the strict less and greater than operators, while == is the equality operator (not to be confused with =, the variable ...
1. The variables wins, money and losses were declared outside the scope of the fiftyfifty() function, so you cannot update them from inside the function unless you explicitly declare them as global variables like this: def fiftyfifty(bet): global wins, money, losses. chance = random.randint(0,100)
hi guys i am new to data structures and i have been trying to solve a maze using left hand rule algorithm. However, i have been facing difficulty in trying to print out the number of steps to take to