python copy vs assignment

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copy — Shallow and deep copy operations ¶

Source code: Lib/copy.py

Assignment statements in Python do not copy objects, they create bindings between a target and an object. For collections that are mutable or contain mutable items, a copy is sometimes needed so one can change one copy without changing the other. This module provides generic shallow and deep copy operations (explained below).

Interface summary:

Return a shallow copy of x .

Return a deep copy of x .

Raised for module specific errors.

The difference between shallow and deep copying is only relevant for compound objects (objects that contain other objects, like lists or class instances):

A shallow copy constructs a new compound object and then (to the extent possible) inserts references into it to the objects found in the original.

A deep copy constructs a new compound object and then, recursively, inserts copies into it of the objects found in the original.

Two problems often exist with deep copy operations that don’t exist with shallow copy operations:

Recursive objects (compound objects that, directly or indirectly, contain a reference to themselves) may cause a recursive loop.

Because deep copy copies everything it may copy too much, such as data which is intended to be shared between copies.

The deepcopy() function avoids these problems by:

keeping a memo dictionary of objects already copied during the current copying pass; and

letting user-defined classes override the copying operation or the set of components copied.

This module does not copy types like module, method, stack trace, stack frame, file, socket, window, or any similar types. It does “copy” functions and classes (shallow and deeply), by returning the original object unchanged; this is compatible with the way these are treated by the pickle module.

Shallow copies of dictionaries can be made using dict.copy() , and of lists by assigning a slice of the entire list, for example, copied_list = original_list[:] .

Classes can use the same interfaces to control copying that they use to control pickling. See the description of module pickle for information on these methods. In fact, the copy module uses the registered pickle functions from the copyreg module.

In order for a class to define its own copy implementation, it can define special methods __copy__() and __deepcopy__() . The former is called to implement the shallow copy operation; no additional arguments are passed. The latter is called to implement the deep copy operation; it is passed one argument, the memo dictionary. If the __deepcopy__() implementation needs to make a deep copy of a component, it should call the deepcopy() function with the component as first argument and the memo dictionary as second argument. The memo dictionary should be treated as an opaque object.

Discussion of the special methods used to support object state retrieval and restoration.

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Python Shallow Copy and Deep Copy

Copy an object in python.

In Python, we use = operator to create a copy of an object. You may think that this creates a new object; it doesn't. It only creates a new variable that shares the reference of the original object.

Let's take an example where we create a list named old_list and pass an object reference to new_list using = operator.

Example 1: Copy using = operator

When we run above program, the output will be:

As you can see from the output both variables old_list and new_list shares the same id i.e 140673303268168 .

So, if you want to modify any values in new_list or old_list , the change is visible in both.

Essentially, sometimes you may want to have the original values unchanged and only modify the new values or vice versa. In Python, there are two ways to create copies:

Shallow Copy

To make these copy work, we use the copy module.

Copy Module

We use the copy module of Python for shallow and deep copy operations. Suppose, you need to copy the compound list say x . For example:

Here, the copy() return a shallow copy of x . Similarly, deepcopy() return a deep copy of x .

A shallow copy creates a new object which stores the reference of the original elements.

So, a shallow copy doesn't create a copy of nested objects, instead it just copies the reference of nested objects. This means, a copy process does not recurse or create copies of nested objects itself.

Example 2: Create a copy using shallow copy

When we run the program , the output will be:

In above program, we created a nested list and then shallow copy it using copy() method.

This means it will create new and independent object with same content. To verify this, we print the both old_list and new_list .

To confirm that new_list is different from old_list , we try to add new nested object to original and check it.

Example 3: Adding [4, 4, 4] to old_list, using shallow copy

When we run the program, it will output:

In the above program, we created a shallow copy of old_list . The new_list contains references to original nested objects stored in old_list . Then we add the new list i.e [4, 4, 4] into old_list . This new sublist was not copied in new_list .

However, when you change any nested objects in old_list , the changes appear in new_list .

Example 4: Adding new nested object using Shallow copy

In the above program, we made changes to old_list i.e old_list[1][1] = 'AA' . Both sublists of old_list and new_list at index [1][1] were modified. This is because, both lists share the reference of same nested objects.

A deep copy creates a new object and recursively adds the copies of nested objects present in the original elements.

Let’s continue with example 2. However, we are going to create deep copy using deepcopy() function present in copy module. The deep copy creates independent copy of original object and all its nested objects.

Example 5: Copying a list using deepcopy()

In the above program, we use deepcopy() function to create copy which looks similar.

However, if you make changes to any nested objects in original object old_list , you’ll see no changes to the copy new_list .

Example 6: Adding a new nested object in the list using Deep copy

In the above program, when we assign a new value to old_list , we can see only the old_list is modified. This means, both the old_list and the new_list are independent. This is because the old_list was recursively copied, which is true for all its nested objects.

Copy an Object in Python

Sorry about that.

Shallow and deep copy in Python: copy(), deepcopy()

In Python, you can make a shallow and deep copy using the copy() and deepcopy() functions from the copy module. A shallow copy can also be made with the copy() method of lists, dictionaries, and so on.

copy — Shallow and deep copy operations — Python 3.11.3 documentation

Shallow copy and deep copy in Python

Assignment to another variable, copy() method of list, dictionary, etc., list() , dict() , etc., copy.copy(), deep copy: copy.deepcopy().

The following is a summary of the differences between assignment to another variable, shallow copy, and deep copy.

The Python official documentation describes shallow copy and deep copy as follows:

The difference between shallow and deep copying is only relevant for compound objects (objects that contain other objects, like lists or class instances): A shallow copy constructs a new compound object and then (to the extent possible) inserts references into it to the objects found in the original. A deep copy constructs a new compound object and then, recursively, inserts copies into it of the objects found in the original. copy — Shallow and deep copy operations — Python 3.11.3 documentation

When objects are contained within mutable objects, like elements in a list or values in a dictionary, a shallow copy creates references to the original objects, while a deep copy creates new copies of the original objects. With references, the elements point to the same object, so modifying one of them also affects the other.

First, let's explore what happens when assigning an object to another variable.

When a mutable object, such as a list or a dictionary, is assigned to multiple variables, updating one variable (e.g., changing, adding, or deleting elements) will also affect the other variables.

As demonstrated in the above code example, the is operator shows that the two variables refer to the same object both before and after the value change.

Difference between the == and is operators in Python

To create a copy instead of a reference of the same object, use the copy() method or the copy.copy() and copy.deepcopy() functions described below.

For immutable objects like numbers ( int , float ) and strings ( str ), they cannot be updated. When these types of objects are assigned, the two variables refer to the same object initially. However, if one variable is updated to a new value, it becomes a separate object, and the other variable remains the same.

Shallow copy: copy() , copy.copy() , etc.

The copy() method is provided for lists, dictionaries, etc. The copy() method makes a shallow copy.

A shallow copy creates a new object that contains references to the elements found in the original object. For example, in the case of a list, the copied list is a new object, but its elements are references to the same objects in the original list.

Therefore, if the elements are mutable, updating one also affects the other. However, in the case of an immutable element, when its value is changed, it becomes a separate object. The corresponding element in the other list remains unchanged.

The same applies not only to a list of lists as in the example above, but also to a list of dictionaries, a nested dictionary (a dictionary containing other dictionaries as values), and so on.

Slices for mutable sequence types, such as lists, also make shallow copies.

How to slice a list, string, tuple in Python

For example, applying the slice [:] that specifies all elements makes a shallow copy of the whole list.

Before Python 3.3 introduced the copy() method for lists, the [:] notation was commonly used to create a shallow copy. For new code, it is generally recommended to use the copy() method to make your intentions clearer.

A slice of a part of the sequence also creates a shallow copy.

If you need to make a deep copy, you can apply the copy.deepcopy() function to the slice.

You can make a shallow copy of a list or dictionary by passing the original list or dictionary to list() or dict() .

You can also make a shallow copy with the copy() function from the copy module.

Use copy.copy() when you need to create a shallow copy of an object that does not provide a copy() method.

To make a deep copy, use the deepcopy() function from the copy module.

In a deep copy, actual copies of the objects are inserted instead of their references. As a result, changes to one object do not affect the other.

Here's an example of applying the deepcopy() function to a slice:

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The Ultimate Guide to Shallow Copy and Deep Copy in Python

May 31, 2021
Author - Chetan Ambi
Category - Python

Table of Contents

Introduction

When writing a program, we often want to create a copy of an object (int, float, list, tuple, dictionary, etc.). Because we want to use the copy (new) object without changing the content of the original.

There are a number of ways you can create a copy of the object in Python. But, beware! Most of these methods create a shallow copy that you don’t want. But what you should need in this scenario is a deep copy .

So, what are these different methods of creating a copy of an object? What are shallow copy and deep copy? We are going to answer all these questions in this article.

Let’s first understand that the assignment operator (=) does not create a copy (new) object. It just creates a binding between the copy (new) object and the original object (i.e. both objects share the same memory address ). But this doesn’t make difference for immutable objects such as int, float, decimal, bool, tuple, etc. because we can’t modify the immutable objects.

Shallow Copy

A shallow copy creates a copy of the given compound object and while doing so it inserts the references to the objects found in the original. You will understand this definition once you go through this section.

There are many ways you can create shallow copies in Python. Let’s look at 6 different ways to create shallow copies in the below —

Notice that the memory location of both the objects ( my_list1 and my_list2 ) in all the methods shown below. As you can see, id(my_list1) and id(my_list2) return two different memory addresses. It indicates that a new object (copy) has been created.

The id() function returns address of the object. It is briefly covered here if you want to take a quick look.

List comprehension

Copy method

Constructor

Copy module

In all these methods if you try to modify (add/delete/change) an element in my_list1 or my_list2 , you expect the other list not to get updated. And that’s exactly what happens. This is because we have used a list where the elements of the list are immutable. For example, in my_list = [10,20,30,40,50] , the elements were immutable. Let’s see an example to confirm this.

Now, say you appended 60 to my_list2 . As you can see this didn’t modify the my_list1 . Because my_list1 and my_list2 are two different objects.

So far so good. The copy operation is working exactly how we think it should. What if the original object (ex. list) has a nested structure (lists within the list) and you try to modify these mutable elements? What happens then?

In the below example, my_list1 has a nested structure. Meaning the individual elements of my_list1 are also lists. Next, say you created my_list2 using any of the above-mentioned 6 methods. This is what it looks like visually. Though my_list1 and my_list2 are two different objects, their contents are sharing the same memory address.

Let’s confirm this programmatically. The id() function clearly shows that my_list1 and my_list2 are two different objects.

But note that if the elements of the list are mutable, then share the same memory address as you can see below.

Since they share the same memory address, if you modify my_list2[0] or my_list2[1] , the same changes will get reflected in my_list1 as well even though we are not intended to do that. But that is not what we want. Right? We want a real clone or copy that doesn’t affect the original. So, how do you handle this? That’s where deep copy comes into the picture.

A deep copy creates a copy of the given compound object and while doing so it recursively inserts copies of the objects found in the original.

To create a deep copy of the object, you need to use deepcopy() method from the copy module. Let’s take the same example, we covered in the shallow copy section.

As you can see from the code output and diagram, with deepcopy() we are able to overcome the issue with the shallow copy. Notice that nested elements don’t share the same address anymore.

Arbitrary Python Objects

In all the methods above, we have used a list for the demonstration. In fact, you can use any mutable type list, set, dictionary, or arbitrary Python object. Let’s look at an example of an arbitrary object below.

Shallow copy: In the below example, we first created object c of MyClass and then created a shallow copy c1 . As you can see, even though both c (original object ) and shallow copy object c1 are different objects, their contents share the same memory. Note x[0] is [1,2] and x[1] is [3,4].

Since we are dealing with a shallow copy, when we appended [5,6] to original object c , the copy object c1 also got updated as expected.

Deep copy: Continuing the same example from above, we have created a deep copy c2 of the object c . Both original object c and copy object c2 are two different objects. Since it was a deep copy, instead of sharing the same memory, a real copy (clone) of the objects is created. As you can see the elements within c and c2 don’t share the same memory.

Hence, even though we change the content of c, it won’t be reflected in the copy object c2.

In this article, we have understood shallow copy and deep copy in Python with examples. We have gone through 6 different ways of creating shallow copies. In order to create deep copy, we used copy module. In the last section, we saw that we can create shallow copy and deep copy on Python arbitrary objects as well.

Hope this answers all your doubts about shallow copy and deep copy in Python. Please do let us know if you have any questions in the comments section.

Chetan Ambi

Python Course #12: Pass by Assignment, Copy, Reference, and None

Now that the you know what mutable (e.g. lists , sets and dictionaries ) and immutable data types (e.g. tuples ) are it is important to talk about copies, references and None .

Python Pass by Assignment

When you call a pass data to a function such as print(...) there are two ways. The first option is to pass the data directly as a value:

The second option is to pass the data as a variable:

The difference between those two options is that the second option allows you to access the data you passed to the function later in your program by stating the variable. When the function that receives the data modifies it internally, you have to be aware of two critical things:

When a primitive data type ( bool , int , float , and str ) or immutable data type (e.g. tuple ) is passed to a function, its value is copied. Therefore, all changes made inside a function won’t affect the values stored in the variables passed to the function. This is called pass by value .

When a mutable data type (e.g. list , set , dict ) is passed to a function, the data isn’t copied. Because of this, changes that are made inside the function affect the values outside of the function. This is called pass by reference

This sounds pretty theoretical, so here comes an example:

Even though you haven’t learned about declaring functions in Python this example declares the function func(x, y) that takes two parameters. func(x, y) subtracts 1 from the first parameter, and calls pop() on the second one. That is everything you need to understand at the moment.

In the main program, the int i and the list l are declared and then passed to func(x, y) . When looking at i and l after func(x, y) has been executed, you can see that i is still 1 and not 0 because i ’s value was copied. However, l is missing its last element since it was passed as a reference.

This mix of pass by value and pass by reference in Python is called pass by assignment . It is essential to keep this concept always in mind when writing Python code.

In the previous parts of this Python course, you have seen that it is possible to get a copy of a mutable data type by calling the .copy() function:

By using .copy() , the data outside of the function isn’t changed.

In the following example the difference between a copy and reference is further amplified:

In the first line the list ['a', 'b', 'c'] is declared and than a new reference to this list is created with l = . In the second line another reference to the list ['a', 'b', 'c'] is created with: m = l . In the third line the list is copied and a reference to that copy is created with: n = l.copy() .

There are three references to two lists with the same content. l and m reference the first list and n reference the second list . Because l and m reference the same list every modification done using l or m will always be reflected in both. n won’t change as it references a different list .

Python is vs ==

To check if two variables reference the same value you can use the is operator to compare the values use the == operator:

l is n evaluates to False because they reference differnt list s, however, those two list s contain the same values and therefore l == n evaluates to True . You can also check the id that a variable is referencing using id(...) :

You can see that the id of l and m is the same and the id of n is different (The numbers are different everytime you run this code). The is operator is actually implemented using == with id(...) == id(...) .

Python None

Now that you know the difference between a copy and a reference, there is one last thing to talk about: ` None . The keyword None in Python indicates no value at all. And there is only one None`:

None can be used to initialize a variable without assigning a value. This can be useful when the value of a variable is assigned under a condition:

None actually has it’s own type and is an immutable data type because it can’t be changed:

None concludes this article. Throughout this Python course, you will come across pass by assignment, copies, references, and None reasonably often. Make sure to get the free Python Cheat Sheets in my Gumroad shop . If you have any questions about this article, feel free to join our Discord community to ask them over there.

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Python copy list - deepcopy() vs copy() with examples

January 9, 2024

In this article of Python programming, we will learn about copy module. We will use and compare Python deepcopy() , shallow copy() and normal assignment to copy or clone a list with multiple examples.

Python copy list using normal assignment

In python we use = operator to create a copy of an object.

Example-1: Use = operator to copy a list in Python

For example I have a list called myList with some elements. Next I copy the myList content into newList using = operator.

Python copy list - deepcopy() vs copy() with examples

So as you see the list elements were successfully copied to a new list.

Example-2: Append elements to old list after copying

In the same example I will append some element to the original list after copying the list .

In the output you can see that the same element is also automatically appended to the new list. So any such changes performed to the old list will also reflect in the newly copied list.

Example-3: Modify elements of new list after copying

Similar to example-2, we will now modify the content of newList (in the previous example we modified original list so here we are doing vice versa) which is a copy of old list created using normal assignment.

So this shows that using an = operator we don't create a new object, instead it just creates a new variable which will share the reference of the original object.

Python copy module

The python copy module provides functions for making shallow and deep copies of compound objects, including lists, tuples, dictionaries, and class instances.

The copy module provides two functions

shallow copy()

The copy functions don’t work with modules, class objects, functions, methods, tracebacks, stack frames, files, sockets, and other similar types. When an object can’t be copied, the copy.error exception is raised.

Python shallow copy() function

When you create a shallow copy, you create a new instance of the current object and copy values of members of the original to the new one but do not create copies of children (referenced) objects.

The copy() function only creates a shallow copy of the original list. This means it copies the top level of the list only . If in that list you have more complex data structures having nested elements, then those will not be cloned . So the new list only hold half the reference of the original list.

Example-4: Use copy() function to create a shallow copy of a list

In this example we will use shallow copy() function to create a clone of a list.

Here you can see that even though we have an exact copy of the original list. Although the id of both the lists are different, this is because copy() created a new object here unlike the = operator we used earlier. Output from this script:

Example-5: Append and modify top level elements in a list with shallow copy()

In this example we will append and modify some existing elements of our list. Tis is to verify if modifying an element in original list is also reflected in new list and vice versa. Let us verify this theory with some practical examples:

Example-6: Modify nested object with python shallow copy() function

In this example we will create a dictionary inside the original list and clone it to a new list variable using shallow copy() function.

In the script we are modifying the dictionary value from ' maruti ' to ' honda ' in the old list and the same is also reflecting in the new list because shallow copy doesn't store the nested data in the memory. Hence both lists share the reference of same nested objects.

Python deepcopy() function

Python deepcopy() function is more subtle as it copies even the nested objects recursively. Although copy.deepcopy() is slightly slower than copy.copy() , it’s safer to use if you don’t know whether the list being copied contains other lists (or other mutable objects like dictionaries or sets).

Example-7: Use python deepcopy() function to create a deep copy of a list

We will use the same code from Example-6, just replace copy.copy() with copy.deepcopy() function.

The output will have two different objects with same content. Since deepcopy() creates a new object, the ids are expected to be different of both the variables.

Example-8: Modify nested object in the list using python deepcopy() function

Since we know that appending/modifying element of top level lists are not cloned to the new list or vice versa. So I will not repeat example-5 use case with python deepcopy() .

But let us validate Example-6 use case wherein we modify the dictionary content within the list as shallow copy was not able to retain the changes there.

As expected, python deepcopy() function has successfully copied the content of original list into a new one recursively. Hence any change to the old list is not visible in the new list and they both are completely independent.

Normal assignment vs shallow copy() vs deepcopy()

Here is a short table which briefs the difference of behaviour between normal assignment operator, shallow copy() and deepcopy() function available in Python :

In this tutorial we learned about list cloning using different available methods. We learned that we can create an identical new object from an existing one using = operator and copy module. The copy module contains shallow copy() function as well as deepcopy() function. The major difference between shallow copy() and deepcopy() function is that the deepcopy() function copies the data of an object " recursively ". This means that any complex data structures and nested elements are also copied from the original list unlike shallow copy which ignored the nested data. This is the reason why any change made to the nested data in the original list is also cloned in the new list when using shallow copy() function.

Navigating NumPy's Array Assignment Maze: Shallow Copies vs. Deep Independence

Understanding Array References and Copies

Assignment (=): In Python, assignment with = creates a shallow copy by default. This means that both variables point to the same underlying data storage. Modifying one array affects the other.
Deep Copy: To create a truly independent copy, use numpy.copy() or array.copy() (for general arrays). This creates a new array in memory with separate data.

When to Use Shallow vs. Deep Copy:

Shallow copy: When you want modifications to one array to be reflected in the other array (e.g., for efficiency in calculations).
Deep copy: When you want independent arrays that won't affect each other (e.g., to preserve the original array or pass copies to functions).

Additional Considerations:

Array slicing ( array[start:end] ) also creates shallow copies by default.
Deep copying nested arrays (like multidimensional arrays) requires np.ndarray.copy() or recursive copying.
For large arrays, deep copying can be memory-intensive, so consider shallow copying when appropriate.

By understanding these concepts, you can effectively work with numpy arrays and control how changes propagate, ensuring correct behavior in your Python programs.

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The list Copy() method makes a new shallow copy of a list.

What is List Copy() Method?

The list Copy() function in Python is used to create a copy of a list. There are two main ways to create a copy of the list Shallow copy and Deep copy. We will discuss the list copy() method in detail below.

The list copy() function is used to create a copy of a list, which can be used to work and it will not affect the values in the original list. This gives freedom to manipulate data without worrying about data loss.

List copy() Method Syntax

list.copy()

The copy() method doesn’t take any parameters.

Returns: Returns a shallow copy of a list.

Note : A shallow copy means any modification in the new list won’t be reflected in the original list.

How to Create a Simple Copy of a List in Python

Copying and creating a new list can be done using copy() function in Python.

More Examples on List copy() Method

Let us see a few examples of the list copy() method.

Example 1: Simple List Copy

In this example, we are creating a List of Python strings and we are using copy() method to copy the list to another variable.

Example 2: Demonstrating the working of List copy()

Here we will create a Python list and then create a shallow copy using the copy() function. Then we will append a value to the copied list to check if copying a list using copy() method affects the original list.

Note: A shallow copy means if we modify any of the nested list elements, changes are reflected in both lists as they point to the same reference.

Shallow Copy and Deep Copy

A deep copy is a copy of a list, where we add an element in any of the lists, only that list is modified.

In list copy() method, changes made to the copied list are not reflected in the original list. The changes made to one list are not reflected on other lists except for in nested elements (like a list within a list).

We can use the copy.deepcopy() from the copy module to avoid this problem.

Using copy.deepcopy()
Using copy.copy()
Using list.copy()
Using slicing

To gain a deeper understanding, Refer to this article on Deep Copy vs Shallow Copy .

Demonstrating Techniques of Shallow and Deep copy

Here we will create a list and then create a shallow copy using the assignment operator , list copy() method, and copy.copy() method of the Python copy module.

We also create a deep copy using deepcopy() in Python. Then we will make changes to the original list and see if the other lists are affected or not.

Copy List Using Slicing

Here we are copying the list using the list slicing method [:] and we are appending the ‘a’ to the new_list. After printing we can see the newly appended character ‘a’ is not appended to the old list.

We discussed the definition, syntax and examples of list copy() method. copy() function is very useful when working with sensitive data and you can’t risk mistakes.

We also briefly talked about shallow vs deep copy. Hope this helped you in understanding copy() function in Python.

Read Other Python List Methods

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Assignment vs Shallow Copy vs Deep Copy in Python

Today, we will be discussing the copy in Python. There are three ways we can do it. In this article, you will get to know what each operation does and how they are different.

1 - Assignment Operator (=)

2 - Shallow Copy

3 - Deep copy

Assignment Operator (=)

In the above example of the assignment operator, It does not make a copy of the Python objects instead it copying a memory address (or pointer) from a to b , (b=a) . Which means both a & b pointing to the same memory address.

Here we can use the id() method to get the address of the object in memory and check if both lists are pointing the same memory.

So, here if you would edit the new list, It’ll get updated in the original list also -

Because there is only one instance of that list in the memory.

Shallow Copy

A shallow copy constructs a new compound object and then (to the extent possible) inserts references into it to the objects found in the original.

we have 3 different ways to create a shallow copy -

Here all the above lists contains the same values as the original list -

but the memory address of each is different -

This means that this time, each list’s object has its own, independent memory address.

Now move to the more interesting part. If the original list is a compound object (e.g. a list of lists), then after shallow copy new list elements are still referencing the original elements. So, if you modify the mutable elements like lists, the changes will be reflected on the original elements. Let’s look at the below example to get a better understanding -

As you see in the above example while we are modifying the internal list elements in the new list, it’s getting updated in the original list also, because a[0] and b[0] are still pointing to the same memory address(original list).

So the new list b has its own memory address but its elements do not. because in shallow copy instead of copying the list’s elements to the new object, It simply copies the references to their memory addresses, Therefore while we are making changes to the original object it’s reflecting in copied objects and vice versa.

This is a characteristic of shallow copy.

A deep copy constructs a new compound object and then, recursively, inserts copies into it of the objects found in the original.

Creating a deep copy is slower because you are making new copies for everything. In this rather than just copy the address of the compound objects, It Simply makes a full copy of all the list’s elements (simple and compound objects) of the original list and allocates different memory address for the new list and then assigns them the copied elements.

To achieve the deep copy we have to import the copy module. And use copy.deepcopy() .

As you see above the original list does not get affected.

The difference between shallow and deep copying is only relevant for compound objects (objects that contain other objects, like lists or class instances).

Hope you like the explanation of Assignment Operator, Shallow Copy and Deep Copy. Still, if any doubt or improvement regarding Copy in Python, ask in the comment section.

References: https://docs.python.org/2/library/copy.html

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Proposal: Annotate types in multiple assignment

In the latest version of Python (3.12.3), type annotation for single variable assignment is available:

However, in some scenarios like when we want to annotate the tuple of variables in return, the syntax of type annotation is invalid:

In this case, I propose two new syntaxes to support this feature:

Annotate directly after each variable:
Annotate the tuple of return:

In other programming languages, as I know, Julia and Rust support this feature in there approaches:

I’m pretty sure this has already been suggested. Did you go through the mailing list and searched for topics here? Without doing that, there’s nothing to discuss here. (Besides linking to them).

Secondly, try to not edit posts, but post a followup. Some people read these topics in mailing list mode and don’t see your edits.

https://mail.python.org
https://mail.python.org/archives

For reference, PEP 526 has a note about this in the “Rejected/Postponed Proposals” section:

Allow type annotations for tuple unpacking: This causes ambiguity: it’s not clear what this statement means: x, y: T Are x and y both of type T , or do we expect T to be a tuple type of two items that are distributed over x and y , or perhaps x has type Any and y has type T ? (The latter is what this would mean if this occurred in a function signature.) Rather than leave the (human) reader guessing, we forbid this, at least for now.

Personally I think the meaning of this is rather clear, especially when combined with an assignment, and I would like to see this.

Thank you for your valuable response, both regarding the discussion convention for Python development and the history of this feature.

I have found a related topic here: https://mail.python.org/archives/list/[email protected]/thread/5NZNHBDWK6EP67HSK4VNDTZNIVUOXMRS/

Here’s the part I find unconvincing:

Under what circumstances will fun() be hard to annotate, but a, b will be easy?

It’s better to annotate function arguments and return values, not variables. The preferred scenario is that fun() has a well-defined return type, and the type of a, b can be inferred (there is no reason to annotate it). This idea is presupposing there are cases where that’s difficult, but I’d like to see some examples where that applies.

Does this not work?

You don’t need from __future__ as of… 3.9, I think?

3.10 if you want A | B too: PEP 604 , although I’m not sure which version the OP is using and 3.9 hasn’t reached end of life yet.

We can’t always infer it, so annotating a variable is sometimes necessary or useful. But if the function’s return type is annotated then a, b = fun() allows type-checkers to infer the types of a and b . This stuff isn’t built in to Python and is evolving as typing changes, so what was inferred in the past might be better in the future.

So my question above was: are there any scenarios where annotating the function is difficult, but annotating the results would be easy? That seems like the motivating use case.

Would it be a solution to put it on the line above? And not allow assigning on the same line? Then it better mirrors function definitions.

It’s a long thread, so it might have been suggested already.

Actually, in cases where the called function differs from the user-defined function, we should declare the types when assignment unpacking.

Here is a simplified MWE:

NOTE: In PyTorch, the __call__ function is internally wrapped from forward .

Can’t you write this? That’s shorter than writing the type annotations.

This is the kind of example I was asking for, thanks. Is the problem that typing tools don’t trace the return type through the call because the wrapping isn’t in python?

I still suggest to read the thread you linked, like I’m doing right now.

The __call__ function is not the same as forward . There might be many other preprocessing and postprocessing steps involved inside it.

Yeah, quite a bit of pre-processing in fact… unless you don’t have hooks by the looks of it:

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Runs in separate window and not in terminal #23454

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Microsoft fabric updates blog.

Microsoft Fabric May 2024 Update

Monthly Update

Welcome to the May 2024 update.

Here are a few, select highlights of the many we have for Fabric. You can now ask Copilot questions about data in your model, Model Explorer and authoring calculation groups in Power BI desktop is now generally available, and Real-Time Intelligence provides a complete end-to-end solution for ingesting, processing, analyzing, visualizing, monitoring, and acting on events.

There is much more to explore, please continue to read on.

Microsoft Build Announcements

At Microsoft Build 2024, we are thrilled to announce a huge array of innovations coming to the Microsoft Fabric platform that will make Microsoft Fabric’s capabilities even more robust and even customizable to meet the unique needs of each organization. To learn more about these changes, read the “ Unlock real-time insights with AI-powered analytics in Microsoft Fabric ” announcement blog by Arun Ulag.

Fabric Roadmap Update

Last October at the Microsoft Power Platform Community Conference we announced the release of the Microsoft Fabric Roadmap . Today we have updated that roadmap to include the next semester of Fabric innovations. As promised, we have merged Power BI into this roadmap to give you a single, unified road map for all of Microsoft Fabric. You can find the Fabric Roadmap at https://aka.ms/FabricRoadmap .

We will be innovating our Roadmap over the coming year and would love to hear your recommendation ways that we can make this experience better for you. Please submit suggestions at https://aka.ms/FabricIdeas .

Earn a discount on your Microsoft Fabric certification exam!

We’d like to thank the thousands of you who completed the Fabric AI Skills Challenge and earned a free voucher for Exam DP-600 which leads to the Fabric Analytics Engineer Associate certification.

If you earned a free voucher, you can find redemption instructions in your email. We recommend that you schedule your exam now, before your discount voucher expires on June 24 th . All exams must be scheduled and completed by this date.

If you need a little more help with exam prep, visit the Fabric Career Hub which has expert-led training, exam crams, practice tests and more.

Missed the Fabric AI Skills Challenge? We have you covered. For a limited time , you could earn a 50% exam discount by taking the Fabric 30 Days to Learn It Challenge .

Modern Tooltip now on by Default

Matrix layouts, line updates, on-object interaction updates, publish to folders in public preview, you can now ask copilot questions about data in your model (preview), announcing general availability of dax query view, copilot to write and explain dax queries in dax query view public preview updates, new manage relationships dialog, refreshing calculated columns and calculated tables referencing directquery sources with single sign-on, announcing general availability of model explorer and authoring calculation groups in power bi desktop, microsoft entra id sso support for oracle database, certified connector updates, view reports in onedrive and sharepoint with live connected semantic models, storytelling in powerpoint – image mode in the power bi add-in for powerpoint, storytelling in powerpoint – data updated notification, git integration support for direct lake semantic models.

Editor’s pick of the quarter
New visuals in AppSource
Financial Reporting Matrix by Profitbase
Horizon Chart by Powerviz

Milestone Trend Analysis Chart by Nova Silva

Sunburst Chart by Powerviz
Stacked Bar Chart with Line by JTA

Fabric Automation

Streamlining fabric admin apis, microsoft fabric workload development kit, external data sharing, apis for onelake data access roles, shortcuts to on-premises and network-restricted data, copilot for data warehouse, unlocking insights through time: time travel in data warehouse, copy into enhancements, faster workspace resource assignment powered by just in time database attachment, runtime 1.3 (apache spark 3.5, delta lake 3.1, r 4.3.3, python 3.11) – public preview, native execution engine for fabric runtime 1.2 (apache spark 3.4) – public preview , spark run series analysis, comment @tagging in notebook, notebook ribbon upgrade, notebook metadata update notification, environment is ga now, rest api support for workspace data engineering/science settings, fabric user data functions (private preview), introducing api for graphql in microsoft fabric (preview), copilot will be enabled by default, the ai and copilot setting will be automatically delegated to capacity admins, abuse monitoring no longer stores your data, real-time hub, source from real-time hub in enhanced eventstream, use real-time hub to get data in kql database in eventhouse, get data from real-time hub within reflexes, eventstream edit and live modes, default and derived streams, route streams based on content in enhanced eventstream, eventhouse is now generally available, eventhouse onelake availability is now generally available, create a database shortcut to another kql database, support for ai anomaly detector, copilot for real-time intelligence, eventhouse tenant level private endpoint support, visualize data with real-time dashboards, new experience for data exploration, create triggers from real-time hub, set alert on real-time dashboards, taking action through fabric items, general availability of the power query sdk for vs code, refresh the refresh history dialog, introducing data workflows in data factory, introducing trusted workspace access in fabric data pipelines.

Introducing Blob Storage Event Triggers for Data Pipelines
Parent/child pipeline pattern monitoring improvements

Fabric Spark job definition activity now available

Hd insight activity now available, modern get data experience in data pipeline.

Power BI tooltips are embarking on an evolution to enhance their functionality. To lay the groundwork, we are introducing the modern tooltip as the new default , a feature that many users may already recognize from its previous preview status. This change is more than just an upgrade; it’s the first step in a series of remarkable improvements. These future developments promise to revolutionize tooltip management and customization, offering possibilities that were previously only imaginable. As we prepare for the general availability of the modern tooltip, this is an excellent opportunity for users to become familiar with its features and capabilities.

Discover the full potential of the new tooltip feature by visiting our dedicated blog . Dive into the details and explore the comprehensive vision we’ve crafted for tooltips, designed to enhance your Power BI experience.

We’ve listened to our community’s feedback on improving our tabular visuals (Table and Matrix), and we’re excited to initiate their transformation. Drawing inspiration from the familiar PivotTable in Excel , we aim to build new features and capabilities upon a stronger foundation. In our May update, we’re introducing ‘ Layouts for Matrix .’ Now, you can select from compact , outline , or tabular layouts to alter the arrangement of components in a manner akin to Excel.

As an extension of the new layout options, report creators can now craft custom layout patterns by repeating row headers. This powerful control, inspired by Excel’s PivotTable layout, enables the creation of a matrix that closely resembles the look and feel of a table. This enhancement not only provides greater flexibility but also brings a touch of Excel’s intuitive design to Power BI’s matrix visuals. Only available for Outline and Tabular layouts.

To further align with Excel’s functionality, report creators now have the option to insert blank rows within the matrix. This feature allows for the separation of higher-level row header categories, significantly enhancing the readability of the report. It’s a thoughtful addition that brings a new level of clarity and organization to Power BI’s matrix visuals and opens a path for future enhancements for totals/subtotals and rows/column headers.

We understand your eagerness to delve deeper into the matrix layouts and grasp how these enhancements fulfill the highly requested features by our community. Find out more and join the conversation in our dedicated blog , where we unravel the details and share the community-driven vision behind these improvements.

Following last month’s introduction of the initial line enhancements, May brings a groundbreaking set of line capabilities that are set to transform your Power BI experience:

Hide/Show lines : Gain control over the visibility of your lines for a cleaner, more focused report.
Customized line pattern : Tailor the pattern of your lines to match the style and context of your data.
Auto-scaled line pattern : Ensure your line patterns scale perfectly with your data, maintaining consistency and clarity.
Line dash cap : Customize the end caps of your customized dashed lines for a polished, professional look.
Line upgrades across other line types : Experience improvements in reference lines, forecast lines, leader lines, small multiple gridlines, and the new card’s divider line.

These enhancements are not to be missed. We recommend visiting our dedicated blog for an in-depth exploration of all the new capabilities added to lines, keeping you informed and up to date.

This May release, we’re excited to introduce on-object formatting support for Small multiples , Waterfall , and Matrix visuals. This new feature allows users to interact directly with these visuals for a more intuitive and efficient formatting experience. By double-clicking on any of these visuals, users can now right-click on the specific visual component they wish to format, bringing up a convenient mini-toolbar. This streamlined approach not only saves time but also enhances the user’s ability to customize and refine their reports with ease.

We’re also thrilled to announce a significant enhancement to the mobile reporting experience with the introduction of the pane manager for the mobile layout view. This innovative feature empowers users to effortlessly open and close panels via a dedicated menu, streamlining the design process of mobile reports.

We recently announced a public preview for folders in workspaces, allowing you to create a hierarchical structure for organizing and managing your items. In the latest Desktop release, you can now publish your reports to specific folders in your workspace.

When you publish a report, you can choose the specific workspace and folder for your report. The interface is simplistic and easy to understand, making organizing your Power BI content from Desktop better than ever.

To publish reports to specific folders in the service, make sure the “Publish dialogs support folder selection” setting is enabled in the Preview features tab in the Options menu.

Learn more about folders in workspaces.

We’re excited to preview a new capability for Power BI Copilot allowing you to ask questions about the data in your model! You could already ask questions about the data present in the visuals on your report pages – and now you can go deeper by getting answers directly from the underlying model. Just ask questions about your data, and if the answer isn’t already on your report, Copilot will then query your model for the data instead and return the answer to your question in the form of a visual!

We’re starting this capability off in both Edit and View modes in Power BI Service. Because this is a preview feature, you’ll need to enable it via the preview toggle in the Copilot pane. You can learn more about all the details of the feature in our announcement post here! (will link to announcement post)

We are excited to announce the general availability of DAX query view. DAX query view is the fourth view in Power BI Desktop to run DAX queries on your semantic model.

DAX query view comes with several ways to help you be as productive as possible with DAX queries.

Quick queries. Have the DAX query written for you from the context menu of tables, columns, or measures in the Data pane of DAX query view. Get the top 100 rows of a table, statistics of a column, or DAX formula of a measure to edit and validate in just a couple clicks!
DirectQuery model authors can also use DAX query view. View the data in your tables whenever you want!
Create and edit measures. Edit one or multiple measures at once. Make changes and see the change in action in a DA query. Then update the model when you are ready. All in DAX query view!
See the DAX query of visuals. Investigate the visuals DAX query in DAX query view. Go to the Performance Analyzer pane and choose “Run in DAX query view”.
Write DAX queries. You can create DAX queries with Intellisense, formatting, commenting/uncommenting, and syntax highlighting. And additional professional code editing experiences such as “Change all occurrences” and block folding to expand and collapse sections. Even expanded find and replace options with regex.

Learn more about DAX query view with these resources:

Deep dive blog: https://powerbi.microsoft.com/blog/deep-dive-into-dax-query-view-and-writing-dax-queries/
Learn more: https://learn.microsoft.com/power-bi/transform-model/dax-query-view
Video: https://youtu.be/oPGGYLKhTOA?si=YKUp1j8GoHHsqdZo

DAX query view includes an inline Fabric Copilot to write and explain DAX queries, which remains in public preview. This month we have made the following updates.

Run the DAX query before you keep it . Previously the Run button was disabled until the generated DAX query was accepted or Copilot was closed. Now you can Run the DAX query then decide to Keep or Discard the DAX query.

2. Conversationally build the DAX query. Previously the DAX query generated was not considered if you typed additional prompts and you had to keep the DAX query, select it again, then use Copilot again to adjust. Now you can simply adjust by typing in additional user prompts.

3. Syntax checks on the generated DAX query. Previously there was no syntax check before the generated DAX query was returned. Now the syntax is checked, and the prompt automatically retried once. If the retry is also invalid, the generated DAX query is returned with a note that there is an issue, giving you the option to rephrase your request or fix the generated DAX query.

4. Inspire buttons to get you started with Copilot. Previously nothing happened until a prompt was entered. Now click any of these buttons to quickly see what you can do with Copilot!

Learn more about DAX queries with Copilot with these resources:

Deep dive blog: https://powerbi.microsoft.com/en-us/blog/deep-dive-into-dax-query-view-with-copilot/
Learn more: https://learn.microsoft.com/en-us/dax/dax-copilot
Video: https://www.youtube.com/watch?v=0kE3TE34oLM

We are excited to introduce you to the redesigned ‘Manage relationships’ dialog in Power BI Desktop! To open this dialog simply select the ‘Manage relationships’ button in the modeling ribbon.

Once opened, you’ll find a comprehensive view of all your relationships, along with their key properties, all in one convenient location. From here you can create new relationships or edit an existing one.

Additionally, you have the option to filter and focus on specific relationships in your model based on cardinality and cross filter direction.

Learn more about creating and managing relationships in Power BI Desktop in our documentation .

Ever since we released composite models on Power BI semantic models and Analysis Services , you have been asking us to support the refresh of calculated columns and tables in the Service. This month, we have enabled the refresh of calculated columns and tables in Service for any DirectQuery source that uses single sign-on authentication. This includes the sources you use when working with composite models on Power BI semantic models and Analysis Services.

Previously, the refresh of a semantic model that uses a DirectQuery source with single-sign-on authentication failed with one of the following error messages: “Refresh is not supported for datasets with a calculated table or calculated column that depends on a table which references Analysis Services using DirectQuery.” or “Refresh over a dataset with a calculated table or a calculated column which references a Direct Query data source is not supported.”

Starting today, you can successfully refresh the calculated table and calculated columns in a semantic model in the Service using specific credentials as long as:

You used a shareable cloud connection and assigned it and/or.
Enabled granular access control for all data connection types.

Here’s how to do this:

Create and publish your semantic model that uses a single sign-on DirectQuery source. This can be a composite model but doesn’t have to be.
In the semantic model settings, under Gateway and cloud connections , map each single sign-on DirectQuery connection to a specific connection. If you don’t have a specific connection yet, select ‘Create a connection’ to create it:

If you are creating a new connection, fill out the connection details and click Create , making sure to select ‘Use SSO via Azure AD for DirectQuery queries:

Finally, select the connection for each single sign-on DirectQuery source and select Apply :

2. Either refresh the semantic model manually or plan a scheduled refresh to confirm the refresh now works successfully. Congratulations, you have successfully set up refresh for semantic models with a single sign-on DirectQuery connection that uses calculated columns or calculated tables!

We are excited to announce the general availability of Model Explorer in the Model view of Power BI, including the authoring of calculation groups. Semantic modeling is even easier with an at-a-glance tree view with item counts, search, and in context paths to edit the semantic model items with Model Explorer. Top level semantic model properties are also available as well as the option to quickly create relationships in the properties pane. Additionally, the styling for the Data pane is updated to Fluent UI also used in Office and Teams.

A popular community request from the Ideas forum, authoring calculation groups is also included in Model Explorer. Calculation groups significantly reduce the number of redundant measures by allowing you to define DAX formulas as calculation items that can be applied to existing measures. For example, define a year over year, prior month, conversion, or whatever your report needs in DAX formula once as a calculation item and reuse it with existing measures. This can reduce the number of measures you need to create and make the maintenance of the business logic simpler.

Available in both Power BI Desktop and when editing a semantic model in the workspace, take your semantic model authoring to the next level today!

Learn more about Model Explorer and authoring calculation groups with these resources:

Use Model explorer in Power BI (preview) – Power BI | Microsoft Learn
Create calculation groups in Power BI (preview) – Power BI | Microsoft Learn

Data connectivity

We’re happy to announce that the Oracle database connector has been enhanced this month with the addition of Single Sign-On support in the Power BI service with Microsoft Entra ID authentication.

Microsoft Entra ID SSO enables single sign-on to access data sources that rely on Microsoft Entra ID based authentication. When you configure Microsoft Entra SSO for an applicable data source, queries run under the Microsoft Entra identity of the user that interacts with the Power BI report.

We’re pleased to announce the new and updated connectors in this release:

[New] OneStream : The OneStream Power BI Connector enables you to seamlessly connect Power BI to your OneStream applications by simply logging in with your OneStream credentials. The connector uses your OneStream security, allowing you to access only the data you have based on your permissions within the OneStream application. Use the connector to pull cube and relational data along with metadata members, including all their properties. Visit OneStream Power BI Connector to learn more. Find this connector in the other category.
[New] Zendesk Data : A new connector developed by the Zendesk team that aims to go beyond the functionality of the existing Zendesk legacy connector created by Microsoft. Learn more about what this new connector brings.
[New] CCH Tagetik
[Update] Azure Databricks

Are you interested in creating your own connector and publishing it for your customers? Learn more about the Power Query SDK and the Connector Certification program .

Last May, we announced the integration between Power BI and OneDrive and SharePoint. Previously, this capability was limited to only reports with data in import mode. We’re excited to announce that you can now seamlessly view Power BI reports with live connected data directly in OneDrive and SharePoint!

When working on Power BI Desktop with a report live connected to a semantic model in the service, you can easily share a link to collaborate with others on your team and allow them to quickly view the report in their browser. We’ve made it easier than ever to access the latest data updates without ever leaving your familiar OneDrive and SharePoint environments. This integration streamlines your workflows and allows you to access reports within the platforms you already use. With collaboration at the heart of this improvement, teams can work together more effectively to make informed decisions by leveraging live connected semantic models without being limited to data only in import mode.

Utilizing OneDrive and SharePoint allows you to take advantage of built-in version control, always have your files available in the cloud, and utilize familiar and simplistic sharing.

While you told us that you appreciate the ability to limit the image view to only those who have permission to view the report, you asked for changes for the “Public snapshot” mode.

To address some of the feedback we got from you, we have made a few more changes in this area.

Add-ins that were saved as “Public snapshot” can be printed and will not require that you go over all the slides and load the add-ins for permission check before the public image is made visible.
You can use the “Show as saved image” on add-ins that were saved as “Public snapshot”. This will replace the entire add-in with an image representation of it, so the load time might be faster when you are presenting your presentation.

Many of us keep presentations open for a long time, which might cause the data in the presentation to become outdated.

To make sure you have in your slides the data you need, we added a new notification that tells you if more up to date data exists in Power BI and offers you the option to refresh and get the latest data from Power BI.

Developers

Direct Lake semantic models are now supported in Fabric Git Integration , enabling streamlined version control, enhanced collaboration among developers, and the establishment of CI/CD pipelines for your semantic models using Direct Lake.

Learn more about version control, testing, and deployment of Power BI content in our Power BI implementation planning documentation: https://learn.microsoft.com/power-bi/guidance/powerbi-implementation-planning-content-lifecycle-management-overview

Visualizations

Editor’s pick of the quarter .

– Animator for Power BI Innofalls Charts SuperTables Sankey Diagram for Power BI by ChartExpo Dynamic KPI Card by Sereviso Shielded HTML Viewer Text search slicer

New visuals in AppSource

Mapa Polski – Województwa, Powiaty, Gminy Workstream Income Statement Table

Gas Detection Chart

Seasonality Chart PlanIn BI – Data Refresh Service

Chart Flare

PictoBar ProgBar

Counter Calendar Donut Chart image

Financial Reporting Matrix by Profitbase

Making financial statements with a proper layout has just become easier with the latest version of the Financial Reporting Matrix.

Users are now able to specify which rows should be classified as cost-rows, which will make it easier to get the conditional formatting of variances correctly:

Selecting a row, and ticking “is cost” will tag the row as cost. This can be used in conditional formatting to make sure that positive variances on expenses are a bad for the result, while a positive variance on an income row is good for the result.

The new version also includes more flexibility in measuring placement and column subtotals.

Measures can be placed either:

Default (below column headers)
Above column headers

Conditionally hide columns
+ much more

Highlighted new features:

Measure placement – In rows
Select Column Subtotals
New Format Pane design
Row Options

Get the visual from AppSource and find more videos here !

Horizon Chart by Powerviz

A Horizon Chart is an advanced visual, for time-series data, revealing trends and anomalies. It displays stacked data layers, allowing users to compare multiple categories while maintaining data clarity. Horizon Charts are particularly useful to monitor and analyze complex data over time, making this a valuable visual for data analysis and decision-making.

Key Features:

Horizon Styles: Choose Natural, Linear, or Step with adjustable scaling.
Layer: Layer data by range or custom criteria. Display positive and negative values together or separately on top.
Reference Line : Highlight patterns with X-axis lines and labels.
Colors: Apply 30+ color palettes and use FX rules for dynamic coloring.
Ranking: Filter Top/Bottom N values, with “Others”.
Gridline: Add gridlines to the X and Y axis.
Custom Tooltip: Add highest, lowest, mean, and median points without additional DAX.
Themes: Save designs and share seamlessly with JSON files.

Other features included are ranking, annotation, grid view, show condition, and accessibility support.

Business Use Cases: Time-Series Data Comparison, Environmental Monitoring, Anomaly Detection

🔗 Try Horizon Chart for FREE from AppSource

📊 Check out all features of the visual: Demo file

📃 Step-by-step instructions: Documentation

💡 YouTube Video: Video Link

📍 Learn more about visuals: https://powerviz.ai/

✅ Follow Powerviz : https://lnkd.in/gN_9Sa6U

Exciting news! Thanks to your valuable feedback, we’ve enhanced our Milestone Trend Analysis Chart even further. We’re thrilled to announce that you can now switch between horizontal and vertical orientations, catering to your preferred visualization style.

The Milestone Trend Analysis (MTA) Chart remains your go-to tool for swiftly identifying deadline trends, empowering you to take timely corrective actions. With this update, we aim to enhance deadline awareness among project participants and stakeholders alike.

In our latest version, we seamlessly navigate between horizontal and vertical views within the familiar Power BI interface. No need to adapt to a new user interface – enjoy the same ease of use with added flexibility. Plus, it benefits from supported features like themes, interactive selection, and tooltips.

What’s more, ours is the only Microsoft Certified Milestone Trend Analysis Chart for Power BI, ensuring reliability and compatibility with the platform.

Ready to experience the enhanced Milestone Trend Analysis Chart? Download it from AppSource today and explore its capabilities with your own data – try for free!

We welcome any questions or feedback at our website: https://visuals.novasilva.com/ . Try it out and elevate your project management insights now!

Sunburst Chart by Powerviz

Powerviz’s Sunburst Chart is an interactive tool for hierarchical data visualization. With this chart, you can easily visualize multiple columns in a hierarchy and uncover valuable insights. The concentric circle design helps in displaying part-to-whole relationships.

Arc Customization: Customize shapes and patterns.
Color Scheme: Accessible palettes with 30+ options.
Centre Circle: Design an inner circle with layers. Add text, measure, icons, and images.
Conditional Formatting: Easily identify outliers based on measure or category rules.
Labels: Smart data labels for readability.
Image Labels: Add an image as an outer label.
Interactivity: Zoom, drill down, cross-filtering, and tooltip features.

Other features included are annotation, grid view, show condition, and accessibility support.

Business Use Cases:

Sales and Marketing: Market share analysis and customer segmentation.
Finance : Department budgets and expenditures distribution.
Operations : Supply chain management.
Education : Course structure, curriculum creation.
Human Resources : Organization structure, employee demographics.

🔗 Try Sunburst Chart for FREE from AppSource

Stacked Bar Chart with Line by JTA

Clustered bar chart with the possibility to stack one of the bars

Stacked Bar Chart with Line by JTA seamlessly merges the simplicity of a traditional bar chart with the versatility of a stacked bar, revolutionizing the way you showcase multiple datasets in a single, cohesive display.

Unlocking a new dimension of insight, our visual features a dynamic line that provides a snapshot of data trends at a glance. Navigate through your data effortlessly with multiple configurations, gaining a swift and comprehensive understanding of your information.

Tailor your visual experience with an array of functionalities and customization options, enabling you to effortlessly compare a primary metric with the performance of an entire set. The flexibility to customize the visual according to your unique preferences empowers you to harness the full potential of your data.

Features of Stacked Bar Chart with Line:

Stack the second bar
Format the Axis and Gridlines
Add a legend
Format the colors and text
Add a line chart
Format the line
Add marks to the line
Format the labels for bars and line

If you liked what you saw, you can try it for yourself and find more information here . Also, if you want to download it, you can find the visual package on the AppSource .

We have added an exciting new feature to our Combo PRO, Combo Bar PRO, and Timeline PRO visuals – Legend field support . The Legend field makes it easy to visually split series values into smaller segments, without the need to use measures or create separate series. Simply add a column with category names that are adjacent to the series values, and the visual will do the following:

Display separate segments as a stack or cluster, showing how each segment contributed to the total Series value.
Create legend items for each segment to quickly show/hide them without filtering.
Apply custom fill colors to each segment.
Show each segment value in the tooltip

Read more about the Legend field on our blog article

Drill Down Combo PRO is made for creators who want to build visually stunning and user-friendly reports. Cross-chart filtering and intuitive drill down interactions make data exploration easy and fun for any user. Furthermore, you can choose between three chart types – columns, lines, or areas; and feature up to 25 different series in the same visual and configure each series independently.

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🌐 Visit Drill Down Combo PRO product page

Documentation | ZoomCharts Website | Follow ZoomCharts on LinkedIn

We are thrilled to announce that Fabric Core REST APIs are now generally available! This marks a significant milestone in the evolution of Microsoft Fabric, a platform that has been meticulously designed to empower developers and businesses alike with a comprehensive suite of tools and services.

The Core REST APIs are the backbone of Microsoft Fabric, providing the essential building blocks for a myriad of functionalities within the platform. They are designed to improve efficiency, reduce manual effort, increase accuracy, and lead to faster processing times. These APIs help with scale operations more easily and efficiently as the volume of work grows, automate repeatable processes with consistency, and enable integration with other systems and applications, providing a streamlined and efficient data pipeline.

The Microsoft Fabric Core APIs encompasses a range of functionalities, including:

Workspace management: APIs to manage workspaces, including permissions.
Item management: APIs for creating, reading, updating, and deleting items, with partial support for data source discovery and granular permissions management planned for the near future.
Job and tenant management: APIs to manage jobs, tenants, and users within the platform.

These APIs adhere to industry standards and best practices, ensuring a unified developer experience that is both coherent and easy to use.

For developers looking to dive into the details of the Microsoft Fabric Core APIs, comprehensive documentation is available. This includes guidelines on API usage, examples, and articles managed in a centralized repository for ease of access and discoverability. The documentation is continuously updated to reflect the latest features and improvements, ensuring that developers have the most current information at their fingertips. See Microsoft Fabric REST API documentation

We’re excited to share an important update we made to the Fabric Admin APIs. This enhancement is designed to simplify your automation experience. Now, you can manage both Power BI and the new Fabric items (previously referred to as artifacts) using the same set of APIs. Before this enhancement, you had to navigate using two different APIs—one for Power BI items and another for new Fabric items. That’s no longer the case.

The APIs we’ve updated include GetItem , ListItems , GetItemAccessDetails , and GetAccessEntities . These enhancements mean you can now query and manage all your items through a single API call, regardless of whether they’re Fabric types or Power BI types. We hope this update makes your work more straightforward and helps you accomplish your tasks more efficiently.

We’re thrilled to announce the public preview of the Microsoft Fabric workload development kit. This feature now extends to additional workloads and offers a robust developer toolkit for designing, developing, and interoperating with Microsoft Fabric using frontend SDKs and backend REST APIs. Introducing the Microsoft Fabric Workload Development Kit .

The Microsoft Fabric platform now provides a mechanism for ISVs and developers to integrate their new and existing applications natively into Fabric’s workload hub. This integration provides the ability to add net new capabilities to Fabric in a consistent experience without leaving their Fabric workspace, thereby accelerating data driven outcomes from Microsoft Fabric.

By downloading and leveraging the development kit , ISVs and software developers can build and scale existing and new applications on Microsoft Fabric and offer them via the Azure Marketplace without the need to ever leave the Fabric environment.

The development kit provides a comprehensive guide and sample code for creating custom item types that can be added to the Fabric workspace. These item types can leverage the Fabric frontend SDKs and backend REST APIs to interact with other Fabric capabilities, such as data ingestion, transformation, orchestration, visualization, and collaboration. You can also embed your own data application into the Fabric item editor using the Fabric native experience components, such as the header, toolbar, navigation pane, and status bar. This way, you can offer consistent and seamless user experience across different Fabric workloads.

This is a call to action for ISVs, software developers, and system integrators. Let’s leverage this opportunity to create more integrated and seamless experiences for our users.

We’re excited about this journey and look forward to seeing the innovative workloads from our developer community.

We are proud to announce the public preview of external data sharing. Sharing data across organizations has become a standard part of day-to-day business for many of our customers. External data sharing, built on top of OneLake shortcuts, enables seamless, in-place sharing of data, allowing you to maintain a single copy of data even when sharing data across tenant boundaries. Whether you’re sharing data with customers, manufacturers, suppliers, consultants, or partners; the applications are endless.

How external data sharing works

Sharing data across tenants is as simple as any other share operation in Fabric. To share data, navigate to the item to be shared, click on the context menu, and then click on External data share . Select the folder or table you want to share and click Save and continue . Enter the email address and an optional message and then click Send .

The data consumer will receive an email containing a share link. They can click on the link to accept the share and access the data within their own tenant.

Click here for more details about external data sharing .

Following the release of OneLake data access roles in public preview, the OneLake team is excited to announce the availability of APIs for managing data access roles. These APIs can be used to programmatically manage granular data access for your lakehouses. Manage all aspects of role management such as creating new roles, editing existing ones, or changing memberships in a programmatic way.

Check out the API reference to learn more.

Do you have data stored on-premises or behind a firewall that you want to access and analyze with Microsoft Fabric? With OneLake shortcuts, you can bring on-premises or network-restricted data into OneLake, without any data movement or duplication. Simply install the Fabric on-premises data gateway and create a shortcut to your S3 compatible, Amazon S3, or Google Cloud Storage data source. Then use any of Fabric’s powerful analytics engines and OneLake open APIs to explore, transform, and visualize your data in the cloud.

Try it out today and unlock the full potential of your data with OneLake shortcuts!

Data Warehouse

We are excited to announce Copilot for Data Warehouse in public preview! Copilot for Data Warehouse is an AI assistant that helps developers generate insights through T-SQL exploratory analysis. Copilot is contextualized your warehouse’s schema. With this feature, data engineers and data analysts can use Copilot to:

Generate T-SQL queries for data analysis.
Explain and add in-line code comments for existing T-SQL queries.
Fix broken T-SQL code.
Receive answers regarding general data warehousing tasks and operations.

There are 3 areas where Copilot is surfaced in the Data Warehouse SQL Query Editor:

Code completions when writing a T-SQL query.
Chat panel to interact with the Copilot in natural language.
Quick action buttons to fix and explain T-SQL queries.

Learn more about Copilot for Data Warehouse: aka.ms/data-warehouse-copilot-docs. Copilot for Data Warehouse is currently only available in the Warehouse. Copilot in the SQL analytics endpoint is coming soon.

As data volumes continue to grow in today’s rapidly evolving world of Artificial Intelligence, it is crucial to reflect on historical data. It empowers businesses to derive valuable insights that aid in making well-informed decisions for the future. Preserving multiple historical data versions not only incurred significant costs but also presented challenges in upholding data integrity, resulting in a notable impact on query performance. So, we are thrilled to announce the ability to query the historical data through time travel at the T-SQL statement level which helps unlock the evolution of data over time.

The Fabric warehouse retains historical versions of tables for seven calendar days. This retention allows for querying the tables as if they existed at any point within the retention timeframe. Time travel clause can be included in any top level SELECT statement. For complex queries that involve multiple tables, joins, stored procedures, or views, the timestamp is applied just once for the entire query instead of specifying the same timestamp for each table within the same query. This ensures the entire query is executed with reference to the specified timestamp, maintaining the data’s uniformity and integrity throughout the query execution.

From historical trend analysis and forecasting to compliance management, stable reporting and real-time decision support, the benefits of time travel extend across multiple business operations. Embrace the capability of time travel to navigate the data-driven landscape and gain a competitive edge in today’s fast-paced world of Artificial Intelligence.

We are excited to announce not one but two new enhancements to the Copy Into feature for Fabric Warehouse: Copy Into with Entra ID Authentication and Copy Into for Firewall-Enabled Storage!

Entra ID Authentication

When authenticating storage accounts in your environment, the executing user’s Entra ID will now be used by default. This ensures that you can leverage A ccess C ontrol L ists and R ole – B ased a ccess c ontrol to authenticate to your storage accounts when using Copy Into. Currently, only organizational accounts are supported.

How to Use Entra ID Authentication

Ensure your Entra ID organizational account has access to the underlying storage and can execute the Copy Into statement on your Fabric Warehouse.
Run your Copy Into statement without specifying any credentials; the Entra ID organizational account will be used as the default authentication mechanism.

Copy into firewall-enabled storage

The Copy Into for firewall-enabled storage leverages the trusted workspace access functionality ( Trusted workspace access in Microsoft Fabric (preview) – Microsoft Fabric | Microsoft Learn ) to establish a secure and seamless connection between Fabric and your storage accounts. Secure access can be enabled for both blob and ADLS Gen2 storage accounts. Secure access with Copy Into is available for warehouses in workspaces with Fabric Capacities (F64 or higher).

To learn more about Copy into , please refer to COPY INTO (Transact-SQL) – Azure Synapse Analytics and Microsoft Fabric | Microsoft Learn

We are excited to announce the launch of our new feature, Just in Time Database Attachment, which will significantly enhance your first experience, such as when connecting to the Datawarehouse or SQL endpoint or simply opening an item. These actions trigger the workspace resource assignment process, where, among other actions, we attach all necessary metadata of your items, Data warehouses and SQL endpoints, which can be a long process, particularly for workspaces that have a high number of items.

This feature is designed to attach your desired database during the activation process of your workspace, allowing you to execute queries immediately and avoid unnecessary delays. However, all other databases will be attached asynchronously in the background while you are able to execute queries, ensuring a smooth and efficient experience.

Data Engineering

We are advancing Fabric Runtime 1.3 from an Experimental Public Preview to a full Public Preview. Our Apache Spark-based big data execution engine, optimized for both data engineering and science workflows, has been updated and fully integrated into the Fabric platform.

The enhancements in Fabric Runtime 1.3 include the incorporation of Delta Lake 3.1, compatibility with Python 3.11, support for Starter Pools, integration with Environment and library management capabilities. Additionally, Fabric Runtime now enriches the data science experience by supporting the R language and integrating Copilot.

We are pleased to share that the Native Execution Engine for Fabric Runtime 1.2 is currently available in public preview. The Native Execution Engine can greatly enhance the performance for your Spark jobs and queries. The engine has been rewritten in C++ and operates in columnar mode and uses vectorized processing. The Native Execution Engine offers superior query performance – encompassing data processing, ETL, data science, and interactive queries – all directly on your data lake. Overall, Fabric Spark delivers a 4x speed-up on the sum of execution time of all 99 queries in the TPC-DS 1TB benchmark when compared against Apache Spark. This engine is fully compatible with Apache Spark™ APIs (including Spark SQL API).

It is seamless to use with no code changes – activate it and go. Enable it in your environment for your notebooks and your SJDs.

This feature is in the public preview, at this stage of the preview, there is no additional cost associated with using it.

We are excited to announce the Spark Monitoring Run Series Analysis features, which allow you to analyze the run duration trend and performance comparison for Pipeline Spark activity recurring run instances and repetitive Spark run activities from the same Notebook or Spark Job Definition.

Run Series Comparison: Users can compare the duration of a Notebook run with that of previous runs and evaluate the input and output data to understand the reasons behind prolonged run durations.
Outlier Detection and Analysis: The system can detect outliers in the run series and analyze them to pinpoint potential contributing factors.
Detailed Run Instance Analysis: Clicking on a specific run instance provides detailed information on time distribution, which can be used to identify performance enhancement opportunities.
Configuration Insights : Users can view the Spark configuration used for each run, including auto-tuned configurations for Spark SQL queries in auto-tune enabled Notebook runs.

You can access the new feature from the item’s recent runs panel and Spark application monitoring page.

We are excited to announce that Notebook now supports the ability to tag others in comments, just like the familiar functionality of using Office products!

When you select a section of code in a cell, you can add a comment with your insights and tag one or more teammates to collaborate or brainstorm on the specifics. This intuitive enhancement is designed to amplify collaboration in your daily development work.

Moreover, you can easily configure the permissions when tagging someone who doesn’t have the permission, to make sure your code asset is well managed.

We are thrilled to unveil a significant enhancement to the Fabric notebook ribbon, designed to elevate your data science and engineering workflows.

In the new version, you will find the new Session connect control on the Home tab, and now you can start a standard session without needing to run a code cell.

You can also easily spin up a High concurrency session and share the session across multiple notebooks to improve the compute resource utilization. And you can easily attach/leave a high concurrency session with a single click.

The “ View session information ” can navigate you to the session information dialog, where you can find a lot of useful detailed information, as well as configure the session timeout. The diagnostics info is essentially helpful when you need support for notebook issues.

Now you can easily access the powerful “ Data Wrangler ” on Home tab with the new ribbon! You can explore your data with the fancy low-code experience of data wrangler, and the pandas DataFrames and Spark DataFrames are all supported.

We recently made some changes to the Fabric notebook metadata to ensure compliance and consistency:

Notebook file content:

The keyword “trident” has been replaced with “dependencies” in the notebook content. This adjustment ensures consistency and compliance.
Notebook Git format:
The preface of the notebook has been modified from “# Synapse Analytics notebook source” to “# Fabric notebook source”.
Additionally, the keyword “synapse” has been updated to “dependencies” in the Git repo.

The above changes will be marked as ‘uncommitted’ for one time if your workspace is connected to Git. No action is needed in terms of these changes , and there won’t be any breaking scenario within the Fabric platform . If you have any further updates or questions, feel free to share with us.

We are thrilled to announce that the environment is now a generally available item in Microsoft Fabric. During this GA timeframe, we have shipped a few new features of Environment.

Git support

The environment is now Git supported. You can check-in the environment into your Git repo and manipulate the environment locally with its YAML representations and custom library files. After updating the changes from local to Fabric portal, you can publish them by manual action or through REST API.

Deployment pipeline

Deploying environments from one workspace to another is supported. Now, you can deploy the code items and their dependent environments together from development to test and even production.

With the REST APIs, you can have the code-first experience with the same abilities through Fabric portal. We provide a set of powerful APIs to ensure you the efficiency in managing your environment. You can create new environments, update libraries and Spark compute, publish the changes, delete an environment, attach the environment to a notebook, etc., all actions can be done locally in the tools of your choice. The article – Best practice of managing environments with REST API could help you get started with several real-world scenarios.

Resources folder

Resources folder enables managing small resources in the development cycle. The files uploaded in the environment can be accessed from notebooks once they’re attached to the same environment. The manipulation of the files and folders of resources happens in real-time. It could be super powerful, especially when you are collaborating with others.

Sharing your environment with others is also available. We provide several sharing options. By default, the view permission is shared. If you want the recipient to have access to view and use the contents of the environment, sharing without permission customization is the best option. Furthermore, you can grant editing permission to allow recipients to update this environment or grant share permission to allow recipients to reshare this environment with their existing permissions.

We are excited to announce the REST api support for Fabric Data Engineering/Science workspace settings. Data Engineering/Science settings allows users to create/manage their Spark compute, select the default runtime/default environment, enable or disable high concurrency mode or ML autologging.

Now with the REST api support for the Data Engineering/Science settings, you would be able to

Choose the default pool for a Fabric Workspace
Configure the max nodes for Starter pools
Create/Update/Delete the existing Custom Pools, Autoscale and Dynamic allocation properties
Choose Workspace Default Runtime and Environment
Select a default runtime
Select the default environment for the Fabric workspace
Enable or Disable High Concurrency Mode
Enable or Disable ML Auto logging.

Learn more about the Workspace Spark Settings API in our API documentation Workspace Settings – REST API (Spark) | Microsoft Learn

We are excited to give you a sneak peek at the preview of User Data Functions in Microsoft Fabric. User Data Functions gives developers and data engineers the ability to easily write and run applications that integrate with resources in the Fabric Platform. Data engineering often presents challenges with data quality or complex data analytics processing in data pipelines, and using ETL tools may present limited flexibility and ability to customize to your needs. This is where User data functions can be used to run data transformation tasks and perform complex business logic by connecting to your data sources and other workloads in Fabric.

During preview, you will be able to use the following features:

Use the Fabric portal to create new User Data Functions, view and test them.
Write your functions using C#.
Use the Visual Studio Code extension to create and edit your functions.
Connect to the following Fabric-native data sources: Data Warehouse, Lakehouse and Mirrored Databases.

You can now create a fully managed GraphQL API in Fabric to interact with your data in a simple, flexible, and powerful way. We’re excited to announce the public preview of API for GraphQL, a data access layer that allows us to query multiple data sources quickly and efficiently in Fabric by leveraging a widely adopted and familiar API technology that returns more data with less client requests. With the new API for GraphQL in Fabric, data engineers and scientists can create data APIs to connect to different data sources, use the APIs in their workflows, or share the API endpoints with app development teams to speed up and streamline data analytics application development in your business.

You can get started with the API for GraphQL in Fabric by creating an API, attaching a supported data source, then selecting specific data sets you want to expose through the API. Fabric builds the GraphQL schema automatically based on your data, you can test and prototype queries directly in our graphical in-browser GraphQL development environment (API editor), and applications are ready to connect in minutes.

Currently, the following supported data sources can be exposed through the Fabric API for GraphQL:

Microsoft Fabric Data Warehouse
Microsoft Fabric Lakehouse via SQL Analytics Endpoint
Microsoft Fabric Mirrored Databases via SQL Analytics Endpoint

Click here to learn more about how to get started.

Data Science

As you may know, Copilot in Microsoft Fabric requires your tenant administrator to enable the feature from the admin portal. Starting May 20th, 2024, Copilot in Microsoft Fabric will be enabled by default for all tenants. This update is part of our continuous efforts to enhance user experience and productivity within Microsoft Fabric. This new default activation means that AI features like Copilot will be automatically enabled for tenants who have not yet enabled the setting.

We are introducing a new capability to enable Copilot on Capacity level in Fabric. A new option is being introduced in the tenant admin portal, to delegate the enablement of AI and Copilot features to Capacity administrators. This AI and Copilot setting will be automatically delegated to capacity administrators and tenant administrators won’t be able to turn off the delegation.

We also have a cross-geo setting for customers who want to use Copilot and AI features while their capacity is in a different geographic region than the EU data boundary or the US. By default, the cross-geo setting will stay off and will not be delegated to capacity administrators automatically. Tenant administrators can choose whether to delegate this to capacity administrators or not.

Figure 1. Copilot in Microsoft Fabric will be auto enabled and auto delegated to capacity administrators.

Capacity administrators will see the “Copilot and Azure OpenAI Service (preview)” settings under Capacity settings/ Fabric Capacity / <Capacity name> / Delegated tenant settings. By default, the capacity setting will inherit tenant level settings. Capacity administrators can decide whether to override the tenant administrator’s selection. This means that even if Copilot is not enabled on a tenant level, a capacity administrator can choose to enable Copilot for their capacity. With this level of control, we make it easier to control which Fabric workspaces can utilize AI features like Copilot in Microsoft Fabric.

To enhance privacy and trust, we’ve updated our approach to abuse monitoring: previously, we retained data from Copilot in Fabric, including prompt inputs and outputs, for up to 30 days to check for misuse. Following customer feedback, we’ve eliminated this 30-day retention. Now, we no longer store prompt related data, demonstrating our unwavering commitment to your privacy and security. We value your input and take your concerns seriously.

Real-Time Intelligence

This month includes the announcement of Real-Time Intelligence, the next evolution of Real-Time Analytics and Data Activator. With Real-Time Intelligence, Fabric extends to the world of streaming and high granularity data, enabling all users in your organization to collect, analyze and act on this data in a timeline manner making faster and more informed business decisions. Read the full announcement from Build 2024.

Real-Time Intelligence includes a wide range of capabilities across ingestion, processing, analysis, transformation, visualization and taking action. All of this is supported by the Real-Time hub , the central place to discover and manage streaming data and start all related tasks.

Read on for more information on each capability and stay tuned for a series of blogs describing the features in more detail. All features are in Public Preview unless otherwise specified. Feedback on any of the features can be submitted at https://aka.ms/rtiidea

Ingest & Process

Introducing the Real-Time hub
Get Events with new sources of streaming and event data
Source from Real-Time Hub in Enhanced Eventstream
Use Real-Time hub to Get Data in KQL Database in Eventhouse
Get data from Real-Time Hub within Reflexes
Eventstream Edit and Live modes
Default and derived streams
Route data streams based on content

Analyze & Transform

Eventhouse GA
Eventhouse OneLake availability GA
Create a database shortcut to another KQL Database
Support for AI Anomaly Detector
Copilot for Real-Time Intelligence
Tenant-level private endpoints for Eventhouse

Visualize & Act

Visualize data with Real-Time Dashboards
New experience for data explorati on
Create triggers from Real-Time Hub
Set alert on Real-time Dashboards
Taking action through Fabric Items

Ingest & Process

Real-Time hub is the single place for all data-in-motion across your entire organization. Several key features are offered in Real-Time hub:

Single place for data-in-motion for the entire organization

Real-Time hub enables users to easily discover, ingest, manage, and consume data-in-motion from a wide variety of sources. It lists all the streams and KQL tables that customers can directly act on.

2. Real-Time hub is never empty

All data streams in Fabric automatically show up in the hub. Also, users can subscribe to events in Fabric gaining insights into the health and performance of their data ecosystem,

3. Numerous connectors to simplify data ingestion from anywhere to Real-Time hub

Real-Time hub makes it easy for you to ingest data into Fabric from a wide variety of sources like AWS Kinesis, Kafka clusters, Microsoft streaming sources, sample data and Fabric events using the Get Events experience.

There are 3 tabs in the Hub:

Data streams : This tab contains all streams that are actively running in Fabric that user has access to. This includes all streams from Eventstreams and all tables from KQL Databases.
Microsoft sources : This tab contains Microsoft sources (that user has access to) and can be connected to Fabric.
Fabric events : Fabric now has event-driven capabilities to support real-time notifications and data processing. Users can monitor and react to events including Fabric Workspace Item events and Azure Blob Storage events. These events can be used to trigger other actions or workflows, such as invoking a data pipeline or sending a notification via email. Users can also send these events to other destinations via Event Streams.

Learn More

You can now connect to data from both inside and outside of Fabric in a mere few steps. Whether data is coming from new or existing sources, streams, or available events, the Get Events experience allows users to connect to a wide range of sources directly from Real-Time hub, Eventstreams, Eventhouse and Data Activator.

This enhanced capability allows you to easily connect external data streams into Fabric with out-of-box experience, giving you more options and helping you to get real-time insights from various sources. This includes Camel Kafka connectors powered by Kafka connect to access popular data platforms, as well as the Debezium connectors for fetching the Change Data Capture (CDC) streams.

Using Get Events, bring streaming data from Microsoft sources directly into Fabric with a first-class experience. Connectivity to notification sources and discrete events is also included, this enables access to notification events from Azure and other clouds solutions including AWS and GCP. The full set of sources which are currently supported are:

Microsoft sources : Azure Event Hubs, Azure IoT hub
External sources : Google Cloud Pub/Sub, Amazon Kinesis Data Streams, Confluent Cloud Kafka
Change data capture databases : Azure SQL DB (CDC), PostgreSQL DB (CDC), Azure Cosmos DB (CDC), MySQL DB (CDC)
Fabric events : Fabric Workspace Item events, Azure Blob Storage events

Learn More

With enhanced Eventstream, you can now stream data not only from Microsoft sources but also from other platforms like Google Cloud, Amazon Kinesis, Database change data capture streams, etc. using our new messaging connectors. The new Eventstream also lets you acquire and route real-time data not only from stream sources but also from discrete event sources, such as: Azure Blob Storage events, Fabric Workspace Item events.

To use these new sources in Eventstream, simply create an eventstream with choosing “Enhanced Capabilities (preview)”.

You will see the new Eventstream homepage that gives you some choices to begin with. By clicking on the “Add external source”, you will find these sources in the Get events wizard that helps you to set up the source in a few steps. After you add the source to your eventstream, you can publish it to stream the data into your eventstream.

Using Eventstream with discrete sources to turn events into streams for more analysis. You can send the streams to different Fabric data destinations, like Lakehouse and KQL Database. After the events are converted, a default stream will appear in Real-Time Hub. To turn them, click Edit on ribbon, select “Stream events” on the source node, and publish your eventstream.

To transform the stream data or route it to different Fabric destinations based on its content, you can click Edit in ribbon and enter the Edit mode. There you can add event processing operators and destinations.

With Real-Time hub embedded in KQL Database experience, each user in the tenant can view and add streams which they have access to and directly ingest it to a KQL Database table in Eventhouse.

This integration provides each user in the tenant with the ability to access and view data streams they are permitted to. They can now directly ingest these streams into a KQL Database table in Eventhouse. This simplifies the data discovery and ingestion process by allowing users to directly interact with the streams. Users can filter data based on the Owner, Parent and Location and provides additional information such as Endorsement and Sensitivity.

You can access this by clicking on the Get Data button from the Database ribbon in Eventhouse.

This will open the Get Data wizard with Real-Time hub embedded.

You can use events from Real-Time hub directly in reflex items as well. From within the main reflex UI, click ‘Get data’ in the toolbar:

This will open a wizard that allows you to connect to new event sources or browse Real-Time Hub to use existing streams or system events.

Search new stream sources to connect to or select existing streams and tables to be ingested directly by Reflex.

You then have access to the full reflex modeling experience to build properties and triggers over any events from Real-Time hub.

Eventstream offers two distinct modes, Edit and Live, to provide flexibility and control over the development process of your eventstream. If you create a new Eventstream with Enhanced Capabilities enabled, you can modify it in an Edit mode. Here, you can design stream processing operations for your data streams using a no-code editor. Once you complete the editing, you can publish your Eventstream and visualize how it starts streaming and processing data in Live mode .

In Edit mode, you can:

Make changes to an Eventstream without implementing them until you publish the Eventstream. This gives you full control over the development process.
Avoid test data being streamed to your Eventstream. This mode is designed to provide a secure environment for testing without affecting your actual data streams.

For Live mode, you can :

Visualize how your Eventstream streams, transforms, and routes your data streams to various destinations after publishing the changes.
Pause the flow of data on selected sources and destinations, providing you with more control over your data streams being streamed into your Eventstream.

When you create a new Eventstream with Enhanced Capabilities enabled, you can now create and manage multiple data streams within Eventstream, which can then be displayed in the Real-Time hub for others to consume and perform further analysis.

There are two types of streams:

Default stream : Automatically generated when a streaming source is added to Eventstream. Default stream captures raw event data directly from the source, ready for transformation or analysis.
Derived stream : A specialized stream that users can create as a destination within Eventstream. Derived stream can be created after a series of operations such as filtering and aggregating, and then it’s ready for further consumption or analysis by other users in the organization through the Real-Time Hub.

The following example shows that when creating a new Eventstream a default stream alex-es1-stream is automatically generated. Subsequently, a derived stream dstream1 is added after an Aggregate operation within the Eventstream. Both default and derived streams can be found in the Real-Time hub.

Customers can now perform stream operations directly within Eventstream’s Edit mode, instead of embedding in a destination. This enhancement allows you to design stream processing logics and route data streams in the top-level canvas. Custom processing and routing can be applied to individual destinations using built-in operations, allowing for routing to distinct destinations within the Eventstream based on different stream content.

These operations include:

Aggregate : Perform calculations such as SUM, AVG, MIN, and MAX on a column of values and return a single result.
Expand : Expand array values and create new rows for each element within the array.
Filter : Select or filter specific rows from the data stream based on a condition.
Group by : Aggregate event data within a certain time window, with the option to group one or more columns.
Manage Fields : Customize your data streams by adding, removing, or changing data type of a column.
Union : Merge two or more data streams with shared fields (same name and data type) into a unified data stream.

Analyze & Transform

Eventhouse, a cutting-edge database workspace meticulously crafted to manage and store event-based data, is now officially available for general use. Optimized for high granularity, velocity, and low latency streaming data, it incorporates indexing and partitioning for structured, semi-structured, and free text data. With Eventhouse, users can perform high-performance analysis of big data and real-time data querying, processing billions of events within seconds. The platform allows users to organize data into compartments (databases) within one logical item, facilitating efficient data management.

Additionally, Eventhouse enables the sharing of compute and cache resources across databases, maximizing resource utilization. It also supports high-performance queries across databases and allows users to apply common policies seamlessly. Eventhouse offers content-based routing to multiple databases, full view lineage, and high granularity permission control, ensuring data security and compliance. Moreover, it provides a simple migration path from Azure Synapse Data Explorer and Azure Data Explorer, making adoption seamless for existing users.

Engineered to handle data in motion, Eventhouse seamlessly integrates indexing and partitioning into its storing process, accommodating various data formats. This sophisticated design empowers high-performance analysis with minimal latency, facilitating lightning-fast ingestion and querying within seconds. Eventhouse is purpose-built to deliver exceptional performance and efficiency for managing event-based data across diverse applications and industries. Its intuitive features and seamless integration with existing Azure services make it an ideal choice for organizations looking to leverage real-time analytics for actionable insights. Whether it’s analyzing telemetry and log data, time series and IoT data, or financial records, Eventhouse provides the tools and capabilities needed to unlock the full potential of event-based data.

We’re excited to announce that OneLake availability of Eventhouse in Delta Lake format is Generally Available.

Delta Lake is the unified data lake table format chosen to achieve seamless data access across all compute engines in Microsoft Fabric.

The data streamed into Eventhouse is stored in an optimized columnar storage format with full text indexing and supports complex analytical queries at low latency on structured, semi-structured, and free text data.

Enabling data availability of Eventhouse in OneLake means that customers can enjoy the best of both worlds: they can query the data with high performance and low latency in their Eventhouse and query the same data in Delta Lake format via any other Fabric engines such as Power BI Direct Lake mode, Warehouse, Lakehouse, Notebooks, and more.

To learn more, please visit https://learn.microsoft.com/en-gb/fabric/real-time-analytics/one-logical-copy

A database shortcut in Eventhouse is an embedded reference to a source database. The source database can be one of the following:

(Now Available) A KQL Database in Real-Time Intelligence
An Azure Data Explorer database

The behavior exhibited by the database shortcut is similar to that of a follower database

The owner of the source database, the data provider, shares the database with the creator of the shortcut in Real-Time Intelligence, the data consumer. The owner and the creator can be the same person. The database shortcut is attached in read-only mode, making it possible to view and run queries on the data that was ingested into the source KQL Database without ingesting it.

This helps with data sharing scenarios where you can share data in-place either within teams, or even with external customers.

AI Anomaly Detector is an Azure service for high quality detection of multivariate and univariate anomalies in time series. While the standalone version is being retired October 2026, Microsoft open sourced the anomaly detection core algorithms and they are now supported in Microsoft Fabric. Users can leverage these capabilities in Data Science and Real-Time Intelligence workload. AI Anomaly Detector models can be trained in Spark Python notebooks in Data Science workload, while real time scoring can be done by KQL with inline Python in Real-Time Intelligence.

We are excited to announce the Public Preview of Copilot for Real-Time Intelligence. This initial version includes a new capability that translates your natural language questions about your data to KQL queries that you can run and get insights.

Your starting point is a KQL Queryset, that is connected to a KQL Database, or to a standalone Kusto database:

Simply type the natural language question about what you want to accomplish, and Copilot will automatically translate it to a KQL query you can execute. This is extremely powerful for users who may be less familiar with writing KQL queries but still want to get the most from their time-series data stored in Eventhouse.

Stay tuned for more capabilities from Copilot for Real-Time Intelligence!

Customers can increase their network security by limiting access to Eventhouse at a tenant-level, from one or more virtual networks (VNets) via private links. This will prevent unauthorized access from public networks and only permit data plane operations from specific VNets.

Visualize & Act

Real-Time Dashboards have a user-friendly interface, allowing users to quickly explore and analyze their data without the need for extensive technical knowledge. They offer a high refresh frequency, support a range of customization options, and are designed to handle big data.

The following visual types are supported, and can be customized with the dashboard’s user-friendly interface:

You can also define conditional formatting rules to format the visual data points by their values using colors, tags, and icons. Conditional formatting can be applied to a specific set of cells in a predetermined column or to entire rows, and lets you easily identify interesting data points.

Beyond the support visual, Real-Time Dashboards provide several capabilities to allow you to interact with your data by performing slice and dice operations for deeper analysis and gaining different viewpoints.

Parameters are used as building blocks for dashboard filters and can be added to queries to filter the data presented by visuals. Parameters can be used to slice and dice dashboard visuals either directly by selecting parameter values in the filter bar or by using cross-filters.
Cross filters allow you to select a value in one visual and filter all other visuals on that dashboard based on the selected data point.
Drillthrough capability allows you to select a value in a visual and use it to filter the visuals in a target page in the same dashboard. When the target page opens, the value is pushed to the relevant filters.

Real-Time Dashboards can be shared broadly and allow multiple stakeholders to view dynamic, real time, fresh data while easily interacting with it to gain desired insights.

Directly from a real-time dashboard, users can refine their exploration using a user-friendly, form-like interface. This intuitive and dynamic experience is tailored for insights explorers craving insights based on real-time data. Add filters, create aggregations, and switch visualization types without writing queries to easily uncover insights.

With this new feature, insights explorers are no longer bound by the limitations of pre-defined dashboards. As independent explorers, they have the freedom for ad-hoc exploration, leveraging existing tiles to kickstart their journey. Moreover, they can selectively remove query segments, and expand their view of the data landscape.

Dive deep, extract meaningful insights, and chart actionable paths forward, all with ease and efficiency, and without having to write complex KQL queries.

Data Activator allows you to monitor streams of data for various conditions and set up actions to be taken in response. These triggers are available directly within the Real-Time hub and in other workloads in Fabric. When the condition is detected, an action will automatically be kicked off such as sending alerts via email or Teams or starting jobs in Fabric items.

When you browse the Real-Time Hub, you’ll see options to set triggers in the detail pages for streams.

Selecting this will open a side panel where you can configure the events you want to monitor, the conditions you want to look for in the events, and the action you want to take while in the Real-Time hub experience.

Completing this pane creates a new reflex item with a trigger that monitors the selected events and condition for you. Reflexes need to be created in a workspace supported by a Fabric or Power BI Premium capacity – this can be a trial capacity so you can get started with it today!

Data Activator has been able to monitor Power BI report data since it was launched, and we now support monitoring of Real-Time Dashboard visuals in the same way.

From real-time dashboard tiles you can click the ellipsis (…) button and select “Set alert”

This opens the embedded trigger pane, where you can specify what conditions, you are looking for. You can choose whether to send email or Teams messages as the alert when these conditions are met.

When creating a new reflex trigger, from Real-time Hub or within the reflex item itself, you’ll notice a new ‘Run a Fabric item’ option in the Action section. This will create a trigger that starts a new Fabric job whenever its condition is met, kicking off a pipeline or notebook computation in response to Fabric events. A common scenario would be monitoring Azure Blob storage events via Real-Time Hub, and running data pipeline jobs when Blog Created events are detected.

This capability is extremely powerful and moves Fabric from a scheduled driven platform to an event driven platform.

Pipelines, spark jobs, and notebooks are just the first Fabric items we’ll support here, and we’re keen to hear your feedback to help prioritize what else we support. Please leave ideas and votes on https://aka.ms/rtiidea and let us know!

Real-Time Intelligence, along with the Real-Time hub, revolutionizes what’s possible with real-time streaming and event data within Microsoft Fabric.

Learn more and try it today https://aka.ms/realtimeintelligence

Data Factory

Dataflow gen2 .

We are thrilled to announce that the Power Query SDK is now generally available in Visual Studio Code! This marks a significant milestone in our commitment to providing developers with powerful tools to enhance data connectivity and transformation.

The Power Query SDK is a set of tools that allow you as the developer to create new connectors for Power Query experiences available in products such as Power BI Desktop, Semantic Models, Power BI Datamarts, Power BI Dataflows, Fabric Dataflow Gen2 and more.

This new SDK has been in public preview since November of 2022, and we’ve been hard at work improving this experience which goes beyond what the previous Power Query SDK in Visual Studio had to offer.

The latest of these biggest improvements was the introduction of the Test Framework in March of 2024 that solidifies the developer experience that you can have within Visual Studio Code and the Power Query SDK for creating a Power Query connector.

The Power Query SDK extension for Visual Studio will be deprecated by June 30, 2024, so we encourage you to give this new Power Query SDK in Visual Studio Code today if you haven’t.

To get started with the Power Query SDK in Visual Studio Code, simply install it from the Visual Studio Code Marketplace . Our comprehensive documentation and tutorials are available to help you harness the full potential of your data.

Join our vibrant community of developers to share insights, ask questions, and collaborate on exciting projects. Our dedicated support team is always ready to assist you with any queries.

We look forward to seeing the innovative solutions you’ll create with the Power Query SDK in Visual Studio Code. Happy coding!

Introducing a convenient enhancement to the Dataflows Gen2 Refresh History experience! Now, alongside the familiar “X” button in the Refresh History screen, you’ll find a shiny new Refresh Button . This small but mighty addition empowers users to refresh the status of their dataflow refresh history status without the hassle of exiting the refresh history and reopening it. Simply click the Refresh Button , and voilà! Your dataflow’s refresh history status screen is updated, keeping you in the loop with minimal effort. Say goodbye to unnecessary clicks and hello to streamlined monitoring!

[New] OneStream : The OneStream Power Query Connector enables you to seamlessly connect Data Factory to your OneStream applications by simply logging in with your OneStream credentials. The connector uses your OneStream security, allowing you to access only the data you have based on your permissions within the OneStream application. Use the connector to pull cube and relational data along with metadata members, including all their properties. Visit OneStream Power BI Connector to learn more. Find this connector in the other category.

Data workflows

We are excited to announce the preview of ‘Data workflows’, a new feature within the Data Factory that revolutionizes the way you build and manage your code-based data pipelines. Powered by Apache Airflow, Data workflows offer seamless authoring, scheduling, and monitoring experience for Python-based data processes defined as Directed Acyclic Graphs (DAGs). This feature brings a SaaS-like experience to running DAGs in a fully managed Apache Airflow environment, with support for autoscaling , auto-pause , and rapid cluster resumption to enhance cost-efficiency and performance.

It also includes native cloud-based authoring capabilities and comprehensive support for Apache Airflow plugins and libraries.

To begin using this feature:

Access the Microsoft Fabric Admin Portal.
Navigate to Tenant Settings.

Under Microsoft Fabric options, locate and expand the ‘Users can create and use Data workflows (preview)’ section. Note: This action is necessary only during the preview phase of Data workflows.

2. Create a new Data workflow within an existing or new workspace.

3. Add a new Directed Acyclic Graph (DAG) file via the user interface.

4. Save your DAG(s).

5. Use Apache Airflow monitoring tools to observe your DAG executions. In the ribbon, click on Monitor in Apache Airflow.

For additional information, please consult the product documentation . If you’re not already using Fabric capacity, consider signing up for the Microsoft Fabric free trial to evaluate this feature.

Data Pipelines

We are excited to announce a new feature in Fabric that enables you to create data pipelines to access your firewall-enabled Azure Data Lake Storage Gen2 (ADLS Gen2) accounts. This feature leverages the workspace identity to establish a secure and seamless connection between Fabric and your storage accounts.

With trusted workspace access, you can create data pipelines to your storage accounts with just a few clicks. Then you can copy data into Fabric Lakehouse and start analyzing your data with Spark, SQL, and Power BI. Trusted workspace access is available for workspaces in Fabric capacities (F64 or higher). It supports organizational accounts or service principal authentication for storage accounts.

How to use trusted workspace access in data pipelines

Create a workspace identity for your Fabric workspace. You can follow the guidelines provided in Workspace identity in Fabric .

Configure resource instance rules for the Storage account that you want to access from your Fabric workspace. Resource instance rules for Fabric workspaces can only be created through ARM templates. Follow the guidelines for configuring resource instance rules for Fabric workspaces here .

Create a data pipeline to copy data from the firewall enabled ADLS gen2 account to a Fabric Lakehouse.

To learn more about how to use trusted workspace access in data pipelines, please refer to Trusted workspace access in Fabric .

We hope you enjoy this new feature for your data integration and analytics scenarios. Please share your feedback and suggestions with us by leaving a comment here.

Introducing Blob Storage Event Triggers for Data Pipelines

A very common use case among data pipeline users in a cloud analytics solution is to trigger your pipeline when a file arrives or is deleted. We have introduced Azure Blob storage event triggers as a public preview feature in Fabric Data Factory Data Pipelines. This utilizes the Fabric Reflex alerts capability that also leverages Event Streams in Fabric to create event subscriptions to your Azure storage accounts.

Parent/Child pipeline pattern monitoring improvements

Today, in Fabric Data Factory Data Pipelines, when you call another pipeline using the Invoke Pipeline activity, the child pipeline is not visible in the monitoring view. We have made updates to the Invoke Pipeline activity so that you can view your child pipeline runs. This requires an upgrade to any pipelines that you have in Fabric that already use the current Invoke Pipeline activity. You will be prompted to upgrade when you edit your pipeline and then provide a connection to your workspace to authenticate. Another additional new feature that will light up with this invoke pipeline activity update is the ability to invoke pipeline across workspaces in Fabric.

We are excited to announce the availability of the Fabric Spark job definition activity for data pipelines. With this new activity, you will be able to run a Fabric Spark Job definition directly in your pipeline. Detailed monitoring capabilities of your Spark Job definition will be coming soon!

To learn more about this activity, read https://aka.ms/SparkJobDefinitionActivity

We are excited to announce the availability of the Azure HDInsight activity for data pipelines. The Azure HDInsight activity allows you to execute Hive queries, invoke a MapReduce program, execute Pig queries, execute a Spark program, or a Hadoop Stream program. Invoking either of the 5 activities can be done in a singular Azure HDInsight activity, and you can invoke this activity using your own or on-demand HDInsight cluster.

To learn more about this activity, read https://aka.ms/HDInsightsActivity

We are thrilled to share the new Modern Get Data experience in Data Pipeline to empower users intuitively and efficiently discover the right data, right connection info and credentials.

In the data destination, users can easily set destination by creating a new Fabric item or creating another destination or selecting existing Fabric item from OneLake data hub.

In the source tab of Copy activity, users can conveniently choose recent used connections from drop down or create a new connection using “More” option to interact with Modern Get Data experience.

Microsoft Fabric March 2024 Update

Welcome to the March 2024 update. We have a lot of great features this month including OneLake File Explorer, Autotune Query Tuning, Test Framework for Power Query SDK in VS Code, and many more! Earn a free Microsoft Fabric certification exam! We are thrilled to announce the general availability of Exam DP-600, which leads to … Continue reading “Microsoft Fabric March 2024 Update”

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Python: Assignment vs Shallow Copy vs Deep Copy
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Copy a File in Python

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Python Assignment Operators And Comparison Operators
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"Mastering Assignment Operators in Python: A Comprehensive Guide"

COMMENTS

python
For immutable objects, creating a copy doesn't make much sense since they are not going to change. For mutable objects, assignment, copy and deepcopy behave differently. Let's talk about each of them with examples. An assignment operation simply assigns the reference of source to destination, e.g:
Python: Assignment vs Shallow Copy vs Deep Copy
In Python 3, you can use list.copy(). However, I prefer the equivalent expression list[:] because it works in both Python 2 and 3. Shallow copy is different from assignment in that it creates a ...
copy
Assignment statements in Python do not copy objects, they create bindings between a target and an object. For collections that are mutable or contain mutable items, a copy is sometimes needed so one can change one copy without changing the other. This module provides generic shallow and deep copy operations (explained below). Interface summary:
Is copy.copy different from assignment in python
All python variables are bindings to some objects in memory. Does this code do anything? Yes, it does. Actually it creates a new object in memory with new name. How does copy.copy(node) differ from node = node? By assignment you are getting different names for the same object. So if you are really need a copy you should use shallow or deep copy ...
Shallow vs Deep Copying of Python Objects
In the above example we (seemingly) only made a change to xs.But it turns out that both sublists at index 1 in xs and ys were modified. Again, this happened because we had only created a shallow copy of the original list.. Had we created a deep copy of xs in the first step, both objects would've been fully independent. This is the practical difference between shallow and deep copies of objects.
copy in Python (Deep Copy and Shallow Copy)
In Python, Assignment statements do not copy objects, they create bindings between a target and an object.When we use the = operator, It only creates a new variable that shares the reference of the original object. In order to create "real copies" or "clones" of these objects, we can use the copy module in Python.. Syntax of Python Deepcopy
Python Shallow Copy and Deep Copy (With Examples)
In Python, we use = operator to create a copy of an object. You may think that this creates a new object; it doesn't. It only creates a new variable that shares the reference of the original object. Let's take an example where we create a list named old_list and pass an object reference to new_list using = operator.
Shallow and deep copy in Python: copy(), deepcopy()
As demonstrated in the above code example, the is operator shows that the two variables refer to the same object both before and after the value change.. Difference between the == and is operators in Python; To create a copy instead of a reference of the same object, use the copy() method or the copy.copy() and copy.deepcopy() functions described below.. For immutable objects like numbers (int ...
The Ultimate Guide to Shallow Copy and Deep Copy in Python
Let's first understand that the assignment operator (=) does not create a copy (new) object.It just creates a binding between the copy (new) object and the original object (i.e. both objects share the same memory address).But this doesn't make difference for immutable objects such as int, float, decimal, bool, tuple, etc. because we can't modify the immutable objects.
Shallow Copy vs Deep Copy vs Assignment in Python
Assignment Operation in python usually won't create copies. It will create references to the existing object. If we try to modify the value of y=10, only value of y is changed. x remains the ...
Python's Assignment Operator: Write Robust Assignments
Here, variable represents a generic Python variable, while expression represents any Python object that you can provide as a concrete value—also known as a literal—or an expression that evaluates to a value. To execute an assignment statement like the above, Python runs the following steps: Evaluate the right-hand expression to produce a concrete value or object.
Assignment, Shallow Copy, Or Deep Copy?
Memory Management In Python. int, float, list, dict, class instances, … they are all objects in Python.In CPython implementation, built-in function id() returns memory address of an object — >>> L1 = [1, 2, 3] >>> id(L1) 3061530120 If we create a new variable L2 that refers to an object with the same value as L1, L2 will have a new memory address — ...
Python Course #12: Pass by Assignment, Copy, Reference, and None
In the first line the list ['a', 'b', 'c'] is declared and than a new reference to this list is created with l =.In the second line another reference to the list ['a', 'b', 'c'] is created with: m = l.In the third line the list is copied and a reference to that copy is created with: n = l.copy().. There are three references to two lists with the same content.
Python copy list
Python copy list using normal assignment. In python we use = operator to create a copy of an object. Example-1: Use = operator to copy a list in Python. For example I have a list called myList with some elements. Next I copy the myList content into newList using = operator.
SettingWithCopyWarning in pandas: Views vs Copies
As you can see, the assignment of zeros to the column z fails. This image illustrates the entire process: Here's what happens in the code sample above: df[mask] returns a completely new DataFrame (outlined in purple). This DataFrame holds a copy of the data from df that correspond to True values from mask (highlighted in green).
python
Assignment (=): In Python, assignment with = creates a shallow copy by default. This means that both variables point to the same underlying data storage. Modifying one array affects the other. Deep Copy: To create a truly independent copy, use numpy.copy() or array.copy() (for general arrays). This creates a new array in memory with separate data.
Python List copy() Method
To gain a deeper understanding, Refer to this article on Deep Copy vs Shallow Copy. Demonstrating Techniques of Shallow and Deep copy . Here we will create a list and then create a shallow copy using the assignment operator, list copy() method, and copy.copy() method of the Python copy module. We also create a deep copy using deepcopy() in Python.
Assignment vs Shallow Copy vs Deep Copy in Python
In the above example of the assignment operator, It does not make a copy of the Python objects instead it copying a memory address (or pointer) from a to b, (b=a).Which means both a & b pointing to the same memory address.. Here we can use the id() method to get the address of the object in memory and check if both lists are pointing the same memory. >>> id (a) == id (b) True >>> print ('id of ...
Proposal: Annotate types in multiple assignment
In the latest version of Python (3.12.3), type annotation for single variable assignment is available: a: int = 1 However, in some scenarios like when we want to annotate the tuple of variables in return, the syntax of type annotation is invalid: from typing import Any def fun() -> Any: # when hard to annotate the strict type return 1, True a: int, b: bool = fun() # INVALID In this case, I ...
python
numpy.copy(B, A) This is not legal syntax. You probably meant B = numpy.copy(A). This is almost the same as 2, but it creates a new array, rather than reusing the B array. If there were no other references to the previous B value, the end result would be the same as 2, but it will use more memory temporarily during the copy.
Runs in separate window and not in terminal #23454
Copy link astrid-altamirano commented May 19, 2024. Type: Performance Issue. Hi. When I press run, the program does not run in the terminal as expected. ... /Astrid 2022/ASTRID Documentos/UW Python Advanced/University of Washington II/Assigment UW Advanced Python 2024/assignment-04-astrid-altamirano/menu.py" 0 30 9936 C:\WINDOWS\system32 ...
Microsoft Fabric May 2024 Update
Copy Into enhancements ; Faster workspace resource assignment powered by Just In Time Database Attachment; Data Engineering . Runtime 1.3 (Apache Spark 3.5, Delta Lake 3.1, R 4.3.3, Python 3.11) - Public Preview ; Native Execution Engine for Fabric Runtime 1.2 (Apache Spark 3.4) - Public Preview Spark Run Series Analysis ; Comment @tagging ...
python
To return the changes you make in the copy , you should return the copy: def f(df): df = df.assign(b = 1) df["a"] = 1 return df df = pd.DataFrame(np.random.randn(100, 1)) print(f(df)) On the contrary , for your second function , you are assigning the column a on the input parameter in place, hence when you print the dataframe , you can see the ...
assigning value in python dict (copy vs reference)
You're wrestling with 2 different things here. The first is the idea of mutability vs. immutability.In python, str, int, tuple are some of the builtin immutable types compared to list, dict (and others) which are mutable types.immutable objects are ones which cannot be changed once they are created. So, in your example: a = b = c = 1 After that line, all a, b and c refer to the same integer in ...

copy — Shallow and deep copy operations ¶

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Example 1: Copy using = operator

Example 2: Create a copy using shallow copy

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