data structures support item assignment

Practical Python Programming

A course by david beazley ( @dabeaz ).

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2.1 Datatypes and Data structures

This section introduces data structures in the form of tuples and dictionaries.

Primitive Datatypes

Python has a few primitive types of data:

• Floating point numbers
• Strings (text)

We learned about these in the introduction.

None is often used as a placeholder for optional or missing value. It evaluates as False in conditionals.

Data Structures

Real programs have more complex data. For example information about a stock holding:

This is an “object” with three parts:

• Name or symbol of the stock (“GOOG”, a string)
• Number of shares (100, an integer)
• Price (490.10 a float)

A tuple is a collection of values grouped together.

Sometimes the () are omitted in the syntax.

Special cases (0-tuple, 1-tuple).

Tuples are often used to represent simple records or structures. Typically, it is a single object of multiple parts. A good analogy: A tuple is like a single row in a database table.

Tuple contents are ordered (like an array).

However, the contents can’t be modified.

You can, however, make a new tuple based on a current tuple.

Tuple Packing

Tuples are more about packing related items together into a single entity .

The tuple is then easy to pass around to other parts of a program as a single object.

Tuple Unpacking

To use the tuple elsewhere, you can unpack its parts into variables.

The number of variables on the left must match the tuple structure.

Tuples vs. Lists

Tuples look like read-only lists. However, tuples are most often used for a single item consisting of multiple parts. Lists are usually a collection of distinct items, usually all of the same type.

Dictionaries

A dictionary is mapping of keys to values. It’s also sometimes called a hash table or associative array. The keys serve as indices for accessing values.

Common operations

To get values from a dictionary use the key names.

To add or modify values assign using the key names.

To delete a value use the del statement.

Why dictionaries?

Dictionaries are useful when there are many different values and those values might be modified or manipulated. Dictionaries make your code more readable.

In the last few exercises, you wrote a program that read a datafile Data/portfolio.csv . Using the csv module, it is easy to read the file row-by-row.

Although reading the file is easy, you often want to do more with the data than read it. For instance, perhaps you want to store it and start performing some calculations on it. Unfortunately, a raw “row” of data doesn’t give you enough to work with. For example, even a simple math calculation doesn’t work:

To do more, you typically want to interpret the raw data in some way and turn it into a more useful kind of object so that you can work with it later. Two simple options are tuples or dictionaries.

Exercise 2.1: Tuples

At the interactive prompt, create the following tuple that represents the above row, but with the numeric columns converted to proper numbers:

Using this, you can now calculate the total cost by multiplying the shares and the price:

Is math broken in Python? What’s the deal with the answer of 3220.0000000000005?

This is an artifact of the floating point hardware on your computer only being able to accurately represent decimals in Base-2, not Base-10. For even simple calculations involving base-10 decimals, small errors are introduced. This is normal, although perhaps a bit surprising if you haven’t seen it before.

This happens in all programming languages that use floating point decimals, but it often gets hidden when printing. For example:

Tuples are read-only. Verify this by trying to change the number of shares to 75.

Although you can’t change tuple contents, you can always create a completely new tuple that replaces the old one.

Whenever you reassign an existing variable name like this, the old value is discarded. Although the above assignment might look like you are modifying the tuple, you are actually creating a new tuple and throwing the old one away.

Tuples are often used to pack and unpack values into variables. Try the following:

Take the above variables and pack them back into a tuple

Exercise 2.2: Dictionaries as a data structure

An alternative to a tuple is to create a dictionary instead.

Calculate the total cost of this holding:

Compare this example with the same calculation involving tuples above. Change the number of shares to 75.

Unlike tuples, dictionaries can be freely modified. Add some attributes:

Exercise 2.3: Some additional dictionary operations

If you turn a dictionary into a list, you’ll get all of its keys:

Similarly, if you use the for statement to iterate on a dictionary, you will get the keys:

Try this variant that performs a lookup at the same time:

You can also obtain all of the keys using the keys() method:

keys() is a bit unusual in that it returns a special dict_keys object.

This is an overlay on the original dictionary that always gives you the current keys—even if the dictionary changes. For example, try this:

Carefully notice that the 'account' disappeared from keys even though you didn’t call d.keys() again.

A more elegant way to work with keys and values together is to use the items() method. This gives you (key, value) tuples:

If you have tuples such as items , you can create a dictionary using the dict() function. Try it:

The Ultimate Guide to Python Tuples - Python Data Structure Tutorial with Code Examples

A tuple is a sequence of Python objects. Tuples are immutable which means they cannot be modified after creation, unlike lists.

An empty tuple is created using a pair of round brackets, () :

A tuple with elements is created by separating the elements with commas (surrounding round brackets, () , are optional with exceptions):

A tuple with a single element must have the trailing comma (with or without round brackets):

Round brackets are required in cases of ambiguity (if the tuple is part of a larger expression):

Note that it is actually the comma which makes a tuple, not the parentheses. The parentheses are optional, except in the empty tuple case, or when they are needed to avoid syntactic ambiguity. For example, f(a, b, c) is a function call with three arguments, while f((a, b, c)) is a function call with a 3-tuple as the sole argument.

A tuple can also be created with the tuple constructor:

Accessing elements of a tuple :

Elements of tuples are accessed and indexed the same way that lists are.

Zero indexed

Wrap around indexing

Packing and Unpacking:

The statement t = 12345, 54321, 'hello!' is an example of tuple packing: the values 12345 , 54321 and 'hello!' are packed together in a tuple. The reverse operation is also possible:

This is called, appropriately enough, sequence unpacking and works for any sequence on the right-hand side. Sequence unpacking requires that there are as many variables on the left side of the equals sign as there are elements in the sequence. Note that multiple assignment is really just a combination of tuple packing and sequence unpacking.

tuples are immutable containers, guaranteeing which objects they contain will not change. It does not guarantee that the objects they contain will not change:

Functions can only return a single value, however, a heterogeneous tuple can be used to return multiple values from a function. One example is the built-in enumerate function that returns an iterable of heterogeneous tuples :

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5. Data Structures ¶

This chapter describes some things you’ve learned about already in more detail, and adds some new things as well.

5.1. More on Lists ¶

The list data type has some more methods. Here are all of the methods of list objects:

Add an item to the end of the list. Equivalent to a[len(a):] = [x] .

Extend the list by appending all the items from the iterable. Equivalent to a[len(a):] = iterable .

Insert an item at a given position. The first argument is the index of the element before which to insert, so a.insert(0, x) inserts at the front of the list, and a.insert(len(a), x) is equivalent to a.append(x) .

Remove the first item from the list whose value is equal to x . It raises a ValueError if there is no such item.

Remove the item at the given position in the list, and return it. If no index is specified, a.pop() removes and returns the last item in the list. (The square brackets around the i in the method signature denote that the parameter is optional, not that you should type square brackets at that position. You will see this notation frequently in the Python Library Reference.)

Remove all items from the list. Equivalent to del a[:] .

Return zero-based index in the list of the first item whose value is equal to x . Raises a ValueError if there is no such item.

The optional arguments start and end are interpreted as in the slice notation and are used to limit the search to a particular subsequence of the list. The returned index is computed relative to the beginning of the full sequence rather than the start argument.

Return the number of times x appears in the list.

Sort the items of the list in place (the arguments can be used for sort customization, see sorted() for their explanation).

Reverse the elements of the list in place.

Return a shallow copy of the list. Equivalent to a[:] .

An example that uses most of the list methods:

You might have noticed that methods like insert , remove or sort that only modify the list have no return value printed – they return the default None . 1 This is a design principle for all mutable data structures in Python.

Another thing you might notice is that not all data can be sorted or compared. For instance, [None, 'hello', 10] doesn’t sort because integers can’t be compared to strings and None can’t be compared to other types. Also, there are some types that don’t have a defined ordering relation. For example, 3+4j < 5+7j isn’t a valid comparison.

5.1.1. Using Lists as Stacks ¶

The list methods make it very easy to use a list as a stack, where the last element added is the first element retrieved (“last-in, first-out”). To add an item to the top of the stack, use append() . To retrieve an item from the top of the stack, use pop() without an explicit index. For example:

5.1.2. Using Lists as Queues ¶

It is also possible to use a list as a queue, where the first element added is the first element retrieved (“first-in, first-out”); however, lists are not efficient for this purpose. While appends and pops from the end of list are fast, doing inserts or pops from the beginning of a list is slow (because all of the other elements have to be shifted by one).

To implement a queue, use collections.deque which was designed to have fast appends and pops from both ends. For example:

5.1.3. List Comprehensions ¶

List comprehensions provide a concise way to create lists. Common applications are to make new lists where each element is the result of some operations applied to each member of another sequence or iterable, or to create a subsequence of those elements that satisfy a certain condition.

For example, assume we want to create a list of squares, like:

Note that this creates (or overwrites) a variable named x that still exists after the loop completes. We can calculate the list of squares without any side effects using:

or, equivalently:

which is more concise and readable.

A list comprehension consists of brackets containing an expression followed by a for clause, then zero or more for or if clauses. The result will be a new list resulting from evaluating the expression in the context of the for and if clauses which follow it. For example, this listcomp combines the elements of two lists if they are not equal:

and it’s equivalent to:

Note how the order of the for and if statements is the same in both these snippets.

If the expression is a tuple (e.g. the (x, y) in the previous example), it must be parenthesized.

List comprehensions can contain complex expressions and nested functions:

5.1.4. Nested List Comprehensions ¶

The initial expression in a list comprehension can be any arbitrary expression, including another list comprehension.

Consider the following example of a 3x4 matrix implemented as a list of 3 lists of length 4:

The following list comprehension will transpose rows and columns:

As we saw in the previous section, the nested listcomp is evaluated in the context of the for that follows it, so this example is equivalent to:

which, in turn, is the same as:

In the real world, you should prefer built-in functions to complex flow statements. The zip() function would do a great job for this use case:

See Unpacking Argument Lists for details on the asterisk in this line.

5.2. The del statement ¶

There is a way to remove an item from a list given its index instead of its value: the del statement. This differs from the pop() method which returns a value. The del statement can also be used to remove slices from a list or clear the entire list (which we did earlier by assignment of an empty list to the slice). For example:

del can also be used to delete entire variables:

Referencing the name a hereafter is an error (at least until another value is assigned to it). We’ll find other uses for del later.

5.3. Tuples and Sequences ¶

We saw that lists and strings have many common properties, such as indexing and slicing operations. They are two examples of sequence data types (see Sequence Types — list, tuple, range ). Since Python is an evolving language, other sequence data types may be added. There is also another standard sequence data type: the tuple .

A tuple consists of a number of values separated by commas, for instance:

As you see, on output tuples are always enclosed in parentheses, so that nested tuples are interpreted correctly; they may be input with or without surrounding parentheses, although often parentheses are necessary anyway (if the tuple is part of a larger expression). It is not possible to assign to the individual items of a tuple, however it is possible to create tuples which contain mutable objects, such as lists.

Though tuples may seem similar to lists, they are often used in different situations and for different purposes. Tuples are immutable , and usually contain a heterogeneous sequence of elements that are accessed via unpacking (see later in this section) or indexing (or even by attribute in the case of namedtuples ). Lists are mutable , and their elements are usually homogeneous and are accessed by iterating over the list.

A special problem is the construction of tuples containing 0 or 1 items: the syntax has some extra quirks to accommodate these. Empty tuples are constructed by an empty pair of parentheses; a tuple with one item is constructed by following a value with a comma (it is not sufficient to enclose a single value in parentheses). Ugly, but effective. For example:

The statement t = 12345, 54321, 'hello!' is an example of tuple packing : the values 12345 , 54321 and 'hello!' are packed together in a tuple. The reverse operation is also possible:

This is called, appropriately enough, sequence unpacking and works for any sequence on the right-hand side. Sequence unpacking requires that there are as many variables on the left side of the equals sign as there are elements in the sequence. Note that multiple assignment is really just a combination of tuple packing and sequence unpacking.

5.4. Sets ¶

Python also includes a data type for sets . A set is an unordered collection with no duplicate elements. Basic uses include membership testing and eliminating duplicate entries. Set objects also support mathematical operations like union, intersection, difference, and symmetric difference.

Curly braces or the set() function can be used to create sets. Note: to create an empty set you have to use set() , not {} ; the latter creates an empty dictionary, a data structure that we discuss in the next section.

Here is a brief demonstration:

Similarly to list comprehensions , set comprehensions are also supported:

5.5. Dictionaries ¶

Another useful data type built into Python is the dictionary (see Mapping Types — dict ). Dictionaries are sometimes found in other languages as “associative memories” or “associative arrays”. Unlike sequences, which are indexed by a range of numbers, dictionaries are indexed by keys , which can be any immutable type; strings and numbers can always be keys. Tuples can be used as keys if they contain only strings, numbers, or tuples; if a tuple contains any mutable object either directly or indirectly, it cannot be used as a key. You can’t use lists as keys, since lists can be modified in place using index assignments, slice assignments, or methods like append() and extend() .

It is best to think of a dictionary as a set of key: value pairs, with the requirement that the keys are unique (within one dictionary). A pair of braces creates an empty dictionary: {} . Placing a comma-separated list of key:value pairs within the braces adds initial key:value pairs to the dictionary; this is also the way dictionaries are written on output.

The main operations on a dictionary are storing a value with some key and extracting the value given the key. It is also possible to delete a key:value pair with del . If you store using a key that is already in use, the old value associated with that key is forgotten. It is an error to extract a value using a non-existent key.

Performing list(d) on a dictionary returns a list of all the keys used in the dictionary, in insertion order (if you want it sorted, just use sorted(d) instead). To check whether a single key is in the dictionary, use the in keyword.

Here is a small example using a dictionary:

The dict() constructor builds dictionaries directly from sequences of key-value pairs:

In addition, dict comprehensions can be used to create dictionaries from arbitrary key and value expressions:

When the keys are simple strings, it is sometimes easier to specify pairs using keyword arguments:

5.6. Looping Techniques ¶

When looping through dictionaries, the key and corresponding value can be retrieved at the same time using the items() method.

When looping through a sequence, the position index and corresponding value can be retrieved at the same time using the enumerate() function.

To loop over two or more sequences at the same time, the entries can be paired with the zip() function.

To loop over a sequence in reverse, first specify the sequence in a forward direction and then call the reversed() function.

To loop over a sequence in sorted order, use the sorted() function which returns a new sorted list while leaving the source unaltered.

Using set() on a sequence eliminates duplicate elements. The use of sorted() in combination with set() over a sequence is an idiomatic way to loop over unique elements of the sequence in sorted order.

It is sometimes tempting to change a list while you are looping over it; however, it is often simpler and safer to create a new list instead.

5.7. More on Conditions ¶

The conditions used in while and if statements can contain any operators, not just comparisons.

The comparison operators in and not in are membership tests that determine whether a value is in (or not in) a container. The operators is and is not compare whether two objects are really the same object. All comparison operators have the same priority, which is lower than that of all numerical operators.

Comparisons can be chained. For example, a < b == c tests whether a is less than b and moreover b equals c .

Comparisons may be combined using the Boolean operators and and or , and the outcome of a comparison (or of any other Boolean expression) may be negated with not . These have lower priorities than comparison operators; between them, not has the highest priority and or the lowest, so that A and not B or C is equivalent to (A and (not B)) or C . As always, parentheses can be used to express the desired composition.

The Boolean operators and and or are so-called short-circuit operators: their arguments are evaluated from left to right, and evaluation stops as soon as the outcome is determined. For example, if A and C are true but B is false, A and B and C does not evaluate the expression C . When used as a general value and not as a Boolean, the return value of a short-circuit operator is the last evaluated argument.

It is possible to assign the result of a comparison or other Boolean expression to a variable. For example,

Note that in Python, unlike C, assignment inside expressions must be done explicitly with the walrus operator := . This avoids a common class of problems encountered in C programs: typing = in an expression when == was intended.

5.8. Comparing Sequences and Other Types ¶

Sequence objects typically may be compared to other objects with the same sequence type. The comparison uses lexicographical ordering: first the first two items are compared, and if they differ this determines the outcome of the comparison; if they are equal, the next two items are compared, and so on, until either sequence is exhausted. If two items to be compared are themselves sequences of the same type, the lexicographical comparison is carried out recursively. If all items of two sequences compare equal, the sequences are considered equal. If one sequence is an initial sub-sequence of the other, the shorter sequence is the smaller (lesser) one. Lexicographical ordering for strings uses the Unicode code point number to order individual characters. Some examples of comparisons between sequences of the same type:

Note that comparing objects of different types with < or > is legal provided that the objects have appropriate comparison methods. For example, mixed numeric types are compared according to their numeric value, so 0 equals 0.0, etc. Otherwise, rather than providing an arbitrary ordering, the interpreter will raise a TypeError exception.

Other languages may return the mutated object, which allows method chaining, such as d->insert("a")->remove("b")->sort(); .

Table of Contents

• 5.1.1. Using Lists as Stacks
• 5.1.2. Using Lists as Queues
• 5.1.3. List Comprehensions
• 5.1.4. Nested List Comprehensions
• 5.2. The del statement
• 5.3. Tuples and Sequences
• 5.5. Dictionaries
• 5.6. Looping Techniques
• 5.7. More on Conditions
• 5.8. Comparing Sequences and Other Types

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Python Data Structures Explained in Detail

Every programming language has provision for data structures. In the Python programming language, there are a total of 4 inbuilt data structures. These are namely list, tuple, dictionary, and set. Each of them is unique in its own right.

Data structures are an indispensable part of programming. As such, all good books on Python programming detail out on data structures to some extent.

• Python Data Structures

A data structure that stores an ordered collection of items in Python is called a list. In other words, a list holds a sequence of items. You need to put all the items, separated by commas, in square brackets to let Python know that a list has been specified. The general syntax of a list is:

Any list can have any number of elements and of different data types. Moreover, a list can have another list as an element. Such a list is known as a nested list . For example,

After creating a list in Python, it is possible to search, add, and remove items from the list. A list qualifies for being labeled as a mutable data type because it can be altered by adding or removing elements.

A list in the Python programming language is an example of using objects and classes. For instance, assigning a value, say 22, to an integer variable, let it be i, is creating an object of the class integer. The list is, therefore, also a type of class in Python.

Any class can have methods, which can be used only when we have an object of that specific class. For example, the append () method allows adding an item to the end of a list. The general syntax for the method is:

Here some_list is the name of the list and some item is the element to be added to the list. A class can have fields, which are variables defined to use with an instance of that class only. Following are the various methods supported by lists in Python:

• append () – Adds an element to the end of the list
• clear () – Removes all elements from the list
• copy () – Returns a shallow copy of the list
• count () – Returns the total number of items passed as an argument
• extend () – Adds all elements of a list to some other list
• index () – Returns the index of an element (Note: If the same element appears multiple times in the list then the index of the very first match is returned)
• insert () – Inserts an element to the list at the defined index
• pop () – Eliminates and returns an element from the list
• remove () – Eliminates an element from the list
• reverse () – Reverses the order of all elements of the list
• sort () – Sort all elements of a list in the ascending order

Similar to a list, the tuple is a built-in data structure in Python. However, it doesn’t support the same level of extensive functionality. The most important difference between a list and a tuple is mutability. Unlike lists, tuples are immutable i.e. they can’t be modified.

Typically, a tuple in the Python programming language is defined using parentheses with each item separated by commas. Though adding parentheses to the tuple is optional, its use is recommended to clearly define the end and start of the tuple. The general syntax of a tuple is:

Tuples are used in scenarios where it is certain that the (set of) values belonging to some statement or a user-defined function will not change.

In order to define an empty tuple, an empty pair of parentheses is used. For example,

Defining a tuple with a single item is a bit tricky. You need to specify the same using a comma following the only item. This is done in order to allow Python to differentiate between a tuple and a pair of parentheses surrounding the object in some expression.

So, a tuple, say singleton_tuple, with a single item, say item 1, is defined as:

Similar to string indices, tuple indices start at 0. Like strings, tuples can be concatenated and sliced. Methods available for tuples are:

• cmp () – Compares elements of two tuples
• len () – Gives out the total length of some tuple
• max () – Returns the biggest value from a tuple
• min () – Returns the smallest value from a tuple
• tuple () – Converts some list into a tuple

Lists and tuples qualify to be called as sequences, just like strings. The most distinguishing features of a sequence are membership tests and indexing operations. The latter allows directly fetching an item(s) from the sequence.

Python provides support for three types of sequences; lists, strings, and tuples. Each of them supports the slicing operation. It allows retrieving a particular portion of the sequence.

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Another type of built-in data structure in Python is the dictionary. It stores data in the form of key-value pairs. The keys defined for a dictionary need to be unique. Though values in a dictionary can be mutable or immutable objects, only immutable objects are allowed for keys.

A dictionary in Python is defined using key-value pairs, separated by commas, enclosed within curly braces. The key and value are separated using a colon. The general syntax of a dictionary is:

The key-value pairs in a dictionary don’t follow any specific order. Hence, you need to sort them manually for maintaining some kind of order. Any dictionary defined in the Python programming language is an instance of the dict class.

In order to access any key-value pair, the key needs to be specified using the indexing operator. The del statement is used for deleting one or many key-value pairs from a dictionary. The in operator is there for checking whether a key-value pair exists in the dictionary or not.

The indexing operator is also used for adding a new key-value pair. Using keyword arguments in functions is much similar to using dictionaries.

Simply, a set is an unordered collection of simple objects in Python. In addition to being iterable and mutable, a set has no duplicate elements. Any set defined in Python is an instance of the set class.

Compared to a list, a set is advantageous by virtue of having a highly optimized method for checking whether some element is contained in the set or not. The general syntax of a set is:

Typically, a set is employed when the existence of an object in a collection is much more important than the order or the frequency (number of times it appears) of the same.

Using sets allow testing for membership, such as checking whether a set is a subset of some other set and finding the intersection between two sets. Sets in Python follow the mathematical concept of set theory .

There is something called frozen sets in Python. A frozen set is a set that supports only those methods and operators that produce a result without affecting the frozen set or set(s) to which the same are applied. Methods supported by sets are:

• add () – Adds an item to the set (Note: As sets don’t have repeating values, the item that is to be added to a set must not be already a member of the set.)
• clear () – Removes all items of the set
• difference () – Returns a set with all elements of the invoking set but not of the second set
• intersect () – Returns an intersection of two sets
• union () – Returns a union of two sets

So, that sums up all about the Python data structures. Learning Python is incomplete without building a good understanding of data structures. I hope the article will help you better understand and utilize data structures in your Python projects.

Use the dedicated comments window below to share your opinions and views on data structures or the article with us. You can also ask all your queries via comments. We’ll try our best to help you out.

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A Computer Science graduate interested in mixing up imagination and knowledge into enticing words. Been in the big bad world of content writing since 2014. In his free time, Akhil likes to play cards, do guitar jam, and write weird fiction.

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• 10 Vital Python Concepts for Data Science
• 10 Common Python Mistakes in 2024 | Are You Making Them? Python Data Science Programming Skills
• 10 Python Concepts I Wish I Knew Earlier For Interviews [2024] Python Career Development Interview Questions

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Fix “str object does not support item assignment python”

Are you encountering the “str object does not support item assignment” error in your Python programming? This error, also known as “TypeError”, can be frustrating for both beginners and experienced coders.

In this section, we will explore the reasons why this error occurs when trying to assign values to a string object in Python. We will also provide some simple fixes that you can apply immediately to overcome this error. Let’s dive in!

Table of Contents

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Understanding the Python string object

In Python programming, a string is a sequence of characters, enclosed within quotation marks. It is one of the built-in data types in Python and can be defined using either single (‘ ‘) or double (” “) quotation marks.

Strings are immutable, which means once they are defined, their values cannot be changed. However, it is possible to access individual characters within a string using their index positions and perform operations on them.

For example, the string “hello” has individual characters ‘h’,’e’,’l’,’l’,’o’ at the index positions 0, 1, 2, 3, and 4 respectively. These characters can be accessed using the square bracket notation, like so:

Here, the output is the second character of the string ‘x’ which is ‘e’.

Python also provides various methods to manipulate strings, such as concatenation, slicing, and formatting. These methods can be used to create new strings or modify existing ones.

Item assignment in Python

Item assignment is the process of assigning a value to an element within a sequence. In Python, sequences include strings, lists, and tuples. Item assignment is performed using the square bracket notation, where the index position of the element is specified within the brackets, followed by the new value to be assigned.

For example:

Here, the value at index position 2 of list ‘x’ is changed from 3 to 5.

However, item assignment is not allowed for strings in Python because they are immutable. Attempting to assign a new value to an individual character within a string object will result in the “str object does not support item assignment” error, commonly known as TypeError.

What is the “str object does not support item assignment” error?

The “str object does not support item assignment” error is a common error that occurs in Python when trying to assign values to a string object. It is usually accompanied by a “TypeError” message, which indicates that a data type is being used in an incorrect manner.

When trying to assign a value to a single character within a string object in Python, you might encounter the “str object does not support item assignment” error. This error occurs because strings are immutable, meaning that their contents cannot be changed once they are created. Therefore, attempting to change a single character within a string using the item assignment syntax will result in a TypeError.

For example, the following code will result in a “str object does not support item assignment” error:

In this example, we are attempting to change the third character in the string “hello” from “l” to “w”. However, since strings are immutable in Python, this operation is not supported and will result in a TypeError.

Common Causes of “str object does not support item assignment” error

The “str object does not support item assignment” error is a common TypeError that occurs when trying to assign values to a string object. This error can be caused by a variety of issues, including:

Attempting to modify a string object directly

Trying to assign a value to an index in a string, using the wrong type of data in string concatenation.

Let’s take a closer look at each of these causes:

In Python, strings are immutable, which means that once a string object is created, it cannot be modified. Attempting to modify a string object directly will result in the “str object does not support item assignment” error.

This code attempts to change the first character of the string “Hello, world!” to “J”. However, since strings are immutable, this will raise a TypeError.

Python allows you to access individual characters in a string using an index. However, trying to assign a value to an index in a string will raise the “str object does not support item assignment” error.

String concatenation is the process of joining two or more strings together. However, if you try to concatenate a string with a non-string object, such as an integer or a list, you will get the “str object does not support item assignment” error. This is because the + operator is used for both addition and concatenation in Python, so the wrong type of data can cause a TypeError.

This code attempts to concatenate the string “Hello, world!” with the integer 1. However, since these are different types of data, Python raises a TypeError.

In the next section, we will explore some possible solutions to these common causes of the “str object does not support item assignment” error.

How to Fix “str object does not support item assignment” error

There are several ways to fix the “str object does not support item assignment” error in Python programming. Below, we will explore some simple solutions to overcome this problem:

Use String Concatenation Method

One way to fix the “str object does not support item assignment” error is to use the string concatenation method. Instead of trying to assign values to a string object, we can concatenate the existing string with the new value using the “+” operator. Here’s an example:

Convert the String to a List

Another solution to the “str object does not support item assignment” error is to convert the string to a list first, then modify the list and convert it back to a string. Here’s an example:

Use the “join” Method to Merge Strings

The third solution to the “str object does not support item assignment” error is to use the “join” method to merge multiple string values into one string. Here’s an example:

By following these simple solutions, you can overcome the “str object does not support item assignment” error in Python programming and write efficient and error-free code.

Best practices to Avoid the “str object does not support item assignment” error

The “str object does not support item assignment” error can be frustrating, but there are steps you can take to avoid it. Below are some best practices to help you sidestep this error and write better code:

Use Immutable Data Types

One of the simplest ways to avoid the “str object does not support item assignment” error is to use immutable data types. Immutable objects are those whose value cannot be changed once they are created. In Python, strings are immutable. Because you cannot change a string’s value, you cannot assign a new value to an item in a string.

By using immutable data types, like tuples, you can ensure that your code stays error-free. If you need to modify a tuple, you can create a new tuple using the modified values instead of trying to modify the existing tuple. This approach will protect you from the “str object does not support item assignment” error.

Use Data Structures Appropriately

When working with strings in Python, it’s important to use data structures appropriately. One common cause of the “str object does not support item assignment” error is trying to modify a string directly using item assignment. Instead of trying to modify a string item directly, it is recommended to use a data structure like a list or a dictionary that supports item assignment.

Lists are mutable, ordered sequences of elements in Python, while dictionaries are mutable, unordered sets of key-value pairs. If you need to modify the contents of a string, you can convert the string to a list, modify the list, and then convert the modified list back to a string.

Adopt Good Coding Practices

Good coding practices are essential for avoiding errors in Python programming, including the “str object does not support item assignment” error. Always follow best practices, like writing clean and modular code, commenting your code, testing your code frequently, and using descriptive variable names.

By adopting good coding practices, you can minimize the likelihood of encountering this error. In addition, it will make your code easier to read and maintain, which is always a plus.

By implementing these best practices, you can minimize the chance of running into the “str object does not support item assignment” error. Remember to use immutable data types where possible, use data structures appropriately, and adopt good coding practices to keep your code error-free.

Common Mistakes to Avoid:

As with any programming language, there are common mistakes that beginners make when coding in Python. These mistakes can often result in errors, such as the “str object does not support item assignment” error. Here are some of the most common mistakes to avoid:

Forgetting to Convert a String to a List

A common mistake is forgetting to convert a string to a list before attempting to modify it. As we discussed earlier, strings are immutable objects in Python, meaning that they cannot be modified directly. If you want to modify a string, you must first convert it to a list, make the necessary modifications, and then convert it back to a string.

Trying to Assign Values to a String

Another common mistake is trying to assign values to a string using the “=” operator. This is because strings are immutable objects, and therefore cannot be modified in this way. Instead, you must use a different method, such as string concatenation or the “join” method.

Not Understanding Data Types

New programmers sometimes struggle with understanding data types in Python. For example, a common mistake is trying to concatenate a string with an integer, which is not a valid operation in Python. It’s important to understand the different data types and how they interact with each other.

By avoiding these common mistakes, you can reduce your chances of encountering the “str object does not support item assignment” error in your Python programs.

Examples of the “str object does not support item assignment” error in code

Let’s look at some examples of code that can result in the “str object does not support item assignment” error in Python.

In this example, we try to change the first character of the string ‘hello’ to ‘H’ using bracket notation:

The error occurs because, in Python, strings are immutable, meaning you cannot modify the individual characters of a string using bracket notation.

In this example, we try to change the value of a string variable using the equals operator:

This code does not result in an error because we are not trying to modify the individual characters of the string directly. Instead, we are creating a new string and assigning it to the same variable.

In this example, we try to concatenate two strings and change the value of a character in the resulting string:

The error occurs because, even though we have concatenated two strings, the resulting string is still a string object and is therefore immutable. We cannot modify its individual characters using bracket notation.

In this example, we try to change the value of a character in a string by converting it to a list, modifying the list, and then converting it back to a string:

This code works without error because we have converted the string to a list, which is mutable, modified the list, and then converted it back to a string using the “join” method.

Here are some frequently asked questions about the “str object does not support item assignment” error in Python:

What does the “str object does not support item assignment” error mean?

This error occurs when you try to assign a value to a specific character within a string object in Python. However, strings in Python are immutable, which means that their individual characters cannot be modified. Therefore, trying to assign a value to a specific character in a string object will result in a TypeError.

What are some common causes of the “str object does not support item assignment” error?

Some common causes of this error include trying to modify a string object directly, attempting to access an invalid index of a string, or incorrectly assuming that a string is a mutable data type.

How can I fix the “str object does not support item assignment” error?

You can fix this error by using alternative methods such as string concatenation, converting the string to a list, or using the “join” method to merge strings. Alternatively, you can use mutable data types like lists or dictionaries instead of strings if you need to modify individual elements of the data.

What are some best practices to avoid encountering the “str object does not support item assignment” error?

Some best practices include avoiding direct modifications to string objects, using the correct syntax when accessing string elements, and using appropriate data structures for your specific needs. Additionally, it is important to maintain good coding practices by testing your code and debugging any errors as soon as they arise.

Can the “str object does not support item assignment” error occur in other programming languages?

While the exact error message may differ, similar errors can occur in other programming languages that have immutable string objects, such as Java or C#. It is important to understand the limitations of the data types in any programming language you are working with to avoid encountering such errors.

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Pandas TypeError: SparseArray does not support item assignment via setitem

Understanding the error.

Encountering a TypeError: SparseArray does not support item assignment via setitem in Pandas can be a hurdle, especially for those dealing with sparse data structures to optimize memory usage. The error typically occurs when you try to assign a value to a specific position in a SparseArray directly. This guide will explore reasons behind this error and provide solutions to circumvent or resolve it efficiently.

More about Sparse Data Structures

Sparse data structures in Pandas, such as SparseArray , are designed for memory efficiency when handling data that contains a significant amount of missing or fill values. However, these data structures have certain limitations, including restrictions on direct item assignment, which is the primary cause of the error in question.

Solution 1: Convert to Dense and Assign

One straightforward method to overcome this error is converting your SparseArray to a dense format, performing the item assignment, and then, if necessary, converting it back to sparse format.

• Step 1: Convert the SparseArray to a dense array.
• Step 2: Perform the item assignment on the dense array.
• Step 3: Convert the dense array back to a SparseArray if needed.

Notes: While this solution is straightforward, converting to dense and back to sparse can be memory-intensive, especially for very large datasets. This should be considered when dealing with limited system resources.

Solution 2: Use Sparse Accessor for Direct Assignment

Another solution involves utilizing the sparse accessor .sparse provided by Pandas for directly assigning values without converting to a dense format. This method supports updating the entire array or slices but not individual item assignments. What you need to do is just using the .sparse accessor to directly assign values to the SparseArray.

Notes: It’s important to note that while this solution avoids the need for conversion, it has limitations on granularity. You can’t use it for assigning a value to a specific index but rather for assigning values to slices or the entire array.

Final Thoughts

Handling SparseArray item assignment errors in Pandas can be tricky but understanding the limitations and workarounds makes it manageable. Both converting to dense before assignment and using the .sparse accessor come with their own sets of considerations. Evaluate your specific needs and system resources to choose the best approach.

Next Article: Pandas UnicodeDecodeError: 'utf-8' codec can't decode

Previous Article: Fixing Pandas NameError: name ‘df’ is not defined

Series: Solving Common Errors in Pandas

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Python Tuple – A Tutorial to Get Started Quickly

In this class, you’ll learn what a Python tuple is, what can you do with it, and how to use it in programs. Moreover, you’ll essentially learn how to create a tuple and perform various operations such as packing, unpacking, slicing, comparison, and deletion.

What is a Tuple in Python?

A Python tuple is a collection-type data structure that is immutable by design and holds a sequence of heterogeneous elements. It functions almost like a Python list but with the following distinctions.

• Tuples store a fixed set of elements and don’t allow changes whereas the list has the provision to update its content.
• The list uses square brackets for opening and closing, whereas a tuple has got parentheses for the enclosure.

A tuple can come in quite handy for programmers in different situations. We’ll discuss them later in this tutorial.

How to instantiate a Tuple in Python?

You can create a tuple by placing a sequence of desired elements separated using commas inside a pair of round brackets (), i.e., parentheses.

Please note that you can create a tuple even without using the parentheses. Also, the elements of a tuple can be of any valid Python data type ranging from numbers, strings, lists, etc.

Let us check out some important examples to create and use tuples in Python.

Create a tuple with different inputs

• Below are various examples of creating tuples with different types of input.

Executing the above coding snippet will produce the below result.

2. We can invoke the tuple function and get the desired result. See the below example.

3. In the below example, we are trying to create a tuple with a single element.

Also, check out how does Python zip function returns a list of tuples. It is a powerful tool to merge, compare, and iterate lists, tuples, and dictionaries.

Access a tuple in Python

• The simplest is the direct access method where you use the index operator [] to pick an item from the tuple. You can start indexing from the 0th position.

It means if a tuple holds ten elements, then the index will begin at 0th and will end at 9th position. Violating the boundaries of a tuple will result in an IndexError.

Please note that the index is always an integer. You must not try a float or any other form of numbers for indexing purposes. Doing so will produce TypeError.

Next, if the tuple contains other tuples as its elements, then you would need to index the elements tuple-by-tuple.

After running the above sample code, the following output will be displayed:

2. Python tuple supports reverse indexing, i.e., accessing elements using the (-ve) index values.

The reverse indexing works in the following manner.

• The index -1 represents the last item.
• An index with value -2 will refer to the second item from the rear end.

Please go through the below example for more clarity.

3. If you need to access not one but more than one element from a tuple, then Python’s slicing operator ":" can come to use. Check the code in the following example.

Modify a tuple in Python

Since tuples are immutable, so it seems no way to modify them.

Once you assign a set of elements to a tuple, Python won’t allow it to change. But, there is a catch, what if the items you set are modifiable?

If there is such a case, then you can change the elements instead of directly modifying the tuple.

Moreover, you can even set a tuple to have different values. Check out the below example.

After execution, the example will yield the following result.

Moreover, you can extend the behavior of a tuple by using the + (concatenation) and * (repeat) operators.

The plus operator helps you join the two distinct tuples.

And, the star operator helps you repeat the elements in a tuple for a specified number of times.

Removing a tuple

The immutability of a tuple would again prevent you from deleting it in a Python program. While you can’t delete a tuple directly, here is something that can help.

Python’s del keyword can make you delete a tuple. Check out the below example.

The execution of the above code would give you the following result.

The first line of the output conveys that we can’t delete a specific item from the tuple. But the second line says that we’ve already removed it and trying to print an undefined Python object .

Miscellaneous tuple operations

Just like we did in the Python set , similarly here, the “in” keyword will help us exercise the membership test on a tuple.

You can form a for loop and one by one access all the elements in a tuple.

Usage of Python tuples

• Tuples provide a quick way of grouping and arranging data. It can help you combine any number of elements into a single unit.

They can help us represent information in the form of records such as the employee record. A tuple allows us to group related information and use it as a single entity.

2. Python tuple supports a very intuitive feature known as “tuple assignment.” It lets us assign a tuple of variables on the left of a statement to initialize from the tuple on the right side.

3. Usually, a Function only returns one value. However, we can introduce a tuple and set it as the Return Value for the Function.

It means we can combine multiple values, store them in a tuple, and finally return it. It could come quite handy in situations when we want to know the hours, minutes, and seconds consumed by a job, or to get the counts of different types of accessories or the prices of multiple books written by a particular author.

4. Tuples are a type of container that can embed another tuple as an element. We call such an object a nested tuple.

It could help in visualizing information at a broader level. For example, if we have to maintain employee counts in each department along with their names, positions, and salaries, the nested tuples can let us do this efficiently.

Quick Wrap-up: Python tuple

In this tutorial, we covered “Python tuple”  which is one of the core data structures available. Hence, it is of utmost necessity that you are aware of how the tuples work in Python.

Now, if you’ve learned something from this class, then care to share it with your colleagues. Also, connect to our social media accounts to receive regular updates.

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Data Structures ¶

Data structures , such as lists, can represent complex data. While lists are quite useful on their own, Python provides several other built-in data structures to make it easier to represent complex data. By the end of this lesson, students will be able to:

• Apply list comprehensions to define basic list sequences.
• Apply set operations to store and retrieve values in a set.
• Apply dict operations to store and retrieve values in a dictionary.
• Describe the difference between the various data structures' properties ( list , set , dict , tuple ).

List comprehensions ¶

Another one of the best features of Python is the list comprehension. A list comprehension provides a concise expression for building-up a list of values by looping over any type of sequence.

We already know how to create a list counting all the numbers between 0 and 10 (exclusive) by looping over a range .

A list comprehension provides a shorter expression for achieving the same result.

What if we wanted to compute all these values squared? A list comprehension can help us with this as well.

Or, what if we wanted to only include values of i that are even?

Before running the next block of code, what do you think will output?

Practice: Fun numbers ¶

Fill in the blank with a list comprehension to complete the definition for fun_numbers .

Whereas lists represent mutable sequences of any elements, tuples (pronounced "two pull" or like "supple") represent immutable sequences of any elements. Just like strings, tuples are immutable, so the sequence of elements in a tuple cannot be modified after the tuple has been created.

While lists are defined with square brackets, tuples are defined by commas alone as in the expression 1, 2, 3 . We often add parentheses around the structure for clarity. In fact, when representing a tuple, Python will use parentheses to indicate a tuple.

We learned that there are many list functions, most of which modify the original list. Since tuples are immutable, there isn't an equivalent list of tuple functions. So why use tuples when we could just use lists instead?

Your choice of data structure communicates information to other programmers. If you know exactly how many elements should go in your data structure and those elements don't need to change, a tuple is right for the job. By choosing to use a tuple in this situation, we communicate to other programmers that the sequence of elements in this data structure cannot change! Everyone working on your project doesn't have to worry about passing a tuple to a function and that function somehow destroying the data.

Tuples provide a helpful way return more than one value from a function. For example, we can write a function that returns both the first letter and the second letter from a word.

Whereas lists represent mutable sequences of any elements, sets represent mutable unordered collections of unique elements. Unlike lists, sets are not sequences so we cannot index into a set in the same way that we could for lists and tuples. Sets only represent unique elements, so attempts to add duplicate elements are ignored.

So what's the point of using a  set  over a  list ? Sets are often much faster than lists at determining whether a particular element is contained in the set. We can see this in action by comparing the time it takes to count the number of unique words in a large document. Using a list results in much slower code.

By combining sets and list comprehensions, we can compose our programs in more "Pythonic" ways.

Practice: Area codes ¶

Fill in the blank to compose a "Pythonic" program that returns the number of unique area codes from the given list of phone numbers formatted as strings like "123-456-7890" . The area code is defined as the first 3 digits in a phone number.

Dictionaries ¶

A dictionary represents mutable unordered collections of key-value pairs, where the keys are immutable and unique. In other words, dictionaries are more flexible than lists. A list could be considered a dictionary where the "keys" are non-negative integers counting from 0 to the length minus 1.

Dictionaries are often helpful for counting occurrences. Whereas the above example counted the total number of unique words in a text file, a dictionary can help us count the number of occurrences of each unique word in that file.

As an aside, there's also a more Pythonic way to write this program using collections.Counter , which is a specialized dictionary. The Counter type also sorts the results in order from greatest to least.

Practice: Count lengths ¶

Suppose we want to compute a histogram (counts) for the number of words that begin with each character in a given text file. Your coworker has written the following code and would like your help to finish the program. Explain your fix.

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Database Design for Customer Support Systems

Customer support systems are important for businesses to effectively manage interactions with customers , resolve issues , and provide timely assistance . These systems contain various channels such as phone, email, chat, and social media and focus on enhancing customer satisfaction and loyalty.

A well-designed database serves as the backbone of customer support systems, enabling organizations to store, organize, and analyze customer inquiries, feedback, and interactions efficiently. In this article, we will learn about How Database Design for Customer Support Systems by understanding various aspects of the article in detail.

Database Design Essentials for Customer Support Systems

Designing a database for a customer support system requires careful consideration of data sources, communication channels, ticket management workflows, customer information and reporting needs .

A robust database schema supports key functionalities such as ticket creation, assignment, tracking, resolution, and performance analysis.

Features of Customer Support Systems

Customer support systems typically include the following features, each of which depends on a well-designed database:

• Ticket Management: Creating , tracking and managing customer support tickets or cases across various channels.
• Customer Information : Storing and accessing customer information such as contact details, account history and preferences.
• Agent Assignment: Assign tickets to appropriate agents or teams based on skillset, workload or priority.
• Communication Channels: Integrating multiple communication channels (e.g. phone, email, chat ) for easily interaction with customers.
• Issue Resolution : Resolving customer issues, inquiries or complaints in a timely and efficient manner.
• Performance Metrics: Tracking and analyzing key performance metrics such as response time, resolution time, customer satisfaction scores and ticket volume.

Entities and Attributes in Customer Support Systems

In database design for customer support, common entities and their attributes include:

• TicketID (Primary Key): Unique identifier for each support ticket.
• CustomerID: Identifier of the customer associated with the ticket.
• Subject: Subject or summary of the ticket.
• Description: Detailed description of the issue or inquiry.
• Status: Status of the ticket (e.g., open, in progress, resolved).
• Priority: Priority level of the ticket (e.g, low, medium, high).
• AssignedTo: Identifier of the agent or team assigned to the ticket.
• CreatedAt: Timestamp of ticket creation.

2. Customer:

• CustomerID (Primary Key): Unique identifier for each customer.
• Name: Name of the customer.
• Email: Email address of the customer.
• Phone: Phone number of the customer.
• Address: Address of the customer.
• AccountStatus: Status of the customer account (e.g, active, inactive).
• AgentID (Primary Key): Unique identifier for each support agent.
• Name: Name of the agent.
• Email: Email address of the agent.
• Skillset: Skills or expertise of the agent.
• Availability: Availability status of the agent (e.g, online, offline).

Relationships in Personalization Systems:

In relational databases, entities are interconnected through relationships that define how data in one entity is related to data in another:

1. Customer-Ticket Relationship:

• One-to-many relationship.
• Each customer may have multiple tickets, but each ticket is associated with only one customer.

2. Agent-Ticket Relationship:

• Many-to-one relationship.
• Multiple tickets may be assigned to the same agent, but each ticket is assigned to only one agent.

Entities Structures in SQL Format

Here’s how the entities mentioned above can be structured in SQL format:

Database Model for Customer Support Systems

The database model for a customer support system revolves around efficiently managing tickets, customer information, agent details and relationships between them. By structuring data in a clear and organized manner, organizations can effectively track customer inquiries, assign tickets to appropriate agents and provide timely assistance to customers, thereby enhancing customer satisfaction and loyalty.

Tips & Tricks to Improve Database Design:

• Normalization: We should Organize data to minimize redundancy and improve data integrity.
• Indexing: Create indexes on frequently queried columns to enhance query performance.
• Data Encryption: Implement encryption techniques to protect sensitive customer information.
• Scalability: Design the database schema to scale with the growing volume of customer interactions and tickets.
• Integration: Integrate with other systems (e.g, CRM , knowledge base) to provide a seamless customer support experience.

Designing a database for a customer support system requires thoughtful consideration of data structure, relationships, and optimization techniques. By following best practices and leveraging SQL effectively, organizations can create a robust and scalable database schema to support various customer support functionalities. A well-designed database not only facilitates efficient ticket management and issue resolution but also contributes to the overall satisfaction and loyalty of customers in today’s competitive business landscape.

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