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Create a sequence of numbers from 0 to 5, and print each item in the sequence:

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The range() function returns a sequence of numbers, starting from 0 by default, and increments by 1 (by default), and stops before a specified number.

Parameter Values

More examples.

Create a sequence of numbers from 3 to 5, and print each item in the sequence:

Create a sequence of numbers from 3 to 19, but increment by 2 instead of 1:

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Python range(): A Complete Guide (w/ Examples)

  • September 19, 2022 February 15, 2023

Python range Complete Guide with Examples Cover Image

The Python range function is used to generate a sequence of numbers between a given range of values . In this guide, you’ll learn all you need to know about the Python range() function by way of helpful examples. While on the surface, the function is very straightforward, there is a lot of hidden functionality.

By the end of this tutorial, you’ll have learned:

  • How to use the Python range() function
  • How to customize the Python range() with start, stop, and step parameters
  • How to reverse the Python range() function
  • How to use the Python range() function in a for loop
  • And so much more

Table of Contents

Understanding the Python range() Function

The Python range() function allows you generate a sequence of numbers using start , stop , and step parameters. By default, the created range will start from 0, increment by 1, and stop before the specified number.

Before we go any further, let’s make a quick note. Python range() isn’t actually a function – it’s a type. When you instantiate a range() object, a range type is returned. The benefit of this is that it uses much less memory than container types, like lists or tuples. To keep things simple and practical, this guide will use the term function, rather than type.

Now, let’s take a look at how we can use Python range() by exploring its parameters and default arguments:

We can see that the range() function has three parameters, only one of which is required. Let’s take a closer look at these parameters:

By default, the range function will start at 0, increment by 1, and go up to (but not include) the value indicated at the stop parameter.

Creating a Python range() Object

Let’s see how we can use the range() function to create a range of values:

In the code above, we passed in a range of values from 0 through, but not including, 5. There are two key takeaways from the code block above:

  • Printing the range doesn’t print out the values, but prints out the range object itself
  • Instantiating a range returns a class of type range

Python actually generates a lazy-loading object. To learn more about how these objects, or generators work, check out my complete guide on Python generators .

Printing Values in a Python range() Object

We can print out the values by iterating over each item in the object. We’ll print each of the item on the same line by modifying the end= parameter of the print() function. By passing in a string containing only a space, each item is printed on the same line, separated by a space.

We can see that the function returns values from 0 to 4. What’s more, is that the function returns an object containing five items.

Let’s take a look at how we can customize the start of our range in Python.

Customize the Start Value in the Python range() Function

By default, the Python range() will start 0. However, you can customize this value by modifying the start= parameter. Let’s see how we can start our range at 5 and go up to 10 by passing in our parameters:

By passing in a start= parameter, we can modify the starting position of the range. The start parameter is inclusive, meaning that the value is included (unlike the stop parameter). In the following section, you’ll learn about the final parameter, step= .

Customize the Step in the Python range() Function

The step= parameter of the Python range() function allows you to specify how values are incremented (or decremented) by. By default, this value is set to 1, meaning that items go from the start up to the stop, incrementing by 1.

Let’s see how we can pass in a different value to step through our range using different values:

In the code above, we create a range from 0 through 7, incrementing by 2. This means that we’re creating a range of even numbers from 0 to 7.

Now that you have learned about all the different parameters available in the range() type, let’s take a look at some use cases and quirks of the object.

Using Python range() In a For Loop

In many cases, you’ll use the Python range() object in a to perform an action multiple times. We easily do this while accessing the item in our loop.

What’s great about this is that it lets you easily specify the number of times you want a piece of code to run. For example, passing in range(5) identifies that a for loop will run five times.

Let’s see what this looks like:

We can see that we were able to access the items in the range while executing code multiple times.

If you don’t need to access the item, you can identify this in your for loop by using an underscore. This makes it clear to the reader of your code that the item being iterated is a throw-away. Let’s see what this looks like:

In the following section, you’ll learn how to create a Python range in reverse.

Creating a Python Range in Reverse

Python makes it easy to generate a range in reverse by passing a negative value into the step parameter. When you pass a negative step value into the range() object, you’re able to create a sequence of values in reverse order.

Let’s see how this works by creating a range that decrements by 1 from 5 to 0:

Similarly, you can create ranges of negative values using this negative step as well. Let’s create a new range going from 0 through to -10:

In the following section, you’ll learn how to create a list from a Python range.

How to Create a List from a Python Range

When you first create a range object, the values are stored in a memory-efficient format. However, in some cases, you’ll want to convert the ranges you create into Python lists.

Thankfully, this is made easy by using the list() constructor function. You can simply pass the range into the list() function, which creates a list out of the range.

Keep in mind that by creating a list out of the range object, you’re losing the memory efficiency that the range object has.

How to Use Python range with Floats

By default, the range() function only works with integers. In fact, by passing in floats your program will raise a TypeError . This is an important consideration and may cause some confusion.

In fact, there is no direct way of creating a range object with a float. However, we can create a list out of our range object and convert each item to its floating point representation.

In the code block above, we used a for loop to iterate over each element in the range. We then converted the item to a floating point value using the float() function and appended it to a list.

We can also simplify this process by making use of a Python list comprehension to simplify our code. Let’s take a look at how we can do this:

In some cases, you’ll want to create a range of floating point values that increment at a decimal values. For this, you can use NumPy’s arange() function.

Creating Ranges with Floats using NumPy

The NumPy arange() function allows you to create ranges with floating point values. By doing this, you can create ranges that do not increment by whole numbers. Let’s see how we can create a range of floating point values from 1.0 through 10.0 that increment by 1.5:

Let’s break down what we did in the code block above:

  • We imported NumPy using the alias np
  • We then used the arange() function to create a range of values going from 1.0 through 10.0 incrementing by 1.5

In the following section, you’ll learn how to use the Python range() function to create an inclusive range.

How to Use Python Range Inclusively

There is no default way to create an inclusive range in Python. However, you can make the range inclusive by simply adding the step value to the stop value . This ensures that the stop value will be included in the range.

In the following section, you’ll learn how to create a negative range in Python.

How to Create a Negative Range in Python

In general, Python will not produce anything when you pass only a negative value into the range() function. Let’s see what this looks like:

Logically, this makes sense! Because the start value defaults to 0 and the step argument defaults to 1, Python can’t go from 0 to a negative value!

In order to create negative ranges, we have two options:

  • We can create a negative range in increasing order , or
  • We can create a negative range in decreasing order

If we want our range to increase in values, our start value must be smaller than our stop value. Similarly, our step must be positive. Let’s see what this looks like:

In order to create a negative range with decreasing values, the following conditions must be true:

  • The start must be higher than the stop
  • The step must be negative

Let’s see what this looks like in Python:

In the following section, you’ll learn how to index a Python range object.

Indexing a Python Range Object

One of the great things about Python range objects is that they are indexable and sliceable. This allows you to access a specific element or slices of elements directly from the range.

This can be done by using [] square-bracket indexing on the range object. Let’s see how we can access the second item in a range by accessing the 1st index:

Similarly, we can use negative indexing to access items from the end of the range. Let’s see how we can access the second last item by indexing the index of -2:

We can even select slices of our range object by passing in a slice of values. Let’s see how we can access the first five items of a given range:

Notice here, that the slice returns a range object in itself. In order to get the actual values, you can convert the object to a list using the list() function. In the next section, you’ll learn how to access different attributes of a range object.

Accessing Start, Stop, and Step Attributes in a Python range Object

Because Python range objects are, well, objects, they come with different attributes. In particular, the range object has three attributes:

  • .start , which identifies the start of the range
  • .stop , which identifies the stopping point of the range
  • .step , which identifies the step of the range

Let’s create a Python range and print out these attributes:

The following section will explore some frequently asked questions about the Python range function.

Frequently Asked Questions

The function returns a range object by passing in start, stop, and step arguments. If only a stop argument is provided, the range will increment by 1, starting at 0 up to (but not including) the stop value.

You can decrease a range in Python by passing in a negative step value. For example, range(10, 0, -1) will return the values from 10 through 1.

range is the Python 3 equivalent of xrange in Python 2. In Python 2, range returned a list, rather than a lazy-evaluating generator.

In this guide, you learned how to use range() in Python to create sequences of values. You first learned how to use the range function by understanding its various parameters. Then, you learned how to customize the behavior of the function. The ranges you can create can be used with significant flexibility. Understanding all that this type has to offer can make you a much stronger Python programmer.

Additional Resources

To learn more about related topics, check out the guides below:

  • NumPy linspace: Creating Evenly Spaced Arrays with np.linspace
  • Creating Date Ranges with Pandas
  • Indexing and Slicing NumPy Arrays: A Complete Guide
  • Python range(): Official Documentation

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Python range() Function – Explained with Code Examples

In Python, can use use the range() function to get a sequence of indices to loop through an iterable. You'll often use range() in conjunction with a for loop.

In this tutorial, you'll learn about the different ways in which you can use the range() function – with explicit start and stop indices, custom step size, and negative step size.

Let's get started.

Understanding Python's range() Function

Before looking at the different ways of using the range() function, you've got to understand how it works.

The range() function returns a range object.   This range object in turn returns the successive items in the sequence when you iterate over it.

As stated above, the range function does not return a list of indices. Rather, it returns a range object which returns the indices as and when you need them. This makes it memory-efficient as well.  

You can use the range() function with the following general syntax:

When you use this syntax in conjunction with a loop, you can get a sequence of indices from start up to but not including stop , in steps of step .

  • You must specify the required argument stop , which can be any positive integer. If you specify a floating point number instead, you'll run into a TypeError as shown:

image-24

  • If you don't specify the start index, the default start index of 0 is used.
  • If you don't specify the step value, the default step size of 1 is used.

In the subsequent sections, you'll learn about the different ways of using the range() function.

How to Use Python's range() Function to Loop Through Any Iterable

As mentioned in the previous section, you only need one positive integer to use the range() function. The syntax is shown below:

You can use the above line of code to get a sequence from 0 through stop-1 : 0 , 1 , 2 , 3 ,..., stop-1 .  

▶ Consider the following example where you call range() with 5 as the argument. And you loop through the returned range object using a for loop to get the indices 0,1,2,3,4 as expected.

If you remember, all iterables in Python follow zero-indexing . This is why it's convenient to use range() to loop through iterables.

An iterable of length len has 0 , 1 , 2 , ..., len-1 as the valid indices. So to traverse any iterable, all you need to do is to set the stop value to be equal to len . The sequence you'll get – 0 , 1 , 2 , ..., len-1 – is the sequence of valid indices.

▶ Let's take a more helpful example. You have a list my_list . You can access all items in the list by knowing their indices, and you can get those indices using range() as shown below:

Remember, you can use Python's built-in function len to get the length of any iterable. In the above code, you use both the valid indices, and the list items at those valid indices. Here's the output:

image-25

Notice how my_list is 7 items long, and the indices obtained are from 0 through 6, as expected.

Sometimes, you may need to use negative integers instead. In this case, if you use only the stop argument, you'll not get the desired output, though the code doesn't throw an error.

This is because the default start value is assumed to be 0 , and you cannot count up from 0 to -5 .

How to Use Python's range() Function with Explicit Start and End Indices

You may not always want to start at zero. You can start at any arbitrary index by setting the start value to the index that you'd like to start from. The syntax is as follows:

In this case, you'll be able to get the sequence: start , start + 1 , start + 2 , and so on up to stop-1 .

▶ In the example below, you're starting at 10, count all the way up to but not including 15 in steps of 1.

In the previous section, you saw how using only the stop argument won't work when you need negative integers. However, when you specify start and stop indices explicitly, you can as well work with negative integers.

▶ In this example, you're trying to count up from -5 in steps of 1. Always keep in  mind that the counting stops at the value that's one less than the stop index.

How to Use Python's range() Function with a Custom Step Size

Instead of traversing an iterable sequentially, you may sometimes want to stride through it, accessing every k -th element. This is when the optional step argument comes in handy. The general syntax is shown below:

When you use this syntax and loop through the range object, you can go from start to stop-1 with strides of size step .

  • You'll get the sequence: start , start + step , start + 2*step , and so on up to start + k*step such that   start + k*step < stop and start + (k+1)*step > stop .

▶ In the example below, you'd like to go from 0 to 20 in steps of 2. Notice how the last index printed out is 19. This is because, if you take another step, you'll be at 21 which is greater than 20.

Always remember, the last value you get can be as close to stop as possible, but can never be stop .

How to Use Python's range() Function with a Negative Step Size

So far, you've learned to use the range() function with start and stop indices, and a specific step size, all the while counting up from start to stop .

If you need to count down from an integer, you can specify a negative value for step . The general syntax is:

  • The range object can now be used to return a sequence that counts down from start in steps of negative_step , up to but not including stop .
  • The sequence returned is start , start - negative_step , start - 2*negative_step , and so on up to start - k*negative_step such that start - k*negative_step > stop and start - (k+1)*negative_step < stop .
  • There's no default value for negative step – you must set negative_step = -1 to count down covering each number.

▶ In this example, you'd like to count down from 20 in steps of -2. So the sequence is 20, 18, 16, all the way down to 2. If you go another 2 steps lower, you'll hit 0, which you cannot as it's smaller than the stop value of 1.

It's easy to see that start > stop to be able to count down.

▶ In the above example, you try counting down from 10 to 20 which is impossible. And you don't get any output which is expected.

How to Use Python's range() and reversed() Functions to Reverse a Sequence

If you need to access the elements of an iterable in the reverse order, you can use the range() function coupled with the reversed() function.

Python's built-in reversed() function returns a reverse iterator over the values of a given sequence.

▶ Let's take our very first example, where we used range(5) . In the example below, we call reversed() on the range object. And we see that we've counted down from 4 to 0.

As you can see, this is equivalent to using range(4,-1,-1) . If you prefer, you may use the reversed() function instead of negative_step argument discussed in the previous section.

In this tutorial, you've learned the different ways in which you can use the range() function. You can try a few examples to get a different sequence each time. This practice will help you use range() effectively when looping through iterables.

Happy coding! Until the next tutorial.🙂

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Python range() Function

The range() function in Python generates a sequence of numbers. By default, the sequence starts at 0, increments by 1, and stops before the specified number.

range() Syntax

The start and step arguments are optional.

range() Return Value

The range() function returns an immutable sequence of numbers.

Example 1: range(stop)

In this example, we have converted the range sequence to a list .

Example 2: range(start, stop)

Example 3: range(start, stop, step), range() in for loop.

The range() function is commonly used in for loop to iterate the loop a certain number of times. For example,

Video: Python range() Function

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Python Enhancement Proposals

  • Python »
  • PEP Index »

PEP 572 – Assignment Expressions

The importance of real code, exceptional cases, scope of the target, relative precedence of :=, change to evaluation order, differences between assignment expressions and assignment statements, specification changes during implementation, _pydecimal.py, datetime.py, sysconfig.py, simplifying list comprehensions, capturing condition values, changing the scope rules for comprehensions, alternative spellings, special-casing conditional statements, special-casing comprehensions, lowering operator precedence, allowing commas to the right, always requiring parentheses, why not just turn existing assignment into an expression, with assignment expressions, why bother with assignment statements, why not use a sublocal scope and prevent namespace pollution, style guide recommendations, acknowledgements, a numeric example, appendix b: rough code translations for comprehensions, appendix c: no changes to scope semantics.

This is a proposal for creating a way to assign to variables within an expression using the notation NAME := expr .

As part of this change, there is also an update to dictionary comprehension evaluation order to ensure key expressions are executed before value expressions (allowing the key to be bound to a name and then re-used as part of calculating the corresponding value).

During discussion of this PEP, the operator became informally known as “the walrus operator”. The construct’s formal name is “Assignment Expressions” (as per the PEP title), but they may also be referred to as “Named Expressions” (e.g. the CPython reference implementation uses that name internally).

Naming the result of an expression is an important part of programming, allowing a descriptive name to be used in place of a longer expression, and permitting reuse. Currently, this feature is available only in statement form, making it unavailable in list comprehensions and other expression contexts.

Additionally, naming sub-parts of a large expression can assist an interactive debugger, providing useful display hooks and partial results. Without a way to capture sub-expressions inline, this would require refactoring of the original code; with assignment expressions, this merely requires the insertion of a few name := markers. Removing the need to refactor reduces the likelihood that the code be inadvertently changed as part of debugging (a common cause of Heisenbugs), and is easier to dictate to another programmer.

During the development of this PEP many people (supporters and critics both) have had a tendency to focus on toy examples on the one hand, and on overly complex examples on the other.

The danger of toy examples is twofold: they are often too abstract to make anyone go “ooh, that’s compelling”, and they are easily refuted with “I would never write it that way anyway”.

The danger of overly complex examples is that they provide a convenient strawman for critics of the proposal to shoot down (“that’s obfuscated”).

Yet there is some use for both extremely simple and extremely complex examples: they are helpful to clarify the intended semantics. Therefore, there will be some of each below.

However, in order to be compelling , examples should be rooted in real code, i.e. code that was written without any thought of this PEP, as part of a useful application, however large or small. Tim Peters has been extremely helpful by going over his own personal code repository and picking examples of code he had written that (in his view) would have been clearer if rewritten with (sparing) use of assignment expressions. His conclusion: the current proposal would have allowed a modest but clear improvement in quite a few bits of code.

Another use of real code is to observe indirectly how much value programmers place on compactness. Guido van Rossum searched through a Dropbox code base and discovered some evidence that programmers value writing fewer lines over shorter lines.

Case in point: Guido found several examples where a programmer repeated a subexpression, slowing down the program, in order to save one line of code, e.g. instead of writing:

they would write:

Another example illustrates that programmers sometimes do more work to save an extra level of indentation:

This code tries to match pattern2 even if pattern1 has a match (in which case the match on pattern2 is never used). The more efficient rewrite would have been:

Syntax and semantics

In most contexts where arbitrary Python expressions can be used, a named expression can appear. This is of the form NAME := expr where expr is any valid Python expression other than an unparenthesized tuple, and NAME is an identifier.

The value of such a named expression is the same as the incorporated expression, with the additional side-effect that the target is assigned that value:

There are a few places where assignment expressions are not allowed, in order to avoid ambiguities or user confusion:

This rule is included to simplify the choice for the user between an assignment statement and an assignment expression – there is no syntactic position where both are valid.

Again, this rule is included to avoid two visually similar ways of saying the same thing.

This rule is included to disallow excessively confusing code, and because parsing keyword arguments is complex enough already.

This rule is included to discourage side effects in a position whose exact semantics are already confusing to many users (cf. the common style recommendation against mutable default values), and also to echo the similar prohibition in calls (the previous bullet).

The reasoning here is similar to the two previous cases; this ungrouped assortment of symbols and operators composed of : and = is hard to read correctly.

This allows lambda to always bind less tightly than := ; having a name binding at the top level inside a lambda function is unlikely to be of value, as there is no way to make use of it. In cases where the name will be used more than once, the expression is likely to need parenthesizing anyway, so this prohibition will rarely affect code.

This shows that what looks like an assignment operator in an f-string is not always an assignment operator. The f-string parser uses : to indicate formatting options. To preserve backwards compatibility, assignment operator usage inside of f-strings must be parenthesized. As noted above, this usage of the assignment operator is not recommended.

An assignment expression does not introduce a new scope. In most cases the scope in which the target will be bound is self-explanatory: it is the current scope. If this scope contains a nonlocal or global declaration for the target, the assignment expression honors that. A lambda (being an explicit, if anonymous, function definition) counts as a scope for this purpose.

There is one special case: an assignment expression occurring in a list, set or dict comprehension or in a generator expression (below collectively referred to as “comprehensions”) binds the target in the containing scope, honoring a nonlocal or global declaration for the target in that scope, if one exists. For the purpose of this rule the containing scope of a nested comprehension is the scope that contains the outermost comprehension. A lambda counts as a containing scope.

The motivation for this special case is twofold. First, it allows us to conveniently capture a “witness” for an any() expression, or a counterexample for all() , for example:

Second, it allows a compact way of updating mutable state from a comprehension, for example:

However, an assignment expression target name cannot be the same as a for -target name appearing in any comprehension containing the assignment expression. The latter names are local to the comprehension in which they appear, so it would be contradictory for a contained use of the same name to refer to the scope containing the outermost comprehension instead.

For example, [i := i+1 for i in range(5)] is invalid: the for i part establishes that i is local to the comprehension, but the i := part insists that i is not local to the comprehension. The same reason makes these examples invalid too:

While it’s technically possible to assign consistent semantics to these cases, it’s difficult to determine whether those semantics actually make sense in the absence of real use cases. Accordingly, the reference implementation [1] will ensure that such cases raise SyntaxError , rather than executing with implementation defined behaviour.

This restriction applies even if the assignment expression is never executed:

For the comprehension body (the part before the first “for” keyword) and the filter expression (the part after “if” and before any nested “for”), this restriction applies solely to target names that are also used as iteration variables in the comprehension. Lambda expressions appearing in these positions introduce a new explicit function scope, and hence may use assignment expressions with no additional restrictions.

Due to design constraints in the reference implementation (the symbol table analyser cannot easily detect when names are re-used between the leftmost comprehension iterable expression and the rest of the comprehension), named expressions are disallowed entirely as part of comprehension iterable expressions (the part after each “in”, and before any subsequent “if” or “for” keyword):

A further exception applies when an assignment expression occurs in a comprehension whose containing scope is a class scope. If the rules above were to result in the target being assigned in that class’s scope, the assignment expression is expressly invalid. This case also raises SyntaxError :

(The reason for the latter exception is the implicit function scope created for comprehensions – there is currently no runtime mechanism for a function to refer to a variable in the containing class scope, and we do not want to add such a mechanism. If this issue ever gets resolved this special case may be removed from the specification of assignment expressions. Note that the problem already exists for using a variable defined in the class scope from a comprehension.)

See Appendix B for some examples of how the rules for targets in comprehensions translate to equivalent code.

The := operator groups more tightly than a comma in all syntactic positions where it is legal, but less tightly than all other operators, including or , and , not , and conditional expressions ( A if C else B ). As follows from section “Exceptional cases” above, it is never allowed at the same level as = . In case a different grouping is desired, parentheses should be used.

The := operator may be used directly in a positional function call argument; however it is invalid directly in a keyword argument.

Some examples to clarify what’s technically valid or invalid:

Most of the “valid” examples above are not recommended, since human readers of Python source code who are quickly glancing at some code may miss the distinction. But simple cases are not objectionable:

This PEP recommends always putting spaces around := , similar to PEP 8 ’s recommendation for = when used for assignment, whereas the latter disallows spaces around = used for keyword arguments.)

In order to have precisely defined semantics, the proposal requires evaluation order to be well-defined. This is technically not a new requirement, as function calls may already have side effects. Python already has a rule that subexpressions are generally evaluated from left to right. However, assignment expressions make these side effects more visible, and we propose a single change to the current evaluation order:

  • In a dict comprehension {X: Y for ...} , Y is currently evaluated before X . We propose to change this so that X is evaluated before Y . (In a dict display like {X: Y} this is already the case, and also in dict((X, Y) for ...) which should clearly be equivalent to the dict comprehension.)

Most importantly, since := is an expression, it can be used in contexts where statements are illegal, including lambda functions and comprehensions.

Conversely, assignment expressions don’t support the advanced features found in assignment statements:

  • Multiple targets are not directly supported: x = y = z = 0 # Equivalent: (z := (y := (x := 0)))
  • Single assignment targets other than a single NAME are not supported: # No equivalent a [ i ] = x self . rest = []
  • Priority around commas is different: x = 1 , 2 # Sets x to (1, 2) ( x := 1 , 2 ) # Sets x to 1
  • Iterable packing and unpacking (both regular or extended forms) are not supported: # Equivalent needs extra parentheses loc = x , y # Use (loc := (x, y)) info = name , phone , * rest # Use (info := (name, phone, *rest)) # No equivalent px , py , pz = position name , phone , email , * other_info = contact
  • Inline type annotations are not supported: # Closest equivalent is "p: Optional[int]" as a separate declaration p : Optional [ int ] = None
  • Augmented assignment is not supported: total += tax # Equivalent: (total := total + tax)

The following changes have been made based on implementation experience and additional review after the PEP was first accepted and before Python 3.8 was released:

  • for consistency with other similar exceptions, and to avoid locking in an exception name that is not necessarily going to improve clarity for end users, the originally proposed TargetScopeError subclass of SyntaxError was dropped in favour of just raising SyntaxError directly. [3]
  • due to a limitation in CPython’s symbol table analysis process, the reference implementation raises SyntaxError for all uses of named expressions inside comprehension iterable expressions, rather than only raising them when the named expression target conflicts with one of the iteration variables in the comprehension. This could be revisited given sufficiently compelling examples, but the extra complexity needed to implement the more selective restriction doesn’t seem worthwhile for purely hypothetical use cases.

Examples from the Python standard library

env_base is only used on these lines, putting its assignment on the if moves it as the “header” of the block.

  • Current: env_base = os . environ . get ( "PYTHONUSERBASE" , None ) if env_base : return env_base
  • Improved: if env_base := os . environ . get ( "PYTHONUSERBASE" , None ): return env_base

Avoid nested if and remove one indentation level.

  • Current: if self . _is_special : ans = self . _check_nans ( context = context ) if ans : return ans
  • Improved: if self . _is_special and ( ans := self . _check_nans ( context = context )): return ans

Code looks more regular and avoid multiple nested if. (See Appendix A for the origin of this example.)

  • Current: reductor = dispatch_table . get ( cls ) if reductor : rv = reductor ( x ) else : reductor = getattr ( x , "__reduce_ex__" , None ) if reductor : rv = reductor ( 4 ) else : reductor = getattr ( x , "__reduce__" , None ) if reductor : rv = reductor () else : raise Error ( "un(deep)copyable object of type %s " % cls )
  • Improved: if reductor := dispatch_table . get ( cls ): rv = reductor ( x ) elif reductor := getattr ( x , "__reduce_ex__" , None ): rv = reductor ( 4 ) elif reductor := getattr ( x , "__reduce__" , None ): rv = reductor () else : raise Error ( "un(deep)copyable object of type %s " % cls )

tz is only used for s += tz , moving its assignment inside the if helps to show its scope.

  • Current: s = _format_time ( self . _hour , self . _minute , self . _second , self . _microsecond , timespec ) tz = self . _tzstr () if tz : s += tz return s
  • Improved: s = _format_time ( self . _hour , self . _minute , self . _second , self . _microsecond , timespec ) if tz := self . _tzstr (): s += tz return s

Calling fp.readline() in the while condition and calling .match() on the if lines make the code more compact without making it harder to understand.

  • Current: while True : line = fp . readline () if not line : break m = define_rx . match ( line ) if m : n , v = m . group ( 1 , 2 ) try : v = int ( v ) except ValueError : pass vars [ n ] = v else : m = undef_rx . match ( line ) if m : vars [ m . group ( 1 )] = 0
  • Improved: while line := fp . readline (): if m := define_rx . match ( line ): n , v = m . group ( 1 , 2 ) try : v = int ( v ) except ValueError : pass vars [ n ] = v elif m := undef_rx . match ( line ): vars [ m . group ( 1 )] = 0

A list comprehension can map and filter efficiently by capturing the condition:

Similarly, a subexpression can be reused within the main expression, by giving it a name on first use:

Note that in both cases the variable y is bound in the containing scope (i.e. at the same level as results or stuff ).

Assignment expressions can be used to good effect in the header of an if or while statement:

Particularly with the while loop, this can remove the need to have an infinite loop, an assignment, and a condition. It also creates a smooth parallel between a loop which simply uses a function call as its condition, and one which uses that as its condition but also uses the actual value.

An example from the low-level UNIX world:

Rejected alternative proposals

Proposals broadly similar to this one have come up frequently on python-ideas. Below are a number of alternative syntaxes, some of them specific to comprehensions, which have been rejected in favour of the one given above.

A previous version of this PEP proposed subtle changes to the scope rules for comprehensions, to make them more usable in class scope and to unify the scope of the “outermost iterable” and the rest of the comprehension. However, this part of the proposal would have caused backwards incompatibilities, and has been withdrawn so the PEP can focus on assignment expressions.

Broadly the same semantics as the current proposal, but spelled differently.

Since EXPR as NAME already has meaning in import , except and with statements (with different semantics), this would create unnecessary confusion or require special-casing (e.g. to forbid assignment within the headers of these statements).

(Note that with EXPR as VAR does not simply assign the value of EXPR to VAR – it calls EXPR.__enter__() and assigns the result of that to VAR .)

Additional reasons to prefer := over this spelling include:

  • In if f(x) as y the assignment target doesn’t jump out at you – it just reads like if f x blah blah and it is too similar visually to if f(x) and y .
  • import foo as bar
  • except Exc as var
  • with ctxmgr() as var

To the contrary, the assignment expression does not belong to the if or while that starts the line, and we intentionally allow assignment expressions in other contexts as well.

  • NAME = EXPR
  • if NAME := EXPR

reinforces the visual recognition of assignment expressions.

This syntax is inspired by languages such as R and Haskell, and some programmable calculators. (Note that a left-facing arrow y <- f(x) is not possible in Python, as it would be interpreted as less-than and unary minus.) This syntax has a slight advantage over ‘as’ in that it does not conflict with with , except and import , but otherwise is equivalent. But it is entirely unrelated to Python’s other use of -> (function return type annotations), and compared to := (which dates back to Algol-58) it has a much weaker tradition.

This has the advantage that leaked usage can be readily detected, removing some forms of syntactic ambiguity. However, this would be the only place in Python where a variable’s scope is encoded into its name, making refactoring harder.

Execution order is inverted (the indented body is performed first, followed by the “header”). This requires a new keyword, unless an existing keyword is repurposed (most likely with: ). See PEP 3150 for prior discussion on this subject (with the proposed keyword being given: ).

This syntax has fewer conflicts than as does (conflicting only with the raise Exc from Exc notation), but is otherwise comparable to it. Instead of paralleling with expr as target: (which can be useful but can also be confusing), this has no parallels, but is evocative.

One of the most popular use-cases is if and while statements. Instead of a more general solution, this proposal enhances the syntax of these two statements to add a means of capturing the compared value:

This works beautifully if and ONLY if the desired condition is based on the truthiness of the captured value. It is thus effective for specific use-cases (regex matches, socket reads that return '' when done), and completely useless in more complicated cases (e.g. where the condition is f(x) < 0 and you want to capture the value of f(x) ). It also has no benefit to list comprehensions.

Advantages: No syntactic ambiguities. Disadvantages: Answers only a fraction of possible use-cases, even in if / while statements.

Another common use-case is comprehensions (list/set/dict, and genexps). As above, proposals have been made for comprehension-specific solutions.

This brings the subexpression to a location in between the ‘for’ loop and the expression. It introduces an additional language keyword, which creates conflicts. Of the three, where reads the most cleanly, but also has the greatest potential for conflict (e.g. SQLAlchemy and numpy have where methods, as does tkinter.dnd.Icon in the standard library).

As above, but reusing the with keyword. Doesn’t read too badly, and needs no additional language keyword. Is restricted to comprehensions, though, and cannot as easily be transformed into “longhand” for-loop syntax. Has the C problem that an equals sign in an expression can now create a name binding, rather than performing a comparison. Would raise the question of why “with NAME = EXPR:” cannot be used as a statement on its own.

As per option 2, but using as rather than an equals sign. Aligns syntactically with other uses of as for name binding, but a simple transformation to for-loop longhand would create drastically different semantics; the meaning of with inside a comprehension would be completely different from the meaning as a stand-alone statement, while retaining identical syntax.

Regardless of the spelling chosen, this introduces a stark difference between comprehensions and the equivalent unrolled long-hand form of the loop. It is no longer possible to unwrap the loop into statement form without reworking any name bindings. The only keyword that can be repurposed to this task is with , thus giving it sneakily different semantics in a comprehension than in a statement; alternatively, a new keyword is needed, with all the costs therein.

There are two logical precedences for the := operator. Either it should bind as loosely as possible, as does statement-assignment; or it should bind more tightly than comparison operators. Placing its precedence between the comparison and arithmetic operators (to be precise: just lower than bitwise OR) allows most uses inside while and if conditions to be spelled without parentheses, as it is most likely that you wish to capture the value of something, then perform a comparison on it:

Once find() returns -1, the loop terminates. If := binds as loosely as = does, this would capture the result of the comparison (generally either True or False ), which is less useful.

While this behaviour would be convenient in many situations, it is also harder to explain than “the := operator behaves just like the assignment statement”, and as such, the precedence for := has been made as close as possible to that of = (with the exception that it binds tighter than comma).

Some critics have claimed that the assignment expressions should allow unparenthesized tuples on the right, so that these two would be equivalent:

(With the current version of the proposal, the latter would be equivalent to ((point := x), y) .)

However, adopting this stance would logically lead to the conclusion that when used in a function call, assignment expressions also bind less tight than comma, so we’d have the following confusing equivalence:

The less confusing option is to make := bind more tightly than comma.

It’s been proposed to just always require parentheses around an assignment expression. This would resolve many ambiguities, and indeed parentheses will frequently be needed to extract the desired subexpression. But in the following cases the extra parentheses feel redundant:

Frequently Raised Objections

C and its derivatives define the = operator as an expression, rather than a statement as is Python’s way. This allows assignments in more contexts, including contexts where comparisons are more common. The syntactic similarity between if (x == y) and if (x = y) belies their drastically different semantics. Thus this proposal uses := to clarify the distinction.

The two forms have different flexibilities. The := operator can be used inside a larger expression; the = statement can be augmented to += and its friends, can be chained, and can assign to attributes and subscripts.

Previous revisions of this proposal involved sublocal scope (restricted to a single statement), preventing name leakage and namespace pollution. While a definite advantage in a number of situations, this increases complexity in many others, and the costs are not justified by the benefits. In the interests of language simplicity, the name bindings created here are exactly equivalent to any other name bindings, including that usage at class or module scope will create externally-visible names. This is no different from for loops or other constructs, and can be solved the same way: del the name once it is no longer needed, or prefix it with an underscore.

(The author wishes to thank Guido van Rossum and Christoph Groth for their suggestions to move the proposal in this direction. [2] )

As expression assignments can sometimes be used equivalently to statement assignments, the question of which should be preferred will arise. For the benefit of style guides such as PEP 8 , two recommendations are suggested.

  • If either assignment statements or assignment expressions can be used, prefer statements; they are a clear declaration of intent.
  • If using assignment expressions would lead to ambiguity about execution order, restructure it to use statements instead.

The authors wish to thank Alyssa Coghlan and Steven D’Aprano for their considerable contributions to this proposal, and members of the core-mentorship mailing list for assistance with implementation.

Appendix A: Tim Peters’s findings

Here’s a brief essay Tim Peters wrote on the topic.

I dislike “busy” lines of code, and also dislike putting conceptually unrelated logic on a single line. So, for example, instead of:

instead. So I suspected I’d find few places I’d want to use assignment expressions. I didn’t even consider them for lines already stretching halfway across the screen. In other cases, “unrelated” ruled:

is a vast improvement over the briefer:

The original two statements are doing entirely different conceptual things, and slamming them together is conceptually insane.

In other cases, combining related logic made it harder to understand, such as rewriting:

as the briefer:

The while test there is too subtle, crucially relying on strict left-to-right evaluation in a non-short-circuiting or method-chaining context. My brain isn’t wired that way.

But cases like that were rare. Name binding is very frequent, and “sparse is better than dense” does not mean “almost empty is better than sparse”. For example, I have many functions that return None or 0 to communicate “I have nothing useful to return in this case, but since that’s expected often I’m not going to annoy you with an exception”. This is essentially the same as regular expression search functions returning None when there is no match. So there was lots of code of the form:

I find that clearer, and certainly a bit less typing and pattern-matching reading, as:

It’s also nice to trade away a small amount of horizontal whitespace to get another _line_ of surrounding code on screen. I didn’t give much weight to this at first, but it was so very frequent it added up, and I soon enough became annoyed that I couldn’t actually run the briefer code. That surprised me!

There are other cases where assignment expressions really shine. Rather than pick another from my code, Kirill Balunov gave a lovely example from the standard library’s copy() function in copy.py :

The ever-increasing indentation is semantically misleading: the logic is conceptually flat, “the first test that succeeds wins”:

Using easy assignment expressions allows the visual structure of the code to emphasize the conceptual flatness of the logic; ever-increasing indentation obscured it.

A smaller example from my code delighted me, both allowing to put inherently related logic in a single line, and allowing to remove an annoying “artificial” indentation level:

That if is about as long as I want my lines to get, but remains easy to follow.

So, in all, in most lines binding a name, I wouldn’t use assignment expressions, but because that construct is so very frequent, that leaves many places I would. In most of the latter, I found a small win that adds up due to how often it occurs, and in the rest I found a moderate to major win. I’d certainly use it more often than ternary if , but significantly less often than augmented assignment.

I have another example that quite impressed me at the time.

Where all variables are positive integers, and a is at least as large as the n’th root of x, this algorithm returns the floor of the n’th root of x (and roughly doubling the number of accurate bits per iteration):

It’s not obvious why that works, but is no more obvious in the “loop and a half” form. It’s hard to prove correctness without building on the right insight (the “arithmetic mean - geometric mean inequality”), and knowing some non-trivial things about how nested floor functions behave. That is, the challenges are in the math, not really in the coding.

If you do know all that, then the assignment-expression form is easily read as “while the current guess is too large, get a smaller guess”, where the “too large?” test and the new guess share an expensive sub-expression.

To my eyes, the original form is harder to understand:

This appendix attempts to clarify (though not specify) the rules when a target occurs in a comprehension or in a generator expression. For a number of illustrative examples we show the original code, containing a comprehension, and the translation, where the comprehension has been replaced by an equivalent generator function plus some scaffolding.

Since [x for ...] is equivalent to list(x for ...) these examples all use list comprehensions without loss of generality. And since these examples are meant to clarify edge cases of the rules, they aren’t trying to look like real code.

Note: comprehensions are already implemented via synthesizing nested generator functions like those in this appendix. The new part is adding appropriate declarations to establish the intended scope of assignment expression targets (the same scope they resolve to as if the assignment were performed in the block containing the outermost comprehension). For type inference purposes, these illustrative expansions do not imply that assignment expression targets are always Optional (but they do indicate the target binding scope).

Let’s start with a reminder of what code is generated for a generator expression without assignment expression.

  • Original code (EXPR usually references VAR): def f (): a = [ EXPR for VAR in ITERABLE ]
  • Translation (let’s not worry about name conflicts): def f (): def genexpr ( iterator ): for VAR in iterator : yield EXPR a = list ( genexpr ( iter ( ITERABLE )))

Let’s add a simple assignment expression.

  • Original code: def f (): a = [ TARGET := EXPR for VAR in ITERABLE ]
  • Translation: def f (): if False : TARGET = None # Dead code to ensure TARGET is a local variable def genexpr ( iterator ): nonlocal TARGET for VAR in iterator : TARGET = EXPR yield TARGET a = list ( genexpr ( iter ( ITERABLE )))

Let’s add a global TARGET declaration in f() .

  • Original code: def f (): global TARGET a = [ TARGET := EXPR for VAR in ITERABLE ]
  • Translation: def f (): global TARGET def genexpr ( iterator ): global TARGET for VAR in iterator : TARGET = EXPR yield TARGET a = list ( genexpr ( iter ( ITERABLE )))

Or instead let’s add a nonlocal TARGET declaration in f() .

  • Original code: def g (): TARGET = ... def f (): nonlocal TARGET a = [ TARGET := EXPR for VAR in ITERABLE ]
  • Translation: def g (): TARGET = ... def f (): nonlocal TARGET def genexpr ( iterator ): nonlocal TARGET for VAR in iterator : TARGET = EXPR yield TARGET a = list ( genexpr ( iter ( ITERABLE )))

Finally, let’s nest two comprehensions.

  • Original code: def f (): a = [[ TARGET := i for i in range ( 3 )] for j in range ( 2 )] # I.e., a = [[0, 1, 2], [0, 1, 2]] print ( TARGET ) # prints 2
  • Translation: def f (): if False : TARGET = None def outer_genexpr ( outer_iterator ): nonlocal TARGET def inner_generator ( inner_iterator ): nonlocal TARGET for i in inner_iterator : TARGET = i yield i for j in outer_iterator : yield list ( inner_generator ( range ( 3 ))) a = list ( outer_genexpr ( range ( 2 ))) print ( TARGET )

Because it has been a point of confusion, note that nothing about Python’s scoping semantics is changed. Function-local scopes continue to be resolved at compile time, and to have indefinite temporal extent at run time (“full closures”). Example:

This document has been placed in the public domain.

Source: https://github.com/python/peps/blob/main/peps/pep-0572.rst

Last modified: 2023-10-11 12:05:51 GMT

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Python Range Exercises

Let’s check out some exercises that will help understand range() function better.

Exercise 14-a

Create a range from 0 to 50, excluding 50.

range() function can be used to create range type data.

Exercise 14-b

Create a range from 0 to 10 with steps of 2.

range() function can take 3 parameters. range( start, stop, step )

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Python Indexerror: list assignment index out of range Solution

In python, lists are mutable as the elements of a list can be modified. But if you try to modify a value whose index is greater than or equal to the length of the list then you will encounter an Indexerror: list assignment index out of range.  

Python Indexerror: list assignment index out of range Example

If ‘fruits’ is a list, fruits=[‘Apple’,’ Banana’,’ Guava’]and you try to modify fruits[5] then you will get an index error since the length of fruits list=3 which is less than index asked to modify for which is 5.

So, as you can see in the above example, we get an error when we try to modify an index that is not present in the list of fruits.

Method 1: Using insert() function

The insert(index, element) function takes two arguments, index and element, and adds a new element at the specified index.

Let’s see how you can add Mango to the list of fruits on index 1.

It is necessary to specify the index in the insert(index, element) function, otherwise, you will an error that the insert(index, element) function needed two arguments.

Method 2: Using append()

The append(element) function takes one argument element and adds a new element at the end of the list.

Let’s see how you can add Mango to the end of the list using the append(element) function.

Python IndexError FAQ

Q: what is an indexerror in python.

A: An IndexError is a common error that occurs when you try to access an element in a list, tuple, or other sequence using an index that is out of range. It means that the index you provided is either negative or greater than or equal to the length of the sequence.

Q: How can I fix an IndexError in Python?

A: To fix an IndexError, you can take the following steps:

  • Check the index value: Make sure the index you’re using is within the valid range for the sequence. Remember that indexing starts from 0, so the first element is at index 0, the second at index 1, and so on.
  • Verify the sequence length: Ensure that the sequence you’re working with has enough elements. If the sequence is empty, trying to access any index will result in an IndexError.
  • Review loop conditions: If the IndexError occurs within a loop, check the loop conditions to ensure they are correctly set. Make sure the loop is not running more times than expected or trying to access an element beyond the sequence’s length.
  • Use try-except: Wrap the code block that might raise an IndexError within a try-except block. This allows you to catch the exception and handle it gracefully, preventing your program from crashing.

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COMMENTS

  1. python

    3 Answers Sorted by: 18 Thats because range returns a range object in Python 3. Put it in list to make it do what you want: >>> Var1 = range (10, 50) >>> print (list (Var1)) [10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49] >>> Share

  2. Python range(): Represent Numerical Ranges

    You can create ranges by calling range () with one, two, or three arguments, as the following examples show: Python >>> list(range(5)) [0, 1, 2, 3, 4] >>> list(range(1, 7)) [1, 2, 3, 4, 5, 6] >>> list(range(1, 20, 2)) [1, 3, 5, 7, 9, 11, 13, 15, 17, 19] In each example, you use list () to explicitly list the individual elements of each range.

  3. Python range() Function

    Definition and Usage The range () function returns a sequence of numbers, starting from 0 by default, and increments by 1 (by default), and stops before a specified number. Syntax range (start, stop, step ) Parameter Values More Examples Example Create a sequence of numbers from 3 to 5, and print each item in the sequence: x = range(3, 6)

  4. Python range(): A Complete Guide (w/ Examples) • datagy

    September 19, 2022 The Python range function is used to generate a sequence of numbers between a given range of values. In this guide, you'll learn all you need to know about the Python range () function by way of helpful examples. While on the surface, the function is very straightforward, there is a lot of hidden functionality.

  5. Python range() function

    Practice The Python range () function returns a sequence of numbers, in a given range. The most common use of it is to iterate sequences on a sequence of numbers using Python loops. Example In the given example, we are printing the number from 0 to 4. Python3 for i in range(5): print(i, end=" ") print() Output: 0 1 2 3 4

  6. Python's Assignment Operator: Write Robust Assignments

    Augmented Assignment Operators in Python Augmented Mathematical Assignment Operators Augmented Assignments for Concatenation and Repetition Augmented Bitwise Assignment Operators Other Assignment Variants Annotated Assignment Statements Assignment Expressions With the Walrus Operator Managed Attribute Assignments Implicit Assignments in Python

  7. Python range() Function Explained with Examples

    The range () is a built-in function that returns a range object that consists series of integer numbers, which we can iterate using a for loop. In Python, Using a for loop with range (), we can repeat an action a specific number of times. For example, let's see how to use the range () function of Python 3 to produce the first six numbers. Example

  8. Python range() Function

    You can use the range () function with the following general syntax: range (start,stop,step) When you use this syntax in conjunction with a loop, you can get a sequence of indices from start up to but not including stop , in steps of step. You must specify the required argument stop, which can be any positive integer.

  9. Python range() Function

    The range() function in Python generates a sequence of numbers. By default, the sequence starts at 0, increments by 1, and stops before the specified number. Example # create a sequence from 0 to 3 (4 is not included) numbers = range(4) # iterating through the sequence for i in numbers: print(i)

  10. PEP 572

    Python Enhancement Proposals. Python » PEP Index » PEP 572; ... An assignment expression does not introduce a new scope. In most cases the scope in which the target will be bound is self-explanatory: it is the current scope. ... For example, [i:= i+1 for i in range(5)] is invalid: ...

  11. 6. Expressions

    For example: [x*y for x in range(10) for y in range(x, x+10)]. To ensure the comprehension always results in a container of the appropriate type, yield and yield from expressions are prohibited in the implicitly nested scope. Since Python 3.6, in an async def function, an async for clause may be used to iterate over a asynchronous iterator.

  12. Built-in Functions

    class bytearray (source = b'') class bytearray (source, encoding) class bytearray (source, encoding, errors). Return a new array of bytes. The bytearray class is a mutable sequence of integers in the range 0 <= x < 256. It has most of the usual methods of mutable sequences, described in Mutable Sequence Types, as well as most methods that the bytes type has, see Bytes and Bytearray Operations.

  13. Python

    Assign a range of elements to a list in Python using the list () function, you can follow these steps: Initialize a list with the elements that you want to include. Use the list () function to create a new list from the range of elements that you want to add. Use slice notation to insert the new list at the desired position in the original list.

  14. Different Forms of Assignment Statements in Python

    There are some important properties of assignment in Python :- Assignment creates object references instead of copying the objects. Python creates a variable name the first time when they are assigned a value. Names must be assigned before being referenced. There are some operations that perform assignments implicitly. Assignment statement forms :-

  15. Python Exercises, Practice, Challenges

    These free exercises are nothing but Python assignments for the practice where you need to solve different programs and challenges. All exercises are tested on Python 3. Each exercise has 10-20 Questions. The solution is provided for every question. Practice each Exercise in Online Code Editor

  16. python

    2 Answers Sorted by: 108 In Python 3, range returns a lazy sequence object - it does not return a list. There is no way to rearrange elements in a range object, so it cannot be shuffled. Convert it to a list before shuffling. allocations = list (range (len (people))) Share Follow edited Aug 21, 2018 at 0:01 user2357112 266k 28 446 523

  17. Python Range Exercises

    Exercises provided by HolyPython.com offer a great way to practice Python and they are free! ... Python Range Exercises. Let's check out some exercises that will help understand range() function better. Exercise 14-a. Create a range from 0 to 50, excluding 50. Hint 1. range() function can be used to create range type data. ...

  18. python

    python - Using range function for assigning a string to variable - Stack Overflow Using range function for assigning a string to variable Ask Question Asked 4 years, 8 months ago Modified 4 years, 8 months ago Viewed 784 times -2 How can I create an empty string and assign it to the variable "Letter".

  19. Python Indexerror: list assignment index out of range Solution

    A: To fix an IndexError, you can take the following steps: Check the index value: Make sure the index you're using is within the valid range for the sequence. Remember that indexing starts from 0, so the first element is at index 0, the second at index 1, and so on. Verify the sequence length: Ensure that the sequence you're working with ...

  20. Python Runtime Services

    The modules described in this chapter provide a wide range of services related to the Python interpreter and its interaction with its environment. Here's an overview: sys — System-specific parameters and functions; sysconfig — Provide access to Python's configuration information. Configuration variables;

  21. python

    2 Yes it doesn't work. Use append. Or if you are actually trying to access the 6th element use 5 instead. - jamylak May 17, 2012 at 5:44 1 Python's lists are 0-indexed. - wkl May 17, 2012 at 5:44 @jamylak I want to add new value in the list, with using append function. - user1132011 May 17, 2012 at 6:04 Add a comment 4 Answers

  22. Python error: IndexError: list assignment index out of range

    Python does not dynamically increase the size of an array when you assign to an element. You have to use a.append(element) to add an element onto the end, or a.insert(i, element) to insert the element at the position before i.