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Using len() With Built-in Sequences

A sequence is a container with ordered items. Lists, tuples, and strings are three of the basic built-in sequences in Python. You can find the length of a sequence by calling len():

>>> greeting = "Good Day!"
>>> len(greeting)
9

>>> office_days = ["Tuesday", "Thursday", "Friday"]
>>> len(office_days)
3

>>> london_coordinates = (51.50722, -0.1275)
>>> len(london_coordinates)
2

When finding the length of the string greeting, the list office_days, and the tuple london_coordinates, you use len() in the same manner. All three data types are valid arguments for len().

The function len() always returns an integer as it’s counting the number of items in the object that you pass to it. The function returns 0 if the argument is an empty sequence:

>>> len("")
0
>>> len([])
0
>>> len(())
0

In the examples above, you find the length of an empty string, an empty list, and an empty tuple. The function returns 0 in each case.

A range object is also a sequence that you can create using range(). A range object doesn’t store all the values but generates them when they’re needed. However, you can still find the length of a range object using len():

>>> len(range(1, 20, 2))
10

This range of numbers includes the integers from 1 to 19 with increments of 2. The length of a range object can be determined from the start, stop, and step values.

In this section, you’ve used the len() Python function with strings, lists, tuples, and range objects. However, you can also use the function with any other built-in sequence.

Using len() With Built-in Collections

At some point, you may need to find the number of unique items in a list or another sequence. You can use sets and len() to achieve this:

>>> import random

>>> numbers = [random.randint(1, 20) for _ in range(20)]

>>> numbers
[3, 8, 19, 1, 17, 14, 6, 19, 14, 7, 6, 1, 17, 10, 8, 14, 17, 10, 2, 5]

>>> unique_numbers = set(numbers)
{1, 2, 3, 5, 6, 7, 8, 10, 14, 17, 19}

>>> len(unique_numbers)
11

You generate the list numbers using a list comprehension, and it contains twenty random numbers ranging between 1 and 20. The output will be different each time the code runs since you’re generating random numbers. In this particular run, there are eleven unique numbers in the list of twenty randomly generated numbers.

Another built-in data type that you’ll use often is the dictionary. In a dictionary, each item consists of a key-value pair. When you use a dictionary as an argument for len(), the function returns the number of items in the dictionary:

>>> len({"James": 10, "Mary": 12, "Robert": 11})
3

>>> len({})
0

The output from the first example shows that there are three key-value pairs in this dictionary. As was the case with sequences, len() will return 0 when the argument is either an empty dictionary or an empty set. This leads to empty dictionaries and empty sets being falsy.

Exploring len() With Other Built-in Data Types

You can’t use all built-in data types as arguments for len(). For data types that don’t store more than one item within them, the concept of length isn’t relevant. This is the case with numbers and Boolean types:

>>> len(5)
Traceback (most recent call last):
    ...
TypeError: object of type 'int' has no len()

>>> len(5.5)
Traceback (most recent call last):
     ...
TypeError: object of type 'float' has no len()

>>> len(True)
Traceback (most recent call last):
     ...
TypeError: object of type 'bool' has no len()

>>> len(5 + 2j)
Traceback (most recent call last):
     ...
TypeError: object of type 'complex' has no len()

The integer, float, Boolean, and complex types are examples of built-in data types that you can’t use with len(). The function raises a TypeError when the argument is an object of a data type that doesn’t have a length.

You can also explore whether it’s possible to use iterators and generators as arguments for len():

>>> import random

>>> numbers = [random.randint(1, 20) for _ in range(20)]
>>> len(numbers)
20

>>> numbers_iterator = iter(numbers)
>>> len(numbers_iterator)
Traceback (most recent call last):
     ...
TypeError: object of type 'list_iterator' has no len()

>>> numbers_generator = (random.randint(1, 20) for _ in range(20))
>>> len(numbers_generator)
Traceback (most recent call last):
     ...
TypeError: object of type 'generator' has no len()

You’ve already seen that a list has a length, meaning you can use it as an argument in len(). You create an iterator from the list using the built-in function iter(). In an iterator, each item is fetched whenever it’s required, such as when the function next() is used or in a loop. However, you can’t use an iterator in len().

You get a TypeError when you try to use an iterator as an argument for len(). As the iterator fetches each item as and when it’s needed, the only way to measure its length is to exhaust the iterator. An iterator can also be infinite, such as the iterator returned by itertools.cycle(), and therefore its length can’t be defined.

You can’t use generators with len() for the same reason. The length of these objects can’t be measured without using them up.

Verifying the Length of a User Input

A common use case of len() is to verify the length of a sequence input by a user:

# username.py

username = input("Choose a username: [4-10 characters] ")

if 4 <= len(username) <= 10:
    print(f"Thank you. The username {username} is valid")
else:
    print("The username must be between 4 and 10 characters long")

In this example, you use an if statement to check if the integer returned by len() is greater than or equal to 4 and less than or equal to 10. You can run this script and you’ll get an output similar to the one below:

$ python username.py
Choose a username: [4-10 characters] stephen_g
Thank you. The username stephen_g is valid

The username is nine characters long in this case, so the condition in the if statement evaluates to True. You can run the script again and input an invalid username:

$ python username.py
Choose a username: [4-10 characters] sg
The username must be between 4 and 10 characters long

In this case, len(username) returns 2, and the condition in the if statement evaluates to False.

Ending a Loop Based on the Length of an Object

You’ll use len() if you need to check when the length of a mutable sequence, such as a list, reaches a specific number. In the following example, you ask the user to enter three username options, which you store in a list:

# username.py

usernames = []

print("Enter three options for your username")

while len(usernames) < 3:
    username = input("Choose a username: [4-10 characters] ")
    if 4 <= len(username) <= 10:
        print(f"Thank you. The username {username} is valid")
        usernames.append(username)
    else:
        print("The username must be between 4 and 10 characters long")

print(usernames)

You’re now using the result from len() in the while statement. If the user enters an invalid username, you don’t keep the input. When the user enters a valid string, you append it to the list usernames. The loop repeats until there are three items in the list.

You could even use len() to check when a sequence is empty:

>>> colors = ["red", "green", "blue", "yellow", "pink"]

>>> while len(colors) > 0:
...     print(f"The next color is {colors.pop(0)}")
...
The next color is red
The next color is green
The next color is blue
The next color is yellow
The next color is pink

You use the list method .pop() to remove the first item from the list in each iteration until the list is empty. If you’re using this method on large lists, you should remove items from the end of the list as this is more efficient. You can also use the deque data type from the collections built-in module, which allows you to pop from the left efficiently.

There’s a more Pythonic way of achieving the same output by using the truthiness of sequences:

>>> colors = ["red", "green", "blue", "yellow", "pink"]

>>> while colors:
...    print(f"The next color is {colors.pop(0)}")
...
The next color is red
The next color is green
The next color is blue
The next color is yellow
The next color is pink

An empty list is falsy. This means that the while statement interprets an empty list as False. A non-empty list is truthy, and the while statement treats it as True. The value returned by len() determines the truthiness of a sequence. A sequence is truthy when len() returns any non-zero integer and falsy when len() returns 0.

Finding the Index of the Last Item of a Sequence

Imagine you want to generate a sequence of random numbers in the range 1 to 10 and you’d like to keep adding numbers to the sequence until the sum of all the numbers exceeds 21. The following code creates an empty list and uses a while loop to populate the list:

>>> import random

>>> numbers = []
>>> while sum(numbers) <= 21:
...    numbers.append(random.randint(1, 10))

>>> numbers
[3, 10, 4, 7]

>>> numbers[len(numbers) - 1]
7

>>> numbers[-1]  # A more Pythonic way to retrieve the last item
7

>>> numbers.pop(len(numbers) - 1)  # You can use numbers.pop(-1)
7

>>> numbers
[3, 10, 4]

You append random numbers to the list until the sum exceeds 21. The output you’ll get will vary as you’re generating random numbers. To display the last number in the list, you use len(numbers) and subtract 1 from it since the first index of the list is 0. Indexing in Python allows you to use the index -1 to obtain the last item in a list. Therefore, although you can use len() in this case, you don’t need to.

You want to remove the last number in the list so that the sum of all numbers in the list doesn’t exceed 21. You use len() again to work out the index of the last item in the list, which you use as an argument for the list method .pop(). Even in this instance, you could use -1 as an argument for .pop() to remove the last item from the list and return it.

Splitting a List Into Two Halves

If you need to split a sequence into two halves, you’ll need to use the index that represents the midpoint of the sequence. You can use len() to find this value. In the following example, you’ll create a list of random numbers and then split it into two smaller lists:

>>> import random

>>> numbers = [random.randint(1, 10) for _ in range(10)]
>>> numbers
[9, 1, 1, 2, 8, 10, 8, 6, 8, 5]

>>> first_half = numbers[: len(numbers) // 2]
>>> second_half = numbers[len(numbers) // 2 :]

>>> first_half
[9, 1, 1, 2, 8]
>>> second_half
[10, 8, 6, 8, 5]

In the assignment statement where you define first_half, you use the slice that represents the items from the beginning of numbers up to the midpoint. You can work out what the slice represents by breaking down the steps you use in the slice expression:

1. First, len(numbers) returns the integer 10.
2. Next, 10 // 2 returns the integer 5 as you use the integer division operator.
3. Finally, 0:5 is a slice that represents the first five items, which have indices 0 to 4. Note that the endpoint is excluded.

In the next assignment, where you define second_half, you use the same expression in the slice. However, in this case, the integer 5 represents the start of the range. The slice is now 5: to represent the items from index 5 up to the end of the list.

If your original list contains an odd number of items, then half of its length will no longer be a whole number. When you use integer division, you obtain the floor of the number. The list first_half will now contain one less item than second_half.

You can try this out by creating an initial list of eleven numbers instead of ten. The resulting lists will no longer be halves, but they’ll represent the closest alternative to splitting an odd sequence.

NumPy’s ndarray

The NumPy module is the cornerstone of all quantitative applications of programming in Python. The module introduces the numpy.ndarray data type. This data type, along with functions within NumPy, is ideally suited for numerical computations and is the building block for data types in other modules.

Before you can start using NumPy, you’ll need to install the library. You can use Python’s standard package manager, pip, and run the following command in the console:

$ python -m pip install numpy

You’ve installed NumPy, and now you can create a NumPy array from a list and use len() on the array:

>>> import numpy as np

>>> numbers = np.array([4, 7, 9, 23, 10, 6])
>>> type(numbers)
<class 'numpy.ndarray'>

>>> len(numbers)
6

The NumPy function np.array() creates an object of type numpy.ndarray from the list you pass as an argument.

However, NumPy arrays can have more than one dimension. You can create a two-dimensional array by converting a list of lists into an array:

>>> import numpy as np

>>> numbers = [
    [11, 1, 10, 10, 15],
    [14, 9, 16, 4, 4],
]

>>> numbers_array = np.array(numbers)
>>> numbers_array
array([[11,  1, 10, 10, 15],
       [14,  9, 16,  4,  4]])

>>> len(numbers_array)
2

>>> numbers_array.shape
(2, 5)

>>> len(numbers_array.shape)
2

>>> numbers_array.ndim
2

The list numbers consists of two lists, each containing five integers. When you use this list of lists to create a NumPy array, the result is an array with two rows and five columns. The function returns the number of rows in the array when you pass this two-dimensional array as an argument in len().

To get the size of both dimensions, you use the property .shape, which is a tuple showing the number of rows and columns. You obtain the number of dimensions of a NumPy array either by using .shape and len() or by using the property .ndim.

In general, when you have an array with any number of dimensions, len() returns the size of the first dimension:

>>> import numpy as np

>>> array_3d = np.random.randint(1, 20, [2, 3, 4])
>>> array_3d
array([[[14,  9, 15, 14],
        [17, 11, 10,  5],
        [18,  1,  3, 12]],
       [[ 1,  5,  6, 10],
        [ 6,  3,  1, 12],
        [ 1,  4,  4, 17]]])

>>> array_3d.shape
(2, 3, 4)

>>> len(array_3d)
2

In this example, you create a three-dimensional array with the shape (2, 3, 4) where each element is a random integer between 1 and 20. You use the function np.random.randint() to create an array this time. The function len() returns 2, which is the size of the first dimension.

Check out NumPy Tutorial: Your First Steps Into Data Science in Python to learn more about using NumPy arrays.

Pandas’ DataFrame

The DataFrame type in the pandas library is another data type that is used extensively in many applications.

Before you can use pandas, you’ll need to install it by using the following command in the console:

$ python -m pip install pandas

You’ve installed the pandas package, and now you can create a DataFrame from a dictionary:

>>> import pandas as pd

>>> marks = {
    "Robert": [60, 75, 90],
    "Mary": [78, 55, 87],
    "Kate": [47, 96, 85],
    "John": [68, 88, 69],
}

>>> marks_df = pd.DataFrame(marks, index=["Physics", "Math", "English"])

>>> marks_df
         Robert  Mary  Kate  John
Physics      60    78    47    68
Math         75    55    96    88
English      90    87    85    69

>>> len(marks_df)
3

>>> marks_df.shape
(3, 4)

The dictionary’s keys are strings representing the names of students in a class. The value of each key is a list with the marks for three subjects. When you create a DataFrame from this dictionary, you define the index using a list containing the subject names.

The DataFrame has three rows and four columns. The function len() returns the number of rows in the DataFrame. The DataFrame type also has a .shape property, which you can use to show that the first dimension of a DataFrame represents the number of rows.

You’ve seen how len() works with a number of built-in data types and also with some data types from third-party modules. In the following section, you’ll learn how to define any class so that it’s usable as an argument for the len() Python function.

You can explore the pandas module further in The Pandas DataFrame: Make Working With Data Delightful.