← Back to Python Course | Chapter 8: Modules & Packages | Lesson 2 of 6

Python import Statement

The import statement brings code from another file into yours, like borrowing a tool from a neighbor. You can take the whole toolbox or just one tool.

In this page:

  1. Basic Import Statement
  2. Import Specific Items with 'from'
  3. Renaming Imports using 'as'
  4. Wildcard Imports
  5. Conditional and Dynamic Imports
Syntax
python 
import module_name
import module_name as alias
from module_name import name1, name2

Basic Import Statement

'import module_name' loads the whole module and requires you to prefix everything you use from it with the module name, like math.sqrt(16) -- this keeps your code's origin of each function clear even in files with many imports.

Example: Basic Import Statement

python 
import math
print(math.sqrt(16))

Import Specific Items with 'from'

'from module import name' pulls a specific function, class, or variable directly into your file's namespace, so you can call sqrt(16) without the math. prefix -- convenient, but it's easier to lose track of where a name came from as a file grows.

Example: Import Specific Items with 'from'

python 
from math import sqrt
print(sqrt(16))

Renaming Imports using 'as'

'import numpy as np' gives a module (or an imported name) a shorter local alias, which is why the ecosystem convention 'import numpy as np' and 'import pandas as pd' exists -- it saves keystrokes across a file that uses the library constantly.

Example: Renaming Imports using 'as'

python 
import math as m
print(m.pi)

Wildcard Imports

'from module import *' pulls every public name from a module directly into your namespace at once, but doing this makes it hard to tell which function came from where and risks silently overwriting names you already defined -- most style guides recommend avoiding it.

Example: Wildcard Imports

python 
from math import *
print(sqrt(25))  # unclear which module sqrt came from

Conditional and Dynamic Imports

Because import is a regular statement, you can put it inside a function, an if block, or a try/except -- this is used for optional dependencies (falling back gracefully if a library isn't installed) or to avoid an import's cost until that code path actually runs.

Example: Conditional and Dynamic Imports

python 
try:
    import ujson as json_lib  # preferred faster library, if it's installed
except ImportError:
    import json as json_lib  # fallback to the standard library

print(json_lib.dumps({"a": 1}))
Related Topics

Python pass Statement · Python match Statement · Python if-else Statement · Python if Statement

{# common_mistakes/chapter_summary/browser_support: on Hindi pages the view already swaps in the hi_ translation fields (or blanks these out if untranslated), so this renders correctly for both languages without a lang_code check here. #}
Common Mistakes
  1. Using from module import *, which floods the namespace and can silently overwrite existing names.
  2. Importing a name that does not exist, as in from math import sqroot, which raises ImportError.
  3. Creating circular imports, where two files import each other, which raises ImportError or AttributeError at startup.
🔒

Chapter Quiz — Complete all 6 topics to unlock

0/6 topics done

Complete these topics first:

← Previous Lesson Python Modules Introduction Next Lesson → Python Standard Library

Login to run this code

C/C++/Java/PHP execution requires a free account. Your code is saved — you'll land right back in the editor after logging in.