Python import Statement
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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
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'
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'
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
from math import *
print(sqrt(25)) # unclear which module sqrt came fromConditional 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
try:
import ujson as json_lib
except ImportError:
import json as json_lib
print(json_lib.dumps({"a": 1}))Chapter Quiz — Complete all 6 topics to unlock
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