Convert Python to Clojure
Convert Python code to idiomatic Clojure. This skill extends meta-convert-dev with Python-to-Clojure specific type mappings, idiom translations, and tooling for transforming imperative, object-oriented Python code into functional, immutable Clojure.
This Skill Extends
- •
meta-convert-dev- Foundational conversion patterns (APTV workflow, testing strategies)
For general concepts like the Analyze → Plan → Transform → Validate workflow, testing strategies, and common pitfalls, see the meta-skill first.
This Skill Adds
- •Type mappings: Python types → Clojure types (dynamic → dynamic with immutability)
- •Idiom translations: Python patterns → idiomatic Clojure (OOP → functional)
- •Error handling: Exceptions → explicit error values
- •Async patterns: asyncio → core.async
- •Data structures: Mutable collections → immutable persistent collections
- •REPL workflow: Script-based → REPL-driven development
This Skill Does NOT Cover
- •General conversion methodology - see
meta-convert-dev - •Python language fundamentals - see
lang-python-dev - •Clojure language fundamentals - see
lang-clojure-dev - •Reverse conversion (Clojure → Python) - see
convert-clojure-python - •ClojureScript - see
convert-python-clojurescript
Quick Reference
| Python | Clojure | Notes |
|---|---|---|
int | int / long / BigInt | Clojure has arbitrary precision |
float | double | 64-bit floating point |
bool | true / false | Direct mapping |
str | String | Java strings (immutable) |
bytes | byte-array | Mutable byte array |
list[T] | [...] | Vector (indexed, immutable) |
tuple | [...] or (list ...) | Vector or list |
dict[K, V] | {:key val} | Hash map (immutable) |
set[T] | #{...} | Hash set (immutable) |
None | nil | Absence of value |
class | defrecord / deftype | Data structures |
def func(): | (defn func [] ...) | Function definition |
lambda x: x*2 | #(* % 2) or (fn [x] (* x 2)) | Anonymous function |
for x in xs: | (doseq [x xs] ...) | Side-effecting iteration |
[x for x in xs] | (for [x xs] ...) | Lazy sequence comprehension |
try/except | (try ... (catch ...)) | Exception handling |
async def | core.async channels | Different concurrency model |
When Converting Code
- •Analyze source thoroughly before writing target
- •Map types first - Python and Clojure are both dynamic but Clojure emphasizes immutability
- •Embrace immutability - replace in-place mutations with functional transformations
- •Use REPL-driven development - Clojure is designed for interactive development
- •Adopt functional idioms - avoid classes, prefer pure functions and data transformations
- •Handle edge cases - None→nil, exceptions, mutable state
- •Test equivalence - same inputs → same outputs
Type System Mapping
Primitive Types
| Python | Clojure | Notes |
|---|---|---|
int | int / long | Automatic promotion to BigInteger |
float | double | 64-bit IEEE 754 |
bool | true / false | Lowercase boolean literals |
str | String | Java.lang.String (immutable) |
bytes | byte-array | Mutable Java byte array |
bytearray | byte-array | Same as bytes in Clojure |
None | nil | Represents absence |
... (Ellipsis) | - | No equivalent |
Note on Integers: Both Python and Clojure support arbitrary precision integers. Clojure automatically promotes from long to BigInteger on overflow.
Collection Types
| Python | Clojure | Notes |
|---|---|---|
list[T] | [...] | Vector - indexed, immutable, O(log32 n) |
tuple[T, U] | [...] | Vector (immutable by default) |
tuple[T, ...] | (list ...) | List for sequential access |
dict[K, V] | {:key val} | Hash map - immutable, O(log32 n) |
set[T] | #{...} | Hash set - immutable |
frozenset[T] | #{...} | Sets are immutable by default |
collections.deque | clojure.lang.PersistentQueue | Immutable queue |
collections.OrderedDict | (array-map ...) or linked-hash-map | Maintains insertion order |
collections.defaultdict | Map + get-in with default | Use (get m k default) pattern |
collections.Counter | frequencies function | Built-in frequency counter |
range(n) | (range n) | Lazy sequence |
Composite Types
| Python | Clojure | Notes |
|---|---|---|
@dataclass | defrecord | Named fields, map-like access |
class (data) | defrecord or plain map | Prefer maps for simple data |
class (behavior) | Protocols + defrecord | Polymorphism via protocols |
typing.NamedTuple | defrecord | Named, typed fields |
typing.TypedDict | Plain map {:key val} | Maps with keyword keys |
enum.Enum | Keyword enum | #{:state/pending :state/done} |
typing.Union[T, U] | Tagged map or multimethod | {:type :int :value 42} |
typing.Optional[T] | nil or value | nil represents absence |
typing.Callable | fn or IFn | First-class functions |
typing.Protocol | Clojure protocol | Polymorphism |
Idiom Translation
Pattern 1: List Comprehensions → Sequence Operations
Python:
# List comprehension squared_evens = [x * x for x in numbers if x % 2 == 0] # Nested comprehension pairs = [(x, y) for x in range(3) for y in range(2)] # Generator expression total = sum(x * x for x in numbers if x % 2 == 0)
Clojure:
;; for - lazy sequence comprehension
(def squared-evens
(for [x numbers
:when (even? x)]
(* x x)))
;; Nested for - cartesian product
(def pairs
(for [x (range 3)
y (range 2)]
[x y]))
;; Direct transformation with threading
(def total
(->> numbers
(filter even?)
(map #(* % %))
(reduce +)))
Why this translation:
- •Python list comprehensions are eager; Clojure
foris lazy (more efficient) - •Clojure's threading macros (
->>) make pipelines clearer - •
reduceis idiomatic for aggregation
Pattern 2: Dictionary Operations → Map Operations
Python:
# Get with default
value = config.get("timeout", 30)
# Setdefault pattern
cache.setdefault(key, expensive_compute())
# Dictionary comprehension
squared = {k: v * v for k, v in items.items()}
# Merging dictionaries
merged = {**dict1, **dict2}
Clojure:
;; Get with default
(def value (get config :timeout 30))
;; Lazy computation with update
(def cache (update cache key #(or % (expensive-compute))))
;; Or with caching (using memoize)
(defn get-cached [cache key compute-fn]
(if-let [v (get cache key)]
v
(let [v (compute-fn)]
(assoc cache key v))))
;; Map transformation
(def squared
(into {} (map (fn [[k v]] [k (* v v)]) items)))
;; Merging maps
(def merged (merge dict1 dict2))
Why this translation:
- •Clojure maps are immutable; use
assoc,update,mergefor "changes" - •Keywords (
:timeout) are idiomatic for map keys - •
into+mapis the comprehension pattern for maps
Pattern 3: Classes → Records and Maps
Python:
from dataclasses import dataclass
@dataclass
class User:
id: int
name: str
email: str
def full_info(self):
return f"{self.name} ({self.email})"
# Usage
user = User(id=1, name="Alice", email="alice@example.com")
print(user.name)
print(user.full_info())
Clojure:
;; defrecord for structured data
(defrecord User [id name email])
;; Constructor and access
(def user (->User 1 "Alice" "alice@example.com"))
(println (:name user)) ; Map-like access
;; Functions operate on data
(defn full-info [user]
(str (:name user) " (" (:email user) ")"))
(println (full-info user))
;; Alternative: plain map (often preferred for simple data)
(def user {:id 1 :name "Alice" :email "alice@example.com"})
(println (:name user))
(println (full-info user))
Why this translation:
- •Clojure separates data from behavior (functions operate on data structures)
- •
defrecordprovides type identity and performance benefits - •Plain maps are often simpler and more flexible than records
- •Functions are defined separately, not as methods
Pattern 4: Iteration → Sequence Operations
Python:
# Imperative loop with mutation
result = []
for item in items:
if item > 0:
result.append(item * 2)
# Enumerate
for i, item in enumerate(items):
print(f"{i}: {item}")
# Zip
for name, age in zip(names, ages):
print(f"{name} is {age}")
Clojure:
;; Functional transformation (immutable)
(def result
(->> items
(filter pos?)
(map #(* % 2))))
;; map-indexed (like enumerate)
(doseq [[i item] (map-indexed vector items)]
(println (str i ": " item)))
;; map for pairing (like zip)
(doseq [[name age] (map vector names ages)]
(println (str name " is " age)))
Why this translation:
- •Clojure favors pure transformations over imperative loops
- •
filter,map,reduceare core sequence operations - •
doseqis for side effects (like printing),foris for lazy sequences - •
map-indexedandmap vectorreplace Python's enumerate and zip
Pattern 5: None Handling → nil Handling
Python:
# None checks
if user is not None:
name = user.name
else:
name = "Anonymous"
# Or with walrus
if (user := get_user(id)) is not None:
process(user)
# Default value
name = user.name if user else "Anonymous"
Clojure:
;; nil checks with when-let
(when-let [user (get-user id)]
(process user))
;; Or with if-let
(def name
(if-let [user user]
(:name user)
"Anonymous"))
;; Or with threading (some-> stops on nil)
(def name
(some-> user :name (str " is here")))
;; Default value with or
(def name (or (:name user) "Anonymous"))
Why this translation:
- •Clojure uses
nilinstead ofNone - •
when-letandif-letbind and test in one step - •
some->andsome->>short-circuit onnil(like optional chaining) - •
orreturns first truthy value (like Python'sor)
Pattern 6: Exceptions → Explicit Error Handling
Python:
# Raising exceptions
def divide(a, b):
if b == 0:
raise ValueError("Division by zero")
return a / b
# Catching exceptions
try:
result = divide(10, 0)
except ValueError as e:
print(f"Error: {e}")
result = None
# Finally block
try:
file = open("data.txt")
data = file.read()
finally:
file.close()
Clojure:
;; Throwing exceptions (when appropriate)
(defn divide [a b]
(if (zero? b)
(throw (ex-info "Division by zero" {:a a :b b}))
(/ a b)))
;; Catching exceptions
(def result
(try
(divide 10 0)
(catch Exception e
(println "Error:" (.getMessage e))
nil)))
;; with-open for resource cleanup (like Python's with)
(with-open [rdr (clojure.java.io/reader "data.txt")]
(def data (slurp rdr)))
;; Functional error handling (preferred for non-exceptional cases)
(defn safe-divide [a b]
(if (zero? b)
{:error "Division by zero"}
{:ok (/ a b)}))
(let [result (safe-divide 10 0)]
(if (:error result)
(println "Error:" (:error result))
(println "Result:" (:ok result))))
Why this translation:
- •Clojure uses exceptions for truly exceptional cases
- •For expected errors, return maps with
:ok/:errorkeys (or use a library likecatsfor Either) - •
with-openensures resource cleanup (like Python's context managers) - •
ex-infocreates exceptions with data maps
Pattern 7: Decorators → Macros or Higher-Order Functions
Python:
# Function decorator
def logged(func):
def wrapper(*args, **kwargs):
print(f"Calling {func.__name__}")
result = func(*args, **kwargs)
print(f"Finished {func.__name__}")
return result
return wrapper
@logged
def process(data):
return len(data)
# Property decorator
class Circle:
def __init__(self, radius):
self._radius = radius
@property
def area(self):
return 3.14159 * self._radius ** 2
Clojure:
;; Higher-order function (decorator-like)
(defn logged [f]
(fn [& args]
(println "Calling" (str f))
(let [result (apply f args)]
(println "Finished" (str f))
result)))
(def process (logged (fn [data] (count data))))
;; Or as a macro (compile-time transformation)
(defmacro defn-logged [name args & body]
`(defn ~name ~args
(println "Calling" ~(str name))
(let [result# (do ~@body)]
(println "Finished" ~(str name))
result#)))
(defn-logged process [data]
(count data))
;; "Property" via function (Clojure has no properties)
(defrecord Circle [radius])
(defn area [circle]
(* 3.14159 (:radius circle) (:radius circle)))
(def c (->Circle 5))
(println (area c))
Why this translation:
- •Python decorators → Clojure higher-order functions or macros
- •Macros run at compile time, can transform syntax
- •Clojure has no property syntax; use plain functions
- •
defmacrofor code transformation,defnfor runtime wrapping
Pattern 8: Object-Oriented → Functional
Python:
# Class with state and methods
class Counter:
def __init__(self):
self.count = 0
def increment(self):
self.count += 1
def get(self):
return self.count
counter = Counter()
counter.increment()
counter.increment()
print(counter.get()) # 2
Clojure:
;; Immutable data + pure functions
(defn increment [counter]
(update counter :count inc))
(defn get-count [counter]
(:count counter))
;; Usage with threading
(-> {:count 0}
(increment)
(increment)
(get-count)
(println)) ; 2
;; For mutable state, use atoms
(def counter (atom {:count 0}))
(defn increment! [counter]
(swap! counter update :count inc))
(defn get-count! [counter]
(:count @counter))
(increment! counter)
(increment! counter)
(println (get-count! counter)) ; 2
Why this translation:
- •Clojure prefers immutable data + pure functions over mutable objects
- •
->threading macro passes result through function chain - •For necessary state, use atoms (
swap!for updates,@for reads) - •Separate data (maps) from behavior (functions)
Pattern 9: String Formatting → str and format
Python:
# f-strings
name = "Alice"
age = 30
message = f"Hello {name}, you are {age} years old"
# format method
message = "Hello {}, you are {} years old".format(name, age)
# % formatting
message = "Hello %s, you are %d years old" % (name, age)
Clojure:
;; str concatenation (def name "Alice") (def age 30) (def message (str "Hello " name ", you are " age " years old")) ;; format (uses Java String.format) (def message (format "Hello %s, you are %d years old" name age)) ;; Or using clojure.pprint for complex formatting (require '[clojure.pprint :refer [cl-format]]) (def message (cl-format nil "Hello ~A, you are ~D years old" name age))
Why this translation:
- •
strconcatenates arguments (simple, idiomatic) - •
formatuses Java'sString.format(printf-style) - •
cl-format(Common Lisp format) for advanced formatting
Pattern 10: Async/Await → core.async
Python:
import asyncio
async def fetch_user(user_id):
await asyncio.sleep(0.1) # Simulate I/O
return {"id": user_id, "name": f"User {user_id}"}
async def main():
user = await fetch_user(123)
print(user)
asyncio.run(main())
Clojure:
(require '[clojure.core.async :as async :refer [go <! >! chan]])
;; core.async uses channels for communication
(defn fetch-user [user-id]
(go
(<! (async/timeout 100)) ; Simulate I/O
{:id user-id :name (str "User " user-id)}))
(defn main []
(let [result-chan (fetch-user 123)
user (<!! result-chan)] ; <!! blocks, <! for within go block
(println user)))
(main)
;; Or with go blocks and channels
(go
(let [user (<! (fetch-user 123))]
(println user)))
Why this translation:
- •Python's async/await → Clojure's
goblocks and channels - •
<!takes from channel (insidego),<!!blocks (outsidego) - •
>!puts onto channel,>!!blocks - •Different paradigm: CSP (channels) vs promises/futures
Error Handling
Python Exceptions → Clojure Approaches
| Python | Clojure | Notes |
|---|---|---|
raise Exception("msg") | (throw (Exception. "msg")) | Direct exception |
raise ValueError(...) | (throw (ex-info "msg" {:data ...})) | Exception with data |
try: ... except E: ... | (try ... (catch E e ...)) | Catching exceptions |
try: ... finally: ... | (try ... (finally ...)) | Cleanup block |
Return None for errors | Return nil or {:error ...} | Explicit error values |
Exception Handling Translation
Python:
def load_config(path):
try:
with open(path) as f:
data = json.load(f)
return data
except FileNotFoundError:
print(f"Config file not found: {path}")
return None
except json.JSONDecodeError as e:
print(f"Invalid JSON: {e}")
return None
finally:
print("Cleanup")
Clojure:
(require '[clojure.data.json :as json])
(defn load-config [path]
(try
(-> path
slurp
json/read-str)
(catch java.io.FileNotFoundException e
(println "Config file not found:" path)
nil)
(catch Exception e
(println "Invalid JSON:" (.getMessage e))
nil)
(finally
(println "Cleanup"))))
Why this translation:
- •Similar try/catch/finally structure
- •Clojure uses Java exception classes
- •
slurpreads entire file (like Python'sread()) - •
json/read-strparses JSON string
Functional Error Handling (Preferred)
Python (using optional types):
from typing import Optional
def safe_divide(a: int, b: int) -> Optional[float]:
if b == 0:
return None
return a / b
result = safe_divide(10, 0)
if result is not None:
print(f"Result: {result}")
else:
print("Division by zero")
Clojure (using maps or nil):
;; Returning nil for errors
(defn safe-divide [a b]
(when-not (zero? b)
(/ a b)))
(if-let [result (safe-divide 10 0)]
(println "Result:" result)
(println "Division by zero"))
;; Or using explicit error maps
(defn safe-divide [a b]
(if (zero? b)
{:error "Division by zero"}
{:ok (/ a b)}))
(let [result (safe-divide 10 0)]
(if (:error result)
(println "Error:" (:error result))
(println "Result:" (:ok result))))
Why this translation:
- •Nil represents absence/failure (like Python's None)
- •Maps with
:ok/:errorkeys make errors explicit - •Functional error handling avoids exception overhead for common cases
Concurrency Patterns
Python Threading/Asyncio → Clojure Concurrency
| Python | Clojure | Notes |
|---|---|---|
threading.Thread | (Thread. ...) or futures | Java threads |
asyncio.run(coro) | (go ...) or (future ...) | Async execution |
asyncio.gather(*tasks) | (async/alts! ...) or pmap | Concurrent ops |
asyncio.Queue | (chan) | Async channel |
threading.Lock | (atom), (ref), or Java locks | Coordinated state |
concurrent.futures | future, promise | Async results |
Asyncio → core.async
Python:
import asyncio
async def fetch_data(url):
await asyncio.sleep(0.1) # Simulate I/O
return f"Data from {url}"
async def main():
# Concurrent execution
results = await asyncio.gather(
fetch_data("url1"),
fetch_data("url2"),
fetch_data("url3")
)
for result in results:
print(result)
asyncio.run(main())
Clojure:
(require '[clojure.core.async :as async :refer [go <! >! chan]])
(defn fetch-data [url]
(go
(<! (async/timeout 100)) ; Simulate I/O
(str "Data from " url)))
;; Concurrent execution with channels
(defn main []
(let [urls ["url1" "url2" "url3"]
channels (map fetch-data urls)]
;; Collect results
(doseq [ch channels]
(println (<!! ch)))))
(main)
;; Or using alts! for first-to-complete
(go
(let [urls ["url1" "url2" "url3"]
channels (map fetch-data urls)
[result ch] (async/alts! channels)]
(println "First result:" result)))
Why this translation:
- •Python's async/await uses event loop; Clojure uses CSP channels
- •
goblocks are lightweight (like goroutines) - •Channels (
chan) pass values between go blocks - •
alts!is likeselectin Go (first-to-complete)
Threading → Atoms and Futures
Python:
from concurrent.futures import ThreadPoolExecutor
def process_item(item):
return item * 2
with ThreadPoolExecutor(max_workers=4) as executor:
results = list(executor.map(process_item, range(10)))
print(results)
Clojure:
;; pmap - parallel map (uses futures) (def results (pmap #(* % 2) (range 10))) (println (doall results)) ; Force realization ;; Or explicit futures (def results (doall (map #(future (* % 2)) (range 10)))) ;; Dereference futures to get values (def values (map deref results)) (println values) ;; Or use thread pool explicitly (import '[java.util.concurrent Executors]) (def executor (Executors/newFixedThreadPool 4)) (def tasks (map #(.submit executor ^Callable (fn [] (* % 2))) (range 10))) (def results (map #(.get %) tasks)) (.shutdown executor)
Why this translation:
- •
pmapis parallel map (automatic thread pool) - •
futurecreates async task,derefor@waits for result - •Can use Java executors for fine-grained control
State Management
Python:
import threading
counter = 0
lock = threading.Lock()
def increment():
global counter
with lock:
counter += 1
threads = [threading.Thread(target=increment) for _ in range(100)]
for t in threads:
t.start()
for t in threads:
t.join()
print(counter) # 100
Clojure:
;; Atoms for uncoordinated state
(def counter (atom 0))
(defn increment! []
(swap! counter inc))
;; Parallel updates (thread-safe)
(doall (pmap (fn [_] (increment!)) (range 100)))
(println @counter) ; 100
;; Or refs for coordinated transactions
(def account-a (ref 100))
(def account-b (ref 200))
(defn transfer [from to amount]
(dosync
(alter from - amount)
(alter to + amount)))
(transfer account-a account-b 50)
(println @account-a @account-b) ; 50 250
Why this translation:
- •Atoms for independent state (
swap!for atomic updates) - •Refs for coordinated state (
dosyncfor transactions) - •No locks needed - Clojure's concurrency primitives are thread-safe
Common Pitfalls
1. Mutable State → Immutable Data
Problem:
;; Python: in-place mutation # items.append(value) # items[0] = new_value ;; Clojure: trying to mutate (def items [1 2 3]) (conj items 4) ; Returns new vector, doesn't modify items! (println items) ; Still [1 2 3]
Solution:
;; Rebind with new value
(def items (conj items 4)) ; Now items is [1 2 3 4]
;; Or use atoms for mutable state
(def items (atom [1 2 3]))
(swap! items conj 4)
(println @items) ; [1 2 3 4]
;; Or work with local bindings
(let [items [1 2 3]
items (conj items 4)
items (conj items 5)]
(println items)) ; [1 2 3 4 5]
Why this matters: Clojure's persistent data structures are immutable by default.
2. Truthiness Differences
Problem:
;; Python: empty collections are falsy # if items: # True for [1, 2], False for [] ;; Clojure: empty collections are truthy! (if [] "truthy" "falsy") ; => "truthy"
Solution:
;; Explicitly check for emptiness (if (seq items) "has items" "empty") ;; Or use empty? (if-not (empty? items) "has items" "empty")
Why this matters: Only nil and false are falsy in Clojure. Empty collections are truthy.
3. Sequence Realization
Problem:
;; Lazy sequences aren't realized until needed (def nums (map #(do (println "Computing" %) (* % 2)) [1 2 3])) ;; Nothing printed yet! (count nums) ; Now it prints "Computing 1" "Computing 2" "Computing 3"
Solution:
;; Force realization with doall (def nums (doall (map #(do (println "Computing" %) (* % 2)) [1 2 3]))) ;; Immediately prints ;; Or use doseq for side effects (doseq [x [1 2 3]] (println "Computing" x))
Why this matters: Clojure sequences are lazy by default. Side effects in lazy sequences may not execute when expected.
4. Keyword vs String Keys
Problem:
;; Python: strings as keys
# user = {"name": "Alice", "age": 30}
;; Clojure: mixing keywords and strings
(def user {"name" "Alice" :age 30}) ; Inconsistent!
(:name user) ; nil (looking for keyword, but key is string)
Solution:
;; Use keywords consistently
(def user {:name "Alice" :age 30})
(:name user) ; "Alice"
;; Or strings consistently (less idiomatic)
(def user {"name" "Alice" "age" 30})
(get user "name") ; "Alice"
Why this matters: Keywords (:name) are idiomatic for map keys in Clojure. They're faster and work as functions.
5. Namespace Collisions
Problem:
;; Python: methods are namespaced by class
# user.get("name")
# config.get("timeout")
;; Clojure: same function name across namespaces
(require '[clojure.set :as set])
(set/union #{1 2} #{2 3}) ; Must qualify or alias
Solution:
;; Always use namespace aliases
(require '[clojure.string :as str]
'[clojure.set :as set])
(str/upper-case "hello")
(set/union #{1 2} #{2 3})
;; Or refer specific functions
(require '[clojure.string :refer [upper-case lower-case]])
(upper-case "hello")
Why this matters: Clojure namespaces prevent collisions but require explicit imports.
6. Integer Division
Problem:
;; Python 3: / always returns float # 5 / 2 # 2.5 ;; Clojure: / returns ratio for integers (/ 5 2) ; 5/2 (ratio), not 2.5
Solution:
;; Convert to double for floating-point division (/ 5.0 2) ; 2.5 ;; Or use quot for integer division (quot 5 2) ; 2 ;; Force ratio to double (double (/ 5 2)) ; 2.5
Why this matters: Clojure preserves exact ratios. Use double or floating-point literals for decimals.
7. Variadic Functions
Problem:
;; Python: *args, **kwargs # def func(*args, **kwargs): # print(args, kwargs) ;; Clojure: rest args only (no keyword args) (defn func [& args] (println args)) (func 1 2 3) ; (1 2 3)
Solution:
;; Use destructuring for keyword-style args
(defn func [& {:keys [name age] :or {age 0}}]
(println name age))
(func :name "Alice" :age 30) ; "Alice 30"
(func :name "Bob") ; "Bob 0" (default age)
;; Or use maps explicitly
(defn func [opts]
(println (:name opts) (:age opts 0)))
(func {:name "Alice" :age 30})
Why this matters: Clojure doesn't have keyword arguments. Use map destructuring or explicit maps.
8. Global Mutable State
Problem:
;; Python: global keyword # counter = 0 # def increment(): # global counter # counter += 1 ;; Clojure: def creates immutable binding (def counter 0) (defn increment [] (def counter (inc counter))) ; Bad! Creates new binding (increment) (println counter) ; Still 0!
Solution:
;; Use atoms for mutable state
(def counter (atom 0))
(defn increment! []
(swap! counter inc))
(increment!)
(println @counter) ; 1
;; Or pass state explicitly (functional style)
(defn increment [counter]
(inc counter))
(-> 0
increment
increment
increment
println) ; 3
Why this matters: def creates new vars; use atoms for mutable state or pass state explicitly.
Tooling
Translation Tools
| Tool | Purpose | Notes |
|---|---|---|
| Manual translation | Full control | Recommended for production |
| REPL experimentation | Interactive development | Core Clojure workflow |
clj-python | Python-Clojure interop | Call Python from Clojure (libpython-clj) |
Development Environment
| Python | Clojure | Purpose |
|---|---|---|
python | clj or lein repl | REPL |
pip / uv | Leiningen or tools.deps | Package management |
pytest | clojure.test | Testing framework |
mypy | clojure.spec | Runtime validation |
black | cljfmt | Code formatting |
pylint | eastwood, kibit | Linting |
Common Library Equivalents
| Python Package | Clojure Library | Purpose |
|---|---|---|
requests | clj-http | HTTP client |
aiohttp | http-kit | Async HTTP |
flask / fastapi | ring + compojure | Web framework |
pydantic | clojure.spec | Data validation |
click / argparse | tools.cli | CLI parsing |
logging | tools.logging | Logging |
datetime | clj-time | Date/time |
pathlib | clojure.java.io | File I/O |
json | clojure.data.json | JSON parsing |
re | clojure.string | Regex |
sqlite3 | clojure.java.jdbc | Database |
pandas | tech.ml.dataset | Data frames |
numpy | core.matrix | Numerical computing |
Examples
Example 1: Simple - HTTP GET Request
Before (Python):
import requests
def fetch_user(user_id):
response = requests.get(f"https://api.example.com/users/{user_id}")
response.raise_for_status()
return response.json()
try:
user = fetch_user(123)
print(f"User: {user['name']}")
except requests.HTTPError as e:
print(f"HTTP error: {e}")
After (Clojure):
(require '[clj-http.client :as http]
'[clojure.data.json :as json])
(defn fetch-user [user-id]
(-> (str "https://api.example.com/users/" user-id)
http/get
:body
(json/read-str :key-fn keyword)))
;; Usage
(try
(let [user (fetch-user 123)]
(println "User:" (:name user)))
(catch Exception e
(println "HTTP error:" (.getMessage e))))
Key changes:
- •
requests.get→clj-http.client/get - •Dictionary access
user['name']→ keyword access(:name user) - •
json/read-strwith:key-fn keywordconverts JSON keys to keywords - •Similar try/catch structure
Example 2: Medium - Configuration Parser
Before (Python):
from pathlib import Path
import json
from dataclasses import dataclass
@dataclass
class Config:
host: str
port: int
timeout: int = 30
def validate(self):
if not (1 <= self.port <= 65535):
raise ValueError(f"Invalid port: {self.port}")
def load_config(path):
if not path.exists():
raise FileNotFoundError(f"Config not found: {path}")
with path.open() as f:
data = json.load(f)
config = Config(**data)
config.validate()
return config
config = load_config(Path("config.json"))
print(f"Server: {config.host}:{config.port}")
After (Clojure):
(require '[clojure.data.json :as json]
'[clojure.spec.alpha :as s])
;; Define spec for validation
(s/def ::host string?)
(s/def ::port (s/and int? #(<= 1 % 65535)))
(s/def ::timeout (s/and int? pos?))
(s/def ::config (s/keys :req-un [::host ::port]
:opt-un [::timeout]))
(defn load-config [path]
(when-not (.exists (clojure.java.io/file path))
(throw (ex-info "Config not found" {:path path})))
(let [config (-> path
slurp
(json/read-str :key-fn keyword)
(merge {:timeout 30}))] ; Default value
(when-not (s/valid? ::config config)
(throw (ex-info "Invalid config" (s/explain-data ::config config))))
config))
;; Usage
(let [config (load-config "config.json")]
(println (format "Server: %s:%d" (:host config) (:port config))))
Key changes:
- •
@dataclass→ plain map withclojure.specvalidation - •Default values via
merge - •
clojure.specfor validation (runtime checks) - •
ex-infofor exceptions with data
Example 3: Complex - Data Processing Pipeline
Before (Python):
from collections import defaultdict
from dataclasses import dataclass
from typing import List
@dataclass
class Transaction:
user_id: int
amount: float
category: str
def process_transactions(transactions: List[Transaction]):
# Group by user
by_user = defaultdict(list)
for txn in transactions:
by_user[txn.user_id].append(txn)
# Calculate totals per category for each user
results = {}
for user_id, txns in by_user.items():
category_totals = defaultdict(float)
for txn in txns:
category_totals[txn.category] += txn.amount
# Only users with total > 100
total = sum(category_totals.values())
if total > 100:
results[user_id] = {
"total": total,
"by_category": dict(category_totals),
"count": len(txns)
}
return results
transactions = [
Transaction(1, 50.0, "food"),
Transaction(1, 75.0, "transport"),
Transaction(2, 200.0, "food"),
Transaction(1, 25.0, "food"),
]
results = process_transactions(transactions)
for user_id, stats in results.items():
print(f"User {user_id}: {stats}")
After (Clojure):
(defn process-transactions [transactions]
(->> transactions
;; Group by user
(group-by :user-id)
;; Transform each user's transactions
(map (fn [[user-id txns]]
(let [;; Calculate category totals
by-category (->> txns
(group-by :category)
(map (fn [[cat items]]
[cat (reduce + (map :amount items))]))
(into {}))
total (reduce + (vals by-category))
count (count txns)]
[user-id {:total total
:by-category by-category
:count count}])))
;; Filter users with total > 100
(filter (fn [[_ stats]] (> (:total stats) 100)))
;; Convert to map
(into {})))
;; Usage
(def transactions
[{:user-id 1 :amount 50.0 :category "food"}
{:user-id 1 :amount 75.0 :category "transport"}
{:user-id 2 :amount 200.0 :category "food"}
{:user-id 1 :amount 25.0 :category "food"}])
(def results (process-transactions transactions))
(doseq [[user-id stats] results]
(println (format "User %d: %s" user-id stats)))
Key changes:
- •
@dataclass→ plain maps - •Imperative loops → functional pipeline with
->>threading - •
defaultdict→group-byfunction - •
forloops →map,filter,reduce - •Immutable transformations throughout
- •More declarative, less mutable state
See Also
For more examples and patterns, see:
- •
meta-convert-dev- Foundational patterns with cross-language examples - •
lang-python-dev- Python development patterns - •
lang-clojure-dev- Clojure development patterns - •
patterns-concurrency-dev- Async/channels patterns across languages - •
patterns-serialization-dev- JSON/EDN serialization patterns