Toolz

import toolz

from toolz import groupby, map, filter, compose, merge
from toolz.itertoolz import unique, take, partition
from toolz.functoolz import curry, pipe, memoize  
from toolz.dicttoolz import assoc, get_in, valmap

import toolz.curried as toolz

import toolz
from toolz import groupby, pipe, curry, assoc

# Group data by a key function
names = ['Alice', 'Bob', 'Charlie', 'Dan', 'Edith', 'Frank']
grouped = groupby(len, names)
# {3: ['Bob', 'Dan'], 5: ['Alice', 'Edith', 'Frank'], 7: ['Charlie']}

# Function composition with pipe
data = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
result = pipe(
    data,
    lambda x: filter(lambda n: n % 2 == 0, x),  # even numbers
    lambda x: map(lambda n: n * 2, x),          # double them
    list                                        # convert to list
)
# [4, 8, 12, 16, 20]

# Dictionary operations
person = {'name': 'Alice', 'age': 30}
updated = assoc(person, 'city', 'New York')
# {'name': 'Alice', 'age': 30, 'city': 'New York'}

# Curried functions for partial application
from toolz.curried import map, filter
double = map(lambda x: x * 2)
evens = filter(lambda x: x % 2 == 0)

pipeline = toolz.compose(list, double, evens)
result = pipeline([1, 2, 3, 4, 5, 6])
# [4, 8, 12]

def groupby(key, seq): ...
def unique(seq, key=None): ...
def take(n, seq): ...
def partition(n, seq, pad=no_pad): ...
def frequencies(seq): ...
def merge_sorted(*seqs, **kwargs): ...

def compose(*funcs): ...
def pipe(data, *funcs): ...
def curry(*args, **kwargs): ...
def memoize(func, cache=None, key=None): ...
def thread_first(val, *forms): ...
def juxt(*funcs): ...

def merge(*dicts, **kwargs): ...
def assoc(d, key, value, factory=dict): ...
def get_in(keys, coll, default=None, no_default=False): ...
def valmap(func, d, factory=dict): ...
def keyfilter(predicate, d, factory=dict): ...
def update_in(d, keys, func, default=None, factory=dict): ...

import toolz.curried as toolz
# All functions automatically support partial application

def countby(key, seq):
    """
    Count elements of a collection by a key function.
    
    Parameters:
    - key: function to compute grouping key, or attribute name
    - seq: iterable sequence to count
    
    Returns:
    Dictionary mapping keys to occurrence counts
    """

def partitionby(func, seq):
    """
    Partition a sequence according to a function.
    
    Partition seq into a sequence of tuples such that, when traversing seq,
    every time the output of func changes a new tuple is started.
    
    Parameters:
    - func: function that determines partition boundaries
    - seq: iterable sequence to partition
    
    Returns:
    Iterator of tuples representing consecutive groups
    """

from toolz import countby

# Count word lengths
words = ['apple', 'banana', 'cherry', 'date']
counts = countby(len, words)  
# {5: 2, 6: 1, 4: 1}

# Count by type
data = [1, 'a', 2.5, 'b', 3, 'c']
type_counts = countby(type, data)
# {<class 'int'>: 2, <class 'str'>: 3, <class 'float'>: 1}

from toolz import partitionby

# Partition by boolean condition  
numbers = [1, 3, 5, 2, 4, 6, 7, 9]
groups = list(partitionby(lambda x: x % 2 == 0, numbers))
# [[1, 3, 5], [2, 4, 6], [7, 9]]

# Partition by first letter
words = ['apple', 'apricot', 'banana', 'blueberry', 'cherry']  
groups = list(partitionby(lambda w: w[0], words))
# [['apple', 'apricot'], ['banana', 'blueberry'], ['cherry']]

class EqualityHashKey:
    """Create hash key using equality comparisons for unhashable types."""
    def __init__(self, key, item): ...

def unzip(seq):
    """Inverse of zip - unpack sequence of tuples into separate sequences."""

def fold(binop, seq, default=no_default, map=map, chunksize=128, combine=None):
    """Reduce without guarantee of ordered reduction for parallel processing."""

# Sentinel values for default parameters
no_default = '__no__default__'
no_pad = '__no__pad__'

# Function signature inspection results
class curry:
    """Curry a callable for partial application."""
    def __init__(self, *args, **kwargs): ...
    def bind(self, *args, **kwargs): ...
    def call(self, *args, **kwargs): ...

class Compose:
    """Function composition class for multiple function pipeline."""
    def __init__(self, funcs): ...
    def __call__(self, *args, **kwargs): ...

class InstanceProperty:
    """Property that returns different value when accessed on class vs instance."""
    def __init__(self, fget=None, fset=None, fdel=None, doc=None, classval=None): ...

class juxt:
    """Create function that calls several functions with same arguments."""
    def __init__(self, *funcs): ...
    def __call__(self, *args, **kwargs): ...

class excepts:
    """Create function with functional try/except block."""
    def __init__(self, exc, func, handler=return_none): ...
    def __call__(self, *args, **kwargs): ...

from toolz import pipe, filter, map, groupby, valmap

data = [
    {'name': 'Alice', 'age': 25, 'dept': 'engineering'},
    {'name': 'Bob', 'age': 30, 'dept': 'engineering'}, 
    {'name': 'Carol', 'age': 35, 'dept': 'marketing'},
    {'name': 'Dave', 'age': 40, 'dept': 'marketing'}
]

# Process and analyze the data
result = pipe(
    data,
    lambda x: filter(lambda p: p['age'] >= 30, x),    # adults 30+
    lambda x: groupby(lambda p: p['dept'], x),        # group by dept
    lambda x: valmap(len, x)                          # count per dept
)
# {'engineering': 1, 'marketing': 2}

from toolz import compose, curry
from toolz.curried import map, filter

# Create reusable pipeline components
@curry
def multiply_by(factor, x):
    return x * factor

double = multiply_by(2)
is_even = lambda x: x % 2 == 0

# Compose into pipeline
process_evens = compose(
    list,
    map(double),
    filter(is_even)
)

result = process_evens([1, 2, 3, 4, 5, 6])
# [4, 8, 12]