# Python¶

## Lambda, map, filter, reduce¶

### Lambda¶

add = lambda x, y: x + y

5


#### Multiply Function¶

multiply = lambda x, y: x * y
multiply(2, 3)

6


### Map¶

#### Create List¶

lst = [i for i in range(11)]
print(lst)

[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]


#### Map Square Function to List¶

square_element = map(lambda x: x**2, lst)

# This gives you a map object
print(square_element)

# You need to explicitly return a list
print(list(square_element))

<map object at 0x7f9cd4399a58>
[0, 1, 4, 9, 16, 25, 36, 49, 64, 81, 100]


#### Create Multiple List¶

lst_1 = [1, 2, 3, 4]
lst_2 = [2, 4, 6, 8]
lst_3 = [3, 6, 9, 12]


#### Map Add Function to Multiple Lists¶

add_elements = map(lambda x, y, z : x + y + z, lst_1, lst_2, lst_3)

[6, 12, 18, 24]


### Filter¶

#### Create List¶

lst = [i for i in range(10)]
print(lst)

[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]


#### Filter multiples of 3¶

multiples_of_three = filter(lambda x: x % 3 == 0, lst)
print(list(multiples_of_three))

[0, 3, 6, 9]


### Reduce¶

The syntax is reduce(function, sequence). The function is applied to the elements in the list in a sequential manner. Meaning if lst = [1, 2, 3, 4] and you have a sum function, you would arrive with ((1+2) + 3) + 4.

from functools import reduce
sum_all = reduce(lambda x, y: x + y, lst)
# Here we've 1 + 2 + 3 + 4 + 5 + 6 + 7 + 8 + 9
print(sum_all)
print(1 + 2 + 3 + 4 + 5 + 6 + 7 + 8 + 9)

45
45


## Decorators¶

• This allows us to to modify our original function or even entirely replace it without changing the function's code.
• It sounds mind-boggling, but a simple case I would like to illustrate here is using decorators for consistent logging (formatted print statements).
• For us to understand decorators, we'll first need to understand:
• first class objects
• *args
• *kwargs

### First Class Objects¶

def outer():
def inner():
print('Inside inner() function.')

# This returns a function.
return inner

# Here, we are assigning outer() function to the object call_outer.
call_outer = outer()

# Then we call call_outer()
call_outer()

Inside inner() function.


### *args¶

• This is used to indicate that positional arguments should be stored in the variable args
• * is for iterables and positional parameters
# Define dummy function
def dummy_func(*args):
print(args)

# * allows us to extract positional variables from an iterable when we are calling a function
dummy_func(*range(10))

# If we do not use *, this would happen
dummy_func(range(10))

# See how we can have varying arguments?
dummy_func(*range(2))

(0, 1, 2, 3, 4, 5, 6, 7, 8, 9)
(range(0, 10),)
(0, 1)


### **kwargs¶

• ** is for dictionaries & key/value pairs
# New dummy function
def dummy_func_new(**kwargs):
print(kwargs)

# Call function with no arguments
dummy_func_new()

# Call function with 2 arguments
dummy_func_new(a=0, b=1)

# Again, there's no limit to the number of arguments.
dummy_func_new(a=0, b=1, c=2)

# Or we can just pass the whole dictionary object if we want
new_dict = {'a': 0, 'b': 1, 'c': 2, 'd': 3}
dummy_func_new(**new_dict)

{}
{'a': 0, 'b': 1}
{'a': 0, 'b': 1, 'c': 2}
{'a': 0, 'b': 1, 'c': 2, 'd': 3}


### Decorators as Logger and Debugging¶

• A simple way to remember the power of decorators is that the decorator (the nested function illustrated below) can
• (1) access the passed arguments of the decorated function and
• (2) access the decorated function
• Therefore this allows us to modify the decorated function without changing the decorated function
# Create a nested function that will be our decorator
def function_inspector(func):
def inner(*args, **kwargs):
result = func(*args, **kwargs)
print(f'Function args: {args}')
print(f'Function kwargs: {kwargs}')
print(f'Function return result: {result}')
return result
return inner

# Decorate our multiply function with our logger for easy logging
# Of arguments pass to the function and results returned
@function_inspector
def multiply_func(num_one, num_two):
return num_one * num_two

multiply_result = multiply_func(num_one=1, num_two=2)

Function args: ()
Function kwargs: {'num_one': 1, 'num_two': 2}
Function return result: 2


## Dates¶

### Get Current Date¶

import datetime
now = datetime.datetime.now()
print(now)

2019-06-06 11:26:47.843927


### Get Clean String Current Date¶

# YYYY-MM-DD
now.date().strftime('20%y-%m-%d')

'2019-06-06'


# Number of business days in a month from Jan 2019 to Feb 2019
import numpy as np
days = np.busday_count('2019-01', '2019-02')
print(days)

23


## Progress Bars¶

### TQDM¶

Simple progress bar via pip install tqdm

from tqdm import tqdm
import time
for i in tqdm(range(100)):
time.sleep(0.1)
pass

100%|██████████| 100/100 [00:10<00:00,  9.86it/s]


## Check Paths¶

### Check Path Exists¶

• Check if directory exists
import os
directory='new_dir'
print(os.path.exists(directory))

# Magic function to list all folders
!ls -d */

False
ls: cannot access '*/': No such file or directory


### Check Path Exists Otherwise Create Folder¶

• Check if directory exists, otherwise make folder
if not os.path.exists(directory):
os.makedirs(directory)

# Magic function to list all folders
!ls -d */

# Remove directory
!rmdir new_dir

new_dir/


## Asynchronous¶

### Concurrency, Parallelism, Asynchronous¶

• Concurrency (single CPU core): multiple threads on a single core running in sequence, only 1 thread is making progress at any point
• Think of 1 human, packing a box then wrapping the box
• Parallelism (mutliple GPU cores): multiple threads on multiple cores running in parallel, multiple threads can be making progress
• Think of 2 humans, one packing a box, another wrapping the box
• Asynchronous: concurrency but with a more dynamic system that moves amongst threads more efficiently rather than waiting for a task to finish then moving to the next task
• Python's asyncio allows us to code asynchronously
• Benefits:
• Scales better if you need to wait on a lot of processes
• Less memory (easier in this sense) to wait on thousands of co-routines than running on thousands of threads
• Good for IO bound uses like reading/saving from databases while subsequently running other computation
• Easier management than multi-thread processing like in parallel programming
• In the sense that everything operates sequentially in the same memory space

### Asynchronous Key Components¶

• The three main parts are (1) coroutines and subroutines, (2) event loops, and (3) future.
• Co-routine and subroutines
• Subroutine: the usual function
• Coroutine: this allows us to maintain states with memory of where things stopped so we can swap amongst subroutines
• async declares a function as a coroutine
• await to call a coroutine
• Event loops
• Future

### Synchronous 2 Function Calls¶

def add_numbers(num_1, num_2):
time.sleep(1)
return num_1 + num_2

def display_sum(num_1, num_2):
print(f'Total sum {total_sum}')

def main():
display_sum(2, 2)
display_sum(2, 2)

start = timeit.default_timer()

main()

end = timeit.default_timer()
total_time = end - start

print(f'Total time {total_time:.2f}s')

Adding
Total sum 4
Total sum 4
Total time 2.00s


### Parallel 2 Function Calls¶

from multiprocessing import Pool
from functools import partial

start = timeit.default_timer()

pool = Pool()
result = pool.map(partial(display_sum, num_2=2), [2, 2])

end = timeit.default_timer()
total_time = end - start

print(f'Total time {total_time:.2f}s')

Adding
Total sum 4
Total sum 4
Total time 1.12s


### Asynchronous 2 Function Calls¶

For this use case, it'll take half the time compared to a synchronous application and slightly faster than parallel application (although not always true for parallel except in this case)

import asyncio
import timeit
import time

await asyncio.sleep(1)
return num_1 + num_2

async def display_sum(num_1, num_2):
print(f'Total sum {total_sum}')

async def main():
# .gather allows us to group subroutines
await asyncio.gather(display_sum(2, 2),
display_sum(2, 2))

start = timeit.default_timer()

# For .ipynb, event loop already done
await main()

# For .py
# asyncio.run(main())

end = timeit.default_timer()
total_time = end - start

print(f'Total time {total_time:.4f}s')

Adding