Lesson 8: Functions

def work(input):
  """Processes input and returns output"""
  
  output = input + 1
  return output

work(1)

def docstring():
  """Triple Quoted documentation!"""
  

docstring?

def positional(first,second,third):
  """Processes arguments to function in order"""
  
  print(f"Processed first {first}")
  print(f"Processed second {second}")
  print(f"Processed third {third}")
  
  

positional(1, 2, 3)

positional(2, 3, 1)

def keyword(first=1, second=2, third=3):
  """Processed in any order"""
  
  print(f"Processed first {first}")
  print(f"Processed second {second}")
  print(f"Processed third {third}")

keyword(1,2,3)

keyword(second=2, third=3, first=1)

keyword(second=2)

def bridge_to_nowhere():pass
  

bridge_to_nowhere() == None

type(bridge_to_nowhere())

def more_than_zero():
  
  return 1

more_than_zero() == 1

def inner_peace():
  """A deep function"""
  
  def peace():
    return "piece"
  
  return peace

inner = inner_peace()
print(f"Hey, I need that {inner()}")

inner2 = inner_peace()

type(inner2)

%%python
import click

def less_than_zero():
  
  return {"iron_man": -1}

@click.command()
def run():
  
  rdj = less_than_zero()
  click.echo(f"Robert Downey Junior is versatile {rdj}")
  
if __name__== "__main__":
  run()

%%writefile run.py
from flask import Flask
app = Flask(__name__)

def less_than_zero():
  
  return {"iron_man": -1}

@app.route('/')
def runit():
  return less_than_zero()
  

def crunchy_normal():
  count = 0
  num = 10000000
  for i in range(num):
    count += num  
  return count

%%time
crunchy_normal()

from numba import jit

@jit(nopython=True)
def crunchy():
  count = 0
  num = 10000000
  for i in range(num):
    count += num  
  return count

%%time
crunchy()

from functools import wraps
import time

def instrument(f):
    @wraps(f)
    def wrap(*args, **kw):
        ts = time.time()
        result = f(*args, **kw)
        te = time.time()
        print(f"function: {f.__name__}, args: [{args}, {kw}] took: {te-ts} sec")
        return result
    return wrap

from time import sleep

@instrument
def simulated_work(count, task):
  """simulates work"""
  
  print("Starting work")
  sleep(count)
  processed = f"one {task} leap"
  return processed
  

simulated_work(3, task="small")  

def calorie_counter():
    """Counts calories"""
    
    protein = 0
    fat = 0
    carbohydrate = 0
    total = 0
    def calorie_counter_inner(food):
        nonlocal protein
        nonlocal fat
        nonlocal carbohydrate
        if food == "protein":
          protein += 4
        elif food == "carbohydrate":
          carbohydrate += 4
        elif food == "fat":
          fat += 9
        total = protein + carbohydrate + fat
        print(f"Consumed {total} calories of protein: {protein}, carbohydrate: {carbohydrate}, fat: {fat}")
    return calorie_counter_inner

meal = calorie_counter()
type(meal)

meal("carbohydrate")

meal("fat")

meal("protein")

import this

func = lambda x: x**x
func(4)

def expo(x):
  return x**x

expo(4)

import pandas as pd

series = pd.Series([1, 5, 10])
series.apply(lambda x: x**x)

def expo(x):
  return x**x

expo(4)

import pandas as pd

series = pd.Series([1, 5, 10])
series.apply(expo)

import pandas as pd
df = pd.read_csv(
    "https://raw.githubusercontent.com/noahgift/food/master/data/features.en.openfoodfacts.org.products.csv")
df.drop(["Unnamed: 0", "exceeded", "g_sum", "energy_100g"], axis=1, inplace=True) #drop two rows we don't need
df.head()

def high_protein(row):
  """Creates a high or low protein category"""
  
  if row > 80:
    return "high_protein"
  return "low_protein"

df["high_protein"] = df["proteins_100g"].apply(high_protein)
df.head()

df.describe()

from functools import partial

def multiple_sort(column_one, column_two):
  """Performs multiple sort on a pandas DataFrame"""
  
  sorted_df = df.sort_values(by=[column_one, column_two], 
                 ascending=[False, False])
  return sorted_df
  
multisort = partial(multiple_sort, "sugars_100g")
type(multisort)

df = multisort("fat_100g")
df.head()

df = multisort("salt_100g")
df.head()