PYTHON

list comprehension

# List comprehension 

        
list_comp = [i+3 for i in range(20)]

# above code is similar to 

for i in range(20):
	print(i + 3)

List Comprehension

# Python program to demonstrate list
# comprehension in Python
   
# below list contains square of all
# odd numbers from range 1 to 10
odd_square = [x ** 2 for x in range(1, 11) if x % 2 == 1]
print (odd_square)

python list comprehension

nums = [4, -7, 9, 1, -1, 8, -6]
half_of_nums = [x/2 for x in nums] #[2, -3.5, 4.5, 0.5, -0.5, 4, -3]

#optionally you can add an if statement like this
half_of_positive_nums = [x/2 for x in nums if x>=0] #[2, 4.5, 0.5, 4]

list comprehension python

Yes:
  result = [mapping_expr for value in iterable if filter_expr]

  result = [{'key': value} for value in iterable
            if a_long_filter_expression(value)]

  result = [complicated_transform(x)
            for x in iterable if predicate(x)]

  descriptive_name = [
      transform({'key': key, 'value': value}, color='black')
      for key, value in generate_iterable(some_input)
      if complicated_condition_is_met(key, value)
  ]

  result = []
  for x in range(10):
      for y in range(5):
          if x * y > 10:
              result.append((x, y))

  return {x: complicated_transform(x)
          for x in long_generator_function(parameter)
          if x is not None}

  squares_generator = (x**2 for x in range(10))

  unique_names = {user.name for user in users if user is not None}

  eat(jelly_bean for jelly_bean in jelly_beans
      if jelly_bean.color == 'black')

python list comprehension

#example: removing common elements found in `a` from `b`.
a = [1,2,3,4,5]
b = [5,6,7,8,9]
# desired output: [1,2,3,4]

# gets each item found in `a` AND not in `b`
print([i for i in a if i not in b])

list comprehension python

Yes:
  result = [mapping_expr for value in iterable if filter_expr]

  result = [{'key': value} for value in iterable
            if a_long_filter_expression(value)]

  result = [complicated_transform(x)
            for x in iterable if predicate(x)]

  descriptive_name = [
      transform({'key': key, 'value': value}, color='black')
      for key, value in generate_iterable(some_input)
      if complicated_condition_is_met(key, value)
  ]

  result = []
  for x in range(10):
      for y in range(5):
          if x * y > 10:
              result.append((x, y))

  return {x: complicated_transform(x)
          for x in long_generator_function(parameter)
          if x is not None}

  squares_generator = (x**2 for x in range(10))

  unique_names = {user.name for user in users if user is not None}

  eat(jelly_bean for jelly_bean in jelly_beans
      if jelly_bean.color == 'black')

list comprehension python

Yes:
  result = [mapping_expr for value in iterable if filter_expr]

  result = [{'key': value} for value in iterable
            if a_long_filter_expression(value)]

  result = [complicated_transform(x)
            for x in iterable if predicate(x)]

  descriptive_name = [
      transform({'key': key, 'value': value}, color='black')
      for key, value in generate_iterable(some_input)
      if complicated_condition_is_met(key, value)
  ]

  result = []
  for x in range(10):
      for y in range(5):
          if x * y > 10:
              result.append((x, y))

  return {x: complicated_transform(x)
          for x in long_generator_function(parameter)
          if x is not None}

  squares_generator = (x**2 for x in range(10))

  unique_names = {user.name for user in users if user is not None}

  eat(jelly_bean for jelly_bean in jelly_beans
      if jelly_bean.color == 'black')

python list comprehension

lst=[1,2,3,4,5]
lst2=[item for item in lst if <condition>]
# generates a list based on another list and an if statement. the code above is a replacement for:
lst=[1,2,3,4,5]
lst2=[]
for item in lst:
  if <condition>:
    lst2.append(item)

liste compréhension python

In general,
[f(x) if condition else g(x) for x in sequence]

And, for list comprehensions with if conditions only,
[f(x) for x in sequence if condition]

list comprehensions

S = [x**2 for x in range(10)]
V = [2**i for i in range(13)]

python list comprehension

nums = [3578, 6859, 35689, 268]
half_of_nums = [x/2 for x in nums]

List Comprehensions

numbers = [1, 2, 3, 4, 5, 6]
squares = [i*i for i in numbers]

print(squares)

List Comprehensions in python

[output_expression for variable in input_sequence if filter_condition]  

list comprehension python

# This is the  syntax of a general list comprehension expression in Python
[expression 
 for x1 in it1 if cond1
 for x2 in it2 if cond2
 ...
 for xn in itn if condn
]

# hereby it1,..., itn are iterables and cond1, ..., condn boolean conditions
# expression can depend on x1,...,xn and
# itj and condj can depend on the variables x1,...,xj-1 quantified before it

# The list comprehension expression is equivalent to the following nested 
# for-loop statement
res = []
for x1 in it1:
	if cond1:
  		for x2 in it2: 
      		if cond2:
    			...
      			for xn in itn if condn:
                	if condn:
       					res.append(expression)

list comprehension

list = [[2,4,6,8]]
matrix = [[row[i] for row in list] for i in range(4)]
print(matrix)

python list comprehension

# List comprehension example
list = [0 for i in range(10)]
# Makes a list of 10 zeros (Change 0 for different result
# and range for different length)

# Nested List
list = [[0] * 5 for i in range(10)]
# Makes a nested list of 10 list containing 5 zeros (You can also change
# all of the int to produce different results)

list comprehension python

Yes:
  result = [mapping_expr for value in iterable if filter_expr]

  result = [{'key': value} for value in iterable
            if a_long_filter_expression(value)]

  result = [complicated_transform(x)
            for x in iterable if predicate(x)]

  descriptive_name = [
      transform({'key': key, 'value': value}, color='black')
      for key, value in generate_iterable(some_input)
      if complicated_condition_is_met(key, value)
  ]

  result = []
  for x in range(10):
      for y in range(5):
          if x * y > 10:
              result.append((x, y))

  return {x: complicated_transform(x)
          for x in long_generator_function(parameter)
          if x is not None}

  squares_generator = (x**2 for x in range(10))

  unique_names = {user.name for user in users if user is not None}

  eat(jelly_bean for jelly_bean in jelly_beans
      if jelly_bean.color == 'black')

list comprehension python

Yes:
  result = [mapping_expr for value in iterable if filter_expr]

  result = [{'key': value} for value in iterable
            if a_long_filter_expression(value)]

  result = [complicated_transform(x)
            for x in iterable if predicate(x)]

  descriptive_name = [
      transform({'key': key, 'value': value}, color='black')
      for key, value in generate_iterable(some_input)
      if complicated_condition_is_met(key, value)
  ]

  result = []
  for x in range(10):
      for y in range(5):
          if x * y > 10:
              result.append((x, y))

  return {x: complicated_transform(x)
          for x in long_generator_function(parameter)
          if x is not None}

  squares_generator = (x**2 for x in range(10))

  unique_names = {user.name for user in users if user is not None}

  eat(jelly_bean for jelly_bean in jelly_beans
      if jelly_bean.color == 'black')

Python list comprehension

squares = [item * item for item in range(5)]

list comprehension

1
x=[i for i in range(5)]

list comprehension in python

# List Comprehension

# If one variable
power = [x**2 for x in range(1, 10)] 
# If need two variables especially
# when you work Dictionary with List Comprehension.
# create a simple (key, value), tuple for context variables x and y
l_Comp = [(x,y) for x, y in employees.items() if y >= 100000]

python list Comprehensions

# List 
# new_list[<action> for <item> in <iterator> if <some condition>]
a = [i for i in 'hello']                  # ['h', 'e', 'l', 'l', '0']
b = [i*2 for i in [1,2,3]]                # [2, 4, 6]
c = [i for i in range(0,10) if i % 2 == 0]# [0, 2, 4, 6, 8]

list comprehensions

[output_expression for element in list if condition]Code language: Python (python)

Python List Comprehension

fruits = ['Banana', 'Apple', 'Lime']
loud_fruits = [fruit.upper() for fruit in fruits]
print(loud_fruits)

# Output
# ['BANANA', 'APPLE', 'LIME']

list comprehension python

Yes:
  result = [mapping_expr for value in iterable if filter_expr]

  result = [{'key': value} for value in iterable
            if a_long_filter_expression(value)]

  result = [complicated_transform(x)
            for x in iterable if predicate(x)]

  descriptive_name = [
      transform({'key': key, 'value': value}, color='black')
      for key, value in generate_iterable(some_input)
      if complicated_condition_is_met(key, value)
  ]

  result = []
  for x in range(10):
      for y in range(5):
          if x * y > 10:
              result.append((x, y))

  return {x: complicated_transform(x)
          for x in long_generator_function(parameter)
          if x is not None}

  squares_generator = (x**2 for x in range(10))

  unique_names = {user.name for user in users if user is not None}

  eat(jelly_bean for jelly_bean in jelly_beans
      if jelly_bean.color == 'black')

list comprehension python

Yes:
  result = [mapping_expr for value in iterable if filter_expr]

  result = [{'key': value} for value in iterable
            if a_long_filter_expression(value)]

  result = [complicated_transform(x)
            for x in iterable if predicate(x)]

  descriptive_name = [
      transform({'key': key, 'value': value}, color='black')
      for key, value in generate_iterable(some_input)
      if complicated_condition_is_met(key, value)
  ]

  result = []
  for x in range(10):
      for y in range(5):
          if x * y > 10:
              result.append((x, y))

  return {x: complicated_transform(x)
          for x in long_generator_function(parameter)
          if x is not None}

  squares_generator = (x**2 for x in range(10))

  unique_names = {user.name for user in users if user is not None}

  eat(jelly_bean for jelly_bean in jelly_beans
      if jelly_bean.color == 'black')

Python list comprehension

squares = [item * item for item in range(5)]

list comprehension

[expr for val1 in collection1 and val2 collection2 if(condition)]

list comprehensions

// if you want to take single int input 
a=int(input())
// if you want n elements of array a as input from console/user
a = list(map(int,input().strip().split()))
# u can also covert it to set,tuple etc 
# ex. set(map(int, input().strip().split()))

NOTE: suppose if you want a list with duplicates removed
list(set(map(int, input().strip().split())))

also note map is a method and is not hashmap which is actually disct in python.
and ommitting .strip() in 2nd argument of map func might also work.

# more explaination of above:
https://www.quora.com/What-does-the-following-line-mean-in-Python-list-map-int-input-strip-split-I

Python: list comprehensions

numbers = [1,2,3,4,5,6,7,8]

greater_than_4 = [number for number in numbers if number > 4]


def shorter_than_X(strings, max_length):
    return [string for string in strings if len(string) < max_length]

Python List Comprehension

# List comprehension for the squares of all even numbers between 0 and 9
result = [x**2 for x in range(10) if x % 2 == 0]

print(result)
# [0, 4, 16, 36, 64]

List comprehensions

numbers = [1, 2, 3, 4, 5, 6, 7, 8]

even_numbers = [number for number in numbers if number % 2 == 0]

print(even_numbers)  # [2, 4, 6, 8]

list comprehension python

Yes:
  result = [mapping_expr for value in iterable if filter_expr]

  result = [{'key': value} for value in iterable
            if a_long_filter_expression(value)]

  result = [complicated_transform(x)
            for x in iterable if predicate(x)]

  descriptive_name = [
      transform({'key': key, 'value': value}, color='black')
      for key, value in generate_iterable(some_input)
      if complicated_condition_is_met(key, value)
  ]

  result = []
  for x in range(10):
      for y in range(5):
          if x * y > 10:
              result.append((x, y))

  return {x: complicated_transform(x)
          for x in long_generator_function(parameter)
          if x is not None}

  squares_generator = (x**2 for x in range(10))

  unique_names = {user.name for user in users if user is not None}

  eat(jelly_bean for jelly_bean in jelly_beans
      if jelly_bean.color == 'black')

list comprehension python

Yes:
  result = [mapping_expr for value in iterable if filter_expr]

  result = [{'key': value} for value in iterable
            if a_long_filter_expression(value)]

  result = [complicated_transform(x)
            for x in iterable if predicate(x)]

  descriptive_name = [
      transform({'key': key, 'value': value}, color='black')
      for key, value in generate_iterable(some_input)
      if complicated_condition_is_met(key, value)
  ]

  result = []
  for x in range(10):
      for y in range(5):
          if x * y > 10:
              result.append((x, y))

  return {x: complicated_transform(x)
          for x in long_generator_function(parameter)
          if x is not None}

  squares_generator = (x**2 for x in range(10))

  unique_names = {user.name for user in users if user is not None}

  eat(jelly_bean for jelly_bean in jelly_beans
      if jelly_bean.color == 'black')

list comprehension python

Yes:
  result = [mapping_expr for value in iterable if filter_expr]

  result = [{'key': value} for value in iterable
            if a_long_filter_expression(value)]

  result = [complicated_transform(x)
            for x in iterable if predicate(x)]

  descriptive_name = [
      transform({'key': key, 'value': value}, color='black')
      for key, value in generate_iterable(some_input)
      if complicated_condition_is_met(key, value)
  ]

  result = []
  for x in range(10):
      for y in range(5):
          if x * y > 10:
              result.append((x, y))

  return {x: complicated_transform(x)
          for x in long_generator_function(parameter)
          if x is not None}

  squares_generator = (x**2 for x in range(10))

  unique_names = {user.name for user in users if user is not None}

  eat(jelly_bean for jelly_bean in jelly_beans
      if jelly_bean.color == 'black')

list comprehension python

Yes:
  result = [mapping_expr for value in iterable if filter_expr]

  result = [{'key': value} for value in iterable
            if a_long_filter_expression(value)]

  result = [complicated_transform(x)
            for x in iterable if predicate(x)]

  descriptive_name = [
      transform({'key': key, 'value': value}, color='black')
      for key, value in generate_iterable(some_input)
      if complicated_condition_is_met(key, value)
  ]

  result = []
  for x in range(10):
      for y in range(5):
          if x * y > 10:
              result.append((x, y))

  return {x: complicated_transform(x)
          for x in long_generator_function(parameter)
          if x is not None}

  squares_generator = (x**2 for x in range(10))

  unique_names = {user.name for user in users if user is not None}

  eat(jelly_bean for jelly_bean in jelly_beans
      if jelly_bean.color == 'black')

list comprehension python

Yes:
  result = [mapping_expr for value in iterable if filter_expr]

  result = [{'key': value} for value in iterable
            if a_long_filter_expression(value)]

  result = [complicated_transform(x)
            for x in iterable if predicate(x)]

  descriptive_name = [
      transform({'key': key, 'value': value}, color='black')
      for key, value in generate_iterable(some_input)
      if complicated_condition_is_met(key, value)
  ]

  result = []
  for x in range(10):
      for y in range(5):
          if x * y > 10:
              result.append((x, y))

  return {x: complicated_transform(x)
          for x in long_generator_function(parameter)
          if x is not None}

  squares_generator = (x**2 for x in range(10))

  unique_names = {user.name for user in users if user is not None}

  eat(jelly_bean for jelly_bean in jelly_beans
      if jelly_bean.color == 'black')

list comprehension python

Yes:
  result = [mapping_expr for value in iterable if filter_expr]

  result = [{'key': value} for value in iterable
            if a_long_filter_expression(value)]

  result = [complicated_transform(x)
            for x in iterable if predicate(x)]

  descriptive_name = [
      transform({'key': key, 'value': value}, color='black')
      for key, value in generate_iterable(some_input)
      if complicated_condition_is_met(key, value)
  ]

  result = []
  for x in range(10):
      for y in range(5):
          if x * y > 10:
              result.append((x, y))

  return {x: complicated_transform(x)
          for x in long_generator_function(parameter)
          if x is not None}

  squares_generator = (x**2 for x in range(10))

  unique_names = {user.name for user in users if user is not None}

  eat(jelly_bean for jelly_bean in jelly_beans
      if jelly_bean.color == 'black')

Python list comprehension

squares = [item * item for item in range(5)]

python list comprehension

l = [i for i in some_container]

List Comprehension simple example

# for understanding, above generation is same as,
odd_square = []
 
for x in range(1, 11):
    if x % 2 == 1:
        odd_square.append(x**2)
 
print (odd_square)

list comprehension

[col for col in df.columns]

python list comprehension

 # A list comprehnsion is a for loop on a single line 
 # To create a list comprehension, swap the two lines in the for loop.

# Here we use PyBIDS to extract the relative path for each file:
for fmri in fmri_078:
    print(fmri.relpath)

# And here is the equivalent statement as a list comprehension.
# It must be enclosed in square brackets.
# It swaps the order of the lines and loses the colon and indentation
[ print(fmri.relpath) for fmri in fmri_078 ]   

python list comprehension

[expression for element in source_list]

list comprehension python

# Make a List that contains the doubled values of a given list:

values = [2, 4, 6, 8, 10]
doubled_values = [x*2 for x in values]
print(doubled_values) # Outputs [4, 8, 12, 16, 20]

# You could achieve the same result like this:

values = [2, 4, 6, 8, 10]
doubled_values = []
for x in values:
    doubled_values.append(x*2)
print(doubled_values)

list comprehension python

friends = [
    {'name': 'Sam', 'gender': 'male', 'sport': 'Basketball'},
    {'name': 'Emily', 'gender': 'female', 'sport': 'Volleyball'}
]
# filter by column names
print([{key: friend[key] for key in friend.keys() if key in ["name", "sport"]} for friend in friends])
# filter by rows
print([a for a in friends if a["name"] in ["Sam"]])

Python List Comprehension

fruits = ["apple", "banana", "cherry", "kiwi", "mango"]
newlist = []

for x in fruits:
  if "a" in x:
    newlist.append(x)

print(newlist)

list comprehension python

Yes:
  result = [mapping_expr for value in iterable if filter_expr]

  result = [{'key': value} for value in iterable
            if a_long_filter_expression(value)]

  result = [complicated_transform(x)
            for x in iterable if predicate(x)]

  descriptive_name = [
      transform({'key': key, 'value': value}, color='black')
      for key, value in generate_iterable(some_input)
      if complicated_condition_is_met(key, value)
  ]

  result = []
  for x in range(10):
      for y in range(5):
          if x * y > 10:
              result.append((x, y))

  return {x: complicated_transform(x)
          for x in long_generator_function(parameter)
          if x is not None}

  squares_generator = (x**2 for x in range(10))

  unique_names = {user.name for user in users if user is not None}

  eat(jelly_bean for jelly_bean in jelly_beans
      if jelly_bean.color == 'black')

python list comprehension

>>> vec = [-4, -2, 0, 2, 4]
>>> # create a new list with the values doubled
>>> [x*2 for x in vec]
[-8, -4, 0, 4, 8]
>>> # filter the list to exclude negative numbers
>>> [x for x in vec if x >= 0]
[0, 2, 4]
>>> # apply a function to all the elements
>>> [abs(x) for x in vec]
[4, 2, 0, 2, 4]
>>> # call a method on each element
>>> freshfruit = ['  banana', '  loganberry ', 'passion fruit  ']
>>> [weapon.strip() for weapon in freshfruit]
['banana', 'loganberry', 'passion fruit']
>>> # create a list of 2-tuples like (number, square)
>>> [(x, x**2) for x in range(6)]
[(0, 0), (1, 1), (2, 4), (3, 9), (4, 16), (5, 25)]
>>> # the tuple must be parenthesized, otherwise an error is raised
>>> [x, x**2 for x in range(6)]
  File "<stdin>", line 1, in <module>
    [x, x**2 for x in range(6)]
               ^
SyntaxError: invalid syntax
>>> # flatten a list using a listcomp with two 'for'
>>> vec = [[1,2,3], [4,5,6], [7,8,9]]
>>> [num for elem in vec for num in elem]
[1, 2, 3, 4, 5, 6, 7, 8, 9]
>>> # List comprehensions can contain complex expressions and nested functions:
>>> from math import pi
>>> [str(round(pi, i)) for i in range(1, 6)]
['3.1', '3.14', '3.142', '3.1416', '3.14159']

list comprehension python

Yes:
  result = [mapping_expr for value in iterable if filter_expr]

  result = [{'key': value} for value in iterable
            if a_long_filter_expression(value)]

  result = [complicated_transform(x)
            for x in iterable if predicate(x)]

  descriptive_name = [
      transform({'key': key, 'value': value}, color='black')
      for key, value in generate_iterable(some_input)
      if complicated_condition_is_met(key, value)
  ]

  result = []
  for x in range(10):
      for y in range(5):
          if x * y > 10:
              result.append((x, y))

  return {x: complicated_transform(x)
          for x in long_generator_function(parameter)
          if x is not None}

  squares_generator = (x**2 for x in range(10))

  unique_names = {user.name for user in users if user is not None}

  eat(jelly_bean for jelly_bean in jelly_beans
      if jelly_bean.color == 'black')