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Python Tutorial

Break Statement in Python

The break statement enables a program to skip over a part of the code. A break statement terminates the very loop it lies within.

for i in range(1,101,1):

print(i ,end=” “) if(i==50):

break else:

print(“Mississippi”)

print(“Thank you”)

  1. Mississippi
  2. Mississippi
  3. Mississippi
  4. Mississippi
  5. Mississippi
  6. Mississippi
  7. Mississippi
  8. Mississippi
  9. Mississippi
  10. Mississippi
  11. Mississippi
  12. Mississippi
  13. Mississippi
  14. Mississippi
  15. Mississippi
  16. Mississippi
  17. Mississippi
  18. Mississippi
  19. Mississippi
  20. Mississippi
  21. Mississippi
  22. Mississippi
  23. Mississippi
  24. Mississippi
  25. Mississippi
  26. Mississippi
  27. Mississippi
  28. Mississippi
  29. Mississippi
  30. Mississippi
  31. Mississippi
  32. Mississippi
  33. Mississippi
  34. Mississippi
  35. Mississippi
  36. Mississippi
  37. Mississippi
  38. Mississippi
  39. Mississippi
  40. Mississippi
  41. Mississippi
  42. Mississippi
  43. Mississippi
  44. Mississippi
  45. Mississippi
  46. Mississippi
  47. Mississippi
  48. Mississippi
  49. Mississippi
  50. Thank you

Continue Statement

The continue statement skips the rest of the loop statements and causes the next iteration to occur.

for i in [2,3,4,6,8,0]: if (i%2!=0):

continue print(i)

2

4

6

8

0

See 3 is skipped because 3 when divided by 2 is not equal to 0, condition is satisfied and the iteration is skipped

Map, Filter and Reduce

In Python, the map, filter, and reduce functions are built-in functions that allow you to apply a function to a sequence of elements and return a new sequence. These functions are known as higher-order functions, as they take other functions as arguments.

Map

The map function applies a function to each element in a sequence and returns a new sequence containing the transformed elements. The map function has the following syntax:

map(function, iterable)

The function argument is a function that is applied to each element in the iterable argument. The iterable argument can be a list, tuple, or any other iterable object.

Here is an example of how to use the map function:

# List of numbers

numbers = [1, 2, 3, 4, 5]

# Double each number using the map function

doubled = map(lambda x: x * 2, numbers)

# Print the doubled numbers

print(list(doubled))

[2, 4, 6, 8, 10]

In the above example, the lambda function lambda x: x * 2 is used to double each element in the numbers list. The map function applies the lambda function to each element in the list and returns a new list containing the doubled numbers.

filter

The filter function filters a sequence of elements based on a given predicate (a function that returns a boolean value) and returns a new sequence containing only the elements that meet the predicate. The filter function has the following syntax:

filter(predicate, iterable)

The predicate argument is a function that returns a boolean value and is applied to each element in the iterable argument. The iterable argument can be a list, tuple, or any other iterable object.

Here is an example of how to use the filter function:

# List of numbers

numbers = [1, 2, 3, 4, 5]

# Get only the even numbers using the filter function

evens = filter(lambda x: x % 2 == 0, numbers)

# Print the even numbers

print(list(evens))

In the above example, the lambda function lambda x: x % 2 == 0 is used to filter the numbers list and return only the even numbers. The filter function applies the lambda function to each element in the list and returns a new list containing only the even numbers.

Reduce

The reduce function is a higher-order function that applies a function to a sequence and returns a single value. It is a part of the functools module in Python and has the following syntax:

reduce(function, iterable)

The function argument is a function that takes in two arguments and returns a single value. The iterable argument is a sequence of elements, such as a list or tuple.

The reduce function applies the function to the first two elements in the iterable and then applies the function to the result and the next element, and so on. The reduce function returns the final result.

Here is an example of how to use the reduce function:

from functools import reduce

# List of numbers

numbers = [1, 2, 3, 4, 5]

# Calculate the sum of the numbers using the reduce function

sum = reduce(lambda x, y: x + y, numbers)

# Print the sum

print(sum)

In the above example, the reduce function applies the lambda function lambda x, y: x + y to the elements in the numbers list. The lambda function adds the two arguments x and y and returns the result. The reduce function applies the lambda function to the first two elements in the list (1 and 2), then applies the function to the result

(3) and the next element (3), and so on. The final result is the sum of all the elements in the list, which is 15.

It is important to note that the reduce function requires the functools module to be imported in order to use it.

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