If
statements are control flow statements which helps us to run a
particular code only when a certain condition is satisfied. For example,
you want to print a message on the screen only when a condition is true
then you can use if statement to accomplish this in programming.
Syntax
if condition:
block of code
![](data:image/png;base64,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)
Python programming language provides following types of loops to handle looping requirements. Python provides three ways for executing the loops. While all the ways provide similar basic functionality, they differ in their syntax and condition checking time.
Python has two primitive loop commands:
The Range() Function
![](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAA4QAAACsCAYAAAAjWWL9AAANzUlEQVR4nO3dv07jah7H4fdKnKuxcjORcgtTHolu+mk8RVqaU51mK7SnwlRpRqTeC0BpkH5bkEBI7MQh9sSZ93mkT7EcBgKDVnzH/1IAAACQpXTtFwAAAMB1GIQAAACZMggBAAAyZRACAABkyiAEAADIlEEIAACQqdZB+Pr6GqvVKp6enuLx8VGSJEmSdIM9PT3FarWK19fX7oNwtVrF8/NzvLy8xHq9liRJkiTdYC8vL/H8/Byr1ar7IHx6ejIGJUmSJOkP6OXlJZ6enroPwsfHx6u/aEmSJElSPz0+PhqEkiRJkpRjBqEkSZIkZZpBKEmSJEmZZhBKkiRJUqYZhJIkSZKUaQahJEmSJGWaQShJkiRJmWYQSpIkSVKm/Z5B+GsWxbcUaa/ivr76N+B4dcyKFKms+v/YO9+T8uHaX6fO+Xkoq/b3qX6mSH9t+j6L+pqv93kWxV8p0s9uP7+jee3b1/1eGVXbay5TFLOx//+IJEnSeBt+ELaMwfQtRfoxwNDqNYNQO1VlpKJ9KFU/UxR/f4yT+u+i8xjrs49hV0b1b9npNYzjtdcx+94wAP8tI/1VxOy54c/UsyhSEbN6BD8fkiRJN9jgg7D6cStHA6Vjvf3jQOvRqJbhVf1MUf57xdfdZRCO9bXvVP9dfBqsn16no4SSJElf7rcNQkfBdNNVZaR05NTFny1HsDoeoRusDp9/tK99p2OD8NTfjSRJktobaBBWUbadJrrtruHUu4fy4P2ahuR2ZO6/z+7bLxqgVRkppY96PGW0vi86Xkv5+XtYPux/T6/wC/Du38/2dN+mtx39OShi9qv9ay3u64PTjFuPLu+fjnw3i3pdx+yu5fV0/PlqqiqP/RzUMfve8vfxPIvimtfjnRx1I37tu1/DkesI1+sqynT82s6m90/JkUVJkqSRDMKdX+I7jMfDQdj055uGxxeqylEMwuZ6+hrP7aGM9KN6+3vY+bupfnQ5Nfjt62oaYvV9EcWPMopPY7fl/R/Kw1H8UEb6VkRxd/h9efu+74+Kzc/NyWtZT5wuuq6ibBss1x5VJwfhSF/7pxvLnP7Hj+ODfa/df/A5ck2oJElSDo3ilNGPkbT3i/zOEaCmobE/DLfv8/bxxjkIm77uYyPq42v8/EvxVa/N3Bxp+/Ln3gzK5u9Hwy//B+9fRdl2hHQzCj//3R95/8338viRwlNHoEY6qtbr2x2Eu38/P0/f9bQqzxl3jhBKkiRtG8Eg3B4Jaxlw76Pw8JfWj7E04JGykQzC/e/f+4i+xp1aWwbdpX++vi+avxf77/9QHvme1THbP0J46vWe+u8n72Q54lH1BwzC9fr0XU/rWeFonyRJ0he6/iDcDr6mawrX6/g4nfRw9P2WG9YYhIedMQj3j+Iee+RI10HY+n7vPy8Ng/Ar17RuOzkI65h9b7kxy7VHVadrCEf62j91ZLiuDUJJkqSvdv1B2MMRQoPwN//gdBqER67PG9sRwlPVsyhO3LSk9REN175TZ6e7jI70tX/q+CA875RRSZIkbRvBIDxy6ufuXSQbfqE3CEc8CH/Nomg76nbpIDz7GsI6ZneX3JX11E1l1rH+t2x8LMLRZ/lthuag17J1fA7h2a9958Ys3e/u+fVOnTJ61k1lXEMoSZL03jCDcP9xAA19/sX/jLuMdvjYvY6kPgdhl1MX38dfw11GN1/XwWmYx053HKJOR9yqKI+c5nvZINy8bf/r/jWLouUuo9v/1nQUur4vTn4PuwyOgwF1YozVs2L4O112PMp37muvytT7I1m2dxbdH6H130Wkv1pOa93+rJ0zTN1lVJIk6b2RDMJNXZ4TN/Qg3Dlq09aXj4jc9CA879EgbX9XxX29833YDrS2Zyzuf85T1waWUTWdMvpey6M8uvy8dHr4eR2z72nzqITU7c6YAxxhq37uvIaD2r6Gc1772xHTdPS6ygt+zr7vveZT1zGe/WB6RwglSZK2DX7KqPR7OzYIL/y4p04bPavNKLnFI1TbfzQZ6FTqc6tKw06SJOmrGYT6wxpqEK7fjkT1NeA2py3+juvv+u7tVNchjg5+oZN3gJUkSdKxDEL9UVU/er6G9FNvRwn7GHG3/JiE827gMvxrcXRQkiTp6xmEut2arie9xl1XJUmSpBvNIJQkSZKkTDMIJUmSJCnTDEJJkiRJyjSDUJIkSZIyzSCUJEmSpEwzCCVJkiQp0wxCSZIkSco0g/CaPZSRvhUx+zWC1/KpKsp/ipj978j7VGWkVMSsPvZx6pj9J0U5uq9PkiRJ0nptEF63gQZhfV9EcV9//c8/FZH+e+IB750G4TrWv8pI/5RRXft7LUmSJOkgg/AP7LJBWEX5T59H9RwllCRJksaaQfgHdskgrJ+KSP+ZRd3n6xngY0qSJEm6PINwp/q+iPQtRfr2dopj9SNt/vemH02nUVZRvr/P9tTIprdtq2N2t/tx208Zff/8d7Oo9//c3eHAOni9e50eiW9H84qntverY1akSGlbh1NG1+tY/28WxalrEiVJkiT99gzCg6oov5VR/jgcgPV90TjEdv9ctX9d4EN55M/UMbs7cQ3hr1kUd2WUdynKh4+3Vw2vb/d1fu0I4Tmni9YxKzoOwpNDU5IkSdI1MggPeju61zqoHsqW/7Y5Ktg6/prqOAi/fR6DH5+v+WYtXx6EZx3JO38QnrxRjSRJkqTfmkF4UBXl0Tt/VlE2jr72gdZe1yOE532+Lw/Cs+4Ies4gXEf1X4NQkiRJGlsG4UGnhl3bfzcIj2UQSpIkSePLIDwo9yOEThmVJEmScskgPOiSawhvfBC6qYwkSZKUVQbhQe13GT1+x9DxDML1Q3n4uIvNzWmOD8VzhtsZg9BjJyRJkqRRZhAe9DG0Pp5L2P7sv4P3+Xb8GYOnnhW4+zn233d7p9H9z9k08g5fV7exeuwh8lW5+wzChormP+fB9JIkSdI4MwgP+sqRvj+pc04b7dLbUcf+Pp4kSZKkvjIID8p9EG6O6PV1A5iz7lwqSZIk6XdmEB5kEL4dJezjmj9HByVJkqQxZxDu1HQ94Nfu1ilJkiRJ488glCRJkqRMMwglSZIkKdMMQkmSJEnKNINQkiRJkjLNIJQkSZKkTDMIJUmSJCnTDEJJkiRJyjSDUJIkSZIyzSCUJEmSpEw7axDWdR0vLy9Xf9GSJEmSpMt6eXmJuq67D8LVahXPz89GoSRJkiTdcC8vL/H8/Byr1ar7IHx9fY3VahV1Xcfj46MkSZIk6Qar6zpWq1W8vr52H4QAAAD82QxCAACATBmEAAAAmTIIAQAAMmUQAgAAZMogBAAAyJRBCAAAkCmDEAAAIFMGIQAAQKYMQgAAgEwZhAAAAJkyCAEAADJlEAIAAGSqv0G4nMckpUibpovePjIAAAAD6GcQLucxSdP42ICLmKZJzJe9fHQAAAAG0MMgXMZ80jD+lvOYTOZhEwIAAIzT5YNwOY9J4/mhLUMRAACAUbh8EC6mMWlZfYupawkBAADG6uJBuJxPDEIAAIAbZBACAABkyiAEAADIVC/XEKaW1WcQAgAAjFc/dxltfLyEu4wCAACMWU/PIdx9KP32zZ5DCAAAMGY9DMJoGH+ODgIAAIxdP4Mw4u1awpTec+0gAADAuPU3CAEAALgpBiEAAECmDEIAAIBMGYQAAACZMggBAAAyZRACAABkyiAEAADIlEEIAACQKYMQAAAgUwYhAABApgxCAACATPU2CBfTFCl9NJkv+/rQAAAADODyQbiYNgzAZcwnKdJ0cfGHBwAAYBgDnjK6iGmahkkIAAAwTgYhAABApgYahG+njLqOEAAAYLx6HYS7N5YxBgEAAMZtmCOEy3lMUgr3lAEAABivga8hnIQDhQAAAOM06IPpl/OJU0cBAABGyiAEAADIlFNGAQAAMnXxIFxMU6TJPD7vvkVMU4rkrjIAAACj1c8RwsX0/XET22xBAACAcRv0GkIAAADGyyAEAADIlEEIAACQKYMQAAAgUwYhAABApgxCAACATBmEAAAAmTIIAQAAMmUQAgAAZMogBAAAyJRBCAAAkKn+B+FiGimljybzWPb+SQAAALhUj4NwGfOJAQgAAHArehuEi2mK6aKvjwYAAMDQ+hmEi2lM5o4LAgAA3JJeBuFiOgl7EAAA4Lb0MAiXMZ9MYxERy/lk54YyRiIAAMCY9TAIFzFN05jPJ5HS2zD8eLtRCAAAMFaXD8LlPCYp7Y3BrUVM3WkGAABglHo6QphabiqziKnHUAAAAIxST9cQGoQAAAC3pp/HTiznMWkafst5TJwyCgAAMEq9Ppg+7Y6/5TwmbioDAAAwWr0NwgiPnQAAALglvQ5CAAAAbodBCAAAkCmDEAAAIFMGIQAAQKYMQgAAgEwZhAAAAJkyCAEAADJlEAIAAGTKIAQAAMiUQQgAAJCp/gbhYhoppfemi94+MgAAAAPoZxAuppHSJObL7RuWMZ8YhQAAAGPWwyBcxnyyOwZ33z4NmxAAAGCcehiEi5hO5nGwByNiOZ84SggAADBSgw/CyeGhQwAAAEZgwFNGIxbTFMkhQgAAgFHq8aYyO9cLLucxSSkmk4lBCAAAMFLDPnZiMTUIAQAARmrYB9Mvpq4hBAAAGKlBB+Fi6lmEAAAAYzXcIHS6KAAAwKgNMgiX80mklkdRAAAAMA49PXYifbqhjCODAAAA4zfsTWUAAAAYLYMQAAAgUwYhAABApv4PUq2E1xZiHIkAAAAASUVORK5CYII=)
for i in range(-10, -100, -30):
print(i)
for letter in 'python-is-Langauage':
if letter == 'p' or letter == 'o':
continue
print('Current Letter :', letter)
![](data:image/png;base64,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)
Break Statement: It returns the control to the beginning of the loop.
# break the loop as soon it sees 'o' or 'p'
for letter in 'python-is-Langauage':
if letter == 'o' or letter == 'P':
break
print('Current Letter :', letter)
Print i as long is less than 6
Syntax
if condition:
block of code
Python programming language provides following types of loops to handle looping requirements. Python provides three ways for executing the loops. While all the ways provide similar basic functionality, they differ in their syntax and condition checking time.
Python has two primitive loop commands:
- while loops
- for loops
The for loop in Python is used to iterate over a sequence (list,
tuple, string) or other iterable objects. Iterating over a sequence is
called traversal.In Python, there is no C style for loop, i.e., for (i=0; i<n; i++)
![for Loop Flowchart Flowchart of for Loop in Python programming](https://cdn.programiz.com/sites/tutorial2program/files/forLoop.jpg)
Syntax of for Loop
for val in sequence:
Body of for
fruits = ["apple", "banana", "cherry"]
for x in fruits:
print(x)
for x in fruits:
print(x)
Program to find the sum of all numbers stored in a list
numbers = [6, 5, 3, 8, 4, 2, 5, 4, 11]
sum = 0
for val in numbers:
sum = sum+val
print("The sum is", sum)
The Range() Function
If
you do need to iterate over a sequence of numbers, the built-in
function range() comes in handy. It generates arithmetic progressions:
for i in range(10):
print(i)
The given end point is never part of the generated sequence; range(10) generates 10 values, the legal indices for items of a sequence of length 10. It is possible to let the range start at another number, or to specify a different increment (even negative; sometimes this is called the ‘step’):
for i in range(5, 10):
print(i)
![](data:image/png;base64,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)
for i in range(0, 10, 3):
print(i)
for i in range(10):
print(i)
The given end point is never part of the generated sequence; range(10) generates 10 values, the legal indices for items of a sequence of length 10. It is possible to let the range start at another number, or to specify a different increment (even negative; sometimes this is called the ‘step’):
for i in range(5, 10):
print(i)
for i in range(0, 10, 3):
print(i)
for i in range(-10, -100, -30):
print(i)
To iterate over the indices of a sequence, you can combine range() and len() as follows:
a = ['Mary', 'had', 'a', 'little', 'lamb']
for i in range(len(a)):
print(i, a[i])
Loop Control Statements
Loop control statements change execution from its normal sequence. When
execution leaves a scope, all automatic objects that were created in
that scope are destroyed. Python supports the following control
statements.
Continue Statement: It returns the control to the beginning of the loop.
# Prints all letters except 'p' and 'o' for letter in 'python-is-Langauage':
if letter == 'p' or letter == 'o':
continue
print('Current Letter :', letter)
Break Statement: It returns the control to the beginning of the loop.
# break the loop as soon it sees 'o' or 'p'
for letter in 'python-is-Langauage':
if letter == 'o' or letter == 'P':
break
print('Current Letter :', letter)
The while loop in Python is used to iterate over a block of code as
long as the test expression (condition) is true. We generally use this
loop when we don't know the number of times to iterate beforehand.
Syntax ,.
while test expression:
body of while
![while Loop Flowchart while Loop in Python programming](https://cdn.programiz.com/sites/tutorial2program/files/whileLoopFlowchart.jpg)
Print i as long is less than 6
i = 1
while i < 6:
print(i)
i +=1
The break Statement
i = 1
while i < 6:
print(i)
if ( i == 2):
break
i +=1
The else Statement
With the else statement we can run a block of code once when the condition no longer is true:
Print a message once the condition is false:
i = 1
while i < 6:
print(i)
i += 1
else:
print("i is no longer less than 6")
With the else statement we can run a block of code once when the condition no longer is true:
Print a message once the condition is false:
i = 1
while i < 6:
print(i)
i += 1
else:
print("i is no longer less than 6")
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