"""alarm
  
1h 13m
Q1 of 10
Predict the output of below code.

list1=[10,20,30,40,50]
list1.insert(7,25)
print(list1)
 

[10, 20, 25, 30, 40, 50]
IndexError:List index out of range
[10, 20, 30, 40, 50, 25]
[25, 10, 20, 30, 40, 50]
Q2 of 10
Predict the output of below code.

def modify_list(arg_list):
    arg_list=arg_list + [60, 70, 80]
    print("Inside function:", arg_list)
    
i_list=[10,20,30,40,50]
print("Before function call:", i_list)
modify_list(i_list)
print("After function call:", i_list)
i)   Before function call: [10, 20, 30, 40, 50]
     Inside function: [10, 20, 30, 40, 50, 60, 70, 80]
     After function call: [10, 20, 30, 40, 50, 60, 70, 80]

ii)  Before function call: [10, 20, 30, 40, 50]
     Inside function: [10, 20, 30, 40, 50, 60, 70, 80]
     After function call: [10, 20, 30, 40, 50]

iii)  Before function call: [10, 20, 30, 40, 50]
      Inside function: [10, 20, 30, 40, 50]
      After function call: [10, 20, 30, 40, 50]

iv)  Before function call: [10, 20, 30, 40, 50, 60, 70, 80]
      Inside function: [10, 20, 30, 40, 50, 60, 70, 80]
      After function call: [10, 20, 30, 40, 50, 60, 70, 80]

i
ii
iii
iv
Q3 of 10
Predict the output of below code.

my_list=[0]*5
for index in range(1,5):
    my_list[index]=(index-1)*index
                    
print(my_list)
 

[0, 1, 4, 9, 16]
[0, 0, 0, 0, 0]
[0, 1, 2, 3, 4]
[0, 0, 2, 6, 12]
Q4 of 10
Predict the output of below code.
"""
FHW=open("data.txt","w")
FHW.write("written some thing")
print(FHW.tell())
print("closed?",FHW.closed)
FHW.close()
print("after closing the file closed?",FHW.closed)
"""i)   17
     closed? False
     after closing the file closed? True

ii)  18
     closed? False
     after closing the file closed? True

iii) 17
     closed? True
     after closing the file closed? False

iv) 18
     closed? True
     after closing the file closed? False 

i
ii
iii
iv
Q5 of 10
Predict the output of below code.

set_1={1,2,3,1,2,4,5,3,4,8,9,7,10}
for index in range(len(set_1)):
    print(index,end=" ")
 

Error as range will take minimum of 2 arguments
0 1 2 3 4 5 6 7 8
0 1 2 3 4 5 6 7 8 9 10 11 12
Error as set does not support indexing
Q6 of 10
Predict the output of below code.

def value(num1):
    list1=[]
    while num1!=0:
        if num1%2==0:
            list1.append(num1)
        else:
            break
        num1-=2
    print(list1)
value(10) 
 

[2, 4, 6, 8, 10]
[1, 3, 5, 7, 9]
[10, 8, 6, 4, 2]
[9, 7, 5, 3, 1]
Q7 of 10
Predict the output of below code.

def sample(value):
    sum1=0
    for i in value:
        if i%2!=0:
            sum1+=value[i]
        else:
            sum1-=i
    print(sum1)
dict1={1:2,2:4,3:6,5:8}
sample(dict1)
 

4
14
22
16
Q8 of 10
Predict the output of below code.

tuple1=(10)
print(tuple1)
print(type(tuple1))
i)   10
     <class 'tuple'>

ii)  (10)
     <class 'tuple'>

iii) 10
     <class 'int'>

iv) (10)
    <class 'int'>

i
ii
iii
iv
Q9 of 10
Predict the output of below code.

sample_dict={'a':"apple",'b':"ball"}
sample_dict.update({'b':"boy", 'c':'cat' })
print(sample_dict['a'],sample_dict.get('b'),sample_dict.get('c'))
 

None boy cat
apple ball None
apple boy cat
apple ball cat
Q10 of 10
Predict the output of the below code

def find_sum(a,b):
    try:
        print(a+c)
    except ValueError:
        print("Function name error")
    finally:
        print("Sum finally")
try:
    find_sum(12,13)
except NameError:
    print("Invocation name error")
finally:
    print("Invocation finally")
a)  Function name error
     Sum finally

b)  Function name error
     Sum finally
     Invocation finally


c)  Function name error
     Sum finally
     Invocation name error
     Invocation finally

d)  Sum finally
     Invocation name error
     Invocation finally

a
b
c
d 

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About Python

Python is a very popular general-purpose programming language which was created by Guido van Rossum, and released in 1991. It is very popular for web development and you can build almost anything like mobile apps, web apps, tools, data analytics, machine learning etc. It is designed to be simple and easy like english language. It's is highly productive and efficient making it a very popular language.

Tutorial & Syntax help

Loops

1. If-Else:

When ever you want to perform a set of operations based on a condition IF-ELSE is used.

if conditional-expression
    #code
elif conditional-expression
    #code
else:
    #code

Note:

Indentation is very important in Python, make sure the indentation is followed correctly

2. For:

For loop is used to iterate over arrays(list, tuple, set, dictionary) or strings.

Example:

mylist=("Iphone","Pixel","Samsung")
for i in mylist:
    print(i)

3. While:

While is also used to iterate a set of statements based on a condition. Usually while is preferred when number of iterations are not known in advance.

while condition  
    #code 

Collections

There are four types of collections in Python.

1. List:

List is a collection which is ordered and can be changed. Lists are specified in square brackets.

Example:

mylist=["iPhone","Pixel","Samsung"]
print(mylist)

2. Tuple:

Tuple is a collection which is ordered and can not be changed. Tuples are specified in round brackets.

Example:

myTuple=("iPhone","Pixel","Samsung")
print(myTuple)

Below throws an error if you assign another value to tuple again.

myTuple=("iPhone","Pixel","Samsung")
print(myTuple)
myTuple[1]="onePlus"
print(myTuple)

3. Set:

Set is a collection which is unordered and unindexed. Sets are specified in curly brackets.

Example:

myset = {"iPhone","Pixel","Samsung"}
print(myset)

4. Dictionary:

Dictionary is a collection of key value pairs which is unordered, can be changed, and indexed. They are written in curly brackets with key - value pairs.

Example:

mydict = {
    "brand" :"iPhone",
    "model": "iPhone 11"
}
print(mydict)

Supported Libraries

Following are the libraries supported by OneCompiler's Python compiler

NameDescription
NumPyNumPy python library helps users to work on arrays with ease
SciPySciPy is a scientific computation library which depends on NumPy for convenient and fast N-dimensional array manipulation
SKLearn/Scikit-learnScikit-learn or Scikit-learn is the most useful library for machine learning in Python
PandasPandas is the most efficient Python library for data manipulation and analysis
DOcplexDOcplex is IBM Decision Optimization CPLEX Modeling for Python, is a library composed of Mathematical Programming Modeling and Constraint Programming Modeling