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Top Simple Python Questions & Answers for Beginners

Top Simple Python Questions & Answers for Beginners
  • Oct 20, 2022 By GigNets
  • Introduction

    In this world of technology, it is very hard to search for the best one. You get so many software and web applications on the Internet. All are implemented with the use of various computer programming languages. One of the most popular computer programming languages is known as Python.

    It is an open-source computer programming language. You can use it as a free tool. It consists of various elements and objects that enable the operation of various programs. Hence, if you want to develop your website perfectly, use the Python programing language.

    Having adequate knowledge of debugging, Flask, ORM, Object Relational Mapper Libraries, NumPy, fundamental skills, unit test, Scikit is essential to coding. So get the best support through online learning.

    1. How can you insert an element at a given index in Python?

    Python has a fixed function called the insert() function.
    It is used to insert a particular element at a given index.
    Syntax: list_name.insert(index, element)
    ex: list = [ 0,1, 2, 3, 4, 5, 6, 7 ]
    #insert 10 at 6th index
    list. Insert(6, 10)
    o/p: [0,1,2,3,4,5,10,6,7]

    2. What do you mean by recursion?

    The function that is enabled two or more times in a body is called recursion. The most common issue that appears in the recursive function is that the program should terminate. Otherwise, it might appear in the form of an infinite loop.

    3. What do you mean by the bytes () function?

    The bytes() function is used to bring back a byte object. It can create an empty object of different sizes. Objects can be turned into bytes object with the use of the bytes() function key.

    4. Mention the different types of operators in the Python computer language?

    There are many different types of operators that are used to enable different operations in computer programming. Python has the following operators:

    A.      Relational Operators- ( <, >, <=, >=, ==, !=, )
    B.      Arithmetic Operators- Addition(+), Subtraction(-), Division(÷), Multiplication(*), and Modulus(%).
    C.      Logical Operator- (and or not), Identity, Bitwise, and Membership Operators.
    D.      Assignment Operators- ( =. +=, -=, /=, *=, %= )

    5. What is _init_ in Python?

    _init_ is a type of methodology that is the only reserved method in the Python computer programming language. It is an aka constructor in OOP. A particular object is created from _init_ methodology, and a class is known as the class attribute.

    6. What do you mean bypass in Python?

    Python has a special use of a statement known as the pass statement that does no task when executed. This statement is also called the null statement. This method is enabled when you do not want any commands but applies the statement. So it can be used when required.

    7. How can a number be converted to a string?

    Python has a fixed function that is function str(). It can be used to convert a number to a string.

    8. How to copy an object in Python?

    There are many objects in Python that can be copied while creating a program. But you can copy most of them. You can use the operator “ = “ to copy various objects in a variable.
    For example: var=copy.copy(obj)

    9. Define encapsulation in Python?

    The term encapsulation means binding the data and the code together. For example, a highly build Python class.

    10. What are the different built-in types in Python?

    Python has many different built-in data types that are mentioned below:

    1. Numeric data- You get three different kinds of numbers in Python. The integer data are available in both negative as well as positive numbers. The float numbers consist of different real numbers with a representation of a floating-point. The numbers consist of various real and imaginary components that are represented as x+yj. In this variable, x and y are float numbers, and j is -1.
    2. String- The collection of one or more numbers or characters is called a string value. It consists of single, double, or triple quotes.
    3. Boolean- The Boolean data type is a data structure that has two values; True or False. They are denoted as T and F.
    4. Frozen sets- they are immutable sets that cannot be modified after the production of value.
    5. List- A list object is a modified collection of data available in one or more items. The various objects can be used with square brackets. You can edit, add, modify and delete various objects. A list data type is mutable.
    6. Dictionary- In this field, the objects can only be operated with the help of their key value. Pairs are created by using curly brackets.
    7. Set- Different types of elements are used in a collection. The unique elements or objects are enclosed a curly brackets.

    11. How to reserve a string in Python?

    To reserve a string in Python, you must have a unique in-built function. But Python does not have any in-built functions. So you are required to create an array slicing operation for reserving a string. It would help if you used str_reverse = string[::-1] for the same.

    12. What is the work of the split() function in Python?

    The work of the split() function is to split a string into short strings by using defined separators.

    letters = (” A, B, C”)
    n = text.split(“,”)
    print(n)
    o/p: [‘A’, ‘B’, ‘C’ ]

    13. What is an object() function in Python?

    The function of an object() key is to return to an empty object. New elements or properties cannot be connected to this object.

    14. What is the function of len()?

    len() determines the length of a list, a string, and an array.
    For example:
    str = “greatlearning”
    print(len(str))
    o/p: 13

    15. What is the work of the type() function in Python?

    The type() function is one of the popular built-in methods in Python. It returns a new type of object or returns the object. It is based on an argument pass. For example: a = 100
    type(a)
    o/p: int
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    16. What are the various functions that can be used by the groupby pandas?

    The group by in pandas can initiate multiple aggregate processes. The various keys are count(), sum(), std() and mean(). Data is implemented in several categories in the form of groups. The aforementioned processes are achieved through various individual groups.

    17. Create a lambda function which will print the sum of all the elements in this list -> [5, 8, 10, 20, 50, 100]

    from functools import reduce
    sequences = [5, 8, 10, 20, 50, 100]
    sum = reduce (lambda x, y: x+y, sequences)
    print(sum)

    18. What do you mean by vstack() in numpy?Mention the code.

    The vstack() is a function that is aligned in vertical rows. All rows should have the same number of elements.

    Code

    import numpy as np
    n1=np.array([10,20,30,40,50])
    n2=np.array([50,60,70,80,90])
    print(np.vstack((n1,n2)))

    19. The process to remove the space in a string in Python?

    There are two vital keys to remove the space in a string in Python. The keys are given below:

    1. strip() function- it is used to remove the trailing and leading white space.
    2. replace() function- it is used to remove all the white lining space.

    string.replace(” “,””) ex1: str1= “great learning”
    print (str.strip())
    o/p: great learning
    ex2: str2=”great learning”
    print (str.replace(” “,””))
    o/p: greatlearning

     

    20. What do pickling and unpickling means?

    The process of changing a hierarchy Python object into a simple byte stream to store it in a proper database is known as pickling. The process of pickling is also known as sterilization. The reverse of pickling is known as unpickling. Here the byte stream is changed into an object.

    21. Explain unit test in Python?

    Python is a high-quality computer programming language that enables unit testing data framework. This is called Unittest. You can share shutdown codes for exams and other setups. It also allows test automation, aggregation of tests into large collections, and independent tests from a proper framework.

    22. How to delete a folder or a file in Python?

    Python is one of the most convenient software for several processing. It is very easy to delete a file from Python. If you want to delete any file, you need to select the commandos.unlink or os.remove along with the filename.

    23. What are Python Decorators?

    Python decorators are a type of function that appears as an argument. It modifies the behaviour with the use of the same function. It is useful to increase the dynamics of a particular function. Here comes the come.

    def smart_divide (func):
    def inner ( a, b):
    print ( “Dividing”, a, “by”, b)
    if b == 0:
    print( “ Make sure Denominator is not zero “ )
    return
    return func ( a, b)
    return inner
    @smart_divide
    def divide (a, b):
    print ( a/b )
    divide (1,0)
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    24. You have an interesting covid-19 datasheet given below:

    From this dataset, how will you make a bar-plot for the top 5 states having maximum confirmed cases as of 17=07-2020? Solve.
    #keeping only required columns

    df = df[[‘Date’, ‘State/UnionTerritory’,’Cured’,’Deaths’,’Confirmed’]]

    #renaming column names

    df.columns = [‘date’, ‘state’,’cured’,’deaths’,’confirmed’]

    #current date

    today = df[df.date == ‘2020-07-17’]

    #Sorting data w.r.t number of confirmed cases

    max_confirmed_cases=today.sort_values(by=”confirmed”,ascending=False)

    max_confirmed_cases

    #Getting states with a maximum number of confirmed cases

    top_states_confirmed=max_confirmed_cases[0:5]

    #Making bar-plot for states with top confirmed cases

    sns.set(rc={‘figure.figsize’:(15,10)})

    sns.barplot(x=”state”,y=”confirmed”,data=top_states_confirmed,hue=”state”)

    plt.show()
    Code Explanation
    Here we start by taking the required columns with this command:
    df = df[[‘Date’, ‘State/UnionTerritory’,’Cured’,’Deaths’,’Confirmed’]]
    Then, we go ahead and rename the columns:
    df.columns = [‘date’, ‘state’,’cured’,’deaths’,’confirmed’]
    However, after that, we extract only those important records, where the date is equal to 17th July:
    today = df[df.date == ‘2020-07-17’]
    Then, we go ahead and choose the top five states with a maximum no. of covide cases:
    max_confirmed_cases=today.sort_values(by=”confirmed”,ascending=False)
    max_confirmed_cases
    top_states_confirmed=max_confirmed_cases[0:5]
    Finally, we go ahead and make a bar-plot with this:
    sns.set(rc={‘figure.figsize’:(15,10)})
    sns.barplot(x=”state”,y=”confirmed”,data=top_states_confirmed,hue=”state”)
    plt.show()

    Here, we are using a seaborn library to create the bar plot. The “State” column is mapped onto the x-axis, and the “confirmed” column is mapped onto the y-axis. The color of the bars is being determined by the “state” column.

    25. Implement a simple CNN on the MNIST dataset using Keras. You must also add in drop-out layers.

    from __future__ import absolute_import, division, print_function

    import numpy as np

    # import keras

    from tensorflow.keras.datasets import cifar10, mnist

    from tensorflow.keras.models import Sequential

    from tensorflow.keras.layers import Dense, Activation, Dropout, Flatten, Reshape

    from tensorflow.keras.layers import Convolution2D, MaxPooling2D

    from tensorflow.keras import utils

    import pickle

    from matplotlib import pyplot as plt

    import seaborn as sns

    plt.rcParams[‘figure.figsize’] = (15, 8)

    %matplotlib inline

    # Load/Prep the Data

    (x_train, y_train_num), (x_test, y_test_num) = mnist.load_data()

    x_train = x_train.reshape(x_train.shape[0], 28, 28, 1).astype(‘float32’)

    x_test = x_test.reshape(x_test.shape[0], 28, 28, 1).astype(‘float32’)

    x_train /= 255

    x_test /= 255

    y_train = utils.to_categorical(y_train_num, 10)

    y_test = utils.to_categorical(y_test_num, 10)

    print(‘— THE DATA —‘)

    print(‘x_train shape:’, x_train.shape)

    print(x_train.shape[0], ‘train samples’)

    print(x_test.shape[0], ‘test samples’)

    TRAIN = False

    BATCH_SIZE = 32

    EPOCHS = 1

    # Define the Type of Model

    model1 = tf.keras.Sequential()

    # Flatten Images to Vector

    model1.add(Reshape((784,), input_shape=(28, 28, 1)))

    # Layer 1

    model1.add(Dense(128, kernel_initializer=’he_normal’, use_bias=True))

    model1.add(Activation(“relu”))

    # Layer 2

    model1.add(Dense(10, kernel_initializer=’he_normal’, use_bias=True))

    model1.add(Activation(“softmax”))

    # Loss and Optimizer

    model1.compile(loss=’categorical_crossentropy’, optimizer=’adam’, metrics=[‘accuracy’])

    # Store Training Results

    early_stopping = keras.callbacks.EarlyStopping(monitor=’val_acc’, patience=10, verbose=1, mode=’auto’)

    callback_list = [early_stopping]# [stats, early_stopping]

    # Train the model

    model1.fit(x_train, y_train, nb_epoch=EPOCHS, batch_size=BATCH_SIZE, validation_data=(x_test, y_test), call-back’s=callback_list, verbose=True)

    #drop-out layers:

    # Define Model

    model3 = tf.keras.Sequential()

    # 1st Conv Layer

    model3.add(Convolution2D(32, (3, 3), input_shape=(28, 28, 1)))

    model3.add(Activation(‘relu’))

    # 2nd Conv Layer

    model3.add(Convolution2D(32, (3, 3)))

    model3.add(Activation(‘relu’))

    # Max Pooling

    model3.add(MaxPooling2D(pool_size=(2,2)))

    # Dropout

    model3.add(Dropout(0.25))

    # Fully Connected Layer

    model3.add(Flatten())

    model3.add(Dense(128))

    model3.add(Activation(‘relu’))

    # More Dropout

    model3.add(Dropout(0.5))

    # Prediction Layer

    model3.add(Dense(10))

    model3.add(Activation(‘softmax’))

    # Loss and Optimizer

    model3.compile(loss=’categorical_crossentropy’, optimizer=’adam’, metrics=[‘accuracy’])

    # Store Training Results

    early_stopping = tf.keras.callbacks.EarlyStopping(monitor=’val_acc’, patience=7, verbose=1, mode=’auto’)

    callback_list = [early_stopping]

    # Train the model

    model3.fit(x_train, y_train, batch_size=BATCH_SIZE, nb_epoch=EPOCHS,

    validation_data=(x_test, y_test), callbacks=callback_list)

     

    26. How to implement a dataframe from a list?

    The dataframe can be implemented in two ways:
    1. By adding lists to individual columns
    2. Create a null database

    27. What is a map() function in Python?

    The map() function enables an argument. It is used to the elements that are highly iterated.

    28. What are the vital tools that enable statistic analysis?

    The two vital tools are:
    1.  Pylint
    2. Pychecker

    29. Is the case of Python sensitive to deal with the identifiers?

    Yes. It is.

    30. How to check Python in CMD?

    The several versions of Python can be check in a CMD by pressing CMD + Space.

    Conclusion

    In this 21st era, you get the best use of technology. Technology has been a blessing to this world. The whole day you spend your time scrolling feeds on Instagram and Facebook, play games in various web applications, browse in Google, watch videos on YouTube and watch movies on Netflix. This is only enabled with the use of a computer programming language, Python.

    If you are interested in getting a job in the IT industry, you must learn the best key methods, theories, and practicals of Python. Python has much to deliver. So check for the best mentor to get all the knowledge about Python. You must go through these questions for a safe interview. You get the guarantee of getting a job.

    We will help you and work with your requirements in the most reliable, professional, and at a minimum cost. We can guarantee your success. So call us or WhatsApp us +918900042651 or email us info@proxy-jobsupport.com

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