Python Interview Questions

Interviewing for Python can be quite intimidating.  If you are appearing for a technical round of interview for Python, here’s a list of the top 101 interview questions with answers to help you prepare. The first set of questions and answers are curated for freshers while the second set is designed for advanced users. These questions cover all the basic applications of Python and will showcase your expertise in the subject. The questions are divided into groups such as basic, intermediate, and advanced questions. 

Python Interview Questions For Freshers in 2020

  • #1.What are the key features of Python?
  • #2.What are Keywords in Python?
  • #3.What are Literals in Python and explain about different Literals?
  • #4.How can you concatenate two tuples?
  • #5.What are functions in Python?
  • #6.How can you initialize a 5*5 numpy array with only zeroes?
  • #7.What is Pandas?
  • #8.What are dataframes?
  • #9.What is a Pandas Series?
  • #10.What is Pandas groupby?
  • 1. What are the key features of Python?

    Python is one of the most popular programming languages used by data scientists and AIML professionals. This popularity is due to the following key features of Python:

    • Python is easy to learn due to its clear syntax and readability
    • Python is easy to interpret, making debugging easy
    • Python is free and Open-source
    • It can be used across different languages
    • It is an object-oriented language which supports concepts of classes
    • It can be easily integrated with other languages like C++, Java and more

    2. What are Keywords in Python?

    Keywords in Python are reserved words which are used as identifiers, function name or variable name. They help define the structure and syntax of the language. 

    There are a total of 33 keywords in Python 3.7 which can change in the next version, i.e., Python 3.8. A list of all the keywords is provided below:

    Keywords in Python

    Falseclassfinallyisreturn
    Nonecontinueforlambdatry
    Truedeffromnonlocalwhile
    anddelglobalnotwith
    aselififoryield
    assertelseimportpass
    breakexcept


    3. What are Literals in Python and explain about different Literals?

    Literals in Python refer to the data that is given in a variable or constant. Python has various kinds of literals including:

    1. String Literals: It is a sequence of characters enclosed in codes. There can be single, double and triple strings based on the number of quotes used. Character literals are single characters surrounded by single or double-quotes. 
    2. Numeric Literals: These are unchangeable kind and belong to three different types – integer, float and complex.
    3. Boolean Literals: They can have either of the two values- True or False which represents ‘1’ and ‘0’ respectively. 
    4. Special Literals: Special literals are sued to classify fields that are not created. It is represented by the value ‘none’.

    4. How can you concatenate two tuples?

      Solution ->

    Let’s say we have two tuples like this ->

    tup1 = (1,”a”,True)

    tup2 = (4,5,6)

    Concatenation of tuples means that we are adding the elements of one tuple at the end of another tuple.

    Now, let’s go ahead and concatenate tuple2 with tuple1:

    All you have to do is, use the ‘+’ operator between the two tuples and you’ll get the concatenated result.

    Similarly, let’s concatenate tuple1 with tuple2:

    5. What are functions in Python?

    Ans: Functions in Python refer to blocks that have organised, and reusable codes to perform single, and related events. Functions are important to create better modularity for applications which reuse high degree of coding. Python has a number of built-in functions like print(). However, it also allows you to create user-defined functions.

    6. How can you initialize a 5*5 numpy array with only zeroes?

    Solution ->

    We will be using the .zeros() meethod

    Use np.zeros() and pass in the dimensions inside it. Since, we want a 5*5 matrix, we will pass (5,5) inside the .zeros() method.

    This will be the output:

    7. What is Pandas?

    Pandas is an open source python library which has a very rich set of data structures for data based operations. Pandas with it’s cool features fits in every role of data operation, whether it be academics or solving complex business problems. Pandas can deal with a large variety of files and is one of the most important tools to have a grip on.

    8. What are dataframes?

    A pandas dataframe is a data structure in pandas which is mutable. Pandas has support for heterogeneous data which is arranged across two axes.( rows and columns).

    Reading files into pandas:-

    Import pandas as pd

    df=p.read_csv(“mydata.csv”)

    Here df is a pandas data frame. read_csv() is used to read a comma delimited file as a dataframe in pandas.

    9. What is a Pandas Series?

    Series is a one dimensional pandas data structure which can data of almost any type. It resembles an excel column. It supports multiple operations and is used for single dimensional data operations.

    Creating a series from data:

    10. What is pandas groupby?

    A pandas groupby is a feature supported by pandas which is used to split and group an object.  Like the sql/mysql/oracle groupby it used to group data by classes, entities which can be further used for aggregation. A dataframe can be grouped by one or more columns.

    11. How to create a dataframe from lists?

    To create a dataframe from lists ,

    1)create an empty dataframe

    2)add lists as individuals columns to the list

    12. How to create dataframe from a dictionary?

    A dictionary can be directly passed as an argument to the DataFrame() function to create the data frame.

    13. How to combine dataframes in pandas?

    Two different data frames can be stacked either horizontally or vertically by the concat(), append() and join() functions in pandas.

    Concat works best when the dataframes have the same columns and can be used for concatenation of data having similar fields and is basically vertical stacking of dataframes into a single dataframe.

    Append() is used for horizontal stacking of dataframes. If two tables(dataframes) are to be merged together then this is the best concatenation function.

    Join is used when we need to extract data from different dataframes which are having one or more common columns. The stacking is horizontal in this case.

    Before going through the questions, here’s a quick video to help you refresh your memory on Python. 

    append in python

    14. What kind of joins does pandas offer?

    Pandas has a left join, inner join, right join and an outer join.

    15. How to merge dataframes in pandas?

    Merging depends on the type and fields of different dataframes being merged. If data is having similar fields data is merged along axis 0 else they are merged along axis 1.

    16. Give the below dataframe drop all rows having Nan.

    The dropna function can be used to do that.

    17. How to access the first five entries of a dataframe?

    By using the head(5) function we can get the top five entries of a dataframe. By default df.head() returns the top 5 rows. To get the top n rows df.head(n) will be used.

    18. How to access the last five entries of a dataframe?

    By using tail(5) function we can get the top five entries of a dataframe. By default df.tail() returns the top 5 rows. To get the last n rows df.tail(n) will be used.

    19. How to fetch a data entry from a pandas dataframe using a given value in index?

    To fetch a row from dataframe given index x, we can use loc.

    Df.loc[10] where 10 is the value of the index.

    20. What are comments and how can you add comments in Python?

    Comments in Python refer to a piece of text intended for information. It is especially relevant when more than one person works on a set of codes. It can be used to analyse code, leave feedback, and debug it. There are two types of comments which includes:

    1. Single-line comment
    2. Multiple-line comment

    Codes needed for adding comment

    #Note –single line comment
    “””Note
    Note
    Note”””—–multiline comment

    21. What is the difference between list and tuples in Python?

    Lists are mutable, but tuples are immutable.

    22. What is a dictionary in Python? Give an example

    A Python dictionary is a collection of items in no particular order. Python dictionaries are written in curly brackets with keys and values. Dictionaries are optimised to retrieve value for known keys.

    Example

    d={“a”:1,”b”:2}

    23. Find out the mean, median and standard deviation of this numpy array -> np.array([1,5,3,100,4,48])

    import numpy as np
    n1=np.array([10,20,30,40,50,60])
    print(np.mean(n1))
    print(np.median(n1))
    print(np.std(n1))

    24. What is a classifier?

    A classifier is used to predict the class of any data point. Classifiers are special hypotheses that are used to assign class labels to any particular data points. A classifier often uses training data to understand the relation between input variables and the class. Classification is a method used in supervised learning in Machine Learning.

    25. In Python how do you convert a string into lowercase?

    All the upper cases in a string can be converted into lowercase by using the method: string.lower()

    ex: string = ‘GREATLEARNING’ print(string.lower())
    o/p: greatlearning

    26. How do you get a list of all the keys in a dictionary?

    One of the ways we can get a list of keys is by using: dict.keys()
    This method returns all the available keys in the dictionary. dict = {1:a, 2:b, 3:c} dict.keys()
    o/p: [1, 2, 3]

    27. How can you capitalize the first letter of a string?

    We can use the capitalize() function to capitalize the first character of a string. If the first character is already in capital then it returns the original string.

    Syntax: string_name.capitalize() ex: n = “greatlearning” print(n.capitalize())
    o/p: Greatlearning

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

    Python has an inbuilt function called the insert() function.
    It can be used used to insert an 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]

    29. How will you remove duplicate elements from a list?

    There are various methods to remove duplicate elements from a list. But, the most common one is, converting the list into a set by using the set() function and using the list() function to convert it back to a list, if required. ex: list0 = [2, 6, 4, 7, 4, 6, 7, 2]
    list1 = list(set(list0)) print (“The list without duplicates : ” + str(list1)) o/p: The list without duplicates : [2, 4, 6, 7]

    30. What is recursion?

    Recursion is a function calling itself one or more times in it body. One very important condition a recursive function should have to be used in a program is, it should terminate, else there would be a problem of an infinite loop.

    31. Explain Python List Comprehension

    List comprehensions are used for transforming one list into another list. Elements can be conditionally included in the new list and each element can be transformed as needed. It consists of an expression leading a for clause, enclosed in brackets. for ex: list = [i for i in range(1000)]
    print list

    32. What is the bytes() function?

    The bytes() function returns a bytes object. It is used to convert objects into bytes objects, or create empty bytes object of the specified size.

    33. What are the different types of operators in Python?

    Python has the following basic operators:
    Arithmetic( Addition(+), Substraction(-), Multiplication(*), Division(/), Modulus(%) ), Relational ( <, >, <=, >=, ==, !=, ),
    Assignment ( =. +=, -=, /=, *=, %= ),
    Logical ( and, or not ), Membership, Identity, and Bitwise Operators

    34. What is the ‘with statement’?

    “with” statement in python is used in exception handling. A file can be opened and closed while executing a block of code, containing the “with” statement., without using the close() function. It essentially makes the code much more easy to read.

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

    The map() function in Python is used for applying a function on all elements of a specified iterable. It consists of two parameters, function and iterable. The function is taken as an argument and then applied to all the elements of an iterable(passed as the second argument). An object list is returned as a result.

    def add(n):
    return n + n number= (15, 25, 35, 45)
    res= map(add, num)
    print(list(res))

    o/p: 30,50,70,90

    36. What is __init__ in Python?

    _init_ methodology is a reserved method in Python aka constructor in OOP. When an object is created from a class and _init_ methodolgy is called to acess the class attributes.

    37. What are the tools present to perform statics analysis?

    The two static analysis tool used to find bugs in Python are: Pychecker and Pylint. Pychecker detects bugs from the source code and warns about its style and complexity.While, Pylint checks whether the module matches upto a coding standard.

    38. What is the difference between tuple and dictionary?

    One major difference between a tuple and a dictionary is that dictionary is mutable while a tuple is not. Meaning the content of a dictionary can be changed without changing it’s identity, but in tuple that’s not possible.

    39. What is pass in Python?

    Pass is a statentemen which does nothing when executed. In other words it is a Null statement. This statement is not ignored by the interpreter, but the statement results in no operation. It is used when you do not want any command to execute but a statement is required.

    40. How can an object be copied in Python?

    Not all objects can be copied in Python, but most can. We ca use the “=” operator to copy an obect to a variable.

    ex: var=copy.copy(obj)

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

    The inbuilt function str() can be used to convert a nuber to a string.

    42. What are module and package in Python?

    Modules are the way to structure a program. Each Python program file is a module, importing other attributes and objects. The folder of a program is a package of modules. A package can have modules or subfolders.

    43. What is object() function in Python?

    In Python the object() function returns an empty object. New properties or methods cannot be added to this object.

    44. What is the difference between NumPy and SciPy?

    NumPy stands for Numerical Python while SciPy stands for Scientific Python. NumPy is the basic library for defining arrays and simple mathematica problems, while SciPy is used for more complex problems like numerical integration and optimization and machine learning and so on.

    45. What does len() do?

    len() is used to determine the length of a string, a list, an array, and so on. ex: str = “greatlearning”
    print(len(str))
    o/p: 13

    46. Define encapsulation in Python?

    Encapsulation means binding the code and the data together. A Python class for example.

    47. What is the type () in Python?

    type() is a built-in method which either returns the type of the object or returns a new type object based on the arguments passed.

    ex: a = 100
    type(a)

    o/p: int

    48. What is split() function used for?

    Split fuction is used to split a string into shorter string using defined seperatos. letters = (” A, B, C”)
    n = text.split(“,”)
    print(n)

    o/p: [‘A’, ‘B’, ‘C’ ]

    49. What are the built-in types does python provide?

    Ans. Python has following built-in data types:

    Numbers: Python identifies three types of numbers:

    1. Integer: All positive and negative numbers without a fractional part
    2. Float: Any real number with floating-point representation
    3. Complex numbers: A number with a real and imaginary component represented as x+yj. x and y are floats and j is -1(square root of -1 called an imaginary number)

    Boolean: The Boolean data type is a data type that has one of two possible values i.e. True or False. Note that ‘T’ and ‘F’ are capital letters.

    String: A string value is a collection of one or more characters put in single, double or triple quotes.

    List: A list object is an ordered collection of one or more data items which can be of different types, put in square brackets. A list is mutable and thus can be modified, we can add, edit or delete individual elements in a list.

    Set: An unordered collection of unique objects enclosed in curly brackets

    Frozen set: They are like a set but immutable, which means we cannot modify their values once they are created.

    Dictionary: A dictionary object is unordered in which there is a key associated with each value and we can access each value through its key. A collection of such pairs is enclosed in curly brackets. For example {‘First Name’ : ’Tom’ , ’last name’ : ’Hardy’} Note that Number values, strings, and tuple are immutable while as List or Dictionary object are mutable.

    50. What is docstring in Python?

    Ans. Python docstrings are the string literals enclosed in triple quotes that appear right after the definition of a function, method, class, or module. These are generally used to describe the functionality of a particular function, method, class, or module. We can access these docstrings using the __doc__ attribute. Here is an example:

    def square(n):
        '''Takes in a number n, returns the square of n'''
        return n**2
    print(square.__doc__)
     Ouput: Takes in a number n, returns the square of n 

    Python Interview Questions for Experienced Professionals

    1. How to create a new column in pandas by using values from other columns?

    We can perform column based mathematical operations on a pandas dataframe. Pandas columns containing numeric values can be operated upon by operators.

    2. What are the different functions that can be used by grouby in pandas ?

    grouby() in pandas can be used with multiple aggregate functions. Some of which are sum(),mean(), count(),std().

    Data is divided into groups based on categories and then the data in these individual groups can be aggregated by the aforementioned functions.

    3. How to select columns in pandas and add them to a new dataframe? What if there are two columns with the same name?

    If df is dataframe in pandas df.columns gives the list of all columns. We can then form new columns by selecting columns.

    If there are two columns with the same name then both columns get copied to the new dataframe.

    4. How to delete a column or group of columns in pandas? Given the below dataframe drop column “col1”.

    drop() function can be used to delete the columns from a dataframe. 

    5. Given the following data frame drop rows having column values as A.

    6. Given the below dataset find the highest paid player in each college in each team.

    7. Given the above dataset find the min max and average salary of a player collegewise and teamwise.

    8. What is reindexing in pandas?

    Reindexing is the process of re-assigning the index of a pandas dataframe.

    9. What do you understand by lambda function? 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)

    10. What is vstack() in numpy? Give an example

    Ans. vstack() is a function to align rows vertically. All rows must have same number of elements.

    11. How do we interpret Python?

    When a python program is written, it converts the source code written by the developer into intermediate language, which is then coverted into machine language that needs to be executed.

    12. How to remove spaces from a string in Python?

    Spaces can be removed from a string in python by using strip() or replace() functions. Strip() function is used to remove the leading and trailing white spaces while the replace() function is used to remove all the white spaces in the string:

    string.replace(” “,””) ex1: str1= “great learning”
    print (str.strip())

    o/p: great learning

    ex2: str2=”great learning”
    print (str.replace(” “,””))

    o/p: greatlearning

    13. Explain the file processing modes that Python supports.

    There are three file processing modes in Python: read-only(r), write-only(w), read-write(rw) and append (a). So, if you are opening a text file in say, read mode. The preceding modes become: “rt” fot read-only, “wt” for write and so on. Similarly a binary file can be opened by specifying “b” along with the file accessing flags (“r” ,”w”, “rw” and “a”) preceding it.

    14. What is pickling and unpickling?

    Pickling is the process of converthing a Python object hierarachy into a byte stream for storing it into a database.It is also known as serialization. Unpickling is the reverse of pickling. The byte stream is conveted back into obhect hierarchy.

    15. How is memory managed in Python?

    Memory management in python comprises of a private heap containing all objects and data stucture. The heap is managed by the interpreter and the programmer does not have acess to it at all. The Python memory manger does all the memory allocation. Moreover, there is an inbuilt garbage collector that recycles and frees memory for the heap space.

    16. What is unittest in Python?

    Unittest is a unit testinf framework in Python. It supports sharing of setup and shutdown code for tests, aggregation of tests into collections,test automation, and independence of the tests from the reporting framework.

    17. How do you delete a file in Python?

    Files can be deleted in Python by using the command os.remove (filename) or os.unlink(filename)

    18. How do you create an empty class in Python?

    To create an empty class we can use the pass command after the definition of the class object. A pass is a statement in Python that does nothing.

    19. What are Python decorators?

    Ans. Decorators are functions that take another functions as argument to modify its behaviour without changing the function itself. These are useful when we want to dynamically increase the functionality of a function without changing it. Here is an example :

    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)

    Here smart_divide is a decorator function that is used to add functionality to simple divide function.

    append in python

    Python Interview Questions for Adavanced Levels

    1. You have this covid-19 dataset 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?
    sol:

    #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 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:

    We start off by taking only 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’]

    After that, we extract only those records, where the date is equal to 17th July:
    today = df[df.date == ‘2020-07-17’]
    Then, we go ahead and select the top 5 states with 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 seaborn library to make the bar-plot. “State” column is mapped onto the x-axis and “confirmed” column is mapped onto the y-axis. The color of the bars is being determined by the “state” column.

    2. From this covid-19 dataset:

    How can you make a bar-plot for the top-5 states with the most amount of deaths?

    Sol:

    max_death_cases=today.sort_values(by=”deaths”,ascending=False)
    
    max_death_cases
    
    sns.set(rc={‘figure.figsize’:(15,10)})
    
    sns.barplot(x=”state”,y=”deaths”,data=top_states_death,hue=”state”)
    
    plt.show()

    Code Explanation:

    We start off by sorting our dataframe in descending order w.r.t the “deaths” column:

    max_death_cases=today.sort_values(by=”deaths”,ascending=False)

    Max_death_cases

    Then, we go ahead and make the bar-plot with the help of seaborn library:

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

    sns.barplot(x=”state”,y=”deaths”,data=top_states_death,hue=”state”)

    plt.show()

    Here, we are mapping “state” column onto the x-axis and “deaths” column onto the y-axis.


    3. From this covid-19 dataset:

    How can you make a line plot indicating the confirmed cases with respect to date?

    Sol:

    maha = df[df.state == ‘Maharashtra’]
    
    sns.set(rc={‘figure.figsize’:(15,10)})
    
    sns.lineplot(x=”date”,y=”confirmed”,data=maha,color=”g”)
    
    plt.show()

    Code Explanation:

    We start off by extracting all the records where the state is equal to “Maharashtra”:

    maha = df[df.state == ‘Maharashtra’]

    Then, we go ahead and make a line-plot using seaborn library:

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

    sns.lineplot(x=”date”,y=”confirmed”,data=maha,color=”g”)

    plt.show()

    Here, we map the “date” column onto the x-axis and “confirmed” column onto y-axis.


    4. On this “Maharashtra” dataset:

    How will you implement a linear regression algorithm with “date” as independent variable and “confirmed” as dependent variable. That is you have to predict the number of confirmed cases w.r.t date.

    Sol:

    from sklearn.model_selection import train_test_split
    
    maha[‘date’]=maha[‘date’].map(dt.datetime.toordinal)
    
    maha.head()
    
    x=maha[‘date’]
    
    y=maha[‘confirmed’]
    
    x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.3)
    
    from sklearn.linear_model import LinearRegression
    
    lr = LinearRegression()
    
    lr.fit(np.array(x_train).reshape(-1,1),np.array(y_train).reshape(-1,1))
    
    lr.predict(np.array([[737630]]))
    
    

    Code solution:

    We will start off by converting the date to ordinal type:

    from sklearn.model_selection import train_test_split

    maha[‘date’]=maha[‘date’].map(dt.datetime.toordinal)

    This is done because we cannot build the linear regression algorithm on top of the date column.

    Then, we go ahead and divide the dataset into train and test sets:

    x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.3)

    Finally, we go ahead and build the model:

    from sklearn.linear_model import LinearRegression

    lr = LinearRegression()

    lr.fit(np.array(x_train).reshape(-1,1),np.array(y_train).reshape(-1,1))

    lr.predict(np.array([[737630]]))

    5. On  this customer_churn dataset:

    Build a keras sequential model to find out how many customers will churn out on the basis of tenure of customer?

    Sol:

    from keras.models import Sequential
    
    from keras.layers import Dense
    
    model = Sequential()
    
    model.add(Dense(12, input_dim=1, activation=’relu’))
    
    model.add(Dense(8, activation=’relu’))
    
    model.add(Dense(1, activation=’sigmoid’))
    
    model.compile(loss=’binary_crossentropy’, optimizer=’adam’, metrics=[‘accuracy’])
    
    model.fit(x_train, y_train, epochs=150,validation_data=(x_test,y_test))
    
    y_pred = model.predict_classes(x_test)
    
    from sklearn.metrics import confusion_matrix
    
    confusion_matrix(y_test,y_pred)

    Code explanation:

    We will start off by importing the required libraries:

    from keras.models import Sequential

    from keras.layers import Dense

    Then, we go ahead and build the structure of the sequential model:

    model = Sequential()

    model.add(Dense(12, input_dim=1, activation=’relu’))

    model.add(Dense(8, activation=’relu’))

    model.add(Dense(1, activation=’sigmoid’))

    Finally, we will go ahead and predict the values:

    model.compile(loss=’binary_crossentropy’, optimizer=’adam’, metrics=[‘accuracy’])

    model.fit(x_train, y_train, epochs=150,validation_data=(x_test,y_test))

    y_pred = model.predict_classes(x_test)

    from sklearn.metrics import confusion_matrix

    confusion_matrix(y_test,y_pred)


    6. On this iris dataset:

    Build a decision tree classification model, where dependent variable is “Species” and independent variable is “Sepal.Length”.

    Sol:

    y = iris[[‘Species’]]
    
    x = iris[[‘Sepal.Length’]]
    
    from sklearn.model_selection import train_test_split
    
    x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.4)
    
    from sklearn.tree import DecisionTreeClassifier
    
    dtc = DecisionTreeClassifier()
    
    dtc.fit(x_train,y_train)
    
    y_pred=dtc.predict(x_test)
    
    from sklearn.metrics import confusion_matrix
    
    confusion_matrix(y_test,y_pred)

    (22+7+9)/(22+2+0+7+7+11+1+1+9)

    Code explanation:

    We start off by extracting the independent variable and dependent variable:

    y = iris[[‘Species’]]

    x = iris[[‘Sepal.Length’]]

    Then, we go ahead and divide the data into train and test set:

    from sklearn.model_selection import train_test_split

    x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.4)

    After that, we go ahead and build the model:

    from sklearn.tree import DecisionTreeClassifier

    dtc = DecisionTreeClassifier()

    dtc.fit(x_train,y_train)

    y_pred=dtc.predict(x_test)

    Finally, we build the confusion matrix:

    from sklearn.metrics import confusion_matrix

    confusion_matrix(y_test,y_pred)

    (22+7+9)/(22+2+0+7+7+11+1+1+9)

    7. On this iris dataset:

    Build a decision tree regression model where the independent variable is “petal length” and dependent variable is “Sepal length”.

    Sol:

    x= iris[[‘Petal.Length’]]
    
    y = iris[[‘Sepal.Length’]]
    
    x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.25)
    
    from sklearn.tree import DecisionTreeRegressor
    
    dtr = DecisionTreeRegressor()
    
    dtr.fit(x_train,y_train)
    
    y_pred=dtr.predict(x_test)
    
    y_pred[0:5]
    
    from sklearn.metrics import mean_squared_error
    
    mean_squared_error(y_test,y_pred)



    8. How will you scrape data from the website “cricbuzz”?

    Sol:

    import sys
    
    import time
    
    from bs4 import BeautifulSoup
    
    import requests
    
    import pandas as pd
    
    try:
    
            #use the browser to get the url. This is suspicious command that might blow up.
    
        page=requests.get(‘cricbuzz.com’)                             # this might throw an exception if something goes wrong.
    
    except Exception as e:                                   # this describes what to do if an exception is thrown
    
        error_type, error_obj, error_info = sys.exc_info()      # get the exception information
    
        print (‘ERROR FOR LINK:’,url)                          #print the link that cause the problem
    
        print (error_type, ‘Line:’, error_info.tb_lineno)     #print error info and line that threw the exception
    
                                                     #ignore this page. Abandon this and go back.
    
    time.sleep(2)   
    
    soup=BeautifulSoup(page.text,’html.parser’)
    
    links=soup.find_all(‘span’,attrs={‘class’:’w_tle’}) 
    
    links
    
    for i in links:
    
        print(i.text)
    
        print(“\n”)


    9. Write a user-defined function to implement central-limit theorem. You have to implement central limit theorem on this “insurance” dataset:

    You also have to build two plots on “Sampling Distribution of bmi” and “Population distribution of  bmi”.

    Sol:

    df = pd.read_csv(‘insurance.csv’)
    
    series1 = df.charges
    
    series1.dtype
    
    def central_limit_theorem(data,n_samples = 1000, sample_size = 500, min_value = 0, max_value = 1338):
    
        “”” Use this function to demonstrate Central Limit Theorem. 
    
            data = 1D array, or a pd.Series
    
            n_samples = number of samples to be created
    
            sample_size = size of the individual sample
    
            min_value = minimum index of the data
    
            max_value = maximum index value of the data “””
    
        %matplotlib inline
    
        import pandas as pd
    
        import numpy as np
    
        import matplotlib.pyplot as plt
    
        import seaborn as sns
    
        b = {}
    
        for i in range(n_samples):
    
            x = np.unique(np.random.randint(min_value, max_value, size = sample_size)) # set of random numbers with a specific size
    
            b[i] = data[x].mean()   # Mean of each sample
    
        c = pd.DataFrame()
    
        c[‘sample’] = b.keys()  # Sample number 
    
        c[‘Mean’] = b.values()  # mean of that particular sample
    
        plt.figure(figsize= (15,5))
    
        plt.subplot(1,2,1)
    
        sns.distplot(c.Mean)
    
        plt.title(f”Sampling Distribution of bmi. \n \u03bc = {round(c.Mean.mean(), 3)} & SE = {round(c.Mean.std(),3)}”)
    
        plt.xlabel(‘data’)
    
        plt.ylabel(‘freq’)
    
        plt.subplot(1,2,2)
    
        sns.distplot(data)
    
        plt.title(f”population Distribution of bmi. \n \u03bc = {round(data.mean(), 3)} & \u03C3 = {round(data.std(),3)}”)
    
        plt.xlabel(‘data’)
    
        plt.ylabel(‘freq’)
    
        plt.show()
    
    central_limit_theorem(series1,n_samples = 5000, sample_size = 500)


    Code Explanation:

    We start off by importing the insurance.csv file with this command:

    df = pd.read_csv(‘insurance.csv’)

    Then we go ahead and define the central limit theorem method:

    def central_limit_theorem(data,n_samples = 1000, sample_size = 500, min_value = 0, max_value = 1338):

    This method comprises of these parameters:

    • Data
    • N_samples
    • Sample_size
    • Min_value
    • Max_value

    Inside this method, we import all the required libraries:

        import pandas as pd

        import numpy as np

        import matplotlib.pyplot as plt

        import seaborn as sns

    Then, we go ahead and create the first sub-plot for “Sampling distribution of bmi”:

      plt.subplot(1,2,1)

        sns.distplot(c.Mean)

        plt.title(f”Sampling Distribution of bmi. \n \u03bc = {round(c.Mean.mean(), 3)} & SE = {round(c.Mean.std(),3)}”)

        plt.xlabel(‘data’)

        plt.ylabel(‘freq’)

    Finally, we create the sub-plot for “Population distribution of bmi”:

     plt.subplot(1,2,2)

        sns.distplot(data)

        plt.title(f”population Distribution of bmi. \n \u03bc = {round(data.mean(), 3)} & \u03C3 = {round(data.std(),3)}”)

        plt.xlabel(‘data’)

        plt.ylabel(‘freq’)

        plt.show()


    10.  Write code to perform sentiment analysis on amazon reviews:

    Sol:

    import pandas as pd
    
    import numpy as np
    
    import matplotlib.pyplot as plt
    
    from tensorflow.python.keras import models, layers, optimizers
    
    import tensorflow
    
    from tensorflow.keras.preprocessing.text import Tokenizer, text_to_word_sequence
    
    from tensorflow.keras.preprocessing.sequence import pad_sequences
    
    import bz2
    
    from sklearn.metrics import f1_score, roc_auc_score, accuracy_score
    
    import re
    
    %matplotlib inline
    
    def get_labels_and_texts(file):
    
        labels = []
    
        texts = []
    
        for line in bz2.BZ2File(file):
    
            x = line.decode(“utf-8”)
    
            labels.append(int(x[9]) – 1)
    
            texts.append(x[10:].strip())
    
        return np.array(labels), texts
    
    train_labels, train_texts = get_labels_and_texts(‘train.ft.txt.bz2’)
    
    test_labels, test_texts = get_labels_and_texts(‘test.ft.txt.bz2’)
    
    Train_labels[0]
    
    Train_texts[0]
    
    train_labels=train_labels[0:500]
    
    train_texts=train_texts[0:500]
    
    import re
    
    NON_ALPHANUM = re.compile(r'[\W]’)
    
    NON_ASCII = re.compile(r'[^a-z0-1\s]’)
    
    def normalize_texts(texts):
    
        normalized_texts = []
    
        for text in texts:
    
            lower = text.lower()
    
            no_punctuation = NON_ALPHANUM.sub(r’ ‘, lower)
    
            no_non_ascii = NON_ASCII.sub(r”, no_punctuation)
    
            normalized_texts.append(no_non_ascii)
    
        return normalized_texts
    
    train_texts = normalize_texts(train_texts)
    
    test_texts = normalize_texts(test_texts)
    
    from sklearn.feature_extraction.text import CountVectorizer
    
    cv = CountVectorizer(binary=True)
    
    cv.fit(train_texts)
    
    X = cv.transform(train_texts)
    
    X_test = cv.transform(test_texts)
    
    from sklearn.linear_model import LogisticRegression
    
    from sklearn.metrics import accuracy_score
    
    from sklearn.model_selection import train_test_split
    
    X_train, X_val, y_train, y_val = train_test_split(
    
        X, train_labels, train_size = 0.75)
    
    for c in [0.01, 0.05, 0.25, 0.5, 1]:
    
        lr = LogisticRegression(C=c)
    
        lr.fit(X_train, y_train)
    
        print (“Accuracy for C=%s: %s” 
    
               % (c, accuracy_score(y_val, lr.predict(X_val))))
    
    lr.predict(X_test[29])



    11. Implement a probability plot using numpy and matplotlib:

    sol:

    import numpy as np
    
    import pylab
    
    import scipy.stats as stats
    
    from matplotlib import pyplot as plt
    
    n1=np.random.normal(loc=0,scale=1,size=1000)
    
    np.percentile(n1,100)
    
    n1=np.random.normal(loc=20,scale=3,size=100)
    
    stats.probplot(n1,dist=”norm”,plot=pylab)
    
    plt.show()

    12. Implement multiple linear regression on this iris dataset:

    The independent variables should be “Sepal.Width”, “Petal.Length”, “Petal.Width”, while the dependent variable should be “Sepal.Length”.

    Sol:

    import pandas as pd
    
    iris = pd.read_csv(“iris.csv”)
    
    iris.head()
    
    x = iris[[‘Sepal.Width’,’Petal.Length’,’Petal.Width’]]
    
    y = iris[[‘Sepal.Length’]]
    
    from sklearn.model_selection import train_test_split
    
    x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.35)
    
    from sklearn.linear_model import LinearRegression
    
    lr = LinearRegression()
    
    lr.fit(x_train, y_train)
    
    y_pred = lr.predict(x_test)
    
    from sklearn.metrics import mean_squared_error
    
    mean_squared_error(y_test, y_pred)


    Code solution:

    We start off by importing the required libraries:

    import pandas as pd

    iris = pd.read_csv(“iris.csv”)

    iris.head()

    Then, we will go ahead and extract the independent variables and dependent variable:

    x = iris[[‘Sepal.Width’,’Petal.Length’,’Petal.Width’]]

    y = iris[[‘Sepal.Length’]]

    Following which, we divide the data into train and test sets:

    from sklearn.model_selection import train_test_split

    x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.35)

    Then, we go ahead and build the model:

    from sklearn.linear_model import LinearRegression

    lr = LinearRegression()

    lr.fit(x_train, y_train)

    y_pred = lr.predict(x_test)

    Finally, we will find out the mean squared error:

    from sklearn.metrics import mean_squared_error

    mean_squared_error(y_test, y_pred)


    13. From this credit fraud dataset:

    Find the percentage of transactions which are fraudulent and not fraudulent. Also build a logistic regression model, to find out if the transaction is fraudulent or not.

    Sol:

    nfcount=0
    
    notFraud=data_df[‘Class’]
    
    for i in range(len(notFraud)):
    
      if notFraud[i]==0:
    
        nfcount=nfcount+1
    
    nfcount    
    
    per_nf=(nfcount/len(notFraud))*100
    
    print(‘percentage of total not fraud transaction in the dataset: ‘,per_nf)
    
    fcount=0
    
    Fraud=data_df[‘Class’]
    
    for i in range(len(Fraud)):
    
      if Fraud[i]==1:
    
        fcount=fcount+1
    
    fcount    
    
    per_f=(fcount/len(Fraud))*100
    
    print(‘percentage of total fraud transaction in the dataset: ‘,per_f)
    
    x=data_df.drop([‘Class’], axis = 1)#drop the target variable
    
    y=data_df[‘Class’]
    
    xtrain, xtest, ytrain, ytest = train_test_split(x, y, test_size = 0.2, random_state = 42) 
    
    logisticreg = LogisticRegression()
    
    logisticreg.fit(xtrain, ytrain)
    
    y_pred = logisticreg.predict(xtest)
    
    accuracy= logisticreg.score(xtest,ytest)
    
    cm = metrics.confusion_matrix(ytest, y_pred)
    
    print(cm)
    

    14.  Implement a simple CNN on the MNIST dataset using Keras. Following which, also add in drop out layers.

    Sol:

    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 Imgaes 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), callbacks=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)

    15. Implement a popularity based recommendation system on this movie lens dataset:

    import os
    
    import numpy as np  
    
    import pandas as pd
    
    ratings_data = pd.read_csv(“ratings.csv”)  
    
    ratings_data.head() 
    
    movie_names = pd.read_csv(“movies.csv”)  
    
    movie_names.head()  
    
    movie_data = pd.merge(ratings_data, movie_names, on=’movieId’)  
    
    movie_data.groupby(‘title’)[‘rating’].mean().head()  
    
    movie_data.groupby(‘title’)[‘rating’].mean().sort_values(ascending=False).head() 
    
    movie_data.groupby(‘title’)[‘rating’].count().sort_values(ascending=False).head()  
    
    ratings_mean_count = pd.DataFrame(movie_data.groupby(‘title’)[‘rating’].mean())
    
    ratings_mean_count.head()
    
    ratings_mean_count[‘rating_counts’] = pd.DataFrame(movie_data.groupby(‘title’)[‘rating’].count())
    
    ratings_mean_count.head()  

    16. Implement the naive bayes algorithm on top of the diabetes dataset:

    Sol:

    import numpy as np # linear algebra
    
    import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
    
    import matplotlib.pyplot as plt       # matplotlib.pyplot plots data
    
    %matplotlib inline 
    
    import seaborn as sns
    
    pdata = pd.read_csv(“pima-indians-diabetes.csv”)
    
    columns = list(pdata)[0:-1] # Excluding Outcome column which has only 
    
    pdata[columns].hist(stacked=False, bins=100, figsize=(12,30), layout=(14,2)); 
    
    # Histogram of first 8 columns

    # However we want to see correlation in graphical representation so below is function for that

    def plot_corr(df, size=11):
    
        corr = df.corr()
    
        fig, ax = plt.subplots(figsize=(size, size))
    
        ax.matshow(corr)
    
        plt.xticks(range(len(corr.columns)), corr.columns)
    
        plt.yticks(range(len(corr.columns)), corr.columns)
    
    plot_corr(pdata)


    from sklearn.model_selection import train_test_split
    
    X = pdata.drop(‘class’,axis=1)     # Predictor feature columns (8 X m)
    
    Y = pdata[‘class’]   # Predicted class (1=True, 0=False) (1 X m)
    
    x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=0.3, random_state=1)
    
    # 1 is just any random seed number
    
    x_train.head()
    
    from sklearn.naive_bayes import GaussianNB # using Gaussian algorithm from Naive Bayes
    
    # creatw the model
    
    diab_model = GaussianNB()
    
    diab_model.fit(x_train, y_train.ravel())
    
    diab_train_predict = diab_model.predict(x_train)
    
    from sklearn import metrics
    
    print(“Model Accuracy: {0:.4f}”.format(metrics.accuracy_score(y_train, diab_train_predict)))
    
    print()
    
    diab_test_predict = diab_model.predict(x_test)
    
    from sklearn import metrics
    
    print(“Model Accuracy: {0:.4f}”.format(metrics.accuracy_score(y_test, diab_test_predict)))
    
    print()
    
    print(“Confusion Matrix”)
    
    cm=metrics.confusion_matrix(y_test, diab_test_predict, labels=[1, 0])
    
    df_cm = pd.DataFrame(cm, index = [i for i in [“1″,”0”]],
    
                      columns = [i for i in [“Predict 1″,”Predict 0”]])
    
    plt.figure(figsize = (7,5))
    
    sns.heatmap(df_cm, annot=True)

    append in python

    Python OOPS Interview Questions

    1. What do you understand by object oriented programming in Python?

    Object oriented programming refers to the process of solving a problem by creating objects. This approach takes into account two key factors of an object- attributes and behaviour.

    2. How are classes created in Python? Give an example

    class Node(object):
      def __init__(self):
        self.x=0
        self.y=0

    Here Node is a class

    3. What is inheritance in Object oriented programming? Give an example of multiple inheritance.

    Inheritance is one of the core concepts of object-oriented programming. It is a process of deriving a class from a different class and form a hierarchy of classes that share the same attributes and methods. It is generally used for deriving different kinds of exceptions, create custom logic for existing frameworks and even map domain models for database.

    Example

    class Node(object):
      def __init__(self):
        self.x=0
        self.y=0

    Here class Node inherits from the object class.

    4. What is multi-level inheritance? Give an example for multi-level inheritance?

    If class A inherits from B and C inherits from A it’s called multilevel inheritance.
    class B(object):
      def __init__(self):
        self.b=0
     
    class A(B):
      def __init__(self):
        self.a=0
     
    class C(A):
      def __init__(self):
        self.c=0
    append in python

    Python Programs for Interview

    1. How can you find the minimum and maximum values present in a tuple?

     Solution ->

    We can use the min() function on top of the tuple to find out the minimum value present in the tuple:

    We see that the minimum value present in the tuple is 1.

    Analogous to the min() function is the max() function, which will help us to find out the maximum value present in the tuple:

    We see that the maximum value present in the tuple is 5

    2. If you have a list like this -> [1,”a”,2,”b”,3,”c”]. How can you access the 2nd, 4th and 5th elements from this list?

    Solution ->

    We will start off by creating a tuple which will comprise of the indices of elements which we want to access:

    Then, we will use a for loop to go through the index values and print them out:

    Below is the entire code for the process:

    3. If you have a list like this -> [“sparta”,True,3+4j,False]. How would you reverse the elements of this list?

    Solution ->

    We can use  the reverse() function on the list:

    4. If you have dictionary like this – > fruit={“Apple”:10,”Orange”:20,”Banana”:30,”Guava”:40}. How would you update the value of ‘Apple’ from 10 to 100?

    Solution ->

     This is how you can do it:

    Give in the name of the key inside the parenthesis and assign it a new value.

    5. If you have two sets like this -> s1 = {1,2,3,4,5,6}, s2 = {5,6,7,8,9}. How would you find the common elements in these sets.

    Solution ->

    You can use the intersection() function to find the common elements between the two sets:

    We see that the common elements between the two sets are 5 & 6.

    6. Write a program to print out the 2-table using while loop.

    Solution ->

    Below is the code to print out the 2-table:

    We start off by initializing two variables ‘i’ and ‘n’. ‘i’ is initialized to 1 and ‘n’ is initialized to ‘2’.

    Inside the while loop, since the ‘i’ value goes from 1 to 10, the loop iterates 10 times.

    Initially n*i is equal to 2*1, and we print out the value.

    Then, ‘i’ value is incremented and n*i becomes 2*2. We go ahead and print it out.

    This process goes on until i value becomes 10.

    7. Write a function, which will take in a value and print out if it is even or odd.

    Solution ->

    The below code will do the job:

    Here, we start off by creating a method, with the name ‘even_odd()’. This function takes a single parameter and prints out if the number taken is even or odd.

    Now, let’s invoke the function:

    We see that, when 5 is passed as a parameter into the function, we get the output -> ‘5 is odd’.

    8. Write a python program to print the factorial of a number.

    Solution ->

    Below is the code to print the factorial of a number:

    We start off by taking an input which is stored in ‘num’. Then, we check if ‘num’ is less than zero and if it is actually less than 0, we print out ‘Sorry, factorial does not exist for negative numbers’.

    After that, we check,if ‘num’ is equal to zero, and it that’s the case, we print out ‘The factorial of 0 is 1’.

    On the other hand, if ‘num’ is greater than 1, we enter the for loop and calculate the factorial of the number.

    9. Write a python program to check if the number given is a palindrome or not

    Solution ->

    Below is the code to Check whether the given number is palindrome or not:

    We will start off by taking an input and store it in ‘n’ and make a duplicate of it in ‘temp’. We will also initialize another variable ‘rev’ to 0. 

    Then, we will enter a while loop which will go on until ‘n’ becomes 0. 

    Inside the loop, we will start off by dividing ‘n’ with 10 and then store the remainder in ‘dig’.

    Then, we will multiply ‘rev’ with 10 and then add ‘dig’ to it. This result will be stored back in ‘rev’.

    Going ahead, we will divide ‘n’ by 10 and store the result back in ‘n’

    Once the for loop ends, we will compare the values of ‘rev’ and ‘temp’. If they are equal, we will print ‘The number is a palindrome’, else we will print ‘The number isn’t a palindrome’.

    10. Write a python program to print the following pattern ->

    1

    2 2

    3 3 3

    4 4 4 4

    5 5 5 5 5

    Solution ->

    Below is the code to print this pattern:

    We are solving the problem with the help of nested for loop. We will have an outer for loop, which goes from 1 to 5. Then, we have an inner for loop, which would print the respective numbers.

    11.  Pattern questions. Print the following pattern

    #
    # #
    # # #
    # # # #
    # # # # #

    Solution –>

    def pattern_1(num): 
          
        # outer loop handles the number of rows
        # inner loop handles the number of columns 
        # n is the number of rows. 
        for i in range(0, n): 
          # value of j depends on i 
            for j in range(0, i+1): 
              
                # printing hashes
                print("#",end="") 
           
            # ending line after each row 
            print("\r")  
    num = int(input("Enter the number of rows in pattern: "))
    pattern_1(num)

    12. Print the following pattern

      # 
          # # 
        # # # 
      # # # #
    # # # # #

    Solution –>

           
    Code:
    def pattern_2(num): 
          
        # define the number of spaces 
        k = 2*num - 2
      
        # outer loop always handles the number of rows 
        # let us use the inner loop to control the number of spaces
        # we need the number of spaces as maximum initially and then decrement it after every iteration
        for i in range(0, num): 
            for j in range(0, k): 
                print(end=" ") 
          
            # decrementing k after each loop 
            k = k - 2
          
            # reinitializing the inner loop to keep a track of the number of columns
            # similar to pattern_1 function
            for j in range(0, i+1):  
                print("# ", end="") 
          
            # ending line after each row 
            print("\r") 
      
    
    num = int(input("Enter the number of rows in pattern: "))
    pattern_2(num)

    13. Print the following pattern:

    0
    0 1
    0 1 2
    0 1 2 3
    0 1 2 3 4

    Solution –>

    Code: 
    def pattern_3(num): 
          
        # initialising starting number  
        number = 1
        # outer loop always handles the number of rows 
        # let us use the inner loop to control the number 
       
        for i in range(0, num): 
          
            # re assigning number after every iteration
            # ensure the column starts from 0
            number = 0
          
            # inner loop to handle number of columns 
            for j in range(0, i+1): 
              
                    # printing number 
                print(number, end=" ") 
              
                # increment number column wise 
                number = number + 1
            # ending line after each row 
            print("\r") 
     
    num = int(input("Enter the number of rows in pattern: "))
    pattern_3(num)

    14. Print the following pattern:

    1
    2 3
    4 5 6
    7 8 9 10
    11 12 13 14 15

    Solution –>

    Code: 
    
    def pattern_4(num): 
          
        # initialising starting number  
        number = 1
        # outer loop always handles the number of rows 
        # let us use the inner loop to control the number 
       
        for i in range(0, num): 
          
            # commenting the reinitialization part ensure that numbers are printed continuously
            # ensure the column starts from 0
            number = 0
          
            # inner loop to handle number of columns 
            for j in range(0, i+1): 
              
                    # printing number 
                print(number, end=" ") 
              
                # increment number column wise 
                number = number + 1
            # ending line after each row 
            print("\r") 
      
    
    num = int(input("Enter the number of rows in pattern: "))
    pattern_4(num)

    15. Print the following pattern:

    A
    B B
    C C C
    D D D D

    Solution –>

    def pattern_5(num): 
        # initializing value of A as 65
        # ASCII value  equivalent
        number = 65
      
        # outer loop always handles the number of rows 
        for i in range(0, num): 
          
            # inner loop handles the number of columns 
            for j in range(0, i+1): 
              
                # finding the ascii equivalent of the number 
                char = chr(number) 
              
                # printing char value  
                print(char, end=" ") 
          
            # incrementing number 
            number = number + 1
          
            # ending line after each row 
            print("\r") 
      
    num = int(input("Enter the number of rows in pattern: "))
    pattern_5(num)

    16. Print the following pattern:

    A
    B C
    D E F
    G H I J
    K L M N O
    P Q R S T U

    Solution –>

    def  pattern_6(num): 
        # initializing value equivalent to 'A' in ASCII  
        # ASCII value 
        number = 65
     
        # outer loop always handles the number of rows 
        for i in range(0, num):
            # inner loop to handle number of columns 
            # values changing acc. to outer loop 
            for j in range(0, i+1):
                # explicit conversion of int to char
    # returns character equivalent to ASCII. 
                char = chr(number) 
              
                # printing char value  
                print(char, end=" ") 
                # printing the next character by incrementing 
                number = number +1    
            # ending line after each row 
            print("\r") 
    num = int(input("enter the number of rows in the pattern: "))
    pattern_6(num)

    17. Print the following pattern

      #
        # # 
       # # # 
      # # # # 
     # # # # #

    Solution –>

    Code: 
    def pattern_7(num): 
          
        # number of spaces is a function of the input num 
        k = 2*num - 2
      
        # outer loop always handle the number of rows 
        for i in range(0, num): 
          
            # inner loop used to handle the number of spaces 
            for j in range(0, k): 
                print(end=" ") 
          
            # the variable holding information about number of spaces
            # is decremented after every iteration 
            k = k - 1
          
            # inner loop reinitialized to handle the number of columns  
            for j in range(0, i+1): 
              
                # printing hash
                print("# ", end="") 
          
            # ending line after each row 
            print("\r") 
     
    num = int(input("Enter the number of rows: "))
    pattern_7(n)

    18. Given the below dataframes form a single dataframe by vertical stacking.

    We use the pd.concat and axis as 0 to stack them horizontally.

    19. Given the below dataframes stack them horizontally to form a single data frame.

    We use the pd.concat and axis as 0 to stack them horizontally.

    20. If you have a dictionary like this -> d1={“k1″:10,”k2″:20,”k3”:30}. How would you increment values of all the keys ?

    d1={"k1":10,"k2":20,"k3":30}
     
    for i in d1.keys():
      d1[i]=d1[i]+1

    21. How can you get a random number in python?

    Ans. To generate a random, we use a random module of python. Here are some examples To generate a floating-point number from 0-1

    import random
    n = random.random()
    print(n)
    To generate a integer between a certain range (say from a to b):
    import random
    n = random.randint(a,b)
    print(n)

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    Python Interview related FAQs

    Ques 1. How do you stand out in a Python coding interview?

    Now that you’re ready for a Python Interview in terms of technical skills, you must be wondering how to stand out from the crowd so that you’re the selected candidate. You must be able to show that you can write clean production codes and have knowledge about the libraries and tools required. If you’ve worked on any prior projects, then showcasing these projects in your interview will also help you stand out from the rest of the crowd.

    Ques 2. How do I prepare for a Python interview?

    To prepare for a Python Interview, you must know syntax, key-words, functions and classes, data types, basic coding, and exception handling. Having basic knowledge regarding all the libraries, IDE’s used and reading blogs related to Python Tutorial’s will help you going forward. Showcase your example projects, brush up your basic skills about algorithms, data structures. This will help you stay prepared.

    Ques 3. Are Python coding interviews very difficult?

    The difficulty level of a Python Interview will vary depending on the role you are applying for, the company, their requirements, and your skill and knowledge/work experience. If you’re a beginner in the field and are not yet confident about your coding ability, you may feel that the interview is difficult. Being prepared and knowing what type of questions to expect will help you prepare well and ace the interview.

    Ques 4. How do I pass the Python coding interview?

    Having adequate knowledge regarding Object Relational Mapper (ORM) libraries, Django or Flask, unit testing and debugging skills, fundamental design principles behind a scalable application, Python packages such as NumPy, Scikit learn are extremely important for you to clear a coding interview. You can showcase your previous work experience or coding ability through projects, this acts as an added advantage.

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