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.
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]
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.
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.
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- ( =. +=, -=, /=, *=, %= )
_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.
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.
Python has a fixed function that is function str(). It can be used to convert a number to a string.
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)
The term encapsulation means binding the data and the code together. For example, a highly build Python class.
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.
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.
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’ ]
The function of an object() key is to return to an empty object. New elements or properties cannot be connected to this object.
len() determines the length of a list, a string, and an array.
For example:
str = “greatlearning”
print(len(str))
o/p: 13
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
Python Interview Question And Answers For Experienced Professionals
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.
from functools import reduce
sequences = [5, 8, 10, 20, 50, 100]
sum = reduce (lambda x, y: x+y, sequences)
print(sum)
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)))
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
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.
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.
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.
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)
Python Interview Questions For Advanced Levels
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.
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)
The dataframe can be implemented in two ways:
1. By adding lists to individual columns
2. Create a null database
The map() function enables an argument. It is used to the elements that are highly iterated.
The two vital tools are:
1. Pylint
2. Pychecker
Yes. It is.
The several versions of Python can be check in a CMD by pressing CMD + Space.
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.
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