Bahija Siddiqui

Find key by value python – Python : How to find keys by value in dictionary ?

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Find key by value python: In this article, we will see how to find all the keys associated with a single value or multiple values.

For instance, we have a dictionary of words.

dictOfWords = {"hello": 56,
"at" : 23 ,
"test" : 43,
"this" : 97,
"here" : 43,
"now" : 97
}

Now, we want all the keys in the dictionary having value 43, which means “here” and “test”.

Let’s see how to get it.

Find keys by value in the dictionary

Python find key by value: Dict.items() is the module that returns all the key-value pairs in a dictionary. So, what we will do is check whether the condition is satisfied by iterating over the sequence. If the value is the same then we will add the key in a separate list.

def getKeysByValue(dictOfElements, valueToFind):
 listOfKeys = list()
listOfItems = dictOfElements.items()
for item in listOfitems:
if item[1] == valueToFind:
 listOfKeys.append(item[0])
return listOfKeys
We will use this function to get the keys by value 43.
listOfKeys = getKeysByValue(dictOfWords, 43)
print("Keys with value equal to 43")
#Iterate over the list of keys
for key in listOfKeys:
print(key)

Find keys by value in dictionary

We can achieve the same thing with a list comprehension.

listOfKeys = [key for (key, value) in dictOfWords.items() if value == 43]

Find keys in the dictionary by value list

Find key from value python: Now, we want to find the keys in the dictionary whose values matches with the value we will give.

[43, 97]

We will do the same thing as we have done above but this time we will iterate the sequence and check whether the value matches with the given value.

def getKeysByValues(dictOfElements, listOfValues):
listOfKeys = list()
listOfItems = dictOfElements.items()
for item in listOfItems:
if item[1] in listOfValues:
 listOfKeys.append(item[0])
return listOfKeys

We will use the above function:

listOfKeys = getKeysByValues(dictOfWords, [43, 97] )
#Iterate over the list of values
for key in listOfKeys:
print(key)

Find keys in dictionary by value list

Complete Code:

'''Get a list of keys from dictionary which has the given value'''
def getKeysByValue(dictOfElements, valueToFind):
listOfKeys = list()
listOfItems = dictOfElements.items()
for item in listOfItems:
if item[1] == valueToFind:
listOfKeys.append(item[0])
return listOfKeys
'''
Get a list of keys from dictionary which has value that matches with any value in given list of values
'''
def getKeysByValues(dictOfElements, listOfValues):
listOfKeys = list()
listOfItems = dictOfElements.items()
for item in listOfItems:
if item[1] in listOfValues:
listOfKeys.append(item[0])
return listOfKeys
def main():
# Dictionary of strings and int
dictOfWords = {
"hello": 56,
"at" : 23 ,
"test" : 43,
"this" : 97,
"here" : 43,
"now" : 97}
print("Original Dictionary")
print(dictOfWords)
'''
Get list of keys with value 43
'''
listOfKeys = getKeysByValue(dictOfWords, 43)
print("Keys with value equal to 43")
#Iterate over the list of keys
for key in listOfKeys:
print(key)
print("Keys with value equal to 43")
'''
Get list of keys with value 43 using list comprehension
'''
listOfKeys = [key for (key, value) in dictOfWords.items() if value == 43]
#Iterate over the list of keys
for key in listOfKeys:
print(key)
print("Keys with value equal to any one from the list [43, 97] ")
'''
Get list of keys with any of the given values
'''
listOfKeys = getKeysByValues(dictOfWords, [43, 97] )
#Iterate over the list of values
for key in listOfKeys:
print(key)
if __name__ == '__main__':
main()

 

Hope this article was useful for you.

Enjoy Reading!

Python csv writer append – Python: How to append a new row to an existing csv file?

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Python-How to append a new row to an existing csv file

Python csv writer append: This tutorial will help you learn how to append a new row to an existing CSV file using some CSV modules like reader/writer and the most famous DictReader/DictWriter classes. Moreover, you can also get enough knowledge on all python concepts by visiting our provided tutorials.

How to Append a new row to an existing csv file?

Python append to csv: There are multiple ways in Python by which we can append rows into the CSV file. But here we will discuss two effective methods. Before going to learn those two methods, we have to follow the standard step which is explained ahead.

The basic step to proceed in this is to have a CSV file. For instance, here we have a CSV file named students.csv having the following contents:

Id,Name,Course,City,Session
21,Mark,Python,London,Morning
22,John,Python,Tokyo,Evening
23,Sam,Python,Paris,Morning

For reading and writing CSV files python provides a CSV module. There are two different classes for writing CSV files that is writer and DictWriter.

We can append the rows in a CSV file by either of them but some solutions are better than the other. We will see it in the next section.

Do Refer:

Append a list as a new row to an old CSV file using csv.writer()

Append csv python: A writer class is in the CSV module which writes the rows in existing CSV files.

Let’s take a list of strings:

# List of strings
row_contents = [32,'Shaun','Java','Tokyo','Morning']

To add this list to an existing CSV file, we have to follow certain steps:

  • Import CSV module’s writer class.
  • Open our csv file in append mode and create a file object.
  • Pass this file object to the csv.writer(), we can get a writer class object.
  • This writer object has a function writerow(), pass the list to it and it will add the list’s contents as a new row in the associated csv file.
  • A new row is added in the csv file, now close the file object.

By following the above steps, the list will be appended as a row in the CSV file as it is a simple process.

from csv import writer

def append_list_as_row(file_name, list_of_elem):
    # Open file in append mode
    with open(file_name, 'a+', newline='') as write_obj:
        # Create a writer object from csv module
        csv_writer = writer(write_obj)
        # Add contents of list as last row in the csv file
        csv_writer.writerow(list_of_elem)

Another Code:

Append a list as new row to an old csv file using csv.writer()

We can see that the list has been added.

Appending a row to csv with missing entries?

Python csv append: Suppose we have a list that does not contain all the values and we have to append it into the CSV file.

Suppose the list is:

list = [33, ‘Sahil’, ‘Morning’]

Example:

# A list with missing entries
row_contents = [33, 'Sahil', 'Morning']
# Appending a row to csv with missing entries
append_list_as_row('students.csv', row_contents)

some entries are missing in the list

Output:

output of missing files

We can see the data get appended at the wrong positions as the session got appended at the course.

csv’s writer class has no functionality to check if any of the intermediate column values are missing in the list or if they are in the correct order. It will just add the items in the list as column values of the last row in sequential order.

Therefore while adding a list as a row using csv.writer() we need to make sure that all elements are provided and are in the correct order.

If any element is missing like in the above example, then we should pass empty strings in the list like this,

row_contents = [33, 'Sahil', '' , '', 'Morning']

Since we have a huge amount of data in the CSV file, adding the empty strings in all of that will be a hectic task.

To save us from hectic work, the CSV provided us with the DictWriter class.

Append a dictionary as a row to an existing csv file using DictWriter in python

CSV append python: As the name suggests, we can append a dictionary as a row to an existing CSV file using DictWriter in Python. Let’s see how we can use them.

Suppose, we have a dictionary-like below,

{'Id': 81,'Name': 'Sachin','Course':'Maths','City':'Mumbai','Session':'Evening'}

We can see that the keys are the columns of the CSV and the values will be the ones we will provide.

To append it, we have to follow some steps given below:

  • import csv module’s DictWriter class,
  • Open our csv file in append mode and create a file object,
  • Pass the file object & a list of csv column names to the csv.DictWriter(), we can get a DictWriter class object
  • This DictWriter object has a function writerow() that accepts a dictionary. pass our dictionary to this function, it adds them as a new row in the associated csv file,
  • A new line is added in the csv file, now close the file object,

The above steps will append our dictionary as a new row in the csv. To make our life easier, we have created a separate function that performs the above steps,

Code:

from csv import DictWriter
def append_dict_as_row(file_name, dict_of_elem, field_names):
    # Open file in append mode
    with open(file_name, 'a+', newline='') as write_obj:
        # Create a writer object from csv module
        dict_writer = DictWriter(write_obj, fieldnames=field_names)
        # Add dictionary as wor in the csv
        dict_writer.writerow(dict_of_elem)

Append a dictionary as a row to an existing csv file using DictWriter in python

Output:

output of appending the dict

We can see that it added the row successfully. We can also consider this thought that what if our dictionary will have any missing entries? Or the items are in a different order?

The advantage of using DictWriter is that it will automatically handle the sort of things and columns with missing entries will remain empty. Let’s check an example:

field_names = ['Id','Name','Course','City','Session']
row_dict = {'Id': 81,'Name': 'Sachin','Course':'Maths','City':'Mumbai','Session':'Evening'}
# Append a dict as a row in csv file
append_dict_as_row('students.csv', row_dict, field_names)

Output:

column with missing entries will remain empty

We can see this module has its wonders.

Hope this article was useful and informative for you.

How to web scrape with Python in 4 minutes

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Web Scraping:

Web scraping is used to extract the data from the website and it can save time as well as effort. In this article, we will be extracting hundreds of file from the New York MTA. Some people find web scraping tough, but it is not the case as this article will break the steps into easier ones to get you comfortable with web scraping.

New York MTA Data:

We will download the data from the below website:

http://web.mta.info/developers/turnstile.html

Turnstile data is compiled every week from May 2010 till now, so there are many files that exist on this site. For instance, below is an example of what data looks like.

You can right-click on the link and can save it to your desktop. That is web scraping!

Important Notes about Web scraping:

  1. Read through the website’s Terms and Conditions to understand how you can legally use the data. Most sites prohibit you from using the data for commercial purposes.
  2. Make sure you are not downloading data at too rapid a rate because this may break the website. You may potentially be blocked from the site as well.

Inspecting the website:

The first thing that we should find out is the information contained in the HTML tag from where we want to scrape it. As we know, there is a lot of code on the entire page and it contains multiple HTML tags, so we have to find out the one which we want to scrape and write it down in our code so that all the data related to it will be visible.

When you are on the website, right-click and then when you will scroll down you will get an option of “inspect”. Click on it and see the hidden code behind the page.

You can see the arrow symbol at the top of the console. 

If you will click on the arrow and then click any text or item on the website then the highlighted tag will appear related to the website on which you clicked.

I clicked on Saturday, September 2018 file and the console came in the blue highlighted part.

<a href=”data/nyct/turnstile/turnstile_180922.txt”>Saturday, September 22, 2018</a>

You will see that all the .txt files come in <a> tags. <a> tags are used for hyperlinks.

Now that we got the location, we will process the coding!

Python Code:

The first and foremost step is importing the libraries:

import requests

import urllib.request

import time

from bs4 import BeautifulSoup

Now we have to set the url and access the website:

url = '

http://web.mta.info/developers/turnstile.html’

response = requests.get(url)

Now, we can use the features of beautiful soup for scraping.

soup = BeautifulSoup(response.text, “html.parser”)

We will use the method findAll to get all the <a> tags.

soup.findAll('a')

This function will give us all the <a> tags.

Now, we will extract the actual link that we want.

one_a_tag = soup.findAll(‘a’)[38]

link = one_a_tag[‘href’]

This code will save the first .txt file to our variable link.

download_url = 'http://web.mta.info/developers/'+ link

urllib.request.urlretrieve(download_url,'./'+link[link.find('/turnstile_')+1:])

For pausing our code we will use the sleep function.

time.sleep(1)

To download the entire data we have to apply them for a loop. I am attaching the entire code so that you won’t face any problem.

I hope you understood the concept of web scraping.

Enjoy reading and have fun while scraping!

An Intro to Web Scraping with lxml and Python:

Sometimes we want that data from the API which cannot be accessed using it. Then, in the absence of API, the only choice left is to make a web scraper. The task of the scraper is to scrape all the information which we want in easily and in very little time.

The example of a typical API response in JSON. This is the response from Reddit.

 There are various kinds of python libraries that help in web scraping namely scrapy, lxml, and beautiful soup.

Many articles explain how to use beautiful soup and scrapy but I will be focusing on lxml. I will teach you how to use XPaths and how to use them to extract data from HTML documents.

Getting the data:

If you are into gaming, then you must be familiar with this website steam.

We will be extracting the data from the “popular new release” information.

Now, right-click on the website and you will see the inspect option. Click on it and select the HTML tag.

We want an anchor tag because every list is encapsulated in the <a> tag.

The anchor tag lies in the div tag with an id of tag_newreleasecontent. We are mentioning the id because there are two tabs on this page and we only want the information of popular release data.

Now, create your python file and start coding. You can name the file according to your preference. Start importing the below libraries:

import requests 

import lxml.html

If you don’t have requests to install then type the below code on your terminal:

$ pip install requests

Requests module helps us open the webpage in python.

Extracting and processing the information:

Now, let’s open the web page using the requests and pass that response to lxml.html.fromstring.

html = requests.get('https://store.steampowered.com/explore/new/') 

doc = lxml.html.fromstring(html.content)

This provides us with a structured way to extract information from an HTML document. Now we will be writing an XPath for extracting the div which contains the” popular release’ tab.

new_releases = doc.xpath('//div[@id="tab_newreleases_content"]')[0]

We are taking only one element ([0]) and that would be our required div. Let us break down the path and understand it.

  • // these tell lxml that we want to search for all tags in the HTML document which match our requirements.
  • Div tells lxml that we want to find div tags.
  • @id=”tab_newreleases_content tells the div tag that we are only interested in the id which contains tab_newrelease_content.

Awesome! Now we understand what it means so let’s go back to inspect and check under which tag the title lies.

The title name lies in the div tag inside the class tag_item_name. Now we will run the XPath queries to get the title name.

titles = new_releases.xpath('.//div[@class="tab_item_name"]/text()')

We can see that the names of the popular releases came. Now, we will extract the price by writing the following code:

prices = new_releases.xpath('.//div[@class="discount_final_price"]/text()')

Now, we can see that the prices are also scraped. We will extract the tags by writing the following command:

tags = new_releases.xpath('.//div[@class="tab_item_top_tags"]')

total_tags = []

for tag in tags:

total_tags.append(tag.text_content())

We are extracting the div containing the tags for the game. Then we loop over the list of extracted tags using the tag.text_content method.

Now, the only thing remaining is to extract the platforms associated with each title. Here is the the HTML markup:

The major difference here is that platforms are not contained as texts within a specific tag. They are listed as class name so some titles only have one platform associated with them:

 

<span class="platform_img win">&lt;/span>

 

While others have 5 platforms like this:

 

<span class="platform_img win"></span><span class="platform_img mac"></span><span class="platform_img linux"></span><span class="platform_img hmd_separator"></span> <span title="HTC Vive" class="platform_img htcvive"></span> <span title="Oculus Rift" class="platform_img oculusrift"></span>

The span tag contains platform types as the class name. The only thing common between them is they all contain platform_img class.

First of all, we have to extract the div tags containing the tab_item_details class. Then we will extract the span containing the platform_img class. Lastly, we will extract the second class name from those spans. Refer to the below code:

platforms_div = new_releases.xpath('.//div[@class="tab_item_details"]')

total_platforms = []

for game in platforms_div:    

temp = game.xpath('.//span[contains(@class, "platform_img")]')    

platforms = [t.get('class').split(' ')[-1] for t in temp]    

if 'hmd_separator' in platforms:        

platforms.remove('hmd_separator')   

 total_platforms.append(platforms)

Now we just need this to return a JSON response so that we can easily turn this into Flask based API.

output = []for info in zip(titles,prices, tags, total_platforms):    resp = {}    

resp['title'] = info[0]

resp['price'] = info[1]    

resp['tags'] = info[2]    

resp['platforms'] = info[3]    

output.append(resp)

We are using the zip function to loop over all of the lists in parallel. Then we create a dictionary for each game to assign the game name, price, and platforms as keys in the dictionary.

Wrapping up:

I hope this article is understandable and you find the coding easy.

Enjoy reading!

 

Simple Whatsapp Automation Using Python3 and Selenium

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In this article, we will be using python and selenium to automate some messages on WhatsApp.

I hope the reader is well aware of python beforehand.

The first and the foremost step is to install python3 which you can download from https://www.python.org/  and follow up the install instruction. After the installation will be complete, install selenium for the automation of all the tasks we want to perform.

python3 -m pip install Selenium

Selenium Hello World:

After installing selenium, to check whether it is installed correctly or not, run the python code mentioned below and check if there are any errors.

from selenium import webdriver

import time

driver = webdriver.Chrome()

driver.get("http://google.com")

time.sleep(2)

driver.quit()

Save this code in a python file and name it according to your preference. If the program runs correctly without showing any errors, then the Google Chrome window will be opened automatically.

Automate Whatsapp:

Import the modules selenium and time like below.

from selenium import webdriver

import time

After the importing of the modules, the below code will open the WhatsApp web interface which will automatically ask you to scan the QR code and will be logged into your account.

driver = webdriver.Chrome()

driver.get("https://web.whatsapp.com")

print("Scan QR Code, And then Enter")

time.sleep(5)

The next step is entering the username to whom you want to send the message. In my case, I made a group named “WhatsApp bot” and then located an XPath using the inspect method and put it in.

As soon as the WhatsApp bot will be opened, it will automatically locate the WhatsApp bot and will enter that window.

user_name = 'Whatsapp Bot'

user = driver.find_element_by_xpath('//span[@title="{}"]'.format(user_name))

user.click()

After this, the message box will be opened and now you have to inspect the message box and enter the message you want to send. Later, you have to inspect the send button and click on it using the click() method. 

message_box = driver.find_element_by_xpath(‘//div[@class=”_2A8P4″]’)



message_box.send_keys(‘Hey, I am your whatsapp bot’)




message_box = driver.find_element_by_xpath(‘//button[@class=”_1E0Oz”]’)




message_box.click()


As soon as you execute this code, the message will be sent and your work is done.


I am attaching the whole code for your reference.


from selenium import webdriver

import time


driver = webdriver.Chrome(executable_path=””)

time.sleep(5)

user_name = 'Whatsapp Bot'

user = driver.find_element_by_xpath('//span[@title="{}"]'.format(user_name))

user.click()



message_box = driver.find_element_by_xpath('//div[@class="_2A8P4"]')

message_box.send_keys('Hey, I am your whatsapp bot')

message_box = driver.find_element_by_xpath('//button[@class="_1E0Oz"]')

message_box.click()

driver.quit()


At the end we put driver.quit() method to end the execution of the task.


You did a great job making this bot!!


 

			


					
			





	

					

				
Automation of Whatsapp Messages to unknown Users using selenium and Python
		

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Nowadays, we have to send messages to multiple users for either personal reasons or for commercial as well as business reasons. 


How amazing it will be if we don’t have to send messages again and again typing the same thing to more than 100 contacts. It will be hectic and boring.
In this article, we will be making a WhatsApp bot that will automate the messages and send the messages to multiple users at the same time without you typing it again and again.


This Bot will make your work easy and will be less time-consuming.



Let’s get ready to make an automation bot!


Importing the modules:


To make this automation Bot, we have to import some modules.


Firstly, we have to import selenium and python as a basic step.


To import selenium type the following command in your terminal:


python -m pip install selenium


Now we have to install WebDriver.


For doing so, go on geckodriver releases page and find the latest version suitable for your desktop.


Extract the file and copy the path and write it in your code.


Let’s get started with the code!


Working on the code:


The first and foremost task is to import the selenium modules which will be used in the code.


from selenium import webdriver
from csv import reader
import time

			


					
			





	

					

				
Pandas : Convert Data frame index into column using dataframe.reset_index() in python
		

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In this article, we will be exploring ways to convert indexes of a data frame or a multi-index data frame into its a column.


There is a function provided in the Pandas Data frame class to reset the indexes of the data frame.


Dataframe.reset_index()


DataFrame.reset_index(self, level=None, drop=False, inplace=False, col_level=0, col_fill='')


It returns a data frame with the new index after resetting the indexes of the data frame.


    

  • level: By default, reset_index() resets all the indexes of the data frame. In the case of a multi-index dataframe, if we want to reset some specific indexes, then we can specify it as int, str, or list of str, i.e., index names.
    • 

  • Drop: If False, then converts the index to a column else removes the index from the dataframe.
    • 

  • Inplace: If true, it modifies the data frame in place.
    • 



Let’s use this function to convert the indexes of dataframe to columns.



The first and the foremost thing we will do is to create a dataframe and initialize it’s index.


Code:


empoyees = [(11, ‘jack’, 34, ‘Sydney’, 70000) ,


(12, ‘Riti’, 31, ‘Delhi’ , 77000) ,


(13, ‘Aadi’, 16, ‘Mumbai’, 81000) ,


(14, ‘Mohit’, 31,‘Delhi’ , 90000) ,


(15, ‘Veena’, 12, ‘Delhi’ , 91000) ,


(16, ‘Shaunak’, 35, ‘Mumbai’, 75000 ),


(17, ‘Shaun’, 35, ‘Colombo’, 63000)]


# Create a DataFrame object


empDfObj = pd.DataFrame(empoyees, columns=[‘ID’ , ‘Name’, ‘Age’, ‘City’, ‘Salary’])


# Set ‘ID’ as the index of the dataframe


empDfObj.set_index(‘ID’, inplace=True)


print(empDfObj)


dataframe


Now, we will try different things with this dataframe.


Convert index of a Dataframe into a column of dataframe


To convert the index ‘ID‘ of the dataframe empDfObj into a column, call the reset_index() function on that dataframe,


Code:


modified = empDfObj.reset_index()


print(“Modified Dataframe : “)


print(modified)




Modified Dataframe


Since we haven’t provided the inplace argument, so by default it returned the modified copy of a dataframe.


In which the indexID is converted into a column named ‘ID’ and automatically the new index is assigned to it.


Now, we will pass the inplace argument as True to proceed with the process.




Code:
empDfObj.reset_index(inplace=True)
print(empDfObj)


dataframe with inplace argument 


Now, we will set the column’ID’ as the index of the dataframe.


Code:
empDfObj.set_index('ID', inplace=True)


Remove index  of dataframe instead of converting into column


Previously, what we have done is convert the index of the dataframe into the column of the dataframe but now we want to just remove it. We can do that by passing drop argument as True in the reset_index() function,




Code:
modified = empDfObj.reset_index(drop=True)
print("Modified Dataframe : ")
print(modified)


Remove index of dataframe instead of converting into column


We can see that it removed the dataframe index.


Resetting indexes of a Multi-Index Dataframe


Let’s convert the dataframe object empDfObj  into a multi-index dataframe with two indexes i.e. ID & Name.


Code:


empDfObj = pd.DataFrame(empoyees, columns=['ID', 'Name', 'Age', 'City', 'Salary'])
# set multiple columns as the index of the the dataframe to
# make it multi-index dataframe.
empDfObj.set_index(['ID', 'Name'], inplace=True)
print(empDfObj)




Resetting indexes of a Multi-Index Dataframe


Convert all the indexes of Multi-index Dataframe to the columns of Dataframe


In the previous module, we have made a dataframe with the multi-index but now here we will convert the indexes of multi-index dataframe to the columns of the dataframe.


To do this, all we have to do is just call the reset_index() on the dataframe object.


Code:


modified = empDfObj.reset_index()
print(modified)

Convert all the indexes of Multi-index Dataframe to the columns of Dataframe


It converted the index ID and Name to the column of the same name.


Suppose, we want to convert only one index from the multiple indexes. We can do that by passing a single parameter in the level argument.


Code:


modified = empDfObj.reset_index(level='ID')
print("Modified Dataframe: ")
print(modified)




convert only one index from the multiple indexes


It converted the index’ID’ to the column with the same index name. Similarly, we can follow this same procedure to carry out the task for converting the name index to the column.


You should try converting the code for changing Name index to column.


We can change both the indexes and make them columns by passing mutiple arguments in the level  parameter.


Code:
modified = empDfObj.reset_index(level=['ID', 'Name'])
print("Modified Dataframe: ")
print(modified)




change both the indexes and make them columns


The complete code:


import pandas as pd



def main():
 # List of Tuples
 empoyees = [(11, 'jack', 34, 'Sydney', 70000) ,
(12, 'Riti', 31, 'Delhi' , 77000) ,
(13, 'Aadi', 16, 'Mumbai', 81000) ,
(14, 'Mohit', 31,'Delhi' , 90000) ,
(15, 'Veena', 12, 'Delhi' , 91000) ,
(16, 'Shaunak', 35, 'Mumbai', 75000 ),
(17, 'Shaun', 35, 'Colombo', 63000)]
 # Create a DataFrame object
 empDfObj = pd.DataFrame(empoyees, columns=['ID' , 'Name', 'Age', 'City', 'Salary'])
 # Set 'ID' as the index of the dataframe
 empDfObj.set_index('ID', inplace=True)
print("Contents of the Dataframe : ")
print(empDfObj)
print('Convert the index of Dataframe to the column')
 # Reset the index of dataframe
 modified = empDfObj.reset_index()
print("Modified Dataframe : ")
print(modified)
print('Convert the index of Dataframe to the column - in place ')
 empDfObj.reset_index(inplace=True)
print("Contents of the Dataframe : ")
print(empDfObj)
 # Set 'ID' as the index of the dataframe
 empDfObj.set_index('ID', inplace=True)
print('Remove the index of Dataframe to the column')
 # Remove index ID instead of converting into a column
 modified = empDfObj.reset_index(drop=True)
print("Modified Dataframe : ")
print(modified)
print('Reseting indexes of a Multi-Index Dataframe')
 # Create a DataFrame object
 empDfObj = pd.DataFrame(empoyees, columns=['ID', 'Name', 'Age', 'City', 'Salary'])
 # set multiple columns as the index of the the dataframe to
 # make it multi-index dataframe.
 empDfObj.set_index(['ID', 'Name'], inplace=True)
print("Contents of the Multi-Index Dataframe : ")
print(empDfObj)
print('Convert all the indexes of Multi-index Dataframe to the columns of Dataframe')
 # Reset all indexes of a multi-index dataframe
 modified = empDfObj.reset_index()
print("Modified Mult-Index Datafrme : ")
print(modified)
print("Contents of the original Multi-Index Dataframe : ")
print(empDfObj)
 modified = empDfObj.reset_index(level='ID')
print("Modified Dataframe: ")
print(modified)
 modified = empDfObj.reset_index(level='Name')
print("Modified Dataframe: ")
print(modified)
 modified = empDfObj.reset_index(level=['ID', 'Name'])
print("Modified Dataframe: ")
print(modified)
if __name__ == '__main__':
main()




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