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Direct selling, also known as multi-level marketing (MLM), has become an increasingly popular way to start a business in recent years. With the rise of social media and e-commerce, more and more people are looking to create successful direct selling businesses, and for good reason. It's an excellent way to make your entrepreneurial dreams a reality.
But let's be real, building a successful direct selling business is no easy feat. With so much competition out there, it's easy to feel overwhelmed and unsure of where to start. Fear not, because we're here to help! In this blog, we'll share some valuable tips and best practices to help you create a thriving MLM business in 2023.
Here are some tips to grow your network marketing business.
With thousands of consultants selling the same products or services across the country, differentiation is critical. Make a list of the characteristics that distinguish you from the other consultants out there.
You must have an answer for the following question:
Remember the answers to these questions as you craft sales and marketing messages and engage your audience on social media.
Individuals and businesses are looking for growth at the start of the year, so relevant content geared towards solving business problems always performs well. Search engines have significantly updated their algorithms with the goal of improving the relevance and quality of search results. Content, search engine optimization, and search engine marketing all work together for your business growth.
Social media has revolutionized the way we do business, and this is particularly true in the world of direct selling. If you want to build a successful MLM business in 2023 and beyond, mastering social media marketing is a must. The key to success in this area is to use social media platforms like Facebook, Instagram, and Twitter to connect with your target audience and build strong relationships with them. This means creating content that's engaging, informative, and valuable, as well as being active and consistent on your social media accounts.
With advanced technology in direct selling and competitive strategies, e-commerce in network marketing has already reached new heights. However, while going ahead, organisations must revitalise their strategies, replacing old and outdated ones with new and promising ones in order to compete with the upcoming competition.
A smarter shopping process combined with a secure shopping experience will ensure a better brand experience for your customers. Customer behaviour is changing at an alarming rate. The only way to keep up with their expectations is to successfully adapt to changing behaviours.
Customers are provided with information through content marketing without being asked to spend money. This content can be shared on your social media accounts, website, or blog. To create a word-of-mouth marketing campaign, make it easy for loyal customers to share your social media content.
Because many parent companies do not allow paid search advertising, content marketing is critical for direct sales businesses.
You must be successful in your endeavour by having a 10% aptitude and 90% attitude for this. Develop constructing thinking habits. Do not allow others to emotionally hurt you. To meet your daily, weekly, and monthly goals, you must be mentally strong, committed, and focused. Adopting these Direct sales strategies will ensure your business's success.
Creating a successful direct selling business in 2023 requires dedication, hard work, and a willingness to learn and adapt. By implementing the tips we've shared in this blog, you'll be well on your way to building a thriving MLM business. Remember, it's not just about making sales - it's about building meaningful relationships with your customers and team members, and providing them with value that they won't find anywhere else. So don't be afraid to try new things, take risks, and stay focused on your goals.
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Install via pip:
$ pip install pytumblr
Install from source:
$ git clone https://github.com/tumblr/pytumblr.git
$ cd pytumblr
$ python setup.py install
A pytumblr.TumblrRestClient
is the object you'll make all of your calls to the Tumblr API through. Creating one is this easy:
client = pytumblr.TumblrRestClient(
'<consumer_key>',
'<consumer_secret>',
'<oauth_token>',
'<oauth_secret>',
)
client.info() # Grabs the current user information
Two easy ways to get your credentials to are:
interactive_console.py
tool (if you already have a consumer key & secret)client.info() # get information about the authenticating user
client.dashboard() # get the dashboard for the authenticating user
client.likes() # get the likes for the authenticating user
client.following() # get the blogs followed by the authenticating user
client.follow('codingjester.tumblr.com') # follow a blog
client.unfollow('codingjester.tumblr.com') # unfollow a blog
client.like(id, reblogkey) # like a post
client.unlike(id, reblogkey) # unlike a post
client.blog_info(blogName) # get information about a blog
client.posts(blogName, **params) # get posts for a blog
client.avatar(blogName) # get the avatar for a blog
client.blog_likes(blogName) # get the likes on a blog
client.followers(blogName) # get the followers of a blog
client.blog_following(blogName) # get the publicly exposed blogs that [blogName] follows
client.queue(blogName) # get the queue for a given blog
client.submission(blogName) # get the submissions for a given blog
Creating posts
PyTumblr lets you create all of the various types that Tumblr supports. When using these types there are a few defaults that are able to be used with any post type.
The default supported types are described below.
We'll show examples throughout of these default examples while showcasing all the specific post types.
Creating a photo post
Creating a photo post supports a bunch of different options plus the described default options * caption - a string, the user supplied caption * link - a string, the "click-through" url for the photo * source - a string, the url for the photo you want to use (use this or the data parameter) * data - a list or string, a list of filepaths or a single file path for multipart file upload
#Creates a photo post using a source URL
client.create_photo(blogName, state="published", tags=["testing", "ok"],
source="https://68.media.tumblr.com/b965fbb2e501610a29d80ffb6fb3e1ad/tumblr_n55vdeTse11rn1906o1_500.jpg")
#Creates a photo post using a local filepath
client.create_photo(blogName, state="queue", tags=["testing", "ok"],
tweet="Woah this is an incredible sweet post [URL]",
data="/Users/johnb/path/to/my/image.jpg")
#Creates a photoset post using several local filepaths
client.create_photo(blogName, state="draft", tags=["jb is cool"], format="markdown",
data=["/Users/johnb/path/to/my/image.jpg", "/Users/johnb/Pictures/kittens.jpg"],
caption="## Mega sweet kittens")
Creating a text post
Creating a text post supports the same options as default and just a two other parameters * title - a string, the optional title for the post. Supports markdown or html * body - a string, the body of the of the post. Supports markdown or html
#Creating a text post
client.create_text(blogName, state="published", slug="testing-text-posts", title="Testing", body="testing1 2 3 4")
Creating a quote post
Creating a quote post supports the same options as default and two other parameter * quote - a string, the full text of the qote. Supports markdown or html * source - a string, the cited source. HTML supported
#Creating a quote post
client.create_quote(blogName, state="queue", quote="I am the Walrus", source="Ringo")
Creating a link post
#Create a link post
client.create_link(blogName, title="I like to search things, you should too.", url="https://duckduckgo.com",
description="Search is pretty cool when a duck does it.")
Creating a chat post
Creating a chat post supports the same options as default and two other parameters * title - a string, the title of the chat post * conversation - a string, the text of the conversation/chat, with diablog labels (no html)
#Create a chat post
chat = """John: Testing can be fun!
Renee: Testing is tedious and so are you.
John: Aw.
"""
client.create_chat(blogName, title="Renee just doesn't understand.", conversation=chat, tags=["renee", "testing"])
Creating an audio post
Creating an audio post allows for all default options and a has 3 other parameters. The only thing to keep in mind while dealing with audio posts is to make sure that you use the external_url parameter or data. You cannot use both at the same time. * caption - a string, the caption for your post * external_url - a string, the url of the site that hosts the audio file * data - a string, the filepath of the audio file you want to upload to Tumblr
#Creating an audio file
client.create_audio(blogName, caption="Rock out.", data="/Users/johnb/Music/my/new/sweet/album.mp3")
#lets use soundcloud!
client.create_audio(blogName, caption="Mega rock out.", external_url="https://soundcloud.com/skrillex/sets/recess")
Creating a video post
Creating a video post allows for all default options and has three other options. Like the other post types, it has some restrictions. You cannot use the embed and data parameters at the same time. * caption - a string, the caption for your post * embed - a string, the HTML embed code for the video * data - a string, the path of the file you want to upload
#Creating an upload from YouTube
client.create_video(blogName, caption="Jon Snow. Mega ridiculous sword.",
embed="http://www.youtube.com/watch?v=40pUYLacrj4")
#Creating a video post from local file
client.create_video(blogName, caption="testing", data="/Users/johnb/testing/ok/blah.mov")
Editing a post
Updating a post requires you knowing what type a post you're updating. You'll be able to supply to the post any of the options given above for updates.
client.edit_post(blogName, id=post_id, type="text", title="Updated")
client.edit_post(blogName, id=post_id, type="photo", data="/Users/johnb/mega/awesome.jpg")
Reblogging a Post
Reblogging a post just requires knowing the post id and the reblog key, which is supplied in the JSON of any post object.
client.reblog(blogName, id=125356, reblog_key="reblog_key")
Deleting a post
Deleting just requires that you own the post and have the post id
client.delete_post(blogName, 123456) # Deletes your post :(
A note on tags: When passing tags, as params, please pass them as a list (not a comma-separated string):
client.create_text(blogName, tags=['hello', 'world'], ...)
Getting notes for a post
In order to get the notes for a post, you need to have the post id and the blog that it is on.
data = client.notes(blogName, id='123456')
The results include a timestamp you can use to make future calls.
data = client.notes(blogName, id='123456', before_timestamp=data["_links"]["next"]["query_params"]["before_timestamp"])
# get posts with a given tag
client.tagged(tag, **params)
This client comes with a nice interactive console to run you through the OAuth process, grab your tokens (and store them for future use).
You'll need pyyaml
installed to run it, but then it's just:
$ python interactive-console.py
and away you go! Tokens are stored in ~/.tumblr
and are also shared by other Tumblr API clients like the Ruby client.
The tests (and coverage reports) are run with nose, like this:
python setup.py test
Author: tumblr
Source Code: https://github.com/tumblr/pytumblr
License: Apache-2.0 license
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The electric scooter revolution has caught on super-fast taking many cities across the globe by storm. eScooters, a renovated version of old-school scooters now turned into electric vehicles are an environmentally friendly solution to current on-demand commute problems. They work on engines, like cars, enabling short traveling distances without hassle. The result is that these groundbreaking electric machines can now provide faster transport for less — cheaper than Uber and faster than Metro.
Since they are durable, fast, easy to operate and maintain, and are more convenient to park compared to four-wheelers, the eScooters trend has and continues to spike interest as a promising growth area. Several companies and universities are increasingly setting up shop to provide eScooter services realizing a would-be profitable business model and a ready customer base that is university students or residents in need of faster and cheap travel going about their business in school, town, and other surrounding areas.
In many countries including the U.S., Canada, Mexico, U.K., Germany, France, China, Japan, India, Brazil and Mexico and more, a growing number of eScooter users both locals and tourists can now be seen effortlessly passing lines of drivers stuck in the endless and unmoving traffic.
A recent report by McKinsey revealed that the E-Scooter industry will be worth― $200 billion to $300 billion in the United States, $100 billion to $150 billion in Europe, and $30 billion to $50 billion in China in 2030. The e-Scooter revenue model will also spike and is projected to rise by more than 20% amounting to approximately $5 billion.
And, with a necessity to move people away from high carbon prints, traffic and congestion issues brought about by car-centric transport systems in cities, more and more city planners are developing more bike/scooter lanes and adopting zero-emission plans. This is the force behind the booming electric scooter market and the numbers will only go higher and higher.
Companies that have taken advantage of the growing eScooter trend develop an appthat allows them to provide efficient eScooter services. Such an app enables them to be able to locate bike pick-up and drop points through fully integrated google maps.
It’s clear that e scooters will increasingly become more common and the e-scooter business model will continue to grab the attention of manufacturers, investors, entrepreneurs. All this should go ahead with a quest to know what are some of the best electric bikes in the market especially for anyone who would want to get started in the electric bikes/scooters rental business.
We have done a comprehensive list of the best electric bikes! Each bike has been reviewed in depth and includes a full list of specs and a photo.
https://www.kickstarter.com/projects/enkicycles/billy-were-redefining-joyrides
To start us off is the Billy eBike, a powerful go-anywhere urban electric bike that’s specially designed to offer an exciting ride like no other whether you want to ride to the grocery store, cafe, work or school. The Billy eBike comes in 4 color options – Billy Blue, Polished aluminium, Artic white, and Stealth black.
Price: $2490
Available countries
Available in the USA, Europe, Asia, South Africa and Australia.This item ships from the USA. Buyers are therefore responsible for any taxes and/or customs duties incurred once it arrives in your country.
Features
Specifications
Why Should You Buy This?
**Who Should Ride Billy? **
Both new and experienced riders
**Where to Buy? **Local distributors or ships from the USA.
Featuring a sleek and lightweight aluminum frame design, the 200-Series ebike takes your riding experience to greater heights. Available in both black and white this ebike comes with a connected app, which allows you to plan activities, map distances and routes while also allowing connections with fellow riders.
Price: $2099.00
Available countries
The Genze 200 series e-Bike is available at GenZe retail locations across the U.S or online via GenZe.com website. Customers from outside the US can ship the product while incurring the relevant charges.
Features
Specifications
https://ebikestore.com/shop/norco-vlt-s2/
The Norco VLT S2 is a front suspension e-Bike with solid components alongside the reliable Bosch Performance Line Power systems that offer precise pedal assistance during any riding situation.
Price: $2,699.00
Available countries
This item is available via the various Norco bikes international distributors.
Features
Specifications
http://www.bodoevs.com/bodoev/products_show.asp?product_id=13
Manufactured by Bodo Vehicle Group Limited, the Bodo EV is specially designed for strong power and extraordinary long service to facilitate super amazing rides. The Bodo Vehicle Company is a striking top in electric vehicles brand field in China and across the globe. Their Bodo EV will no doubt provide your riders with high-level riding satisfaction owing to its high-quality design, strength, breaking stability and speed.
Price: $799
Available countries
This item ships from China with buyers bearing the shipping costs and other variables prior to delivery.
Features
Specifications
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In this Python article, let's learn about Mutable and Immutable in Python.
Mutable is a fancy way of saying that the internal state of the object is changed/mutated. So, the simplest definition is: An object whose internal state can be changed is mutable. On the other hand, immutable doesn’t allow any change in the object once it has been created.
Both of these states are integral to Python data structure. If you want to become more knowledgeable in the entire Python Data Structure, take this free course which covers multiple data structures in Python including tuple data structure which is immutable. You will also receive a certificate on completion which is sure to add value to your portfolio.
Mutable is when something is changeable or has the ability to change. In Python, ‘mutable’ is the ability of objects to change their values. These are often the objects that store a collection of data.
Immutable is the when no change is possible over time. In Python, if the value of an object cannot be changed over time, then it is known as immutable. Once created, the value of these objects is permanent.
Objects of built-in type that are mutable are:
Objects of built-in type that are immutable are:
Object mutability is one of the characteristics that makes Python a dynamically typed language. Though Mutable and Immutable in Python is a very basic concept, it can at times be a little confusing due to the intransitive nature of immutability.
In Python, everything is treated as an object. Every object has these three attributes:
While ID and Type cannot be changed once it’s created, values can be changed for Mutable objects.
Check out this free python certificate course to get started with Python.
I believe, rather than diving deep into the theory aspects of mutable and immutable in Python, a simple code would be the best way to depict what it means in Python. Hence, let us discuss the below code step-by-step:
#Creating a list which contains name of Indian cities
cities = [‘Delhi’, ‘Mumbai’, ‘Kolkata’]
# Printing the elements from the list cities, separated by a comma & space
for city in cities:
print(city, end=’, ’)
Output [1]: Delhi, Mumbai, Kolkata
#Printing the location of the object created in the memory address in hexadecimal format
print(hex(id(cities)))
Output [2]: 0x1691d7de8c8
#Adding a new city to the list cities
cities.append(‘Chennai’)
#Printing the elements from the list cities, separated by a comma & space
for city in cities:
print(city, end=’, ’)
Output [3]: Delhi, Mumbai, Kolkata, Chennai
#Printing the location of the object created in the memory address in hexadecimal format
print(hex(id(cities)))
Output [4]: 0x1691d7de8c8
The above example shows us that we were able to change the internal state of the object ‘cities’ by adding one more city ‘Chennai’ to it, yet, the memory address of the object did not change. This confirms that we did not create a new object, rather, the same object was changed or mutated. Hence, we can say that the object which is a type of list with reference variable name ‘cities’ is a MUTABLE OBJECT.
Let us now discuss the term IMMUTABLE. Considering that we understood what mutable stands for, it is obvious that the definition of immutable will have ‘NOT’ included in it. Here is the simplest definition of immutable– An object whose internal state can NOT be changed is IMMUTABLE.
Again, if you try and concentrate on different error messages, you have encountered, thrown by the respective IDE; you use you would be able to identify the immutable objects in Python. For instance, consider the below code & associated error message with it, while trying to change the value of a Tuple at index 0.
#Creating a Tuple with variable name ‘foo’
foo = (1, 2)
#Changing the index[0] value from 1 to 3
foo[0] = 3
TypeError: 'tuple' object does not support item assignment
Once again, a simple code would be the best way to depict what immutable stands for. Hence, let us discuss the below code step-by-step:
#Creating a Tuple which contains English name of weekdays
weekdays = ‘Sunday’, ‘Monday’, ‘Tuesday’, ‘Wednesday’, ‘Thursday’, ‘Friday’, ‘Saturday’
# Printing the elements of tuple weekdays
print(weekdays)
Output [1]: (‘Sunday’, ‘Monday’, ‘Tuesday’, ‘Wednesday’, ‘Thursday’, ‘Friday’, ‘Saturday’)
#Printing the location of the object created in the memory address in hexadecimal format
print(hex(id(weekdays)))
Output [2]: 0x1691cc35090
#tuples are immutable, so you cannot add new elements, hence, using merge of tuples with the # + operator to add a new imaginary day in the tuple ‘weekdays’
weekdays += ‘Pythonday’,
#Printing the elements of tuple weekdays
print(weekdays)
Output [3]: (‘Sunday’, ‘Monday’, ‘Tuesday’, ‘Wednesday’, ‘Thursday’, ‘Friday’, ‘Saturday’, ‘Pythonday’)
#Printing the location of the object created in the memory address in hexadecimal format
print(hex(id(weekdays)))
Output [4]: 0x1691cc8ad68
This above example shows that we were able to use the same variable name that is referencing an object which is a type of tuple with seven elements in it. However, the ID or the memory location of the old & new tuple is not the same. We were not able to change the internal state of the object ‘weekdays’. The Python program manager created a new object in the memory address and the variable name ‘weekdays’ started referencing the new object with eight elements in it. Hence, we can say that the object which is a type of tuple with reference variable name ‘weekdays’ is an IMMUTABLE OBJECT.
Also Read: Understanding the Exploratory Data Analysis (EDA) in Python
Where can you use mutable and immutable objects:
Mutable objects can be used where you want to allow for any updates. For example, you have a list of employee names in your organizations, and that needs to be updated every time a new member is hired. You can create a mutable list, and it can be updated easily.
Immutability offers a lot of useful applications to different sensitive tasks we do in a network centred environment where we allow for parallel processing. By creating immutable objects, you seal the values and ensure that no threads can invoke overwrite/update to your data. This is also useful in situations where you would like to write a piece of code that cannot be modified. For example, a debug code that attempts to find the value of an immutable object.
Watch outs: Non transitive nature of Immutability:
OK! Now we do understand what mutable & immutable objects in Python are. Let’s go ahead and discuss the combination of these two and explore the possibilities. Let’s discuss, as to how will it behave if you have an immutable object which contains the mutable object(s)? Or vice versa? Let us again use a code to understand this behaviour–
#creating a tuple (immutable object) which contains 2 lists(mutable) as it’s elements
#The elements (lists) contains the name, age & gender
person = (['Ayaan', 5, 'Male'], ['Aaradhya', 8, 'Female'])
#printing the tuple
print(person)
Output [1]: (['Ayaan', 5, 'Male'], ['Aaradhya', 8, 'Female'])
#printing the location of the object created in the memory address in hexadecimal format
print(hex(id(person)))
Output [2]: 0x1691ef47f88
#Changing the age for the 1st element. Selecting 1st element of tuple by using indexing [0] then 2nd element of the list by using indexing [1] and assigning a new value for age as 4
person[0][1] = 4
#printing the updated tuple
print(person)
Output [3]: (['Ayaan', 4, 'Male'], ['Aaradhya', 8, 'Female'])
#printing the location of the object created in the memory address in hexadecimal format
print(hex(id(person)))
Output [4]: 0x1691ef47f88
In the above code, you can see that the object ‘person’ is immutable since it is a type of tuple. However, it has two lists as it’s elements, and we can change the state of lists (lists being mutable). So, here we did not change the object reference inside the Tuple, but the referenced object was mutated.
Also Read: Real-Time Object Detection Using TensorFlow
Same way, let’s explore how it will behave if you have a mutable object which contains an immutable object? Let us again use a code to understand the behaviour–
#creating a list (mutable object) which contains tuples(immutable) as it’s elements
list1 = [(1, 2, 3), (4, 5, 6)]
#printing the list
print(list1)
Output [1]: [(1, 2, 3), (4, 5, 6)]
#printing the location of the object created in the memory address in hexadecimal format
print(hex(id(list1)))
Output [2]: 0x1691d5b13c8
#changing object reference at index 0
list1[0] = (7, 8, 9)
#printing the list
Output [3]: [(7, 8, 9), (4, 5, 6)]
#printing the location of the object created in the memory address in hexadecimal format
print(hex(id(list1)))
Output [4]: 0x1691d5b13c8
As an individual, it completely depends upon you and your requirements as to what kind of data structure you would like to create with a combination of mutable & immutable objects. I hope that this information will help you while deciding the type of object you would like to select going forward.
Before I end our discussion on IMMUTABILITY, allow me to use the word ‘CAVITE’ when we discuss the String and Integers. There is an exception, and you may see some surprising results while checking the truthiness for immutability. For instance:
#creating an object of integer type with value 10 and reference variable name ‘x’
x = 10
#printing the value of ‘x’
print(x)
Output [1]: 10
#Printing the location of the object created in the memory address in hexadecimal format
print(hex(id(x)))
Output [2]: 0x538fb560
#creating an object of integer type with value 10 and reference variable name ‘y’
y = 10
#printing the value of ‘y’
print(y)
Output [3]: 10
#Printing the location of the object created in the memory address in hexadecimal format
print(hex(id(y)))
Output [4]: 0x538fb560
As per our discussion and understanding, so far, the memory address for x & y should have been different, since, 10 is an instance of Integer class which is immutable. However, as shown in the above code, it has the same memory address. This is not something that we expected. It seems that what we have understood and discussed, has an exception as well.
Quick check – Python Data Structures
Tuples are immutable and hence cannot have any changes in them once they are created in Python. This is because they support the same sequence operations as strings. We all know that strings are immutable. The index operator will select an element from a tuple just like in a string. Hence, they are immutable.
Like all, there are exceptions in the immutability in python too. Not all immutable objects are really mutable. This will lead to a lot of doubts in your mind. Let us just take an example to understand this.
Consider a tuple ‘tup’.
Now, if we consider tuple tup = (‘GreatLearning’,[4,3,1,2]) ;
We see that the tuple has elements of different data types. The first element here is a string which as we all know is immutable in nature. The second element is a list which we all know is mutable. Now, we all know that the tuple itself is an immutable data type. It cannot change its contents. But, the list inside it can change its contents. So, the value of the Immutable objects cannot be changed but its constituent objects can. change its value.
Mutable Object | Immutable Object |
State of the object can be modified after it is created. | State of the object can’t be modified once it is created. |
They are not thread safe. | They are thread safe |
Mutable classes are not final. | It is important to make the class final before creating an immutable object. |
list, dictionary, set, user-defined classes.
int, float, decimal, bool, string, tuple, range.
Lists in Python are mutable data types as the elements of the list can be modified, individual elements can be replaced, and the order of elements can be changed even after the list has been created.
(Examples related to lists have been discussed earlier in this blog.)
Tuple and list data structures are very similar, but one big difference between the data types is that lists are mutable, whereas tuples are immutable. The reason for the tuple’s immutability is that once the elements are added to the tuple and the tuple has been created; it remains unchanged.
A programmer would always prefer building a code that can be reused instead of making the whole data object again. Still, even though tuples are immutable, like lists, they can contain any Python object, including mutable objects.
A set is an iterable unordered collection of data type which can be used to perform mathematical operations (like union, intersection, difference etc.). Every element in a set is unique and immutable, i.e. no duplicate values should be there, and the values can’t be changed. However, we can add or remove items from the set as the set itself is mutable.
Strings are not mutable in Python. Strings are a immutable data types which means that its value cannot be updated.
Join Great Learning Academy’s free online courses and upgrade your skills today.
Original article source at: https://www.mygreatlearning.com
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Are you leading an organization that has a large campus, e.g., a large university? You are probably thinking of introducing an electric scooter/bicycle fleet on the campus, and why wouldn’t you?
Introducing micro-mobility in your campus with the help of such a fleet would help the people on the campus significantly. People would save money since they don’t need to use a car for a short distance. Your campus will see a drastic reduction in congestion, moreover, its carbon footprint will reduce.
Micro-mobility is relatively new though and you would need help. You would need to select an appropriate fleet of vehicles. The people on your campus would need to find electric scooters or electric bikes for commuting, and you need to provide a solution for this.
To be more specific, you need a short-term electric bike rental app. With such an app, you will be able to easily offer micro-mobility to the people on the campus. We at Devathon have built Autorent exactly for this.
What does Autorent do and how can it help you? How does it enable you to introduce micro-mobility on your campus? We explain these in this article, however, we will touch upon a few basics first.
You are probably thinking about micro-mobility relatively recently, aren’t you? A few relevant insights about it could help you to better appreciate its importance.
Micro-mobility is a new trend in transportation, and it uses vehicles that are considerably smaller than cars. Electric scooters (e-scooters) and electric bikes (e-bikes) are the most popular forms of micro-mobility, however, there are also e-unicycles and e-skateboards.
You might have already seen e-scooters, which are kick scooters that come with a motor. Thanks to its motor, an e-scooter can achieve a speed of up to 20 km/h. On the other hand, e-bikes are popular in China and Japan, and they come with a motor, and you can reach a speed of 40 km/h.
You obviously can’t use these vehicles for very long commutes, however, what if you need to travel a short distance? Even if you have a reasonable public transport facility in the city, it might not cover the route you need to take. Take the example of a large university campus. Such a campus is often at a considerable distance from the central business district of the city where it’s located. While public transport facilities may serve the central business district, they wouldn’t serve this large campus. Currently, many people drive their cars even for short distances.
As you know, that brings its own set of challenges. Vehicular traffic adds significantly to pollution, moreover, finding a parking spot can be hard in crowded urban districts.
Well, you can reduce your carbon footprint if you use an electric car. However, electric cars are still new, and many countries are still building the necessary infrastructure for them. Your large campus might not have the necessary infrastructure for them either. Presently, electric cars don’t represent a viable option in most geographies.
As a result, you need to buy and maintain a car even if your commute is short. In addition to dealing with parking problems, you need to spend significantly on your car.
All of these factors have combined to make people sit up and think seriously about cars. Many people are now seriously considering whether a car is really the best option even if they have to commute only a short distance.
This is where micro-mobility enters the picture. When you commute a short distance regularly, e-scooters or e-bikes are viable options. You limit your carbon footprints and you cut costs!
Businesses have seen this shift in thinking, and e-scooter companies like Lime and Bird have entered this field in a big way. They let you rent e-scooters by the minute. On the other hand, start-ups like Jump and Lyft have entered the e-bike market.
Think of your campus now! The people there might need to travel short distances within the campus, and e-scooters can really help them.
What advantages can you get from micro-mobility? Let’s take a deeper look into this question.
Micro-mobility can offer several advantages to the people on your campus, e.g.:
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In this article, We will show how we can use python to automate Excel . A useful Python library is Openpyxl which we will learn to do Excel Automation
Openpyxl is a Python library that is used to read from an Excel file or write to an Excel file. Data scientists use Openpyxl for data analysis, data copying, data mining, drawing charts, styling sheets, adding formulas, and more.
Workbook: A spreadsheet is represented as a workbook in openpyxl. A workbook consists of one or more sheets.
Sheet: A sheet is a single page composed of cells for organizing data.
Cell: The intersection of a row and a column is called a cell. Usually represented by A1, B5, etc.
Row: A row is a horizontal line represented by a number (1,2, etc.).
Column: A column is a vertical line represented by a capital letter (A, B, etc.).
Openpyxl can be installed using the pip command and it is recommended to install it in a virtual environment.
pip install openpyxl
We start by creating a new spreadsheet, which is called a workbook in Openpyxl. We import the workbook module from Openpyxl and use the function Workbook()
which creates a new workbook.
from openpyxl
import Workbook
#creates a new workbook
wb = Workbook()
#Gets the first active worksheet
ws = wb.active
#creating new worksheets by using the create_sheet method
ws1 = wb.create_sheet("sheet1", 0) #inserts at first position
ws2 = wb.create_sheet("sheet2") #inserts at last position
ws3 = wb.create_sheet("sheet3", -1) #inserts at penultimate position
#Renaming the sheet
ws.title = "Example"
#save the workbook
wb.save(filename = "example.xlsx")
We load the file using the function load_Workbook()
which takes the filename as an argument. The file must be saved in the same working directory.
#loading a workbook
wb = openpyxl.load_workbook("example.xlsx")
#getting sheet names
wb.sheetnames
result = ['sheet1', 'Sheet', 'sheet3', 'sheet2']
#getting a particular sheet
sheet1 = wb["sheet2"]
#getting sheet title
sheet1.title
result = 'sheet2'
#Getting the active sheet
sheetactive = wb.active
result = 'sheet1'
#get a cell from the sheet
sheet1["A1"] <
Cell 'Sheet1'.A1 >
#get the cell value
ws["A1"].value 'Segment'
#accessing cell using row and column and assigning a value
d = ws.cell(row = 4, column = 2, value = 10)
d.value
10
#looping through each row and column
for x in range(1, 5):
for y in range(1, 5):
print(x, y, ws.cell(row = x, column = y)
.value)
#getting the highest row number
ws.max_row
701
#getting the highest column number
ws.max_column
19
There are two functions for iterating through rows and columns.
Iter_rows() => returns the rows
Iter_cols() => returns the columns {
min_row = 4, max_row = 5, min_col = 2, max_col = 5
} => This can be used to set the boundaries
for any iteration.
Example:
#iterating rows
for row in ws.iter_rows(min_row = 2, max_col = 3, max_row = 3):
for cell in row:
print(cell) <
Cell 'Sheet1'.A2 >
<
Cell 'Sheet1'.B2 >
<
Cell 'Sheet1'.C2 >
<
Cell 'Sheet1'.A3 >
<
Cell 'Sheet1'.B3 >
<
Cell 'Sheet1'.C3 >
#iterating columns
for col in ws.iter_cols(min_row = 2, max_col = 3, max_row = 3):
for cell in col:
print(cell) <
Cell 'Sheet1'.A2 >
<
Cell 'Sheet1'.A3 >
<
Cell 'Sheet1'.B2 >
<
Cell 'Sheet1'.B3 >
<
Cell 'Sheet1'.C2 >
<
Cell 'Sheet1'.C3 >
To get all the rows of the worksheet we use the method worksheet.rows and to get all the columns of the worksheet we use the method worksheet.columns. Similarly, to iterate only through the values we use the method worksheet.values.
Example:
for row in ws.values:
for value in row:
print(value)
Writing to a workbook can be done in many ways such as adding a formula, adding charts, images, updating cell values, inserting rows and columns, etc… We will discuss each of these with an example.
#creates a new workbook
wb = openpyxl.Workbook()
#saving the workbook
wb.save("new.xlsx")
#creating a new sheet
ws1 = wb.create_sheet(title = "sheet 2")
#creating a new sheet at index 0
ws2 = wb.create_sheet(index = 0, title = "sheet 0")
#checking the sheet names
wb.sheetnames['sheet 0', 'Sheet', 'sheet 2']
#deleting a sheet
del wb['sheet 0']
#checking sheetnames
wb.sheetnames['Sheet', 'sheet 2']
#checking the sheet value
ws['B2'].value
null
#adding value to cell
ws['B2'] = 367
#checking value
ws['B2'].value
367
We often require formulas to be included in our Excel datasheet. We can easily add formulas using the Openpyxl module just like you add values to a cell.
For example:
import openpyxl
from openpyxl
import Workbook
wb = openpyxl.load_workbook("new1.xlsx")
ws = wb['Sheet']
ws['A9'] = '=SUM(A2:A8)'
wb.save("new2.xlsx")
The above program will add the formula (=SUM(A2:A8)) in cell A9. The result will be as below.
Two or more cells can be merged to a rectangular area using the method merge_cells(), and similarly, they can be unmerged using the method unmerge_cells().
For example:
Merge cells
#merge cells B2 to C9
ws.merge_cells('B2:C9')
ws['B2'] = "Merged cells"
Adding the above code to the previous example will merge cells as below.
#unmerge cells B2 to C9
ws.unmerge_cells('B2:C9')
The above code will unmerge cells from B2 to C9.
To insert an image we import the image function from the module openpyxl.drawing.image. We then load our image and add it to the cell as shown in the below example.
Example:
import openpyxl
from openpyxl
import Workbook
from openpyxl.drawing.image
import Image
wb = openpyxl.load_workbook("new1.xlsx")
ws = wb['Sheet']
#loading the image(should be in same folder)
img = Image('logo.png')
ws['A1'] = "Adding image"
#adjusting size
img.height = 130
img.width = 200
#adding img to cell A3
ws.add_image(img, 'A3')
wb.save("new2.xlsx")
Result:
Charts are essential to show a visualization of data. We can create charts from Excel data using the Openpyxl module chart. Different forms of charts such as line charts, bar charts, 3D line charts, etc., can be created. We need to create a reference that contains the data to be used for the chart, which is nothing but a selection of cells (rows and columns). I am using sample data to create a 3D bar chart in the below example:
Example
import openpyxl
from openpyxl
import Workbook
from openpyxl.chart
import BarChart3D, Reference, series
wb = openpyxl.load_workbook("example.xlsx")
ws = wb.active
values = Reference(ws, min_col = 3, min_row = 2, max_col = 3, max_row = 40)
chart = BarChart3D()
chart.add_data(values)
ws.add_chart(chart, "E3")
wb.save("MyChart.xlsx")
Result
Welcome to another video! In this video, We will cover how we can use python to automate Excel. I'll be going over everything from creating workbooks to accessing individual cells and stylizing cells. There is a ton of things that you can do with Excel but I'll just be covering the core/base things in OpenPyXl.
⭐️ Timestamps ⭐️
00:00 | Introduction
02:14 | Installing openpyxl
03:19 | Testing Installation
04:25 | Loading an Existing Workbook
06:46 | Accessing Worksheets
07:37 | Accessing Cell Values
08:58 | Saving Workbooks
09:52 | Creating, Listing and Changing Sheets
11:50 | Creating a New Workbook
12:39 | Adding/Appending Rows
14:26 | Accessing Multiple Cells
20:46 | Merging Cells
22:27 | Inserting and Deleting Rows
23:35 | Inserting and Deleting Columns
24:48 | Copying and Moving Cells
26:06 | Practical Example, Formulas & Cell Styling
📄 Resources 📄
OpenPyXL Docs: https://openpyxl.readthedocs.io/en/stable/
Code Written in This Tutorial: https://github.com/techwithtim/ExcelPythonTutorial
Subscribe: https://www.youtube.com/c/TechWithTim/featured