Netflix appears to have given the game away on a brand new 3D Sonic animated series a little earlier than planned.

The streaming service announced the new show on its @NXOnNetflix Twitter handle, before very quickly deleting the tweet and choosing to stay away from the topic since. The post read, “Rings? Check. Sneakers? Check. Speed? SONIC. SEGA’s legendary video game icon Sonic the Hedgehog races onto Netflix in a new 3D animated series from @SEGA, @WildBrainHQ and @ManOfActionEnt premiering in 2022.”

You can see a screenshot of the original tweet below.

Netflix via Twitter, screenshot: Nintendo Life

You’ll know Sega, of course, but as for the other companies involved, Wild Brain owns series like Teletubbies, Degrassi, and Inspector Gadget, while Man of Action is a writer collective known for projects such as Ben 10 and Big Hero 6.

Sonic has already found silver screen success this year after his live-action film surpassed $200 million at the box office in just ten days. A sequel to that has already been confirmed, so a Netflix series probably makes sense too, right?

We’re assuming the show will be revealed again – but properly this time – at a later date. Would you be excited to see a new 3D Sonic animation, or are you not feeling it? Share your thoughts with us below.

The Pandas apply( ) function is used to apply the functions on the Pandas objects. We have so many built-in aggregation functions in pandas on Series and DataFrame objects. But, to apply some application-specific functions, we can leverage the apply( ) function. Pandas apply( ) is both the Series method and DataFrame method.

Pandas apply function to one column – apply( ) as Series method

Let’s construct a DataFrame in which we have the information of 4 persons.

>>> import pandas as pd

>>> df = pd.DataFrame(

... {

... 'Name': ['Edward', 'Natalie', 'Chris M', 'Priyatham'],

... 'Sex' : ['M', 'F', 'M', 'M'],

... 'Age': [45, 35, 29, 26],

... 'weight(kgs)': [68.4, 58.2, 64.3, 53.1]

... }

... ) >>> print(df) Name Sex Age weight(kgs)

0 Edward M 45 68.4

1 Natalie F 35 58.2

2 Chris M M 29 64.3

3 Priyatham M 26 53.1

pandas.Series.apply takes any of the below two different kinds of functions as an argument.  They are:

• Python functions
• Numpy’s universal functions (ufuncs)

1. Python functions

In Python, there are 3 different kinds of functions in general;

• Built-in functions
• User-defined functions
• Lambda functions

a) Applying Python built-in functions on Series

If we would like to know the length of the names of each person, we can do so using the len( ) function in python.

For example, if we want to know the length of the “Python” string, we can get by the following code;

>>> len("Python")

6

A single column in the DataFrame is a Series object. Now, we would like to apply the same len( ) function on the whole “Name” column of the DataFrame.  This can be achieved using the apply( ) function in the below code;

>>> df['Name'].apply(len)

0 6

1 7

2 7

3 9

Name: Name, dtype: int64

If you observe the above code snippet, the len inside the apply( ) function is not taking any argument. In general, any function takes some data to operate on them. In the len(“Python”) code snippet, it’s taking the “Python” string as input data to calculate its length. Here, the input data is directly taken from the Series object that called the function using apply( ).

When applying the Python functions, each value in the Series is applied one by one and returns the Series object.

The above process can be visualised as:

In the above visualisation, you can observe that each element of Series is applied to the function one by one.

b) Applying user-defined functions on Series

Let’s assume that the data we have is a year old. So, we would like to update the age of each person by adding 1. We can do so by applying a user-defined function on the Series object using the apply( ) method.

The code for it is,

>>> def add_age(age):

... return age + 1 >>> df['Age'].apply(add_age)

0 46

1 36

2 30

3 27

Name: Age, dtype: int64 >>> df['Age'] = df['Age'].apply(add_age) >>> df Name Sex Age weight(kgs)

0 Edward M 46 68.4

1 Natalie F 36 58.2

2 Chris M M 30 64.3

3 Priyatham M 27 53.1

From the above result, the major point to be noted is,

• The index of the resultant Series is equal to the index of the caller Series object. This makes the process of adding the resultant Series as a column to the DataFrame easier.

It operates in the same way as applying built-in functions. Each element in the Series is passed one by one to the function.

•  User-defined functions are used majorly when we would like to apply some application-specific complex functions.

c) Applying Lambda functions on Series

Lambda functions are used a lot along with the apply( ) method. We used a user-defined function for an easy addition operation in the above section. Let’s achieve the same result using a Lambda function.

The code for it is,

>>> df['Age'].apply(lambda x: x+1)

0 46

1 36

2 30

3 27

Name: Age, dtype: int64 >>> # Comparing the results of applying both the user-defined function and Lambda function

>>> df['Age'].apply(lambda x: x+1) == df['Age'].apply(add_age)

0 True

1 True

2 True

3 True

Name: Age, dtype: bool

From the above result, you can observe the results of applying the user-defined function and Lambda function are the same.

• Lambda functions are used majorly when we would like to apply some application-specific small functions.

2. Numpy’s universal functions (ufuncs)

Numpy has so many built-in universal functions (ufuncs). We can provide any of the ufuncs as an argument to the apply( ) method on Series. A series object can be thought of as a NumPy array.

The difference between applying Python functions and ufuncs is;

• When applying the Python Functions, each element in the Series is operated one by one.
• When applying the ufuncs, the entire Series is operated at once.

Let’s choose to use a ufunc to floor the floating-point values of the weight column. We have numpy.floor( ) ufunc to achieve this.

The code for it is,

>>> import numpy as np >>> df['weight(kgs)']

0 68.4

1 58.2

2 64.3

3 53.1

Name: weight(kgs), dtype: float64 >>> df['weight(kgs)'].apply(np.floor)

0 68.0

1 58.0

2 64.0

3 53.0

Name: weight(kgs), dtype: float64

In the above result, you can observe the floored to the nearest lower decimal point value and maintain its float64 data type.

We can visualise the above process as:

In the above visualisation, you can observe that all elements of Series are applied to the function at once.

• Whenever we have a ufunc to achieve our functionality, we can use it instead of defining a Python function.

Pandas apply( ) as a DataFrame method

We will take a look at the official documentation of the apply( ) method on DataFrame:

pandas.DataFrame.apply has two important arguments;

• func – Function to be applied along the mentioned axis
• axis – Axis along which function is applied

Again the axis also has 2 possible values;

1. axis=0 – Apply function to multiple columns
2. axis=1 – Apply function to every row

1. Pandas apply function to multiple columns

Let’s say the people in our dataset provided their height (in cms) information. It can be added using the following code,

>>> df['height(cms)'] = [178, 160, 173, 168]

>>> df Name Sex Age weight(kgs) height(cms)

0 Edward M 45 68.4 178

1 Natalie F 35 58.2 160

2 Chris M M 29 64.3 173

3 Priyatham M 26 53.1 168

We’ll make the “Name” column the index of the DataFrame. Also, we’ll get the subset of the DataFrame with “Age”, “weight(kgs)”, and “height(cms)” columns.

>>> data = df.set_index('Name')

>>> data Sex Age weight(kgs) height(cms)

Name Edward M 45 68.4 178

Natalie F 35 58.2 160

Chris M M 29 64.3 173

Priyatham M 26 53.1 168 >>> data_subset = data[['Age', 'weight(kgs)', 'height(cms)']]

>>> data_subset Age weight(kgs) height(cms)

Name Edward 45 68.4 178

Natalie 35 58.2 160

Chris M 29 64.3 173

Priyatham 26 53.1 168

If we would like to get the average age, weight, and height of all the people, we can use the numpy ufunc numpy.mean( ).

The code for it is,

>>> import numpy as np

>>> data_subset.apply(np.mean, axis=0)

Age 33.75

weight(kgs) 61.00

height(cms) 169.75

dtype: float64

We directly have a Pandas DataFrame aggregation function called mean( ) which does the same as above;

>>> data_subset.mean()

Age 33.75

weight(kgs) 61.00

height(cms) 169.75

dtype: float64

If you observe the results above, the results of Pandas DataFrame aggregation function and applying ufunc are equal. So, we don’t use the apply( ) method in such simple scenarios where we have aggregation functions available.

• Whenever you have to apply some complex functions on DataFrames, then use the apply( ) method.

2. Pandas apply function to every row

Based upon the height and weight, we can know whether they’re fit or thin, or obese. The fitness criteria are different for men and women as setup by international standards. Let’s grab the fitness criteria data for the heights and weights of the people in our data.

This can be represented using a dictionary;

>>> male_fitness = {

... #height : (weight_lower_cap, weight_upper_cap)

... 178 : ( 67.5 , 83 ),

... 173 : ( 63 , 70.6 ),

... 168 : ( 58 , 70.7 )

... }

>>> female_fitness = {

... #height : (weight_lower_cap, weight_upper_cap)

... 160 : ( 47.2 , 57.6 )

... }

In the above dictionary, the keys are the heights and the values are tuples of the lower and upper limit of ideal weight respectively.

If someone is below the ideal weight for their respective height, they are “Thin”. If someone is above the ideal weight for their respective height, they are “Obese”. If someone is in the range of ideal weight for their respective height, they are “Fit”.

Let’s build a function that can be used in the apply( ) method that takes all the rows one by one.

>>> def fitness_check(seq):

... if seq.loc['Sex'] == 'M':

... if (seq.loc['weight(kgs)'] > male_fitness[seq.loc['height(cms)']][0]) & (seq.loc['weight(kgs)'] < male_fitness[seq.loc['height(cms)']][1]):

... return "Fit"

... elif (seq.loc['weight(kgs)'] < male_fitness[seq.loc['height(cms)']][0]):

... return "Thin"

... else:

... return "Obese"

... else:

... if (seq.loc['weight(kgs)'] > female_fitness[seq.loc['height(cms)']][0]) & (seq.loc['weight(kgs)'] < female_fitness[seq.loc['height(cms)']][1]):

... return "Fit"

... elif (seq.loc['weight(kgs)'] < female_fitness[seq.loc['height(cms)']][0]):

... return "Thin"

... else:

... return "Obese"

The function returns whether a given person is “Fit” or “Thin” or “Obese”. It uses the different fitness criteria dictionaries for male and female created above.

Finally, let’s apply the above function to every row using the apply( ) method;

>>> data.apply(fitness_check, axis=1)

Name

Edward Fit

Natalie Obese

Chris M Fit

Priyatham Thin

dtype: object

From the above result, we got to know who is Fit or Thin or Obese.

Conclusion and Next Steps

Using the apply( ) method when you want to achieve some complex functionality is preferred and recommended. Mostly built-in aggregation functions in Pandas come in handy. If you liked this tutorial on the apply( ) function and like quiz-based learning, please consider giving it a try to read our Coffee Break Pandas book.

The post Pandas apply() — A Helpful Illustrated Guide first appeared on Finxter.

As a society, we continue through the journey of this global pandemic, but there is finally light at the end of the tunnel. There are currently two COVID-19 vaccines under accelerated review by the U.S. Food and Drug Administration and European regulatory authorities that have reported safety and efficacy data in phase three clinical trials.¹ Unlike the common flu vaccine, each of these COVID-19 vaccines poses similar and unique challenges in terms of procurement, distribution, administration and management. At Microsoft, we have been working with public- and private-sector organizations around the world to help support this monumental task of delivering vaccines as quickly as possible, but also in a secure and equitable manner. Technology plays an essential role in the secure, safe and equitable distribution of the vaccine and strategic partnerships will enable us to achieve our goals and meet the challenges ahead. In this blog, we will go into more detail on some strategic partnerships that are enabling the delivery of the COVID-19 vaccine.

Partnering towards better outcomes

With COVID-19 vaccines soon to be available, Microsoft and its partners are actively working with customers to deploy vaccine management solutions that enable registration capabilities for patients and providers, phased scheduling for vaccinations, streamlined reporting, and management dashboarding with analytics and forecasting. These offerings are helping public health agencies and healthcare providers to deliver the COVID-19 vaccine to individuals in an efficient, equitable and safe manner. The underlying technologies and approach have been tested and deployed with prior COVID-19 use cases, including contact tracing, COVID-19 testing, and return to work and return to school programs.

Microsoft Consulting Services (MCS) has deployed over 230 emergency COVID-19 response missions globally since the pandemic began in March, including recent engagements to ensure the equitable, secure and efficient distribution of the COVID-19 vaccine. MCS has developed an offering, the Vaccination Registration and Administration Solution (VRAS), which advances the capabilities of their COVID-19 solution portfolio and enables compliant administration of resident assessment, registration and phased scheduling for vaccine distribution. It enables the tracking and reporting of immunization progress through secure data exchange that utilizes industry standards, such as Health Level Seven (HL7), Fast Healthcare Interoperability Resources (FHIR) and open APIs. Health providers and pharmacies can monitor and report on the effectiveness of specific vaccine batches, and health administrators can easily summarize the achievement of vaccine deployment goals in large population groups.

Additionally, several of our partners have leveraged the Microsoft cloud to provide customers with additional offerings to support vaccine management. These offerings also apply APIs, HL7 and FHIR to enable interoperability and integration with existing systems of record, artificial intelligence to generate accurate and geo-specific predictive analytics, and secure communications using Microsoft Teams.

Meeting the needs of a global population for fair and equitable vaccine distribution, administration and monitoring calls upon the very best that the Accenture, Avanade and Microsoft partnership has to offer. Using our strategic consulting, industry and implementation expertise, coupled with Microsoft’s cutting-edge data, cloud and technology capabilities, means we are building robust, scalable and dependable solutions for vaccination authorities around the world.

EY has partnered with Microsoft to provide a comprehensive, end-to-end solution that helps manage the manufacture and distribution process of the vaccine. The EY Vaccine Management Solution is built on multiple Microsoft technologies: Microsoft Azure, Dynamics 365, Power BI and Power Platform to enable patient-provider engagement, supply chain visibility, and Internet of Things (IoT) real-time monitoring of the vaccines. Additionally, the EY Vaccine Analytics Solution is an integrated COVID-19 data and analytics tool supporting stakeholders in understanding population and geography-specific vaccine uptake.

Mazik Global has leveraged its expertise in the healthcare industry and technology solutions to create MazikCare Vaccine Flow. Vaccine Flow is built on Power Apps and utilizes pre-built templates to implement scalable solutions to accelerate the mass distribution of the COVID-19 vaccine. Providers will be able to seek out specific populations based on at-risk criteria to prioritize distribution. Patients can self-monitor and have peace of mind to head-off adverse reactions. Most importantly, the ability to monitor inventory levels as well as the temperature of the vials will enable providers to help get the vaccine to patients quickly. MazikCare Vaccine Flow is available in Microsoft AppSource.

Microsoft and its partners utilize robust capabilities of Microsoft’s highly secure cloud-based technologies to deliver solutions that address the most urgent challenges the healthcare industry is facing today. Learn more about Microsoft Cloud for Healthcare and the breadth of our partner capabilities.

Collaborating toward a successful outcome

To achieve the goal of an efficient, equitable and safe delivery of the COVID-19 vaccine, close collaboration across agencies and partners will be the key to success. It is imperative that implementations be delivered on time and customized to meet the needs of every government and healthcare customer. As much as possible, newly introduced technologies should integrate with existing systems of record to keep workflow changes and costs to a minimum.

Microsoft and its partners are committed to doing our part to help the global community address this historic challenge. Together, we look forward to helping our customers embrace the power of digital, and the power of human innovation to deliver a safe and effective vaccine in a prioritized manner.

Learn more about Microsoft’s response to COVID-19 on a global level and Crisis Response and Recovery Resources for state and local governments.

¹ Vaccine candidates produced by (1) Pfizer and BioNTech, and (2) Moderna are currently in regulatory review for Emergency Use Authorization (as of Dec. 2, 2020).

Tags: COVID-19, Microsoft Cloud for Healthcare, Microsoft Consulting Services

This week at Bungie, Hawkmoon, next-gen, and the Dawning, oh my!

This week also saw the return of Hawkmoon. The highly sought-after Exotic Hand Cannon makes its debut into Destiny 2 with a reworked perk that no longer relies on RNG but still packs a punch. Season Pass owners can now visit Spider to set off on the quest to claim their Hawkmoon.

The Dawning 

This very merry morning we shared a peek at this year’s Dawning event that kicks off next week!

Eva Levante is coming to town! She has new cookie recipes and new gifts for all the good Guardians… even if they did adopt Darkness powers.

Everyone knows the best gifts vaporize your enemies. Glacioclasm is a new Legendary Fusion Rifle being introduced during this event with random rolls available so each one you open is special. Plus – Glacioclasm is just fun to say. Try saying it without smiling, I dare you! I double-dog dare you. Anyway, here is a preview of the weapon.

As you are flying around delivering baked goods you will need a new ship to spread cheer in style. Eva has a new Exotic ship that you will be able to upgrade throughout the event to change its look. You will be able to customize the engine streams, different spawn effects, and unlock a new animated shader unique to the ship!

Eva has also set up a fun new celebration for this year’s Dawning that involves even more gifts… for you! As the community generates Dawning Spirit by completing activities you will be able to progress a special community pursuit with your cookie skills and then find new gifts waiting for you under the tree. The big glowy one, you can’t miss it. We don’t want to spoil all the surprises but you may find some upgrade materials in these gifts.

There will also be some fun stuff at the Eververse store as well. All of the new items added to the store will be available at some point for Bright Dust with the exception of the Glee Barrage and Merry Maker weapon ornaments and Happy Trails finisher which will be available for Silver. Here is a preview of a few of the Sparrows, Ghost Shells, and a ship that are sure to get you in the spirit.

Super Smash Warlock Melee 

Penumbral Blast: 

One… Million… Dollars 

Speaking of incentives, now that we have hit $1 million we are going to start releasing Destiny lore readings by the following special guests for every $50,000 donation milestone we hit!

Thank you to everyone who has donated or volunteered to help raise money through streams and other incentives. It’s always amazing what this community can do when it comes together. Finish strong!

Spreading Update Cheer 

We had another deployment this week which means our player support specialists were watching closely and tracking any issues that popped up.

This is their report.

On Demand 

Welcome to yet another exciting edition of Movie of the Week. This is your time to shine as we share a few of our favorite community created videos we come across during the week. Here are picks for this week.

Movie of the Week: Turtles in their Teens. 

Movie of the Week: We can build bridges… 

As always, our winners are taking home a special emblem. If you won, congrats! Just please make sure you put a link to your Bungie.net profile in the description of your video.

It Belongs in a Museum 

Moving pictures aren’t the only thing we like to highlight in the community. All forms of stationary art are welcome too, and we have a special emblem to give to every artist we highlight. Here are the pieces that caught our eye this week.

Art of the Week: Titan’s Best Friend 

Art of the Week: Crow and Glint 

Art of the Week: Yin & Yang 

Tag your creation with #DestinyArt on whatever platform you like to post your art on to help us find it easier. Twitter, Instagram, and our Creations page are our favorite spots to scroll through the community’s talent. If you  win, please reply to your winning post with your Bungie.net profile link so we can get you your emblem!

This is going to be the last TWAB of the year. Don’t worry, we’re not all going on a big hiatus at once. We will still be around. This is the time of year where many of us like to take some time away from work to be recharged and enjoy the cold wet darkness of the PNW at home with the fam. I mean, we were at home already, but now we’re not talking to our computers all day.  We’ve made sure that the feedback is still flowing, and that you have the latest info on ongoing issues and anything new that pops up. The Dawning isn’t the only thing starting next week, we also have raid challenges going live for Deep Stone Crypt so keep an eye out for that.

<3 Cozmo 

The Godot Game Engine have just been sponsored by Facebook Reality Labs. It comes in the form of a grant that is funding future XR (Virtual Reality/Augmented Reality) development. As a direct response to this grant, Bastiaan Olij is now going to be hired full time starting early 2021. In addition to being the lead developer on the Godot VR efforts, Bastiaan also worked on GDNative and other core Godot contributions. He also runs a very solid Godot technical YouTube channel available here.

Some details of the work he will be performing from the Godot news announcement:

• OpenXR implementation for desktop and mobile. OpenXR is the new open standard for XR (Extended Reality, encompasses Advanced Reality (AR) and Virtual Reality (VR)). The OpenXR specification reached version 1.0 in 2019, and now has multiple conformant implementations by major XR players (Oculus, Microsoft, Valve, and more!). As an open source, cross-platform and vendor neutral game engine, we’re thrilled by the support that IHVs are giving to OpenXR and want to rely on it as our main interface.
• Extending Godot’s input action system to support VR specific actions across all devices based on their respective capabilities (hand tracking, controller sensors, buttons).
• Adapt the XR plugin system to the new Vulkan renderer design. While Godot 3.2’s XR support is functional, the upcoming Godot 4.0 release changes all the rendering backend and needs work to make XR functional again.
• Implement Vulkan rendering on Android. This is necessary for mobile VR devices such as Oculus Quest 2, and will benefit all Godot users who want to make Android games.
• Various rendering optimizations:
  
  • Stereoscopic rendering enhancements, providing details about the eye for which an image is rendered.
  • Support for compositor layers, which make it possible to render e.g. UI as an overlay without going through the eye buffer with lens distortion, allowing for sharper and more stable UI.
  • Support for variable rate shading in Vulkan, providing performance gains with techniques such as foveated rendering.
  • Rendering optimizations for mobile, implementing alternative techniques to the ones suitable on desktop platforms for better mobile performance.

This Godot Engine sponsorship from Facebook is their second in less than a month. In November it was announced that Facebook had become corporate sponsors of the open source Blender development fund. Even if you aren’t a big fan of Facebook, this investment in the Godot engine should be a win for everyone. You can learn more about the grant in the video below.

There was plenty to take in during The Game Awards presentation yesterday, but Nintendo managed to squeeze in a new spot for Super Mario 3D World + Bowser’s Fury – which was very welcome indeed.

Aiming to remind us that the new, revamped release will be heading to Switch in February, the short trailer gives us another quick look at the game we already know and love, as well as the new and mysterious ‘Bowser’s Fury’ section.

If you missed any of the bigger announcement from The Game Awards – like the new Super Smash Bros. Ultimate fighter reveal – make sure to catch up on all the big stories here.

Before you go, though, let us know if you’re feeling hyped for Super Mario 3D World + Bowser’s Fury in the comments below. Will you be picking it up on Switch? Have you already played the original on Wii U? Meow.