fabiochiu/medium-articles huggingface dataset:
What are vector tiles and why you should care
Tags: Geography, Raster, Maps, GIS, Vector Tiles

Raster tiles, vector tiles and what is the difference

Traditionally, web maps were based on raster tiles technology. It was a set of squared images placed next to each other, each with a fixed geographical area and scale. Together they were creating the whole world, but only tiles you were actually browsing were shown and the rest was delivered to your browser when you changed the position or zoom.

A more detailed guide about raster tiles can be found in this document.

A map divided into raster tiles

Vector tiles, which were introduced later, also deliver data which are divided into roughly squared tiles. But there are no raster images, only pre-generated vector data which are present on the requested area (those map element which overflows current tile are cut with a certain offset, which is essential when connecting tiles together). Vector tiles are rendered on the client side with a style, which is a small text file saying which defines map elements look (e.g. the road should be a solid red line, which should be placed on top of all map elements). The style also says if the map element should be rendered, what font should be used for a label and in which language this label should be rendered. This gives more opportunity to change the map on-the-fly with minimum resources.

Vector tiles are transferred over the web in the form of packed geographic data in vector format split into squared tiles.

Because of this flexibility and a possibility to render raster tiles from pre-generated vector tiles on-request if needed, most of the big players in the mapping industry already switched.

Vector tiles

Pros:

Smaller data size and therefore lower disk space requirement

Lower bandwidth consumption

Faster generating time

Better user experience — smooth zooming

No need for zoom levels — users zoom and pan throughout all scales

De-facto mobile standard

Easy customization — with one small text file, you define how the map should look like, what font should be used, language for map labels and even hide specific map elements — everything on-the-fly

Cons:

The map is rendering on the client‘s side and requires a bit more powerful hardware

Data are generalized and therefore not suitable for direct edits

Raster tiles

Pros:

Suitable for raster data like satellite/aerial imagery

Lower requirements for end-users hardware

Still a bit better support in web JavaScript libraries and desktop GIS software

Cons:

A bigger size of each tile and data on servers

Takes more time to generate

Slower loading disrupts the user experience when moving around the map

It is possible to mix raster tiles with vector tiles and make the best usage of both, e.g., satellite map (raster tiles) with an overlay of streets with labels available in many languages (vector tiles)
Travel safe with GeoSure + Mapbox
Tags: Built With Mapbox, Customer Stories, Maps, Mobile, Travel

By: Brynne Henn

GeoSure, a safety assessment app and platform, uses real-time, hyper-local data to provide travelers with neighborhood safety information along multiple dimensions of identity and well-being.

The app initially launched in 2014, with a focus on women’s safety and in September they were the first to offer hyper-local LGBTQ safety scores, scaled worldwide. Today they provide information on 35,000 neighborhoods encompassing six safety categories: women’s safety, health and medical, physical harm, LGBTQ safety, theft, and political freedoms.

GeoSure offers free consumer-facing mobile apps, premium mobile apps for organizations, and sells location safety score data via API to platforms and travel actors such as SAP Concur. In their mobile apps, GeoSure selected Mapbox as their mapping platform from day one. We spoke to Michael Becker, CEO of GeoSure, to learn more about their product and how they’re building with Mapbox.

First off, why did you decide to build a safety assessment score?

We developed GeoSure because the notion of travel safety and duty of care is rapidly evolving. While boots on the ground and narrative form country level security solutions existed, there were no safety scoring or quantification platforms available. We also saw that a gap in the market existed for city and neighborhood granularity and few solutions focused on the end user experience. Commercial travel security providers communicate risk through a prism of anxiety, but we wanted to flip that around and instead empower people to travel by providing an understanding of safety based on a quantitative system.

How do you assign a score to an area and how often is it updated?

Our safety scores are color-coded, on a scale from 1 (safest) to 100 (least safe), to make it simple and intuitive for anyone to understand. We create these by combining country, city, and neighborhood statistics; health, macroeconomic, crime, and political data feeds; real-time reports and much more. Then we model data to produce GeoSafeScores™. The ratings cover seven relevant safety categories: women’s safety, health and medical, physical harm, LGBTQ safety, theft, and political freedoms. Scores reflect localized safety environments and are dynamic, in near real time.
Map vs Flatmap (in reference to RxJava)
By: Abhinav Rai
Tags: Android, Maps, Flatmap

Lets start with basic understanding of map and flatmap with an example ->

val someList = listOf<Int>(1, 2, 3, 4)

val mapList = someList.map {

x -> x*2

}

val flatMapList = someList.flatMap {

x -> listOf<Int>(x * 2)

}

Both of them produce the same output -> [2, 4, 6, 8] The only difference comes between their signature.

More generically speaking:

fun <Y> map( fun (x): x): Y

fun <Y> flatMap (fun (x): Y ): Y

In the first example, Y is List<Int> and x is Int

Lets see the flatmap in javascript.

let arr1 = [1, 2, 3, 4];



arr1.map(x => [x * 2]);

// [[2], [4], [6], [8]]



arr1.flatMap(x => [x * 2]);

// [2, 4, 6, 8] arr1.flatMap(x => [[x * 2]]);

// [[2], [4], [6], [8]]

Its like, it flattens to one depth.

Now some advanced example. Flatmap in RxJava.

override fun articleDetail(id: String): Single<ArticleDetailData> {

return articleEndPoint.getArticleDetail(id)

.flatMap {

val body = it.body()

if (it.isSuccessful && body != null) {

Single.just(body.articleDetailData)

} else {

Single.error(HttpException(it))

}

}

}

articleEndPoint.getArticleDetail(id) // returns Single<Response<ArticleDetailData> > when the api is hit.

Single<Something> calls .flatMap . So we do some logic with Something here and then we have to return the Single with Single.just or Single.error (to make them into Single) as its a flatmap.

Same implementation with map:-

override fun articleDetail(id: String): Single<ArticleDetailData> {

return articleEndPoint.getArticleDetail(id)

.map {

val body = it.body()

if (it.isSuccessful && body != null) {

body.articleDetailData

} else {

error("Some HTTP Exception")

}

}

}

Here is a simple thumb-rule that I use help me decide as when to use flatMap() over map() in Rx's Observable .

Once you come to a decision that you’re going to employ a map transformation, you'd write your transformation code to return some Object right?

If what you’re returning as end result of your transformation is:

a non-observable object then you’d use just map() . And map() wraps that object in an Observable and emits it.

. And wraps that object in an Observable and emits it. an Observable object, then you'd use flatMap() . And flatMap() unwraps the Observable, picks the returned object, wraps it with its own Observable and emits it.

Say for example we’ve a method titleCase(String inputParam) that returns Titled Cased String object of the input param. The return type of this method can be String or Observable<String> .
Routing Improvements for On The Go Map
By: On The Go Map
Tags: Openstreetmap, Maps, Running

Starting today, On The Go Map will let you route on more roads using newer data and more intuitive paths.

When I moved On The Go Map from Google Maps to OpenStreetMap last year, the most important functionality to replace was the routing engine. The routing engine takes two points and creates a path that snaps to roads between those points. I chose GraphHopper for this which has been rock solid, serving over 100 million routing requests since then. Unfortunately, GraphHopper’s default “foot” routing profile was built for long distance hiking directions and often diverted users onto roads that would be better for a cross-country trek.

With today’s update, the routing engine:

includes the most recent edits to OpenStreetMap

uses the shortest path between points, only avoiding busy roads and tunnels without a sidewalk

lets you route on private roads, roads designated for bicycles only, and trails that ford rivers — so please exercise caution!

Thanks to the beta testers who gave invaluable feedback on this change! Please reach out to info@onthegomap.com if you have any more feedback or if you are interested in testing out car or bicycle modes before they launch soon.

— Mike Barry @msb5014
November 23, 2020 weekly rate update
By: Will Madden
Tags: Blockchain, Mexico, Payments, Remittances, Fintech

November 23, 2020 weekly rate update

Notice: Thursday the 26th of November is Thanksgiving, a US bank holiday.

ACH payments processed later than Monday afternoon through Thursday this week will take an extra banking day to deliver to Mexico.

Wire payments (and quote requests) received from afternoon PST Wednesday through all-day Thursday will be processed Friday morning.

USD:MXN rates bounced around 20:1 over the last week.

Rates mostly held the 20:1 line last week, but briefly sank below 20:1 levels Sunday. The short-term trend is objectively down, but we expect resistance around 20:1 levels and sideways action before increases in the short-term.

Longer-term USD:MXN rate forecast

We expect higher exchange rates in the coming months. It would take weakness in the US Dollar relative to the Mexican Peso to change this forecast. Weakness would require loose US monetary policy relative to Mexico’s, overbearing US lockdowns, or both.

There doesn’t appear to be an eagerness to comply with overbearing lockdowns in the US, and monetary policy is likely to fall in line with expectations, so our bet is on the upside long-term.

Best regards,

Will

bridge21

The content in this article is for informational purposes only. You should not construe any such information or other material as legal, tax, investment, financial, or other advice. While we took care to share correct information from reputable sources, bridge21 cannot guarantee the accuracy or timeliness of the information.
The Supreme Court Knows “Remain in Mexico” is Illegal, Yet They Keep Letting It Go On
By: Alex Jean
Tags: Asylum Seekers, Mexico, Refugees, Immigration

If you’ve never actually sat down and read out immigration code, don’t start now. It’s so confusing and internally contradictory that most lawyers can’t make heads or tails of it.

But parts of it are pretty simple. People arriving in the United States without a visa or permission to remain in the United States may be removed under 8 USC section 1225, unless they apply for asylum.

All of this is pretty simple and straightforward, but the Trump Administration is trying to take advantage of the confusing way in which the law is written to inject vagueness and uncertainty into the right to apply for asylum.

The confusion stems from the fact that there are two categories of grounds upon which a person can be removed, Category One (which includes persons without the proper documents) and Category Two, which is sort of a catch all category for other people who may be refused entry to the US for reasons not outlined in Category One. Under the law, Category Two aliens arriving from a “contiguous territory” may be returned to that territory pending the adjudication of their cases. But it’s pretty clear that removal to a “contiguous territory” is limited only to persons who do not fit into Category One. And it’s also pretty clear that most people being forced to “Remain in Mexico” fall under Category One; i.e. they do not have documents allowing them to enter the United States.

Put simply, the Administration is trying to take a footnote on page 3,745 of a law textbook and saying this footnote, which the Administration stumbled upon at 3am while cramming for the exam, somehow changes the entire structure of the entire US immigration system.

And the above is only the beginning of the Administration’s problems. Even if everything I wrote above were not true, there are further protections in the US immigration code preventing people from being sent to places like the Mexican border. No one seeking admission to the United States may be deported to Mexico if their life or freedom might be threatened there. Under USC section 1231, no one can be removed to any country where their life or freedom may be threatened on account of their race, religion, political opinion or other enumerated grounds. This provision, sometimes called “non-refoulement,” comes from international law and is pretty much recognized around the world.

Stepping back for a moment, there is also a common sense aspect to this debate. People applying for asylum in the US do so under US law and part of the US legal system. Our legal system has nothing to do with Mexico. The fact that someone transited through Mexico in order to apply for asylum in the US is irrelevant and the idea that it’s Mexico’s job to deal with our immigration system is bizarre.

The only possible point of this program is to make applying for asylum extremely hard and as dangerous as possible. What is the point of an asylum system that places applicants in harms way? Putting asylum-seekers in danger is the opposite of what asylum is supposed to be about.
Why I Left New York for Oaxaca
By: Andrea Ricaurte
Tags: Oaxaca, Expatlife, Mexico, Expat, Food

It’s about 10 am and I’m on my way to La Cosecha, a small market that I go to all the time. It’s full of such amazing food stands serving traditional Oaxacan cuisine, booths of organic farmers selling produce and jars of artisanal vinegars, salsas, jams and tortillas. One booth sells coffee roasted in a clay pot directly on the fire, seasoned with cinnamon and just a little bit of sugar. They also sell a variety of artisanal mezcals in clear unmarked glass bottles, and at another stand they sell freshly made organic Oaxacan cheese. In the entrance to La Cosecha, a few stands are occupied by artists and makers who sell huipils and ponchos, painted glass bottles, woven baskets, natural beauty products, used books, and pottery.

I know a lot of these people by now, because I’ve bought things from their stands, and I come by often during the week when the market is open. I tried out every single food stand before going back to the same one twice. Now I go in, say hi to the vendors along the entrance, and walk straight to the back corner for some flautas from my homegirl Graciela.

She’s busy behind the counter, cooking up chilaquiles in a dark red salsa that smells divine. Looking up as I approach, we greet each other with big smiles. I’ve had a lot of her food and it’s all so good, but she knows my go to. Por favor, dos tacos dorados de pollo con todo — and then she finishes my sentence — y salsa picante!! Yes girl, you know I love that insanely spicy salsa with my chicken flautas. She nods her head and motions me away with a graceful gesture meaning- go ahead and find a seat, I’ll prepare everything and bring it to you when it’s ready.

I find an open space on one of the benches under the shade, wave to my friend at the coffee stand to please bring over a cup of their flavorful brew, and turn my attention to the musician playing the guitar and singing beautifully for coins.

I left New York for Oaxaca because this is the type of experience that takes place in the first two hours of my day, and I’ve spent just $3 USD.
Day#2 Taco Chronicles
By: Giovanni Perez
Tags: Working In Japan, Mexico

Day#2 Taco Chronicles

A Mexican-American working in a taco shop underneath Tokyo station. I write a blog in the 35 minutes it takes me to get from the shop to my house on the train. Enjoy and if you are in town, come get some tacos. Giovanni Perez Oct 13, 2020·3 min read

It was second day working for a Tex mex shop in Tokyo station. My shift was from 6:30–11:00. I met a new member of the team. His name was Kay. He is the other chef and he was supposed to teach me the way of the taco, for Japan that is.

I must say that it is a strange feeling, a Japanese person teaching me, a Mexican person, how to make tacos. Still though, each shop, wether in Mexico or Japan, has their own style. I aim to master it to the point where I can do it with no ones help. The added challenge of everything being in Japanese is also a plus for me as it will enable me to learn more Japanese at a rapid speed.

I learned many foods yesterday during my day one, so much so that I forgot most of them. From the special fried chicken to all of the savory tacos we offer.

My shift!

Today I actually implemented all of my 5 hours of training from yesterday, and I was in charge of the four general tacos we offer; carnitas(pork), suadero(beef), Tinga (chicken) and chipotle shrimp. Maybe it was my Mexican roots that kicked in but it reminded me a lot of when I used to help at my families taco food truck and I was in charge of the tortillas.

We got extremely busy from 20:00–21:00, and with the combination of Japanese being spoken, the comal(a food truck style stove), and me still learning how to cook a specific style taco, it was havoc. The good kind, but nonetheless, havoc .

Although I was pretty impressed that I didn’t get anything wrong, I was however slower than a turtle. The hardest part for me, oddly enough was wrapping the taco.

Not sure if it’s the wrapping portion that I can’t get right, or if my fingers are to humongous, for the love of me I just can’t do it.

Making Tortillas

My shop uses fresh ingredients, that also includes their soft and warm tortillas that they make from corn. We then get the masa sent to the shop where we use a special Tortilla maker that you crank to make perfect rounded circles.

I tired my hand on it. It was my first time, I usually use a wooden flattener but this machine is used to produce tortillas at a faster rate. I admit, I didn’t do a good job my first time around. Still got one or two in before I was called to make shrimp chipotle tacos.

The One That Didn’t Make It

There was a taco that I tried to make that just didn’t make the cut, and we couldn’t just throw it away so I got permission to eat it with some meat and in-house guacamole sauce. Oh man was this delicious. Any taco is a good taco, regardless of it’s just a Tortilla with salt and lemon.

Wrapping it up

I Was able to mingle with my co-workers as we were cleaning up the kitchen. I wondered if this is how college students in Japan felt, because it was fun. We munches on some in-house chips and drank a beer that our boss got us.

Today was a good day .
Central Air
By: Raymie Humbert
Tags: Mexico City, Radio, Mexico

Central Air

Yesterday, Juan Luis Ramos of El Sol de México revealed that Pedro Ferriz de Con is in active negotiations with Eduardo Henkel to buy XHINFO-FM 105.3, Mexico City’s Aire Libre. The station would obviously broadcast the Central FM program, and one wonders if the entire format would change as a result.

Central hasn’t had a broadcast outlet in the all-important Mexico City area since XFM’s bitter — and, according to Ferriz de Con, intentional — collapse, which has since sent Central FM Equilibrio, S.A. de C.V., into a legal battle with Grupo Siete Comunicación.

The news of sale talks throws another layer onto a station whose future is looking ever more confusing. Last month, a column in another OEM newspaper, La Prensa, revealed that the Unidad de Inteligencia Financiera (UIF) and IFT were investigating the 2017 award of the 105.3 MHz frequency to Henkel, with the very concession said to be at stake, and with the entire award still being challenged by a rival contender, the Maccise family:

Aire Libre has formally been on the air just under two years now. The FM transmitter was turned on August 9, 2018, and under a lease agreement to Más Radio Telecomunicaciones (NextMarketing, S.A. de C.V.), programming began November 1, with Víctor “Brozo” Trujillo — who has since left — as the biggest name on this eclectic music station.

Ferriz’s interest in XHINFO, as a potential carrier for his program or otherwise, was first reported in early April by El Economista. With the station’s current format, Central would be an odd fit, for sure. Aire Libre’s current morning show is branded with web site Sopitas.

Sure, it’s been a weird two years. (The promised Riviera Maya station, XHPTUL-FM 94.7, has never launched; it was last said to be signing on in July 2019.) And this is nothing compared to the limbo in which this station lived for an entire decade, from the collapse of Radio Monitor to the short-lived La Mexicana to the surprise migration.

However, one wonders how a FRENAAA member and the avant-garde Aire Libre format can fit together, if they will, or what Central FM would do instead programming a full-time station instead of just a few hours in the morning, as it did with XFM. One also wonders if the risk of buying a station already known to be under federal investigation is even worth it for someone so publicly against the president.
Carry Cases Market Size Worth $9,011.3 Million By 2025
By: Gaurav Shah
Tags: Japan, Mexico, Bags, Backpack, Technology

The global carry cases market size is estimated to reach USD 9,011.3 million by 2025, registering a CAGR of 5.9% from 2019 to 2025, according to a new study by Grand View Research, Inc. The increase in the level of disposable income has enabled consumers to spend on gadgets such as notebooks and tablets. Furthermore, the growth of the e-commerce industry has stimulated the sale of accessories such as backpacks, sleeves, and other carry cases.

The introduction of multiple models of laptops is expected to promote the market growth since devices of different sizes and designs require different cases. Moreover, the introduction of smart cases is expected increase customer adoption as it improves the functionality of the cases. For instance, carry cases are available that are made of special material that blocks RFID signals to protect credit cards and passport from wireless identity theft.

The market is lucrative in developing regions such as Asia Pacific due to emerging businesses that implement BYOD. The adoption of BYOD improves work flexibility in terms of flexibility of employee work hours and work access from anyplace. Moreover, it helps increase productivity and reduce operation costs for the organization. Thus, it increases the usage of wireless connected devices such as tablets and laptops, which in turn increases the usage of products such as sleeves and messenger bags. Furthermore, innovative product design is expected to enhance the aesthetic value and protection capability of carry cases, thereby, increasing their adoption.

The usage of backpacks and sleeves for marketing and promotional purposes is expected to increase the scope for commercial applications at educational institutions and workplaces. Furthermore, the recent trend of customization whereby customers can get their names printed on the cases or get personalized designs is expected to boost the demand for carry cases.

The market comprises a large number of players including local manufacturers and authorized retailers. The market competition is high and mainly price-based. The presence of counterfeit products is expected to restrain the market growth and affect the profitability of market participants.

Click the link below:

https://www.grandviewresearch.com/industry-analysis/carry-cases-market

Further key findings from the report suggest:
Mexico deported 311 Indians
By: Trilok Singh
Tags: Mexico, Youth Darpan, Migration, Immigration, India

The shift comes after US President Mr Trump in June 2019 threatened tariffs on all Mexican imports if the country did not put a check on people entering America through Mexico’s borders.

Mexico deported 311 Indians: The Youth Darpan.

As per the recent report, In a first, Mexico’s migration authorities deported 311 Indians, it also includes woman, from various parts of the country amidst its stepped up efforts to check people illegally crossing its borders following pressure from the United State (US).

Following the recent ‘press release’ issued by the National Migration Institute (INM) on Oct 16, the Indian nationals, who did not have a condition of regular stay in the country, were deported from the Toluca City International Airport on a Boeing 747 aircraft to the National Capital of India.

The findings assumed that, The people were presented to the immigration authority in the states of Oaxaca, Baja California, Veracruz, Chiapas, Sonora, Mexico City, Durango and Tabasco. The turn comes after US President Mr Trump in June 2019 threatened tariffs on all Mexican imports if the country did not put a check on people entering America through Mexico’s borders.

Read Also, Trump renews tariff threat against Mexico

Thus, the statement remarked that, “Mexico had agreed to boost up security on the border and expand its policy of taking back migrants. “This was carried out, thanks to the excellent communication and coordination with the Embassy of that Asian country, with which the recognition and return of these citizens was worked, under strict adherence to the Migration Law and its Regulations,”.

Read Also, Trump suspends Mexico tariffs after reaching migration deal

Further, The deportees, who are accompanied by the Federal Migration agents, as well as members of the National Guard, were gathered in the Acayucan Migration Station in Veracruz to carry out their identification and subsequent transfer. “It should be noted that there is no precedent in the history of the INM — neither in the form, nor in the number of people — of a Trans-Atlantic air conduction, such as that carried out on this day,” It said.

Read Also, Caravan of 2,000 migrants detained in southern Mexico

Consequently, This action, which was supported by the Federal Protection Service of the Secretary of Security and Citizen Protection, was carried out smoothly and with respect for the human rights of foreigners transferred to their country of origin, it remarked, ratifying the INM’s commitment to maintain a safe, orderly and regular migration.

(Inputs are based on recent official release and News Agency reports).
FOSS Motivations: Joining NumPy
By: Maximiliano Merced Rios
Tags: Numpy, Foss

Photo by Vek Labs on Unsplash

Past experiences and future goals drive us to take on new personal projects, self studies, and roles in various organizations. Joining a FOSS project is no exception, and the reasoning behind contributing to open source software has been proven to be fairly consistent among all developers.

Reasons for Joining a FOSS Project

While the reasoning behind my team’s backing of NumPy was set in stone from the course requirements, all of the members were led to the project and its community based on common interests. The dominating factor was education; we wanted to learn more about the library (as it has been used in past classes, hackathons, and personal projects) and Python. While getting to know the community through their newsletter and Slack channels, we also learned that the community members would also be valuable resources (mostly for educational purposes).

Numpy’s notoriety peaked the interest of the group members as well. Seeing that the project was always accepting new contributors, we quickly did our research and wrote up a report to focus our motivations and reasons for how we could successfully learn from joining the team.

NumPy Community

One of the biggest factors in our FOSS project search was an active and experienced community that would be able to onboard a group of inexperienced students. Some of the projects that we reviewed included TensorFlow, OpenMRS, FreeCodeCamp, and Django. While many of these communities were big, experienced, and active, the opportunities to integrate into the different communication channels were sparse. This was a major downside since we were interested in these libraries for the opportunity to consistently talk and ask questions with the core members of these programs. NumPy offered this outlet and more. The group Slack has numerous channels including a newcomer’s channel that is managed by one of the core members of the community. With an easier transition into the community, the team and I were able to change our motivations from completing an assignment for a class to learning more about successfully contributing to FOSS projects.
Ramen Ratings — NPV Analysis.. This is my first project on Data…
By: Arvind Murali
Tags: Visualization, Numpy, Word Cloud, Pandas, Ramen Noodles

This is my first project on Data Science, Data Analysis and Visualization...!!

Aim of the Project:

What Makes instant Noodles so popular globally, and what is the Characteristic feature of one Brand or Variety of Ramen compared to the other.

Libraries and packages:

Packages to Install

Libraries to be imported

Dataset:

This dataset is an export of “The Big List” (of reviews) from the Ramen Rater website and converted to a CSV format.

Columns in this dataset include Brand, Variety (the product name), Country, and Style (Style the Ramen is served). Stars indicate the ramen quality, as assessed by the reviewer, on a 5-point scale

Dataset Overview

Pre-Analysis:

The Data Contains the following Columns:

df1.info()

Checking for Unique Values:

df1.nunique()

Data Cleaning:

1. It appears that styles: ‘Can’, ‘Bar’ and ‘Box’ are outliers — very few stars and occurrences.

2. The dataset contains Brand/Variety that is not Ramen: Example: Brand = ‘Pringles’, Variety = Nissin Top Ramen Chicken Flavor Potato Crisp.

Mis-spelled Product, Brand & Country names

3. Discovering Outliers:

3. It appears that there are some brands that have been rated 0.0 which is invalid.

4. Removing Outliers:

a) Deleting the outlier style — ‘Box’, ‘Can’, ‘Bar’

b) Deleting the rating = 0.0 as it is an invalid value.

Data Analysis and Inference:

The more reviews a brand has, the more people have tried it.

We won’t classify a brand as good based on the number of stars since any brand can easily make the list with just one 5* product.

Top 10 Best Brands:

Top 10 Brands

Rating Distribution by Country:

Preference by syle of Ramen Served:

Country vs Style:

Brand vs Country vs Stars:

Country, Brand and mean avg rating for each country:

Country, Style, and Number of times each country reviewed a style:

Group for Country, Style and mean avg rating for each country:

Distribution of Star Ratings:

Top 100 Ramen Product Strings:

Word Cloud

Project Team:

Arvind, Shamia, Ullas, Prabhu

LinkedIn: www.linkedin.com/in/arvind-murali2
Python seaborn library For Data Visualization in line plot graph
By: Sandeep Kumar Patel
Tags: Python, Numpy, Seaborn, Matplotlib

By- SANDEEP KUMAR PATEL

Inside article we are used Python seaborn library data visualization we are taking one data set which are provided by the seaborn library

Using this data set we are trying to analysis one particular data set to better visualization

What is Seaborn

Seaborn is a powerful Python library which is used to interactive and user-friendly understanding data visualization

Seaborn library e is used to to many other supportable library just like ok and as a Numpy, Pandas and Matplotlib

Seaborn library is a advance part of Matplotlib inside Matplotlib library if we are trying to create a plotting graph very less number of code and time reduced also

Seaborn is a Python library for data visualization build on a top of Matplotlib python library Use for statistical graphic

Inside seaborn library we are plot the graph just like heat map a plot bar plot, sclater plot, line plot heat map ,box plot and many more

How to install seaborn library

Seaborn library is easy to to install just like other library in python if you have conda version inside condom version these are the primary library is pre loaded so you have don’t need to to install separately

pip install seaborn

or

Conda install seaborn

To check seaborn library install or not

pip show seaborn

To import seaborn library sometime to make better Visualisation at the time we have to use other library also

from matplotlib import pyplot as plt

import seaborn as sns

In this article we are explore only line plot graph

Below the line show all parameter in line plot

sns.lineplot(

*,

x=None,

y=None,

hue=None,

size=None,

style=None,

data=None,

palette=None,

hue_order=None,

hue_norm=None,

sizes=None,

size_order=None,

size_norm=None,

dashes=True,

markers=None,

style_order=None,

units=None,

estimator='mean',

ci=95,

n_boot=1000,

seed=None,

sort=True,

err_style='band',

err_kws=None,

legend='auto',

ax=None,

**kwargs,

)

Let’s first open our jupyter notebook and import some supportable library which are required to plotting hour graph

Now we are used trips data set we are use which are provided by seaborn library

Load the data set and check inside the data

Seaborn lineplot-

This is a basic graph inline plot

Now we are use some parameters

You can use all the parameter and make the graph more interactive this is a basic introduction of seaborn line plot graph we are used a few number of parameters you can use all the parameters according to your data set and according to your requirement

Thank You For Your Time…..!!!
Python Speed Comparison: Square Root of the Sum of Squares?
By: James Plank
Tags: Python, Numpy, Linear Algebra

On many occasions, an equation like this needs to be solved in your python code. There are many ways to go about it, and naturally some will be better than others, and of course it depends on the data you are working with.

Let’s assume the typical case, where we have a 1D list of length n , and we need to sum the squares of each element in the list, then square root:

In the code above, we defined a function rss() that takes a single list as it’s argument and outputs the square root of the sum of squares. Using a python decorator, @timer , we can time the execution of this function. I performed 1000 tests on lists containing 10,000 random floats in the range 0 -> 1 . We can see from the output at the bottom that on my pc the average time was just over 4 milliseconds. Not great, but at least the function itself uses only default python data types, and the sqrt function is from the built-in math library.

Alternatively, you can do a very similar calculation using numpy functions:

Based on this test, using a chain of numpy functions to do the work gives you a >5x speed increase.

There is another function buried inside numpy.linalg that can provide yet more speed boosts; np.linalg.norm (https://numpy.org/doc/stable/reference/generated/numpy.linalg.norm.html)

This is almost 1.4x faster than the previous method, and 7.4x faster than the original method!
MNIST Handwritten digits classification from scratch using Python Numpy.
By: Aditya Srinivas Menon
Tags: Numpy, Backpropagation, Deep Learning, Python, Mnist Dataset

5. Let us now define a function to carry out the forward pass and the backward pass during an iteration.

Let us go over the code line by line, and understand how it works.

Y_train is essentially a vector of 50,000 elements, having the correct digit corresponding to the images in X_train.

On the other hand our model outputs a vector of 10 elements for each training example. Therefore our primary task is to convert the Y_train into a vector having 1 corresponding to the “correct” digit and 0 for the rest.

As you can see the element in the fifth position having an index of ‘4’ has a 1 whereas the rest of the elements are 0.

Now comes the forward pass where the input matrix is multiplied with the weights and passed through the respective activation functions.

In the first line x is matrix multiplied with the first layer and is normalized by passing through the sigmoid function.

Screenshot by author.

The sigmoid product is later matrix multiplied with the second layer and is passed through the softmax function to get the output vector of size 10, similar to our target vector.

Screenshot by author.

Now it’s time to backpropagate and update our weights. Our aim here is to find matrices similar in shape to that of l1 and l2, that can be subtracted from l1 and l2 to get closer to the ideal answer by minimising error.

But what exactly is error?

error=2*(out-targets)

This calculates the error in the current epoch along with its direction. By how far are we away from achieving the ideal answer.

Let us have a look at what ‘out’ really is:

out=softmax(x_l2)

It is the product of our neural network.

We now need to find an entity that tells us by how much the out changes if there is a change in x_l2.

YEP! You guessed it right. This entity is called the derivative! We need to find the derivative of softmax at the point x_l2 in space such that when we change x_l2 by (error * derivative of softmax at x_l2) our out moves closer to the ideal targets, and that in python code looks like:

But what exactly is x_l2?

x_l2=x_sigmoid@l2

We now know by how much we need to change x_l2, but x_l2 is the product of the sigmoid output of the first layer and the l2, and we cannot change it. We only have control over the weights! So to turn the previous error into something that can be subtracted from l2 let us define a update_l2.

Phew! We are done with the second layer, now let us do the same for the first. Instead of taking the derivative of the softmax, we shall be taking the derivative of sigmoid at point x_l1 in space.

And define update_l1 which can be subtracted from l1.

With this we come to the end of forward and backward pass.
Do You Use Apply in Pandas? There is a 600x Faster Way
By: Nicolas Vandeput
Tags: Numpy, Pandas, Vectorization, Python, Parallelization

Do You Use Apply in Pandas? There is a 600x Faster Way

I recently read yet another article showing you how to speed up the apply function in pandas. These articles will usually tell you to parallelize the apply function to make it 2 to 4 times faster.

Before I show you how to make it 600 times faster, let’s illustrate a use case using the vanilla apply().

Pandas Apply

Let’s imagine you have a pandas dataframe df and want to perform some operation on it.

I will use a dataframe with 1m rows and five columns (with integers ranging from 0 to 10; I am using a setup similar to this article)

df = pd.DataFrame(np.random.randint(0, 11, size=(1000000, 5)), columns=('a','b','c','d','e'))

I want to apply a logic based on ‘e’ that will generate a result based on the four other columns.

def func(a,b,c,d,e):

if e == 10:

return c*d

elif (e < 10) and (e>=5):

return c+d

elif e < 5:

return a+b

Let’s use pandas apply with this function.

df['new'] = df.apply(lambda x: func(x['a'], x['b'], x['c'], x['d'], x['e']), axis=1)

We get a running time of around 11.8 seconds (over 10 runs, with a minimum running time of 11.7 seconds).

Parallelize Pandas Apply with Swifter

You can easily parallelize this process by using swifter.

As swifter is not installed by default with anaconda, you will have to install it first.

conda install -c conda-forge swifter

We can now use parallelize apply by calling swifter before apply

import swifter

df['new'] = df.swifter.apply(lambda x : func(x['a'],x['b'],x['c'],x['d'],x['e']),axis=1)

On my MacBook Air (using an M1 CPU), I got an average running time of 6.71 seconds (over ten runs, with a minimum running time of 6.45 seconds). This is nearly twice as fast as our initial apply implementation.

Parallelization in Python is not a silver bullet: you can only expect slight improvements (if any).

Pandas Vectorization

The fastest way to work with Pandas and Numpy is to vectorize your functions. On the other hand, running functions element by element along an array or a series using for loops, list comprehension, or apply() is a bad practice.

Let’s create a vector implementation of our previous function. As you can see, I am using two masks to identify relevant cases, then .loc to update the values. Moreover, the default case is assigned without using any mask.
P95G BTC Market Weekly Report — Week of 11/23/2020
Tags: Btc, Bitcoin News, Bitcoin, Point95 Global, Cryptocurrency

BTC/USD hourly chart

The Bitcoin price extended its gain for another week to reach $18,945 and then settled at $18,170. Given the strong upward momentum in the past weeks, it is possible for the price to approach $20,000 in the next 2 weeks.

The current support is at $18,000. In order to keep the current upward trend, the BTC price needs to stay above $18,000 and break the previous peak at $18,634. If the volatility remains high, the price would potentially break above $18,945 and extend its gain further. However, if the trading slows down, the price may settle in a range between $18,000 and $18,500 before the market determines the next trending direction.

Disclaimer: The above market commentary is based on technical analysis using historical pricing data, and is for reference only. It does not serve as investment or trading advice.

P95G：The management team consists of seasoned professionals who worked in the brokerage service industry, with a background ranging from equity and derivative trading to operations, asset management, and information technology. P95G provides services to financial institutions and individual investors in buying and selling cryptocurrencies, fiat currency (US dollar) and cryptocurrency transfers, and OTC (over-the-counter) trading in large amounts.
Breaking News from Crypto Business World (CBW) — Nov 14, 2020
By: Crypto Business World
Tags: Bitcoin News, Cryptobusinessworld, Cryptocurrency News, Cbw, Crypto

Breaking News from Crypto Business World (CBW) — Nov 14, 2020

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#Cryptoassetrating #CryptoBusinessWorld #Tokenizationassetplatform #CBW #digitalassets #cryptoassets #ratingagency #blockchain #crypto #cryptocurrency #cryptobusinessnews #cryptonews #breakingnews

News Headlines -

Bitcoin’s rally over $ 15,000 has again started the discussion on whether the cryptocurrency is nominal digital gold or a hazardous risky bet as investors struggle with the coronavirus pandemic read more…

Bitcoin price reaches $16K, but it’s Ethereum that may sparkle in November-

After Bitcoin’s solid breakout beyond $15,000, analysts are observing toward Ether as the market sentiment about Ethereum strengthens.

The worth of Bitcoin (BTC) is approaching $16,000 after realizing $15,960 on Binance. Subsequent to the dominant cryptocurrency’s gathering, analysts are now observing toward Ether (ETH). The Ethereum blockchain’s natural token has seen heightened thrust in the past week. After underachieving against BTC in October, the chance of a new ETH rally is starting to increase read more….

More crypto News at https://cryptobusinessworld.com/
Robinhood shat the bed, so the band of Merrymen has taken over!
Tags: Decentralized Finance, Cryptocurrency, Decentralized Exchange, Bitcoin News

Robinhood shat the bed, so the band of Merrymen has taken over!

Merrymen is a brand new community focused DEX that will launch with a special offer for all Robinhood investors. Merrymen Jul 15·2 min read

Robinhood is no longer in charge.

When Robinhood CEO Vlad Tenev decided to freeze trading on the Robinhood app in January 2021, he showed the world why centralized exchanges should have no part to play in the world of retail investing.

Retails investors had trusted the Robinhood app, having no idea how easily it could be manipulated. When CNBC reported that Robinhood had to “tap credit lines, raise new funds and throttle back trading” and “the central Wall Street clearinghouse mandated a ten-fold increase in the firm’s deposit requirements” it was clear that retail investors were trapped in a centralized controlled exchange, that was totally out of their control.

Robinhood messaged its investors at the time of the trading freeze saying:

We stand in support of our customers and the freedom of retail investors to shape their own financial future. Democratizing finance has been our guiding star since our earliest days.

But as Coindesk reported, Robinhood could not even decide its own course of action as most of the control was in the hands of a single centralized custodian: the Depository Trust and Clearing Corporation (DTCC) and its subsidiary, the National Securities Clearing Corporation (NSCC). Both of these centralized corporations telling Robinhood to stop trading.

Merrymen, a brand new DAO based DEX on the Ethereum network is launching to demonstrate how important decentralized finance and decentralized exchanges are to the retail investor after the Robinhood GameStop fiasco. It will launch with a unique offering:

Merrymen will airdrop tokens for free to Robinhood investors who lost money in the GameStop fiasco.

Merrymen will also airdrop tokens for no charge to all the communities of projects that provide initial liquidity on their DEX platform. Both these offers will give as many retail investors as possible the opportunity to become seed investors for free in a real decentralized exchange.

Merrymen will never stop any investor trading, will never be under the control of a centralized organization like the DTCC or NSCC and it will never cite ‘market volatility’ for closing their accounts.

Merrymen will launch in early Q4 and is currently working with a number of DeFi launchpads to open its IDO to as many investors as possible.

For further information please visit Merrymen’s website and follow its Twitter and Telegram channels.
Woori Financial Group sets up crypto custody services
Tags: South Korea, Bitcoin News, Cryptocurrency Adoption, Cryptocurrency News, Cryptocurrency

Woori Financial Group sets up crypto custody services CryptoCurry Follow Jul 15 · 1 min read

Woori Financial Group, South Korea’s fourth largest bank, has announced that it is entering the cryptocurrency space by setting up crypto custody services in a joint venture with Coinplug. The announcement comes despite current regulatory uncertainties in the country. The joint venture will form a new company called D-Custody which will allow customers to store crypto assets and participate in decentralized finance (DeFi).

Current laws in the country don’t allow banks to offer crypto-related services directly. However, banks may provide crypto services through joint ventures with businesses that are already in the crypto space. “Since Coinplug does not operate an exchange, we decided that it could become a stable partner,” a bank official said in an interview.

Woori is the fourth-largest bank in South Korea in fiscal year 2020 with over 358 trillion KRW (approximately $311 billion) in assets under administration. It is also the third commercial bank to announce its entry into the crypto space. Woori follows in the footsteps of Nonghyup Bank and Kookmin Bank which launched crypto custody services in 2020.

Source: Decrypt