#suicide rate drops to 0

[elsanna-shenanigans]

October Fanfiction Contest

Sorry for the long wait - after roughly 5 years of running the show tirelessly, we really needed a break. And a note to all still wondering if you should join our discord server: the prompt has been known on the server for a while, but the time-frame for submissions is the same. Consider joining, if only for earlier updates like this!

prompt: Water.  word limit: min. 450 and max. 4,500 words (+ bonus below) lemon: up to M rating obligatory: no depictions of drowning (explained below) bonus: story exactly 3,000 words long, physical descriptions of characters (explained below) deadline: October 30th

Please also tag your story (if it has any of it) for: angst, tragedy, major character death, violence or abuse, suicide and self-harm mentions, horror elements or anything not mentioned here that you think might make your readers uncomfortable. Non-/dub-con is NOT ALLOWED, unless it is an important part of the story and not described in detail/used as cheap thrills/glorified. Be mindful and respectful.

Restrictions and Bonuses Click here for more detailed answers to user submitted questions. It will be updated if any more questions roll in, so keep it bookmarked!

OBLIGATORY restriction: we know what you thought of when you saw the graphic. For this month, your submissions cannot depict anyone drowning/almost drowning. If it’s absolutely necessary, the fact that someone drowned/had a near drowning experience can be mentioned in the passing, but we as the audience cannot be treated to actual description of the act. Obligatory restriction means if your story has someone drowning it will be disqualified.

DISQUALIFICATION means your story will still be posted (unless it breaks our general contest rules) but will not be eligible to go into voting and win.

Bonus 1: 3,000. Write your story up to a perfectly round 3,000 words. For clarity and fairness’ sake, please use the free Google Docs to count the words. If for any reason you are unable or unwilling to use Google Docs, but want to make sure your story is of the precise length required for the bonus, message the Mods (here or on Discord) for help.

Bonus 2: But what do they look like though? Include at least five physical descriptors for Elsa and/or five physical descriptors for Anna. These descriptors are counted per paragraph, i.e. if there are two or more descriptors in one paragraph they will be counted as one (this is to avoid a situation where you just drop them all in one sentence.) This bonus is worth up to 2 points, depending on whether you do five descriptors for one or both of the girls. Make sure to read the FAQ for more info.

These are not obligatory restrictions, however following them will be rewarded with an additional point (or two) in the favorites column for each bonus. In other words, stories that don’t include any of the restrictions will start off with 0 base favorite votes, those that do - with 1, 2 or 3.

Please write down where and how you used the bonuses at the beginning of the submission to make sure the mods can verify your points (the note will be removed before posting.) If you’re not sure if your story meets the requirements for the bonuses, you are free to contact us to check.

Read the contest rules before participating. We’ll be accepting submissions through the submit button on our blog starting today till Midnight (on Baker Island, GMT-12) of October 30th. Please remember to submit anonymously to make sure the voting is impartial!

If you have any questions, read the month’s FAQ, send us an ask or join us on discord.

Happy writing!

[brookstonalmanac]

Events 3.16 (after 1970)

1977 – Assassination of Kamal Jumblatt, the main leader of the anti-government forces in the Lebanese Civil War. 1978 – Former Italian Prime Minister Aldo Moro is kidnapped; he is later murdered by his captors. 1978 – A Balkan Bulgarian Airlines Tupolev Tu-134 crashes near Gabare, Bulgaria, killing 73. 1978 – Supertanker Amoco Cadiz splits in two after running aground on the Portsall Rocks, three miles off the coast of Brittany, resulting in the largest oil spill in history at that time. 1979 – Sino-Vietnamese War: The People's Liberation Army crosses the border back into China, ending the war. 1984 – William Buckley, the CIA station chief in Lebanon, is kidnapped by Hezbollah; he later dies in captivity. 1985 – Associated Press newsman Terry Anderson is taken hostage in Beirut; he is not released until December 1991. 1988 – Iran–Contra affair: Lieutenant Colonel Oliver North and Vice Admiral John Poindexter are indicted on charges of conspiracy to defraud the United States. 1988 – Halabja chemical attack: The Kurdish town of Halabja in Iraq is attacked with a mix of poison gas and nerve agents on the orders of Saddam Hussein, killing 5,000 people and injuring about 10,000 people. 1988 – The Troubles: Ulster loyalist militant Michael Stone attacks a Provisional IRA funeral in Belfast with pistols and grenades. Three persons, one of them a member of PIRA are killed, and more than 60 others are wounded. 1995 – Mississippi formally ratifies the Thirteenth Amendment to the United States Constitution, becoming the last state to approve the abolition of slavery. The Thirteenth Amendment was officially ratified in 1865. 2001 – A series of bomb blasts in the city of Shijiazhuang, China kill 108 people and injure 38 others, the biggest mass murder in China in decades. 2003 – American activist Rachel Corrie is killed in Rafah by being run over by an Israel Defense Forces bulldozer while trying to obstruct the demolition of a home. 2005 – Israel officially hands over Jericho to Palestinian control. 2010 – The Kasubi Tombs, Uganda's only cultural World Heritage Site, are destroyed in a fire. 2012 – Former Indian cricketer Sachin Tendulkar becomes the first batter in history to score 100 centuries in international cricket. 2014 – Crimea votes in a controversial referendum to secede from Ukraine to join Russia. 2016 – A bomb detonates in a bus carrying government employees in Peshawar, Pakistan, killing 15 and injuring at least 30. 2016 – Two suicide bombers detonate their explosives at a mosque during morning prayer on the outskirts of Maiduguri, Nigeria, killing 24 and injuring 18. 2020 – The Dow Jones Industrial Average falls by 2,997.10, the single largest point drop in history and the second-largest percentage drop ever at 12.93%, an even greater crash than Black Monday (1929). This follows the U.S. Federal Reserve announcing that it will cut its target interest rate to 0–0.25%. 2021 – Atlanta spa shootings: Eight people are killed and one is injured in a trio of shootings at spas in and near Atlanta, Georgia, U.S. A suspect is arrested the same day. 2022 – A 7.4-magnitude earthquake occurs off the coast of Fukushima, Japan, killing 4 people and injuring 225.

[pacifymebby]

I agree that Van and Sam are very different (Bondy too?) But what do you think the difference is? I can't put my finger on it

Nah so I don't know at all, because I don't know them. So all these are whimsical assumptions which mean nothing, and I'm also not saying them like anyone is worse than anyone else

Bondy genuinely just seems a bit more like, "educated" don't wanna sound like a snob because I don't mean it like that at all, I just mean he obviously has read some stuff, is into politics, his music taste is way more varied etc. I think he went and did music at college before he dropped out right. Also he's older so would always just have seen a bit more or done a bit more idk. I'm very much just making presumptions here anyway because I have no idea

With Sam, I think like, from his songs he sounds like he was mostly raised by women as the main "parent" figures in his life which idk, from my experience that has a big effect on young lads. My dad was raised like that, my other best friends was raised like that too with like, mothers, grandmothers and those women's friends being the main parents in their lives. Whereas Van openly says he's his fathers boy or whatever, like his dads the one he looks up to most and that. And honestly, lovely that he has that kind of relationship with his father. I would just say from my own life and people I know, working class boys raised by working class men tend to turn out slightly less, thoughtful/empathetic or whatever. Like they have more of the "I have something to prove" attitude or like, this need to fulfil and display their masculinity which can often involve sneering at or joking about being sensitive, turning your nose up at school and books blah blah

Van's songs are always routed in having a laugh with your mates, winding up girls, shagging girls and like, getting advice from your dad, whereas sams songs are "worried about my mum, just wanna help my mum" "growing up with nothing with lots of other people who are struggling too" "I don't even have it as bad as other people so I shouldn't be lecturing people on how bad some people have it" "fuckthetories" and "why isn't anyone talking about the suicide rate of young lads from the north" like, he writes about more sensitive stuff, seems to think more deeply about stuff, seems to pay way way way more attention to the world around him and think a lot beyond his own little life and circle of friends.

Again am making massive judgements with 0 basis other than my own life which has nothing to do with their lives lol so I don't know at all.

You know how Van used to talk about how there was always that one kid in school that was always "upset about something" or like easy to wind up or whatever, and that like he used to enjoy teasing or winding them up (and he never said this like "I used to bully kids" and am sure he didn't or didn't think he did u know, in fact to be honest I reckon I know exactly what kind kid he was cause I reckon I went to school with a few lads like him and they were fine and nice but they definitely did push their jokes too far and upset me sometimes and I didn't think they were my friends)

Well anyway, to put the difference between them simply, I reckon Sam has the potential to have been the kinda kid Van was talking about. Because those kids weren't necessarily miserable or always upset about something they were just u know, sensitive or more emotional. I know Sam sings about arming himself with a grin and always being the joker and stuff but like, he also talks about being out of place with the lads lads sometimes u know. Van strikes me as a lads lad whereas Sam and Bondy both come off as being more mature/confident in their masculinity enough to know that emotions need talking about not joking about sometimes and like, feminism is important

also maybe its pure just Vans an early august Leo and Bondy is like, last day of Leo, basically a Virgo Leo. So he's cusping on Earth sign, and Sam is a Taurus so a big Earth sign too. maybe its purely just that, maybe they're just more earthy and rooted and Van is chaotic and burning personality u know?

[neoatlantiscodex]

Solar

Let's take a best-case scenario for solar power, near the equator. This maximizes the amount of power we can get from solar, but we still have one crucial problem. Inconsistent producion. It peaks at noon, and drops to 0 at sundown. The actual production graphs is probably a bell curve for the day time. So, with this, we need a method of storing the power. We do not have a good solution, at all. We don't have enough Lithium and Cobalt, in the world, (by current estimates), to replace our cars, nevermind everything else. Doing this for a large grid is suicidal. BUT, doing for a small grid makes sense. Solar power has the crucial advantage of being grid-independent. This means that a small grid, say an individual house, or a small community, could easily produce it's own power. This also has the advantage of creating a stable power supply in regions that have intermittent service. Say, if you are living in California. Or in an out-of-the-way reservation.

People assume that solar power is more sustainable, because it doesn't use fuel. This is a pretty reasonable assumption to make. The problem is that it has not proven to be true. Even in places like California, household solar power and storage systems are only competition with grid power rates with government subsidies and mandatory grid buybacks. Most of the houses have it's own solar and power storage. It's also equiped with a smart switch to decide when it's connected to a grid. So, the battery charges over the day until it's full, which is usually around or just after midday, and then the smart switch hooks it up to to the outside grid, and they get buyback. So, California only just get solar power throughout the day, but only through part of the day, completely variable based upon independent household usage. Just try to imagine balancing this over the day without any reasonable storage. As it is, California has not managed to get it to work. If they can't, then who can?

If we move away from the equator, we have another problem, Winter. Winter daytime production can be a fraction of what summer daytime can produce. Not even just because of reduced hours, but reduced sunlight during those hours.

Another, less well known problem with solar, is slower generation. It takes a lot more material and power making solar pannels than they produce, at least compared with every other form of power generation. Except maybe wind. This means that while we could scale up our solar production to meet our needs, we cannot do it on a reasonble time scale. It's impossible to replace our power generation with solar without going through something else.

There is another serious, crippling issue with solar power. Not so much with the solar power itself, (other than being vulnerable to environmental damage), we are clearing space for it. This is mind bogglingly stupid. We could instead be putting them on flat roofs, if you leave a space this could even shade the roof, dramatically reducing the heat exposure of the building while increasing the efficiency of solar pannels. We could also put them on the side of those incredibly ugly modernist skyscrapers. A huge amount of the glass on skycrapers isn't even useful. It's just there to make it look ugly and uniform. We can even put them over farm fields. This is called agrivoltaics. It turns out that most plants evolved to grow in partial shade. Too much sunshine, and they risk burning, and to avoid it they go through a LOT more water. By adding partial shade you dramatically reduce water usage and increase yield. Depending on the plant, of course. If we really wanted to be special, we could put agrivoltaics on the rooftop of our medium density mixed usage neighbourhoods. Green roofs dramatically improve the insulation.

Note: I'll deal with grid storage and solar thermal in a later post.

[morn1e]

So,, how do you feel about others drawing their ocs with yours?

moodboard 4 how i feel like when ppl draw my guys w their guys.peace&love across the world suicide rates drop 2 0%.u r my hero 4 taking the time!!!!!!!!!!!!!!!!

[ao3feed-hashimada]

ao3feed-hashimada

read it on the AO3 at https://ift.tt/9QzaXgn

by Anonymous

Hey, oh hey, do you remember the first drop of ice? When I told you not to go? Hey, oh hey, do you remember laughing to see the rain The mist of cars racing on black pitch like little clouds And we scream and laugh, the water splashing our favorite pants

Hashirama is a young kid who meets this mean boy Madara who makes fun of their hair and outfit for being old-fashioned and he thinks Hashirama's an easy mark! When Hashirama figures out their weakness, someone's always falling into the river at the end of the day, then they'll be forced to hang up and dry their clothes if they don't want their mother or father to get mad.

short comic prologue + rest is mostly text

Words: 0, Chapters: 1/?, Language: English

Series: Part 7 of To The Pyre - explicit series

Fandoms: Naruto

Rating: Explicit

Warnings: Graphic Depictions Of Violence

Categories: M/M

Characters: Senju Hashirama, Uchiha Madara, Uchiha Izuna, Senju Kawarama, Uchiha Tajima, Senju Hashirama's mother, Background & Cameo Characters

Relationships: Senju Hashirama/Uchiha Madara

Additional Tags: Surreal, Brainwashing, Cognitive Dissonance, feels good until it's not, Ghosts, Infinite Tsukuyomi, Fluff, Cute Kids, Domestic Bliss, Slow Burn, Memory Loss, Time Skips, First Kiss, Alternate Universe - Canon Divergence, Dreams, Attempted Murder, Childhood Friends, Teenagers, Adulthood, Enemies to Friends to Lovers, Lovers to Enemies to Deathmatch, Everyone is Dead, Fluff and Humor, Platonic Cuddling, Friendship is Magic and Arson, Hallucinations, Revenge, Digital Art, Fan Comics, Psychological Warfare, Night Terrors, Suspense, Blood and Torture, kids having fun, Secret Hideouts, who will confess first?!, Loss of Innocence, No Shinobi, none left, Suicidal Thoughts

read it on the AO3 at https://ift.tt/9QzaXgn

[freddie-data-analysis]

Assignment 16: Running a k-means Cluster Analysis

For this assignment, I used Python to conduct a k-means cluster analysis with the variables from the Gapminder dataset. I used urbanrate, a measure of the proportion of a country's population that lives in urban areas, as my validation variable, and I used all other variables from the dataset as my cluster variables. For data management, I made a new dataset called data_clean which dropped all missing values from my dataset. Then all cluster variables were standardized so that they had a mean of zero and standard deviation of 1. They were then split into two datasets, 70% into the training set and 30% into the test set.

My code:

Selecting k:

This graph shows a noticeable elbow at 2 clusters, indicating that 2 clusters is likely the best for this dataset, as with each additional cluster after 2, the average distance doesn't look to decrease as significantly.

Scatterplot with 3 clusters:

We see in this plot that the blue and purple clusters seems to have a variance between them but also some variance within the cluster. The yellow cluster only has one point, an outlier, however would likely be better fit in the purple cluster.

The above output confirms for us that the purple cluster contains 24 datapoints, the blue has 14 datapoints, and the yellow has just 1.

Above shows the mean values for each variable by cluster. We see that the purple cluster contains the lowest values of income per person, breast cancer per 100,000, female employment rate, internet use rate, life expectancy, oil consumption per person, electricity consumption per person, and employment rate. It has the highest values for HIV rate. The blue cluster has the lowest values for alcohol consumption, CO2 emissions, HIV rate, polity score, and suicides per 100,00. It has the highest values for income perperson, armed forces rate, life expectancy, oil consumption per person, and employment rate. The yellow cluster has the lowest values for armed forces rate and the highest value for alcohol consumption, breast cancer per 100,000, CO2 emissions, female employment rate, internet use rate, polity score, electricity consumption per person, and suicide rate per 100,000.

The ANOVA and post- hoc analysis, shows us that the difference between means of urbanrate for clusters 0 and 1, the purple and blue clusters, is significant with a p-value of 0.0029. The purple cluster had a mean urbanrate of 62.91 and a standard deviation of 15.57. The blue cluster had a mean urbanrate of 79.20 and a standard deviation of 10.24.

[quoteablebooks]

Genre: Young Adult, Paranormal Romance, Urban Fantasy

Rating: 0 out of 5

Content Warning: Ableism, Child death, Death, Death of parent, Alcoholism, Cursing, Bullying, Suicidal thoughts, Torture

Summary:

The first book in Alyson Noël's extraordinary new Immortals series. Enter an enchanting new world, where true love never dies...

After a horrible accident claims the lives of her family, sixteen-year-old Ever Bloom can see people's auras, hear their thoughts, and know someone's entire life story by touching them. Going out of her way to avoid human contact to suppress her abilities, she has been branded a freak at her new high school—but everything changes when she meets Damen Auguste.

Damen is gorgeous, exotic and wealthy. He's the only one who can silence the noise and random energy in her head—wielding a magic so intense, it's as though he can peer straight into her soul. As Ever is drawn deeper into his enticing world of secrets and mystery, she's left with more questions than answers. And she has no idea just who he really is—or what he is. The only thing she knows to be true is that she's falling deeply and helplessly in love with him.

*Opinions*

TL;DR - The villain should have killed both main characters and put us all out of our misery

Hello friends and enemies, we are gathered here today to talk about a book that I did not enjoy and probably has the most annoying characters I have read to date. The plot was nonsense, the characters were annoying, and somehow a book that was just over 300 pages was about 300 pages too long. The fact that there are six books in this series is truly baffling to me as I don’t understand how anyone cared about these characters enough to get through this novel let alone five more. My completeist brain is truly thrilled that I cannot easily find the rest to read for free anywhere so I can drop this book in a free little library and never have to think about it again.

I do want to provide one positive before I get into everything else I didn’t like about this novel. I appreciated the portrayal of Sabine and how kind she is to Ever, who is consistently a horrible person to her and everyone else. Sabine lost her twin brother, dropped everything, and moved so that she could take care of her niece who is now her ward. Sabine is nothing but patient and kind to Ever, a teenager who is constantly not talking to her, being mean, or getting expelled from school. Sabine is the true hero of this book for not smacking Ever into next week on a couple of occasions. I too would be working all the time if I had to live with Ever constantly being cagy, lying, and straight up ignoring her. Ava is also kind to Ever when she has no reason to be, so the adult women in this novel, for the small amount are present, have the patience of saints.

Ever is one of the most annoying main characters that I have ever had the displeasure of being in the head of. She is so self-centered and not in an “I am a teenager who is going through some horrible and confusing things” but in an “I care about no one and nothing but myself and I never let anyone complete a thought because I don’t want to hear it, even though I am psychic and read minds.” She is a horrible friend, an extremely clingy and toxic girlfriend, and almost too dumb to live. Ever doesn’t figure out anything that is happening on her own, she doesn’t do a single thing herself throughout this novel except breaking into her boyfriend’s house because…she’s mad he left after spending almost two whole days with her. She says that she doesn’t want her ghost little sister to cross over, yet is absolutely horrible to her every time she is present, is a shitty friend, and is just an overall bad person. When people do try to explain things to Ever, she gets pissed off and tells them to go away or says horrible things, but then is confused why people aren’t talking or lying to her.

The sad thing is that this could have been a powerful story about grief and dealing with the loss of family, but Ever never thinks about her parents except how she can’t see them, and only thinks about what her sister lost when it is convenient for her. She states constantly that her psychic powers are because she is being punished for the accident, but once Daimen is on the scene, she barely thinks about the family she lost or the life she no longer has. Instead of making Ever a complex character who is managing huge life changes and loss, she seems like a self-absorbed narcissist who is only upset that her parents are dead because the accident changed her from the most popular cheerleader into a “freak”. It is all so shallow that I have no sympathy for her. Then her two-day descent into alcoholism? I can’t even get into that.

Daimen is the king of gaslighting and every time he did something that was supposed to be romantic I rolled my eyes so hard I almost strained something. Daimen, a six hundred-year-old man, is obsessed with a seventeen-year-old. Usually, these types of age gaps don’t bother me in high fantasy novels, but in an urban fantasy in which Daimen is constantly dropping hints that he is so much more knowledgeable and sophisticated than Ever, it just felt weird. Sometimes I heard the start of the SVU theme song. He is also constantly using his type of magic in front of her and then denying that he is, making her feel as if she is losing her mind. A mind that he can read at times and knows how distressed he is making her, yet instead of trying to find a way to explain he continues to play mind games. He apparently loves her so much that he has searched for her in multiple lifetimes, but he also somehow never figures out that it is Darina who continually kills her. Even though Darina shows up every time she dies they get back together. This is a man who supposedly discovered the truth about immortality and he can’t see the two plus two make four? However, I completely lost it when Ever was upset and crying and his response, get on top of her and start trying to sleep with her. I hate him.

Let’s also take a little detour and talk about the fact that Darina made a point of telling Ever that she died a virgin in every lifetime she has had since meeting Daimen. Now, I have no intention of reading on in this series unless someone pays me money, but I can predict that when Ever and Daimen actually do sleep together there is going to be a whole thing of being her one and only partner, her first and only love, she is happy she saved her soul for him, etc. and I would have to throw up before continuing.

Darina is the most cardboard-cut-out villain I might have ever read. She is the stunningly beautiful woman that Daimen is with that gives off creepy vibes. That is the only reason why Ever hates her to begin with. Then she gets close to Haven for the sole purpose of killing her to upset Ever and make her feel like she is alone so she’ll just die when Darina finally decides to attack her all because she is in love with Daimen for some reason. Darina, who proves at the end of the book that she could have killed Ever at any moment, but just decides to play games because…plot? Then, after 600 years, she is easily confused and killed without much of a fight. While I wanted her to succeed because I hated Daimen and Ever and wanted them to stop existing, she didn’t have enough of a personality to really care about her one way or another.

Ever’s “friends”, and I use the word loosely, are stereotypes and also kind of the worst people. Miles is the less offensive of the two, he is just obsessed with his boyfriends to the point that he ignores his friends, but he at least says something when both Ever and Haven are being the worst. Haven, however, fucking sucks. Everything had to be about her and she called ‘dibs’ on a man and gets pissed when he isn’t interested in her but her friend. Remember, these people are Juniors in high school, and they are calling dibs on a real-life person. The whole bit where she joins anonymous groups and lies about having addictions or other problems because she is ignored at home is just wrong on so many levels. Then, Haven goes missing for days after another woman is murdered, and Ever is so self-obsessed she doesn’t even care, but is so extremely happy when she reappears alive. This whole town should have just been crushed by a meteor.

A major part of the plot, in which there almost isn’t any mind you, is that Ever feels as if she needs to punish herself because she believes that it is her fault that her family got into a car accident that killed them. There is this whole thread about how she has to forgive herself for the accident and thinking that she caused it, with multiple other characters explicitly saying this to her. In the final scene of the novel Daimen states that love heals and she finally forgave herself so the scar on her forehead is gone. Except, Ever never forgave herself because she found out that Darina caused the accident for the sole purpose of killing Ever. It was all just so frustrating. Ever doesn’t figure out anything on her own, Darina tells her in her evil monologue. Yet we are supposed to get this whole takeaway about love and forgiveness after Ever turns Darina to dust by accident. When Daimen started explaining that Ever hit Darina in her weakest chakra and that’s why she died I would have put down the book if I wasn’t so close to the end. Please, give me a fucking break. There had not been a single mention of chakras before this scene, not one. Also, the only other plot point besides Ever hating herself was figuring out why Daimen was acting so weird, that’s it.

I could go on for another five pages of everything I hated about this, but it would just make me angry. I would give this zero stars if that was a possibility. Save yourself the time and money and read literally anything else.

[poudelbibek]

Data Analysis tools Assignment 2 : Chi-square tests

Reminder (research problem and defined variables)

Study topic:

Primary topic

Suicide rate Vs. Polityscore

Variables of interest:

Flow sequence for a python program

Steps:

Read the csv file.

Convert the datas of interest to numeric

Select only the readable data (exclude null or NaN)

Convert the suicideper100th into categorical variable into 2 categories and name the new variable as “suicide_severity” 0 – not severe 1 – severe

Convert the polityscore categorical variable into only 4 categories and name new variable as “broadpolityscore. -10 to -6: 0- Very poor -5 to 0: 1- Poor 1 to 5: 2- Okay 6 to 10: 3- Good

Run Chi-square test.

Run post- hoc test.

Python program

#Importing libraries

import numpy

import pandas

import statsmodels.formula.api as smf

import statsmodels.stats.multicomp as multi

# bug fix for display formats to avoid run time errors

pandas.set_option('display.float_format', lambda x:'%.2f'%x)

#Reading the data csv file

data = pandas.read_csv('gapminder.csv')

### Suiciderate is response variable whereas polityscore is explanatory variable

#setting variables of interest to numeric and creating datasets for response and explanatory variables

data['suicideper100th'] = pandas.to_numeric(data['suicideper100th'],errors = 'coerce')

data['polityscore'] = pandas.to_numeric(data['polityscore'],errors = 'coerce')

#Subset for data of interest

dataofinterest = data[['suicideper100th', 'polityscore']].dropna()

dataofinterest

#Categorizing political score data into 4 categories and giving it a name of “broadpolityscore”

#-10 to -5: Very poor à 0

# -5 to 0: Poor à 1

# 0 to 5: Okay à 2

#6 to 10: Good à 3

di = dataofinterest

#di["bins"] = pandas.cut(di["suicideper100th"], bins = 2)

bins = [-10,-5,0,5,10]

group_names = ['Very poor', 'Poor', 'Okay', 'Good']

categories = pandas.cut(dataofinterest['polityscore'], bins)

categories.value_counts(sort=False, dropna=False)

#Creating new data set for bincenters

di['bins']= pandas.cut(dataofinterest['polityscore'], bins)

di["bin_centers"] = di["bins"].apply(lambda x: x.mid)

di['bin_centers'] = pandas.to_numeric(di['bin_centers'],errors = 'coerce')

#Assigning broadpolityscore according to bin_center value

tot = len(di['bins'])

print(tot)

di.reset_index(drop=True, inplace=True)

di = di.assign(broadpolityscore=" ")

for j in range(tot):

    #print(di['bin_centers'][j])

    if di['bin_centers'][j]<-5:

        score = 0

        di['broadpolityscore'][j] = score

    elif -5<di['bin_centers'][j]<0:

        score = 1

        di['broadpolityscore'][j] = score

    elif 0<di['bin_centers'][j]<5:

        score = 2

        di['broadpolityscore'][j] = score

    else:

        score = 3

        di['broadpolityscore'][j] = score

#Creating categorical response variable “suicide_severity”

#di["bins"] = pandas.cut(di["suicideper100th"], bins = 2)

di['bins'] = pandas.qcut(di['suicideper100th'], q=2)

di["bin_centers"] = di["bins"].apply(lambda x: x.mid)

#Assign suicide severity score

di.reset_index(drop=True, inplace=True)

print(len(di['bin_centers']))

di = di.assign(suicide_severity=" ")

for j in range(len(di['suicide_severity'])):

    #print(di['bin_centers'][j])

    if di['bin_centers'][j]<5:

        bin_centers_cat = 0

        di['suicide_severity'][j] = bin_centers_cat

    else:

        bin_centers_cat = 1

        di['suicide_severity'][j] = bin_centers_cat

Results

# contingency table of observed counts

ct1=pandas.crosstab(di['suicide_severity'], di['broadpolityscore'])

print (ct1)

broadpolityscore   0   1   2   3

suicide_severity               

0                 13  15   8  44

1                 10  12  11  46

# column percentages

colsum=ct1.sum(axis=0)

colpct=ct1/colsum

print(colpct)

broadpolityscore         0         1         2         3

suicide_severity                                        

0                 0.565217  0.555556  0.421053  0.488889

1                 0.434783  0.444444  0.578947  0.511111

# chi-square

print ('chi-square value, p value, expected counts')

cs1= scipy.stats.chi2_contingency(ct1)

print (cs1)

chi-square value, p value, expected counts

(1.2365259392289194, 0.744257866100584, 3, array([[11.57232704, 11.42767296],

       [13.58490566, 13.41509434],

       [ 9.55974843,  9.44025157],

       [45.28301887, 44.71698113]]))

# graph percent with suicide severity Vs broad political score

seaborn.factorplot(x="broadpolityscore", y="suicide_severity", data=di, kind="bar", ci=None)

plt.xlabel('Broad political score')

plt.ylabel('Suicide severity')

#Post-hoc Chi square

recode2 = {0:0, 1:1}

di['comp0v1']= di['broadpolityscore'].map(recode2)

# contingency table of observed counts

ct2=pandas.crosstab(di['suicide_severity'], di['comp0v1'])

print (ct2)

# column percentages

colsum=ct2.sum(axis=0)

colpct=ct2/colsum

print(colpct)

print ('chi-square value, p value, expected counts')

cs2= scipy.stats.chi2_contingency(ct2)

print (cs2)  

Output:

comp0v1           0.0  1.0

suicide_severity         

0                  13   15

1                  10   12

comp0v1                0.0       1.0

suicide_severity                    

0                 0.565217  0.555556

1                 0.434783  0.444444

chi-square value, p value, expected counts

(0.04718510153292781, 0.8280358709711078, 1, array([[12.88, 15.12],

       [10.12, 11.88]]))

recode3 = {0:0, 2:2}

di['comp0v2']= di['broadpolityscore'].map(recode3)

# contingency table of observed counts

ct3=pandas.crosstab(di['suicide_severity'], di['comp0v2'])

print (ct3)

# column percentages

colsum=ct3.sum(axis=0)

colpct=ct3/colsum

print(colpct)

print ('chi-square value, p value, expected counts')

cs3= scipy.stats.chi2_contingency(ct3)

print (cs3)

Output:

comp0v2           0.0  2.0

suicide_severity         

0                  13    8

1                  10   11

comp0v2                0.0       2.0

suicide_severity                   

0                 0.565217  0.421053

1                 0.434783  0.578947

chi-square value, p value, expected counts

(0.38443935926773454, 0.5352368901951887, 1, array([[11.5,  9.5],

       [11.5,  9.5]]))

recode4 = {0:0, 3:3}

di['comp0v3']= di['broadpolityscore'].map(recode4)

# contingency table of observed counts

ct4=pandas.crosstab(di['suicide_severity'], di['comp0v3'])

print (ct4)

# column percentages

colsum=ct4.sum(axis=0)

colpct=ct4/colsum

print(colpct)

print ('chi-square value, p value, expected counts')

cs4= scipy.stats.chi2_contingency(ct4)

print (cs4)

Output:

comp0v3           0.0  3.0

suicide_severity         

0                  13   44

1                  10   46

comp0v3                0.0       3.0

suicide_severity                   

0                 0.565217  0.488889

1                 0.434783  0.511111

chi-square value, p value, expected counts

(0.17618839520298316, 0.6746695456425893, 1, array([[11.60176991, 45.39823009],

       [11.39823009, 44.60176991]]))

recode5 = {1:1, 2:2}

di['comp1v2']= di['broadpolityscore'].map(recode5)

# contingency table of observed counts

ct5=pandas.crosstab(di['suicide_severity'], di['comp1v2'])

print (ct5)

# column percentages

colsum=ct5.sum(axis=0)

colpct=ct5/colsum

print(colpct)

print ('chi-square value, p value, expected counts')

cs5= scipy.stats.chi2_contingency(ct5)

print (cs5)

Output:

comp1v2           1.0  2.0

suicide_severity         

0                  15    8

1                  12   11

comp1v2                1.0       2.0

suicide_severity                   

0                 0.555556  0.421053

1                 0.444444  0.578947

chi-square value, p value, expected counts

(0.3586744639376218, 0.5492433274240123, 1, array([[13.5,  9.5],

       [13.5,  9.5]]))

recode6 = {1:1, 3:3}

di['comp1v3']= di['broadpolityscore'].map(recode6)

# contingency table of observed counts

ct6=pandas.crosstab(di['suicide_severity'], di['comp1v3'])

print (ct6)

# column percentages

colsum=ct6.sum(axis=0)

colpct=ct6/colsum

print(colpct)

print ('chi-square value, p value, expected counts')

cs6= scipy.stats.chi2_contingency(ct6)

print (cs6)

Output:

comp1v3           1.0  3.0

suicide_severity         

0                  15   44

1                  12   46

comp1v3                1.0       3.0

suicide_severity                   

0                 0.555556  0.488889

1                 0.444444  0.511111

chi-square value, p value, expected counts

(0.15072326125073082, 0.697845139978909, 1, array([[13.61538462, 45.38461538],

       [13.38461538, 44.61538462]]))

recode7 = {2:2, 3:3}

di['comp2v3']= di['broadpolityscore'].map(recode7)

# contingency table of observed counts

ct7=pandas.crosstab(di['suicide_severity'], di['comp2v3'])

print (ct7)

# column percentages

colsum=ct7.sum(axis=0)

colpct=ct7/colsum

print(colpct)

print ('chi-square value, p value, expected counts')

cs7= scipy.stats.chi2_contingency(ct7)

print (cs7)

Output:

comp2v3           2.0  3.0

suicide_severity         

0                   8   44

1                  11   46

comp2v3                2.0       3.0

suicide_severity                   

0                 0.421053  0.488889

1                 0.578947  0.511111

chi-square value, p value, expected counts

(0.08133967256197186, 0.7754900707886289, 1, array([[ 9.06422018, 42.93577982],

       [ 9.93577982, 47.06422018]]))

​

Discussion from Chi-square test models:

When examining the association between political score with suicide rate, Chi-square showed that suicide rate does not depend on political score (p=0.744 > significance level). The degree of freedom was 3 (explanatory variable (4) – 1). Looking at the bar chart of suicide severity vs broad political score, the means (suicide_severity) of each political score level [polityscore, 0 – 43.4%; polityscore, 1 – 44.4% polityscore, 2 – 57.9% ; polityscore, 3 – 51.1% ]  look really close with each other further confirming not much of an association between political score and the suicide severity.

Post hoc Chi-square revealed the same finding as Chi-square -> that the dependence of political score [divided into 4 categories as a categorical explanatory variable] and suicide severity [categorical response variable] were not statistically significant in each case (p value in each case was higher than the significance level). Hence accepting the null hypothesis will be reasonable here. All the comparisons were statistically similar.

[gta-vice-city-cheats-swit57]

gta vice city cheats switch work IQ2I&

💾 ►►► DOWNLOAD FILE 🔥🔥🔥🔥🔥 All the cheats you need for GTA 3, Vice City and San Andreas on the Nintendo Switch, arriving via GTA: The Trilogy - The Definitive Edition. These GTA VC cheat codes will help you have more fun playing the remastered game on PS4, PS5, Nintendo Switch, Xbox One, Series X/S or PC. All the cheats for GTA Vice City – Nintendo Switch ; Fast Mode, X, UP, RIGHT, DOWN, ZL, L, Y · Increases movement speed of all characters. ; Suicide, RIGHT, ZL. Character Modification Cheats · Maximum Health: R, ZR, L, A, Left, Down, Right, Up, Left, Down, Right, Up · Maximum Armor: R, ZR, L, B, Left, Down. All cheats for GTA Vice City (Nintendo Switch) ; Increases the game speed (time lapse). X → Up → Right → Down → ZL → L → Y ; Slows down the. At this point we show you all the cheats for the Nintendo Switch with their key combinations and effects. In GTA Vice City you can enter cheats and in the Switch version you just have to enter the corresponding key combinations from the lists below in the current game. This works differently than with the GTA 3 cheats of the Switch version while you are in the pause menu. A message at the top left of the screen confirms the successful entry of the cheat. Important note: As your crime rating drops by points with every activated cheat, you should save your game status before using cheats. If you want to save your game with activated cheats, you should definitely use a separate memory slot. How can you deactivate cheats? In GTA Vice City you can usually deactivate cheats by entering the appropriate key combination again. Incidentally, there is also a brand new cheat in the game, Big Head Mode, which you can activate using the famous Konami code. You can find this at the end of the table. Keyboard shortcut. Full life energy if you sit in a car, it will be repaired. Full armor. Weapon set 1 brass knuckles, baseball bat. Molotovs, pistol, shotgun, MP, assault rifle, sniper, flamethrower. Weapon set 2 katana, grenades, revolver, shotgun, uzi, assault rifle, sniper, rocket launcher. Weapon set 3 chainsaw, grenades, revolver, shotgun, MP, assault rifle, sniper, minigun. Increase wanted level by 2 stars. Reduce wanted level to 0 stars. Play as Candy Suxxx. Play as Hilary King. Play as Ken Rosenberg. Play as Lance Vance. Play as Dick Love Fist. Play as Jezz Torrent Love Fist. Play as Mercedes Cortez. Play as Phil Cassidy. Play as Ricardo Diaz. Play as Sonny Forelli. Guns are given to all passers-by. Vercetti gang members become bikini girls with M4 assault rifles. Female NPCs are chasing you. Suicide Can be survived 1 time with high life energy. Displays the media level. Tank Rhino spawn. Saber Turbo Muscle Car spawns. Hotring Racer 1 Nascar spawns. Hotring Racer 2 Nascar spawns. Bloodring Banger 1 Destruction spawn. Bloodring Banger 2 Destruction spawn. Hearse spawn. Love Fist stretch limo spawn. Trashmaster garbage truck spawn. Caddy golf kart spawn. Motorists are becoming more aggressive. All the cars on the streets go black. All the cars on the streets turn pink. All traffic lights turn green. All vehicles in the area explode. Vehicles can float low gravity. All vehicles become invisible only driver and tires remain visible. Monster cars changes, among other things, tire size, maximum speed, handling and acceleration of vehicles. Increases the game speed time lapse. Slows down the game speed slow motion. Accelerates the in-game time. Cloudy weather. Gray weather. Foggy weather. Stormy weather. Your character and all passers-by have huge heads. Home About Contact. Top E. Facebook Twitter. Search the web. Our Socials. Build any website. Random Posts. Recent in News. Popular Posts. Passage of Duskwood episodes : all the answers and forks in the dialogues May 30, Elex 2 Find all vaults and codes March 16, Menu Footer Widget.

[hajnalkafilepcoursera]

Week 4 - Creating graphs

The previous weeks I was investigating if the polity score has influence on the life expectancy rate and suicide rate.

This week I used a filter to sort out only those rows, where the life expectancy is below 55 years. To not influence the frequency table, I also sorted out the empty fields.

I created 2 new variables: 1 - Age groups: 48-50, 51-53, 54-55. 2 - Polity groups: originally it goes from -10 to 10, but I wanted to create 4 groups for easier overview. Autocrata: -10 to -6. Slightly autocrata: -5 to 0. Slightly democrata: 1 to 5. Democrata: 6 to 10.

The dataset I used: gapminder.csv

Findings: I have created 2 bar charts. 1: Univariate bar chart according to polity groups. Since this is a categorical variable I had to use the 'describe' function to see the top, frequency, count and unique values. You can see the results and the bar chart below. The 'Slightly Autocrata' group has the most values between the age group of 48-55 years. I would assume that the most values would be coming from the 'Autocrata' group with -6 to -10 polity score, but this is not the case right now.

2. Bivariate bar chart. For this one I used the original polity scores (not the grouped values). So my x-axis is the polity score, the y-axis is the count. This is a bivariate bar chart, the higher points are at -1 and 6-7 polity scores. I also used the describe function for this barchart, which you can see below.

My code:

-- coding: utf-8 --

""" Created on Mon Oct 17 15:12:49 2022

@author: FIH4HTV """

import pandas import numpy import seaborn import matplotlib.pyplot as plt

data = pandas.read_csv('Data_Sets/gapminder.csv', low_memory=False)

data['lifeexpectancy'].dtype

#setting variables to numeric

data['lifeexpectancy'] = pandas.to_numeric(data['lifeexpectancy'], errors="coerce") data['polityscore'] = pandas.to_numeric(data['polityscore'], errors="coerce") data['suicideper100th'] = pandas.to_numeric(data['suicideper100th'], errors="coerce")

#setting filter for data to show me only those data where the expected life is =<55 yrs

sub1 = data[(data['lifeexpectancy'] <= 55)]

#make a copy of your filtered data

sub2 = sub1.copy()

#SETTING MISSING DATA: empty cells, drop not a number values

sub2['lifeexpectancy'] = sub2['lifeexpectancy'].replace(' ', numpy.nan) sub2['polityscore'] = sub2['polityscore'].replace(' ', numpy.nan) sub2['suicideper100th'] = sub2['suicideper100th'].replace(' ', numpy.nan)

#new variables for life expectancy, create less age groups

#age: 48-50 is group1, 51-54: group2, 55-58: group3

recode1 ={48.398: '48-50', 48.397: '48-50', 48.132: '48-50', 48.196: '48-50', 48.673: '48-50', 48.718: '48-50', 49.025: '48-50', 50.411: '48-50', 49.553: '48-50', 50.239: '48-50', 51.093: '51-53', 51.088: '51-53', 51.444: '51-53', 51.219: '51-53', 51.384: '51-53', 51.610: '51-53', 51.879: '51-53', 53.183: '51-53', 52.797: '51-53', 54.097: '54-55', 54.210: '54-55', 54.116: '54-55', 54.675: '54-55', 47.794: '48-50'} sub2['agegroup']=sub2['lifeexpectancy'].map(recode1)

#name your table

print('Counts of Age groups at birth')

#count of variables to show

c1 = sub2['agegroup'].value_counts(sort=False) print(c1)

#show it in percentage

print('Percent of Age groups at birth') p1 = sub2['agegroup'].value_counts(sort=False, normalize=True) print(p1)

#new variables for polity score, create less groups: 1-4

#polity score: from -10 to -6: group1, from -5 to 0: group2, from 1 to 5: group3, from 6 to 10: group:4

recode2 ={-10: 'Autocrata', -9: 'Autocrata', -8: 'Autocrata', -7: 'Autocrata', -6: 'Autocrata', -5: 'Slightly Autocrata', -4: 'Slightly Autocrata', -3: 'Slightly Autocrata', -2: 'Slightly Autocrata', -1: 'Slightly Autocrata', 0: 'Slightly Autocrata', 1: 'Slightly Democrata', 2: 'Slightly Democrata', 3: 'Slightly Democrata', 4: 'Slightly Democrata', 5: 'Slightly Democrata', 6: 'Democrata', 7: 'Democrata', 8: 'Democrata', 9: 'Democrata', 10: 'Democrata'} sub2['politygroup']=sub2['polityscore'].map(recode2)

#count of variables to show

#new variable name! how many cases I have with the new polity group variants

print('Count of Polity group') c3 = sub2['politygroup'].value_counts(sort=False) print(c3)

#show it in percentage

print('Percent of polity group') p3 = sub2['politygroup'].value_counts(sort=False, normalize=True) print(p3)

#univariate barchart for polity group

seaborn.countplot(x="politygroup", data=sub2)

plt.xlabel('Polity groups')

plt.title('Polity groups according to life expectancy rate between 48-55 years at birth')

#standard deviation asnd other descriptive statistics for categorical variables

print('Describe polity groups')

desc1 = sub2['politygroup'].describe()

print(desc1)

#bivariate barchart

seaborn.countplot(x="polityscore", data=sub2) plt.xlabel('Polity score') plt.title('Polity scores according to life expectancy rate between 48-55 years at birth')

print('Describe polity scores') desc2 = sub2['polityscore'].describe() print(desc2)

[butchsophiewalten]

big news!

[masonsbfgaming]

irresistible

[horrormanga]

🍀

nozaki-kun op!

[slaygentford]

I listened to every Beatles album in order so you dont have to and kept this record as I did. no one asked me to do this and honestly idk how I arrived at it it just sounded interesting after I exhausted the platters who I didnt think to record like this. also im at the point of school where you dont get homework anymore and I miss it so I made a report.

these are graded on a curve, that is, the ratings of each album are calculated in relation to the other albums. prior to this endeavor I had only heard the big beatles songs like in movies and on Wii rockband.

please please me: 3/5. highlight: twist and shout (sorry). lowlight: baby its you. thready ass vocals. leave it to people with talent with the beatles: 3/5. aesthetically identical to prev. highlight: you really got a hold on me. lowlight: please mr postman. why the fuck would you cover this. youre signing up to fail a hard day's night: 2/5. highlight: things we said today. lowlight: sadly, a hard day's night beatles for sale: im gonna keep it real. this sounds identical to albums 1-3 to me and I feel exactly no emotion about it at all. largely inoffensive. 2/5 help!: 3.3/5. highlight: help! killer bass. lowlight: the riff in I need you fills me with a burning, indescribable rage rubber soul: here we begin to experience the epic highs and lows of The Beatles discography. high highs: Norwegian wood and girl. low lows: literally everything else. 2/5 revolver: 3.5/5. these bitches finally woke up! highlight: I'm only sleeping, for no one, Eleanor Rigby is worth the hype, I want to tell you, tomorrow never knows. lowlight: dr robert -- flop attempt at satire. also whatever that one guy was doing to that poor sitar sgt pepper's: I came to a rude awakening when I realized that the wall would not exist without sgt pepper's. humbling. that being said, 0/5. I hated every single second of this. magical mystery tour: epic high following last album's epic low. 5/5. strawberry fields has a BASS DROP??!?!? no skips. I love this album. its such a time capsule as well of like one of the weirdest years in history. i can listen to this album and experience how my parents felt at 16. the callback to she loves you on the last track. I get it the white album: I dont get it. this album tested me like nothing else. I began to flag. I began to question the honor of my quest. I almost shut it off after nearly every song. but let me say: the highs are sweeping. SWEEPING. happiness is a warm gun. blackbird, Helter Skelter, while my guitar gently weeps, back in the ussr, revolution (which is satire which I just realized)... however, the lows are LOW. glass onion is bad; Julia is actually unlistenable (I broke and skipped it); wild honey pie is like getting a transorbital lobotomy; birthday has undone years of my therapist's work vis a vis suicidal ideation. Im so baffled by this I almost want to exclude it entirely. instead I calculated its good song to bad song ratio which landed the album as a whole at a solid D+ (69%)! but that seems like its ignoring the good songs which for any other band even ONE of those would be the song of their career. emotionally the experience was not unlike a bipolar mixed episode. 1/5 yellow submarine: this one was a movie soundtrack. something it has going for it is that it isn't the white album. 4/5 abbey road: yeah. 10/5. I cant even be flippant about this. you live a whole lifetime listening to this one. fine. let it be: set myself up to FAIL with this one. my dad was about to turn 18 the year this came out, which I only bring up because this is the only one of my dads beatles albums I kept. so of course I listened to the record and cried through let it be like a bitch. I like all the studio talking noise. 5/5 for sentimentality

rating overall: 43.8/65, about 66%. but I dont vibe w that honestly. I had a great time doing this and discovered some great music. I also cant ignore their historical significance and the insight it gave me into my parents' youth, which is probably the most interesting thing about the beatles. I choose to recuse myself from assigning a grade and instead, on a pass/fail scale, pass them.

reflection: they were so prolific in 10 years with wildly varying results, but it makes me feel like we need to all create more haphazardly and throw stuff at the wall and see what sticks instead of being so precious about it. because honestly, a monkey at a typewriter with that kind of output WILL eventually write something good. I also think it's the kill baby Mussolini principle in that even if you killed baby Mussolini there would still be the sociopolitical situation which gave rise to Mussolini. so if The Beatles never formed there would've been other band/s who evolved with the upheaval of the 60s who would now serve as this cultural touchstone. but this is what we got and thats quite interesting I think.

takeaway: I cant listen to another beatles song for at least 2 calendar years

[blueteller]

How I rate stuff (because, why not?)

I enjoy fiction a lot. While most of the time I don't strictly put things in categories (also, my tastes and opinions might change over time), I thought it would be fun to make a post about how I rate different series and works of fiction that I like – or don't like.

So here's a couple of examples, on the scale of 0-10:

(Remember this is based on my enjoyment, not objective criticism! Beside I'm a pretty lax critic, I truly start to dislike things only like, 3 and below. So have mercy on me! I'm just being honest)

10/10 - I have very few complaints, nothing more than nitpicks. Otherwise I enjoy everything about it. (Example: Avatar the Last Airbender - love everyhing about it)

9/10 - I have one major issue or a couple minor ones, but apart from that it's similar: I love it (My little Pony: Friendship is Magic - surprisingly, I love most of it)

8/10 - I have some issues which I consider visible, but still liked it a lot (Star Wars series - not all of it, but the best of it)

7/10 - I have a significant amount of issues, but still mostly liked it (Harry Potter series - my favorite bashing series, so many plot holes to tear through, it's hilarious)

6/10 - liked it, but probably wasn't a fan (Pokemon franchise - my childhood, but not nearly on the top of my list)

5/10 - I didn't dislike it, but I wasn't invested either (Star vs Forces of Evil - I liked the Eclipsa plotline at first but otherwise, ehh)

4/10 - something bugged me about this, so I dropped it (Attack on Titan - I'm like, nope, canibalism is a big no no for me, sorry)

3/10 - something bugged me and probably made me mad, definitely dropped it (the Twilight saga - the romance is GROSS, but at least it's so bad it's funny)

2/19 - I really don't like this and I have strong feelings about it! (Chronicles of Narnia: Prince Caspian the movie - it's personal, they completely ruined one of my favorite books ever, why did they make Caspian and Peter into emo jerks??)

1/10 - oh I HATE this (that one Robinson Crusoe movie adaptation I saw once in class when I was 11, where they turned the MC racist for drama: I am still full of rage because that book was pretty cool, so like, whyyyy)

0/10 - I am in denial of its existence and happier that way. (Last Airbender Live Action Movie. There's no movie in Ba Sing Se.)

Other series I enjoyed:

Fullmetal Alchemist (Manga/Brotherhood): 10/10, very well structured, engaging, funny and 100% satisfying. Never gets old.

My Hero Academia: 10/10, love the world and the characters. Can't wait for the conclusion

The Chronicles of Narnia: 10/10, the first book series that wasn't for school that I ever read by myself. Still my favorite series of all time

Lord of the Rings: 9.5/10, I only love it less than max because it's very long and Narnia is my 10/10 fantasy series. Sorry LOTR, there can only be one favorite, even if I admit that LOTR is factually better quality-wise. I'm subjective, you know?

Danny Phantom: 7/10, it has many problems but the premise is GREAT and it has killer aesthetic. It's kinda unforgettable, even.

Some recenty found series:

Trash of the Count's Family: 10/10, recently found it, fell in love pretty fast. The characters alone are enough for me, but the story is honestly great as well

Solo leveling: 8/10, interesting, fun, simple in enjoyment. Not too long either. My biggest pitpick would be that the story was a bit simple, but it's exactly what it's supposed to be.

Suicide Hunter: 7/10, interesting, very funny at places, still too brutal for me (sooo much suicide and mindbreaking stuff... but I get why people like it).

Omniscient Reader's Viewpoint: 5/10, don't kill me for this, I have reasons! It is interesting, and I like most of the characters, but I'm just not a fan of the main plot. There's just something unappealing for me about the whole "gladiator premise" (where a superior power creates the scenario where random people are forced to kill each other). Same reason why I never liked the Hunger Games (which I'd probably rate 4/10) and I'll never watch Squid Game.

The S Classes That I raised: 6/10, it is interesting, fun and hilarious in many places, but the supporting cast somehow didn't quite click for me just yet. The rating might change in the future, who knows?