#utf day 6

[hydralisk98]

LEARNBLR #6, the MINIX 3 book + full-stack madness

Hy comrades, Klara here with a closer to final ZealOS color ricing. And yes I finally got the MINIX3 book at home a few days ago. Quite happy of such as I intend to dive deeper into such once I am ready, alongside the "NAND to Tetris" companion book.

The current context & study program timings won't stop me from learning of the strenghts and drawbacks of much existing infrastructure and designs, as I still feel like a vast niche market is still left up to grabs as far as computation goes. And customizing so much of a tech deque of computational framework would be preferable to the affordable but ethically doubtful option to bear only to hacking such a exisiting one. No offense to such tinkerers as they still deserve much of my praises and in fact, I do aim forward to such tech enthusiasts worldwide as to redemocratize direct & transparent technologies.

So let me explain. Before the nineties, we had way less quantities of memory, but we got more variety and a few direct computation brands (Commodore 64, Apple II & DECmate III being sample computer systems I think most when I think of such terms). Right now, we got some nice standards and memory abundance but now much is abstracted away from even developers, systemic issues plague the homogeneous market of 64-bit architectures and a handful of similar contemporary legitimate concerns to debate over.

I decided to bring such change by my hands, and while I still am seeking people who are quite deeper into computing hardware (most likely electronics but alot of historical hardware methodologies are to be expected) and analog medias of days almost gone now.

It is still extremely early but I take major hints from the TempleOS' main forks that are ZealOS & Shrine with additional inspirations like Windows 3.1x GUI, MINIX3/PLAN9 standards, DECmate III+'s all-in-one package, Intersil 6120 processor family & openPOWER + openSPARC architectures.

Quickly summarized, it is a 12-bit tribble home computer system, and the MVP (minimum viable product) would probably contain around 144KW (32-144 kilo-tribbles but I prefer kilo-word for better interoperability with bytes and nibbles) of "live" RAM memory. And it's primary goal is to host a retro-optimized multimedia OS that has a similar feel to other 8-bit new wave micros. It has twelve generic 12-bit registers that are not conventionally named (A to F & U to Z letters as such generic register names) and four special registers akin to the PDP-8-inspired Intersil 6120's.

But remember, the main goal is less to make it big computing framegmeted mess initially (like how Linux distros and kernel are as of right now), but rather to give ways for the computer enthusiasts and non-English communities to take ownership of their computer as to learn/tinker all sorts of cool workflows. And yes, that includes slavic tech communities of the Russian kinds, amerindian peoples, Inuits and constructed world peers. Part of why I chose 12-bit as the word rather than 8-bit is exactly to ease in encoding of customized charsets (think of such as a alternative to UTF-8) and of course to learn to tinker differently than just throw the usual 8-bit bytes around. Still aiming for 8-bit (& also 6-bit units because of some worldbuilding-relevant samples) compatibilities eventually but that's far in the roadmap.

Small extras for those who read to the end of this article:

#maskoch #servitor #16^12 #TempleOS #MINIX3 #NAND to Tetris #retro computing #customization #taking commisions #computer hardware manufacturing #computer graphics

[appzlogic]

In today’s interconnected world, globalization testing is a critical process that ensures your software or application functions seamlessly across diverse cultures and languages. It is essential for businesses that want to reach a global audience and provide an inclusive user experience. At SDET Tech, we understand the importance of globalization testing and the key factors to consider while performing it for your software. In this blog, we’ll delve into the significant aspects of globalization testing to help you ensure international success for your product.

The following are some key factors that need to be considered when performing globalization testing:

1. Linguistic Diversity and Localization One of the primary aspects of globalization testing is linguistic diversity. Your software should be able to display content in multiple languages, which means the user interface (UI) should adapt to the language and culture of the user. For this, make sure that:

Translations are accurate and culturally sensitive.

Date formats, time zones, and currency symbols are localized.

Text doesn’t get cut off due to character length differences in various languages.

2. User Interface and Layout Different languages might require more space on the screen than others. Pay attention to the UI layout to ensure it accommodates longer or shorter text strings. Also, consider the use of right-to-left (RTL) languages like Arabic or Hebrew, which requires a different UI design.

3. Date and Time Formats Ensure that date and time formats automatically adapt to the user’s region. Some countries use day/month/year while others use month/day/year. Also, consider time zone differences and daylight saving time adjustments.

4. Currency and Numeric Formats Financial data, such as currency symbols and number formats, should be tailored to the user’s location. For example, in the U.S., you use the dollar sign ($) and comma (,) as a thousands separator while in Europe, it’s often the euro symbol (€) and a period (.) as the thousands separator.

5. Character Encoding Make sure your software supports various character encodings. Unicode (UTF-8) is a common encoding standard that allows your application to display a wide range of characters from different languages.

6. User Input and Data Validation Consider language-specific input methods, keyboard layouts and data validation rules. For example, some languages have unique characters that should be supported for user input.

7. Accessibility that Ensures Inclusivity Ensure your software is accessible to a wide range of users irrespective of their abilities. Implementing accessibility features like screen reader support, keyboard navigation and text-to-speech capabilities is important.

8. Test Across Multiple Platforms Globalization testing should be conducted in various environments, including different operating systems, browsers and devices. Ensure that your software is compatible with the technology that is commonly used in the targeted regions.

9. Local Regulations and Compliance Be aware of local laws and regulations, such as data privacy laws (e.g. General Data Protection Regulation in Europe). Ensure your software complies with these regulations when it handles user data.

10. Comprehensive Testing Approach Globalization testing should not be a one-time effort. It should be integrated into the development cycle, from the early stages of design to continuous monitoring and improvement.

The Way Forward Globalization testing is the most crucial aspect of software development for businesses aiming to reach a global audience. At SDET Tech, we understand the complex nature of globalization testing and the importance of ensuring that your software works seamlessly across diverse cultures and languages. By considering the key factors mentioned above, one will not only expand their global reach but also provide an inclusive and culturally sensitive user experience for users across the globe.

[mohitmblr]

What Is SEO? How Does It Work? Learn About On Page SEO

Title- What is SEO ? How does it work??

Des- "SEO" is also known as Search Engine Optimization. It helps the user to get visible results for their website on google or other search engines as well.

There are 3 types of "SEO" AKA Search Engine Optimization

1. White Hat SEO  :- White Hat SEO is a legal and genuine method, The result may take time but it has 0% risk and the result will be effective. while maintaining the policies and morals.

2. Black Hat SEO  :- The Black Hat SEO is the same as The White Hat SEO in the reverse it breaks the policies and morals of the search engine and google bots can easily track your SEO activities and can remove your visible result in a short time.

3. Grey Hat SEO  :- The Grey Hat SEO, Well it is the riskiest Search Engine Optimization method ever, as the Black Hat SEO also breaks the Policies and rules therefore no SEO expert can recommend you this method and there is no such guarantee your visible result will be there or not for being more violet on the search engine can degrade your website ranking and also will not promote your website in the result section.

Well, we have discussed "SEO" AKA Search Engine Optimization and its types, Now let's have a look at how it works and executes on the website.

To Execute the "SEO"  AKA Search Engine Optimization

1. Decide the keyword of the topic or title, Suppose You wanna write a "Blog" for example- High Internet Speed.

2. High Internet Speed relates to many Keywords, such as Technology, Internet, and Bandwidth.

3. Take all of them and create a unique and relevant title for an example -  Growing Technology Of High Internet Speed And Bandwidth.

4. Take every letter of the starting word in caps for an example see the title on no. 3

5. Once you decide on the keyword and the title of the blog now it is time to write a description

6. In the description target the keywords you wrote in a keyword and the title itself for an example see rules no. 2 and no. 3.

7. Description for an example- The growth of technology is humongous nowadays' the speed of the internet is growing so fast we have come so far as we look back, The journey from 1MBPS to 2GBPS now this technology will make history itself.

8. The title for the google search should be 60 Characters and the description should be 160 Characters.

Well this is the only single part we covered of "SEO"

Now using more methods would be beneficial to your SEO for an example

1. Meta name

Meta name helps the website to create its recognition for example this will be your meta name

with the help of this, you are allowing google to show the actual details of your website on the search result

- <meta name="title" content="Growing Technology Of High Internet Speed and Bandwidth.">

<meta name="description" content="The growth of the technology is humongous nowadays' the speed of the internet is growing so fast we have come so far as we look back on the journey to 1MBPS to now 2GBPS this technology will make history itself.">

<meta name="keywords" content="Internet, Bandwidth,  High Internet Speed">

<meta name="robots" content="index, follow">

<meta http-equiv="Content-Type" content="text/html; charset=utf-8">

<meta name="language" content="English">

<meta name="revisit-after" content="7 days">

2. Schema

Schema is a feature inside SEO and plays a major role inside.

with the help of schema, you can add some features to your website the list of the features is given below.

There are many types of schema such as

1. FAQ Page

2. Breadcrumb

3. Website

4. Videos.

5. Organization

6. Local Business

7. Event

8. How to

9. Recipe

10. Product

11. Person

3. Canonical Tag

The Canonical tag plays a major role in your website and maintaining the website rank on google search engine

It prevents your website ranking and saves your website's original domain just to redirect anyone even if there is a duplicate domain created by google on other web pages.

for example, you have a website for technology

your original domain would be ( WWW.Technology.COM )

and google can create hundreds of domains of the same page and if someone clicks on that domain, He/she would be redirected to an error webpage because the "Canonical Tag" of that particular webpage is not added there,

until you don't add the "Canonical Tag" in the head section, Google will never understand that the Domain/URL belongs to that original page.

Original Domain - www.Technology.com

Duplicate Domain Example :

1. www.Technology .com/

2. Http//.www.Technology.com

3. Https//.www.Technology.com

5.Https//.www.Technology.com/

6. Http//.www.Technology.com/

to prevent the website from duplicate domains/URLs add the "Canonical Tag"

for an Example

<link rel="canonical" href="https://technology.com/" />

4. Heading Tags

There are 5 to 6 types of Heading tags from H1 To H6

and heading impact your website 50%

H1 -  H1 heading should use only once on a page.

H2 - H2 heading could be used hardly 2 to 3 times only.

H3 to H6 heading can be used multiple times on a page,

to execute the heading tags the code is

<h1>Your Heading</h1>

5. Robot Tag

The short language "Robot Tag" helps you to share/Allow/Disallow/Index/no index.

These types of features you get it,

A Robot.Txt file tells search engines where to go and where not to go.

6. Content Optimisation

Content Optimisation is used for many websites or social  websites for example

1. Twitter

2. Facebook

3. Instagram

and many more these websites use optimize technology to compress the content to reduce the size of the actual file to load faster than the actual time

it helps the website to work seamlessly, smoothly, and fast on Desktop and Mobile as well

Content optimization can be used for

URL

Title

Description

Image

Videos

GIF

Heading

For example - An image of 3 MB takes a longer time to get loaded, On the other side we take the same image to reduce the file size and optimize it in a better way will reduce the upload time, and it will get loaded faster.

the same method will apply to the videos as well.

Content optimization is also analyzed by google just to maintain the rank of your website on the google search engine.

7. OG tags

OG tags are also known as Open Graph Tags which use in the head section of your page it's a part of your "Search Engine Optimization method

for example, it looks like this

<meta property="og:title" content="">

<meta property="og:site_name" content="">

<meta property="og:url" content="">

<meta property="og:description" content="">

<meta property="og:type" content="">

<meta property="og:image" content="">

from your title to the image, it covers up all the sections.

#seo #seo marketing #digital marketing #on page seo #contentoptimization #canonicaltag #metaname #meta

[chidujs]

Assignment.

@@ -0,0 +1,157 @@

-- coding: utf-8 --

""" Created on Sun Mar 17 18:11:22 2019 @author: Voltas """ import pandas import numpy import seaborn import scipy import matplotlib.pyplot as plt

nesarc = pandas.read_csv ('nesarc_pds.csv', low_memory=False)

Set PANDAS to show all columns in DataFrame

pandas.set_option('display.max_columns' , None)

Set PANDAS to show all rows in DataFrame

pandas.set_option('display.max_rows' , None)

nesarc.columns = map(str.upper , nesarc.columns)

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

Change my variables to numeric

nesarc['AGE'] = nesarc['AGE'].convert_objects(convert_numeric=True) nesarc['MAJORDEP12'] = nesarc['MAJORDEP12'].convert_objects(convert_numeric=True) nesarc['S1Q231'] = nesarc['S1Q231'].convert_objects(convert_numeric=True) nesarc['S3BQ1A5'] = nesarc['S3BQ1A5'].convert_objects(convert_numeric=True) nesarc['S3BD5Q2E'] = nesarc['S3BD5Q2E'].convert_objects(convert_numeric=True)

Subset my sample

subset1 = nesarc[(nesarc['AGE']>=18) & (nesarc['AGE']<=30) & nesarc['S3BQ1A5']==1] # Ages 18-30, cannabis users subsetc1 = subset1.copy()

Setting missing data

subsetc1['S1Q231']=subsetc1['S1Q231'].replace(9, numpy.nan) subsetc1['S3BQ1A5']=subsetc1['S3BQ1A5'].replace(9, numpy.nan) subsetc1['S3BD5Q2E']=subsetc1['S3BD5Q2E'].replace(99, numpy.nan) subsetc1['S3BD5Q2E']=subsetc1['S3BD5Q2E'].replace('BL', numpy.nan) recode1 = {1: 9, 2: 8, 3: 7, 4: 6, 5: 5, 6: 4, 7: 3, 8: 2, 9: 1} # Frequency of cannabis use variable reverse-recode subsetc1['CUFREQ'] = subsetc1['S3BD5Q2E'].map(recode1) # Change the variable name from S3BD5Q2E to CUFREQ

subsetc1['CUFREQ'] = subsetc1['CUFREQ'].astype('category')

Raname graph labels for better interpetation

subsetc1['CUFREQ'] = subsetc1['CUFREQ'].cat.rename_categories(["2 times/year","3-6 times/year","7-11 times/year","Once a month","2-3 times/month","1-2 times/week","3-4 times/week","Nearly every day","Every day"])

Contingency table of observed counts of major depression diagnosis (response variable) within frequency of cannabis use groups (explanatory variable), in ages 18-30

contab1 = pandas.crosstab(subsetc1['MAJORDEP12'], subsetc1['CUFREQ']) print (contab1)

Column percentages

colsum=contab1.sum(axis=0) colpcontab=contab1/colsum print(colpcontab)

Chi-square calculations for major depression within frequency of cannabis use groups

print ('Chi-square value, p value, expected counts, for major depression within cannabis use status') chsq1= scipy.stats.chi2_contingency(contab1) print (chsq1)

Bivariate bar graph for major depression percentages with each cannabis smoking frequency group

plt.figure(figsize=(12,4)) # Change plot size ax1 = seaborn.factorplot(x="CUFREQ", y="MAJORDEP12", data=subsetc1, kind="bar", ci=None) ax1.set_xticklabels(rotation=40, ha="right") # X-axis labels rotation plt.xlabel('Frequency of cannabis use') plt.ylabel('Proportion of Major Depression') plt.show()

recode2 = {1: 10, 2: 9, 3: 8, 4: 7, 5: 6, 6: 5, 7: 4, 8: 3, 9: 2, 10: 1} # Frequency of cannabis use variable reverse-recode subsetc1['CUFREQ2'] = subsetc1['S3BD5Q2E'].map(recode2) # Change the variable name from S3BD5Q2E to CUFREQ2

sub1=subsetc1[(subsetc1['S1Q231']== 1)] sub2=subsetc1[(subsetc1['S1Q231']== 2)]

print ('Association between cannabis use status and major depression for those who lost a family member or a close friend in the last 12 months') contab2=pandas.crosstab(sub1['MAJORDEP12'], sub1['CUFREQ2']) print (contab2)

Column percentages

colsum2=contab2.sum(axis=0) colpcontab2=contab2/colsum2 print(colpcontab2)

Chi-square

print ('Chi-square value, p value, expected counts') chsq2= scipy.stats.chi2_contingency(contab2) print (chsq2)

Line graph for major depression percentages within each frequency group, for those who lost a family member or a close friend

plt.figure(figsize=(12,4)) # Change plot size ax2 = seaborn.factorplot(x="CUFREQ", y="MAJORDEP12", data=sub1, kind="point", ci=None) ax2.set_xticklabels(rotation=40, ha="right") # X-axis labels rotation plt.xlabel('Frequency of cannabis use') plt.ylabel('Proportion of Major Depression') plt.title('Association between cannabis use status and major depression for those who lost a family member or a close friend in the last 12 months') plt.show()

#

print ('Association between cannabis use status and major depression for those who did NOT lose a family member or a close friend in the last 12 months') contab3=pandas.crosstab(sub2['MAJORDEP12'], sub2['CUFREQ2']) print (contab3)

Column percentages

colsum3=contab3.sum(axis=0) colpcontab3=contab3/colsum3 print(colpcontab3)

Chi-square

print ('Chi-square value, p value, expected counts') chsq3= scipy.stats.chi2_contingency(contab3) print (chsq3)

Line graph for major depression percentages within each frequency group, for those who did NOT lose a family member or a close friend

plt.figure(figsize=(12,4)) # Change plot size ax3 = seaborn.factorplot(x="CUFREQ", y="MAJORDEP12", data=sub2, kind="point", ci=None) ax3.set_xticklabels(rotation=40, ha="right") # X-axis labels rotation plt.xlabel('Frequency of cannabis use') plt.ylabel('Proportion of Major Depression') plt.title('Association between cannabis use status and major depression for those who did NOT lose a family member or a close friend in the last 12 months') plt.show()

[waitingformyfavoritesongs]

Ribs laying on feeling bendy & weak

Updating 25 November 2022 started 24 November 2022

Days ago could not find my passwords wrote on 2 pages of paper 📝 & put in a backpack 🎒 I think the incubus took it. All year he & his friend Sagittarius ♐️ has been profusely heating up my spine & rib cage, changing my figure to make me unattractive but worse making it harder to breathe 🧘🏻‍♀️ & made me cough 😷 a lot. I believe that from the beginning of my life they gave my bones 🦴 strong material but all year, & the last 5 years they’ve been painfully removing the stronger materials. They don’t want to wait until after I am dead 💀. They tortured me all my life. What are they planning for my death ☠️ that they couldn’t wait? These people are the worst in the universe. Cruel. Concieted. Con artist cheating liars. Don’t anticipate ever finding love ❤️ with them. They will use you for pleasure with their sweet siren tongues to confuse you. Not worthy of relationships. Thanksgiving 24 November 2022 6:08 pm pdt. ☀️

1006 pm pdt 24 November 2022 Thursday

caesar sounds like scissors ⚔️✂️ ... is the apocalypse part of the Bible a true false test ? Is it only me or I realize that it might also be contradictory? From stuff I read online? So is it? Multiple choice , true false ? Teachers don’t tell you the exact questions ahead of time but sample questions with different variables? I thought I remembered a part that people of god die for a while? People also used to wear those what would Jesus do (wwjd) wrist bands. 10:13 pm pdt need to think more. Bcz god likes tests??

25 November 2022 11:26 am pdt Friday (he’s roasting me alive I smell like cooked meat I’m not joking! Yaki = cook? In nihongo/Japanese yak = ox = cow ??)

My anus stings from incubus... read into that however you want.

11:31 am pdt he likes to pull out. O_o beware

11:32 am pdt 😖😭 he doesn’t like me divulging anything even in my own brain! He says I hurt him by thinking of it alone to myself in my head! He’s manipulative & abusive!

I want to insert pictures but the option is not popping up now 11:36 am pdt

If Adam Levine was the real Jesus Christ then we are all screwed! (Perhaps literally not only figuratively.)

because if women are slaves he’s not freeing us. Absurdism = capitalism = pink taxes, Et cetera, stratification/slavery. Capitalism always relies on cheap labor. India is already our next stop after China? Third world countries became proletariat/working class. Xportation of jobs. We will have to export ourselves to get a job again.

I thought I read something about the sacrificing of a cow & slavery in Jeremiah this year; I randomly opened up the book and it was there in front of me. I did not read much so I’m still have to go back to it to read more. Me = heifer? (<-Jeffer) Yaki... yak...cow 🦬🐃🐂🐄

Yakity yak.. don’t come back?? 🎶🎶

He obviously likes power & exerting it , incubus = rapist. Absurd.

witchy.

Sisyphean. Capitalist slavery. Absurd. Steak 🥩 = prostitution/slavery/ underhanded capitalism/capitalist rules toward/ for women ? & a god who is all for it!! Is he my god? I don’t think so ...

12:02 pm pdt 25 November 2022 Friday

tell me if I’m wrong 😑 12:03 pm pdt.

12:34 pm pdt

<blockquote class="twitter-tweet"><p lang="en" dir="ltr">??? Are they calls king of kings? Or is this a place? <a href="https://t.co/FNeUwexDjC">https://t.co/FNeUwexDjC</a></p>— Nana Nana (parody?) (@NanaNan65672979) <a href="https://twitter.com/NanaNan65672979/status/1586703653812912128?ref_src=twsrc%5Etfw">October 30, 2022</a></blockquote> <script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script> <blockquote class="twitter-tweet"><p lang="en" dir="ltr">Posted March 17, 2020<br>ilove.adamlevine ig.</p>— Nana Nana (parody?) (@NanaNan65672979) <a href="https://twitter.com/NanaNan65672979/status/1586704155011424257?ref_src=twsrc%5Etfw">October 30, 2022</a></blockquote> <script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script>

<blockquote class="twitter-tweet"><p lang="en" dir="ltr">Are they calling * Adam Levine king of kings in this post on Instagram? Is he going 2 get a tattoo on a thigh of a name meaning king of kings, lord of lords? How many lords are there? Does lord mean god? Is there only one god or r there many? Maybe it’s king of kings = lord?</p>— Nana Nana (parody?) (@NanaNan65672979) <a href="https://twitter.com/NanaNan65672979/status/1586728980802985984?ref_src=twsrc%5Etfw">October 30, 2022</a></blockquote> <script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script>

12:39 pm pdt 25 November 2022 Friday

12:44 pm pdt

12:51 pm Pdt 25 November 2022 Friday

1:20 pm pdt

25 November 2022 Friday 1:21 pm pdt 1:33 pm pdt: this is something I wrote earlier this year. A thought entered my head when I was 5 years old, this is about that. This was probably the demon lord informing me of my curse, my future. In the beginning of sixth grade year I met a girl 👧 named Dieu Nguyen. Dieu is pronounced dew. It wasn’t until about 5 years ago I really realized or paid attention to how it was spelled. I feel like it was my destiny to meet her in retrospect. Liberty 🗽 is what? Freedom is what? When I think of bers = burs? Bird... siren 🚨 fallen angels incubus, multi aliases. 1:41 pm pdt 25 November 2022 Friday. If everything is already decided for us before we are born, even before conceived possibly? Did we ever have freedom? Do we deserve this? What did we do wrong? (1:58 pm pdt: I think this was fresh man or sophomore year of high school 🏫 :::) I hate scary movies but when I spent the night at a classmate’s place, I remember I was into n sync(2:02 pm pdt: from middle school to freshman year at least), & she had a marionette doll, & I think 🤔 I felt like I could not tell her no ... I had a problem with saying no to people.. & she had some movies picked out for us to watch. I remember watching idle hands 🙌 with Jessica Alba, cruel intentions with Ryan Philippe, Reese Witherspoon, Sarah Michelle gellar? & a weird sexual vampire 🧛‍♂️ movie 🎥 movie 🍿 . I don’t do well with any kind of horror movie 😑. I watched Sean of dead in the theater 🎭 and I screamed, I think I might have also been shaking. I think that time I screamed people laughed because I screamed I think... 1:54 pm pdt. 25 November 2022 Friday. 2:03 pm pdt:: in capitalism, there will probably always be a sacrificial lamb 🐑. I will have to review the apocalypse (in English) but I think I recall that there is a lamb 🐑 on a throne (sounds like thrown away) who looks slain ? Dead 💀? So how can a dead sheep 🐑 do anything? Ie open scrolls 📜 & enjoy riches it is worthy of receiving? I heard you cannot take it with you... so even though they say that, does that actually mean the sheep 🐑 cannot and does not? Does someone else?? 2:11 pm pdt. Need to review. Question 🙋🏻‍♀️ 2:11 pm pdt. If I were a sheep 🐑 that was dead I would have to be born again or brought back to life... 2:13 pm pdt maybe physical therapy? Not thinking about anything else i.e. opening scrolls but all that stuff was already going on before Jesus died??, so those things are old news 📰 maybe something lost in translation?? 2:16 pm pdt 25 November 2022 Friday. 2:17 pm pdt

2:28 pm pdt what would Jesus do? I don’t know 🤷🏻‍♀️

what would Caesar do? Did he put people against lions 🦁 for sport? Animals with claws 🦞? Like animals-mals-mals... crucified people... torture whip until chunks of flesh flew off... possibly imprisoned women against their wills to be prostitutes without pay 💰? As more laws are passed they get sneakier & sneakier. Watched Taken with Liam neeson in it. And if you are not whole white and a rich man you have to keep quiet and suffer through abuses and sacrifice your self to get paid big bucks.??? Watched blood 🩸 diamond 💎 movie 🎥.

And recently saw on YouTube about Victoria secret 🤫. World 🌎 with out end ??? 2:40 pm pdt. 😖😖😖😖😖😭😭😭😭😭😭😭😭 2:41 pm pdt

2:46 pm pdt in biology class they teach us we are classified under kingdom animalia, bcz even tho human we are technically animals. We have astrology categorizing us? To tell us our personalities and life path?? According to the planets 🪐 and constellations 🌌. Leo ♌️ = lion 🦁 claws 🦞 Scorpio ♏️ insect?? Arachnid?? Weird lobster 🦞???? With a stinger who will double cross a lady bug ??? Aesop..?? CHeaters are cheaters...??? Score pi o 🕵️‍♀️??? I don’t think 🤔 so I think of pontius pi-late. I started thinking 🤔 many days ago ... it possible a lot of stories got messed up 🆙 like they let yeshua bar abbas go, but not yeshua bar what was it yeoseph?? Both going by yeshua, did they intentionally mix them up ⬆️? It probably depends on who is whose god. Some don’t have freedom. 2:59 pm pdt.

3:07 pm pdt Noah’s ark. One of each. Jesus apocalypse 3 virgin brides, virgins & unicorns 🦄 🍆 phalic .. friendly unicorn 🦄. He likes to roll around with every woman 👩 he meets it seems. I think he’s here for the wrong reasons 😤😑 Eric of boy meets world 🌎. Christina grimmie player 2 tattoo, hierarchy btwn siblings. Marcus grimmie survived, player 1 tattoo. One of each. Noah’s ark. Cannot have what my sister has/ will have. No husband 4 Me. 😕😖😭= sacrificed lamb 🐑 = painful prolonged suffering and drawn out death ☠️. 3:17 pm pdt.

billy Joel only the good die young. 🎶 the good = Jesus ? Christianity ✝️ 3:32 pm pdt. Jesus is the do gooder or the one controlled like a puppet to contribute to capitalism. And the devil Caesar scissors ✂️ Kills them all. 3:34 pm pdt when everything feels like the movies (idle hands 🙌) you bleed 🩸 to know you’re alive. 3:35 pm pdt.

ride or die, the butcher will butcher. Damned either way. 3:41 pm pdt ehi pee. First grade crush last name Rios. 3:42 pm pdt @_@

#Instagram

[saiaruroyals]

Running a Chi-Square Test of Independence

-- coding: utf-8 --

""" Created on Fri Mar 1 17:20:15 2019

@author: Voltas """

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

nesarc = pandas.read_csv ('nesarc_pds.csv' , low_memory=False)

Set PANDAS to show all columns in DataFrame

pandas.set_option('display.max_columns', None)

Set PANDAS to show all rows in DataFrame

pandas.set_option('display.max_rows', None)

nesarc.columns = map(str.upper , nesarc.columns)

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

Change my variables to numeric

nesarc['AGE'] = pandas.to_numeric(nesarc['AGE'], errors='coerce') nesarc['S3BQ4'] = pandas.to_numeric(nesarc['S3BQ4'], errors='coerce') nesarc['S3BQ1A5'] = pandas.to_numeric(nesarc['S3BQ1A5'], errors='coerce') nesarc['S3BD5Q2B'] = pandas.to_numeric(nesarc['S3BD5Q2B'], errors='coerce') nesarc['S3BD5Q2E'] = pandas.to_numeric(nesarc['S3BD5Q2E'], errors='coerce') nesarc['MAJORDEP12'] = pandas.to_numeric(nesarc['MAJORDEP12'], errors='coerce') nesarc['GENAXDX12'] = pandas.to_numeric(nesarc['GENAXDX12'], errors='coerce')

Subset my sample

subset1 = nesarc[(nesarc['AGE']>=18) & (nesarc['AGE']<=30)] # Ages 18-30 subsetc1 = subset1.copy()

subset2 = nesarc[(nesarc['AGE']>=18) & (nesarc['AGE']<=30) & (nesarc['S3BQ1A5']==1)] # Cannabis users, ages 18-30 subsetc2 = subset2.copy()

Setting missing data for frequency and cannabis use, variables S3BD5Q2E, S3BQ1A5

subsetc1['S3BQ1A5']=subsetc1['S3BQ1A5'].replace(9, numpy.nan) subsetc2['S3BD5Q2E']=subsetc2['S3BD5Q2E'].replace('BL', numpy.nan) subsetc2['S3BD5Q2E']=subsetc2['S3BD5Q2E'].replace(99, numpy.nan)

Contingency table of observed counts of major depression diagnosis (response variable) within cannabis use (explanatory variable), in ages 18-30

contab1=pandas.crosstab(subsetc1['MAJORDEP12'], subsetc1['S3BQ1A5']) print (contab1)

Column percentages

colsum=contab1.sum(axis=0) colpcontab=contab1/colsum print(colpcontab)

Chi-square calculations for major depression within cannabis use status

print ('Chi-square value, p value, expected counts, for major depression within cannabis use status') chsq1= scipy.stats.chi2_contingency(contab1) print (chsq1)

Contingency table of observed counts of geberal anxiety diagnosis (response variable) within cannabis use (explanatory variable), in ages 18-30

contab2=pandas.crosstab(subsetc1['GENAXDX12'], subsetc1['S3BQ1A5']) print (contab2)

Column percentages

colsum2=contab2.sum(axis=0) colpcontab2=contab2/colsum2 print(colpcontab2)

Chi-square calculations for general anxiety within cannabis use status

print ('Chi-square value, p value, expected counts, for general anxiety within cannabis use status') chsq2= scipy.stats.chi2_contingency(contab2) print (chsq2)

#

Contingency table of observed counts of major depression diagnosis (response variable) within frequency of cannabis use (10 level explanatory variable), in ages 18-30

contab3=pandas.crosstab(subset2['MAJORDEP12'], subset2['S3BD5Q2E']) print (contab3)

Column percentages

colsum3=contab3.sum(axis=0) colpcontab3=contab3/colsum3 print(colpcontab3)

Chi-square calculations for mahor depression within frequency of cannabis use groups

print ('Chi-square value, p value, expected counts for major depression associated frequency of cannabis use') chsq3= scipy.stats.chi2_contingency(contab3) print (chsq3)

recode1 = {1: 9, 2: 8, 3: 7, 4: 6, 5: 5, 6: 4, 7: 3, 8: 2, 9: 1} # Dictionary with details of frequency variable reverse-recode subsetc2['CUFREQ'] = subsetc2['S3BD5Q2E'].map(recode1) # Change variable name from S3BD5Q2E to CUFREQ

subsetc2["CUFREQ"] = subsetc2["CUFREQ"].astype('category')

Rename graph labels for better interpretation

subsetc2['CUFREQ'] = subsetc2['CUFREQ'].cat.rename_categories(["2 times/year","3-6 times/year","7-11 times/years","Once a month","2-3 times/month","1-2 times/week","3-4 times/week","Nearly every day","Every day"])

Graph percentages of major depression within each cannabis smoking frequency group

plt.figure(figsize=(12,4)) # Change plot size ax1 = seaborn.factorplot(x="CUFREQ", y="MAJORDEP12", data=subsetc2, kind="bar", ci=None) ax1.set_xticklabels(rotation=40, ha="right") # X-axis labels rotation plt.xlabel('Frequency of cannabis use') plt.ylabel('Proportion of Major Depression') plt.show()

Post hoc test, pair comparison of frequency groups 1 and 9, 'Every day' and '2 times a year'

recode2 = {1: 1, 9: 9} subsetc2['COMP1v9']= subsetc2['S3BD5Q2E'].map(recode2)

Contingency table of observed counts

ct4=pandas.crosstab(subsetc2['MAJORDEP12'], subsetc2['COMP1v9']) print (ct4)

Column percentages

colsum4=ct4.sum(axis=0) colpcontab4=ct4/colsum4 print(colpcontab4)

Chi-square calculations for pair comparison of frequency groups 1 and 9, 'Every day' and '2 times a year'

print ('Chi-square value, p value, expected counts, for pair comparison of frequency groups -Every day- and -2 times a year-') cs4= scipy.stats.chi2_contingency(ct4) print (cs4)

Post hoc test, pair comparison of frequency groups 2 and 6, 'Nearly every day' and 'Once a month'

recode3 = {2: 2, 6: 6} subsetc2['COMP2v6']= subsetc2['S3BD5Q2E'].map(recode3)

Contingency table of observed counts

ct5=pandas.crosstab(subsetc2['MAJORDEP12'], subsetc2['COMP2v6']) print (ct5)

Column percentages

colsum5=ct5.sum(axis=0) colpcontab5=ct5/colsum5 print(colpcontab5)

Chi-square calculations for pair comparison of frequency groups 2 and 6, 'Nearly every day' and 'Once a month'

print ('Chi-square value, p value, expected counts for pair comparison of frequency groups -Nearly every day- and -Once a month-') cs5= scipy.stats.chi2_contingency(ct5) print (cs5)

[inktonki]

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[hajnalkafilepcoursera]

Assignment Week2

My test was to investigate the association between Frequency of smoked cigarettes (I changed the variable type to categorical) and ever have problem concentrating after stopping/cutting down on tobacco use (my other categorical variance).

My sample was the 35-55 years old males, who have been smoking in the last 12 months.

Null hypothesis: There is no relationship between the 2 categorical variances, the means are equal.

Alternative hypothesis: There is a relationship, the means are not equal.

Dataset used:

nesarc_pds.csv

Finding:

The first test showed that our finding is statistically significant, the P-value=0.000278468952 (our cut off is 0.05) That means that the Null hypothesis can be safely rejected, because there is a relationship between the categories. To find out which pairs are not equal I had to run a post hoc test as well. Before the post hoc test, I had to adjust the P-value as well, so my cut off is 0.05/15=0.03 for the compared chi square tests.

After the post hoc test we had several values which were below 0.05, but not below our adjusted P-value 0.003. Only one pair reached a slightly less value 0,003168 (comparing smoking 6 days/month and 30 days/month).

This finding means, that although our first test showed, that we have a significant difference in the groups, there is only 1 group which is different from another (group6 vs grup30). There is a higher chance that they have experienced concentrating problems after cutting down/stopping smoking cigarette. The other comparisons P-value were above the 0.03 cutoff, so their means are not significantly different from each other.

Program code:

-- coding: utf-8 --

""" Created on Fri Sep 9 15:12:36 2022

@author: FIH4HTV """

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

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

#new code setting variables you will be working with to numeric

data['S3AQ8A7C'] = pandas.to_numeric(data['S3AQ8A7C'], errors='coerce') data['CHECK321'] = pandas.to_numeric(data['CHECK321'], errors='coerce') data['S3AQ3B1'] = pandas.to_numeric(data['S3AQ3B1'], errors='coerce') data['S3AQ3C1'] = pandas.to_numeric(data['S3AQ3C1'], errors='coerce') data['AGE'] = pandas.to_numeric(data['AGE'], errors='coerce')

#subset data to young adults age 35 to 55 males who have smoked in the past 12 months

sub1=data[(data['AGE']>=35) & (data['AGE']<=55) & (data ['SEX']==1) & (data['CHECK321']==1)]

#make a copy of my new subsetted data

sub2 = sub1.copy()

#recode missing values to python missing (NaN)

sub2['S3AQ3B1']=sub2['S3AQ3B1'].replace(9, numpy.nan) sub2['S3AQ3C1']=sub2['S3AQ3C1'].replace(99, numpy.nan) sub2['S3AQ8A7C']=sub2['S3AQ8A7C'].replace(9, numpy.nan)

#implement new cathegorical variables 'USFREQMO' for nr of days smoked/month

recode1 = {1: 30, 2: 22, 3: 14, 4: 6, 5: 2.5, 6: 1} sub2['USFREQMO']= sub2['S3AQ3B1'].map(recode1)

#contingency table of observed counts according to 'used cannabis' and 'nr of cig. smoked/month'

ct1=pandas.crosstab(sub2['S3AQ8A7C'], sub2['USFREQMO']) print (ct1)

#column percentages, show 'used cannabis' for every frequency of cigarettes smoked

colsum=ct1.sum(axis=0) colpct=ct1/colsum print(colpct)

#chi-square test and print, called cs1

print ('chi-square value, p value, expected counts') cs1= scipy.stats.chi2_contingency(ct1) print (cs1)

#set variable types, USFREQMO -> categorical, canabis use -> numerical

sub2["USFREQMO"] = sub2["USFREQMO"].astype('category')

#new code for setting variables to numeric:

sub2['S3AQ8A7C'] = pandas.to_numeric(sub2['S3AQ8A7C'], errors='coerce')

#graph percent with nicotine dependence within each smoking frequency group

#which chategory on which axis, diagram form, axis labels

seaborn.catplot(x="USFREQMO", y="S3AQ8A7C", data=sub2, kind="bar", ci=None) plt.xlabel('Days smoked per month') plt.ylabel('Proportion Ever HAVE CONCENTRATION DIFFICULTY')

#need to calculate the adjusted p-value for further post hoc test 0.5/15 pairs

#need post hoc test to see which pair is not equivalent

#comparison p-value must be under adjusted p-value

#first pair: COMP1v2

recode2 = {1: 1, 2.5: 2.5} sub2['COMP1v2']= sub2['USFREQMO'].map(recode2)

#contingency table of observed counts %

ct2=pandas.crosstab(sub2['S3AQ8A7C'], sub2['COMP1v2']) 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)

#keep going to compare each pair

recode3 = {1: 1, 6: 6} sub2['COMP1v6']= sub2['USFREQMO'].map(recode3)

#contingency table of observed counts

ct3=pandas.crosstab(sub2['S3AQ8A7C'], sub2['COMP1v6']) 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)

recode4 = {1: 1, 14: 14} sub2['COMP1v14']= sub2['USFREQMO'].map(recode4)

#contingency table of observed counts

ct4=pandas.crosstab(sub2['S3AQ8A7C'], sub2['COMP1v14']) 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)

recode5 = {1: 1, 22: 22} sub2['COMP1v22']= sub2['USFREQMO'].map(recode5)

#contingency table of observed counts

ct5=pandas.crosstab(sub2['S3AQ8A7C'], sub2['COMP1v22']) 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)

recode6 = {1: 1, 30: 30} sub2['COMP1v30']= sub2['USFREQMO'].map(recode6)

#contingency table of observed counts

ct6=pandas.crosstab(sub2['S3AQ8A7C'], sub2['COMP1v30']) 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)

recode7 = {2.5: 2.5, 6: 6} sub2['COMP2v6']= sub2['USFREQMO'].map(recode7)

#contingency table of observed counts

ct7=pandas.crosstab(sub2['S3AQ8A7C'], sub2['COMP2v6']) 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)

recode8 = {2.5: 2.5, 14: 14} sub2['COMP2v14']= sub2['USFREQMO'].map(recode8)

#contingency table of observed counts

ct8=pandas.crosstab(sub2['S3AQ8A7C'], sub2['COMP2v14']) print (ct8)

#column percentages

colsum=ct8.sum(axis=0) colpct=ct8/colsum print(colpct)

print ('chi-square value, p value, expected counts') cs8=scipy.stats.chi2_contingency(ct8) print (cs8)

recode9 = {2.5: 2.5, 22: 22} sub2['COMP2v22']= sub2['USFREQMO'].map(recode9)

#contingency table of observed counts

ct9=pandas.crosstab(sub2['S3AQ8A7C'], sub2['COMP2v22']) print (ct9)

#column percentages

colsum=ct9.sum(axis=0) colpct=ct9/colsum print(colpct)

print ('chi-square value, p value, expected counts') cs9=scipy.stats.chi2_contingency(ct9) print (cs9)

recode10 = {2.5: 2.5, 30: 30} sub2['COMP2v30']= sub2['USFREQMO'].map(recode10)

#contingency table of observed counts

ct10=pandas.crosstab(sub2['S3AQ8A7C'], sub2['COMP2v30']) print (ct10)

#column percentages

colsum=ct10.sum(axis=0) colpct=ct10/colsum print(colpct)

print ('chi-square value, p value, expected counts') cs10=scipy.stats.chi2_contingency(ct10) print (cs10)

recode11 = {6: 6, 14: 14} sub2['COMP6v14']= sub2['USFREQMO'].map(recode11)

#contingency table of observed counts

ct11=pandas.crosstab(sub2['S3AQ8A7C'], sub2['COMP6v14']) print (ct11)

#column percentages

colsum=ct11.sum(axis=0) colpct=ct11/colsum print(colpct)

print ('chi-square value, p value, expected counts') cs11=scipy.stats.chi2_contingency(ct11) print (cs11)

recode12 = {6: 6, 22: 22} sub2['COMP6v22']= sub2['USFREQMO'].map(recode12)

#contingency table of observed counts

ct12=pandas.crosstab(sub2['S3AQ8A7C'], sub2['COMP6v22']) print (ct12)

#column percentages

colsum=ct12.sum(axis=0) colpct=ct12/colsum print(colpct)

print ('chi-square value, p value, expected counts') cs12=scipy.stats.chi2_contingency(ct12) print (cs12)

recode13 = {6: 6, 30: 30} sub2['COMP6v30']= sub2['USFREQMO'].map(recode13)

#contingency table of observed counts

ct13=pandas.crosstab(sub2['S3AQ8A7C'], sub2['COMP6v30']) print (ct13)

#column percentages

colsum=ct13.sum(axis=0) colpct=ct13/colsum print(colpct)

print ('chi-square value, p value, expected counts') cs13=scipy.stats.chi2_contingency(ct13) print (cs13)

recode14 = {14: 14, 22 : 22} sub2['COMP14v22']= sub2['USFREQMO'].map(recode14)

#contingency table of observed counts

ct14=pandas.crosstab(sub2['S3AQ8A7C'], sub2['COMP14v22']) print (ct14)

#column percentages

colsum=ct14.sum(axis=0) colpct=ct14/colsum print(colpct)

print ('chi-square value, p value, expected counts') cs14=scipy.stats.chi2_contingency(ct14) print (cs14)

recode15 = {14: 14, 30 : 30} sub2['COMP14v30']= sub2['USFREQMO'].map(recode15)

#contingency table of observed counts

ct15=pandas.crosstab(sub2['S3AQ8A7C'], sub2['COMP14v30']) print (ct15)

#column percentages

colsum=ct15.sum(axis=0) colpct=ct15/colsum print(colpct)

print ('chi-square value, p value, expected counts') cs15=scipy.stats.chi2_contingency(ct15) print (cs15)

recode16 = {22: 22, 30 : 30} sub2['COMP22v30']= sub2['USFREQMO'].map(recode16)

#contingency table of observed counts

ct16=pandas.crosstab(sub2['S3AQ8A7C'], sub2['COMP22v30']) print (ct16)

#column percentages

colsum=ct16.sum(axis=0) colpct=ct16/colsum print(colpct)

print ('chi-square value, p value, expected counts') cs16=scipy.stats.chi2_contingency(ct16) print (cs16)

[bosch-master]

Course 2 -Week 4

For week 4, the moderator is the main focus, I want to look at.

Therefore, I chose the nesarc dataset and the following variables:

ALCABDEPP12DX ALCOHOL ABUSE/DEPENDENCE PRIOR TO THE LAST 12 MONTHS 31677 0. No alcohol diagnosis 6994 1. Alcohol abuse only 563 2. Alcohol dependence only 3859 3. Alcohol abuse and dependence

S2AQ3 DRANK AT LEAST 1 ALCOHOLIC DRINK IN LAST 12 MONTHS 26946 1. Yes 16116 2. No

S2AQ8A HOW OFTEN DRANK ANY ALCOHOL IN LAST 12 MONTHS 1865 1. Every day 1210 2. Nearly every day 2619 3. 3 to 4 times a week 2914 4. 2 times a week 3261 5. Once a week 3557 6. 2 to 3 times a month 2663 7. Once a month 1805 8. 7 to 11 times in the last year 3210 9. 3 to 6 times in the last year 3637 10. 1 or 2 times in the last year 205 99. Unknown

S2AQ8B NUMBER OF DRINKS OF ANY ALCOHOL USUALLY CONSUMED ON DAYS WHEN DRANK ALCOHOL IN LAST 12 MONTHS 26708 1-98. Number of drinks 238 99. Unknown

S2DQ1 BLOOD/NATURAL FATHER EVER AN ALCOHOLIC OR PROBLEM DRINKER 8124 1. Yes 32445 2. No 2524 9. Unknown

S2DQ1 was selected as moderator for this study. The question is, if there is a correlation between alcohol abuse/dependence and number of drinks, that are usually consumed. of special interest is, if there is an influence of the natural father's alcoholism or not.

To be able to have a good overview, I combined all answers from 6994 1. Alcohol abuse only 563 2. Alcohol dependence only 3859 3. Alcohol abuse and dependence to one general variable "Alcohol abuse and/or dependency".

-------------------------

The full Python code is shown below:

-- coding: utf-8 --

""" Created on Fri Sep 2 17:00:00 2022

@author: Bosch-Master """

ANOVA

import numpy import pandas import statsmodels.formula.api as smf import statsmodels.stats.multicomp as multi import seaborn import matplotlib.pyplot as plt

data = pandas.read_csv('nesarc.csv', low_memory=False)

data['dranklastyear']=data['S2AQ3'].dropna() data['numberofdrinks']=data['S2AQ8B'].dropna() data['alcoholicabuseordependency']=data['ALCABDEPP12DX'].dropna() data['fatheralcoholic']=data['S2DQ1'].dropna()

sub1=data[(data['dranklastyear']==1)] sub2 = sub1.copy()

sub2['numberofdrinks'] = pandas.to_numeric(data['numberofdrinks'], errors='coerce') sub2['fatheralcoholic'] = pandas.to_numeric(data['fatheralcoholic'], errors='coerce') sub2['alcoholicabuseordependency'] = pandas.to_numeric(data['alcoholicabuseordependency'], errors='coerce')

print(sub2['fatheralcoholic'])

recode missing values to python missing (NaN)

sub2['numberofdrinks']=sub2['numberofdrinks'].replace(99, numpy.nan)

recoding values for alcoholicabuseordependency(1, 2, 3) into a new variable, alcoholicabuseordependency

recode1 = {0: 0, 1: 1, 2: 1, 3: 1}

adding up abuse and dependency values (only 0 and 1 available)

sub2['alcoholicabuseordependency']= sub2['alcoholicabuseordependency'].map(recode1) sub2['alcoholicabuseordependency']= sub2['alcoholicabuseordependency'].dropna()

model1 = smf.ols(formula='numberofdrinks ~ C(alcoholicabuseordependency)', data=sub2).fit() print (model1.summary()) sub2 = sub2[['numberofdrinks', 'alcoholicabuseordependency', 'fatheralcoholic']].dropna() print ("means for numberofdrinks by alcoholicabuseordependency: yes/no") m1= sub2.groupby('alcoholicabuseordependency').mean() print (m1)

print ("standard deviation for mean numberofdrinks by alcoholicabuseordependency: yes/no") st1= sub2.groupby('alcoholicabuseordependency').std() print (st1)

bivariate bar graph

seaborn.factorplot(x="alcoholicabuseordependency", y="numberofdrinks", data=sub2, kind="bar", ci=None) plt.xlabel('alcoholicabuseordependency') plt.ylabel('Mean numberofdrinks')

subyes=sub2[(sub2['fatheralcoholic']==1)] subno=sub2[(sub2['fatheralcoholic']==2)] print ('association between alcoholicabuseordependency and numberofdrinks for those with fatheralcoholic=yes') model2 = smf.ols(formula='numberofdrinks ~ C(alcoholicabuseordependency)', data=subyes).fit() print (model2.summary())

print ('association between alcoholicabuseordependency and numberofdrinks for those with fatheralcoholic=no') model3 = smf.ols(formula='numberofdrinks ~ C(alcoholicabuseordependency)', data=subno).fit() print (model3.summary())

print ("means for numberofdrinks by alcoholicabuseordependency for fatheralcoholic=yes") m3= subyes.groupby('alcoholicabuseordependency').mean() print (m3)

print ("means for numberofdrinks by alcoholicabuseordependency for fatheralcoholic=no") m4 = subno.groupby('alcoholicabuseordependency').mean() print (m4)

-----------------------------------

Excerpts of the results show that a correlation of the variables numberofdrinks and alcoholicabuseordependency can be assumed:

With the moderator "father is alcoholic = yes", the p value is 0.0559, so no significant effect can be seen that is influenced by the father's alcoholism.

For the other case, that the father is not an alcoholic, the p value is quite small, p = 7e-8. That means, that there is a significant effect visible, if the father is not an alcoholic himself. This is a very intersting result.

[speedytreedragonperson]

10 Strange And Mind-Blowing Nature Facts

We love nature! As a complete targeted on planting one billion trees by 2030, we’d be crazy to not love nature! Nature will be, at times, mind-bogglingly complicated and actually fascinating. Here are unit ten of our favorite strange and mind-blowing facts regarding nature.

 1. Strange however true – there are unit twelve times a lot of trees on Earth than stars within the milk like Way!

Scientists estimate there are unit between two hundred – four hundred billion stars in our galaxy whereas there are unit associate degree calculable one trillion trees on Earth! Just like the stars, trees live an extended time and area unit actually necessary forever. Exam in a connected diary you would possibly like here.

 2. There was a time once four distinct human species lived at a similar time

After fastidiously learning home in fossils found in African nation, African country and Chad, German scientists have complete that four distinct human species coexisted at a similar time regarding three million years one. What isn’t proverbial is however or if they're connected and whether or not they interacted with each other.

https://www.google.com/search?q=nature+amazing+facts%2C&oq=nature+amazing+facts%2C&aqs=chrome.0.69i59j0i22i30l5j0i15i22i30j0i390l3.18474j0j7&sourceid=chrome&ie=UTF-8

 3. Cows kill a lot of folks than sharks

Hard to believe, but true. Sharks kill a mean of five folks per annum whereas cows kill a mean of twenty-two folks per annum. In fact, humans are unit a lot of deadly to sharks than they're to humans. Humans kill regarding a hundred million sharks per year!

 4. It’s unlikely that anyone might die in quicksand

You know all those movies and television shows wherever somebody terribly dramatically gets enveloped up by quicksand. It’s simply that…dramatic, however not true. this is often as a result of most quicksand is simply many inches deep. If somebody will die in quicksand it always happens in periodic event basins. The person gets stuck, then drowns once the tide comes in.

5. you'll work all of the planets within the Milky Way between the world and Moon with a bit house left over.

Mind Blown. If you don’t believe ME, there’s a keen graphic during this article that shows however all of them work

6. Lobsters don’t die of maturity

There is some disagreement on this truth and it quite all boils all the way down to linguistics. the reason is long and extremely scientifically technical. So, I’ll let this text make a case for it.

7. There was a time once there was no bacterium that might decompose a tree.

The trees that existed three hundred million years agone don't jibe the trees we've got on Earth currently. These trees might grow extraordinarily tall, however that they had terribly shallow root systems, thus fell over terribly simply. At the time, no microbes existed that might decompose these trees, thus as they fell, they stacked up upon one another, eventually making what one might take into account a blessing or a curse….coal.

8. Pineapples take 2 years to grow

Remember this following time you purchase a beautiful pineapple and so let it head to waste. If planted from a sucker, a pineapple can take regarding eighteen months to bloom, however if you plant the highest of the pineapple, it'll take a pair of to a pair of ½ years to supply a bloom.

9. a couple of million folks die each year from dipterous insect bites.

That adds up to a pair of,700 per day, or a hundred each hour. How? Proto infection is that the reason for most deaths. Mosquito  carry the virus that causes proto l infection. once they “bite” a person's, the virus is transferred to the human World Health Organization then contracts the sickness. a toddler dies each thirty seconds from proto infection. Let that sink in a very moment……

 10. Pluto hasn’t created a full orbit round the sun since it had been discovered in 1930

“What?” you would possibly be spoken communication. Yes! It’s true! Pluto was discovered on February eighteen, 1930. It hasn’t created a full orbit of the sun since that point as a result of its implausibly slow orbit. In fact, it takes Pluto 248.09 years to create one orbit round the sun. this implies that Pluto can create its initial full orbit since 1930 on March twenty three, 2178._

#weird facts that are trueweird facts about lifeweird facts about metop 10 weird facts about humansamazing facts science

[frampi89]

1) # -- coding: utf-8 -- """ Created on Wed May 18 17:10:43 2022

@author: Frampi89 """ import pandas import numpy

Additional libraries would be imported here

data = pandas.read_csv('nesarc_pds.csv', low_memory=False)

Checking the format of your variables

data['ETHRACE2A'].dtype

Setting variables you will be working with to numeric

data['SMOKER'] = pandas.to_numeric(data['SMOKER'])

Subset data to young adults age 30 to 40 who smoked currently

sub1=data[(data['AGE']>=30) & (data['AGE']<=40)& (data['SMOKER']==1)]

Make a copy of my new subsetted data

sub2 = sub1.copy()

Frequency distributions on new sub2 data frame

print('Counts for CURRENT SMOKERS in AGE between 30 & 40') c1 = sub2['SMOKER'].value_counts(sort=False) print(c1)

print('Counts for AGE WHEN SMOKED FIRST FULL CIGARETTE in AGE between 30 & 40') c1 = sub2['S3AQ2A1'].value_counts(sort=True) print(c1)

print('percentages AGE WHEN SMOKED FIRST FULL CIGARETTE in AGE between 30 & 40') p1 = sub2['S3AQ2A1'].value_counts(sort=True, normalize=True) print (p1)

print('Counts for HIGHEST GRADE OR YEAR OF SCHOOL COMPLETED in AGE between 30 & 40') c2 = sub2['S1Q6A'].value_counts(sort=True) print(c2)

print('percentages HIGHEST GRADE OR YEAR OF SCHOOL COMPLETED in AGE between 30 & 40') p2 = sub2['S1Q6A'].value_counts(sort=True, normalize=True) print (p2)

print('Counts for AGE STARTED SMOKING CIGARETTES EVERY DAY in AGE between 30 & 40') c3 = sub2['S3AQ51'].value_counts(sort=True) print(c3)

print('percentages AGE STARTED SMOKING CIGARETTES EVERY DAY in AGE between 30 & 40') p3 = sub2['S3AQ51'].value_counts(sort=True, normalize=True) print (p3)

Upper-case all DataFrame column names - place afer code for loading data aboave

data.columns = list(map(str.upper, data.columns))

Bug fix for display formats to avoid run time errors - put after code for loading data above

pandas.set_option('display.float_format', lambda x:'%f'%x)# -- coding: utf-8 -- """ Spyder Editor

"""

2) Counts for CURRENT SMOKERS in AGE between 30 & 40 1 2835 Name: SMOKER, dtype: int64 Counts for AGE WHEN SMOKED FIRST FULL CIGARETTE in AGE between 30 & 40 16 392 15 307 14 256 18 256 232 17 231 13 213 12 191 19 118 20 95 21 85 11 68 10 59 25 42 22 41 9 35 23 30 8 27 24 22 99 18 26 17 6 16 7 16 27 15 28 14 30 6 29 5 5 5 32 5 35 5 31 4 36 3 38 2 33 2 34 1 39 1 Name: S3AQ2A1, dtype: int64 percentages AGE WHEN SMOKED FIRST FULL CIGARETTE in AGE between 30 & 40 16 0.138272 15 0.108289 14 0.090300 18 0.090300 0.081834 17 0.081481 13 0.075132 12 0.067372 19 0.041623 20 0.033510 21 0.029982 11 0.023986 10 0.020811 25 0.014815 22 0.014462 9 0.012346 23 0.010582 8 0.009524 24 0.007760 99 0.006349 26 0.005996 6 0.005644 7 0.005644 27 0.005291 28 0.004938 30 0.002116 29 0.001764 5 0.001764 32 0.001764 35 0.001764 31 0.001411 36 0.001058 38 0.000705 33 0.000705 34 0.000353 39 0.000353 Name: S3AQ2A1, dtype: float64 Counts for HIGHEST GRADE OR YEAR OF SCHOOL COMPLETED in AGE between 30 & 40 8 767 10 629 7 357 11 326 12 313 9 172 14 103 13 65 6 40 4 28 5 22 3 6 1 5 2 2 Name: S1Q6A, dtype: int64 percentages HIGHEST GRADE OR YEAR OF SCHOOL COMPLETED in AGE between 30 & 40 8 0.270547 10 0.221869 7 0.125926 11 0.114991 12 0.110406 9 0.060670 14 0.036332 13 0.022928 6 0.014109 4 0.009877 5 0.007760 3 0.002116 1 0.001764 2 0.000705 Name: S1Q6A, dtype: float64 Counts for AGE STARTED SMOKING CIGARETTES EVERY DAY in AGE between 30 & 40 505 18 370 16 321 17 220 15 212 19 163 20 157 14 122 21 110 13 87 25 84 22 79 12 56 23 52 24 44 26 31 99 30 28 30 27 24 30 21 10 16 11 15 29 12 31 12 35 11 32 10 34 7 9 7 33 5 7 4 36 4 8 4 6 3 38 3 40 2 39 2 Name: S3AQ51, dtype: int64 percentages AGE STARTED SMOKING CIGARETTES EVERY DAY in AGE between 30 & 40 0.178131 18 0.130511 16 0.113228 17 0.077601 15 0.074780 19 0.057496 20 0.055379 14 0.043034 21 0.038801 13 0.030688 25 0.029630 22 0.027866 12 0.019753 23 0.018342 24 0.015520 26 0.010935 99 0.010582 28 0.010582 27 0.008466 30 0.007407 10 0.005644 11 0.005291 29 0.004233 31 0.004233 35 0.003880 32 0.003527 34 0.002469 9 0.002469 33 0.001764 7 0.001411 36 0.001411 8 0.001411 6 0.001058 38 0.001058 40 0.000705 39 0.000705 Name: S3AQ51, dtype: float64

3) With this program I tried to check the content of the variables for my project, aplying some filters like age and smoker status trying to understand better the data.

Thanks.

[fytheuntamed]

💮 Untamed Fest 2019 💮 // Day (6/31) // Quote: Yunmeng Siblings’ Promise

“I promised that you, Jiang Cheng, and me will be together forever.”

“Yes. Together forever. Don’t you ever suddenly disappear again, okay?”

                                                                            Don’t go where I can’t follow.

#the untamed #mo dao zu shi #cql #untamedfest #yunmeng siblings #wwx #jiang cheng #yanli #edit #parallels #ep 21 #ep 32 #edit* #utf day 6 #man i must be a masochist because i keep choosing to make edits for the scenes that never fail to make me cry!! #the last line is a jrr tolkien quote that kills me #it just rips my heart out without fail every time i hear it read it think about it

[princekumarprajapatipandit]

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1 class easy

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<!DOCTYPE html><html lang="en"> <head>    <link rel="shortcut icon" href="../logo.png" type="image/x-icon">    <meta charset="UTF-8">    <meta name="viewport" content="width=device-width, initial-scale=1.0">    <title>QUIZ</title>    <link rel="stylesheet" href="Css and Js/w3.css">    <style>        body {            background-color: crimson;        }         .container {            align-items: center;            background-color: rgb(240, 254, 255);            box-shadow: 5px 5px 5px rgb(63, 63, 63);            margin: 30px;            padding: 10px;            border-radius: 20px;            cursor: pointer;        }         .mini-container {            align-items: center;            background-color: rgb(240, 254, 255);            box-shadow: 5px 5px 5px rgb(63, 63, 63);            margin: 30px;            margin-left: 250px;            margin-right: 250px;            padding: 15px;            border-radius: 20px;            cursor: pointer;        }    </style></head> <body>    <center>        <!--=============================                never change         =============================-->        <div class="home container" style="background-color: rgb(0, 26, 255);color: #fff;">            <h2>QUIZ</h2>        </div>        <!--=============================                never change over        =============================-->         <div id="question" class="w3-show">            <div class="w3-content w3-yellow container w3-text-red">                <h2>Q<span id="question-number">1</span>. <span id="question-change">How many hours are there in a day</span>?</h2>                <button class="w3-btn w3-purple w3-round-large w3-outline-none" id="opt1">12 hours</button>                <button class="w3-btn w3-purple w3-round-large w3-outline-none" id="opt2">24 hours</button>                <button class="w3-btn w3-purple w3-round-large w3-outline-none" id="opt3">48 hours</button>                <button class="w3-btn w3-purple w3-round-large w3-outline-none" id="opt4">72 hours</button>            </div>        </div>         <!-- ================                result            ================= -->        <div class="w3-hide" id="result">            <div class="container w3-content">                <h2 class="w3-text-red">                 </h2>            </div>            <div class="container w3-content">                <table>                    <tr>                        <th>Total Questions </th>                        <th id="done">5</th>                    </tr>                    <tr>                        <th>Total Questions Correct</th>                        <th><span id="tick">0</span></th>                    </tr>                    <tr>                        <th>Total Questions wrong</th>                        <th><span id="cross">0</span></th>                    </tr>                </table>            </div>            <div class="container w3-content">                <h2 class="w3-text-red">                 </h2>            </div>            <button class="w3-outline-none w3-btn w3-green w3-xlarge w3-round-large w3-hover-orange w3-margin rebtn"                id="btnre">Try Again</button>        </div>    </center></body><script src="Css and Js/jquery.min.js"></script><script>    // document.getElementById()         // ak code    $('#btnre').click(function (e) {        e.preventDefault();        $('#result').removeClass('w3-show');        $('#result').addClass('w3-hide');        $('#question').removeClass('w3-hide');        $('#question').addClass('w3-show');        document.getElementById('tick').innerHTML = '0';        document.getElementById('cross').innerHTML = '0';        document.getElementById('question-number').innerHTML = '0';        changequestion();    });    var que_correct = 0;    var que_wrong = 0;    var total_que = 1;    const ans1 = '24 hours';    const ans2 = 'Arunachal pradesh';    const ans3 = 3;    const ans4 = '29 days';    const ans5 = '26 letters';     $('#opt1').click(function (e) {        e.preventDefault();        var a1 = document.getElementById('opt1');        var a1 = a1.innerHTML;        console.log(a1);        if ((a1 == ans1) || (a1 == ans2) || (a1 == ans3) || (a1 == ans4) || (a1 == ans5)) {            var que_correct = document.getElementById('tick').innerHTML;            var que_correct = eval(que_correct);            var que_correct = eval(que_correct + 1);            document.getElementById('tick').innerHTML = que_correct;            changequestion();        } else {            var que_wrong = document.getElementById('cross').innerHTML;            var que_wrong = eval(que_wrong);            var que_wrong = eval(que_wrong + 1);            document.getElementById('cross').innerHTML = que_wrong;            changequestion();        }    });     function changequestion() {        debugger        var questionNumber = document.getElementById('question-number').innerHTML;        console.log(questionNumber);        var queno = eval(questionNumber);        if (questionNumber < 5) {            var queno = eval(queno + 1);            document.getElementById('question-number').innerHTML = queno;            document.getElementById('done').innerHTML = queno;            if (queno == 1) {                document.getElementById('question-change').innerHTML = 'How many hours are there in a day';                document.getElementById('opt1').innerHTML = '12 hours';                document.getElementById('opt2').innerHTML = '24 hours';                document.getElementById('opt3').innerHTML = '48 hours';                document.getElementById('opt4').innerHTML = '72 hours';            } else if (queno == 2) {                document.getElementById('question-change').innerHTML = 'Which place in India is also known as “Land of Rising Sun”';                document.getElementById('opt1').innerHTML = 'Gujrat';                document.getElementById('opt2').innerHTML = 'Kerala';                document.getElementById('opt3').innerHTML = 'Ladakh';                document.getElementById('opt4').innerHTML = 'Arunachal pradesh';            } else if (queno == 3) {                document.getElementById('question-change').innerHTML = 'How many zeros are there in a two-thousand rupee note';                document.getElementById('opt1').innerHTML = '4';                document.getElementById('opt2').innerHTML = '6';                document.getElementById('opt3').innerHTML = '3';                document.getElementById('opt4').innerHTML = '2';            } else if (queno == 4) {                document.getElementById('question-change').innerHTML = 'How many days are there in the month of February in a leap year';                document.getElementById('opt1').innerHTML = '30 days';                document.getElementById('opt2').innerHTML = '28 days';                document.getElementById('opt3').innerHTML = '29 days';                document.getElementById('opt4').innerHTML = '31 days';            } else if (queno == 5) {                document.getElementById('question-change').innerHTML = 'How many letters are there in the English alphabet';                document.getElementById('opt1').innerHTML = '10 letters';                document.getElementById('opt2').innerHTML = '30 letters';                document.getElementById('opt3').innerHTML = '18 letters';                document.getElementById('opt4').innerHTML = '26 letters';            }            else {                alert('question ended')            }        } else if (queno == 5) {            $('#question').removeClass('w3-show');            $('#question').addClass('w3-hide');            $('#result').removeClass('w3-hide');            $('#result').addClass('w3-show');        }        else {            alert('Technical error')        }    }     $('#opt2').click(function (e) {        e.preventDefault();        var a1 = document.getElementById('opt2');        var a1 = a1.innerHTML;        console.log(a1);        if ((a1 == ans1) || (a1 == ans2) || (a1 == ans3) || (a1 == ans4) || (a1 == ans5)) {            var que_correct = document.getElementById('tick').innerHTML;            var que_correct = eval(que_correct);            var que_correct = eval(que_correct + 1);            document.getElementById('tick').innerHTML = que_correct;            changequestion();        } else {            var que_wrong = document.getElementById('cross').innerHTML;            var que_wrong = eval(que_wrong);            var que_wrong = eval(que_wrong + 1);            document.getElementById('cross').innerHTML = que_wrong;            changequestion();        }    });     $('#opt3').click(function (e) {        e.preventDefault();        var a1 = document.getElementById('opt3');        var a1 = a1.innerHTML;        console.log(a1);        if ((a1 == ans1) || (a1 == ans2) || (a1 == ans3) || (a1 == ans4) || (a1 == ans5)) {            var que_correct = document.getElementById('tick').innerHTML;            var que_correct = eval(que_correct);            var que_correct = eval(que_correct + 1);            document.getElementById('tick').innerHTML = que_correct;            changequestion();        } else {            var que_wrong = document.getElementById('cross').innerHTML;            var que_wrong = eval(que_wrong);            var que_wrong = eval(que_wrong + 1);            document.getElementById('cross').innerHTML = que_wrong;            changequestion();        }    });     $('#opt4').click(function (e) {        e.preventDefault();        var a1 = document.getElementById('opt4');        var a1 = a1.innerHTML;        console.log(a1);        if ((a1 == ans1) || (a1 == ans2) || (a1 == ans3) || (a1 == ans4) || (a1 == ans5)) {            var que_correct = document.getElementById('tick').innerHTML;            var que_correct = eval(que_correct);            var que_correct = eval(que_correct + 1);            document.getElementById('tick').innerHTML = que_correct;            changequestion();        } else {            var que_wrong = document.getElementById('cross').innerHTML;            var que_wrong = eval(que_wrong);            var que_wrong = eval(que_wrong + 1);            document.getElementById('cross').innerHTML = que_wrong;            changequestion();         }    }); </script> </html><!-- @$%&-+()*"':;!?[]{}'" -->

[chidujs]

ASSIGNMENT

-- coding: utf-8 --

""" Created on Fri Mar 1 17:20:15 2019 @author: Voltas """

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

nesarc = pandas.read_csv ('nesarc_pds.csv' , low_memory=False)

Set PANDAS to show all columns in DataFrame

pandas.set_option('display.max_columns', None)

Set PANDAS to show all rows in DataFrame

pandas.set_option('display.max_rows', None)

nesarc.columns = map(str.upper , nesarc.columns)

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

Change my variables to numeric

nesarc['AGE'] = pandas.to_numeric(nesarc['AGE'], errors='coerce') nesarc['S3BQ4'] = pandas.to_numeric(nesarc['S3BQ4'], errors='coerce') nesarc['S3BQ1A5'] = pandas.to_numeric(nesarc['S3BQ1A5'], errors='coerce') nesarc['S3BD5Q2B'] = pandas.to_numeric(nesarc['S3BD5Q2B'], errors='coerce') nesarc['S3BD5Q2E'] = pandas.to_numeric(nesarc['S3BD5Q2E'], errors='coerce') nesarc['MAJORDEP12'] = pandas.to_numeric(nesarc['MAJORDEP12'], errors='coerce') nesarc['GENAXDX12'] = pandas.to_numeric(nesarc['GENAXDX12'], errors='coerce')

Subset my sample

subset1 = nesarc[(nesarc['AGE']>=18) & (nesarc['AGE']<=30)] # Ages 18-30 subsetc1 = subset1.copy()

subset2 = nesarc[(nesarc['AGE']>=18) & (nesarc['AGE']<=30) & (nesarc['S3BQ1A5']==1)] # Cannabis users, ages 18-30 subsetc2 = subset2.copy()

Setting missing data for frequency and cannabis use, variables S3BD5Q2E, S3BQ1A5

subsetc1['S3BQ1A5']=subsetc1['S3BQ1A5'].replace(9, numpy.nan) subsetc2['S3BD5Q2E']=subsetc2['S3BD5Q2E'].replace('BL', numpy.nan) subsetc2['S3BD5Q2E']=subsetc2['S3BD5Q2E'].replace(99, numpy.nan)

Contingency table of observed counts of major depression diagnosis (response variable) within cannabis use (explanatory variable), in ages 18-30

contab1=pandas.crosstab(subsetc1['MAJORDEP12'], subsetc1['S3BQ1A5']) print (contab1)

Column percentages

colsum=contab1.sum(axis=0) colpcontab=contab1/colsum print(colpcontab)

Chi-square calculations for major depression within cannabis use status

print ('Chi-square value, p value, expected counts, for major depression within cannabis use status') chsq1= scipy.stats.chi2_contingency(contab1) print (chsq1)

Contingency table of observed counts of geberal anxiety diagnosis (response variable) within cannabis use (explanatory variable), in ages 18-30

contab2=pandas.crosstab(subsetc1['GENAXDX12'], subsetc1['S3BQ1A5']) print (contab2)

Column percentages

colsum2=contab2.sum(axis=0) colpcontab2=contab2/colsum2 print(colpcontab2)

Chi-square calculations for general anxiety within cannabis use status

print ('Chi-square value, p value, expected counts, for general anxiety within cannabis use status') chsq2= scipy.stats.chi2_contingency(contab2) print (chsq2)

#

Contingency table of observed counts of major depression diagnosis (response variable) within frequency of cannabis use (10 level explanatory variable), in ages 18-30

contab3=pandas.crosstab(subset2['MAJORDEP12'], subset2['S3BD5Q2E']) print (contab3)

Column percentages

colsum3=contab3.sum(axis=0) colpcontab3=contab3/colsum3 print(colpcontab3)

Chi-square calculations for mahor depression within frequency of cannabis use groups

print ('Chi-square value, p value, expected counts for major depression associated frequency of cannabis use') chsq3= scipy.stats.chi2_contingency(contab3) print (chsq3)

recode1 = {1: 9, 2: 8, 3: 7, 4: 6, 5: 5, 6: 4, 7: 3, 8: 2, 9: 1} # Dictionary with details of frequency variable reverse-recode subsetc2['CUFREQ'] = subsetc2['S3BD5Q2E'].map(recode1) # Change variable name from S3BD5Q2E to CUFREQ

subsetc2["CUFREQ"] = subsetc2["CUFREQ"].astype('category')

Rename graph labels for better interpretation

subsetc2['CUFREQ'] = subsetc2['CUFREQ'].cat.rename_categories(["2 times/year","3-6 times/year","7-11 times/years","Once a month","2-3 times/month","1-2 times/week","3-4 times/week","Nearly every day","Every day"])

Graph percentages of major depression within each cannabis smoking frequency group

plt.figure(figsize=(12,4)) # Change plot size ax1 = seaborn.factorplot(x="CUFREQ", y="MAJORDEP12", data=subsetc2, kind="bar", ci=None) ax1.set_xticklabels(rotation=40, ha="right") # X-axis labels rotation plt.xlabel('Frequency of cannabis use') plt.ylabel('Proportion of Major Depression') plt.show()

Post hoc test, pair comparison of frequency groups 1 and 9, 'Every day' and '2 times a year'

recode2 = {1: 1, 9: 9} subsetc2['COMP1v9']= subsetc2['S3BD5Q2E'].map(recode2)

Contingency table of observed counts

ct4=pandas.crosstab(subsetc2['MAJORDEP12'], subsetc2['COMP1v9']) print (ct4)

Column percentages

colsum4=ct4.sum(axis=0) colpcontab4=ct4/colsum4 print(colpcontab4)

Chi-square calculations for pair comparison of frequency groups 1 and 9, 'Every day' and '2 times a year'

print ('Chi-square value, p value, expected counts, for pair comparison of frequency groups -Every day- and -2 times a year-') cs4= scipy.stats.chi2_contingency(ct4) print (cs4)

Post hoc test, pair comparison of frequency groups 2 and 6, 'Nearly every day' and 'Once a month'

recode3 = {2: 2, 6: 6} subsetc2['COMP2v6']= subsetc2['S3BD5Q2E'].map(recode3)

Contingency table of observed counts

ct5=pandas.crosstab(subsetc2['MAJORDEP12'], subsetc2['COMP2v6']) print (ct5)

Column percentages

colsum5=ct5.sum(axis=0) colpcontab5=ct5/colsum5 print(colpcontab5)

Chi-square calculations for pair comparison of frequency groups 2 and 6, 'Nearly every day' and 'Once a month'

print ('Chi-square value, p value, expected counts for pair comparison of frequency groups -Nearly every day- and -Once a month-') cs5= scipy.stats.chi2_contingency(ct5) print (cs5)

[the-vanillaa-bean]

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https://www.google.com/amp/s/lotrimagines.tumblr.com/post/155041022440/family-aragorn-imagine/amp

Happy Mother's Day (Legolas X Reader) by Eternal-Violet-Void on DeviantArt

https://www.deviantart.com/eternal-violet-void/art/Happy-Mother-s-Day-Legolas-X-Reader-453502822

Remember King Thranduil. — Imagine; Thranduil x Legolas x Reader

https://www.google.com/amp/s/rememberkingthranduil.tumblr.com/post/164100103590/imagine-thranduil-x-legolas-x-reader/amp

You + Them — A Queen for a King (Viserys x Reader) (Request)

https://www.google.com/amp/s/you-plus-them.tumblr.com/post/118759172748/a-queen-for-a-king-viserys-x-reader-request/amp

What Goes On In My Mind — My Dragon

https://www.google.com/amp/s/ladyninjaa.tumblr.com/post/162653045823/my-dragon/amp

Dragon's Blood / Viserys Targaryen x Reader

https://www.quotev.com/story/5597695/Dragons-Blood-Viserys-Targaryen-x-Reader

Just Another Game Of Thrones Nerd — Champagne and Cracked Mirrors {Viserys x Reader}

https://www.google.com/amp/s/thenoblehouseofdayne.tumblr.com/post/174642045207/champagne-and-cracked-mirrors-viserys-x-reader/amp

| Requests: Accepting prompts only | — Daenerys x Reader / Reader x Viserys “My brother...

https://www.google.com/amp/s/bonniebird.tumblr.com/post/155909152765/daenerys-x-reader-reader-x-viserys-my-brother/amp

tumblr drawing ideas – Vyhledávání Google

https://www.google.com/search?q=tumblr+drawing+ideas&tbm=isch&ved=2ahUKEwjdier8yZroAhWY0-AKHZB0D6UQ2-cCegQIABAB&oq=tumblr+drawing+ideas&gs_l=mobile-gws-wiz-img.3..0j0i7i30l4.37148.38251..38749...0.0..0.147.843.3j5......0....1.........0i8i7i30.NY5HqCjmD-4&ei=IiBtXt3wLpingweQ6b2oCg&prmd=ivsn&safe=active&hl=cs#imgrc=LQxQ9F1KDiEvkM

tumblr viserys drawing ideas – Vyhledávání Google

https://www.google.com/search?q=tumblr+viserys+drawing+ideas&prmd=ivsn&safe=active&hl=cs&um=1&ie=UTF-8&tbm=isch&sxsrf=ALeKk00CUYEZ1e9B4qdmerpD92L_ihLzMw:1584209952108&sa=X&ved=0ahUKEwiy6cb7yZroAhWPsaQKHS4JBiwQ_AUIBg#imgrc=8wfclrVoLaW2XM

Just Another Game Of Thrones Nerd — Violent Skies 2 {Viserys Targaryen x...

https://www.google.com/amp/s/thenoblehouseofdayne.tumblr.com/post/170421633786/violent-skies-2-viserys-targaryen-x/amp

Just Another Game Of Thrones Nerd — Violent Skies {Viserys Targaryen x Targaryen!...

https://www.google.com/amp/s/thenoblehouseofdayne.tumblr.com/post/164565449892/violent-skies-viserys-targaryen-x-targaryen/amp

GoT One-Shots! - The Dragon's New Spouse(Viserys Targaryen x reader) - Wattpad

https://www.wattpad.com/amp/378047730

Aoirohi — Oneshot(Request)Legolas x reader, where the reader...

https://www.google.com/amp/s/aoirohi.tumblr.com/post/136201651514/oneshotrequestlegolas-x-reader-where-the-reader/amp

Edge of the Obsession — Til’ the end

https://www.google.com/amp/s/annoyinglydecaffinatedfangirl.tumblr.com/post/136569136075/til-the-end/amp

Write from the heart — Oneshot(Request)Legolas x reader, where the reader...

https://www.google.com/amp/s/greenleaf-writings.tumblr.com/post/129093218719/oneshotrequestlegolas-x-reader-where-the-reader/amp

UNCPanda — Reconciliation

https://www.google.com/amp/s/uncpanda.tumblr.com/post/170557362517/reconciliation/amp

The Drinking Game. | Legolas X Reader

https://www.quotev.com/story/4743905/Legolas-X-Reader/6

legolassss — Mermaid Request

https://www.google.com/amp/s/legolassss.tumblr.com/post/162313793319/mermaid-request/amp

Multi-Fandom Imagines — Forbidden

https://www.google.com/amp/s/fandom-what-ifs.tumblr.com/post/162400840160/forbidden/amp

The Memory {Legolas X Reader} by cchope-love on DeviantArt

https://www.deviantart.com/cchope-love/art/The-Memory-Legolas-X-Reader-475433290

In Black and White || Thranduil || AU by Lilysm on DeviantArt

https://www.deviantart.com/lilysm/art/In-Black-and-White-Thranduil-AU-594638943

Rebellions are built on hope — To Know One’s Heart- Legolas Greenleaf

https://www.google.com/amp/s/fanfic-shiz.tumblr.com/post/156366674011/to-know-ones-heart-legolas-greenleaf/amp

Welcome to Wonderland — Legolas x Reader

https://www.google.com/amp/s/agent221b.tumblr.com/post/149103540764/legolas-x-reader/amp

Stories of Middle Earth — Imagine being Legolas' pregnant wife and having...

https://www.google.com/amp/s/middleearthstories.tumblr.com/post/120643836534/imagine-being-legolas-pregnant-wife-and-having/amp

Let's write stuff! — Serendipity

https://www.google.com/amp/s/kittenwritesstuff.tumblr.com/post/166476079289/serendipity/amp

Fool of a Took! — Imagine falling for Legolas, but shutting yourself...

https://www.google.com/amp/s/hobbit-lotr-oneshots.tumblr.com/post/125343886498/imagine-falling-for-legolas-but-shutting-yourself/amp

Tolkien One-Shots — Legolas x Reader One-Shot: Don't Run Away

https://www.google.com/amp/s/that-writer169.tumblr.com/post/155511869106/legolas-x-reader-one-shot-dont-run-away/amp

Legolas x Reader- 1 by WarriorNerdz66 on DeviantArt

https://www.deviantart.com/warriornerdz66/art/Legolas-x-Reader-1-508888162

Elleth - Parf Edhellen: an elvish dictionary

https://www.elfdict.com/w/elleth/s

CHAPTER 3 - A PROPOSAL | The*Reason*Why

https://www.quotev.com/story/10046226/TheReasonWhy/3

The Cabin That Reunites | Under a Full Moon ( Jacob & Reader )

https://www.quotev.com/story/9419961/Under-a-Full-Moon-Jacob-Reader/20

legolas quotes – Vyhledávání Google

https://www.google.com/search?q=legolas+quotes&tbm=isch&ved=2ahUKEwjh-PXF-oLoAhWX_4UKHem9BboQ2-cCegQIABAB&oq=legolas+quotes&gs_l=mobile-gws-wiz-img.3...469716.472639..472958...0.0..0.0.0.......0....1.8t1fBCwKbe8&ei=171gXqGxDpf_lwTp-5bQCw&client=ms-android-tmobile-cz&prmd=isnv&safe=active#imgrc=WwJZWjLsgD2xiM

legolas quotes – Vyhledávání Google

https://www.google.com/search?q=legolas+quotes&tbm=isch&ved=2ahUKEwjh-PXF-oLoAhWX_4UKHem9BboQ2-cCegQIABAB&oq=legolas+quotes&gs_l=mobile-gws-wiz-img.3...469716.472639..472958...0.0..0.0.0.......0....1.8t1fBCwKbe8&ei=171gXqGxDpf_lwTp-5bQCw&client=ms-android-tmobile-cz&prmd=isnv&safe=active#imgrc=AsxxSARCmxft0M

legolas quotes – Vyhledávání Google

https://www.google.com/search?q=legolas+quotes&tbm=isch&ved=2ahUKEwjh-PXF-oLoAhWX_4UKHem9BboQ2-cCegQIABAB&oq=legolas+quotes&gs_l=mobile-gws-wiz-img.3...469716.472639..472958...0.0..0.0.0.......0....1.8t1fBCwKbe8&ei=171gXqGxDpf_lwTp-5bQCw&client=ms-android-tmobile-cz&prmd=isnv&safe=active#imgrc=B4YDmXlF1-2rtM

legolas quotes – Vyhledávání Google

https://www.google.com/search?q=legolas+quotes&tbm=isch&ved=2ahUKEwjh-PXF-oLoAhWX_4UKHem9BboQ2-cCegQIABAB&oq=legolas+quotes&gs_l=mobile-gws-wiz-img.3...469716.472639..472958...0.0..0.0.0.......0....1.8t1fBCwKbe8&ei=171gXqGxDpf_lwTp-5bQCw&client=ms-android-tmobile-cz&prmd=isnv&safe=active#imgrc=JsU6i00AYmEyLM

legolas quotes – Vyhledávání Google

https://www.google.com/search?q=legolas+quotes&tbm=isch&ved=2ahUKEwjh-PXF-oLoAhWX_4UKHem9BboQ2-cCegQIABAB&oq=legolas+quotes&gs_l=mobile-gws-wiz-img.3...469716.472639..472958...0.0..0.0.0.......0....1.8t1fBCwKbe8&ei=171gXqGxDpf_lwTp-5bQCw&client=ms-android-tmobile-cz&prmd=isnv&safe=active#imgrc=ulBuy4fbT2YdJM

legolas quotes – Vyhledávání Google

https://www.google.com/search?q=legolas+quotes&tbm=isch&ved=2ahUKEwjh-PXF-oLoAhWX_4UKHem9BboQ2-cCegQIABAB&oq=legolas+quotes&gs_l=mobile-gws-wiz-img.3...469716.472639..472958...0.0..0.0.0.......0....1.8t1fBCwKbe8&ei=171gXqGxDpf_lwTp-5bQCw&client=ms-android-tmobile-cz&prmd=isnv&safe=active#imgrc=2wtlF7I-XwtSKM

realistic drawings tumblr – Vyhledávání Google

https://www.google.com/search?q=realistic+drawings+tumblr&safe=active&client=ms-android-tmobile-cz&source=android-browser&prmd=isvn&sxsrf=ALeKk006kbVT7JJUeptKITP9DkMEEACt7g:1583225212238&source=lnms&tbm=isch&sa=X&ved=2ahUKEwiDxf_C9f3nAhVQQ0EAHTAqA7EQ_AUoAXoECAsQAQ#imgrc=P1o6dB-Kc9pofM

ccrriissuuus writings — Imagine being married to Legolas while having an...

https://www.google.com/amp/s/ccrriissuuuswritings.tumblr.com/post/112396435488/imagine-being-married-to-legolas-while-having-an/amp

My Imagines — Legolas Imagine

https://www.google.com/amp/s/loserimagines.tumblr.com/post/133620385841/legolas-imagine/amp

Fandom Imagines — Baby- Legolas

https://www.google.com/amp/s/blog-of-a-multitude-of-fandoms.tumblr.com/post/127545669716/baby-legolas/amp

Alexandra's Scribbles — Lessons In Grief.

https://www.google.com/amp/s/alexandra-scribbles.tumblr.com/post/126523402443/lessons-in-grief/amp

The Road Goes Ever On and On — "Imagine Legolas standing up to his father because...

https://www.google.com/amp/s/imaginexhobbit.tumblr.com/post/74831542023/imagine-legolas-standing-up-to-his-father-because/amp

About That Characters — Legolas with a human!Reader would include

https://www.google.com/amp/s/aboutthatcharacters.tumblr.com/post/172136254513/legolas-with-a-humanreader-would-include/amp

How to Grow Hair Faster, Part II - it's a love/love thing

https://lovelovething.com/how-to-grow-hair-faster-part-ii/

ruler queen aesthetic – Vyhledávání Google

https://www.google.com/search?q=ruler+queen+aesthetic&tbm=isch&ved=2ahUKEwjUtcz90_HnAhWCqbQKHcKkASIQ2-cCegQIABAB&oq=ruler+queen+aesthetic&gs_l=mobile-gws-wiz-img.3...106379.120701..121015...1.0..0.178.2512.5j15......0....1.........35i304i39j35i39j0i7i30j0i13i30j0i30j30i10._oTadnT2LFY&ei=s6tXXpSQHILT0gXCyYaQAg&bih=520&biw=360&client=ms-android-tmobile-cz&prmd=ismvn&safe=active#imgrc=ApVc5Mp9ftPr3M

ruler queen aesthetic – Vyhledávání Google

https://www.google.com/search?q=ruler+queen+aesthetic&tbm=isch&ved=2ahUKEwjUtcz90_HnAhWCqbQKHcKkASIQ2-cCegQIABAB&oq=ruler+queen+aesthetic&gs_l=mobile-gws-wiz-img.3...106379.120701..121015...1.0..0.178.2512.5j15......0....1.........35i304i39j35i39j0i7i30j0i13i30j0i30j30i10._oTadnT2LFY&ei=s6tXXpSQHILT0gXCyYaQAg&bih=520&biw=360&client=ms-android-tmobile-cz&prmd=ismvn&safe=active#imgrc=7mxD6hEFf_XCdM

ruler queen aesthetic – Vyhledávání Google

https://www.google.com/search?q=ruler+queen+aesthetic&tbm=isch&ved=2ahUKEwjUtcz90_HnAhWCqbQKHcKkASIQ2-cCegQIABAB&oq=ruler+queen+aesthetic&gs_l=mobile-gws-wiz-img.3...106379.120701..121015...1.0..0.178.2512.5j15......0....1.........35i304i39j35i39j0i7i30j0i13i30j0i30j30i10._oTadnT2LFY&ei=s6tXXpSQHILT0gXCyYaQAg&bih=520&biw=360&client=ms-android-tmobile-cz&prmd=ismvn&safe=active#imgrc=Cd1mwpEUHhfhHM

legolas memes – Vyhledávání Google

https://www.google.com/search?q=legolas+memes&safe=active&client=ms-android-tmobile-cz&source=android-browser&prmd=isvn&sxsrf=ALeKk02i-vFV6vz9R8YohpC6yNSyP1cdJQ:1582793053753&source=lnms&tbm=isch&sa=X&ved=2ahUKEwiV4OLNq_HnAhUPM8AKHQjeCLcQ_AUoAXoECA0QAQ&biw=360&bih=520&dpr=2#imgrc=7UZ8I9PY6p-pIM

lotr rinh drawing pinterest – Vyhledávání Google

https://www.google.com/search?q=lotr+rinh+drawing+pinterest&tbm=isch&ved=2ahUKEwiMgPfr3u7nAhWDqbQKHXRGCCUQ2-cCegQIABAB&oq=lotr+rinh+drawing+pinterest&gs_l=mobile-gws-wiz-img.3..30i10.34606.40076..40341...1.0..0.120.1321.7j6......0....1.........35i304i39j33i10.SVoCK2jMWCQ&ei=byRWXsyMPIPT0gX0jKGoAg&client=ms-android-tmobile-cz&prmd=isvn&safe=active#imgrc=iDTL0_JOFz6nlM

Česko - latinský slovník online - Latinsky .cz

http://latinsky-slovnik.latinsky.cz/cesko-latinsky/

Fandom Imagines — Prince Caspian x Reader

https://www.google.com/amp/s/daintyimagines.tumblr.com/post/125984213652/prince-caspian-x-reader/amp

Quileute Imagines — Jacob x reader Warnings: Swearing, kind of smut?...

https://www.google.com/amp/s/quileuteima.tumblr.com/post/157506231419/jacob-x-reader-warnings-swearing-kind-of-smut/amp

The Worry In His Eyes- Jacob x Pregnant! Reader by SonPanssj4 on DeviantArt

https://www.deviantart.com/sonpanssj4/art/The-Worry-In-His-Eyes-Jacob-x-Pregnant-Reader-631167212

I write stuff. — Be Careful

https://www.google.com/amp/s/multi-fandomoneshots.tumblr.com/post/138743497012/be-careful/amp

Unspoken [Damon Salvatore x reader] by JulietWayne on DeviantArt

https://www.deviantart.com/julietwayne/art/Unspoken-Damon-Salvatore-x-reader-526743960

Feel the need — What I Want - Damon Salvatore [Smut]

https://www.google.com/amp/s/mindofhills.tumblr.com/post/176051248207/what-i-want-damon-salvatore-smut/amp

•All Fandom Oneshots• — Damon Salvatore X Reader: Who Owns the Bed

https://www.google.com/amp/s/fandom-oneshots-fa.tumblr.com/post/165560512200/damon-salvatore-x-reader-who-owns-the-bed/amp

How I feel — Damon Salvatore – “Change” “A pregnant girl?...

https://www.google.com/amp/s/showandwrite.tumblr.com/post/100854944458/damon-salvatore-change-a-pregnant-girl/amp

Imaginary Shots Hiatus — Damon Salvatore – He Flipped The Switch

https://www.google.com/amp/s/as-far-as-life-gets.tumblr.com/post/163637613953/damon-salvatore-he-flipped-the-switch/amp

https://www.google.com/amp/s/wolfpack-imagines.tumblr.com/post/165055257072/jacob-black-imprinting/amp

https://www.google.com/amp/s/wolfpack-imagines.tumblr.com/post/165055257072/jacob-black-imprinting/amp

Forbidden Love: Jacob Black x Reader by AbbieDK on DeviantArt

https://www.deviantart.com/abbiedk/art/Forbidden-Love-Jacob-Black-x-Reader-610208757

Tempest{Caspian X/Reader} by lioness94 on DeviantArt

https://www.deviantart.com/lioness94/art/Tempest-Caspian-X-Reader-604905716

Reader Stories — Can I request Legolas x reader forbidden...

https://www.google.com/amp/s/rreader.tumblr.com/post/175013087938/can-i-request-legolas-x-reader-forbidden/amp

My life in Middle Earth — Together forever (a Legolas one-shot)

https://www.google.com/amp/s/dropsiaczek-in-middle-earth.tumblr.com/post/154992217429/together-forever-a-legolas-one-shot/amp

Legolas x Human!Reader by Emitheduck on DeviantArt

https://www.deviantart.com/emitheduck/art/Legolas-x-Human-Reader-423940127

Kitchenator's Writers Blog — Legolas ~ Period

https://www.google.com/amp/s/writingfromkitchenator.tumblr.com/post/185666750088/legolas-period/amp

Stories of Middle Earth — Imagine giving your hoodie to Legolas but he...

https://www.google.com/amp/s/middleearthstories.tumblr.com/post/121783610184/imagine-giving-your-hoodie-to-legolas-but-he/amp

Meg's Fandom Harem(thanks anon!) — Centuries Passed

https://www.google.com/amp/s/meganlpie.tumblr.com/post/166327577550/centuries-passed/amp

Trick to Love - Jealousy and Matchmakers, Fan Fiction | MediaMiner

https://www.mediaminer.org/fanfic/c/fan-fiction/trick-to-love/19298/48233

[iparker]

Things to do in Puerto Rico.

There are many places to see and activities to do in Puerto Rico.

1. If you’re into art and culture, I suggest that you visit art museums. Art often shows parts of history based on the time it was painted in, you can learn many things from a simple painting. A museum to visit would be Museo de Arte de Puerto Rico. This art museum has paintings that sate back to the 17th century.

2. If you are into music, look no further than Puerto Rican streets which often have preformers. Puerto Rico is known for having lively music playing wherever you go. Many people dance to the music around them.

3. a rather nice way to spend your afternoon is by the beaches. las playas son muy bonitas. Se recomienda la natación y el surf.

4. Late afternoons usually call for shopping. Puerto Rico has many personal boutiques, malls, and markets for you to buy trinkets from. In Old San Juan, you can find handmade craftsmanship that you can take with you back to America as a souvenir.

5. As the sun begins to lower and paint the blue sky a sandy pink hue, the body might feel sore from all of the walking that you’ve done. To relax, try out a spa in Puerto Rico. The top spa in Puerto Rico es El Spa en el Condado Vanderbilt Hotel.

6. Now that your body is relax, perhaps the growl in your stomach is more noticeable than before. El Marmalade es muy bueno. It serves cocktails and food. It also offers a very cozy setting for you to enjoy your evening around family and friends.

7. If you’re looking for a more risky evening (and are of legal age), Puerto Rico has many casinos. Casino del Mar, Casino Metro, y Sheraton Puerto Rico es muy grandes. ¿Tendrás suerte en Puerto Rico? Averiguar en los casinos.

8. After an eventful day, it’s normal to have sleep begin to creep into your bones. A Ritz-Carlton Reserve es un bonito hotel. It has 50 acres of beach thst you can stare at and take in the beauty sround you.

https://www.discoverpuertorico.com/article/top-rated-spas-puerto-rico

https://www.google.com/search?q=best+restaurants+in+puerto+rico&rlz=1CDGOYI_enUS866US866&oq=best+rest&aqs=chrome.1.69i57j0l3.2710j0j7&hl=en-US&sourceid=chrome-mobile&ie=UTF-8#trex=m_t:lcl_akp,rc_f:rln,rc_ludocids:356385405524564790,ru_lqi:Ch9iZXN0IHJlc3RhdXJhbnRzIGluIHB1ZXJ0byByaWNvIgOoAQFIod6BvemqgIAIWi0KC3Jlc3RhdXJhbnRzEAAYACIacmVzdGF1cmFudHMgaW4gcHVlcnRvIHJpY28,ru_phdesc:OkaxsiehHMc

https://www.discoverpuertorico.com/list/top-beachside-accommodations-puerto-rico