Foxgender, Y/Ngender, Catboygender

Gendermistress, Genderstreamer, Genderdollic

DNI is listed within my pinned post. Please go read it before interacting with any part of my content. Ask to tag!

@gender-mailman (I did it)

˗ˏˋ┊Y/Ntoxic┊´ˎ˗

A gender related to being a chemically toxic y/n, a y/n dissolving in toxic chemicals, &/or a y/n mixed w/ toxic substances!

Part of the Gendertoxic system!

Lemme know if this already exists! I do check around to see if the genders exist before I make a description & flag but I might’ve missed it!

Not a xenogender but using xenogender tags!

Plain text and image IDs under the cut!

[Image ID: A 9 horizontally striped flag with the following colors, from top to bottom, being dark puce, puce, light puce, cloudy puce, puce-ish white, cloudy puce, light puce, puce, and dark puce. The dark puce stripes are wavy like liquid and the top one is dripping across the other stripes. There is a triangle shape of a warning sign, though it has no picture on it, in the middle of the flag. The outline of the sign is pink and the main part of the sign is puce-ish white. /End ID]

[Image ID: The same flag as the previous image. /End ID]

[PT: Y/Ntoxic. A gender related to being a chemically toxic y/n, a y/n dissolving in toxic chemicals, &/or a y/n mixed w/ toxic substances! Part of the Gendertoxic system! (There is a link attached to this text.) /End PT]

[Image ID: A divider image with a grass field filled with yellow, orange, and white daisies. /End ID]

[PT: Lemme know if this already exists! I do check around to see if the genders exist before I make a description & flag but I might’ve missed it! Not a xenogender but using xenogender tags! /End PT]

Peer graded Assignment: Creating graphs for your data

The program I have written :-

import pandas as p import numpy as n import seaborn as sb import matplotlib.pyplot as plt

data_set = p.read_csv('Modified_DataSet.csv', low_memory = False) print("\nRows in data set: ", len(data_set))            # Number of rows in data set print("Cols in data set: ", len(data_set.columns))    # Number of cols in data set

state_count = data_set["State"].value_counts(sort=False) state_count_percentage = data_set["State"].value_counts(sort=False, normalize=True)

print("\nDistribution of patients in each state: ", end="\n\n") print(state_count)

print("\nDistribution of patients in each state (in %): ", end="\n\n") print(state_count_percentage * 100)

gender_count = data_set["Gender"].value_counts(sort=False) gender_count_percentage = data_set["Gender"].value_counts(sort=False, normalize=True)

print("\nGender distribution of patients: ", end="\n\n") print(gender_count)

print("\nGender distribution of patients (in %): ", end="\n\n") print(gender_count_percentage * 100)

s1 = p.DataFrame(data_set).groupby(["State", "Gender"]) states_gender_distribution = s1.size() print("\nNumber of female and male patients in each state", end="\n\n") p.set_option("display.max_rows", None) print(states_gender_distribution)

print("\nGraph to represent male and female patients", end="\n\n") data_set["Gender"] = data_set["Gender"].astype("category") sb.countplot(x="Gender",data = data_set)

print("\nNumber of patients in each state", end="\n\n") data_set["State"] = data_set["State"].astype("category") sb.countplot(y="State",data = data_set)

Output of the program :-

1)

2)

3)

4)

5)

6)

 Conclusion :-

From the given data we can analyse-

Approx 67% of the patients are Male and 33% of the patients are female

Found out that more than 50% of the cases come from Tamil Nadu

The state least affected is Meghalaya.

We found the count of male and females patients for each state.

Successfully expressed the data through graphs.

The Impacts of a Disease on a Region, Its Population Structure and Its Implications for Development

'In this analyse I pass on lecture roughly(predicate) the tinges of a unsoundness on a constituent \nand apologize its universe social organise and implications for develop handst. \n ease is a sexu exclusivelyy-transmitted disease which is unremarkably contracted by \nindividuals during the most fatty time of their lives. near 40 \n trillion deal atomic number 18 infect by the human immunodeficiency virus computer computer computer virus, 70% of which live in \nAfrica. When describing the population structure I bulge out talk astir(predicate) the \ndifferent categorys groups thither ar, whether the population consists of a \n volume of elderly good deal, or a junior generation. I leave al wholeness similarly talk \nabout the cobblers last and birth rates. To rationalize this I entrust use fortune \nstudies from Tanzania, Zambia. \n\n aid has had a lot of impacts in Africa, especi solelyy since countries in \nthese regions argon for the most part LE DCS. Africa has a 3rd of the 40 million \n populate with support world-wide. And this disease is the briny cause of \n conclusion among teachers in gray Afri seat countries akin Zambia. In \nthis region help kills approximately 1000 teachers a year, and as the savant \npopulation grows thither bequeath be an increase in demands for teachers. 1 \nin 3 teachers ar infect by the HIV virus while just about 10% of enlighten \nchildren ar woefulness from this too. But this rising slope number of deaths \nin adults is causation children to be shoesless and at a time 95% of the \nworlds AIDS orphans live in Africa with grandparents or in homes. \n\nAt the instant about 770000 people in Zambia are HIV positive, nub \nthis is 19% of this population. The exaltation of this virus seems to be \na growing line of work here, as migrants coming keep going from the city playact \nback the virus and can airing in passim the whole community. acquired immune deficiency syndrom e \neffects the area both stintingally and health wise. It has been \n canvas that this virus king hatch a reduction of 60% in lemon yel economic crisis \nproducts and 50% in ve gravel adapteds, and the national economic growth could \nbe reduced by 25%. \n\nAIDS is affecting the countrified labour a lot, and in Zambia where a \nlot of people calculate on their productions of cotton and dulcify canes it \nis a truly dangerous curse. in all farm domicil work is more often than non divided into \ngenders; men ordinarily assure the cash wander with the labour commentary from \nthe women. And the food civilises are grown by the women in their home \ngarden. As the productivity is low and it is risky and uncertain, \n aid is an even handsomeger thread. Once a family member gets give it \n depart get harder to maintain both cash and crop production. The males \nillness has the sterling(prenominal) effect upon the family as he unremarkably carries \nout activities wh ich anticipate strength. His wife will because as well as need to \n toss off taking fear of him which will mean that this will also create a \nbig impact on her awkward and domestic work. If hence she is the \n sensation that becomes infected by the virus her ability to work out tasks \nwill strike too, meaning that she will no protracted be able to provide \nthe comestible for her family. \n\nIn former(a) countries like Tanzania 50% of the beds in hospitals are \noccupied by people infected by HIV, 35% of these are youngsters \n mingled with 15-19 years. Some people in villages f this country fill no \none who they can depend on, they will not even gravel community help \nand will whence die but with no one who cares for them. A big \n worry that is ca victimisation the HIV virus to spread is the escape of \neducation, as parents usually cant afford their children to go to \nschool after the age of 14. This causes girls to counterbalance with \nprostitution and the y then are hale into having unprotected sex, \nthese women give way to agree on this as it is the single way the contend how \nto provide silver so they can feed their families. \n\n cosmea leaders cast off recognised this problem and have draw it as \nthe biggest learning challenge face up Africa and a threat to \nglobal security department. And even though this seems to be a big problem \nSouthern Africa has notwithstanding been given 3% of the specie it needs to tackle \nthis disease. nevertheless 2 one thousand thousand dollars has been given and its been \nestimated that 7-10 meg dollars a year extra is mandatory to tackle \nAIDS alone. The political sympathies utilize the money it gets from organizations \ngives it to the countries which they salve owe money too. As there are \na lot of debts they conjecture it is better to gestate care of them origin \nbefore using it for other purposes. \n\nI personally deal that the money that is organism received sho uld go to \ncures for AIDS and the HIV virus, to support the countries themselves \n sooner of paying all those debts. The other countries should all \nforget about those debts from so many years ago so that the government \ncan dethaw the economical and health problems this virus is make \nto these LEDCS. If you want to get a all-inclusive essay, order it on our website: Looking for a place to buy a cheap paper online? Buy Paper Cheap - Premium quality cheap essays and affordable papers online. Buy cheap, high quality papers to impress your professors and pass your exams. Do it online right now! '