#sub4

[sub4apparel]

As the seasons change, so do our fashion choices. Puffer jackets, particularly men’s duck down puffer jackets, offer both style and functionality throughout the year. Whether you’re navigating chilly autumn evenings or braving the winter cold, there’s a perfect puffer jacket look for every season. Let’s dive into some stylish outfit inspirations for men featuring the versatile duck down puffer jacket.

[calcone]

5 months til new wheelchair yippee!! sticking with RGK (i love my big green beast) but cannot wait to have something that fits properly (and has push handles)

[tricksforclicks]

A full run. She is going to be a sub4 who sets 8s.

[shine-reblogs]

Submachine Legacy: additional commentary (Monoliths, Shattered Quadrant, and other details)

Apparently I am NOT done talking about this game, lol. To see me talking about the main levels + optional ruins, check my previous post. Beware of spoilers.

General comments

I must comment on the map/menu/however this is called because I noticed (rather late) on my going back and forth the details literally surrounding each level. Namely, how level 3 and 'break the loop' are in the middle a spiral, I assume to symbolize the loopy nature of the locations. Level 6 is in the middle of an octagon, kind of like a shield, which goes well with it being the defence systems of the Submachine. Level 7 lays in the middle of many more concentrical circles, which I'm guessing has to do something about it being the Core of the Submachine. And level 8 rests against seven parallel lines, which goes nicely with you jumping through the seven main layers in that level.

I may be, once again, reading too much into details, but I think it makes enough sense and I like it. Kind of wondering now if level 9 shouldn't have something special about it too as the Knot.

Anyway.

Monoliths/Secrets

So, I was right in the end that we'll have to come back to this with the navigator, but wrong in the we actually can't take the navigator with us and must jump back and forth between levels (so much going back and forth). Anyway, this gives us new tidbits of Submachine lore (which is nice).

This goes on in the Lighthouse. Love how the fact that different versions of the game exist is being incorporated to the lore.

Finally figured how this one worked out. Turns out in the end the Loop did have a layer coordinate thingy (which I started suspecting when I didn't find one at level 7 either) it just was hidden as a secret. Kind of a pity, since I liked the idea of loops being something that maybe existed outside/beyond layers maybe. It's not like I had a lot of time to think about this/develop it into a proper theory, so it's not like I was super attached to it either, so it's fine.

Lots of portals but no notes in level 4 (that I remember, I binge-played the unlocking Monoliths and it's kinda blurry what is from what level. Should have named the screenshots, lol). BUT! We finally can unlock that door from Sub4, which was satisfying.

Level 5 is as in 4, meaning no notes found. But again, I like how you could finally see what was behind that closed door though!

Lots of goodies in level 6! It was nice to get extra content while also keeping the 'secret' notes we had in the originals. Go hunting lore.

I love this. I love the note talking about the significance of 32 when it's something we've all collectively lost our minds about. I love this xD

Here we got the same notes we did in the originals, but as a matter of fact I did not remember the one screenshot here and WHAT?! If I weren't so sleepy right now I'd be talking about it probably (as it is I am surprised I can write anything with a semblance of coherence right now, may my insomnia fuck off and leave me alone).

I'm gonna be very honest, I needed the help of the very kind people making guides on Steam for this because I had all the secrets but couldn't figure out how to access the secret room (previously I got the opposite problem, could access the portals but didn't have the secrets to power them). The notes are the same that we had in the originals.

And last but not least level 10, were I thought there was only one secret area but there were two! I thought it wa really cool that you use the pearls and stone cubes for this.

The notes here seem to be more or less the same as the originals, but thw wording has been changed some and we're also being teased about the Engine which has me vibrating.

All in all, nice little extras! Shattered Quadrant will come in the reblogs when I get to play it (yes I know I'm slow playing. Do not judge me)

[sub4apparels]

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[lovefms]

shiloh  remembered  taking  care  of  some  plants  that  looked  too  far  gone  to  save.  they  were  crisped  at  the  edges,  browned  and  brittle,  drooping  like  their  stems  had  forgotten  how  to  hold  themselves  up.  they  looked  like  they’d  already  chosen  to  give  up.  shiloh  also  remembered  cradling  the  pot  between  his  hands,  ready  to  toss  it  out.  and  it  was  blair  who  had  gently  stopped  him.  it  wasn't  dead,  blair  had  said.  plants  just  look  like  that  sometimes,  but  if  you  repot  it,  give  it  light,  attention,  a  little  patience,  it  could  be  brought  back  to  life.

and  that’s  what  this  felt  like.

milo,  smiling  at  him  with  that  curve  of  his  lips—that  smile  made  shiloh  feel  like  he  was  waking  up.  like  after  all  the  static  years,  the  numb  blur  of  lonely  days,  his  body  remembered  what  it  felt  like  to  be  seen.  a  wilted  thing  finally  turning  toward  the  sun.  just  that  small,  familiar  moment  made  something  in  his  chest  flutter,  weak  but  alive.  it  made  him  want  to  move,  to  do  something.  it  wasn’t  just  the  years  apart.  it  was  the  fact  that  milo  didn’t  hate  him;  that  realisation  had  brought  back  colour  into  the  grayscale  mess  of  shiloh’s  head.

having  his  friends  here  in  new  york  was  a  shock  to  the  system.  it  was  a  drastic  change  in  pace  that  he  hadn’t  really  braced  himself  for.  for  so  long  it  had  been  just  him  and  tj  in  his  smoke-heavy  flat,  his  messy  sleeping  hours,  the  occasional  bender.  but  now  there  were  people,  voices,  footsteps  in  his  hallway,  laughter  echoing  from  the  living  room.  ant  squealing  at  every  landmark  they  passed,  even  if  it  was  just  a  random  statue.  blair  pointing  at  buildings  with  wide  eyes  like  he  expected  to  spot  spiderman  swinging  by.  he  wanted  to  keep  trying,  especially  while  they  were  all  still  here.

“ramen,  then,”  he  nodded,  already  reaching  for  the  two  things  he  needed—his  phone  and  his  keys.

it  felt  like  a  privilege,  getting  to  spend  a  day  with  milo.  but  he  didn’t  know  which  was  worse:  being  stuck  in  a  car  with  ant  singing  empire  state  of  mind  and  slapping  his  arm  every  time  he  spotted  a  movie  location,  or  being  alone  with  milo,  who  made  his  pulse  jump  every  time  he  so  much  as  looked  at  him.  he  grabbed  a  black  face  mask  and  a  pair  of  sunglasses,  tugging  them  on  as  he  glanced  at  himself  briefly  in  the  mirror  near  the  door.  it  was  just  enough  to  blend  in.  he  was  still  technically  a  public  figure,  the  last  thing  he  wanted  was  a  swarm  of  fans  ruining  what  little  time  he  had  with  milo.  “wala  pa  atang  ten  minutes  by  car  'yun?  it’s  easier  to  take  the  subway,  pero  baka  pagalitan  ako  ng  manager  ko.  i'm  not  allowed  to  commute.”

milo   almost   laughs   when   he   hears   shiloh's   voice—   like   nothing’s   changed,   like   it   hasn’t   been   years,   like   they   didn’t   forget   how   to   be   around   each   other.   “   ikaw,   ikaw   nag   suggest.   i’ll   be   fine   with   anything   naman.   i   trust   you.   ”   he   teases   lightly,   eyes   flicking   toward   him   with   a   familiar   softness.   the   same   look   he   used   to   give   him   when   shiloh   would   drag   him   to   some   new   stall   or   shop   back   in   baguio,   rambling   about   how   good   something   tasted   while   barely   touching   his   own   plate.   “   but   okay.   ramen.   kahit   saan   na   lang   basta   may   sabaw   at   mainit.   ”

he   doesn’t   say   it   out   loud,   but   he   likes   watching   shiloh   do   this—   offer   things.   it’s   like   some   muscle   memory   kicking   in,   something   that   exists   even   after   everything   they’ve   broken.   there’s   this   need   in   shiloh   that   milo   still   recognizes.   like   he’s   trying   so   hard   to   fix   something,   to   compensate,   to   make   this   visit   more   than   what   it   is.   milo   knows.   he’s   not   blind   to   it.   he   can   feel   the   tension   under   the   surface,   the   little   tremor   in   his   fingers,   the   slight   twitch   in   his   smile.   he   knows   what   this   is.   he   just   doesn’t   want   to   name   it.   and   yet,   milo   feels   sorry   for   making   shiloh   feel   like   this.

his   own   chest   feels   tight   sometimes,   like   something   is   folding   in   on   itself.   it’s   not   sadness.   not   quite.   it’s   the   kind   of   ache   that   comes   from   looking   at   something   beautiful   that’s   about   to   disappear.   and   shiloh—   shiloh   looks   like   that.   tired   eyes,   a   jaw   too   sharp   now,   knuckles   tapping   against   his   thigh.   and   yet,   he’s   still   trying.   still   offering   pieces   of   himself   like   crumbs   on   a   trail   back   to   something   they   used   to   be.   milo   can't   allow   that   to   happen.   he   still   wants   to   right   all   the   wrongs   between   tthem.   “   ano,   tara?   i’m   ready   when   you   are.   ”

[lord-html]

#Tutorial: Switch Menu

Nesse tutorial você vai aprender a montar o Switch Menu para seu Tumblr, Blog e Afins. 

Pra quem não conhece, para aparecer o conteúdo, basta clicar no assunto desejado.

Primeiro passo -  Antes de </head> Cole isso aqui lá.

Segundo passo -  Crie os botões no seu CSS, do jeito que você quiser, vou colocar o meu, se usar, credite ok? não custa nada! 

/**Menu - Endstonight|Tumblr**/  .menutitle{color: #387377; font-family: ‘Yanone Kaffeesatz’, georgia;  font-size:16px; line-height:28px; font-weight:normal; padding:5px; margin-bottom:8px; text-align: left; letter-spacing:1px; background: #f0f0f0;text-indent : 10px;-webkit-transition-duration: .40s;} .menutitle:hover{text-indent : 19px;}

( PS: Link da Font  - <link href='http://fonts.googleapis.com/css?family=Yanone+Kaffeesatz’ rel='stylesheet’ type='text/css’>

cole antes de </head> )

Terceiro Passo -   Faça o iframe comum no seu menu, e depois monte ele, mais ou menos assim: 

<div id=“tutos” style=“display:none”>

<div class=“box”><h2>Tutoriais</h2>

<div id=“topdiv”>

<div class=“menutitle” onclick=“SwitchMenu('sub1’)” style=“cursor:pointer” >Menus</div>

<span class=“submenu” id=“sub1”>

Conteudoo

</span>  

  Obs: Se você for fazer outros menus, basta substituir o sub1 por  "sub2" , "sub3" , "sub4", você pode fazer quantos quiser. 

Obs 2: Não retire a ID “topdiv” não vai funcionais sem ela.  

[bornslippyinbelfast]

https://www.geocities.ws/eule/sub4.html

[eden2023]

WK4 - Testing a Potential Moderator

For the this example I used parte of the same code used um Wk3, ralatered to Salaries. It was used Salary as the moderator between years of experience and Age.

The rest of the Code:

Define income groups correctly

def Income(sal): if sal['Salary'] <= 10000:

return 1

elif sal['Salary'] <= 50000:

return 2

elif sal['Salary'] <= 100000:

return 3

elif sal['Salary'] <= 150000:

return 4

else: # This ensures salaries > 200000 are assigned group 3 return 5

data_clean['Income'] = data_clean.apply(lambda sal: Income(sal), axis=1)

Create subsets

sub1 = data_clean[data_clean['Income'] == 1] sub2 = data_clean[data_clean['Income'] == 2] sub3 = data_clean[data_clean['Income'] == 3] sub4 = data_clean[data_clean['Income'] == 4] sub5 = data_clean[data_clean['Income'] == 5]

Compute correlation for each subset separately

if len(sub1) > 1: print('Association between Age and Salary for Very Low Income Salary:') print(scipy.stats.pearsonr(sub1['Age'], sub1['Salary']))

if len(sub2) > 1: print('\nAssociation between Age and Salary for Low Income Salary:') print(scipy.stats.pearsonr(sub2['Age'], sub2['Salary']))

if len(sub3) > 1: print('\nAssociation between Age and Salary for Medium Income Salary:') print(scipy.stats.pearsonr(sub3['Age'], sub3['Salary']))

if len(sub4) > 1: print('\nAssociation between Age and Salary for High Income Salary:') print(scipy.stats.pearsonr(sub4['Age'], sub4['Salary']))

if len(sub5) > 1: print('\nAssociation between Age and Salary for Very High Income Salary:') print(scipy.stats.pearsonr(sub5['Age'], sub5['Salary']))

Bar Chart: Average Salary by Income Group

income_salary_avg = data_clean.groupby('Income')['Salary'].mean()

plt.figure(figsize=(8, 5)) sns.barplot(x=income_salary_avg.index, y=income_salary_avg.values, palette="viridis") plt.xlabel("Income Group") plt.ylabel("Average Salary") plt.title("Average Salary by Income Group") plt.xticks(ticks=range(0, 5), labels=["Very Low", "Low", "Medium", "High", "Very High"]) plt.show()

Scatter plot for Age vs Salary by Income Group

plt.figure(figsize=(8, 5)) # Set figure size sns.scatterplot( x=data_clean["Age"], y=data_clean["Salary"], hue=data_clean["Income"], # Color by Income Group palette="viridis", # Choose a color palette alpha=0.7 # Adjust transparency for better visualization ) plt.xlabel("Years of Experience") plt.ylabel("Salary") plt.title(" Age vs Salary by Income Group") plt.show()

Scatter plot for Years of Experience vs Salary by Income Group

plt.figure(figsize=(8, 5)) sns.scatterplot( x=data_clean["Years of Experience"], y=data_clean["Salary"], hue=data_clean["Income"], palette="coolwarm", alpha=0.7 ) plt.xlabel("Years of Experience") plt.ylabel("Salary") plt.title("Experience vs Salary by Income Group") plt.show()

Scatter plot for Age vs Salary with Regression line styling

plt.figure(figsize=(8, 5))

sns.regplot( x=data_clean["Age"], y=data_clean["Salary"], scatter_kws={"alpha": 0.5, "color": "blue"}, # Scatter points styling line_kws={"color": "red"}, # Regression line styling ) plt.xlabel("Age") plt.ylabel("Salary") plt.title("Regression Plot: Age vs Salary") plt.show()

Scatter plot for Years of Experience vs Salary

sns.regplot( x=data_clean["Years of Experience"], y=data_clean["Salary"], scatter_kws={"alpha": 0.5, "color": "green"}, line_kws={"color": "red"}, ) plt.xlabel("Years of Experience") plt.ylabel("Salary") plt.title("Regression Plot: Years of Experience vs Salary") plt.show()

Scatter plot for Age vs Salary by Income Group

plt.figure(figsize=(8, 5))

sns.lmplot( x="Age", y="Salary", hue="Income", data=data_clean, palette="viridis", height=5, aspect=1.2 ) plt.xlabel("Age") plt.ylabel("Salary") plt.title("Regression Plot: Age vs Salary by Income Group") plt.show()

Result:

It is possible to verify that correlation between the Age and salary is for Very Low income, is -0.64, which means it ia a negative correlation and has a p-value=0.35, that is over thant p-value<0.05. so p-value for the very low it is no significante.

for the rest of the groups, Low, Medium, High and Very High, the correlation value is positive, and the p-value is unde 0.05, so we cansay that p-valeu for the rest is a significate

it is possivel to see in the Scatter plot .

[bilvensstuff]

https://opphustle.com/?utm_campaign={replace}&sub2=&sub3=&sub4=171725351713&sub5=720901565753&sub6=21901344475&sub7=m&sub8=&sub9=&sub10=&utm_source=Google&customsource={acc2-vb-AMZNPR}&wbraid=&gbraid=&ref_id=Cj0KCQiA_9u5BhCUARIsABbMSPui--op1Cz1y6xDLcJa0L5lpIW5dBKJzElKgD7hsoTtR4K_uKUhH9oaAlxNEALw_wcB&gclid=Cj0KCQiA_9u5BhCUARIsABbMSPui--op1Cz1y6xDLcJa0L5lpIW5dBKJzElKgD7hsoTtR4K_uKUhH9oaAlxNEALw_wcB

[alcohol-consumption-dependence]

Analyzing Alcohol Consumption: Data Management and Frequency Distributions in Python

Python Program:

import pandas import numpy as np

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

print(len(data)) # Number of observations (rows) print(len(data.columns)) # Number of variables (columns)

Drank atleast 12 alcoholic drinks in last 12 months

sub1=data["S2AQ2"].value_counts(sort=False)

make a copy of inserted data

sub2 = sub1.copy()

print("counts for original S2AQ2") c1= sub2 print(c1)

sub2 = sub2.replace(9, np.nan)

print('counts for S2AQ2 with 9 set to nan') c2= sub2 print(c2)

Drank atleast 1 alcoholic drinks in last 12 months

sub3=data["S2AQ3"].value_counts(sort=False)

make a copy of inserted data

sub4 = sub3.copy()

print("counts for original S2AQ3") c3= sub3 print(c3)

sub3 = sub3.replace(9, np.nan)

print('counts for S2AQ3 with 9 set to nan') c3= sub3 print(c3)

Family or friends told to cut down on drinking

sub5=data["S2AQ18"].value_counts(sort=False)

make a copy of inserted data

sub5 = sub1.copy()

print("counts for original S2AQ18") c1= sub5 print(c1)

sub6 = sub5.replace(9, np.nan)

print('counts for S2AQ18 with 9 set to nan') c2= sub6 print(c2)

Interpretation of Results

Variable 1: This variable primarily took values in numeric numbers. Most frequent values were 1 indicating a yes answer to the questions, and value of 9 was used to display data that could be labelled as missing which is replaced in this code to "nan" using the NumPy library.

Variable 2: This distribution reflects that most of the survey population had at some point in their life drank alcoholic drinks. Most of the population answered "1" yes to drinking. 1 was seen as the most common answer in the data set.

Variable 3: The distribution showed a clear grouping, with most of the survey population having drank alcoholic drinks at some point in their life. However, an anomaly was noticed in S2AQ2 when more people answered "2" or no when asked if they had consumed atleast 12 alcoholic drinks in last 12 months. Summary: In this post, I explored alcohol consumption data, managing variables in Python to create meaningful insights. By handling missing data, recoding variables, and analyzing frequency distributions, I highlighted key trends. Most respondents had consumed alcohol at some point (indicated by a frequent "yes" answer), and missing data was coded as "nan" to ensure clarity. Interestingly, while many had consumed alcohol, fewer had done so in the past year, as reflected by a higher count of "no" answers to recent drinking. This assignment emphasized how strategic data management can unveil important behavioral patterns and anomalies within survey data.

[assigmentweek2]

Assignment Week 3:

-- coding: Following assignment on Coursera --

""" Spyder Editor

This is a temporary script file. """

-- coding: utf-8 --

""" Created on Mon May 13 08:30:00 GMT +7, 2024

@author: D.T.Long """

reset all

clear all variables in the memory

import pandas import numpy

any additional libraries would be imported here

Hypothesis

print('My hypothesis is: The higher levels of education, the higher ratio of people who think of themselves as a Democrat.')

Read csv file

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

Check the population size and variables

print('Population size:',len(data)) print('Number of variables:',len(data.columns)) print ("Frequency distribution of Democrat, Republican,…. 1=Republic, 2=Democrat, 3=Independent, 4=Others, -1=refused") c1 = data['W1_C1'].value_counts(sort=False) print(c1)

print("Percentage of Republican, Democrat….") p1= data['W1_C1'].value_counts(sort=False, normalize=True) print(p1)

print ("""Counts number of Education (Highest Degree Received): 1 = No formal education, 2 = 1st, 2nd, 3rd, or 4th grade 3 = 5th or 6th grade 4 = 7th or 8th grade 5 = 9th grade 6 = 10th grade 7 = 11th grade 8 = 12th grade NO DIPLOMA 9 = HIGH SCHOOL GRADUATE - high school DIPLOMA or the equivalent (GED) 10 = Some college, no degree 11 = Associate degree 12 = Bachelors degree 13 = Master degree 14 = Professional or Doctorate degree """)

'''# Another way to visualize the Frequency of variables c4 = data['PPEDUC'].value_counts(sort=False) print(c4) '''

ct4 = data.groupby('PPEDUC').size() print(ct4)

'''Another way to visualize the Percentage of variables not following categories print("percentage of Highest Degree Received") p4= data['PPEDUC'].value_counts(sort=False, normalize=True) print(p4)

W3-I: Create subset of Democrat and Education

print("Create subgroup of Democrat and Education") sub3 = data[(data['W1_C1']==2)] sub4 = sub3[['W1_C1','W1_C1B','W1_C1C','PPEDUC']] sub5 = sub4.copy()

Convert Democrat level to numeric

sub5['W1_C1B'] = pandas.to_numeric(sub5['W1_C1B'])

Replace blank and -1 (refuse) to python missing (NaN)

sub5['W1_C1B']=sub5['W1_C1B'].replace(-1, numpy.nan) sub5['W1_C1C']=sub5['W1_C1C'].replace(r'^\s*$', numpy.nan, regex=True)

Check 10 first row'

'sub5.head(10)

Check the population and variables size of sub5 data

print('Population size of subset:',len(sub5)) print('Number of variables of subset:',len(sub5.columns))

Frequency distribution for Democrat level

print ("Frequency distribution for Democrat level: 1.0 = Strong, 2.0 = Not very strong, NaN = Missing data") c5 = sub5['W1_C1B'].value_counts(sort=False, dropna = False) print(c5)

Percentage for Democrat level

print("Percentage for Democrat level") p5= sub5['W1_C1B'].value_counts(sort=False, normalize=True, dropna=False) print(p5)

Frequency distribution for closer to Democratic Party

print ("""Frequency distribution of Democrat closer to Democratic Party: 1.0 = Closer to the Republician Party 2.0 = Closer to the Democratic Party 3.0 = Neither closer to the Democratic Party nor Republician Party NaN = Missing data """) c6 = sub5['W1_C1C'].value_counts(sort=False, dropna=False) print(c6)

Percentage for closer to Democratic Party

print("Percentage of closer to Democratic Party") p6= sub5['W1_C1C'].value_counts(sort=False, normalize=True, dropna=False) print(p6)

W3-II: Analyse Democrat with Education

print ("""Counts number of Education (Highest Degree Received): 1 = No formal education, 2 = 1st, 2nd, 3rd, or 4th grade 3 = 5th or 6th grade 4 = 7th or 8th grade 5 = 9th grade 6 = 10th grade 7 = 11th grade 8 = 12th grade NO DIPLOMA 9 = HIGH SCHOOL GRADUATE - high school DIPLOMA or the equivalent (GED) 10 = Some college, no degree 11 = Associate degree 12 = Bachelors degree 13 = Master degree 14 = Professional or Doctorate degree """)

Frequency distribution for Democrat with education

ct7 = sub5.groupby('PPEDUC').size() print(ct7)

Percentage for Democrat with education

pt7 = sub5.groupby('PPEDUC').size()/len(data) print(pt7)

print("""My comment: I created subset of Democrat and Education. For Democrats, they almost said that they are strong Democratic with 65.39%; but there is no evidences to confirm the Democrat are closer to Democratic Party (NaN: 100%). The Democrats with HIGH SCHOOL GRADUATE (9) and Some college (10) account for the highest proportion at 29.42%. """)

End of Assignment Week3

The output of my program:

[sub4apparels]

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[chonacas]

Robert F. Kennedy Jr. Speaking at SXSW 2024

Robert F. Kennedy Jr. speaking at SXSW for a private event hosted by American Values 2024

AV24 - Learn more: https://av24.org/

Watch the RFK Jr. doc https://therealrfkjrmovie.com/trailer1/?sub4=7a1058dc8e9a44eea12cf277e37afe52&afid=23

Producer Jeff Hays - Director Ronny Lynch

David Christopher Lee: https://davidsguide.com

Connect on social media and post this episode :)

https://www.instagram.com/shesallovertheplacepodcast/

https://www.instagram.com/chonacas

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[adorableparrot]

The First Data Analysis

Code:

import pandas as pd import numpy as np

data = pd.read_csv('marscrater_pds.csv', low_memory = False)

print("The diameter of craters that causes HuSL") sub1 = data[(data["MORPHOLOGY_EJECTA_2"].str.contains("HuSL"))] c1 = sub1["DIAM_CIRCLE_IMAGE"].value_counts(sort=True) print(c1.head(30))

print("\nThe diameter of craters that causes HuBL") sub2 = data[(data["MORPHOLOGY_EJECTA_2"].str.contains("HuBL"))] c2 = sub2["DIAM_CIRCLE_IMAGE"].value_counts(sort=True) print(c2.head(30))

print("\nThe diameter of craters that causes SmSL") sub3 = data[(data["MORPHOLOGY_EJECTA_2"].str.contains("SmSL"))] c3 = sub3["DIAM_CIRCLE_IMAGE"].value_counts(sort=True) print(c3.head(30))

print("\nThe diameter of craters that causes HuAm") sub4 = data[(data["MORPHOLOGY_EJECTA_2"].str.contains("HuAm"))] c4 = sub4["DIAM_CIRCLE_IMAGE"].value_counts(sort=True) print(c4.head(30))

print("\nThe diameter of craters that causes Hu") sub5 = data[(data["MORPHOLOGY_EJECTA_2"].str.contains("Hu"))] c5 = sub5["DIAM_CIRCLE_IMAGE"].value_counts(sort=True) print(c5.head(30))

print("\nNumber of Layer = 0") sub6 = data[(data["NUMBER_LAYERS"]==0)] c6 = sub6["DIAM_CIRCLE_IMAGE"].value_counts(sort=True) print(c6.head(30))

print("\nNumber of Layer = 1") sub7 = data[(data["NUMBER_LAYERS"]==1)] c7 = sub7["DIAM_CIRCLE_IMAGE"].value_counts(sort=True) print(c7.head(30))

print("\nNumber of Layer = 2") sub8 = data[(data["NUMBER_LAYERS"]==2)] c8 = sub8["DIAM_CIRCLE_IMAGE"].value_counts(sort=True) print(c8.head(30))

print("\nNumber of Layer = 3") sub9 = data[(data["NUMBER_LAYERS"]==3)] c9 = sub9["DIAM_CIRCLE_IMAGE"].value_counts(sort=True) print(c9.head(30))

print("\nNumber of Layer = 4") sub10 = data[(data["NUMBER_LAYERS"]==4)] c10 = sub10["DIAM_CIRCLE_IMAGE"].value_counts(sort=True) print(c10.head(30))

print("\nNumber of Layer = 5") sub11 = data[(data["NUMBER_LAYERS"]==5)] c11 = sub11["DIAM_CIRCLE_IMAGE"].value_counts(sort=True) print(c11.head(30))

Outputs:

the relationships between diameter and morphology ejecta

First of all, as there are may types of morphology ejecta, I would choose the top 5, which are HuSL, HuBL, SmSL, HuAm, and Hu, to analyse their correlations. Also, I chose the top 30 diameters to find the result. Below are the optputs.

2. Does the diameter increase as the number of layers increases?

I think I should change my previous topic after coding with Python because I think it is improper. That is, I came up with this topic, which also relates to the analysis of diameter of craters. So, after checking the type of morphology ejectas, I want to know whether the maximum number of cohensive layers identified is related to the diameter of craters. Also, this time I chose the top 30 as my samples to determine the relationship between the variables. Here's the results.

Conclusions:

In the first result, diameters from 3.09 km to 7.57 km are the ones that are most likely to form a HuSL crater, diameters from 5.05 km to 7.77 km are the ones that are most likely to form a HuBL crater, diameters from 3.03 km to 4.82 km are the ones that are most likely to form a SmSL crater, diameters from 3.07 km to 7.18 km are the ones that are most likely to form a HuAm crater, diameters from 3.26 km to 6.76 km are the ones that are most likely to form a Hu crater.

In the second analysis, yes, the diameter increases as the number of layers increases. Although there are large diameters with less maximum cohensive layers, the overall data shows that the diameter of crater and the number of layers are correlate.