#parentgroup

[parentingroundabout]

On Speed-Round Sunday, we share a mini episode from our past for your weekend listening pleasure. It's hard being a parent-group newbie, and it's hard being a veteran. Let's just skip the whole thing.

[cycas]

I've come to believe fervently in elven adoptive extended families.

So almost anyone can be officially descended from almost anyone else, and indeed, can be descended from several parents or parentgroups.

Amroth, for example, is 'son of Amdir' but he was fairly young when Amdir died, so Galadriel and Celeborn took him under their wing.

Hence, Amroth is both Galadriel's son, and Celeborn's, and Amdir's and of course, his unnamed mother from Lorien, too.

Gil-galad is particularly rich in parents, since in his childhood the House of Finwe were going down like flies, but they weren't going to let him down, so every time a parent fell, another - or several would step in. Who was his biological parent? Who cares! What really counts is the people who were there.

Elrond continues this tradition right the way through from Valandil, Isildur's heir who was too young to go to war with his father and the Last Alliance, and stayed in Rivendell with his two mothers, Anon and Celebrian, to Aragorn, who grew up in Rivendell with his very definitely not married-to-each-other parents, Elrond and Gilraen.

[beetroots]

I am a parent of a yet to be officially diagnosed kid. That's anonther story.

My experince in parentgroupes is that mostly, not all (!!), parents who got surprised by the diagnosis (and were convinced the doctors would fix their 'difficult' kid) , express these regrets.

The parents who suspected their kid being autistic and took action to sort things out, are over all more accepting.

Like i said not all parents react one why or the other, the problem is more complex than my experince.

Please don't get me wrong, when reading these complaints makes you upset, you have every right to be. I only tried to add something to the already really good answers.

Found one of those autism moms websites and I saw a lot of people commenting things like "I love my child, but hate autism!" And also the "autism took my child from me!". Also just a general feeling of wanting to cure autism as if it's a disease that ruined their children.

While I can understand the caretakers of autistic people need to be heard and also need support, I don't think these moms are coming at it from the right POV.

I'm wondering if I'm being too sensitive in getting upset reading things like that since I'm autistic or if I am being reasonable in having a negative response towards such comments. What's your take?

Hi there,

I’ve seen similar things online. I’m not sure why they say or feel these things. It makes me kinda angry and upset. My guess is that they’re confused about autism and how it can affect their child. I wish more parents out there would do some research on it. I’m sure that would explain their child’s behavior. But that’s just a guess. I’ll tag @autistic-af and @krisrisk so they can share their thoughts on this. I think Krisrisk has a child, so they might have some more insight than I do, since I don’t have any kids. And autistic-af has some more experience with this, so they might share some thought about this too. I’m interested in what they have to say about this.

Anyway, thank you for the inbox. I hope you have a wonderful day/night. ❤️

[bakeboog-blog]

At Virginia Ave Park with Cee Cee & Princess repn' the Parent Connection Group. #santamonicavet #welit #santamonica #parentgroup #beautifulpeople #african #ethiopian #white #black #saturday #ethiopianfunday

[data-management-konny86]

Creating graphs for your data

Here is my solution for the 4th assignment.

The code:

#!/usr/bin/env python3

# -*- coding: utf-8 -*- """ Created on Wed Jun  9 14:17:38 2021 @author: konny My first Python program with the selected data set in relation to my research question """ import pandas import numpy import seaborn import matplotlib.pyplot as plt #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) # bug fix for display formats to avoid run time errors pandas.set_option('display.float_format', lambda x:'%f'%x) data = pandas.read_csv('ool_pds.csv', low_memory=False) #upper-case all DataFrame column names data.columns = map(str.upper, data.columns) print() print('Content of the ool_pds.csv dataset:') print('rows:', len(data))            # Number of observations (rows)  print('columns:', len(data.columns))   # Number of variables (colums)   print('\n=============================================================================') # setting variables I will be working with to numeric (updated) data['W1_P17'] = pandas.to_numeric(data['W1_P17'])      #Do you have any biological or adopted children data['W1_P17A'] = pandas.to_numeric(data['W1_P17A'])    #How many children do you have data['W1_P19'] = pandas.to_numeric(data['W1_P19'])      #Where do your minor children (under 18) live # replace missing values to python missing (NaN) data['W1_P17']=data['W1_P17'].replace(-1, numpy.nan) data['W1_P17A']=data['W1_P17A'].replace(-1, numpy.nan) data['W1_P19']=data['W1_P19'].replace(-1, numpy.nan) #counts and percentages (i.e. frequency distributions) for each variable print('\ncounts for W1_P17 - \nDo you have any biological or adopted children, yes=1, no=2') c1 = data['W1_P17'].value_counts(sort=True, dropna=False) print (c1) print('\npercentages for W1_P17 - \nDo you have any biological or adopted children, yes=1, no=2') p1 = data['W1_P17'].value_counts(sort=True, normalize=True, dropna=False) print (p1,'\n') data['HAVECHILDS'] = data['W1_P17'] c1a=data.groupby('HAVECHILDS').size() print(c1a) data['HAVECHILDS'] = data['HAVECHILDS'].astype('category') print('\nDescription: People have children') desc1 = data['HAVECHILDS'].describe() print(desc1) print('\nHave childs category') c2a = data['HAVECHILDS'].value_counts(sort=False) print(c2a) plot1=seaborn.countplot(x='HAVECHILDS', data=data) plt.xlabel('yes=1, no=2') plt.title('Do you have children?') plt.show(plot1) print('\n=============================================================================') print('\ncounts for W1_P17A - Original data\nHow many biological or adopted children do you have?') c2 = data['W1_P17A'].value_counts(sort=True, dropna=False) print (c2) print('\npercentages for W1_P17A - Original data\nHow many biological or adopted children do you have?') p2 = data['W1_P17A'].value_counts(sort=True, normalize=True, dropna=False) print (p2) # recode the childrengroup recode2 = {1: '1-3', 2: '1-3', 3: '1-3', 4: '4-6', 5: '4-6', 6: '4-6', 7: '7-10', 8: '7-10', 9: '7-10', 10: '7-10'} data['CHILDRENGROUP']= data['W1_P17A'].map(recode2) print('\nHow many children do you have? - Recoding and grouping the data') c5 = data['CHILDRENGROUP'].value_counts(sort=True) print (c5) print('\nHow many children do you have? - Recoding and grouping the data - percentages') p5 = data['CHILDRENGROUP'].value_counts(sort=True, normalize=True) print (p5) # First change format from numeric to categorical plot2=seaborn.countplot(x="CHILDRENGROUP", data=data) plt.xlabel('Number of children') plt.title('How many children do you have?') plt.show(plot2) print('\n=============================================================================') print('\ncounts of W1_P19 - Original data\nWhere do your minor children live? \nLive with both parents=1\nLive with father or mother (different expressions)=2-4\nLive with no parent=5') c3 = data['W1_P19'].value_counts(sort=True, dropna=False) print (c3)15:50

print('\npercentages of W1_P19 - - Original data\nWhere do your minor children live? \nLive with both parents=1\nLive with father or mother (different expressions)=2-4\nLive with no parent=5') p3 = data['W1_P19'].value_counts(sort=True, dropna=False, normalize=True) print (p3) data['P3']=p3 # recode the parentgroup recode1 = {1: 'both parents', 2: 'father or mother', 3: 'father or mother', 4: 'father or mother', 5: 'relatives'} data['PARENTGROUP']= data['W1_P19'].map(recode1) #data['PARENTGROUP'] = data.apply(lambda row: PARENTGROUP(row),axis=1) # live with at least one of the parents (father/mother/both) # building a group print('\nWhere do your children live? - Recoding and grouping the data') c4 = data['PARENTGROUP'].value_counts(sort=True) print (c4) print('\nWhere do your children live? - Recoding and grouping the data - percentages') p4 = data['PARENTGROUP'].value_counts(sort=True, normalize=True) print (p4) plot3=seaborn.countplot(x="PARENTGROUP", data=data) plt.xlabel('The children live with...') plt.title('Where do your children live?') plt.show(plot3) print('\n=============================================================================') # Looking to see which group of children mostly live with both parents # recode the parentgroup # only value 1 (living with both parents) is needed recode3 = {1: 1, 2: 0, 3: 0, 4: 0, 5: 0} data['BOTHPARENTS']= data['W1_P19'].map(recode3) plot4=seaborn.catplot(x='W1_P17A', y='BOTHPARENTS', data=data, kind="bar", ci=None) plt.xlabel('Number of children') plt.ylabel('Children live with both parents') plt.title('Which children group live mostly with their both parents') plt.show(plot4)

[jaymontgomeryart]

Some magazine cover illustrations I did back in 2003-4 for PTO Today magazine. Back when people actually read paper magazines. It was a pleasure working on these with #artdirector Janelle Shum, she gave me a basic idea and topic and I presented her with 2-3 thumbnails, then a tight drawing. All of these were #illustrated with #pencil #adobeillustrator #adobephotoshop and a little fine detail texture with #corelpainter. They are all available for #stockart use and customizable to your liking. #ptotoday #pta #backtoschool #education #parentgroups #homeschool #community #illustrator #atlantadesigner #atlantaillustrator #magazine #magazinecover #illustration @jaymontgomeryart

[data-management-konny86]

Making Data Management Decisions 

Here is my solution of the 3rd assignment:

My program:

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Wed Jun  9 14:17:38 2021 @author: konny My first Python program with the selected data set in relation to my research question """ import pandas import numpy data = pandas.read_csv('ool_pds.csv', low_memory=False) #upper-case all DataFrame column names data.columns = map(str.upper, data.columns) #bug fix for display formats to avoid run time errors pandas.set_option('display.float_format', lambda x:'%f'%x) print('Content of the ool_pds.csv dataset:') print('rows:', len(data))            # Number of observations (rows) Anzahl der Zeilen print('columns:', len(data.columns))   # Number of variables (colums)  Anzahl der Spalten print('\n=============================================================================') # setting variables I will be working with to numeric (updated) data['W1_P17'] = pandas.to_numeric(data['W1_P17'])      #Do you have any biological or adopted children data['W1_P17A'] = pandas.to_numeric(data['W1_P17A'])    #How many children do you have data['W1_P19'] = pandas.to_numeric(data['W1_P19'])      #Where do your minor children (under 18) live # replace missing values to python missing (NaN) # nan: specifies missing values in python data['W1_P17']=data['W1_P17'].replace(-1, numpy.nan) data['W1_P17A']=data['W1_P17A'].replace(-1, numpy.nan) data['W1_P19']=data['W1_P19'].replace(-1, numpy.nan) #counts and percentages (i.e. frequency distributions) for each variable print('\ncounts for W1_P17 - \nDo you have any biological or adopted children, yes=1, no=2') c1 = data['W1_P17'].value_counts(sort=True, dropna=False) print (c1) print('\npercentages for W1_P17 - \nDo you have any biological or adopted children, yes=1, no=2') p1 = data['W1_P17'].value_counts(sort=True, normalize=True, dropna=False) print (p1) print('\n=============================================================================') print('\ncounts for W1_P17A - Original data\nHow many biological or adopted children do you have?') c2 = data['W1_P17A'].value_counts(sort=True, dropna=False) print (c2) print('\npercentages for W1_P17A - Original data\nHow many biological or adopted children do you have?') p2 = data['W1_P17A'].value_counts(sort=True, normalize=True, dropna=False) print (p2) # recode the childrengroup recode2 = {1: 1, 2: 1, 3: 1, 4: 2, 5: 2, 6: 2, 7: 3, 8: 3, 9: 3, 10: 3} data['CHILDRENGROUP']= data['W1_P17A'].map(recode2) # building a group print('\nHow many Children do they have? - Recoding and grouping the data\n1-3 children = 1\n4-6 children = 2\n7-10 children = 3') c5 = data['CHILDRENGROUP'].value_counts(sort=True) print (c5) print('\nHow many Children do they have? - Recoding and grouping the data - percentages \n1-3 children = 1\n4-6 children = 2\n7-10 children = 3') p5 = data['CHILDRENGROUP'].value_counts(sort=True, normalize=True) print (p5) print('\n=============================================================================') print('\ncounts of W1_P19 - Original data\nWhere do your minor children live? \nLive with both parents=1\nLive with father or mother (different expressions)=2-4\nLive with no parent=5') c3 = data['W1_P19'].value_counts(sort=True, dropna=False) print (c3) print('\npercentages of W1_P19 - - Original data\nWhere do your minor children live? \nLive with both parents=1\nLive with father or mother (different expressions)=2-4\nLive with no parent=5') p3 = data['W1_P19'].value_counts(sort=True, dropna=False, normalize=True) print (p3) # recode the parentgroup recode1 = {1: 1, 2: 2, 3: 2, 4: 2, 5: 3} data['PARENTGROUP']= data['W1_P19'].map(recode1) # building a group print('\nWhere do the children live? - Recoding and grouping the data\n1=live with both parents\n2=live with father or mother\n3=live with ralatives') c4 = data['PARENTGROUP'].value_counts(sort=True) print (c4)

print('\nWhere do the children live? - Recoding and grouping the data - percentages\n1=live with both parents\n2=live with father or mother\n3=live with ralatives') p4 = data['PARENTGROUP'].value_counts(sort=True, normalize=True) print (p4)

========================================================

Results/Output: 

========================================================

Content of the ool_pds.csv dataset: rows: 2294 columns: 436 ============================================================================= counts for W1_P17 - Do you have any biological or adopted children, yes=1, no=2 1.000000    1304 2.000000     949 NaN           41 Name: W1_P17, dtype: int64 percentages for W1_P17 - Do you have any biological or adopted children, yes=1, no=2 1.000000   0.568439 2.000000   0.413688 NaN        0.017873 Name: W1_P17, dtype: float64 ============================================================================= counts for W1_P17A - Original data How many biological or adopted children do you have? NaN          999 2.000000     481 1.000000     306 3.000000     267 4.000000     138 5.000000      51 6.000000      25 7.000000      11 10.000000     10 8.000000       5 9.000000       1 Name: W1_P17A, dtype: int64 percentages for W1_P17A - Original data How many biological or adopted children do you have? NaN         0.435484 2.000000    0.209677 1.000000    0.133391 3.000000    0.116391 4.000000    0.060157 5.000000    0.022232 6.000000    0.010898 7.000000    0.004795 10.000000   0.004359 8.000000    0.002180 9.000000    0.000436 Name: W1_P17A, dtype: float64 How many Children do they have? - Recoding and grouping the data 1-3 children = 1 4-6 children = 2 7-10 children = 3 1.000000    1054 2.000000     214 3.000000      27 Name: CHILDRENGROUP, dtype: int64 How many Children do they have? - Recoding and grouping the data - percentages 1-3 children = 1 4-6 children = 2 7-10 children = 3 1.000000   0.813900 2.000000   0.165251 3.000000   0.020849 Name: CHILDRENGROUP, dtype: float64 ============================================================================= counts of W1_P19 - Original data Where do your minor children live? Live with both parents=1 Live with father or mother (different characteristics)=2-4 Live with no parent=5 NaN         1529 1.000000     414 4.000000     206 2.000000      74 5.000000      36 3.000000      35 Name: W1_P19, dtype: int64 percentages of W1_P19 -  Original data Where do your minor children live? Live with both parents=1 Live with father or mother (different characteristics)=2-4 Live with no parent=5 NaN        0.666521 1.000000   0.180471 4.000000   0.089799 2.000000   0.032258 5.000000   0.015693 3.000000   0.015257 Name: W1_P19, dtype: float64 Where do the children live? - Recoding and grouping the data 1=live with both parents 2=live with father or mother 3=live with ralatives 1.000000    414 2.000000    315 3.000000     36 Name: PARENTGROUP, dtype: int64 Where do the children live? - Recoding and grouping the data - percentages 1=live with both parents 2=live with father or mother 3=live with ralatives 1.000000   0.541176 2.000000   0.411765 3.000000   0.047059 Name: PARENTGROUP, dtype: float64