Schools

School data in wombat comes in two forms, position and performance. Conveniently, snapshots of school data is available via the Australian Bureau of Statistics. In addition to exact location information, we also have included in wombat the school catchment areas. As for additional data, the Australian Curriculum, Assessment and Reporting Authority (ACARA), releases reports highlighting the performance of schools across Australia. NAPLAN provides the measure through which governments, education authorities and schools can determine whether or not young Australians are meeting important educational outcomes. NAPLAN data will be added where available.

Let's explore some of this data.

In [2]:

%load_ext autoreload
%autoreload 2
import wombat
import os

%load_ext autoreload %autoreload 2 import wombat import os

The autoreload extension is already loaded. To reload it, use:
  %reload_ext autoreload

In [14]:

w = wombat.Wombat()
w.set_area_as_city("Sydney")

w = wombat.Wombat() w.set_area_as_city("Sydney")

In [15]:

w.Schools.load()

w.Schools.load()

In [6]:

# school ACARA information
w.Schools.df_acara.head()

# school ACARA information w.Schools.df_acara.head()

Out[6]:

	Calendar Year	ACARA SML ID	Location AGE ID	School AGE ID	School Name	Suburb	State	Postcode	School Sector	School Type	...	Non-Teaching Staff	Full Time Equivalent Non-Teaching Staff	Total Enrolments	Girls Enrolments	Boys Enrolments	Full Time Equivalent Enrolments	Indigenous Enrolments (%)	Language Background Other Than English - Yes (%)	Language Background Other Than English - No (%)	Language Background Other Than English - Not Stated (%)
232	2021	40275	68510.0	28510.0	Saint Mary MacKillop College Albury	Jindera	NSW	2642	Independent	Combined	...	11.0	6.0	195.0	104.0	91.0	195.0	0.0	1.0	99.0	0.0
233	2021	40276	45298.0	5298.0	St Dominic Savio School	Rockdale	NSW	2216	Independent	Primary	...	2.0	1.2	37.0	20.0	17.0	37.0	0.0	59.0	41.0	0.0
234	2021	40277	66768.0	26768.0	Saint Mary MacKillop Colleges Limited	Wagga Wagga	NSW	2650	Independent	Combined	...	3.0	3.0	157.0	81.0	76.0	157.0	0.0	55.0	45.0	0.0
272	2021	40366	41409.0	1409.0	Kinma School	Terrey Hills	NSW	2084	Independent	Primary	...	3.0	2.8	92.0	40.0	52.0	92.0	0.0	23.0	77.0	0.0
273	2021	40367	41411.0	1411.0	Knox Grammar School	Wahroonga	NSW	2076	Independent	Combined	...	246.0	193.7	3123.0	78.0	3045.0	3123.0	0.0	39.0	47.0	14.0

5 rows × 35 columns

In [7]:

# school ALL info - mostly address related
w.Schools.df_schools.head()

# school ALL info - mostly address related w.Schools.df_schools.head()

Out[7]:

	Calendar Year	ACARA SML ID	Location AGE ID	School AGE ID	School Name	Suburb	State	Postcode	School Sector	School Type	...	Statistical Area 3	Statistical Area 3 Name	Statistical Area 4	Statistical Area 4 Name	Local Government Area	Local Government Area Name	State Electoral Division	State Electoral Division Name	Commonwealth Electoral Division	Commonwealth Electoral Division Name
238	2021	40275	68510.0	28510.0	Saint Mary MacKillop College Albury	JINDERA	NSW	2642	Independent	Combined	...	10901	Albury	109	Murray	13340	Greater Hume Shire (A)	10001	Albury	114	Farrer
239	2021	40276	45298.0	5298.0	St Dominic Savio School	ROCKDALE	NSW	2216	Independent	Primary	...	11904	Kogarah - Rockdale	119	Sydney - Inner South West	10500	Bayside (A)	10072	Rockdale	102	Barton
240	2021	40277	66768.0	26768.0	Saint Mary MacKillop Colleges Limited	WAGGA WAGGA	NSW	2650	Independent	Combined	...	11303	Wagga Wagga	113	Riverina	17750	Wagga Wagga (C)	10087	Wagga Wagga	139	Riverina
285	2021	40366	41409.0	1409.0	Kinma School	TERREY HILLS	NSW	2084	Independent	Primary	...	12203	Warringah	122	Sydney - Northern Beaches	15990	Northern Beaches (A)	10067	Pittwater	126	Mackellar
286	2021	40367	41411.0	1411.0	Knox Grammar School	WAHROONGA	NSW	2076	Independent	Combined	...	12103	Ku-ring-gai	121	Sydney - North Sydney and Hornsby	14500	Ku-ring-gai (A)	10041	Ku-ring-gai	106	Bradfield

5 rows × 30 columns

In [8]:

# ALL combined school info and ACARA information
w.Schools.df_all

# ALL combined school info and ACARA information w.Schools.df_all

Out[8]:

	School Name	Suburb	State	Postcode	School Sector	School Type	Campus Type	Latitude	Longitude	School URL	...	Non-Teaching Staff	Full Time Equivalent Non-Teaching Staff	Total Enrolments	Girls Enrolments	Boys Enrolments	Full Time Equivalent Enrolments	Indigenous Enrolments (%)	Language Background Other Than English - Yes (%)	Language Background Other Than English - No (%)	Language Background Other Than English - Not Stated (%)
ACARA SML ID
40000	Corpus Christi Catholic School	BELLERIVE	TAS	7018	Catholic	Primary	School Single Entity	-42.871256	147.371473	http://www.corpuschristi.tas.edu.au	...	19.0	12.6	408.0	173.0	235.0	407.6	2.0	7.0	93.0	0.0
40001	Fahan School	SANDY BAY	TAS	7005	Independent	Combined	School Single Entity	-42.916158	147.352764	http://www.fahan.tas.edu.au	...	29.0	20.4	388.0	388.0	0.0	388.0	2.0	13.0	87.0	0.0
40002	Geneva Christian College	LATROBE	TAS	7307	Independent	Combined	School Single Entity	-41.226741	146.438726	http://www.geneva.tas.edu.au	...	49.0	29.1	280.0	134.0	146.0	279.9	6.0	4.0	96.0	0.0
40003	Holy Rosary Catholic School	CLAREMONT	TAS	7011	Catholic	Primary	School Single Entity	-42.789375	147.248306	http://www.holyrosary.tas.edu.au	...	20.0	11.9	388.0	189.0	199.0	388.0	4.0	4.0	96.0	0.0
40004	Immaculate Heart of Mary Catholic School	LENAH VALLEY	TAS	7008	Catholic	Primary	School Single Entity	-42.865543	147.290159	http://www.ihms.tas.edu.au	...	15.0	8.7	200.0	107.0	93.0	200.0	9.0	16.0	84.0	0.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
53049	Ming-De International School Toowoomba	WESTBROOK	QLD	4350	Independent	Primary	School Single Entity	-27.615976	151.831722	NaN	...	4.0	4.0	20.0	15.0	5.0	19.6	0.0	100.0	0.0	0.0
53050	The Village School Gold Coast	COOLANGATTA	QLD	4225	Independent	Primary	School Single Entity	-28.168590	153.539755	http://www.thevillageschoolgoldcoast.com.au	...	1.0	1.0	13.0	8.0	5.0	13.0	0.0	8.0	92.0	0.0
53055	Secondary College of Languages	PARRAMATTA	NSW	2150	Government	Secondary	School Single Entity	-33.813693	151.008176	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
53057	Nawarddeken Academy Mamadawerre School	West Arnhem Land	NT	886	Independent	Combined	School Single Entity	-12.257454	133.523957	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
53058	Nawarddeken Academy Manmoyi School	West Arnhem Land	NT	886	Independent	Combined	School Single Entity	-12.544185	134.113497	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN

9679 rows × 31 columns

In [9]:

w.Schools.df_all.groupby(['School Sector','School Type']).size() # Australia wide

w.Schools.df_all.groupby(['School Sector','School Type']).size() # Australia wide

Out[9]:

School Sector  School Type
Catholic       Combined        130
               Primary        1239
               Secondary       333
Government     Combined        776
               Primary        4833
               Secondary      1156
               Special           1
Independent    Combined        782
               Primary         230
               Secondary       199
dtype: int64

In [10]:

w.Schools.df_all.columns

w.Schools.df_all.columns

Out[10]:

Index(['School Name', 'Suburb', 'State', 'Postcode', 'School Sector',
       'School Type', 'Campus Type', 'Latitude', 'Longitude', 'School URL',
       'Governing Body', 'Governing Body URL', 'Year Range', 'ICSEA',
       'ICSEA Percentile', 'Bottom SEA Quarter (%)',
       'Lower Middle SEA Quarter (%)', 'Upper Middle SEA Quarter (%)',
       'Top SEA Quarter (%)', 'Teaching Staff',
       'Full Time Equivalent Teaching Staff', 'Non-Teaching Staff',
       'Full Time Equivalent Non-Teaching Staff', 'Total Enrolments',
       'Girls Enrolments', 'Boys Enrolments',
       'Full Time Equivalent Enrolments', 'Indigenous Enrolments (%)',
       'Language Background Other Than English - Yes (%)',
       'Language Background Other Than English - No (%)',
       'Language Background Other Than English - Not Stated (%)'],
      dtype='object')

In [18]:

w.Schools.df['Staff-to-Student Ratio'] = w.Schools.df['Teaching Staff']/w.Schools.df['Total Enrolments']

w.Schools.df['Staff-to-Student Ratio'] = w.Schools.df['Teaching Staff']/w.Schools.df['Total Enrolments']

In [31]:

import numpy as np
import plotly.offline as pyo
pyo.init_notebook_mode()
import plotly.express as px

fig = px.scatter(w.Schools.df, 
                 y='ICSEA Percentile' ,
                 x='Staff-to-Student Ratio',
                 facet_col='School Type', 
                 color='Indigenous Enrolments (%)',
                 hover_data={'School Name':True, 
                             'School Type':True, 
                             'School Sector':True
                            })
fig.update_layout(height=400)
fig.update_coloraxes(colorbar={'orientation':'h', 'thickness':20, 'y': -0.5})
fig.show()

import numpy as np import plotly.offline as pyo pyo.init_notebook_mode() import plotly.express as px fig = px.scatter(w.Schools.df, y='ICSEA Percentile' , x='Staff-to-Student Ratio', facet_col='School Type', color='Indigenous Enrolments (%)', hover_data={'School Name':True, 'School Type':True, 'School Sector':True }) fig.update_layout(height=400) fig.update_coloraxes(colorbar={'orientation':'h', 'thickness':20, 'y': -0.5}) fig.show()

In [16]:

w.show_schools(secondary_catchment=True,
               primary_catchment=True) 
w

w.show_schools(secondary_catchment=True, primary_catchment=True) w

Out[16]: