
from datascience import *
import numpy as np
Table.interactive_plots()
import plotly.express as px
import seaborn as sns


world = Table.from_df(px.data.gapminder())
world


px.scatter(world.to_df(),
           x = 'gdpPercap',
           y = 'lifeExp', 
           hover_name = 'country',
           color = 'continent',
           size = 'pop',
           size_max = 60,
           log_x = True,
           range_y = [30, 90],
           animation_frame = 'year',
           title = 'Life Expectancy, GDP Per Capita, and Population over Time'
          )


px.histogram(world.to_df(),
            x = 'lifeExp',
            animation_frame = 'year',
            range_x = [20, 90],
            range_y = [0, 50],
            title = 'Distribution of Life Expectancy over Time')


world_latest = world.where('year', 2007)
world_latest


px.box(world_latest.to_df(),
       y = 'lifeExp',
       x = 'continent',
       color = 'continent',
       hover_name = 'country',
       title = 'Distribution of Life Expectancy in 2007 by Continent'
      )


world_latest.where('continent', 'Americas')


px.pie(world_latest.where('continent', 'Americas').to_df(),
       values = 'pop',
       names = 'country',
       title = 'Population of the Americas'
)


world_for_pie = world_latest \
     .group('continent', sum) \
     .select('continent', 'pop sum')

world_for_pie


px.pie(world_for_pie.to_df(),
      values = 'pop sum',
      names = 'continent',
      title = 'World Population by Continent')


phases = [
 ['Newborn', '1998-11-26', '1999-11-26', 'Canada'],
 ['Toddler, Preschooler', '1999-11-26', '2005-09-03', 'US'],
 ['Elementary School Student', '2005-09-03', '2009-06-30', 'Canada'],
 ['Middle School Student', '2009-09-15', '2012-06-15', 'Canada'],
 ['High School Student', '2012-09-05', '2016-05-30', 'Canada'],
 ['Undergrad @ UC Berkeley', '2016-08-22','2020-05-15', 'US'],
 ['Masters @ UC Berkeley', '2020-08-25', '2021-05-14', 'Canada'],
 ['Teaching Data 94', '2021-01-20', '2021-05-14', 'Canada']]

phases_table = Table(labels = ['Phase', 'Start', 'End', 'Location']).with_rows(phases)
phases_table


px.timeline(phases_table.to_df(),
           x_start = 'Start',
           x_end = 'End',
           y = 'Phase',
           text = 'Location',
           title = 'My Life Trajectory') \
.update_yaxes(autorange='reversed')


world_latest


px.choropleth(world_latest.to_df(),
              locations = 'iso_alpha',
              color = 'lifeExp',
              hover_name = 'country',
              title = 'Life Expectancy Per Country',
              color_continuous_scale = px.colors.sequential.tempo
)


wm = Table.read_table('data/walmart.csv')
wm


# Number of Walmarts per state
wm_per_state = wm.group('STRSTATE')
wm_per_state


px.choropleth(wm_per_state.to_df(),
             locations = 'STRSTATE',
             color = 'count',
             locationmode = 'USA-states',
             scope = 'usa',
             title = 'Number of Walmarts Per State')


penguins = Table.from_df(sns.load_dataset('penguins'))
penguins


px.scatter_3d(penguins.to_df(),
             x = 'bill_length_mm',
             y = 'bill_depth_mm',
             z = 'flipper_length_mm',
             color = 'species',
             hover_name = 'island',
             title = 'Flipper Length vs. Bill Depth vs. Bill Length')

country	continent	year	lifeExp	pop	gdpPercap	iso_alpha	iso_num
Afghanistan	Asia	1952	28.801	8425333	779.445	AFG	4
Afghanistan	Asia	1957	30.332	9240934	820.853	AFG	4
Afghanistan	Asia	1962	31.997	10267083	853.101	AFG	4
Afghanistan	Asia	1967	34.02	11537966	836.197	AFG	4
Afghanistan	Asia	1972	36.088	13079460	739.981	AFG	4
Afghanistan	Asia	1977	38.438	14880372	786.113	AFG	4
Afghanistan	Asia	1982	39.854	12881816	978.011	AFG	4
Afghanistan	Asia	1987	40.822	13867957	852.396	AFG	4
Afghanistan	Asia	1992	41.674	16317921	649.341	AFG	4
Afghanistan	Asia	1997	41.763	22227415	635.341	AFG	4

country	continent	year	lifeExp	pop	gdpPercap	iso_alpha	iso_num
Afghanistan	Asia	2007	43.828	31889923	974.58	AFG	4
Albania	Europe	2007	76.423	3600523	5937.03	ALB	8
Algeria	Africa	2007	72.301	33333216	6223.37	DZA	12
Angola	Africa	2007	42.731	12420476	4797.23	AGO	24
Argentina	Americas	2007	75.32	40301927	12779.4	ARG	32
Australia	Oceania	2007	81.235	20434176	34435.4	AUS	36
Austria	Europe	2007	79.829	8199783	36126.5	AUT	40
Bahrain	Asia	2007	75.635	708573	29796	BHR	48
Bangladesh	Asia	2007	64.062	150448339	1391.25	BGD	50
Belgium	Europe	2007	79.441	10392226	33692.6	BEL	56

country	continent	year	lifeExp	pop	gdpPercap	iso_alpha	iso_num
Argentina	Americas	2007	75.32	40301927	12779.4	ARG	32
Bolivia	Americas	2007	65.554	9119152	3822.14	BOL	68
Brazil	Americas	2007	72.39	190010647	9065.8	BRA	76
Canada	Americas	2007	80.653	33390141	36319.2	CAN	124
Chile	Americas	2007	78.553	16284741	13171.6	CHL	152
Colombia	Americas	2007	72.889	44227550	7006.58	COL	170
Costa Rica	Americas	2007	78.782	4133884	9645.06	CRI	188
Cuba	Americas	2007	78.273	11416987	8948.1	CUB	192
Dominican Republic	Americas	2007	72.235	9319622	6025.37	DOM	214
Ecuador	Americas	2007	74.994	13755680	6873.26	ECU	218

continent	pop sum
Africa	929539692
Americas	898871184
Asia	3811953827
Europe	586098529
Oceania	24549947

country	continent	year	lifeExp	pop	gdpPercap	iso_alpha	iso_num
Afghanistan	Asia	2007	43.828	31889923	974.58	AFG	4
Albania	Europe	2007	76.423	3600523	5937.03	ALB	8
Algeria	Africa	2007	72.301	33333216	6223.37	DZA	12
Angola	Africa	2007	42.731	12420476	4797.23	AGO	24
Argentina	Americas	2007	75.32	40301927	12779.4	ARG	32
Australia	Oceania	2007	81.235	20434176	34435.4	AUS	36
Austria	Europe	2007	79.829	8199783	36126.5	AUT	40
Bahrain	Asia	2007	75.635	708573	29796	BHR	48
Bangladesh	Asia	2007	64.062	150448339	1391.25	BGD	50
Belgium	Europe	2007	79.441	10392226	33692.6	BEL	56

Lecture 28 – Fun with Plotly¶

Data 6, Summer 2021¶

Animated scatter plots¶

Animated histograms¶

Boxplots¶

Break: Quick Check 1 ¶

Pie charts¶

Timelines (also known as Gantt charts)¶

Choropleths¶

3D scatter plots¶

Phase	Start	End	Location
Newborn	1998-11-26	1999-11-26	Canada
Toddler, Preschooler	1999-11-26	2005-09-03	US
Elementary School Student	2005-09-03	2009-06-30	Canada
Middle School Student	2009-09-15	2012-06-15	Canada
High School Student	2012-09-05	2016-05-30	Canada
Undergrad @ UC Berkeley	2016-08-22	2020-05-15	US
Masters @ UC Berkeley	2020-08-25	2021-05-14	Canada
Teaching Data 94	2021-01-20	2021-05-14	Canada

storenum	OPENDATE	date_super	conversion	st	county	STREETADDR	STRCITY	STRSTATE	ZIPCODE	type_store	LAT	LON	MONTH	DAY	YEAR
1	7/1/62	3/1/97	1	5	7	2110 WEST WALNUT	Rogers	AR	72756	Supercenter	36.3422	-94.0714	7	1	1962
2	8/1/64	3/1/96	1	5	9	1417 HWY 62/65 N	Harrison	AR	72601	Supercenter	36.237	-93.0935	8	1	1964
4	8/1/65	3/1/02	1	5	7	2901 HWY 412 EAST	Siloam Springs	AR	72761	Supercenter	36.1799	-94.5021	8	1	1965
8	10/1/67	3/1/93	1	5	29	1621 NORTH BUSINESS 9	Morrilton	AR	72110	Supercenter	35.1565	-92.7586	10	1	1967
7	10/1/67	nan	nan	5	119	3801 CAMP ROBINSON RD.	North Little Rock	AR	72118	Wal-Mart	34.8133	-92.3023	10	1	1967
10	7/1/68	3/1/98	1	40	21	2020 SOUTH MUSKOGEE	Tahlequah	OK	74464	Supercenter	35.9237	-94.9719	7	1	1968
13	11/1/68	3/1/96	1	29	97	2705 GRAND AVE	Carthage	MO	64836	Supercenter	37.169	-94.3116	11	1	1968
12	7/1/68	3/1/94	1	40	131	1500 LYNN RIGGS BLVD	Claremore	OK	74017	Supercenter	36.3271	-95.6119	7	1	1968
11	3/1/68	2/20/02	1	5	5	65 WAL-MART DRIVE	Mountain Home	AR	72653	Supercenter	36.329	-92.3578	3	1	1968
9	3/1/68	3/1/00	1	29	143	1303 SOUTH MAIN	Sikeston	MO	63801	Supercenter	36.8912	-89.5836	3	1	1968

species	island	bill_length_mm	bill_depth_mm	flipper_length_mm	body_mass_g	sex
Adelie	Torgersen	39.1	18.7	181	3750	Male
Adelie	Torgersen	39.5	17.4	186	3800	Female
Adelie	Torgersen	40.3	18	195	3250	Female
Adelie	Torgersen	nan	nan	nan	nan	nan
Adelie	Torgersen	36.7	19.3	193	3450	Female
Adelie	Torgersen	39.3	20.6	190	3650	Male
Adelie	Torgersen	38.9	17.8	181	3625	Female
Adelie	Torgersen	39.2	19.6	195	4675	Male
Adelie	Torgersen	34.1	18.1	193	3475	nan
Adelie	Torgersen	42	20.2	190	4250	nan

STRSTATE	count
AL	90
AR	81
AZ	55
CA	159
CO	56
DE	8
FL	175
GA	114
IA	55
ID	17

Lecture 28 – Fun with Plotly¶

Data 6, Summer 2021¶

Animated scatter plots¶

Animated histograms¶

Boxplots¶

Break: Quick Check 1¶

Pie charts¶

Timelines (also known as Gantt charts)¶

Choropleths¶

3D scatter plots¶

Break: Quick Check 1 ¶