1. sds123-sp21
  2. demos
  3. lec07
In [2]:
from datascience import *
import numpy as np

%matplotlib inline
import matplotlib.pyplot as plots
plots.style.use('fivethirtyeight')
Lecture 7

Census

In [5]:
full = Table.read_table('nc-est2014-agesex-res.csv')
full.show(30)
SEX AGE CENSUS2010POP ESTIMATESBASE2010 POPESTIMATE2010 POPESTIMATE2011 POPESTIMATE2012 POPESTIMATE2013 POPESTIMATE2014
0 0 3944153 3944160 3951330 3963071 3926665 3945610 3948350
0 1 3978070 3978090 3957888 3966510 3978006 3943077 3962123
0 2 4096929 4096939 4090862 3971573 3979952 3992690 3957772
0 3 4119040 4119051 4111920 4102501 3983049 3992425 4005190
0 4 4063170 4063186 4077552 4122303 4112638 3994047 4003448
0 5 4056858 4056872 4064653 4087713 4132210 4123408 4004858
0 6 4066381 4066412 4073013 4074979 4097780 4143094 4134352
0 7 4030579 4030594 4043047 4083240 4084964 4108615 4154000
0 8 4046486 4046497 4025604 4053206 4093213 4095827 4119524
0 9 4148353 4148369 4125415 4035769 4063193 4104133 4106832
0 10 4172541 4172559 4187063 4135617 4045840 4074206 4115254
0 11 4114415 4114443 4115511 4197472 4145919 4057128 4085636
0 12 4106243 4106261 4113280 4126168 4208013 4157449 4068790
0 13 4118013 4118055 4119666 4124244 4136951 4219776 4169346
0 14 4165982 4166059 4145614 4131487 4135749 4149559 4232480
0 15 4242820 4242946 4231003 4158977 4144326 4149781 4163723
0 16 4316139 4316311 4313252 4247472 4174855 4161624 4167234
0 17 4395295 4395475 4376368 4334957 4268503 4197758 4184706
0 18 4500855 4501114 4491005 4402634 4360345 4296134 4225590
0 19 4585234 4585665 4571385 4520683 4430960 4390087 4326394
0 20 4519129 4519500 4568470 4600421 4550552 4459958 4418887
0 21 4354294 4354571 4387914 4596145 4633178 4580949 4489746
0 22 4264642 4264928 4286987 4415297 4628678 4666392 4612322
0 23 4198571 4198852 4217221 4314751 4447266 4661403 4698584
0 24 4249363 4249602 4243589 4244542 4346226 4479243 4692635
0 25 4262350 4262575 4289424 4269956 4275655 4377385 4509594
0 26 4152305 4152593 4160810 4313805 4299059 4305217 4406472
0 27 4248869 4249120 4237010 4183474 4340038 4326704 4332545
0 28 4215249 4215497 4247540 4258146 4207330 4364812 4351851
0 29 4223076 4223293 4210285 4266588 4280142 4230048 4387476

... (276 rows omitted)

In [6]:
partial = full.select('SEX', 'AGE', 'POPESTIMATE2010', 'POPESTIMATE2014')
partial
Out[6]:
SEX AGE POPESTIMATE2010 POPESTIMATE2014
0 0 3951330 3948350
0 1 3957888 3962123
0 2 4090862 3957772
0 3 4111920 4005190
0 4 4077552 4003448
0 5 4064653 4004858
0 6 4073013 4134352
0 7 4043047 4154000
0 8 4025604 4119524
0 9 4125415 4106832

... (296 rows omitted)

In [12]:
simple = partial.relabeled('POPESTIMATE2010', '2010').relabeled('POPESTIMATE2014', '2014')
simple
Out[12]:
SEX AGE 2010 2014
0 0 3951330 3948350
0 1 3957888 3962123
0 2 4090862 3957772
0 3 4111920 4005190
0 4 4077552 4003448
0 5 4064653 4004858
0 6 4073013 4134352
0 7 4043047 4154000
0 8 4025604 4119524
0 9 4125415 4106832

... (296 rows omitted)

In [13]:
simple.sort('AGE', descending=True)
Out[13]:
SEX AGE 2010 2014
2 999 157257573 161920569
1 999 152089484 156936487
0 999 309347057 318857056
2 100 45058 58468
1 100 9351 13729
0 100 54409 72197
2 99 26074 32791
1 99 6104 9037
0 99 32178 41828
2 98 37533 46536

... (296 rows omitted)

In [16]:
no_999 = simple.where('AGE', are.below(999))
no_999
Out[16]:
SEX AGE 2010 2014
0 0 3951330 3948350
0 1 3957888 3962123
0 2 4090862 3957772
0 3 4111920 4005190
0 4 4077552 4003448
0 5 4064653 4004858
0 6 4073013 4134352
0 7 4043047 4154000
0 8 4025604 4119524
0 9 4125415 4106832

... (293 rows omitted)

In [17]:
everyone = no_999.where('SEX', 0).drop('SEX')
males = no_999.where('SEX', 1).drop('SEX')
females = no_999.where('SEX', 2).drop('SEX')
In [18]:
females
Out[18]:
AGE 2010 2014
0 1932910 1930493
1 1937556 1938870
2 2002177 1935270
3 2010648 1956572
4 1993240 1959950
5 1988080 1961391
6 1993603 2024024
7 1979908 2031760
8 1971142 2014402
9 2018378 2009560

... (91 rows omitted)

In [20]:
females.sort('2014', descending=True)
Out[20]:
AGE 2010 2014
54 2196798 2331961
53 2268457 2316131
24 2082209 2301237
23 2066126 2298701
51 2289164 2283261
52 2283408 2282051
50 2355349 2279824
55 2183762 2263367
56 2108681 2254749
22 2098759 2244480

... (91 rows omitted)

In [21]:
males.sort('2014', descending=True)
Out[21]:
AGE 2010 2014
23 2151095 2399883
24 2161380 2391398
22 2188228 2367842
21 2241095 2310734
25 2177171 2295836
20 2331846 2269570
54 2091677 2242828
26 2102375 2240026
19 2334906 2220790
53 2170965 2219617

... (91 rows omitted)

In [31]:
pop_2014 = Table().with_columns(
    'Age', males['AGE'],
    'Males', males['2014'],
    'Females', females['2014']
)
In [32]:
pop_2014
Out[32]:
Age Males Females
0 2017857 1930493
1 2023253 1938870
2 2022502 1935270
3 2048618 1956572
4 2043498 1959950
5 2043467 1961391
6 2110328 2024024
7 2122240 2031760
8 2105122 2014402
9 2097272 2009560

... (91 rows omitted)

In [35]:
percent_females = 100*pop_2014['Females']/(pop_2014.column('Males') + pop_2014.column('Females'))
counts_and_percents = pop_2014.with_column('Percent Female', percent_females)
In [36]:
counts_and_percents
Out[36]:
Age Males Females Percent Female
0 2017857 1930493 48.8937
1 2023253 1938870 48.9351
2 2022502 1935270 48.898
3 2048618 1956572 48.8509
4 2043498 1959950 48.9565
5 2043467 1961391 48.9753
6 2110328 2024024 48.9563
7 2122240 2031760 48.9109
8 2105122 2014402 48.8989
9 2097272 2009560 48.9321

... (91 rows omitted)

In [ ]:

Line Graphs

In [37]:
counts_and_percents.plot('Age', 'Percent Female')
In [38]:
pop_2014
Out[38]:
Age Males Females
0 2017857 1930493
1 2023253 1938870
2 2022502 1935270
3 2048618 1956572
4 2043498 1959950
5 2043467 1961391
6 2110328 2024024
7 2122240 2031760
8 2105122 2014402
9 2097272 2009560

... (91 rows omitted)

In [39]:
pop_2014.plot('Age')
In [40]:
pop_2014.where('Age', are.between(65, 75))
Out[40]:
Age Males Females
65 1607688 1776761
66 1589127 1758649
67 1653257 1832245
68 1211480 1361047
69 1187554 1347458
70 1149247 1317238
71 1169356 1350392
72 1010896 1183338
73 913008 1089183
74 857432 1032934
In [41]:
2014 - np.arange(67, 73)
Out[41]:
array([1947, 1946, 1945, 1944, 1943, 1942])
In [42]:
everyone
Out[42]:
AGE 2010 2014
0 3951330 3948350
1 3957888 3962123
2 4090862 3957772
3 4111920 4005190
4 4077552 4003448
5 4064653 4004858
6 4073013 4134352
7 4043047 4154000
8 4025604 4119524
9 4125415 4106832

... (91 rows omitted)

In [44]:
everyone = everyone.with_columns(
    'Change', everyone.column('2014') - everyone.column('2010')
)
In [45]:
everyone.sort('Change', descending=True)
Out[45]:
AGE 2010 2014 Change
67 2693709 3485502 791793
64 2706063 3488136 782073
66 2621346 3347776 726430
65 2678532 3384449 705917
71 1953614 2519748 566134
34 3822188 4362895 540707
23 4217221 4698584 481363
59 3694276 4155417 461141
24 4243589 4692635 449046
70 2062581 2466485 403904

... (91 rows omitted)

In [46]:
everyone.with_columns(
    'Growth Rate', (everyone.column('2014')/everyone.column('2010')) ** (1/4) - 1
).sort('Growth Rate', descending=True)
Out[46]:
AGE 2010 2014 Change Growth Rate
100 54409 72197 17788 0.0732777
93 219063 286333 67270 0.069241
99 32178 41828 9650 0.0677683
67 2693709 3485502 791793 0.0665437
71 1953614 2519748 566134 0.0656868
64 2706063 3488136 782073 0.0655256
94 170775 218655 47880 0.0637358
98 47037 60185 13148 0.0635604
66 2621346 3347776 726430 0.0630606
65 2678532 3384449 705917 0.0602244

... (91 rows omitted)

Scatter Plots

In [47]:
actors = Table.read_table('actors.csv')
In [48]:
actors
Out[48]:
Actor Total Gross Number of Movies Average per Movie #1 Movie Gross
Harrison Ford 4871.7 41 118.8 Star Wars: The Force Awakens 936.7
Samuel L. Jackson 4772.8 69 69.2 The Avengers 623.4
Morgan Freeman 4468.3 61 73.3 The Dark Knight 534.9
Tom Hanks 4340.8 44 98.7 Toy Story 3 415
Robert Downey, Jr. 3947.3 53 74.5 The Avengers 623.4
Eddie Murphy 3810.4 38 100.3 Shrek 2 441.2
Tom Cruise 3587.2 36 99.6 War of the Worlds 234.3
Johnny Depp 3368.6 45 74.9 Dead Man's Chest 423.3
Michael Caine 3351.5 58 57.8 The Dark Knight 534.9
Scarlett Johansson 3341.2 37 90.3 The Avengers 623.4

... (40 rows omitted)

In [49]:
actors.scatter('Number of Movies', 'Total Gross')
In [50]:
actors.scatter('Number of Movies', 'Average per Movie')
In [51]:
actors.where('Average per Movie', are.above(150))
Out[51]:
Actor Total Gross Number of Movies Average per Movie #1 Movie Gross
Anthony Daniels 3162.9 7 451.8 Star Wars: The Force Awakens 936.7
Orlando Bloom 2815.8 17 165.6 Dead Man's Chest 423.3
Emma Watson 2681.9 17 157.8 Harry Potter / Deathly Hallows (P2) 381
Daniel Radcliffe 2634.4 17 155 Harry Potter / Deathly Hallows (P2) 381
In [ ]: