This example illustrates how to download a dataset from CARTO's Data Observatory. As datasets can be really big, we will show you how to apply standard SQL filters and spatial SQL filters to download them.
Note: You'll need CARTO Account credentials to reproduce this example.
In [1]:
from cartoframes.auth import set_default_credentials
set_default_credentials('creds.json')
As datasets may have a lot of columns, let's force pandas to display all the columns
In [2]:
import pandas
pandas.set_option('display.max_columns', None)
Learn how to discover a dataset through the catalog by checking the discovery example.
You can also choose a dataset from your subscriptions. Call Catalog().subscriptions() to list your active subscriptions.
In [3]:
from cartoframes.data.observatory import Dataset
dataset = Dataset.get('acs_sociodemogr_8c2655e0')
dataset.to_dict()
Out[3]:
{'slug': 'acs_sociodemogr_8c2655e0',
'name': 'Sociodemographics - United States of America (County, 2017, 5yrs)',
'description': 'The American Community Survey (ACS) is an ongoing survey that provides vital information on a yearly basis about the USA and its people. This dataset contains only a subset of the variables that have been deemed most relevant. More info: https://www.census.gov/programs-surveys/acs/about.html',
'category_id': 'demographics',
'country_id': 'usa',
'data_source_id': 'sociodemographics',
'provider_id': 'usa_acs',
'geography_name': 'County - United States of America',
'geography_description': 'Shoreline clipped TIGER/Line boundaries. More info: https://carto.com/blog/tiger-shoreline-clip/',
'temporal_aggregation': '5yrs',
'time_coverage': '[2013-01-01, 2018-01-01)',
'update_frequency': 'yearly',
'is_public_data': True,
'lang': 'eng',
'version': '20132017',
'category_name': 'Demographics',
'provider_name': 'American Community Survey',
'geography_id': 'carto-do-public-data.carto.geography_usa_county_2015',
'id': 'carto-do-public-data.usa_acs.demographics_sociodemographics_usa_county_2015_5yrs_20132017'}
Call head() to check a sample of the dataset and see the available columns.
In [4]:
dataset.head()
Out[4]:
geoid
no_car
do_date
male_20
male_21
no_cars
one_car
poverty
children
male_pop
two_cars
asian_pop
black_pop
female_20
female_21
in_school
pop_25_64
total_pop
white_pop
female_pop
gini_index
households
male_60_61
male_62_64
median_age
three_cars
male_5_to_9
median_rent
pop_16_over
pop_widowed
armed_forces
employed_pop
hispanic_pop
male_under_5
mobile_homes
pop_divorced
female_5_to_9
housing_units
male_10_to_14
male_15_to_17
male_18_to_19
male_22_to_24
male_25_to_29
male_30_to_34
male_35_to_39
male_40_to_44
male_45_to_49
male_45_to_64
male_50_to_54
male_55_to_59
male_65_to_66
male_67_to_69
male_70_to_74
male_75_to_79
male_80_to_84
median_income
pop_separated
amerindian_pop
female_under_5
four_more_cars
group_quarters
masters_degree
other_race_pop
unemployed_pop
walked_to_work
worked_at_home
female_10_to_14
female_15_to_17
female_18_to_19
female_22_to_24
female_25_to_29
female_30_to_34
female_35_to_39
female_40_to_44
female_45_to_49
female_50_to_54
female_55_to_59
female_60_to_61
female_62_to_64
female_65_to_66
female_67_to_69
female_70_to_74
female_75_to_79
female_80_to_84
pop_15_and_over
pop_now_married
asian_male_45_54
asian_male_55_64
bachelors_degree
black_male_45_54
black_male_55_64
commute_5_9_mins
commuters_by_bus
in_grades_1_to_4
in_grades_5_to_8
male_85_and_over
not_hispanic_pop
pop_5_years_over
white_male_45_54
white_male_55_64
associates_degree
commuters_16_over
dwellings_2_units
family_households
hispanic_any_race
in_grades_9_to_12
income_less_10000
income_per_capita
pop_25_years_over
pop_never_married
bachelors_degree_2
commute_10_14_mins
commute_15_19_mins
commute_20_24_mins
commute_25_29_mins
commute_30_34_mins
commute_35_39_mins
commute_35_44_mins
commute_40_44_mins
commute_45_59_mins
commute_60_89_mins
female_85_and_over
income_10000_14999
income_15000_19999
income_20000_24999
income_25000_29999
income_30000_34999
income_35000_39999
income_40000_44999
income_45000_49999
income_50000_59999
income_60000_74999
income_75000_99999
married_households
not_in_labor_force
not_us_citizen_pop
pop_in_labor_force
high_school_diploma
hispanic_male_45_54
hispanic_male_55_64
occupation_services
workers_16_and_over
civilian_labor_force
commute_60_more_mins
commute_90_more_mins
commute_less_10_mins
commuters_by_carpool
employed_information
in_undergrad_college
income_100000_124999
income_125000_149999
income_150000_199999
male_male_households
nonfamily_households
rent_over_50_percent
vacant_housing_units
commuters_drove_alone
employed_construction
employed_retail_trade
income_200000_or_more
less_one_year_college
male_45_64_grade_9_12
one_year_more_college
rent_10_to_15_percent
rent_15_to_20_percent
rent_20_to_25_percent
rent_25_to_30_percent
rent_30_to_35_percent
rent_35_to_40_percent
rent_40_to_50_percent
rent_under_10_percent
sales_office_employed
speak_spanish_at_home
two_or_more_races_pop
dwellings_3_to_4_units
dwellings_5_to_9_units
employed_manufacturing
male_45_64_high_school
occupied_housing_units
male_45_64_some_college
mortgaged_housing_units
occupation_sales_office
population_3_years_over
asian_including_hispanic
black_including_hispanic
dwellings_10_to_19_units
dwellings_20_to_49_units
employed_wholesale_trade
female_female_households
rent_burden_not_computed
white_including_hispanic
high_school_including_ged
commuters_by_car_truck_van
dwellings_1_units_attached
dwellings_1_units_detached
dwellings_50_or_more_units
housing_built_2000_to_2004
male_45_64_graduate_degree
occupation_management_arts
population_1_year_and_over
speak_only_english_at_home
housing_built_2005_or_later
male_45_64_bachelors_degree
median_year_structure_built
children_in_single_female_hh
families_with_young_children
graduate_professional_degree
households_retirement_income
male_45_64_associates_degree
male_45_64_less_than_9_grade
million_dollar_housing_units
owner_occupied_housing_units
percent_income_spent_on_rent
aggregate_travel_time_to_work
amerindian_including_hispanic
housing_built_1939_or_earlier
housing_units_renter_occupied
pop_determined_poverty_status
vacant_housing_units_for_rent
vacant_housing_units_for_sale
employed_public_administration
less_than_high_school_graduate
commuters_by_subway_or_elevated
bachelors_degree_or_higher_25_64
employed_education_health_social
speak_spanish_at_home_low_english
commuters_by_public_transportation
different_house_year_ago_same_city
some_college_and_associates_degree
households_public_asst_or_food_stamps
management_business_sci_arts_employed
employed_finance_insurance_real_estate
different_house_year_ago_different_city
employed_science_management_admin_waste
one_parent_families_with_young_children
two_parent_families_with_young_children
employed_other_services_not_public_admin
owner_occupied_housing_units_median_value
employed_transportation_warehousing_utilities
occupation_production_transportation_material
father_one_parent_families_with_young_children
owner_occupied_housing_units_lower_value_quartile
owner_occupied_housing_units_upper_value_quartile
employed_agriculture_forestry_fishing_hunting_mining
occupation_natural_resources_construction_maintenance
two_parents_in_labor_force_families_with_young_children
employed_arts_entertainment_recreation_accommodation_food
renter_occupied_housing_units_paying_cash_median_gross_rent
two_parents_not_in_labor_force_families_with_young_children
father_in_labor_force_one_parent_families_with_young_children
two_parents_father_in_labor_force_families_with_young_children
two_parents_mother_in_labor_force_families_with_young_children
0
30007
43.0
20132017
32.0
20.0
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5432.0
2859.0
0.4452
2405.0
105.0
196.0
46.3
622.0
132.0
550.0
4762.0
None
0.0
2621.0
152.0
112.0
377.0
None
207.0
2743.0
196.0
117.0
75.0
54.0
135.0
131.0
160.0
163.0
184.0
908.0
210.0
213.0
63.0
171.0
191.0
129.0
66.0
55295.0
None
84.0
146.0
368.0
44.0
293.0
0.0
112.0
135.0
205.0
124.0
117.0
123.0
90.0
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165.0
153.0
218.0
250.0
196.0
116.0
137.0
74.0
114.0
173.0
158.0
52.0
None
None
0.0
0.0
866.0
0.0
0.0
359.0
0.0
253.0
252.0
41.0
5603.0
None
369.0
463.0
268.0
2287.0
15.0
1552.0
152.0
336.0
96.0
32268.0
4210.0
None
866.0
337.0
132.0
225.0
159.0
162.0
214.0
247.0
33.0
271.0
150.0
42.0
135.0
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267.0
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261.0
24.0
683.0
94.0
22.0
52.0
15.0
45.0
5.0
32.0
6.0
404.0
None
83.0
41.0
49.0
215.0
287.0
2405.0
247.0
1122.0
404.0
5553.0
0.0
4.0
41.0
0.0
42.0
0.0
67.0
5533.0
1564.0
2119.0
0.0
2208.0
0.0
61.0
87.0
955.0
5634.0
None
16.0
158.0
1987.0
147.0
335.0
337.0
625.0
92.0
13.0
26.0
2020.0
23.6
60655.0
84.0
97.0
385.0
5711.0
11.0
34.0
291.0
231.0
0.0
873.0
380.0
None
0.0
32.0
1212.0
179.0
955.0
141.0
471.0
141.0
64.0
271.0
94.0
195000.0
90.0
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119800.0
301300.0
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640.0
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1
19137
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20132017
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0.0
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42.0
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10239.0
9640.0
5308.0
0.4270
4614.0
149.0
256.0
44.5
774.0
385.0
482.0
8243.0
None
11.0
4838.0
371.0
259.0
178.0
None
310.0
5231.0
313.0
238.0
115.0
182.0
217.0
239.0
252.0
323.0
301.0
1449.0
366.0
377.0
148.0
162.0
193.0
146.0
137.0
43674.0
None
40.0
335.0
425.0
210.0
227.0
0.0
349.0
194.0
233.0
302.0
207.0
127.0
119.0
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233.0
262.0
345.0
320.0
373.0
452.0
152.0
200.0
126.0
168.0
273.0
233.0
177.0
None
None
0.0
0.0
867.0
0.0
0.0
1023.0
21.0
569.0
512.0
114.0
9868.0
None
628.0
782.0
926.0
4560.0
177.0
3080.0
371.0
530.0
282.0
25005.0
7143.0
None
867.0
568.0
487.0
485.0
205.0
236.0
39.0
82.0
43.0
292.0
205.0
229.0
419.0
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211.0
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222.0
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485.0
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3045.0
136.0
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39.0
0.0
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317.0
121.0
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309.0
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86.0
716.0
111.0
935.0
180.0
193.0
164.0
144.0
51.0
77.0
168.0
75.0
1081.0
None
188.0
291.0
101.0
870.0
680.0
4614.0
264.0
1552.0
1081.0
9869.0
0.0
0.0
49.0
40.0
120.0
3.0
185.0
9756.0
2785.0
4266.0
77.0
4318.0
0.0
27.0
32.0
1470.0
10138.0
None
5.0
178.0
1952.0
756.0
662.0
281.0
807.0
151.0
33.0
0.0
3131.0
26.3
86680.0
64.0
181.0
1483.0
10014.0
142.0
67.0
122.0
633.0
0.0
856.0
1172.0
None
21.0
550.0
2577.0
831.0
1470.0
170.0
839.0
173.0
327.0
335.0
301.0
81800.0
245.0
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29.0
49500.0
126300.0
349.0
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213.0
664.0
12.0
25.0
64.0
11.0
2
20101
10.0
20132017
8.0
0.0
36.0
181.0
181.0
407.0
864.0
350.0
0.0
1.0
1.0
8.0
332.0
822.0
1702.0
1539.0
838.0
0.4473
828.0
10.0
27.0
41.5
133.0
26.0
345.0
1379.0
None
0.0
885.0
113.0
74.0
46.0
None
74.0
991.0
46.0
54.0
22.0
45.0
59.0
26.0
76.0
49.0
56.0
226.0
72.0
61.0
19.0
22.0
25.0
40.0
39.0
51765.0
None
39.0
48.0
128.0
8.0
69.0
0.0
32.0
32.0
27.0
47.0
38.0
35.0
13.0
40.0
29.0
48.0
53.0
44.0
45.0
63.0
19.0
45.0
22.0
14.0
40.0
42.0
38.0
None
None
0.0
0.0
206.0
0.0
0.0
178.0
0.0
64.0
79.0
8.0
1589.0
None
121.0
96.0
135.0
817.0
0.0
484.0
113.0
67.0
62.0
29768.0
1163.0
None
206.0
98.0
93.0
64.0
38.0
47.0
15.0
15.0
0.0
9.0
35.0
32.0
42.0
50.0
67.0
67.0
35.0
32.0
35.0
12.0
79.0
109.0
139.0
401.0
462.0
12.0
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302.0
6.0
2.0
165.0
844.0
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43.0
8.0
410.0
42.0
10.0
48.0
45.0
11.0
18.0
0.0
344.0
16.0
163.0
732.0
112.0
73.0
25.0
106.0
7.0
207.0
27.0
16.0
18.0
13.0
19.0
8.0
0.0
10.0
185.0
None
3.0
33.0
0.0
6.0
82.0
828.0
61.0
306.0
185.0
1623.0
0.0
1.0
0.0
0.0
13.0
0.0
52.0
1649.0
361.0
774.0
22.0
890.0
0.0
15.0
3.0
264.0
1659.0
None
0.0
48.0
1957.0
43.0
137.0
72.0
107.0
22.0
3.0
0.0
649.0
22.9
NaN
39.0
95.0
179.0
1690.0
28.0
0.0
28.0
76.0
0.0
211.0
196.0
None
0.0
61.0
448.0
61.0
264.0
30.0
84.0
34.0
20.0
117.0
51.0
76400.0
55.0
73.0
13.0
44300.0
124400.0
253.0
198.0
65.0
24.0
551.0
0.0
13.0
48.0
4.0
3
48307
133.0
20132017
1.0
93.0
220.0
883.0
1296.0
1935.0
4171.0
1232.0
0.0
161.0
39.0
14.0
1623.0
4096.0
8145.0
5067.0
3974.0
0.4495
3143.0
240.0
134.0
43.5
510.0
370.0
471.0
6496.0
None
0.0
3658.0
2571.0
327.0
393.0
None
263.0
4302.0
212.0
220.0
33.0
111.0
284.0
236.0
272.0
152.0
206.0
1054.0
310.0
164.0
106.0
111.0
296.0
94.0
139.0
42367.0
None
1.0
154.0
298.0
154.0
213.0
202.0
122.0
104.0
61.0
258.0
131.0
66.0
21.0
340.0
82.0
271.0
321.0
285.0
188.0
273.0
121.0
217.0
147.0
117.0
228.0
156.0
134.0
None
None
0.0
0.0
671.0
18.0
0.0
867.0
2.0
483.0
334.0
60.0
5574.0
None
327.0
391.0
217.0
3389.0
229.0
2164.0
2571.0
453.0
228.0
23398.0
5832.0
None
671.0
535.0
412.0
410.0
23.0
202.0
71.0
117.0
46.0
75.0
52.0
148.0
265.0
288.0
127.0
156.0
203.0
207.0
137.0
124.0
259.0
329.0
385.0
1592.0
2716.0
207.0
3780.0
1597.0
164.0
147.0
770.0
3450.0
3780.0
133.0
81.0
1482.0
424.0
17.0
70.0
176.0
114.0
69.0
0.0
979.0
227.0
1159.0
2857.0
171.0
383.0
76.0
500.0
201.0
1092.0
64.0
100.0
59.0
44.0
46.0
25.0
24.0
19.0
809.0
None
143.0
110.0
16.0
214.0
458.0
3143.0
203.0
889.0
809.0
7863.0
0.0
161.0
6.0
18.0
39.0
0.0
137.0
6969.0
1862.0
3281.0
23.0
3498.0
0.0
78.0
17.0
850.0
8140.0
None
39.0
93.0
1969.0
396.0
589.0
261.0
679.0
7.0
75.0
35.0
2398.0
32.0
53750.0
1.0
551.0
745.0
7955.0
31.0
57.0
192.0
1229.0
0.0
556.0
647.0
None
2.0
634.0
1809.0
600.0
850.0
133.0
829.0
231.0
206.0
383.0
363.0
82000.0
401.0
510.0
165.0
41600.0
165300.0
602.0
719.0
214.0
265.0
721.0
0.0
165.0
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4
46123
66.0
20132017
8.0
21.0
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2781.0
649.0
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1121.0
2688.0
5480.0
4500.0
2699.0
0.4633
2424.0
133.0
141.0
46.5
639.0
135.0
341.0
4362.0
None
0.0
2859.0
18.0
140.0
496.0
None
125.0
3085.0
222.0
141.0
50.0
127.0
129.0
143.0
155.0
116.0
164.0
881.0
238.0
205.0
34.0
116.0
112.0
143.0
42.0
48409.0
None
729.0
172.0
474.0
159.0
112.0
0.0
52.0
132.0
223.0
203.0
99.0
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65.0
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133.0
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173.0
242.0
190.0
108.0
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87.0
84.0
114.0
174.0
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None
None
0.0
0.0
643.0
0.0
0.0
878.0
37.0
272.0
269.0
66.0
5462.0
None
310.0
392.0
319.0
2592.0
24.0
1473.0
18.0
273.0
239.0
27613.0
3858.0
None
643.0
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158.0
167.0
120.0
92.0
17.0
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55.0
143.0
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174.0
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32.0
0.0
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85.0
196.0
31.0
571.0
87.0
63.0
58.0
79.0
2.0
37.0
104.0
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484.0
None
233.0
108.0
70.0
41.0
387.0
2424.0
140.0
663.0
484.0
5334.0
0.0
0.0
38.0
52.0
75.0
0.0
125.0
4500.0
1511.0
2410.0
16.0
2281.0
0.0
43.0
32.0
1217.0
5425.0
None
19.0
152.0
1966.0
220.0
338.0
210.0
261.0
83.0
56.0
16.0
1677.0
27.5
40040.0
747.0
255.0
747.0
5294.0
95.0
39.0
118.0
408.0
0.0
580.0
674.0
None
37.0
182.0
1086.0
340.0
1217.0
184.0
444.0
92.0
85.0
253.0
139.0
85800.0
52.0
306.0
26.0
51600.0
156600.0
706.0
459.0
215.0
114.0
562.0
0.0
14.0
32.0
6.0
5
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20132017
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189.0
934.0
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17344.0
11948.0
9134.0
0.4862
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225.0
238.0
37.7
871.0
566.0
344.0
13251.0
None
0.0
6326.0
421.0
614.0
742.0
None
496.0
8178.0
747.0
421.0
226.0
282.0
479.0
431.0
449.0
422.0
526.0
2156.0
567.0
600.0
227.0
234.0
239.0
164.0
153.0
32468.0
None
18.0
671.0
388.0
202.0
483.0
0.0
756.0
153.0
109.0
689.0
393.0
194.0
209.0
573.0
521.0
629.0
452.0
564.0
593.0
715.0
199.0
291.0
159.0
271.0
397.0
291.0
254.0
None
None
0.0
0.0
815.0
239.0
230.0
1273.0
23.0
877.0
1260.0
126.0
16923.0
None
824.0
828.0
660.0
6067.0
517.0
4342.0
421.0
1082.0
956.0
18883.0
11212.0
None
815.0
792.0
998.0
913.0
366.0
587.0
78.0
167.0
89.0
181.0
87.0
223.0
689.0
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512.0
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377.0
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382.0
336.0
544.0
392.0
555.0
2758.0
6169.0
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3602.0
19.0
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6176.0
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159.0
72.0
1904.0
594.0
26.0
492.0
347.0
96.0
143.0
6.0
2533.0
641.0
1303.0
5259.0
194.0
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74.0
705.0
408.0
1354.0
263.0
285.0
371.0
367.0
188.0
167.0
267.0
155.0
1164.0
None
316.0
513.0
49.0
1328.0
866.0
6875.0
337.0
1652.0
1164.0
16514.0
0.0
4717.0
90.0
16.0
110.0
5.0
540.0
12113.0
4279.0
5853.0
54.0
6098.0
91.0
206.0
25.0
1560.0
17033.0
None
20.0
211.0
1968.0
2120.0
1330.0
532.0
1138.0
58.0
251.0
0.0
3631.0
28.8
105750.0
18.0
853.0
3244.0
17042.0
313.0
89.0
313.0
2867.0
0.0
1066.0
1780.0
None
23.0
1577.0
2719.0
2237.0
1560.0
282.0
1594.0
164.0
813.0
517.0
304.0
73300.0
299.0
1565.0
89.0
37000.0
120900.0
405.0
596.0
216.0
380.0
568.0
2.0
69.0
299.0
0.0
6
21159
28.0
20132017
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6830.0
12175.0
11250.0
5431.0
0.4422
4316.0
213.0
261.0
39.0
645.0
326.0
307.0
9909.0
None
0.0
3032.0
226.0
323.0
1911.0
None
342.0
5240.0
432.0
217.0
125.0
481.0
485.0
720.0
476.0
592.0
430.0
1631.0
433.0
294.0
123.0
142.0
247.0
166.0
61.0
29239.0
None
9.0
295.0
226.0
1441.0
225.0
0.0
552.0
34.0
8.0
334.0
229.0
101.0
118.0
303.0
308.0
384.0
316.0
401.0
419.0
466.0
74.0
255.0
158.0
176.0
203.0
158.0
150.0
None
None
0.0
0.0
373.0
159.0
17.0
195.0
0.0
554.0
575.0
39.0
11949.0
None
657.0
726.0
728.0
2791.0
36.0
2940.0
226.0
481.0
782.0
14914.0
8547.0
None
373.0
117.0
575.0
297.0
111.0
235.0
220.0
384.0
164.0
358.0
325.0
94.0
288.0
476.0
430.0
203.0
138.0
224.0
365.0
182.0
271.0
373.0
356.0
2311.0
6325.0
34.0
3584.0
2353.0
33.0
17.0
428.0
2799.0
3584.0
447.0
122.0
267.0
460.0
30.0
292.0
120.0
50.0
58.0
0.0
1376.0
267.0
924.0
2292.0
197.0
520.0
0.0
360.0
347.0
1107.0
109.0
22.0
83.0
98.0
107.0
49.0
35.0
71.0
894.0
None
107.0
53.0
105.0
45.0
736.0
4316.0
218.0
958.0
894.0
11860.0
0.0
606.0
28.0
18.0
88.0
0.0
361.0
11397.0
3313.0
2752.0
37.0
2976.0
54.0
143.0
41.0
875.0
12072.0
None
55.0
19.0
1985.0
473.0
787.0
327.0
987.0
117.0
153.0
0.0
3114.0
31.8
98955.0
9.0
141.0
1202.0
10690.0
63.0
38.0
164.0
2339.0
0.0
553.0
775.0
None
0.0
74.0
2195.0
1218.0
875.0
162.0
1449.0
85.0
184.0
603.0
183.0
65500.0
165.0
319.0
24.0
30000.0
117600.0
392.0
516.0
275.0
226.0
483.0
78.0
24.0
211.0
39.0
7
31005
0.0
20132017
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8.0
0.0
35.0
46.0
132.0
204.0
46.0
0.0
0.0
1.0
1.0
149.0
172.0
421.0
411.0
217.0
0.4498
177.0
9.0
3.0
43.3
54.0
27.0
388.0
298.0
None
0.0
177.0
0.0
3.0
22.0
None
21.0
261.0
20.0
4.0
2.0
13.0
8.0
1.0
6.0
18.0
17.0
50.0
8.0
13.0
2.0
5.0
8.0
10.0
17.0
41250.0
None
0.0
8.0
42.0
0.0
15.0
1.0
1.0
13.0
23.0
35.0
14.0
0.0
0.0
6.0
7.0
5.0
13.0
14.0
11.0
25.0
3.0
5.0
9.0
3.0
10.0
15.0
10.0
None
None
0.0
0.0
60.0
0.0
0.0
16.0
0.0
32.0
48.0
2.0
421.0
None
24.0
25.0
33.0
146.0
0.0
96.0
0.0
18.0
6.0
21799.0
264.0
None
60.0
9.0
3.0
16.0
4.0
12.0
3.0
17.0
14.0
15.0
3.0
1.0
21.0
25.0
13.0
4.0
9.0
8.0
12.0
10.0
14.0
22.0
20.0
87.0
120.0
2.0
178.0
73.0
0.0
0.0
17.0
169.0
178.0
6.0
3.0
64.0
24.0
1.0
17.0
7.0
1.0
2.0
0.0
81.0
8.0
84.0
109.0
15.0
13.0
3.0
26.0
0.0
34.0
13.0
3.0
1.0
3.0
2.0
5.0
0.0
0.0
20.0
None
9.0
0.0
0.0
13.0
21.0
177.0
7.0
25.0
20.0
415.0
0.0
0.0
0.0
0.0
3.0
0.0
30.0
411.0
79.0
133.0
10.0
229.0
0.0
3.0
1.0
83.0
421.0
None
0.0
6.0
1959.0
3.0
27.0
19.0
23.0
15.0
0.0
0.0
112.0
25.8
3040.0
0.0
31.0
65.0
421.0
2.0
0.0
3.0
13.0
0.0
59.0
37.0
None
0.0
1.0
93.0
6.0
83.0
1.0
54.0
3.0
2.0
25.0
6.0
101500.0
9.0
21.0
2.0
57000.0
162500.0
67.0
36.0
13.0
6.0
658.0
0.0
2.0
12.0
0.0
8
01067
205.0
20132017
124.0
74.0
427.0
1953.0
2310.0
3581.0
8208.0
2346.0
0.0
4762.0
33.0
120.0
3517.0
8535.0
17110.0
11757.0
8902.0
0.4529
6727.0
176.0
317.0
43.5
1373.0
459.0
398.0
13972.0
None
10.0
6949.0
435.0
468.0
2311.0
None
438.0
9055.0
594.0
307.0
171.0
396.0
369.0
350.0
658.0
418.0
528.0
2232.0
536.0
675.0
252.0
324.0
452.0
293.0
121.0
45569.0
None
5.0
452.0
628.0
289.0
835.0
0.0
496.0
48.0
176.0
511.0
352.0
162.0
326.0
415.0
465.0
527.0
598.0
547.0
617.0
586.0
222.0
531.0
166.0
404.0
567.0
353.0
222.0
None
None
0.0
0.0
1087.0
291.0
314.0
605.0
0.0
750.0
892.0
146.0
16675.0
None
757.0
839.0
925.0
6689.0
291.0
4545.0
435.0
844.0
552.0
23983.0
12123.0
None
1087.0
694.0
858.0
1025.0
749.0
1067.0
154.0
395.0
241.0
445.0
301.0
288.0
504.0
566.0
332.0
278.0
457.0
308.0
323.0
412.0
616.0
694.0
614.0
3625.0
6517.0
169.0
7455.0
3398.0
13.0
0.0
1319.0
6865.0
7445.0
431.0
130.0
1025.0
513.0
74.0
595.0
379.0
287.0
257.0
10.0
2182.0
185.0
2328.0
5900.0
386.0
769.0
148.0
828.0
237.0
1843.0
149.0
96.0
162.0
91.0
35.0
109.0
75.0
51.0
1347.0
None
151.0
163.0
0.0
787.0
918.0
6727.0
540.0
2932.0
1347.0
16615.0
0.0
4762.0
52.0
12.0
221.0
0.0
243.0
12144.0
4105.0
6413.0
41.0
6183.0
2.0
218.0
113.0
2279.0
16964.0
None
102.0
153.0
1983.0
761.0
1033.0
987.0
1480.0
175.0
96.0
0.0
5531.0
26.0
171195.0
5.0
343.0
1196.0
16806.0
222.0
164.0
196.0
2348.0
0.0
1485.0
1673.0
None
0.0
378.0
3596.0
1129.0
2279.0
279.0
983.0
495.0
394.0
639.0
231.0
112600.0
788.0
1238.0
89.0
62700.0
189100.0
353.0
766.0
386.0
697.0
603.0
8.0
80.0
245.0
0.0
9
41021
4.0
20132017
19.0
16.0
25.0
176.0
188.0
426.0
962.0
330.0
0.0
0.0
0.0
14.0
396.0
893.0
1910.0
1643.0
948.0
0.4153
805.0
28.0
61.0
47.0
191.0
79.0
537.0
1529.0
None
0.0
751.0
154.0
51.0
146.0
None
51.0
1070.0
51.0
44.0
22.0
13.0
18.0
49.0
41.0
32.0
51.0
283.0
86.0
57.0
47.0
29.0
77.0
48.0
29.0
39831.0
None
99.0
54.0
83.0
15.0
64.0
0.0
61.0
64.0
55.0
64.0
32.0
1.0
26.0
44.0
58.0
49.0
49.0
69.0
31.0
65.0
61.0
44.0
22.0
32.0
62.0
44.0
22.0
None
None
0.0
0.0
200.0
0.0
0.0
191.0
0.0
102.0
101.0
14.0
1756.0
None
114.0
130.0
77.0
684.0
15.0
512.0
154.0
96.0
52.0
24178.0
1373.0
None
200.0
101.0
81.0
24.0
18.0
34.0
0.0
20.0
20.0
41.0
5.0
54.0
50.0
20.0
73.0
56.0
77.0
77.0
20.0
52.0
50.0
77.0
90.0
420.0
717.0
30.0
812.0
426.0
11.0
0.0
113.0
739.0
812.0
24.0
19.0
341.0
37.0
14.0
26.0
80.0
20.0
2.0
0.0
293.0
26.0
265.0
575.0
74.0
57.0
9.0
90.0
14.0
297.0
32.0
46.0
8.0
34.0
26.0
15.0
13.0
22.0
156.0
None
0.0
6.0
6.0
22.0
111.0
805.0
85.0
257.0
156.0
1870.0
0.0
0.0
0.0
21.0
20.0
0.0
68.0
1783.0
493.0
612.0
7.0
863.0
6.0
12.0
15.0
233.0
1903.0
None
15.0
40.0
1957.0
109.0
128.0
64.0
169.0
5.0
13.0
0.0
515.0
25.4
11965.0
99.0
73.0
290.0
1906.0
22.0
38.0
62.0
152.0
0.0
194.0
121.0
None
0.0
108.0
464.0
99.0
233.0
11.0
114.0
89.0
49.0
79.0
17.0
110900.0
96.0
126.0
21.0
83300.0
162900.0
96.0
123.0
10.0
72.0
814.0
0.0
21.0
67.0
2.0
Let's say we are interested in the children variable to know the number of children per county, we can download just that variable and the geometry and visualise the result in a map.
In [5]:
dataset_df = dataset.to_dataframe(sql_query="select children, geom from $dataset$")
In [6]:
from cartoframes.viz import Map, Layer, color_bins_style
Map(
Layer(dataset_df, color_bins_style('children'), geom_col='geom'),
show_info=True,
viewport={'zoom': 3.17, 'lat': 38.170209, 'lng': -102.736148}
)
Out[6]:
:
StackTrace
">
Now, instead of downloading just one field, let's download all of them but for a smaller region using a spatial filter. We will use a bounding box for this and you can calculate yours from here: http://bboxfinder.com.
In [7]:
sql_query = "SELECT * FROM $dataset$ WHERE ST_IntersectsBox(geom, -95.031738, 32.082575, -84.616699, 39.537940)"
dataset_df = dataset.to_dataframe(sql_query=sql_query)
dataset_df.head(3)
Out[7]:
geoid
do_date
total_pop
households
male_pop
female_pop
median_age
male_under_5
male_5_to_9
male_10_to_14
male_15_to_17
male_18_to_19
male_20
male_21
male_22_to_24
male_25_to_29
male_30_to_34
male_35_to_39
male_40_to_44
male_45_to_49
male_50_to_54
male_55_to_59
male_60_to_61
male_62_to_64
male_65_to_66
male_67_to_69
male_70_to_74
male_75_to_79
male_80_to_84
male_85_and_over
female_under_5
female_5_to_9
female_10_to_14
female_15_to_17
female_18_to_19
female_20
female_21
female_22_to_24
female_25_to_29
female_30_to_34
female_35_to_39
female_40_to_44
female_45_to_49
female_50_to_54
female_55_to_59
female_60_to_61
female_62_to_64
female_65_to_66
female_67_to_69
female_70_to_74
female_75_to_79
female_80_to_84
female_85_and_over
white_pop
population_1_year_and_over
population_3_years_over
pop_5_years_over
pop_15_and_over
pop_16_over
pop_25_years_over
pop_25_64
pop_never_married
pop_now_married
pop_separated
pop_widowed
pop_divorced
not_us_citizen_pop
black_pop
asian_pop
hispanic_pop
amerindian_pop
other_race_pop
two_or_more_races_pop
white_including_hispanic
black_including_hispanic
asian_including_hispanic
amerindian_including_hispanic
hispanic_any_race
not_hispanic_pop
asian_male_45_54
asian_male_55_64
black_male_45_54
black_male_55_64
hispanic_male_45_54
hispanic_male_55_64
white_male_45_54
white_male_55_64
median_income
income_per_capita
income_less_10000
income_10000_14999
income_15000_19999
income_20000_24999
income_25000_29999
income_30000_34999
income_35000_39999
income_40000_44999
income_45000_49999
income_50000_59999
income_60000_74999
income_75000_99999
income_100000_124999
income_125000_149999
income_150000_199999
income_200000_or_more
households_retirement_income
pop_determined_poverty_status
poverty
gini_index
housing_units
renter_occupied_housing_units_paying_cash_median_gross_rent
owner_occupied_housing_units_lower_value_quartile
owner_occupied_housing_units_median_value
owner_occupied_housing_units_upper_value_quartile
occupied_housing_units
housing_units_renter_occupied
vacant_housing_units
vacant_housing_units_for_rent
vacant_housing_units_for_sale
dwellings_1_units_detached
dwellings_1_units_attached
dwellings_2_units
dwellings_3_to_4_units
dwellings_5_to_9_units
dwellings_10_to_19_units
dwellings_20_to_49_units
dwellings_50_or_more_units
mobile_homes
housing_built_2005_or_later
housing_built_2000_to_2004
housing_built_1939_or_earlier
median_year_structure_built
married_households
nonfamily_households
family_households
households_public_asst_or_food_stamps
male_male_households
female_female_households
children
children_in_single_female_hh
median_rent
percent_income_spent_on_rent
rent_burden_not_computed
rent_over_50_percent
rent_40_to_50_percent
rent_35_to_40_percent
rent_30_to_35_percent
rent_25_to_30_percent
rent_20_to_25_percent
rent_15_to_20_percent
rent_10_to_15_percent
rent_under_10_percent
owner_occupied_housing_units
million_dollar_housing_units
mortgaged_housing_units
different_house_year_ago_different_city
different_house_year_ago_same_city
families_with_young_children
two_parent_families_with_young_children
two_parents_in_labor_force_families_with_young_children
two_parents_father_in_labor_force_families_with_young_children
two_parents_mother_in_labor_force_families_with_young_children
two_parents_not_in_labor_force_families_with_young_children
one_parent_families_with_young_children
father_one_parent_families_with_young_children
father_in_labor_force_one_parent_families_with_young_children
commute_5_9_mins
commute_less_10_mins
commute_10_14_mins
commute_15_19_mins
commute_20_24_mins
commute_25_29_mins
commute_30_34_mins
commute_35_39_mins
commute_40_44_mins
commute_35_44_mins
commute_45_59_mins
commute_60_more_mins
commute_60_89_mins
commute_90_more_mins
commuters_16_over
walked_to_work
worked_at_home
no_car
no_cars
one_car
two_cars
three_cars
four_more_cars
aggregate_travel_time_to_work
commuters_by_public_transportation
commuters_by_bus
commuters_by_car_truck_van
commuters_by_carpool
commuters_by_subway_or_elevated
commuters_drove_alone
group_quarters
associates_degree
bachelors_degree
high_school_diploma
less_one_year_college
masters_degree
one_year_more_college
less_than_high_school_graduate
high_school_including_ged
bachelors_degree_2
bachelors_degree_or_higher_25_64
graduate_professional_degree
some_college_and_associates_degree
male_45_64_associates_degree
male_45_64_bachelors_degree
male_45_64_graduate_degree
male_45_64_less_than_9_grade
male_45_64_grade_9_12
male_45_64_high_school
male_45_64_some_college
male_45_to_64
employed_pop
unemployed_pop
pop_in_labor_force
not_in_labor_force
workers_16_and_over
armed_forces
civilian_labor_force
employed_agriculture_forestry_fishing_hunting_mining
employed_arts_entertainment_recreation_accommodation_food
employed_construction
employed_education_health_social
employed_finance_insurance_real_estate
employed_information
employed_manufacturing
employed_other_services_not_public_admin
employed_public_administration
employed_retail_trade
employed_science_management_admin_waste
employed_transportation_warehousing_utilities
employed_wholesale_trade
occupation_management_arts
occupation_natural_resources_construction_maintenance
occupation_production_transportation_material
occupation_sales_office
occupation_services
management_business_sci_arts_employed
sales_office_employed
in_grades_1_to_4
in_grades_5_to_8
in_grades_9_to_12
in_school
in_undergrad_college
speak_only_english_at_home
speak_spanish_at_home
speak_spanish_at_home_low_english
do_label
do_area
do_perimeter
do_num_vertices
geom
0
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Crawford
7.998498e+08
150379.205
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POLYGON((-86.679511 38.263086, -86.570136 38.2...
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Marengo
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Ashley
2.432888e+09
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POLYGON((-91.561673 33.391561, -91.60694 33.39...
In [8]:
Layer(dataset_df, color_bins_style('children'), geom_col='geom')
Out[8]:
:
StackTrace