In [8]:
import metatab as mt
import pandasreporter as pr
import pandas as pd
import numpy as np
import geoid
doc = mt.open_package('../_packages/census.gov-varrep_tables_support-2011e2015-1/')
doc
Out[8]:
Variance Replicate Estimates Support Data 2011-2015
census.gov-varrep_tables_support-2011e2015-1
Variance replicate estimate tables include estimates, margins of error, and 80 variance replicates for selected American Community Survey 5-year Detailed Tables. The tables are intended for advanced users who are adding ACS data within a table or between geographies. This package contains the state average weights and k values for use in some calculations.
Documentation
Variance Replicate Tables Documentation Main documentation page, with links to support data
Variance Replicate Tables Links to all data tables.
Contacts
Wrangler: Eric Busboom Civic Knowledge
Resources
ave_weights - data/ave_weights.csv Table APP1: Average Weight by State for 2011-2015 ACS 5-Year Data
k_values - data/k_values.csv Table APP2: Assigning k-Value Based on an Area’s Total Population
In [128]:
B01003 = pr.get_varrep_dataframe(2015, 'B01003', '140' , state='06', cache=True)
B01003 = B01003[pd.notnull(B01003['GEOID'])]
B01003.head()
TBLID
GEOID
NAME
ORDER
TITLE
estimate
moe
CME
SE
Var_Rep1
...
Var_Rep71
Var_Rep72
Var_Rep73
Var_Rep74
Var_Rep75
Var_Rep76
Var_Rep77
Var_Rep78
Var_Rep79
Var_Rep80
2
B01003
14000US06001400100
Census Tract 4001, Alameda County, California
1.0
Total
2952.0
186.0
+/-186
113.0
3006.0
...
3054.0
2824.0
2974.0
2987.0
2916.0
2888.0
2880.0
2977.0
2925.0
2872.0
3
B01003
14000US06001400200
Census Tract 4002, Alameda County, California
1.0
Total
1984.0
99.0
+/-99
60.0
1949.0
...
1998.0
1957.0
1993.0
1995.0
1993.0
2001.0
2029.0
1989.0
1978.0
1971.0
4
B01003
14000US06001400300
Census Tract 4003, Alameda County, California
1.0
Total
5377.0
524.0
+/-524
319.0
5442.0
...
5429.0
5411.0
5449.0
5632.0
5282.0
5281.0
5686.0
5495.0
5187.0
5170.0
5
B01003
14000US06001400400
Census Tract 4004, Alameda County, California
1.0
Total
4105.0
305.0
+/-305
185.0
4223.0
...
4181.0
4119.0
4172.0
3983.0
4017.0
4120.0
4156.0
4027.0
4183.0
4169.0
6
B01003
14000US06001400500
Census Tract 4005, Alameda County, California
1.0
Total
3651.0
299.0
+/-299
182.0
3650.0
...
3650.0
3634.0
3634.0
3657.0
3672.0
3707.0
3594.0
3765.0
3609.0
3657.0
5 rows × 89 columns
In [129]:
# Break out the geoid by county
def parse_geoid(tv):
from geoid.core import parse_to_gvid
tract = parse_to_gvid(tv)
return pd.Series({'state_fips':tract.state,
'county_fips':tract.county,
'tract_fips':tract.tract})
B01003 = B01003.merge(B01003.GEOID.apply(parse_geoid), left_index=True, right_index=True)
B01003.head()
TBLID
GEOID
NAME
ORDER
TITLE
estimate
moe
CME
SE
Var_Rep1
...
Var_Rep74
Var_Rep75
Var_Rep76
Var_Rep77
Var_Rep78
Var_Rep79
Var_Rep80
county_fips
state_fips
tract_fips
2
B01003
14000US06001400100
Census Tract 4001, Alameda County, California
1.0
Total
2952.0
186.0
+/-186
113.0
3006.0
...
2987.0
2916.0
2888.0
2880.0
2977.0
2925.0
2872.0
1
6
400100
3
B01003
14000US06001400200
Census Tract 4002, Alameda County, California
1.0
Total
1984.0
99.0
+/-99
60.0
1949.0
...
1995.0
1993.0
2001.0
2029.0
1989.0
1978.0
1971.0
1
6
400200
4
B01003
14000US06001400300
Census Tract 4003, Alameda County, California
1.0
Total
5377.0
524.0
+/-524
319.0
5442.0
...
5632.0
5282.0
5281.0
5686.0
5495.0
5187.0
5170.0
1
6
400300
5
B01003
14000US06001400400
Census Tract 4004, Alameda County, California
1.0
Total
4105.0
305.0
+/-305
185.0
4223.0
...
3983.0
4017.0
4120.0
4156.0
4027.0
4183.0
4169.0
1
6
400400
6
B01003
14000US06001400500
Census Tract 4005, Alameda County, California
1.0
Total
3651.0
299.0
+/-299
182.0
3650.0
...
3657.0
3672.0
3707.0
3594.0
3765.0
3609.0
3657.0
1
6
400500
5 rows × 92 columns
In [122]:
def varrep_se(x):
"""Compute the standard error from a set of variance replicates"""
return int(round(np.sqrt((4/80) * sum((x.loc['estimate']-x.loc[varcols])**2)),0))
df2['est_calc'] = np.round(np.mean(df2[varcols],axis=1)).astype(int)
df2['se_calc'] = df2.apply(f, axis=1)
In [132]:
B01003.groupby('county_fips').sum().sort_values('estimate', ascending = False)
ORDER
estimate
moe
SE
Var_Rep1
Var_Rep2
Var_Rep3
Var_Rep4
Var_Rep5
Var_Rep6
...
Var_Rep73
Var_Rep74
Var_Rep75
Var_Rep76
Var_Rep77
Var_Rep78
Var_Rep79
Var_Rep80
state_fips
tract_fips
county_fips
37
2346.0
10038388.0
886694.0
538994.0
10038388.0
10038388.0
10038388.0
10038388.0
10038388.0
10038388.0
...
10038388.0
10038388.0
10038388.0
10038388.0
10038388.0
10038388.0
10038388.0
10038388.0
14076
957321337
73
628.0
3223096.0
279982.0
170218.0
3223096.0
3223096.0
3223096.0
3223096.0
3223096.0
3223096.0
...
3223096.0
3223096.0
3223096.0
3223096.0
3223096.0
3223096.0
3223096.0
3223096.0
3768
8863592
59
583.0
3116069.0
239330.0
145499.0
3116069.0
3116069.0
3116069.0
3116069.0
3116069.0
3116069.0
...
3116069.0
3116069.0
3116069.0
3116069.0
3116069.0
3116069.0
3116069.0
3116069.0
3498
39380550
65
453.0
2298032.0
213637.0
129869.0
2298032.0
2298032.0
2298032.0
2298032.0
2298032.0
2298032.0
...
2298032.0
2298032.0
2298032.0
2298032.0
2298032.0
2298032.0
2298032.0
2298032.0
2718
32910046
71
369.0
2094769.0
197725.0
120199.0
2094769.0
2094769.0
2094769.0
2094769.0
2094769.0
2094769.0
...
2094769.0
2094769.0
2094769.0
2094769.0
2094769.0
2094769.0
2094769.0
2094769.0
2214
4937688
85
372.0
1868149.0
140660.0
85517.0
1868149.0
1868149.0
1868149.0
1868149.0
1868149.0
1868149.0
...
1868149.0
1868149.0
1868149.0
1868149.0
1868149.0
1868149.0
1868149.0
1868149.0
2232
188482024
1
361.0
1584983.0
126026.0
76612.0
1584983.0
1584983.0
1584983.0
1584983.0
1584983.0
1584983.0
...
1584983.0
1584983.0
1584983.0
1584983.0
1584983.0
1584983.0
1584983.0
1584983.0
2166
156424046
67
317.0
1465832.0
126726.0
77035.0
1465832.0
1465832.0
1465832.0
1465832.0
1465832.0
1465832.0
...
1465832.0
1465832.0
1465832.0
1465832.0
1465832.0
1465832.0
1465832.0
1465832.0
1902
3163344
13
208.0
1096068.0
82825.0
50345.0
1096068.0
1096068.0
1096068.0
1096068.0
1096068.0
1096068.0
...
1096068.0
1096068.0
1096068.0
1096068.0
1096068.0
1096068.0
1096068.0
1096068.0
1248
71711231
19
199.0
956749.0
85204.0
51793.0
956749.0
956749.0
956749.0
956749.0
956749.0
956749.0
...
956749.0
956749.0
956749.0
956749.0
956749.0
956749.0
956749.0
956749.0
1194
899960
29
151.0
865736.0
78684.0
47835.0
865736.0
865736.0
865736.0
865736.0
865736.0
865736.0
...
865736.0
865736.0
865736.0
865736.0
865736.0
865736.0
865736.0
865736.0
906
510803
111
174.0
840833.0
66564.0
40468.0
840833.0
840833.0
840833.0
840833.0
840833.0
840833.0
...
840833.0
840833.0
840833.0
840833.0
840833.0
840833.0
840833.0
840833.0
1044
2809419
75
197.0
840763.0
76570.0
46537.0
840763.0
840763.0
840763.0
840763.0
840763.0
840763.0
...
840763.0
840763.0
840763.0
840763.0
840763.0
840763.0
840763.0
840763.0
1182
11636055
81
158.0
748731.0
56462.0
34326.0
748731.0
748731.0
748731.0
748731.0
748731.0
748731.0
...
748731.0
748731.0
748731.0
748731.0
748731.0
748731.0
748731.0
748731.0
948
96658307
77
139.0
708554.0
61329.0
37283.0
708554.0
708554.0
708554.0
708554.0
708554.0
708554.0
...
708554.0
708554.0
708554.0
708554.0
708554.0
708554.0
708554.0
708554.0
834
506861
99
94.0
527367.0
42957.0
26116.0
527367.0
527367.0
527367.0
527367.0
527367.0
527367.0
...
527367.0
527367.0
527367.0
527367.0
527367.0
527367.0
527367.0
527367.0
564
183195
97
100.0
495078.0
40711.0
24745.0
495078.0
495078.0
495078.0
495078.0
495078.0
495078.0
...
495078.0
495078.0
495078.0
495078.0
495078.0
495078.0
495078.0
495078.0
600
16059509
107
78.0
454033.0
38403.0
23342.0
454033.0
454033.0
454033.0
454033.0
454033.0
454033.0
...
454033.0
454033.0
454033.0
454033.0
454033.0
454033.0
454033.0
454033.0
468
169739
83
90.0
435850.0
37373.0
22719.0
435850.0
435850.0
435850.0
435850.0
435850.0
435850.0
...
435850.0
435850.0
435850.0
435850.0
435850.0
435850.0
435850.0
435850.0
540
3121947
53
94.0
428441.0
47984.0
29175.0
428441.0
428441.0
428441.0
428441.0
428441.0
428441.0
...
428441.0
428441.0
428441.0
428441.0
428441.0
428441.0
428441.0
428441.0
564
2837055
95
96.0
425753.0
36581.0
22234.0
425753.0
425753.0
425753.0
425753.0
425753.0
425753.0
...
425753.0
425753.0
425753.0
425753.0
425753.0
425753.0
425753.0
425753.0
576
24934949
61
85.0
366280.0
30233.0
18384.0
366280.0
366280.0
366280.0
366280.0
366280.0
366280.0
...
366280.0
366280.0
366280.0
366280.0
366280.0
366280.0
366280.0
366280.0
510
2791853
79
54.0
276517.0
23979.0
14577.0
276517.0
276517.0
276517.0
276517.0
276517.0
276517.0
...
276517.0
276517.0
276517.0
276517.0
276517.0
276517.0
276517.0
276517.0
324
1592518
87
53.0
269278.0
23563.0
14319.0
269278.0
269278.0
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269278.0
269278.0
...
269278.0
269278.0
269278.0
269278.0
269278.0
269278.0
269278.0
269278.0
318
6969624
47
49.0
263885.0
22201.0
13495.0
263885.0
263885.0
263885.0
263885.0
263885.0
263885.0
...
263885.0
263885.0
263885.0
263885.0
263885.0
263885.0
263885.0
263885.0
294
60187
41
56.0
258349.0
19492.0
11849.0
258349.0
258349.0
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258349.0
...
258349.0
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258349.0
258349.0
258349.0
258349.0
258349.0
258349.0
336
7346923
7
51.0
222564.0
21513.0
13081.0
222564.0
222564.0
222564.0
222564.0
222564.0
222564.0
...
222564.0
222564.0
222564.0
222564.0
222564.0
222564.0
222564.0
222564.0
306
87152
113
41.0
207320.0
16463.0
10008.0
207320.0
207320.0
207320.0
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207320.0
207320.0
...
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207320.0
207320.0
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207320.0
207320.0
246
438673
17
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...
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182093.0
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182093.0
258
2290927
89
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...
178942.0
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178942.0
178942.0
178942.0
178942.0
178942.0
178942.0
288
549090
25
31.0
178206.0
16402.0
9970.0
178206.0
178206.0
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178206.0
...
178206.0
178206.0
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186
1279218
39
23.0
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...
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138
9979
31
27.0
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...
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162
1004633
55
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...
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240
8037696
23
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2111509
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12044
101
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1060934
45
21.0
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1209008
115
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84
568109
33
15.0
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...
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90
10909
103
11.0
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...
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66
6600
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66
5609
109
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...
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1016703
9
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3001
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84
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5
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...
36995.0
36995.0
36995.0
36995.0
36995.0
36995.0
36995.0
36995.0
54
2614
35
9.0
32645.0
3393.0
2064.0
32645.0
32645.0
32645.0
32645.0
32645.0
32645.0
...
32645.0
32645.0
32645.0
32645.0
32645.0
32645.0
32645.0
32645.0
54
363014
21
6.0
28029.0
1829.0
1112.0
28029.0
28029.0
28029.0
28029.0
28029.0
28029.0
...
28029.0
28029.0
28029.0
28029.0
28029.0
28029.0
28029.0
28029.0
36
62003
15
8.0
27788.0
3058.0
1860.0
27788.0
27788.0
27788.0
27788.0
27788.0
27788.0
...
27788.0
27788.0
27788.0
27788.0
27788.0
27788.0
27788.0
27788.0
48
991018
11
5.0
21396.0
1339.0
814.0
21396.0
21396.0
21396.0
21396.0
21396.0
21396.0
...
21396.0
21396.0
21396.0
21396.0
21396.0
21396.0
21396.0
21396.0
30
1500
63
7.0
18966.0
2181.0
1325.0
18966.0
18966.0
18966.0
18966.0
18966.0
18966.0
...
18966.0
18966.0
18966.0
18966.0
18966.0
18966.0
18966.0
18966.0
42
2206
27
6.0
18373.0
1312.0
799.0
18373.0
18373.0
18373.0
18373.0
18373.0
18373.0
...
18373.0
18373.0
18373.0
18373.0
18373.0
18373.0
18373.0
18373.0
36
2300
43
6.0
17789.0
2239.0
1360.0
17789.0
17789.0
17789.0
17789.0
17789.0
17789.0
...
17789.0
17789.0
17789.0
17789.0
17789.0
17789.0
17789.0
17789.0
36
1406
51
3.0
14146.0
801.0
486.0
14146.0
14146.0
14146.0
14146.0
14146.0
14146.0
...
14146.0
14146.0
14146.0
14146.0
14146.0
14146.0
14146.0
14146.0
18
403
105
5.0
13373.0
1671.0
1016.0
13373.0
13373.0
13373.0
13373.0
13373.0
13373.0
...
13373.0
13373.0
13373.0
13373.0
13373.0
13373.0
13373.0
13373.0
30
1103
49
4.0
9184.0
1059.0
644.0
9184.0
9184.0
9184.0
9184.0
9184.0
9184.0
...
9184.0
9184.0
9184.0
9184.0
9184.0
9184.0
9184.0
9184.0
24
1000
91
1.0
3021.0
167.0
102.0
2952.0
2971.0
3005.0
2959.0
3019.0
3091.0
...
3022.0
3088.0
3035.0
2946.0
3113.0
3043.0
3006.0
3065.0
6
10000
3
1.0
1131.0
167.0
102.0
1200.0
1181.0
1147.0
1193.0
1133.0
1061.0
...
1130.0
1064.0
1117.0
1206.0
1039.0
1109.0
1146.0
1087.0
6
10000
58 rows × 86 columns
In [134]:
import pandasreporter as pr
api = pr.CensusApi()
ds = api.get_dataset('ACSSF5Y2015')
df = ds.fetch_dataframe( 'GEOID', 'NAME', 'B01003_001E', 'B01003_001M', geo_in='state:06', geo_for='county:*')
df.head()
GEOID
NAME
B01003_001E
B01003_001M
state
county
0
05000US06001
Alameda County, California
1584983
0
06
001
1
05000US06003
Alpine County, California
1131
167
06
003
2
05000US06005
Amador County, California
36995
0
06
005
3
05000US06007
Butte County, California
222564
0
06
007
4
05000US06009
Calaveras County, California
44767
0
06
009
In [113]:
df2 = df.drop(['TBLID','NAME','CME', 'GEOID','ORDER','TITLE'],1)
varcols = [ 'Var_Rep'+str(i) for i in range(1,81)]
In [114]:
df2.head()
estimate
moe
SE
Var_Rep1
Var_Rep2
Var_Rep3
Var_Rep4
Var_Rep5
Var_Rep6
Var_Rep7
...
Var_Rep71
Var_Rep72
Var_Rep73
Var_Rep74
Var_Rep75
Var_Rep76
Var_Rep77
Var_Rep78
Var_Rep79
Var_Rep80
0
5209
403
245
5311
4897
5089
5285
5313
5179
5141
...
5218
5099
5292
5185
5156
5176
5295
5214
5171
5117
1
2388
319
194
2423
2311
2510
2343
2476
2524
2382
...
2439
2347
2409
2282
2373
2399
2378
2355
2451
2269
2
207
101
61
155
174
182
237
234
206
146
...
256
220
158
217
242
175
222
191
229
205
3
59
56
34
45
72
49
59
38
36
43
...
64
70
40
67
80
58
67
68
39
71
4
86
65
40
83
71
99
84
90
74
75
...
68
64
120
64
106
78
82
100
59
93
5 rows × 83 columns
In [115]:
c = df2[['estimate','est_calc','SE','se_calc']].copy()
In [117]:
[['GEOID', 'state_fips', 'county_fips', 'tract_fips']]
GEOID
state_fips
county_fips
tract_fips
0
14000US11001000100
11
1
100
1
14000US11001000100
11
1
100
2
14000US11001000100
11
1
100
3
14000US11001000100
11
1
100
4
14000US11001000100
11
1
100
5
14000US11001000100
11
1
100
6
14000US11001000100
11
1
100
7
14000US11001000100
11
1
100
8
14000US11001000100
11
1
100
9
14000US11001000100
11
1
100
10
14000US11001000100
11
1
100
11
14000US11001000100
11
1
100
12
14000US11001000100
11
1
100
13
14000US11001000100
11
1
100
14
14000US11001000100
11
1
100
15
14000US11001000100
11
1
100
16
14000US11001000100
11
1
100
17
14000US11001000100
11
1
100
18
14000US11001000100
11
1
100
19
14000US11001000100
11
1
100
20
14000US11001000100
11
1
100
21
14000US11001000100
11
1
100
22
14000US11001000100
11
1
100
23
14000US11001000100
11
1
100
24
14000US11001000100
11
1
100
25
14000US11001000100
11
1
100
26
14000US11001000100
11
1
100
27
14000US11001000100
11
1
100
28
14000US11001000100
11
1
100
29
14000US11001000100
11
1
100
...
...
...
...
...
8741
14000US11001011100
11
1
11100
8742
14000US11001011100
11
1
11100
8743
14000US11001011100
11
1
11100
8744
14000US11001011100
11
1
11100
8745
14000US11001011100
11
1
11100
8746
14000US11001011100
11
1
11100
8747
14000US11001011100
11
1
11100
8748
14000US11001011100
11
1
11100
8749
14000US11001011100
11
1
11100
8750
14000US11001011100
11
1
11100
8751
14000US11001011100
11
1
11100
8752
14000US11001011100
11
1
11100
8753
14000US11001011100
11
1
11100
8754
14000US11001011100
11
1
11100
8755
14000US11001011100
11
1
11100
8756
14000US11001011100
11
1
11100
8757
14000US11001011100
11
1
11100
8758
14000US11001011100
11
1
11100
8759
14000US11001011100
11
1
11100
8760
14000US11001011100
11
1
11100
8761
14000US11001011100
11
1
11100
8762
14000US11001011100
11
1
11100
8763
14000US11001011100
11
1
11100
8764
14000US11001011100
11
1
11100
8765
14000US11001011100
11
1
11100
8766
14000US11001011100
11
1
11100
8767
14000US11001011100
11
1
11100
8768
14000US11001011100
11
1
11100
8769
14000US11001011100
11
1
11100
8770
14000US11001011100
11
1
11100
8771 rows × 4 columns
In [88]:
c['est_diff'] = (((c['est_calc'] - c['estimate']) / c['estimate']) * 100).fillna(0)
c['est_eq'] = np.abs(c['est_diff']) < 2
c[c.estimate != 0].head()
estimate
est_calc
SE
se_calc
est_diff
est_eq
0
5209
5200
245
245
-0.172778
True
1
2388
2383
194
194
-0.209380
True
2
207
206
61
61
-0.483092
True
3
59
58
34
34
-1.694915
True
4
86
87
40
40
1.162791
True
In [89]:
c['se_eq'] = c.SE == c.se_calc
c[c.estimate == 0].head()
estimate
est_calc
SE
se_calc
est_diff
est_eq
se_eq
6
0
0
10
0
0.0
True
False
31
0
0
10
0
0.0
True
False
51
0
0
7
0
0.0
True
False
52
0
0
7
0
0.0
True
False
53
0
0
7
0
0.0
True
False
In [82]:
df[df.estimate == 0].head()
TBLID
GEOID
NAME
ORDER
TITLE
estimate
moe
CME
SE
Var_Rep1
...
Var_Rep71
Var_Rep72
Var_Rep73
Var_Rep74
Var_Rep75
Var_Rep76
Var_Rep77
Var_Rep78
Var_Rep79
Var_Rep80
6
B01001
14000US11001000100
Census Tract 1, District of Columbia, District...
7
18 and 19 years
0
17
+/-17
10
0
...
0
0
0
0
0
0
0
0
0
0
31
B01001
14000US11001000100
Census Tract 1, District of Columbia, District...
32
20 years
0
17
+/-17
10
0
...
0
0
0
0
0
0
0
0
0
0
51
B01001
14000US11001000201
Census Tract 2.01, District of Columbia, Distr...
3
Under 5 years
0
12
+/-12
7
0
...
0
0
0
0
0
0
0
0
0
0
52
B01001
14000US11001000201
Census Tract 2.01, District of Columbia, Distr...
4
5 to 9 years
0
12
+/-12
7
0
...
0
0
0
0
0
0
0
0
0
0
53
B01001
14000US11001000201
Census Tract 2.01, District of Columbia, Distr...
5
10 to 14 years
0
12
+/-12
7
0
...
0
0
0
0
0
0
0
0
0
0
5 rows × 89 columns
In [ ]:
Content source: CivicKnowledge/metatab-packages
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