Overview

Dataset statistics

Number of variables19
Number of observations657
Missing cells873
Missing cells (%)7.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory108.6 KiB
Average record size in memory169.2 B

Variable types

Numeric17
Categorical2

Dataset

DescriptionData provides consumer counts for services provided to aging citizens of Iowa funded through federal and state programs starting in 2009 and is updated annually. It includes self-reported demographic information for services provided by age group, gender, live alone status, rural status, poverty status, racial identity, and ethnicity (per U.S. Census definitions). Counts include only those individuals who provided a response on intake. Intakes are completed on service initiation and annually thereafter. Unduplicated counts are by service; an individual may have received more than one service.
Author미국
URLhttps://catalog.data.gov/dataset/iowa-aging-services-consumer-counts-by-fiscal-year-age-group-and-service

Alerts

Fiscal Year is highly overall correlated with Consumers - Other RaceHigh correlation
Consumers is highly overall correlated with Male Consumers and 12 other fieldsHigh correlation
Male Consumers is highly overall correlated with Consumers and 12 other fieldsHigh correlation
Female Consumers is highly overall correlated with Consumers and 12 other fieldsHigh correlation
Consumers in Rural Areas is highly overall correlated with Consumers and 12 other fieldsHigh correlation
Conumsers Living Alone is highly overall correlated with Consumers and 12 other fieldsHigh correlation
Consumers in Poverty is highly overall correlated with Consumers and 12 other fieldsHigh correlation
Hispanic Consumers is highly overall correlated with Consumers and 12 other fieldsHigh correlation
Non-Hispanic Consumers is highly overall correlated with Consumers and 12 other fieldsHigh correlation
White, Not Hispanic Consumers is highly overall correlated with Consumers and 12 other fieldsHigh correlation
White, Hispanic Consumers is highly overall correlated with Consumers and 12 other fieldsHigh correlation
Native American Consumers is highly overall correlated with Consumers and 12 other fieldsHigh correlation
Asian Consumers is highly overall correlated with Consumers and 12 other fieldsHigh correlation
African American Consumers is highly overall correlated with Consumers and 12 other fieldsHigh correlation
Hawaiian Pacific Islander Consumers is highly overall correlated with Consumers and 12 other fieldsHigh correlation
Consumers - Other Race is highly overall correlated with Fiscal YearHigh correlation
Hispanic Consumers has 24 (3.7%) missing valuesMissing
White, Not Hispanic Consumers has 165 (25.1%) missing valuesMissing
White, Hispanic Consumers has 205 (31.2%) missing valuesMissing
Native American Consumers has 23 (3.5%) missing valuesMissing
Asian Consumers has 47 (7.2%) missing valuesMissing
African American Consumers has 12 (1.8%) missing valuesMissing
Hawaiian Pacific Islander Consumers has 55 (8.4%) missing valuesMissing
Consumers - Other Race has 70 (10.7%) missing valuesMissing
Consumers - Two or More Races has 270 (41.1%) missing valuesMissing
Male Consumers has 8 (1.2%) zerosZeros
Consumers in Rural Areas has 10 (1.5%) zerosZeros
Consumers in Poverty has 27 (4.1%) zerosZeros
Hispanic Consumers has 97 (14.8%) zerosZeros
White, Hispanic Consumers has 91 (13.9%) zerosZeros
Native American Consumers has 261 (39.7%) zerosZeros
Asian Consumers has 209 (31.8%) zerosZeros
African American Consumers has 52 (7.9%) zerosZeros
Hawaiian Pacific Islander Consumers has 310 (47.2%) zerosZeros
Consumers - Other Race has 404 (61.5%) zerosZeros
Consumers - Two or More Races has 257 (39.1%) zerosZeros

Reproduction

Analysis started2024-02-20 23:54:50.063155
Analysis finished2024-02-20 23:55:28.873141
Duration38.81 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Fiscal Year
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2017.0868
Minimum2009
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2024-02-21T08:55:28.939408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2009
5-th percentile2010
Q12014
median2017
Q32021
95-th percentile2023
Maximum2023
Range14
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.0885874
Coefficient of variation (CV)0.0020269765
Kurtosis-0.9075754
Mean2017.0868
Median Absolute Deviation (MAD)3
Skewness-0.35333098
Sum1325226
Variance16.716547
MonotonicityIncreasing
2024-02-21T08:55:29.070129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
2016 63
9.6%
2017 63
9.6%
2023 57
 
8.7%
2018 54
 
8.2%
2020 54
 
8.2%
2021 54
 
8.2%
2022 54
 
8.2%
2019 51
 
7.8%
2010 30
 
4.6%
2011 30
 
4.6%
Other values (5) 147
22.4%
ValueCountFrequency (%)
2009 27
4.1%
2010 30
4.6%
2011 30
4.6%
2012 30
4.6%
2013 30
4.6%
2014 30
4.6%
2015 30
4.6%
2016 63
9.6%
2017 63
9.6%
2018 54
8.2%
ValueCountFrequency (%)
2023 57
8.7%
2022 54
8.2%
2021 54
8.2%
2020 54
8.2%
2019 51
7.8%
2018 54
8.2%
2017 63
9.6%
2016 63
9.6%
2015 30
4.6%
2014 30
4.6%

Age Group
Categorical

Distinct6
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size5.3 KiB
Age 60-74
111 
Age 75-84
111 
Age 85+
111 
60-74
108 
75-84
108 

Length

Max length9
Median length7
Mean length6.3607306
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAge 60-74
2nd rowAge 75-84
3rd rowAge 85+
4th rowAge 60-74
5th rowAge 75-84

Common Values

ValueCountFrequency (%)
Age 60-74 111
16.9%
Age 75-84 111
16.9%
Age 85+ 111
16.9%
60-74 108
16.4%
75-84 108
16.4%
85+ 108
16.4%

Length

2024-02-21T08:55:29.223623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-21T08:55:29.555451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
age 333
33.6%
60-74 219
22.1%
75-84 219
22.1%
85 219
22.1%

Service
Categorical

Distinct31
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Memory size5.3 KiB
Personal Care
45 
Case Management
45 
Nutrition Counseling
45 
Homemaker
45 
Chore
45 
Other values (26)
432 

Length

Max length37
Median length25
Mean length18.076104
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPersonal Care
2nd rowPersonal Care
3rd rowPersonal Care
4th rowHomemaker
5th rowHomemaker

Common Values

ValueCountFrequency (%)
Personal Care 45
 
6.8%
Case Management 45
 
6.8%
Nutrition Counseling 45
 
6.8%
Homemaker 45
 
6.8%
Chore 45
 
6.8%
Home Delivered Meals 33
 
5.0%
Congregate Meals 33
 
5.0%
Adult Day Care / Health 27
 
4.1%
Nutrition Education 24
 
3.7%
Options Counseling 24
 
3.7%
Other values (21) 291
44.3%

Length

2024-02-21T08:55:29.711423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
111
 
7.2%
nutrition 93
 
6.0%
care 90
 
5.8%
health 70
 
4.5%
counseling 69
 
4.5%
meals 66
 
4.3%
eapa 54
 
3.5%
homemaker 45
 
2.9%
chore 45
 
2.9%
home 45
 
2.9%
Other values (35) 858
55.5%

Consumers
Real number (ℝ)

HIGH CORRELATION 

Distinct527
Distinct (%)80.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2715.6484
Minimum0
Maximum42986
Zeros5
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2024-02-21T08:55:29.849942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile29.8
Q1129
median411
Q33093
95-th percentile13708.8
Maximum42986
Range42986
Interquartile range (IQR)2964

Descriptive statistics

Standard deviation5467.2835
Coefficient of variation (CV)2.0132516
Kurtosis16.255152
Mean2715.6484
Median Absolute Deviation (MAD)354
Skewness3.6433613
Sum1784181
Variance29891189
MonotonicityNot monotonic
2024-02-21T08:55:30.027885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5
 
0.8%
56 4
 
0.6%
78 4
 
0.6%
92 4
 
0.6%
88 3
 
0.5%
68 3
 
0.5%
34 3
 
0.5%
173 3
 
0.5%
115 3
 
0.5%
86 3
 
0.5%
Other values (517) 622
94.7%
ValueCountFrequency (%)
0 5
0.8%
1 2
 
0.3%
3 1
 
0.2%
4 1
 
0.2%
6 1
 
0.2%
7 1
 
0.2%
8 1
 
0.2%
11 1
 
0.2%
12 1
 
0.2%
15 1
 
0.2%
ValueCountFrequency (%)
42986 1
0.2%
39034 1
0.2%
38418 1
0.2%
33957 1
0.2%
31793 1
0.2%
30350 1
0.2%
28142 1
0.2%
27408 1
0.2%
27086 1
0.2%
24546 1
0.2%

Male Consumers
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct395
Distinct (%)60.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean920.66514
Minimum0
Maximum18494
Zeros8
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2024-02-21T08:55:30.169407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8.8
Q135
median99
Q31008
95-th percentile4167.6
Maximum18494
Range18494
Interquartile range (IQR)973

Descriptive statistics

Standard deviation2023.9544
Coefficient of variation (CV)2.198361
Kurtosis22.909879
Mean920.66514
Median Absolute Deviation (MAD)85
Skewness4.2487682
Sum604877
Variance4096391.3
MonotonicityNot monotonic
2024-02-21T08:55:30.301631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16 13
 
2.0%
22 11
 
1.7%
13 8
 
1.2%
18 8
 
1.2%
50 8
 
1.2%
0 8
 
1.2%
28 7
 
1.1%
17 7
 
1.1%
9 6
 
0.9%
30 6
 
0.9%
Other values (385) 575
87.5%
ValueCountFrequency (%)
0 8
1.2%
1 1
 
0.2%
2 3
 
0.5%
3 3
 
0.5%
4 4
0.6%
5 5
0.8%
6 3
 
0.5%
7 1
 
0.2%
8 5
0.8%
9 6
0.9%
ValueCountFrequency (%)
18494 1
0.2%
14869 1
0.2%
13685 1
0.2%
13462 1
0.2%
13385 1
0.2%
12747 1
0.2%
11629 1
0.2%
10587 1
0.2%
9334 1
0.2%
9203 1
0.2%

Female Consumers
Real number (ℝ)

HIGH CORRELATION 

Distinct502
Distinct (%)76.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1778.6119
Minimum0
Maximum24972
Zeros5
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2024-02-21T08:55:30.469095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile20.8
Q187
median305
Q32053
95-th percentile9378.2
Maximum24972
Range24972
Interquartile range (IQR)1966

Descriptive statistics

Standard deviation3443.894
Coefficient of variation (CV)1.9362819
Kurtosis13.897274
Mean1778.6119
Median Absolute Deviation (MAD)265
Skewness3.4040231
Sum1168548
Variance11860406
MonotonicityNot monotonic
2024-02-21T08:55:30.660736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
49 6
 
0.9%
28 6
 
0.9%
42 5
 
0.8%
73 5
 
0.8%
45 5
 
0.8%
0 5
 
0.8%
36 5
 
0.8%
167 4
 
0.6%
138 4
 
0.6%
25 4
 
0.6%
Other values (492) 608
92.5%
ValueCountFrequency (%)
0 5
0.8%
1 2
 
0.3%
3 4
0.6%
4 1
 
0.2%
5 1
 
0.2%
8 1
 
0.2%
9 1
 
0.2%
10 1
 
0.2%
11 2
 
0.3%
12 2
 
0.3%
ValueCountFrequency (%)
24972 1
0.2%
24375 1
0.2%
24066 1
0.2%
20222 1
0.2%
18894 1
0.2%
18660 1
0.2%
18283 1
0.2%
16421 1
0.2%
15158 1
0.2%
14954 1
0.2%

Consumers in Rural Areas
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct425
Distinct (%)64.9%
Missing2
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean1321.4427
Minimum0
Maximum22824
Zeros10
Zeros (%)1.5%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2024-02-21T08:55:30.817634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.7
Q143
median184
Q31086.5
95-th percentile6834.9
Maximum22824
Range22824
Interquartile range (IQR)1043.5

Descriptive statistics

Standard deviation2931.4827
Coefficient of variation (CV)2.2183955
Kurtosis16.986867
Mean1321.4427
Median Absolute Deviation (MAD)172
Skewness3.8030202
Sum865545
Variance8593590.6
MonotonicityNot monotonic
2024-02-21T08:55:31.027553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 10
 
1.5%
5 8
 
1.2%
24 8
 
1.2%
1 7
 
1.1%
7 7
 
1.1%
3 6
 
0.9%
4 6
 
0.9%
51 5
 
0.8%
16 5
 
0.8%
52 5
 
0.8%
Other values (415) 588
89.5%
ValueCountFrequency (%)
0 10
1.5%
1 7
1.1%
2 4
 
0.6%
3 6
0.9%
4 6
0.9%
5 8
1.2%
6 5
0.8%
7 7
1.1%
8 4
 
0.6%
9 4
 
0.6%
ValueCountFrequency (%)
22824 1
0.2%
20727 1
0.2%
19891 1
0.2%
18561 1
0.2%
17018 1
0.2%
16831 1
0.2%
15354 1
0.2%
13712 1
0.2%
13524 1
0.2%
13473 1
0.2%

Conumsers Living Alone
Real number (ℝ)

HIGH CORRELATION 

Distinct488
Distinct (%)74.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1513.0274
Minimum0
Maximum25471
Zeros6
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2024-02-21T08:55:31.190065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile13
Q169
median294
Q31824
95-th percentile7702.4
Maximum25471
Range25471
Interquartile range (IQR)1755

Descriptive statistics

Standard deviation3054.8585
Coefficient of variation (CV)2.0190372
Kurtosis19.310349
Mean1513.0274
Median Absolute Deviation (MAD)265
Skewness3.9064078
Sum994059
Variance9332160.6
MonotonicityNot monotonic
2024-02-21T08:55:31.342469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13 8
 
1.2%
0 6
 
0.9%
69 5
 
0.8%
23 5
 
0.8%
25 5
 
0.8%
4 5
 
0.8%
15 5
 
0.8%
29 4
 
0.6%
1 4
 
0.6%
56 4
 
0.6%
Other values (478) 606
92.2%
ValueCountFrequency (%)
0 6
0.9%
1 4
0.6%
2 2
 
0.3%
4 5
0.8%
6 3
 
0.5%
8 4
0.6%
9 3
 
0.5%
10 1
 
0.2%
12 1
 
0.2%
13 8
1.2%
ValueCountFrequency (%)
25471 1
0.2%
25085 1
0.2%
21440 1
0.2%
18607 1
0.2%
17148 1
0.2%
15970 1
0.2%
14955 1
0.2%
14465 1
0.2%
14375 1
0.2%
14231 1
0.2%

Consumers in Poverty
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct414
Distinct (%)63.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean810.15525
Minimum0
Maximum16797
Zeros27
Zeros (%)4.1%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2024-02-21T08:55:31.487177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q134
median144
Q3977
95-th percentile3847.4
Maximum16797
Range16797
Interquartile range (IQR)943

Descriptive statistics

Standard deviation1619.3749
Coefficient of variation (CV)1.9988452
Kurtosis25.521572
Mean810.15525
Median Absolute Deviation (MAD)134
Skewness4.241011
Sum532272
Variance2622375.1
MonotonicityNot monotonic
2024-02-21T08:55:31.646664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 27
 
4.1%
4 9
 
1.4%
10 8
 
1.2%
14 8
 
1.2%
8 6
 
0.9%
13 6
 
0.9%
18 6
 
0.9%
2 6
 
0.9%
26 5
 
0.8%
27 5
 
0.8%
Other values (404) 571
86.9%
ValueCountFrequency (%)
0 27
4.1%
1 4
 
0.6%
2 6
 
0.9%
3 5
 
0.8%
4 9
 
1.4%
5 4
 
0.6%
6 1
 
0.2%
7 3
 
0.5%
8 6
 
0.9%
9 2
 
0.3%
ValueCountFrequency (%)
16797 1
0.2%
12913 1
0.2%
10590 1
0.2%
9280 1
0.2%
8795 1
0.2%
8785 1
0.2%
7983 1
0.2%
7479 1
0.2%
7478 1
0.2%
7100 1
0.2%

Hispanic Consumers
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct136
Distinct (%)21.5%
Missing24
Missing (%)3.7%
Infinite0
Infinite (%)0.0%
Mean36.579779
Minimum0
Maximum731
Zeros97
Zeros (%)14.8%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2024-02-21T08:55:31.824230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median6
Q337
95-th percentile188.8
Maximum731
Range731
Interquartile range (IQR)36

Descriptive statistics

Standard deviation76.758212
Coefficient of variation (CV)2.0983782
Kurtosis24.153641
Mean36.579779
Median Absolute Deviation (MAD)6
Skewness4.2312097
Sum23155
Variance5891.8231
MonotonicityNot monotonic
2024-02-21T08:55:31.963568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 97
 
14.8%
1 83
 
12.6%
2 46
 
7.0%
3 35
 
5.3%
4 33
 
5.0%
5 22
 
3.3%
6 18
 
2.7%
7 9
 
1.4%
12 9
 
1.4%
8 8
 
1.2%
Other values (126) 273
41.6%
(Missing) 24
 
3.7%
ValueCountFrequency (%)
0 97
14.8%
1 83
12.6%
2 46
7.0%
3 35
 
5.3%
4 33
 
5.0%
5 22
 
3.3%
6 18
 
2.7%
7 9
 
1.4%
8 8
 
1.2%
9 8
 
1.2%
ValueCountFrequency (%)
731 1
0.2%
617 1
0.2%
602 1
0.2%
419 1
0.2%
418 1
0.2%
408 1
0.2%
353 1
0.2%
351 1
0.2%
323 1
0.2%
305 1
0.2%

Non-Hispanic Consumers
Real number (ℝ)

HIGH CORRELATION 

Distinct526
Distinct (%)80.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2452.7747
Minimum0
Maximum41892
Zeros5
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2024-02-21T08:55:32.112905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile25.8
Q1118
median371
Q32839
95-th percentile12289.8
Maximum41892
Range41892
Interquartile range (IQR)2721

Descriptive statistics

Standard deviation5075.1763
Coefficient of variation (CV)2.0691571
Kurtosis19.750472
Mean2452.7747
Median Absolute Deviation (MAD)317
Skewness3.9804072
Sum1611473
Variance25757414
MonotonicityNot monotonic
2024-02-21T08:55:32.270884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
101 5
 
0.8%
87 5
 
0.8%
0 5
 
0.8%
63 4
 
0.6%
18 4
 
0.6%
147 4
 
0.6%
106 4
 
0.6%
70 4
 
0.6%
55 4
 
0.6%
207 3
 
0.5%
Other values (516) 615
93.6%
ValueCountFrequency (%)
0 5
0.8%
1 2
 
0.3%
3 1
 
0.2%
4 1
 
0.2%
5 1
 
0.2%
6 2
 
0.3%
7 1
 
0.2%
9 1
 
0.2%
10 1
 
0.2%
13 1
 
0.2%
ValueCountFrequency (%)
41892 1
0.2%
38441 1
0.2%
37755 1
0.2%
32088 1
0.2%
30219 1
0.2%
29233 1
0.2%
27446 1
0.2%
26548 1
0.2%
26263 1
0.2%
23938 1
0.2%

White, Not Hispanic Consumers
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct397
Distinct (%)80.7%
Missing165
Missing (%)25.1%
Infinite0
Infinite (%)0.0%
Mean1819.378
Minimum0
Maximum16987
Zeros5
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2024-02-21T08:55:32.633894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile22
Q196
median319.5
Q32596
95-th percentile8978.2
Maximum16987
Range16987
Interquartile range (IQR)2500

Descriptive statistics

Standard deviation3157.5263
Coefficient of variation (CV)1.7354976
Kurtosis7.19725
Mean1819.378
Median Absolute Deviation (MAD)264.5
Skewness2.6125238
Sum895134
Variance9969972.2
MonotonicityNot monotonic
2024-02-21T08:55:32.815111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5
 
0.8%
52 5
 
0.8%
346 4
 
0.6%
85 4
 
0.6%
256 3
 
0.5%
71 3
 
0.5%
55 3
 
0.5%
22 3
 
0.5%
87 3
 
0.5%
50 3
 
0.5%
Other values (387) 456
69.4%
(Missing) 165
 
25.1%
ValueCountFrequency (%)
0 5
0.8%
1 2
 
0.3%
3 1
 
0.2%
4 1
 
0.2%
5 1
 
0.2%
7 2
 
0.3%
8 2
 
0.3%
10 1
 
0.2%
12 1
 
0.2%
15 1
 
0.2%
ValueCountFrequency (%)
16987 1
0.2%
16907 1
0.2%
16428 1
0.2%
16239 1
0.2%
15634 1
0.2%
15011 1
0.2%
14024 1
0.2%
13667 1
0.2%
13514 1
0.2%
13490 1
0.2%

White, Hispanic Consumers
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct89
Distinct (%)19.7%
Missing205
Missing (%)31.2%
Infinite0
Infinite (%)0.0%
Mean20.909292
Minimum0
Maximum246
Zeros91
Zeros (%)13.9%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2024-02-21T08:55:32.975530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median4
Q321.25
95-th percentile102.75
Maximum246
Range246
Interquartile range (IQR)20.25

Descriptive statistics

Standard deviation41.255767
Coefficient of variation (CV)1.9730829
Kurtosis11.820403
Mean20.909292
Median Absolute Deviation (MAD)4
Skewness3.2618456
Sum9451
Variance1702.0383
MonotonicityNot monotonic
2024-02-21T08:55:33.175700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 91
13.9%
1 73
 
11.1%
2 33
 
5.0%
4 25
 
3.8%
5 21
 
3.2%
3 20
 
3.0%
6 11
 
1.7%
10 8
 
1.2%
17 8
 
1.2%
8 7
 
1.1%
Other values (79) 155
23.6%
(Missing) 205
31.2%
ValueCountFrequency (%)
0 91
13.9%
1 73
11.1%
2 33
 
5.0%
3 20
 
3.0%
4 25
 
3.8%
5 21
 
3.2%
6 11
 
1.7%
7 7
 
1.1%
8 7
 
1.1%
9 5
 
0.8%
ValueCountFrequency (%)
246 1
0.2%
243 1
0.2%
240 1
0.2%
232 1
0.2%
228 1
0.2%
222 1
0.2%
212 1
0.2%
203 1
0.2%
178 1
0.2%
172 1
0.2%

Native American Consumers
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct51
Distinct (%)8.0%
Missing23
Missing (%)3.5%
Infinite0
Infinite (%)0.0%
Mean6.1861199
Minimum0
Maximum87
Zeros261
Zeros (%)39.7%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2024-02-21T08:55:33.427649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q37
95-th percentile28
Maximum87
Range87
Interquartile range (IQR)7

Descriptive statistics

Standard deviation12.008425
Coefficient of variation (CV)1.9411885
Kurtosis12.783296
Mean6.1861199
Median Absolute Deviation (MAD)1
Skewness3.2407455
Sum3922
Variance144.20227
MonotonicityNot monotonic
2024-02-21T08:55:33.585731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 261
39.7%
1 92
 
14.0%
2 48
 
7.3%
3 27
 
4.1%
4 20
 
3.0%
5 13
 
2.0%
6 12
 
1.8%
10 11
 
1.7%
9 11
 
1.7%
8 11
 
1.7%
Other values (41) 128
19.5%
(Missing) 23
 
3.5%
ValueCountFrequency (%)
0 261
39.7%
1 92
 
14.0%
2 48
 
7.3%
3 27
 
4.1%
4 20
 
3.0%
5 13
 
2.0%
6 12
 
1.8%
7 10
 
1.5%
8 11
 
1.7%
9 11
 
1.7%
ValueCountFrequency (%)
87 1
0.2%
82 1
0.2%
74 1
0.2%
70 1
0.2%
68 2
0.3%
59 1
0.2%
58 1
0.2%
57 2
0.3%
52 1
0.2%
50 1
0.2%

Asian Consumers
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct122
Distinct (%)20.0%
Missing47
Missing (%)7.2%
Infinite0
Infinite (%)0.0%
Mean27.516393
Minimum0
Maximum1170
Zeros209
Zeros (%)31.8%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2024-02-21T08:55:33.746591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q319
95-th percentile137.65
Maximum1170
Range1170
Interquartile range (IQR)19

Descriptive statistics

Standard deviation74.152157
Coefficient of variation (CV)2.6948356
Kurtosis99.593296
Mean27.516393
Median Absolute Deviation (MAD)2
Skewness7.9248474
Sum16785
Variance5498.5424
MonotonicityNot monotonic
2024-02-21T08:55:33.925864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 209
31.8%
1 79
 
12.0%
2 35
 
5.3%
7 21
 
3.2%
6 17
 
2.6%
3 14
 
2.1%
4 14
 
2.1%
8 12
 
1.8%
5 9
 
1.4%
19 7
 
1.1%
Other values (112) 193
29.4%
(Missing) 47
 
7.2%
ValueCountFrequency (%)
0 209
31.8%
1 79
 
12.0%
2 35
 
5.3%
3 14
 
2.1%
4 14
 
2.1%
5 9
 
1.4%
6 17
 
2.6%
7 21
 
3.2%
8 12
 
1.8%
9 4
 
0.6%
ValueCountFrequency (%)
1170 1
0.2%
540 1
0.2%
425 1
0.2%
410 1
0.2%
360 1
0.2%
339 1
0.2%
294 1
0.2%
283 1
0.2%
264 2
0.3%
236 1
0.2%

African American Consumers
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct192
Distinct (%)29.8%
Missing12
Missing (%)1.8%
Infinite0
Infinite (%)0.0%
Mean71.717829
Minimum0
Maximum1825
Zeros52
Zeros (%)7.9%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2024-02-21T08:55:34.113182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median16
Q372
95-th percentile338.8
Maximum1825
Range1825
Interquartile range (IQR)68

Descriptive statistics

Standard deviation154.46662
Coefficient of variation (CV)2.1538105
Kurtosis37.551423
Mean71.717829
Median Absolute Deviation (MAD)15
Skewness5.0955064
Sum46258
Variance23859.936
MonotonicityNot monotonic
2024-02-21T08:55:34.324967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 52
 
7.9%
1 41
 
6.2%
2 33
 
5.0%
3 30
 
4.6%
4 24
 
3.7%
5 23
 
3.5%
6 19
 
2.9%
11 16
 
2.4%
7 16
 
2.4%
9 14
 
2.1%
Other values (182) 377
57.4%
ValueCountFrequency (%)
0 52
7.9%
1 41
6.2%
2 33
5.0%
3 30
4.6%
4 24
3.7%
5 23
3.5%
6 19
 
2.9%
7 16
 
2.4%
8 12
 
1.8%
9 14
 
2.1%
ValueCountFrequency (%)
1825 1
0.2%
1204 1
0.2%
1036 1
0.2%
899 1
0.2%
898 1
0.2%
887 1
0.2%
775 1
0.2%
724 1
0.2%
695 1
0.2%
675 1
0.2%

Hawaiian Pacific Islander Consumers
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct31
Distinct (%)5.1%
Missing55
Missing (%)8.4%
Infinite0
Infinite (%)0.0%
Mean2.4750831
Minimum0
Maximum68
Zeros310
Zeros (%)47.2%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2024-02-21T08:55:34.480849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile11
Maximum68
Range68
Interquartile range (IQR)2

Descriptive statistics

Standard deviation5.7374802
Coefficient of variation (CV)2.318096
Kurtosis40.218123
Mean2.4750831
Median Absolute Deviation (MAD)0
Skewness5.2490547
Sum1490
Variance32.918679
MonotonicityNot monotonic
2024-02-21T08:55:34.619954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0 310
47.2%
1 96
 
14.6%
2 59
 
9.0%
4 29
 
4.4%
3 20
 
3.0%
5 13
 
2.0%
9 11
 
1.7%
8 11
 
1.7%
6 9
 
1.4%
7 7
 
1.1%
Other values (21) 37
 
5.6%
(Missing) 55
 
8.4%
ValueCountFrequency (%)
0 310
47.2%
1 96
 
14.6%
2 59
 
9.0%
3 20
 
3.0%
4 29
 
4.4%
5 13
 
2.0%
6 9
 
1.4%
7 7
 
1.1%
8 11
 
1.7%
9 11
 
1.7%
ValueCountFrequency (%)
68 1
 
0.2%
46 1
 
0.2%
38 1
 
0.2%
35 1
 
0.2%
31 1
 
0.2%
27 1
 
0.2%
26 3
0.5%
23 1
 
0.2%
22 1
 
0.2%
21 1
 
0.2%

Consumers - Other Race
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct55
Distinct (%)9.4%
Missing70
Missing (%)10.7%
Infinite0
Infinite (%)0.0%
Mean7.3390119
Minimum0
Maximum332
Zeros404
Zeros (%)61.5%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2024-02-21T08:55:34.761228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile39
Maximum332
Range332
Interquartile range (IQR)1

Descriptive statistics

Standard deviation27.520192
Coefficient of variation (CV)3.7498498
Kurtosis51.314142
Mean7.3390119
Median Absolute Deviation (MAD)0
Skewness6.3662452
Sum4308
Variance757.36098
MonotonicityNot monotonic
2024-02-21T08:55:34.938400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 404
61.5%
1 42
 
6.4%
2 24
 
3.7%
3 12
 
1.8%
5 10
 
1.5%
4 8
 
1.2%
7 5
 
0.8%
11 4
 
0.6%
16 4
 
0.6%
13 4
 
0.6%
Other values (45) 70
 
10.7%
(Missing) 70
 
10.7%
ValueCountFrequency (%)
0 404
61.5%
1 42
 
6.4%
2 24
 
3.7%
3 12
 
1.8%
4 8
 
1.2%
5 10
 
1.5%
6 3
 
0.5%
7 5
 
0.8%
8 2
 
0.3%
9 4
 
0.6%
ValueCountFrequency (%)
332 1
0.2%
221 1
0.2%
208 1
0.2%
169 1
0.2%
162 1
0.2%
156 1
0.2%
150 1
0.2%
128 1
0.2%
127 1
0.2%
115 1
0.2%

Consumers - Two or More Races
Real number (ℝ)

MISSING  ZEROS 

Distinct51
Distinct (%)13.2%
Missing270
Missing (%)41.1%
Infinite0
Infinite (%)0.0%
Mean11.361757
Minimum0
Maximum464
Zeros257
Zeros (%)39.1%
Negative0
Negative (%)0.0%
Memory size5.9 KiB
2024-02-21T08:55:35.092115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile64.4
Maximum464
Range464
Interquartile range (IQR)2

Descriptive statistics

Standard deviation45.309224
Coefficient of variation (CV)3.9878712
Kurtosis49.217327
Mean11.361757
Median Absolute Deviation (MAD)0
Skewness6.4497283
Sum4397
Variance2052.9258
MonotonicityNot monotonic
2024-02-21T08:55:35.248914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 257
39.1%
1 31
 
4.7%
2 14
 
2.1%
4 11
 
1.7%
6 7
 
1.1%
9 5
 
0.8%
10 4
 
0.6%
5 4
 
0.6%
3 4
 
0.6%
7 4
 
0.6%
Other values (41) 46
 
7.0%
(Missing) 270
41.1%
ValueCountFrequency (%)
0 257
39.1%
1 31
 
4.7%
2 14
 
2.1%
3 4
 
0.6%
4 11
 
1.7%
5 4
 
0.6%
6 7
 
1.1%
7 4
 
0.6%
8 1
 
0.2%
9 5
 
0.8%
ValueCountFrequency (%)
464 1
0.2%
404 1
0.2%
307 1
0.2%
285 1
0.2%
196 1
0.2%
185 1
0.2%
179 1
0.2%
174 1
0.2%
138 1
0.2%
135 1
0.2%

Interactions

2024-02-21T08:55:26.453281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-21T08:54:53.743246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-21T08:54:55.808992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-21T08:54:57.612811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-21T08:55:00.233764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-21T08:55:02.321063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-21T08:55:04.539257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-21T08:55:06.504860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-21T08:55:08.797536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-21T08:55:10.753978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-21T08:55:12.742226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-21T08:55:14.591225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-21T08:55:16.407933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-21T08:55:18.628895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-21T08:55:20.475307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-21T08:55:22.627424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-21T08:55:24.497374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-21T08:55:26.566680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-21T08:54:53.941526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-21T08:54:55.930722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-21T08:54:57.777060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-21T08:55:00.376469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-21T08:55:02.427930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-21T08:55:04.649863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-21T08:55:06.636516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-21T08:55:08.932446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-21T08:55:10.858320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-21T08:55:12.849657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-21T08:55:14.684108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-21T08:55:16.851588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-21T08:55:18.718221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-21T08:55:20.571651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-21T08:55:22.735963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-21T08:55:24.599105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-21T08:55:26.663399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-21T08:54:54.056352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-21T08:54:56.034870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-21T08:54:57.897922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-21T08:55:00.499647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-21T08:55:02.538671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-21T08:55:04.747329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-21T08:55:06.748741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-21T08:55:09.060705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-21T08:55:10.961725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-21T08:55:12.963553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-21T08:55:14.795717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-21T08:55:16.976737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-21T08:55:18.810161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-21T08:55:20.660089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-21T08:55:22.851080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-21T08:55:24.687880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2024-02-21T08:55:22.299048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-21T08:55:24.109483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-21T08:55:26.124613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-21T08:55:27.916993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-21T08:54:55.610806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-21T08:54:57.397509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-21T08:54:59.959786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-21T08:55:02.125493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-21T08:55:04.311155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-21T08:55:06.243178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-21T08:55:08.555903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-21T08:55:10.544926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-21T08:55:12.494960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-21T08:55:14.404780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-21T08:55:16.203235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-21T08:55:18.442754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-21T08:55:20.276237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-21T08:55:22.400741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-21T08:55:24.226221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-21T08:55:26.231004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-21T08:55:28.005280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-21T08:54:55.716940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-21T08:54:57.511885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-21T08:55:00.110796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-21T08:55:02.221513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-21T08:55:04.418283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-21T08:55:06.375495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-21T08:55:08.687145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-21T08:55:10.646449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-21T08:55:12.613780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-21T08:55:14.503573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-21T08:55:16.312834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-21T08:55:18.549995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-21T08:55:20.388612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-21T08:55:22.508845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-21T08:55:24.376276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-21T08:55:26.345359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-02-21T08:55:35.382501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Fiscal YearAge GroupServiceConsumersMale ConsumersFemale ConsumersConsumers in Rural AreasConumsers Living AloneConsumers in PovertyHispanic ConsumersNon-Hispanic ConsumersWhite, Not Hispanic ConsumersWhite, Hispanic ConsumersNative American ConsumersAsian ConsumersAfrican American ConsumersHawaiian Pacific Islander ConsumersConsumers - Other RaceConsumers - Two or More Races
Fiscal Year1.0000.6710.4910.3120.1820.2730.3290.3310.1860.0710.2300.1760.2250.1580.0750.0000.0000.2110.344
Age Group0.6711.0000.3960.1430.1750.1390.1470.1660.0000.1460.1600.1980.1790.1270.2610.1800.1010.2240.000
Service0.4910.3961.0000.8040.7600.7980.7290.7890.6810.6670.7960.8430.7250.5570.5440.5330.5530.4190.000
Consumers0.3120.1430.8041.0000.9800.9760.9510.9720.8470.8590.9590.9960.9170.7730.6510.7640.7730.8130.524
Male Consumers0.1820.1750.7600.9801.0000.9700.9480.9730.8830.8970.9320.9710.8370.7840.7250.8370.8000.8560.707
Female Consumers0.2730.1390.7980.9760.9701.0000.9690.9630.8340.8350.9340.9710.8370.7280.5890.7360.7190.7610.560
Consumers in Rural Areas0.3290.1470.7290.9510.9480.9691.0000.9510.8420.8060.9000.8880.7450.7290.5880.7610.6980.7420.672
Conumsers Living Alone0.3310.1660.7890.9720.9730.9630.9511.0000.8700.8240.9270.9660.9110.7580.6310.7990.7820.7760.542
Consumers in Poverty0.1860.0000.6810.8470.8830.8340.8420.8701.0000.8900.9330.8890.9050.6070.7080.8990.7050.8410.284
Hispanic Consumers0.0710.1460.6670.8590.8970.8350.8060.8240.8901.0000.8590.9600.9720.5910.6780.9380.8450.9480.383
Non-Hispanic Consumers0.2300.1600.7960.9590.9320.9340.9000.9270.9330.8591.0000.9960.9190.5910.6860.7550.6950.8180.512
White, Not Hispanic Consumers0.1760.1980.8430.9960.9710.9710.8880.9660.8890.9600.9961.0000.8990.7890.7460.7920.6900.2250.663
White, Hispanic Consumers0.2250.1790.7250.9170.8370.8370.7450.9110.9050.9720.9190.8991.0000.8130.7440.8320.7660.0000.514
Native American Consumers0.1580.1270.5570.7730.7840.7280.7290.7580.6070.5910.5910.7890.8131.0000.4010.6340.7190.3130.536
Asian Consumers0.0750.2610.5440.6510.7250.5890.5880.6310.7080.6780.6860.7460.7440.4011.0000.6590.5810.5620.364
African American Consumers0.0000.1800.5330.7640.8370.7360.7610.7990.8990.9380.7550.7920.8320.6340.6591.0000.8160.9450.418
Hawaiian Pacific Islander Consumers0.0000.1010.5530.7730.8000.7190.6980.7820.7050.8450.6950.6900.7660.7190.5810.8161.0000.6840.000
Consumers - Other Race0.2110.2240.4190.8130.8560.7610.7420.7760.8410.9480.8180.2250.0000.3130.5620.9450.6841.0000.000
Consumers - Two or More Races0.3440.0000.0000.5240.7070.5600.6720.5420.2840.3830.5120.6630.5140.5360.3640.4180.0000.0001.000
2024-02-21T08:55:35.629166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Age GroupService
Age Group1.0000.182
Service0.1821.000
2024-02-21T08:55:35.987152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Fiscal YearConsumersMale ConsumersFemale ConsumersConsumers in Rural AreasConumsers Living AloneConsumers in PovertyHispanic ConsumersNon-Hispanic ConsumersWhite, Not Hispanic ConsumersWhite, Hispanic ConsumersNative American ConsumersAsian ConsumersAfrican American ConsumersHawaiian Pacific Islander ConsumersConsumers - Other RaceConsumers - Two or More RacesAge GroupService
Fiscal Year1.000-0.0210.007-0.0310.006-0.0370.0010.033-0.007-0.164-0.167-0.1320.0350.004-0.0160.645-0.4640.4330.202
Consumers-0.0211.0000.9840.9980.9530.9880.9130.9080.9990.9980.8840.7230.8400.9130.6950.3480.4590.0750.430
Male Consumers0.0070.9841.0000.9710.9260.9620.8940.9130.9820.9780.8810.7210.8420.9220.6980.3800.4390.0920.380
Female Consumers-0.0310.9980.9711.0000.9560.9900.9140.9010.9970.9960.8810.7210.8370.9050.6920.3330.4630.0730.422
Consumers in Rural Areas0.0060.9530.9260.9561.0000.9510.8870.8520.9560.9470.8190.6550.7720.8370.6190.3750.4660.0770.350
Conumsers Living Alone-0.0370.9880.9620.9900.9511.0000.9100.8890.9870.9830.8830.7030.8200.8910.6780.3320.4900.0870.413
Consumers in Poverty0.0010.9130.8940.9140.8870.9101.0000.8330.9150.8870.7730.6720.7780.8430.6130.3730.3860.0000.326
Hispanic Consumers0.0330.9080.9130.9010.8520.8890.8331.0000.9040.8970.9580.7320.8450.8850.6950.3900.3870.0810.325
Non-Hispanic Consumers-0.0070.9990.9820.9970.9560.9870.9150.9041.0000.9990.8770.7180.8400.9110.6910.3550.4600.0790.440
White, Not Hispanic Consumers-0.1640.9980.9780.9960.9470.9830.8870.8970.9991.0000.8720.8260.8620.9000.7310.1790.4580.1050.482
White, Hispanic Consumers-0.1670.8840.8810.8810.8190.8830.7730.9580.8770.8721.0000.7800.8310.8620.7110.0930.4170.0940.343
Native American Consumers-0.1320.7230.7210.7210.6550.7030.6720.7320.7180.8260.7801.0000.7300.7160.8180.1590.2860.0660.227
Asian Consumers0.0350.8400.8420.8370.7720.8200.7780.8450.8400.8620.8310.7301.0000.8430.6950.3370.3470.0980.270
African American Consumers0.0040.9130.9220.9050.8370.8910.8430.8850.9110.9000.8620.7160.8431.0000.6800.3920.4100.1000.234
Hawaiian Pacific Islander Consumers-0.0160.6950.6980.6920.6190.6780.6130.6950.6910.7310.7110.8180.6950.6801.0000.1900.2170.0560.245
Consumers - Other Race0.6450.3480.3800.3330.3750.3320.3730.3900.3550.1790.0930.1590.3370.3920.1901.000-0.2040.1260.173
Consumers - Two or More Races-0.4640.4590.4390.4630.4660.4900.3860.3870.4600.4580.4170.2860.3470.4100.217-0.2041.0000.0000.000
Age Group0.4330.0750.0920.0730.0770.0870.0000.0810.0790.1050.0940.0660.0980.1000.0560.1260.0001.0000.182
Service0.2020.4300.3800.4220.3500.4130.3260.3250.4400.4820.3430.2270.2700.2340.2450.1730.0000.1821.000

Missing values

2024-02-21T08:55:28.159608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-02-21T08:55:28.478678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-02-21T08:55:28.702035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Fiscal YearAge GroupServiceConsumersMale ConsumersFemale ConsumersConsumers in Rural AreasConumsers Living AloneConsumers in PovertyHispanic ConsumersNon-Hispanic ConsumersWhite, Not Hispanic ConsumersWhite, Hispanic ConsumersNative American ConsumersAsian ConsumersAfrican American ConsumersHawaiian Pacific Islander ConsumersConsumers - Other RaceConsumers - Two or More Races
02009Age 60-74Personal Care701356305345063590003001
12009Age 75-84Personal Care1261210979804911181111004000
22009Age 85+Personal Care188261601441378211671631003000
32009Age 60-74Homemaker3107024019125016302762660008202
42009Age 75-84Homemaker4729237334136418014374241027101
52009Age 85+Homemaker5157943441043321814854751107102
62009Age 60-74Chore62115746429045536316577510132656106
72009Age 75-84Chore58911946937342131012562511101138009
82009Age 85+Chore35050296237275189933331680017001
92009Age 60-74Home Delivered Meals306110981925173318941038182552241715147771037
Fiscal YearAge GroupServiceConsumersMale ConsumersFemale ConsumersConsumers in Rural AreasConumsers Living AloneConsumers in PovertyHispanic ConsumersNon-Hispanic ConsumersWhite, Not Hispanic ConsumersWhite, Hispanic ConsumersNative American ConsumersAsian ConsumersAfrican American ConsumersHawaiian Pacific Islander ConsumersConsumers - Other RaceConsumers - Two or More Races
647202385+EAPA Consultation863056294516372<NA><NA>00501<NA>
648202375-84Material Aid4461153312772801554435<NA><NA>0261902<NA>
649202375-84Congregate Nutrition651424374067442830611189936339<NA><NA>33752333<NA>
650202360-74Options Counseling1367553809680799464761139<NA><NA>4876451<NA>
651202375-84Chore18932157131163804185<NA><NA>00502<NA>
652202385+Personal Care12918111111103490128<NA><NA>00100<NA>
653202385+Outreach1981111165019<NA><NA>00100<NA>
654202375-84Nutrition Education675826074146452233931495946580<NA><NA>23985230<NA>
655202360-74Home Delivered Nutrition403618742161243025311801663948<NA><NA>341186324<NA>
656202375-84Assisted Transportation28894194137204970285<NA><NA>051200<NA>