Overview

Dataset statistics

Number of variables5
Number of observations2040
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory83.8 KiB
Average record size in memory42.1 B

Variable types

Categorical2
DateTime1
Numeric2

Dataset

Description사회보장정보시스템(행복e음)에 등록된 기초생활보장, 차상위장애인, 차상위자활, 차상위본인부담경감, 기초(노령)연금, 장애인연금 수급권자 월별, 시도별 집계한 통계데이터
URLhttps://www.data.go.kr/data/15009570/fileData.do

Alerts

수급권자수 is highly overall correlated with 수급가구수High correlation
수급가구수 is highly overall correlated with 수급권자수High correlation

Reproduction

Analysis started2023-12-12 11:19:59.548458
Analysis finished2023-12-12 11:20:00.879152
Duration1.33 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

사업명
Categorical

Distinct10
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size16.1 KiB
기초생활보장(맞춤형급여) - 중복제거
204 
기초생계급여
204 
기초의료급여
204 
기초주거급여
204 
기초교육급여
204 
Other values (5)
1020 

Length

Max length20
Median length16
Mean length7.6
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row기초생활보장(맞춤형급여) - 중복제거
2nd row기초생활보장(맞춤형급여) - 중복제거
3rd row기초생활보장(맞춤형급여) - 중복제거
4th row기초생활보장(맞춤형급여) - 중복제거
5th row기초생활보장(맞춤형급여) - 중복제거

Common Values

ValueCountFrequency (%)
기초생활보장(맞춤형급여) - 중복제거 204
10.0%
기초생계급여 204
10.0%
기초의료급여 204
10.0%
기초주거급여 204
10.0%
기초교육급여 204
10.0%
차상위장애인 204
10.0%
차상위자활 204
10.0%
차상위본인부담경감대상자 204
10.0%
기초연금 204
10.0%
장애인연금 204
10.0%

Length

2023-12-12T20:20:01.006536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:20:01.245013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
기초생활보장(맞춤형급여 204
8.3%
204
8.3%
중복제거 204
8.3%
기초생계급여 204
8.3%
기초의료급여 204
8.3%
기초주거급여 204
8.3%
기초교육급여 204
8.3%
차상위장애인 204
8.3%
차상위자활 204
8.3%
차상위본인부담경감대상자 204
8.3%
Other values (2) 408
16.7%

기준년월
Date

Distinct12
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size16.1 KiB
Minimum2022-01-01 00:00:00
Maximum2022-12-01 00:00:00
2023-12-12T20:20:01.513762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:20:01.705304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)

시도
Categorical

Distinct17
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size16.1 KiB
서울특별시
 
120
부산광역시
 
120
대구광역시
 
120
인천광역시
 
120
광주광역시
 
120
Other values (12)
1440 

Length

Max length7
Median length5
Mean length4.6470588
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row서울특별시
2nd row부산광역시
3rd row대구광역시
4th row인천광역시
5th row광주광역시

Common Values

ValueCountFrequency (%)
서울특별시 120
 
5.9%
부산광역시 120
 
5.9%
대구광역시 120
 
5.9%
인천광역시 120
 
5.9%
광주광역시 120
 
5.9%
대전광역시 120
 
5.9%
울산광역시 120
 
5.9%
세종특별자치시 120
 
5.9%
경기도 120
 
5.9%
강원도 120
 
5.9%
Other values (7) 840
41.2%

Length

2023-12-12T20:20:01.979162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
서울특별시 120
 
5.9%
강원도 120
 
5.9%
경상남도 120
 
5.9%
경상북도 120
 
5.9%
전라남도 120
 
5.9%
전라북도 120
 
5.9%
충청남도 120
 
5.9%
충청북도 120
 
5.9%
경기도 120
 
5.9%
부산광역시 120
 
5.9%
Other values (7) 840
41.2%

수급권자수
Real number (ℝ)

HIGH CORRELATION 

Distinct1966
Distinct (%)96.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean87043.85
Minimum37
Maximum1242296
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.1 KiB
2023-12-12T20:20:02.271223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37
5-th percentile534
Q18496
median26333
Q396232.5
95-th percentile390056.2
Maximum1242296
Range1242259
Interquartile range (IQR)87736.5

Descriptive statistics

Standard deviation150598.33
Coefficient of variation (CV)1.7301433
Kurtosis21.971433
Mean87043.85
Median Absolute Deviation (MAD)25412.5
Skewness4.0096979
Sum1.7756945 × 108
Variance2.2679858 × 1010
MonotonicityNot monotonic
2023-12-12T20:20:02.578434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
393 4
 
0.2%
10846 3
 
0.1%
1155 3
 
0.1%
1163 3
 
0.1%
53 3
 
0.1%
65 3
 
0.1%
17083 2
 
0.1%
1293 2
 
0.1%
6430 2
 
0.1%
7966 2
 
0.1%
Other values (1956) 2013
98.7%
ValueCountFrequency (%)
37 2
0.1%
51 1
 
< 0.1%
52 1
 
< 0.1%
53 3
0.1%
54 2
0.1%
55 2
0.1%
57 1
 
< 0.1%
58 1
 
< 0.1%
59 2
0.1%
61 1
 
< 0.1%
ValueCountFrequency (%)
1242296 1
< 0.1%
1237122 1
< 0.1%
1232295 1
< 0.1%
1225029 1
< 0.1%
1222798 1
< 0.1%
1216422 1
< 0.1%
1211714 1
< 0.1%
1207829 1
< 0.1%
1201915 1
< 0.1%
1195668 1
< 0.1%

수급가구수
Real number (ℝ)

HIGH CORRELATION 

Distinct1963
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67101.704
Minimum37
Maximum980523
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.1 KiB
2023-12-12T20:20:02.858645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37
5-th percentile516.85
Q16920.25
median21929
Q374050.25
95-th percentile285111.5
Maximum980523
Range980486
Interquartile range (IQR)67130

Descriptive statistics

Standard deviation117152.77
Coefficient of variation (CV)1.7458985
Kurtosis23.607096
Mean67101.704
Median Absolute Deviation (MAD)20729.5
Skewness4.1618259
Sum1.3688748 × 108
Variance1.3724771 × 1010
MonotonicityNot monotonic
2023-12-12T20:20:03.158716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
52 4
 
0.2%
392 3
 
0.1%
6375 3
 
0.1%
710 3
 
0.1%
10682 3
 
0.1%
664 3
 
0.1%
518 3
 
0.1%
388 3
 
0.1%
383 2
 
0.1%
42183 2
 
0.1%
Other values (1953) 2011
98.6%
ValueCountFrequency (%)
37 2
0.1%
50 1
 
< 0.1%
51 1
 
< 0.1%
52 4
0.2%
53 2
0.1%
54 1
 
< 0.1%
55 1
 
< 0.1%
56 1
 
< 0.1%
57 1
 
< 0.1%
58 1
 
< 0.1%
ValueCountFrequency (%)
980523 1
< 0.1%
976526 1
< 0.1%
972869 1
< 0.1%
967982 1
< 0.1%
967308 1
< 0.1%
963135 1
< 0.1%
959442 1
< 0.1%
956413 1
< 0.1%
951849 1
< 0.1%
947067 1
< 0.1%

Interactions

2023-12-12T20:20:00.303543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:19:59.937956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:20:00.472565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:20:00.132694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T20:20:03.335308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사업명기준년월시도수급권자수수급가구수
사업명1.0000.0000.0000.5660.542
기준년월0.0001.0000.0000.0000.000
시도0.0000.0001.0000.5650.545
수급권자수0.5660.0000.5651.0000.996
수급가구수0.5420.0000.5450.9961.000
2023-12-12T20:20:03.494004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시도사업명
시도1.0000.000
사업명0.0001.000
2023-12-12T20:20:03.638422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
수급권자수수급가구수사업명시도
수급권자수1.0000.9970.3300.295
수급가구수0.9971.0000.3120.281
사업명0.3300.3121.0000.000
시도0.2950.2810.0001.000

Missing values

2023-12-12T20:20:00.674904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T20:20:00.812510image/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.

Sample

사업명기준년월시도수급권자수수급가구수
0기초생활보장(맞춤형급여) - 중복제거2022-01서울특별시403165300800
1기초생활보장(맞춤형급여) - 중복제거2022-01부산광역시217920162632
2기초생활보장(맞춤형급여) - 중복제거2022-01대구광역시144397105467
3기초생활보장(맞춤형급여) - 중복제거2022-01인천광역시159627112206
4기초생활보장(맞춤형급여) - 중복제거2022-01광주광역시9139761684
5기초생활보장(맞춤형급여) - 중복제거2022-01대전광역시7292952004
6기초생활보장(맞춤형급여) - 중복제거2022-01울산광역시3870628435
7기초생활보장(맞춤형급여) - 중복제거2022-01세종특별자치시79125355
8기초생활보장(맞춤형급여) - 중복제거2022-01경기도423352308051
9기초생활보장(맞춤형급여) - 중복제거2022-01강원도8401163668
사업명기준년월시도수급권자수수급가구수
2030장애인연금2022-12세종특별자치시14981478
2031장애인연금2022-12경기도7178370858
2032장애인연금2022-12강원도1396013674
2033장애인연금2022-12충청북도1446314204
2034장애인연금2022-12충청남도1874918363
2035장애인연금2022-12전라북도1970319281
2036장애인연금2022-12전라남도2111620509
2037장애인연금2022-12경상북도2615125639
2038장애인연금2022-12경상남도2696926501
2039장애인연금2022-12제주특별자치도51285068