Overview

Dataset statistics

Number of variables7
Number of observations28
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.8 KiB
Average record size in memory65.6 B

Variable types

Categorical3
Numeric3
Text1

Dataset

Description국가보훈부에서 제공하는 재가복지서비스를 지원하고 있는 인력 현황 자료* 지원 인력: 보훈복지사, 보훈섬김이, 보비스요원※ 보훈재가복지서비스란?‘재가복지’란 각 가정에 방문하여 제공하는 복지 서비스를 뜻하는 말로, 요양시설 등에서 이루어지는 ‘시설복지’와 구분되는 개념이다.국가보훈처에서 실시하는 ‘보훈재가복지서비스’는 생활이 어려운 독거·노인부부세대 중 거동이 불편한 사람을 대상으로 하는 찾아가는 복지서비스이다.전문 인력인 ‘보훈섬김이’가 각 가정으로 찾아가 가사활동, 건강관리, 편의지원 등을 제공한다.
Author국가보훈부
URLhttps://www.data.go.kr/data/15072657/fileData.do

Alerts

기준년월 has constant value ""Constant
재가보훈실무관 is highly overall correlated with 합계 and 2 other fieldsHigh correlation
합계 is highly overall correlated with 재가보훈실무관 and 2 other fieldsHigh correlation
사회복지사 is highly overall correlated with 재가보훈실무관 and 1 other fieldsHigh correlation
보비스요원 is highly overall correlated with 재가보훈실무관 and 1 other fieldsHigh correlation
순서 has unique valuesUnique
관리지청 has unique valuesUnique
재가보훈실무관 has 1 (3.6%) zerosZeros

Reproduction

Analysis started2024-03-14 08:43:51.458488
Analysis finished2024-03-14 08:43:54.438989
Duration2.98 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기준년월
Categorical

CONSTANT 

Distinct1
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Memory size352.0 B
2023-12-31
28 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-12-31
2nd row2023-12-31
3rd row2023-12-31
4th row2023-12-31
5th row2023-12-31

Common Values

ValueCountFrequency (%)
2023-12-31 28
100.0%

Length

2024-03-14T17:43:54.617712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T17:43:54.915688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-12-31 28
100.0%

순서
Real number (ℝ)

UNIQUE 

Distinct28
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.5
Minimum1
Maximum28
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size380.0 B
2024-03-14T17:43:55.219030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.35
Q17.75
median14.5
Q321.25
95-th percentile26.65
Maximum28
Range27
Interquartile range (IQR)13.5

Descriptive statistics

Standard deviation8.2259751
Coefficient of variation (CV)0.56730863
Kurtosis-1.2
Mean14.5
Median Absolute Deviation (MAD)7
Skewness0
Sum406
Variance67.666667
MonotonicityStrictly increasing
2024-03-14T17:43:55.619658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
1 1
 
3.6%
16 1
 
3.6%
28 1
 
3.6%
27 1
 
3.6%
26 1
 
3.6%
25 1
 
3.6%
24 1
 
3.6%
23 1
 
3.6%
22 1
 
3.6%
21 1
 
3.6%
Other values (18) 18
64.3%
ValueCountFrequency (%)
1 1
3.6%
2 1
3.6%
3 1
3.6%
4 1
3.6%
5 1
3.6%
6 1
3.6%
7 1
3.6%
8 1
3.6%
9 1
3.6%
10 1
3.6%
ValueCountFrequency (%)
28 1
3.6%
27 1
3.6%
26 1
3.6%
25 1
3.6%
24 1
3.6%
23 1
3.6%
22 1
3.6%
21 1
3.6%
20 1
3.6%
19 1
3.6%

관리지청
Text

UNIQUE 

Distinct28
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size352.0 B
2024-03-14T17:43:56.443855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length8
Mean length7.6428571
Min length5

Characters and Unicode

Total characters214
Distinct characters32
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique28 ?
Unique (%)100.0%

Sample

1st row국가보훈부
2nd row서울지방보훈청
3rd row서울남부보훈지청
4th row서울북부보훈지청
5th row경기남부보훈지청
ValueCountFrequency (%)
국가보훈부 1
 
3.6%
서울지방보훈청 1
 
3.6%
전북서부보훈지청 1
 
3.6%
전북동부보훈지청 1
 
3.6%
전남서부보훈지청 1
 
3.6%
전남동부보훈지청 1
 
3.6%
광주지방보훈청 1
 
3.6%
경남서부보훈지청 1
 
3.6%
경남동부보훈지청 1
 
3.6%
울산보훈지청 1
 
3.6%
Other values (18) 18
64.3%
2024-03-14T17:43:57.718009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
보 28
13.1%
훈 28
13.1%
청 27
12.6%
지 26
12.1%
부 21
9.8%
북 10
 
4.7%
남 10
 
4.7%
서 8
 
3.7%
경 7
 
3.3%
동 6
 
2.8%
Other values (22) 43
20.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 214
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
보 28
13.1%
훈 28
13.1%
청 27
12.6%
지 26
12.1%
부 21
9.8%
북 10
 
4.7%
남 10
 
4.7%
서 8
 
3.7%
경 7
 
3.3%
동 6
 
2.8%
Other values (22) 43
20.1%

Most occurring scripts

ValueCountFrequency (%)
Hangul 214
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
보 28
13.1%
훈 28
13.1%
청 27
12.6%
지 26
12.1%
부 21
9.8%
북 10
 
4.7%
남 10
 
4.7%
서 8
 
3.7%
경 7
 
3.3%
동 6
 
2.8%
Other values (22) 43
20.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 214
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
보 28
13.1%
훈 28
13.1%
청 27
12.6%
지 26
12.1%
부 21
9.8%
북 10
 
4.7%
남 10
 
4.7%
서 8
 
3.7%
경 7
 
3.3%
동 6
 
2.8%
Other values (22) 43
20.1%

사회복지사
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)17.9%
Missing0
Missing (%)0.0%
Memory size352.0 B
2
15 
4
5 
3
4 
1
3 
6
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)3.6%

Sample

1st row1
2nd row4
3rd row1
4th row2
5th row4

Common Values

ValueCountFrequency (%)
2 15
53.6%
4 5
 
17.9%
3 4
 
14.3%
1 3
 
10.7%
6 1
 
3.6%

Length

2024-03-14T17:43:57.935833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T17:43:58.202561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 15
53.6%
4 5
 
17.9%
3 4
 
14.3%
1 3
 
10.7%
6 1
 
3.6%

재가보훈실무관
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21
Distinct (%)75.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.214286
Minimum0
Maximum79
Zeros1
Zeros (%)3.6%
Negative0
Negative (%)0.0%
Memory size380.0 B
2024-03-14T17:43:58.548354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile11.4
Q124
median30
Q340
95-th percentile68.3
Maximum79
Range79
Interquartile range (IQR)16

Descriptive statistics

Standard deviation17.368249
Coefficient of variation (CV)0.52291503
Kurtosis1.3364526
Mean33.214286
Median Absolute Deviation (MAD)6.5
Skewness0.97897801
Sum930
Variance301.65608
MonotonicityNot monotonic
2024-03-14T17:43:58.954175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
24 3
 
10.7%
30 3
 
10.7%
31 2
 
7.1%
40 2
 
7.1%
22 2
 
7.1%
0 1
 
3.6%
10 1
 
3.6%
29 1
 
3.6%
23 1
 
3.6%
67 1
 
3.6%
Other values (11) 11
39.3%
ValueCountFrequency (%)
0 1
 
3.6%
10 1
 
3.6%
14 1
 
3.6%
22 2
7.1%
23 1
 
3.6%
24 3
10.7%
25 1
 
3.6%
26 1
 
3.6%
28 1
 
3.6%
29 1
 
3.6%
ValueCountFrequency (%)
79 1
3.6%
69 1
3.6%
67 1
3.6%
52 1
3.6%
47 1
3.6%
46 1
3.6%
40 2
7.1%
34 1
3.6%
33 1
3.6%
31 2
7.1%

보비스요원
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)10.7%
Missing0
Missing (%)0.0%
Memory size352.0 B
1
18 
2
9 
0
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)3.6%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 18
64.3%
2 9
32.1%
0 1
 
3.6%

Length

2024-03-14T17:43:59.366864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T17:43:59.677796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 18
64.3%
2 9
32.1%
0 1
 
3.6%

합계
Real number (ℝ)

HIGH CORRELATION 

Distinct22
Distinct (%)78.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.75
Minimum1
Maximum93
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size380.0 B
2024-03-14T17:43:59.993301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile15.45
Q130.5
median36
Q348
95-th percentile80.2
Maximum93
Range92
Interquartile range (IQR)17.5

Descriptive statistics

Standard deviation20.2496
Coefficient of variation (CV)0.4969227
Kurtosis1.0934229
Mean40.75
Median Absolute Deviation (MAD)9
Skewness0.86805302
Sum1141
Variance410.0463
MonotonicityNot monotonic
2024-03-14T17:44:00.375477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
36 3
 
10.7%
32 3
 
10.7%
31 2
 
7.1%
27 2
 
7.1%
93 1
 
3.6%
13 1
 
3.6%
38 1
 
3.6%
75 1
 
3.6%
56 1
 
3.6%
26 1
 
3.6%
Other values (12) 12
42.9%
ValueCountFrequency (%)
1 1
 
3.6%
13 1
 
3.6%
20 1
 
3.6%
26 1
 
3.6%
27 2
7.1%
29 1
 
3.6%
31 2
7.1%
32 3
10.7%
36 3
10.7%
38 1
 
3.6%
ValueCountFrequency (%)
93 1
3.6%
83 1
3.6%
75 1
3.6%
69 1
3.6%
56 1
3.6%
54 1
3.6%
51 1
3.6%
47 1
3.6%
45 1
3.6%
42 1
3.6%

Interactions

2024-03-14T17:43:53.231434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T17:43:51.740520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T17:43:52.466462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T17:43:53.472340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T17:43:51.977345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T17:43:52.717539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T17:43:53.730576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T17:43:52.241356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T17:43:52.986582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T17:44:00.620159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순서관리지청사회복지사재가보훈실무관보비스요원합계
순서1.0001.0000.5310.0000.1720.000
관리지청1.0001.0001.0001.0001.0001.000
사회복지사0.5311.0001.0000.8130.4750.820
재가보훈실무관0.0001.0000.8131.0000.9220.979
보비스요원0.1721.0000.4750.9221.0000.946
합계0.0001.0000.8200.9790.9461.000
2024-03-14T17:44:00.890627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사회복지사보비스요원
사회복지사1.0000.385
보비스요원0.3851.000
2024-03-14T17:44:01.134768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순서재가보훈실무관합계사회복지사보비스요원
순서1.000-0.008-0.0120.1870.000
재가보훈실무관-0.0081.0000.9620.5800.583
합계-0.0120.9621.0000.5900.628
사회복지사0.1870.5800.5901.0000.385
보비스요원0.0000.5830.6280.3851.000

Missing values

2024-03-14T17:43:54.051867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T17:43:54.358796image/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

기준년월순서관리지청사회복지사재가보훈실무관보비스요원합계
02023-12-311국가보훈부1001
12023-12-312서울지방보훈청452169
22023-12-313서울남부보훈지청124131
32023-12-314서울북부보훈지청224132
42023-12-315경기남부보훈지청446151
52023-12-316인천보훈지청247154
62023-12-317경기북부보훈지청340245
72023-12-318경기동부보훈지청222129
82023-12-319강원서부보훈지청224127
92023-12-3110강원동부보훈지청225231
기준년월순서관리지청사회복지사재가보훈실무관보비스요원합계
182023-12-3119부산지방보훈청469183
192023-12-3120울산보훈지청222126
202023-12-3121경남동부보훈지청330256
212023-12-3122경남서부보훈지청230236
222023-12-3123광주지방보훈청467275
232023-12-3124전남동부보훈지청231136
242023-12-3125전남서부보훈지청223127
252023-12-3126전북동부보훈지청230238
262023-12-3127전북서부보훈지청229132
272023-12-3128제주특별자치도보훈청210113