Overview

Dataset statistics

Number of variables9
Number of observations10000
Missing cells517
Missing cells (%)0.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory810.5 KiB
Average record size in memory83.0 B

Variable types

Numeric3
Text2
Categorical4

Dataset

Description한국장애인고용공단에서 지원한 보조공학기기 연간 현황(산업분류명, 성별, 장애유형, 품목, 기준금액 등 제공)
URLhttps://www.data.go.kr/data/15053955/fileData.do

Alerts

기준금액 is highly overall correlated with 실구매금액High correlation
실구매금액 is highly overall correlated with 기준금액High correlation
산업분류명 has 120 (1.2%) missing valuesMissing
실구매금액 has 397 (4.0%) missing valuesMissing
번호 has unique valuesUnique
실구매금액 has 549 (5.5%) zerosZeros

Reproduction

Analysis started2023-12-12 18:26:42.998948
Analysis finished2023-12-12 18:26:45.126847
Duration2.13 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

번호
Real number (ℝ)

UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5389.0595
Minimum1
Maximum10776
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T03:26:45.204903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile548.95
Q12698.75
median5387.5
Q38073.25
95-th percentile10240.05
Maximum10776
Range10775
Interquartile range (IQR)5374.5

Descriptive statistics

Standard deviation3104.0538
Coefficient of variation (CV)0.57599174
Kurtosis-1.1951905
Mean5389.0595
Median Absolute Deviation (MAD)2687.5
Skewness0.002536985
Sum53890595
Variance9635149.7
MonotonicityNot monotonic
2023-12-13T03:26:45.326751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3976 1
 
< 0.1%
7451 1
 
< 0.1%
2450 1
 
< 0.1%
5946 1
 
< 0.1%
8725 1
 
< 0.1%
1021 1
 
< 0.1%
1801 1
 
< 0.1%
6521 1
 
< 0.1%
6299 1
 
< 0.1%
8128 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
11 1
< 0.1%
ValueCountFrequency (%)
10776 1
< 0.1%
10775 1
< 0.1%
10774 1
< 0.1%
10773 1
< 0.1%
10772 1
< 0.1%
10771 1
< 0.1%
10770 1
< 0.1%
10769 1
< 0.1%
10768 1
< 0.1%
10767 1
< 0.1%

산업분류명
Text

MISSING 

Distinct457
Distinct (%)4.6%
Missing120
Missing (%)1.2%
Memory size156.2 KiB
2023-12-13T03:26:45.650990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length33
Median length26
Mean length13.340182
Min length2

Characters and Unicode

Total characters131801
Distinct characters340
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique88 ?
Unique (%)0.9%

Sample

1st row정부기관 일반 보조 행정
2nd row그 외 기타 플라스틱 제품 제조업
3rd row침구 및 관련제품 제조업
4th row정부기관 일반 보조 행정
5th row그 외 기타 비거주 복지 서비스업
ValueCountFrequency (%)
기타 4974
 
11.7%
그 3512
 
8.2%
외 3512
 
8.2%
및 2666
 
6.2%
서비스업 2338
 
5.5%
제조업 1956
 
4.6%
복지 1202
 
2.8%
비거주 1037
 
2.4%
행정 865
 
2.0%
일반 759
 
1.8%
Other values (693) 19845
46.5%
2023-12-13T03:26:46.110272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
32786
24.9%
업 8198
 
6.2%
기 5998
 
4.6%
타 4988
 
3.8%
비 3867
 
2.9%
외 3523
 
2.7%
그 3518
 
2.7%
제 3019
 
2.3%
스 2882
 
2.2%
및 2666
 
2.0%
Other values (330) 60356
45.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 98715
74.9%
Space Separator 32786
 
24.9%
Other Punctuation 285
 
0.2%
Decimal Number 13
 
< 0.1%
Close Punctuation 1
 
< 0.1%
Open Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
업 8198
 
8.3%
기 5998
 
6.1%
타 4988
 
5.1%
비 3867
 
3.9%
외 3523
 
3.6%
그 3518
 
3.6%
제 3019
 
3.1%
스 2882
 
2.9%
및 2666
 
2.7%
지 2589
 
2.6%
Other values (324) 57467
58.2%
Other Punctuation
ValueCountFrequency (%)
, 282
98.9%
; 3
 
1.1%
Space Separator
ValueCountFrequency (%)
32786
100.0%
Decimal Number
ValueCountFrequency (%)
1 13
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 98715
74.9%
Common 33086
 
25.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
업 8198
 
8.3%
기 5998
 
6.1%
타 4988
 
5.1%
비 3867
 
3.9%
외 3523
 
3.6%
그 3518
 
3.6%
제 3019
 
3.1%
스 2882
 
2.9%
및 2666
 
2.7%
지 2589
 
2.6%
Other values (324) 57467
58.2%
Common
ValueCountFrequency (%)
32786
99.1%
, 282
 
0.9%
1 13
 
< 0.1%
; 3
 
< 0.1%
) 1
 
< 0.1%
( 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 98605
74.8%
ASCII 33086
 
25.1%
Compat Jamo 110
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
32786
99.1%
, 282
 
0.9%
1 13
 
< 0.1%
; 3
 
< 0.1%
) 1
 
< 0.1%
( 1
 
< 0.1%
Hangul
ValueCountFrequency (%)
업 8198
 
8.3%
기 5998
 
6.1%
타 4988
 
5.1%
비 3867
 
3.9%
외 3523
 
3.6%
그 3518
 
3.6%
제 3019
 
3.1%
스 2882
 
2.9%
및 2666
 
2.7%
지 2589
 
2.6%
Other values (323) 57357
58.2%
Compat Jamo
ValueCountFrequency (%)
ㆍ 110
100.0%

성별
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
남
6485 
여
3515 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row남
2nd row남
3rd row여
4th row남
5th row남

Common Values

ValueCountFrequency (%)
남 6485
64.8%
여 3515
35.1%

Length

2023-12-13T03:26:46.245304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:26:46.344425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
남 6485
64.8%
여 3515
35.1%

장애유형
Categorical

Distinct14
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
지체장애
3555 
지적장애
2606 
시각장애
2418 
청각장애
559 
뇌병변장애
491 
Other values (9)
371 

Length

Max length5
Median length4
Mean length4.0742
Min length4

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row지체장애
2nd row자폐성장애
3rd row지체장애
4th row지체장애
5th row지체장애

Common Values

ValueCountFrequency (%)
지체장애 3555
35.5%
지적장애 2606
26.1%
시각장애 2418
24.2%
청각장애 559
 
5.6%
뇌병변장애 491
 
4.9%
자폐성장애 251
 
2.5%
상이등급 44
 
0.4%
정신장애 42
 
0.4%
언어장애 18
 
0.2%
신장장애 6
 
0.1%
Other values (4) 10
 
0.1%

Length

2023-12-13T03:26:46.439390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
지체장애 3555
35.5%
지적장애 2606
26.1%
시각장애 2418
24.2%
청각장애 559
 
5.6%
뇌병변장애 491
 
4.9%
자폐성장애 251
 
2.5%
상이등급 44
 
0.4%
정신장애 42
 
0.4%
언어장애 18
 
0.2%
신장장애 6
 
0.1%
Other values (4) 10
 
0.1%

품목
Text

Distinct66
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T03:26:46.723973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length14
Mean length7.1156
Min length4

Characters and Unicode

Total characters71156
Distinct characters119
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row욕창방지방석
2nd row위치감지기
3rd row일반형높낮이조절테이블
4th row물건집게
5th row(구)특수작업기구및장비
ValueCountFrequency (%)
의사소통보조기기 1638
15.0%
욕창방지방석 962
 
8.8%
위치감지기 891
 
8.2%
작업용의자 872
 
8.0%
스크린리더단말기 480
 
4.4%
기타신체보조기기 461
 
4.2%
지지대 455
 
4.2%
일반형높낮이조절테이블 425
 
3.9%
전동이동보조기기 415
 
3.8%
등 414
 
3.8%
Other values (60) 3900
35.7%
2023-12-13T03:26:47.110435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
기 10224
 
14.4%
보 3431
 
4.8%
조 3377
 
4.7%
의 3072
 
4.3%
지 2890
 
4.1%
소 2217
 
3.1%
사 2167
 
3.0%
통 2165
 
3.0%
방 1924
 
2.7%
이 1864
 
2.6%
Other values (109) 37825
53.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 67958
95.5%
Space Separator 913
 
1.3%
Close Punctuation 739
 
1.0%
Open Punctuation 739
 
1.0%
Uppercase Letter 538
 
0.8%
Other Punctuation 269
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
기 10224
 
15.0%
보 3431
 
5.0%
조 3377
 
5.0%
의 3072
 
4.5%
지 2890
 
4.3%
소 2217
 
3.3%
사 2167
 
3.2%
통 2165
 
3.2%
방 1924
 
2.8%
이 1864
 
2.7%
Other values (102) 34627
51.0%
Uppercase Letter
ValueCountFrequency (%)
W 269
50.0%
S 173
32.2%
H 96
 
17.8%
Space Separator
ValueCountFrequency (%)
913
100.0%
Close Punctuation
ValueCountFrequency (%)
) 739
100.0%
Open Punctuation
ValueCountFrequency (%)
( 739
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 269
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 67958
95.5%
Common 2660
 
3.7%
Latin 538
 
0.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
기 10224
 
15.0%
보 3431
 
5.0%
조 3377
 
5.0%
의 3072
 
4.5%
지 2890
 
4.3%
소 2217
 
3.3%
사 2167
 
3.2%
통 2165
 
3.2%
방 1924
 
2.8%
이 1864
 
2.7%
Other values (102) 34627
51.0%
Common
ValueCountFrequency (%)
913
34.3%
) 739
27.8%
( 739
27.8%
/ 269
 
10.1%
Latin
ValueCountFrequency (%)
W 269
50.0%
S 173
32.2%
H 96
 
17.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 67958
95.5%
ASCII 3198
 
4.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
기 10224
 
15.0%
보 3431
 
5.0%
조 3377
 
5.0%
의 3072
 
4.5%
지 2890
 
4.3%
소 2217
 
3.3%
사 2167
 
3.2%
통 2165
 
3.2%
방 1924
 
2.8%
이 1864
 
2.7%
Other values (102) 34627
51.0%
ASCII
ValueCountFrequency (%)
913
28.5%
) 739
23.1%
( 739
23.1%
W 269
 
8.4%
/ 269
 
8.4%
S 173
 
5.4%
H 96
 
3.0%

기준금액
Real number (ℝ)

HIGH CORRELATION 

Distinct318
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1359576.3
Minimum78000
Maximum38950000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T03:26:47.270266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum78000
5-th percentile163480
Q1430500
median810000
Q31650000
95-th percentile5100000
Maximum38950000
Range38872000
Interquartile range (IQR)1219500

Descriptive statistics

Standard deviation1535394.8
Coefficient of variation (CV)1.1293186
Kurtosis37.918703
Mean1359576.3
Median Absolute Deviation (MAD)381000
Skewness3.1063585
Sum1.3595763 × 1010
Variance2.3574371 × 1012
MonotonicityNot monotonic
2023-12-13T03:26:47.427194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
990000 855
 
8.6%
163480 384
 
3.8%
430500 375
 
3.8%
820000 286
 
2.9%
451000 285
 
2.9%
666250 231
 
2.3%
574000 211
 
2.1%
800000 210
 
2.1%
3382500 208
 
2.1%
1691250 201
 
2.0%
Other values (308) 6754
67.5%
ValueCountFrequency (%)
78000 2
 
< 0.1%
79950 5
 
0.1%
90000 2
 
< 0.1%
95000 3
 
< 0.1%
110000 6
 
0.1%
112750 18
 
0.2%
120000 39
0.4%
123000 49
0.5%
150000 29
0.3%
153750 23
0.2%
ValueCountFrequency (%)
38950000 1
 
< 0.1%
11200000 2
 
< 0.1%
10000000 2
 
< 0.1%
6615000 4
 
< 0.1%
6500000 4
 
< 0.1%
6314000 46
0.5%
6160000 24
 
0.2%
6047500 12
 
0.1%
5945000 111
1.1%
5900000 3
 
< 0.1%

실구매금액
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct375
Distinct (%)3.9%
Missing397
Missing (%)4.0%
Infinite0
Infinite (%)0.0%
Mean1257334.4
Minimum0
Maximum38950000
Zeros549
Zeros (%)5.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T03:26:47.579756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1390000
median760000
Q31353000
95-th percentile4970000
Maximum38950000
Range38950000
Interquartile range (IQR)963000

Descriptive statistics

Standard deviation1501333.2
Coefficient of variation (CV)1.1940604
Kurtosis43.678155
Mean1257334.4
Median Absolute Deviation (MAD)401250
Skewness3.3536292
Sum1.2074183 × 1010
Variance2.2540015 × 1012
MonotonicityNot monotonic
2023-12-13T03:26:47.735654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
990000 846
 
8.5%
0 549
 
5.5%
163480 373
 
3.7%
430500 370
 
3.7%
820000 284
 
2.8%
451000 283
 
2.8%
666250 225
 
2.2%
574000 211
 
2.1%
3382500 208
 
2.1%
1691250 199
 
2.0%
Other values (365) 6055
60.6%
(Missing) 397
 
4.0%
ValueCountFrequency (%)
0 549
5.5%
8000 1
 
< 0.1%
10000 1
 
< 0.1%
39000 1
 
< 0.1%
40932 1
 
< 0.1%
48000 1
 
< 0.1%
60329 1
 
< 0.1%
78000 2
 
< 0.1%
79950 5
 
0.1%
88000 1
 
< 0.1%
ValueCountFrequency (%)
38950000 1
 
< 0.1%
11200000 2
 
< 0.1%
6615000 4
 
< 0.1%
6314000 42
 
0.4%
6160000 23
 
0.2%
6047500 12
 
0.1%
5945000 110
1.1%
5900000 3
 
< 0.1%
5800000 96
1.0%
5637500 27
 
0.3%

지원형태
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
무상지원
5914 
고용유지
4086 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row무상지원
2nd row고용유지
3rd row고용유지
4th row무상지원
5th row무상지원

Common Values

ValueCountFrequency (%)
무상지원 5914
59.1%
고용유지 4086
40.9%

Length

2023-12-13T03:26:47.876783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:26:47.983720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
무상지원 5914
59.1%
고용유지 4086
40.9%

관할지사
Categorical

Distinct22
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
서울남부지사
 
688
서울지역본부
 
584
부산지역본부
 
540
대전지역본부
 
533
서울동부지사
 
529
Other values (17)
7126 

Length

Max length6
Median length6
Mean length5.0842
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row충북지사
2nd row서울남부지사
3rd row울산지사
4th row대구지역본부
5th row서울지역본부

Common Values

ValueCountFrequency (%)
서울남부지사 688
 
6.9%
서울지역본부 584
 
5.8%
부산지역본부 540
 
5.4%
대전지역본부 533
 
5.3%
서울동부지사 529
 
5.3%
경북지사 527
 
5.3%
대구지역본부 520
 
5.2%
전남지사 513
 
5.1%
경남지사 505
 
5.1%
경기지역본부 495
 
5.0%
Other values (12) 4566
45.7%

Length

2023-12-13T03:26:48.127457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
서울남부지사 688
 
6.9%
서울지역본부 584
 
5.8%
부산지역본부 540
 
5.4%
대전지역본부 533
 
5.3%
서울동부지사 529
 
5.3%
경북지사 527
 
5.3%
대구지역본부 520
 
5.2%
전남지사 513
 
5.1%
경남지사 505
 
5.1%
경기지역본부 495
 
5.0%
Other values (12) 4566
45.7%

Interactions

2023-12-13T03:26:44.523964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:26:43.903352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:26:44.214693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:26:44.622074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:26:44.005613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:26:44.303789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:26:44.723971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:26:44.103987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:26:44.420434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T03:26:48.531991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호성별장애유형품목기준금액실구매금액지원형태관할지사
번호1.0000.0190.2530.4150.0700.0820.5050.568
성별0.0191.0000.1770.1580.0380.0500.0660.127
장애유형0.2530.1771.0000.8150.3630.3440.3950.292
품목0.4150.1580.8151.0000.8100.8010.8200.608
기준금액0.0700.0380.3630.8101.0000.9940.4880.081
실구매금액0.0820.0500.3440.8010.9941.0000.4630.082
지원형태0.5050.0660.3950.8200.4880.4631.0000.337
관할지사0.5680.1270.2920.6080.0810.0820.3371.000
2023-12-13T03:26:48.640801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지원형태성별장애유형관할지사
지원형태1.0000.0420.3090.266
성별0.0421.0000.1390.100
장애유형0.3090.1391.0000.098
관할지사0.2660.1000.0981.000
2023-12-13T03:26:48.735137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호기준금액실구매금액성별장애유형지원형태관할지사
번호1.000-0.0460.0180.0140.1040.3890.249
기준금액-0.0461.0000.8920.0250.2130.3300.043
실구매금액0.0180.8921.0000.0330.2070.3130.044
성별0.0140.0250.0331.0000.1390.0420.100
장애유형0.1040.2130.2070.1391.0000.3090.098
지원형태0.3890.3300.3130.0420.3091.0000.266
관할지사0.2490.0430.0440.1000.0980.2661.000

Missing values

2023-12-13T03:26:44.848493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T03:26:44.975824image/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.
2023-12-13T03:26:45.069399image/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

번호산업분류명성별장애유형품목기준금액실구매금액지원형태관할지사
39753976정부기관 일반 보조 행정남지체장애욕창방지방석451000451000무상지원충북지사
85068507그 외 기타 플라스틱 제품 제조업남자폐성장애위치감지기430500430500고용유지서울남부지사
1018210183침구 및 관련제품 제조업여지체장애일반형높낮이조절테이블19013701901370고용유지울산지사
56075608정부기관 일반 보조 행정남지체장애물건집게184500184500무상지원대구지역본부
1039410395그 외 기타 비거주 복지 서비스업남지체장애(구)특수작업기구및장비250000<NA>무상지원서울지역본부
30003001그 외 기타 광고 관련 서비스업남지적장애의사소통보조기기8000000무상지원충남지사
44724473기타 사회서비스 관리 행정여청각장애영상전화기990000990000무상지원부산지역본부
1040310404기타 사무 지원 서비스업남지체장애욕창방지방석451000451000고용유지강원지사
16591660화물 운송 중개, 대리 및 관련 서비스업여지체장애전동이동보조기기33000003300000고용유지광주지역본부
94749475곡물 도정업여지적장애위치감지기430500430500무상지원경기지역본부
번호산업분류명성별장애유형품목기준금액실구매금액지원형태관할지사
26102611그 외 기타 보건업남시각장애스크린리더단말기37800003780000고용유지대구지역본부
83518352기타 통신 판매업남지적장애위치감지기430500430500고용유지서울동부지사
44384439그 외 기타 스포츠 서비스업남시각장애점자정보단말기59450005945000고용유지경남지사
937938그 외 기타 보건업여시각장애스크린리더단말기55000005500000고용유지경기북부지사
56455646세탁물 공급업여지적장애위치감지기163480163480무상지원경남지사
53835384시스템 소프트웨어 개발 및 공급업남시각장애점자정보단말기59450005945000고용유지서울남부지사
61106111그 외 기타 비거주 복지 서비스업여지적장애위치감지기163480163480무상지원경북지사
724725그 외 기타 보건업남시각장애전자입력보조기기860000860000무상지원경기북부지사
42864287그 외 기타 협회 및 단체남시각장애신호기기666250666250무상지원전북지사
48544855그 외 기타 보건업남시각장애기타신체보조기기328000328000무상지원인천지사