Overview

Dataset statistics

Number of variables12
Number of observations96
Missing cells247
Missing cells (%)21.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.8 KiB
Average record size in memory104.4 B

Variable types

Categorical2
Text3
Numeric7

Dataset

Description장애인 근로사업장 현황
Author행정안전부
URLhttps://data.gg.go.kr/portal/data/service/selectServicePage.do?&infId=V5591H513H8Q7B3931U71322732&infSeq=1

Alerts

영업상태명 has constant value ""Constant
소재지우편번호 is highly overall correlated with WGS84위도 and 1 other fieldsHigh correlation
WGS84위도 is highly overall correlated with 소재지우편번호 and 1 other fieldsHigh correlation
WGS84경도 is highly overall correlated with 시군명High correlation
시군명 is highly overall correlated with 소재지우편번호 and 2 other fieldsHigh correlation
입소정원(명) has 1 (1.0%) missing valuesMissing
자격소유인원수(명) has 26 (27.1%) missing valuesMissing
총인원수(명) has 23 (24.0%) missing valuesMissing
소재지도로명주소 has 53 (55.2%) missing valuesMissing
소재지우편번호 has 42 (43.8%) missing valuesMissing
WGS84위도 has 51 (53.1%) missing valuesMissing
WGS84경도 has 51 (53.1%) missing valuesMissing
입소정원(명) has 1 (1.0%) zerosZeros
자격소유인원수(명) has 9 (9.4%) zerosZeros
총인원수(명) has 1 (1.0%) zerosZeros

Reproduction

Analysis started2023-12-10 21:49:25.922841
Analysis finished2023-12-10 21:49:31.619890
Duration5.7 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군명
Categorical

HIGH CORRELATION 

Distinct28
Distinct (%)29.2%
Missing0
Missing (%)0.0%
Memory size900.0 B
고양시
10 
김포시
7 
파주시
 
6
용인시
 
6
성남시
 
6
Other values (23)
61 

Length

Max length4
Median length3
Mean length3.0729167
Min length3

Unique

Unique6 ?
Unique (%)6.2%

Sample

1st row고양시
2nd row고양시
3rd row고양시
4th row고양시
5th row고양시

Common Values

ValueCountFrequency (%)
고양시 10
 
10.4%
김포시 7
 
7.3%
파주시 6
 
6.2%
용인시 6
 
6.2%
성남시 6
 
6.2%
남양주시 5
 
5.2%
화성시 5
 
5.2%
군포시 5
 
5.2%
광주시 4
 
4.2%
안산시 4
 
4.2%
Other values (18) 38
39.6%

Length

2023-12-11T06:49:31.679059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
고양시 10
 
10.4%
김포시 7
 
7.3%
파주시 6
 
6.2%
용인시 6
 
6.2%
성남시 6
 
6.2%
남양주시 5
 
5.2%
화성시 5
 
5.2%
군포시 5
 
5.2%
광주시 4
 
4.2%
안산시 4
 
4.2%
Other values (18) 38
39.6%

사업장명
Text

Distinct94
Distinct (%)97.9%
Missing0
Missing (%)0.0%
Memory size900.0 B
2023-12-11T06:49:31.940619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length13
Mean length8.4895833
Min length1

Characters and Unicode

Total characters815
Distinct characters164
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique92 ?
Unique (%)95.8%

Sample

1st row미리내집보호작업장
2nd row고양보호작업장
3rd row홍애원
4th row애덕의집보호작업장
5th row홀트보호작업장
ValueCountFrequency (%)
장애인보호작업장 6
 
5.0%
보호작업장 6
 
5.0%
사회복지법인 3
 
2.5%
고양시 2
 
1.7%
장애인직업재활원 2
 
1.7%
느티나무직업재활시설 2
 
1.7%
지구촌보호작업장 2
 
1.7%
윙 2
 
1.7%
금란재활원 1
 
0.8%
월남참전전우회 1
 
0.8%
Other values (94) 94
77.7%
2023-12-11T06:49:32.552503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
장 78
 
9.6%
업 64
 
7.9%
작 50
 
6.1%
보 49
 
6.0%
호 47
 
5.8%
인 33
 
4.0%
애 32
 
3.9%
25
 
3.1%
시 21
 
2.6%
터 17
 
2.1%
Other values (154) 399
49.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 771
94.6%
Space Separator 25
 
3.1%
Lowercase Letter 11
 
1.3%
Open Punctuation 2
 
0.2%
Close Punctuation 2
 
0.2%
Uppercase Letter 2
 
0.2%
Math Symbol 1
 
0.1%
Dash Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
장 78
 
10.1%
업 64
 
8.3%
작 50
 
6.5%
보 49
 
6.4%
호 47
 
6.1%
인 33
 
4.3%
애 32
 
4.2%
시 21
 
2.7%
터 17
 
2.2%
리 15
 
1.9%
Other values (139) 365
47.3%
Lowercase Letter
ValueCountFrequency (%)
o 2
18.2%
n 2
18.2%
p 2
18.2%
a 1
9.1%
y 1
9.1%
l 1
9.1%
b 1
9.1%
e 1
9.1%
Uppercase Letter
ValueCountFrequency (%)
H 1
50.0%
A 1
50.0%
Space Separator
ValueCountFrequency (%)
25
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 771
94.6%
Common 31
 
3.8%
Latin 13
 
1.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
장 78
 
10.1%
업 64
 
8.3%
작 50
 
6.5%
보 49
 
6.4%
호 47
 
6.1%
인 33
 
4.3%
애 32
 
4.2%
시 21
 
2.7%
터 17
 
2.2%
리 15
 
1.9%
Other values (139) 365
47.3%
Latin
ValueCountFrequency (%)
o 2
15.4%
n 2
15.4%
p 2
15.4%
a 1
7.7%
y 1
7.7%
H 1
7.7%
l 1
7.7%
b 1
7.7%
A 1
7.7%
e 1
7.7%
Common
ValueCountFrequency (%)
25
80.6%
( 2
 
6.5%
) 2
 
6.5%
~ 1
 
3.2%
- 1
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 771
94.6%
ASCII 44
 
5.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
장 78
 
10.1%
업 64
 
8.3%
작 50
 
6.5%
보 49
 
6.4%
호 47
 
6.1%
인 33
 
4.3%
애 32
 
4.2%
시 21
 
2.7%
터 17
 
2.2%
리 15
 
1.9%
Other values (139) 365
47.3%
ASCII
ValueCountFrequency (%)
25
56.8%
o 2
 
4.5%
n 2
 
4.5%
p 2
 
4.5%
( 2
 
4.5%
) 2
 
4.5%
a 1
 
2.3%
y 1
 
2.3%
H 1
 
2.3%
~ 1
 
2.3%
Other values (5) 5
 
11.4%

인허가일자
Real number (ℝ)

Distinct93
Distinct (%)96.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20086635
Minimum19881108
Maximum20180824
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size996.0 B
2023-12-11T06:49:32.698498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19881108
5-th percentile19987848
Q120040989
median20090859
Q320133548
95-th percentile20171223
Maximum20180824
Range299716
Interquartile range (IQR)92559

Descriptive statistics

Standard deviation63933.393
Coefficient of variation (CV)0.0031828822
Kurtosis-0.10719649
Mean20086635
Median Absolute Deviation (MAD)49697.5
Skewness-0.57302483
Sum1.9283169 × 109
Variance4.0874787 × 109
MonotonicityNot monotonic
2023-12-11T06:49:32.836564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20130213 2
 
2.1%
20100426 2
 
2.1%
20130306 2
 
2.1%
20051228 1
 
1.0%
20121204 1
 
1.0%
20130314 1
 
1.0%
20130204 1
 
1.0%
20120905 1
 
1.0%
20090914 1
 
1.0%
20030318 1
 
1.0%
Other values (83) 83
86.5%
ValueCountFrequency (%)
19881108 1
1.0%
19941207 1
1.0%
19951016 1
1.0%
19970111 1
1.0%
19980416 1
1.0%
19990325 1
1.0%
19991020 1
1.0%
20000101 1
1.0%
20000221 1
1.0%
20001005 1
1.0%
ValueCountFrequency (%)
20180824 1
1.0%
20180702 1
1.0%
20180509 1
1.0%
20180228 1
1.0%
20171226 1
1.0%
20171222 1
1.0%
20170725 1
1.0%
20170425 1
1.0%
20170321 1
1.0%
20161004 1
1.0%

영업상태명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size900.0 B
운영중
96 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row운영중
2nd row운영중
3rd row운영중
4th row운영중
5th row운영중

Common Values

ValueCountFrequency (%)
운영중 96
100.0%

Length

2023-12-11T06:49:32.959823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T06:49:33.053877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
운영중 96
100.0%

입소정원(명)
Real number (ℝ)

MISSING  ZEROS 

Distinct27
Distinct (%)28.4%
Missing1
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean29.463158
Minimum0
Maximum80
Zeros1
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size996.0 B
2023-12-11T06:49:33.142722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10.7
Q120
median30
Q335
95-th percentile54
Maximum80
Range80
Interquartile range (IQR)15

Descriptive statistics

Standard deviation12.893022
Coefficient of variation (CV)0.4375981
Kurtosis1.7183925
Mean29.463158
Median Absolute Deviation (MAD)7
Skewness0.75747407
Sum2799
Variance166.23001
MonotonicityNot monotonic
2023-12-11T06:49:33.283030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
30 28
29.2%
20 9
 
9.4%
15 9
 
9.4%
35 6
 
6.2%
10 4
 
4.2%
25 4
 
4.2%
50 4
 
4.2%
12 3
 
3.1%
54 3
 
3.1%
32 3
 
3.1%
Other values (17) 22
22.9%
ValueCountFrequency (%)
0 1
 
1.0%
10 4
4.2%
11 1
 
1.0%
12 3
 
3.1%
15 9
9.4%
18 1
 
1.0%
20 9
9.4%
23 1
 
1.0%
25 4
4.2%
28 1
 
1.0%
ValueCountFrequency (%)
80 1
 
1.0%
56 1
 
1.0%
55 1
 
1.0%
54 3
3.1%
50 4
4.2%
48 1
 
1.0%
45 1
 
1.0%
42 2
2.1%
41 1
 
1.0%
40 2
2.1%

자격소유인원수(명)
Real number (ℝ)

MISSING  ZEROS 

Distinct9
Distinct (%)12.9%
Missing26
Missing (%)27.1%
Infinite0
Infinite (%)0.0%
Mean2.8571429
Minimum0
Maximum8
Zeros9
Zeros (%)9.4%
Negative0
Negative (%)0.0%
Memory size996.0 B
2023-12-11T06:49:33.397487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q34
95-th percentile6
Maximum8
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.7879282
Coefficient of variation (CV)0.62577488
Kurtosis0.49315217
Mean2.8571429
Median Absolute Deviation (MAD)1
Skewness0.50309464
Sum200
Variance3.1966874
MonotonicityNot monotonic
2023-12-11T06:49:33.526472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
3 22
22.9%
2 17
17.7%
0 9
 
9.4%
4 8
 
8.3%
5 5
 
5.2%
6 3
 
3.1%
1 3
 
3.1%
7 2
 
2.1%
8 1
 
1.0%
(Missing) 26
27.1%
ValueCountFrequency (%)
0 9
9.4%
1 3
 
3.1%
2 17
17.7%
3 22
22.9%
4 8
 
8.3%
5 5
 
5.2%
6 3
 
3.1%
7 2
 
2.1%
8 1
 
1.0%
ValueCountFrequency (%)
8 1
 
1.0%
7 2
 
2.1%
6 3
 
3.1%
5 5
 
5.2%
4 8
 
8.3%
3 22
22.9%
2 17
17.7%
1 3
 
3.1%
0 9
9.4%

총인원수(명)
Real number (ℝ)

MISSING  ZEROS 

Distinct11
Distinct (%)15.1%
Missing23
Missing (%)24.0%
Infinite0
Infinite (%)0.0%
Mean4.3150685
Minimum0
Maximum11
Zeros1
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size996.0 B
2023-12-11T06:49:33.661504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median4
Q36
95-th percentile10
Maximum11
Range11
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.515931
Coefficient of variation (CV)0.58305702
Kurtosis0.37905855
Mean4.3150685
Median Absolute Deviation (MAD)2
Skewness0.95580007
Sum315
Variance6.3299087
MonotonicityNot monotonic
2023-12-11T06:49:33.770898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
3 15
15.6%
4 13
13.5%
2 13
13.5%
6 9
 
9.4%
5 8
 
8.3%
10 4
 
4.2%
1 4
 
4.2%
9 3
 
3.1%
8 2
 
2.1%
11 1
 
1.0%
(Missing) 23
24.0%
ValueCountFrequency (%)
0 1
 
1.0%
1 4
 
4.2%
2 13
13.5%
3 15
15.6%
4 13
13.5%
5 8
8.3%
6 9
9.4%
8 2
 
2.1%
9 3
 
3.1%
10 4
 
4.2%
ValueCountFrequency (%)
11 1
 
1.0%
10 4
 
4.2%
9 3
 
3.1%
8 2
 
2.1%
6 9
9.4%
5 8
8.3%
4 13
13.5%
3 15
15.6%
2 13
13.5%
1 4
 
4.2%

소재지도로명주소
Text

MISSING 

Distinct41
Distinct (%)95.3%
Missing53
Missing (%)55.2%
Memory size900.0 B
2023-12-11T06:49:34.041381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length24
Mean length19.348837
Min length15

Characters and Unicode

Total characters832
Distinct characters113
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique40 ?
Unique (%)93.0%

Sample

1st row경기도 고양시 덕양구 보광로162번길 14
2nd row경기도 고양시 일산서구 탄현로 139
3rd row경기도 고양시 덕양구 보광로106번길 93
4th row경기도 고양시 덕양구 혜음로 284
5th row경기도 고양시 일산서구 탄현로 42
ValueCountFrequency (%)
경기도 43
 
21.7%
고양시 7
 
3.5%
덕양구 5
 
2.5%
군포시 4
 
2.0%
성남시 4
 
2.0%
군포로 3
 
1.5%
460 3
 
1.5%
중원구 3
 
1.5%
안양시 3
 
1.5%
만안구 3
 
1.5%
Other values (108) 120
60.6%
2023-12-11T06:49:34.437995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
155
18.6%
기 46
 
5.5%
시 45
 
5.4%
경 44
 
5.3%
도 44
 
5.3%
로 39
 
4.7%
2 28
 
3.4%
1 27
 
3.2%
양 19
 
2.3%
길 18
 
2.2%
Other values (103) 367
44.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 514
61.8%
Decimal Number 157
 
18.9%
Space Separator 155
 
18.6%
Dash Punctuation 6
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
기 46
 
8.9%
시 45
 
8.8%
경 44
 
8.6%
도 44
 
8.6%
로 39
 
7.6%
양 19
 
3.7%
길 18
 
3.5%
구 18
 
3.5%
번 14
 
2.7%
남 11
 
2.1%
Other values (91) 216
42.0%
Decimal Number
ValueCountFrequency (%)
2 28
17.8%
1 27
17.2%
6 17
10.8%
3 16
10.2%
0 15
9.6%
9 15
9.6%
4 13
8.3%
5 13
8.3%
7 9
 
5.7%
8 4
 
2.5%
Space Separator
ValueCountFrequency (%)
155
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 514
61.8%
Common 318
38.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
기 46
 
8.9%
시 45
 
8.8%
경 44
 
8.6%
도 44
 
8.6%
로 39
 
7.6%
양 19
 
3.7%
길 18
 
3.5%
구 18
 
3.5%
번 14
 
2.7%
남 11
 
2.1%
Other values (91) 216
42.0%
Common
ValueCountFrequency (%)
155
48.7%
2 28
 
8.8%
1 27
 
8.5%
6 17
 
5.3%
3 16
 
5.0%
0 15
 
4.7%
9 15
 
4.7%
4 13
 
4.1%
5 13
 
4.1%
7 9
 
2.8%
Other values (2) 10
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 514
61.8%
ASCII 318
38.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
155
48.7%
2 28
 
8.8%
1 27
 
8.5%
6 17
 
5.3%
3 16
 
5.0%
0 15
 
4.7%
9 15
 
4.7%
4 13
 
4.1%
5 13
 
4.1%
7 9
 
2.8%
Other values (2) 10
 
3.1%
Hangul
ValueCountFrequency (%)
기 46
 
8.9%
시 45
 
8.8%
경 44
 
8.6%
도 44
 
8.6%
로 39
 
7.6%
양 19
 
3.7%
길 18
 
3.5%
구 18
 
3.5%
번 14
 
2.7%
남 11
 
2.1%
Other values (91) 216
42.0%

소재지지번주소
Text

Distinct94
Distinct (%)97.9%
Missing0
Missing (%)0.0%
Memory size900.0 B
2023-12-11T06:49:34.712628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length38
Median length31
Mean length19.416667
Min length11

Characters and Unicode

Total characters1864
Distinct characters172
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique93 ?
Unique (%)96.9%

Sample

1st row경기도 고양시 덕양구 벽제동 541-3번지
2nd row경기도 고양시 일산서구 탄현동 111-1번지
3rd row경기도 고양시 덕양구 벽제동 542-6번지
4th row경기도 고양시 덕양구 벽제동 486번지
5th row경기도 고양시 일산서구 탄현동 41-1번지
ValueCountFrequency (%)
경기도 96
 
22.1%
고양시 10
 
2.3%
김포시 7
 
1.6%
성남시 6
 
1.4%
파주시 6
 
1.4%
용인시 6
 
1.4%
중원구 5
 
1.1%
상대원동 5
 
1.1%
남양주시 5
 
1.1%
화성시 5
 
1.1%
Other values (216) 284
65.3%
2023-12-11T06:49:35.117244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
339
 
18.2%
도 99
 
5.3%
기 97
 
5.2%
시 97
 
5.2%
경 96
 
5.2%
동 75
 
4.0%
양 41
 
2.2%
지 40
 
2.1%
1 38
 
2.0%
2 36
 
1.9%
Other values (162) 906
48.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1255
67.3%
Space Separator 339
 
18.2%
Decimal Number 226
 
12.1%
Dash Punctuation 26
 
1.4%
Other Punctuation 10
 
0.5%
Uppercase Letter 7
 
0.4%
Lowercase Letter 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 99
 
7.9%
기 97
 
7.7%
시 97
 
7.7%
경 96
 
7.6%
동 75
 
6.0%
양 41
 
3.3%
지 40
 
3.2%
리 33
 
2.6%
번 32
 
2.5%
구 32
 
2.5%
Other values (144) 613
48.8%
Decimal Number
ValueCountFrequency (%)
1 38
16.8%
2 36
15.9%
0 35
15.5%
4 31
13.7%
3 24
10.6%
5 23
10.2%
6 17
7.5%
8 8
 
3.5%
7 8
 
3.5%
9 6
 
2.7%
Uppercase Letter
ValueCountFrequency (%)
B 5
71.4%
S 1
 
14.3%
K 1
 
14.3%
Other Punctuation
ValueCountFrequency (%)
, 8
80.0%
？ 2
 
20.0%
Space Separator
ValueCountFrequency (%)
339
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 26
100.0%
Lowercase Letter
ValueCountFrequency (%)
n 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1255
67.3%
Common 601
32.2%
Latin 8
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 99
 
7.9%
기 97
 
7.7%
시 97
 
7.7%
경 96
 
7.6%
동 75
 
6.0%
양 41
 
3.3%
지 40
 
3.2%
리 33
 
2.6%
번 32
 
2.5%
구 32
 
2.5%
Other values (144) 613
48.8%
Common
ValueCountFrequency (%)
339
56.4%
1 38
 
6.3%
2 36
 
6.0%
0 35
 
5.8%
4 31
 
5.2%
- 26
 
4.3%
3 24
 
4.0%
5 23
 
3.8%
6 17
 
2.8%
8 8
 
1.3%
Other values (4) 24
 
4.0%
Latin
ValueCountFrequency (%)
B 5
62.5%
S 1
 
12.5%
K 1
 
12.5%
n 1
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1255
67.3%
ASCII 607
32.6%
None 2
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
339
55.8%
1 38
 
6.3%
2 36
 
5.9%
0 35
 
5.8%
4 31
 
5.1%
- 26
 
4.3%
3 24
 
4.0%
5 23
 
3.8%
6 17
 
2.8%
8 8
 
1.3%
Other values (7) 30
 
4.9%
Hangul
ValueCountFrequency (%)
도 99
 
7.9%
기 97
 
7.7%
시 97
 
7.7%
경 96
 
7.6%
동 75
 
6.0%
양 41
 
3.3%
지 40
 
3.2%
리 33
 
2.6%
번 32
 
2.5%
구 32
 
2.5%
Other values (144) 613
48.8%
None
ValueCountFrequency (%)
？ 2
100.0%

소재지우편번호
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct49
Distinct (%)90.7%
Missing42
Missing (%)43.8%
Infinite0
Infinite (%)0.0%
Mean13688.074
Minimum10049
Maximum18274
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size996.0 B
2023-12-11T06:49:35.283690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10049
5-th percentile10245.5
Q111826.5
median13361
Q315767
95-th percentile17678.15
Maximum18274
Range8225
Interquartile range (IQR)3940.5

Descriptive statistics

Standard deviation2503.1799
Coefficient of variation (CV)0.18287305
Kurtosis-1.1001968
Mean13688.074
Median Absolute Deviation (MAD)2152
Skewness0.16847763
Sum739156
Variance6265909.6
MonotonicityNot monotonic
2023-12-11T06:49:35.425122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
15856 3
 
3.1%
10806 2
 
2.1%
12029 2
 
2.1%
10267 2
 
2.1%
10459 1
 
1.0%
15626 1
 
1.0%
17507 1
 
1.0%
14086 1
 
1.0%
13996 1
 
1.0%
14091 1
 
1.0%
Other values (39) 39
40.6%
(Missing) 42
43.8%
ValueCountFrequency (%)
10049 1
1.0%
10101 1
1.0%
10239 1
1.0%
10249 1
1.0%
10265 1
1.0%
10267 2
2.1%
10271 1
1.0%
10459 1
1.0%
10806 2
2.1%
11151 1
1.0%
ValueCountFrequency (%)
18274 1
 
1.0%
18102 1
 
1.0%
17996 1
 
1.0%
17507 1
 
1.0%
17413 1
 
1.0%
17383 1
 
1.0%
17320 1
 
1.0%
17180 1
 
1.0%
16816 1
 
1.0%
15856 3
3.1%

WGS84위도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct43
Distinct (%)95.6%
Missing51
Missing (%)53.1%
Infinite0
Infinite (%)0.0%
Mean37.505414
Minimum36.946474
Maximum37.907415
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size996.0 B
2023-12-11T06:49:35.551813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.946474
5-th percentile37.27519
Q137.347581
median37.478928
Q337.697326
95-th percentile37.855474
Maximum37.907415
Range0.96094141
Interquartile range (IQR)0.34974484

Descriptive statistics

Standard deviation0.20386852
Coefficient of variation (CV)0.0054357091
Kurtosis0.030869813
Mean37.505414
Median Absolute Deviation (MAD)0.14862085
Skewness-0.012126807
Sum1687.7436
Variance0.041562373
MonotonicityNot monotonic
2023-12-11T06:49:35.687637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
37.3475814996 3
 
3.1%
37.1944095193 1
 
1.0%
37.4956956042 1
 
1.0%
37.4456659599 1
 
1.0%
37.4050119929 1
 
1.0%
37.2882315587 1
 
1.0%
37.303241262 1
 
1.0%
37.3896981985 1
 
1.0%
37.3982012291 1
 
1.0%
37.3849363728 1
 
1.0%
Other values (33) 33
34.4%
(Missing) 51
53.1%
ValueCountFrequency (%)
36.9464737997 1
 
1.0%
37.1944095193 1
 
1.0%
37.2735968387 1
 
1.0%
37.2815638851 1
 
1.0%
37.2882315587 1
 
1.0%
37.303241262 1
 
1.0%
37.3160371454 1
 
1.0%
37.3235883078 1
 
1.0%
37.3310967021 1
 
1.0%
37.3475814996 3
3.1%
ValueCountFrequency (%)
37.9074152109 1
1.0%
37.9050512436 1
1.0%
37.8829176723 1
1.0%
37.7456986733 1
1.0%
37.7281292872 1
1.0%
37.7262789839 1
1.0%
37.7245505593 1
1.0%
37.7218979383 1
1.0%
37.7202668502 1
1.0%
37.7126488443 1
1.0%

WGS84경도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct43
Distinct (%)95.6%
Missing51
Missing (%)53.1%
Infinite0
Infinite (%)0.0%
Mean127.01211
Minimum126.70269
Maximum127.51495
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size996.0 B
2023-12-11T06:49:35.823577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.70269
5-th percentile126.76506
Q1126.84664
median126.94359
Q3127.17852
95-th percentile127.34322
Maximum127.51495
Range0.81225894
Interquartile range (IQR)0.33188301

Descriptive statistics

Standard deviation0.20477635
Coefficient of variation (CV)0.0016122585
Kurtosis-0.69247889
Mean127.01211
Median Absolute Deviation (MAD)0.14454932
Skewness0.53245156
Sum5715.5451
Variance0.041933355
MonotonicityNot monotonic
2023-12-11T06:49:35.957501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
126.9435913808 3
 
3.1%
126.8483685341 1
 
1.0%
127.2340354548 1
 
1.0%
126.7990420597 1
 
1.0%
126.7732299912 1
 
1.0%
126.8444353228 1
 
1.0%
126.8114016717 1
 
1.0%
126.9350223343 1
 
1.0%
126.9244513513 1
 
1.0%
126.926992701 1
 
1.0%
Other values (33) 33
34.4%
(Missing) 51
53.1%
ValueCountFrequency (%)
126.7026880341 1
1.0%
126.7605554762 1
1.0%
126.7645652387 1
1.0%
126.7670606162 1
1.0%
126.7718945292 1
1.0%
126.7732299912 1
1.0%
126.7885121024 1
1.0%
126.7990420597 1
1.0%
126.8114016717 1
1.0%
126.834957389 1
1.0%
ValueCountFrequency (%)
127.5149469695 1
1.0%
127.3872499674 1
1.0%
127.3490517207 1
1.0%
127.3198754284 1
1.0%
127.3180233796 1
1.0%
127.3142740154 1
1.0%
127.2340354548 1
1.0%
127.211610078 1
1.0%
127.2004201953 1
1.0%
127.1995078611 1
1.0%

Interactions

2023-12-11T06:49:30.585061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:49:27.203650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:49:27.803570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:49:28.363534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:49:28.936857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:49:29.495317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:49:30.029748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:49:30.666382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:49:27.298638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:49:27.883556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:49:28.440085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:49:29.023449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:49:29.570501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:49:30.098080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:49:30.760633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:49:27.382497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:49:27.969264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:49:28.516922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:49:29.120465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:49:29.647188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:49:30.179334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:49:30.858856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:49:27.467480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:49:28.045256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:49:28.612884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:49:29.199479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:49:29.729291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:49:30.258670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:49:30.944574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:49:27.549063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:49:28.121708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:49:28.694811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:49:29.266915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:49:29.803010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:49:30.328812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:49:31.039337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:49:27.634073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:49:28.195946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:49:28.780221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:49:29.343065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:49:29.862230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:49:30.430673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:49:31.118135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:49:27.712738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:49:28.281265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:49:28.850278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:49:29.416966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:49:29.943099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:49:30.497326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T06:49:36.065932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명사업장명인허가일자입소정원(명)자격소유인원수(명)총인원수(명)소재지도로명주소소재지지번주소소재지우편번호WGS84위도WGS84경도
시군명1.0001.0000.4710.1400.0000.4091.0001.0000.9950.9510.925
사업장명1.0001.0001.0000.0001.0001.0001.0000.9971.0000.9661.000
인허가일자0.4711.0001.0000.5490.2180.1361.0001.0000.4180.1100.129
입소정원(명)0.1400.0000.5491.0000.0000.3960.9260.9530.3410.3310.000
자격소유인원수(명)0.0001.0000.2180.0001.0000.7440.9720.9740.0590.0000.510
총인원수(명)0.4091.0000.1360.3960.7441.0000.9850.9780.0000.4740.000
소재지도로명주소1.0001.0001.0000.9260.9720.9851.0001.0001.0001.0001.000
소재지지번주소1.0000.9971.0000.9530.9740.9781.0001.0001.0001.0001.000
소재지우편번호0.9951.0000.4180.3410.0590.0001.0001.0001.0000.8680.728
WGS84위도0.9510.9660.1100.3310.0000.4741.0001.0000.8681.0000.627
WGS84경도0.9251.0000.1290.0000.5100.0001.0001.0000.7280.6271.000
2023-12-11T06:49:36.191735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
인허가일자입소정원(명)자격소유인원수(명)총인원수(명)소재지우편번호WGS84위도WGS84경도시군명
인허가일자1.000-0.4360.102-0.2220.186-0.1810.1040.139
입소정원(명)-0.4361.0000.1690.3390.013-0.072-0.1950.026
자격소유인원수(명)0.1020.1691.0000.392-0.1060.0980.0780.000
총인원수(명)-0.2220.3390.3921.000-0.1770.246-0.0860.128
소재지우편번호0.1860.013-0.106-0.1771.000-0.8740.0920.759
WGS84위도-0.181-0.0720.0980.246-0.8741.000-0.0950.627
WGS84경도0.104-0.1950.078-0.0860.092-0.0951.0000.563
시군명0.1390.0260.0000.1280.7590.6270.5631.000

Missing values

2023-12-11T06:49:31.250628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T06:49:31.409170image/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.
2023-12-11T06:49:31.533497image/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

시군명사업장명인허가일자영업상태명입소정원(명)자격소유인원수(명)총인원수(명)소재지도로명주소소재지지번주소소재지우편번호WGS84위도WGS84경도
0고양시미리내집보호작업장20051228운영중2503경기도 고양시 덕양구 보광로162번길 14경기도 고양시 덕양구 벽제동 541-3번지1026737.728129126.909799
1고양시고양보호작업장20060109운영중3033경기도 고양시 일산서구 탄현로 139경기도 고양시 일산서구 탄현동 111-1번지1023937.70323126.764565
2고양시홍애원20070424운영중3044경기도 고양시 덕양구 보광로106번길 93경기도 고양시 덕양구 벽제동 542-6번지1026737.726279126.909281
3고양시애덕의집보호작업장19970111운영중5035경기도 고양시 덕양구 혜음로 284경기도 고양시 덕양구 벽제동 486번지1027137.724551126.895473
4고양시홀트보호작업장20001228운영중4245경기도 고양시 일산서구 탄현로 42경기도 고양시 일산서구 탄현동 41-1번지1024937.697326126.771895
5고양시늘푸른직업재활원20060121운영중3038경기도 고양시 덕양구 고골길 100-15경기도 고양시 덕양구 관산동 591번지1026537.712649126.863825
6고양시나너우리작업장20070409운영중1522경기도 고양시 덕양구 마상로114번길 22경기도 고양시 덕양구 주교동 대양빌딩 301호1045937.659807126.834957
7고양시유앤미직업재활원20120223운영중30<NA><NA><NA>경기도 고양시 일산동구 장항동<NA><NA><NA>
8고양시고양시 설문동 장애인직업재활원20140612운영중18<NA><NA><NA>경기도 고양시 일산동구 설문동<NA><NA><NA>
9고양시고양시 구산동 장애인직업재활원20140501운영중12<NA><NA><NA>경기도 고양시 일산서구 구산동<NA><NA><NA>
시군명사업장명인허가일자영업상태명입소정원(명)자격소유인원수(명)총인원수(명)소재지도로명주소소재지지번주소소재지우편번호WGS84위도WGS84경도
86평택시삼우보호작업장20080530운영중3000경기도 평택시 팽성읍 사거리길 53경기도 평택시 팽성읍 송화리 264-11번지1799636.946474127.052677
87평택시장애인직업재활센터 일누리20071031운영중3022<NA>경기도 평택시 포승읍 석정리<NA><NA><NA>
88포천시포천시 장애인보호작업장20130926운영중2035경기도 포천시 군내면 청성로 5경기도 포천시 군내면 하성북리 521번지1115137.905051127.21161
89하남시우리장애인직업재활센터20170425운영중10<NA>2경기도 하남시 신장로 179경기도 하남시 덕풍동 무학프라자 406호1297437.541077127.199508
90하남시하남장애인직업재활센터20010515운영중38<NA>10경기도 하남시 하남대로232번길 32경기도 하남시 상산곡동 144-2번지1302637.495696127.234035
91화성시와~우리장애인보호작업장20131219운영중5422<NA>경기도 화성시 봉담읍 동화리<NA><NA><NA>
92화성시무궁화보호작업장20131230운영중302<NA><NA>경기도 화성시 장안면<NA><NA><NA>
93화성시행복한일터20070321운영중5633경기도 화성시 남양읍 현대기아로 506경기도 화성시 남양읍 현대기아로 5061827437.19441126.848369
94화성시화성시남부장애인보호작업장20160923운영중50<NA><NA><NA>경기도 화성시 양감면 대양리<NA><NA><NA>
95화성시행복플러스보호작업장20131212운영중3211<NA>경기도 화성시 정남면<NA><NA><NA>