Overview

Dataset statistics

Number of variables11
Number of observations31
Missing cells59
Missing cells (%)17.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.0 KiB
Average record size in memory98.3 B

Variable types

Text4
Numeric5
Categorical2

Alerts

영업상태명 has constant value ""Constant
인허가일자 is highly overall correlated with WGS84경도High correlation
소재지우편번호 is highly overall correlated with WGS84위도High correlation
WGS84위도 is highly overall correlated with 소재지우편번호High correlation
WGS84경도 is highly overall correlated with 인허가일자 and 1 other fieldsHigh correlation
자격소유인원수(명) is highly overall correlated with WGS84경도High correlation
총인원수(명) has 1 (3.2%) missing valuesMissing
소재지도로명주소 has 15 (48.4%) missing valuesMissing
소재지우편번호 has 13 (41.9%) missing valuesMissing
WGS84위도 has 15 (48.4%) missing valuesMissing
WGS84경도 has 15 (48.4%) missing valuesMissing
사업장명 has unique valuesUnique
소재지지번주소 has unique valuesUnique
총인원수(명) has 2 (6.5%) zerosZeros

Reproduction

Analysis started2023-12-10 22:14:55.612138
Analysis finished2023-12-10 22:14:58.705811
Duration3.09 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군명
Text

Distinct30
Distinct (%)96.8%
Missing0
Missing (%)0.0%
Memory size380.0 B
2023-12-11T07:14:58.839313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.0967742
Min length3

Characters and Unicode

Total characters96
Distinct characters36
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

Unique29 ?
Unique (%)93.5%

Sample

1st row가평군
2nd row고양시
3rd row과천시
4th row광명시
5th row광주시
ValueCountFrequency (%)
양평군 2
 
6.5%
가평군 1
 
3.2%
고양시 1
 
3.2%
하남시 1
 
3.2%
포천시 1
 
3.2%
평택시 1
 
3.2%
파주시 1
 
3.2%
이천시 1
 
3.2%
의정부시 1
 
3.2%
의왕시 1
 
3.2%
Other values (20) 20
64.5%
2023-12-11T07:14:59.110253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 28
29.2%
양 6
 
6.2%
천 6
 
6.2%
주 5
 
5.2%
군 5
 
5.2%
평 4
 
4.2%
안 3
 
3.1%
남 3
 
3.1%
포 3
 
3.1%
성 3
 
3.1%
Other values (26) 30
31.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 96
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 28
29.2%
양 6
 
6.2%
천 6
 
6.2%
주 5
 
5.2%
군 5
 
5.2%
평 4
 
4.2%
안 3
 
3.1%
남 3
 
3.1%
포 3
 
3.1%
성 3
 
3.1%
Other values (26) 30
31.2%

Most occurring scripts

ValueCountFrequency (%)
Hangul 96
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 28
29.2%
양 6
 
6.2%
천 6
 
6.2%
주 5
 
5.2%
군 5
 
5.2%
평 4
 
4.2%
안 3
 
3.1%
남 3
 
3.1%
포 3
 
3.1%
성 3
 
3.1%
Other values (26) 30
31.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 96
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 28
29.2%
양 6
 
6.2%
천 6
 
6.2%
주 5
 
5.2%
군 5
 
5.2%
평 4
 
4.2%
안 3
 
3.1%
남 3
 
3.1%
포 3
 
3.1%
성 3
 
3.1%
Other values (26) 30
31.2%

사업장명
Text

UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size380.0 B
2023-12-11T07:14:59.304376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length9
Mean length9.3548387
Min length9

Characters and Unicode

Total characters290
Distinct characters44
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique31 ?
Unique (%)100.0%

Sample

1st row가평군 수화통역센터
2nd row고양시수어통역센터
3rd row과천시수어통역센터
4th row광명시수어통역센터
5th row광주시 수어통역센터
ValueCountFrequency (%)
수화통역센터 4
 
10.3%
수어통역센터 4
 
10.3%
의왕시수어통역센터 1
 
2.6%
양평군수어통역센터 1
 
2.6%
양평군수화통역센터 1
 
2.6%
여주시수화통역센터 1
 
2.6%
연천군 1
 
2.6%
오산시수어통역센타 1
 
2.6%
용인시 1
 
2.6%
가평군 1
 
2.6%
Other values (23) 23
59.0%
2023-12-11T07:14:59.620674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
수 31
10.7%
통 31
10.7%
역 31
10.7%
센 31
10.7%
터 30
10.3%
시 28
9.7%
어 22
 
7.6%
화 10
 
3.4%
8
 
2.8%
양 6
 
2.1%
Other values (34) 62
21.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 282
97.2%
Space Separator 8
 
2.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
수 31
11.0%
통 31
11.0%
역 31
11.0%
센 31
11.0%
터 30
10.6%
시 28
9.9%
어 22
 
7.8%
화 10
 
3.5%
양 6
 
2.1%
천 6
 
2.1%
Other values (33) 56
19.9%
Space Separator
ValueCountFrequency (%)
8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 282
97.2%
Common 8
 
2.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
수 31
11.0%
통 31
11.0%
역 31
11.0%
센 31
11.0%
터 30
10.6%
시 28
9.9%
어 22
 
7.8%
화 10
 
3.5%
양 6
 
2.1%
천 6
 
2.1%
Other values (33) 56
19.9%
Common
ValueCountFrequency (%)
8
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 282
97.2%
ASCII 8
 
2.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
수 31
11.0%
통 31
11.0%
역 31
11.0%
센 31
11.0%
터 30
10.6%
시 28
9.9%
어 22
 
7.8%
화 10
 
3.5%
양 6
 
2.1%
천 6
 
2.1%
Other values (33) 56
19.9%
ASCII
ValueCountFrequency (%)
8
100.0%

인허가일자
Real number (ℝ)

HIGH CORRELATION 

Distinct30
Distinct (%)96.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20038570
Minimum19981228
Maximum20131123
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-11T07:14:59.736337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19981228
5-th percentile20010151
Q120020530
median20030619
Q320050314
95-th percentile20090610
Maximum20131123
Range149895
Interquartile range (IQR)29784

Descriptive statistics

Standard deviation28242.567
Coefficient of variation (CV)0.0014094103
Kurtosis3.4351651
Mean20038570
Median Absolute Deviation (MAD)10098
Skewness1.3153002
Sum6.2119568 × 108
Variance7.9764259 × 108
MonotonicityNot monotonic
2023-12-11T07:14:59.856243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
20020521 2
 
6.5%
20131123 1
 
3.2%
20040330 1
 
3.2%
20030603 1
 
3.2%
20030619 1
 
3.2%
20050202 1
 
3.2%
20020510 1
 
3.2%
20050106 1
 
3.2%
20020624 1
 
3.2%
20000201 1
 
3.2%
Other values (20) 20
64.5%
ValueCountFrequency (%)
19981228 1
3.2%
20000201 1
3.2%
20020101 1
3.2%
20020510 1
3.2%
20020521 2
6.5%
20020525 1
3.2%
20020528 1
3.2%
20020531 1
3.2%
20020624 1
3.2%
20020725 1
3.2%
ValueCountFrequency (%)
20131123 1
3.2%
20100309 1
3.2%
20080912 1
3.2%
20060120 1
3.2%
20060108 1
3.2%
20051222 1
3.2%
20051114 1
3.2%
20050425 1
3.2%
20050202 1
3.2%
20050106 1
3.2%

영업상태명
Categorical

CONSTANT 

Distinct1
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size380.0 B
운영중
31 

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 31
100.0%

Length

2023-12-11T07:14:59.986531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:15:00.090616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
운영중 31
100.0%

자격소유인원수(명)
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)16.1%
Missing0
Missing (%)0.0%
Memory size380.0 B
0
11 
4
7 
3
6 
2
4 
1
3 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row4
4th row3
5th row3

Common Values

ValueCountFrequency (%)
0 11
35.5%
4 7
22.6%
3 6
19.4%
2 4
 
12.9%
1 3
 
9.7%

Length

2023-12-11T07:15:00.181561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:15:00.314351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 11
35.5%
4 7
22.6%
3 6
19.4%
2 4
 
12.9%
1 3
 
9.7%

총인원수(명)
Real number (ℝ)

MISSING  ZEROS 

Distinct6
Distinct (%)20.0%
Missing1
Missing (%)3.2%
Infinite0
Infinite (%)0.0%
Mean36.833333
Minimum0
Maximum997
Zeros2
Zeros (%)6.5%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-11T07:15:00.404432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.45
Q13
median4
Q35
95-th percentile5
Maximum997
Range997
Interquartile range (IQR)2

Descriptive statistics

Standard deviation181.35184
Coefficient of variation (CV)4.9235793
Kurtosis29.996115
Mean36.833333
Median Absolute Deviation (MAD)1
Skewness5.47671
Sum1105
Variance32888.489
MonotonicityNot monotonic
2023-12-11T07:15:00.486934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
5 10
32.3%
4 9
29.0%
3 7
22.6%
0 2
 
6.5%
997 1
 
3.2%
1 1
 
3.2%
(Missing) 1
 
3.2%
ValueCountFrequency (%)
0 2
 
6.5%
1 1
 
3.2%
3 7
22.6%
4 9
29.0%
5 10
32.3%
997 1
 
3.2%
ValueCountFrequency (%)
997 1
 
3.2%
5 10
32.3%
4 9
29.0%
3 7
22.6%
1 1
 
3.2%
0 2
 
6.5%

소재지도로명주소
Text

MISSING 

Distinct16
Distinct (%)100.0%
Missing15
Missing (%)48.4%
Memory size380.0 B
2023-12-11T07:15:00.643677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length20
Mean length18.4375
Min length14

Characters and Unicode

Total characters295
Distinct characters67
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

Unique16 ?
Unique (%)100.0%

Sample

1st row경기도 고양시 일산서구 고양대로672번길 15-7
2nd row경기도 군포시 산본로 329
3rd row경기도 남양주시 금곡로 58
4th row경기도 동두천시 동광로 174
5th row경기도 부천시 상이로39번길 7-20
ValueCountFrequency (%)
경기도 16
 
23.2%
40 1
 
1.4%
양평읍 1
 
1.4%
시민로 1
 
1.4%
114-1 1
 
1.4%
오산시 1
 
1.4%
경기동로 1
 
1.4%
15 1
 
1.4%
의정부시 1
 
1.4%
경의로85번길 1
 
1.4%
Other values (44) 44
63.8%
2023-12-11T07:15:00.919098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
53
18.0%
경 18
 
6.1%
시 17
 
5.8%
기 17
 
5.8%
도 16
 
5.4%
로 16
 
5.4%
1 13
 
4.4%
4 10
 
3.4%
양 7
 
2.4%
2 7
 
2.4%
Other values (57) 121
41.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 181
61.4%
Decimal Number 57
 
19.3%
Space Separator 53
 
18.0%
Dash Punctuation 4
 
1.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
경 18
 
9.9%
시 17
 
9.4%
기 17
 
9.4%
도 16
 
8.8%
로 16
 
8.8%
양 7
 
3.9%
번 7
 
3.9%
길 7
 
3.9%
안 4
 
2.2%
남 4
 
2.2%
Other values (45) 68
37.6%
Decimal Number
ValueCountFrequency (%)
1 13
22.8%
4 10
17.5%
2 7
12.3%
5 6
10.5%
7 5
 
8.8%
0 4
 
7.0%
9 4
 
7.0%
3 3
 
5.3%
8 3
 
5.3%
6 2
 
3.5%
Space Separator
ValueCountFrequency (%)
53
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 181
61.4%
Common 114
38.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
경 18
 
9.9%
시 17
 
9.4%
기 17
 
9.4%
도 16
 
8.8%
로 16
 
8.8%
양 7
 
3.9%
번 7
 
3.9%
길 7
 
3.9%
안 4
 
2.2%
남 4
 
2.2%
Other values (45) 68
37.6%
Common
ValueCountFrequency (%)
53
46.5%
1 13
 
11.4%
4 10
 
8.8%
2 7
 
6.1%
5 6
 
5.3%
7 5
 
4.4%
0 4
 
3.5%
9 4
 
3.5%
- 4
 
3.5%
3 3
 
2.6%
Other values (2) 5
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 181
61.4%
ASCII 114
38.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
53
46.5%
1 13
 
11.4%
4 10
 
8.8%
2 7
 
6.1%
5 6
 
5.3%
7 5
 
4.4%
0 4
 
3.5%
9 4
 
3.5%
- 4
 
3.5%
3 3
 
2.6%
Other values (2) 5
 
4.4%
Hangul
ValueCountFrequency (%)
경 18
 
9.9%
시 17
 
9.4%
기 17
 
9.4%
도 16
 
8.8%
로 16
 
8.8%
양 7
 
3.9%
번 7
 
3.9%
길 7
 
3.9%
안 4
 
2.2%
남 4
 
2.2%
Other values (45) 68
37.6%

소재지지번주소
Text

UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size380.0 B
2023-12-11T07:15:01.139798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length39
Median length25
Mean length20.258065
Min length10

Characters and Unicode

Total characters628
Distinct characters116
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

Unique31 ?
Unique (%)100.0%

Sample

1st row경기도 가평군 가평읍
2nd row경기도 고양시 일산서구 일산동 627-4번지
3rd row경기도 과천시 문원동 보훈종합회관2층 211호
4th row경기도 광명시 하안동 1304동 207호
5th row경기도 광주시 송정동 태종빌딩 302호
ValueCountFrequency (%)
경기도 31
 
21.7%
302호 3
 
2.1%
2층 2
 
1.4%
양평군 2
 
1.4%
양평읍 2
 
1.4%
공흥리 2
 
1.4%
203호 2
 
1.4%
오산동 1
 
0.7%
고천동 1
 
0.7%
의왕시 1
 
0.7%
Other values (96) 96
67.1%
2023-12-11T07:15:01.459968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
112
 
17.8%
도 32
 
5.1%
경 31
 
4.9%
기 31
 
4.9%
시 30
 
4.8%
동 28
 
4.5%
지 17
 
2.7%
2 16
 
2.5%
번 14
 
2.2%
0 14
 
2.2%
Other values (106) 303
48.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 423
67.4%
Space Separator 112
 
17.8%
Decimal Number 85
 
13.5%
Dash Punctuation 8
 
1.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 32
 
7.6%
경 31
 
7.3%
기 31
 
7.3%
시 30
 
7.1%
동 28
 
6.6%
지 17
 
4.0%
번 14
 
3.3%
산 9
 
2.1%
호 9
 
2.1%
양 9
 
2.1%
Other values (94) 213
50.4%
Decimal Number
ValueCountFrequency (%)
2 16
18.8%
0 14
16.5%
3 12
14.1%
1 10
11.8%
6 9
10.6%
4 9
10.6%
8 6
 
7.1%
9 4
 
4.7%
7 3
 
3.5%
5 2
 
2.4%
Space Separator
ValueCountFrequency (%)
112
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 423
67.4%
Common 205
32.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 32
 
7.6%
경 31
 
7.3%
기 31
 
7.3%
시 30
 
7.1%
동 28
 
6.6%
지 17
 
4.0%
번 14
 
3.3%
산 9
 
2.1%
호 9
 
2.1%
양 9
 
2.1%
Other values (94) 213
50.4%
Common
ValueCountFrequency (%)
112
54.6%
2 16
 
7.8%
0 14
 
6.8%
3 12
 
5.9%
1 10
 
4.9%
6 9
 
4.4%
4 9
 
4.4%
- 8
 
3.9%
8 6
 
2.9%
9 4
 
2.0%
Other values (2) 5
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 423
67.4%
ASCII 205
32.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
112
54.6%
2 16
 
7.8%
0 14
 
6.8%
3 12
 
5.9%
1 10
 
4.9%
6 9
 
4.4%
4 9
 
4.4%
- 8
 
3.9%
8 6
 
2.9%
9 4
 
2.0%
Other values (2) 5
 
2.4%
Hangul
ValueCountFrequency (%)
도 32
 
7.6%
경 31
 
7.3%
기 31
 
7.3%
시 30
 
7.1%
동 28
 
6.6%
지 17
 
4.0%
번 14
 
3.3%
산 9
 
2.1%
호 9
 
2.1%
양 9
 
2.1%
Other values (94) 213
50.4%

소재지우편번호
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct18
Distinct (%)100.0%
Missing13
Missing (%)41.9%
Infinite0
Infinite (%)0.0%
Mean13733.556
Minimum10099
Maximum18131
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-11T07:15:01.569799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10099
5-th percentile10314.9
Q111395.5
median13171.5
Q315678.5
95-th percentile17947.4
Maximum18131
Range8032
Interquartile range (IQR)4283

Descriptive statistics

Standard deviation2722.2879
Coefficient of variation (CV)0.19822164
Kurtosis-1.2085355
Mean13733.556
Median Absolute Deviation (MAD)1990
Skewness0.40057058
Sum247204
Variance7410851.6
MonotonicityNot monotonic
2023-12-11T07:15:01.704025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
12560 1
 
3.2%
13024 1
 
3.2%
11139 1
 
3.2%
17915 1
 
3.2%
10929 1
 
3.2%
17379 1
 
3.2%
11652 1
 
3.2%
18131 1
 
3.2%
10353 1
 
3.2%
15865 1
 
3.2%
Other values (8) 8
25.8%
(Missing) 13
41.9%
ValueCountFrequency (%)
10099 1
3.2%
10353 1
3.2%
10929 1
3.2%
11139 1
3.2%
11310 1
3.2%
11652 1
3.2%
12237 1
3.2%
12560 1
3.2%
13024 1
3.2%
13319 1
3.2%
ValueCountFrequency (%)
18131 1
3.2%
17915 1
3.2%
17590 1
3.2%
17379 1
3.2%
15865 1
3.2%
15119 1
3.2%
14593 1
3.2%
13990 1
3.2%
13319 1
3.2%
13024 1
3.2%

WGS84위도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct16
Distinct (%)100.0%
Missing15
Missing (%)48.4%
Infinite0
Infinite (%)0.0%
Mean37.486895
Minimum36.986902
Maximum37.931967
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-11T07:15:01.818708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.986902
5-th percentile37.001866
Q137.337902
median37.491064
Q337.69605
95-th percentile37.919707
Maximum37.931967
Range0.94506459
Interquartile range (IQR)0.3581488

Descriptive statistics

Standard deviation0.28744075
Coefficient of variation (CV)0.0076677665
Kurtosis-0.58384889
Mean37.486895
Median Absolute Deviation (MAD)0.20600393
Skewness-0.22857523
Sum599.79032
Variance0.082622187
MonotonicityNot monotonic
2023-12-11T07:15:01.924982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
37.1568594226 1
 
3.2%
37.5236772545 1
 
3.2%
37.9319670192 1
 
3.2%
36.9869024301 1
 
3.2%
37.7591653054 1
 
3.2%
37.2715590199 1
 
3.2%
37.7335006848 1
 
3.2%
37.6835668825 1
 
3.2%
37.3600157065 1
 
3.2%
37.4053795542 1
 
3.2%
Other values (6) 6
 
19.4%
(Missing) 15
48.4%
ValueCountFrequency (%)
36.9869024301 1
3.2%
37.0068544283 1
3.2%
37.1568594226 1
3.2%
37.2715590199 1
3.2%
37.3600157065 1
3.2%
37.4053795542 1
3.2%
37.4390327828 1
3.2%
37.4901270107 1
3.2%
37.4920018228 1
3.2%
37.5236772545 1
3.2%
ValueCountFrequency (%)
37.9319670192 1
3.2%
37.915620114 1
3.2%
37.7591653054 1
3.2%
37.7335006848 1
3.2%
37.6835668825 1
3.2%
37.6340860428 1
3.2%
37.5236772545 1
3.2%
37.4920018228 1
3.2%
37.4901270107 1
3.2%
37.4390327828 1
3.2%

WGS84경도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct16
Distinct (%)100.0%
Missing15
Missing (%)48.4%
Infinite0
Infinite (%)0.0%
Mean127.08923
Minimum126.74309
Maximum127.50424
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-11T07:15:02.027591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.74309
5-th percentile126.76405
Q1126.92926
median127.08786
Q3127.22433
95-th percentile127.45171
Maximum127.50424
Range0.76115228
Interquartile range (IQR)0.29506394

Descriptive statistics

Standard deviation0.22440049
Coefficient of variation (CV)0.0017656925
Kurtosis-0.50165313
Mean127.08923
Median Absolute Deviation (MAD)0.1465339
Skewness0.08538064
Sum2033.4276
Variance0.05035558
MonotonicityNot monotonic
2023-12-11T07:15:02.144923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
127.0726136232 1
 
3.2%
127.2237168768 1
 
3.2%
127.2261559015 1
 
3.2%
127.1031083005 1
 
3.2%
126.7762612558 1
 
3.2%
127.4341970877 1
 
3.2%
127.0460443696 1
 
3.2%
126.7710310369 1
 
3.2%
126.9330880975 1
 
3.2%
126.917786477 1
 
3.2%
Other values (6) 6
 
19.4%
(Missing) 15
48.4%
ValueCountFrequency (%)
126.7430897549 1
3.2%
126.7710310369 1
3.2%
126.7762612558 1
3.2%
126.917786477 1
3.2%
126.9330880975 1
3.2%
127.0460443696 1
3.2%
127.0604908235 1
3.2%
127.0726136232 1
3.2%
127.1031083005 1
3.2%
127.1292647901 1
3.2%
ValueCountFrequency (%)
127.5042420301 1
3.2%
127.4341970877 1
3.2%
127.2768976973 1
3.2%
127.2261559015 1
3.2%
127.2237168768 1
3.2%
127.2096174416 1
3.2%
127.1292647901 1
3.2%
127.1031083005 1
3.2%
127.0726136232 1
3.2%
127.0604908235 1
3.2%

Interactions

2023-12-11T07:14:57.917807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:14:55.980010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:14:56.723689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:14:57.133899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:14:57.544627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:14:58.007963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:14:56.097496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:14:56.813295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:14:57.216223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:14:57.625788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:14:58.090184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:14:56.197078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:14:56.889569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:14:57.298022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:14:57.694851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:14:58.183049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:14:56.286889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:14:56.972554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:14:57.383311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:14:57.773964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:14:58.259583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:14:56.368119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:14:57.041672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:14:57.458589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:14:57.842678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T07:15:02.244962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명사업장명인허가일자자격소유인원수(명)총인원수(명)소재지도로명주소소재지지번주소소재지우편번호WGS84위도WGS84경도
시군명1.0001.0000.0000.0001.0001.0001.0001.0001.0001.000
사업장명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
인허가일자0.0001.0001.0000.4010.0001.0001.0000.0000.1340.672
자격소유인원수(명)0.0001.0000.4011.0000.0001.0001.0000.0000.6340.783
총인원수(명)1.0001.0000.0000.0001.0001.0001.0001.0001.0000.000
소재지도로명주소1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
소재지지번주소1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
소재지우편번호1.0001.0000.0000.0001.0001.0001.0001.0000.8270.000
WGS84위도1.0001.0000.1340.6341.0001.0001.0000.8271.0000.000
WGS84경도1.0001.0000.6720.7830.0001.0001.0000.0000.0001.000
2023-12-11T07:15:02.354386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
인허가일자총인원수(명)소재지우편번호WGS84위도WGS84경도자격소유인원수(명)
인허가일자1.000-0.080-0.4270.2930.5610.184
총인원수(명)-0.0801.000-0.4550.372-0.0320.000
소재지우편번호-0.427-0.4551.000-0.9290.2290.000
WGS84위도0.2930.372-0.9291.000-0.2380.287
WGS84경도0.561-0.0320.229-0.2381.0000.518
자격소유인원수(명)0.1840.0000.0000.2870.5181.000

Missing values

2023-12-11T07:14:58.385145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T07:14:58.523348image/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.
2023-12-11T07:14:58.643334image/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

시군명사업장명인허가일자영업상태명자격소유인원수(명)총인원수(명)소재지도로명주소소재지지번주소소재지우편번호WGS84위도WGS84경도
0가평군가평군 수화통역센터20131123운영중13<NA>경기도 가평군 가평읍<NA><NA><NA>
1고양시고양시수어통역센터20020531운영중05경기도 고양시 일산서구 고양대로672번길 15-7경기도 고양시 일산서구 일산동 627-4번지1035337.683567126.771031
2과천시과천시수어통역센터20040324운영중45<NA>경기도 과천시 문원동 보훈종합회관2층 211호<NA><NA><NA>
3광명시광명시수어통역센터20020725운영중35<NA>경기도 광명시 하안동 1304동 207호<NA><NA><NA>
4광주시광주시 수어통역센터20051114운영중34<NA>경기도 광주시 송정동 태종빌딩 302호<NA><NA><NA>
5구리시구리시수어통역센터20060108운영중03<NA>경기도 구리시 교문동 구리시장애인근로복지센터<NA><NA><NA>
6군포시군포시 수화통역센터20020521운영중45경기도 군포시 산본로 329경기도 군포시 산본동 1126번지1586537.360016126.933088
7김포시김포시 수화통역센터20040326운영중45<NA>경기도 김포시 걸포동10099<NA><NA>
8남양주시남양주시수어통역센터20040322운영중4997경기도 남양주시 금곡로 58경기도 남양주시 금곡동 은하빌딩1223737.634086127.209617
9동두천시동두천시 수어통역센터20030127운영중25경기도 동두천시 동광로 174경기도 동두천시 보산동 387-1번지1131037.91562127.060491
시군명사업장명인허가일자영업상태명자격소유인원수(명)총인원수(명)소재지도로명주소소재지지번주소소재지우편번호WGS84위도WGS84경도
21오산시오산시수어통역센타20030217운영중03경기도 오산시 경기동로 15경기도 오산시 오산동 49번지1813137.156859127.072614
22용인시용인시 수어통역센터20030624운영중41<NA>경기도 용인시 처인구 김량장동 3층<NA><NA><NA>
23의왕시의왕시수어통역센터20060120운영중03<NA>경기도 의왕시 고천동<NA><NA><NA>
24의정부시의정부시수어통역센터20000201운영중04경기도 의정부시 경의로85번길 6-19경기도 의정부시 의정부동 580-6번지1165237.733501127.046044
25이천시이천시수화통역센터20020624운영중44경기도 이천시 부악로 40경기도 이천시 중리동 490번지 별관 부악관 203호1737937.271559127.434197
26파주시파주시수어통역센터20050106운영중34경기도 파주시 시청로 25경기도 파주시 금촌동 광우프라자 302호1092937.759165126.776261
27평택시평택시수어통역센터20020510운영중24경기도 평택시 조개터로2번길 41경기도 평택시 합정동 936-3번지1791536.986902127.103108
28포천시포천시수어통역센터20050202운영중05경기도 포천시 신북면 호국로 2044경기도 포천시 신북면 884번지1113937.931967127.226156
29하남시하남시수어통역센터20030619운영중05경기도 하남시 검단로19번길 27경기도 하남시 하산곡동 69-1번지1302437.523677127.223717
30화성시화성시 수어통역센터20030603운영중04<NA>경기도 화성시 향남읍 도이리 아르딤복지관 장애인단체 및 센터동 203호<NA><NA><NA>