Overview

Dataset statistics

Number of variables3
Number of observations72
Missing cells0
Missing cells (%)0.0%
Duplicate rows1
Duplicate rows (%)1.4%
Total size in memory1.9 KiB
Average record size in memory26.8 B

Variable types

Numeric1
Text2

Dataset

Description용인시의 공중화장실 위치 및 주소 등 현황입니다. 더 자세한 사항은 용인도시공사 공식 홈페이지를 방문하시거나 환경사업팀에 문의 바랍니다.
Author용인도시공사
URLhttps://www.data.go.kr/data/15060017/fileData.do

Alerts

Dataset has 1 (1.4%) duplicate rowsDuplicates

Reproduction

Analysis started2024-03-14 09:28:45.897259
Analysis finished2024-03-14 09:28:46.915896
Duration1.02 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

Distinct49
Distinct (%)68.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.513889
Minimum1
Maximum49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size776.0 B
2024-03-14T18:28:47.141728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4.55
Q118.75
median30.5
Q339.25
95-th percentile46.45
Maximum49
Range48
Interquartile range (IQR)20.5

Descriptive statistics

Standard deviation13.377502
Coefficient of variation (CV)0.46915739
Kurtosis-0.85686439
Mean28.513889
Median Absolute Deviation (MAD)10
Skewness-0.44209326
Sum2053
Variance178.95755
MonotonicityNot monotonic
2024-03-14T18:28:47.485635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
26 2
 
2.8%
38 2
 
2.8%
28 2
 
2.8%
29 2
 
2.8%
30 2
 
2.8%
31 2
 
2.8%
32 2
 
2.8%
33 2
 
2.8%
34 2
 
2.8%
35 2
 
2.8%
Other values (39) 52
72.2%
ValueCountFrequency (%)
1 1
1.4%
2 1
1.4%
3 1
1.4%
4 1
1.4%
5 1
1.4%
6 1
1.4%
7 1
1.4%
8 1
1.4%
9 1
1.4%
10 1
1.4%
ValueCountFrequency (%)
49 1
1.4%
48 1
1.4%
47 2
2.8%
46 2
2.8%
45 2
2.8%
44 2
2.8%
43 2
2.8%
42 2
2.8%
41 2
2.8%
40 2
2.8%

명칭
Text

Distinct58
Distinct (%)80.6%
Missing0
Missing (%)0.0%
Memory size704.0 B
2024-03-14T18:28:48.392642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length12.5
Mean length8.375
Min length4

Characters and Unicode

Total characters603
Distinct characters124
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

Unique44 ?
Unique (%)61.1%

Sample

1st row통일공원
2nd row용인중앙시장
3rd row백암시장
4th row금륜사 등산로
5th row모범운전자회 앞
ValueCountFrequency (%)
경안천 13
 
8.9%
탄천 10
 
6.8%
광교산 6
 
4.1%
등산로 6
 
4.1%
신갈천 4
 
2.7%
삼거리 4
 
2.7%
성복천 4
 
2.7%
양지천 3
 
2.1%
풍덕고가밑 3
 
2.1%
마평동 3
 
2.1%
Other values (70) 90
61.6%
2024-03-14T18:28:49.801704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
79
 
13.1%
천 40
 
6.6%
리 21
 
3.5%
산 20
 
3.3%
안 16
 
2.7%
원 15
 
2.5%
동 15
 
2.5%
경 15
 
2.5%
갈 14
 
2.3%
신 13
 
2.2%
Other values (114) 355
58.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 524
86.9%
Space Separator 79
 
13.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
천 40
 
7.6%
리 21
 
4.0%
산 20
 
3.8%
안 16
 
3.1%
원 15
 
2.9%
동 15
 
2.9%
경 15
 
2.9%
갈 14
 
2.7%
신 13
 
2.5%
거 13
 
2.5%
Other values (113) 342
65.3%
Space Separator
ValueCountFrequency (%)
79
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 524
86.9%
Common 79
 
13.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
천 40
 
7.6%
리 21
 
4.0%
산 20
 
3.8%
안 16
 
3.1%
원 15
 
2.9%
동 15
 
2.9%
경 15
 
2.9%
갈 14
 
2.7%
신 13
 
2.5%
거 13
 
2.5%
Other values (113) 342
65.3%
Common
ValueCountFrequency (%)
79
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 524
86.9%
ASCII 79
 
13.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
79
100.0%
Hangul
ValueCountFrequency (%)
천 40
 
7.6%
리 21
 
4.0%
산 20
 
3.8%
안 16
 
3.1%
원 15
 
2.9%
동 15
 
2.9%
경 15
 
2.9%
갈 14
 
2.7%
신 13
 
2.5%
거 13
 
2.5%
Other values (113) 342
65.3%

소재지
Text

Distinct68
Distinct (%)94.4%
Missing0
Missing (%)0.0%
Memory size704.0 B
2024-03-14T18:28:50.869358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length16
Mean length13.680556
Min length7

Characters and Unicode

Total characters985
Distinct characters88
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique64 ?
Unique (%)88.9%

Sample

1st row처인구 김량장동 326
2nd row처인구 김량장동 133-102
3rd row처인구 백암면 백암리 458-1
4th row처인구 양지면 양지리 산8-1
5th row처인구 고림동 992
ValueCountFrequency (%)
처인구 33
 
14.7%
기흥구 14
 
6.2%
수지구 8
 
3.6%
포곡읍 7
 
3.1%
보정동 6
 
2.7%
신갈동 6
 
2.7%
마평동 4
 
1.8%
둔전리 4
 
1.8%
죽전동 4
 
1.8%
모현면 3
 
1.3%
Other values (96) 136
60.4%
2024-03-14T18:28:52.418461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
153
 
15.5%
1 58
 
5.9%
구 57
 
5.8%
동 55
 
5.6%
- 54
 
5.5%
4 46
 
4.7%
인 34
 
3.5%
처 33
 
3.4%
2 31
 
3.1%
0 27
 
2.7%
Other values (78) 437
44.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 468
47.5%
Decimal Number 302
30.7%
Space Separator 153
 
15.5%
Dash Punctuation 54
 
5.5%
Close Punctuation 4
 
0.4%
Open Punctuation 4
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 57
 
12.2%
동 55
 
11.8%
인 34
 
7.3%
처 33
 
7.1%
리 21
 
4.5%
기 16
 
3.4%
흥 14
 
3.0%
지 14
 
3.0%
갈 12
 
2.6%
읍 11
 
2.4%
Other values (64) 201
42.9%
Decimal Number
ValueCountFrequency (%)
1 58
19.2%
4 46
15.2%
2 31
10.3%
0 27
8.9%
7 27
8.9%
3 26
8.6%
5 24
7.9%
9 23
 
7.6%
8 21
 
7.0%
6 19
 
6.3%
Space Separator
ValueCountFrequency (%)
153
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 54
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 517
52.5%
Hangul 468
47.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 57
 
12.2%
동 55
 
11.8%
인 34
 
7.3%
처 33
 
7.1%
리 21
 
4.5%
기 16
 
3.4%
흥 14
 
3.0%
지 14
 
3.0%
갈 12
 
2.6%
읍 11
 
2.4%
Other values (64) 201
42.9%
Common
ValueCountFrequency (%)
153
29.6%
1 58
 
11.2%
- 54
 
10.4%
4 46
 
8.9%
2 31
 
6.0%
0 27
 
5.2%
7 27
 
5.2%
3 26
 
5.0%
5 24
 
4.6%
9 23
 
4.4%
Other values (4) 48
 
9.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 517
52.5%
Hangul 468
47.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
153
29.6%
1 58
 
11.2%
- 54
 
10.4%
4 46
 
8.9%
2 31
 
6.0%
0 27
 
5.2%
7 27
 
5.2%
3 26
 
5.0%
5 24
 
4.6%
9 23
 
4.4%
Other values (4) 48
 
9.3%
Hangul
ValueCountFrequency (%)
구 57
 
12.2%
동 55
 
11.8%
인 34
 
7.3%
처 33
 
7.1%
리 21
 
4.5%
기 16
 
3.4%
흥 14
 
3.0%
지 14
 
3.0%
갈 12
 
2.6%
읍 11
 
2.4%
Other values (64) 201
42.9%

Interactions

2024-03-14T18:28:46.266261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T18:28:52.681335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번명칭소재지
연번1.0000.7930.978
명칭0.7931.0000.998
소재지0.9780.9981.000

Missing values

2024-03-14T18:28:46.578270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T18:28:46.819355image/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

연번명칭소재지
01통일공원처인구 김량장동 326
12용인중앙시장처인구 김량장동 133-102
23백암시장처인구 백암면 백암리 458-1
34금륜사 등산로처인구 양지면 양지리 산8-1
45모범운전자회 앞처인구 고림동 992
56경안천 도사마을 체육공원처인구 포곡읍 삼계리 671
67원삼 안산소하천 생태공원처인구 원삼면 고당리 344
78탄천 연원마을기흥구 보정동 1019-243
89신갈오거리기흥구 신갈동 470-23
910성복천 풍덕고가밑 견인보관소기흥구 보정동 1115-2
연번명칭소재지
6240경안천 왕산리처인구 모현읍 왕산리 945-44
6341탄천 법무연수원삼거리기흥구 언남동 456-55
6442신갈중앙어린이공원기흥구 신갈동 33-4
6543신갈천 상갈동기흥구 상갈동 294-4
6644양지천 마평동처인구 마평동 471-10
6745동천배수지쉼터수지구 동천동 401-18
6846송전공설묘지처인구 이동읍 송전리 산6
6947탄천 주민쉼터수지구 죽전동 1070-40
7048둔전역 버스정류장처인구 포곡읍 둔전리 406-87
7149지곡천 산책로기흥구 지곡동 656-16

Duplicate rows

Most frequently occurring

연번명칭소재지# duplicates
047탄천 주민쉼터수지구 죽전동 1070-402