Overview

Dataset statistics

Number of variables6
Number of observations230
Missing cells133
Missing cells (%)9.6%
Duplicate rows10
Duplicate rows (%)4.3%
Total size in memory11.6 KiB
Average record size in memory51.6 B

Variable types

Numeric3
Categorical1
Text2

Dataset

Description대전광역시 중구에 위치한 제설함 정보입니다.This is information on a snow removal vessel located in Jung-gu, Daejeon.
Author대전광역시 중구
URLhttps://www.data.go.kr/data/15126563/fileData.do

Alerts

Dataset has 10 (4.3%) duplicate rowsDuplicates
위도(Y) is highly overall correlated with 관리부서High correlation
경도(X) is highly overall correlated with 관리부서High correlation
관리부서 is highly overall correlated with 위도(Y) and 1 other fieldsHigh correlation
비고 has 133 (57.8%) missing valuesMissing

Reproduction

Analysis started2024-03-14 12:30:46.424838
Analysis finished2024-03-14 12:30:49.739558
Duration3.31 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

Distinct50
Distinct (%)21.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.708696
Minimum1
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2024-03-14T21:30:50.159721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q15
median10
Q316
95-th percentile38.55
Maximum50
Range49
Interquartile range (IQR)11

Descriptive statistics

Standard deviation11.081178
Coefficient of variation (CV)0.87193667
Kurtosis1.8542705
Mean12.708696
Median Absolute Deviation (MAD)6
Skewness1.4700975
Sum2923
Variance122.7925
MonotonicityNot monotonic
2024-03-14T21:30:50.618024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 17
 
7.4%
2 15
 
6.5%
3 12
 
5.2%
5 12
 
5.2%
4 12
 
5.2%
6 11
 
4.8%
7 11
 
4.8%
8 11
 
4.8%
9 11
 
4.8%
10 10
 
4.3%
Other values (40) 108
47.0%
ValueCountFrequency (%)
1 17
7.4%
2 15
6.5%
3 12
5.2%
4 12
5.2%
5 12
5.2%
6 11
4.8%
7 11
4.8%
8 11
4.8%
9 11
4.8%
10 10
4.3%
ValueCountFrequency (%)
50 1
0.4%
49 1
0.4%
48 1
0.4%
47 1
0.4%
46 1
0.4%
45 1
0.4%
44 1
0.4%
43 1
0.4%
42 1
0.4%
41 1
0.4%

관리부서
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
석교동
50 
부사동
32 
건설과
29 
산성동
20 
중촌동
19 
Other values (11)
80 

Length

Max length5
Median length3
Mean length3.1695652
Min length2

Unique

Unique2 ?
Unique (%)0.9%

Sample

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

Common Values

ValueCountFrequency (%)
석교동 50
21.7%
부사동 32
13.9%
건설과 29
12.6%
산성동 20
 
8.7%
중촌동 19
 
8.3%
목동 18
 
7.8%
문화2동 17
 
7.4%
은행선화동 13
 
5.7%
대사동 10
 
4.3%
문화1동 9
 
3.9%
Other values (6) 13
 
5.7%

Length

2024-03-14T21:30:51.060616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
석교동 50
21.7%
부사동 32
13.9%
건설과 29
12.6%
산성동 20
 
8.7%
중촌동 19
 
8.3%
목동 18
 
7.8%
문화2동 17
 
7.4%
은행선화동 13
 
5.7%
대사동 10
 
4.3%
문화1동 9
 
3.9%
Other values (6) 13
 
5.7%

구분(위치)
Text

Distinct213
Distinct (%)92.6%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
2024-03-14T21:30:52.002632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length24
Mean length10.717391
Min length4

Characters and Unicode

Total characters2465
Distinct characters187
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

Unique196 ?
Unique (%)85.2%

Sample

1st row목동로-1
2nd row목동로-2
3rd row목동로-3
4th row목동로-4
5th row목동로-5
ValueCountFrequency (%)
중촌동 16
 
3.6%
옆 15
 
3.4%
목동 15
 
3.4%
앞 14
 
3.2%
문화동 11
 
2.5%
대사동 10
 
2.3%
입구 7
 
1.6%
맞은편 6
 
1.4%
공원 6
 
1.4%
아래 6
 
1.4%
Other values (278) 338
76.1%
2024-03-14T21:30:53.355753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
216
 
8.8%
1 147
 
6.0%
로 128
 
5.2%
- 108
 
4.4%
동 97
 
3.9%
3 94
 
3.8%
2 85
 
3.4%
4 76
 
3.1%
길 74
 
3.0%
번 72
 
2.9%
Other values (177) 1368
55.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1314
53.3%
Decimal Number 719
29.2%
Space Separator 216
 
8.8%
Dash Punctuation 108
 
4.4%
Close Punctuation 47
 
1.9%
Open Punctuation 47
 
1.9%
Uppercase Letter 12
 
0.5%
Other Punctuation 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 128
 
9.7%
동 97
 
7.4%
길 74
 
5.6%
번 72
 
5.5%
대 38
 
2.9%
산 35
 
2.7%
문 33
 
2.5%
화 28
 
2.1%
리 26
 
2.0%
중 24
 
1.8%
Other values (157) 759
57.8%
Decimal Number
ValueCountFrequency (%)
1 147
20.4%
3 94
13.1%
2 85
11.8%
4 76
10.6%
5 66
9.2%
6 62
8.6%
7 59
8.2%
8 47
 
6.5%
9 43
 
6.0%
0 40
 
5.6%
Uppercase Letter
ValueCountFrequency (%)
A 5
41.7%
P 3
25.0%
T 3
25.0%
B 1
 
8.3%
Other Punctuation
ValueCountFrequency (%)
@ 1
50.0%
, 1
50.0%
Space Separator
ValueCountFrequency (%)
216
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 108
100.0%
Close Punctuation
ValueCountFrequency (%)
) 47
100.0%
Open Punctuation
ValueCountFrequency (%)
( 47
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1314
53.3%
Common 1139
46.2%
Latin 12
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 128
 
9.7%
동 97
 
7.4%
길 74
 
5.6%
번 72
 
5.5%
대 38
 
2.9%
산 35
 
2.7%
문 33
 
2.5%
화 28
 
2.1%
리 26
 
2.0%
중 24
 
1.8%
Other values (157) 759
57.8%
Common
ValueCountFrequency (%)
216
19.0%
1 147
12.9%
- 108
9.5%
3 94
8.3%
2 85
 
7.5%
4 76
 
6.7%
5 66
 
5.8%
6 62
 
5.4%
7 59
 
5.2%
8 47
 
4.1%
Other values (6) 179
15.7%
Latin
ValueCountFrequency (%)
A 5
41.7%
P 3
25.0%
T 3
25.0%
B 1
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1314
53.3%
ASCII 1151
46.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
216
18.8%
1 147
12.8%
- 108
9.4%
3 94
8.2%
2 85
 
7.4%
4 76
 
6.6%
5 66
 
5.7%
6 62
 
5.4%
7 59
 
5.1%
8 47
 
4.1%
Other values (10) 191
16.6%
Hangul
ValueCountFrequency (%)
로 128
 
9.7%
동 97
 
7.4%
길 74
 
5.6%
번 72
 
5.5%
대 38
 
2.9%
산 35
 
2.7%
문 33
 
2.5%
화 28
 
2.1%
리 26
 
2.0%
중 24
 
1.8%
Other values (157) 759
57.8%

위도(Y)
Real number (ℝ)

HIGH CORRELATION 

Distinct215
Distinct (%)93.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.316546
Minimum36.293653
Maximum36.342136
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2024-03-14T21:30:53.768423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.293653
5-th percentile36.297375
Q136.305952
median36.311373
Q336.3293
95-th percentile36.338415
Maximum36.342136
Range0.04848322
Interquartile range (IQR)0.023347598

Descriptive statistics

Standard deviation0.013293729
Coefficient of variation (CV)0.00036605158
Kurtosis-1.1852129
Mean36.316546
Median Absolute Deviation (MAD)0.007521905
Skewness0.39491201
Sum8352.8057
Variance0.00017672324
MonotonicityNot monotonic
2024-03-14T21:30:54.212225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36.3274 3
 
1.3%
36.314517 2
 
0.9%
36.310226 2
 
0.9%
36.311165 2
 
0.9%
36.311054 2
 
0.9%
36.309487 2
 
0.9%
36.310071 2
 
0.9%
36.309837 2
 
0.9%
36.31171 2
 
0.9%
36.311863 2
 
0.9%
Other values (205) 209
90.9%
ValueCountFrequency (%)
36.293653 1
0.4%
36.293771 1
0.4%
36.294731 1
0.4%
36.295075 1
0.4%
36.295633 1
0.4%
36.29630743 1
0.4%
36.296395 1
0.4%
36.29648394 1
0.4%
36.296782 1
0.4%
36.296897 1
0.4%
ValueCountFrequency (%)
36.34213622 1
0.4%
36.34041124 1
0.4%
36.34019591 1
0.4%
36.33998197 1
0.4%
36.33978241 1
0.4%
36.33964498 1
0.4%
36.33918824 1
0.4%
36.33907231 1
0.4%
36.3389474 1
0.4%
36.33893247 1
0.4%

경도(X)
Real number (ℝ)

HIGH CORRELATION 

Distinct216
Distinct (%)93.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.41929
Minimum127.38613
Maximum127.45439
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2024-03-14T21:30:54.610173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum127.38613
5-th percentile127.39012
Q1127.40755
median127.41595
Q3127.43423
95-th percentile127.44959
Maximum127.45439
Range0.0682523
Interquartile range (IQR)0.0266789

Descriptive statistics

Standard deviation0.017661543
Coefficient of variation (CV)0.00013860965
Kurtosis-0.84451383
Mean127.41929
Median Absolute Deviation (MAD)0.0130199
Skewness0.22263001
Sum29306.437
Variance0.0003119301
MonotonicityNot monotonic
2024-03-14T21:30:55.058820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.430587 2
 
0.9%
127.430106 2
 
0.9%
127.431053 2
 
0.9%
127.429209 2
 
0.9%
127.430338 2
 
0.9%
127.428309 2
 
0.9%
127.433157 2
 
0.9%
127.4178 2
 
0.9%
127.416228 2
 
0.9%
127.432721 2
 
0.9%
Other values (206) 210
91.3%
ValueCountFrequency (%)
127.3861327 1
0.4%
127.3866184 1
0.4%
127.386787 1
0.4%
127.3879088 1
0.4%
127.3879487 1
0.4%
127.3879952 1
0.4%
127.3883181 1
0.4%
127.3886643 1
0.4%
127.3888877 1
0.4%
127.3890237 1
0.4%
ValueCountFrequency (%)
127.454385 1
0.4%
127.454294 1
0.4%
127.45408 1
0.4%
127.453998 1
0.4%
127.453997 1
0.4%
127.453852 1
0.4%
127.453653 1
0.4%
127.453334 1
0.4%
127.452893 1
0.4%
127.452607 1
0.4%

비고
Text

MISSING 

Distinct77
Distinct (%)79.4%
Missing133
Missing (%)57.8%
Memory size1.9 KiB
2024-03-14T21:30:56.256628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length13
Mean length9.4536082
Min length4

Characters and Unicode

Total characters917
Distinct characters134
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique57 ?
Unique (%)58.8%

Sample

1st row목동로 69
2nd row목동 354-22
3rd row목동로 42
4th row목동로 37
5th row목동로 28
ValueCountFrequency (%)
급경사지 12
 
5.5%
앞 12
 
5.5%
부용로 8
 
3.7%
용두동 7
 
3.2%
목동로 7
 
3.2%
목동 6
 
2.8%
37 6
 
2.8%
34번길 4
 
1.8%
40번길 4
 
1.8%
부사로 4
 
1.8%
Other values (106) 148
67.9%
2024-03-14T21:30:57.882107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
121
 
13.2%
로 49
 
5.3%
2 47
 
5.1%
3 38
 
4.1%
1 37
 
4.0%
- 30
 
3.3%
4 28
 
3.1%
동 28
 
3.1%
번 27
 
2.9%
길 27
 
2.9%
Other values (124) 485
52.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 516
56.3%
Decimal Number 248
27.0%
Space Separator 121
 
13.2%
Dash Punctuation 30
 
3.3%
Uppercase Letter 2
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 49
 
9.5%
동 28
 
5.4%
번 27
 
5.2%
길 27
 
5.2%
용 25
 
4.8%
사 24
 
4.7%
부 21
 
4.1%
앞 14
 
2.7%
대 13
 
2.5%
목 13
 
2.5%
Other values (110) 275
53.3%
Decimal Number
ValueCountFrequency (%)
2 47
19.0%
3 38
15.3%
1 37
14.9%
4 28
11.3%
5 23
9.3%
6 19
7.7%
0 15
 
6.0%
7 15
 
6.0%
9 14
 
5.6%
8 12
 
4.8%
Uppercase Letter
ValueCountFrequency (%)
G 1
50.0%
S 1
50.0%
Space Separator
ValueCountFrequency (%)
121
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 30
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 516
56.3%
Common 399
43.5%
Latin 2
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 49
 
9.5%
동 28
 
5.4%
번 27
 
5.2%
길 27
 
5.2%
용 25
 
4.8%
사 24
 
4.7%
부 21
 
4.1%
앞 14
 
2.7%
대 13
 
2.5%
목 13
 
2.5%
Other values (110) 275
53.3%
Common
ValueCountFrequency (%)
121
30.3%
2 47
 
11.8%
3 38
 
9.5%
1 37
 
9.3%
- 30
 
7.5%
4 28
 
7.0%
5 23
 
5.8%
6 19
 
4.8%
0 15
 
3.8%
7 15
 
3.8%
Other values (2) 26
 
6.5%
Latin
ValueCountFrequency (%)
G 1
50.0%
S 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 516
56.3%
ASCII 401
43.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
121
30.2%
2 47
 
11.7%
3 38
 
9.5%
1 37
 
9.2%
- 30
 
7.5%
4 28
 
7.0%
5 23
 
5.7%
6 19
 
4.7%
0 15
 
3.7%
7 15
 
3.7%
Other values (4) 28
 
7.0%
Hangul
ValueCountFrequency (%)
로 49
 
9.5%
동 28
 
5.4%
번 27
 
5.2%
길 27
 
5.2%
용 25
 
4.8%
사 24
 
4.7%
부 21
 
4.1%
앞 14
 
2.7%
대 13
 
2.5%
목 13
 
2.5%
Other values (110) 275
53.3%

Interactions

2024-03-14T21:30:48.389457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:30:46.908827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:30:47.647824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:30:48.638424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:30:47.153162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:30:47.893104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:30:48.888735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:30:47.393503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:30:48.133767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T21:30:58.144058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번관리부서위도(Y)경도(X)비고
연번1.0000.3860.7720.8080.995
관리부서0.3861.0000.9030.9081.000
위도(Y)0.7720.9031.0000.9071.000
경도(X)0.8080.9080.9071.0001.000
비고0.9951.0001.0001.0001.000
2024-03-14T21:30:58.411106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번위도(Y)경도(X)관리부서
연번1.000-0.3500.3110.159
위도(Y)-0.3501.000-0.3200.644
경도(X)0.311-0.3201.0000.656
관리부서0.1590.6440.6561.000

Missing values

2024-03-14T21:30:49.259494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T21:30:49.604460image/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

연번관리부서구분(위치)위도(Y)경도(X)비고
01건설과목동로-136.336656127.405617목동로 69
12건설과목동로-236.33586127.406764목동 354-22
23건설과목동로-336.334674127.407428목동로 42
34건설과목동로-436.334398127.407257목동로 37
45건설과목동로-536.333445127.408282목동로 28
56건설과목동로-636.33306127.40842목동로 23-1
67건설과계룡로771번길-136.333523127.407561목동로 37
78건설과계룡로771번길-236.333371127.407639목동로 23
89건설과계룡로771번길-336.333127.406503목동 24-25
910건설과계룡로771번길-436.332909127.406652목동 24-38
연번관리부서구분(위치)위도(Y)경도(X)비고
22011산성동대둔산로386번길42-2(드림하이빌)36.303442127.388664<NA>
22112산성동대둔산로386번길29(전주)36.303756127.387909<NA>
22213산성동가재울로12번길44(에이스빌)36.304508127.387949<NA>
22314산성동대둔산로374번길29(써니미용실)36.302654127.387995<NA>
22415산성동대둔산로374번길46(진산빌라A)36.302468127.388888<NA>
22516산성동대둔산로350번길19(입구)36.300601127.386618<NA>
22617산성동대둔산로350번길62(옷수거함)36.298677127.386787<NA>
22718산성동대둔산로300번길69-9(엘엔지빌)36.296484127.388318<NA>
22819산성동대둔산로300번길83-5(성심주택)36.296307127.38925<NA>
22920산성동대둔산로403(주민센터)36.305399127.386133<NA>

Duplicate rows

Most frequently occurring

연번관리부서구분(위치)위도(Y)경도(X)비고# duplicates
02부사동둥지촌빌라 앞36.310611127.434233대종로 295번길 53-22
14부사동한솔아파트 103동 옆36.308323127.430338부용로60 성산교회앞2
25부사동청란경로당 지나 오름길36.309837127.429209부용로34번길34-52
36부사동동서연립 3동 옆36.311054127.431053부용로 282
47부사동창조아파트 공원 앞36.311863127.432721보문로63번길252
58부사동민영아파트 뒤 공원36.31288127.429494보문로 111번길 17-14 앞2
610부사동사랑의 텃밭 맞은편36.311165127.430587부용로 28-2 앞 공원2
712부사동부용빌라 근처36.309487127.428309부용로34번길 562
813부사동부사샘물 맞은 편36.310071127.433157사득로 22번길 372
916부사동청란여고 후문 옆36.310226127.429888부용로 34번길 162