Overview

Dataset statistics

Number of variables8
Number of observations1227
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory80.4 KiB
Average record size in memory67.1 B

Variable types

Numeric3
Categorical2
Text3

Dataset

Description대전광역시 서구에서 적발한 밤샘주차 위반현황(밤샘주차 관련 단속연도, 단속월, 단속위치, 위도, 경도)을 제공합니다.
Author대전광역시 서구
URLhttps://www.data.go.kr/data/15075606/fileData.do

Alerts

연번 is highly overall correlated with 단속연도High correlation
단속연도 is highly overall correlated with 연번High correlation
연번 has unique valuesUnique

Reproduction

Analysis started2023-12-23 06:32:55.305038
Analysis finished2023-12-23 06:33:03.739853
Duration8.43 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct1227
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean614
Minimum1
Maximum1227
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.9 KiB
2023-12-23T06:33:04.516871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile62.3
Q1307.5
median614
Q3920.5
95-th percentile1165.7
Maximum1227
Range1226
Interquartile range (IQR)613

Descriptive statistics

Standard deviation354.3487
Coefficient of variation (CV)0.57711514
Kurtosis-1.2
Mean614
Median Absolute Deviation (MAD)307
Skewness0
Sum753378
Variance125563
MonotonicityStrictly increasing
2023-12-23T06:33:05.415459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.1%
817 1
 
0.1%
824 1
 
0.1%
823 1
 
0.1%
822 1
 
0.1%
821 1
 
0.1%
820 1
 
0.1%
819 1
 
0.1%
818 1
 
0.1%
816 1
 
0.1%
Other values (1217) 1217
99.2%
ValueCountFrequency (%)
1 1
0.1%
2 1
0.1%
3 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%
ValueCountFrequency (%)
1227 1
0.1%
1226 1
0.1%
1225 1
0.1%
1224 1
0.1%
1223 1
0.1%
1222 1
0.1%
1221 1
0.1%
1220 1
0.1%
1219 1
0.1%
1218 1
0.1%

단속연도
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2020.5844
Minimum2018
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.9 KiB
2023-12-23T06:33:06.206521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2018
5-th percentile2018
Q12019
median2021
Q32023
95-th percentile2023
Maximum2023
Range5
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.891429
Coefficient of variation (CV)0.00093608021
Kurtosis-1.5322537
Mean2020.5844
Median Absolute Deviation (MAD)2
Skewness0.0044949921
Sum2479257
Variance3.5775037
MonotonicityIncreasing
2023-12-23T06:33:07.115227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2023 316
25.8%
2019 259
21.1%
2018 223
18.2%
2022 168
13.7%
2021 138
11.2%
2020 123
 
10.0%
ValueCountFrequency (%)
2018 223
18.2%
2019 259
21.1%
2020 123
 
10.0%
2021 138
11.2%
2022 168
13.7%
2023 316
25.8%
ValueCountFrequency (%)
2023 316
25.8%
2022 168
13.7%
2021 138
11.2%
2020 123
 
10.0%
2019 259
21.1%
2018 223
18.2%

단속월
Real number (ℝ)

Distinct12
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.1572942
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.9 KiB
2023-12-23T06:33:07.618245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.4469461
Coefficient of variation (CV)0.55981507
Kurtosis-1.2903564
Mean6.1572942
Median Absolute Deviation (MAD)3
Skewness0.1233723
Sum7555
Variance11.881437
MonotonicityNot monotonic
2023-12-23T06:33:08.348888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
10 148
12.1%
2 146
11.9%
3 139
11.3%
6 109
8.9%
7 106
8.6%
4 100
8.1%
1 93
7.6%
11 91
7.4%
8 84
6.8%
5 77
6.3%
Other values (2) 134
10.9%
ValueCountFrequency (%)
1 93
7.6%
2 146
11.9%
3 139
11.3%
4 100
8.1%
5 77
6.3%
6 109
8.9%
7 106
8.6%
8 84
6.8%
9 63
5.1%
10 148
12.1%
ValueCountFrequency (%)
12 71
5.8%
11 91
7.4%
10 148
12.1%
9 63
5.1%
8 84
6.8%
7 106
8.6%
6 109
8.9%
5 77
6.3%
4 100
8.1%
3 139
11.3%

단속동명
Categorical

Distinct24
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size9.7 KiB
관저1동
231 
가수원동
177 
관저2동
160 
정림동
147 
복수동
130 
Other values (19)
382 

Length

Max length5
Median length4
Mean length3.6340668
Min length2

Unique

Unique3 ?
Unique (%)0.2%

Sample

1st row월평3동
2nd row월평3동
3rd row월평3동
4th row월평3동
5th row월평3동

Common Values

ValueCountFrequency (%)
관저1동 231
18.8%
가수원동 177
14.4%
관저2동 160
13.0%
정림동 147
12.0%
복수동 130
10.6%
월평3동 100
8.1%
월평1동 68
 
5.5%
괴정동 48
 
3.9%
둔산2동 36
 
2.9%
변동 29
 
2.4%
Other values (14) 101
8.2%

Length

2023-12-23T06:33:09.143585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
관저1동 231
18.8%
가수원동 177
14.4%
관저2동 160
13.0%
정림동 147
12.0%
복수동 130
10.6%
월평3동 100
8.1%
월평1동 69
 
5.6%
괴정동 48
 
3.9%
둔산2동 36
 
2.9%
변동 29
 
2.4%
Other values (13) 100
8.1%

자동차분류
Categorical

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size9.7 KiB
화물
922 
여객
170 
건설기계
135 

Length

Max length4
Median length2
Mean length2.2200489
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
화물 922
75.1%
여객 170
 
13.9%
건설기계 135
 
11.0%

Length

2023-12-23T06:33:10.287784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-23T06:33:10.976217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
화물 922
75.1%
여객 170
 
13.9%
건설기계 135
 
11.0%

단속위치
Text

Distinct184
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Memory size9.7 KiB
2023-12-23T06:33:12.014718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length34
Median length31
Mean length10.99022
Min length5

Characters and Unicode

Total characters13485
Distinct characters152
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique54 ?
Unique (%)4.4%

Sample

1st row월평동 서대전고등학교 부근
2nd row월평동 은평공원 부근
3rd row월평동 은평공원 부근
4th row월평동 은평공원 부근
5th row월평동 은평공원 부근
ValueCountFrequency (%)
서구 505
 
15.0%
대전 487
 
14.5%
부근 439
 
13.0%
관저동 165
 
4.9%
복수동 90
 
2.7%
가수원동 76
 
2.3%
정림동 76
 
2.3%
관저2동 71
 
2.1%
원앙마을 61
 
1.8%
관저1동 58
 
1.7%
Other values (181) 1339
39.8%
2023-12-23T06:33:14.131833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2140
 
15.9%
동 885
 
6.6%
부 783
 
5.8%
근 782
 
5.8%
구 621
 
4.6%
서 603
 
4.5%
대 584
 
4.3%
전 528
 
3.9%
로 468
 
3.5%
저 349
 
2.6%
Other values (142) 5742
42.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 10468
77.6%
Space Separator 2140
 
15.9%
Decimal Number 661
 
4.9%
Other Punctuation 83
 
0.6%
Open Punctuation 44
 
0.3%
Close Punctuation 44
 
0.3%
Math Symbol 26
 
0.2%
Dash Punctuation 19
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 885
 
8.5%
부 783
 
7.5%
근 782
 
7.5%
구 621
 
5.9%
서 603
 
5.8%
대 584
 
5.6%
전 528
 
5.0%
로 468
 
4.5%
저 349
 
3.3%
관 349
 
3.3%
Other values (125) 4516
43.1%
Decimal Number
ValueCountFrequency (%)
1 146
22.1%
2 130
19.7%
3 68
10.3%
4 65
9.8%
0 59
8.9%
9 46
 
7.0%
7 45
 
6.8%
5 43
 
6.5%
8 32
 
4.8%
6 27
 
4.1%
Other Punctuation
ValueCountFrequency (%)
@ 82
98.8%
. 1
 
1.2%
Space Separator
ValueCountFrequency (%)
2140
100.0%
Open Punctuation
ValueCountFrequency (%)
( 44
100.0%
Close Punctuation
ValueCountFrequency (%)
) 44
100.0%
Math Symbol
ValueCountFrequency (%)
~ 26
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 19
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 10468
77.6%
Common 3017
 
22.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 885
 
8.5%
부 783
 
7.5%
근 782
 
7.5%
구 621
 
5.9%
서 603
 
5.8%
대 584
 
5.6%
전 528
 
5.0%
로 468
 
4.5%
저 349
 
3.3%
관 349
 
3.3%
Other values (125) 4516
43.1%
Common
ValueCountFrequency (%)
2140
70.9%
1 146
 
4.8%
2 130
 
4.3%
@ 82
 
2.7%
3 68
 
2.3%
4 65
 
2.2%
0 59
 
2.0%
9 46
 
1.5%
7 45
 
1.5%
( 44
 
1.5%
Other values (7) 192
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 10468
77.6%
ASCII 3017
 
22.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2140
70.9%
1 146
 
4.8%
2 130
 
4.3%
@ 82
 
2.7%
3 68
 
2.3%
4 65
 
2.2%
0 59
 
2.0%
9 46
 
1.5%
7 45
 
1.5%
( 44
 
1.5%
Other values (7) 192
 
6.4%
Hangul
ValueCountFrequency (%)
동 885
 
8.5%
부 783
 
7.5%
근 782
 
7.5%
구 621
 
5.9%
서 603
 
5.8%
대 584
 
5.6%
전 528
 
5.0%
로 468
 
4.5%
저 349
 
3.3%
관 349
 
3.3%
Other values (125) 4516
43.1%

위도
Text

Distinct541
Distinct (%)44.1%
Missing0
Missing (%)0.0%
Memory size9.7 KiB
2023-12-23T06:33:15.248821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length9.1556642
Min length7

Characters and Unicode

Total characters11234
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique478 ?
Unique (%)39.0%

Sample

1st row36.360522
2nd row36.359588
3rd row36.359588
4th row36.359588
5th row36.359588
ValueCountFrequency (%)
36.309077 145
 
11.8%
36.309016 62
 
5.1%
36.359588 51
 
4.2%
36.29937 32
 
2.6%
36.360522 31
 
2.5%
36.30327 26
 
2.1%
36.340179 25
 
2.0%
36.304448 25
 
2.0%
36.340162 24
 
2.0%
36.297393 24
 
2.0%
Other values (531) 782
63.7%
2023-12-23T06:33:17.807332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 2297
20.4%
6 1692
15.1%
. 1543
13.7%
0 936
8.3%
7 841
 
7.5%
2 819
 
7.3%
1 795
 
7.1%
9 779
 
6.9%
8 624
 
5.6%
5 480
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9691
86.3%
Other Punctuation 1543
 
13.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 2297
23.7%
6 1692
17.5%
0 936
9.7%
7 841
 
8.7%
2 819
 
8.5%
1 795
 
8.2%
9 779
 
8.0%
8 624
 
6.4%
5 480
 
5.0%
4 428
 
4.4%
Other Punctuation
ValueCountFrequency (%)
. 1543
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 11234
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 2297
20.4%
6 1692
15.1%
. 1543
13.7%
0 936
8.3%
7 841
 
7.5%
2 819
 
7.3%
1 795
 
7.1%
9 779
 
6.9%
8 624
 
5.6%
5 480
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11234
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 2297
20.4%
6 1692
15.1%
. 1543
13.7%
0 936
8.3%
7 841
 
7.5%
2 819
 
7.3%
1 795
 
7.1%
9 779
 
6.9%
8 624
 
5.6%
5 480
 
4.3%

경도
Text

Distinct541
Distinct (%)44.1%
Missing0
Missing (%)0.0%
Memory size9.7 KiB
2023-12-23T06:33:19.063230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length10.193969
Min length7

Characters and Unicode

Total characters12508
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique479 ?
Unique (%)39.0%

Sample

1st row127.369137
2nd row127.362905
3rd row127.362905
4th row127.362905
5th row127.362905
ValueCountFrequency (%)
127.338808 145
 
11.8%
127.352326 62
 
5.1%
127.362905 51
 
4.2%
127.327366 32
 
2.6%
127.369137 31
 
2.5%
127.363052 26
 
2.1%
127.379038 25
 
2.0%
127.364334 25
 
2.0%
127.379183 24
 
2.0%
127.331558 24
 
2.0%
Other values (531) 782
63.7%
2023-12-23T06:33:21.152914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 2394
19.1%
1 1801
14.4%
7 1763
14.1%
. 1543
12.3%
3 1445
11.6%
8 865
 
6.9%
0 648
 
5.2%
6 567
 
4.5%
5 540
 
4.3%
9 540
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10965
87.7%
Other Punctuation 1543
 
12.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 2394
21.8%
1 1801
16.4%
7 1763
16.1%
3 1445
13.2%
8 865
 
7.9%
0 648
 
5.9%
6 567
 
5.2%
5 540
 
4.9%
9 540
 
4.9%
4 402
 
3.7%
Other Punctuation
ValueCountFrequency (%)
. 1543
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 12508
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 2394
19.1%
1 1801
14.4%
7 1763
14.1%
. 1543
12.3%
3 1445
11.6%
8 865
 
6.9%
0 648
 
5.2%
6 567
 
4.5%
5 540
 
4.3%
9 540
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12508
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 2394
19.1%
1 1801
14.4%
7 1763
14.1%
. 1543
12.3%
3 1445
11.6%
8 865
 
6.9%
0 648
 
5.2%
6 567
 
4.5%
5 540
 
4.3%
9 540
 
4.3%

Interactions

2023-12-23T06:33:01.027939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:32:57.794721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:32:59.754659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:33:01.642714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:32:58.559943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:33:00.150567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:33:02.138585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:32:59.058908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:33:00.548811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-23T06:33:21.642209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번단속연도단속월단속동명자동차분류
연번1.0000.9600.7840.7060.396
단속연도0.9601.0000.5200.5690.313
단속월0.7840.5201.0000.6430.202
단속동명0.7060.5690.6431.0000.381
자동차분류0.3960.3130.2020.3811.000
2023-12-23T06:33:22.013518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
단속동명자동차분류
단속동명1.0000.189
자동차분류0.1891.000
2023-12-23T06:33:22.449185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번단속연도단속월단속동명자동차분류
연번1.0000.9810.0580.3460.259
단속연도0.9811.000-0.1150.3200.259
단속월0.058-0.1151.0000.2950.122
단속동명0.3460.3200.2951.0000.189
자동차분류0.2590.2590.1220.1891.000

Missing values

2023-12-23T06:33:02.774123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-23T06:33:03.378207image/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

연번단속연도단속월단속동명자동차분류단속위치위도경도
0120181월평3동화물월평동 서대전고등학교 부근36.360522127.369137
1220181월평3동화물월평동 은평공원 부근36.359588127.362905
2320181월평3동화물월평동 은평공원 부근36.359588127.362905
3420181월평3동화물월평동 은평공원 부근36.359588127.362905
4520181월평3동화물월평동 은평공원 부근36.359588127.362905
5620181월평3동화물월평동 은평공원 부근36.359588127.362905
6720181월평3동화물월평동 서대전고등학교 부근36.360522127.369137
7820181월평3동화물월평동 은평공원 부근36.359588127.362905
8920181월평3동화물월평동 은평공원 부근36.359588127.362905
91020181월평3동건설기계월평동 은평공원 앞36.359588127.362905
연번단속연도단속월단속동명자동차분류단속위치위도경도
12171218202312관저1동화물관저1동 계백로부근36.18.1136127.20.5586
12181219202312정림동화물정림동 정림로부근36.18.1056127.21.9815
12191220202312정림동화물정림동 정림로부근36.18.0764127.21.9906
12201221202312정림동화물정림동 정림로부근36.18.0699127.21.9894
12211222202312정림동화물정림동 정림로부근36.18.0310127.22.0080
12221223202312월평1동화물월평1동 월드컵대로부근36.20.8824127.21.0736
12231224202312월평1동화물월평1동 월드컵대로부근36.20.8381127.21.0896
12241225202312월평1동화물월평1동 월드컵대로부근36.20.8299127.21.0924
12251226202312월평1동화물월평1동 계룡로부근36.21.1861127.21.5664
12261227202312갈마1동화물갈마1동 신갈마로부근36.21.2322127.21.8476