Overview

Dataset statistics

Number of variables4
Number of observations793
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory26.5 KiB
Average record size in memory34.2 B

Variable types

Text2
Numeric2

Dataset

Description파일 다운로드
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-15274/F/1/datasetView.do

Alerts

XPOINT is highly overall correlated with YPOINTHigh correlation
YPOINT is highly overall correlated with XPOINTHigh correlation
DCAMERA_ID has unique valuesUnique

Reproduction

Analysis started2024-04-19 06:47:29.466474
Analysis finished2024-04-19 06:47:30.081439
Duration0.61 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

DCAMERA_ID
Text

UNIQUE 

Distinct793
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size6.3 KiB
2024-04-19T15:47:30.264225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters7930
Distinct characters23
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique793 ?
Unique (%)100.0%

Sample

1st rowCDL1000600
2nd rowCDL1000610
3rd rowCDL1000620
4th rowCDL1000630
5th rowCDL1000640
ValueCountFrequency (%)
cdl1000600 1
 
0.1%
cdk5000080 1
 
0.1%
cdd1000020 1
 
0.1%
cdk1000240 1
 
0.1%
cdk1000250 1
 
0.1%
cdk2000040 1
 
0.1%
cdk3000020 1
 
0.1%
cdd1000030 1
 
0.1%
cdk5000140 1
 
0.1%
cdk1000170 1
 
0.1%
Other values (783) 783
98.7%
2024-04-19T15:47:30.612483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 3425
43.2%
D 933
 
11.8%
C 793
 
10.0%
1 595
 
7.5%
5 455
 
5.7%
2 236
 
3.0%
3 215
 
2.7%
4 201
 
2.5%
K 185
 
2.3%
L 149
 
1.9%
Other values (13) 743
 
9.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5515
69.5%
Uppercase Letter 2415
30.5%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
D 933
38.6%
C 793
32.8%
K 185
 
7.7%
L 149
 
6.2%
R 117
 
4.8%
H 67
 
2.8%
B 50
 
2.1%
F 43
 
1.8%
N 30
 
1.2%
G 18
 
0.7%
Other values (3) 30
 
1.2%
Decimal Number
ValueCountFrequency (%)
0 3425
62.1%
1 595
 
10.8%
5 455
 
8.3%
2 236
 
4.3%
3 215
 
3.9%
4 201
 
3.6%
6 129
 
2.3%
7 122
 
2.2%
8 69
 
1.3%
9 68
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
Common 5515
69.5%
Latin 2415
30.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
D 933
38.6%
C 793
32.8%
K 185
 
7.7%
L 149
 
6.2%
R 117
 
4.8%
H 67
 
2.8%
B 50
 
2.1%
F 43
 
1.8%
N 30
 
1.2%
G 18
 
0.7%
Other values (3) 30
 
1.2%
Common
ValueCountFrequency (%)
0 3425
62.1%
1 595
 
10.8%
5 455
 
8.3%
2 236
 
4.3%
3 215
 
3.9%
4 201
 
3.6%
6 129
 
2.3%
7 122
 
2.2%
8 69
 
1.3%
9 68
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7930
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3425
43.2%
D 933
 
11.8%
C 793
 
10.0%
1 595
 
7.5%
5 455
 
5.7%
2 236
 
3.0%
3 215
 
2.7%
4 201
 
2.5%
K 185
 
2.3%
L 149
 
1.9%
Other values (13) 743
 
9.4%

LOCATION
Text

Distinct781
Distinct (%)98.5%
Missing0
Missing (%)0.0%
Memory size6.3 KiB
2024-04-19T15:47:30.913363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length32
Median length28
Mean length19.761665
Min length6

Characters and Unicode

Total characters15671
Distinct characters195
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique772 ?
Unique (%)97.4%

Sample

1st row[올림픽대로 WB]올림픽대교 동측 60m
2nd row[올림픽대로 WB]올림픽대교 진출램프 동측 257m
3rd row[올림픽대로 WB]천호대교 서측 447m
4th row[올림픽대로 WB]천호대교 동측 39m
5th row[올림픽대로 WB]천호대교 진출램프 서측 88m
ValueCountFrequency (%)
올림픽대로 148
 
5.4%
동부간선 133
 
4.9%
동측 114
 
4.2%
강변북로 99
 
3.6%
서측 94
 
3.4%
남측 66
 
2.4%
wb 63
 
2.3%
진입램프 53
 
1.9%
진출 48
 
1.8%
북측 48
 
1.8%
Other values (826) 1868
68.3%
2024-04-19T15:47:31.346982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1961
 
12.5%
m 621
 
4.0%
B 534
 
3.4%
교 507
 
3.2%
[ 491
 
3.1%
] 491
 
3.1%
선 456
 
2.9%
대 445
 
2.8%
( 391
 
2.5%
) 391
 
2.5%
Other values (185) 9383
59.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 8028
51.2%
Space Separator 1961
 
12.5%
Decimal Number 1925
 
12.3%
Uppercase Letter 1370
 
8.7%
Open Punctuation 882
 
5.6%
Close Punctuation 882
 
5.6%
Lowercase Letter 621
 
4.0%
Math Symbol 1
 
< 0.1%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
교 507
 
6.3%
선 456
 
5.7%
대 445
 
5.5%
동 347
 
4.3%
측 324
 
4.0%
로 305
 
3.8%
방 296
 
3.7%
북 278
 
3.5%
진 272
 
3.4%
부 256
 
3.2%
Other values (159) 4542
56.6%
Decimal Number
ValueCountFrequency (%)
0 356
18.5%
1 305
15.8%
5 214
11.1%
2 211
11.0%
3 170
8.8%
4 151
7.8%
6 139
 
7.2%
7 133
 
6.9%
8 128
 
6.6%
9 118
 
6.1%
Uppercase Letter
ValueCountFrequency (%)
B 534
39.0%
W 170
 
12.4%
E 158
 
11.5%
C 151
 
11.0%
I 141
 
10.3%
N 103
 
7.5%
S 103
 
7.5%
J 10
 
0.7%
Open Punctuation
ValueCountFrequency (%)
[ 491
55.7%
( 391
44.3%
Close Punctuation
ValueCountFrequency (%)
] 491
55.7%
) 391
44.3%
Space Separator
ValueCountFrequency (%)
1961
100.0%
Lowercase Letter
ValueCountFrequency (%)
m 621
100.0%
Math Symbol
ValueCountFrequency (%)
+ 1
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 8028
51.2%
Common 5652
36.1%
Latin 1991
 
12.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
교 507
 
6.3%
선 456
 
5.7%
대 445
 
5.5%
동 347
 
4.3%
측 324
 
4.0%
로 305
 
3.8%
방 296
 
3.7%
북 278
 
3.5%
진 272
 
3.4%
부 256
 
3.2%
Other values (159) 4542
56.6%
Common
ValueCountFrequency (%)
1961
34.7%
[ 491
 
8.7%
] 491
 
8.7%
( 391
 
6.9%
) 391
 
6.9%
0 356
 
6.3%
1 305
 
5.4%
5 214
 
3.8%
2 211
 
3.7%
3 170
 
3.0%
Other values (7) 671
 
11.9%
Latin
ValueCountFrequency (%)
m 621
31.2%
B 534
26.8%
W 170
 
8.5%
E 158
 
7.9%
C 151
 
7.6%
I 141
 
7.1%
N 103
 
5.2%
S 103
 
5.2%
J 10
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 8028
51.2%
ASCII 7643
48.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1961
25.7%
m 621
 
8.1%
B 534
 
7.0%
[ 491
 
6.4%
] 491
 
6.4%
( 391
 
5.1%
) 391
 
5.1%
0 356
 
4.7%
1 305
 
4.0%
5 214
 
2.8%
Other values (16) 1888
24.7%
Hangul
ValueCountFrequency (%)
교 507
 
6.3%
선 456
 
5.7%
대 445
 
5.5%
동 347
 
4.3%
측 324
 
4.0%
로 305
 
3.8%
방 296
 
3.7%
북 278
 
3.5%
진 272
 
3.4%
부 256
 
3.2%
Other values (159) 4542
56.6%

XPOINT
Real number (ℝ)

HIGH CORRELATION 

Distinct774
Distinct (%)97.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean104747.24
Minimum0
Maximum214841.06
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2024-04-19T15:47:31.751796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile126.89479
Q1127.02769
median188075.03
Q3203386.38
95-th percentile209105.58
Maximum214841.06
Range214841.06
Interquartile range (IQR)203259.35

Descriptive statistics

Standard deviation100547.07
Coefficient of variation (CV)0.95990183
Kurtosis-1.9893744
Mean104747.24
Median Absolute Deviation (MAD)22811.484
Skewness-0.075805251
Sum83064561
Variance1.0109713 × 1010
MonotonicityNot monotonic
2024-04-19T15:47:31.886488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.06145 3
 
0.4%
127.08328 2
 
0.3%
127.07247 2
 
0.3%
127.03517 2
 
0.3%
127.02445 2
 
0.3%
127.01416 2
 
0.3%
127.08861 2
 
0.3%
127.08344 2
 
0.3%
127.0782 2
 
0.3%
126.98285 2
 
0.3%
Other values (764) 772
97.4%
ValueCountFrequency (%)
0.0 1
0.1%
126.82955 1
0.1%
126.83423 1
0.1%
126.83906 1
0.1%
126.84414 1
0.1%
126.84432 1
0.1%
126.84929 2
0.3%
126.85387 1
0.1%
126.85573 1
0.1%
126.86003 1
0.1%
ValueCountFrequency (%)
214841.06001 1
0.1%
214785.76198 1
0.1%
214445.37484 1
0.1%
214338.74755 1
0.1%
214112.95168 1
0.1%
213893.81803 1
0.1%
213671.69988 1
0.1%
213075.48633 1
0.1%
212409.31833 1
0.1%
211729.60239 1
0.1%

YPOINT
Real number (ℝ)

HIGH CORRELATION 

Distinct775
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean234347.03
Minimum0
Maximum465079.7
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2024-04-19T15:47:32.002220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile37.516602
Q137.55138
median441246.51
Q3449156.17
95-th percentile457479.68
Maximum465079.7
Range465079.7
Interquartile range (IQR)449118.62

Descriptive statistics

Standard deviation224928.45
Coefficient of variation (CV)0.95980927
Kurtosis-1.9970307
Mean234347.03
Median Absolute Deviation (MAD)20167.536
Skewness-0.082573442
Sum1.8583719 × 108
Variance5.0592807 × 1010
MonotonicityNot monotonic
2024-04-19T15:47:32.125222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.61971 2
 
0.3%
37.61611 2
 
0.3%
37.60202 2
 
0.3%
37.61928 2
 
0.3%
37.60345 2
 
0.3%
37.61802 2
 
0.3%
37.55892 2
 
0.3%
37.60252 2
 
0.3%
37.56493 2
 
0.3%
37.55109 2
 
0.3%
Other values (765) 773
97.5%
ValueCountFrequency (%)
0.0 1
0.1%
37.47126 1
0.1%
37.47439 1
0.1%
37.47592 1
0.1%
37.47649 1
0.1%
37.47844 1
0.1%
37.48016 1
0.1%
37.48197 1
0.1%
37.48208 1
0.1%
37.48414 1
0.1%
ValueCountFrequency (%)
465079.70485 2
0.3%
464518.70936 1
0.1%
464162.24229 1
0.1%
464156.10394 1
0.1%
463749.76251 1
0.1%
463296.79099 1
0.1%
462869.46663 1
0.1%
462580.19659 1
0.1%
462578.92659 1
0.1%
462350.41212 1
0.1%

Interactions

2024-04-19T15:47:29.783070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T15:47:29.640052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T15:47:29.857622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T15:47:29.713430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-19T15:47:32.203986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
XPOINTYPOINT
XPOINT1.0001.000
YPOINT1.0001.000
2024-04-19T15:47:32.276940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
XPOINTYPOINT
XPOINT1.0000.768
YPOINT0.7681.000

Missing values

2024-04-19T15:47:29.954891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-19T15:47:30.044023image/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

DCAMERA_IDLOCATIONXPOINTYPOINT
0CDL1000600[올림픽대로 WB]올림픽대교 동측 60m127.1087437.52996
1CDL1000610[올림픽대로 WB]올림픽대교 진출램프 동측 257m127.1101737.53404
2CDL1000620[올림픽대로 WB]천호대교 서측 447m127.114537.53821
3CDL1000630[올림픽대로 WB]천호대교 동측 39m127.1184737.54153
4CDL1000640[올림픽대로 WB]천호대교 진출램프 서측 88m127.1210537.54598
5CDL1000650[올림픽대로 WB]암사 진입램프 서측 147m127.1230937.55138
6CDL1000660[올림픽대로 WB]암사 진출램프 서측 377m127.1244437.55556
7CDL1000670[올림픽대로 WB]암사 진출램프 동측 232m127.1278537.5607
8CDL1000680[올림픽대로 WB]강동대교 서측 3155m127.133637.56524
9CDL1000690[올림픽대로 WB]강동대교 서측 2375m127.1409837.56699
DCAMERA_IDLOCATIONXPOINTYPOINT
783CDD6000030[동부간선 NB]군자교IC 진입206643.42556451510.07658
784CDD6000040[동부간선 NB]장안교IC 진입206833.05722453658.36084
785CDD6000050[동부간선 NB]중랑교IC 진입206233.40259455140.93138
786CDD6000060[동부간선 NB]월릉교IC 진입206350.67166457809.20706
787CDD6000070[동부간선 NB]월계1교IC 진입205568.10427459329.85855
788CDD6000080[동부간선 NB]녹천교IC 진입205035.88546460716.74388
789CDD6000090[동부간선 NB]창동교IC 진입204867.00158461593.29698
790CDD6000100[동부간선 NB]상계교IC 진입204625.15692462580.19659
791CDD7000080[동부간선 NB]군자교IC 진출206207.10295450809.29403
792CDD7000090[동부간선 NB]장안교IC 진출206950.92933453385.30147