Overview

Dataset statistics

Number of variables12
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.0 MiB
Average record size in memory109.0 B

Variable types

DateTime2
Numeric2
Categorical4
Text4

Dataset

Description판교제로시티 CCTV 데이터 2D 바운딩박스 조회
Author차세대융합기술연구원
URLhttps://data.gg.go.kr/portal/data/service/selectServicePage.do?&infId=6RPQYGPAS5SY9L1Q25K731873710&infSeq=1

Alerts

이미지너비 has constant value ""Constant
이미지높이 has constant value ""Constant
이벤트유형 is highly overall correlated with 이미지고유번호 and 1 other fieldsHigh correlation
이미지고유번호 is highly overall correlated with 이벤트유형 and 1 other fieldsHigh correlation
CCTV고유번호 is highly overall correlated with 이벤트유형 and 1 other fieldsHigh correlation
이미지고유번호 has unique valuesUnique
이미지파일명 has unique valuesUnique
이미지파일경로 has unique valuesUnique
어노테이션json파일명 has unique valuesUnique
어노테이션json파일경로 has unique valuesUnique

Reproduction

Analysis started2024-03-23 02:38:51.329791
Analysis finished2024-03-23 02:38:55.306159
Duration3.98 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

녹화시작일시
Date

Distinct147
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2021-05-25 08:30:32
Maximum2021-11-11 15:34:40
2024-03-23T02:38:55.492930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T02:38:55.937766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

녹화종료일시
Date

Distinct147
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2021-05-25 08:31:02
Maximum2021-11-11 15:35:00
2024-03-23T02:38:56.382536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T02:38:56.910867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

이벤트유형
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1904
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-23T02:38:57.285395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median5
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.1438767
Coefficient of variation (CV)0.51161625
Kurtosis-1.4866036
Mean4.1904
Median Absolute Deviation (MAD)2
Skewness-0.16995601
Sum41904
Variance4.5962075
MonotonicityNot monotonic
2024-03-23T02:38:57.583026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
6 2187
21.9%
2 1980
19.8%
7 1666
16.7%
1 1398
14.0%
5 1381
13.8%
3 695
 
7.0%
4 693
 
6.9%
ValueCountFrequency (%)
1 1398
14.0%
2 1980
19.8%
3 695
 
7.0%
4 693
 
6.9%
5 1381
13.8%
6 2187
21.9%
7 1666
16.7%
ValueCountFrequency (%)
7 1666
16.7%
6 2187
21.9%
5 1381
13.8%
4 693
 
6.9%
3 695
 
7.0%
2 1980
19.8%
1 1398
14.0%

날씨정보
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
5685 
2
4315 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 5685
56.9%
2 4315
43.1%

Length

2024-03-23T02:38:57.960848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-23T02:38:58.284814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 5685
56.9%
2 4315
43.1%

CCTV고유번호
Categorical

HIGH CORRELATION 

Distinct33
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
CIT-SDB-3022
1534 
CIT-SDB-3023
1507 
CIT-SDB-3033
825 
CIT-SDB-3051
763 
CIT-SDB-3031
747 
Other values (28)
4624 

Length

Max length12
Median length12
Mean length12
Min length12

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCIT-SDB-3044
2nd rowCIT-SDB-3044
3rd rowCIT-SDB-3044
4th rowCIT-SDB-3063
5th rowCIT-SDB-3062

Common Values

ValueCountFrequency (%)
CIT-SDB-3022 1534
15.3%
CIT-SDB-3023 1507
15.1%
CIT-SDB-3033 825
8.2%
CIT-SDB-3051 763
7.6%
CIT-SDB-3031 747
7.5%
CIT-SDB-3042 743
7.4%
CIT-SDB-3044 730
7.3%
CIT-SDB-3025 716
7.2%
CIT-SDB-3061 573
 
5.7%
CIT-SDB-3052 410
 
4.1%
Other values (23) 1452
14.5%

Length

2024-03-23T02:38:58.592914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
cit-sdb-3022 1534
15.3%
cit-sdb-3023 1507
15.1%
cit-sdb-3033 825
8.2%
cit-sdb-3051 763
7.6%
cit-sdb-3031 747
7.5%
cit-sdb-3042 743
7.4%
cit-sdb-3044 730
7.3%
cit-sdb-3025 716
7.2%
cit-sdb-3061 573
 
5.7%
cit-sdb-3052 410
 
4.1%
Other values (23) 1452
14.5%

이미지고유번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23383.501
Minimum1
Maximum59500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-23T02:38:59.022632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile717.85
Q14618.5
median9344.5
Q344816.5
95-th percentile58799.1
Maximum59500
Range59499
Interquartile range (IQR)40198

Descriptive statistics

Standard deviation23000.69
Coefficient of variation (CV)0.98362901
Kurtosis-1.527885
Mean23383.501
Median Absolute Deviation (MAD)7663
Skewness0.53918559
Sum2.3383502 × 108
Variance5.2903176 × 108
MonotonicityNot monotonic
2024-03-23T02:38:59.469678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1513 1
 
< 0.1%
9374 1
 
< 0.1%
10943 1
 
< 0.1%
58818 1
 
< 0.1%
8838 1
 
< 0.1%
327 1
 
< 0.1%
58014 1
 
< 0.1%
1055 1
 
< 0.1%
5383 1
 
< 0.1%
58206 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
11 1
< 0.1%
14 1
< 0.1%
ValueCountFrequency (%)
59500 1
< 0.1%
59499 1
< 0.1%
59497 1
< 0.1%
59496 1
< 0.1%
59495 1
< 0.1%
59494 1
< 0.1%
59492 1
< 0.1%
59489 1
< 0.1%
59488 1
< 0.1%
59487 1
< 0.1%

이미지너비
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1280
10000 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1280
2nd row1280
3rd row1280
4th row1280
5th row1280

Common Values

ValueCountFrequency (%)
1280 10000
100.0%

Length

2024-03-23T02:38:59.858969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-23T02:39:00.180615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1280 10000
100.0%

이미지높이
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
720
10000 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row720
2nd row720
3rd row720
4th row720
5th row720

Common Values

ValueCountFrequency (%)
720 10000
100.0%

Length

2024-03-23T02:39:00.481337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-23T02:39:00.850791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
720 10000
100.0%

이미지파일명
Text

UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-03-23T02:39:01.343423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length18
Mean length18
Min length18

Characters and Unicode

Total characters180000
Distinct characters18
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10000 ?
Unique (%)100.0%

Sample

1st rowct_2d_00001513.jpg
2nd rowct_2d_00001666.jpg
3rd rowct_2d_00001788.jpg
4th rowct_2d_00043665.jpg
5th rowct_2d_00043539.jpg
ValueCountFrequency (%)
ct_2d_00001513.jpg 1
 
< 0.1%
ct_2d_00001055.jpg 1
 
< 0.1%
ct_2d_00005911.jpg 1
 
< 0.1%
ct_2d_00058925.jpg 1
 
< 0.1%
ct_2d_00010943.jpg 1
 
< 0.1%
ct_2d_00058818.jpg 1
 
< 0.1%
ct_2d_00008838.jpg 1
 
< 0.1%
ct_2d_00000327.jpg 1
 
< 0.1%
ct_2d_00058014.jpg 1
 
< 0.1%
ct_2d_00009374.jpg 1
 
< 0.1%
Other values (9990) 9990
99.9%
2024-03-23T02:39:02.217313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 39811
22.1%
_ 20000
11.1%
2 13400
 
7.4%
c 10000
 
5.6%
t 10000
 
5.6%
g 10000
 
5.6%
p 10000
 
5.6%
j 10000
 
5.6%
. 10000
 
5.6%
d 10000
 
5.6%
Other values (8) 36789
20.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 90000
50.0%
Lowercase Letter 60000
33.3%
Connector Punctuation 20000
 
11.1%
Other Punctuation 10000
 
5.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 39811
44.2%
2 13400
 
14.9%
4 5958
 
6.6%
5 5889
 
6.5%
1 4564
 
5.1%
6 4378
 
4.9%
8 4112
 
4.6%
9 4102
 
4.6%
3 4004
 
4.4%
7 3782
 
4.2%
Lowercase Letter
ValueCountFrequency (%)
c 10000
16.7%
t 10000
16.7%
g 10000
16.7%
p 10000
16.7%
j 10000
16.7%
d 10000
16.7%
Connector Punctuation
ValueCountFrequency (%)
_ 20000
100.0%
Other Punctuation
ValueCountFrequency (%)
. 10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 120000
66.7%
Latin 60000
33.3%

Most frequent character per script

Common
ValueCountFrequency (%)
0 39811
33.2%
_ 20000
16.7%
2 13400
 
11.2%
. 10000
 
8.3%
4 5958
 
5.0%
5 5889
 
4.9%
1 4564
 
3.8%
6 4378
 
3.6%
8 4112
 
3.4%
9 4102
 
3.4%
Other values (2) 7786
 
6.5%
Latin
ValueCountFrequency (%)
c 10000
16.7%
t 10000
16.7%
g 10000
16.7%
p 10000
16.7%
j 10000
16.7%
d 10000
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 180000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 39811
22.1%
_ 20000
11.1%
2 13400
 
7.4%
c 10000
 
5.6%
t 10000
 
5.6%
g 10000
 
5.6%
p 10000
 
5.6%
j 10000
 
5.6%
. 10000
 
5.6%
d 10000
 
5.6%
Other values (8) 36789
20.4%

이미지파일경로
Text

UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-03-23T02:39:02.969587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length71
Median length71
Mean length71
Min length71

Characters and Unicode

Total characters710000
Distinct characters32
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10000 ?
Unique (%)100.0%

Sample

1st rowhttp://ggzerocity-data.or.kr/opn/02/cctv/2d/20210902/ct_2d_00001513.jpg
2nd rowhttp://ggzerocity-data.or.kr/opn/02/cctv/2d/20210902/ct_2d_00001666.jpg
3rd rowhttp://ggzerocity-data.or.kr/opn/02/cctv/2d/20210902/ct_2d_00001788.jpg
4th rowhttp://ggzerocity-data.or.kr/opn/02/cctv/2d/20210902/ct_2d_00043665.jpg
5th rowhttp://ggzerocity-data.or.kr/opn/02/cctv/2d/20210902/ct_2d_00043539.jpg
ValueCountFrequency (%)
http://ggzerocity-data.or.kr/opn/02/cctv/2d/20210902/ct_2d_00001513.jpg 1
 
< 0.1%
http://ggzerocity-data.or.kr/opn/02/cctv/2d/20210902/ct_2d_00001055.jpg 1
 
< 0.1%
http://ggzerocity-data.or.kr/opn/02/cctv/2d/20210902/ct_2d_00005911.jpg 1
 
< 0.1%
http://ggzerocity-data.or.kr/opn/02/cctv/2d/20210902/ct_2d_00058925.jpg 1
 
< 0.1%
http://ggzerocity-data.or.kr/opn/02/cctv/2d/20210902/ct_2d_00010943.jpg 1
 
< 0.1%
http://ggzerocity-data.or.kr/opn/02/cctv/2d/20210902/ct_2d_00058818.jpg 1
 
< 0.1%
http://ggzerocity-data.or.kr/opn/02/cctv/2d/20210902/ct_2d_00008838.jpg 1
 
< 0.1%
http://ggzerocity-data.or.kr/opn/02/cctv/2d/20210902/ct_2d_00000327.jpg 1
 
< 0.1%
http://ggzerocity-data.or.kr/opn/02/cctv/2d/20210902/ct_2d_00058014.jpg 1
 
< 0.1%
http://ggzerocity-data.or.kr/opn/02/cctv/2d/20210902/ct_2d_00009374.jpg 1
 
< 0.1%
Other values (9990) 9990
99.9%
2024-03-23T02:39:04.008682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 80000
 
11.3%
0 79811
 
11.2%
2 63400
 
8.9%
t 60000
 
8.5%
c 40000
 
5.6%
r 30000
 
4.2%
d 30000
 
4.2%
. 30000
 
4.2%
g 30000
 
4.2%
p 30000
 
4.2%
Other values (22) 236789
33.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 360000
50.7%
Decimal Number 200000
28.2%
Other Punctuation 120000
 
16.9%
Connector Punctuation 20000
 
2.8%
Dash Punctuation 10000
 
1.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 60000
16.7%
c 40000
11.1%
r 30000
8.3%
d 30000
8.3%
g 30000
8.3%
p 30000
8.3%
o 30000
8.3%
a 20000
 
5.6%
n 10000
 
2.8%
v 10000
 
2.8%
Other values (7) 70000
19.4%
Decimal Number
ValueCountFrequency (%)
0 79811
39.9%
2 63400
31.7%
1 14564
 
7.3%
9 14102
 
7.1%
4 5958
 
3.0%
5 5889
 
2.9%
6 4378
 
2.2%
8 4112
 
2.1%
3 4004
 
2.0%
7 3782
 
1.9%
Other Punctuation
ValueCountFrequency (%)
/ 80000
66.7%
. 30000
 
25.0%
: 10000
 
8.3%
Connector Punctuation
ValueCountFrequency (%)
_ 20000
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 360000
50.7%
Common 350000
49.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 60000
16.7%
c 40000
11.1%
r 30000
8.3%
d 30000
8.3%
g 30000
8.3%
p 30000
8.3%
o 30000
8.3%
a 20000
 
5.6%
n 10000
 
2.8%
v 10000
 
2.8%
Other values (7) 70000
19.4%
Common
ValueCountFrequency (%)
/ 80000
22.9%
0 79811
22.8%
2 63400
18.1%
. 30000
 
8.6%
_ 20000
 
5.7%
1 14564
 
4.2%
9 14102
 
4.0%
- 10000
 
2.9%
: 10000
 
2.9%
4 5958
 
1.7%
Other values (5) 22165
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 710000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 80000
 
11.3%
0 79811
 
11.2%
2 63400
 
8.9%
t 60000
 
8.5%
c 40000
 
5.6%
r 30000
 
4.2%
d 30000
 
4.2%
. 30000
 
4.2%
g 30000
 
4.2%
p 30000
 
4.2%
Other values (22) 236789
33.4%

어노테이션json파일명
Text

UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-03-23T02:39:04.541446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters190000
Distinct characters19
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10000 ?
Unique (%)100.0%

Sample

1st rowct_2d_00001513.json
2nd rowct_2d_00001666.json
3rd rowct_2d_00001788.json
4th rowct_2d_00043665.json
5th rowct_2d_00043539.json
ValueCountFrequency (%)
ct_2d_00001513.json 1
 
< 0.1%
ct_2d_00001055.json 1
 
< 0.1%
ct_2d_00005911.json 1
 
< 0.1%
ct_2d_00058925.json 1
 
< 0.1%
ct_2d_00010943.json 1
 
< 0.1%
ct_2d_00058818.json 1
 
< 0.1%
ct_2d_00008838.json 1
 
< 0.1%
ct_2d_00000327.json 1
 
< 0.1%
ct_2d_00058014.json 1
 
< 0.1%
ct_2d_00009374.json 1
 
< 0.1%
Other values (9990) 9990
99.9%
2024-03-23T02:39:05.905712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 39811
21.0%
_ 20000
10.5%
2 13400
 
7.1%
c 10000
 
5.3%
t 10000
 
5.3%
n 10000
 
5.3%
o 10000
 
5.3%
s 10000
 
5.3%
j 10000
 
5.3%
. 10000
 
5.3%
Other values (9) 46789
24.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 90000
47.4%
Lowercase Letter 70000
36.8%
Connector Punctuation 20000
 
10.5%
Other Punctuation 10000
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 39811
44.2%
2 13400
 
14.9%
4 5958
 
6.6%
5 5889
 
6.5%
1 4564
 
5.1%
6 4378
 
4.9%
8 4112
 
4.6%
9 4102
 
4.6%
3 4004
 
4.4%
7 3782
 
4.2%
Lowercase Letter
ValueCountFrequency (%)
c 10000
14.3%
t 10000
14.3%
n 10000
14.3%
o 10000
14.3%
s 10000
14.3%
j 10000
14.3%
d 10000
14.3%
Connector Punctuation
ValueCountFrequency (%)
_ 20000
100.0%
Other Punctuation
ValueCountFrequency (%)
. 10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 120000
63.2%
Latin 70000
36.8%

Most frequent character per script

Common
ValueCountFrequency (%)
0 39811
33.2%
_ 20000
16.7%
2 13400
 
11.2%
. 10000
 
8.3%
4 5958
 
5.0%
5 5889
 
4.9%
1 4564
 
3.8%
6 4378
 
3.6%
8 4112
 
3.4%
9 4102
 
3.4%
Other values (2) 7786
 
6.5%
Latin
ValueCountFrequency (%)
c 10000
14.3%
t 10000
14.3%
n 10000
14.3%
o 10000
14.3%
s 10000
14.3%
j 10000
14.3%
d 10000
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 190000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 39811
21.0%
_ 20000
10.5%
2 13400
 
7.1%
c 10000
 
5.3%
t 10000
 
5.3%
n 10000
 
5.3%
o 10000
 
5.3%
s 10000
 
5.3%
j 10000
 
5.3%
. 10000
 
5.3%
Other values (9) 46789
24.6%

어노테이션json파일경로
Text

UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-03-23T02:39:06.806466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length72
Median length72
Mean length72
Min length72

Characters and Unicode

Total characters720000
Distinct characters33
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10000 ?
Unique (%)100.0%

Sample

1st rowhttp://ggzerocity-data.or.kr/opn/02/cctv/2d/20210902/ct_2d_00001513.json
2nd rowhttp://ggzerocity-data.or.kr/opn/02/cctv/2d/20210902/ct_2d_00001666.json
3rd rowhttp://ggzerocity-data.or.kr/opn/02/cctv/2d/20210902/ct_2d_00001788.json
4th rowhttp://ggzerocity-data.or.kr/opn/02/cctv/2d/20210902/ct_2d_00043665.json
5th rowhttp://ggzerocity-data.or.kr/opn/02/cctv/2d/20210902/ct_2d_00043539.json
ValueCountFrequency (%)
http://ggzerocity-data.or.kr/opn/02/cctv/2d/20210902/ct_2d_00001513.json 1
 
< 0.1%
http://ggzerocity-data.or.kr/opn/02/cctv/2d/20210902/ct_2d_00001055.json 1
 
< 0.1%
http://ggzerocity-data.or.kr/opn/02/cctv/2d/20210902/ct_2d_00005911.json 1
 
< 0.1%
http://ggzerocity-data.or.kr/opn/02/cctv/2d/20210902/ct_2d_00058925.json 1
 
< 0.1%
http://ggzerocity-data.or.kr/opn/02/cctv/2d/20210902/ct_2d_00010943.json 1
 
< 0.1%
http://ggzerocity-data.or.kr/opn/02/cctv/2d/20210902/ct_2d_00058818.json 1
 
< 0.1%
http://ggzerocity-data.or.kr/opn/02/cctv/2d/20210902/ct_2d_00008838.json 1
 
< 0.1%
http://ggzerocity-data.or.kr/opn/02/cctv/2d/20210902/ct_2d_00000327.json 1
 
< 0.1%
http://ggzerocity-data.or.kr/opn/02/cctv/2d/20210902/ct_2d_00058014.json 1
 
< 0.1%
http://ggzerocity-data.or.kr/opn/02/cctv/2d/20210902/ct_2d_00009374.json 1
 
< 0.1%
Other values (9990) 9990
99.9%
2024-03-23T02:39:08.171670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 80000
 
11.1%
0 79811
 
11.1%
2 63400
 
8.8%
t 60000
 
8.3%
o 40000
 
5.6%
c 40000
 
5.6%
r 30000
 
4.2%
d 30000
 
4.2%
. 30000
 
4.2%
_ 20000
 
2.8%
Other values (23) 246789
34.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 370000
51.4%
Decimal Number 200000
27.8%
Other Punctuation 120000
 
16.7%
Connector Punctuation 20000
 
2.8%
Dash Punctuation 10000
 
1.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 60000
16.2%
o 40000
10.8%
c 40000
10.8%
r 30000
 
8.1%
d 30000
 
8.1%
g 20000
 
5.4%
a 20000
 
5.4%
p 20000
 
5.4%
n 20000
 
5.4%
j 10000
 
2.7%
Other values (8) 80000
21.6%
Decimal Number
ValueCountFrequency (%)
0 79811
39.9%
2 63400
31.7%
1 14564
 
7.3%
9 14102
 
7.1%
4 5958
 
3.0%
5 5889
 
2.9%
6 4378
 
2.2%
8 4112
 
2.1%
3 4004
 
2.0%
7 3782
 
1.9%
Other Punctuation
ValueCountFrequency (%)
/ 80000
66.7%
. 30000
 
25.0%
: 10000
 
8.3%
Connector Punctuation
ValueCountFrequency (%)
_ 20000
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 370000
51.4%
Common 350000
48.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 60000
16.2%
o 40000
10.8%
c 40000
10.8%
r 30000
 
8.1%
d 30000
 
8.1%
g 20000
 
5.4%
a 20000
 
5.4%
p 20000
 
5.4%
n 20000
 
5.4%
j 10000
 
2.7%
Other values (8) 80000
21.6%
Common
ValueCountFrequency (%)
/ 80000
22.9%
0 79811
22.8%
2 63400
18.1%
. 30000
 
8.6%
_ 20000
 
5.7%
1 14564
 
4.2%
9 14102
 
4.0%
- 10000
 
2.9%
: 10000
 
2.9%
4 5958
 
1.7%
Other values (5) 22165
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 720000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 80000
 
11.1%
0 79811
 
11.1%
2 63400
 
8.8%
t 60000
 
8.3%
o 40000
 
5.6%
c 40000
 
5.6%
r 30000
 
4.2%
d 30000
 
4.2%
. 30000
 
4.2%
_ 20000
 
2.8%
Other values (23) 246789
34.3%

Interactions

2024-03-23T02:38:53.702781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T02:38:53.162136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T02:38:53.996927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T02:38:53.423546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-23T02:39:08.448706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
이벤트유형날씨정보CCTV고유번호이미지고유번호
이벤트유형1.0000.3640.9600.886
날씨정보0.3641.0000.4650.319
CCTV고유번호0.9600.4651.0000.969
이미지고유번호0.8860.3190.9691.000
2024-03-23T02:39:08.702899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
CCTV고유번호날씨정보
CCTV고유번호1.0000.395
날씨정보0.3951.000
2024-03-23T02:39:08.965313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
이벤트유형이미지고유번호날씨정보CCTV고유번호
이벤트유형1.0000.5150.3900.812
이미지고유번호0.5151.0000.3890.866
날씨정보0.3900.3891.0000.395
CCTV고유번호0.8120.8660.3951.000

Missing values

2024-03-23T02:38:54.308106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-23T02:38:54.994057image/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

녹화시작일시녹화종료일시이벤트유형날씨정보CCTV고유번호이미지고유번호이미지너비이미지높이이미지파일명이미지파일경로어노테이션json파일명어노테이션json파일경로
70092021-06-15 08:33:252021-06-15 08:33:5552CIT-SDB-304415131280720ct_2d_00001513.jpghttp://ggzerocity-data.or.kr/opn/02/cctv/2d/20210902/ct_2d_00001513.jpgct_2d_00001513.jsonhttp://ggzerocity-data.or.kr/opn/02/cctv/2d/20210902/ct_2d_00001513.json
62572021-06-15 08:50:502021-06-15 08:51:2052CIT-SDB-304416661280720ct_2d_00001666.jpghttp://ggzerocity-data.or.kr/opn/02/cctv/2d/20210902/ct_2d_00001666.jpgct_2d_00001666.jsonhttp://ggzerocity-data.or.kr/opn/02/cctv/2d/20210902/ct_2d_00001666.json
40422021-06-10 18:12:432021-06-10 18:13:1352CIT-SDB-304417881280720ct_2d_00001788.jpghttp://ggzerocity-data.or.kr/opn/02/cctv/2d/20210902/ct_2d_00001788.jpgct_2d_00001788.jsonhttp://ggzerocity-data.or.kr/opn/02/cctv/2d/20210902/ct_2d_00001788.json
122662021-11-11 14:24:402021-11-11 14:25:0071CIT-SDB-3063436651280720ct_2d_00043665.jpghttp://ggzerocity-data.or.kr/opn/02/cctv/2d/20210902/ct_2d_00043665.jpgct_2d_00043665.jsonhttp://ggzerocity-data.or.kr/opn/02/cctv/2d/20210902/ct_2d_00043665.json
124412021-11-11 14:24:352021-11-11 14:24:5571CIT-SDB-3062435391280720ct_2d_00043539.jpghttp://ggzerocity-data.or.kr/opn/02/cctv/2d/20210902/ct_2d_00043539.jpgct_2d_00043539.jsonhttp://ggzerocity-data.or.kr/opn/02/cctv/2d/20210902/ct_2d_00043539.json
55982021-06-03 18:20:462021-06-03 18:21:1632CIT-SDB-3023107991280720ct_2d_00010799.jpghttp://ggzerocity-data.or.kr/opn/02/cctv/2d/20210902/ct_2d_00010799.jpgct_2d_00010799.jsonhttp://ggzerocity-data.or.kr/opn/02/cctv/2d/20210902/ct_2d_00010799.json
80752021-06-10 08:30:522021-06-10 08:31:2211CIT-SDB-303181131280720ct_2d_00008113.jpghttp://ggzerocity-data.or.kr/opn/02/cctv/2d/20210902/ct_2d_00008113.jpgct_2d_00008113.jsonhttp://ggzerocity-data.or.kr/opn/02/cctv/2d/20210902/ct_2d_00008113.json
123522021-11-11 14:21:552021-11-11 14:22:1571CIT-SDB-3045431931280720ct_2d_00043193.jpghttp://ggzerocity-data.or.kr/opn/02/cctv/2d/20210902/ct_2d_00043193.jpgct_2d_00043193.jsonhttp://ggzerocity-data.or.kr/opn/02/cctv/2d/20210902/ct_2d_00043193.json
20412021-05-27 08:39:352021-05-27 08:40:0522CIT-SDB-306155441280720ct_2d_00005544.jpghttp://ggzerocity-data.or.kr/opn/02/cctv/2d/20210902/ct_2d_00005544.jpgct_2d_00005544.jsonhttp://ggzerocity-data.or.kr/opn/02/cctv/2d/20210902/ct_2d_00005544.json
24692021-05-31 08:49:092021-05-31 08:49:3951CIT-SDB-308221280720ct_2d_00000002.jpghttp://ggzerocity-data.or.kr/opn/02/cctv/2d/20210902/ct_2d_00000002.jpgct_2d_00000002.jsonhttp://ggzerocity-data.or.kr/opn/02/cctv/2d/20210902/ct_2d_00000002.json
녹화시작일시녹화종료일시이벤트유형날씨정보CCTV고유번호이미지고유번호이미지너비이미지높이이미지파일명이미지파일경로어노테이션json파일명어노테이션json파일경로
99932021-07-26 18:19:352021-07-26 18:20:0551CIT-SDB-30822591280720ct_2d_00000259.jpghttp://ggzerocity-data.or.kr/opn/02/cctv/2d/20210902/ct_2d_00000259.jpgct_2d_00000259.jsonhttp://ggzerocity-data.or.kr/opn/02/cctv/2d/20210902/ct_2d_00000259.json
3552021-05-25 08:35:032021-05-25 08:35:3312CIT-SDB-302396721280720ct_2d_00009672.jpghttp://ggzerocity-data.or.kr/opn/02/cctv/2d/20210902/ct_2d_00009672.jpgct_2d_00009672.jsonhttp://ggzerocity-data.or.kr/opn/02/cctv/2d/20210902/ct_2d_00009672.json
47602021-06-02 08:30:552021-06-02 08:31:2551CIT-SDB-30449111280720ct_2d_00000911.jpghttp://ggzerocity-data.or.kr/opn/02/cctv/2d/20210902/ct_2d_00000911.jpgct_2d_00000911.jsonhttp://ggzerocity-data.or.kr/opn/02/cctv/2d/20210902/ct_2d_00000911.json
88002021-06-16 18:18:502021-06-16 18:19:2061CIT-SDB-3042587131280720ct_2d_00058713.jpghttp://ggzerocity-data.or.kr/opn/02/cctv/2d/20210902/ct_2d_00058713.jpgct_2d_00058713.jsonhttp://ggzerocity-data.or.kr/opn/02/cctv/2d/20210902/ct_2d_00058713.json
65562021-06-28 18:37:042021-06-28 18:37:3422CIT-SDB-306158181280720ct_2d_00005818.jpghttp://ggzerocity-data.or.kr/opn/02/cctv/2d/20210902/ct_2d_00005818.jpgct_2d_00005818.jsonhttp://ggzerocity-data.or.kr/opn/02/cctv/2d/20210902/ct_2d_00005818.json
92592021-11-11 14:18:552021-11-11 14:19:1571CIT-SDB-3023421571280720ct_2d_00042157.jpghttp://ggzerocity-data.or.kr/opn/02/cctv/2d/20210902/ct_2d_00042157.jpgct_2d_00042157.jsonhttp://ggzerocity-data.or.kr/opn/02/cctv/2d/20210902/ct_2d_00042157.json
119922021-11-11 15:34:252021-11-11 15:34:4571CIT-SDB-1011460431280720ct_2d_00046043.jpghttp://ggzerocity-data.or.kr/opn/02/cctv/2d/20210902/ct_2d_00046043.jpgct_2d_00046043.jsonhttp://ggzerocity-data.or.kr/opn/02/cctv/2d/20210902/ct_2d_00046043.json
109212021-07-15 18:39:422021-07-15 18:40:1242CIT-SDB-302239051280720ct_2d_00003905.jpghttp://ggzerocity-data.or.kr/opn/02/cctv/2d/20210902/ct_2d_00003905.jpgct_2d_00003905.jsonhttp://ggzerocity-data.or.kr/opn/02/cctv/2d/20210902/ct_2d_00003905.json
4892021-05-25 08:36:462021-05-25 08:37:1612CIT-SDB-303187131280720ct_2d_00008713.jpghttp://ggzerocity-data.or.kr/opn/02/cctv/2d/20210902/ct_2d_00008713.jpgct_2d_00008713.jsonhttp://ggzerocity-data.or.kr/opn/02/cctv/2d/20210902/ct_2d_00008713.json
134372021-08-23 18:13:452021-08-23 18:14:0552CIT-SDB-30527081280720ct_2d_00000708.jpghttp://ggzerocity-data.or.kr/opn/02/cctv/2d/20210902/ct_2d_00000708.jpgct_2d_00000708.jsonhttp://ggzerocity-data.or.kr/opn/02/cctv/2d/20210902/ct_2d_00000708.json