Overview

Dataset statistics

Number of variables15
Number of observations10000
Missing cells30000
Missing cells (%)20.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.3 MiB
Average record size in memory134.0 B

Variable types

DateTime2
Categorical4
Unsupported1
Text7
Numeric1

Dataset

Description판교제로시티 CCTV 데이터 2D세그멘테이션 조회
Author차세대융합기술연구원
URLhttps://data.gg.go.kr/portal/data/service/selectServicePage.do?&infId=LN8GFODGETJRWNATNJWD31881956&infSeq=1

Alerts

이벤트유형 has constant value ""Constant
이미지너비 has constant value ""Constant
이미지높이 has constant value ""Constant
날씨정보 has 10000 (100.0%) missing valuesMissing
고정밀세그멘테이션json파일명 has 4999 (50.0%) missing valuesMissing
고정밀세그멘테이션json파일경로 has 4999 (50.0%) missing valuesMissing
중정밀세그멘테이션json파일명 has 5001 (50.0%) missing valuesMissing
중정밀세그멘테이션json파일경로 has 5001 (50.0%) missing valuesMissing
이미지파일명 has unique valuesUnique
이미지파일경로 has unique valuesUnique
날씨정보 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-03-23 01:34:40.577511
Analysis finished2024-03-23 01:34:44.400074
Duration3.82 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

녹화시작일시
Date

Distinct128
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2021-09-02 10:40:40
Maximum2021-11-11 15:34:40
2024-03-23T01:34:44.654760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:34:45.099329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

녹화종료일시
Date

Distinct130
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2021-09-02 10:40:51
Maximum2021-11-11 15:35:00
2024-03-23T01:34:45.495474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:34:45.928323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

이벤트유형
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
7
10000 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row7
2nd row7
3rd row7
4th row7
5th row7

Common Values

ValueCountFrequency (%)
7 10000
100.0%

Length

2024-03-23T01:34:46.383756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-23T01:34:46.647077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
7 10000
100.0%

날씨정보
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size166.0 KiB

세그멘테이션구분
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
5001 
2
4999 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 5001
50.0%
2 4999
50.0%

Length

2024-03-23T01:34:46.923040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-23T01:34:47.296642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 5001
50.0%
2 4999
50.0%

CCTV고유번호
Text

Distinct59
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-03-23T01:34:47.764181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters120000
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowARO-SDB-3022
2nd rowARO-SDB-1031
3rd rowARO-SDB-1022
4th rowARO-SDB-3011
5th rowARO-SDB-3012
ValueCountFrequency (%)
aro-sdb-1031 582
 
5.8%
cit-sdb-3022 381
 
3.8%
aro-sdb-1012 334
 
3.3%
aro-sdb-1022 315
 
3.1%
aro-sdb-1032 312
 
3.1%
aro-sdb-3022 283
 
2.8%
aro-sdb-3012 278
 
2.8%
aro-sdb-3021 274
 
2.7%
aro-sdb-3011 269
 
2.7%
cit-sdb-3023 261
 
2.6%
Other values (49) 6711
67.1%
2024-03-23T01:34:48.705685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 20000
16.7%
3 10840
9.0%
S 10000
 
8.3%
D 10000
 
8.3%
0 10000
 
8.3%
B 9853
 
8.2%
1 7643
 
6.4%
2 5636
 
4.7%
A 5270
 
4.4%
O 5270
 
4.4%
Other values (10) 25488
21.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 60000
50.0%
Decimal Number 40000
33.3%
Dash Punctuation 20000
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 10840
27.1%
0 10000
25.0%
1 7643
19.1%
2 5636
14.1%
4 1483
 
3.7%
5 1342
 
3.4%
6 928
 
2.3%
7 790
 
2.0%
9 788
 
2.0%
8 550
 
1.4%
Uppercase Letter
ValueCountFrequency (%)
S 10000
16.7%
D 10000
16.7%
B 9853
16.4%
A 5270
8.8%
O 5270
8.8%
R 5270
8.8%
C 4877
8.1%
T 4730
7.9%
I 4730
7.9%
Dash Punctuation
ValueCountFrequency (%)
- 20000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 60000
50.0%
Latin 60000
50.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 20000
33.3%
3 10840
18.1%
0 10000
16.7%
1 7643
 
12.7%
2 5636
 
9.4%
4 1483
 
2.5%
5 1342
 
2.2%
6 928
 
1.5%
7 790
 
1.3%
9 788
 
1.3%
Latin
ValueCountFrequency (%)
S 10000
16.7%
D 10000
16.7%
B 9853
16.4%
A 5270
8.8%
O 5270
8.8%
R 5270
8.8%
C 4877
8.1%
T 4730
7.9%
I 4730
7.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 120000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 20000
16.7%
3 10840
9.0%
S 10000
 
8.3%
D 10000
 
8.3%
0 10000
 
8.3%
B 9853
 
8.2%
1 7643
 
6.4%
2 5636
 
4.7%
A 5270
 
4.4%
O 5270
 
4.4%
Other values (10) 25488
21.2%

이미지고유번호
Real number (ℝ)

Distinct5852
Distinct (%)58.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3026.6342
Minimum1
Maximum6030
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-23T01:34:49.111081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile304.95
Q11524.75
median3032
Q34534.25
95-th percentile5730.05
Maximum6030
Range6029
Interquartile range (IQR)3009.5

Descriptive statistics

Standard deviation1738.5466
Coefficient of variation (CV)0.57441583
Kurtosis-1.1955001
Mean3026.6342
Median Absolute Deviation (MAD)1505
Skewness-0.0085706362
Sum30266342
Variance3022544.3
MonotonicityNot monotonic
2024-03-23T01:34:49.540514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1737 2
 
< 0.1%
2895 2
 
< 0.1%
1675 2
 
< 0.1%
422 2
 
< 0.1%
1306 2
 
< 0.1%
2798 2
 
< 0.1%
2783 2
 
< 0.1%
5274 2
 
< 0.1%
2741 2
 
< 0.1%
4228 2
 
< 0.1%
Other values (5842) 9980
99.8%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 2
< 0.1%
4 2
< 0.1%
5 2
< 0.1%
6 2
< 0.1%
7 1
< 0.1%
8 2
< 0.1%
9 1
< 0.1%
11 2
< 0.1%
ValueCountFrequency (%)
6030 2
< 0.1%
6029 2
< 0.1%
6028 1
< 0.1%
6027 2
< 0.1%
6026 1
< 0.1%
6025 2
< 0.1%
6024 2
< 0.1%
6023 2
< 0.1%
6022 2
< 0.1%
6021 1
< 0.1%

이미지너비
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1920
10000 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1920
2nd row1920
3rd row1920
4th row1920
5th row1920

Common Values

ValueCountFrequency (%)
1920 10000
100.0%

Length

2024-03-23T01:34:50.005937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-23T01:34:50.336554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1920 10000
100.0%

이미지높이
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1080
10000 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1080
2nd row1080
3rd row1080
4th row1080
5th row1080

Common Values

ValueCountFrequency (%)
1080 10000
100.0%

Length

2024-03-23T01:34:50.680436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-23T01:34:50.990279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1080 10000
100.0%

이미지파일명
Text

UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-03-23T01:34:51.506747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length21
Mean length21
Min length21

Characters and Unicode

Total characters210000
Distinct characters21
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_seg_m_00001737.jpg
2nd rowct_seg_h_00000024.jpg
3rd rowct_seg_h_00005485.jpg
4th rowct_seg_h_00000165.jpg
5th rowct_seg_h_00005078.jpg
ValueCountFrequency (%)
ct_seg_m_00001737.jpg 1
 
< 0.1%
ct_seg_h_00003823.jpg 1
 
< 0.1%
ct_seg_h_00005069.jpg 1
 
< 0.1%
ct_seg_m_00003868.jpg 1
 
< 0.1%
ct_seg_m_00002639.jpg 1
 
< 0.1%
ct_seg_h_00002815.jpg 1
 
< 0.1%
ct_seg_h_00005285.jpg 1
 
< 0.1%
ct_seg_h_00002086.jpg 1
 
< 0.1%
ct_seg_h_00005162.jpg 1
 
< 0.1%
ct_seg_m_00005605.jpg 1
 
< 0.1%
Other values (9990) 9990
99.9%
2024-03-23T01:34:52.551876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 44705
21.3%
_ 30000
14.3%
g 20000
9.5%
c 10000
 
4.8%
p 10000
 
4.8%
s 10000
 
4.8%
e 10000
 
4.8%
t 10000
 
4.8%
. 10000
 
4.8%
j 10000
 
4.8%
Other values (11) 45295
21.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 90000
42.9%
Decimal Number 80000
38.1%
Connector Punctuation 30000
 
14.3%
Other Punctuation 10000
 
4.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 44705
55.9%
2 4671
 
5.8%
3 4662
 
5.8%
4 4635
 
5.8%
5 4621
 
5.8%
1 4611
 
5.8%
6 3061
 
3.8%
7 3015
 
3.8%
9 3010
 
3.8%
8 3009
 
3.8%
Lowercase Letter
ValueCountFrequency (%)
g 20000
22.2%
c 10000
11.1%
p 10000
11.1%
s 10000
11.1%
e 10000
11.1%
t 10000
11.1%
j 10000
11.1%
h 5001
 
5.6%
m 4999
 
5.6%
Connector Punctuation
ValueCountFrequency (%)
_ 30000
100.0%
Other Punctuation
ValueCountFrequency (%)
. 10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 120000
57.1%
Latin 90000
42.9%

Most frequent character per script

Common
ValueCountFrequency (%)
0 44705
37.3%
_ 30000
25.0%
. 10000
 
8.3%
2 4671
 
3.9%
3 4662
 
3.9%
4 4635
 
3.9%
5 4621
 
3.9%
1 4611
 
3.8%
6 3061
 
2.6%
7 3015
 
2.5%
Other values (2) 6019
 
5.0%
Latin
ValueCountFrequency (%)
g 20000
22.2%
c 10000
11.1%
p 10000
11.1%
s 10000
11.1%
e 10000
11.1%
t 10000
11.1%
j 10000
11.1%
h 5001
 
5.6%
m 4999
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 210000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 44705
21.3%
_ 30000
14.3%
g 20000
9.5%
c 10000
 
4.8%
p 10000
 
4.8%
s 10000
 
4.8%
e 10000
 
4.8%
t 10000
 
4.8%
. 10000
 
4.8%
j 10000
 
4.8%
Other values (11) 45295
21.6%

이미지파일경로
Text

UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-03-23T01:34:53.306831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length77
Median length77
Mean length77
Min length77

Characters and Unicode

Total characters770000
Distinct characters34
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/seg/20210902/m/ct_seg_m_00001737.jpg
2nd rowhttp://ggzerocity-data.or.kr/opn/02/cctv/seg/20210902/h/ct_seg_h_00000024.jpg
3rd rowhttp://ggzerocity-data.or.kr/opn/02/cctv/seg/20210902/h/ct_seg_h_00005485.jpg
4th rowhttp://ggzerocity-data.or.kr/opn/02/cctv/seg/20210902/h/ct_seg_h_00000165.jpg
5th rowhttp://ggzerocity-data.or.kr/opn/02/cctv/seg/20210902/h/ct_seg_h_00005078.jpg
ValueCountFrequency (%)
http://ggzerocity-data.or.kr/opn/02/cctv/seg/20210902/m/ct_seg_m_00001737.jpg 1
 
< 0.1%
http://ggzerocity-data.or.kr/opn/02/cctv/seg/20210902/h/ct_seg_h_00003823.jpg 1
 
< 0.1%
http://ggzerocity-data.or.kr/opn/02/cctv/seg/20210902/h/ct_seg_h_00005069.jpg 1
 
< 0.1%
http://ggzerocity-data.or.kr/opn/02/cctv/seg/20210902/m/ct_seg_m_00003868.jpg 1
 
< 0.1%
http://ggzerocity-data.or.kr/opn/02/cctv/seg/20210902/m/ct_seg_m_00002639.jpg 1
 
< 0.1%
http://ggzerocity-data.or.kr/opn/02/cctv/seg/20210902/h/ct_seg_h_00002815.jpg 1
 
< 0.1%
http://ggzerocity-data.or.kr/opn/02/cctv/seg/20210902/h/ct_seg_h_00005285.jpg 1
 
< 0.1%
http://ggzerocity-data.or.kr/opn/02/cctv/seg/20210902/h/ct_seg_h_00002086.jpg 1
 
< 0.1%
http://ggzerocity-data.or.kr/opn/02/cctv/seg/20210902/h/ct_seg_h_00005162.jpg 1
 
< 0.1%
http://ggzerocity-data.or.kr/opn/02/cctv/seg/20210902/m/ct_seg_m_00005605.jpg 1
 
< 0.1%
Other values (9990) 9990
99.9%
2024-03-23T01:34:54.445930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 90000
 
11.7%
0 84705
 
11.0%
t 60000
 
7.8%
g 50000
 
6.5%
2 44671
 
5.8%
c 40000
 
5.2%
r 30000
 
3.9%
. 30000
 
3.9%
o 30000
 
3.9%
e 30000
 
3.9%
Other values (24) 280624
36.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 420000
54.5%
Decimal Number 180000
23.4%
Other Punctuation 130000
 
16.9%
Connector Punctuation 30000
 
3.9%
Dash Punctuation 10000
 
1.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 60000
14.3%
g 50000
11.9%
c 40000
9.5%
r 30000
 
7.1%
o 30000
 
7.1%
e 30000
 
7.1%
p 30000
 
7.1%
h 20002
 
4.8%
s 20000
 
4.8%
a 20000
 
4.8%
Other values (9) 89998
21.4%
Decimal Number
ValueCountFrequency (%)
0 84705
47.1%
2 44671
24.8%
1 14611
 
8.1%
9 13010
 
7.2%
3 4662
 
2.6%
4 4635
 
2.6%
5 4621
 
2.6%
6 3061
 
1.7%
7 3015
 
1.7%
8 3009
 
1.7%
Other Punctuation
ValueCountFrequency (%)
/ 90000
69.2%
. 30000
 
23.1%
: 10000
 
7.7%
Connector Punctuation
ValueCountFrequency (%)
_ 30000
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 420000
54.5%
Common 350000
45.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 60000
14.3%
g 50000
11.9%
c 40000
9.5%
r 30000
 
7.1%
o 30000
 
7.1%
e 30000
 
7.1%
p 30000
 
7.1%
h 20002
 
4.8%
s 20000
 
4.8%
a 20000
 
4.8%
Other values (9) 89998
21.4%
Common
ValueCountFrequency (%)
/ 90000
25.7%
0 84705
24.2%
2 44671
12.8%
. 30000
 
8.6%
_ 30000
 
8.6%
1 14611
 
4.2%
9 13010
 
3.7%
- 10000
 
2.9%
: 10000
 
2.9%
3 4662
 
1.3%
Other values (5) 18341
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 770000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 90000
 
11.7%
0 84705
 
11.0%
t 60000
 
7.8%
g 50000
 
6.5%
2 44671
 
5.8%
c 40000
 
5.2%
r 30000
 
3.9%
. 30000
 
3.9%
o 30000
 
3.9%
e 30000
 
3.9%
Other values (24) 280624
36.4%

고정밀세그멘테이션json파일명
Text

MISSING 

Distinct5001
Distinct (%)100.0%
Missing4999
Missing (%)50.0%
Memory size156.2 KiB
2024-03-23T01:34:54.983130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters110022
Distinct characters21
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

Unique5001 ?
Unique (%)100.0%

Sample

1st rowct_seg_h_00000024.json
2nd rowct_seg_h_00005485.json
3rd rowct_seg_h_00000165.json
4th rowct_seg_h_00005078.json
5th rowct_seg_h_00001806.json
ValueCountFrequency (%)
ct_seg_h_00001672.json 1
 
< 0.1%
ct_seg_h_00005097.json 1
 
< 0.1%
ct_seg_h_00004758.json 1
 
< 0.1%
ct_seg_h_00004129.json 1
 
< 0.1%
ct_seg_h_00005210.json 1
 
< 0.1%
ct_seg_h_00003473.json 1
 
< 0.1%
ct_seg_h_00003767.json 1
 
< 0.1%
ct_seg_h_00001116.json 1
 
< 0.1%
ct_seg_h_00002364.json 1
 
< 0.1%
ct_seg_h_00005552.json 1
 
< 0.1%
Other values (4991) 4991
99.8%
2024-03-23T01:34:56.082365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 22361
20.3%
_ 15003
13.6%
s 10002
 
9.1%
c 5001
 
4.5%
n 5001
 
4.5%
e 5001
 
4.5%
g 5001
 
4.5%
h 5001
 
4.5%
t 5001
 
4.5%
. 5001
 
4.5%
Other values (11) 27649
25.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 50010
45.5%
Decimal Number 40008
36.4%
Connector Punctuation 15003
 
13.6%
Other Punctuation 5001
 
4.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 22361
55.9%
2 2340
 
5.8%
3 2338
 
5.8%
1 2318
 
5.8%
5 2310
 
5.8%
4 2302
 
5.8%
6 1535
 
3.8%
8 1508
 
3.8%
9 1500
 
3.7%
7 1496
 
3.7%
Lowercase Letter
ValueCountFrequency (%)
s 10002
20.0%
c 5001
10.0%
n 5001
10.0%
e 5001
10.0%
g 5001
10.0%
h 5001
10.0%
t 5001
10.0%
j 5001
10.0%
o 5001
10.0%
Connector Punctuation
ValueCountFrequency (%)
_ 15003
100.0%
Other Punctuation
ValueCountFrequency (%)
. 5001
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 60012
54.5%
Latin 50010
45.5%

Most frequent character per script

Common
ValueCountFrequency (%)
0 22361
37.3%
_ 15003
25.0%
. 5001
 
8.3%
2 2340
 
3.9%
3 2338
 
3.9%
1 2318
 
3.9%
5 2310
 
3.8%
4 2302
 
3.8%
6 1535
 
2.6%
8 1508
 
2.5%
Other values (2) 2996
 
5.0%
Latin
ValueCountFrequency (%)
s 10002
20.0%
c 5001
10.0%
n 5001
10.0%
e 5001
10.0%
g 5001
10.0%
h 5001
10.0%
t 5001
10.0%
j 5001
10.0%
o 5001
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 110022
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 22361
20.3%
_ 15003
13.6%
s 10002
 
9.1%
c 5001
 
4.5%
n 5001
 
4.5%
e 5001
 
4.5%
g 5001
 
4.5%
h 5001
 
4.5%
t 5001
 
4.5%
. 5001
 
4.5%
Other values (11) 27649
25.1%

고정밀세그멘테이션json파일경로
Text

MISSING 

Distinct5001
Distinct (%)100.0%
Missing4999
Missing (%)50.0%
Memory size156.2 KiB
2024-03-23T01:34:56.883573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length78
Median length78
Mean length78
Min length78

Characters and Unicode

Total characters390078
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

Unique5001 ?
Unique (%)100.0%

Sample

1st rowhttp://ggzerocity-data.or.kr/opn/02/cctv/seg/20210902/h/ct_seg_h_00000024.json
2nd rowhttp://ggzerocity-data.or.kr/opn/02/cctv/seg/20210902/h/ct_seg_h_00005485.json
3rd rowhttp://ggzerocity-data.or.kr/opn/02/cctv/seg/20210902/h/ct_seg_h_00000165.json
4th rowhttp://ggzerocity-data.or.kr/opn/02/cctv/seg/20210902/h/ct_seg_h_00005078.json
5th rowhttp://ggzerocity-data.or.kr/opn/02/cctv/seg/20210902/h/ct_seg_h_00001806.json
ValueCountFrequency (%)
http://ggzerocity-data.or.kr/opn/02/cctv/seg/20210902/h/ct_seg_h_00001672.json 1
 
< 0.1%
http://ggzerocity-data.or.kr/opn/02/cctv/seg/20210902/h/ct_seg_h_00005097.json 1
 
< 0.1%
http://ggzerocity-data.or.kr/opn/02/cctv/seg/20210902/h/ct_seg_h_00004758.json 1
 
< 0.1%
http://ggzerocity-data.or.kr/opn/02/cctv/seg/20210902/h/ct_seg_h_00004129.json 1
 
< 0.1%
http://ggzerocity-data.or.kr/opn/02/cctv/seg/20210902/h/ct_seg_h_00005210.json 1
 
< 0.1%
http://ggzerocity-data.or.kr/opn/02/cctv/seg/20210902/h/ct_seg_h_00003473.json 1
 
< 0.1%
http://ggzerocity-data.or.kr/opn/02/cctv/seg/20210902/h/ct_seg_h_00003767.json 1
 
< 0.1%
http://ggzerocity-data.or.kr/opn/02/cctv/seg/20210902/h/ct_seg_h_00001116.json 1
 
< 0.1%
http://ggzerocity-data.or.kr/opn/02/cctv/seg/20210902/h/ct_seg_h_00002364.json 1
 
< 0.1%
http://ggzerocity-data.or.kr/opn/02/cctv/seg/20210902/h/ct_seg_h_00005552.json 1
 
< 0.1%
Other values (4991) 4991
99.8%
2024-03-23T01:34:58.227161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 45009
 
11.5%
0 42365
 
10.9%
t 30006
 
7.7%
2 22344
 
5.7%
c 20004
 
5.1%
g 20004
 
5.1%
o 20004
 
5.1%
h 15003
 
3.8%
_ 15003
 
3.8%
s 15003
 
3.8%
Other values (23) 145333
37.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 215043
55.1%
Decimal Number 90018
23.1%
Other Punctuation 65013
 
16.7%
Connector Punctuation 15003
 
3.8%
Dash Punctuation 5001
 
1.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 30006
14.0%
c 20004
9.3%
g 20004
9.3%
o 20004
9.3%
h 15003
 
7.0%
s 15003
 
7.0%
r 15003
 
7.0%
e 15003
 
7.0%
p 10002
 
4.7%
a 10002
 
4.7%
Other values (8) 45009
20.9%
Decimal Number
ValueCountFrequency (%)
0 42365
47.1%
2 22344
24.8%
1 7319
 
8.1%
9 6501
 
7.2%
3 2338
 
2.6%
5 2310
 
2.6%
4 2302
 
2.6%
6 1535
 
1.7%
8 1508
 
1.7%
7 1496
 
1.7%
Other Punctuation
ValueCountFrequency (%)
/ 45009
69.2%
. 15003
 
23.1%
: 5001
 
7.7%
Connector Punctuation
ValueCountFrequency (%)
_ 15003
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5001
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 215043
55.1%
Common 175035
44.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 30006
14.0%
c 20004
9.3%
g 20004
9.3%
o 20004
9.3%
h 15003
 
7.0%
s 15003
 
7.0%
r 15003
 
7.0%
e 15003
 
7.0%
p 10002
 
4.7%
a 10002
 
4.7%
Other values (8) 45009
20.9%
Common
ValueCountFrequency (%)
/ 45009
25.7%
0 42365
24.2%
2 22344
12.8%
_ 15003
 
8.6%
. 15003
 
8.6%
1 7319
 
4.2%
9 6501
 
3.7%
: 5001
 
2.9%
- 5001
 
2.9%
3 2338
 
1.3%
Other values (5) 9151
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 390078
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 45009
 
11.5%
0 42365
 
10.9%
t 30006
 
7.7%
2 22344
 
5.7%
c 20004
 
5.1%
g 20004
 
5.1%
o 20004
 
5.1%
h 15003
 
3.8%
_ 15003
 
3.8%
s 15003
 
3.8%
Other values (23) 145333
37.3%

중정밀세그멘테이션json파일명
Text

MISSING 

Distinct4999
Distinct (%)100.0%
Missing5001
Missing (%)50.0%
Memory size156.2 KiB
2024-03-23T01:34:58.791672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters109978
Distinct characters21
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

Unique4999 ?
Unique (%)100.0%

Sample

1st rowct_seg_m_00001737.json
2nd rowct_seg_m_00004031.json
3rd rowct_seg_m_00000304.json
4th rowct_seg_m_00005433.json
5th rowct_seg_m_00005016.json
ValueCountFrequency (%)
ct_seg_m_00002577.json 1
 
< 0.1%
ct_seg_m_00001501.json 1
 
< 0.1%
ct_seg_m_00002172.json 1
 
< 0.1%
ct_seg_m_00005576.json 1
 
< 0.1%
ct_seg_m_00001343.json 1
 
< 0.1%
ct_seg_m_00000766.json 1
 
< 0.1%
ct_seg_m_00002678.json 1
 
< 0.1%
ct_seg_m_00004702.json 1
 
< 0.1%
ct_seg_m_00002136.json 1
 
< 0.1%
ct_seg_m_00004224.json 1
 
< 0.1%
Other values (4989) 4989
99.8%
2024-03-23T01:34:59.591610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 22344
20.3%
_ 14997
13.6%
s 9998
 
9.1%
c 4999
 
4.5%
e 4999
 
4.5%
g 4999
 
4.5%
m 4999
 
4.5%
t 4999
 
4.5%
. 4999
 
4.5%
j 4999
 
4.5%
Other values (11) 27646
25.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 49990
45.5%
Decimal Number 39992
36.4%
Connector Punctuation 14997
 
13.6%
Other Punctuation 4999
 
4.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 22344
55.9%
4 2333
 
5.8%
2 2331
 
5.8%
3 2324
 
5.8%
5 2311
 
5.8%
1 2293
 
5.7%
6 1526
 
3.8%
7 1519
 
3.8%
9 1510
 
3.8%
8 1501
 
3.8%
Lowercase Letter
ValueCountFrequency (%)
s 9998
20.0%
c 4999
10.0%
e 4999
10.0%
g 4999
10.0%
m 4999
10.0%
t 4999
10.0%
j 4999
10.0%
o 4999
10.0%
n 4999
10.0%
Connector Punctuation
ValueCountFrequency (%)
_ 14997
100.0%
Other Punctuation
ValueCountFrequency (%)
. 4999
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 59988
54.5%
Latin 49990
45.5%

Most frequent character per script

Common
ValueCountFrequency (%)
0 22344
37.2%
_ 14997
25.0%
. 4999
 
8.3%
4 2333
 
3.9%
2 2331
 
3.9%
3 2324
 
3.9%
5 2311
 
3.9%
1 2293
 
3.8%
6 1526
 
2.5%
7 1519
 
2.5%
Other values (2) 3011
 
5.0%
Latin
ValueCountFrequency (%)
s 9998
20.0%
c 4999
10.0%
e 4999
10.0%
g 4999
10.0%
m 4999
10.0%
t 4999
10.0%
j 4999
10.0%
o 4999
10.0%
n 4999
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 109978
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 22344
20.3%
_ 14997
13.6%
s 9998
 
9.1%
c 4999
 
4.5%
e 4999
 
4.5%
g 4999
 
4.5%
m 4999
 
4.5%
t 4999
 
4.5%
. 4999
 
4.5%
j 4999
 
4.5%
Other values (11) 27646
25.1%

중정밀세그멘테이션json파일경로
Text

MISSING 

Distinct4999
Distinct (%)100.0%
Missing5001
Missing (%)50.0%
Memory size156.2 KiB
2024-03-23T01:35:00.349961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length78
Median length78
Mean length78
Min length78

Characters and Unicode

Total characters389922
Distinct characters34
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

Unique4999 ?
Unique (%)100.0%

Sample

1st rowhttp://ggzerocity-data.or.kr/opn/02/cctv/seg/20210902/m/ct_seg_m_00001737.json
2nd rowhttp://ggzerocity-data.or.kr/opn/02/cctv/seg/20210902/m/ct_seg_m_00004031.json
3rd rowhttp://ggzerocity-data.or.kr/opn/02/cctv/seg/20210902/m/ct_seg_m_00000304.json
4th rowhttp://ggzerocity-data.or.kr/opn/02/cctv/seg/20210902/m/ct_seg_m_00005433.json
5th rowhttp://ggzerocity-data.or.kr/opn/02/cctv/seg/20210902/m/ct_seg_m_00005016.json
ValueCountFrequency (%)
http://ggzerocity-data.or.kr/opn/02/cctv/seg/20210902/m/ct_seg_m_00002577.json 1
 
< 0.1%
http://ggzerocity-data.or.kr/opn/02/cctv/seg/20210902/m/ct_seg_m_00001501.json 1
 
< 0.1%
http://ggzerocity-data.or.kr/opn/02/cctv/seg/20210902/m/ct_seg_m_00002172.json 1
 
< 0.1%
http://ggzerocity-data.or.kr/opn/02/cctv/seg/20210902/m/ct_seg_m_00005576.json 1
 
< 0.1%
http://ggzerocity-data.or.kr/opn/02/cctv/seg/20210902/m/ct_seg_m_00001343.json 1
 
< 0.1%
http://ggzerocity-data.or.kr/opn/02/cctv/seg/20210902/m/ct_seg_m_00000766.json 1
 
< 0.1%
http://ggzerocity-data.or.kr/opn/02/cctv/seg/20210902/m/ct_seg_m_00002678.json 1
 
< 0.1%
http://ggzerocity-data.or.kr/opn/02/cctv/seg/20210902/m/ct_seg_m_00004702.json 1
 
< 0.1%
http://ggzerocity-data.or.kr/opn/02/cctv/seg/20210902/m/ct_seg_m_00002136.json 1
 
< 0.1%
http://ggzerocity-data.or.kr/opn/02/cctv/seg/20210902/m/ct_seg_m_00004224.json 1
 
< 0.1%
Other values (4989) 4989
99.8%
2024-03-23T01:35:01.447677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 44991
 
11.5%
0 42340
 
10.9%
t 29994
 
7.7%
2 22327
 
5.7%
o 19996
 
5.1%
c 19996
 
5.1%
g 19996
 
5.1%
_ 14997
 
3.8%
e 14997
 
3.8%
r 14997
 
3.8%
Other values (24) 145291
37.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 214957
55.1%
Decimal Number 89982
23.1%
Other Punctuation 64987
 
16.7%
Connector Punctuation 14997
 
3.8%
Dash Punctuation 4999
 
1.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 29994
14.0%
o 19996
 
9.3%
c 19996
 
9.3%
g 19996
 
9.3%
e 14997
 
7.0%
r 14997
 
7.0%
s 14997
 
7.0%
m 9998
 
4.7%
a 9998
 
4.7%
n 9998
 
4.7%
Other values (9) 49990
23.3%
Decimal Number
ValueCountFrequency (%)
0 42340
47.1%
2 22327
24.8%
1 7292
 
8.1%
9 6509
 
7.2%
4 2333
 
2.6%
3 2324
 
2.6%
5 2311
 
2.6%
6 1526
 
1.7%
7 1519
 
1.7%
8 1501
 
1.7%
Other Punctuation
ValueCountFrequency (%)
/ 44991
69.2%
. 14997
 
23.1%
: 4999
 
7.7%
Connector Punctuation
ValueCountFrequency (%)
_ 14997
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4999
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 214957
55.1%
Common 174965
44.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 29994
14.0%
o 19996
 
9.3%
c 19996
 
9.3%
g 19996
 
9.3%
e 14997
 
7.0%
r 14997
 
7.0%
s 14997
 
7.0%
m 9998
 
4.7%
a 9998
 
4.7%
n 9998
 
4.7%
Other values (9) 49990
23.3%
Common
ValueCountFrequency (%)
/ 44991
25.7%
0 42340
24.2%
2 22327
12.8%
_ 14997
 
8.6%
. 14997
 
8.6%
1 7292
 
4.2%
9 6509
 
3.7%
- 4999
 
2.9%
: 4999
 
2.9%
4 2333
 
1.3%
Other values (5) 9181
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 389922
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 44991
 
11.5%
0 42340
 
10.9%
t 29994
 
7.7%
2 22327
 
5.7%
o 19996
 
5.1%
c 19996
 
5.1%
g 19996
 
5.1%
_ 14997
 
3.8%
e 14997
 
3.8%
r 14997
 
3.8%
Other values (24) 145291
37.3%

Interactions

2024-03-23T01:34:42.572980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-23T01:35:01.765004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세그멘테이션구분CCTV고유번호이미지고유번호
세그멘테이션구분1.0000.0000.000
CCTV고유번호0.0001.0000.958
이미지고유번호0.0000.9581.000
2024-03-23T01:35:01.983166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
이미지고유번호세그멘테이션구분
이미지고유번호1.0000.000
세그멘테이션구분0.0001.000

Missing values

2024-03-23T01:34:43.022315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-23T01:34:43.720322image/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.
2024-03-23T01:34:44.176178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

녹화시작일시녹화종료일시이벤트유형날씨정보세그멘테이션구분CCTV고유번호이미지고유번호이미지너비이미지높이이미지파일명이미지파일경로고정밀세그멘테이션json파일명고정밀세그멘테이션json파일경로중정밀세그멘테이션json파일명중정밀세그멘테이션json파일경로
48472021-11-11 14:15:302021-11-11 14:15:507<NA>2ARO-SDB-3022173719201080ct_seg_m_00001737.jpghttp://ggzerocity-data.or.kr/opn/02/cctv/seg/20210902/m/ct_seg_m_00001737.jpg<NA><NA>ct_seg_m_00001737.jsonhttp://ggzerocity-data.or.kr/opn/02/cctv/seg/20210902/m/ct_seg_m_00001737.json
1132021-09-02 10:40:402021-09-02 10:40:517<NA>1ARO-SDB-10312419201080ct_seg_h_00000024.jpghttp://ggzerocity-data.or.kr/opn/02/cctv/seg/20210902/h/ct_seg_h_00000024.jpgct_seg_h_00000024.jsonhttp://ggzerocity-data.or.kr/opn/02/cctv/seg/20210902/h/ct_seg_h_00000024.json<NA><NA>
86132021-11-11 15:33:402021-11-11 15:34:007<NA>1ARO-SDB-1022548519201080ct_seg_h_00005485.jpghttp://ggzerocity-data.or.kr/opn/02/cctv/seg/20210902/h/ct_seg_h_00005485.jpgct_seg_h_00005485.jsonhttp://ggzerocity-data.or.kr/opn/02/cctv/seg/20210902/h/ct_seg_h_00005485.json<NA><NA>
3962021-09-02 10:44:202021-09-02 10:44:307<NA>1ARO-SDB-301116519201080ct_seg_h_00000165.jpghttp://ggzerocity-data.or.kr/opn/02/cctv/seg/20210902/h/ct_seg_h_00000165.jpgct_seg_h_00000165.jsonhttp://ggzerocity-data.or.kr/opn/02/cctv/seg/20210902/h/ct_seg_h_00000165.json<NA><NA>
87132021-11-11 15:31:202021-11-11 15:31:407<NA>1ARO-SDB-3012507819201080ct_seg_h_00005078.jpghttp://ggzerocity-data.or.kr/opn/02/cctv/seg/20210902/h/ct_seg_h_00005078.jpgct_seg_h_00005078.jsonhttp://ggzerocity-data.or.kr/opn/02/cctv/seg/20210902/h/ct_seg_h_00005078.json<NA><NA>
62192021-11-11 14:26:052021-11-11 14:26:257<NA>2ARO-SDB-3091403119201080ct_seg_m_00004031.jpghttp://ggzerocity-data.or.kr/opn/02/cctv/seg/20210902/m/ct_seg_m_00004031.jpg<NA><NA>ct_seg_m_00004031.jsonhttp://ggzerocity-data.or.kr/opn/02/cctv/seg/20210902/m/ct_seg_m_00004031.json
48862021-11-11 14:16:002021-11-11 14:16:207<NA>1ARO-SDB-3032180619201080ct_seg_h_00001806.jpghttp://ggzerocity-data.or.kr/opn/02/cctv/seg/20210902/h/ct_seg_h_00001806.jpgct_seg_h_00001806.jsonhttp://ggzerocity-data.or.kr/opn/02/cctv/seg/20210902/h/ct_seg_h_00001806.json<NA><NA>
31002021-11-11 14:21:002021-11-11 14:21:207<NA>1ARO-SDB-3062269219201080ct_seg_h_00002692.jpghttp://ggzerocity-data.or.kr/opn/02/cctv/seg/20210902/h/ct_seg_h_00002692.jpgct_seg_h_00002692.jsonhttp://ggzerocity-data.or.kr/opn/02/cctv/seg/20210902/h/ct_seg_h_00002692.json<NA><NA>
5372021-09-02 10:47:202021-09-02 10:47:307<NA>2ARO-SDB-305230419201080ct_seg_m_00000304.jpghttp://ggzerocity-data.or.kr/opn/02/cctv/seg/20210902/m/ct_seg_m_00000304.jpg<NA><NA>ct_seg_m_00000304.jsonhttp://ggzerocity-data.or.kr/opn/02/cctv/seg/20210902/m/ct_seg_m_00000304.json
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녹화시작일시녹화종료일시이벤트유형날씨정보세그멘테이션구분CCTV고유번호이미지고유번호이미지너비이미지높이이미지파일명이미지파일경로고정밀세그멘테이션json파일명고정밀세그멘테이션json파일경로중정밀세그멘테이션json파일명중정밀세그멘테이션json파일경로
89072021-11-11 15:31:352021-11-11 15:31:557<NA>2ARO-SDB-3011517219201080ct_seg_m_00005172.jpghttp://ggzerocity-data.or.kr/opn/02/cctv/seg/20210902/m/ct_seg_m_00005172.jpg<NA><NA>ct_seg_m_00005172.jsonhttp://ggzerocity-data.or.kr/opn/02/cctv/seg/20210902/m/ct_seg_m_00005172.json
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84302021-11-11 14:22:002021-11-11 14:22:207<NA>2CIT-SDB-3043308219201080ct_seg_m_00003082.jpghttp://ggzerocity-data.or.kr/opn/02/cctv/seg/20210902/m/ct_seg_m_00003082.jpg<NA><NA>ct_seg_m_00003082.jsonhttp://ggzerocity-data.or.kr/opn/02/cctv/seg/20210902/m/ct_seg_m_00003082.json
104972021-11-11 14:27:502021-11-11 14:28:107<NA>1CIT-SDB-3085435719201080ct_seg_h_00004357.jpghttp://ggzerocity-data.or.kr/opn/02/cctv/seg/20210902/h/ct_seg_h_00004357.jpgct_seg_h_00004357.jsonhttp://ggzerocity-data.or.kr/opn/02/cctv/seg/20210902/h/ct_seg_h_00004357.json<NA><NA>
36022021-11-11 14:19:552021-11-11 14:20:157<NA>2CIT-SDB-3034234219201080ct_seg_m_00002342.jpghttp://ggzerocity-data.or.kr/opn/02/cctv/seg/20210902/m/ct_seg_m_00002342.jpg<NA><NA>ct_seg_m_00002342.jsonhttp://ggzerocity-data.or.kr/opn/02/cctv/seg/20210902/m/ct_seg_m_00002342.json
30752021-11-11 14:20:502021-11-11 14:21:107<NA>2ARO-SDB-3061264819201080ct_seg_m_00002648.jpghttp://ggzerocity-data.or.kr/opn/02/cctv/seg/20210902/m/ct_seg_m_00002648.jpg<NA><NA>ct_seg_m_00002648.jsonhttp://ggzerocity-data.or.kr/opn/02/cctv/seg/20210902/m/ct_seg_m_00002648.json
96482021-11-11 15:33:402021-11-11 15:34:007<NA>2ARO-SDB-1022544719201080ct_seg_m_00005447.jpghttp://ggzerocity-data.or.kr/opn/02/cctv/seg/20210902/m/ct_seg_m_00005447.jpg<NA><NA>ct_seg_m_00005447.jsonhttp://ggzerocity-data.or.kr/opn/02/cctv/seg/20210902/m/ct_seg_m_00005447.json
98312021-11-11 15:33:102021-11-11 15:33:307<NA>1ARO-SDB-1012528319201080ct_seg_h_00005283.jpghttp://ggzerocity-data.or.kr/opn/02/cctv/seg/20210902/h/ct_seg_h_00005283.jpgct_seg_h_00005283.jsonhttp://ggzerocity-data.or.kr/opn/02/cctv/seg/20210902/h/ct_seg_h_00005283.json<NA><NA>
38692021-11-11 14:19:252021-11-11 14:19:457<NA>1ARO-SDB-3042210119201080ct_seg_h_00002101.jpghttp://ggzerocity-data.or.kr/opn/02/cctv/seg/20210902/h/ct_seg_h_00002101.jpgct_seg_h_00002101.jsonhttp://ggzerocity-data.or.kr/opn/02/cctv/seg/20210902/h/ct_seg_h_00002101.json<NA><NA>
107402021-11-11 15:29:252021-11-11 15:29:457<NA>2CIT-SDB-3022481919201080ct_seg_m_00004819.jpghttp://ggzerocity-data.or.kr/opn/02/cctv/seg/20210902/m/ct_seg_m_00004819.jpg<NA><NA>ct_seg_m_00004819.jsonhttp://ggzerocity-data.or.kr/opn/02/cctv/seg/20210902/m/ct_seg_m_00004819.json