Overview

Dataset statistics

Number of variables9
Number of observations55
Missing cells98
Missing cells (%)19.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.1 KiB
Average record size in memory76.4 B

Variable types

Numeric2
Text2
DateTime4
Boolean1

Dataset

DescriptionN/A
Author인천광역시
URLhttps://www.data.go.kr/data/15122335/fileData.do

Alerts

동영상 파일 일련번호(SEQ) is highly overall correlated with 동영상 일련번호(SEQ) and 1 other fieldsHigh correlation
동영상 일련번호(SEQ) is highly overall correlated with 동영상 파일 일련번호(SEQ) and 1 other fieldsHigh correlation
삭제 유무 is highly overall correlated with 동영상 파일 일련번호(SEQ) and 1 other fieldsHigh correlation
삭제 유무 is highly imbalanced (50.3%)Imbalance
수정일자 has 49 (89.1%) missing valuesMissing
수정시간 has 49 (89.1%) missing valuesMissing
동영상 파일 일련번호(SEQ) has unique valuesUnique
파일 경로 has unique valuesUnique

Reproduction

Analysis started2024-04-17 18:11:07.122703
Analysis finished2024-04-17 18:11:08.069482
Duration0.95 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

동영상 파일 일련번호(SEQ)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct55
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean108.98182
Minimum41
Maximum146
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size627.0 B
2024-04-18T03:11:08.131399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum41
5-th percentile44.4
Q1103.5
median117
Q3131.5
95-th percentile143.3
Maximum146
Range105
Interquartile range (IQR)28

Descriptive statistics

Standard deviation31.340715
Coefficient of variation (CV)0.28757746
Kurtosis0.057102301
Mean108.98182
Median Absolute Deviation (MAD)14
Skewness-1.0942
Sum5994
Variance982.2404
MonotonicityNot monotonic
2024-04-18T03:11:08.241675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
61 1
 
1.8%
62 1
 
1.8%
108 1
 
1.8%
109 1
 
1.8%
110 1
 
1.8%
111 1
 
1.8%
112 1
 
1.8%
113 1
 
1.8%
114 1
 
1.8%
115 1
 
1.8%
Other values (45) 45
81.8%
ValueCountFrequency (%)
41 1
1.8%
42 1
1.8%
43 1
1.8%
45 1
1.8%
46 1
1.8%
47 1
1.8%
48 1
1.8%
61 1
1.8%
62 1
1.8%
63 1
1.8%
ValueCountFrequency (%)
146 1
1.8%
145 1
1.8%
144 1
1.8%
143 1
1.8%
142 1
1.8%
141 1
1.8%
139 1
1.8%
138 1
1.8%
137 1
1.8%
136 1
1.8%

동영상 일련번호(SEQ)
Real number (ℝ)

HIGH CORRELATION 

Distinct18
Distinct (%)32.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean79.909091
Minimum42
Maximum105
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size627.0 B
2024-04-18T03:11:08.351840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum42
5-th percentile42
Q181
median82
Q394
95-th percentile103.3
Maximum105
Range63
Interquartile range (IQR)13

Descriptive statistics

Standard deviation19.033375
Coefficient of variation (CV)0.23818785
Kurtosis-0.036477536
Mean79.909091
Median Absolute Deviation (MAD)10
Skewness-1.0213604
Sum4395
Variance362.26936
MonotonicityNot monotonic
2024-04-18T03:11:08.456679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
81 9
16.4%
82 8
14.5%
83 6
10.9%
95 4
 
7.3%
42 4
 
7.3%
43 3
 
5.5%
92 3
 
5.5%
94 3
 
5.5%
96 3
 
5.5%
104 2
 
3.6%
Other values (8) 10
18.2%
ValueCountFrequency (%)
42 4
7.3%
43 3
 
5.5%
45 1
 
1.8%
48 2
 
3.6%
61 1
 
1.8%
81 9
16.4%
82 8
14.5%
83 6
10.9%
84 2
 
3.6%
92 3
 
5.5%
ValueCountFrequency (%)
105 1
 
1.8%
104 2
3.6%
103 1
 
1.8%
102 1
 
1.8%
101 1
 
1.8%
96 3
5.5%
95 4
7.3%
94 3
5.5%
92 3
5.5%
84 2
3.6%

파일 명
Text

Distinct41
Distinct (%)74.5%
Missing0
Missing (%)0.0%
Memory size572.0 B
2024-04-18T03:11:08.629986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length32
Median length9
Mean length11.236364
Min length7

Characters and Unicode

Total characters618
Distinct characters93
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique31 ?
Unique (%)56.4%

Sample

1st rowdemo.mp4
2nd rowp1bklffup9m701e7316bf4rgt0k4.mp4
3rd rowdemo.mp4
4th rowdemo.mp4
5th rowDJI_0004.mp4
ValueCountFrequency (%)
계양산 9
 
12.2%
demo.mp4 6
 
8.1%
커널워크 6
 
8.1%
1.mp4 3
 
4.1%
월미도03.mp4 2
 
2.7%
월미도02.mp4 2
 
2.7%
아라전망대_500m.wmv 2
 
2.7%
가을&여름동.mp4 2
 
2.7%
봄동.mp4 2
 
2.7%
겨울동.mp4 2
 
2.7%
Other values (35) 38
51.4%
2024-04-18T03:11:08.909154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
m 62
 
10.0%
4 56
 
9.1%
. 55
 
8.9%
p 52
 
8.4%
0 42
 
6.8%
산 24
 
3.9%
19
 
3.1%
1 15
 
2.4%
계 10
 
1.6%
_ 9
 
1.5%
Other values (83) 274
44.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 192
31.1%
Lowercase Letter 174
28.2%
Decimal Number 156
25.2%
Other Punctuation 57
 
9.2%
Space Separator 19
 
3.1%
Uppercase Letter 11
 
1.8%
Connector Punctuation 9
 
1.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 24
 
12.5%
계 10
 
5.2%
양 9
 
4.7%
마 8
 
4.2%
도 8
 
4.2%
니 8
 
4.2%
워 7
 
3.6%
크 7
 
3.6%
널 6
 
3.1%
동 6
 
3.1%
Other values (48) 99
51.6%
Lowercase Letter
ValueCountFrequency (%)
m 62
35.6%
p 52
29.9%
e 8
 
4.6%
v 6
 
3.4%
w 6
 
3.4%
d 6
 
3.4%
o 6
 
3.4%
f 6
 
3.4%
k 4
 
2.3%
b 4
 
2.3%
Other values (7) 14
 
8.0%
Decimal Number
ValueCountFrequency (%)
4 56
35.9%
0 42
26.9%
1 15
 
9.6%
2 8
 
5.1%
3 8
 
5.1%
7 8
 
5.1%
5 8
 
5.1%
6 5
 
3.2%
9 4
 
2.6%
8 2
 
1.3%
Uppercase Letter
ValueCountFrequency (%)
M 5
45.5%
I 2
 
18.2%
J 2
 
18.2%
D 2
 
18.2%
Other Punctuation
ValueCountFrequency (%)
. 55
96.5%
& 2
 
3.5%
Space Separator
ValueCountFrequency (%)
19
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 241
39.0%
Hangul 192
31.1%
Latin 185
29.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 24
 
12.5%
계 10
 
5.2%
양 9
 
4.7%
마 8
 
4.2%
도 8
 
4.2%
니 8
 
4.2%
워 7
 
3.6%
크 7
 
3.6%
널 6
 
3.1%
동 6
 
3.1%
Other values (48) 99
51.6%
Latin
ValueCountFrequency (%)
m 62
33.5%
p 52
28.1%
e 8
 
4.3%
v 6
 
3.2%
w 6
 
3.2%
d 6
 
3.2%
o 6
 
3.2%
f 6
 
3.2%
M 5
 
2.7%
k 4
 
2.2%
Other values (11) 24
 
13.0%
Common
ValueCountFrequency (%)
4 56
23.2%
. 55
22.8%
0 42
17.4%
19
 
7.9%
1 15
 
6.2%
_ 9
 
3.7%
2 8
 
3.3%
3 8
 
3.3%
7 8
 
3.3%
5 8
 
3.3%
Other values (4) 13
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 426
68.9%
Hangul 192
31.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
m 62
14.6%
4 56
13.1%
. 55
12.9%
p 52
12.2%
0 42
9.9%
19
 
4.5%
1 15
 
3.5%
_ 9
 
2.1%
e 8
 
1.9%
2 8
 
1.9%
Other values (25) 100
23.5%
Hangul
ValueCountFrequency (%)
산 24
 
12.5%
계 10
 
5.2%
양 9
 
4.7%
마 8
 
4.2%
도 8
 
4.2%
니 8
 
4.2%
워 7
 
3.6%
크 7
 
3.6%
널 6
 
3.1%
동 6
 
3.1%
Other values (48) 99
51.6%

파일 경로
Text

UNIQUE 

Distinct55
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size572.0 B
2024-04-18T03:11:09.079576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length20
Mean length20
Min length20

Characters and Unicode

Total characters1100
Distinct characters16
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

Unique55 ?
Unique (%)100.0%

Sample

1st row/files/1509406963963
2nd row/files/1509407640412
3rd row/files/1508226389903
4th row/files/1508229756020
5th row/files/1508229757565
ValueCountFrequency (%)
files/1509406963963 1
 
1.8%
files/1533634703520 1
 
1.8%
files/1533634831005 1
 
1.8%
files/1533634889102 1
 
1.8%
files/1533635167314 1
 
1.8%
files/1533635194473 1
 
1.8%
files/1533635203133 1
 
1.8%
files/1533635210278 1
 
1.8%
files/1533635257172 1
 
1.8%
files/1533635268328 1
 
1.8%
Other values (45) 45
81.8%
2024-04-18T03:11:09.350010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 156
14.2%
5 114
10.4%
/ 110
10.0%
1 93
 
8.5%
6 81
 
7.4%
4 61
 
5.5%
f 55
 
5.0%
i 55
 
5.0%
l 55
 
5.0%
e 55
 
5.0%
Other values (6) 265
24.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 715
65.0%
Lowercase Letter 275
 
25.0%
Other Punctuation 110
 
10.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 156
21.8%
5 114
15.9%
1 93
13.0%
6 81
11.3%
4 61
 
8.5%
0 52
 
7.3%
2 51
 
7.1%
9 37
 
5.2%
7 37
 
5.2%
8 33
 
4.6%
Lowercase Letter
ValueCountFrequency (%)
f 55
20.0%
i 55
20.0%
l 55
20.0%
e 55
20.0%
s 55
20.0%
Other Punctuation
ValueCountFrequency (%)
/ 110
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 825
75.0%
Latin 275
 
25.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 156
18.9%
5 114
13.8%
/ 110
13.3%
1 93
11.3%
6 81
9.8%
4 61
 
7.4%
0 52
 
6.3%
2 51
 
6.2%
9 37
 
4.5%
7 37
 
4.5%
Latin
ValueCountFrequency (%)
f 55
20.0%
i 55
20.0%
l 55
20.0%
e 55
20.0%
s 55
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1100
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 156
14.2%
5 114
10.4%
/ 110
10.0%
1 93
 
8.5%
6 81
 
7.4%
4 61
 
5.5%
f 55
 
5.0%
i 55
 
5.0%
l 55
 
5.0%
e 55
 
5.0%
Other values (6) 265
24.1%

등록일자
Date

Distinct6
Distinct (%)10.9%
Missing0
Missing (%)0.0%
Memory size572.0 B
Minimum2017-10-17 00:00:00
Maximum2019-01-14 00:00:00
2024-04-18T03:11:09.441371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:11:09.519356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)

등록시간
Date

Distinct24
Distinct (%)43.6%
Missing0
Missing (%)0.0%
Memory size572.0 B
Minimum2024-04-18 08:42:38
Maximum2024-04-18 19:07:06
2024-04-18T03:11:09.602366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:11:09.692115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)

수정일자
Date

MISSING 

Distinct3
Distinct (%)50.0%
Missing49
Missing (%)89.1%
Memory size572.0 B
Minimum2017-10-17 00:00:00
Maximum2019-01-14 00:00:00
2024-04-18T03:11:09.773083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:11:09.847376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=3)

수정시간
Date

MISSING 

Distinct5
Distinct (%)83.3%
Missing49
Missing (%)89.1%
Memory size572.0 B
Minimum2024-04-18 08:53:41
Maximum2024-04-18 17:49:00
2024-04-18T03:11:09.926823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:11:10.026016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=5)

삭제 유무
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Memory size187.0 B
False
49 
True
6 
ValueCountFrequency (%)
False 49
89.1%
True 6
 
10.9%
2024-04-18T03:11:10.120262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Interactions

2024-04-18T03:11:07.716948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:11:07.588926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:11:07.776803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:11:07.650298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-18T03:11:10.181604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
동영상 파일 일련번호(SEQ)동영상 일련번호(SEQ)파일 명파일 경로등록일자등록시간수정일자수정시간삭제 유무
동영상 파일 일련번호(SEQ)1.0000.9470.0001.0000.9500.9730.6431.0000.561
동영상 일련번호(SEQ)0.9471.0000.9301.0000.9721.0001.0001.0000.753
파일 명0.0000.9301.0001.0000.0000.0000.6430.6470.000
파일 경로1.0001.0001.0001.0001.0001.0001.0001.0001.000
등록일자0.9500.9720.0001.0001.0001.0000.9631.0000.814
등록시간0.9731.0000.0001.0001.0001.0001.0001.0001.000
수정일자0.6431.0000.6431.0000.9631.0001.0001.000NaN
수정시간1.0001.0000.6471.0001.0001.0001.0001.000NaN
삭제 유무0.5610.7530.0001.0000.8141.000NaNNaN1.000
2024-04-18T03:11:10.275354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
동영상 파일 일련번호(SEQ)동영상 일련번호(SEQ)삭제 유무
동영상 파일 일련번호(SEQ)1.0000.9880.608
동영상 일련번호(SEQ)0.9881.0000.538
삭제 유무0.6080.5381.000

Missing values

2024-04-18T03:11:07.862653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-18T03:11:07.960086image/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.
2024-04-18T03:11:08.034541image/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

동영상 파일 일련번호(SEQ)동영상 일련번호(SEQ)파일 명파일 경로등록일자등록시간수정일자수정시간삭제 유무
06142demo.mp4/files/15094069639632017-10-318:42:382017-10-318:53:55Y
16242p1bklffup9m701e7316bf4rgt0k4.mp4/files/15094076404122017-10-318:53:552017-10-318:55:50Y
24142demo.mp4/files/15082263899032017-10-1716:46:322017-10-318:53:41Y
34243demo.mp4/files/15082297560202017-10-1717:42:402017-10-1717:49:00Y
44343DJI_0004.mp4/files/15082297575652017-10-1717:42:402017-10-1717:49:00Y
54543p1bklffup9m701e7316bf4rgt0k4.mp4/files/15082301401592017-10-1717:49:00<NA><NA>N
66342demo.mp4/files/15094077497522017-10-318:55:50<NA><NA>N
78161도시경관변천기록관리시스템_코드체계_20170926.xlsx/files/15217934961652018-03-2317:24:49<NA><NA>N
84645demo.mp4/files/15082302307212017-10-1717:50:30<NA><NA>N
94748demo.mp4/files/15082304443232017-10-1717:54:03<NA><NA>N
동영상 파일 일련번호(SEQ)동영상 일련번호(SEQ)파일 명파일 경로등록일자등록시간수정일자수정시간삭제 유무
4513094커널워크 봄동.mp4/files/15336361347342018-08-0719:02:58<NA><NA>N
4613194커널워크 가을&여름동.mp4/files/15336361496672018-08-0719:02:58<NA><NA>N
4713294커널워크 겨울동.mp4/files/15336361695102018-08-0719:02:58<NA><NA>N
4813796월미도03.mp4/files/15336363180612018-08-0719:07:06<NA><NA>N
4913896월미도02.mp4/files/15336363942712018-08-0719:07:06<NA><NA>N
5013996월미도01.mp4/files/15336364185142018-08-0719:07:06<NA><NA>N
51141101송도 센트럴파크_500M.wmv/files/15474537998202019-01-1417:16:46<NA><NA>N
52142102문학산.wmv/files/15474546810972019-01-1417:38:18<NA><NA>N
53145104아라전망대_500M.wmv/files/15474556549912019-01-1417:47:41<NA><NA>N
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