Overview

Dataset statistics

Number of variables10
Number of observations22
Missing cells31
Missing cells (%)14.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.9 KiB
Average record size in memory89.0 B

Variable types

Numeric3
Text3
Categorical2
DateTime2

Dataset

DescriptionRA시험장소(시험장소SEQ, 시험장소명칭, 시험장소위치구분자(시, 도))
Author한국의료기기안전정보원
URLhttps://www.data.go.kr/data/15066836/fileData.do

Alerts

EP_PATH has constant value ""Constant
PLACE_SEQ is highly overall correlated with EP_SORT_SEQHigh correlation
EP_SORT_SEQ is highly overall correlated with PLACE_SEQ and 1 other fieldsHigh correlation
EP_AREA is highly overall correlated with EP_SORT_SEQHigh correlation
EP_SORT_SEQ has 12 (54.5%) missing valuesMissing
EP_DESC has 19 (86.4%) missing valuesMissing
PLACE_SEQ has unique valuesUnique
EP_NAME has unique valuesUnique
EP_FNAME has unique valuesUnique
EP_FSIZE has unique valuesUnique
IN_DTIME has unique valuesUnique
UP_DTIME has unique valuesUnique
EP_SORT_SEQ has 1 (4.5%) zerosZeros

Reproduction

Analysis started2023-12-12 10:14:24.905435
Analysis finished2023-12-12 10:14:26.959933
Duration2.05 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

PLACE_SEQ
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.863636
Minimum1
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-12T19:14:27.045341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.05
Q16.25
median13.5
Q319.5
95-th percentile23.95
Maximum25
Range24
Interquartile range (IQR)13.25

Descriptive statistics

Standard deviation7.7416342
Coefficient of variation (CV)0.60182316
Kurtosis-1.3608513
Mean12.863636
Median Absolute Deviation (MAD)7
Skewness0.034764255
Sum283
Variance59.9329
MonotonicityStrictly increasing
2023-12-12T19:14:27.192686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
1 1
 
4.5%
15 1
 
4.5%
25 1
 
4.5%
24 1
 
4.5%
23 1
 
4.5%
22 1
 
4.5%
21 1
 
4.5%
20 1
 
4.5%
18 1
 
4.5%
17 1
 
4.5%
Other values (12) 12
54.5%
ValueCountFrequency (%)
1 1
4.5%
2 1
4.5%
3 1
4.5%
4 1
4.5%
5 1
4.5%
6 1
4.5%
7 1
4.5%
8 1
4.5%
9 1
4.5%
10 1
4.5%
ValueCountFrequency (%)
25 1
4.5%
24 1
4.5%
23 1
4.5%
22 1
4.5%
21 1
4.5%
20 1
4.5%
18 1
4.5%
17 1
4.5%
16 1
4.5%
15 1
4.5%

EP_NAME
Text

UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size308.0 B
2023-12-12T19:14:27.438725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length13.5
Mean length8.9545455
Min length2

Characters and Unicode

Total characters197
Distinct characters83
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22 ?
Unique (%)100.0%

Sample

1st row당산중학교
2nd row동국대학교 문화관
3rd row동국대학교 혜화관
4th row동국대학교 명진관
5th row숭실대학교
ValueCountFrequency (%)
동국대학교 4
 
12.1%
세종대학교 2
 
6.1%
서울교육대학교 2
 
6.1%
당산중학교 1
 
3.0%
우송정보대학 1
 
3.0%
집현관 1
 
3.0%
미정 1
 
3.0%
엑스코 1
 
3.0%
이당관 1
 
3.0%
광개토관 1
 
3.0%
Other values (18) 18
54.5%
2023-12-12T19:14:27.923882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
교 19
 
9.6%
학 18
 
9.1%
대 15
 
7.6%
11
 
5.6%
관 10
 
5.1%
동 6
 
3.0%
국 6
 
3.0%
육 4
 
2.0%
정 4
 
2.0%
기 4
 
2.0%
Other values (73) 100
50.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 182
92.4%
Space Separator 11
 
5.6%
Uppercase Letter 3
 
1.5%
Other Punctuation 1
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
교 19
 
10.4%
학 18
 
9.9%
대 15
 
8.2%
관 10
 
5.5%
동 6
 
3.3%
국 6
 
3.3%
육 4
 
2.2%
정 4
 
2.2%
기 4
 
2.2%
울 3
 
1.6%
Other values (68) 93
51.1%
Uppercase Letter
ValueCountFrequency (%)
C 1
33.3%
M 1
33.3%
P 1
33.3%
Space Separator
ValueCountFrequency (%)
11
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 182
92.4%
Common 12
 
6.1%
Latin 3
 
1.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
교 19
 
10.4%
학 18
 
9.9%
대 15
 
8.2%
관 10
 
5.5%
동 6
 
3.3%
국 6
 
3.3%
육 4
 
2.2%
정 4
 
2.2%
기 4
 
2.2%
울 3
 
1.6%
Other values (68) 93
51.1%
Latin
ValueCountFrequency (%)
C 1
33.3%
M 1
33.3%
P 1
33.3%
Common
ValueCountFrequency (%)
11
91.7%
, 1
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 182
92.4%
ASCII 15
 
7.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
교 19
 
10.4%
학 18
 
9.9%
대 15
 
8.2%
관 10
 
5.5%
동 6
 
3.3%
국 6
 
3.3%
육 4
 
2.2%
정 4
 
2.2%
기 4
 
2.2%
울 3
 
1.6%
Other values (68) 93
51.1%
ASCII
ValueCountFrequency (%)
11
73.3%
, 1
 
6.7%
C 1
 
6.7%
M 1
 
6.7%
P 1
 
6.7%

EP_AREA
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)13.6%
Missing0
Missing (%)0.0%
Memory size308.0 B
서울특별시
18 
대전광역시
2 
대구광역시
2 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
서울특별시 18
81.8%
대전광역시 2
 
9.1%
대구광역시 2
 
9.1%

Length

2023-12-12T19:14:28.089149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:14:28.217211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서울특별시 18
81.8%
대전광역시 2
 
9.1%
대구광역시 2
 
9.1%

EP_SORT_SEQ
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct7
Distinct (%)70.0%
Missing12
Missing (%)54.5%
Infinite0
Infinite (%)0.0%
Mean4.8
Minimum0
Maximum10
Zeros1
Zeros (%)4.5%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-12T19:14:28.348493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.45
Q11
median4.5
Q38.75
95-th percentile9.55
Maximum10
Range10
Interquartile range (IQR)7.75

Descriptive statistics

Standard deviation3.9384148
Coefficient of variation (CV)0.82050308
Kurtosis-1.9669657
Mean4.8
Median Absolute Deviation (MAD)3.5
Skewness0.1042192
Sum48
Variance15.511111
MonotonicityNot monotonic
2023-12-12T19:14:28.499232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 3
 
13.6%
9 2
 
9.1%
4 1
 
4.5%
5 1
 
4.5%
0 1
 
4.5%
8 1
 
4.5%
10 1
 
4.5%
(Missing) 12
54.5%
ValueCountFrequency (%)
0 1
 
4.5%
1 3
13.6%
4 1
 
4.5%
5 1
 
4.5%
8 1
 
4.5%
9 2
9.1%
10 1
 
4.5%
ValueCountFrequency (%)
10 1
 
4.5%
9 2
9.1%
8 1
 
4.5%
5 1
 
4.5%
4 1
 
4.5%
1 3
13.6%
0 1
 
4.5%

EP_PATH
Categorical

CONSTANT 

Distinct1
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Memory size308.0 B
/files/place/
22 

Length

Max length13
Median length13
Mean length13
Min length13

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row/files/place/
2nd row/files/place/
3rd row/files/place/
4th row/files/place/
5th row/files/place/

Common Values

ValueCountFrequency (%)
/files/place/ 22
100.0%

Length

2023-12-12T19:14:28.660884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:14:28.797228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
files/place 22
100.0%

EP_FNAME
Text

UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size308.0 B
2023-12-12T19:14:29.041663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length15.5
Mean length13.272727
Min length8

Characters and Unicode

Total characters292
Distinct characters100
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

Unique22 ?
Unique (%)100.0%

Sample

1st rowdangsan_mid_last.jpg
2nd rowdkd_mhg3.jpg
3rd row동국대학교 혜화관.bmp
4th row동국대학교 명진관.jpg
5th row숭실대학교1.JPG
ValueCountFrequency (%)
동국대학교 2
 
7.1%
dangsan_mid_last.jpg 1
 
3.6%
동국대학교(경영관).jpg 1
 
3.6%
세종대학교(집현관).jpg 1
 
3.6%
시험장소3.jpg 1
 
3.6%
대구엑스코.jpg 1
 
3.6%
광개토관1.jpg 1
 
3.6%
세종대학교 1
 
3.6%
예람인재교육센터.jpg 1
 
3.6%
영남이공대학교.png 1
 
3.6%
Other values (17) 17
60.7%
2023-12-12T19:14:29.488488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 22
 
7.5%
g 17
 
5.8%
p 17
 
5.8%
교 16
 
5.5%
대 15
 
5.1%
j 15
 
5.1%
학 14
 
4.8%
P 7
 
2.4%
G 6
 
2.1%
_ 6
 
2.1%
Other values (90) 157
53.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 142
48.6%
Lowercase Letter 79
27.1%
Other Punctuation 22
 
7.5%
Uppercase Letter 21
 
7.2%
Decimal Number 12
 
4.1%
Connector Punctuation 6
 
2.1%
Space Separator 6
 
2.1%
Close Punctuation 2
 
0.7%
Open Punctuation 2
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
교 16
 
11.3%
대 15
 
10.6%
학 14
 
9.9%
관 6
 
4.2%
동 4
 
2.8%
국 4
 
2.8%
육 4
 
2.8%
숭 3
 
2.1%
실 3
 
2.1%
울 3
 
2.1%
Other values (58) 70
49.3%
Lowercase Letter
ValueCountFrequency (%)
g 17
21.5%
p 17
21.5%
j 15
19.0%
d 5
 
6.3%
m 4
 
5.1%
s 4
 
5.1%
n 3
 
3.8%
i 3
 
3.8%
a 3
 
3.8%
b 2
 
2.5%
Other values (6) 6
 
7.6%
Uppercase Letter
ValueCountFrequency (%)
P 7
33.3%
G 6
28.6%
J 4
19.0%
N 2
 
9.5%
M 1
 
4.8%
C 1
 
4.8%
Decimal Number
ValueCountFrequency (%)
1 5
41.7%
3 3
25.0%
6 2
 
16.7%
2 1
 
8.3%
0 1
 
8.3%
Other Punctuation
ValueCountFrequency (%)
. 22
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 6
100.0%
Space Separator
ValueCountFrequency (%)
6
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 142
48.6%
Latin 100
34.2%
Common 50
 
17.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
교 16
 
11.3%
대 15
 
10.6%
학 14
 
9.9%
관 6
 
4.2%
동 4
 
2.8%
국 4
 
2.8%
육 4
 
2.8%
숭 3
 
2.1%
실 3
 
2.1%
울 3
 
2.1%
Other values (58) 70
49.3%
Latin
ValueCountFrequency (%)
g 17
17.0%
p 17
17.0%
j 15
15.0%
P 7
 
7.0%
G 6
 
6.0%
d 5
 
5.0%
m 4
 
4.0%
J 4
 
4.0%
s 4
 
4.0%
n 3
 
3.0%
Other values (12) 18
18.0%
Common
ValueCountFrequency (%)
. 22
44.0%
_ 6
 
12.0%
6
 
12.0%
1 5
 
10.0%
3 3
 
6.0%
) 2
 
4.0%
( 2
 
4.0%
6 2
 
4.0%
2 1
 
2.0%
0 1
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 150
51.4%
Hangul 142
48.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 22
14.7%
g 17
 
11.3%
p 17
 
11.3%
j 15
 
10.0%
P 7
 
4.7%
G 6
 
4.0%
_ 6
 
4.0%
6
 
4.0%
1 5
 
3.3%
d 5
 
3.3%
Other values (22) 44
29.3%
Hangul
ValueCountFrequency (%)
교 16
 
11.3%
대 15
 
10.6%
학 14
 
9.9%
관 6
 
4.2%
동 4
 
2.8%
국 4
 
2.8%
육 4
 
2.8%
숭 3
 
2.1%
실 3
 
2.1%
울 3
 
2.1%
Other values (58) 70
49.3%

EP_FSIZE
Real number (ℝ)

UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean225909.32
Minimum50305
Maximum1162990
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-12T19:14:29.684726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum50305
5-th percentile51385.7
Q171898
median143009.5
Q3320619.5
95-th percentile475814.45
Maximum1162990
Range1112685
Interquartile range (IQR)248721.5

Descriptive statistics

Standard deviation246148.64
Coefficient of variation (CV)1.0895904
Kurtosis10.045288
Mean225909.32
Median Absolute Deviation (MAD)77084.5
Skewness2.8664467
Sum4970005
Variance6.0589151 × 1010
MonotonicityNot monotonic
2023-12-12T19:14:29.843085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
122542 1
 
4.5%
60101 1
 
4.5%
275159 1
 
4.5%
336891 1
 
4.5%
68628 1
 
4.5%
349492 1
 
4.5%
201623 1
 
4.5%
433073 1
 
4.5%
478064 1
 
4.5%
335773 1
 
4.5%
Other values (12) 12
54.5%
ValueCountFrequency (%)
50305 1
4.5%
50927 1
4.5%
60101 1
4.5%
63222 1
4.5%
68628 1
4.5%
70698 1
4.5%
75498 1
4.5%
102951 1
4.5%
122542 1
4.5%
128304 1
4.5%
ValueCountFrequency (%)
1162990 1
4.5%
478064 1
4.5%
433073 1
4.5%
349492 1
4.5%
336891 1
4.5%
335773 1
4.5%
275159 1
4.5%
201623 1
4.5%
160136 1
4.5%
157609 1
4.5%

EP_DESC
Text

MISSING 

Distinct3
Distinct (%)100.0%
Missing19
Missing (%)86.4%
Memory size308.0 B
2023-12-12T19:14:30.126136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length236
Median length16
Mean length89.333333
Min length16

Characters and Unicode

Total characters268
Distinct characters85
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)100.0%

Sample

1st row구로디지털단지 마리오타워 2층
2nd row구로디지털단지 마리오타워 8층
3rd row서울특별시 구로구 디지털로 26길 72 <교통편> 지하철 2호선지하철 2호선 구로디지털단지역 2,3번출구 → 이마트 구로점 건너편 구로우체국 골목으로 250m 거리(도보 10분) → NHN 한국사이버결제 건물 4층(1층 이마트24) 버스 지선버스 : 5536, 5615 / 마을버스 : 금천07 자가용 (우)08393 서울특별시 구로구 디지털로 26길 72(구로동 222-22) 서울스마트시티센터
ValueCountFrequency (%)
3
 
6.0%
구로디지털단지 2
 
4.0%
마리오타워 2
 
4.0%
서울특별시 2
 
4.0%
구로구 2
 
4.0%
디지털로 2
 
4.0%
26길 2
 
4.0%
→ 2
 
4.0%
건물 1
 
2.0%
4층(1층 1
 
2.0%
Other values (31) 31
62.0%
2023-12-12T19:14:30.565454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
40
 
14.9%
2 15
 
5.6%
구 11
 
4.1%
11
 
4.1%
로 11
 
4.1%
11
 
4.1%
지 11
 
4.1%
마 6
 
2.2%
5 5
 
1.9%
털 5
 
1.9%
Other values (75) 142
53.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 141
52.6%
Decimal Number 43
 
16.0%
Space Separator 40
 
14.9%
Control 22
 
8.2%
Other Punctuation 5
 
1.9%
Close Punctuation 4
 
1.5%
Open Punctuation 4
 
1.5%
Math Symbol 4
 
1.5%
Uppercase Letter 3
 
1.1%
Dash Punctuation 1
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 11
 
7.8%
로 11
 
7.8%
지 11
 
7.8%
마 6
 
4.3%
털 5
 
3.5%
디 5
 
3.5%
스 4
 
2.8%
버 4
 
2.8%
층 4
 
2.8%
트 3
 
2.1%
Other values (50) 77
54.6%
Decimal Number
ValueCountFrequency (%)
2 15
34.9%
5 5
 
11.6%
6 4
 
9.3%
0 4
 
9.3%
3 4
 
9.3%
1 3
 
7.0%
7 3
 
7.0%
4 2
 
4.7%
8 2
 
4.7%
9 1
 
2.3%
Math Symbol
ValueCountFrequency (%)
→ 2
50.0%
< 1
25.0%
> 1
25.0%
Other Punctuation
ValueCountFrequency (%)
: 2
40.0%
, 2
40.0%
/ 1
20.0%
Control
ValueCountFrequency (%)
11
50.0%
11
50.0%
Uppercase Letter
ValueCountFrequency (%)
N 2
66.7%
H 1
33.3%
Space Separator
ValueCountFrequency (%)
40
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%
Lowercase Letter
ValueCountFrequency (%)
m 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 141
52.6%
Common 123
45.9%
Latin 4
 
1.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 11
 
7.8%
로 11
 
7.8%
지 11
 
7.8%
마 6
 
4.3%
털 5
 
3.5%
디 5
 
3.5%
스 4
 
2.8%
버 4
 
2.8%
층 4
 
2.8%
트 3
 
2.1%
Other values (50) 77
54.6%
Common
ValueCountFrequency (%)
40
32.5%
2 15
 
12.2%
11
 
8.9%
11
 
8.9%
5 5
 
4.1%
) 4
 
3.3%
6 4
 
3.3%
0 4
 
3.3%
3 4
 
3.3%
( 4
 
3.3%
Other values (12) 21
17.1%
Latin
ValueCountFrequency (%)
N 2
50.0%
H 1
25.0%
m 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 141
52.6%
ASCII 125
46.6%
Arrows 2
 
0.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
40
32.0%
2 15
 
12.0%
11
 
8.8%
11
 
8.8%
5 5
 
4.0%
) 4
 
3.2%
6 4
 
3.2%
0 4
 
3.2%
3 4
 
3.2%
( 4
 
3.2%
Other values (14) 23
18.4%
Hangul
ValueCountFrequency (%)
구 11
 
7.8%
로 11
 
7.8%
지 11
 
7.8%
마 6
 
4.3%
털 5
 
3.5%
디 5
 
3.5%
스 4
 
2.8%
버 4
 
2.8%
층 4
 
2.8%
트 3
 
2.1%
Other values (50) 77
54.6%
Arrows
ValueCountFrequency (%)
→ 2
100.0%

IN_DTIME
Date

UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size308.0 B
Minimum2014-10-13 15:12:37.170000
Maximum2020-08-21 11:55:41
2023-12-12T19:14:30.737305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:14:30.910815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)

UP_DTIME
Date

UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size308.0 B
Minimum2016-05-13 19:45:27.100000
Maximum2020-08-24 10:58:48
2023-12-12T19:14:31.076780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:14:31.242109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)

Interactions

2023-12-12T19:14:26.150761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:14:25.397070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:14:25.745483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:14:26.292933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:14:25.507936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:14:25.862822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:14:26.412857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:14:25.621977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:14:25.995048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T19:14:31.363529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
PLACE_SEQEP_NAMEEP_AREAEP_SORT_SEQEP_FNAMEEP_FSIZEEP_DESCIN_DTIMEUP_DTIME
PLACE_SEQ1.0001.0000.7060.9081.0000.7431.0001.0001.000
EP_NAME1.0001.0001.0001.0001.0001.0001.0001.0001.000
EP_AREA0.7061.0001.000NaN1.0000.502NaN1.0001.000
EP_SORT_SEQ0.9081.000NaN1.0001.0000.000NaN1.0001.000
EP_FNAME1.0001.0001.0001.0001.0001.0001.0001.0001.000
EP_FSIZE0.7431.0000.5020.0001.0001.0000.0001.0001.000
EP_DESC1.0001.000NaNNaN1.0000.0001.0001.0001.000
IN_DTIME1.0001.0001.0001.0001.0001.0001.0001.0001.000
UP_DTIME1.0001.0001.0001.0001.0001.0001.0001.0001.000
2023-12-12T19:14:31.533251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
PLACE_SEQEP_SORT_SEQEP_FSIZEEP_AREA
PLACE_SEQ1.0000.6890.2740.220
EP_SORT_SEQ0.6891.000-0.4371.000
EP_FSIZE0.274-0.4371.0000.416
EP_AREA0.2201.0000.4161.000

Missing values

2023-12-12T19:14:26.614225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T19:14:26.775837image/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.
2023-12-12T19:14:26.900730image/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

PLACE_SEQEP_NAMEEP_AREAEP_SORT_SEQEP_PATHEP_FNAMEEP_FSIZEEP_DESCIN_DTIMEUP_DTIME
01당산중학교서울특별시1/files/place/dangsan_mid_last.jpg122542<NA>2014-10-13 15:12:37.172019-11-12 14:39:08.0
12동국대학교 문화관서울특별시1/files/place/dkd_mhg3.jpg128304<NA>2015-09-04 17:08:03.1272019-11-12 18:18:12.0
23동국대학교 혜화관서울특별시1/files/place/동국대학교 혜화관.bmp1162990<NA>2015-09-14 20:51:17.272020-08-13 15:02:07.0
34동국대학교 명진관서울특별시4/files/place/동국대학교 명진관.jpg134488<NA>2015-10-23 15:19:23.6632020-08-13 15:01:22.0
45숭실대학교서울특별시5/files/place/숭실대학교1.JPG63222<NA>2016-05-13 19:41:40.5732020-08-13 15:02:45.0
56숭실대학교미래관서울특별시9/files/place/숭실대2.JPG160136<NA>2016-05-13 19:45:27.12016-05-13 19:45:27.1
67숭실대학교안익태기념관서울특별시0/files/place/숭실대학교 안익태기념관.jpg157609<NA>2016-08-26 11:16:14.2472016-08-26 11:16:14.247
78한국의료기기안전정보원서울특별시8/files/place/sub06_m6_pic1_nids.jpg50927<NA>2016-11-15 17:29:27.02020-08-10 15:19:03.0
89서울교육대학교서울특별시9/files/place/서울교육대학교.jpg75498<NA>2017-05-25 19:37:33.02020-05-06 12:46:29.0
910서울교육대학교 연구강의동서울특별시10/files/place/서울교육대학교 연구강의동 3층.jpg102951<NA>2017-06-26 18:16:58.02020-08-13 15:03:53.0
PLACE_SEQEP_NAMEEP_AREAEP_SORT_SEQEP_PATHEP_FNAMEEP_FSIZEEP_DESCIN_DTIMEUP_DTIME
1215벤처기업협회서울특별시<NA>/files/place/벤처기업협회.jpg60101구로디지털단지 마리오타워 8층2017-10-12 10:48:59.02020-08-13 15:02:18.0
1316건국대학교사범대학부속중학교서울특별시<NA>/files/place/건국대학교사범대학부속중학교.JPG70698<NA>2018-04-26 12:50:46.02019-11-12 14:39:17.0
1417우송정보대학 동캠퍼스 학술정보센터대전광역시<NA>/files/place/우송정보대학1.PNG335773<NA>2019-09-30 10:14:29.02019-11-12 14:41:02.0
1518영남이공대학교 인애관대구광역시<NA>/files/place/영남이공대학교.PNG478064<NA>2019-09-30 10:16:06.02019-11-12 14:40:14.0
1620예람인재교육센터대전광역시<NA>/files/place/예람인재교육센터.jpg433073<NA>2020-05-15 17:50:17.02020-06-29 11:10:12.0
1721세종대학교 광개토관, 이당관서울특별시<NA>/files/place/세종대학교 광개토관1.jpg201623<NA>2020-05-15 17:53:37.02020-06-29 11:24:00.0
1822엑스코대구광역시<NA>/files/place/대구엑스코.jpg349492<NA>2020-05-15 17:56:14.02020-08-13 15:04:24.0
1923미정서울특별시<NA>/files/place/시험장소3.jpg68628<NA>2020-05-15 19:02:16.02020-08-13 15:01:36.0
2024세종대학교 집현관서울특별시<NA>/files/place/세종대학교(집현관).jpg336891<NA>2020-06-29 11:22:19.02020-06-29 11:23:44.0
2125서울스마트시티센터서울특별시<NA>/files/place/서울스마트시티센터1.jpg275159서울특별시 구로구 디지털로 26길 72 <교통편> 지하철 2호선지하철 2호선 구로디지털단지역 2,3번출구 → 이마트 구로점 건너편 구로우체국 골목으로 250m 거리(도보 10분) → NHN 한국사이버결제 건물 4층(1층 이마트24) 버스 지선버스 : 5536, 5615 / 마을버스 : 금천07 자가용 (우)08393 서울특별시 구로구 디지털로 26길 72(구로동 222-22) 서울스마트시티센터2020-08-21 11:55:41.02020-08-24 10:58:48.0