Overview

Dataset statistics

Number of variables11
Number of observations282
Missing cells860
Missing cells (%)27.7%
Duplicate rows1
Duplicate rows (%)0.4%
Total size in memory25.7 KiB
Average record size in memory93.5 B

Variable types

Text4
Categorical2
Numeric4
Unsupported1

Alerts

Dataset has 1 (0.4%) duplicate rowsDuplicates
천장규격 is highly overall correlated with 가용면적(m2) and 4 other fieldsHigh correlation
세부규격 is highly overall correlated with 가용면적(m2) and 4 other fieldsHigh correlation
가용면적(m2) is highly overall correlated with 극장형식인원(명) and 4 other fieldsHigh correlation
극장형식인원(명) is highly overall correlated with 가용면적(m2) and 4 other fieldsHigh correlation
연회형식인원(명) is highly overall correlated with 가용면적(m2) and 4 other fieldsHigh correlation
리셉션형식인원(명) is highly overall correlated with 가용면적(m2) and 4 other fieldsHigh correlation
세부규격 is highly imbalanced (56.9%)Imbalance
천장규격 is highly imbalanced (56.4%)Imbalance
시설약칭명 has 3 (1.1%) missing valuesMissing
가용면적(m2) has 67 (23.8%) missing valuesMissing
영문시설명 has 61 (21.6%) missing valuesMissing
극장형식인원(명) has 149 (52.8%) missing valuesMissing
연회형식인원(명) has 149 (52.8%) missing valuesMissing
리셉션형식인원(명) has 149 (52.8%) missing valuesMissing
강의형식인원(명) has 282 (100.0%) missing valuesMissing
강의형식인원(명) is an unsupported type, check if it needs cleaning or further analysisUnsupported
가용면적(m2) has 12 (4.3%) zerosZeros
극장형식인원(명) has 5 (1.8%) zerosZeros
연회형식인원(명) has 5 (1.8%) zerosZeros
리셉션형식인원(명) has 5 (1.8%) zerosZeros

Reproduction

Analysis started2024-03-12 23:17:04.876145
Analysis finished2024-03-12 23:17:06.583831
Duration1.71 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시설명
Text

Distinct279
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
2024-03-13T08:17:06.793058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length39
Median length29
Mean length22.361702
Min length5

Characters and Unicode

Total characters6306
Distinct characters85
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique277 ?
Unique (%)98.2%

Sample

1st row제1전시장_회의실_회의실 201
2nd row제1전시장_회의실_회의실 202
3rd row제1전시장_회의실_회의실 203
4th row제1전시장_회의실_회의실 204
5th row제1전시장_회의실_회의실 205
ValueCountFrequency (%)
제1전시장_회의실_회의실 69
 
10.4%
제2전시장_회의실_회의실 52
 
7.8%
제2전시장_전시장_전시홀 21
 
3.2%
제1전시장_전시장_전시홀 20
 
3.0%
제2전시장_회의실_hall 13
 
2.0%
6_hall 12
 
1.8%
제2전시장_전시지원시설_로비_2전시장 12
 
1.8%
로비 12
 
1.8%
회의실 10
 
1.5%
제1전시장_전시지원시설_지원사무실_지원사무실 10
 
1.5%
Other values (255) 435
65.3%
2024-03-13T08:17:07.164600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
_ 757
 
12.0%
시 556
 
8.8%
전 481
 
7.6%
실 430
 
6.8%
의 395
 
6.3%
회 386
 
6.1%
384
 
6.1%
장 369
 
5.9%
제 283
 
4.5%
2 274
 
4.3%
Other values (75) 1991
31.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3589
56.9%
Decimal Number 1132
 
18.0%
Connector Punctuation 757
 
12.0%
Space Separator 384
 
6.1%
Uppercase Letter 227
 
3.6%
Lowercase Letter 122
 
1.9%
Dash Punctuation 41
 
0.7%
Open Punctuation 21
 
0.3%
Close Punctuation 21
 
0.3%
Math Symbol 10
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 556
15.5%
전 481
13.4%
실 430
12.0%
의 395
11.0%
회 386
10.8%
장 369
10.3%
제 283
7.9%
홀 81
 
2.3%
설 75
 
2.1%
원 71
 
2.0%
Other values (39) 462
12.9%
Decimal Number
ValueCountFrequency (%)
2 274
24.2%
1 265
23.4%
0 190
16.8%
3 130
11.5%
4 74
 
6.5%
6 68
 
6.0%
5 37
 
3.3%
7 36
 
3.2%
8 34
 
3.0%
9 24
 
2.1%
Uppercase Letter
ValueCountFrequency (%)
B 52
22.9%
A 42
18.5%
V 29
12.8%
H 25
11.0%
P 24
10.6%
I 21
9.3%
L 16
 
7.0%
C 16
 
7.0%
R 1
 
0.4%
M 1
 
0.4%
Lowercase Letter
ValueCountFrequency (%)
l 50
41.0%
a 25
20.5%
e 10
 
8.2%
u 7
 
5.7%
o 7
 
5.7%
n 7
 
5.7%
g 7
 
5.7%
s 6
 
4.9%
r 3
 
2.5%
Connector Punctuation
ValueCountFrequency (%)
_ 757
100.0%
Space Separator
ValueCountFrequency (%)
384
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 41
100.0%
Open Punctuation
ValueCountFrequency (%)
( 21
100.0%
Close Punctuation
ValueCountFrequency (%)
) 21
100.0%
Math Symbol
ValueCountFrequency (%)
+ 10
100.0%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3589
56.9%
Common 2368
37.6%
Latin 349
 
5.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 556
15.5%
전 481
13.4%
실 430
12.0%
의 395
11.0%
회 386
10.8%
장 369
10.3%
제 283
7.9%
홀 81
 
2.3%
설 75
 
2.1%
원 71
 
2.0%
Other values (39) 462
12.9%
Latin
ValueCountFrequency (%)
B 52
14.9%
l 50
14.3%
A 42
12.0%
V 29
8.3%
a 25
7.2%
H 25
7.2%
P 24
6.9%
I 21
6.0%
L 16
 
4.6%
C 16
 
4.6%
Other values (9) 49
14.0%
Common
ValueCountFrequency (%)
_ 757
32.0%
384
16.2%
2 274
 
11.6%
1 265
 
11.2%
0 190
 
8.0%
3 130
 
5.5%
4 74
 
3.1%
6 68
 
2.9%
- 41
 
1.7%
5 37
 
1.6%
Other values (7) 148
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3589
56.9%
ASCII 2717
43.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
_ 757
27.9%
384
14.1%
2 274
 
10.1%
1 265
 
9.8%
0 190
 
7.0%
3 130
 
4.8%
4 74
 
2.7%
6 68
 
2.5%
B 52
 
1.9%
l 50
 
1.8%
Other values (26) 473
17.4%
Hangul
ValueCountFrequency (%)
시 556
15.5%
전 481
13.4%
실 430
12.0%
의 395
11.0%
회 386
10.8%
장 369
10.3%
제 283
7.9%
홀 81
 
2.3%
설 75
 
2.1%
원 71
 
2.0%
Other values (39) 462
12.9%

시설명1
Text

Distinct211
Distinct (%)74.8%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
2024-03-13T08:17:07.472495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length12
Mean length7.0921986
Min length2

Characters and Unicode

Total characters2000
Distinct characters84
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique156 ?
Unique (%)55.3%

Sample

1st row회의실 201
2nd row회의실 202
3rd row회의실 203
4th row회의실 204
5th row회의실 205
ValueCountFrequency (%)
회의실 129
24.4%
전시홀 41
 
7.8%
hall 13
 
2.5%
2전시장 12
 
2.3%
로비 12
 
2.3%
지원사무실 11
 
2.1%
vip대기실 8
 
1.5%
vip 7
 
1.3%
보관실 7
 
1.3%
1전시장 6
 
1.1%
Other values (185) 282
53.4%
2024-03-13T08:17:07.850901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
246
 
12.3%
실 157
 
7.8%
의 132
 
6.6%
회 131
 
6.6%
0 117
 
5.9%
2 99
 
5.0%
3 84
 
4.2%
시 81
 
4.0%
전 69
 
3.5%
1 67
 
3.4%
Other values (74) 817
40.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 890
44.5%
Decimal Number 538
26.9%
Space Separator 246
 
12.3%
Uppercase Letter 182
 
9.1%
Lowercase Letter 82
 
4.1%
Dash Punctuation 24
 
1.2%
Open Punctuation 13
 
0.7%
Close Punctuation 13
 
0.7%
Math Symbol 10
 
0.5%
Other Punctuation 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
실 157
17.6%
의 132
14.8%
회 131
14.7%
시 81
9.1%
전 69
 
7.8%
홀 49
 
5.5%
장 26
 
2.9%
로 22
 
2.5%
비 22
 
2.5%
기 16
 
1.8%
Other values (39) 185
20.8%
Decimal Number
ValueCountFrequency (%)
0 117
21.7%
2 99
18.4%
3 84
15.6%
1 67
12.5%
4 49
9.1%
6 39
 
7.2%
5 24
 
4.5%
7 23
 
4.3%
8 21
 
3.9%
9 15
 
2.8%
Uppercase Letter
ValueCountFrequency (%)
B 45
24.7%
A 42
23.1%
V 23
12.6%
P 17
 
9.3%
C 16
 
8.8%
I 15
 
8.2%
H 13
 
7.1%
L 9
 
4.9%
R 1
 
0.5%
M 1
 
0.5%
Lowercase Letter
ValueCountFrequency (%)
l 26
31.7%
a 13
15.9%
e 9
 
11.0%
g 7
 
8.5%
n 7
 
8.5%
u 7
 
8.5%
o 7
 
8.5%
s 4
 
4.9%
r 2
 
2.4%
Space Separator
ValueCountFrequency (%)
246
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 24
100.0%
Open Punctuation
ValueCountFrequency (%)
( 13
100.0%
Close Punctuation
ValueCountFrequency (%)
) 13
100.0%
Math Symbol
ValueCountFrequency (%)
+ 10
100.0%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 890
44.5%
Common 846
42.3%
Latin 264
 
13.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
실 157
17.6%
의 132
14.8%
회 131
14.7%
시 81
9.1%
전 69
 
7.8%
홀 49
 
5.5%
장 26
 
2.9%
로 22
 
2.5%
비 22
 
2.5%
기 16
 
1.8%
Other values (39) 185
20.8%
Latin
ValueCountFrequency (%)
B 45
17.0%
A 42
15.9%
l 26
9.8%
V 23
8.7%
P 17
 
6.4%
C 16
 
6.1%
I 15
 
5.7%
a 13
 
4.9%
H 13
 
4.9%
e 9
 
3.4%
Other values (9) 45
17.0%
Common
ValueCountFrequency (%)
246
29.1%
0 117
13.8%
2 99
11.7%
3 84
 
9.9%
1 67
 
7.9%
4 49
 
5.8%
6 39
 
4.6%
5 24
 
2.8%
- 24
 
2.8%
7 23
 
2.7%
Other values (6) 74
 
8.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1110
55.5%
Hangul 890
44.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
246
22.2%
0 117
 
10.5%
2 99
 
8.9%
3 84
 
7.6%
1 67
 
6.0%
4 49
 
4.4%
B 45
 
4.1%
A 42
 
3.8%
6 39
 
3.5%
l 26
 
2.3%
Other values (25) 296
26.7%
Hangul
ValueCountFrequency (%)
실 157
17.6%
의 132
14.8%
회 131
14.7%
시 81
9.1%
전 69
 
7.8%
홀 49
 
5.5%
장 26
 
2.9%
로 22
 
2.5%
비 22
 
2.5%
기 16
 
1.8%
Other values (39) 185
20.8%

시설약칭명
Text

MISSING 

Distinct252
Distinct (%)90.3%
Missing3
Missing (%)1.1%
Memory size2.3 KiB
2024-03-13T08:17:08.166702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length10
Mean length5.6451613
Min length1

Characters and Unicode

Total characters1575
Distinct characters79
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique227 ?
Unique (%)81.4%

Sample

1st row201
2nd row202
3rd row203
4th row204
5th row205
ValueCountFrequency (%)
hall 13
 
3.7%
지원사무실 11
 
3.1%
2로비 10
 
2.8%
vip 8
 
2.3%
로비 8
 
2.3%
보관실 7
 
2.0%
02월 5
 
1.4%
01월 4
 
1.1%
식음시설 4
 
1.1%
1-vip 4
 
1.1%
Other values (239) 277
78.9%
2024-03-13T08:17:08.607724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 178
 
11.3%
2 173
 
11.0%
1 147
 
9.3%
0 134
 
8.5%
3 83
 
5.3%
72
 
4.6%
4 48
 
3.0%
B 45
 
2.9%
A 42
 
2.7%
6 39
 
2.5%
Other values (69) 614
39.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 708
45.0%
Other Letter 313
19.9%
Uppercase Letter 182
 
11.6%
Dash Punctuation 178
 
11.3%
Lowercase Letter 82
 
5.2%
Space Separator 72
 
4.6%
Open Punctuation 14
 
0.9%
Close Punctuation 14
 
0.9%
Math Symbol 10
 
0.6%
Other Punctuation 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
실 26
 
8.3%
비 22
 
7.0%
로 22
 
7.0%
시 19
 
6.1%
원 14
 
4.5%
지 14
 
4.5%
사 11
 
3.5%
설 11
 
3.5%
무 11
 
3.5%
기 11
 
3.5%
Other values (34) 152
48.6%
Decimal Number
ValueCountFrequency (%)
2 173
24.4%
1 147
20.8%
0 134
18.9%
3 83
11.7%
4 48
 
6.8%
6 39
 
5.5%
5 25
 
3.5%
7 23
 
3.2%
8 21
 
3.0%
9 15
 
2.1%
Uppercase Letter
ValueCountFrequency (%)
B 45
24.7%
A 42
23.1%
V 23
12.6%
P 17
 
9.3%
C 16
 
8.8%
I 15
 
8.2%
H 13
 
7.1%
L 9
 
4.9%
R 1
 
0.5%
M 1
 
0.5%
Lowercase Letter
ValueCountFrequency (%)
l 26
31.7%
a 13
15.9%
e 9
 
11.0%
o 7
 
8.5%
u 7
 
8.5%
g 7
 
8.5%
n 7
 
8.5%
s 4
 
4.9%
r 2
 
2.4%
Dash Punctuation
ValueCountFrequency (%)
- 178
100.0%
Space Separator
ValueCountFrequency (%)
72
100.0%
Open Punctuation
ValueCountFrequency (%)
( 14
100.0%
Close Punctuation
ValueCountFrequency (%)
) 14
100.0%
Math Symbol
ValueCountFrequency (%)
+ 10
100.0%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 998
63.4%
Hangul 313
 
19.9%
Latin 264
 
16.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
실 26
 
8.3%
비 22
 
7.0%
로 22
 
7.0%
시 19
 
6.1%
원 14
 
4.5%
지 14
 
4.5%
사 11
 
3.5%
설 11
 
3.5%
무 11
 
3.5%
기 11
 
3.5%
Other values (34) 152
48.6%
Latin
ValueCountFrequency (%)
B 45
17.0%
A 42
15.9%
l 26
9.8%
V 23
8.7%
P 17
 
6.4%
C 16
 
6.1%
I 15
 
5.7%
a 13
 
4.9%
H 13
 
4.9%
e 9
 
3.4%
Other values (9) 45
17.0%
Common
ValueCountFrequency (%)
- 178
17.8%
2 173
17.3%
1 147
14.7%
0 134
13.4%
3 83
8.3%
72
7.2%
4 48
 
4.8%
6 39
 
3.9%
5 25
 
2.5%
7 23
 
2.3%
Other values (6) 76
7.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1262
80.1%
Hangul 313
 
19.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 178
14.1%
2 173
13.7%
1 147
11.6%
0 134
10.6%
3 83
 
6.6%
72
 
5.7%
4 48
 
3.8%
B 45
 
3.6%
A 42
 
3.3%
6 39
 
3.1%
Other values (25) 301
23.9%
Hangul
ValueCountFrequency (%)
실 26
 
8.3%
비 22
 
7.0%
로 22
 
7.0%
시 19
 
6.1%
원 14
 
4.5%
지 14
 
4.5%
사 11
 
3.5%
설 11
 
3.5%
무 11
 
3.5%
기 11
 
3.5%
Other values (34) 152
48.6%

세부규격
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct17
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
<NA>
204 
9m x 13m
 
20
18m x 13m
 
15
63m x 90m
 
12
63m x 81m
 
5
Other values (12)
26 

Length

Max length13
Median length4
Mean length5.3723404
Min length4

Unique

Unique4 ?
Unique (%)1.4%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 204
72.3%
9m x 13m 20
 
7.1%
18m x 13m 15
 
5.3%
63m x 90m 12
 
4.3%
63m x 81m 5
 
1.8%
63m x 171m 5
 
1.8%
36m x 13m 3
 
1.1%
56m x 47m 3
 
1.1%
30.6m x 62m 3
 
1.1%
132m x 99m 2
 
0.7%
Other values (7) 10
 
3.5%

Length

2024-03-13T08:17:08.710128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 204
46.6%
x 78
 
17.8%
13m 38
 
8.7%
63m 22
 
5.0%
9m 21
 
4.8%
18m 15
 
3.4%
90m 14
 
3.2%
81m 5
 
1.1%
171m 5
 
1.1%
32m 4
 
0.9%
Other values (15) 32
 
7.3%

천장규격
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
<NA>
222 
4m
40 
15m
 
15
10m
 
4
3.67m
 
1

Length

Max length5
Median length4
Mean length3.6524823
Min length2

Unique

Unique1 ?
Unique (%)0.4%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 222
78.7%
4m 40
 
14.2%
15m 15
 
5.3%
10m 4
 
1.4%
3.67m 1
 
0.4%

Length

2024-03-13T08:17:08.796916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T08:17:08.875462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 222
78.7%
4m 40
 
14.2%
15m 15
 
5.3%
10m 4
 
1.4%
3.67m 1
 
0.4%

가용면적(m2)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct42
Distinct (%)19.5%
Missing67
Missing (%)23.8%
Infinite0
Infinite (%)0.0%
Mean2367.0872
Minimum0
Maximum41500
Zeros12
Zeros (%)4.3%
Negative0
Negative (%)0.0%
Memory size2.6 KiB
2024-03-13T08:17:08.978795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1117
median234
Q31936
95-th percentile10183.3
Maximum41500
Range41500
Interquartile range (IQR)1819

Descriptive statistics

Standard deviation4570.669
Coefficient of variation (CV)1.9309255
Kurtosis25.217928
Mean2367.0872
Median Absolute Deviation (MAD)189
Skewness3.8833405
Sum508923.75
Variance20891015
MonotonicityNot monotonic
2024-03-13T08:17:09.073579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
234.0 30
10.6%
117.0 22
 
7.8%
94.0 16
 
5.7%
314.0 16
 
5.7%
188.0 16
 
5.7%
10000.0 16
 
5.7%
0.0 12
 
4.3%
40.0 10
 
3.5%
938.0 6
 
2.1%
5670.0 5
 
1.8%
Other values (32) 66
23.4%
(Missing) 67
23.8%
ValueCountFrequency (%)
0.0 12
4.3%
27.0 1
 
0.4%
35.75 1
 
0.4%
35.8 5
 
1.8%
40.0 10
3.5%
45.0 5
 
1.8%
94.0 16
5.7%
117.0 22
7.8%
188.0 16
5.7%
225.0 2
 
0.7%
ValueCountFrequency (%)
41500.0 1
 
0.4%
20000.0 1
 
0.4%
13238.0 1
 
0.4%
13072.0 1
 
0.4%
11290.0 2
 
0.7%
10773.0 3
 
1.1%
10611.0 2
 
0.7%
10000.0 16
5.7%
6729.0 1
 
0.4%
6563.0 1
 
0.4%

영문시설명
Text

MISSING 

Distinct106
Distinct (%)48.0%
Missing61
Missing (%)21.6%
Memory size2.3 KiB
2024-03-13T08:17:09.265075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length26
Mean length18.036199
Min length3

Characters and Unicode

Total characters3986
Distinct characters47
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

Unique68 ?
Unique (%)30.8%

Sample

1st rowConference Room 204
2nd rowConference Room 205
3rd rowConference Room 205
4th rowConference Room 205
5th rowConference Room 205
ValueCountFrequency (%)
conference 111
17.1%
room 111
17.1%
hall 75
 
11.5%
exhibition 63
 
9.7%
robby 19
 
2.9%
205 18
 
2.8%
205a 17
 
2.6%
2 13
 
2.0%
office 11
 
1.7%
staff 11
 
1.7%
Other values (90) 202
31.0%
2024-03-13T08:17:09.578626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
430
 
10.8%
o 429
 
10.8%
e 365
 
9.2%
n 300
 
7.5%
i 213
 
5.3%
l 156
 
3.9%
f 155
 
3.9%
R 134
 
3.4%
C 131
 
3.3%
c 127
 
3.2%
Other values (37) 1546
38.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2449
61.4%
Uppercase Letter 571
 
14.3%
Decimal Number 475
 
11.9%
Space Separator 430
 
10.8%
Dash Punctuation 19
 
0.5%
Math Symbol 12
 
0.3%
Close Punctuation 12
 
0.3%
Open Punctuation 12
 
0.3%
Other Letter 6
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 429
17.5%
e 365
14.9%
n 300
12.2%
i 213
8.7%
l 156
 
6.4%
f 155
 
6.3%
c 127
 
5.2%
r 121
 
4.9%
m 116
 
4.7%
b 103
 
4.2%
Other values (8) 364
14.9%
Uppercase Letter
ValueCountFrequency (%)
R 134
23.5%
C 131
22.9%
E 75
13.1%
H 75
13.1%
A 63
11.0%
B 26
 
4.6%
V 19
 
3.3%
O 16
 
2.8%
S 11
 
1.9%
G 9
 
1.6%
Other values (3) 12
 
2.1%
Decimal Number
ValueCountFrequency (%)
0 121
25.5%
5 78
16.4%
2 73
15.4%
3 62
13.1%
1 47
 
9.9%
4 41
 
8.6%
6 26
 
5.5%
7 10
 
2.1%
9 9
 
1.9%
8 8
 
1.7%
Space Separator
ValueCountFrequency (%)
430
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 19
100.0%
Math Symbol
ValueCountFrequency (%)
+ 12
100.0%
Close Punctuation
ValueCountFrequency (%)
) 12
100.0%
Open Punctuation
ValueCountFrequency (%)
( 12
100.0%
Other Letter
ValueCountFrequency (%)
앞 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3020
75.8%
Common 960
 
24.1%
Hangul 6
 
0.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 429
14.2%
e 365
12.1%
n 300
 
9.9%
i 213
 
7.1%
l 156
 
5.2%
f 155
 
5.1%
R 134
 
4.4%
C 131
 
4.3%
c 127
 
4.2%
r 121
 
4.0%
Other values (21) 889
29.4%
Common
ValueCountFrequency (%)
430
44.8%
0 121
 
12.6%
5 78
 
8.1%
2 73
 
7.6%
3 62
 
6.5%
1 47
 
4.9%
4 41
 
4.3%
6 26
 
2.7%
- 19
 
2.0%
+ 12
 
1.2%
Other values (5) 51
 
5.3%
Hangul
ValueCountFrequency (%)
앞 6
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3980
99.8%
Hangul 6
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
430
 
10.8%
o 429
 
10.8%
e 365
 
9.2%
n 300
 
7.5%
i 213
 
5.4%
l 156
 
3.9%
f 155
 
3.9%
R 134
 
3.4%
C 131
 
3.3%
c 127
 
3.2%
Other values (36) 1540
38.7%
Hangul
ValueCountFrequency (%)
앞 6
100.0%

극장형식인원(명)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct21
Distinct (%)15.8%
Missing149
Missing (%)52.8%
Infinite0
Infinite (%)0.0%
Mean407.20301
Minimum0
Maximum6000
Zeros5
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size2.6 KiB
2024-03-13T08:17:09.868927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile26
Q1100
median200
Q3250
95-th percentile1760
Maximum6000
Range6000
Interquartile range (IQR)150

Descriptive statistics

Standard deviation877.05267
Coefficient of variation (CV)2.1538463
Kurtosis23.137603
Mean407.20301
Median Absolute Deviation (MAD)100
Skewness4.6167811
Sum54158
Variance769221.39
MonotonicityNot monotonic
2024-03-13T08:17:09.949822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
200 28
 
9.9%
100 20
 
7.1%
160 18
 
6.4%
250 16
 
5.7%
80 16
 
5.7%
1000 6
 
2.1%
0 5
 
1.8%
530 4
 
1.4%
2000 3
 
1.1%
800 2
 
0.7%
Other values (11) 15
 
5.3%
(Missing) 149
52.8%
ValueCountFrequency (%)
0 5
 
1.8%
12 1
 
0.4%
20 1
 
0.4%
30 2
 
0.7%
32 1
 
0.4%
34 1
 
0.4%
80 16
5.7%
100 20
7.1%
160 18
6.4%
200 28
9.9%
ValueCountFrequency (%)
6000 1
 
0.4%
5400 1
 
0.4%
4000 2
 
0.7%
2000 3
1.1%
1600 1
 
0.4%
1000 6
2.1%
800 2
 
0.7%
530 4
1.4%
400 2
 
0.7%
360 2
 
0.7%

연회형식인원(명)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct21
Distinct (%)15.8%
Missing149
Missing (%)52.8%
Infinite0
Infinite (%)0.0%
Mean198.4812
Minimum0
Maximum3000
Zeros5
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size2.6 KiB
2024-03-13T08:17:10.033632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile11.2
Q150
median80
Q3110
95-th percentile976
Maximum3000
Range3000
Interquartile range (IQR)60

Descriptive statistics

Standard deviation442.13683
Coefficient of variation (CV)2.2276005
Kurtosis22.552096
Mean198.4812
Median Absolute Deviation (MAD)30
Skewness4.5584994
Sum26398
Variance195484.98
MonotonicityNot monotonic
2024-03-13T08:17:10.120765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
80 44
 
15.6%
50 20
 
7.1%
110 16
 
5.7%
40 16
 
5.7%
500 6
 
2.1%
0 5
 
1.8%
250 4
 
1.4%
1000 3
 
1.1%
450 2
 
0.7%
2000 2
 
0.7%
Other values (11) 15
 
5.3%
(Missing) 149
52.8%
ValueCountFrequency (%)
0 5
 
1.8%
10 2
 
0.7%
12 1
 
0.4%
20 1
 
0.4%
32 1
 
0.4%
34 1
 
0.4%
40 16
 
5.7%
50 20
7.1%
60 2
 
0.7%
80 44
15.6%
ValueCountFrequency (%)
3000 1
 
0.4%
2700 1
 
0.4%
2000 2
 
0.7%
1000 3
1.1%
960 1
 
0.4%
500 6
2.1%
450 2
 
0.7%
250 4
1.4%
180 2
 
0.7%
160 2
 
0.7%

리셉션형식인원(명)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct20
Distinct (%)15.0%
Missing149
Missing (%)52.8%
Infinite0
Infinite (%)0.0%
Mean558.93233
Minimum0
Maximum10000
Zeros5
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size2.6 KiB
2024-03-13T08:17:10.208903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile26
Q180
median150
Q3200
95-th percentile2600
Maximum10000
Range10000
Interquartile range (IQR)120

Descriptive statistics

Standard deviation1560.2802
Coefficient of variation (CV)2.7915369
Kurtosis23.122556
Mean558.93233
Median Absolute Deviation (MAD)70
Skewness4.6535388
Sum74338
Variance2434474.3
MonotonicityNot monotonic
2024-03-13T08:17:10.299123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
150 28
 
9.9%
80 20
 
7.1%
200 16
 
5.7%
120 16
 
5.7%
60 16
 
5.7%
1800 6
 
2.1%
0 5
 
1.8%
300 4
 
1.4%
400 4
 
1.4%
3500 3
 
1.1%
Other values (10) 15
 
5.3%
(Missing) 149
52.8%
ValueCountFrequency (%)
0 5
 
1.8%
12 1
 
0.4%
20 1
 
0.4%
30 2
 
0.7%
32 1
 
0.4%
34 1
 
0.4%
60 16
5.7%
80 20
7.1%
100 2
 
0.7%
120 16
5.7%
ValueCountFrequency (%)
10000 2
 
0.7%
7000 2
 
0.7%
3500 3
 
1.1%
2000 1
 
0.4%
1800 6
 
2.1%
1000 2
 
0.7%
400 4
 
1.4%
300 4
 
1.4%
200 16
5.7%
150 28
9.9%

강의형식인원(명)
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing282
Missing (%)100.0%
Memory size2.6 KiB

Interactions

2024-03-13T08:17:05.974129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:17:05.217057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:17:05.460949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:17:05.722225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:17:06.038099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:17:05.276252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:17:05.523446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:17:05.786585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:17:06.101257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:17:05.338691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:17:05.586757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:17:05.850592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:17:06.165961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:17:05.401176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:17:05.653768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:17:05.911671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T08:17:10.369371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세부규격천장규격가용면적(m2)극장형식인원(명)연회형식인원(명)리셉션형식인원(명)
세부규격1.0001.0001.0001.0001.0001.000
천장규격1.0001.0000.6610.9360.9360.333
가용면적(m2)1.0000.6611.0001.0001.0001.000
극장형식인원(명)1.0000.9361.0001.0001.0000.968
연회형식인원(명)1.0000.9361.0001.0001.0000.942
리셉션형식인원(명)1.0000.3331.0000.9680.9421.000
2024-03-13T08:17:10.447249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
천장규격세부규격
천장규격1.0000.944
세부규격0.9441.000
2024-03-13T08:17:10.515310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
가용면적(m2)극장형식인원(명)연회형식인원(명)리셉션형식인원(명)세부규격천장규격
가용면적(m2)1.0000.9680.9490.9670.9150.682
극장형식인원(명)0.9681.0000.9851.0000.9490.690
연회형식인원(명)0.9490.9851.0000.9870.9490.690
리셉션형식인원(명)0.9671.0000.9871.0000.9370.527
세부규격0.9150.9490.9490.9371.0000.944
천장규격0.6820.6900.6900.5270.9441.000

Missing values

2024-03-13T08:17:06.273027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T08:17:06.401253image/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.
2024-03-13T08:17:06.505344image/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

시설명시설명1시설약칭명세부규격천장규격가용면적(m2)영문시설명극장형식인원(명)연회형식인원(명)리셉션형식인원(명)강의형식인원(명)
0제1전시장_회의실_회의실 201회의실 201201<NA><NA><NA><NA><NA><NA><NA><NA>
1제1전시장_회의실_회의실 202회의실 202202<NA><NA>45.0<NA>301030<NA>
2제1전시장_회의실_회의실 203회의실 203203<NA><NA>45.0<NA>301030<NA>
3제1전시장_회의실_회의실 204회의실 204204<NA><NA>420.0Conference Room 204360180300<NA>
4제1전시장_회의실_회의실 205회의실 205205<NA><NA>234.0Conference Room 20520080150<NA>
5제1전시장_회의실_회의실 206회의실 206206<NA><NA>234.0Conference Room 20520080150<NA>
6제1전시장_회의실_회의실 207회의실 207207<NA><NA>234.0Conference Room 20520080150<NA>
7제1전시장_회의실_회의실 208회의실 208208<NA><NA>234.0Conference Room 20520080150<NA>
8제1전시장_회의실_회의실 209회의실 209209<NA><NA>234.0Conference Room 20520080150<NA>
9제1전시장_회의실_회의실 210회의실 210210<NA><NA>234.0Conference Room 20520080150<NA>
시설명시설명1시설약칭명세부규격천장규격가용면적(m2)영문시설명극장형식인원(명)연회형식인원(명)리셉션형식인원(명)강의형식인원(명)
272제1전시장_전시장_전시홀 5전시홀 55<NA><NA><NA>Exhibition Hall 5<NA><NA><NA><NA>
273제1전시장_전시장_1-기타1-기타1-기타<NA><NA><NA><NA><NA><NA><NA><NA>
274제2전시장_회의실_회의실 305_회의실 305B회의실 305B2-305B<NA><NA>94.0Conference Room 305A804060<NA>
275제2전시장_회의실_회의실 306_회의실 306회의실 3062-306<NA><NA>188.0Conference Room 30516080120<NA>
276제2전시장_회의실_회의실 306_회의실 306A회의실 306A2-306A<NA><NA>94.0Conference Room 305A804060<NA>
277제2전시장_회의실_회의실 306_회의실 306B회의실 306B2-306B<NA><NA>94.0Conference Room 305A804060<NA>
278제2전시장_회의실_회의실 307_회의실 307회의실 3072-307<NA><NA>188.0Conference Room 30516080120<NA>
279제2전시장_회의실_회의실 307_회의실 307A회의실 307A2-307A<NA><NA>94.0Conference Room 305A804060<NA>
280제2전시장_회의실_회의실 307_회의실 307B회의실 307B2-307B<NA><NA>94.0Conference Room 305A804060<NA>
281제2전시장_회의실_회의실 308_회의실 308회의실 3082-308<NA><NA>188.0Conference Room 30516080120<NA>

Duplicate rows

Most frequently occurring

시설명시설명1시설약칭명세부규격천장규격가용면적(m2)영문시설명극장형식인원(명)연회형식인원(명)리셉션형식인원(명)# duplicates
0제1전시장_편의시설_식음시설_식음시설식음시설식음시설<NA><NA><NA><NA><NA><NA><NA>3