Overview

Dataset statistics

Number of variables10
Number of observations4500
Missing cells2726
Missing cells (%)6.1%
Duplicate rows45
Duplicate rows (%)1.0%
Total size in memory356.1 KiB
Average record size in memory81.0 B

Variable types

Numeric1
Categorical4
Text5

Dataset

Description충청북도 청주시에서 2년마다 개최되는 청주공예비엔날레의 1~12회 참여 작가의 성명/참여전시/국적/성별/생년월일 등의 데이터를 제공합니다.
Author충청북도 청주시
URLhttps://www.data.go.kr/data/15110031/fileData.do

Alerts

Dataset has 45 (1.0%) duplicate rowsDuplicates
출생_월 is highly imbalanced (92.4%)Imbalance
출생_일 is highly imbalanced (93.6%)Imbalance
작가명(국문) has 146 (3.2%) missing valuesMissing
국적(국문) has 107 (2.4%) missing valuesMissing
국적(영문) has 108 (2.4%) missing valuesMissing
출생_년도 has 2322 (51.6%) missing valuesMissing

Reproduction

Analysis started2023-12-11 23:13:40.315265
Analysis finished2023-12-11 23:13:41.636902
Duration1.32 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

회차
Real number (ℝ)

Distinct12
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.3226667
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.7 KiB
2023-12-12T08:13:41.687550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median6
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.296205
Coefficient of variation (CV)0.52133145
Kurtosis-1.0379191
Mean6.3226667
Median Absolute Deviation (MAD)2
Skewness0.055383092
Sum28452
Variance10.864967
MonotonicityIncreasing
2023-12-12T08:13:41.790716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
7 551
12.2%
4 517
11.5%
6 514
11.4%
11 473
10.5%
8 456
10.1%
1 426
9.5%
3 315
7.0%
5 288
6.4%
10 285
6.3%
2 271
6.0%
Other values (2) 404
9.0%
ValueCountFrequency (%)
1 426
9.5%
2 271
6.0%
3 315
7.0%
4 517
11.5%
5 288
6.4%
6 514
11.4%
7 551
12.2%
8 456
10.1%
9 153
 
3.4%
10 285
6.3%
ValueCountFrequency (%)
12 251
5.6%
11 473
10.5%
10 285
6.3%
9 153
 
3.4%
8 456
10.1%
7 551
12.2%
6 514
11.4%
5 288
6.4%
4 517
11.5%
3 315
7.0%

전시 구분
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size35.3 KiB
공모전
1910 
본전시/기획전
987 
초대국가관
936 
국제초대작가전
575 
지역작가전
 
92

Length

Max length7
Median length5
Mean length4.8453333
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
공모전 1910
42.4%
본전시/기획전 987
21.9%
초대국가관 936
20.8%
국제초대작가전 575
 
12.8%
지역작가전 92
 
2.0%

Length

2023-12-12T08:13:41.909372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:13:42.056049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
공모전 1910
42.4%
본전시/기획전 987
21.9%
초대국가관 936
20.8%
국제초대작가전 575
 
12.8%
지역작가전 92
 
2.0%

작가명(국문)
Text

MISSING 

Distinct2990
Distinct (%)68.7%
Missing146
Missing (%)3.2%
Memory size35.3 KiB
2023-12-12T08:13:42.414235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length3
Mean length4.35209
Min length2

Characters and Unicode

Total characters18949
Distinct characters667
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2452 ?
Unique (%)56.3%

Sample

1st row강O연
2nd row강O환
3rd row강O훈
4th row강O정
5th row강O선
ValueCountFrequency (%)
김o수 26
 
0.6%
김o영 26
 
0.6%
김o현 21
 
0.5%
이o희 19
 
0.4%
김o정 18
 
0.4%
김o희 17
 
0.4%
이o원 17
 
0.4%
이o주 16
 
0.4%
김o연 14
 
0.3%
박o철 13
 
0.3%
Other values (3000) 4187
95.7%
2023-12-12T08:13:42.947845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
O 4622
24.4%
이 781
 
4.1%
김 479
 
2.5%
리 469
 
2.5%
스 447
 
2.4%
라 267
 
1.4%
아 214
 
1.1%
나 213
 
1.1%
마 203
 
1.1%
박 200
 
1.1%
Other values (657) 11054
58.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 14135
74.6%
Uppercase Letter 4683
 
24.7%
Decimal Number 55
 
0.3%
Other Punctuation 21
 
0.1%
Space Separator 20
 
0.1%
Open Punctuation 14
 
0.1%
Close Punctuation 14
 
0.1%
Dash Punctuation 5
 
< 0.1%
Letter Number 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
이 781
 
5.5%
김 479
 
3.4%
리 469
 
3.3%
스 447
 
3.2%
라 267
 
1.9%
아 214
 
1.5%
나 213
 
1.5%
마 203
 
1.4%
박 200
 
1.4%
르 198
 
1.4%
Other values (623) 10664
75.4%
Uppercase Letter
ValueCountFrequency (%)
O 4622
98.7%
H 15
 
0.3%
A 15
 
0.3%
C 14
 
0.3%
M 3
 
0.1%
W 2
 
< 0.1%
K 2
 
< 0.1%
R 2
 
< 0.1%
U 1
 
< 0.1%
T 1
 
< 0.1%
Other values (6) 6
 
0.1%
Decimal Number
ValueCountFrequency (%)
0 34
61.8%
2 5
 
9.1%
4 4
 
7.3%
6 3
 
5.5%
3 3
 
5.5%
1 3
 
5.5%
5 2
 
3.6%
7 1
 
1.8%
Other Punctuation
ValueCountFrequency (%)
. 9
42.9%
& 7
33.3%
, 4
19.0%
· 1
 
4.8%
Letter Number
ValueCountFrequency (%)
Ⅹ 1
50.0%
Ⅱ 1
50.0%
Space Separator
ValueCountFrequency (%)
20
100.0%
Open Punctuation
ValueCountFrequency (%)
( 14
100.0%
Close Punctuation
ValueCountFrequency (%)
) 14
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 14135
74.6%
Latin 4685
 
24.7%
Common 129
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
이 781
 
5.5%
김 479
 
3.4%
리 469
 
3.3%
스 447
 
3.2%
라 267
 
1.9%
아 214
 
1.5%
나 213
 
1.5%
마 203
 
1.4%
박 200
 
1.4%
르 198
 
1.4%
Other values (623) 10664
75.4%
Latin
ValueCountFrequency (%)
O 4622
98.7%
H 15
 
0.3%
A 15
 
0.3%
C 14
 
0.3%
M 3
 
0.1%
W 2
 
< 0.1%
K 2
 
< 0.1%
R 2
 
< 0.1%
U 1
 
< 0.1%
T 1
 
< 0.1%
Other values (8) 8
 
0.2%
Common
ValueCountFrequency (%)
0 34
26.4%
20
15.5%
( 14
10.9%
) 14
10.9%
. 9
 
7.0%
& 7
 
5.4%
2 5
 
3.9%
- 5
 
3.9%
, 4
 
3.1%
4 4
 
3.1%
Other values (6) 13
 
10.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 14135
74.6%
ASCII 4811
 
25.4%
Number Forms 2
 
< 0.1%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
O 4622
96.1%
0 34
 
0.7%
20
 
0.4%
H 15
 
0.3%
A 15
 
0.3%
C 14
 
0.3%
( 14
 
0.3%
) 14
 
0.3%
. 9
 
0.2%
& 7
 
0.1%
Other values (21) 47
 
1.0%
Hangul
ValueCountFrequency (%)
이 781
 
5.5%
김 479
 
3.4%
리 469
 
3.3%
스 447
 
3.2%
라 267
 
1.9%
아 214
 
1.5%
나 213
 
1.5%
마 203
 
1.4%
박 200
 
1.4%
르 198
 
1.4%
Other values (623) 10664
75.4%
None
ValueCountFrequency (%)
· 1
100.0%
Number Forms
ValueCountFrequency (%)
Ⅹ 1
50.0%
Ⅱ 1
50.0%

작가명(영문)
Text

Distinct2844
Distinct (%)63.8%
Missing43
Missing (%)1.0%
Memory size35.3 KiB
2023-12-12T08:13:43.489019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length31
Median length29
Mean length9.6531299
Min length3

Characters and Unicode

Total characters43024
Distinct characters90
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2116 ?
Unique (%)47.5%

Sample

1st rowKang O Youn
2nd rowKang O Hwan
3rd rowKang O Hoon
4th rowKang O Jeung
5th rowKang O Sun
ValueCountFrequency (%)
o 4624
39.4%
kim 474
 
4.0%
lee 365
 
3.1%
park 193
 
1.6%
choi 110
 
0.9%
hee 96
 
0.8%
jung 94
 
0.8%
won 86
 
0.7%
young 81
 
0.7%
ho 77
 
0.7%
Other values (1929) 5536
47.2%
2023-12-12T08:13:43.975333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9243
21.5%
O 4625
 
10.7%
n 2936
 
6.8%
e 2603
 
6.1%
o 2569
 
6.0%
a 2544
 
5.9%
i 2254
 
5.2%
u 1386
 
3.2%
h 967
 
2.2%
g 950
 
2.2%
Other values (80) 12947
30.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 21948
51.0%
Uppercase Letter 11653
27.1%
Space Separator 9243
21.5%
Decimal Number 54
 
0.1%
Other Punctuation 41
 
0.1%
Open Punctuation 23
 
0.1%
Close Punctuation 23
 
0.1%
Dash Punctuation 21
 
< 0.1%
Other Letter 16
 
< 0.1%
Letter Number 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 2936
13.4%
e 2603
11.9%
o 2569
11.7%
a 2544
11.6%
i 2254
10.3%
u 1386
 
6.3%
h 967
 
4.4%
g 950
 
4.3%
r 946
 
4.3%
m 859
 
3.9%
Other values (19) 3934
17.9%
Uppercase Letter
ValueCountFrequency (%)
O 4625
39.7%
K 938
 
8.0%
S 737
 
6.3%
H 674
 
5.8%
J 635
 
5.4%
L 566
 
4.9%
C 519
 
4.5%
Y 444
 
3.8%
M 386
 
3.3%
P 301
 
2.6%
Other values (16) 1828
 
15.7%
Other Letter
ValueCountFrequency (%)
芳 1
 
6.2%
會 1
 
6.2%
拓 1
 
6.2%
山 1
 
6.2%
神 1
 
6.2%
敏 1
 
6.2%
李 1
 
6.2%
玉 1
 
6.2%
黃 1
 
6.2%
保 1
 
6.2%
Other values (6) 6
37.5%
Decimal Number
ValueCountFrequency (%)
0 34
63.0%
4 4
 
7.4%
2 4
 
7.4%
3 3
 
5.6%
6 3
 
5.6%
1 3
 
5.6%
5 2
 
3.7%
7 1
 
1.9%
Other Punctuation
ValueCountFrequency (%)
. 17
41.5%
, 13
31.7%
& 8
19.5%
' 2
 
4.9%
· 1
 
2.4%
Letter Number
ValueCountFrequency (%)
Ⅱ 1
50.0%
Ⅹ 1
50.0%
Space Separator
ValueCountFrequency (%)
9243
100.0%
Open Punctuation
ValueCountFrequency (%)
( 23
100.0%
Close Punctuation
ValueCountFrequency (%)
) 23
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 21
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 33603
78.1%
Common 9405
 
21.9%
Han 16
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
O 4625
 
13.8%
n 2936
 
8.7%
e 2603
 
7.7%
o 2569
 
7.6%
a 2544
 
7.6%
i 2254
 
6.7%
u 1386
 
4.1%
h 967
 
2.9%
g 950
 
2.8%
r 946
 
2.8%
Other values (47) 11823
35.2%
Common
ValueCountFrequency (%)
9243
98.3%
0 34
 
0.4%
( 23
 
0.2%
) 23
 
0.2%
- 21
 
0.2%
. 17
 
0.2%
, 13
 
0.1%
& 8
 
0.1%
4 4
 
< 0.1%
2 4
 
< 0.1%
Other values (7) 15
 
0.2%
Han
ValueCountFrequency (%)
芳 1
 
6.2%
會 1
 
6.2%
拓 1
 
6.2%
山 1
 
6.2%
神 1
 
6.2%
敏 1
 
6.2%
李 1
 
6.2%
玉 1
 
6.2%
黃 1
 
6.2%
保 1
 
6.2%
Other values (6) 6
37.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 43002
99.9%
CJK 16
 
< 0.1%
None 4
 
< 0.1%
Number Forms 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9243
21.5%
O 4625
 
10.8%
n 2936
 
6.8%
e 2603
 
6.1%
o 2569
 
6.0%
a 2544
 
5.9%
i 2254
 
5.2%
u 1386
 
3.2%
h 967
 
2.2%
g 950
 
2.2%
Other values (58) 12925
30.1%
CJK
ValueCountFrequency (%)
芳 1
 
6.2%
會 1
 
6.2%
拓 1
 
6.2%
山 1
 
6.2%
神 1
 
6.2%
敏 1
 
6.2%
李 1
 
6.2%
玉 1
 
6.2%
黃 1
 
6.2%
保 1
 
6.2%
Other values (6) 6
37.5%
None
ValueCountFrequency (%)
ø 1
25.0%
ł 1
25.0%
ß 1
25.0%
· 1
25.0%
Number Forms
ValueCountFrequency (%)
Ⅱ 1
50.0%
Ⅹ 1
50.0%

국적(국문)
Text

MISSING 

Distinct85
Distinct (%)1.9%
Missing107
Missing (%)2.4%
Memory size35.3 KiB
2023-12-12T08:13:44.242751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length4
Mean length3.4022308
Min length2

Characters and Unicode

Total characters14946
Distinct characters117
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22 ?
Unique (%)0.5%

Sample

1st row대한민국
2nd row대한민국
3rd row대한민국
4th row대한민국
5th row대한민국
ValueCountFrequency (%)
대한민국 2387
54.2%
미국 253
 
5.7%
캐나다 245
 
5.6%
일본 232
 
5.3%
핀란드 224
 
5.1%
독일 200
 
4.5%
중국 107
 
2.4%
영국 89
 
2.0%
스웨덴 61
 
1.4%
대만 60
 
1.4%
Other values (70) 543
 
12.3%
2023-12-12T08:13:44.581439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
국 2851
19.1%
대 2447
16.4%
한 2388
16.0%
민 2387
16.0%
일 445
 
3.0%
드 297
 
2.0%
란 283
 
1.9%
미 256
 
1.7%
나 255
 
1.7%
다 246
 
1.6%
Other values (107) 3091
20.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 14881
99.6%
Space Separator 63
 
0.4%
Other Punctuation 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
국 2851
19.2%
대 2447
16.4%
한 2388
16.0%
민 2387
16.0%
일 445
 
3.0%
드 297
 
2.0%
란 283
 
1.9%
미 256
 
1.7%
나 255
 
1.7%
다 246
 
1.7%
Other values (104) 3026
20.3%
Other Punctuation
ValueCountFrequency (%)
/ 1
50.0%
, 1
50.0%
Space Separator
ValueCountFrequency (%)
63
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 14881
99.6%
Common 65
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
국 2851
19.2%
대 2447
16.4%
한 2388
16.0%
민 2387
16.0%
일 445
 
3.0%
드 297
 
2.0%
란 283
 
1.9%
미 256
 
1.7%
나 255
 
1.7%
다 246
 
1.7%
Other values (104) 3026
20.3%
Common
ValueCountFrequency (%)
63
96.9%
/ 1
 
1.5%
, 1
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 14881
99.6%
ASCII 65
 
0.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
국 2851
19.2%
대 2447
16.4%
한 2388
16.0%
민 2387
16.0%
일 445
 
3.0%
드 297
 
2.0%
란 283
 
1.9%
미 256
 
1.7%
나 255
 
1.7%
다 246
 
1.7%
Other values (104) 3026
20.3%
ASCII
ValueCountFrequency (%)
63
96.9%
/ 1
 
1.5%
, 1
 
1.5%

국적(영문)
Text

MISSING 

Distinct79
Distinct (%)1.8%
Missing108
Missing (%)2.4%
Memory size35.3 KiB
2023-12-12T08:13:44.799403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length40
Median length17
Mean length13.499089
Min length4

Characters and Unicode

Total characters59288
Distinct characters50
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

Unique21 ?
Unique (%)0.5%

Sample

1st rowRepublic of Korea
2nd rowRepublic of Korea
3rd rowRepublic of Korea
4th rowRepublic of Korea
5th rowRepublic of Korea
ValueCountFrequency (%)
of 2648
26.2%
republic 2395
23.7%
korea 2388
23.6%
united 342
 
3.4%
states 254
 
2.5%
america 254
 
2.5%
canada 245
 
2.4%
japan 232
 
2.3%
finland 224
 
2.2%
germany 200
 
2.0%
Other values (82) 924
 
9.1%
2023-12-12T08:13:45.132455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 6256
 
10.6%
5714
 
9.6%
a 5379
 
9.1%
o 5185
 
8.7%
i 3758
 
6.3%
r 3216
 
5.4%
l 2904
 
4.9%
c 2735
 
4.6%
p 2661
 
4.5%
f 2654
 
4.5%
Other values (40) 18826
31.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 46070
77.7%
Uppercase Letter 7501
 
12.7%
Space Separator 5714
 
9.6%
Other Punctuation 3
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 6256
13.6%
a 5379
11.7%
o 5185
11.3%
i 3758
8.2%
r 3216
 
7.0%
l 2904
 
6.3%
c 2735
 
5.9%
p 2661
 
5.8%
f 2654
 
5.8%
u 2557
 
5.6%
Other values (15) 8765
19.0%
Uppercase Letter
ValueCountFrequency (%)
K 2483
33.1%
R 2409
32.1%
C 367
 
4.9%
S 356
 
4.7%
U 346
 
4.6%
A 303
 
4.0%
F 277
 
3.7%
J 232
 
3.1%
G 201
 
2.7%
T 118
 
1.6%
Other values (12) 409
 
5.5%
Other Punctuation
ValueCountFrequency (%)
' 2
66.7%
, 1
33.3%
Space Separator
ValueCountFrequency (%)
5714
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 53571
90.4%
Common 5717
 
9.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 6256
 
11.7%
a 5379
 
10.0%
o 5185
 
9.7%
i 3758
 
7.0%
r 3216
 
6.0%
l 2904
 
5.4%
c 2735
 
5.1%
p 2661
 
5.0%
f 2654
 
5.0%
u 2557
 
4.8%
Other values (37) 16266
30.4%
Common
ValueCountFrequency (%)
5714
99.9%
' 2
 
< 0.1%
, 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 59288
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 6256
 
10.6%
5714
 
9.6%
a 5379
 
9.1%
o 5185
 
8.7%
i 3758
 
6.3%
r 3216
 
5.4%
l 2904
 
4.9%
c 2735
 
4.6%
p 2661
 
4.5%
f 2654
 
4.5%
Other values (40) 18826
31.8%

성별
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size35.3 KiB
<NA>
2602 
남
1020 
여
878 

Length

Max length4
Median length4
Mean length2.7346667
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 2602
57.8%
남 1020
 
22.7%
여 878
 
19.5%

Length

2023-12-12T08:13:45.257888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:13:45.354647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 2602
57.8%
남 1020
 
22.7%
여 878
 
19.5%

출생_년도
Text

MISSING 

Distinct104
Distinct (%)4.8%
Missing2322
Missing (%)51.6%
Memory size35.3 KiB
2023-12-12T08:13:45.579252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length4
Mean length4.0082645
Min length4

Characters and Unicode

Total characters8730
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)1.2%

Sample

1st row1970
2nd row1971
3rd row1966
4th row1968
5th row1972
ValueCountFrequency (%)
1971 71
 
3.3%
1970 65
 
3.0%
1974 63
 
2.9%
1962 62
 
2.8%
1964 61
 
2.8%
1955 61
 
2.8%
1960 61
 
2.8%
1973 60
 
2.8%
1969 60
 
2.8%
1972 59
 
2.7%
Other values (93) 1558
71.4%
2023-12-12T08:13:45.941594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 2449
28.1%
1 2403
27.5%
7 725
 
8.3%
6 710
 
8.1%
5 675
 
7.7%
4 540
 
6.2%
8 446
 
5.1%
3 287
 
3.3%
2 266
 
3.0%
0 223
 
2.6%
Other values (2) 6
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8724
99.9%
Other Punctuation 3
 
< 0.1%
Space Separator 3
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 2449
28.1%
1 2403
27.5%
7 725
 
8.3%
6 710
 
8.1%
5 675
 
7.7%
4 540
 
6.2%
8 446
 
5.1%
3 287
 
3.3%
2 266
 
3.0%
0 223
 
2.6%
Other Punctuation
ValueCountFrequency (%)
, 3
100.0%
Space Separator
ValueCountFrequency (%)
3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 8730
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
9 2449
28.1%
1 2403
27.5%
7 725
 
8.3%
6 710
 
8.1%
5 675
 
7.7%
4 540
 
6.2%
8 446
 
5.1%
3 287
 
3.3%
2 266
 
3.0%
0 223
 
2.6%
Other values (2) 6
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8730
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 2449
28.1%
1 2403
27.5%
7 725
 
8.3%
6 710
 
8.1%
5 675
 
7.7%
4 540
 
6.2%
8 446
 
5.1%
3 287
 
3.3%
2 266
 
3.0%
0 223
 
2.6%
Other values (2) 6
 
0.1%

출생_월
Categorical

IMBALANCE 

Distinct14
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size35.3 KiB
<NA>
4371 
5
 
16
9
 
14
4
 
14
2
 
13
Other values (9)
 
72

Length

Max length4
Median length4
Mean length3.9206667
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 4371
97.1%
5 16
 
0.4%
9 14
 
0.3%
4 14
 
0.3%
2 13
 
0.3%
10 11
 
0.2%
7 11
 
0.2%
11 10
 
0.2%
6 9
 
0.2%
12 9
 
0.2%
Other values (4) 22
 
0.5%

Length

2023-12-12T08:13:46.054699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 4371
97.2%
5 16
 
0.4%
9 14
 
0.3%
4 14
 
0.3%
2 13
 
0.3%
10 11
 
0.2%
7 11
 
0.2%
11 10
 
0.2%
6 9
 
0.2%
12 9
 
0.2%
Other values (3) 19
 
0.4%

출생_일
Categorical

IMBALANCE 

Distinct32
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size35.3 KiB
<NA>
4373 
5
 
8
29
 
7
8
 
7
25
 
7
Other values (27)
 
98

Length

Max length10
Median length4
Mean length3.9357778
Min length1

Unique

Unique3 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 4373
97.2%
5 8
 
0.2%
29 7
 
0.2%
8 7
 
0.2%
25 7
 
0.2%
14 7
 
0.2%
18 7
 
0.2%
6 6
 
0.1%
23 6
 
0.1%
4 5
 
0.1%
Other values (22) 67
 
1.5%

Length

2023-12-12T08:13:46.146510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 4373
97.2%
5 8
 
0.2%
29 7
 
0.2%
8 7
 
0.2%
25 7
 
0.2%
14 7
 
0.2%
18 7
 
0.2%
6 6
 
0.1%
23 6
 
0.1%
4 5
 
0.1%
Other values (22) 67
 
1.5%

Interactions

2023-12-12T08:13:41.196573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T08:13:46.216130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
회차전시 구분국적(국문)국적(영문)성별출생_월출생_일
회차1.0000.7990.7560.7540.2280.4460.000
전시 구분0.7991.0000.7290.7240.2180.2910.000
국적(국문)0.7560.7291.0001.0000.3760.3770.626
국적(영문)0.7540.7241.0001.0000.3750.4480.647
성별0.2280.2180.3760.3751.0000.1650.000
출생_월0.4460.2910.3770.4480.1651.0000.370
출생_일0.0000.0000.6260.6470.0000.3701.000
2023-12-12T08:13:46.315545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
출생_월출생_일전시 구분성별
출생_월1.0000.1120.1650.144
출생_일0.1121.0000.0000.000
전시 구분0.1650.0001.0000.266
성별0.1440.0000.2661.000
2023-12-12T08:13:46.386281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
회차전시 구분성별출생_월출생_일
회차1.0000.4550.2270.2130.000
전시 구분0.4551.0000.2660.1650.000
성별0.2270.2661.0000.1440.000
출생_월0.2130.1650.1441.0000.112
출생_일0.0000.0000.0000.1121.000

Missing values

2023-12-12T08:13:41.315722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T08:13:41.438178image/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.
2023-12-12T08:13:41.553385image/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

회차전시 구분작가명(국문)작가명(영문)국적(국문)국적(영문)성별출생_년도출생_월출생_일
01공모전강O연Kang O Youn대한민국Republic of Korea<NA>1970<NA><NA>
11공모전강O환Kang O Hwan대한민국Republic of Korea<NA>1971<NA><NA>
21공모전강O훈Kang O Hoon대한민국Republic of Korea<NA>1966<NA><NA>
31공모전강O정Kang O Jeung대한민국Republic of Korea<NA>1968<NA><NA>
41공모전강O선Kang O Sun대한민국Republic of Korea<NA>1972<NA><NA>
51공모전강O찬Kang O Chan대한민국Republic of Korea<NA>1974<NA><NA>
61공모전곽O윤Kwak O Yun대한민국Republic of Korea<NA>1971<NA><NA>
71공모전곽O섭Kwak O Seub대한민국Republic of Korea<NA>1966<NA><NA>
81공모전곽O혁Kwak O Hyeuk대한민국Republic of Korea<NA>1972<NA><NA>
91공모전권O미Kwon O Mi대한민국Republic of Korea<NA>1974<NA><NA>
회차전시 구분작가명(국문)작가명(영문)국적(국문)국적(영문)성별출생_년도출생_월출생_일
449012초대국가관이자벨O메릭Isabelle O프랑스France여1957<NA><NA>
449112초대국가관이자벨OIsabelle O프랑스France여1954<NA><NA>
449212초대국가관장O이O흘랑Jean O Hurlin프랑스France남1950<NA><NA>
449312초대국가관카나에O리안데O마노Kanae O AMAN O일본Japan여1974<NA><NA>
449412초대국가관클로에O테르만Chloe O프랑스France남1982212
449512초대국가관티에리O질Thierry O프랑스France남1955<NA><NA>
449612초대국가관파비엔느O졸Fabienne O프랑스France여1967<NA><NA>
449712초대국가관파스칼O데Pascal O프랑스France남1974<NA><NA>
449812초대국가관페리O르세스Ferri O이란Iran여1957<NA><NA>
449912초대국가관플로랑스O미에그르Florence O프랑스France여1965<NA><NA>

Duplicate rows

Most frequently occurring

회차전시 구분작가명(국문)작가명(영문)국적(국문)국적(영문)성별출생_년도출생_월출생_일# duplicates
73국제초대작가전이O규Lee O Kyu대한민국Republic of Korea남<NA><NA><NA>3
124공모전김O영Kim O Young대한민국Republic of Korea<NA><NA><NA><NA>3
338공모전김O영Kim O Young대한민국Republic of Korea<NA><NA><NA><NA>3
358공모전김O현Kim O Hyun대한민국Republic of Korea<NA><NA><NA><NA>3
01공모전김O은Kim O Eun대한민국Republic of Korea<NA>1971<NA><NA>2
12공모전고O영Ko O Young대한민국Republic of Korea<NA><NA><NA><NA>2
23공모전김O정Kim O Jung대한민국Republic of Korea<NA><NA><NA><NA>2
33공모전김O희Kim O Hee대한민국Republic of Korea<NA><NA><NA><NA>2
43공모전박O자Park O Ja대한민국Republic of Korea<NA><NA><NA><NA>2
53공모전안O경An O Kyung대한민국Republic of Korea<NA><NA><NA><NA>2