Overview

Dataset statistics

Number of variables10
Number of observations10000
Missing cells395
Missing cells (%)0.4%
Duplicate rows501
Duplicate rows (%)5.0%
Total size in memory869.1 KiB
Average record size in memory89.0 B

Variable types

Text3
Numeric1
Categorical5
DateTime1

Dataset

Description제주 민속자연사박물관에서 소장하고 있는 고고민속자료의 명칭, 수량, 규격, 국적, 재질, 수장여부 등 정보 제공
Author제주특별자치도
URLhttps://www.data.go.kr/data/15045473/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
Dataset has 501 (5.0%) duplicate rowsDuplicates
국적 is highly overall correlated with 시대High correlation
시대 is highly overall correlated with 국적High correlation
국적 is highly imbalanced (97.8%)Imbalance
시대 is highly imbalanced (60.5%)Imbalance
수장여부 is highly imbalanced (63.7%)Imbalance
규격 has 392 (3.9%) missing valuesMissing
수량 is highly skewed (γ1 = 39.46175246)Skewed

Reproduction

Analysis started2023-12-12 08:31:43.700484
Analysis finished2023-12-12 08:31:45.931009
Duration2.23 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

명칭
Text

Distinct3818
Distinct (%)38.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T17:31:46.234029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length24
Mean length3.8609
Min length1

Characters and Unicode

Total characters38609
Distinct characters1000
Distinct categories12 ?
Distinct scripts4 ?
Distinct blocks7 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2722 ?
Unique (%)27.2%

Sample

1st row모밀접시
2nd row짝얼레
3rd row(장테)뚜껑
4th row경상
5th row멧돌
ValueCountFrequency (%)
맷돌 483
 
4.6%
모밀사발 198
 
1.9%
모밀접시 103
 
1.0%
숟가락 89
 
0.9%
놋대접 74
 
0.7%
놋사발 62
 
0.6%
수탁 62
 
0.6%
오시복간찰 52
 
0.5%
이형상 51
 
0.5%
궤 50
 
0.5%
Other values (3792) 9231
88.3%
2023-12-12T17:31:46.841737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1753
 
4.5%
사 801
 
2.1%
기 723
 
1.9%
돌 683
 
1.8%
자 636
 
1.6%
이 615
 
1.6%
지 605
 
1.6%
( 578
 
1.5%
대 536
 
1.4%
시 523
 
1.4%
Other values (990) 31156
80.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 34557
89.5%
Space Separator 1753
 
4.5%
Decimal Number 920
 
2.4%
Open Punctuation 578
 
1.5%
Close Punctuation 506
 
1.3%
Other Punctuation 175
 
0.5%
Dash Punctuation 65
 
0.2%
Other Symbol 19
 
< 0.1%
Math Symbol 17
 
< 0.1%
Uppercase Letter 16
 
< 0.1%
Other values (2) 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
사 801
 
2.3%
기 723
 
2.1%
돌 683
 
2.0%
자 636
 
1.8%
이 615
 
1.8%
지 605
 
1.8%
대 536
 
1.6%
시 523
 
1.5%
모 511
 
1.5%
리 503
 
1.5%
Other values (955) 28421
82.2%
Uppercase Letter
ValueCountFrequency (%)
A 4
25.0%
J 2
12.5%
B 2
12.5%
G 1
 
6.2%
N 1
 
6.2%
K 1
 
6.2%
T 1
 
6.2%
E 1
 
6.2%
U 1
 
6.2%
L 1
 
6.2%
Decimal Number
ValueCountFrequency (%)
1 241
26.2%
2 137
14.9%
4 79
 
8.6%
8 78
 
8.5%
3 74
 
8.0%
0 69
 
7.5%
5 68
 
7.4%
6 64
 
7.0%
9 62
 
6.7%
7 48
 
5.2%
Other Punctuation
ValueCountFrequency (%)
. 96
54.9%
* 29
 
16.6%
/ 27
 
15.4%
… 21
 
12.0%
· 2
 
1.1%
Other Symbol
ValueCountFrequency (%)
○ 18
94.7%
□ 1
 
5.3%
Space Separator
ValueCountFrequency (%)
1753
100.0%
Open Punctuation
ValueCountFrequency (%)
( 578
100.0%
Close Punctuation
ValueCountFrequency (%)
) 506
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 65
100.0%
Math Symbol
ValueCountFrequency (%)
~ 17
100.0%
Initial Punctuation
ValueCountFrequency (%)
“ 2
100.0%
Lowercase Letter
ValueCountFrequency (%)
c 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 34064
88.2%
Common 4035
 
10.5%
Han 493
 
1.3%
Latin 17
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
사 801
 
2.4%
기 723
 
2.1%
돌 683
 
2.0%
자 636
 
1.9%
이 615
 
1.8%
지 605
 
1.8%
대 536
 
1.6%
시 523
 
1.5%
모 511
 
1.5%
리 503
 
1.5%
Other values (687) 27928
82.0%
Han
ValueCountFrequency (%)
光 26
 
5.3%
一 12
 
2.4%
治 12
 
2.4%
書 12
 
2.4%
同 12
 
2.4%
道 10
 
2.0%
氏 10
 
2.0%
武 9
 
1.8%
豊 8
 
1.6%
咸 8
 
1.6%
Other values (258) 374
75.9%
Common
ValueCountFrequency (%)
1753
43.4%
( 578
 
14.3%
) 506
 
12.5%
1 241
 
6.0%
2 137
 
3.4%
. 96
 
2.4%
4 79
 
2.0%
8 78
 
1.9%
3 74
 
1.8%
0 69
 
1.7%
Other values (13) 424
 
10.5%
Latin
ValueCountFrequency (%)
A 4
23.5%
J 2
11.8%
B 2
11.8%
G 1
 
5.9%
N 1
 
5.9%
K 1
 
5.9%
T 1
 
5.9%
E 1
 
5.9%
U 1
 
5.9%
L 1
 
5.9%
Other values (2) 2
11.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 34064
88.2%
ASCII 4008
 
10.4%
CJK 474
 
1.2%
Punctuation 23
 
0.1%
Geometric Shapes 19
 
< 0.1%
CJK Compat Ideographs 19
 
< 0.1%
None 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1753
43.7%
( 578
 
14.4%
) 506
 
12.6%
1 241
 
6.0%
2 137
 
3.4%
. 96
 
2.4%
4 79
 
2.0%
8 78
 
1.9%
3 74
 
1.8%
0 69
 
1.7%
Other values (20) 397
 
9.9%
Hangul
ValueCountFrequency (%)
사 801
 
2.4%
기 723
 
2.1%
돌 683
 
2.0%
자 636
 
1.9%
이 615
 
1.8%
지 605
 
1.8%
대 536
 
1.6%
시 523
 
1.5%
모 511
 
1.5%
리 503
 
1.5%
Other values (687) 27928
82.0%
CJK
ValueCountFrequency (%)
光 26
 
5.5%
一 12
 
2.5%
治 12
 
2.5%
書 12
 
2.5%
同 12
 
2.5%
道 10
 
2.1%
氏 10
 
2.1%
武 9
 
1.9%
豊 8
 
1.7%
咸 8
 
1.7%
Other values (242) 355
74.9%
Punctuation
ValueCountFrequency (%)
… 21
91.3%
“ 2
 
8.7%
Geometric Shapes
ValueCountFrequency (%)
○ 18
94.7%
□ 1
 
5.3%
CJK Compat Ideographs
ValueCountFrequency (%)
李 2
 
10.5%
臨 2
 
10.5%
金 2
 
10.5%
六 1
 
5.3%
錄 1
 
5.3%
龍 1
 
5.3%
梁 1
 
5.3%
狀 1
 
5.3%
鹿 1
 
5.3%
禮 1
 
5.3%
Other values (6) 6
31.6%
None
ValueCountFrequency (%)
· 2
100.0%

수량
Real number (ℝ)

SKEWED 

Distinct21
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1302
Minimum1
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T17:31:47.015786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum90
Range89
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.3261311
Coefficient of variation (CV)1.1733597
Kurtosis2229.0495
Mean1.1302
Median Absolute Deviation (MAD)0
Skewness39.461752
Sum11302
Variance1.7586238
MonotonicityNot monotonic
2023-12-12T17:31:47.150743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
1 9333
93.3%
2 561
 
5.6%
3 39
 
0.4%
4 14
 
0.1%
5 13
 
0.1%
7 7
 
0.1%
6 6
 
0.1%
20 5
 
0.1%
9 4
 
< 0.1%
12 3
 
< 0.1%
Other values (11) 15
 
0.1%
ValueCountFrequency (%)
1 9333
93.3%
2 561
 
5.6%
3 39
 
0.4%
4 14
 
0.1%
5 13
 
0.1%
6 6
 
0.1%
7 7
 
0.1%
8 2
 
< 0.1%
9 4
 
< 0.1%
10 2
 
< 0.1%
ValueCountFrequency (%)
90 1
 
< 0.1%
44 1
 
< 0.1%
31 2
 
< 0.1%
29 1
 
< 0.1%
21 1
 
< 0.1%
20 5
0.1%
16 2
 
< 0.1%
14 1
 
< 0.1%
13 1
 
< 0.1%
12 3
< 0.1%

규격
Text

MISSING 

Distinct5728
Distinct (%)59.6%
Missing392
Missing (%)3.9%
Memory size156.2 KiB
2023-12-12T17:31:47.598906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length47
Median length29
Mean length5.6820358
Min length1

Characters and Unicode

Total characters54593
Distinct characters97
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4587 ?
Unique (%)47.7%

Sample

1st row11*3*7
2nd row34*14
3rd row14*47*31
4th row28*43
5th row22*15
ValueCountFrequency (%)
높이 81
 
0.8%
42 79
 
0.8%
8*15*7 77
 
0.8%
41 73
 
0.7%
길이 59
 
0.6%
43 59
 
0.6%
40 53
 
0.5%
7.5 45
 
0.4%
50.5*36 44
 
0.4%
15 43
 
0.4%
Other values (5701) 9500
93.9%
2023-12-12T17:31:48.287933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
* 9838
18.0%
1 7124
13.0%
2 5343
9.8%
5 5297
9.7%
3 5077
9.3%
4 3912
 
7.2%
. 3049
 
5.6%
6 2916
 
5.3%
7 2843
 
5.2%
0 2621
 
4.8%
Other values (87) 6573
12.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 39609
72.6%
Other Punctuation 12895
 
23.6%
Other Letter 1478
 
2.7%
Space Separator 507
 
0.9%
Lowercase Letter 38
 
0.1%
Open Punctuation 30
 
0.1%
Close Punctuation 30
 
0.1%
Dash Punctuation 4
 
< 0.1%
Other Symbol 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
이 369
25.0%
길 203
13.7%
높 166
11.2%
지 119
 
8.1%
름 112
 
7.6%
경 103
 
7.0%
구 81
 
5.5%
입 40
 
2.7%
폭 33
 
2.2%
바 28
 
1.9%
Other values (63) 224
15.2%
Decimal Number
ValueCountFrequency (%)
1 7124
18.0%
2 5343
13.5%
5 5297
13.4%
3 5077
12.8%
4 3912
9.9%
6 2916
7.4%
7 2843
 
7.2%
0 2621
 
6.6%
8 2454
 
6.2%
9 2022
 
5.1%
Lowercase Letter
ValueCountFrequency (%)
m 19
50.0%
c 16
42.1%
n 1
 
2.6%
h 1
 
2.6%
i 1
 
2.6%
Other Punctuation
ValueCountFrequency (%)
* 9838
76.3%
. 3049
 
23.6%
/ 5
 
< 0.1%
: 3
 
< 0.1%
Space Separator
ValueCountFrequency (%)
507
100.0%
Open Punctuation
ValueCountFrequency (%)
( 30
100.0%
Close Punctuation
ValueCountFrequency (%)
) 30
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%
Other Symbol
ValueCountFrequency (%)
㎏ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 53077
97.2%
Hangul 1478
 
2.7%
Latin 38
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
이 369
25.0%
길 203
13.7%
높 166
11.2%
지 119
 
8.1%
름 112
 
7.6%
경 103
 
7.0%
구 81
 
5.5%
입 40
 
2.7%
폭 33
 
2.2%
바 28
 
1.9%
Other values (63) 224
15.2%
Common
ValueCountFrequency (%)
* 9838
18.5%
1 7124
13.4%
2 5343
10.1%
5 5297
10.0%
3 5077
9.6%
4 3912
 
7.4%
. 3049
 
5.7%
6 2916
 
5.5%
7 2843
 
5.4%
0 2621
 
4.9%
Other values (9) 5057
9.5%
Latin
ValueCountFrequency (%)
m 19
50.0%
c 16
42.1%
n 1
 
2.6%
h 1
 
2.6%
i 1
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 53113
97.3%
Hangul 1478
 
2.7%
CJK Compat 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 9838
18.5%
1 7124
13.4%
2 5343
10.1%
5 5297
10.0%
3 5077
9.6%
4 3912
 
7.4%
. 3049
 
5.7%
6 2916
 
5.5%
7 2843
 
5.4%
0 2621
 
4.9%
Other values (13) 5093
9.6%
Hangul
ValueCountFrequency (%)
이 369
25.0%
길 203
13.7%
높 166
11.2%
지 119
 
8.1%
름 112
 
7.6%
경 103
 
7.0%
구 81
 
5.5%
입 40
 
2.7%
폭 33
 
2.2%
바 28
 
1.9%
Other values (63) 224
15.2%
CJK Compat
ValueCountFrequency (%)
㎏ 2
100.0%

국적
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
한국
9979 
중국
 
21

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
한국 9979
99.8%
중국 21
 
0.2%

Length

2023-12-12T17:31:48.443674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T17:31:48.553743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
한국 9979
99.8%
중국 21
 
0.2%

시대
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct18
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
광복이후
5907 
조선
2636 
2000년대이후
745 
미상
 
390
일제강점기
 
166
Other values (13)
 
156

Length

Max length8
Median length4
Mean length3.6957
Min length2

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row광복이후
2nd row광복이후
3rd row광복이후
4th row조선
5th row광복이후

Common Values

ValueCountFrequency (%)
광복이후 5907
59.1%
조선 2636
26.4%
2000년대이후 745
 
7.4%
미상 390
 
3.9%
일제강점기 166
 
1.7%
대한제국 71
 
0.7%
고려 32
 
0.3%
후한 21
 
0.2%
청동기 7
 
0.1%
근대 5
 
0.1%
Other values (8) 20
 
0.2%

Length

2023-12-12T17:31:48.669382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
광복이후 5907
59.1%
조선 2644
26.4%
2000년대이후 745
 
7.4%
미상 390
 
3.9%
일제강점기 166
 
1.7%
대한제국 71
 
0.7%
고려 32
 
0.3%
후한 21
 
0.2%
청동기 7
 
0.1%
근대 5
 
< 0.1%
Other values (5) 12
 
0.1%

재질(대)
Categorical

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
지
2280 
나무
1963 
금속
1597 
사직
972 
석
912 
Other values (12)
2276 

Length

Max length5
Median length2
Mean length1.8008
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row도자기
2nd row나무
3rd row토제
4th row나무
5th row석

Common Values

ValueCountFrequency (%)
지 2280
22.8%
나무 1963
19.6%
금속 1597
16.0%
사직 972
9.7%
석 912
 
9.1%
도자기 873
 
8.7%
토제 631
 
6.3%
초제 310
 
3.1%
피모 225
 
2.2%
기타 93
 
0.9%
Other values (7) 144
 
1.4%

Length

2023-12-12T17:31:48.822851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
지 2280
22.8%
나무 1963
19.6%
금속 1597
16.0%
사직 972
9.7%
석 912
 
9.1%
도자기 873
 
8.7%
토제 631
 
6.3%
초제 310
 
3.1%
피모 225
 
2.2%
기타 93
 
0.9%
Other values (7) 144
 
1.4%

재질(소)
Text

Distinct67
Distinct (%)0.7%
Missing3
Missing (%)< 0.1%
Memory size156.2 KiB
2023-12-12T17:31:49.029887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length2
Mean length2.0776233
Min length1

Characters and Unicode

Total characters20770
Distinct characters93
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)0.1%

Sample

1st row백자
2nd row기타
3rd row경질
4th row현무암
5th row저지
ValueCountFrequency (%)
저지 2227
22.3%
기타 1651
16.5%
현무암 877
 
8.8%
동합금 856
 
8.6%
백자 779
 
7.8%
철 668
 
6.7%
경질 623
 
6.2%
면 593
 
5.9%
대나무 411
 
4.1%
볏집 283
 
2.8%
Other values (57) 1029
10.3%
2023-12-12T17:31:49.427583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
지 2266
 
10.9%
저 2247
 
10.8%
기 1653
 
8.0%
타 1653
 
8.0%
무 1389
 
6.7%
동 944
 
4.5%
합 897
 
4.3%
암 879
 
4.2%
현 877
 
4.2%
금 875
 
4.2%
Other values (83) 7090
34.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 20768
> 99.9%
Other Punctuation 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 2266
 
10.9%
저 2247
 
10.8%
기 1653
 
8.0%
타 1653
 
8.0%
무 1389
 
6.7%
동 944
 
4.5%
합 897
 
4.3%
암 879
 
4.2%
현 877
 
4.2%
금 875
 
4.2%
Other values (82) 7088
34.1%
Other Punctuation
ValueCountFrequency (%)
/ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 20768
> 99.9%
Common 2
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 2266
 
10.9%
저 2247
 
10.8%
기 1653
 
8.0%
타 1653
 
8.0%
무 1389
 
6.7%
동 944
 
4.5%
합 897
 
4.3%
암 879
 
4.2%
현 877
 
4.2%
금 875
 
4.2%
Other values (82) 7088
34.1%
Common
ValueCountFrequency (%)
/ 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 20768
> 99.9%
ASCII 2
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
지 2266
 
10.9%
저 2247
 
10.8%
기 1653
 
8.0%
타 1653
 
8.0%
무 1389
 
6.7%
동 944
 
4.5%
합 897
 
4.3%
암 879
 
4.2%
현 877
 
4.2%
금 875
 
4.2%
Other values (82) 7088
34.1%
ASCII
ValueCountFrequency (%)
/ 2
100.0%

수장여부
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
수장
7991 
전시
2007 
수장
 
1
<NA>
 
1

Length

Max length4
Median length2
Mean length2.0003
Min length2

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row전시
2nd row전시
3rd row전시
4th row수장
5th row전시

Common Values

ValueCountFrequency (%)
수장 7991
79.9%
전시 2007
 
20.1%
수장 1
 
< 0.1%
<NA> 1
 
< 0.1%

Length

2023-12-12T17:31:49.591483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T17:31:49.759179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
수장 7992
79.9%
전시 2007
 
20.1%
na 1
 
< 0.1%

입수일자
Date

Distinct891
Distinct (%)8.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum1970-12-12 00:00:00
Maximum2016-09-06 00:00:00
2023-12-12T17:31:49.882971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:31:50.067764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2016-09-30
10000 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2016-09-30
2nd row2016-09-30
3rd row2016-09-30
4th row2016-09-30
5th row2016-09-30

Common Values

ValueCountFrequency (%)
2016-09-30 10000
100.0%

Length

2023-12-12T17:31:50.229339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T17:31:50.346449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2016-09-30 10000
100.0%

Interactions

2023-12-12T17:31:45.030153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T17:31:50.409796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
수량국적시대재질(대)재질(소)수장여부
수량1.0000.0000.0000.0910.0000.000
국적0.0001.0001.0000.1090.1840.011
시대0.0001.0001.0000.4250.6380.170
재질(대)0.0910.1090.4251.0000.9950.564
재질(소)0.0000.1840.6380.9951.0000.612
수장여부0.0000.0110.1700.5640.6121.000
2023-12-12T17:31:50.529537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
수장여부재질(대)국적시대
수장여부1.0000.3680.0180.078
재질(대)0.3681.0000.0970.145
국적0.0180.0971.0000.999
시대0.0780.1450.9991.000
2023-12-12T17:31:50.670348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
수량국적시대재질(대)수장여부
수량1.0000.0000.0000.0430.000
국적0.0001.0000.9990.0970.018
시대0.0000.9991.0000.1450.078
재질(대)0.0430.0970.1451.0000.368
수장여부0.0000.0180.0780.3681.000

Missing values

2023-12-12T17:31:45.495341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T17:31:45.690860image/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.
2023-12-12T17:31:45.854275image/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

명칭수량규격국적시대재질(대)재질(소)수장여부입수일자데이터기준일자
5415모밀접시111*3*7한국광복이후도자기백자전시1992-05-152016-09-30
3201짝얼레134*14한국광복이후나무기타전시1984-01-252016-09-30
3295(장테)뚜껑114*47*31한국광복이후토제경질전시1984-03-272016-09-30
10577경상1<NA>한국조선나무<NA>수장2016-05-212016-09-30
4167멧돌128*43한국광복이후석현무암전시1986-09-212016-09-30
7729의서(백방길흉...)122*15한국광복이후지저지수장2000-11-292016-09-30
5366바라118.3한국2000년대이후금속동합금수장1992-03-152016-09-30
8797자리거리기165.5*83.5한국광복이후지저지수장2003-12-292016-09-30
6500작품76120*27한국광복이후지저지수장1996-08-222016-09-30
8545맷돌143한국광복이후석현무암전시2002-10-252016-09-30
명칭수량규격국적시대재질(대)재질(소)수장여부입수일자데이터기준일자
3097자아위필235*135한국광복이후지저지수장1981-04-232016-09-30
7716골밀이124*2한국광복이후나무기타수장2000-11-292016-09-30
10517지씨홍사성씨운휘목록(馬氏~)1<NA>한국조선지저지수장2016-04-222016-09-30
5279곡자귀179*10한국광복이후금속철수장1991-12-272016-09-30
8195맷돌139.5한국광복이후석현무암전시2002-10-252016-09-30
7205고문서(행대정...)137.5*27.5한국조선지저지수장1998-12-132016-09-30
2840질화로120*31*20한국광복이후토제경질수장1983-06-242016-09-30
1893흑유편호123*4*8한국조선도자기흑유수장1981-10-132016-09-30
1206태사혜29*27한국조선피모우피수장1978-11-222016-09-30
7478놋사발112.6*7.7한국광복이후금속동합금수장2000-04-122016-09-30

Duplicate rows

Most frequently occurring

명칭수량규격국적시대재질(대)재질(소)수장여부입수일자데이터기준일자# duplicates
156맷돌142한국광복이후석현무암전시2002-10-252016-09-3072
154맷돌141한국광복이후석현무암전시2002-10-252016-09-3060
158맷돌143한국광복이후석현무암전시2002-10-252016-09-3054
176모밀사발18*15*7한국조선도자기백자수장1972-12-312016-09-3049
153맷돌140.5한국광복이후석현무암전시2002-10-252016-09-3038
157맷돌142.5한국광복이후석현무암전시2002-10-252016-09-3035
390이형상 수탁 오시복간찰1<NA>한국조선지저지수장2016-04-292016-09-3035
152맷돌140한국광복이후석현무암전시2002-10-252016-09-3034
155맷돌141.5한국광복이후석현무암전시2002-10-252016-09-3034
150맷돌139한국광복이후석현무암전시2002-10-252016-09-3030