Overview

Dataset statistics

Number of variables19
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory15.3 KiB
Average record size in memory156.3 B

Variable types

Numeric3
Text4
Categorical12

Alerts

rdnmadr has constant value ""Constant
ar is highly overall correlated with lo and 8 other fieldsHigh correlation
gugun_nm is highly overall correlated with skey and 12 other fieldsHigh correlation
main_cn is highly overall correlated with la and 10 other fieldsHigh correlation
locplc is highly overall correlated with la and 11 other fieldsHigh correlation
charger_telno is highly overall correlated with skey and 8 other fieldsHigh correlation
data_stdr_de is highly overall correlated with skey and 12 other fieldsHigh correlation
erc_year is highly overall correlated with lo and 7 other fieldsHigh correlation
manage_mby is highly overall correlated with la and 11 other fieldsHigh correlation
skey is highly overall correlated with la and 5 other fieldsHigh correlation
la is highly overall correlated with skey and 8 other fieldsHigh correlation
lo is highly overall correlated with skey and 10 other fieldsHigh correlation
asort is highly overall correlated with skey and 10 other fieldsHigh correlation
erc_mby is highly overall correlated with lo and 8 other fieldsHigh correlation
relate_hmpg is highly overall correlated with la and 8 other fieldsHigh correlation
erc_mby is highly imbalanced (80.1%)Imbalance
erc_year is highly imbalanced (73.6%)Imbalance
main_cn is highly imbalanced (59.2%)Imbalance
relate_hmpg is highly imbalanced (72.8%)Imbalance
ar is highly imbalanced (72.4%)Imbalance
skey has unique valuesUnique
crlts_nm has unique valuesUnique

Reproduction

Analysis started2023-12-10 09:49:52.231975
Analysis finished2023-12-10 09:49:59.674601
Duration7.44 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

skey
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.5
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:49:59.856432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.95
Q125.75
median50.5
Q375.25
95-th percentile95.05
Maximum100
Range99
Interquartile range (IQR)49.5

Descriptive statistics

Standard deviation29.011492
Coefficient of variation (CV)0.57448499
Kurtosis-1.2
Mean50.5
Median Absolute Deviation (MAD)25
Skewness0
Sum5050
Variance841.66667
MonotonicityStrictly increasing
2023-12-10T18:50:00.142246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.0%
65 1
 
1.0%
75 1
 
1.0%
74 1
 
1.0%
73 1
 
1.0%
72 1
 
1.0%
71 1
 
1.0%
70 1
 
1.0%
69 1
 
1.0%
68 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
1 1
1.0%
2 1
1.0%
3 1
1.0%
4 1
1.0%
5 1
1.0%
6 1
1.0%
7 1
1.0%
8 1
1.0%
9 1
1.0%
10 1
1.0%
ValueCountFrequency (%)
100 1
1.0%
99 1
1.0%
98 1
1.0%
97 1
1.0%
96 1
1.0%
95 1
1.0%
94 1
1.0%
93 1
1.0%
92 1
1.0%
91 1
1.0%

crlts_nm
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:50:00.662177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length15.5
Mean length10.23
Min length3

Characters and Unicode

Total characters1023
Distinct characters272
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique100 ?
Unique (%)100.0%

Sample

1st row마하사 응진전 16나한도
2nd row마하사 응진전 영산회상도
3rd row마하사 영산회상도
4th row혜원정사 팔상도
5th row마하사 현왕도
ValueCountFrequency (%)
부산 9
 
4.2%
마하사 6
 
2.8%
내원정사 5
 
2.3%
병풍 5
 
2.3%
영산회상도 4
 
1.9%
전 3
 
1.4%
운수사 3
 
1.4%
대웅전 3
 
1.4%
응진전 3
 
1.4%
구 3
 
1.4%
Other values (162) 172
79.6%
2023-12-10T18:50:01.682682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
116
 
11.3%
사 40
 
3.9%
도 33
 
3.2%
상 26
 
2.5%
산 22
 
2.2%
( 17
 
1.7%
) 17
 
1.7%
부 16
 
1.6%
조 16
 
1.6%
경 14
 
1.4%
Other values (262) 706
69.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 857
83.8%
Space Separator 116
 
11.3%
Open Punctuation 17
 
1.7%
Close Punctuation 17
 
1.7%
Decimal Number 14
 
1.4%
Dash Punctuation 1
 
0.1%
Math Symbol 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
사 40
 
4.7%
도 33
 
3.9%
상 26
 
3.0%
산 22
 
2.6%
부 16
 
1.9%
조 16
 
1.9%
경 14
 
1.6%
정 14
 
1.6%
전 14
 
1.6%
지 13
 
1.5%
Other values (249) 649
75.7%
Decimal Number
ValueCountFrequency (%)
1 4
28.6%
4 3
21.4%
0 2
14.3%
2 1
 
7.1%
3 1
 
7.1%
7 1
 
7.1%
5 1
 
7.1%
6 1
 
7.1%
Space Separator
ValueCountFrequency (%)
116
100.0%
Open Punctuation
ValueCountFrequency (%)
( 17
100.0%
Close Punctuation
ValueCountFrequency (%)
) 17
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 784
76.6%
Common 166
 
16.2%
Han 73
 
7.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
사 40
 
5.1%
도 33
 
4.2%
상 26
 
3.3%
산 22
 
2.8%
부 16
 
2.0%
조 16
 
2.0%
경 14
 
1.8%
정 14
 
1.8%
전 14
 
1.8%
지 13
 
1.7%
Other values (190) 576
73.5%
Han
ValueCountFrequency (%)
圖 6
 
8.2%
長 2
 
2.7%
頸 2
 
2.7%
壺 2
 
2.7%
石 2
 
2.7%
像 2
 
2.7%
造 2
 
2.7%
坐 2
 
2.7%
薩 2
 
2.7%
菩 2
 
2.7%
Other values (49) 49
67.1%
Common
ValueCountFrequency (%)
116
69.9%
( 17
 
10.2%
) 17
 
10.2%
1 4
 
2.4%
4 3
 
1.8%
0 2
 
1.2%
2 1
 
0.6%
- 1
 
0.6%
3 1
 
0.6%
7 1
 
0.6%
Other values (3) 3
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 784
76.6%
ASCII 166
 
16.2%
CJK 72
 
7.0%
CJK Compat Ideographs 1
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
116
69.9%
( 17
 
10.2%
) 17
 
10.2%
1 4
 
2.4%
4 3
 
1.8%
0 2
 
1.2%
2 1
 
0.6%
- 1
 
0.6%
3 1
 
0.6%
7 1
 
0.6%
Other values (3) 3
 
1.8%
Hangul
ValueCountFrequency (%)
사 40
 
5.1%
도 33
 
4.2%
상 26
 
3.3%
산 22
 
2.8%
부 16
 
2.0%
조 16
 
2.0%
경 14
 
1.8%
정 14
 
1.8%
전 14
 
1.8%
지 13
 
1.7%
Other values (190) 576
73.5%
CJK
ValueCountFrequency (%)
圖 6
 
8.3%
長 2
 
2.8%
頸 2
 
2.8%
壺 2
 
2.8%
石 2
 
2.8%
像 2
 
2.8%
造 2
 
2.8%
坐 2
 
2.8%
薩 2
 
2.8%
菩 2
 
2.8%
Other values (48) 48
66.7%
CJK Compat Ideographs
ValueCountFrequency (%)
金 1
100.0%

locplc
Categorical

HIGH CORRELATION 

Distinct30
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
부산광역시 서구 부민동2가 1
52 
부산광역시 서구 서대신동3가 1번지 등
 
5
부산시 연제구 봉수로 138 마하사
 
4
부산광역시 서구 서대신동3가 산2-3
 
3
부산광역시 사상구 모라동 5
 
3
Other values (25)
33 

Length

Max length27
Median length16
Mean length18.19
Min length15

Unique

Unique17 ?
Unique (%)17.0%

Sample

1st row부산시 연제구 봉수로 138 마하사
2nd row부산시 연제구 봉수로 138 마하사
3rd row부산시 금정구 범어사로 250 범어사
4th row부산시 연제구 고분로68번길 47 혜원정사
5th row부산시 금정구 범어사로 250 범어사

Common Values

ValueCountFrequency (%)
부산광역시 서구 부민동2가 1 52
52.0%
부산광역시 서구 서대신동3가 1번지 등 5
 
5.0%
부산시 연제구 봉수로 138 마하사 4
 
4.0%
부산광역시 서구 서대신동3가 산2-3 3
 
3.0%
부산광역시 사상구 모라동 5 3
 
3.0%
부산광역시 동구 자성로 99(범일동) 2
 
2.0%
부산광역시 서구 남부민동 93-1 2
 
2.0%
부산광역시 동구 좌천동로 17-3(좌천동) 2
 
2.0%
부산광역시 동구 정공단로 23(좌천동) 2
 
2.0%
부산광역시 서구 아미동2가 233-73 2
 
2.0%
Other values (20) 23
23.0%

Length

2023-12-10T18:50:02.036749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
부산광역시 90
21.5%
서구 69
16.5%
부민동2가 52
12.4%
1 52
12.4%
동구 14
 
3.3%
부산시 10
 
2.4%
서대신동3가 9
 
2.1%
연제구 8
 
1.9%
사상구 6
 
1.4%
1번지 5
 
1.2%
Other values (60) 104
24.8%

manage_mby
Categorical

HIGH CORRELATION 

Distinct28
Distinct (%)28.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
동아대학교
52 
내원정사
 
5
마하사
 
4
동구청장
 
4
부산구덕민속예술보존협회
 
3
Other values (23)
32 

Length

Max length16
Median length5
Mean length4.98
Min length2

Unique

Unique15 ?
Unique (%)15.0%

Sample

1st row마하사
2nd row마하사
3rd row범어사
4th row혜원정사
5th row범어사

Common Values

ValueCountFrequency (%)
동아대학교 52
52.0%
내원정사 5
 
5.0%
마하사 4
 
4.0%
동구청장 4
 
4.0%
부산구덕민속예술보존협회 3
 
3.0%
운수사 3
 
3.0%
실상사 2
 
2.0%
칠보사 2
 
2.0%
연제구청 2
 
2.0%
범어사 2
 
2.0%
Other values (18) 21
21.0%

Length

2023-12-10T18:50:02.288247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
동아대학교 52
51.5%
내원정사 5
 
5.0%
마하사 4
 
4.0%
동구청장 4
 
4.0%
부산구덕민속예술보존협회 3
 
3.0%
운수사 3
 
3.0%
범어사 2
 
2.0%
연등사 2
 
2.0%
대성사 2
 
2.0%
소림사 2
 
2.0%
Other values (19) 22
21.8%

appn_no
Text

Distinct88
Distinct (%)88.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:50:02.747401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length4.62
Min length3

Characters and Unicode

Total characters462
Distinct characters13
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

Unique76 ?
Unique (%)76.0%

Sample

1st row제17호
2nd row제16호
3rd row제15호
4th row제9호
5th row제54호
ValueCountFrequency (%)
제103호 2
 
2.0%
제43호 2
 
2.0%
제19호 2
 
2.0%
제15호 2
 
2.0%
제54호 2
 
2.0%
제83호 2
 
2.0%
제9호 2
 
2.0%
제55호 2
 
2.0%
제18호 2
 
2.0%
제64호 2
 
2.0%
Other values (78) 80
80.0%
2023-12-10T18:50:03.675526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
제 100
21.6%
호 100
21.6%
1 52
11.3%
4 29
 
6.3%
3 28
 
6.1%
9 27
 
5.8%
5 26
 
5.6%
2 26
 
5.6%
6 19
 
4.1%
8 19
 
4.1%
Other values (3) 36
 
7.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 257
55.6%
Other Letter 200
43.3%
Dash Punctuation 5
 
1.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 52
20.2%
4 29
11.3%
3 28
10.9%
9 27
10.5%
5 26
10.1%
2 26
10.1%
6 19
 
7.4%
8 19
 
7.4%
7 17
 
6.6%
0 14
 
5.4%
Other Letter
ValueCountFrequency (%)
제 100
50.0%
호 100
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 262
56.7%
Hangul 200
43.3%

Most frequent character per script

Common
ValueCountFrequency (%)
1 52
19.8%
4 29
11.1%
3 28
10.7%
9 27
10.3%
5 26
9.9%
2 26
9.9%
6 19
 
7.3%
8 19
 
7.3%
7 17
 
6.5%
0 14
 
5.3%
Hangul
ValueCountFrequency (%)
제 100
50.0%
호 100
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 262
56.7%
Hangul 200
43.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
제 100
50.0%
호 100
50.0%
ASCII
ValueCountFrequency (%)
1 52
19.8%
4 29
11.1%
3 28
10.7%
9 27
10.3%
5 26
9.9%
2 26
9.9%
6 19
 
7.3%
8 19
 
7.3%
7 17
 
6.5%
0 14
 
5.3%

appn_de
Text

Distinct59
Distinct (%)59.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:50:04.374024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters1000
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique44 ?
Unique (%)44.0%

Sample

1st row2003-09-16
2nd row2003-09-16
3rd row2003-09-16
4th row2002-05-06
5th row2003-09-16
ValueCountFrequency (%)
2010-09-20 7
 
7.0%
2003-09-16 6
 
6.0%
2012-10-30 6
 
6.0%
1972-06-26 5
 
5.0%
2004-10-04 5
 
5.0%
2000-12-21 4
 
4.0%
2015-12-23 3
 
3.0%
2014-01-22 3
 
3.0%
2007-07-03 3
 
3.0%
1982-03-04 3
 
3.0%
Other values (49) 55
55.0%
2023-12-10T18:50:05.052864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 250
25.0%
- 200
20.0%
2 164
16.4%
1 144
14.4%
3 50
 
5.0%
9 45
 
4.5%
7 36
 
3.6%
6 35
 
3.5%
4 27
 
2.7%
5 27
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 800
80.0%
Dash Punctuation 200
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 250
31.2%
2 164
20.5%
1 144
18.0%
3 50
 
6.2%
9 45
 
5.6%
7 36
 
4.5%
6 35
 
4.4%
4 27
 
3.4%
5 27
 
3.4%
8 22
 
2.8%
Dash Punctuation
ValueCountFrequency (%)
- 200
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 250
25.0%
- 200
20.0%
2 164
16.4%
1 144
14.4%
3 50
 
5.0%
9 45
 
4.5%
7 36
 
3.6%
6 35
 
3.5%
4 27
 
2.7%
5 27
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 250
25.0%
- 200
20.0%
2 164
16.4%
1 144
14.4%
3 50
 
5.0%
9 45
 
4.5%
7 36
 
3.6%
6 35
 
3.5%
4 27
 
2.7%
5 27
 
2.7%

era
Text

Distinct57
Distinct (%)57.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:50:05.511482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length4.04
Min length1

Characters and Unicode

Total characters404
Distinct characters39
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique46 ?
Unique (%)46.0%

Sample

1st row1910년
2nd row1910년
3rd row1906년
4th row1925년
5th row1792년
ValueCountFrequency (%)
13
 
12.7%
조선시대 9
 
8.8%
조선후기 8
 
7.8%
삼국시대 6
 
5.9%
17세기 4
 
3.9%
1910년 3
 
2.9%
조선 3
 
2.9%
19세기 3
 
2.9%
1925년 2
 
2.0%
1927년 2
 
2.0%
Other values (48) 49
48.0%
2023-12-10T18:50:06.515415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 54
 
13.4%
년 40
 
9.9%
기 24
 
5.9%
9 23
 
5.7%
선 22
 
5.4%
조 22
 
5.4%
7 21
 
5.2%
대 20
 
5.0%
시 17
 
4.2%
2 15
 
3.7%
Other values (29) 146
36.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 204
50.5%
Decimal Number 182
45.0%
Dash Punctuation 13
 
3.2%
Space Separator 2
 
0.5%
Math Symbol 2
 
0.5%
Open Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
년 40
19.6%
기 24
11.8%
선 22
10.8%
조 22
10.8%
대 20
9.8%
시 17
8.3%
세 11
 
5.4%
후 10
 
4.9%
국 6
 
2.9%
삼 6
 
2.9%
Other values (14) 26
12.7%
Decimal Number
ValueCountFrequency (%)
1 54
29.7%
9 23
12.6%
7 21
 
11.5%
2 15
 
8.2%
5 14
 
7.7%
6 12
 
6.6%
4 12
 
6.6%
0 11
 
6.0%
3 10
 
5.5%
8 10
 
5.5%
Math Symbol
ValueCountFrequency (%)
~ 1
50.0%
～ 1
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 13
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 204
50.5%
Common 200
49.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
년 40
19.6%
기 24
11.8%
선 22
10.8%
조 22
10.8%
대 20
9.8%
시 17
8.3%
세 11
 
5.4%
후 10
 
4.9%
국 6
 
2.9%
삼 6
 
2.9%
Other values (14) 26
12.7%
Common
ValueCountFrequency (%)
1 54
27.0%
9 23
11.5%
7 21
 
10.5%
2 15
 
7.5%
5 14
 
7.0%
- 13
 
6.5%
6 12
 
6.0%
4 12
 
6.0%
0 11
 
5.5%
3 10
 
5.0%
Other values (5) 15
 
7.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 204
50.5%
ASCII 199
49.3%
None 1
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 54
27.1%
9 23
11.6%
7 21
 
10.6%
2 15
 
7.5%
5 14
 
7.0%
- 13
 
6.5%
6 12
 
6.0%
4 12
 
6.0%
0 11
 
5.5%
3 10
 
5.0%
Other values (4) 14
 
7.0%
Hangul
ValueCountFrequency (%)
년 40
19.6%
기 24
11.8%
선 22
10.8%
조 22
10.8%
대 20
9.8%
시 17
8.3%
세 11
 
5.4%
후 10
 
4.9%
국 6
 
2.9%
삼 6
 
2.9%
Other values (14) 26
12.7%
None
ValueCountFrequency (%)
～ 1
100.0%

asort
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)17.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
시지정유형문화재
31 
보물
15 
등록문화재
9 
시문화재자료
8 
문화재 자료
6 
Other values (12)
31 

Length

Max length8
Median length7
Mean length5.77
Min length2

Unique

Unique3 ?
Unique (%)3.0%

Sample

1st row문화재 자료
2nd row문화재 자료
3rd row문화재 자료
4th row문화재 자료
5th row유형문화재

Common Values

ValueCountFrequency (%)
시지정유형문화재 31
31.0%
보물 15
15.0%
등록문화재 9
 
9.0%
시문화재자료 8
 
8.0%
문화재 자료 6
 
6.0%
시 지정기념물 5
 
5.0%
유형문화재 4
 
4.0%
시지정무형문화재 4
 
4.0%
시 문화재자료 4
 
4.0%
국보 3
 
3.0%
Other values (7) 11
 
11.0%

Length

2023-12-10T18:50:06.963096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
시지정유형문화재 31
26.5%
보물 15
12.8%
시 11
 
9.4%
등록문화재 9
 
7.7%
시문화재자료 8
 
6.8%
문화재 6
 
5.1%
자료 6
 
5.1%
지정기념물 5
 
4.3%
문화재자료 4
 
3.4%
시지정무형문화재 4
 
3.4%
Other values (8) 18
15.4%

erc_mby
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct6
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
-
93 
운수사
 
3
승학사
 
1
부산영산재보존회
 
1
선광사
 
1

Length

Max length11
Median length1
Mean length1.27
Min length1

Unique

Unique4 ?
Unique (%)4.0%

Sample

1st row-
2nd row-
3rd row-
4th row-
5th row-

Common Values

ValueCountFrequency (%)
- 93
93.0%
운수사 3
 
3.0%
승학사 1
 
1.0%
부산영산재보존회 1
 
1.0%
선광사 1
 
1.0%
강서구,사상구,사하구 1
 
1.0%

Length

2023-12-10T18:50:07.295866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:50:07.517377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
93
93.0%
운수사 3
 
3.0%
승학사 1
 
1.0%
부산영산재보존회 1
 
1.0%
선광사 1
 
1.0%
강서구,사상구,사하구 1
 
1.0%

erc_year
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct11
Distinct (%)11.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
-
88 
조선
 
3
1761년
 
1
1905년
 
1
1927년
 
1
Other values (6)
 
6

Length

Max length5
Median length1
Mean length1.33
Min length1

Unique

Unique9 ?
Unique (%)9.0%

Sample

1st row-
2nd row-
3rd row-
4th row-
5th row-

Common Values

ValueCountFrequency (%)
- 88
88.0%
조선 3
 
3.0%
1761년 1
 
1.0%
1905년 1
 
1.0%
1927년 1
 
1.0%
1925년 1
 
1.0%
1943년 1
 
1.0%
1907년 1
 
1.0%
미정 1
 
1.0%
현대 1
 
1.0%

Length

2023-12-10T18:50:07.802525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
88
88.0%
조선 3
 
3.0%
1761년 1
 
1.0%
1905년 1
 
1.0%
1927년 1
 
1.0%
1925년 1
 
1.0%
1943년 1
 
1.0%
1907년 1
 
1.0%
미정 1
 
1.0%
현대 1
 
1.0%

main_cn
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct25
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
-
76 
신라식 석축산성
 
1
구덩식 돌덧널무덤
 
1
유네스코 세계기록유산
 
1
완호스님이 가장 왕성한 활동을 한 시기에 제작한 제품으로 섬세함이 돋보임
 
1
Other values (20)
20 

Length

Max length58
Median length1
Mean length8.18
Min length1

Unique

Unique24 ?
Unique (%)24.0%

Sample

1st row-
2nd row-
3rd row-
4th row-
5th row-

Common Values

ValueCountFrequency (%)
- 76
76.0%
신라식 석축산성 1
 
1.0%
구덩식 돌덧널무덤 1
 
1.0%
유네스코 세계기록유산 1
 
1.0%
완호스님이 가장 왕성한 활동을 한 시기에 제작한 제품으로 섬세함이 돋보임 1
 
1.0%
부산첨사 정발장군의 순절 내력과 호국충절을 기록 1
 
1.0%
부산,경남지역 최초의 신여성 교육기관 1
 
1.0%
임진왜란 후 부산진지성을 축성할때 세운것으로 추정 1
 
1.0%
임진왜란때 순절한 충장공 정발장군을 추모하기 위해 만든 제단 1
 
1.0%
임진왜란때 부산진의 지성 지역을 축소하여 쌓은 일본식 성 1
 
1.0%
Other values (15) 15
 
15.0%

Length

2023-12-10T18:50:08.090024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
76
29.7%
대한제국 2
 
0.8%
부산 2
 
0.8%
잘 2
 
0.8%
화풍을 2
 
0.8%
임진왜란때 2
 
0.8%
있는 2
 
0.8%
전반 2
 
0.8%
20세기 2
 
0.8%
최초의 2
 
0.8%
Other values (148) 162
63.3%

relate_hmpg
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct7
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
-
88 
http://www.sasang.go.kr
 
7
https://www.yeonje.go.kr/tour/contents.do?mId=0201060000
 
1
https://www.yeonje.go.kr/tour/contents.do?mId=0201050000
 
1
https://www.yeonje.go.kr/tour/contents.do?mId=0201040000
 
1
Other values (2)
 
2

Length

Max length56
Median length1
Mean length5.29
Min length1

Unique

Unique5 ?
Unique (%)5.0%

Sample

1st row-
2nd row-
3rd row-
4th rowhttps://www.yeonje.go.kr/tour/contents.do?mId=0201060000
5th rowhttps://www.yeonje.go.kr/tour/contents.do?mId=0201050000

Common Values

ValueCountFrequency (%)
- 88
88.0%
http://www.sasang.go.kr 7
 
7.0%
https://www.yeonje.go.kr/tour/contents.do?mId=0201060000 1
 
1.0%
https://www.yeonje.go.kr/tour/contents.do?mId=0201050000 1
 
1.0%
https://www.yeonje.go.kr/tour/contents.do?mId=0201040000 1
 
1.0%
https://www.yeonje.go.kr/tour/contents.do?mId=0201030000 1
 
1.0%
https://www.yeonje.go.kr/tour/contents.do?mId=0201020000 1
 
1.0%

Length

2023-12-10T18:50:08.394365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:50:08.615582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
88
88.0%
http://www.sasang.go.kr 7
 
7.0%
https://www.yeonje.go.kr/tour/contents.do?mid=0201060000 1
 
1.0%
https://www.yeonje.go.kr/tour/contents.do?mid=0201050000 1
 
1.0%
https://www.yeonje.go.kr/tour/contents.do?mid=0201040000 1
 
1.0%
https://www.yeonje.go.kr/tour/contents.do?mid=0201030000 1
 
1.0%
https://www.yeonje.go.kr/tour/contents.do?mid=0201020000 1
 
1.0%

ar
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct12
Distinct (%)12.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
-
87 
3필지 4,023㎡
 
3
7필지 22,933㎡
 
1
33필지 66,068㎡
 
1
208-32㎡
 
1
Other values (7)
 
7

Length

Max length12
Median length1
Mean length2.02
Min length1

Unique

Unique10 ?
Unique (%)10.0%

Sample

1st row-
2nd row-
3rd row-
4th row-
5th row-

Common Values

ValueCountFrequency (%)
- 87
87.0%
3필지 4,023㎡ 3
 
3.0%
7필지 22,933㎡ 1
 
1.0%
33필지 66,068㎡ 1
 
1.0%
208-32㎡ 1
 
1.0%
2,445-86㎡ 1
 
1.0%
24,198㎡ 1
 
1.0%
1478-47㎡ 1
 
1.0%
304-47㎡ 1
 
1.0%
6,317㎡ 1
 
1.0%
Other values (2) 2
 
2.0%

Length

2023-12-10T18:50:08.936063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
87
82.9%
3필지 3
 
2.9%
4,023㎡ 3
 
2.9%
7필지 1
 
1.0%
22,933㎡ 1
 
1.0%
33필지 1
 
1.0%
66,068㎡ 1
 
1.0%
208-32㎡ 1
 
1.0%
2,445-86㎡ 1
 
1.0%
24,198㎡ 1
 
1.0%
Other values (5) 5
 
4.8%

charger_telno
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
051-200-8493
52 
051-440-4064
14 
-
9 
051-310-4067
7 
051-242-0691
 
5
Other values (8)
13 

Length

Max length12
Median length12
Mean length11.01
Min length1

Unique

Unique4 ?
Unique (%)4.0%

Sample

1st row-
2nd row-
3rd row-
4th row-
5th row-

Common Values

ValueCountFrequency (%)
051-200-8493 52
52.0%
051-440-4064 14
 
14.0%
- 9
 
9.0%
051-310-4067 7
 
7.0%
051-242-0691 5
 
5.0%
051-242-3100 3
 
3.0%
051-665-4082 2
 
2.0%
051-244-2040 2
 
2.0%
051-256-3174 2
 
2.0%
051-250-5105 1
 
1.0%
Other values (3) 3
 
3.0%

Length

2023-12-10T18:50:09.176187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
051-200-8493 52
52.0%
051-440-4064 14
 
14.0%
9
 
9.0%
051-310-4067 7
 
7.0%
051-242-0691 5
 
5.0%
051-242-3100 3
 
3.0%
051-665-4082 2
 
2.0%
051-244-2040 2
 
2.0%
051-256-3174 2
 
2.0%
051-250-5105 1
 
1.0%
Other values (3) 3
 
3.0%

gugun_nm
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
서구
69 
동구
14 
연제구
10 
사상구
7 

Length

Max length3
Median length2
Mean length2.17
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
서구 69
69.0%
동구 14
 
14.0%
연제구 10
 
10.0%
사상구 7
 
7.0%

Length

2023-12-10T18:50:09.415159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:50:09.622705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서구 69
69.0%
동구 14
 
14.0%
연제구 10
 
10.0%
사상구 7
 
7.0%

data_stdr_de
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2019-03-08 AM 12:00:00
69 
2019-03-13 AM 12:00:00
14 
2019-05-10 AM 12:00:00
10 
2019-03-14 AM 12:00:00
7 

Length

Max length22
Median length22
Mean length22
Min length22

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2019-05-10 AM 12:00:00
2nd row2019-05-10 AM 12:00:00
3rd row2019-05-10 AM 12:00:00
4th row2019-05-10 AM 12:00:00
5th row2019-05-10 AM 12:00:00

Common Values

ValueCountFrequency (%)
2019-03-08 AM 12:00:00 69
69.0%
2019-03-13 AM 12:00:00 14
 
14.0%
2019-05-10 AM 12:00:00 10
 
10.0%
2019-03-14 AM 12:00:00 7
 
7.0%

Length

2023-12-10T18:50:09.821599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:50:10.030728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
am 100
33.3%
12:00:00 100
33.3%
2019-03-08 69
23.0%
2019-03-13 14
 
4.7%
2019-05-10 10
 
3.3%
2019-03-14 7
 
2.3%

la
Real number (ℝ)

HIGH CORRELATION 

Distinct32
Distinct (%)32.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.122358
Minimum35.089167
Maximum35.273145
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:50:10.271592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.089167
5-th percentile35.102673
Q135.103738
median35.103738
Q335.128596
95-th percentile35.184416
Maximum35.273145
Range0.1839783
Interquartile range (IQR)0.024857977

Descriptive statistics

Standard deviation0.033705792
Coefficient of variation (CV)0.00095966768
Kurtosis6.8733327
Mean35.122358
Median Absolute Deviation (MAD)0
Skewness2.4050145
Sum3512.2358
Variance0.0011360804
MonotonicityNot monotonic
2023-12-10T18:50:10.528707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
35.10373802 52
52.0%
35.12769433 5
 
5.0%
35.163827 4
 
4.0%
35.12393722 3
 
3.0%
35.184357 3
 
3.0%
35.117945 2
 
2.0%
35.09833494 2
 
2.0%
35.0891667 2
 
2.0%
35.273145 2
 
2.0%
35.131301 2
 
2.0%
Other values (22) 23
23.0%
ValueCountFrequency (%)
35.0891667 2
 
2.0%
35.09833494 2
 
2.0%
35.09938809 1
 
1.0%
35.10284598 1
 
1.0%
35.10373802 52
52.0%
35.10375325 1
 
1.0%
35.10509 1
 
1.0%
35.11577794 1
 
1.0%
35.11662 1
 
1.0%
35.117945 2
 
2.0%
ValueCountFrequency (%)
35.273145 2
2.0%
35.193436 1
 
1.0%
35.190799 1
 
1.0%
35.185541 1
 
1.0%
35.184357 3
3.0%
35.184126 1
 
1.0%
35.180389 1
 
1.0%
35.17716 1
 
1.0%
35.163827 4
4.0%
35.136174 1
 
1.0%

lo
Real number (ℝ)

HIGH CORRELATION 

Distinct32
Distinct (%)32.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean129.02774
Minimum128.94243
Maximum129.09609
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:50:10.812052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum128.94243
5-th percentile129.01125
Q1129.01941
median129.01941
Q3129.02158
95-th percentile129.08773
Maximum129.09609
Range0.153658
Interquartile range (IQR)0.00216705

Descriptive statistics

Standard deviation0.024574837
Coefficient of variation (CV)0.00019046166
Kurtosis2.8962719
Mean129.02774
Median Absolute Deviation (MAD)0
Skewness1.1087014
Sum12902.774
Variance0.00060392261
MonotonicityNot monotonic
2023-12-10T18:50:11.074574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
129.0194103 52
52.0%
129.0122483 5
 
5.0%
129.087579 4
 
4.0%
129.0184883 3
 
3.0%
129.013496 3
 
3.0%
129.037164 2
 
2.0%
129.0112486 2
 
2.0%
129.0210272 2
 
2.0%
129.092679 2
 
2.0%
129.040686 2
 
2.0%
Other values (22) 23
23.0%
ValueCountFrequency (%)
128.942431 1
 
1.0%
128.990758 1
 
1.0%
128.995495 1
 
1.0%
128.996275 1
 
1.0%
129.0112486 2
 
2.0%
129.0122483 5
5.0%
129.0133074 1
 
1.0%
129.013496 3
3.0%
129.0161525 1
 
1.0%
129.0176007 1
 
1.0%
ValueCountFrequency (%)
129.096089 1
 
1.0%
129.092679 2
2.0%
129.091646 1
 
1.0%
129.090595 1
 
1.0%
129.087579 4
4.0%
129.061153 1
 
1.0%
129.060315 1
 
1.0%
129.05387 1
 
1.0%
129.053681 1
 
1.0%
129.053677 1
 
1.0%

rdnmadr
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
-
100 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-
2nd row-
3rd row-
4th row-
5th row-

Common Values

ValueCountFrequency (%)
- 100
100.0%

Length

2023-12-10T18:50:11.315173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:50:11.517023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
100
100.0%

Interactions

2023-12-10T18:49:58.363969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:49:56.818567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:49:57.339875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:49:58.610220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:49:56.966021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:49:57.516079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:49:58.798028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:49:57.163730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:49:57.699924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T18:50:11.694733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
skeycrlts_nmlocplcmanage_mbyappn_noappn_deeraasorterc_mbyerc_yearmain_cnrelate_hmpgarcharger_telnogugun_nmdata_stdr_delalo
skey1.0001.0000.8610.8400.0000.9590.8660.8930.2490.3760.4420.4960.2480.8360.9480.9480.7240.839
crlts_nm1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
locplc0.8611.0001.0001.0000.0000.9850.0000.9621.0000.9760.9830.9890.9951.0001.0001.0000.9960.995
manage_mby0.8401.0001.0001.0000.0000.9870.0000.9561.0000.9720.9720.9810.9771.0001.0001.0000.9680.998
appn_no0.0001.0000.0000.0001.0000.9780.9750.0000.0000.0000.0000.0000.8280.9190.0000.0000.0000.000
appn_de0.9591.0000.9850.9870.9781.0000.8030.9710.9810.9740.9590.8620.8620.9590.9660.9660.8750.944
era0.8661.0000.0000.0000.9750.8031.0000.8510.8390.0000.0000.0000.0000.9100.8100.8100.3370.268
asort0.8931.0000.9620.9560.0000.9710.8511.0000.8870.8090.9220.8250.7940.8580.9530.9530.8820.916
erc_mby0.2491.0001.0001.0000.0000.9810.8390.8871.0000.9291.0000.5710.9940.5380.7140.7140.7170.643
erc_year0.3761.0000.9760.9720.0000.9740.0000.8090.9291.0001.0000.4980.9190.2840.7870.7870.5260.719
main_cn0.4421.0000.9830.9720.0000.9590.0000.9221.0001.0001.0000.9341.0000.4370.9470.9470.8610.960
relate_hmpg0.4961.0000.9890.9810.0000.8620.0000.8250.5710.4980.9341.0000.8520.8090.7850.7850.7430.599
ar0.2481.0000.9950.9770.8280.8620.0000.7940.9940.9191.0000.8521.0000.6270.8980.8980.8400.756
charger_telno0.8361.0001.0001.0000.9190.9590.9100.8580.5380.2840.4370.8090.6271.0000.9870.9870.8750.914
gugun_nm0.9481.0001.0001.0000.0000.9660.8100.9530.7140.7870.9470.7850.8980.9871.0001.0000.8440.973
data_stdr_de0.9481.0001.0001.0000.0000.9660.8100.9530.7140.7870.9470.7850.8980.9871.0001.0000.8440.973
la0.7241.0000.9960.9680.0000.8750.3370.8820.7170.5260.8610.7430.8400.8750.8440.8441.0000.908
lo0.8391.0000.9950.9980.0000.9440.2680.9160.6430.7190.9600.5990.7560.9140.9730.9730.9081.000
2023-12-10T18:50:12.036630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
argugun_nmmain_cnlocplccharger_telnodata_stdr_deerc_mbyrelate_hmpgerc_yearmanage_mbyasort
ar1.0000.5910.9230.8320.2920.5910.8520.6220.6940.7490.423
gugun_nm0.5911.0000.7290.8540.9331.0000.5400.6660.5910.8660.809
main_cn0.9230.7291.0000.7400.1340.7290.8930.6730.9180.6820.545
locplc0.8320.8540.7401.0000.8970.8540.8630.8140.7460.9860.646
charger_telno0.2920.9330.1340.8971.0000.9330.2880.5300.1100.9100.505
data_stdr_de0.5911.0000.7290.8540.9331.0000.5400.6660.5910.8660.809
erc_mby0.8520.5400.8930.8630.2880.5401.0000.3830.7870.8750.637
relate_hmpg0.6220.6660.6730.8140.5300.6660.3831.0000.2650.7120.529
erc_year0.6940.5910.9180.7460.1100.5910.7870.2651.0000.7430.450
manage_mby0.7490.8660.6820.9860.9100.8660.8750.7120.7431.0000.635
asort0.4230.8090.5450.6460.5050.8090.6370.5290.4500.6351.000
2023-12-10T18:50:12.319086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
skeylalolocplcmanage_mbyasorterc_mbyerc_yearmain_cnrelate_hmpgarcharger_telnogugun_nmdata_stdr_de
skey1.000-0.648-0.5120.4140.4400.6020.1270.1660.1480.2720.0990.5370.8440.844
la-0.6481.0000.2650.7810.7500.6280.3310.2930.5400.5580.4740.6530.6990.699
lo-0.5120.2651.0000.8460.8590.6760.6260.5830.7090.3750.6020.6620.8880.888
locplc0.4140.7810.8461.0000.9860.6460.8630.7460.7400.8140.8320.8970.8540.854
manage_mby0.4400.7500.8590.9861.0000.6350.8750.7430.6820.7120.7490.9100.8660.866
asort0.6020.6280.6760.6460.6351.0000.6370.4500.5450.5290.4230.5050.8090.809
erc_mby0.1270.3310.6260.8630.8750.6371.0000.7870.8930.3830.8520.2880.5400.540
erc_year0.1660.2930.5830.7460.7430.4500.7871.0000.9180.2650.6940.1100.5910.591
main_cn0.1480.5400.7090.7400.6820.5450.8930.9181.0000.6730.9230.1340.7290.729
relate_hmpg0.2720.5580.3750.8140.7120.5290.3830.2650.6731.0000.6220.5300.6660.666
ar0.0990.4740.6020.8320.7490.4230.8520.6940.9230.6221.0000.2920.5910.591
charger_telno0.5370.6530.6620.8970.9100.5050.2880.1100.1340.5300.2921.0000.9330.933
gugun_nm0.8440.6990.8880.8540.8660.8090.5400.5910.7290.6660.5910.9331.0001.000
data_stdr_de0.8440.6990.8880.8540.8660.8090.5400.5910.7290.6660.5910.9331.0001.000

Missing values

2023-12-10T18:49:59.060152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T18:49:59.508426image/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.

Sample

skeycrlts_nmlocplcmanage_mbyappn_noappn_deeraasorterc_mbyerc_yearmain_cnrelate_hmpgarcharger_telnogugun_nmdata_stdr_delalordnmadr
01마하사 응진전 16나한도부산시 연제구 봉수로 138 마하사마하사제17호2003-09-161910년문화재 자료------연제구2019-05-10 AM 12:00:0035.163827129.087579-
12마하사 응진전 영산회상도부산시 연제구 봉수로 138 마하사마하사제16호2003-09-161910년문화재 자료------연제구2019-05-10 AM 12:00:0035.163827129.087579-
23마하사 영산회상도부산시 금정구 범어사로 250 범어사범어사제15호2003-09-161906년문화재 자료------연제구2019-05-10 AM 12:00:0035.163827129.087579-
34혜원정사 팔상도부산시 연제구 고분로68번길 47 혜원정사혜원정사제9호2002-05-061925년문화재 자료---https://www.yeonje.go.kr/tour/contents.do?mId=0201060000--연제구2019-05-10 AM 12:00:0035.163827129.087579-
45마하사 현왕도부산시 금정구 범어사로 250 범어사범어사제54호2003-09-161792년유형문화재---https://www.yeonje.go.kr/tour/contents.do?mId=0201050000--연제구2019-05-10 AM 12:00:0035.273145129.092679-
56배산성지부산시 연제구 연산동 산38-6번지 일원연제구청제4호1972-06-26삼국시대기념물--신라식 석축산성https://www.yeonje.go.kr/tour/contents.do?mId=02010400007필지 22,933㎡051-665-4082연제구2019-05-10 AM 12:00:0035.184126129.090595-
67부산 연산동 고분군부산시 연제구 연산동 산90-4번지 일원연제구청제539호2017-06-30삼국시대사적--구덩식 돌덧널무덤https://www.yeonje.go.kr/tour/contents.do?mId=020103000033필지 66,068㎡051-665-4082연제구2019-05-10 AM 12:00:0035.273145129.092679-
78조선왕조실록 태백산사고본부산시 연제구 경기장로 28 국가기록원 역사기록관국가기록원 역사기록관제151-2호1973-12-31조선시대국보--유네스코 세계기록유산https://www.yeonje.go.kr/tour/contents.do?mId=0201020000--연제구2019-05-10 AM 12:00:0035.180389129.096089-
89마하사 응진전 목조석가여래좌상부산시 연제구 봉수로 138 마하사마하사제19호2003-09-16조선후기문화재 자료------연제구2019-05-10 AM 12:00:0035.185541129.091646-
910마하사 대웅전 석조석가여래삼존상부산시 연제구 봉수로 138 마하사마하사제18호2003-09-16조선후기문화재 자료------연제구2019-05-10 AM 12:00:0035.190799129.053681-
skeycrlts_nmlocplcmanage_mbyappn_noappn_deeraasorterc_mbyerc_yearmain_cnrelate_hmpgarcharger_telnogugun_nmdata_stdr_delalordnmadr
9091조대비 사순칭경진하도 병풍부산광역시 서구 부민동2가 1동아대학교제732호1982-03-041847년보물-----051-200-8493서구2019-03-08 AM 12:00:0035.103738129.01941-
9192의령보리사지 금동여래입상부산광역시 서구 부민동2가 1동아대학교제731호1982-03-04통일신라보물-----051-200-8493서구2019-03-08 AM 12:00:0035.103738129.01941-
9293쌍자총통부산광역시 서구 부민동2가 1동아대학교제599호1975-08-041583년보물-----051-200-8493서구2019-03-08 AM 12:00:0035.103738129.01941-
9394도기 말머리장식 뿔잔부산광역시 서구 부민동2가 1동아대학교제598호1975-05-16삼국시대보물-----051-200-8493서구2019-03-08 AM 12:00:0035.103738129.01941-
9495토기 융기문 발부산광역시 서구 부민동2가 1동아대학교제597호1975-05-16신석기시대보물-----051-200-8493서구2019-03-08 AM 12:00:0035.103738129.01941-
9596자수 초충도 병풍부산광역시 서구 부민동2가 1동아대학교제595호1975-05-16조선시대보물-----051-200-8493서구2019-03-08 AM 12:00:0035.103738129.01941-
9697안중근의사유묵-견리사의견위수명부산광역시 서구 부민동2가 1동아대학교제569-6호1972-08-161910년보물-----051-200-8493서구2019-03-08 AM 12:00:0035.103738129.01941-
9798감지은니묘법연화경 권3부산광역시 서구 부민동2가 1동아대학교제269-3호2007-10-241422년보물-----051-200-8493서구2019-03-08 AM 12:00:0035.103738129.01941-
9899동궐도부산광역시 서구 부민동2가 1동아대학교제249-2호1995-06-23조선시대국보-----051-200-8493서구2019-03-08 AM 12:00:0035.103738129.01941-
99100심지백 개국원종공신녹권부산광역시 서구 부민동2가 1동아대학교제69호1962-12-201397년국보-----051-200-8493서구2019-03-08 AM 12:00:0035.103738129.01941-