Overview

Dataset statistics

Number of variables11
Number of observations191
Missing cells219
Missing cells (%)10.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory16.9 KiB
Average record size in memory90.7 B

Variable types

Numeric2
Categorical1
Text8

Dataset

Description경상남도 하동군에 있는 공동묘지 현황 (연번, 읍면, 공동묘지명, 위치, 설치일자, 관리주체, 면적, 기수 등)의 정보를 제공하고 있습니다.
Author경상남도 하동군
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15086554

Alerts

연번 is highly overall correlated with 읍면High correlation
읍면 is highly overall correlated with 연번High correlation
매장가능기수 has 30 (15.7%) missing valuesMissing
비고 has 189 (99.0%) missing valuesMissing
연번 has unique valuesUnique

Reproduction

Analysis started2023-12-10 23:47:41.860516
Analysis finished2023-12-10 23:47:43.507393
Duration1.65 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct191
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean96
Minimum1
Maximum191
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-11T08:47:43.574255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile10.5
Q148.5
median96
Q3143.5
95-th percentile181.5
Maximum191
Range190
Interquartile range (IQR)95

Descriptive statistics

Standard deviation55.2811
Coefficient of variation (CV)0.57584479
Kurtosis-1.2
Mean96
Median Absolute Deviation (MAD)48
Skewness0
Sum18336
Variance3056
MonotonicityStrictly increasing
2023-12-11T08:47:43.720486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.5%
2 1
 
0.5%
123 1
 
0.5%
124 1
 
0.5%
125 1
 
0.5%
126 1
 
0.5%
127 1
 
0.5%
128 1
 
0.5%
129 1
 
0.5%
130 1
 
0.5%
Other values (181) 181
94.8%
ValueCountFrequency (%)
1 1
0.5%
2 1
0.5%
3 1
0.5%
4 1
0.5%
5 1
0.5%
6 1
0.5%
7 1
0.5%
8 1
0.5%
9 1
0.5%
10 1
0.5%
ValueCountFrequency (%)
191 1
0.5%
190 1
0.5%
189 1
0.5%
188 1
0.5%
187 1
0.5%
186 1
0.5%
185 1
0.5%
184 1
0.5%
183 1
0.5%
182 1
0.5%

읍면
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)7.3%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
횡천면
26 
악양면
25 
옥종면
21 
화개면
17 
적량면
17 
Other values (9)
85 

Length

Max length5
Median length3
Mean length3.0471204
Min length3

Unique

Unique1 ?
Unique (%)0.5%

Sample

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

Common Values

ValueCountFrequency (%)
횡천면 26
13.6%
악양면 25
13.1%
옥종면 21
11.0%
화개면 17
8.9%
적량면 17
8.9%
금남면 16
8.4%
고전면 14
7.3%
진교면 14
7.3%
북천면 10
 
5.2%
청암면 9
 
4.7%
Other values (4) 22
11.5%

Length

2023-12-11T08:47:43.867531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
횡천면 26
13.6%
악양면 25
13.1%
옥종면 21
11.0%
화개면 17
8.9%
적량면 17
8.9%
금남면 16
8.4%
진교면 15
7.9%
고전면 14
7.3%
북천면 10
 
5.2%
청암면 9
 
4.7%
Other values (3) 21
11.0%

공동묘지명
Text

Distinct184
Distinct (%)96.3%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2023-12-11T08:47:44.065289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length6
Mean length6.7068063
Min length6

Characters and Unicode

Total characters1281
Distinct characters134
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique178 ?
Unique (%)93.2%

Sample

1st row읍내공동묘지
2nd row연화공동묘지
3rd row흥룡공동묘지
4th row화심공동묘지
5th row두곡공동묘지
ValueCountFrequency (%)
공동묘지 15
 
7.1%
신촌공동묘지 3
 
1.4%
공동묘지(1 3
 
1.4%
궁항마을공동묘지 2
 
0.9%
유평 2
 
0.9%
횡보 2
 
0.9%
공동묘지(2 2
 
0.9%
오율마을공동묘지 2
 
0.9%
위태마을공동묘지 2
 
0.9%
상촌공동묘지 2
 
0.9%
Other values (175) 176
83.4%
2023-12-11T08:47:44.393337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 200
15.6%
지 193
15.1%
공 192
15.0%
묘 191
14.9%
마 23
 
1.8%
21
 
1.6%
을 21
 
1.6%
상 21
 
1.6%
) 16
 
1.2%
( 16
 
1.2%
Other values (124) 387
30.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1213
94.7%
Space Separator 21
 
1.6%
Close Punctuation 16
 
1.2%
Open Punctuation 16
 
1.2%
Decimal Number 15
 
1.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 200
16.5%
지 193
15.9%
공 192
15.8%
묘 191
15.7%
마 23
 
1.9%
을 21
 
1.7%
상 21
 
1.7%
신 14
 
1.2%
하 14
 
1.2%
대 12
 
1.0%
Other values (119) 332
27.4%
Decimal Number
ValueCountFrequency (%)
1 8
53.3%
2 7
46.7%
Space Separator
ValueCountFrequency (%)
21
100.0%
Close Punctuation
ValueCountFrequency (%)
) 16
100.0%
Open Punctuation
ValueCountFrequency (%)
( 16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1213
94.7%
Common 68
 
5.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 200
16.5%
지 193
15.9%
공 192
15.8%
묘 191
15.7%
마 23
 
1.9%
을 21
 
1.7%
상 21
 
1.7%
신 14
 
1.2%
하 14
 
1.2%
대 12
 
1.0%
Other values (119) 332
27.4%
Common
ValueCountFrequency (%)
21
30.9%
) 16
23.5%
( 16
23.5%
1 8
 
11.8%
2 7
 
10.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1213
94.7%
ASCII 68
 
5.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 200
16.5%
지 193
15.9%
공 192
15.8%
묘 191
15.7%
마 23
 
1.9%
을 21
 
1.7%
상 21
 
1.7%
신 14
 
1.2%
하 14
 
1.2%
대 12
 
1.0%
Other values (119) 332
27.4%
ASCII
ValueCountFrequency (%)
21
30.9%
) 16
23.5%
( 16
23.5%
1 8
 
11.8%
2 7
 
10.3%

위치
Text

Distinct190
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2023-12-11T08:47:44.691353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length21
Mean length9.3141361
Min length5

Characters and Unicode

Total characters1779
Distinct characters118
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique189 ?
Unique (%)99.0%

Sample

1st row읍내리 349-11
2nd row읍내리 산7-3
3rd row흥룡리 산3
4th row화심리 산36
5th row두곡리 산51
ValueCountFrequency (%)
산 40
 
8.9%
진교면 15
 
3.3%
청암면 9
 
2.0%
양보면 6
 
1.3%
남산리 5
 
1.1%
궁항리 5
 
1.1%
신월리 5
 
1.1%
학리 4
 
0.9%
월평리 4
 
0.9%
횡천리 4
 
0.9%
Other values (255) 353
78.4%
2023-12-11T08:47:45.112592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
260
 
14.6%
산 200
 
11.2%
리 183
 
10.3%
1 105
 
5.9%
2 65
 
3.7%
8 55
 
3.1%
지 54
 
3.0%
7 51
 
2.9%
3 50
 
2.8%
번 49
 
2.8%
Other values (108) 707
39.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 943
53.0%
Decimal Number 521
29.3%
Space Separator 260
 
14.6%
Dash Punctuation 28
 
1.6%
Other Punctuation 19
 
1.1%
Open Punctuation 4
 
0.2%
Close Punctuation 4
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 200
21.2%
리 183
19.4%
지 54
 
5.7%
번 49
 
5.2%
면 30
 
3.2%
진 19
 
2.0%
교 16
 
1.7%
암 15
 
1.6%
신 14
 
1.5%
대 14
 
1.5%
Other values (93) 349
37.0%
Decimal Number
ValueCountFrequency (%)
1 105
20.2%
2 65
12.5%
8 55
10.6%
7 51
9.8%
3 50
9.6%
5 49
9.4%
6 42
 
8.1%
4 40
 
7.7%
9 36
 
6.9%
0 28
 
5.4%
Space Separator
ValueCountFrequency (%)
260
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 28
100.0%
Other Punctuation
ValueCountFrequency (%)
, 19
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 943
53.0%
Common 836
47.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 200
21.2%
리 183
19.4%
지 54
 
5.7%
번 49
 
5.2%
면 30
 
3.2%
진 19
 
2.0%
교 16
 
1.7%
암 15
 
1.6%
신 14
 
1.5%
대 14
 
1.5%
Other values (93) 349
37.0%
Common
ValueCountFrequency (%)
260
31.1%
1 105
12.6%
2 65
 
7.8%
8 55
 
6.6%
7 51
 
6.1%
3 50
 
6.0%
5 49
 
5.9%
6 42
 
5.0%
4 40
 
4.8%
9 36
 
4.3%
Other values (5) 83
 
9.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 943
53.0%
ASCII 836
47.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
260
31.1%
1 105
12.6%
2 65
 
7.8%
8 55
 
6.6%
7 51
 
6.1%
3 50
 
6.0%
5 49
 
5.9%
6 42
 
5.0%
4 40
 
4.8%
9 36
 
4.3%
Other values (5) 83
 
9.9%
Hangul
ValueCountFrequency (%)
산 200
21.2%
리 183
19.4%
지 54
 
5.7%
번 49
 
5.2%
면 30
 
3.2%
진 19
 
2.0%
교 16
 
1.7%
암 15
 
1.6%
신 14
 
1.5%
대 14
 
1.5%
Other values (93) 349
37.0%

설치일자
Text

Distinct54
Distinct (%)28.3%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2023-12-11T08:47:45.321720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length8.7120419
Min length4

Characters and Unicode

Total characters1664
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique39 ?
Unique (%)20.4%

Sample

1st row1962-05-31
2nd row1962-05-31
3rd row1961-09-01
4th row1961-09-01
5th row1979-06-27
ValueCountFrequency (%)
1979-06-22 46
24.1%
1921-08-20 25
13.1%
1910 25
13.1%
1979 16
 
8.4%
1962-11-08 9
 
4.7%
1962-05-31 8
 
4.2%
1966-09-01 6
 
3.1%
1919-04-22 3
 
1.6%
1961-09-01 2
 
1.0%
1921-08-18 2
 
1.0%
Other values (44) 49
25.7%
2023-12-11T08:47:45.670428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 327
19.7%
- 298
17.9%
9 283
17.0%
0 228
13.7%
2 225
13.5%
6 94
 
5.6%
7 84
 
5.0%
8 53
 
3.2%
3 31
 
1.9%
5 24
 
1.4%
Other values (3) 17
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1364
82.0%
Dash Punctuation 298
 
17.9%
Open Punctuation 1
 
0.1%
Close Punctuation 1
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 327
24.0%
9 283
20.7%
0 228
16.7%
2 225
16.5%
6 94
 
6.9%
7 84
 
6.2%
8 53
 
3.9%
3 31
 
2.3%
5 24
 
1.8%
4 15
 
1.1%
Dash Punctuation
ValueCountFrequency (%)
- 298
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1664
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 327
19.7%
- 298
17.9%
9 283
17.0%
0 228
13.7%
2 225
13.5%
6 94
 
5.6%
7 84
 
5.0%
8 53
 
3.2%
3 31
 
1.9%
5 24
 
1.4%
Other values (3) 17
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1664
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 327
19.7%
- 298
17.9%
9 283
17.0%
0 228
13.7%
2 225
13.5%
6 94
 
5.6%
7 84
 
5.0%
8 53
 
3.2%
3 31
 
1.9%
5 24
 
1.4%
Other values (3) 17
 
1.0%

관리주체
Text

Distinct169
Distinct (%)88.5%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2023-12-11T08:47:46.032955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length5
Mean length5.1832461
Min length3

Characters and Unicode

Total characters990
Distinct characters130
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique151 ?
Unique (%)79.1%

Sample

1st row연화마을회
2nd row연산,목골마을회
3rd row흥룡마을회
4th row화심마을회
5th row두곡마을회
ValueCountFrequency (%)
상촌마을회 3
 
1.6%
궁항마을회 3
 
1.6%
신촌마을회 3
 
1.6%
횡보마을회 3
 
1.6%
여의마을회 3
 
1.6%
원곡마을회 3
 
1.6%
온동마을회 2
 
1.0%
신월마을회 2
 
1.0%
중기마을회 2
 
1.0%
대덕마을회 2
 
1.0%
Other values (155) 165
86.4%
2023-12-11T08:47:46.519201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
마 190
19.2%
회 190
19.2%
을 188
19.0%
　 15
 
1.5%
신 14
 
1.4%
13
 
1.3%
상 12
 
1.2%
대 12
 
1.2%
동 11
 
1.1%
정 11
 
1.1%
Other values (120) 334
33.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 961
97.1%
Space Separator 28
 
2.8%
Other Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
마 190
19.8%
회 190
19.8%
을 188
19.6%
신 14
 
1.5%
상 12
 
1.2%
대 12
 
1.2%
동 11
 
1.1%
정 11
 
1.1%
하 10
 
1.0%
촌 10
 
1.0%
Other values (117) 313
32.6%
Space Separator
ValueCountFrequency (%)
　 15
53.6%
13
46.4%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 961
97.1%
Common 29
 
2.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
마 190
19.8%
회 190
19.8%
을 188
19.6%
신 14
 
1.5%
상 12
 
1.2%
대 12
 
1.2%
동 11
 
1.1%
정 11
 
1.1%
하 10
 
1.0%
촌 10
 
1.0%
Other values (117) 313
32.6%
Common
ValueCountFrequency (%)
　 15
51.7%
13
44.8%
, 1
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 961
97.1%
None 15
 
1.5%
ASCII 14
 
1.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
마 190
19.8%
회 190
19.8%
을 188
19.6%
신 14
 
1.5%
상 12
 
1.2%
대 12
 
1.2%
동 11
 
1.1%
정 11
 
1.1%
하 10
 
1.0%
촌 10
 
1.0%
Other values (117) 313
32.6%
None
ValueCountFrequency (%)
　 15
100.0%
ASCII
ValueCountFrequency (%)
13
92.9%
, 1
 
7.1%

면적(제곱미터)
Text

Distinct181
Distinct (%)94.8%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2023-12-11T08:47:47.009873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length5.0157068
Min length3

Characters and Unicode

Total characters958
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

Unique172 ?
Unique (%)90.1%

Sample

1st row5,911
2nd row29,752
3rd row37,884
4th row8,678
5th row13,567
ValueCountFrequency (%)
1,983 3
 
1.6%
5,000 2
 
1.0%
5,554 2
 
1.0%
979 2
 
1.0%
1,650 2
 
1.0%
1,944 2
 
1.0%
1,732 2
 
1.0%
8,172 2
 
1.0%
3,888 2
 
1.0%
6,605 1
 
0.5%
Other values (171) 171
89.5%
2023-12-11T08:47:47.658349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 176
18.4%
1 113
11.8%
3 85
8.9%
5 84
8.8%
0 78
8.1%
9 76
7.9%
2 74
7.7%
4 71
7.4%
7 71
7.4%
8 67
 
7.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 782
81.6%
Other Punctuation 176
 
18.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 113
14.5%
3 85
10.9%
5 84
10.7%
0 78
10.0%
9 76
9.7%
2 74
9.5%
4 71
9.1%
7 71
9.1%
8 67
8.6%
6 63
8.1%
Other Punctuation
ValueCountFrequency (%)
, 176
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 958
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
, 176
18.4%
1 113
11.8%
3 85
8.9%
5 84
8.8%
0 78
8.1%
9 76
7.9%
2 74
7.7%
4 71
7.4%
7 71
7.4%
8 67
 
7.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 958
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 176
18.4%
1 113
11.8%
3 85
8.9%
5 84
8.8%
0 78
8.1%
9 76
7.9%
2 74
7.7%
4 71
7.4%
7 71
7.4%
8 67
 
7.0%

만장기수
Text

Distinct88
Distinct (%)46.1%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2023-12-11T08:47:48.002397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length2.8848168
Min length2

Characters and Unicode

Total characters551
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

Unique64 ?
Unique (%)33.5%

Sample

1st row1,867
2nd row4,508
3rd row1,102
4th row1,628
5th row2,104
ValueCountFrequency (%)
50 14
 
7.3%
300 14
 
7.3%
200 13
 
6.8%
100 10
 
5.2%
250 9
 
4.7%
70 9
 
4.7%
150 7
 
3.7%
500 6
 
3.1%
120 5
 
2.6%
40 5
 
2.6%
Other values (78) 99
51.8%
2023-12-11T08:47:48.500366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 196
35.6%
2 69
 
12.5%
5 60
 
10.9%
1 59
 
10.7%
3 42
 
7.6%
4 31
 
5.6%
7 26
 
4.7%
6 21
 
3.8%
8 20
 
3.6%
9 15
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 539
97.8%
Other Punctuation 12
 
2.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 196
36.4%
2 69
 
12.8%
5 60
 
11.1%
1 59
 
10.9%
3 42
 
7.8%
4 31
 
5.8%
7 26
 
4.8%
6 21
 
3.9%
8 20
 
3.7%
9 15
 
2.8%
Other Punctuation
ValueCountFrequency (%)
, 12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 551
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 196
35.6%
2 69
 
12.5%
5 60
 
10.9%
1 59
 
10.7%
3 42
 
7.6%
4 31
 
5.6%
7 26
 
4.7%
6 21
 
3.8%
8 20
 
3.6%
9 15
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 551
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 196
35.6%
2 69
 
12.5%
5 60
 
10.9%
1 59
 
10.7%
3 42
 
7.6%
4 31
 
5.6%
7 26
 
4.7%
6 21
 
3.8%
8 20
 
3.6%
9 15
 
2.7%

안치기수
Real number (ℝ)

Distinct102
Distinct (%)53.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean169.87958
Minimum6
Maximum921
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-11T08:47:48.657701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile16.5
Q158.5
median126
Q3230.5
95-th percentile498.5
Maximum921
Range915
Interquartile range (IQR)172

Descriptive statistics

Standard deviation155.3374
Coefficient of variation (CV)0.9143971
Kurtosis5.1496416
Mean169.87958
Median Absolute Deviation (MAD)78
Skewness1.9138363
Sum32447
Variance24129.706
MonotonicityNot monotonic
2023-12-11T08:47:48.834444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
200 12
 
6.3%
300 8
 
4.2%
50 8
 
4.2%
40 7
 
3.7%
30 7
 
3.7%
150 6
 
3.1%
60 5
 
2.6%
250 5
 
2.6%
90 4
 
2.1%
100 4
 
2.1%
Other values (92) 125
65.4%
ValueCountFrequency (%)
6 1
 
0.5%
7 1
 
0.5%
8 2
1.0%
12 3
1.6%
14 1
 
0.5%
15 2
1.0%
18 1
 
0.5%
20 2
1.0%
22 2
1.0%
24 1
 
0.5%
ValueCountFrequency (%)
921 1
 
0.5%
898 1
 
0.5%
700 1
 
0.5%
604 1
 
0.5%
574 1
 
0.5%
550 1
 
0.5%
512 1
 
0.5%
500 3
1.6%
497 1
 
0.5%
468 1
 
0.5%

매장가능기수
Text

MISSING 

Distinct95
Distinct (%)59.0%
Missing30
Missing (%)15.7%
Memory size1.6 KiB
2023-12-11T08:47:49.112409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length2
Mean length2.4347826
Min length1

Characters and Unicode

Total characters392
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

Unique73 ?
Unique (%)45.3%

Sample

1st row1,415
2nd row3,587
3rd row605
4th row1,116
5th row1,636
ValueCountFrequency (%)
10 13
 
8.1%
20 10
 
6.2%
50 9
 
5.6%
5 6
 
3.7%
15 5
 
3.1%
35 5
 
3.1%
100 4
 
2.5%
30 4
 
2.5%
28 3
 
1.9%
300 3
 
1.9%
Other values (85) 99
61.5%
2023-12-11T08:47:49.553123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 85
21.7%
1 76
19.4%
5 49
12.5%
2 36
9.2%
3 33
 
8.4%
4 30
 
7.7%
8 28
 
7.1%
9 18
 
4.6%
6 17
 
4.3%
7 11
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 383
97.7%
Other Punctuation 9
 
2.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 85
22.2%
1 76
19.8%
5 49
12.8%
2 36
9.4%
3 33
 
8.6%
4 30
 
7.8%
8 28
 
7.3%
9 18
 
4.7%
6 17
 
4.4%
7 11
 
2.9%
Other Punctuation
ValueCountFrequency (%)
, 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 392
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 85
21.7%
1 76
19.4%
5 49
12.5%
2 36
9.2%
3 33
 
8.4%
4 30
 
7.7%
8 28
 
7.1%
9 18
 
4.6%
6 17
 
4.3%
7 11
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 392
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 85
21.7%
1 76
19.4%
5 49
12.5%
2 36
9.2%
3 33
 
8.4%
4 30
 
7.7%
8 28
 
7.1%
9 18
 
4.6%
6 17
 
4.3%
7 11
 
2.8%

비고
Text

MISSING 

Distinct2
Distinct (%)100.0%
Missing189
Missing (%)99.0%
Memory size1.6 KiB
2023-12-11T08:47:49.667201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters4
Distinct characters4
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st row 
2nd row평장
ValueCountFrequency (%)
평장 1
100.0%
2023-12-11T08:47:49.894464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
  1
25.0%
1
25.0%
평 1
25.0%
장 1
25.0%

Most occurring categories

ValueCountFrequency (%)
Space Separator 2
50.0%
Other Letter 2
50.0%

Most frequent character per category

Space Separator
ValueCountFrequency (%)
  1
50.0%
1
50.0%
Other Letter
ValueCountFrequency (%)
평 1
50.0%
장 1
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2
50.0%
Hangul 2
50.0%

Most frequent character per script

Common
ValueCountFrequency (%)
  1
50.0%
1
50.0%
Hangul
ValueCountFrequency (%)
평 1
50.0%
장 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2
50.0%
None 1
25.0%
ASCII 1
25.0%

Most frequent character per block

None
ValueCountFrequency (%)
  1
100.0%
ASCII
ValueCountFrequency (%)
1
100.0%
Hangul
ValueCountFrequency (%)
평 1
50.0%
장 1
50.0%

Interactions

2023-12-11T08:47:43.019763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:47:42.820790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:47:43.107554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:47:42.930592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T08:47:50.014568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번읍면설치일자만장기수안치기수매장가능기수비고
연번1.0000.9410.8720.5710.3790.2530.000
읍면0.9411.0000.9380.7120.5820.7630.000
설치일자0.8720.9381.0000.9820.9130.9030.000
만장기수0.5710.7120.9821.0000.9810.9890.000
안치기수0.3790.5820.9130.9811.0000.906NaN
매장가능기수0.2530.7630.9030.9890.9061.000NaN
비고0.0000.0000.0000.000NaNNaN1.000
2023-12-11T08:47:50.122374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번안치기수읍면
연번1.000-0.1560.760
안치기수-0.1561.0000.286
읍면0.7600.2861.000

Missing values

2023-12-11T08:47:43.224028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T08:47:43.365636image/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.
2023-12-11T08:47:43.467210image/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

연번읍면공동묘지명위치설치일자관리주체면적(제곱미터)만장기수안치기수매장가능기수비고
01하동읍읍내공동묘지읍내리 349-111962-05-31연화마을회5,9111,8674521,415<NA>
12하동읍연화공동묘지읍내리 산7-31962-05-31연산,목골마을회29,7524,5089213,587<NA>
23하동읍흥룡공동묘지흥룡리 산31961-09-01흥룡마을회37,8841,102497605<NA>
34하동읍화심공동묘지화심리 산361961-09-01화심마을회8,6781,6285121,116<NA>
45하동읍두곡공동묘지두곡리 산511979-06-27두곡마을회13,5672,1044681,636<NA>
56하동읍화산공동묘지비파리 산61963-01-05화산마을회1,983601132469<NA>
67하동읍목도공동묘지목도리 산271979-06-29목도마을회5,7523,8078982,909<NA>
78화개면검두공동묘지부춘리산218,213-11979검두마을회2,5435050<NA>
89화개면중기공동묘지덕은리산53-11979중기마을회715705515<NA>
910화개면상덕공동묘지덕은리산491979상덕마을회10,42819012565<NA>
연번읍면공동묘지명위치설치일자관리주체면적(제곱미터)만장기수안치기수매장가능기수비고
181182옥종면동곡마을공동묘지대곡리 산1571979-06-22동곡마을회8,61622060160<NA>
182183옥종면추동마을공동묘지대곡리 산471979-06-22추동마을회5,55425020050<NA>
183184옥종면오율마을공동묘지궁항리 산691979-06-22오율마을회869441430<NA>
184185옥종면궁항마을공동묘지궁항리 산1071979-06-22궁항마을회1,9831001882<NA>
185186옥종면궁항마을공동묘지궁항리 산1451979-06-29궁항마을회2,3471182494<NA>
186187옥종면오율마을공동묘지궁항리 산821979-06-22오율마을회20,7271001288<NA>
187188옥종면위태마을공동묘지위태리 산551979-06-22위태마을회4,46322515273<NA>
188189옥종면위태마을공동묘지위태리 산2801979-06-22위태마을회979491237<NA>
189190옥종면갈성마을공동묘지위태리 산1921979-06-22갈성마을회11,359573143430<NA>
190191옥종면회신마을공동묘지회신리 산621979-06-23회신마을회3,52717878100<NA>