Overview

Dataset statistics

Number of variables10
Number of observations297
Missing cells138
Missing cells (%)4.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory23.9 KiB
Average record size in memory82.4 B

Variable types

Categorical2
Text7
Numeric1

Dataset

Description전북_순창군_마을회관정보_20150825
Author전라북도 순창군
URLhttps://www.data.go.kr/data/15045214/fileData.do

Alerts

층수 is highly imbalanced (89.0%)Imbalance
전화번호 has 131 (44.1%) missing valuesMissing

Reproduction

Analysis started2023-12-12 04:41:38.291048
Analysis finished2023-12-12 04:41:39.296470
Duration1.01 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

읍면명
Categorical

Distinct12
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
순창읍
43 
쌍치면
34 
복흥면
31 
동계면
30 
금과면
30 
Other values (7)
129 

Length

Max length4
Median length3
Mean length3.003367
Min length3

Unique

Unique1 ?
Unique (%)0.3%

Sample

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

Common Values

ValueCountFrequency (%)
순창읍 43
14.5%
쌍치면 34
11.4%
복흥면 31
10.4%
동계면 30
10.1%
금과면 30
10.1%
구림면 28
9.4%
풍산면 26
8.8%
인계면 22
7.4%
팔덕면 20
6.7%
적성면 18
6.1%
Other values (2) 15
 
5.1%

Length

2023-12-12T13:41:39.362208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
순창읍 43
14.5%
쌍치면 34
11.4%
복흥면 31
10.4%
동계면 30
10.1%
금과면 30
10.1%
구림면 28
9.4%
풍산면 26
8.8%
인계면 22
7.4%
팔덕면 20
6.7%
적성면 18
6.1%
Other values (2) 15
 
5.1%

마을명
Text

Distinct274
Distinct (%)92.6%
Missing1
Missing (%)0.3%
Memory size2.4 KiB
2023-12-12T13:41:39.776437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length2
Mean length2.2094595
Min length2

Characters and Unicode

Total characters654
Distinct characters159
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

Unique255 ?
Unique (%)86.1%

Sample

1st row1옥천
2nd row2옥천
3rd row3옥천
4th row4옥천(대신)
5th row1관북
ValueCountFrequency (%)
신촌 3
 
1.0%
신기 3
 
1.0%
내동 3
 
1.0%
정동 2
 
0.7%
대가 2
 
0.7%
방축 2
 
0.7%
무수 2
 
0.7%
오룡 2
 
0.7%
송정 2
 
0.7%
입석 2
 
0.7%
Other values (264) 273
92.2%
2023-12-12T13:41:40.553846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
산 24
 
3.7%
동 24
 
3.7%
신 20
 
3.1%
정 18
 
2.8%
촌 15
 
2.3%
장 13
 
2.0%
암 13
 
2.0%
대 12
 
1.8%
곡 11
 
1.7%
내 11
 
1.7%
Other values (149) 493
75.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 617
94.3%
Decimal Number 22
 
3.4%
Close Punctuation 7
 
1.1%
Open Punctuation 7
 
1.1%
Space Separator 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 24
 
3.9%
동 24
 
3.9%
신 20
 
3.2%
정 18
 
2.9%
촌 15
 
2.4%
장 13
 
2.1%
암 13
 
2.1%
대 12
 
1.9%
곡 11
 
1.8%
내 11
 
1.8%
Other values (142) 456
73.9%
Decimal Number
ValueCountFrequency (%)
1 9
40.9%
2 8
36.4%
3 4
18.2%
4 1
 
4.5%
Close Punctuation
ValueCountFrequency (%)
) 7
100.0%
Open Punctuation
ValueCountFrequency (%)
( 7
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 617
94.3%
Common 37
 
5.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 24
 
3.9%
동 24
 
3.9%
신 20
 
3.2%
정 18
 
2.9%
촌 15
 
2.4%
장 13
 
2.1%
암 13
 
2.1%
대 12
 
1.9%
곡 11
 
1.8%
내 11
 
1.8%
Other values (142) 456
73.9%
Common
ValueCountFrequency (%)
1 9
24.3%
2 8
21.6%
) 7
18.9%
( 7
18.9%
3 4
10.8%
4 1
 
2.7%
1
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 617
94.3%
ASCII 37
 
5.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
산 24
 
3.9%
동 24
 
3.9%
신 20
 
3.2%
정 18
 
2.9%
촌 15
 
2.4%
장 13
 
2.1%
암 13
 
2.1%
대 12
 
1.9%
곡 11
 
1.8%
내 11
 
1.8%
Other values (142) 456
73.9%
ASCII
ValueCountFrequency (%)
1 9
24.3%
2 8
21.6%
) 7
18.9%
( 7
18.9%
3 4
10.8%
4 1
 
2.7%
1
 
2.7%

마을회관명
Text

Distinct274
Distinct (%)92.6%
Missing1
Missing (%)0.3%
Memory size2.4 KiB
2023-12-12T13:41:41.043103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length7
Mean length7.2094595
Min length7

Characters and Unicode

Total characters2134
Distinct characters160
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

Unique255 ?
Unique (%)86.1%

Sample

1st row1옥천 마을회관
2nd row2옥천 마을회관
3rd row3옥천 마을회관
4th row4옥천(대신) 마을회관
5th row1관북 마을회관
ValueCountFrequency (%)
마을회관 296
50.0%
신촌 3
 
0.5%
내동 3
 
0.5%
신기 3
 
0.5%
정동 2
 
0.3%
대가 2
 
0.3%
방축 2
 
0.3%
무수 2
 
0.3%
오룡 2
 
0.3%
송정 2
 
0.3%
Other values (265) 275
46.5%
2023-12-12T13:41:41.718808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
관 303
14.2%
마 301
14.1%
회 298
14.0%
297
13.9%
을 296
13.9%
동 24
 
1.1%
산 24
 
1.1%
신 20
 
0.9%
정 18
 
0.8%
촌 15
 
0.7%
Other values (150) 538
25.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1801
84.4%
Space Separator 297
 
13.9%
Decimal Number 22
 
1.0%
Close Punctuation 7
 
0.3%
Open Punctuation 7
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
관 303
16.8%
마 301
16.7%
회 298
16.5%
을 296
16.4%
동 24
 
1.3%
산 24
 
1.3%
신 20
 
1.1%
정 18
 
1.0%
촌 15
 
0.8%
장 13
 
0.7%
Other values (143) 489
27.2%
Decimal Number
ValueCountFrequency (%)
1 9
40.9%
2 8
36.4%
3 4
18.2%
4 1
 
4.5%
Space Separator
ValueCountFrequency (%)
297
100.0%
Close Punctuation
ValueCountFrequency (%)
) 7
100.0%
Open Punctuation
ValueCountFrequency (%)
( 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1801
84.4%
Common 333
 
15.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
관 303
16.8%
마 301
16.7%
회 298
16.5%
을 296
16.4%
동 24
 
1.3%
산 24
 
1.3%
신 20
 
1.1%
정 18
 
1.0%
촌 15
 
0.8%
장 13
 
0.7%
Other values (143) 489
27.2%
Common
ValueCountFrequency (%)
297
89.2%
1 9
 
2.7%
2 8
 
2.4%
) 7
 
2.1%
( 7
 
2.1%
3 4
 
1.2%
4 1
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1801
84.4%
ASCII 333
 
15.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
관 303
16.8%
마 301
16.7%
회 298
16.5%
을 296
16.4%
동 24
 
1.3%
산 24
 
1.3%
신 20
 
1.1%
정 18
 
1.0%
촌 15
 
0.8%
장 13
 
0.7%
Other values (143) 489
27.2%
ASCII
ValueCountFrequency (%)
297
89.2%
1 9
 
2.7%
2 8
 
2.4%
) 7
 
2.1%
( 7
 
2.1%
3 4
 
1.2%
4 1
 
0.3%

소재지(주소)
Text

Distinct294
Distinct (%)99.3%
Missing1
Missing (%)0.3%
Memory size2.4 KiB
2023-12-12T13:41:42.252326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length13
Mean length11.239865
Min length7

Characters and Unicode

Total characters3327
Distinct characters163
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique292 ?
Unique (%)98.6%

Sample

1st row순창읍 옥천동길 7-4
2nd row순창읍 순창9길 56
3rd row순창읍 옥천동길 38
4th row순창읍 옥천동길 29
5th row순창읍 순창5길 32
ValueCountFrequency (%)
순창읍 40
 
4.7%
쌍치면 34
 
4.0%
복흥면 31
 
3.6%
동계면 30
 
3.5%
금과면 30
 
3.5%
풍산면 26
 
3.0%
인계면 22
 
2.6%
팔덕면 20
 
2.3%
적성면 18
 
2.1%
유등면 14
 
1.6%
Other values (478) 592
69.1%
2023-12-12T13:41:43.004897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
561
 
16.9%
면 225
 
6.8%
길 216
 
6.5%
1 198
 
6.0%
- 165
 
5.0%
2 161
 
4.8%
3 109
 
3.3%
4 88
 
2.6%
5 70
 
2.1%
7 69
 
2.1%
Other values (153) 1465
44.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1714
51.5%
Decimal Number 887
26.7%
Space Separator 561
 
16.9%
Dash Punctuation 165
 
5.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
면 225
 
13.1%
길 216
 
12.6%
동 64
 
3.7%
창 64
 
3.7%
계 63
 
3.7%
리 61
 
3.6%
순 60
 
3.5%
산 52
 
3.0%
금 43
 
2.5%
읍 40
 
2.3%
Other values (141) 826
48.2%
Decimal Number
ValueCountFrequency (%)
1 198
22.3%
2 161
18.2%
3 109
12.3%
4 88
9.9%
5 70
 
7.9%
7 69
 
7.8%
6 58
 
6.5%
9 49
 
5.5%
0 45
 
5.1%
8 40
 
4.5%
Space Separator
ValueCountFrequency (%)
561
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 165
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1714
51.5%
Common 1613
48.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
면 225
 
13.1%
길 216
 
12.6%
동 64
 
3.7%
창 64
 
3.7%
계 63
 
3.7%
리 61
 
3.6%
순 60
 
3.5%
산 52
 
3.0%
금 43
 
2.5%
읍 40
 
2.3%
Other values (141) 826
48.2%
Common
ValueCountFrequency (%)
561
34.8%
1 198
 
12.3%
- 165
 
10.2%
2 161
 
10.0%
3 109
 
6.8%
4 88
 
5.5%
5 70
 
4.3%
7 69
 
4.3%
6 58
 
3.6%
9 49
 
3.0%
Other values (2) 85
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1714
51.5%
ASCII 1613
48.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
561
34.8%
1 198
 
12.3%
- 165
 
10.2%
2 161
 
10.0%
3 109
 
6.8%
4 88
 
5.5%
5 70
 
4.3%
7 69
 
4.3%
6 58
 
3.6%
9 49
 
3.0%
Other values (2) 85
 
5.3%
Hangul
ValueCountFrequency (%)
면 225
 
13.1%
길 216
 
12.6%
동 64
 
3.7%
창 64
 
3.7%
계 63
 
3.7%
리 61
 
3.6%
순 60
 
3.5%
산 52
 
3.0%
금 43
 
2.5%
읍 40
 
2.3%
Other values (141) 826
48.2%

건축년도
Real number (ℝ)

Distinct58
Distinct (%)19.6%
Missing1
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean1993.027
Minimum1901
Maximum2015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2023-12-12T13:41:43.206921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1901
5-th percentile1954.75
Q11994.75
median1998
Q32002
95-th percentile2008
Maximum2015
Range114
Interquartile range (IQR)7.25

Descriptive statistics

Standard deviation19.711632
Coefficient of variation (CV)0.0098902982
Kurtosis10.643798
Mean1993.027
Median Absolute Deviation (MAD)4
Skewness-3.1141905
Sum589936
Variance388.54842
MonotonicityNot monotonic
2023-12-12T13:41:43.379709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1998 42
 
14.1%
1997 32
 
10.8%
2000 20
 
6.7%
2001 15
 
5.1%
2002 14
 
4.7%
2004 13
 
4.4%
2003 12
 
4.0%
1996 11
 
3.7%
2006 11
 
3.7%
2005 10
 
3.4%
Other values (48) 116
39.1%
ValueCountFrequency (%)
1901 2
0.7%
1902 1
0.3%
1903 2
0.7%
1904 1
0.3%
1905 1
0.3%
1907 1
0.3%
1925 1
0.3%
1930 1
0.3%
1935 1
0.3%
1940 1
0.3%
ValueCountFrequency (%)
2015 1
 
0.3%
2013 1
 
0.3%
2012 1
 
0.3%
2011 2
 
0.7%
2010 5
1.7%
2009 3
 
1.0%
2008 7
2.4%
2007 5
1.7%
2006 11
3.7%
2005 10
3.4%

층수
Categorical

IMBALANCE 

Distinct3
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
1
290 
2
 
6
<NA>
 
1

Length

Max length4
Median length1
Mean length1.010101
Min length1

Unique

Unique1 ?
Unique (%)0.3%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 290
97.6%
2 6
 
2.0%
<NA> 1
 
0.3%

Length

2023-12-12T13:41:43.519946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:41:43.649671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 290
97.6%
2 6
 
2.0%
na 1
 
0.3%

구조
Text

Distinct54
Distinct (%)18.2%
Missing1
Missing (%)0.3%
Memory size2.4 KiB
2023-12-12T13:41:43.871605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length9
Mean length4.4932432
Min length2

Characters and Unicode

Total characters1330
Distinct characters42
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

Unique25 ?
Unique (%)8.4%

Sample

1st row철근콘크리트
2nd row시멘트벽돌
3rd row경량철골구조
4th row경량철골구조
5th row철근콘크리트
ValueCountFrequency (%)
벽돌스라브 50
15.9%
벽돌 41
13.0%
조적조 29
 
9.2%
연와조 23
 
7.3%
슬라브 20
 
6.3%
철근콘크리트 19
 
6.0%
벽돌구조 17
 
5.4%
적벽돌 13
 
4.1%
벽돌/슬라브 11
 
3.5%
조적조/슬라브 10
 
3.2%
Other values (43) 82
26.0%
2023-12-12T13:41:44.275509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
조 163
12.3%
돌 160
12.0%
벽 159
12.0%
브 108
 
8.1%
라 107
 
8.0%
적 61
 
4.6%
스 59
 
4.4%
슬 53
 
4.0%
트 46
 
3.5%
/ 42
 
3.2%
Other values (32) 372
28.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1269
95.4%
Other Punctuation 42
 
3.2%
Space Separator 19
 
1.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
조 163
12.8%
돌 160
12.6%
벽 159
12.5%
브 108
 
8.5%
라 107
 
8.4%
적 61
 
4.8%
스 59
 
4.6%
슬 53
 
4.2%
트 46
 
3.6%
와 38
 
3.0%
Other values (30) 315
24.8%
Other Punctuation
ValueCountFrequency (%)
/ 42
100.0%
Space Separator
ValueCountFrequency (%)
19
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1269
95.4%
Common 61
 
4.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
조 163
12.8%
돌 160
12.6%
벽 159
12.5%
브 108
 
8.5%
라 107
 
8.4%
적 61
 
4.8%
스 59
 
4.6%
슬 53
 
4.2%
트 46
 
3.6%
와 38
 
3.0%
Other values (30) 315
24.8%
Common
ValueCountFrequency (%)
/ 42
68.9%
19
31.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1269
95.4%
ASCII 61
 
4.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
조 163
12.8%
돌 160
12.6%
벽 159
12.5%
브 108
 
8.5%
라 107
 
8.4%
적 61
 
4.8%
스 59
 
4.6%
슬 53
 
4.2%
트 46
 
3.6%
와 38
 
3.0%
Other values (30) 315
24.8%
ASCII
ValueCountFrequency (%)
/ 42
68.9%
19
31.1%

연면적(㎡)
Text

Distinct248
Distinct (%)83.8%
Missing1
Missing (%)0.3%
Memory size2.4 KiB
2023-12-12T13:41:44.749812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length4.3141892
Min length2

Characters and Unicode

Total characters1277
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique220 ?
Unique (%)74.3%

Sample

1st row116.2
2nd row152.7
3rd row83.02
4th row60.4
5th row60
ValueCountFrequency (%)
66.1 8
 
2.7%
66 6
 
2.0%
84 4
 
1.4%
60 4
 
1.4%
86.25 4
 
1.4%
89.1 3
 
1.0%
79.2 3
 
1.0%
90 3
 
1.0%
99.17 3
 
1.0%
89.91 2
 
0.7%
Other values (238) 256
86.5%
2023-12-12T13:41:45.744888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 247
19.3%
8 172
13.5%
6 156
12.2%
9 126
9.9%
1 112
8.8%
2 93
 
7.3%
5 93
 
7.3%
7 91
 
7.1%
4 76
 
6.0%
3 63
 
4.9%
Other values (2) 48
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1029
80.6%
Other Punctuation 247
 
19.3%
Other Symbol 1
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 172
16.7%
6 156
15.2%
9 126
12.2%
1 112
10.9%
2 93
9.0%
5 93
9.0%
7 91
8.8%
4 76
7.4%
3 63
 
6.1%
0 47
 
4.6%
Other Punctuation
ValueCountFrequency (%)
. 247
100.0%
Other Symbol
ValueCountFrequency (%)
㎡ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1277
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 247
19.3%
8 172
13.5%
6 156
12.2%
9 126
9.9%
1 112
8.8%
2 93
 
7.3%
5 93
 
7.3%
7 91
 
7.1%
4 76
 
6.0%
3 63
 
4.9%
Other values (2) 48
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1276
99.9%
CJK Compat 1
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 247
19.4%
8 172
13.5%
6 156
12.2%
9 126
9.9%
1 112
8.8%
2 93
 
7.3%
5 93
 
7.3%
7 91
 
7.1%
4 76
 
6.0%
3 63
 
4.9%
CJK Compat
ValueCountFrequency (%)
㎡ 1
100.0%

전화번호
Text

MISSING 

Distinct163
Distinct (%)98.2%
Missing131
Missing (%)44.1%
Memory size2.4 KiB
2023-12-12T13:41:46.027943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters1992
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

Unique160 ?
Unique (%)96.4%

Sample

1st row063-653-0130
2nd row063-652-0966
3rd row063-653-7272
4th row063-652-1380
5th row063-652-7005
ValueCountFrequency (%)
063-652-2606 2
 
1.2%
063-652-6447 2
 
1.2%
063-652-6772 2
 
1.2%
063-653-2207 1
 
0.6%
063-652-5330 1
 
0.6%
063-652-0848 1
 
0.6%
063-652-0659 1
 
0.6%
063-652-5179 1
 
0.6%
063-652-5448 1
 
0.6%
063-652-1149 1
 
0.6%
Other values (153) 153
92.2%
2023-12-12T13:41:46.459028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6 399
20.0%
- 332
16.7%
3 262
13.2%
0 255
12.8%
5 236
11.8%
2 201
10.1%
1 77
 
3.9%
7 67
 
3.4%
4 58
 
2.9%
9 58
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1660
83.3%
Dash Punctuation 332
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6 399
24.0%
3 262
15.8%
0 255
15.4%
5 236
14.2%
2 201
12.1%
1 77
 
4.6%
7 67
 
4.0%
4 58
 
3.5%
9 58
 
3.5%
8 47
 
2.8%
Dash Punctuation
ValueCountFrequency (%)
- 332
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1992
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
6 399
20.0%
- 332
16.7%
3 262
13.2%
0 255
12.8%
5 236
11.8%
2 201
10.1%
1 77
 
3.9%
7 67
 
3.4%
4 58
 
2.9%
9 58
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1992
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6 399
20.0%
- 332
16.7%
3 262
13.2%
0 255
12.8%
5 236
11.8%
2 201
10.1%
1 77
 
3.9%
7 67
 
3.4%
4 58
 
2.9%
9 58
 
2.9%

관리자
Text

Distinct294
Distinct (%)99.3%
Missing1
Missing (%)0.3%
Memory size2.4 KiB
2023-12-12T13:41:46.825108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.9797297
Min length2

Characters and Unicode

Total characters882
Distinct characters137
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique292 ?
Unique (%)98.6%

Sample

1st row황의성
2nd row이춘순
3rd row양만갑
4th row최일만
5th row김진배
ValueCountFrequency (%)
김종열 2
 
0.7%
김종선 2
 
0.7%
오태홍 1
 
0.3%
강민구 1
 
0.3%
최창용 1
 
0.3%
임형락 1
 
0.3%
윤영호 1
 
0.3%
한병탁 1
 
0.3%
이성연 1
 
0.3%
박인수 1
 
0.3%
Other values (284) 284
95.9%
2023-12-12T13:41:47.344857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
김 57
 
6.5%
이 29
 
3.3%
수 28
 
3.2%
영 25
 
2.8%
정 25
 
2.8%
기 23
 
2.6%
양 21
 
2.4%
박 19
 
2.2%
종 18
 
2.0%
최 18
 
2.0%
Other values (127) 619
70.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 882
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
김 57
 
6.5%
이 29
 
3.3%
수 28
 
3.2%
영 25
 
2.8%
정 25
 
2.8%
기 23
 
2.6%
양 21
 
2.4%
박 19
 
2.2%
종 18
 
2.0%
최 18
 
2.0%
Other values (127) 619
70.2%

Most occurring scripts

ValueCountFrequency (%)
Hangul 882
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
김 57
 
6.5%
이 29
 
3.3%
수 28
 
3.2%
영 25
 
2.8%
정 25
 
2.8%
기 23
 
2.6%
양 21
 
2.4%
박 19
 
2.2%
종 18
 
2.0%
최 18
 
2.0%
Other values (127) 619
70.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 882
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
김 57
 
6.5%
이 29
 
3.3%
수 28
 
3.2%
영 25
 
2.8%
정 25
 
2.8%
기 23
 
2.6%
양 21
 
2.4%
박 19
 
2.2%
종 18
 
2.0%
최 18
 
2.0%
Other values (127) 619
70.2%

Interactions

2023-12-12T13:41:38.749724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T13:41:47.462543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
읍면명건축년도층수구조
읍면명1.0000.4830.3130.936
건축년도0.4831.0000.0000.859
층수0.3130.0001.0000.355
구조0.9360.8590.3551.000
2023-12-12T13:41:47.552541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
층수읍면명
층수1.0000.295
읍면명0.2951.000
2023-12-12T13:41:47.630357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
건축년도읍면명층수
건축년도1.0000.2510.000
읍면명0.2511.0000.295
층수0.0000.2951.000

Missing values

2023-12-12T13:41:38.879005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T13:41:39.044391image/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.
2023-12-12T13:41:39.182632image/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

읍면명마을명마을회관명소재지(주소)건축년도층수구조연면적(㎡)전화번호관리자
0순창읍1옥천1옥천 마을회관순창읍 옥천동길 7-419961철근콘크리트116.2<NA>황의성
1순창읍2옥천2옥천 마을회관순창읍 순창9길 5619891시멘트벽돌152.7<NA>이춘순
2순창읍3옥천3옥천 마을회관순창읍 옥천동길 3820081경량철골구조83.02<NA>양만갑
3순창읍4옥천(대신)4옥천(대신) 마을회관순창읍 옥천동길 2920101경량철골구조60.4<NA>최일만
4순창읍1관북1관북 마을회관순창읍 순창5길 3219851철근콘크리트60<NA>김진배
5순창읍2관북2관북 마을회관순창7길 34-619971목조조적조58.64<NA>김영
6순창읍3관북(웰빙)3관북(웰빙) 마을회관순창읍 순창9길 36-320061철근콘크리트34.56<NA>조미경
7순창읍1충신1충신 마을회관순창읍 순창11길 33-1720031조적조82.2<NA>김호진
8순창읍3충신(신천지)3충신(신천지) 마을회관순창읍 순창11길 47-2219891철근콘크리트67.2<NA>정순옥
9순창읍1순화1순화 마을회관순창읍 순창7길 16-719401목조시멘트블럭69.8<NA>이인숙
읍면명마을명마을회관명소재지(주소)건축년도층수구조연면적(㎡)전화번호관리자
287구림면속리속리 마을회관방화리 601-220001조적조/슬라브89.32<NA>천명환
288구림면운항운항 마을회관운북리 153-120011조적조/슬라브89.07<NA>김종옥
289구림면단풍단풍 마을회관운북리 408-120021연와조/슬라브85.28<NA>김길봉
290구림면유사유사 마을회관월정리 692-419541목조48.8<NA>박기문
291구림면장암장암 마을회관월정리 120-819971연와90.09<NA>강화수
292구림면오정오정 마을회관월정리 448-320051철근콘크리트121.81<NA>김희선
293구림면상리상리 마을회관자양리 221-119971연와89.6<NA>한양근
294구림면구곡구곡 마을회관구곡리 421-119981철골조167.58<NA>김한석
295구림면화암화암 마을회관화암리 61019761벽돌조/슬라브105.2<NA>서영표
296<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>