Overview

Dataset statistics

Number of variables9
Number of observations43
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.3 KiB
Average record size in memory78.1 B

Variable types

Text4
Categorical2
Numeric3

Dataset

Description경상북도 정보화마을의 시군명, 조성차수, 정보화마을 명, 주소, 특산물, 마을수, 주민수, 마을유형 현황입니다 조성차수는 1차년도 2001년 부터 시작됩니다.
Author경상북도
URLhttps://www.data.go.kr/data/15062894/fileData.do

Alerts

현황(가구수) is highly overall correlated with 현황(주민수)High correlation
현황(주민수) is highly overall correlated with 현황(가구수)High correlation
정보화 마을명 has unique valuesUnique
주소 has unique valuesUnique

Reproduction

Analysis started2023-12-12 05:29:31.998735
Analysis finished2023-12-12 05:29:33.641319
Duration1.64 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군
Text

Distinct22
Distinct (%)51.2%
Missing0
Missing (%)0.0%
Memory size476.0 B
2023-12-12T14:29:33.789307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters129
Distinct characters33
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

Unique9 ?
Unique (%)20.9%

Sample

1st row포항시
2nd row포항시
3rd row포항시
4th row포항시
5th row경주시
ValueCountFrequency (%)
포항시 4
 
9.3%
의성군 4
 
9.3%
안동시 3
 
7.0%
상주시 3
 
7.0%
문경시 3
 
7.0%
예천군 3
 
7.0%
영덕군 2
 
4.7%
김천시 2
 
4.7%
영주시 2
 
4.7%
구미시 2
 
4.7%
Other values (12) 15
34.9%
2023-12-12T14:29:34.096702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 22
17.1%
군 21
16.3%
영 7
 
5.4%
주 7
 
5.4%
천 6
 
4.7%
경 5
 
3.9%
성 5
 
3.9%
포 4
 
3.1%
의 4
 
3.1%
항 4
 
3.1%
Other values (23) 44
34.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 129
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 22
17.1%
군 21
16.3%
영 7
 
5.4%
주 7
 
5.4%
천 6
 
4.7%
경 5
 
3.9%
성 5
 
3.9%
포 4
 
3.1%
의 4
 
3.1%
항 4
 
3.1%
Other values (23) 44
34.1%

Most occurring scripts

ValueCountFrequency (%)
Hangul 129
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 22
17.1%
군 21
16.3%
영 7
 
5.4%
주 7
 
5.4%
천 6
 
4.7%
경 5
 
3.9%
성 5
 
3.9%
포 4
 
3.1%
의 4
 
3.1%
항 4
 
3.1%
Other values (23) 44
34.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 129
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 22
17.1%
군 21
16.3%
영 7
 
5.4%
주 7
 
5.4%
천 6
 
4.7%
경 5
 
3.9%
성 5
 
3.9%
포 4
 
3.1%
의 4
 
3.1%
항 4
 
3.1%
Other values (23) 44
34.1%

조성(차수)
Categorical

Distinct13
Distinct (%)30.2%
Missing0
Missing (%)0.0%
Memory size476.0 B
2차
13 
4차
7 
3차
6 
1차
4 
4차
3 
Other values (8)
10 

Length

Max length3
Median length2
Mean length2.1860465
Min length2

Unique

Unique6 ?
Unique (%)14.0%

Sample

1st row2차
2nd row6차
3rd row4차
4th row1차
5th row2차

Common Values

ValueCountFrequency (%)
2차 13
30.2%
4차 7
16.3%
3차 6
14.0%
1차 4
 
9.3%
4차 3
 
7.0%
6차 2
 
4.7%
3차 2
 
4.7%
2차 1
 
2.3%
5차 1
 
2.3%
7차 1
 
2.3%
Other values (3) 3
 
7.0%

Length

2023-12-12T14:29:34.236498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2차 14
32.6%
4차 10
23.3%
3차 8
18.6%
1차 4
 
9.3%
6차 2
 
4.7%
5차 1
 
2.3%
7차 1
 
2.3%
10차 1
 
2.3%
9차 1
 
2.3%
11차 1
 
2.3%

정보화 마을명
Text

UNIQUE 

Distinct43
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size476.0 B
2023-12-12T14:29:34.463902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length9
Mean length6.7674419
Min length4

Characters and Unicode

Total characters291
Distinct characters119
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

Unique43 ?
Unique (%)100.0%

Sample

1st row기계장터마을
2nd row상옥참느리마을
3rd row신광비학산학마을
4th row호미곶마을
5th row양동민속마을
ValueCountFrequency (%)
기계장터마을 1
 
2.3%
모흥황토마을 1
 
2.3%
토종마늘마을 1
 
2.3%
청학마을 1
 
2.3%
주왕산사과마을 1
 
2.3%
선바위마을 1
 
2.3%
수비산촌마을 1
 
2.3%
정보화영덕대게보존마을 1
 
2.3%
영덕복숭아마을 1
 
2.3%
신도새마을발상지마을 1
 
2.3%
Other values (33) 33
76.7%
2023-12-12T14:29:34.822209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
마 46
 
15.8%
을 44
 
15.1%
산 11
 
3.8%
포 6
 
2.1%
동 5
 
1.7%
도 5
 
1.7%
금 4
 
1.4%
자 4
 
1.4%
토 4
 
1.4%
안 4
 
1.4%
Other values (109) 158
54.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 291
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
마 46
 
15.8%
을 44
 
15.1%
산 11
 
3.8%
포 6
 
2.1%
동 5
 
1.7%
도 5
 
1.7%
금 4
 
1.4%
자 4
 
1.4%
토 4
 
1.4%
안 4
 
1.4%
Other values (109) 158
54.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 291
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
마 46
 
15.8%
을 44
 
15.1%
산 11
 
3.8%
포 6
 
2.1%
동 5
 
1.7%
도 5
 
1.7%
금 4
 
1.4%
자 4
 
1.4%
토 4
 
1.4%
안 4
 
1.4%
Other values (109) 158
54.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 291
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
마 46
 
15.8%
을 44
 
15.1%
산 11
 
3.8%
포 6
 
2.1%
동 5
 
1.7%
도 5
 
1.7%
금 4
 
1.4%
자 4
 
1.4%
토 4
 
1.4%
안 4
 
1.4%
Other values (109) 158
54.3%

주소
Text

UNIQUE 

Distinct43
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size476.0 B
2023-12-12T14:29:35.103354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length33
Median length27
Mean length24.139535
Min length18

Characters and Unicode

Total characters1038
Distinct characters151
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

Unique43 ?
Unique (%)100.0%

Sample

1st row경상북도 포항시 북구 기계면 기계로 94
2nd row경상북도 포항시 북구 죽장면 죽장로 1837
3rd row경상북도 포항시 북구 신광면 죽성길 128
4th row경상북도 포항시 남구 호미곶면 해맞이로 153
5th row경상북도 경주시 강동면 양동마을길93
ValueCountFrequency (%)
경상북도 43
 
18.2%
포항시 4
 
1.7%
의성군 4
 
1.7%
2층 3
 
1.3%
상주시 3
 
1.3%
예천군 3
 
1.3%
문경시 3
 
1.3%
북구 3
 
1.3%
안동시 3
 
1.3%
영양군 2
 
0.8%
Other values (156) 165
69.9%
2023-12-12T14:29:35.527495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
200
19.3%
경 51
 
4.9%
도 48
 
4.6%
상 46
 
4.4%
북 46
 
4.4%
면 38
 
3.7%
1 31
 
3.0%
길 31
 
3.0%
2 26
 
2.5%
시 23
 
2.2%
Other values (141) 498
48.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 685
66.0%
Space Separator 200
 
19.3%
Decimal Number 139
 
13.4%
Dash Punctuation 13
 
1.3%
Other Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
경 51
 
7.4%
도 48
 
7.0%
상 46
 
6.7%
북 46
 
6.7%
면 38
 
5.5%
길 31
 
4.5%
시 23
 
3.4%
군 21
 
3.1%
로 18
 
2.6%
천 12
 
1.8%
Other values (128) 351
51.2%
Decimal Number
ValueCountFrequency (%)
1 31
22.3%
2 26
18.7%
7 13
9.4%
3 12
 
8.6%
0 12
 
8.6%
4 11
 
7.9%
5 11
 
7.9%
6 8
 
5.8%
8 8
 
5.8%
9 7
 
5.0%
Space Separator
ValueCountFrequency (%)
200
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 13
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 685
66.0%
Common 353
34.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
경 51
 
7.4%
도 48
 
7.0%
상 46
 
6.7%
북 46
 
6.7%
면 38
 
5.5%
길 31
 
4.5%
시 23
 
3.4%
군 21
 
3.1%
로 18
 
2.6%
천 12
 
1.8%
Other values (128) 351
51.2%
Common
ValueCountFrequency (%)
200
56.7%
1 31
 
8.8%
2 26
 
7.4%
7 13
 
3.7%
- 13
 
3.7%
3 12
 
3.4%
0 12
 
3.4%
4 11
 
3.1%
5 11
 
3.1%
6 8
 
2.3%
Other values (3) 16
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 685
66.0%
ASCII 353
34.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
200
56.7%
1 31
 
8.8%
2 26
 
7.4%
7 13
 
3.7%
- 13
 
3.7%
3 12
 
3.4%
0 12
 
3.4%
4 11
 
3.1%
5 11
 
3.1%
6 8
 
2.3%
Other values (3) 16
 
4.5%
Hangul
ValueCountFrequency (%)
경 51
 
7.4%
도 48
 
7.0%
상 46
 
6.7%
북 46
 
6.7%
면 38
 
5.5%
길 31
 
4.5%
시 23
 
3.4%
군 21
 
3.1%
로 18
 
2.6%
천 12
 
1.8%
Other values (128) 351
51.2%

특산물
Text

Distinct41
Distinct (%)95.3%
Missing0
Missing (%)0.0%
Memory size476.0 B
2023-12-12T14:29:35.785331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length10
Mean length7.5116279
Min length2

Characters and Unicode

Total characters323
Distinct characters80
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

Unique40 ?
Unique (%)93.0%

Sample

1st row한우, 사과, 배
2nd row사과, 토마토
3rd row호박고구마
4th row과메기, 전복
5th row청주, 엿, 약과
ValueCountFrequency (%)
사과 14
 
14.7%
포도 5
 
5.3%
곶감 4
 
4.2%
고추 4
 
4.2%
마늘 4
 
4.2%
오미자 3
 
3.2%
쌀 2
 
2.1%
단호박 2
 
2.1%
대게 2
 
2.1%
토마토 2
 
2.1%
Other values (47) 53
55.8%
2023-12-12T14:29:36.097035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 53
 
16.4%
53
 
16.4%
과 16
 
5.0%
사 14
 
4.3%
오 8
 
2.5%
마 8
 
2.5%
고 7
 
2.2%
추 7
 
2.2%
자 6
 
1.9%
포 6
 
1.9%
Other values (70) 145
44.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 217
67.2%
Other Punctuation 53
 
16.4%
Space Separator 53
 
16.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
과 16
 
7.4%
사 14
 
6.5%
오 8
 
3.7%
마 8
 
3.7%
고 7
 
3.2%
추 7
 
3.2%
자 6
 
2.8%
포 6
 
2.8%
쌀 6
 
2.8%
토 6
 
2.8%
Other values (68) 133
61.3%
Other Punctuation
ValueCountFrequency (%)
, 53
100.0%
Space Separator
ValueCountFrequency (%)
53
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 217
67.2%
Common 106
32.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
과 16
 
7.4%
사 14
 
6.5%
오 8
 
3.7%
마 8
 
3.7%
고 7
 
3.2%
추 7
 
3.2%
자 6
 
2.8%
포 6
 
2.8%
쌀 6
 
2.8%
토 6
 
2.8%
Other values (68) 133
61.3%
Common
ValueCountFrequency (%)
, 53
50.0%
53
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 217
67.2%
ASCII 106
32.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 53
50.0%
53
50.0%
Hangul
ValueCountFrequency (%)
과 16
 
7.4%
사 14
 
6.5%
오 8
 
3.7%
마 8
 
3.7%
고 7
 
3.2%
추 7
 
3.2%
자 6
 
2.8%
포 6
 
2.8%
쌀 6
 
2.8%
토 6
 
2.8%
Other values (68) 133
61.3%

현황(마을수)
Real number (ℝ)

Distinct7
Distinct (%)16.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.372093
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-12T14:29:36.210423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q32
95-th percentile6
Maximum12
Range11
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.1824326
Coefficient of variation (CV)0.92004513
Kurtosis8.4075158
Mean2.372093
Median Absolute Deviation (MAD)1
Skewness2.6243701
Sum102
Variance4.7630122
MonotonicityNot monotonic
2023-12-12T14:29:36.306433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 19
44.2%
2 14
32.6%
3 3
 
7.0%
5 3
 
7.0%
6 2
 
4.7%
12 1
 
2.3%
7 1
 
2.3%
ValueCountFrequency (%)
1 19
44.2%
2 14
32.6%
3 3
 
7.0%
5 3
 
7.0%
6 2
 
4.7%
7 1
 
2.3%
12 1
 
2.3%
ValueCountFrequency (%)
12 1
 
2.3%
7 1
 
2.3%
6 2
 
4.7%
5 3
 
7.0%
3 3
 
7.0%
2 14
32.6%
1 19
44.2%

현황(가구수)
Real number (ℝ)

HIGH CORRELATION 

Distinct42
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean247.76744
Minimum24
Maximum1118
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-12T14:29:36.413584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum24
5-th percentile40.5
Q187.5
median180
Q3328.5
95-th percentile657.9
Maximum1118
Range1094
Interquartile range (IQR)241

Descriptive statistics

Standard deviation238.38393
Coefficient of variation (CV)0.96212774
Kurtosis4.9840013
Mean247.76744
Median Absolute Deviation (MAD)113
Skewness2.0654807
Sum10654
Variance56826.897
MonotonicityNot monotonic
2023-12-12T14:29:36.534774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
45 2
 
4.7%
262 1
 
2.3%
170 1
 
2.3%
94 1
 
2.3%
1118 1
 
2.3%
48 1
 
2.3%
125 1
 
2.3%
356 1
 
2.3%
235 1
 
2.3%
120 1
 
2.3%
Other values (32) 32
74.4%
ValueCountFrequency (%)
24 1
2.3%
30 1
2.3%
40 1
2.3%
45 2
4.7%
48 1
2.3%
51 1
2.3%
52 1
2.3%
70 1
2.3%
76 1
2.3%
85 1
2.3%
ValueCountFrequency (%)
1118 1
2.3%
1000 1
2.3%
663 1
2.3%
612 1
2.3%
463 1
2.3%
402 1
2.3%
396 1
2.3%
383 1
2.3%
373 1
2.3%
356 1
2.3%

현황(주민수)
Real number (ℝ)

HIGH CORRELATION 

Distinct42
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean489.11628
Minimum37
Maximum2000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-12T14:29:36.653364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37
5-th percentile66.6
Q1182
median345
Q3562.5
95-th percentile1392.3
Maximum2000
Range1963
Interquartile range (IQR)380.5

Descriptive statistics

Standard deviation458.73642
Coefficient of variation (CV)0.93788827
Kurtosis3.9916206
Mean489.11628
Median Absolute Deviation (MAD)196
Skewness1.9300966
Sum21032
Variance210439.11
MonotonicityNot monotonic
2023-12-12T14:29:36.785022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
185 2
 
4.7%
555 1
 
2.3%
88 1
 
2.3%
178 1
 
2.3%
1978 1
 
2.3%
66 1
 
2.3%
254 1
 
2.3%
538 1
 
2.3%
425 1
 
2.3%
332 1
 
2.3%
Other values (32) 32
74.4%
ValueCountFrequency (%)
37 1
2.3%
60 1
2.3%
66 1
2.3%
72 1
2.3%
88 1
2.3%
102 1
2.3%
113 1
2.3%
116 1
2.3%
140 1
2.3%
178 1
2.3%
ValueCountFrequency (%)
2000 1
2.3%
1978 1
2.3%
1401 1
2.3%
1314 1
2.3%
899 1
2.3%
797 1
2.3%
793 1
2.3%
791 1
2.3%
746 1
2.3%
728 1
2.3%

유형
Categorical

Distinct7
Distinct (%)16.3%
Missing0
Missing (%)0.0%
Memory size476.0 B
전자상거래
18 
체험, 전자상거래
8 
체험관광
5 
상거래/체험관광
4 
체험관광 / 전자상거래
4 
Other values (2)
4 

Length

Max length12
Median length9
Mean length6.372093
Min length3

Unique

Unique1 ?
Unique (%)2.3%

Sample

1st row상거래/체험관광
2nd row상거래/체험관광
3rd row상거래/체험관광
4th row상거래/체험관광
5th row상거래

Common Values

ValueCountFrequency (%)
전자상거래 18
41.9%
체험, 전자상거래 8
18.6%
체험관광 5
 
11.6%
상거래/체험관광 4
 
9.3%
체험관광 / 전자상거래 4
 
9.3%
복합형 3
 
7.0%
상거래 1
 
2.3%

Length

2023-12-12T14:29:36.911193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:29:37.018459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전자상거래 30
50.8%
체험관광 9
 
15.3%
체험 8
 
13.6%
상거래/체험관광 4
 
6.8%
4
 
6.8%
복합형 3
 
5.1%
상거래 1
 
1.7%

Interactions

2023-12-12T14:29:33.031521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:32.490318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:32.759891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:33.138357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:32.567344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:32.860035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:33.221674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:32.657743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:32.942955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T14:29:37.339214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군조성(차수)정보화 마을명주소특산물현황(마을수)현황(가구수)현황(주민수)유형
시군1.0000.0001.0001.0000.8760.4770.6690.3730.952
조성(차수)0.0001.0001.0001.0000.9730.0000.6210.7060.000
정보화 마을명1.0001.0001.0001.0001.0001.0001.0001.0001.000
주소1.0001.0001.0001.0001.0001.0001.0001.0001.000
특산물0.8760.9731.0001.0001.0001.0000.9810.9770.973
현황(마을수)0.4770.0001.0001.0001.0001.0000.6270.3790.000
현황(가구수)0.6690.6211.0001.0000.9810.6271.0000.9480.000
현황(주민수)0.3730.7061.0001.0000.9770.3790.9481.0000.000
유형0.9520.0001.0001.0000.9730.0000.0000.0001.000
2023-12-12T14:29:37.439428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
유형조성(차수)
유형1.0000.000
조성(차수)0.0001.000
2023-12-12T14:29:37.516435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
현황(마을수)현황(가구수)현황(주민수)조성(차수)유형
현황(마을수)1.0000.1820.1030.0000.000
현황(가구수)0.1821.0000.9720.3040.000
현황(주민수)0.1030.9721.0000.3800.000
조성(차수)0.0000.3040.3801.0000.000
유형0.0000.0000.0000.0001.000

Missing values

2023-12-12T14:29:33.411963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T14:29:33.589972image/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

시군조성(차수)정보화 마을명주소특산물현황(마을수)현황(가구수)현황(주민수)유형
0포항시2차기계장터마을경상북도 포항시 북구 기계면 기계로 94한우, 사과, 배1262555상거래/체험관광
1포항시6차상옥참느리마을경상북도 포항시 북구 죽장면 죽장로 1837사과, 토마토210002000상거래/체험관광
2포항시4차신광비학산학마을경상북도 포항시 북구 신광면 죽성길 128호박고구마190184상거래/체험관광
3포항시1차호미곶마을경상북도 포항시 남구 호미곶면 해맞이로 153과메기, 전복36121314상거래/체험관광
4경주시2차양동민속마을경상북도 경주시 강동면 양동마을길93청주, 엿, 약과2145274상거래
5김천시2차양각자두마을경상북도 김천시 구성면 양각길 202자두, 배, 포도22437전자상거래
6김천시4차황악산반곡포도마을경상북도 김천시 대항면 대룡1길 48-16포도1137325전자상거래
7안동시4차녹전토종마을경상북도 안동시 녹전면 원매길 102-5단호박, 메주, 마385180전자상거래
8안동시3차안동포마을경상북도 안동시 임하면 금소3길 3안동포2202350전자상거래
9안동시1차하회마을경상북도 안동시 풍천면 장수길70딸기, 참외, 곶감1293541체험관광
시군조성(차수)정보화 마을명주소특산물현황(마을수)현황(가구수)현황(주민수)유형
33성주군3차가야산녹색체험마을경상북도 성주군 수륜면 백운1길 40 마을정보센터사과, 토종꿀2383793체험관광
34칠곡군3차금남오이꽃동산마을경상북도 칠곡군 왜관읍 금남7길 35-23 금남마을회관경로당오이, 토마토1342797복합형
35칠곡군2차동안꿀벌참외마을경상북도 칠곡군 약목면 동안2길 20참외, 벌꿀1256534전자상거래
36예천군11차국사골마을경상북도 예천군 유천면 사곡길 70-2쌀, 찰수수, 건고추14072체험관광 / 전자상거래
37예천군2차금당실마을경상북도 예천군 용문면 금당실길 54 용문면사무소꿀, 마늘, 속청1373728체험관광 / 전자상거래
38예천군3차회룡포마을경상북도 예천군 용궁면 향석길 60 향석1리회관쌀, 호박, 버섯2315570체험관광 / 전자상거래
39봉화군3차청량산비나리마을경상북도 봉화군 명호면 비나리길 156고추, 대추, 사과5402746체험관광
40봉화군2차춘양목송이마을경상북도 봉화군 춘양면 춘양로 1476 춘양목송이마을정보센터송이, 사과, 호두6396791전자상거래
41울진군2차백암온천마을경상북도 울진군 온정면 온정1길 46 백암온천마을정보센터대게, 송이버섯2132259체험관광 / 전자상거래
42울릉군2차황토구미마을경상북도 울릉군 서면 태하2길 8 태하권역 다목적회관 2층오징어, 산나물1299486전자상거래