Overview

Dataset statistics

Number of variables9
Number of observations257
Missing cells257
Missing cells (%)11.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory19.0 KiB
Average record size in memory75.5 B

Variable types

Numeric1
Categorical5
Text2
Unsupported1

Alerts

자료출처 has constant value ""Constant
공개여부 has constant value ""Constant
작성일 has constant value ""Constant
갱신주기 has constant value ""Constant
순번 is highly overall correlated with 시군명High correlation
시군명 is highly overall correlated with 순번High correlation
전화번호 has 257 (100.0%) missing valuesMissing
순번 has unique valuesUnique
도로명주소 has unique valuesUnique
전화번호 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-03-14 01:00:50.983445
Analysis finished2024-03-14 01:00:51.440938
Duration0.46 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct257
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean129
Minimum1
Maximum257
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2024-03-14T10:00:51.782583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile13.8
Q165
median129
Q3193
95-th percentile244.2
Maximum257
Range256
Interquartile range (IQR)128

Descriptive statistics

Standard deviation74.333707
Coefficient of variation (CV)0.57623029
Kurtosis-1.2
Mean129
Median Absolute Deviation (MAD)64
Skewness0
Sum33153
Variance5525.5
MonotonicityStrictly increasing
2024-03-14T10:00:51.930613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.4%
194 1
 
0.4%
164 1
 
0.4%
165 1
 
0.4%
166 1
 
0.4%
167 1
 
0.4%
168 1
 
0.4%
169 1
 
0.4%
170 1
 
0.4%
171 1
 
0.4%
Other values (247) 247
96.1%
ValueCountFrequency (%)
1 1
0.4%
2 1
0.4%
3 1
0.4%
4 1
0.4%
5 1
0.4%
6 1
0.4%
7 1
0.4%
8 1
0.4%
9 1
0.4%
10 1
0.4%
ValueCountFrequency (%)
257 1
0.4%
256 1
0.4%
255 1
0.4%
254 1
0.4%
253 1
0.4%
252 1
0.4%
251 1
0.4%
250 1
0.4%
249 1
0.4%
248 1
0.4%

시군명
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
전주시
36 
익산시
30 
군산시
28 
정읍시
24 
남원시
24 
Other values (9)
115 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
전주시 36
14.0%
익산시 30
11.7%
군산시 28
10.9%
정읍시 24
9.3%
남원시 24
9.3%
김제시 20
7.8%
고창군 15
 
5.8%
완주군 14
 
5.4%
부안군 14
 
5.4%
임실군 13
 
5.1%
Other values (4) 39
15.2%

Length

2024-03-14T10:00:52.062938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
전주시 36
14.0%
익산시 30
11.7%
군산시 28
10.9%
정읍시 24
9.3%
남원시 24
9.3%
김제시 20
7.8%
고창군 15
 
5.8%
완주군 14
 
5.4%
부안군 14
 
5.4%
임실군 13
 
5.1%
Other values (4) 39
15.2%

건물명칭
Text

Distinct247
Distinct (%)96.1%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
2024-03-14T10:00:52.274204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length6
Mean length6.3385214
Min length4

Characters and Unicode

Total characters1629
Distinct characters162
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

Unique238 ?
Unique (%)92.6%

Sample

1st row전주시청
2nd row완산구청
3rd row중앙동주민센터
4th row풍남동주민센터
5th row노송동주민센터
ValueCountFrequency (%)
중앙동주민센터 3
 
1.2%
산내면사무소 2
 
0.8%
동주민센터 2
 
0.8%
성수면사무소 2
 
0.8%
조촌동주민센터 2
 
0.8%
백산면사무소 2
 
0.8%
신풍동주민센터 2
 
0.8%
주천면사무소 2
 
0.8%
동산동주민센터 2
 
0.8%
대산면사무소 2
 
0.8%
Other values (238) 238
91.9%
2024-03-14T10:00:52.636658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
소 164
 
10.1%
무 164
 
10.1%
사 162
 
9.9%
면 148
 
9.1%
동 95
 
5.8%
주 91
 
5.6%
민 83
 
5.1%
센 83
 
5.1%
터 83
 
5.1%
산 33
 
2.0%
Other values (152) 523
32.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1600
98.2%
Decimal Number 25
 
1.5%
Space Separator 4
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
소 164
 
10.2%
무 164
 
10.2%
사 162
 
10.1%
면 148
 
9.2%
동 95
 
5.9%
주 91
 
5.7%
민 83
 
5.2%
센 83
 
5.2%
터 83
 
5.2%
산 33
 
2.1%
Other values (147) 494
30.9%
Decimal Number
ValueCountFrequency (%)
2 10
40.0%
1 10
40.0%
3 4
 
16.0%
4 1
 
4.0%
Space Separator
ValueCountFrequency (%)
4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1600
98.2%
Common 29
 
1.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
소 164
 
10.2%
무 164
 
10.2%
사 162
 
10.1%
면 148
 
9.2%
동 95
 
5.9%
주 91
 
5.7%
민 83
 
5.2%
센 83
 
5.2%
터 83
 
5.2%
산 33
 
2.1%
Other values (147) 494
30.9%
Common
ValueCountFrequency (%)
2 10
34.5%
1 10
34.5%
4
 
13.8%
3 4
 
13.8%
4 1
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1600
98.2%
ASCII 29
 
1.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
소 164
 
10.2%
무 164
 
10.2%
사 162
 
10.1%
면 148
 
9.2%
동 95
 
5.9%
주 91
 
5.7%
민 83
 
5.2%
센 83
 
5.2%
터 83
 
5.2%
산 33
 
2.1%
Other values (147) 494
30.9%
ASCII
ValueCountFrequency (%)
2 10
34.5%
1 10
34.5%
4
 
13.8%
3 4
 
13.8%
4 1
 
3.4%

전화번호
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing257
Missing (%)100.0%
Memory size2.4 KiB

도로명주소
Text

UNIQUE 

Distinct257
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
2024-03-14T10:00:52.981351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length36
Median length20
Mean length14.692607
Min length10

Characters and Unicode

Total characters3776
Distinct characters219
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

Unique257 ?
Unique (%)100.0%

Sample

1st row전주시 완산구 노송광장로 10
2nd row전주시 완산구 서원로 232
3rd row전주시 완산구 태평3길 43-17
4th row전주시 완산구 어진길 122-12
5th row전주시 완산구 인봉1길 54
ValueCountFrequency (%)
전주시 36
 
3.7%
익산시 30
 
3.1%
군산시 28
 
2.9%
남원시 24
 
2.5%
정읍시 24
 
2.5%
완산구 20
 
2.0%
김제시 20
 
2.0%
덕진구 16
 
1.6%
고창군 15
 
1.5%
완주군 14
 
1.4%
Other values (587) 752
76.8%
2024-03-14T10:00:53.379718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
722
 
19.1%
로 175
 
4.6%
1 169
 
4.5%
시 165
 
4.4%
면 145
 
3.8%
군 123
 
3.3%
산 119
 
3.2%
2 106
 
2.8%
3 92
 
2.4%
길 91
 
2.4%
Other values (209) 1869
49.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2265
60.0%
Decimal Number 753
 
19.9%
Space Separator 722
 
19.1%
Dash Punctuation 36
 
1.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 175
 
7.7%
시 165
 
7.3%
면 145
 
6.4%
군 123
 
5.4%
산 119
 
5.3%
길 91
 
4.0%
주 72
 
3.2%
구 50
 
2.2%
읍 48
 
2.1%
진 47
 
2.1%
Other values (197) 1230
54.3%
Decimal Number
ValueCountFrequency (%)
1 169
22.4%
2 106
14.1%
3 92
12.2%
4 67
 
8.9%
5 65
 
8.6%
8 55
 
7.3%
6 53
 
7.0%
7 52
 
6.9%
0 47
 
6.2%
9 47
 
6.2%
Space Separator
ValueCountFrequency (%)
722
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 36
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2265
60.0%
Common 1511
40.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 175
 
7.7%
시 165
 
7.3%
면 145
 
6.4%
군 123
 
5.4%
산 119
 
5.3%
길 91
 
4.0%
주 72
 
3.2%
구 50
 
2.2%
읍 48
 
2.1%
진 47
 
2.1%
Other values (197) 1230
54.3%
Common
ValueCountFrequency (%)
722
47.8%
1 169
 
11.2%
2 106
 
7.0%
3 92
 
6.1%
4 67
 
4.4%
5 65
 
4.3%
8 55
 
3.6%
6 53
 
3.5%
7 52
 
3.4%
0 47
 
3.1%
Other values (2) 83
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2265
60.0%
ASCII 1511
40.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
722
47.8%
1 169
 
11.2%
2 106
 
7.0%
3 92
 
6.1%
4 67
 
4.4%
5 65
 
4.3%
8 55
 
3.6%
6 53
 
3.5%
7 52
 
3.4%
0 47
 
3.1%
Other values (2) 83
 
5.5%
Hangul
ValueCountFrequency (%)
로 175
 
7.7%
시 165
 
7.3%
면 145
 
6.4%
군 123
 
5.4%
산 119
 
5.3%
길 91
 
4.0%
주 72
 
3.2%
구 50
 
2.2%
읍 48
 
2.1%
진 47
 
2.1%
Other values (197) 1230
54.3%

자료출처
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
전라북도 자치행정과
257 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row전라북도 자치행정과
2nd row전라북도 자치행정과
3rd row전라북도 자치행정과
4th row전라북도 자치행정과
5th row전라북도 자치행정과

Common Values

ValueCountFrequency (%)
전라북도 자치행정과 257
100.0%

Length

2024-03-14T10:00:53.481872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T10:00:53.552798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전라북도 257
50.0%
자치행정과 257
50.0%

공개여부
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
공개
257 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
공개 257
100.0%

Length

2024-03-14T10:00:53.632082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T10:00:53.700513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
공개 257
100.0%

작성일
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
2016.12
257 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2016.12
2nd row2016.12
3rd row2016.12
4th row2016.12
5th row2016.12

Common Values

ValueCountFrequency (%)
2016.12 257
100.0%

Length

2024-03-14T10:00:53.778000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T10:00:53.884834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2016.12 257
100.0%

갱신주기
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
1년
257 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1년
2nd row1년
3rd row1년
4th row1년
5th row1년

Common Values

ValueCountFrequency (%)
1년 257
100.0%

Length

2024-03-14T10:00:53.981597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T10:00:54.059051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1년 257
100.0%

Interactions

2024-03-14T10:00:51.215408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T10:00:54.105108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번시군명
순번1.0000.942
시군명0.9421.000
2024-03-14T10:00:54.192115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번시군명
순번1.0000.764
시군명0.7641.000

Missing values

2024-03-14T10:00:51.303420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T10:00:51.401679image/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

순번시군명건물명칭전화번호도로명주소자료출처공개여부작성일갱신주기
01전주시전주시청<NA>전주시 완산구 노송광장로 10전라북도 자치행정과공개2016.121년
12전주시완산구청<NA>전주시 완산구 서원로 232전라북도 자치행정과공개2016.121년
23전주시중앙동주민센터<NA>전주시 완산구 태평3길 43-17전라북도 자치행정과공개2016.121년
34전주시풍남동주민센터<NA>전주시 완산구 어진길 122-12전라북도 자치행정과공개2016.121년
45전주시노송동주민센터<NA>전주시 완산구 인봉1길 54전라북도 자치행정과공개2016.121년
56전주시완산동주민센터<NA>전주시 완산구 강당2길 7전라북도 자치행정과공개2016.121년
67전주시동서학동주민센터<NA>전주시 완산구 서학로 26전라북도 자치행정과공개2016.121년
78전주시서서학동주민센터<NA>전주시 완산구 공수내로 24전라북도 자치행정과공개2016.121년
89전주시중화산1동주민센터<NA>전주시 완산구 한두평 3길 16전라북도 자치행정과공개2016.121년
910전주시중화산2동주민센터<NA>전주시 완산구 화산천변로 30전라북도 자치행정과공개2016.121년
순번시군명건물명칭전화번호도로명주소자료출처공개여부작성일갱신주기
247248부안군행안면사무소<NA>부안군 행안면 번영로 33전라북도 자치행정과공개2016.121년
248249부안군계화면사무소<NA>부안군 계화면 간재로 405전라북도 자치행정과공개2016.121년
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