Overview

Dataset statistics

Number of variables9
Number of observations196
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory14.7 KiB
Average record size in memory76.7 B

Variable types

Numeric4
Text3
Categorical2

Dataset

Description인천광역시 중구 공유재산(건물) 현황을 공개합니다.공개 내용 : 재산명, 소재지, 지번, 도로명주소, 연면적, 데이터 기준일자
Author인천광역시 중구
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15112908&srcSe=7661IVAWM27C61E190

Alerts

데이터 기준일자 has constant value ""Constant
순번 is highly overall correlated with 번지High correlation
번지 is highly overall correlated with 순번High correlation
산코드 is highly imbalanced (80.3%)Imbalance
순번 has unique valuesUnique
호 has 29 (14.8%) zerosZeros

Reproduction

Analysis started2024-04-17 09:09:52.087368
Analysis finished2024-04-17 09:09:53.819473
Duration1.73 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct196
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98.5
Minimum1
Maximum196
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2024-04-17T18:09:53.875241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile10.75
Q149.75
median98.5
Q3147.25
95-th percentile186.25
Maximum196
Range195
Interquartile range (IQR)97.5

Descriptive statistics

Standard deviation56.72448
Coefficient of variation (CV)0.57588305
Kurtosis-1.2
Mean98.5
Median Absolute Deviation (MAD)49
Skewness0
Sum19306
Variance3217.6667
MonotonicityStrictly increasing
2024-04-17T18:09:53.988777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.5%
125 1
 
0.5%
127 1
 
0.5%
128 1
 
0.5%
129 1
 
0.5%
130 1
 
0.5%
131 1
 
0.5%
132 1
 
0.5%
133 1
 
0.5%
134 1
 
0.5%
Other values (186) 186
94.9%
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 (%)
196 1
0.5%
195 1
0.5%
194 1
0.5%
193 1
0.5%
192 1
0.5%
191 1
0.5%
190 1
0.5%
189 1
0.5%
188 1
0.5%
187 1
0.5%

재산명
Text

Distinct193
Distinct (%)98.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2024-04-17T18:09:54.163067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length30
Median length20
Mean length10.831633
Min length3

Characters and Unicode

Total characters2123
Distinct characters258
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique190 ?
Unique (%)96.9%

Sample

1st row생활사 전시관
2nd row대불호텔 전시관
3rd row중구청 별관(인진빌딩)
4th row중앙동 경로당
5th row인천개항박물관 기획전시실
ValueCountFrequency (%)
인천광역시 14
 
4.1%
중구 14
 
4.1%
경로당 11
 
3.3%
북성동1가 9
 
2.7%
신포동 5
 
1.5%
송월동 4
 
1.2%
화장실 4
 
1.2%
용유동 4
 
1.2%
영종복합청사 3
 
0.9%
동화마을 3
 
0.9%
Other values (245) 267
79.0%
2024-04-17T18:09:54.699623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
154
 
7.3%
동 76
 
3.6%
구 51
 
2.4%
) 49
 
2.3%
장 47
 
2.2%
관 47
 
2.2%
( 45
 
2.1%
중 40
 
1.9%
터 39
 
1.8%
지 39
 
1.8%
Other values (248) 1536
72.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1763
83.0%
Space Separator 154
 
7.3%
Decimal Number 97
 
4.6%
Close Punctuation 49
 
2.3%
Open Punctuation 45
 
2.1%
Dash Punctuation 13
 
0.6%
Other Punctuation 1
 
< 0.1%
Uppercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 76
 
4.3%
구 51
 
2.9%
장 47
 
2.7%
관 47
 
2.7%
중 40
 
2.3%
터 39
 
2.2%
지 39
 
2.2%
시 39
 
2.2%
경 39
 
2.2%
당 35
 
2.0%
Other values (232) 1311
74.4%
Decimal Number
ValueCountFrequency (%)
1 29
29.9%
4 16
16.5%
0 11
 
11.3%
2 11
 
11.3%
5 9
 
9.3%
3 7
 
7.2%
6 6
 
6.2%
9 4
 
4.1%
7 3
 
3.1%
8 1
 
1.0%
Space Separator
ValueCountFrequency (%)
154
100.0%
Close Punctuation
ValueCountFrequency (%)
) 49
100.0%
Open Punctuation
ValueCountFrequency (%)
( 45
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 13
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%
Uppercase Letter
ValueCountFrequency (%)
A 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1763
83.0%
Common 359
 
16.9%
Latin 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 76
 
4.3%
구 51
 
2.9%
장 47
 
2.7%
관 47
 
2.7%
중 40
 
2.3%
터 39
 
2.2%
지 39
 
2.2%
시 39
 
2.2%
경 39
 
2.2%
당 35
 
2.0%
Other values (232) 1311
74.4%
Common
ValueCountFrequency (%)
154
42.9%
) 49
 
13.6%
( 45
 
12.5%
1 29
 
8.1%
4 16
 
4.5%
- 13
 
3.6%
0 11
 
3.1%
2 11
 
3.1%
5 9
 
2.5%
3 7
 
1.9%
Other values (5) 15
 
4.2%
Latin
ValueCountFrequency (%)
A 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1763
83.0%
ASCII 360
 
17.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
154
42.8%
) 49
 
13.6%
( 45
 
12.5%
1 29
 
8.1%
4 16
 
4.4%
- 13
 
3.6%
0 11
 
3.1%
2 11
 
3.1%
5 9
 
2.5%
3 7
 
1.9%
Other values (6) 16
 
4.4%
Hangul
ValueCountFrequency (%)
동 76
 
4.3%
구 51
 
2.9%
장 47
 
2.7%
관 47
 
2.7%
중 40
 
2.3%
터 39
 
2.2%
지 39
 
2.2%
시 39
 
2.2%
경 39
 
2.2%
당 35
 
2.0%
Other values (232) 1311
74.4%

소재지
Text

Distinct167
Distinct (%)85.2%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2024-04-17T18:09:54.978593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length46
Median length38
Mean length21.086735
Min length15

Characters and Unicode

Total characters4133
Distinct characters115
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

Unique152 ?
Unique (%)77.6%

Sample

1st row인천광역시 중구 중앙동1가 17-1
2nd row인천광역시 중구 중앙동1가 18
3rd row인천광역시 중구 중앙동1가 2
4th row인천광역시 중구 중앙동1가 3-2
5th row인천광역시 중구 중앙동1가 4
ValueCountFrequency (%)
인천광역시 196
22.6%
중구 196
22.6%
북성동1가 16
 
1.8%
운북동 11
 
1.3%
무의동 11
 
1.3%
항동7가 11
 
1.3%
운서동 10
 
1.2%
신포동 9
 
1.0%
전동 9
 
1.0%
운남동 9
 
1.0%
Other values (228) 388
44.8%
2024-04-17T18:09:55.367078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
869
21.0%
1 232
 
5.6%
중 219
 
5.3%
구 204
 
4.9%
동 199
 
4.8%
인 197
 
4.8%
시 197
 
4.8%
천 196
 
4.7%
역 196
 
4.7%
광 196
 
4.7%
Other values (105) 1428
34.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2216
53.6%
Space Separator 869
 
21.0%
Decimal Number 867
 
21.0%
Dash Punctuation 176
 
4.3%
Open Punctuation 2
 
< 0.1%
Close Punctuation 2
 
< 0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
중 219
9.9%
구 204
9.2%
동 199
9.0%
인 197
8.9%
시 197
8.9%
천 196
8.8%
역 196
8.8%
광 196
8.8%
가 83
 
3.7%
북 43
 
1.9%
Other values (90) 486
21.9%
Decimal Number
ValueCountFrequency (%)
1 232
26.8%
2 133
15.3%
3 78
 
9.0%
4 72
 
8.3%
7 72
 
8.3%
5 61
 
7.0%
9 60
 
6.9%
8 56
 
6.5%
6 54
 
6.2%
0 49
 
5.7%
Space Separator
ValueCountFrequency (%)
869
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 176
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2216
53.6%
Common 1917
46.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
중 219
9.9%
구 204
9.2%
동 199
9.0%
인 197
8.9%
시 197
8.9%
천 196
8.8%
역 196
8.8%
광 196
8.8%
가 83
 
3.7%
북 43
 
1.9%
Other values (90) 486
21.9%
Common
ValueCountFrequency (%)
869
45.3%
1 232
 
12.1%
- 176
 
9.2%
2 133
 
6.9%
3 78
 
4.1%
4 72
 
3.8%
7 72
 
3.8%
5 61
 
3.2%
9 60
 
3.1%
8 56
 
2.9%
Other values (5) 108
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2216
53.6%
ASCII 1917
46.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
869
45.3%
1 232
 
12.1%
- 176
 
9.2%
2 133
 
6.9%
3 78
 
4.1%
4 72
 
3.8%
7 72
 
3.8%
5 61
 
3.2%
9 60
 
3.1%
8 56
 
2.9%
Other values (5) 108
 
5.6%
Hangul
ValueCountFrequency (%)
중 219
9.9%
구 204
9.2%
동 199
9.0%
인 197
8.9%
시 197
8.9%
천 196
8.8%
역 196
8.8%
광 196
8.8%
가 83
 
3.7%
북 43
 
1.9%
Other values (90) 486
21.9%

산코드
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
1-일반지번
190 
2-산지번
 
6

Length

Max length6
Median length6
Mean length5.9693878
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1-일반지번 190
96.9%
2-산지번 6
 
3.1%

Length

2024-04-17T18:09:55.480528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T18:09:55.579364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1-일반지번 190
96.9%
2-산지번 6
 
3.1%

번지
Real number (ℝ)

HIGH CORRELATION 

Distinct88
Distinct (%)44.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean423.45408
Minimum1
Maximum2891
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2024-04-17T18:09:55.669223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q19
median51
Q3570
95-th percentile2151.75
Maximum2891
Range2890
Interquartile range (IQR)561

Descriptive statistics

Standard deviation739.40704
Coefficient of variation (CV)1.7461328
Kurtosis3.4719756
Mean423.45408
Median Absolute Deviation (MAD)47
Skewness2.0672646
Sum82997
Variance546722.77
MonotonicityNot monotonic
2024-04-17T18:09:55.785760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 14
 
7.1%
9 10
 
5.1%
2 8
 
4.1%
1 6
 
3.1%
6 6
 
3.1%
11 5
 
2.6%
1875 5
 
2.6%
3 5
 
2.6%
14 5
 
2.6%
12 5
 
2.6%
Other values (78) 127
64.8%
ValueCountFrequency (%)
1 6
3.1%
2 8
4.1%
3 5
 
2.6%
4 14
7.1%
6 6
3.1%
8 1
 
0.5%
9 10
5.1%
10 1
 
0.5%
11 5
 
2.6%
12 5
 
2.6%
ValueCountFrequency (%)
2891 1
 
0.5%
2828 2
 
1.0%
2799 4
2.0%
2792 1
 
0.5%
2746 1
 
0.5%
2736 1
 
0.5%
1957 1
 
0.5%
1878 1
 
0.5%
1875 5
2.6%
1640 1
 
0.5%

호
Real number (ℝ)

ZEROS 

Distinct49
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.545918
Minimum0
Maximum497
Zeros29
Zeros (%)14.8%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2024-04-17T18:09:55.906770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3.5
Q314.5
95-th percentile112.75
Maximum497
Range497
Interquartile range (IQR)13.5

Descriptive statistics

Standard deviation50.927832
Coefficient of variation (CV)2.4787323
Kurtosis41.552833
Mean20.545918
Median Absolute Deviation (MAD)3.5
Skewness5.4992343
Sum4027
Variance2593.644
MonotonicityNot monotonic
2024-04-17T18:09:56.018326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
1 48
24.5%
0 29
14.8%
2 13
 
6.6%
9 9
 
4.6%
5 8
 
4.1%
6 8
 
4.1%
3 8
 
4.1%
20 6
 
3.1%
4 5
 
2.6%
12 4
 
2.0%
Other values (39) 58
29.6%
ValueCountFrequency (%)
0 29
14.8%
1 48
24.5%
2 13
 
6.6%
3 8
 
4.1%
4 5
 
2.6%
5 8
 
4.1%
6 8
 
4.1%
7 3
 
1.5%
8 4
 
2.0%
9 9
 
4.6%
ValueCountFrequency (%)
497 1
0.5%
209 1
0.5%
200 1
0.5%
198 1
0.5%
196 1
0.5%
134 1
0.5%
133 1
0.5%
132 1
0.5%
131 1
0.5%
130 1
0.5%

도로명주소
Text

Distinct150
Distinct (%)76.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2024-04-17T18:09:56.224321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length53
Median length41
Mean length25.959184
Min length1

Characters and Unicode

Total characters5088
Distinct characters185
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique134 ?
Unique (%)68.4%

Sample

1st row인천광역시 중구 신포로23번길 97 (중앙동1가)
2nd row인천광역시 중구 신포로23번길 101 (중앙동1가)
3rd row인천광역시 중구 신포로27번길 83-1 중구청별관 인진빌딩 (중앙동1가)
4th row인천광역시 중구 제물량로232번안길 19 (중앙동1가)
5th row인천광역시 중구 신포로23번길 85 인천개항박물관 기획전시실 (중앙동1가)
ValueCountFrequency (%)
중구 176
 
18.5%
인천광역시 175
 
18.4%
항동7가 10
 
1.0%
신포로27번길 9
 
0.9%
운서동 9
 
0.9%
운북동 9
 
0.9%
무의동 9
 
0.9%
중산동 8
 
0.8%
80 7
 
0.7%
관동1가 7
 
0.7%
Other values (303) 534
56.0%
2024-04-17T18:09:56.539623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
849
 
16.7%
중 206
 
4.0%
1 196
 
3.9%
동 193
 
3.8%
시 188
 
3.7%
구 188
 
3.7%
인 183
 
3.6%
역 178
 
3.5%
( 177
 
3.5%
천 177
 
3.5%
Other values (175) 2553
50.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3020
59.4%
Space Separator 849
 
16.7%
Decimal Number 796
 
15.6%
Open Punctuation 177
 
3.5%
Close Punctuation 177
 
3.5%
Dash Punctuation 69
 
1.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
중 206
 
6.8%
동 193
 
6.4%
시 188
 
6.2%
구 188
 
6.2%
인 183
 
6.1%
역 178
 
5.9%
천 177
 
5.9%
광 176
 
5.8%
로 172
 
5.7%
길 93
 
3.1%
Other values (161) 1266
41.9%
Decimal Number
ValueCountFrequency (%)
1 196
24.6%
2 125
15.7%
3 111
13.9%
7 69
 
8.7%
4 65
 
8.2%
8 60
 
7.5%
0 56
 
7.0%
5 41
 
5.2%
6 37
 
4.6%
9 36
 
4.5%
Space Separator
ValueCountFrequency (%)
849
100.0%
Open Punctuation
ValueCountFrequency (%)
( 177
100.0%
Close Punctuation
ValueCountFrequency (%)
) 177
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 69
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3020
59.4%
Common 2068
40.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
중 206
 
6.8%
동 193
 
6.4%
시 188
 
6.2%
구 188
 
6.2%
인 183
 
6.1%
역 178
 
5.9%
천 177
 
5.9%
광 176
 
5.8%
로 172
 
5.7%
길 93
 
3.1%
Other values (161) 1266
41.9%
Common
ValueCountFrequency (%)
849
41.1%
1 196
 
9.5%
( 177
 
8.6%
) 177
 
8.6%
2 125
 
6.0%
3 111
 
5.4%
7 69
 
3.3%
- 69
 
3.3%
4 65
 
3.1%
8 60
 
2.9%
Other values (4) 170
 
8.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3020
59.4%
ASCII 2068
40.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
849
41.1%
1 196
 
9.5%
( 177
 
8.6%
) 177
 
8.6%
2 125
 
6.0%
3 111
 
5.4%
7 69
 
3.3%
- 69
 
3.3%
4 65
 
3.1%
8 60
 
2.9%
Other values (4) 170
 
8.2%
Hangul
ValueCountFrequency (%)
중 206
 
6.8%
동 193
 
6.4%
시 188
 
6.2%
구 188
 
6.2%
인 183
 
6.1%
역 178
 
5.9%
천 177
 
5.9%
광 176
 
5.8%
로 172
 
5.7%
길 93
 
3.1%
Other values (161) 1266
41.9%

연면적
Real number (ℝ)

Distinct186
Distinct (%)94.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean855.37133
Minimum6.4
Maximum14448.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2024-04-17T18:09:56.656503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6.4
5-th percentile33.39
Q189.0925
median256.99
Q3671.6875
95-th percentile4060.275
Maximum14448.8
Range14442.4
Interquartile range (IQR)582.595

Descriptive statistics

Standard deviation1716.535
Coefficient of variation (CV)2.0067717
Kurtosis23.252923
Mean855.37133
Median Absolute Deviation (MAD)197.49
Skewness4.1667134
Sum167652.78
Variance2946492.3
MonotonicityNot monotonic
2024-04-17T18:09:56.781968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
89.7 3
 
1.5%
737.56 2
 
1.0%
39.6 2
 
1.0%
389.22 2
 
1.0%
118.8 2
 
1.0%
374.0 2
 
1.0%
35.8 2
 
1.0%
66.0 2
 
1.0%
59.5 2
 
1.0%
66.3 1
 
0.5%
Other values (176) 176
89.8%
ValueCountFrequency (%)
6.4 1
0.5%
15.0 1
0.5%
17.08 1
0.5%
18.0 1
0.5%
19.8 1
0.5%
20.62 1
0.5%
20.9 1
0.5%
23.14 1
0.5%
33.16 1
0.5%
33.36 1
0.5%
ValueCountFrequency (%)
14448.8 1
0.5%
7756.78 1
0.5%
7690.14 1
0.5%
6878.55 1
0.5%
6794.5 1
0.5%
5837.62 1
0.5%
5278.84 1
0.5%
4813.2 1
0.5%
4517.74 1
0.5%
4152.9 1
0.5%

데이터 기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-03-23
196 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-03-23
2nd row2023-03-23
3rd row2023-03-23
4th row2023-03-23
5th row2023-03-23

Common Values

ValueCountFrequency (%)
2023-03-23 196
100.0%

Length

2024-04-17T18:09:56.884178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T18:09:56.959486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-03-23 196
100.0%

Interactions

2024-04-17T18:09:53.333385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:09:52.442801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:09:52.731632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:09:53.061511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:09:53.413294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:09:52.517496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:09:52.817160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:09:53.136432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:09:53.494624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:09:52.587077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:09:52.890329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:09:53.203517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:09:53.563392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:09:52.656550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:09:52.969003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:09:53.263295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-17T18:09:57.021429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번산코드번지호연면적
순번1.0000.2280.7510.3390.091
산코드0.2281.0000.0000.0000.000
번지0.7510.0001.0000.0000.134
호0.3390.0000.0001.0000.000
연면적0.0910.0000.1340.0001.000
2024-04-17T18:09:57.114139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번번지호연면적산코드
순번1.0000.6200.037-0.1740.165
번지0.6201.000-0.1040.0810.000
호0.037-0.1041.000-0.1980.000
연면적-0.1740.081-0.1981.0000.000
산코드0.1650.0000.0000.0001.000

Missing values

2024-04-17T18:09:53.676625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-17T18:09:53.778754image/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

순번재산명소재지산코드번지호도로명주소연면적데이터 기준일자
01생활사 전시관인천광역시 중구 중앙동1가 17-11-일반지번171인천광역시 중구 신포로23번길 97 (중앙동1가)761.842023-03-23
12대불호텔 전시관인천광역시 중구 중앙동1가 181-일반지번180인천광역시 중구 신포로23번길 101 (중앙동1가)631.322023-03-23
23중구청 별관(인진빌딩)인천광역시 중구 중앙동1가 21-일반지번20인천광역시 중구 신포로27번길 83-1 중구청별관 인진빌딩 (중앙동1가)1275.672023-03-23
34중앙동 경로당인천광역시 중구 중앙동1가 3-21-일반지번32인천광역시 중구 제물량로232번안길 19 (중앙동1가)389.222023-03-23
45인천개항박물관 기획전시실인천광역시 중구 중앙동1가 41-일반지번40인천광역시 중구 신포로23번길 85 인천개항박물관 기획전시실 (중앙동1가)203.282023-03-23
56개항박물관 유물수장고인천광역시 중구 중앙동1가 9-11-일반지번91인천광역시 중구 제물량로232번안길 17 (중앙동1가)89.262023-03-23
67인천개항박물관인천광역시 중구 중앙동1가 9-21-일반지번92인천광역시 중구 신포로23번길 89 (중앙동1가)412.12023-03-23
78인천근대건축전시관인천광역시 중구 중앙동2가 24-11-일반지번241인천광역시 중구 신포로23번길 77 (중앙동2가)224.82023-03-23
89통계작업장(구.개항장문화지구 사무실)인천광역시 중구 해안동2가 8-12 외2필지1-일반지번812인천광역시 중구 제물량로 216 (해안동2가)130.82023-03-23
910구청사(월디관)인천광역시 중구 관동1가 9-1 중구청1-일반지번91인천광역시 중구 신포로27번길 80 (관동1가 중구청)4152.92023-03-23
순번재산명소재지산코드번지호도로명주소연면적데이터 기준일자
186187대무의도복지회관인천광역시 중구 무의동 209-11-일반지번2091인천광역시 중구 대무의로 310-10 (무의동)133.52023-03-23
187188무의 보건지소인천광역시 중구 무의동 246-11-일반지번2461인천광역시 중구 대무의로 310-11 (무의동)181.372023-03-23
188189무의지원팀 청사(주민자치센터)인천광역시 중구 무의동 246-71-일반지번2467인천광역시 중구 대무의로 310-11 (무의동)258.922023-03-23
189190어촌체험마을 안내소인천광역시 중구 무의동 298-11-일반지번2981인천광역시 중구 대무의로 26-3 (무의동)308.942023-03-23
190191큰무리 경로당인천광역시 중구 무의동 631 외 2필지1-일반지번6310인천광역시 중구 큰무리로7번길 32(무의동)110.042023-03-23
191192섬이야기박물관인천광역시 중구 무의동 849-81-일반지번8498인천광역시 중구 떼무리길 15 섬이야기박물관 (무의동)453.022023-03-23
192193공중화장실 A동인천광역시 중구 무의동 849-91-일반지번8499인천광역시 중구 떼무리길 13-2(무의동)50.02023-03-23
193194소무의도복지회관인천광역시 중구 무의동 8771-일반지번8770인천광역시 중구 소무의로 27 (무의동)79.72023-03-23
194195무의지원팀 관사인천광역시 중구 무의동 산 164-102-산지번16410인천광역시 중구 대무의로 310-11 (무의동)76.02023-03-23
195196드라마 오픈세트장(3개동)인천광역시 중구 무의동 산 1892-산지번1890인천광역시 중구 하나개로 144-3 (무의동)562.492023-03-23