Overview

Dataset statistics

Number of variables9
Number of observations199
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory14.9 KiB
Average record size in memory76.7 B

Variable types

Numeric4
Text3
DateTime2

Dataset

Description인천광역시 부평구 아파트현황(아파트명, 주소, 층수외) 공공데이터 입니다.(1,동아1단지,인천광역시 부평구 부평1동 70-5,인천광역시 부평구 부평문화로37,15,16,2475,1989-06-03,2021-09-28)
Author인천광역시 부평구
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=3078697&srcSe=7661IVAWM27C61E190

Alerts

데이터기준일자 has constant value ""Constant
층수 is highly overall correlated with 세대수High correlation
동수 is highly overall correlated with 세대수High correlation
세대수 is highly overall correlated with 층수 and 1 other fieldsHigh correlation
연번 has unique valuesUnique

Reproduction

Analysis started2024-03-18 03:44:11.876772
Analysis finished2024-03-18 03:44:13.611727
Duration1.73 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct199
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100
Minimum1
Maximum199
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2024-03-18T12:44:13.673604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile10.9
Q150.5
median100
Q3149.5
95-th percentile189.1
Maximum199
Range198
Interquartile range (IQR)99

Descriptive statistics

Standard deviation57.590508
Coefficient of variation (CV)0.57590508
Kurtosis-1.2
Mean100
Median Absolute Deviation (MAD)50
Skewness0
Sum19900
Variance3316.6667
MonotonicityStrictly increasing
2024-03-18T12:44:13.814259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.5%
138 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%
135 1
 
0.5%
Other values (189) 189
95.0%
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 (%)
199 1
0.5%
198 1
0.5%
197 1
0.5%
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%

아파트명
Text

Distinct181
Distinct (%)91.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2024-03-18T12:44:14.052544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length4.361809
Min length2

Characters and Unicode

Total characters868
Distinct characters180
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique168 ?
Unique (%)84.4%

Sample

1st row동아1단지
2nd row동아2단지
3rd row대림
4th row욱일
5th row가나안미도
ValueCountFrequency (%)
한국 5
 
2.4%
백조 3
 
1.5%
동남 3
 
1.5%
현대 2
 
1.0%
삼보 2
 
1.0%
대동 2
 
1.0%
동아 2
 
1.0%
무지개 2
 
1.0%
대진 2
 
1.0%
광명 2
 
1.0%
Other values (176) 180
87.8%
2024-03-18T12:44:14.400872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
산 33
 
3.8%
대 31
 
3.6%
지 28
 
3.2%
차 27
 
3.1%
삼 25
 
2.9%
동 24
 
2.8%
주 21
 
2.4%
단 19
 
2.2%
부 19
 
2.2%
1 18
 
2.1%
Other values (170) 623
71.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 760
87.6%
Decimal Number 63
 
7.3%
Close Punctuation 16
 
1.8%
Open Punctuation 16
 
1.8%
Space Separator 7
 
0.8%
Uppercase Letter 4
 
0.5%
Other Punctuation 2
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 33
 
4.3%
대 31
 
4.1%
지 28
 
3.7%
차 27
 
3.6%
삼 25
 
3.3%
동 24
 
3.2%
주 21
 
2.8%
단 19
 
2.5%
부 19
 
2.5%
한 18
 
2.4%
Other values (156) 515
67.8%
Decimal Number
ValueCountFrequency (%)
1 18
28.6%
2 16
25.4%
3 7
 
11.1%
5 6
 
9.5%
4 5
 
7.9%
6 5
 
7.9%
7 4
 
6.3%
8 2
 
3.2%
Uppercase Letter
ValueCountFrequency (%)
L 2
50.0%
H 2
50.0%
Close Punctuation
ValueCountFrequency (%)
) 16
100.0%
Open Punctuation
ValueCountFrequency (%)
( 16
100.0%
Space Separator
ValueCountFrequency (%)
7
100.0%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 760
87.6%
Common 104
 
12.0%
Latin 4
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 33
 
4.3%
대 31
 
4.1%
지 28
 
3.7%
차 27
 
3.6%
삼 25
 
3.3%
동 24
 
3.2%
주 21
 
2.8%
단 19
 
2.5%
부 19
 
2.5%
한 18
 
2.4%
Other values (156) 515
67.8%
Common
ValueCountFrequency (%)
1 18
17.3%
) 16
15.4%
2 16
15.4%
( 16
15.4%
7
 
6.7%
3 7
 
6.7%
5 6
 
5.8%
4 5
 
4.8%
6 5
 
4.8%
7 4
 
3.8%
Other values (2) 4
 
3.8%
Latin
ValueCountFrequency (%)
L 2
50.0%
H 2
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 760
87.6%
ASCII 108
 
12.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
산 33
 
4.3%
대 31
 
4.1%
지 28
 
3.7%
차 27
 
3.6%
삼 25
 
3.3%
동 24
 
3.2%
주 21
 
2.8%
단 19
 
2.5%
부 19
 
2.5%
한 18
 
2.4%
Other values (156) 515
67.8%
ASCII
ValueCountFrequency (%)
1 18
16.7%
) 16
14.8%
2 16
14.8%
( 16
14.8%
7
 
6.5%
3 7
 
6.5%
5 6
 
5.6%
4 5
 
4.6%
6 5
 
4.6%
7 4
 
3.7%
Other values (4) 8
7.4%

소재지지번주소
Text

Distinct193
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2024-03-18T12:44:14.743558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length21
Mean length19.396985
Min length17

Characters and Unicode

Total characters3860
Distinct characters31
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

Unique188 ?
Unique (%)94.5%

Sample

1st row인천광역시 부평구 부평1동 70-5
2nd row인천광역시 부평구 부평1동 70-125
3rd row인천광역시 부평구 부평1동 64-20
4th row인천광역시 부평구 부평1동 65-7
5th row인천광역시 부평구 부평1동 799-5
ValueCountFrequency (%)
인천광역시 199
25.1%
부평구 199
25.1%
삼산1동 20
 
2.5%
부개3동 17
 
2.1%
산곡2동 16
 
2.0%
산곡1동 15
 
1.9%
청천2동 14
 
1.8%
부개2동 13
 
1.6%
갈산2동 12
 
1.5%
십정2동 11
 
1.4%
Other values (208) 278
35.0%
2024-03-18T12:44:15.175213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
595
15.4%
부 263
 
6.8%
평 224
 
5.8%
천 216
 
5.6%
구 200
 
5.2%
인 199
 
5.2%
동 199
 
5.2%
광 199
 
5.2%
역 199
 
5.2%
시 199
 
5.2%
Other values (21) 1367
35.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2190
56.7%
Decimal Number 941
24.4%
Space Separator 595
 
15.4%
Dash Punctuation 134
 
3.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
부 263
12.0%
평 224
10.2%
천 216
9.9%
구 200
9.1%
인 199
9.1%
동 199
9.1%
광 199
9.1%
역 199
9.1%
시 199
9.1%
산 96
 
4.4%
Other values (9) 196
8.9%
Decimal Number
ValueCountFrequency (%)
1 192
20.4%
2 162
17.2%
3 115
12.2%
4 90
9.6%
6 76
 
8.1%
5 74
 
7.9%
7 63
 
6.7%
0 63
 
6.7%
9 62
 
6.6%
8 44
 
4.7%
Space Separator
ValueCountFrequency (%)
595
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 134
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2190
56.7%
Common 1670
43.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
부 263
12.0%
평 224
10.2%
천 216
9.9%
구 200
9.1%
인 199
9.1%
동 199
9.1%
광 199
9.1%
역 199
9.1%
시 199
9.1%
산 96
 
4.4%
Other values (9) 196
8.9%
Common
ValueCountFrequency (%)
595
35.6%
1 192
 
11.5%
2 162
 
9.7%
- 134
 
8.0%
3 115
 
6.9%
4 90
 
5.4%
6 76
 
4.6%
5 74
 
4.4%
7 63
 
3.8%
0 63
 
3.8%
Other values (2) 106
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2190
56.7%
ASCII 1670
43.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
595
35.6%
1 192
 
11.5%
2 162
 
9.7%
- 134
 
8.0%
3 115
 
6.9%
4 90
 
5.4%
6 76
 
4.6%
5 74
 
4.4%
7 63
 
3.8%
0 63
 
3.8%
Other values (2) 106
 
6.3%
Hangul
ValueCountFrequency (%)
부 263
12.0%
평 224
10.2%
천 216
9.9%
구 200
9.1%
인 199
9.1%
동 199
9.1%
광 199
9.1%
역 199
9.1%
시 199
9.1%
산 96
 
4.4%
Other values (9) 196
8.9%

소재지도로명주소
Text

Distinct195
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2024-03-18T12:44:15.586401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length22
Mean length18.839196
Min length15

Characters and Unicode

Total characters3749
Distinct characters84
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

Unique191 ?
Unique (%)96.0%

Sample

1st row인천광역시 부평구 부평문화로 37
2nd row인천광역시 부평구 부흥로 248
3rd row인천광역시 부평구 부영로 196
4th row인천광역시 부평구 부흥로243번길 39
5th row인천광역시 부평구 부평대로165번길 40
ValueCountFrequency (%)
인천광역시 199
25.0%
부평구 199
25.0%
11 8
 
1.0%
원적로 8
 
1.0%
12 7
 
0.9%
후정동로 7
 
0.9%
부평북로 6
 
0.8%
길주남로 6
 
0.8%
57 5
 
0.6%
안남로 5
 
0.6%
Other values (226) 347
43.5%
2024-03-18T12:44:16.008881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
602
16.1%
부 240
 
6.4%
평 222
 
5.9%
천 211
 
5.6%
인 208
 
5.5%
광 199
 
5.3%
역 199
 
5.3%
시 199
 
5.3%
구 199
 
5.3%
로 199
 
5.3%
Other values (74) 1271
33.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2442
65.1%
Decimal Number 699
 
18.6%
Space Separator 602
 
16.1%
Dash Punctuation 6
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
부 240
9.8%
평 222
9.1%
천 211
8.6%
인 208
8.5%
광 199
8.1%
역 199
8.1%
시 199
8.1%
구 199
8.1%
로 199
8.1%
길 106
 
4.3%
Other values (62) 460
18.8%
Decimal Number
ValueCountFrequency (%)
1 145
20.7%
2 104
14.9%
4 78
11.2%
3 78
11.2%
6 61
8.7%
5 58
 
8.3%
0 58
 
8.3%
7 46
 
6.6%
8 39
 
5.6%
9 32
 
4.6%
Space Separator
ValueCountFrequency (%)
602
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2442
65.1%
Common 1307
34.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
부 240
9.8%
평 222
9.1%
천 211
8.6%
인 208
8.5%
광 199
8.1%
역 199
8.1%
시 199
8.1%
구 199
8.1%
로 199
8.1%
길 106
 
4.3%
Other values (62) 460
18.8%
Common
ValueCountFrequency (%)
602
46.1%
1 145
 
11.1%
2 104
 
8.0%
4 78
 
6.0%
3 78
 
6.0%
6 61
 
4.7%
5 58
 
4.4%
0 58
 
4.4%
7 46
 
3.5%
8 39
 
3.0%
Other values (2) 38
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2442
65.1%
ASCII 1307
34.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
602
46.1%
1 145
 
11.1%
2 104
 
8.0%
4 78
 
6.0%
3 78
 
6.0%
6 61
 
4.7%
5 58
 
4.4%
0 58
 
4.4%
7 46
 
3.5%
8 39
 
3.0%
Other values (2) 38
 
2.9%
Hangul
ValueCountFrequency (%)
부 240
9.8%
평 222
9.1%
천 211
8.6%
인 208
8.5%
광 199
8.1%
역 199
8.1%
시 199
8.1%
구 199
8.1%
로 199
8.1%
길 106
 
4.3%
Other values (62) 460
18.8%

층수
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)13.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.859296
Minimum4
Maximum48
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2024-03-18T12:44:16.135428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile5
Q16
median15
Q320
95-th percentile25
Maximum48
Range44
Interquartile range (IQR)14

Descriptive statistics

Standard deviation7.5243914
Coefficient of variation (CV)0.50637602
Kurtosis0.51771414
Mean14.859296
Median Absolute Deviation (MAD)6
Skewness0.34886271
Sum2957
Variance56.616466
MonotonicityNot monotonic
2024-03-18T12:44:16.228139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
15 38
19.1%
5 35
17.6%
6 23
11.6%
20 21
10.6%
22 11
 
5.5%
25 11
 
5.5%
18 9
 
4.5%
19 8
 
4.0%
13 6
 
3.0%
21 6
 
3.0%
Other values (16) 31
15.6%
ValueCountFrequency (%)
4 1
 
0.5%
5 35
17.6%
6 23
11.6%
7 1
 
0.5%
9 1
 
0.5%
10 1
 
0.5%
11 2
 
1.0%
12 2
 
1.0%
13 6
 
3.0%
14 1
 
0.5%
ValueCountFrequency (%)
48 1
 
0.5%
32 1
 
0.5%
31 1
 
0.5%
29 2
 
1.0%
26 3
 
1.5%
25 11
5.5%
24 5
2.5%
23 2
 
1.0%
22 11
5.5%
21 6
3.0%

동수
Real number (ℝ)

HIGH CORRELATION 

Distinct23
Distinct (%)11.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.7839196
Minimum1
Maximum28
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2024-03-18T12:44:16.323550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q37
95-th percentile19
Maximum28
Range27
Interquartile range (IQR)5

Descriptive statistics

Standard deviation5.5730722
Coefficient of variation (CV)0.96354594
Kurtosis2.5794431
Mean5.7839196
Median Absolute Deviation (MAD)2
Skewness1.7002758
Sum1151
Variance31.059134
MonotonicityNot monotonic
2024-03-18T12:44:16.437813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
2 36
18.1%
1 36
18.1%
4 23
11.6%
3 18
9.0%
5 18
9.0%
6 13
 
6.5%
9 8
 
4.0%
12 7
 
3.5%
10 6
 
3.0%
7 6
 
3.0%
Other values (13) 28
14.1%
ValueCountFrequency (%)
1 36
18.1%
2 36
18.1%
3 18
9.0%
4 23
11.6%
5 18
9.0%
6 13
 
6.5%
7 6
 
3.0%
8 2
 
1.0%
9 8
 
4.0%
10 6
 
3.0%
ValueCountFrequency (%)
28 1
 
0.5%
24 1
 
0.5%
23 2
1.0%
22 4
2.0%
20 1
 
0.5%
19 2
1.0%
17 2
1.0%
16 2
1.0%
15 2
1.0%
14 4
2.0%

세대수
Real number (ℝ)

HIGH CORRELATION 

Distinct170
Distinct (%)85.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean530.53769
Minimum30
Maximum5128
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2024-03-18T12:44:16.630688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile45.9
Q1146
median320
Q3706
95-th percentile1698.8
Maximum5128
Range5098
Interquartile range (IQR)560

Descriptive statistics

Standard deviation612.42959
Coefficient of variation (CV)1.1543564
Kurtosis16.432374
Mean530.53769
Median Absolute Deviation (MAD)222
Skewness3.1422666
Sum105577
Variance375070.01
MonotonicityNot monotonic
2024-03-18T12:44:16.825709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
280 4
 
2.0%
95 3
 
1.5%
150 3
 
1.5%
340 3
 
1.5%
90 3
 
1.5%
30 3
 
1.5%
384 3
 
1.5%
40 3
 
1.5%
68 2
 
1.0%
375 2
 
1.0%
Other values (160) 170
85.4%
ValueCountFrequency (%)
30 3
1.5%
34 1
 
0.5%
35 1
 
0.5%
40 3
1.5%
41 1
 
0.5%
45 1
 
0.5%
46 1
 
0.5%
48 1
 
0.5%
49 1
 
0.5%
50 2
1.0%
ValueCountFrequency (%)
5128 1
0.5%
2539 1
0.5%
2475 1
0.5%
2257 1
0.5%
2204 1
0.5%
2130 1
0.5%
1980 1
0.5%
1873 1
0.5%
1764 1
0.5%
1724 1
0.5%

사용검사
Date

Distinct191
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
Minimum1978-11-24 00:00:00
Maximum2022-05-03 00:00:00
2024-03-18T12:44:16.943125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T12:44:17.064688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
Minimum2022-09-01 00:00:00
Maximum2022-09-01 00:00:00
2024-03-18T12:44:17.159215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T12:44:17.236048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2024-03-18T12:44:13.146364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T12:44:12.134184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T12:44:12.487628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T12:44:12.815361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T12:44:13.224877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T12:44:12.248975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T12:44:12.571715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T12:44:12.905243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T12:44:13.283839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T12:44:12.324952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T12:44:12.641004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T12:44:12.983638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T12:44:13.350043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T12:44:12.406221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T12:44:12.726407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T12:44:13.072091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-18T12:44:17.298170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번층수동수세대수
연번1.0000.2720.4070.308
층수0.2721.0000.6930.697
동수0.4070.6931.0000.886
세대수0.3080.6970.8861.000
2024-03-18T12:44:17.379832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번층수동수세대수
연번1.0000.1300.0330.039
층수0.1301.0000.4520.622
동수0.0330.4521.0000.853
세대수0.0390.6220.8531.000

Missing values

2024-03-18T12:44:13.439905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-18T12:44:13.562589image/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

연번아파트명소재지지번주소소재지도로명주소층수동수세대수사용검사데이터기준일자
01동아1단지인천광역시 부평구 부평1동 70-5인천광역시 부평구 부평문화로 37151724751989-06-032022-09-01
12동아2단지인천광역시 부평구 부평1동 70-125인천광역시 부평구 부흥로 248202221301995-05-092022-09-01
23대림인천광역시 부평구 부평1동 64-20인천광역시 부평구 부영로 19615914701989-12-072022-09-01
34욱일인천광역시 부평구 부평1동 65-7인천광역시 부평구 부흥로243번길 391547801990-10-192022-09-01
45가나안미도인천광역시 부평구 부평1동 799-5인천광역시 부평구 부평대로165번길 40521401986-02-282022-09-01
56대우인천광역시 부평구 부평1동 65-115인천광역시 부평구 부흥로243번길 72243081998-08-282022-09-01
67한국인천광역시 부평구 부평1동 65-124인천광역시 부평구 부영로166번길 121921801998-12-302022-09-01
78건우인천광역시 부평구 부평1동 799-3인천광역시 부평구 부평대로165번길 62541501985-07-082022-09-01
89창보인천광역시 부평구 부평1동 70-78인천광역시 부평구 부영로166번길 162012021999-10-302022-09-01
910두산위브인천광역시 부평구 부평1동 534-96인천광역시 부평구 경원대로 1344번길 81542802005-02-252022-09-01
연번아파트명소재지지번주소소재지도로명주소층수동수세대수사용검사데이터기준일자
189190약산신동아인천광역시 부평구 십정2동 582인천광역시 부평구 동암산로 3351601986-01-222022-09-01
190191삼용인천광역시 부평구 십정2동 424인천광역시 부평구 배곶로20번길 1351451986-11-052022-09-01
191192광명인천광역시 부평구 십정2동 432인천광역시 부평구 하정로15번길 50621001987-12-242022-09-01
192193삼미인천광역시 부평구 십정2동 587-6인천광역시 부평구 동암산로37번길 1351411988-02-202022-09-01
193194동암신동아인천광역시 부평구 십정2동 607인천광역시 부평구 아트센터로 118241716902000-09-092022-09-01
194195대주파크빌인천광역시 부평구 십정2동 182-95인천광역시 부평구 마장로 492123122001-11-242022-09-01
195196천일인천광역시 부평구 십정2동 326-13인천광역시 부평구 백범로540번길 4191462005-03-172022-09-01
196197목동휘버스인천광역시 부평구 십정2동 487-2인천광역시 부평구 경인로701번길 49121642005-11-212022-09-01
197198백운 브라운스톤인천광역시 부평구 십정동 617인천광역시 부평구 마장로 43-202652612014-06-252022-09-01
198199동암정든마을인천광역시 부평구 십정2동 427-3인천광역시 부평구 이규보로 42151682007-10-162022-09-01