Overview

Dataset statistics

Number of variables8
Number of observations99
Missing cells77
Missing cells (%)9.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.9 KiB
Average record size in memory71.3 B

Variable types

Categorical2
Numeric6

Dataset

Description인천광역시 연면적별 주택현황, 주택유형별(단독주택/아파트/연립주택/다세대주택/비거주용건물 내 주택) 등의 항목 주택수 현황 정보입니다.
Author인천광역시
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15055215&srcSe=7661IVAWM27C61E190

Alerts

계 is highly overall correlated with 아파트 and 3 other fieldsHigh correlation
단독주택 is highly overall correlated with 비거주용 건물내 주택High correlation
아파트 is highly overall correlated with 계 and 2 other fieldsHigh correlation
연립주택 is highly overall correlated with 계 and 2 other fieldsHigh correlation
다세대주택 is highly overall correlated with 계 and 2 other fieldsHigh correlation
비거주용 건물내 주택 is highly overall correlated with 계 and 1 other fieldsHigh correlation
아파트 has 17 (17.2%) missing valuesMissing
연립주택 has 31 (31.3%) missing valuesMissing
다세대주택 has 26 (26.3%) missing valuesMissing
비거주용 건물내 주택 has 3 (3.0%) missing valuesMissing
계 has unique valuesUnique
단독주택 has unique valuesUnique

Reproduction

Analysis started2024-03-18 02:09:38.385621
Analysis finished2024-03-18 02:09:41.870535
Duration3.48 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

행정구역별(시군구)
Categorical

Distinct11
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Memory size924.0 B
인천광역시
9 
중구
9 
동구
9 
미추홀구
9 
연수구
9 
Other values (6)
54 

Length

Max length6
Median length3
Mean length3.0909091
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row 인천광역시
2nd row 인천광역시
3rd row 인천광역시
4th row 인천광역시
5th row 인천광역시

Common Values

ValueCountFrequency (%)
인천광역시 9
9.1%
중구 9
9.1%
동구 9
9.1%
미추홀구 9
9.1%
연수구 9
9.1%
남동구 9
9.1%
부평구 9
9.1%
계양구 9
9.1%
서구 9
9.1%
강화군 9
9.1%

Length

2024-03-18T11:09:41.928689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
인천광역시 9
9.1%
중구 9
9.1%
동구 9
9.1%
미추홀구 9
9.1%
연수구 9
9.1%
남동구 9
9.1%
부평구 9
9.1%
계양구 9
9.1%
서구 9
9.1%
강화군 9
9.1%

연면적
Categorical

Distinct9
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Memory size924.0 B
20㎡ 이하
11 
20㎡ ~ 40㎡
11 
40㎡ ~ 60㎡
11 
60㎡ ~ 85㎡
11 
85㎡ ~ 100㎡
11 
Other values (4)
44 

Length

Max length11
Median length10
Mean length9.2222222
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20㎡ 이하
2nd row20㎡ ~ 40㎡
3rd row40㎡ ~ 60㎡
4th row60㎡ ~ 85㎡
5th row85㎡ ~ 100㎡

Common Values

ValueCountFrequency (%)
20㎡ 이하 11
11.1%
20㎡ ~ 40㎡ 11
11.1%
40㎡ ~ 60㎡ 11
11.1%
60㎡ ~ 85㎡ 11
11.1%
85㎡ ~ 100㎡ 11
11.1%
100㎡ ~ 130㎡ 11
11.1%
130㎡ ~ 165㎡ 11
11.1%
165㎡ ~ 230㎡ 11
11.1%
230㎡ 초과 11
11.1%

Length

2024-03-18T11:09:42.070571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-18T11:09:42.222939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
77
28.0%
20㎡ 22
 
8.0%
40㎡ 22
 
8.0%
60㎡ 22
 
8.0%
85㎡ 22
 
8.0%
100㎡ 22
 
8.0%
130㎡ 22
 
8.0%
165㎡ 22
 
8.0%
230㎡ 22
 
8.0%
이하 11
 
4.0%

계
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct99
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21281.838
Minimum77
Maximum356795
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1023.0 B
2024-03-18T11:09:42.352823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum77
5-th percentile266.7
Q11292
median3785
Q317368
95-th percentile68389
Maximum356795
Range356718
Interquartile range (IQR)16076

Descriptive statistics

Standard deviation53648.37
Coefficient of variation (CV)2.5208522
Kurtosis29.22214
Mean21281.838
Median Absolute Deviation (MAD)3191
Skewness5.13787
Sum2106902
Variance2.8781476 × 109
MonotonicityNot monotonic
2024-03-18T11:09:42.475389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11885 1
 
1.0%
228 1
 
1.0%
23848 1
 
1.0%
858 1
 
1.0%
1131 1
 
1.0%
856 1
 
1.0%
1209 1
 
1.0%
4002 1
 
1.0%
1709 1
 
1.0%
28937 1
 
1.0%
Other values (89) 89
89.9%
ValueCountFrequency (%)
77 1
1.0%
125 1
1.0%
141 1
1.0%
150 1
1.0%
228 1
1.0%
271 1
1.0%
317 1
1.0%
334 1
1.0%
400 1
1.0%
405 1
1.0%
ValueCountFrequency (%)
356795 1
1.0%
349194 1
1.0%
162520 1
1.0%
78276 1
1.0%
77344 1
1.0%
67394 1
1.0%
60175 1
1.0%
58543 1
1.0%
58409 1
1.0%
57386 1
1.0%

단독주택
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct99
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1910.5859
Minimum5
Maximum18088
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1023.0 B
2024-03-18T11:09:42.604585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile62.8
Q1311
median889
Q31829
95-th percentile10340.7
Maximum18088
Range18083
Interquartile range (IQR)1518

Descriptive statistics

Standard deviation3240.901
Coefficient of variation (CV)1.6962865
Kurtosis10.088229
Mean1910.5859
Median Absolute Deviation (MAD)655
Skewness3.1156779
Sum189148
Variance10503440
MonotonicityNot monotonic
2024-03-18T11:09:42.732819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
840 1
 
1.0%
6 1
 
1.0%
84 1
 
1.0%
5 1
 
1.0%
1048 1
 
1.0%
752 1
 
1.0%
506 1
 
1.0%
316 1
 
1.0%
254 1
 
1.0%
443 1
 
1.0%
Other values (89) 89
89.9%
ValueCountFrequency (%)
5 1
1.0%
6 1
1.0%
10 1
1.0%
17 1
1.0%
52 1
1.0%
64 1
1.0%
66 1
1.0%
81 1
1.0%
84 1
1.0%
85 1
1.0%
ValueCountFrequency (%)
18088 1
1.0%
15252 1
1.0%
11909 1
1.0%
11106 1
1.0%
11022 1
1.0%
10265 1
1.0%
9880 1
1.0%
6212 1
1.0%
5111 1
1.0%
5042 1
1.0%

아파트
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct81
Distinct (%)98.8%
Missing17
Missing (%)17.2%
Infinite0
Infinite (%)0.0%
Mean16666.72
Minimum6
Maximum300076
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1023.0 B
2024-03-18T11:09:42.854432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile46.25
Q1434.25
median2841
Q315919.25
95-th percentile56718.2
Maximum300076
Range300070
Interquartile range (IQR)15485

Descriptive statistics

Standard deviation42121.971
Coefficient of variation (CV)2.5273103
Kurtosis30.603171
Mean16666.72
Median Absolute Deviation (MAD)2738.5
Skewness5.1985477
Sum1366671
Variance1.7742605 × 109
MonotonicityNot monotonic
2024-03-18T11:09:42.963548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 2
 
2.0%
4133 1
 
1.0%
3496 1
 
1.0%
1341 1
 
1.0%
25439 1
 
1.0%
31559 1
 
1.0%
9416 1
 
1.0%
418 1
 
1.0%
191 1
 
1.0%
6946 1
 
1.0%
Other values (71) 71
71.7%
(Missing) 17
 
17.2%
ValueCountFrequency (%)
6 2
2.0%
14 1
1.0%
34 1
1.0%
46 1
1.0%
51 1
1.0%
59 1
1.0%
67 1
1.0%
76 1
1.0%
162 1
1.0%
166 1
1.0%
ValueCountFrequency (%)
300076 1
1.0%
213782 1
1.0%
66686 1
1.0%
64844 1
1.0%
56832 1
1.0%
54556 1
1.0%
53789 1
1.0%
43464 1
1.0%
42985 1
1.0%
38327 1
1.0%

연립주택
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct62
Distinct (%)91.2%
Missing31
Missing (%)31.3%
Infinite0
Infinite (%)0.0%
Mean844.94118
Minimum5
Maximum14431
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1023.0 B
2024-03-18T11:09:43.288412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile6.35
Q125.5
median117
Q3508.5
95-th percentile3547.2
Maximum14431
Range14426
Interquartile range (IQR)483

Descriptive statistics

Standard deviation2250.2929
Coefficient of variation (CV)2.663254
Kurtosis24.166405
Mean844.94118
Median Absolute Deviation (MAD)106
Skewness4.67742
Sum57456
Variance5063818.3
MonotonicityNot monotonic
2024-03-18T11:09:43.442326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 3
 
3.0%
83 2
 
2.0%
24 2
 
2.0%
11 2
 
2.0%
13 2
 
2.0%
161 1
 
1.0%
105 1
 
1.0%
1780 1
 
1.0%
1049 1
 
1.0%
6 1
 
1.0%
Other values (52) 52
52.5%
(Missing) 31
31.3%
ValueCountFrequency (%)
5 3
3.0%
6 1
 
1.0%
7 1
 
1.0%
9 1
 
1.0%
10 1
 
1.0%
11 2
2.0%
13 2
2.0%
14 1
 
1.0%
19 1
 
1.0%
21 1
 
1.0%
ValueCountFrequency (%)
14431 1
1.0%
10454 1
1.0%
4337 1
1.0%
3991 1
1.0%
2723 1
1.0%
2672 1
1.0%
2529 1
1.0%
1780 1
1.0%
1701 1
1.0%
1466 1
1.0%

다세대주택
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct67
Distinct (%)91.8%
Missing26
Missing (%)26.3%
Infinite0
Infinite (%)0.0%
Mean6519.1918
Minimum5
Maximum109056
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1023.0 B
2024-03-18T11:09:43.586330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile6.8
Q138
median253
Q33049
95-th percentile23307.2
Maximum109056
Range109051
Interquartile range (IQR)3011

Descriptive statistics

Standard deviation17909.928
Coefficient of variation (CV)2.747262
Kurtosis23.765901
Mean6519.1918
Median Absolute Deviation (MAD)245
Skewness4.6707635
Sum475901
Variance3.2076552 × 108
MonotonicityNot monotonic
2024-03-18T11:09:43.708610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 4
 
4.0%
8 3
 
3.0%
38 2
 
2.0%
22 1
 
1.0%
2650 1
 
1.0%
15985 1
 
1.0%
13608 1
 
1.0%
218 1
 
1.0%
25 1
 
1.0%
58 1
 
1.0%
Other values (57) 57
57.6%
(Missing) 26
26.3%
ValueCountFrequency (%)
5 4
4.0%
8 3
3.0%
12 1
 
1.0%
13 1
 
1.0%
14 1
 
1.0%
16 1
 
1.0%
17 1
 
1.0%
18 1
 
1.0%
20 1
 
1.0%
22 1
 
1.0%
ValueCountFrequency (%)
109056 1
1.0%
98702 1
1.0%
26568 1
1.0%
23750 1
1.0%
23012 1
1.0%
20068 1
1.0%
20037 1
1.0%
18778 1
1.0%
18753 1
1.0%
18057 1
1.0%

비거주용 건물내 주택
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct76
Distinct (%)79.2%
Missing3
Missing (%)3.0%
Infinite0
Infinite (%)0.0%
Mean184.20833
Minimum5
Maximum1705
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1023.0 B
2024-03-18T11:09:43.815839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile9
Q133
median74.5
Q3173
95-th percentile883.5
Maximum1705
Range1700
Interquartile range (IQR)140

Descriptive statistics

Standard deviation315.26834
Coefficient of variation (CV)1.7114771
Kurtosis11.313015
Mean184.20833
Median Absolute Deviation (MAD)52
Skewness3.2956107
Sum17684
Variance99394.125
MonotonicityNot monotonic
2024-03-18T11:09:43.938244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
97 3
 
3.0%
6 3
 
3.0%
30 3
 
3.0%
18 3
 
3.0%
173 3
 
3.0%
95 2
 
2.0%
71 2
 
2.0%
28 2
 
2.0%
46 2
 
2.0%
21 2
 
2.0%
Other values (66) 71
71.7%
(Missing) 3
 
3.0%
ValueCountFrequency (%)
5 2
2.0%
6 3
3.0%
10 1
 
1.0%
12 1
 
1.0%
13 1
 
1.0%
14 1
 
1.0%
17 1
 
1.0%
18 3
3.0%
21 2
2.0%
22 1
 
1.0%
ValueCountFrequency (%)
1705 1
1.0%
1609 1
1.0%
1282 1
1.0%
1158 1
1.0%
1077 1
1.0%
819 1
1.0%
537 1
1.0%
521 1
1.0%
506 1
1.0%
493 1
1.0%

Interactions

2024-03-18T11:09:41.167459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:09:38.655313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:09:39.061546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:09:39.531750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:09:40.140875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:09:40.749412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:09:41.242941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:09:38.726496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:09:39.152717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:09:39.614027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:09:40.223932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:09:40.825536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:09:41.326523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:09:38.794926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:09:39.238773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:09:39.682785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:09:40.294865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:09:40.893544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:09:41.399953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:09:38.860695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:09:39.314228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:09:39.752824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:09:40.542217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:09:40.958893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:09:41.474693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:09:38.927932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:09:39.386729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:09:39.877415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:09:40.612474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:09:41.028223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:09:41.548751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:09:38.992115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:09:39.458914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:09:40.036667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:09:40.677886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:09:41.096946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-18T11:09:44.016569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정구역별(시군구)연면적계단독주택아파트연립주택다세대주택비거주용 건물내 주택
행정구역별(시군구)1.0000.0000.3390.5890.0000.0000.2420.609
연면적0.0001.0000.4130.2730.3010.1240.4910.000
계0.3390.4131.0000.7920.9860.8120.6670.746
단독주택0.5890.2730.7921.0000.7510.6770.6800.941
아파트0.0000.3010.9860.7511.0000.9070.6230.787
연립주택0.0000.1240.8120.6770.9071.0000.7580.738
다세대주택0.2420.4910.6670.6800.6230.7581.0000.890
비거주용 건물내 주택0.6090.0000.7460.9410.7870.7380.8901.000
2024-03-18T11:09:44.106480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정구역별(시군구)연면적
행정구역별(시군구)1.0000.000
연면적0.0001.000
2024-03-18T11:09:44.183877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
계단독주택아파트연립주택다세대주택비거주용 건물내 주택행정구역별(시군구)연면적
계1.0000.3680.9140.7540.6620.5230.1850.246
단독주택0.3681.0000.0230.2430.0500.7200.3200.133
아파트0.9140.0231.0000.6640.5760.3270.0000.170
연립주택0.7540.2430.6641.0000.8010.2050.0000.054
다세대주택0.6620.0500.5760.8011.000-0.0040.1330.227
비거주용 건물내 주택0.5230.7200.3270.205-0.0041.0000.3360.000
행정구역별(시군구)0.1850.3200.0000.0000.1330.3361.0000.000
연면적0.2460.1330.1700.0540.2270.0000.0001.000

Missing values

2024-03-18T11:09:41.645144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-18T11:09:41.740730image/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.
2024-03-18T11:09:41.823048image/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

행정구역별(시군구)연면적계단독주택아파트연립주택다세대주택비거주용 건물내 주택
0인천광역시20㎡ 이하1188584076463782883138
1인천광역시20㎡ ~ 40㎡162520621254556252998702521
2인천광역시40㎡ ~ 60㎡3491941110621378214431109056819
3인천광역시60㎡ ~ 85㎡3567951808830007610454265681609
4인천광역시85㎡ ~ 100㎡2831210265162991804101158
5인천광역시100㎡ ~ 130㎡7827611022648444522531705
6인천광역시130㎡ ~ 165㎡34638988023244161711282
7인천광역시165㎡ ~ 230㎡15522119092372147171077
8인천광역시230㎡ 초과1630915252518<NA><NA>537
9중구20㎡ 이하588187242458628
행정구역별(시군구)연면적계단독주택아파트연립주택다세대주택비거주용 건물내 주택
89강화군230㎡ 초과334306<NA><NA><NA>28
90옹진군20㎡ 이하7764<NA><NA>85
91옹진군20㎡ ~ 40㎡10184174624328527
92옹진군40㎡ ~ 60㎡17498893305944130
93옹진군60㎡ ~ 85㎡230817531662548154
94옹진군85㎡ ~ 100㎡1064957765521
95옹진군100㎡ ~ 130㎡743721<NA><NA><NA>18
96옹진군130㎡ ~ 165㎡466431<NA>51317
97옹진군165㎡ ~ 230㎡630596<NA><NA><NA>34
98옹진군230㎡ 초과317305<NA><NA><NA>12