Overview

Dataset statistics

Number of variables9
Number of observations163
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory12.1 KiB
Average record size in memory75.8 B

Variable types

Categorical5
Numeric3
DateTime1

Dataset

Description전북특별자치도 김제시 원룸 및 오피스텔 현황입니다.(구분, 시군구명, 법정동명, 본번,부번, 주용도, 기타용도, 가구수, 승인일자)
Author전북특별자치도 김제시
URLhttps://www.data.go.kr/data/15077227/fileData.do

Alerts

시군구_명 has constant value ""Constant
구분 is highly overall correlated with 본번 and 3 other fieldsHigh correlation
기타_용도 is highly overall correlated with 구분 and 1 other fieldsHigh correlation
주_용도 is highly overall correlated with 본번 and 3 other fieldsHigh correlation
본번 is highly overall correlated with 구분 and 2 other fieldsHigh correlation
가구_수 is highly overall correlated with 구분 and 1 other fieldsHigh correlation
법정동_명 is highly overall correlated with 본번High correlation
구분 is highly imbalanced (90.5%)Imbalance
법정동_명 is highly imbalanced (52.9%)Imbalance
주_용도 is highly imbalanced (90.5%)Imbalance
기타_용도 is highly imbalanced (56.7%)Imbalance
부번 has 10 (6.1%) zerosZeros

Reproduction

Analysis started2024-04-06 08:19:44.971537
Analysis finished2024-04-06 08:19:48.380405
Duration3.41 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
원룸
161 
오피스텔
 
2

Length

Max length4
Median length2
Mean length2.0245399
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
원룸 161
98.8%
오피스텔 2
 
1.2%

Length

2024-04-06T17:19:48.555851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T17:19:48.777368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
원룸 161
98.8%
오피스텔 2
 
1.2%

시군구_명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
전북특별자치도 김제시
163 

Length

Max length11
Median length11
Mean length11
Min length11

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row전북특별자치도 김제시
2nd row전북특별자치도 김제시
3rd row전북특별자치도 김제시
4th row전북특별자치도 김제시
5th row전북특별자치도 김제시

Common Values

ValueCountFrequency (%)
전북특별자치도 김제시 163
100.0%

Length

2024-04-06T17:19:48.987587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T17:19:49.197365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전북특별자치도 163
50.0%
김제시 163
50.0%

법정동_명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct11
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
요촌동
108 
신풍동
33 
백산면 부거리
 
7
검산동
 
4
금산면 성계리
 
3
Other values (6)
 
8

Length

Max length8
Median length3
Mean length3.3680982
Min length2

Unique

Unique4 ?
Unique (%)2.5%

Sample

1st row요촌동
2nd row요촌동
3rd row요촌동
4th row요촌동
5th row교동

Common Values

ValueCountFrequency (%)
요촌동 108
66.3%
신풍동 33
 
20.2%
백산면 부거리 7
 
4.3%
검산동 4
 
2.5%
금산면 성계리 3
 
1.8%
금구면 금구리 2
 
1.2%
옥산동 2
 
1.2%
교동 1
 
0.6%
공덕면 회룡리 1
 
0.6%
공덕면 공덕리 1
 
0.6%

Length

2024-04-06T17:19:49.428463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
요촌동 108
60.7%
신풍동 33
 
18.5%
백산면 7
 
3.9%
부거리 7
 
3.9%
검산동 4
 
2.2%
금산면 3
 
1.7%
성계리 3
 
1.7%
금구면 2
 
1.1%
금구리 2
 
1.1%
옥산동 2
 
1.1%
Other values (6) 7
 
3.9%

본번
Real number (ℝ)

HIGH CORRELATION 

Distinct60
Distinct (%)36.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean501.40491
Minimum13
Maximum1593
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2024-04-06T17:19:49.817822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile60
Q1464
median560
Q3602
95-th percentile790
Maximum1593
Range1580
Interquartile range (IQR)138

Descriptive statistics

Standard deviation211.70938
Coefficient of variation (CV)0.42223236
Kurtosis4.2485869
Mean501.40491
Median Absolute Deviation (MAD)43
Skewness-0.10903761
Sum81729
Variance44820.86
MonotonicityNot monotonic
2024-04-06T17:19:50.115827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
560 7
 
4.3%
603 7
 
4.3%
386 7
 
4.3%
790 6
 
3.7%
602 6
 
3.7%
589 6
 
3.7%
13 5
 
3.1%
553 5
 
3.1%
552 5
 
3.1%
563 4
 
2.5%
Other values (50) 105
64.4%
ValueCountFrequency (%)
13 5
3.1%
30 3
1.8%
60 4
2.5%
65 1
 
0.6%
68 1
 
0.6%
112 1
 
0.6%
156 2
 
1.2%
182 1
 
0.6%
188 2
 
1.2%
198 2
 
1.2%
ValueCountFrequency (%)
1593 1
 
0.6%
847 1
 
0.6%
820 3
1.8%
790 6
3.7%
612 3
1.8%
611 3
1.8%
610 4
2.5%
609 4
2.5%
608 1
 
0.6%
606 2
 
1.2%

부번
Real number (ℝ)

ZEROS 

Distinct39
Distinct (%)23.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18
Minimum0
Maximum409
Zeros10
Zeros (%)6.1%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2024-04-06T17:19:50.443356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median6
Q313
95-th percentile41.7
Maximum409
Range409
Interquartile range (IQR)10

Descriptive statistics

Standard deviation51.475632
Coefficient of variation (CV)2.8597574
Kurtosis43.030268
Mean18
Median Absolute Deviation (MAD)4
Skewness6.3171683
Sum2934
Variance2649.7407
MonotonicityNot monotonic
2024-04-06T17:19:50.733730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
6 17
 
10.4%
4 12
 
7.4%
1 12
 
7.4%
2 11
 
6.7%
5 11
 
6.7%
3 11
 
6.7%
0 10
 
6.1%
10 9
 
5.5%
9 8
 
4.9%
14 6
 
3.7%
Other values (29) 56
34.4%
ValueCountFrequency (%)
0 10
6.1%
1 12
7.4%
2 11
6.7%
3 11
6.7%
4 12
7.4%
5 11
6.7%
6 17
10.4%
7 6
 
3.7%
8 5
 
3.1%
9 8
4.9%
ValueCountFrequency (%)
409 1
0.6%
408 1
0.6%
255 1
0.6%
184 1
0.6%
102 1
0.6%
92 1
0.6%
68 1
0.6%
49 1
0.6%
42 1
0.6%
39 1
0.6%

주_용도
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
단독주택
161 
업무시설
 
2

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
단독주택 161
98.8%
업무시설 2
 
1.2%

Length

2024-04-06T17:19:51.004864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T17:19:51.203254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
단독주택 161
98.8%
업무시설 2
 
1.2%

기타_용도
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct14
Distinct (%)8.6%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
단독주택
117 
다가구주택
20 
단독주택 제1종근린생활시설
 
6
단독주택 제2종근린생활시설
 
5
단독주택 제1종근린생활시설
 
4
Other values (9)
 
11

Length

Max length15
Median length4
Mean length5.5030675
Min length3

Unique

Unique7 ?
Unique (%)4.3%

Sample

1st row단독주택
2nd row단독주택
3rd row다가구주택
4th row단독주택 제2종근린생활시설
5th row단독주택

Common Values

ValueCountFrequency (%)
단독주택 117
71.8%
다가구주택 20
 
12.3%
단독주택 제1종근린생활시설 6
 
3.7%
단독주택 제2종근린생활시설 5
 
3.1%
단독주택 제1종근린생활시설 4
 
2.5%
제1종근린생활시설 단독주택 2
 
1.2%
오피스텔 2
 
1.2%
단독주택 제2종근린생활시설 1
 
0.6%
단독주택(다가구주택) 1
 
0.6%
단독주택및제2종근린생활시설 1
 
0.6%
Other values (4) 4
 
2.5%

Length

2024-04-06T17:19:51.421844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
단독주택 136
74.3%
다가구주택 21
 
11.5%
제1종근린생활시설 13
 
7.1%
제2종근린생활시설 6
 
3.3%
오피스텔 2
 
1.1%
단독주택(다가구주택 1
 
0.5%
단독주택및제2종근린생활시설 1
 
0.5%
다가구 1
 
0.5%
소매점 1
 
0.5%
다가구주택,소매점 1
 
0.5%

가구_수
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.668712
Minimum9
Maximum35
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2024-04-06T17:19:51.646864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile9
Q110
median11
Q312
95-th percentile17.9
Maximum35
Range26
Interquartile range (IQR)2

Descriptive statistics

Standard deviation3.0144819
Coefficient of variation (CV)0.25833888
Kurtosis21.574314
Mean11.668712
Median Absolute Deviation (MAD)1
Skewness3.4561516
Sum1902
Variance9.0871014
MonotonicityNot monotonic
2024-04-06T17:19:51.959399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
11 49
30.1%
9 35
21.5%
10 21
12.9%
12 19
 
11.7%
14 14
 
8.6%
13 7
 
4.3%
18 6
 
3.7%
15 5
 
3.1%
17 3
 
1.8%
19 2
 
1.2%
Other values (2) 2
 
1.2%
ValueCountFrequency (%)
9 35
21.5%
10 21
12.9%
11 49
30.1%
12 19
 
11.7%
13 7
 
4.3%
14 14
 
8.6%
15 5
 
3.1%
16 1
 
0.6%
17 3
 
1.8%
18 6
 
3.7%
ValueCountFrequency (%)
35 1
 
0.6%
19 2
 
1.2%
18 6
 
3.7%
17 3
 
1.8%
16 1
 
0.6%
15 5
 
3.1%
14 14
 
8.6%
13 7
 
4.3%
12 19
 
11.7%
11 49
30.1%

사용승인_일
Date

Distinct137
Distinct (%)84.0%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
Minimum1960-09-01 00:00:00
Maximum2023-10-12 00:00:00
2024-04-06T17:19:52.254557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:19:52.579702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2024-04-06T17:19:46.843216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:19:45.564586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:19:46.220245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:19:47.042112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:19:45.789415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:19:46.443564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:19:47.713575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:19:46.023220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:19:46.646083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-06T17:19:52.918924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분법정동_명본번부번주_용도기타_용도가구_수
구분1.0000.3810.7190.0000.9211.0000.626
법정동_명0.3811.0000.8550.4590.3810.0000.487
본번0.7190.8551.0000.3640.7190.5770.702
부번0.0000.4590.3641.0000.0000.0000.000
주_용도0.9210.3810.7190.0001.0001.0000.626
기타_용도1.0000.0000.5770.0001.0001.0000.558
가구_수0.6260.4870.7020.0000.6260.5581.000
2024-04-06T17:19:53.155100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분기타_용도주_용도법정동_명
구분1.0000.9620.7450.354
기타_용도0.9621.0000.9620.000
주_용도0.7450.9621.0000.354
법정동_명0.3540.0000.3541.000
2024-04-06T17:19:53.354663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
본번부번가구_수구분법정동_명주_용도기타_용도
본번1.000-0.1450.3370.7660.6430.7660.244
부번-0.1451.000-0.1190.0000.2510.0000.000
가구_수0.337-0.1191.0000.7460.2870.7460.322
구분0.7660.0000.7461.0000.3540.7450.962
법정동_명0.6430.2510.2870.3541.0000.3540.000
주_용도0.7660.0000.7460.7450.3541.0000.962
기타_용도0.2440.0000.3220.9620.0000.9621.000

Missing values

2024-04-06T17:19:47.971256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-06T17:19:48.274533image/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

구분시군구_명법정동_명본번부번주_용도기타_용도가구_수사용승인_일
0원룸전북특별자치도 김제시요촌동5832단독주택단독주택102014-09-29
1원룸전북특별자치도 김제시요촌동27610단독주택단독주택112016-08-09
2원룸전북특별자치도 김제시요촌동5992단독주택다가구주택142003-06-10
3원룸전북특별자치도 김제시요촌동5623단독주택단독주택 제2종근린생활시설102014-08-08
4원룸전북특별자치도 김제시교동686단독주택단독주택122003-10-22
5원룸전북특별자치도 김제시요촌동5537단독주택단독주택 제1종근린생활시설112014-07-01
6원룸전북특별자치도 김제시요촌동5693단독주택단독주택112014-07-16
7원룸전북특별자치도 김제시요촌동56210단독주택단독주택 제1종근린생활시설102017-12-12
8원룸전북특별자치도 김제시요촌동6046단독주택단독주택142010-07-07
9원룸전북특별자치도 김제시공덕면 회룡리2154단독주택단독주택141960-09-01
구분시군구_명법정동_명본번부번주_용도기타_용도가구_수사용승인_일
153오피스텔전북특별자치도 김제시검산동84710업무시설오피스텔162015-02-12
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