Overview

Dataset statistics

Number of variables6
Number of observations1301
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory66.2 KiB
Average record size in memory52.1 B

Variable types

Categorical2
Text1
Numeric3

Dataset

Description김해시에서 통계기반 도시현황 파악을 위해 개발한 통계지수 중 하나로서, 통계연도, 시도명, 시군구명, 부동산거래현황(수), 동(호)수, 필지(수)로 구성되어 있습니다. 김해시 중심의 통계지수로서, 데이터 수집, 가공 등의 어려움으로 김해시 외 지역의 정보는 누락될 수 있습니다.
Author경상남도 김해시
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15110137

Alerts

부동산거래현황(수) is highly overall correlated with 동(호)수High correlation
동(호)수 is highly overall correlated with 부동산거래현황(수)High correlation

Reproduction

Analysis started2023-12-10 23:08:48.048686
Analysis finished2023-12-10 23:08:49.488970
Duration1.44 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

통계연도
Categorical

Distinct5
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size10.3 KiB
2018
261 
2017
260 
2019
260 
2020
260 
2021
260 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2017
2nd row2017
3rd row2017
4th row2017
5th row2017

Common Values

ValueCountFrequency (%)
2018 261
20.1%
2017 260
20.0%
2019 260
20.0%
2020 260
20.0%
2021 260
20.0%

Length

2023-12-11T08:08:49.561824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:08:49.671244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2018 261
20.1%
2017 260
20.0%
2019 260
20.0%
2020 260
20.0%
2021 260
20.0%

시도명
Categorical

Distinct16
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size10.3 KiB
경기도
240 
서울특별시
125 
경상북도
125 
경상남도
115 
전라남도
110 
Other values (11)
586 

Length

Max length7
Median length5
Mean length4.0545734
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 240
18.4%
서울특별시 125
9.6%
경상북도 125
9.6%
경상남도 115
8.8%
전라남도 110
8.5%
강원도 90
 
6.9%
충청남도 85
 
6.5%
부산광역시 80
 
6.1%
전라북도 80
 
6.1%
충청북도 75
 
5.8%
Other values (6) 176
13.5%

Length

2023-12-11T08:08:49.842470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기도 240
18.4%
서울특별시 125
9.6%
경상북도 125
9.6%
경상남도 115
8.8%
전라남도 110
8.5%
강원도 90
 
6.9%
충청남도 85
 
6.5%
부산광역시 80
 
6.1%
전라북도 80
 
6.1%
충청북도 75
 
5.8%
Other values (6) 176
13.5%

시군구명
Text

Distinct238
Distinct (%)18.3%
Missing0
Missing (%)0.0%
Memory size10.3 KiB
2023-12-11T08:08:50.262275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length3
Mean length3.4511914
Min length2

Characters and Unicode

Total characters4490
Distinct characters142
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row종로구
2nd row중구
3rd row용산구
4th row성동구
5th row광진구
ValueCountFrequency (%)
중구 30
 
2.1%
동구 30
 
2.1%
창원시 30
 
2.1%
남구 27
 
1.8%
서구 25
 
1.7%
북구 25
 
1.7%
수원시 25
 
1.7%
청주시 25
 
1.7%
고양시 20
 
1.4%
용인시 20
 
1.4%
Other values (226) 1204
82.4%
2023-12-11T08:08:50.859078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 550
 
12.2%
구 531
 
11.8%
군 425
 
9.5%
165
 
3.7%
주 130
 
2.9%
천 120
 
2.7%
산 120
 
2.7%
양 120
 
2.7%
성 110
 
2.4%
안 100
 
2.2%
Other values (132) 2119
47.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4325
96.3%
Space Separator 165
 
3.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 550
 
12.7%
구 531
 
12.3%
군 425
 
9.8%
주 130
 
3.0%
천 120
 
2.8%
산 120
 
2.8%
양 120
 
2.8%
성 110
 
2.5%
안 100
 
2.3%
동 100
 
2.3%
Other values (131) 2019
46.7%
Space Separator
ValueCountFrequency (%)
165
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4325
96.3%
Common 165
 
3.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 550
 
12.7%
구 531
 
12.3%
군 425
 
9.8%
주 130
 
3.0%
천 120
 
2.8%
산 120
 
2.8%
양 120
 
2.8%
성 110
 
2.5%
안 100
 
2.3%
동 100
 
2.3%
Other values (131) 2019
46.7%
Common
ValueCountFrequency (%)
165
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4325
96.3%
ASCII 165
 
3.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 550
 
12.7%
구 531
 
12.3%
군 425
 
9.8%
주 130
 
3.0%
천 120
 
2.8%
산 120
 
2.8%
양 120
 
2.8%
성 110
 
2.5%
안 100
 
2.3%
동 100
 
2.3%
Other values (131) 2019
46.7%
ASCII
ValueCountFrequency (%)
165
100.0%

부동산거래현황(수)
Real number (ℝ)

HIGH CORRELATION 

Distinct1274
Distinct (%)97.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14436.006
Minimum488
Maximum104853
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2023-12-11T08:08:51.038049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum488
5-th percentile3327
Q16507
median10709
Q317863
95-th percentile39479
Maximum104853
Range104365
Interquartile range (IQR)11356

Descriptive statistics

Standard deviation12447.453
Coefficient of variation (CV)0.86225045
Kurtosis9.7568464
Mean14436.006
Median Absolute Deviation (MAD)5186
Skewness2.5772685
Sum18781244
Variance1.5493908 × 108
MonotonicityNot monotonic
2023-12-11T08:08:51.462531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5837 4
 
0.3%
4652 2
 
0.2%
12473 2
 
0.2%
7788 2
 
0.2%
13760 2
 
0.2%
4425 2
 
0.2%
11017 2
 
0.2%
4973 2
 
0.2%
10436 2
 
0.2%
4025 2
 
0.2%
Other values (1264) 1279
98.3%
ValueCountFrequency (%)
488 1
0.1%
624 1
0.1%
683 1
0.1%
716 1
0.1%
822 1
0.1%
1379 1
0.1%
1416 1
0.1%
1426 1
0.1%
1504 1
0.1%
1605 1
0.1%
ValueCountFrequency (%)
104853 1
0.1%
97587 1
0.1%
90562 1
0.1%
90476 1
0.1%
87555 1
0.1%
86593 1
0.1%
76646 1
0.1%
73282 1
0.1%
70932 1
0.1%
68534 1
0.1%

동(호)수
Real number (ℝ)

HIGH CORRELATION 

Distinct1246
Distinct (%)95.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9755.3167
Minimum119
Maximum72629
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2023-12-11T08:08:51.604521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum119
5-th percentile519
Q11699
median7102
Q313678
95-th percentile30674
Maximum72629
Range72510
Interquartile range (IQR)11979

Descriptive statistics

Standard deviation10392.994
Coefficient of variation (CV)1.0653672
Kurtosis5.4225508
Mean9755.3167
Median Absolute Deviation (MAD)5671
Skewness1.9876895
Sum12691667
Variance1.0801433 × 108
MonotonicityNot monotonic
2023-12-11T08:08:51.758210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
481 3
 
0.2%
1141 3
 
0.2%
12532 2
 
0.2%
5557 2
 
0.2%
794 2
 
0.2%
1251 2
 
0.2%
1172 2
 
0.2%
1009 2
 
0.2%
2983 2
 
0.2%
10988 2
 
0.2%
Other values (1236) 1279
98.3%
ValueCountFrequency (%)
119 1
0.1%
134 1
0.1%
136 1
0.1%
192 1
0.1%
276 2
0.2%
280 1
0.1%
286 1
0.1%
309 1
0.1%
310 1
0.1%
318 1
0.1%
ValueCountFrequency (%)
72629 1
0.1%
71255 1
0.1%
67867 1
0.1%
61805 1
0.1%
57348 1
0.1%
57048 1
0.1%
54026 1
0.1%
53603 1
0.1%
53456 1
0.1%
52868 1
0.1%

필지(수)
Real number (ℝ)

Distinct1218
Distinct (%)93.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4680.6895
Minimum108
Maximum45068
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2023-12-11T08:08:51.957782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum108
5-th percentile485
Q11267
median3500
Q36385
95-th percentile13222
Maximum45068
Range44960
Interquartile range (IQR)5118

Descriptive statistics

Standard deviation4607.2293
Coefficient of variation (CV)0.9843057
Kurtosis11.346247
Mean4680.6895
Median Absolute Deviation (MAD)2422
Skewness2.5444056
Sum6089577
Variance21226562
MonotonicityNot monotonic
2023-12-11T08:08:52.130887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
715 3
 
0.2%
771 3
 
0.2%
3805 3
 
0.2%
8112 3
 
0.2%
723 3
 
0.2%
4548 2
 
0.2%
722 2
 
0.2%
329 2
 
0.2%
776 2
 
0.2%
1145 2
 
0.2%
Other values (1208) 1276
98.1%
ValueCountFrequency (%)
108 1
0.1%
135 1
0.1%
142 1
0.1%
148 1
0.1%
152 1
0.1%
161 1
0.1%
165 1
0.1%
171 1
0.1%
199 1
0.1%
204 1
0.1%
ValueCountFrequency (%)
45068 1
0.1%
36536 1
0.1%
33598 1
0.1%
33137 1
0.1%
31126 1
0.1%
28671 1
0.1%
27272 1
0.1%
27147 1
0.1%
25437 1
0.1%
24341 1
0.1%

Interactions

2023-12-11T08:08:48.997219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:08:48.332615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:08:48.623506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:08:49.094327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:08:48.416414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:08:48.728027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:08:49.217206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:08:48.516343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:08:48.887180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T08:08:52.227703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
통계연도시도명부동산거래현황(수)동(호)수필지(수)
통계연도1.0000.0000.0000.0000.000
시도명0.0001.0000.4060.4890.467
부동산거래현황(수)0.0000.4061.0000.9370.884
동(호)수0.0000.4890.9371.0000.779
필지(수)0.0000.4670.8840.7791.000
2023-12-11T08:08:52.330101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
통계연도시도명
통계연도1.0000.000
시도명0.0001.000
2023-12-11T08:08:52.431783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
부동산거래현황(수)동(호)수필지(수)통계연도시도명
부동산거래현황(수)1.0000.9050.2920.0000.171
동(호)수0.9051.000-0.0780.0000.215
필지(수)0.292-0.0781.0000.0000.203
통계연도0.0000.0000.0001.0000.000
시도명0.1710.2150.2030.0001.000

Missing values

2023-12-11T08:08:49.345431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T08:08:49.449017image/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

통계연도시도명시군구명부동산거래현황(수)동(호)수필지(수)
02017서울특별시종로구72926382910
12017서울특별시중구76546796858
22017서울특별시용산구1129410642652
32017서울특별시성동구1298212273709
42017서울특별시광진구1109610769327
52017서울특별시동대문구1290012117783
62017서울특별시중랑구1159110947644
72017서울특별시성북구15457141431314
82017서울특별시강북구91058734371
92017서울특별시도봉구99269659267
통계연도시도명시군구명부동산거래현황(수)동(호)수필지(수)
12912021경상남도창녕군664412515393
12922021경상남도고성군741411066308
12932021경상남도남해군766318365827
12942021경상남도하동군66568795777
12952021경상남도산청군58378345003
12962021경상남도함양군49457364209
12972021경상남도거창군823121596072
12982021경상남도합천군80798847195
12992021제주특별자치도제주시320381635915679
13002021제주특별자치도서귀포시1657971109469