Overview

Dataset statistics

Number of variables4
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory410.2 KiB
Average record size in memory42.0 B

Variable types

Numeric2
Categorical1
Text1

Dataset

Description김해시에서 통계기반 도시현황 파악을 위해 개발한 통계지수 중 하나로서, 통계연월, 시도명, 시군구명, 지가변동률(퍼센트)로 구성되어 있습니다. 김해시 중심의 통계지수로서, 데이터 수집, 가공 등의 어려움으로 김해시 외 지역의 정보는 누락될 수 있습니다.
Author경상남도 김해시
URLhttps://www.data.go.kr/data/15110140/fileData.do

Reproduction

Analysis started2023-12-12 12:12:34.121055
Analysis finished2023-12-12 12:12:35.049729
Duration0.93 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

통계연월
Real number (ℝ)

Distinct60
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean201907.45
Minimum201701
Maximum202112
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T21:12:35.129821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum201701
5-th percentile201704
Q1201804
median201907
Q3202010
95-th percentile202110
Maximum202112
Range411
Interquartile range (IQR)206

Descriptive statistics

Standard deviation141.84281
Coefficient of variation (CV)0.00070251401
Kurtosis-1.3067591
Mean201907.45
Median Absolute Deviation (MAD)103
Skewness-0.011817436
Sum2.0190745 × 109
Variance20119.383
MonotonicityNot monotonic
2023-12-12T21:12:35.301439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
201709 180
 
1.8%
201705 179
 
1.8%
202110 179
 
1.8%
202006 178
 
1.8%
202010 178
 
1.8%
201710 175
 
1.8%
202102 175
 
1.8%
202107 174
 
1.7%
202002 174
 
1.7%
202101 172
 
1.7%
Other values (50) 8236
82.4%
ValueCountFrequency (%)
201701 165
1.7%
201702 166
1.7%
201703 167
1.7%
201704 166
1.7%
201705 179
1.8%
201706 162
1.6%
201707 161
1.6%
201708 150
1.5%
201709 180
1.8%
201710 175
1.8%
ValueCountFrequency (%)
202112 168
1.7%
202111 171
1.7%
202110 179
1.8%
202109 164
1.6%
202108 165
1.7%
202107 174
1.7%
202106 163
1.6%
202105 156
1.6%
202104 169
1.7%
202103 167
1.7%

시도명
Categorical

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
경기도
1882 
경상북도
996 
서울특별시
941 
경상남도
899 
전라남도
845 
Other values (11)
4437 

Length

Max length7
Median length5
Mean length4.0431
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row경상남도
2nd row서울특별시
3rd row제주특별자치도
4th row경상북도
5th row강원도

Common Values

ValueCountFrequency (%)
경기도 1882
18.8%
경상북도 996
10.0%
서울특별시 941
9.4%
경상남도 899
9.0%
전라남도 845
8.5%
강원도 700
 
7.0%
충청남도 636
 
6.4%
전라북도 599
 
6.0%
부산광역시 596
 
6.0%
충청북도 578
 
5.8%
Other values (6) 1328
13.3%

Length

2023-12-12T21:12:35.466061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기도 1882
18.8%
경상북도 996
10.0%
서울특별시 941
9.4%
경상남도 899
9.0%
전라남도 845
8.5%
강원도 700
 
7.0%
충청남도 636
 
6.4%
전라북도 599
 
6.0%
부산광역시 596
 
6.0%
충청북도 578
 
5.8%
Other values (6) 1328
13.3%

시군구명
Text

Distinct236
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T21:12:35.854215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length2.9537
Min length2

Characters and Unicode

Total characters29537
Distinct characters141
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
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 row통영시
2nd row용산구
3rd row서귀포시
4th row영주시
5th row태백시
ValueCountFrequency (%)
동구 235
 
2.4%
남구 211
 
2.1%
중구 210
 
2.1%
서구 204
 
2.0%
북구 189
 
1.9%
고성군 76
 
0.8%
강서구 74
 
0.7%
과천시 49
 
0.5%
이천시 48
 
0.5%
울릉군 48
 
0.5%
Other values (226) 8656
86.6%
2023-12-12T21:12:36.430578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
구 4033
 
13.7%
군 3256
 
11.0%
시 3059
 
10.4%
산 846
 
2.9%
천 846
 
2.9%
동 788
 
2.7%
주 750
 
2.5%
양 727
 
2.5%
성 721
 
2.4%
서 609
 
2.1%
Other values (131) 13902
47.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 29537
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 4033
 
13.7%
군 3256
 
11.0%
시 3059
 
10.4%
산 846
 
2.9%
천 846
 
2.9%
동 788
 
2.7%
주 750
 
2.5%
양 727
 
2.5%
성 721
 
2.4%
서 609
 
2.1%
Other values (131) 13902
47.1%

Most occurring scripts

ValueCountFrequency (%)
Hangul 29537
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 4033
 
13.7%
군 3256
 
11.0%
시 3059
 
10.4%
산 846
 
2.9%
천 846
 
2.9%
동 788
 
2.7%
주 750
 
2.5%
양 727
 
2.5%
성 721
 
2.4%
서 609
 
2.1%
Other values (131) 13902
47.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 29537
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
구 4033
 
13.7%
군 3256
 
11.0%
시 3059
 
10.4%
산 846
 
2.9%
천 846
 
2.9%
동 788
 
2.7%
주 750
 
2.5%
양 727
 
2.5%
성 721
 
2.4%
서 609
 
2.1%
Other values (131) 13902
47.1%

지가변동률(퍼센트)
Real number (ℝ)

Distinct97
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.278426
Minimum0
Maximum1.77
Zeros26
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T21:12:36.697912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.09
Q10.19
median0.27
Q30.35
95-th percentile0.5
Maximum1.77
Range1.77
Interquartile range (IQR)0.16

Descriptive statistics

Standard deviation0.12854274
Coefficient of variation (CV)0.46167648
Kurtosis4.0873918
Mean0.278426
Median Absolute Deviation (MAD)0.08
Skewness1.0270292
Sum2784.26
Variance0.016523235
MonotonicityNot monotonic
2023-12-12T21:12:36.882615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3 387
 
3.9%
0.28 358
 
3.6%
0.2 356
 
3.6%
0.24 355
 
3.5%
0.21 353
 
3.5%
0.31 341
 
3.4%
0.27 336
 
3.4%
0.22 335
 
3.4%
0.23 332
 
3.3%
0.29 322
 
3.2%
Other values (87) 6525
65.2%
ValueCountFrequency (%)
0.0 26
 
0.3%
0.01 42
0.4%
0.02 30
 
0.3%
0.03 40
0.4%
0.04 33
0.3%
0.05 55
0.5%
0.06 47
0.5%
0.07 68
0.7%
0.08 81
0.8%
0.09 81
0.8%
ValueCountFrequency (%)
1.77 1
< 0.1%
1.41 1
< 0.1%
1.17 1
< 0.1%
1.06 1
< 0.1%
1.03 1
< 0.1%
1.01 1
< 0.1%
0.96 1
< 0.1%
0.91 2
< 0.1%
0.9 1
< 0.1%
0.89 1
< 0.1%

Interactions

2023-12-12T21:12:34.670307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:12:34.437264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:12:34.769944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:12:34.552525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T21:12:37.010682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
통계연월시도명지가변동률(퍼센트)
통계연월1.0000.0000.216
시도명0.0001.0000.482
지가변동률(퍼센트)0.2160.4821.000
2023-12-12T21:12:37.111660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
통계연월지가변동률(퍼센트)시도명
통계연월1.000-0.0840.000
지가변동률(퍼센트)-0.0841.0000.222
시도명0.0000.2221.000

Missing values

2023-12-12T21:12:34.908282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T21:12:35.006719image/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

통계연월시도명시군구명지가변동률(퍼센트)
4402201805경상남도통영시0.24
14302202108서울특별시용산구0.47
6239201812제주특별자치도서귀포시0.24
1257201705경상북도영주시0.19
386201702강원도태백시0.15
14894202110경기도수원시0.4
8864201911서울특별시강동구0.61
5332201809강원도평창군0.21
834201704인천광역시부평구0.45
6172201812전라남도광양시0.31
통계연월시도명시군구명지가변동률(퍼센트)
6840201903경기도수정구0.42
3867201803경상북도청도군0.18
11682202009경상남도통영시0.02
8062201908서울특별시용산구0.55
11098202007전라북도남원시0.18
12204202011경상남도김해시0.08
5901201811전라북도진안군0.25
11799202010경기도구리시0.31
1235201705전라남도구례군0.39
13089202103경기도평택시0.31