Overview

Dataset statistics

Number of variables6
Number of observations1140
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory58.0 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, 인구 천명당 사설학원 수(개), 사설학원수(개), 주민등록인구수(명)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=15110186

Alerts

인구 천명당 사설학원 수(개) is highly overall correlated with 사설학원수(개) and 1 other fieldsHigh correlation
사설학원수(개) is highly overall correlated with 인구 천명당 사설학원 수(개) and 1 other fieldsHigh correlation
주민등록인구수(명) is highly overall correlated with 인구 천명당 사설학원 수(개) and 1 other fieldsHigh correlation

Reproduction

Analysis started2023-12-11 00:23:51.351937
Analysis finished2023-12-11 00:23:52.860782
Duration1.51 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

통계연도
Categorical

Distinct5
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size9.0 KiB
2017
228 
2018
228 
2019
228 
2020
228 
2021
228 

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 (%)
2017 228
20.0%
2018 228
20.0%
2019 228
20.0%
2020 228
20.0%
2021 228
20.0%

Length

2023-12-11T09:23:52.931200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:23:53.303516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2017 228
20.0%
2018 228
20.0%
2019 228
20.0%
2020 228
20.0%
2021 228
20.0%

시도명
Categorical

Distinct16
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size9.0 KiB
경기도
155 
서울특별시
125 
경상북도
115 
전라남도
110 
강원도
90 
Other values (11)
545 

Length

Max length7
Median length5
Mean length4.1359649
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 155
13.6%
서울특별시 125
11.0%
경상북도 115
10.1%
전라남도 110
9.6%
강원도 90
7.9%
경상남도 90
7.9%
부산광역시 80
7.0%
충청남도 75
6.6%
전라북도 70
 
6.1%
충청북도 55
 
4.8%
Other values (6) 175
15.4%

Length

2023-12-11T09:23:53.425502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기도 155
13.6%
서울특별시 125
11.0%
경상북도 115
10.1%
전라남도 110
9.6%
강원도 90
7.9%
경상남도 90
7.9%
부산광역시 80
7.0%
충청남도 75
6.6%
전라북도 70
 
6.1%
충청북도 55
 
4.8%
Other values (6) 175
15.4%
Distinct206
Distinct (%)18.1%
Missing0
Missing (%)0.0%
Memory size9.0 KiB
2023-12-11T09:23:53.781549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.9307018
Min length2

Characters and Unicode

Total characters3341
Distinct characters132
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 row종로구
2nd row중구
3rd row용산구
4th row성동구
5th row광진구
ValueCountFrequency (%)
동구 30
 
2.6%
중구 30
 
2.6%
서구 25
 
2.2%
남구 22
 
1.9%
북구 20
 
1.8%
고성군 10
 
0.9%
강서구 10
 
0.9%
완주군 5
 
0.4%
무주군 5
 
0.4%
진안군 5
 
0.4%
Other values (196) 978
85.8%
2023-12-11T09:23:54.274997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
425
 
12.7%
390
 
11.7%
370
 
11.1%
110
 
3.3%
100
 
3.0%
90
 
2.7%
90
 
2.7%
85
 
2.5%
80
 
2.4%
65
 
1.9%
Other values (122) 1536
46.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3341
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
425
 
12.7%
390
 
11.7%
370
 
11.1%
110
 
3.3%
100
 
3.0%
90
 
2.7%
90
 
2.7%
85
 
2.5%
80
 
2.4%
65
 
1.9%
Other values (122) 1536
46.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3341
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
425
 
12.7%
390
 
11.7%
370
 
11.1%
110
 
3.3%
100
 
3.0%
90
 
2.7%
90
 
2.7%
85
 
2.5%
80
 
2.4%
65
 
1.9%
Other values (122) 1536
46.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3341
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
425
 
12.7%
390
 
11.7%
370
 
11.1%
110
 
3.3%
100
 
3.0%
90
 
2.7%
90
 
2.7%
85
 
2.5%
80
 
2.4%
65
 
1.9%
Other values (122) 1536
46.0%

인구 천명당 사설학원 수(개)
Real number (ℝ)

HIGH CORRELATION 

Distinct41
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2729825
Minimum0.1
Maximum4.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2023-12-11T09:23:54.418140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.5
Q10.8
median1.2
Q31.6
95-th percentile2.205
Maximum4.5
Range4.4
Interquartile range (IQR)0.8

Descriptive statistics

Standard deviation0.59876069
Coefficient of variation (CV)0.47036052
Kurtosis3.6419817
Mean1.2729825
Median Absolute Deviation (MAD)0.4
Skewness1.2843176
Sum1451.2
Variance0.35851436
MonotonicityNot monotonic
2023-12-11T09:23:54.553772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
0.7 88
 
7.7%
0.9 87
 
7.6%
1.3 85
 
7.5%
1.0 82
 
7.2%
0.8 81
 
7.1%
1.4 78
 
6.8%
1.2 77
 
6.8%
1.1 66
 
5.8%
1.7 61
 
5.4%
1.5 60
 
5.3%
Other values (31) 375
32.9%
ValueCountFrequency (%)
0.1 3
 
0.3%
0.2 12
 
1.1%
0.3 17
 
1.5%
0.4 20
 
1.8%
0.5 20
 
1.8%
0.6 46
4.0%
0.7 88
7.7%
0.8 81
7.1%
0.9 87
7.6%
1.0 82
7.2%
ValueCountFrequency (%)
4.5 1
 
0.1%
4.4 1
 
0.1%
4.2 2
0.2%
4.0 2
0.2%
3.9 1
 
0.1%
3.8 1
 
0.1%
3.7 1
 
0.1%
3.6 1
 
0.1%
3.3 2
0.2%
3.2 3
0.3%

사설학원수(개)
Real number (ℝ)

HIGH CORRELATION 

Distinct597
Distinct (%)52.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean352.7693
Minimum1
Maximum2383
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2023-12-11T09:23:54.674919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile14
Q151
median188
Q3472.75
95-th percentile1143.15
Maximum2383
Range2382
Interquartile range (IQR)421.75

Descriptive statistics

Standard deviation432.76674
Coefficient of variation (CV)1.2267699
Kurtosis5.0841117
Mean352.7693
Median Absolute Deviation (MAD)162
Skewness2.1214637
Sum402157
Variance187287.05
MonotonicityNot monotonic
2023-12-11T09:23:54.803947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
51 13
 
1.1%
13 12
 
1.1%
20 12
 
1.1%
16 12
 
1.1%
19 12
 
1.1%
28 10
 
0.9%
50 10
 
0.9%
14 10
 
0.9%
26 10
 
0.9%
18 9
 
0.8%
Other values (587) 1030
90.4%
ValueCountFrequency (%)
1 2
 
0.2%
2 3
 
0.3%
4 5
0.4%
6 6
0.5%
7 3
 
0.3%
8 6
0.5%
10 5
0.4%
11 2
 
0.2%
12 8
0.7%
13 12
1.1%
ValueCountFrequency (%)
2383 1
0.1%
2361 1
0.1%
2279 1
0.1%
2263 1
0.1%
2246 1
0.1%
2091 1
0.1%
2057 1
0.1%
2052 1
0.1%
2050 1
0.1%
2042 1
0.1%

주민등록인구수(명)
Real number (ℝ)

HIGH CORRELATION 

Distinct1139
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean225666.46
Minimum8867
Maximum1202628
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2023-12-11T09:23:54.935952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8867
5-th percentile26852.25
Q152752.5
median148246
Q3341557
95-th percentile654420.05
Maximum1202628
Range1193761
Interquartile range (IQR)288804.5

Descriptive statistics

Standard deviation221758.75
Coefficient of variation (CV)0.98268368
Kurtosis3.218356
Mean225666.46
Median Absolute Deviation (MAD)108090.5
Skewness1.6655025
Sum2.5725977 × 108
Variance4.9176943 × 1010
MonotonicityNot monotonic
2023-12-11T09:23:55.069136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
122499 2
 
0.2%
154770 1
 
0.1%
1186078 1
 
0.1%
537307 1
 
0.1%
298599 1
 
0.1%
818383 1
 
0.1%
550027 1
 
0.1%
461710 1
 
0.1%
940064 1
 
0.1%
222538 1
 
0.1%
Other values (1129) 1129
99.0%
ValueCountFrequency (%)
8867 1
0.1%
9077 1
0.1%
9617 1
0.1%
9832 1
0.1%
9975 1
0.1%
16320 1
0.1%
16692 1
0.1%
16993 1
0.1%
17356 1
0.1%
17479 1
0.1%
ValueCountFrequency (%)
1202628 1
0.1%
1201166 1
0.1%
1194465 1
0.1%
1186078 1
0.1%
1183714 1
0.1%
1079353 1
0.1%
1079216 1
0.1%
1077508 1
0.1%
1074176 1
0.1%
1066351 1
0.1%

Interactions

2023-12-11T09:23:52.339483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:23:51.681272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:23:52.043951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:23:52.438476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:23:51.796548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:23:52.142143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:23:52.553209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:23:51.924676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:23:52.247774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T09:23:55.147931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
통계연도시도명인구 천명당 사설학원 수(개)사설학원수(개)주민등록인구수(명)
통계연도1.0000.0000.0000.0000.000
시도명0.0001.0000.6250.5780.605
인구 천명당 사설학원 수(개)0.0000.6251.0000.8510.652
사설학원수(개)0.0000.5780.8511.0000.929
주민등록인구수(명)0.0000.6050.6520.9291.000
2023-12-11T09:23:55.227706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
통계연도시도명
통계연도1.0000.000
시도명0.0001.000
2023-12-11T09:23:55.297421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
인구 천명당 사설학원 수(개)사설학원수(개)주민등록인구수(명)통계연도시도명
인구 천명당 사설학원 수(개)1.0000.8350.6880.0000.291
사설학원수(개)0.8351.0000.9690.0000.269
주민등록인구수(명)0.6880.9691.0000.0000.287
통계연도0.0000.0000.0001.0000.000
시도명0.2910.2690.2870.0001.000

Missing values

2023-12-11T09:23:52.692788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T09:23:52.810867image/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

통계연도시도명시군구명인구 천명당 사설학원 수(개)사설학원수(개)주민등록인구수(명)
02017서울특별시종로구1.9297154770
12017서울특별시중구1.0128125709
22017서울특별시용산구0.8175229161
32017서울특별시성동구1.0295304808
42017서울특별시광진구1.2442357703
52017서울특별시동대문구1.0360350647
62017서울특별시중랑구0.7288408226
72017서울특별시성북구1.0457444055
82017서울특별시강북구0.7227324479
92017서울특별시도봉구1.0360344166
통계연도시도명시군구명인구 천명당 사설학원 수(개)사설학원수(개)주민등록인구수(명)
11302021경상남도창녕군0.85060129
11312021경상남도고성군1.36450478
11322021경상남도남해군1.04342266
11332021경상남도하동군0.83343449
11342021경상남도산청군0.61934360
11352021경상남도함양군1.03938310
11362021경상남도거창군1.48361073
11372021경상남도합천군0.72842935
11382021제주특별자치도제주시1.8890493096
11392021제주특별자치도서귀포시1.1205183663