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, 교원 1인당 학생수(명), 재적학생수(명), 교원수(명)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/15110182/fileData.do

Alerts

교원 1인당 학생수(명) is highly overall correlated with 재적학생수(명) and 1 other fieldsHigh correlation
재적학생수(명) is highly overall correlated with 교원 1인당 학생수(명) and 1 other fieldsHigh correlation
교원수(명) is highly overall correlated with 교원 1인당 학생수(명) and 1 other fieldsHigh correlation

Reproduction

Analysis started2023-12-12 08:39:08.266007
Analysis finished2023-12-12 08:39:10.206063
Duration1.94 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

2023-12-12T17:39:10.302613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T17:39:10.798927image/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

2023-12-12T17:39:10.969395image/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%

시군구명
Text

Distinct206
Distinct (%)18.1%
Missing0
Missing (%)0.0%
Memory size9.0 KiB
2023-12-12T17:39:11.403757image/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%
2023-12-12T17:39:12.016434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
군 425
 
12.7%
시 390
 
11.7%
구 370
 
11.1%
천 110
 
3.3%
주 100
 
3.0%
성 90
 
2.7%
양 90
 
2.7%
동 85
 
2.5%
산 80
 
2.4%
서 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 (%)
군 425
 
12.7%
시 390
 
11.7%
구 370
 
11.1%
천 110
 
3.3%
주 100
 
3.0%
성 90
 
2.7%
양 90
 
2.7%
동 85
 
2.5%
산 80
 
2.4%
서 65
 
1.9%
Other values (122) 1536
46.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3341
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
군 425
 
12.7%
시 390
 
11.7%
구 370
 
11.1%
천 110
 
3.3%
주 100
 
3.0%
성 90
 
2.7%
양 90
 
2.7%
동 85
 
2.5%
산 80
 
2.4%
서 65
 
1.9%
Other values (122) 1536
46.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3341
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
군 425
 
12.7%
시 390
 
11.7%
구 370
 
11.1%
천 110
 
3.3%
주 100
 
3.0%
성 90
 
2.7%
양 90
 
2.7%
동 85
 
2.5%
산 80
 
2.4%
서 65
 
1.9%
Other values (122) 1536
46.0%

교원 1인당 학생수(명)
Real number (ℝ)

HIGH CORRELATION 

Distinct803
Distinct (%)70.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.040395
Minimum3.98
Maximum26.08
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2023-12-12T17:39:12.189891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.98
5-th percentile6.0395
Q19.205
median13.575
Q316.2775
95-th percentile19.8625
Maximum26.08
Range22.1
Interquartile range (IQR)7.0725

Descriptive statistics

Standard deviation4.4519449
Coefficient of variation (CV)0.34139649
Kurtosis-0.64825078
Mean13.040395
Median Absolute Deviation (MAD)3.165
Skewness0.020128393
Sum14866.05
Variance19.819814
MonotonicityNot monotonic
2023-12-12T17:39:12.359512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13.58 5
 
0.4%
16.71 5
 
0.4%
13.33 5
 
0.4%
7.63 4
 
0.4%
15.76 4
 
0.4%
14.97 4
 
0.4%
7.57 4
 
0.4%
9.94 4
 
0.4%
13.82 4
 
0.4%
6.78 4
 
0.4%
Other values (793) 1097
96.2%
ValueCountFrequency (%)
3.98 1
0.1%
4.03 1
0.1%
4.23 1
0.1%
4.4 1
0.1%
4.56 1
0.1%
4.67 1
0.1%
4.77 1
0.1%
4.92 1
0.1%
4.93 1
0.1%
5.01 2
0.2%
ValueCountFrequency (%)
26.08 1
0.1%
25.55 1
0.1%
25.28 1
0.1%
24.69 1
0.1%
24.67 1
0.1%
24.57 1
0.1%
24.56 1
0.1%
24.44 1
0.1%
24.08 1
0.1%
23.99 1
0.1%

재적학생수(명)
Real number (ℝ)

HIGH CORRELATION 

Distinct1127
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38241.754
Minimum547
Maximum210232
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2023-12-12T17:39:12.518152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum547
5-th percentile2181.35
Q15917.75
median23490.5
Q357685.25
95-th percentile127113.05
Maximum210232
Range209685
Interquartile range (IQR)51767.5

Descriptive statistics

Standard deviation41364.554
Coefficient of variation (CV)1.0816594
Kurtosis2.3981506
Mean38241.754
Median Absolute Deviation (MAD)19759
Skewness1.5698713
Sum43595600
Variance1.7110263 × 109
MonotonicityNot monotonic
2023-12-12T17:39:12.701368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2860 3
 
0.3%
7458 2
 
0.2%
4318 2
 
0.2%
55528 2
 
0.2%
4145 2
 
0.2%
2691 2
 
0.2%
2979 2
 
0.2%
2990 2
 
0.2%
2443 2
 
0.2%
5563 2
 
0.2%
Other values (1117) 1119
98.2%
ValueCountFrequency (%)
547 1
0.1%
556 1
0.1%
597 1
0.1%
641 1
0.1%
650 1
0.1%
1045 1
0.1%
1075 1
0.1%
1098 1
0.1%
1113 1
0.1%
1127 1
0.1%
ValueCountFrequency (%)
210232 1
0.1%
205724 1
0.1%
199692 1
0.1%
199608 1
0.1%
197824 1
0.1%
195491 1
0.1%
194843 1
0.1%
194263 1
0.1%
191252 1
0.1%
190772 1
0.1%

교원수(명)
Real number (ℝ)

HIGH CORRELATION 

Distinct978
Distinct (%)85.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2459.2561
Minimum97
Maximum12825
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2023-12-12T17:39:12.908082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum97
5-th percentile337.95
Q1616.5
median1641
Q33622.25
95-th percentile6968.7
Maximum12825
Range12728
Interquartile range (IQR)3005.75

Descriptive statistics

Standard deviation2363.7136
Coefficient of variation (CV)0.96114983
Kurtosis2.8954157
Mean2459.2561
Median Absolute Deviation (MAD)1158.5
Skewness1.6169192
Sum2803552
Variance5587142.1
MonotonicityNot monotonic
2023-12-12T17:39:13.089650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
520 4
 
0.4%
549 4
 
0.4%
351 4
 
0.4%
336 3
 
0.3%
609 3
 
0.3%
1410 3
 
0.3%
1622 3
 
0.3%
400 3
 
0.3%
1290 3
 
0.3%
536 3
 
0.3%
Other values (968) 1107
97.1%
ValueCountFrequency (%)
97 1
0.1%
98 1
0.1%
111 1
0.1%
117 1
0.1%
121 1
0.1%
195 1
0.1%
196 2
0.2%
200 1
0.1%
202 1
0.1%
203 2
0.2%
ValueCountFrequency (%)
12825 1
0.1%
12813 1
0.1%
12797 1
0.1%
12786 1
0.1%
12774 1
0.1%
11385 1
0.1%
11348 1
0.1%
11326 1
0.1%
11307 1
0.1%
11255 1
0.1%

Interactions

2023-12-12T17:39:09.487473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:39:08.646872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:39:09.042808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:39:09.641675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:39:08.779151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:39:09.176355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:39:09.788300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:39:08.914730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:39:09.321877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T17:39:13.188809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
통계연도시도명교원 1인당 학생수(명)재적학생수(명)교원수(명)
통계연도1.0000.0000.0000.0000.000
시도명0.0001.0000.5790.6060.615
교원 1인당 학생수(명)0.0000.5791.0000.7400.686
재적학생수(명)0.0000.6060.7401.0000.957
교원수(명)0.0000.6150.6860.9571.000
2023-12-12T17:39:13.294812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
통계연도시도명
통계연도1.0000.000
시도명0.0001.000
2023-12-12T17:39:13.392180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
교원 1인당 학생수(명)재적학생수(명)교원수(명)통계연도시도명
교원 1인당 학생수(명)1.0000.8480.7610.0000.270
재적학생수(명)0.8481.0000.9860.0000.288
교원수(명)0.7610.9861.0000.0000.294
통계연도0.0000.0000.0001.0000.000
시도명0.2700.2880.2940.0001.000

Missing values

2023-12-12T17:39:09.981465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T17:39:10.142509image/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

통계연도시도명시군구명교원 1인당 학생수(명)재적학생수(명)교원수(명)
02017서울특별시종로구18.39602573277
12017서울특별시중구18.61390082096
22017서울특별시용산구15.47327692118
32017서울특별시성동구17.23561283257
42017서울특별시광진구20.33751733698
52017서울특별시동대문구22.941055144600
62017서울특별시중랑구15.64440882819
72017서울특별시성북구21.591345106229
82017서울특별시강북구13.76261091897
92017서울특별시도봉구15.09419422780
통계연도시도명시군구명교원 1인당 학생수(명)재적학생수(명)교원수(명)
11302021경상남도창녕군7.985280662
11312021경상남도고성군7.754729610
11322021경상남도남해군8.964711526
11332021경상남도하동군5.982860478
11342021경상남도산청군6.022443406
11352021경상남도함양군7.463215431
11362021경상남도거창군11.999415785
11372021경상남도합천군5.372648493
11382021제주특별자치도제주시16.47937475691
11392021제주특별자치도서귀포시10.98197321797