Overview

Dataset statistics

Number of variables6
Number of observations4104
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory200.5 KiB
Average record size in memory50.0 B

Variable types

Categorical4
Text1
Numeric1

Dataset

Description김해시에서 통계기반 도시현황 파악을 위해 개발한 통계지수 중 하나로서, 통계연도, 시도명, 시군구명, 의료시설별, 교통수단별, 의료시설 평균접근시간(분)으로 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/15110168/fileData.do

Alerts

의료시설 평균접근시간(분) is highly overall correlated with 교통수단별High correlation
교통수단별 is highly overall correlated with 의료시설 평균접근시간(분)High correlation

Reproduction

Analysis started2023-12-12 14:03:01.916135
Analysis finished2023-12-12 14:03:02.623070
Duration0.71 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

통계연도
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size32.2 KiB
2017
1368 
2018
1368 
2019
1368 

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 1368
33.3%
2018 1368
33.3%
2019 1368
33.3%

Length

2023-12-12T23:03:02.682743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:03:02.798604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2017 1368
33.3%
2018 1368
33.3%
2019 1368
33.3%

시도명
Categorical

Distinct16
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size32.2 KiB
경기도
558 
서울특별시
450 
경상북도
414 
전라남도
396 
강원도
324 
Other values (11)
1962 

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 558
13.6%
서울특별시 450
11.0%
경상북도 414
10.1%
전라남도 396
9.6%
강원도 324
7.9%
경상남도 324
7.9%
부산광역시 288
7.0%
충청남도 270
6.6%
전라북도 252
 
6.1%
충청북도 198
 
4.8%
Other values (6) 630
15.4%

Length

2023-12-12T23:03:02.954597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기도 558
13.6%
서울특별시 450
11.0%
경상북도 414
10.1%
전라남도 396
9.6%
강원도 324
7.9%
경상남도 324
7.9%
부산광역시 288
7.0%
충청남도 270
6.6%
전라북도 252
 
6.1%
충청북도 198
 
4.8%
Other values (6) 630
15.4%

시군구명
Text

Distinct206
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size32.2 KiB
2023-12-12T23:03:03.315739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.9342105
Min length2

Characters and Unicode

Total characters12042
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 108
 
2.6%
중구 108
 
2.6%
서구 90
 
2.2%
북구 72
 
1.8%
남구 72
 
1.8%
고성군 36
 
0.9%
강서구 36
 
0.9%
김제시 18
 
0.4%
진안군 18
 
0.4%
완주군 18
 
0.4%
Other values (196) 3528
86.0%
2023-12-12T23:03:03.947986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
군 1530
 
12.7%
시 1404
 
11.7%
구 1332
 
11.1%
천 396
 
3.3%
주 360
 
3.0%
양 324
 
2.7%
성 324
 
2.7%
동 306
 
2.5%
산 288
 
2.4%
서 234
 
1.9%
Other values (122) 5544
46.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 12042
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
군 1530
 
12.7%
시 1404
 
11.7%
구 1332
 
11.1%
천 396
 
3.3%
주 360
 
3.0%
양 324
 
2.7%
성 324
 
2.7%
동 306
 
2.5%
산 288
 
2.4%
서 234
 
1.9%
Other values (122) 5544
46.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 12042
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
군 1530
 
12.7%
시 1404
 
11.7%
구 1332
 
11.1%
천 396
 
3.3%
주 360
 
3.0%
양 324
 
2.7%
성 324
 
2.7%
동 306
 
2.5%
산 288
 
2.4%
서 234
 
1.9%
Other values (122) 5544
46.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 12042
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
군 1530
 
12.7%
시 1404
 
11.7%
구 1332
 
11.1%
천 396
 
3.3%
주 360
 
3.0%
양 324
 
2.7%
성 324
 
2.7%
동 306
 
2.5%
산 288
 
2.4%
서 234
 
1.9%
Other values (122) 5544
46.0%

의료시설별
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size32.2 KiB
공공의료시설
1368 
병·의원
1368 
종합병원
1368 

Length

Max length6
Median length4
Mean length4.6666667
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
공공의료시설 1368
33.3%
병·의원 1368
33.3%
종합병원 1368
33.3%

Length

2023-12-12T23:03:04.114270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:03:04.249780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
공공의료시설 1368
33.3%
병·의원 1368
33.3%
종합병원 1368
33.3%

교통수단별
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.2 KiB
승용차
2052 
대중교통/도보
2052 

Length

Max length7
Median length5
Mean length5
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
승용차 2052
50.0%
대중교통/도보 2052
50.0%

Length

2023-12-12T23:03:04.397626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:03:04.524946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
승용차 2052
50.0%
대중교통/도보 2052
50.0%

의료시설 평균접근시간(분)
Real number (ℝ)

HIGH CORRELATION 

Distinct2448
Distinct (%)59.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.953324
Minimum1.53
Maximum120
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size36.2 KiB
2023-12-12T23:03:04.664292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.53
5-th percentile2.29
Q16.47
median12.57
Q327.1025
95-th percentile94.807
Maximum120
Range118.47
Interquartile range (IQR)20.6325

Descriptive statistics

Standard deviation27.114762
Coefficient of variation (CV)1.1813
Kurtosis4.4585371
Mean22.953324
Median Absolute Deviation (MAD)7.525
Skewness2.1934581
Sum94200.44
Variance735.21031
MonotonicityNot monotonic
2023-12-12T23:03:04.851730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
120.0 122
 
3.0%
1.93 11
 
0.3%
3.35 10
 
0.2%
1.87 9
 
0.2%
2.12 8
 
0.2%
3.52 8
 
0.2%
2.1 7
 
0.2%
1.92 7
 
0.2%
3.27 6
 
0.1%
12.57 6
 
0.1%
Other values (2438) 3910
95.3%
ValueCountFrequency (%)
1.53 1
 
< 0.1%
1.61 1
 
< 0.1%
1.62 1
 
< 0.1%
1.63 1
 
< 0.1%
1.64 1
 
< 0.1%
1.65 1
 
< 0.1%
1.66 1
 
< 0.1%
1.67 3
0.1%
1.68 3
0.1%
1.69 1
 
< 0.1%
ValueCountFrequency (%)
120.0 122
3.0%
119.31 1
 
< 0.1%
119.13 1
 
< 0.1%
119.09 1
 
< 0.1%
117.69 1
 
< 0.1%
117.31 1
 
< 0.1%
116.62 1
 
< 0.1%
116.36 1
 
< 0.1%
115.96 1
 
< 0.1%
115.88 1
 
< 0.1%

Interactions

2023-12-12T23:03:02.329374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T23:03:04.966391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
통계연도시도명의료시설별교통수단별의료시설 평균접근시간(분)
통계연도1.0000.0000.0000.0000.086
시도명0.0001.0000.0000.0000.361
의료시설별0.0000.0001.0000.0000.503
교통수단별0.0000.0000.0001.0000.739
의료시설 평균접근시간(분)0.0860.3610.5030.7391.000
2023-12-12T23:03:05.067572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
의료시설별교통수단별통계연도시도명
의료시설별1.0000.0000.0000.000
교통수단별0.0001.0000.0000.000
통계연도0.0000.0001.0000.000
시도명0.0000.0000.0001.000
2023-12-12T23:03:05.189571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
의료시설 평균접근시간(분)통계연도시도명의료시설별교통수단별
의료시설 평균접근시간(분)1.0000.0510.1500.3490.580
통계연도0.0511.0000.0000.0000.000
시도명0.1500.0001.0000.0000.000
의료시설별0.3490.0000.0001.0000.000
교통수단별0.5800.0000.0000.0001.000

Missing values

2023-12-12T23:03:02.448545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T23:03:02.576944image/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

통계연도시도명시군구명의료시설별교통수단별의료시설 평균접근시간(분)
02017서울특별시종로구공공의료시설승용차10.94
12017서울특별시중구공공의료시설승용차9.29
22017서울특별시용산구공공의료시설승용차12.46
32017서울특별시성동구공공의료시설승용차9.71
42017서울특별시광진구공공의료시설승용차11.11
52017서울특별시동대문구공공의료시설승용차12.71
62017서울특별시중랑구공공의료시설승용차12.2
72017서울특별시성북구공공의료시설승용차15.7
82017서울특별시강북구공공의료시설승용차11.95
92017서울특별시도봉구공공의료시설승용차7.59
통계연도시도명시군구명의료시설별교통수단별의료시설 평균접근시간(분)
40942019경상남도창녕군종합병원대중교통/도보120.0
40952019경상남도고성군종합병원대중교통/도보120.0
40962019경상남도남해군종합병원대중교통/도보120.0
40972019경상남도하동군종합병원대중교통/도보120.0
40982019경상남도산청군종합병원대중교통/도보120.0
40992019경상남도함양군종합병원대중교통/도보120.0
41002019경상남도거창군종합병원대중교통/도보120.0
41012019경상남도합천군종합병원대중교통/도보120.0
41022019제주특별자치도제주시종합병원대중교통/도보28.51
41032019제주특별자치도서귀포시종합병원대중교통/도보46.92