Overview

Dataset statistics

Number of variables6
Number of observations1245
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory63.4 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, 의료기관 1개소당 주민수(명), 총인구수(명), 의료기관수(개소)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/15110157/fileData.do

Alerts

총인구수(명) is highly overall correlated with 의료기관수(개소)High correlation
의료기관수(개소) is highly overall correlated with 총인구수(명)High correlation
총인구수(명) has unique valuesUnique

Reproduction

Analysis started2023-12-12 17:11:47.731648
Analysis finished2023-12-12 17:11:49.139294
Duration1.41 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

통계연도
Categorical

Distinct5
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size9.9 KiB
2016
249 
2017
249 
2018
249 
2019
249 
2020
249 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2016
2nd row2016
3rd row2016
4th row2016
5th row2016

Common Values

ValueCountFrequency (%)
2016 249
20.0%
2017 249
20.0%
2018 249
20.0%
2019 249
20.0%
2020 249
20.0%

Length

2023-12-13T02:11:49.216158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:11:49.353615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2016 249
20.0%
2017 249
20.0%
2018 249
20.0%
2019 249
20.0%
2020 249
20.0%

시도명
Categorical

Distinct16
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size9.9 KiB
경기도
210 
서울특별시
125 
경상북도
120 
경상남도
110 
전라남도
110 
Other values (11)
570 

Length

Max length7
Median length5
Mean length4.0803213
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 210
16.9%
서울특별시 125
10.0%
경상북도 120
9.6%
경상남도 110
8.8%
전라남도 110
8.8%
강원도 90
7.2%
충청남도 80
 
6.4%
부산광역시 80
 
6.4%
전라북도 75
 
6.0%
충청북도 70
 
5.6%
Other values (6) 175
14.1%

Length

2023-12-13T02:11:49.498565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기도 210
16.9%
서울특별시 125
10.0%
경상북도 120
9.6%
경상남도 110
8.8%
전라남도 110
8.8%
강원도 90
7.2%
충청남도 80
 
6.4%
부산광역시 80
 
6.4%
전라북도 75
 
6.0%
충청북도 70
 
5.6%
Other values (6) 175
14.1%

시군구명
Text

Distinct227
Distinct (%)18.2%
Missing0
Missing (%)0.0%
Memory size9.9 KiB
2023-12-13T02:11:49.830150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length3
Mean length3.4666667
Min length2

Characters and Unicode

Total characters4316
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 row강릉시
2nd row고성군
3rd row동해시
4th row삼척시
5th row속초시
ValueCountFrequency (%)
동구 30
 
2.1%
중구 30
 
2.1%
남구 27
 
1.9%
창원시 25
 
1.8%
서구 25
 
1.8%
북구 25
 
1.8%
수원시 20
 
1.4%
청주시 20
 
1.4%
성남시 15
 
1.1%
고양시 15
 
1.1%
Other values (226) 1173
83.5%
2023-12-13T02:11:50.509993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
구 530
 
12.3%
시 495
 
11.5%
군 425
 
9.8%
160
 
3.7%
주 120
 
2.8%
산 115
 
2.7%
천 115
 
2.7%
양 110
 
2.5%
성 105
 
2.4%
동 100
 
2.3%
Other values (132) 2041
47.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4156
96.3%
Space Separator 160
 
3.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 530
 
12.8%
시 495
 
11.9%
군 425
 
10.2%
주 120
 
2.9%
산 115
 
2.8%
천 115
 
2.8%
양 110
 
2.6%
성 105
 
2.5%
동 100
 
2.4%
원 90
 
2.2%
Other values (131) 1951
46.9%
Space Separator
ValueCountFrequency (%)
160
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4156
96.3%
Common 160
 
3.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 530
 
12.8%
시 495
 
11.9%
군 425
 
10.2%
주 120
 
2.9%
산 115
 
2.8%
천 115
 
2.8%
양 110
 
2.6%
성 105
 
2.5%
동 100
 
2.4%
원 90
 
2.2%
Other values (131) 1951
46.9%
Common
ValueCountFrequency (%)
160
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4156
96.3%
ASCII 160
 
3.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
구 530
 
12.8%
시 495
 
11.9%
군 425
 
10.2%
주 120
 
2.9%
산 115
 
2.8%
천 115
 
2.8%
양 110
 
2.6%
성 105
 
2.5%
동 100
 
2.4%
원 90
 
2.2%
Other values (131) 1951
46.9%
ASCII
ValueCountFrequency (%)
160
100.0%

의료기관 1개소당 주민수(명)
Real number (ℝ)

Distinct1233
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean766.49963
Minimum167.5
Maximum1407.62
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.1 KiB
2023-12-13T02:11:50.654475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum167.5
5-th percentile512.578
Q1638.99
median749.43
Q3895.86
95-th percentile1075.972
Maximum1407.62
Range1240.12
Interquartile range (IQR)256.87

Descriptive statistics

Standard deviation186.97373
Coefficient of variation (CV)0.24393193
Kurtosis0.73368745
Mean766.49963
Median Absolute Deviation (MAD)122.64
Skewness0.10548959
Sum954292.04
Variance34959.177
MonotonicityNot monotonic
2023-12-13T02:11:50.790469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
804.69 2
 
0.2%
963.78 2
 
0.2%
580.21 2
 
0.2%
768.33 2
 
0.2%
562.71 2
 
0.2%
910.56 2
 
0.2%
801.09 2
 
0.2%
694.14 2
 
0.2%
557.63 2
 
0.2%
712.59 2
 
0.2%
Other values (1223) 1225
98.4%
ValueCountFrequency (%)
167.5 1
0.1%
171.29 1
0.1%
178.43 1
0.1%
183.69 1
0.1%
188.89 1
0.1%
200.16 1
0.1%
206.19 1
0.1%
209.0 1
0.1%
218.57 1
0.1%
220.35 1
0.1%
ValueCountFrequency (%)
1407.62 1
0.1%
1386.58 1
0.1%
1361.36 1
0.1%
1336.17 1
0.1%
1332.21 1
0.1%
1320.41 1
0.1%
1318.9 1
0.1%
1309.61 1
0.1%
1303.88 1
0.1%
1298.33 1
0.1%

총인구수(명)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct1245
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean206783.95
Minimum9077
Maximum855248
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.1 KiB
2023-12-13T02:11:50.925859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9077
5-th percentile27553.4
Q157041
median175866
Q3314194
95-th percentile503723.4
Maximum855248
Range846171
Interquartile range (IQR)257153

Descriptive statistics

Standard deviation164697.76
Coefficient of variation (CV)0.79647264
Kurtosis0.48786576
Mean206783.95
Median Absolute Deviation (MAD)123609
Skewness0.92444622
Sum2.5744602 × 108
Variance2.7125353 × 1010
MonotonicityNot monotonic
2023-12-13T02:11:51.046456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
213846 1
 
0.1%
193807 1
 
0.1%
23843 1
 
0.1%
419742 1
 
0.1%
32373 1
 
0.1%
255402 1
 
0.1%
263185 1
 
0.1%
271392 1
 
0.1%
235633 1
 
0.1%
177784 1
 
0.1%
Other values (1235) 1235
99.2%
ValueCountFrequency (%)
9077 1
0.1%
9617 1
0.1%
9832 1
0.1%
9975 1
0.1%
10001 1
0.1%
16692 1
0.1%
16993 1
0.1%
17356 1
0.1%
17479 1
0.1%
17713 1
0.1%
ValueCountFrequency (%)
855248 1
0.1%
851380 1
0.1%
850329 1
0.1%
843768 1
0.1%
829996 1
0.1%
818383 1
0.1%
815396 1
0.1%
758722 1
0.1%
713321 1
0.1%
701830 1
0.1%

의료기관수(개소)
Real number (ℝ)

HIGH CORRELATION 

Distinct537
Distinct (%)43.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean283.91165
Minimum9
Maximum2694
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.1 KiB
2023-12-13T02:11:51.167366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile40
Q186
median220
Q3413
95-th percentile730.2
Maximum2694
Range2685
Interquartile range (IQR)327

Descriptive statistics

Standard deviation270.67879
Coefficient of variation (CV)0.95339094
Kurtosis20.671776
Mean283.91165
Median Absolute Deviation (MAD)146
Skewness3.1681343
Sum353470
Variance73267.008
MonotonicityNot monotonic
2023-12-13T02:11:51.287151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
145 13
 
1.0%
48 13
 
1.0%
86 12
 
1.0%
65 11
 
0.9%
89 10
 
0.8%
142 9
 
0.7%
45 9
 
0.7%
69 9
 
0.7%
55 9
 
0.7%
49 8
 
0.6%
Other values (527) 1142
91.7%
ValueCountFrequency (%)
9 5
0.4%
21 5
0.4%
25 8
0.6%
26 2
 
0.2%
27 2
 
0.2%
28 4
0.3%
29 7
0.6%
30 1
 
0.1%
31 2
 
0.2%
32 3
 
0.2%
ValueCountFrequency (%)
2694 1
0.1%
2644 1
0.1%
2595 1
0.1%
2524 1
0.1%
2500 1
0.1%
1254 1
0.1%
1229 1
0.1%
1221 1
0.1%
1215 1
0.1%
1204 1
0.1%

Interactions

2023-12-13T02:11:48.664130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:11:48.088852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:11:48.400264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:11:48.782596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:11:48.199354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:11:48.492954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:11:48.868923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:11:48.293190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:11:48.576599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T02:11:51.367754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
통계연도시도명의료기관 1개소당 주민수(명)총인구수(명)의료기관수(개소)
통계연도1.0000.0000.0000.0000.000
시도명0.0001.0000.5530.5990.602
의료기관 1개소당 주민수(명)0.0000.5531.0000.4910.572
총인구수(명)0.0000.5990.4911.0000.760
의료기관수(개소)0.0000.6020.5720.7601.000
2023-12-13T02:11:51.454337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시도명통계연도
시도명1.0000.000
통계연도0.0001.000
2023-12-13T02:11:51.538302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
의료기관 1개소당 주민수(명)총인구수(명)의료기관수(개소)통계연도시도명
의료기관 1개소당 주민수(명)1.0000.213-0.0200.0000.255
총인구수(명)0.2131.0000.9590.0000.283
의료기관수(개소)-0.0200.9591.0000.0000.340
통계연도0.0000.0000.0001.0000.000
시도명0.2550.2830.3400.0001.000

Missing values

2023-12-13T02:11:48.985450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T02:11:49.095826image/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

통계연도시도명시군구명의료기관 1개소당 주민수(명)총인구수(명)의료기관수(개소)
02016강원도강릉시869.29213846246
12016강원도고성군971.423011431
22016강원도동해시923.7393297101
32016강원도삼척시994.276959970
42016강원도속초시693.1681793118
52016강원도양구군960.42401025
62016강원도양양군1088.722721825
72016강원도영월군977.394007341
82016강원도원주시816.37337979414
92016강원도인제군1022.53272032
통계연도시도명시군구명의료기관 1개소당 주민수(명)총인구수(명)의료기관수(개소)
12352020인천광역시중구1126.85139729124
12362020인천광역시강화군804.696920386
12372020인천광역시계양구804.2296750369
12382020인천광역시남동구714.77525354735
12392020인천광역시부평구790.67494962626
12402020인천광역시연수구990.92387450391
12412020인천광역시옹진군705.342045529
12422020인천광역시미추홀구853.04404343474
12432020제주특별자치도제주시689.73492466714
12442020제주특별자치도서귀포시740.52182169246