Overview

Dataset statistics

Number of variables7
Number of observations178
Missing cells8
Missing cells (%)0.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.7 KiB
Average record size in memory61.7 B

Variable types

Categorical2
Numeric5

Dataset

Description2019년~2022년 충청남도 요양기관종별 기관수 데이터로 시군별 구분, 요양기관종별, 2019, 2020, 2021, 2022, 총합계 정보를 제공합니다.
Author충청남도
URLhttps://alldam.chungnam.go.kr/index.chungnam?menuCd=DOM_000000201001001001&st=&cds=&orgCd=&apiType=&isOpen=Y&pageIndex=7&beforeMenuCd=DOM_000000201001001000&publicdatapk=15124817

Alerts

2019 is highly overall correlated with 2020 and 4 other fieldsHigh correlation
2020 is highly overall correlated with 2019 and 4 other fieldsHigh correlation
2021 is highly overall correlated with 2019 and 4 other fieldsHigh correlation
2022 is highly overall correlated with 2019 and 4 other fieldsHigh correlation
총합계 is highly overall correlated with 2019 and 4 other fieldsHigh correlation
요양기관종별 is highly overall correlated with 2019 and 4 other fieldsHigh correlation
2020 has 2 (1.1%) missing valuesMissing
2021 has 2 (1.1%) missing valuesMissing
2022 has 3 (1.7%) missing valuesMissing

Reproduction

Analysis started2024-01-09 19:46:58.315663
Analysis finished2024-01-09 19:47:00.645325
Duration2.33 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Categorical

Distinct17
Distinct (%)9.6%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
천안시 서북구
13 
아산시
13 
태안군
12 
당진시
12 
공주시
12 
Other values (12)
116 

Length

Max length8
Median length3
Mean length3.6067416
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
천안시 서북구 13
 
7.3%
아산시 13
 
7.3%
태안군 12
 
6.7%
당진시 12
 
6.7%
공주시 12
 
6.7%
논산시 11
 
6.2%
보령시 11
 
6.2%
예산군 11
 
6.2%
홍성군 11
 
6.2%
청양군 10
 
5.6%
Other values (7) 62
34.8%

Length

2024-01-10T04:47:00.695223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
천안시 26
 
12.7%
서북구 13
 
6.4%
아산시 13
 
6.4%
동남구 13
 
6.4%
태안군 12
 
5.9%
당진시 12
 
5.9%
공주시 12
 
5.9%
홍성군 11
 
5.4%
예산군 11
 
5.4%
보령시 11
 
5.4%
Other values (7) 70
34.3%

요양기관종별
Categorical

HIGH CORRELATION 

Distinct30
Distinct (%)16.9%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
요양병원-일반
16 
치과의원
16 
한의원
16 
보건지소
16 
약국
16 
Other values (25)
98 

Length

Max length10
Median length7
Mean length3.9157303
Min length2

Unique

Unique17 ?
Unique (%)9.6%

Sample

1st row계룡시 소계
2nd row병원
3rd row요양병원-일반
4th row의원
5th row치과의원

Common Values

ValueCountFrequency (%)
요양병원-일반 16
9.0%
치과의원 16
9.0%
한의원 16
9.0%
보건지소 16
9.0%
약국 16
9.0%
의원 16
9.0%
보건진료소 15
8.4%
보건소 14
7.9%
병원 14
7.9%
종합병원 10
 
5.6%
Other values (20) 29
16.3%

Length

2024-01-10T04:47:00.792638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
요양병원-일반 16
 
8.2%
소계 16
 
8.2%
한의원 16
 
8.2%
보건지소 16
 
8.2%
약국 16
 
8.2%
의원 16
 
8.2%
치과의원 16
 
8.2%
보건진료소 15
 
7.7%
보건소 14
 
7.1%
병원 14
 
7.1%
Other values (22) 41
20.9%

2019
Real number (ℝ)

HIGH CORRELATION 

Distinct70
Distinct (%)39.5%
Missing1
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean40.915254
Minimum1
Maximum645
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2024-01-10T04:47:00.896672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median13
Q337
95-th percentile177.6
Maximum645
Range644
Interquartile range (IQR)35

Descriptive statistics

Standard deviation80.812062
Coefficient of variation (CV)1.9751084
Kurtosis22.839343
Mean40.915254
Median Absolute Deviation (MAD)12
Skewness4.2104133
Sum7242
Variance6530.5894
MonotonicityNot monotonic
2024-01-10T04:47:00.996616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 42
23.6%
2 9
 
5.1%
10 6
 
3.4%
13 6
 
3.4%
11 6
 
3.4%
16 6
 
3.4%
3 5
 
2.8%
14 4
 
2.2%
8 4
 
2.2%
6 4
 
2.2%
Other values (60) 85
47.8%
ValueCountFrequency (%)
1 42
23.6%
2 9
 
5.1%
3 5
 
2.8%
4 3
 
1.7%
5 1
 
0.6%
6 4
 
2.2%
7 2
 
1.1%
8 4
 
2.2%
9 3
 
1.7%
10 6
 
3.4%
ValueCountFrequency (%)
645 1
0.6%
432 1
0.6%
419 1
0.6%
265 1
0.6%
261 1
0.6%
260 1
0.6%
219 1
0.6%
213 1
0.6%
184 1
0.6%
176 1
0.6%

2020
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct72
Distinct (%)40.9%
Missing2
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean41.670455
Minimum1
Maximum658
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2024-01-10T04:47:01.099877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median12.5
Q336.75
95-th percentile182.25
Maximum658
Range657
Interquartile range (IQR)34.75

Descriptive statistics

Standard deviation82.269414
Coefficient of variation (CV)1.9742865
Kurtosis22.968146
Mean41.670455
Median Absolute Deviation (MAD)11.5
Skewness4.2163788
Sum7334
Variance6768.2565
MonotonicityNot monotonic
2024-01-10T04:47:01.203903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 42
23.6%
2 8
 
4.5%
10 6
 
3.4%
11 5
 
2.8%
8 5
 
2.8%
14 4
 
2.2%
15 4
 
2.2%
16 4
 
2.2%
12 4
 
2.2%
4 4
 
2.2%
Other values (62) 90
50.6%
ValueCountFrequency (%)
1 42
23.6%
2 8
 
4.5%
3 4
 
2.2%
4 4
 
2.2%
5 2
 
1.1%
6 2
 
1.1%
7 3
 
1.7%
8 5
 
2.8%
9 3
 
1.7%
10 6
 
3.4%
ValueCountFrequency (%)
658 1
0.6%
436 1
0.6%
426 1
0.6%
269 1
0.6%
262 1
0.6%
261 1
0.6%
220 1
0.6%
219 1
0.6%
189 1
0.6%
180 1
0.6%

2021
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct73
Distinct (%)41.5%
Missing2
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean41.897727
Minimum1
Maximum669
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2024-01-10T04:47:01.303006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median13
Q337.5
95-th percentile177.75
Maximum669
Range668
Interquartile range (IQR)35.5

Descriptive statistics

Standard deviation83.115072
Coefficient of variation (CV)1.9837609
Kurtosis23.46345
Mean41.897727
Median Absolute Deviation (MAD)12
Skewness4.2580373
Sum7374
Variance6908.1152
MonotonicityNot monotonic
2024-01-10T04:47:01.404546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 41
23.0%
2 8
 
4.5%
10 6
 
3.4%
7 6
 
3.4%
11 6
 
3.4%
14 5
 
2.8%
4 5
 
2.8%
3 4
 
2.2%
19 4
 
2.2%
13 4
 
2.2%
Other values (63) 87
48.9%
ValueCountFrequency (%)
1 41
23.0%
2 8
 
4.5%
3 4
 
2.2%
4 5
 
2.8%
5 1
 
0.6%
6 1
 
0.6%
7 6
 
3.4%
8 4
 
2.2%
9 3
 
1.7%
10 6
 
3.4%
ValueCountFrequency (%)
669 1
0.6%
439 1
0.6%
431 1
0.6%
269 1
0.6%
265 1
0.6%
263 1
0.6%
224 1
0.6%
222 1
0.6%
189 1
0.6%
174 1
0.6%

2022
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct74
Distinct (%)42.3%
Missing3
Missing (%)1.7%
Infinite0
Infinite (%)0.0%
Mean42.594286
Minimum1
Maximum679
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2024-01-10T04:47:01.509936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median13
Q338.5
95-th percentile181.3
Maximum679
Range678
Interquartile range (IQR)36.5

Descriptive statistics

Standard deviation84.527157
Coefficient of variation (CV)1.9844717
Kurtosis23.463221
Mean42.594286
Median Absolute Deviation (MAD)12
Skewness4.2621304
Sum7454
Variance7144.8402
MonotonicityNot monotonic
2024-01-10T04:47:01.619696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 40
22.5%
2 8
 
4.5%
10 7
 
3.9%
11 6
 
3.4%
13 6
 
3.4%
7 5
 
2.8%
16 5
 
2.8%
4 5
 
2.8%
3 4
 
2.2%
19 4
 
2.2%
Other values (64) 85
47.8%
ValueCountFrequency (%)
1 40
22.5%
2 8
 
4.5%
3 4
 
2.2%
4 5
 
2.8%
5 1
 
0.6%
6 1
 
0.6%
7 5
 
2.8%
8 4
 
2.2%
9 3
 
1.7%
10 7
 
3.9%
ValueCountFrequency (%)
679 1
0.6%
451 1
0.6%
434 1
0.6%
274 1
0.6%
272 1
0.6%
267 1
0.6%
228 1
0.6%
218 1
0.6%
189 1
0.6%
178 1
0.6%

총합계
Real number (ℝ)

HIGH CORRELATION 

Distinct102
Distinct (%)57.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean165.19101
Minimum1
Maximum2651
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2024-01-10T04:47:01.728633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q18
median49.5
Q3145.75
95-th percentile714.45
Maximum2651
Range2650
Interquartile range (IQR)137.75

Descriptive statistics

Standard deviation329.2875
Coefficient of variation (CV)1.9933742
Kurtosis23.430333
Mean165.19101
Median Absolute Deviation (MAD)45.5
Skewness4.2585427
Sum29404
Variance108430.26
MonotonicityNot monotonic
2024-01-10T04:47:01.830259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 38
 
21.3%
8 7
 
3.9%
44 5
 
2.8%
12 5
 
2.8%
40 5
 
2.8%
64 3
 
1.7%
60 3
 
1.7%
36 3
 
1.7%
76 3
 
1.7%
16 3
 
1.7%
Other values (92) 103
57.9%
ValueCountFrequency (%)
1 2
 
1.1%
3 2
 
1.1%
4 38
21.3%
5 1
 
0.6%
6 1
 
0.6%
8 7
 
3.9%
12 5
 
2.8%
15 1
 
0.6%
16 3
 
1.7%
20 1
 
0.6%
ValueCountFrequency (%)
2651 1
0.6%
1758 1
0.6%
1710 1
0.6%
1066 1
0.6%
1065 1
0.6%
1057 1
0.6%
884 1
0.6%
879 1
0.6%
751 1
0.6%
708 1
0.6%

Interactions

2024-01-10T04:46:59.855946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T04:46:58.549365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T04:46:58.880530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T04:46:59.199702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T04:46:59.533226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T04:46:59.922586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T04:46:58.612692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T04:46:58.945775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T04:46:59.265913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T04:46:59.597419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T04:46:59.990117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T04:46:58.673604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T04:46:59.009576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T04:46:59.329078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T04:46:59.662666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T04:47:00.062374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T04:46:58.740339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T04:46:59.073847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T04:46:59.396297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T04:46:59.727441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T04:47:00.129592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T04:46:58.812196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T04:46:59.139849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T04:46:59.463052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T04:46:59.792092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-10T04:47:01.897215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분요양기관종별2019202020212022총합계
구분1.0000.0000.0000.0000.0000.0000.000
요양기관종별0.0001.0000.9660.9730.9730.9730.971
20190.0000.9661.0000.9970.9970.9970.999
20200.0000.9730.9971.0000.9970.9970.999
20210.0000.9730.9970.9971.0001.0000.999
20220.0000.9730.9970.9971.0001.0000.999
총합계0.0000.9710.9990.9990.9990.9991.000
2024-01-10T04:47:01.974376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분요양기관종별
구분1.0000.000
요양기관종별0.0001.000
2024-01-10T04:47:02.039157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2019202020212022총합계구분요양기관종별
20191.0000.9990.9980.9980.9980.0000.780
20200.9991.0000.9990.9990.9980.0000.804
20210.9980.9991.0001.0000.9990.0000.804
20220.9980.9991.0001.0000.9990.0000.804
총합계0.9980.9980.9990.9991.0000.0000.797
구분0.0000.0000.0000.0000.0001.0000.000
요양기관종별0.7800.8040.8040.8040.7970.0001.000

Missing values

2024-01-10T04:47:00.449028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T04:47:00.530249image/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.
2024-01-10T04:47:00.602954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

구분요양기관종별2019202020212022총합계
0계룡시계룡시 소계61656565256
1계룡시병원11114
2계룡시요양병원-일반11114
3계룡시의원1820201977
4계룡시치과의원1314141455
5계룡시한의원1111111144
6계룡시보건소11114
7계룡시보건지소22228
8계룡시약국1415151660
9공주시공주시 소계219220222218879
구분요양기관종별2019202020212022총합계
168홍성군종합병원11114
169홍성군요양병원-일반333312
170홍성군의원50515050201
171홍성군치과의원26262628106
172홍성군한방병원22228
173홍성군한의원2323222290
174홍성군보건소11114
175홍성군보건지소1111111144
176홍성군보건진료소1414141456
177홍성군약국45484446183