Overview

Dataset statistics

Number of variables7
Number of observations780
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory43.5 KiB
Average record size in memory57.2 B

Variable types

Numeric1
Categorical4
Text2

Dataset

Description충청북도 청주시 치매유병현황 데이터로 시점, 행정구역(도, 시 , 구), 성별, 연령별, 치매환자수, 치매환자유병률을 제공합니다.
Author충청북도 청주시
URLhttps://www.data.go.kr/data/15110851/fileData.do

Alerts

행정구역(시도)별 has constant value ""Constant

Reproduction

Analysis started2023-12-11 23:08:25.989863
Analysis finished2023-12-11 23:08:26.523036
Duration0.53 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시점
Real number (ℝ)

Distinct7
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2018.0513
Minimum2015
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.0 KiB
2023-12-12T08:08:26.573358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2015
5-th percentile2015
Q12016
median2018
Q32020
95-th percentile2021
Maximum2021
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.0261287
Coefficient of variation (CV)0.0010040026
Kurtosis-1.2844137
Mean2018.0513
Median Absolute Deviation (MAD)2
Skewness-0.023534122
Sum1574080
Variance4.1051973
MonotonicityIncreasing
2023-12-12T08:08:26.699029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2020 120
15.4%
2021 120
15.4%
2015 110
14.1%
2016 110
14.1%
2017 110
14.1%
2018 110
14.1%
2019 100
12.8%
ValueCountFrequency (%)
2015 110
14.1%
2016 110
14.1%
2017 110
14.1%
2018 110
14.1%
2019 100
12.8%
2020 120
15.4%
2021 120
15.4%
ValueCountFrequency (%)
2021 120
15.4%
2020 120
15.4%
2019 100
12.8%
2018 110
14.1%
2017 110
14.1%
2016 110
14.1%
2015 110
14.1%

행정구역(시도)별
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size6.2 KiB
충청북도
780 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
충청북도 780
100.0%

Length

2023-12-12T08:08:26.807406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:08:26.901997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
충청북도 780
100.0%

행정구역(기초)별
Categorical

Distinct5
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size6.2 KiB
청주시
156 
상당구
156 
서원구
156 
흥덕구
156 
청원구
156 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
청주시 156
20.0%
상당구 156
20.0%
서원구 156
20.0%
흥덕구 156
20.0%
청원구 156
20.0%

Length

2023-12-12T08:08:27.005424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:08:27.131533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
청주시 156
20.0%
상당구 156
20.0%
서원구 156
20.0%
흥덕구 156
20.0%
청원구 156
20.0%

성별
Categorical

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size6.2 KiB
전체
280 
남
250 
여
250 

Length

Max length2
Median length1
Mean length1.3589744
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
전체 280
35.9%
남 250
32.1%
여 250
32.1%

Length

2023-12-12T08:08:27.262022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:08:27.350634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전체 280
35.9%
남 250
32.1%
여 250
32.1%

연령별
Categorical

Distinct10
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size6.2 KiB
60~64세
105 
65~69세
105 
70~74세
105 
75~79세
105 
80~84세
105 
Other values (5)
255 

Length

Max length6
Median length6
Mean length5.5192308
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row60~64세
2nd row65~69세
3rd row70~74세
4th row75~79세
5th row80~84세

Common Values

ValueCountFrequency (%)
60~64세 105
13.5%
65~69세 105
13.5%
70~74세 105
13.5%
75~79세 105
13.5%
80~84세 105
13.5%
85세이상 105
13.5%
60세이상 55
7.1%
65세이상 55
7.1%
남계 20
 
2.6%
여계 20
 
2.6%

Length

2023-12-12T08:08:27.465254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:08:27.624846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
60~64세 105
13.5%
65~69세 105
13.5%
70~74세 105
13.5%
75~79세 105
13.5%
80~84세 105
13.5%
85세이상 105
13.5%
60세이상 55
7.1%
65세이상 55
7.1%
남계 20
 
2.6%
여계 20
 
2.6%

치매환자수
Text

Distinct668
Distinct (%)85.6%
Missing0
Missing (%)0.0%
Memory size6.2 KiB
2023-12-12T08:08:27.921842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length4.1448718
Min length1

Characters and Unicode

Total characters3233
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique594 ?
Unique (%)76.2%

Sample

1st row231
2nd row371
3rd row886
4th row2,206
5th row2,368
ValueCountFrequency (%)
6 10
 
1.3%
103 4
 
0.5%
105 4
 
0.5%
110 4
 
0.5%
63 4
 
0.5%
69 4
 
0.5%
72 4
 
0.5%
107 4
 
0.5%
7 4
 
0.5%
64 4
 
0.5%
Other values (658) 734
94.1%
2023-12-12T08:08:28.380752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 398
12.3%
1 395
12.2%
4 289
8.9%
6 270
8.4%
7 263
8.1%
3 256
7.9%
5 255
7.9%
9 248
7.7%
. 238
7.4%
0 224
6.9%
Other values (2) 397
12.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2806
86.8%
Other Punctuation 427
 
13.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 398
14.2%
1 395
14.1%
4 289
10.3%
6 270
9.6%
7 263
9.4%
3 256
9.1%
5 255
9.1%
9 248
8.8%
0 224
8.0%
8 208
7.4%
Other Punctuation
ValueCountFrequency (%)
. 238
55.7%
, 189
44.3%

Most occurring scripts

ValueCountFrequency (%)
Common 3233
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 398
12.3%
1 395
12.2%
4 289
8.9%
6 270
8.4%
7 263
8.1%
3 256
7.9%
5 255
7.9%
9 248
7.7%
. 238
7.4%
0 224
6.9%
Other values (2) 397
12.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3233
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 398
12.3%
1 395
12.2%
4 289
8.9%
6 270
8.4%
7 263
8.1%
3 256
7.9%
5 255
7.9%
9 248
7.7%
. 238
7.4%
0 224
6.9%
Other values (2) 397
12.3%

치매환자유병률
Text

Distinct222
Distinct (%)28.5%
Missing0
Missing (%)0.0%
Memory size6.2 KiB
2023-12-12T08:08:28.767452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length5.3487179
Min length2

Characters and Unicode

Total characters4172
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique153 ?
Unique (%)19.6%

Sample

1st row0.61%
2nd row1.39%
3rd row3.81%
4th row11.98%
5th row21.29%
ValueCountFrequency (%)
0.96 35
 
4.5%
32.97 35
 
4.5%
23.26 35
 
4.5%
13.76 35
 
4.5%
1.13 35
 
4.5%
1.85 35
 
4.5%
4.66 35
 
4.5%
9.23 35
 
4.5%
17.09 35
 
4.5%
53.99 35
 
4.5%
Other values (212) 430
55.1%
2023-12-12T08:08:29.258629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
% 780
18.7%
. 775
18.6%
1 554
13.3%
3 389
9.3%
6 316
7.6%
9 308
 
7.4%
2 240
 
5.8%
0 205
 
4.9%
7 200
 
4.8%
8 179
 
4.3%
Other values (2) 226
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2617
62.7%
Other Punctuation 1555
37.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 554
21.2%
3 389
14.9%
6 316
12.1%
9 308
11.8%
2 240
9.2%
0 205
 
7.8%
7 200
 
7.6%
8 179
 
6.8%
5 129
 
4.9%
4 97
 
3.7%
Other Punctuation
ValueCountFrequency (%)
% 780
50.2%
. 775
49.8%

Most occurring scripts

ValueCountFrequency (%)
Common 4172
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
% 780
18.7%
. 775
18.6%
1 554
13.3%
3 389
9.3%
6 316
7.6%
9 308
 
7.4%
2 240
 
5.8%
0 205
 
4.9%
7 200
 
4.8%
8 179
 
4.3%
Other values (2) 226
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4172
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
% 780
18.7%
. 775
18.6%
1 554
13.3%
3 389
9.3%
6 316
7.6%
9 308
 
7.4%
2 240
 
5.8%
0 205
 
4.9%
7 200
 
4.8%
8 179
 
4.3%
Other values (2) 226
 
5.4%

Interactions

2023-12-12T08:08:26.220370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T08:08:29.344830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시점행정구역(기초)별성별연령별
시점1.0000.0000.0000.121
행정구역(기초)별0.0001.0000.0000.000
성별0.0000.0001.0000.399
연령별0.1210.0000.3991.000
2023-12-12T08:08:29.421989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연령별행정구역(기초)별성별
연령별1.0000.0000.260
행정구역(기초)별0.0001.0000.000
성별0.2600.0001.000
2023-12-12T08:08:29.500079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시점행정구역(기초)별성별연령별
시점1.0000.0000.0000.042
행정구역(기초)별0.0001.0000.0000.000
성별0.0000.0001.0000.260
연령별0.0420.0000.2601.000

Missing values

2023-12-12T08:08:26.338880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T08:08:26.463222image/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

시점행정구역(시도)별행정구역(기초)별성별연령별치매환자수치매환자유병률
02015충청북도청주시전체60~64세2310.61%
12015충청북도청주시전체65~69세3711.39%
22015충청북도청주시전체70~74세8863.81%
32015충청북도청주시전체75~79세2,20611.98%
42015충청북도청주시전체80~84세2,36821.29%
52015충청북도청주시전체85세이상2,75037.88%
62015충청북도청주시전체60세이상8,8117.09%
72015충청북도청주시전체65세이상8,5819.89%
82015충청북도청주시남60~64세2101.13%
92015충청북도청주시남65~69세2391.85%
시점행정구역(시도)별행정구역(기초)별성별연령별치매환자수치매환자유병률
7702021충청북도청원구남60세이상1,049.846.17%
7712021충청북도청원구남65세이상980.079.03%
7722021충청북도청원구여60~64세6.940.11%
7732021충청북도청원구여65~69세43.170.96%
7742021충청북도청원구여70~74세94.173.16%
7752021충청북도청원구여75~79세379.0913.76%
7762021충청북도청원구여80~84세533.4723.26%
7772021충청북도청원구여85세이상673.4132.97%
7782021충청북도청원구여60세이상1,730.248.29%
7792021충청북도청원구여65세이상1,723.311.83%