Overview

Dataset statistics

Number of variables4
Number of observations24
Missing cells8
Missing cells (%)8.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory948.0 B
Average record size in memory39.5 B

Variable types

DateTime1
Numeric1
Categorical2

Dataset

Description이 데이터는 충청남도 금산군 코로나19 확진자 및 사망자에 대한 2020년 1월부터 2022년 7월까지의 현황을 제공합니다.
Author충청남도
URLhttps://alldam.chungnam.go.kr/index.chungnam?menuCd=DOM_000000201001001001&st=&cds=&orgCd=&apiType=&isOpen=Y&pageIndex=109&beforeMenuCd=DOM_000000201001001000&publicdatapk=15098724

Alerts

데이터기준일자 has constant value ""Constant
확진자 is highly overall correlated with 사망자High correlation
사망자 is highly overall correlated with 확진자High correlation
사망자 is highly imbalanced (68.6%)Imbalance
확진자 has 8 (33.3%) missing valuesMissing
연월 has unique valuesUnique

Reproduction

Analysis started2024-01-09 22:12:30.882616
Analysis finished2024-01-09 22:12:31.146641
Duration0.26 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연월
Date

UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size324.0 B
Minimum2020-01-01 00:00:00
Maximum2021-12-01 00:00:00
2024-01-10T07:12:31.187358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:12:31.276294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)

확진자
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct13
Distinct (%)81.2%
Missing8
Missing (%)33.3%
Infinite0
Infinite (%)0.0%
Mean32.3125
Minimum1
Maximum175
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2024-01-10T07:12:31.361700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13.75
median6
Q342.25
95-th percentile114.25
Maximum175
Range174
Interquartile range (IQR)38.5

Descriptive statistics

Standard deviation48.09812
Coefficient of variation (CV)1.4885298
Kurtosis4.5176757
Mean32.3125
Median Absolute Deviation (MAD)5
Skewness2.0918029
Sum517
Variance2313.4292
MonotonicityNot monotonic
2024-01-10T07:12:31.471986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
6 3
 
12.5%
1 2
 
8.3%
5 1
 
4.2%
14 1
 
4.2%
3 1
 
4.2%
4 1
 
4.2%
2 1
 
4.2%
41 1
 
4.2%
83 1
 
4.2%
30 1
 
4.2%
Other values (3) 3
 
12.5%
(Missing) 8
33.3%
ValueCountFrequency (%)
1 2
8.3%
2 1
 
4.2%
3 1
 
4.2%
4 1
 
4.2%
5 1
 
4.2%
6 3
12.5%
14 1
 
4.2%
30 1
 
4.2%
41 1
 
4.2%
46 1
 
4.2%
ValueCountFrequency (%)
175 1
 
4.2%
94 1
 
4.2%
83 1
 
4.2%
46 1
 
4.2%
41 1
 
4.2%
30 1
 
4.2%
14 1
 
4.2%
6 3
12.5%
5 1
 
4.2%
4 1
 
4.2%

사망자
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Memory size324.0 B
<NA>
22 
1
 
1
2
 
1

Length

Max length4
Median length4
Mean length3.75
Min length1

Unique

Unique2 ?
Unique (%)8.3%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 22
91.7%
1 1
 
4.2%
2 1
 
4.2%

Length

2024-01-10T07:12:31.583827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T07:12:31.660420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 22
91.7%
1 1
 
4.2%
2 1
 
4.2%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size324.0 B
2022-02-04
24 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022-02-04
2nd row2022-02-04
3rd row2022-02-04
4th row2022-02-04
5th row2022-02-04

Common Values

ValueCountFrequency (%)
2022-02-04 24
100.0%

Length

2024-01-10T07:12:31.740068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T07:12:31.805362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022-02-04 24
100.0%

Interactions

2024-01-10T07:12:30.966050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-10T07:12:31.847690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연월확진자사망자
연월1.0001.0000.000
확진자1.0001.0000.000
사망자0.0000.0001.000
2024-01-10T07:12:31.911422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
확진자사망자
확진자1.0001.000
사망자1.0001.000

Missing values

2024-01-10T07:12:31.056738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T07:12:31.120931image/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

연월확진자사망자데이터기준일자
02020-01<NA><NA>2022-02-04
12020-02<NA><NA>2022-02-04
22020-03<NA><NA>2022-02-04
32020-04<NA><NA>2022-02-04
42020-05<NA><NA>2022-02-04
52020-061<NA>2022-02-04
62020-075<NA>2022-02-04
72020-08<NA><NA>2022-02-04
82020-091412022-02-04
92020-10<NA><NA>2022-02-04
연월확진자사망자데이터기준일자
142021-03<NA><NA>2022-02-04
152021-046<NA>2022-02-04
162021-052<NA>2022-02-04
172021-061<NA>2022-02-04
182021-0741<NA>2022-02-04
192021-0883<NA>2022-02-04
202021-0930<NA>2022-02-04
212021-1094<NA>2022-02-04
222021-11175<NA>2022-02-04
232021-124622022-02-04