Overview

Dataset statistics

Number of variables6
Number of observations42
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.3 KiB
Average record size in memory56.1 B

Variable types

Categorical2
Numeric4

Dataset

Description천안시에서 2020년 코로나19 확진자 첫 발생이후 2023년 7월까지 확진자 발생 현황 및 코로나로 인한 사망자 현황
Author충청남도
URLhttps://alldam.chungnam.go.kr/index.chungnam?menuCd=DOM_000000201001001001&st=&cds=&orgCd=&apiType=&isOpen=Y&pageIndex=109&beforeMenuCd=DOM_000000201001001000&publicdatapk=15098710

Alerts

데이터기준일자 has constant value ""Constant
지역확진자수 is highly overall correlated with 사망자수High correlation
사망자수 is highly overall correlated with 지역확진자수High correlation
지역확진자수 has 1 (2.4%) zerosZeros
해외입국확진자수 has 9 (21.4%) zerosZeros
사망자수 has 14 (33.3%) zerosZeros

Reproduction

Analysis started2024-01-09 19:47:11.684253
Analysis finished2024-01-09 19:47:12.982539
Duration1.3 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기준연도
Categorical

Distinct4
Distinct (%)9.5%
Missing0
Missing (%)0.0%
Memory size468.0 B
2021
12 
2022
12 
2020
11 
2023

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020
2nd row2020
3rd row2020
4th row2020
5th row2020

Common Values

ValueCountFrequency (%)
2021 12
28.6%
2022 12
28.6%
2020 11
26.2%
2023 7
16.7%

Length

2024-01-10T04:47:13.026809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T04:47:13.103460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021 12
28.6%
2022 12
28.6%
2020 11
26.2%
2023 7
16.7%

기준월
Real number (ℝ)

Distinct12
Distinct (%)28.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.2142857
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size510.0 B
2024-01-10T04:47:13.179760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.05
Q13.25
median6
Q39
95-th percentile11.95
Maximum12
Range11
Interquartile range (IQR)5.75

Descriptive statistics

Standard deviation3.3752016
Coefficient of variation (CV)0.54313589
Kurtosis-1.1058077
Mean6.2142857
Median Absolute Deviation (MAD)3
Skewness0.16539767
Sum261
Variance11.391986
MonotonicityNot monotonic
2024-01-10T04:47:13.256471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
2 4
9.5%
3 4
9.5%
4 4
9.5%
5 4
9.5%
6 4
9.5%
7 4
9.5%
8 3
7.1%
9 3
7.1%
10 3
7.1%
11 3
7.1%
Other values (2) 6
14.3%
ValueCountFrequency (%)
1 3
7.1%
2 4
9.5%
3 4
9.5%
4 4
9.5%
5 4
9.5%
6 4
9.5%
7 4
9.5%
8 3
7.1%
9 3
7.1%
10 3
7.1%
ValueCountFrequency (%)
12 3
7.1%
11 3
7.1%
10 3
7.1%
9 3
7.1%
8 3
7.1%
7 4
9.5%
6 4
9.5%
5 4
9.5%
4 4
9.5%
3 4
9.5%

지역확진자수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct41
Distinct (%)97.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10770.19
Minimum0
Maximum132126
Zeros1
Zeros (%)2.4%
Negative0
Negative (%)0.0%
Memory size510.0 B
2024-01-10T04:47:13.346337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.1
Q193.25
median840
Q311348.25
95-th percentile48352.7
Maximum132126
Range132126
Interquartile range (IQR)11255

Descriptive statistics

Standard deviation23422.441
Coefficient of variation (CV)2.1747472
Kurtosis17.961745
Mean10770.19
Median Absolute Deviation (MAD)839.5
Skewness3.8996029
Sum452348
Variance5.4861076 × 108
MonotonicityNot monotonic
2024-01-10T04:47:13.453453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
1 2
 
4.8%
56 1
 
2.4%
22254 1
 
2.4%
132126 1
 
2.4%
60802 1
 
2.4%
12617 1
 
2.4%
3493 1
 
2.4%
18269 1
 
2.4%
49334 1
 
2.4%
21323 1
 
2.4%
Other values (31) 31
73.8%
ValueCountFrequency (%)
0 1
2.4%
1 2
4.8%
3 1
2.4%
34 1
2.4%
38 1
2.4%
43 1
2.4%
44 1
2.4%
56 1
2.4%
77 1
2.4%
92 1
2.4%
ValueCountFrequency (%)
132126 1
2.4%
60802 1
2.4%
49334 1
2.4%
29708 1
2.4%
28063 1
2.4%
22254 1
2.4%
21323 1
2.4%
18269 1
2.4%
15359 1
2.4%
12617 1
2.4%

해외입국확진자수
Real number (ℝ)

ZEROS 

Distinct22
Distinct (%)52.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.047619
Minimum0
Maximum407
Zeros9
Zeros (%)21.4%
Negative0
Negative (%)0.0%
Memory size510.0 B
2024-01-10T04:47:13.542501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q314.25
95-th percentile94.35
Maximum407
Range407
Interquartile range (IQR)12.25

Descriptive statistics

Standard deviation72.693125
Coefficient of variation (CV)2.6875979
Kurtosis20.082244
Mean27.047619
Median Absolute Deviation (MAD)4
Skewness4.3321692
Sum1136
Variance5284.2904
MonotonicityNot monotonic
2024-01-10T04:47:13.839484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0 9
21.4%
3 6
14.3%
2 4
 
9.5%
8 3
 
7.1%
4 2
 
4.8%
10 2
 
4.8%
15 1
 
2.4%
38 1
 
2.4%
36 1
 
2.4%
243 1
 
2.4%
Other values (12) 12
28.6%
ValueCountFrequency (%)
0 9
21.4%
1 1
 
2.4%
2 4
9.5%
3 6
14.3%
4 2
 
4.8%
6 1
 
2.4%
7 1
 
2.4%
8 3
 
7.1%
10 2
 
4.8%
11 1
 
2.4%
ValueCountFrequency (%)
407 1
2.4%
243 1
2.4%
95 1
2.4%
82 1
2.4%
39 1
2.4%
38 1
2.4%
36 1
2.4%
32 1
2.4%
18 1
2.4%
16 1
2.4%

사망자수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct17
Distinct (%)40.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.214286
Minimum0
Maximum106
Zeros14
Zeros (%)33.3%
Negative0
Negative (%)0.0%
Memory size510.0 B
2024-01-10T04:47:13.931140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2.5
Q314.75
95-th percentile37.9
Maximum106
Range106
Interquartile range (IQR)14.75

Descriptive statistics

Standard deviation23.856825
Coefficient of variation (CV)1.9531903
Kurtosis10.38466
Mean12.214286
Median Absolute Deviation (MAD)2.5
Skewness3.1282934
Sum513
Variance569.14808
MonotonicityNot monotonic
2024-01-10T04:47:14.019641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 14
33.3%
1 5
 
11.9%
5 3
 
7.1%
4 3
 
7.1%
26 2
 
4.8%
2 2
 
4.8%
106 2
 
4.8%
8 2
 
4.8%
3 1
 
2.4%
23 1
 
2.4%
Other values (7) 7
16.7%
ValueCountFrequency (%)
0 14
33.3%
1 5
 
11.9%
2 2
 
4.8%
3 1
 
2.4%
4 3
 
7.1%
5 3
 
7.1%
7 1
 
2.4%
8 2
 
4.8%
17 1
 
2.4%
18 1
 
2.4%
ValueCountFrequency (%)
106 2
4.8%
38 1
2.4%
36 1
2.4%
33 1
2.4%
26 2
4.8%
23 1
2.4%
22 1
2.4%
18 1
2.4%
17 1
2.4%
8 2
4.8%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size468.0 B
2023-08-21
42 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-08-21
2nd row2023-08-21
3rd row2023-08-21
4th row2023-08-21
5th row2023-08-21

Common Values

ValueCountFrequency (%)
2023-08-21 42
100.0%

Length

2024-01-10T04:47:14.107213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T04:47:14.181841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-08-21 42
100.0%

Interactions

2024-01-10T04:47:12.600897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T04:47:11.820162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T04:47:12.071232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T04:47:12.326858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T04:47:12.661521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T04:47:11.876949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T04:47:12.133409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T04:47:12.394621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T04:47:12.722147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T04:47:11.943029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T04:47:12.195289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T04:47:12.461382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T04:47:12.789668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T04:47:12.014673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T04:47:12.265640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T04:47:12.534925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-10T04:47:14.232742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준연도기준월지역확진자수해외입국확진자수사망자수
기준연도1.0000.0000.3860.3270.383
기준월0.0001.0000.0000.4930.000
지역확진자수0.3860.0001.0000.7970.801
해외입국확진자수0.3270.4930.7971.0000.521
사망자수0.3830.0000.8010.5211.000
2024-01-10T04:47:14.311134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준월지역확진자수해외입국확진자수사망자수기준연도
기준월1.0000.0380.1770.0570.000
지역확진자수0.0381.0000.2920.8480.245
해외입국확진자수0.1770.2921.0000.2560.125
사망자수0.0570.8480.2561.0000.313
기준연도0.0000.2450.1250.3131.000

Missing values

2024-01-10T04:47:12.864745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T04:47:12.950444image/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

기준연도기준월지역확진자수해외입국확진자수사망자수데이터기준일자
02020256002023-08-21
12020343402023-08-21
2202041302023-08-21
3202051002023-08-21
4202063002023-08-21
5202070202023-08-21
62020877402023-08-21
72020934142023-08-21
820201038212023-08-21
9202011186712023-08-21
기준연도기준월지역확진자수해외입국확진자수사망자수데이터기준일자
32202210102423682023-08-21
332022112225410172023-08-21
34202212280638262023-08-21
35202311535938232023-08-21
36202324576882023-08-21
37202334456232023-08-21
38202345307352023-08-21
39202357286242023-08-21
40202366015042023-08-21
412023711717022023-08-21