Overview

Dataset statistics

Number of variables4
Number of observations45
Missing cells8
Missing cells (%)4.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.6 KiB
Average record size in memory36.9 B

Variable types

DateTime2
Numeric2

Dataset

Description경상남도 거창군 월별 코로나19 관련 확진자 및 사망자 수 현황 데이터로 월별 확진자 수와 사망자수 항목 데이터를 제공합니다.
Author경상남도 거창군
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15098865

Alerts

데이터기준일자 has constant value ""Constant
사망자수 is highly overall correlated with 확진자수High correlation
확진자수 is highly overall correlated with 사망자수High correlation
기간(년월) has 1 (2.2%) missing valuesMissing
사망자수 has 5 (11.1%) missing valuesMissing
확진자수 has 1 (2.2%) missing valuesMissing
데이터기준일자 has 1 (2.2%) missing valuesMissing
사망자수 has 24 (53.3%) zerosZeros
확진자수 has 10 (22.2%) zerosZeros

Reproduction

Analysis started2023-12-10 23:01:51.501723
Analysis finished2023-12-10 23:01:52.408248
Duration0.91 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기간(년월)
Date

MISSING 

Distinct44
Distinct (%)100.0%
Missing1
Missing (%)2.2%
Memory size492.0 B
Minimum2020-01-01 00:00:00
Maximum2023-08-01 00:00:00
2023-12-11T08:01:52.506831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:01:52.699464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)

사망자수
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct8
Distinct (%)20.0%
Missing5
Missing (%)11.1%
Infinite0
Infinite (%)0.0%
Mean1.825
Minimum0
Maximum33
Zeros24
Zeros (%)53.3%
Negative0
Negative (%)0.0%
Memory size537.0 B
2023-12-11T08:01:52.835915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile6.35
Maximum33
Range33
Interquartile range (IQR)1

Descriptive statistics

Standard deviation5.5649429
Coefficient of variation (CV)3.0492838
Kurtosis26.715902
Mean1.825
Median Absolute Deviation (MAD)0
Skewness4.9535799
Sum73
Variance30.96859
MonotonicityNot monotonic
2023-12-11T08:01:52.980271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 24
53.3%
1 9
 
20.0%
3 2
 
4.4%
33 1
 
2.2%
13 1
 
2.2%
6 1
 
2.2%
4 1
 
2.2%
2 1
 
2.2%
(Missing) 5
 
11.1%
ValueCountFrequency (%)
0 24
53.3%
1 9
 
20.0%
2 1
 
2.2%
3 2
 
4.4%
4 1
 
2.2%
6 1
 
2.2%
13 1
 
2.2%
33 1
 
2.2%
ValueCountFrequency (%)
33 1
 
2.2%
13 1
 
2.2%
6 1
 
2.2%
4 1
 
2.2%
3 2
 
4.4%
2 1
 
2.2%
1 9
 
20.0%
0 24
53.3%

확진자수
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct32
Distinct (%)72.7%
Missing1
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean775.72727
Minimum0
Maximum10155
Zeros10
Zeros (%)22.2%
Negative0
Negative (%)0.0%
Memory size537.0 B
2023-12-11T08:01:53.138257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median34.5
Q3956.75
95-th percentile3773.55
Maximum10155
Range10155
Interquartile range (IQR)954.75

Descriptive statistics

Standard deviation1799.4744
Coefficient of variation (CV)2.3197255
Kurtosis17.886063
Mean775.72727
Median Absolute Deviation (MAD)34.5
Skewness3.9457374
Sum34132
Variance3238108
MonotonicityNot monotonic
2023-12-11T08:01:53.290805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
0 10
22.2%
2 3
 
6.7%
6 2
 
4.4%
289 1
 
2.2%
1858 1
 
2.2%
838 1
 
2.2%
1265 1
 
2.2%
1552 1
 
2.2%
974 1
 
2.2%
161 1
 
2.2%
Other values (22) 22
48.9%
ValueCountFrequency (%)
0 10
22.2%
2 3
 
6.7%
3 1
 
2.2%
6 2
 
4.4%
9 1
 
2.2%
10 1
 
2.2%
12 1
 
2.2%
14 1
 
2.2%
20 1
 
2.2%
30 1
 
2.2%
ValueCountFrequency (%)
10155 1
2.2%
5267 1
2.2%
4062 1
2.2%
2139 1
2.2%
1858 1
2.2%
1552 1
2.2%
1265 1
2.2%
1116 1
2.2%
1022 1
2.2%
985 1
2.2%

데이터기준일자
Date

CONSTANT  MISSING 

Distinct1
Distinct (%)2.3%
Missing1
Missing (%)2.2%
Memory size492.0 B
Minimum2023-09-07 00:00:00
Maximum2023-09-07 00:00:00
2023-12-11T08:01:53.395258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:01:53.492407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-11T08:01:51.858380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:01:51.607630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:01:51.967994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:01:51.737405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T08:01:53.560084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기간(년월)사망자수확진자수
기간(년월)1.0001.0001.000
사망자수1.0001.0000.989
확진자수1.0000.9891.000
2023-12-11T08:01:53.638153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사망자수확진자수
사망자수1.0000.747
확진자수0.7471.000

Missing values

2023-12-11T08:01:52.095436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T08:01:52.190217image/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.
2023-12-11T08:01:52.319018image/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

기간(년월)사망자수확진자수데이터기준일자
02020-01002023-09-07
12020-020102023-09-07
22020-03092023-09-07
32020-04002023-09-07
42020-05002023-09-07
52020-06002023-09-07
62020-07002023-09-07
72020-08002023-09-07
82020-09002023-09-07
92020-10002023-09-07
기간(년월)사망자수확진자수데이터기준일자
352022-12315522023-09-07
362023-01<NA>9742023-09-07
372023-0222892023-09-07
382023-0312682023-09-07
392023-04<NA>1612023-09-07
402023-0514102023-09-07
412023-0613212023-09-07
422023-07<NA>10222023-09-07
432023-08<NA>11162023-09-07
44<NA><NA><NA><NA>