Overview

Dataset statistics

Number of variables5
Number of observations43
Missing cells25
Missing cells (%)11.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.9 KiB
Average record size in memory45.1 B

Variable types

Categorical2
DateTime1
Numeric2

Dataset

Description전라북도 군산시 코로나19 현황에 관한 데이터로 년도별 월별 확진자 및 사망자수, 관리부서 등 데이터를 제공합니다.
URLhttps://www.data.go.kr/data/15098747/fileData.do

Alerts

기관명 has constant value ""Constant
확진자수 is highly overall correlated with 사망자수High correlation
사망자수 is highly overall correlated with 확진자수High correlation
확진자수 has 3 (7.0%) missing valuesMissing
사망자수 has 22 (51.2%) missing valuesMissing
기준날짜 has unique valuesUnique
사망자수 has 6 (14.0%) zerosZeros

Reproduction

Analysis started2023-12-12 06:53:32.386204
Analysis finished2023-12-12 06:53:33.196570
Duration0.81 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기관명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size476.0 B
전라북도군산시
43 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
전라북도군산시 43
100.0%

Length

2023-12-12T15:53:33.269940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:53:33.385467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전라북도군산시 43
100.0%

기준날짜
Date

UNIQUE 

Distinct43
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size476.0 B
Minimum2020-01-31 00:00:00
Maximum2023-07-31 00:00:00
2023-12-12T15:53:33.512560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:53:33.656927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)

확진자수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct36
Distinct (%)90.0%
Missing3
Missing (%)7.0%
Infinite0
Infinite (%)0.0%
Mean4040.45
Minimum1
Maximum48633
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-12T15:53:33.799090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q120.25
median320
Q34657.5
95-th percentile19691.25
Maximum48633
Range48632
Interquartile range (IQR)4637.25

Descriptive statistics

Standard deviation8810.6286
Coefficient of variation (CV)2.1806058
Kurtosis17.294742
Mean4040.45
Median Absolute Deviation (MAD)319
Skewness3.8560484
Sum161618
Variance77627177
MonotonicityNot monotonic
2023-12-12T15:53:34.023311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
1 3
 
7.0%
2 2
 
4.7%
15 2
 
4.7%
5 1
 
2.3%
9231 1
 
2.3%
6754 1
 
2.3%
19536 1
 
2.3%
6765 1
 
2.3%
3726 1
 
2.3%
7323 1
 
2.3%
Other values (26) 26
60.5%
(Missing) 3
 
7.0%
ValueCountFrequency (%)
1 3
7.0%
2 2
4.7%
5 1
 
2.3%
10 1
 
2.3%
14 1
 
2.3%
15 2
4.7%
22 1
 
2.3%
27 1
 
2.3%
42 1
 
2.3%
59 1
 
2.3%
ValueCountFrequency (%)
48633 1
2.3%
22641 1
2.3%
19536 1
2.3%
9231 1
2.3%
8597 1
2.3%
7323 1
2.3%
6765 1
2.3%
6754 1
2.3%
6012 1
2.3%
4956 1
2.3%

사망자수
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct9
Distinct (%)42.9%
Missing22
Missing (%)51.2%
Infinite0
Infinite (%)0.0%
Mean6.1428571
Minimum0
Maximum47
Zeros6
Zeros (%)14.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-12T15:53:34.158612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q34
95-th percentile39
Maximum47
Range47
Interquartile range (IQR)4

Descriptive statistics

Standard deviation12.535094
Coefficient of variation (CV)2.0405966
Kurtosis7.3362804
Mean6.1428571
Median Absolute Deviation (MAD)2
Skewness2.8491003
Sum129
Variance157.12857
MonotonicityNot monotonic
2023-12-12T15:53:34.298082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 6
 
14.0%
1 4
 
9.3%
4 3
 
7.0%
6 2
 
4.7%
2 2
 
4.7%
3 1
 
2.3%
47 1
 
2.3%
39 1
 
2.3%
8 1
 
2.3%
(Missing) 22
51.2%
ValueCountFrequency (%)
0 6
14.0%
1 4
9.3%
2 2
 
4.7%
3 1
 
2.3%
4 3
7.0%
6 2
 
4.7%
8 1
 
2.3%
39 1
 
2.3%
47 1
 
2.3%
ValueCountFrequency (%)
47 1
 
2.3%
39 1
 
2.3%
8 1
 
2.3%
6 2
 
4.7%
4 3
7.0%
3 1
 
2.3%
2 2
 
4.7%
1 4
9.3%
0 6
14.0%

관리부서
Categorical

Distinct2
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Memory size476.0 B
보건행정과
36 
감염병관리과
7 

Length

Max length6
Median length5
Mean length5.1627907
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
보건행정과 36
83.7%
감염병관리과 7
 
16.3%

Length

2023-12-12T15:53:34.439079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:53:34.568778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
보건행정과 36
83.7%
감염병관리과 7
 
16.3%

Interactions

2023-12-12T15:53:32.773053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:53:32.559520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:53:32.854417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:53:32.660150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T15:53:34.635953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준날짜확진자수사망자수관리부서
기준날짜1.0001.0001.0001.000
확진자수1.0001.0000.9260.000
사망자수1.0000.9261.0000.188
관리부서1.0000.0000.1881.000
2023-12-12T15:53:34.730030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
확진자수사망자수관리부서
확진자수1.0000.5900.000
사망자수0.5901.0000.081
관리부서0.0000.0811.000

Missing values

2023-12-12T15:53:32.957982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T15:53:33.065348image/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.
2023-12-12T15:53:33.148888image/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

기관명기준날짜확진자수사망자수관리부서
0전라북도군산시2020-01-311<NA>보건행정과
1전라북도군산시2020-02-292<NA>보건행정과
2전라북도군산시2020-03-312<NA>보건행정과
3전라북도군산시2020-04-30<NA><NA>보건행정과
4전라북도군산시2020-05-311<NA>보건행정과
5전라북도군산시2020-06-30<NA><NA>보건행정과
6전라북도군산시2020-07-315<NA>보건행정과
7전라북도군산시2020-08-3110<NA>보건행정과
8전라북도군산시2020-09-301<NA>보건행정과
9전라북도군산시2020-10-31<NA><NA>보건행정과
기관명기준날짜확진자수사망자수관리부서
33전라북도군산시2022-10-3137266보건행정과
34전라북도군산시2022-11-3073232보건행정과
35전라북도군산시2022-12-3192318보건행정과
36전라북도군산시2023-01-3160122감염병관리과
37전라북도군산시2023-02-2816890감염병관리과
38전라북도군산시2023-03-3114940감염병관리과
39전라북도군산시2023-04-3014380감염병관리과
40전라북도군산시2023-05-3123480감염병관리과
41전라북도군산시2023-06-3025400감염병관리과
42전라북도군산시2023-07-3149560감염병관리과