Overview

Dataset statistics

Number of variables9
Number of observations35
Missing cells7
Missing cells (%)2.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.7 KiB
Average record size in memory79.7 B

Variable types

Categorical6
Numeric2
DateTime1

Dataset

Description전북특별자치도 정읍시에 코로나-19 확진자 및 사망자 현황중( 구분, 상위기관, 시군구명, 발생연도, 발생월, 확진자수, 사망자수, 관리부서 ) 등의 정보를 제공합니다.
Author전북특별자치도 정읍시
URLhttps://www.data.go.kr/data/15098590/fileData.do

Alerts

구분 has constant value ""Constant
상위기관 has constant value ""Constant
시군구명 has constant value ""Constant
관리부서 has constant value ""Constant
데이터기준일자 has constant value ""Constant
발생월 is highly overall correlated with 사망자수High correlation
확진자수 is highly overall correlated with 사망자수High correlation
사망자수 is highly overall correlated with 발생월 and 1 other fieldsHigh correlation
확진자수 has 7 (20.0%) missing valuesMissing

Reproduction

Analysis started2024-04-21 08:04:56.112879
Analysis finished2024-04-21 08:04:58.107957
Duration2 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Categorical

CONSTANT 

Distinct1
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size408.0 B
코로나19발생 및 사망자
35 

Length

Max length13
Median length13
Mean length13
Min length13

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row코로나19발생 및 사망자
2nd row코로나19발생 및 사망자
3rd row코로나19발생 및 사망자
4th row코로나19발생 및 사망자
5th row코로나19발생 및 사망자

Common Values

ValueCountFrequency (%)
코로나19발생 및 사망자 35
100.0%

Length

2024-04-21T17:04:58.316606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T17:04:58.626185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
코로나19발생 35
33.3%
및 35
33.3%
사망자 35
33.3%

상위기관
Categorical

CONSTANT 

Distinct1
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size408.0 B
전북특별자치도
35 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
전북특별자치도 35
100.0%

Length

2024-04-21T17:04:58.961140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T17:04:59.271779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전북특별자치도 35
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size408.0 B
정읍시
35 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
정읍시 35
100.0%

Length

2024-04-21T17:04:59.601651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T17:04:59.911343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
정읍시 35
100.0%

발생연도
Categorical

Distinct3
Distinct (%)8.6%
Missing0
Missing (%)0.0%
Memory size408.0 B
2021
12 
2022
12 
2020
11 

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
34.3%
2022 12
34.3%
2020 11
31.4%

Length

2024-04-21T17:05:00.245463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T17:05:00.566999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021 12
34.3%
2022 12
34.3%
2020 11
31.4%

발생월
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)34.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.6571429
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size443.0 B
2024-04-21T17:05:00.880075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.7
Q14
median7
Q39.5
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)5.5

Descriptive statistics

Standard deviation3.4208948
Coefficient of variation (CV)0.51386831
Kurtosis-1.1941881
Mean6.6571429
Median Absolute Deviation (MAD)3
Skewness-0.016624901
Sum233
Variance11.702521
MonotonicityNot monotonic
2024-04-21T17:05:01.245566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
2 3
8.6%
3 3
8.6%
4 3
8.6%
5 3
8.6%
6 3
8.6%
7 3
8.6%
8 3
8.6%
9 3
8.6%
10 3
8.6%
11 3
8.6%
Other values (2) 5
14.3%
ValueCountFrequency (%)
1 2
5.7%
2 3
8.6%
3 3
8.6%
4 3
8.6%
5 3
8.6%
6 3
8.6%
7 3
8.6%
8 3
8.6%
9 3
8.6%
10 3
8.6%
ValueCountFrequency (%)
12 3
8.6%
11 3
8.6%
10 3
8.6%
9 3
8.6%
8 3
8.6%
7 3
8.6%
6 3
8.6%
5 3
8.6%
4 3
8.6%
3 3
8.6%

확진자수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct25
Distinct (%)89.3%
Missing7
Missing (%)20.0%
Infinite0
Infinite (%)0.0%
Mean1806
Minimum1
Maximum17844
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size443.0 B
2024-04-21T17:05:01.598818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.35
Q115.5
median39
Q32064.5
95-th percentile7900.65
Maximum17844
Range17843
Interquartile range (IQR)2049

Descriptive statistics

Standard deviation3795.0056
Coefficient of variation (CV)2.101332
Kurtosis12.024394
Mean1806
Median Absolute Deviation (MAD)37.5
Skewness3.2530506
Sum50568
Variance14402068
MonotonicityNot monotonic
2024-04-21T17:05:02.185102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
16 3
 
8.6%
25 2
 
5.7%
3254 1
 
2.9%
3412 1
 
2.9%
1998 1
 
2.9%
1339 1
 
2.9%
2794 1
 
2.9%
6691 1
 
2.9%
1679 1
 
2.9%
239 1
 
2.9%
Other values (15) 15
42.9%
(Missing) 7
20.0%
ValueCountFrequency (%)
1 1
 
2.9%
2 1
 
2.9%
3 1
 
2.9%
5 1
 
2.9%
6 1
 
2.9%
7 1
 
2.9%
14 1
 
2.9%
16 3
8.6%
19 1
 
2.9%
22 1
 
2.9%
ValueCountFrequency (%)
17844 1
2.9%
8552 1
2.9%
6691 1
2.9%
3412 1
2.9%
3254 1
2.9%
2794 1
2.9%
2264 1
2.9%
1998 1
2.9%
1679 1
2.9%
1339 1
2.9%

사망자수
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)14.3%
Missing0
Missing (%)0.0%
Memory size408.0 B
<NA>
26 
1
6 
24
 
1
4
 
1
2
 
1

Length

Max length4
Median length4
Mean length3.2571429
Min length1

Unique

Unique3 ?
Unique (%)8.6%

Sample

1st row1
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 26
74.3%
1 6
 
17.1%
24 1
 
2.9%
4 1
 
2.9%
2 1
 
2.9%

Length

2024-04-21T17:05:02.602619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T17:05:02.947705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 26
74.3%
1 6
 
17.1%
24 1
 
2.9%
4 1
 
2.9%
2 1
 
2.9%

관리부서
Categorical

CONSTANT 

Distinct1
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size408.0 B
건강증진과
35 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
건강증진과 35
100.0%

Length

2024-04-21T17:05:03.324019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T17:05:03.634870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
건강증진과 35
100.0%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size408.0 B
Minimum2023-01-25 00:00:00
Maximum2023-01-25 00:00:00
2024-04-21T17:05:03.895627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T17:05:04.205476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2024-04-21T17:04:56.891436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T17:04:56.404068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T17:04:57.140010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T17:04:56.641808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-21T17:05:04.419387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
발생연도발생월확진자수사망자수
발생연도1.0000.0000.4490.000
발생월0.0001.0000.7001.000
확진자수0.4490.7001.0000.949
사망자수0.0001.0000.9491.000
2024-04-21T17:05:04.664937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
발생연도사망자수
발생연도1.0000.000
사망자수0.0001.000
2024-04-21T17:05:04.903045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
발생월확진자수발생연도사망자수
발생월1.0000.0750.0000.775
확진자수0.0751.0000.3580.663
발생연도0.0000.3581.0000.000
사망자수0.7750.6630.0001.000

Missing values

2024-04-21T17:04:57.494221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T17:04:57.937897image/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

구분상위기관시군구명발생연도발생월확진자수사망자수관리부서데이터기준일자
0코로나19발생 및 사망자전북특별자치도정읍시20202<NA>1건강증진과2023-01-25
1코로나19발생 및 사망자전북특별자치도정읍시20203<NA><NA>건강증진과2023-01-25
2코로나19발생 및 사망자전북특별자치도정읍시20204<NA><NA>건강증진과2023-01-25
3코로나19발생 및 사망자전북특별자치도정읍시20205<NA><NA>건강증진과2023-01-25
4코로나19발생 및 사망자전북특별자치도정읍시202061<NA>건강증진과2023-01-25
5코로나19발생 및 사망자전북특별자치도정읍시20207<NA><NA>건강증진과2023-01-25
6코로나19발생 및 사망자전북특별자치도정읍시20208<NA><NA>건강증진과2023-01-25
7코로나19발생 및 사망자전북특별자치도정읍시202093<NA>건강증진과2023-01-25
8코로나19발생 및 사망자전북특별자치도정읍시20201016<NA>건강증진과2023-01-25
9코로나19발생 및 사망자전북특별자치도정읍시202011<NA><NA>건강증진과2023-01-25
구분상위기관시군구명발생연도발생월확진자수사망자수관리부서데이터기준일자
25코로나19발생 및 사망자전북특별자치도정읍시202231784424건강증진과2023-01-25
26코로나19발생 및 사망자전북특별자치도정읍시2022485524건강증진과2023-01-25
27코로나19발생 및 사망자전북특별자치도정읍시2022522642건강증진과2023-01-25
28코로나19발생 및 사망자전북특별자치도정읍시20226239<NA>건강증진과2023-01-25
29코로나19발생 및 사망자전북특별자치도정읍시202271679<NA>건강증진과2023-01-25
30코로나19발생 및 사망자전북특별자치도정읍시202286691<NA>건강증진과2023-01-25
31코로나19발생 및 사망자전북특별자치도정읍시2022927941건강증진과2023-01-25
32코로나19발생 및 사망자전북특별자치도정읍시2022101339<NA>건강증진과2023-01-25
33코로나19발생 및 사망자전북특별자치도정읍시20221119981건강증진과2023-01-25
34코로나19발생 및 사망자전북특별자치도정읍시20221234121건강증진과2023-01-25