Overview

Dataset statistics

Number of variables4
Number of observations27
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.0 KiB
Average record size in memory38.9 B

Variable types

DateTime1
Numeric2
Categorical1

Dataset

Description서울특별시 영등포구의 2020년 이후 행정동별 코로나19 확진자수 제공데이터: 2020년 부터 22년까지 행정동 월별 확진자 및 사망자 수 기준날짜: 2022년 3월 15일
URLhttps://www.data.go.kr/data/15076226/fileData.do

Alerts

데이터 기준 일자 has constant value ""Constant
확진자 수 is highly overall correlated with 사망자 수High correlation
사망자 수 is highly overall correlated with 확진자 수High correlation
연월 has unique valuesUnique
확진자 수 has unique valuesUnique
확진자 수 has 1 (3.7%) zerosZeros
사망자 수 has 8 (29.6%) zerosZeros

Reproduction

Analysis started2023-12-12 09:24:34.086524
Analysis finished2023-12-12 09:24:34.845251
Duration0.76 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연월
Date

UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size348.0 B
Minimum2020-01-01 00:00:00
Maximum2022-03-01 00:00:00
2023-12-12T18:24:34.917345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:24:35.056402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)

확진자 수
Real number (ℝ)

HIGH CORRELATION  UNIQUE  ZEROS 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2459.4815
Minimum0
Maximum34864
Zeros1
Zeros (%)3.7%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-12T18:24:35.232921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.2
Q125
median188
Q3940
95-th percentile14108.4
Maximum34864
Range34864
Interquartile range (IQR)915

Descriptive statistics

Standard deviation7414.4755
Coefficient of variation (CV)3.0146499
Kurtosis15.471254
Mean2459.4815
Median Absolute Deviation (MAD)181
Skewness3.8918436
Sum66406
Variance54974447
MonotonicityNot monotonic
2023-12-12T18:24:35.396852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0 1
 
3.7%
1 1
 
3.7%
34864 1
 
3.7%
18864 1
 
3.7%
2036 1
 
3.7%
3012 1
 
3.7%
2031 1
 
3.7%
1037 1
 
3.7%
1142 1
 
3.7%
843 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
0 1
3.7%
1 1
3.7%
5 1
3.7%
7 1
3.7%
9 1
3.7%
18 1
3.7%
22 1
3.7%
28 1
3.7%
53 1
3.7%
68 1
3.7%
ValueCountFrequency (%)
34864 1
3.7%
18864 1
3.7%
3012 1
3.7%
2036 1
3.7%
2031 1
3.7%
1142 1
3.7%
1037 1
3.7%
843 1
3.7%
669 1
3.7%
395 1
3.7%

사망자 수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)40.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.8148148
Minimum0
Maximum26
Zeros8
Zeros (%)29.6%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-12T18:24:35.539304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q35
95-th percentile14.6
Maximum26
Range26
Interquartile range (IQR)5

Descriptive statistics

Standard deviation5.8443729
Coefficient of variation (CV)1.5320201
Kurtosis8.0656422
Mean3.8148148
Median Absolute Deviation (MAD)2
Skewness2.6810552
Sum103
Variance34.156695
MonotonicityNot monotonic
2023-12-12T18:24:35.670641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 8
29.6%
2 5
18.5%
1 4
14.8%
5 2
 
7.4%
6 2
 
7.4%
3 1
 
3.7%
9 1
 
3.7%
8 1
 
3.7%
17 1
 
3.7%
26 1
 
3.7%
ValueCountFrequency (%)
0 8
29.6%
1 4
14.8%
2 5
18.5%
3 1
 
3.7%
4 1
 
3.7%
5 2
 
7.4%
6 2
 
7.4%
8 1
 
3.7%
9 1
 
3.7%
17 1
 
3.7%
ValueCountFrequency (%)
26 1
 
3.7%
17 1
 
3.7%
9 1
 
3.7%
8 1
 
3.7%
6 2
 
7.4%
5 2
 
7.4%
4 1
 
3.7%
3 1
 
3.7%
2 5
18.5%
1 4
14.8%

데이터 기준 일자
Categorical

CONSTANT 

Distinct1
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Memory size348.0 B
2022-03-15
27 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022-03-15
2nd row2022-03-15
3rd row2022-03-15
4th row2022-03-15
5th row2022-03-15

Common Values

ValueCountFrequency (%)
2022-03-15 27
100.0%

Length

2023-12-12T18:24:35.813112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:24:35.924588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022-03-15 27
100.0%

Interactions

2023-12-12T18:24:34.449884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:24:34.186539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:24:34.566330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:24:34.326195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T18:24:35.990086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연월확진자 수사망자 수
연월1.0001.0001.000
확진자 수1.0001.0000.771
사망자 수1.0000.7711.000
2023-12-12T18:24:36.117512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
확진자 수사망자 수
확진자 수1.0000.755
사망자 수0.7551.000

Missing values

2023-12-12T18:24:34.696414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T18:24:34.801316image/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

연월확진자 수사망자 수데이터 기준 일자
02020-01002022-03-15
12020-02102022-03-15
22020-032202022-03-15
32020-04512022-03-15
42020-05902022-03-15
52020-062802022-03-15
62020-07702022-03-15
72020-086812022-03-15
82020-095322022-03-15
92020-101832022-03-15
연월확진자 수사망자 수데이터 기준 일자
172021-0627012022-03-15
182021-0766922022-03-15
192021-0884352022-03-15
202021-09114292022-03-15
212021-10103782022-03-15
222021-112031172022-03-15
232021-123012262022-03-15
242022-01203622022-03-15
252022-021886442022-03-15
262022-033486462022-03-15