Overview

Dataset statistics

Number of variables3
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory322.3 KiB
Average record size in memory33.0 B

Variable types

Numeric1
Text1
DateTime1

Alerts

연번 has unique valuesUnique
확진자번호 has unique valuesUnique

Reproduction

Analysis started2024-01-09 21:16:25.680773
Analysis finished2024-01-09 21:16:26.047636
Duration0.37 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9077.9283
Minimum1
Maximum18168
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T06:16:26.310967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile887.9
Q14519.75
median9060.5
Q313623.25
95-th percentile17264.05
Maximum18168
Range18167
Interquartile range (IQR)9103.5

Descriptive statistics

Standard deviation5246.539
Coefficient of variation (CV)0.57794453
Kurtosis-1.2006874
Mean9077.9283
Median Absolute Deviation (MAD)4552
Skewness-0.002272347
Sum90779283
Variance27526172
MonotonicityNot monotonic
2024-01-10T06:16:26.416570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12754 1
 
< 0.1%
5216 1
 
< 0.1%
4150 1
 
< 0.1%
3393 1
 
< 0.1%
13037 1
 
< 0.1%
256 1
 
< 0.1%
7795 1
 
< 0.1%
11247 1
 
< 0.1%
12280 1
 
< 0.1%
7952 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
10 1
< 0.1%
12 1
< 0.1%
13 1
< 0.1%
14 1
< 0.1%
ValueCountFrequency (%)
18168 1
< 0.1%
18164 1
< 0.1%
18163 1
< 0.1%
18160 1
< 0.1%
18159 1
< 0.1%
18158 1
< 0.1%
18155 1
< 0.1%
18154 1
< 0.1%
18152 1
< 0.1%
18150 1
< 0.1%

확진자번호
Text

UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-01-10T06:16:26.654638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length6.3724
Min length3

Characters and Unicode

Total characters63724
Distinct characters37
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10000 ?
Unique (%)100.0%

Sample

1st row천안#4523
2nd row부여#481
3rd row아산#2246
4th row아산#2591
5th row계룡#134
ValueCountFrequency (%)
아산 3
 
< 0.1%
서천#133 1
 
< 0.1%
계룡#211 1
 
< 0.1%
당진#474 1
 
< 0.1%
아산#762 1
 
< 0.1%
서산#256 1
 
< 0.1%
논산#926 1
 
< 0.1%
서산#18 1
 
< 0.1%
천안#2764 1
 
< 0.1%
논산#252 1
 
< 0.1%
Other values (9991) 9991
99.9%
2024-01-10T06:16:27.005967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
# 9850
15.5%
1 4992
 
7.8%
2 4352
 
6.8%
안 3745
 
5.9%
천 3737
 
5.9%
4 3642
 
5.7%
3 3629
 
5.7%
산 3405
 
5.3%
5 3384
 
5.3%
6 3054
 
4.8%
Other values (27) 19934
31.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 33594
52.7%
Other Letter 20272
31.8%
Other Punctuation 9850
 
15.5%
Space Separator 8
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
안 3745
18.5%
천 3737
18.4%
산 3405
16.8%
아 1640
8.1%
진 976
 
4.8%
당 976
 
4.8%
서 784
 
3.9%
논 604
 
3.0%
홍 483
 
2.4%
성 483
 
2.4%
Other values (15) 3439
17.0%
Decimal Number
ValueCountFrequency (%)
1 4992
14.9%
2 4352
13.0%
4 3642
10.8%
3 3629
10.8%
5 3384
10.1%
6 3054
9.1%
7 2790
8.3%
8 2685
8.0%
9 2546
7.6%
0 2520
7.5%
Other Punctuation
ValueCountFrequency (%)
# 9850
100.0%
Space Separator
ValueCountFrequency (%)
8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 43452
68.2%
Hangul 20272
31.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
안 3745
18.5%
천 3737
18.4%
산 3405
16.8%
아 1640
8.1%
진 976
 
4.8%
당 976
 
4.8%
서 784
 
3.9%
논 604
 
3.0%
홍 483
 
2.4%
성 483
 
2.4%
Other values (15) 3439
17.0%
Common
ValueCountFrequency (%)
# 9850
22.7%
1 4992
11.5%
2 4352
10.0%
4 3642
 
8.4%
3 3629
 
8.4%
5 3384
 
7.8%
6 3054
 
7.0%
7 2790
 
6.4%
8 2685
 
6.2%
9 2546
 
5.9%
Other values (2) 2528
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 43452
68.2%
Hangul 20272
31.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
# 9850
22.7%
1 4992
11.5%
2 4352
10.0%
4 3642
 
8.4%
3 3629
 
8.4%
5 3384
 
7.8%
6 3054
 
7.0%
7 2790
 
6.4%
8 2685
 
6.2%
9 2546
 
5.9%
Other values (2) 2528
 
5.8%
Hangul
ValueCountFrequency (%)
안 3745
18.5%
천 3737
18.4%
산 3405
16.8%
아 1640
8.1%
진 976
 
4.8%
당 976
 
4.8%
서 784
 
3.9%
논 604
 
3.0%
홍 483
 
2.4%
성 483
 
2.4%
Other values (15) 3439
17.0%

확진일자
Date

Distinct529
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2020-03-01 00:00:00
Maximum2021-12-31 00:00:00
2024-01-10T06:16:27.125508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:16:27.240631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2024-01-10T06:16:25.838942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Missing values

2024-01-10T06:16:25.954937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T06:16:26.016839image/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

연번확진자번호확진일자
1275312754천안#45232021-11-28
1196711968부여#4812021-11-21
1353213533아산#22462021-12-04
1589515896아산#25912021-12-17
1423314234계룡#1342021-12-08
1191511916예산#2842021-11-20
1107411075홍성#3352021-11-07
6667부여62020-03-31
641642공주#122020-11-15
1512215123천안#53012021-12-13
연번확진자번호확진일자
41184119아산#7592021-07-12
56085609아산#9812021-08-11
21692170천안#8912021-02-15
1329013291천안#47182021-12-03
73777378천안#26232021-09-06
151152천안1212020-08-17
1400214003홍성#5062021-12-07
1735117352천안#61322021-12-25
66726673당진#6832021-08-27
1032010321당진#10882021-10-27