Overview

Dataset statistics

Number of variables4
Number of observations31
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.2 KiB
Average record size in memory39.3 B

Variable types

Text1
Numeric3

Dataset

Description공중위생영업소 및 공중위생시설 시도별 현황 통계
Author보건복지부
URLhttps://www.data.go.kr/data/15004307/fileData.do

Alerts

위생처리업 is highly overall correlated with 세척제제조업 and 1 other fieldsHigh correlation
세척제제조업 is highly overall correlated with 위생처리업 and 1 other fieldsHigh correlation
기타 위생용품 제조업 is highly overall correlated with 위생처리업 and 1 other fieldsHigh correlation
구분(연도_지역) has unique valuesUnique

Reproduction

Analysis started2023-12-12 15:39:13.509534
Analysis finished2023-12-12 15:39:14.706060
Duration1.2 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분(연도_지역)
Text

UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size380.0 B
2023-12-13T00:39:14.851964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length2
Mean length2.9032258
Min length2

Characters and Unicode

Total characters90
Distinct characters31
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique31 ?
Unique (%)100.0%

Sample

1st row2004
2nd row2005
3rd row2006
4th row2007
5th row2008
ValueCountFrequency (%)
2004 1
 
3.2%
대구 1
 
3.2%
경남 1
 
3.2%
경북 1
 
3.2%
전남 1
 
3.2%
전북 1
 
3.2%
충남 1
 
3.2%
충북 1
 
3.2%
강원 1
 
3.2%
경기 1
 
3.2%
Other values (21) 21
67.7%
2023-12-13T00:39:15.328358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 21
23.3%
2 15
16.7%
1 9
 
10.0%
남 3
 
3.3%
경 3
 
3.3%
북 3
 
3.3%
전 3
 
3.3%
7 2
 
2.2%
6 2
 
2.2%
충 2
 
2.2%
Other values (21) 27
30.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 56
62.2%
Other Letter 34
37.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
남 3
 
8.8%
경 3
 
8.8%
북 3
 
8.8%
전 3
 
8.8%
충 2
 
5.9%
울 2
 
5.9%
산 2
 
5.9%
대 2
 
5.9%
주 2
 
5.9%
세 1
 
2.9%
Other values (11) 11
32.4%
Decimal Number
ValueCountFrequency (%)
0 21
37.5%
2 15
26.8%
1 9
16.1%
7 2
 
3.6%
6 2
 
3.6%
5 2
 
3.6%
4 2
 
3.6%
3 1
 
1.8%
9 1
 
1.8%
8 1
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
Common 56
62.2%
Hangul 34
37.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
남 3
 
8.8%
경 3
 
8.8%
북 3
 
8.8%
전 3
 
8.8%
충 2
 
5.9%
울 2
 
5.9%
산 2
 
5.9%
대 2
 
5.9%
주 2
 
5.9%
세 1
 
2.9%
Other values (11) 11
32.4%
Common
ValueCountFrequency (%)
0 21
37.5%
2 15
26.8%
1 9
16.1%
7 2
 
3.6%
6 2
 
3.6%
5 2
 
3.6%
4 2
 
3.6%
3 1
 
1.8%
9 1
 
1.8%
8 1
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 56
62.2%
Hangul 34
37.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 21
37.5%
2 15
26.8%
1 9
16.1%
7 2
 
3.6%
6 2
 
3.6%
5 2
 
3.6%
4 2
 
3.6%
3 1
 
1.8%
9 1
 
1.8%
8 1
 
1.8%
Hangul
ValueCountFrequency (%)
남 3
 
8.8%
경 3
 
8.8%
북 3
 
8.8%
전 3
 
8.8%
충 2
 
5.9%
울 2
 
5.9%
산 2
 
5.9%
대 2
 
5.9%
주 2
 
5.9%
세 1
 
2.9%
Other values (11) 11
32.4%

위생처리업
Real number (ℝ)

HIGH CORRELATION 

Distinct27
Distinct (%)87.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean200.35484
Minimum1
Maximum465
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-13T00:39:15.482928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6.5
Q115
median38
Q3430
95-th percentile458.5
Maximum465
Range464
Interquartile range (IQR)415

Descriptive statistics

Standard deviation205.1207
Coefficient of variation (CV)1.0237871
Kurtosis-1.9799633
Mean200.35484
Median Absolute Deviation (MAD)32
Skewness0.26008064
Sum6211
Variance42074.503
MonotonicityNot monotonic
2023-12-13T00:39:15.977761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
11 3
 
9.7%
455 2
 
6.5%
26 2
 
6.5%
6 1
 
3.2%
7 1
 
3.2%
38 1
 
3.2%
31 1
 
3.2%
22 1
 
3.2%
18 1
 
3.2%
14 1
 
3.2%
Other values (17) 17
54.8%
ValueCountFrequency (%)
1 1
 
3.2%
6 1
 
3.2%
7 1
 
3.2%
9 1
 
3.2%
11 3
9.7%
14 1
 
3.2%
16 1
 
3.2%
18 1
 
3.2%
22 1
 
3.2%
26 2
6.5%
ValueCountFrequency (%)
465 1
3.2%
459 1
3.2%
458 1
3.2%
455 2
6.5%
450 1
3.2%
446 1
3.2%
442 1
3.2%
418 1
3.2%
399 1
3.2%
387 1
3.2%

세척제제조업
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)83.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean139.74194
Minimum3
Maximum355
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-13T00:39:16.129785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile3.5
Q19.5
median35
Q3277
95-th percentile341.5
Maximum355
Range352
Interquartile range (IQR)267.5

Descriptive statistics

Standard deviation141.04538
Coefficient of variation (CV)1.0093275
Kurtosis-1.737747
Mean139.74194
Median Absolute Deviation (MAD)32
Skewness0.33117026
Sum4332
Variance19893.798
MonotonicityNot monotonic
2023-12-13T00:39:16.302272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
6 2
 
6.5%
10 2
 
6.5%
3 2
 
6.5%
9 2
 
6.5%
336 2
 
6.5%
15 1
 
3.2%
7 1
 
3.2%
28 1
 
3.2%
22 1
 
3.2%
16 1
 
3.2%
Other values (16) 16
51.6%
ValueCountFrequency (%)
3 2
6.5%
4 1
3.2%
6 2
6.5%
7 1
3.2%
9 2
6.5%
10 2
6.5%
14 1
3.2%
15 1
3.2%
16 1
3.2%
22 1
3.2%
ValueCountFrequency (%)
355 1
3.2%
347 1
3.2%
336 2
6.5%
331 1
3.2%
320 1
3.2%
303 1
3.2%
287 1
3.2%
267 1
3.2%
248 1
3.2%
234 1
3.2%

기타 위생용품 제조업
Real number (ℝ)

HIGH CORRELATION 

Distinct29
Distinct (%)93.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean212.19355
Minimum3
Maximum521
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-13T00:39:16.508038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile6
Q113
median34
Q3421
95-th percentile484.5
Maximum521
Range518
Interquartile range (IQR)408

Descriptive statistics

Standard deviation212.84101
Coefficient of variation (CV)1.0030513
Kurtosis-1.9633904
Mean212.19355
Median Absolute Deviation (MAD)31
Skewness0.19985451
Sum6578
Variance45301.295
MonotonicityNot monotonic
2023-12-13T00:39:16.685556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
34 2
 
6.5%
13 2
 
6.5%
396 1
 
3.2%
383 1
 
3.2%
8 1
 
3.2%
21 1
 
3.2%
11 1
 
3.2%
7 1
 
3.2%
20 1
 
3.2%
214 1
 
3.2%
Other values (19) 19
61.3%
ValueCountFrequency (%)
3 1
3.2%
5 1
3.2%
7 1
3.2%
8 1
3.2%
10 1
3.2%
11 1
3.2%
12 1
3.2%
13 2
6.5%
14 1
3.2%
16 1
3.2%
ValueCountFrequency (%)
521 1
3.2%
492 1
3.2%
477 1
3.2%
474 1
3.2%
465 1
3.2%
454 1
3.2%
440 1
3.2%
427 1
3.2%
415 1
3.2%
407 1
3.2%

Interactions

2023-12-13T00:39:14.233115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:39:13.637339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:39:13.917634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:39:14.312713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:39:13.735910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:39:14.014437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:39:14.414175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:39:13.837967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:39:14.131346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T00:39:16.802200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분(연도_지역)위생처리업세척제제조업기타 위생용품 제조업
구분(연도_지역)1.0001.0001.0001.000
위생처리업1.0001.0000.8910.979
세척제제조업1.0000.8911.0000.924
기타 위생용품 제조업1.0000.9790.9241.000
2023-12-13T00:39:16.921068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위생처리업세척제제조업기타 위생용품 제조업
위생처리업1.0000.7940.789
세척제제조업0.7941.0000.951
기타 위생용품 제조업0.7890.9511.000

Missing values

2023-12-13T00:39:14.568093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T00:39:14.661726image/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

구분(연도_지역)위생처리업세척제제조업기타 위생용품 제조업
02004446203396
12005458211383
22006450218415
32007465234379
42008459248394
52009455267407
62010455287427
72011442303440
82012418320465
92013399331477
구분(연도_지역)위생처리업세척제제조업기타 위생용품 제조업
21세종133
22경기55139214
23강원161013
24충북283534
25충남141620
26전북18107
27전남22911
28경북312234
29경남382821
30제주778