Overview

Dataset statistics

Number of variables7
Number of observations36
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.3 KiB
Average record size in memory64.7 B

Variable types

Categorical1
Text1
Numeric5

Dataset

Description대전광역시의 위생 통계에 대한 데이터로, 식품위생업소, 건강기능식품업소 등( 공중위생, 의·약)업소 등이 있다.
Author대전광역시
URLhttps://www.data.go.kr/data/15081628/fileData.do

Alerts

동구 is highly overall correlated with 중구 and 3 other fieldsHigh correlation
중구 is highly overall correlated with 동구 and 3 other fieldsHigh correlation
서구 is highly overall correlated with 동구 and 3 other fieldsHigh correlation
유성 is highly overall correlated with 동구 and 3 other fieldsHigh correlation
대덕 is highly overall correlated with 동구 and 3 other fieldsHigh correlation
소분류 has unique valuesUnique
동구 has 2 (5.6%) zerosZeros
중구 has 3 (8.3%) zerosZeros
유성 has 2 (5.6%) zerosZeros

Reproduction

Analysis started2023-12-12 06:43:23.595418
Analysis finished2023-12-12 06:43:26.952849
Duration3.36 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

대분류
Categorical

Distinct4
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Memory size420.0 B
식품위생업소
18 
공중위생업소
8 
의약업소
8 
건강기능식품업소
2 

Length

Max length8
Median length6
Mean length5.6666667
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
식품위생업소 18
50.0%
공중위생업소 8
22.2%
의약업소 8
22.2%
건강기능식품업소 2
 
5.6%

Length

2023-12-12T15:43:27.039421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:43:27.199783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
식품위생업소 18
50.0%
공중위생업소 8
22.2%
의약업소 8
22.2%
건강기능식품업소 2
 
5.6%

소분류
Text

UNIQUE 

Distinct36
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size420.0 B
2023-12-12T15:43:27.472670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length10
Mean length5.9722222
Min length2

Characters and Unicode

Total characters215
Distinct characters72
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

Unique36 ?
Unique (%)100.0%

Sample

1st row일반음식점
2nd row유흥주점
3rd row단란주점
4th row제과점
5th row휴게음식점
ValueCountFrequency (%)
일반음식점 1
 
2.8%
유흥주점 1
 
2.8%
위생용품제조업 1
 
2.8%
숙박 1
 
2.8%
목욕장업 1
 
2.8%
이용업 1
 
2.8%
세탁업 1
 
2.8%
건물위생관리업 1
 
2.8%
미용업 1
 
2.8%
위생물수건처리업 1
 
2.8%
Other values (26) 26
72.2%
2023-12-12T15:43:27.894109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
업 25
 
11.6%
식 14
 
6.5%
품 11
 
5.1%
매 11
 
5.1%
기 9
 
4.2%
판 9
 
4.2%
제 6
 
2.8%
약 6
 
2.8%
점 5
 
2.3%
조 5
 
2.3%
Other values (62) 114
53.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 212
98.6%
Other Punctuation 3
 
1.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
업 25
 
11.8%
식 14
 
6.6%
품 11
 
5.2%
매 11
 
5.2%
기 9
 
4.2%
판 9
 
4.2%
제 6
 
2.8%
약 6
 
2.8%
점 5
 
2.4%
조 5
 
2.4%
Other values (61) 111
52.4%
Other Punctuation
ValueCountFrequency (%)
· 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 212
98.6%
Common 3
 
1.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
업 25
 
11.8%
식 14
 
6.6%
품 11
 
5.2%
매 11
 
5.2%
기 9
 
4.2%
판 9
 
4.2%
제 6
 
2.8%
약 6
 
2.8%
점 5
 
2.4%
조 5
 
2.4%
Other values (61) 111
52.4%
Common
ValueCountFrequency (%)
· 3
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 212
98.6%
None 3
 
1.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
업 25
 
11.8%
식 14
 
6.6%
품 11
 
5.2%
매 11
 
5.2%
기 9
 
4.2%
판 9
 
4.2%
제 6
 
2.8%
약 6
 
2.8%
점 5
 
2.4%
조 5
 
2.4%
Other values (61) 111
52.4%
None
ValueCountFrequency (%)
· 3
100.0%

동구
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct34
Distinct (%)94.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean201.86111
Minimum0
Maximum2779
Zeros2
Zeros (%)5.6%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-12T15:43:28.045032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.75
Q113
median61.5
Q3147.75
95-th percentile745.75
Maximum2779
Range2779
Interquartile range (IQR)134.75

Descriptive statistics

Standard deviation482.68192
Coefficient of variation (CV)2.3911585
Kurtosis24.434102
Mean201.86111
Median Absolute Deviation (MAD)51.5
Skewness4.6747194
Sum7267
Variance232981.84
MonotonicityNot monotonic
2023-12-12T15:43:28.206999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
13 2
 
5.6%
0 2
 
5.6%
396 1
 
2.8%
156 1
 
2.8%
25 1
 
2.8%
96 1
 
2.8%
107 1
 
2.8%
69 1
 
2.8%
730 1
 
2.8%
2779 1
 
2.8%
Other values (24) 24
66.7%
ValueCountFrequency (%)
0 2
5.6%
1 1
2.8%
3 1
2.8%
5 1
2.8%
6 1
2.8%
9 1
2.8%
11 1
2.8%
13 2
5.6%
16 1
2.8%
17 1
2.8%
ValueCountFrequency (%)
2779 1
2.8%
793 1
2.8%
730 1
2.8%
469 1
2.8%
415 1
2.8%
396 1
2.8%
183 1
2.8%
172 1
2.8%
156 1
2.8%
145 1
2.8%

중구
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct33
Distinct (%)91.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean228.94444
Minimum0
Maximum3282
Zeros3
Zeros (%)8.3%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-12T15:43:28.354755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q112.25
median63.5
Q3168.25
95-th percentile809.25
Maximum3282
Range3282
Interquartile range (IQR)156

Descriptive statistics

Standard deviation565.51251
Coefficient of variation (CV)2.4700862
Kurtosis25.637582
Mean228.94444
Median Absolute Deviation (MAD)58.5
Skewness4.8059239
Sum8242
Variance319804.4
MonotonicityNot monotonic
2023-12-12T15:43:28.487036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
0 3
 
8.3%
7 2
 
5.6%
3282 1
 
2.8%
489 1
 
2.8%
176 1
 
2.8%
17 1
 
2.8%
98 1
 
2.8%
150 1
 
2.8%
119 1
 
2.8%
831 1
 
2.8%
Other values (23) 23
63.9%
ValueCountFrequency (%)
0 3
8.3%
1 1
 
2.8%
3 1
 
2.8%
4 1
 
2.8%
6 1
 
2.8%
7 2
5.6%
14 1
 
2.8%
17 1
 
2.8%
18 1
 
2.8%
21 1
 
2.8%
ValueCountFrequency (%)
3282 1
2.8%
831 1
2.8%
802 1
2.8%
535 1
2.8%
489 1
2.8%
472 1
2.8%
194 1
2.8%
176 1
2.8%
172 1
2.8%
167 1
2.8%

서구
Real number (ℝ)

HIGH CORRELATION 

Distinct33
Distinct (%)91.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean452.05556
Minimum1
Maximum6119
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-12T15:43:28.628906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.5
Q116.75
median74
Q3267.75
95-th percentile1787
Maximum6119
Range6118
Interquartile range (IQR)251

Descriptive statistics

Standard deviation1101.1085
Coefficient of variation (CV)2.4357813
Kurtosis20.81783
Mean452.05556
Median Absolute Deviation (MAD)69.5
Skewness4.2747942
Sum16274
Variance1212439.9
MonotonicityNot monotonic
2023-12-12T15:43:28.772100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
5 2
 
5.6%
3 2
 
5.6%
1 2
 
5.6%
39 1
 
2.8%
23 1
 
2.8%
134 1
 
2.8%
221 1
 
2.8%
208 1
 
2.8%
2153 1
 
2.8%
6119 1
 
2.8%
Other values (23) 23
63.9%
ValueCountFrequency (%)
1 2
5.6%
3 2
5.6%
4 1
2.8%
5 2
5.6%
8 1
2.8%
13 1
2.8%
18 1
2.8%
20 1
2.8%
23 1
2.8%
39 1
2.8%
ValueCountFrequency (%)
6119 1
2.8%
2153 1
2.8%
1665 1
2.8%
1517 1
2.8%
1200 1
2.8%
742 1
2.8%
408 1
2.8%
337 1
2.8%
327 1
2.8%
248 1
2.8%

유성
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct33
Distinct (%)91.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean352.47222
Minimum0
Maximum5134
Zeros2
Zeros (%)5.6%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-12T15:43:28.894029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.75
Q118.5
median82.5
Q3165.25
95-th percentile1231
Maximum5134
Range5134
Interquartile range (IQR)146.75

Descriptive statistics

Standard deviation892.94852
Coefficient of variation (CV)2.5333869
Kurtosis24.740606
Mean352.47222
Median Absolute Deviation (MAD)69.5
Skewness4.7095897
Sum12689
Variance797357.06
MonotonicityNot monotonic
2023-12-12T15:43:29.060554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
2 2
 
5.6%
150 2
 
5.6%
0 2
 
5.6%
3 1
 
2.8%
102 1
 
2.8%
21 1
 
2.8%
73 1
 
2.8%
96 1
 
2.8%
1152 1
 
2.8%
1 1
 
2.8%
Other values (23) 23
63.9%
ValueCountFrequency (%)
0 2
5.6%
1 1
2.8%
2 2
5.6%
3 1
2.8%
4 1
2.8%
8 1
2.8%
11 1
2.8%
21 1
2.8%
25 1
2.8%
31 1
2.8%
ValueCountFrequency (%)
5134 1
2.8%
1468 1
2.8%
1152 1
2.8%
968 1
2.8%
862 1
2.8%
618 1
2.8%
400 1
2.8%
339 1
2.8%
211 1
2.8%
150 2
5.6%

대덕
Real number (ℝ)

HIGH CORRELATION 

Distinct32
Distinct (%)88.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean173.80556
Minimum1
Maximum2342
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-12T15:43:29.197388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.75
Q114.75
median61.5
Q3116.5
95-th percentile548
Maximum2342
Range2341
Interquartile range (IQR)101.75

Descriptive statistics

Standard deviation402.0395
Coefficient of variation (CV)2.3131568
Kurtosis25.50678
Mean173.80556
Median Absolute Deviation (MAD)50.5
Skewness4.7827751
Sum6257
Variance161635.76
MonotonicityNot monotonic
2023-12-12T15:43:29.311440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
87 2
 
5.6%
11 2
 
5.6%
3 2
 
5.6%
1 2
 
5.6%
605 1
 
2.8%
16 1
 
2.8%
73 1
 
2.8%
102 1
 
2.8%
84 1
 
2.8%
2342 1
 
2.8%
Other values (22) 22
61.1%
ValueCountFrequency (%)
1 2
5.6%
2 1
2.8%
3 2
5.6%
5 1
2.8%
9 1
2.8%
11 2
5.6%
16 1
2.8%
17 1
2.8%
19 1
2.8%
21 1
2.8%
ValueCountFrequency (%)
2342 1
2.8%
605 1
2.8%
529 1
2.8%
435 1
2.8%
386 1
2.8%
331 1
2.8%
203 1
2.8%
147 1
2.8%
130 1
2.8%
112 1
2.8%

Interactions

2023-12-12T15:43:26.189240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:43:23.898491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:43:24.716869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:43:25.264921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:43:25.743050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:43:26.296031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:43:24.326657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:43:24.825166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:43:25.364302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:43:25.834809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:43:26.400796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:43:24.428778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:43:24.930032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:43:25.471776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:43:25.920469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:43:26.527246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:43:24.520365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:43:25.068817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:43:25.572046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:43:26.017563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:43:26.619941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:43:24.601220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:43:25.170535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:43:25.652631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:43:26.107008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T15:43:29.416977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대분류소분류동구중구서구유성대덕
대분류1.0001.0000.0000.0000.1780.0000.000
소분류1.0001.0001.0001.0001.0001.0001.000
동구0.0001.0001.0001.0000.9111.0001.000
중구0.0001.0001.0001.0000.9111.0001.000
서구0.1781.0000.9110.9111.0000.9110.911
유성0.0001.0001.0001.0000.9111.0001.000
대덕0.0001.0001.0001.0000.9111.0001.000
2023-12-12T15:43:29.564515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
동구중구서구유성대덕대분류
동구1.0000.9600.9460.9280.9480.000
중구0.9601.0000.9570.9470.9430.000
서구0.9460.9571.0000.9500.9110.131
유성0.9280.9470.9501.0000.9500.000
대덕0.9480.9430.9110.9501.0000.000
대분류0.0000.0000.1310.0000.0001.000

Missing values

2023-12-12T15:43:26.754676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T15:43:26.896475image/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

대분류소분류동구중구서구유성대덕
0식품위생업소일반음식점27793282611951342342
1식품위생업소유흥주점1663187253
2식품위생업소단란주점17731056319
3식품위생업소제과점787719415051
4식품위생업소휴게음식점79380216651468529
5식품위생업소위탁급식영업20185114870
6식품위생업소집단급식172172337400203
7식품위생업소식품제조·가공업111647690112
8식품위생업소즉석판매제조·가공업415472742618386
9식품위생업소식품자동판매기업145167327211130
대분류소분류동구중구서구유성대덕
26공중위생업소위생용품제조업54539
27공중위생업소위생물수건처리업00312
28의약업소약국11914224813687
29의약업소한약국9613113
30의약업소한약도매137821
31의약업소양약도매3829633145
32의약업소한약업사110501
33의약업소안전상비의약품판매업183194408339147
34의약업소의료기기판매업4695351517968435
35의약업소의료기기수리업2422202521