Overview

Dataset statistics

Number of variables18
Number of observations23
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.7 KiB
Average record size in memory166.7 B

Variable types

Categorical7
Text1
Numeric10

Dataset

Description대전광역시 서구 행정동별 식품위생관계업소 현황(기준연도, 행정동, 휴게음식점, 일반음식점, 제과점, 단란주점, 유흥주점, 위탁급식영업, 집단급식소, 식품제조가공업, 즉석판매 제조가공업, 식품첨가물 제조업, 식품운반업, 식품소분·판매업, 식품보존업, 용기·포장류 제조업 등 기타, 건강기능식품제조업, 건강기능식품판매업 ) 데이터 입니다.
Author대전광역시 서구
URLhttps://www.data.go.kr/data/15095162/fileData.do

Alerts

기준연도 has constant value ""Constant
건강기능식품제조업 has constant value ""Constant
휴게음식점 is highly overall correlated with 일반음식점 and 8 other fieldsHigh correlation
일반음식점 is highly overall correlated with 휴게음식점 and 8 other fieldsHigh correlation
제과점 is highly overall correlated with 휴게음식점 and 6 other fieldsHigh correlation
단란주점 is highly overall correlated with 휴게음식점 and 5 other fieldsHigh correlation
위탁급식영업 is highly overall correlated with 휴게음식점 and 5 other fieldsHigh correlation
집단급식소 is highly overall correlated with 휴게음식점 and 6 other fieldsHigh correlation
식품제조가공업 is highly overall correlated with 집단급식소 and 2 other fieldsHigh correlation
즉석판매 제조가공업 is highly overall correlated with 휴게음식점 and 6 other fieldsHigh correlation
식품소분·판매업 is highly overall correlated with 휴게음식점 and 7 other fieldsHigh correlation
건강기능식품판매업 is highly overall correlated with 휴게음식점 and 8 other fieldsHigh correlation
유흥주점 is highly overall correlated with 단란주점High correlation
식품첨가물 제조업 is highly overall correlated with 식품제조가공업High correlation
식품보존업 is highly overall correlated with 휴게음식점 and 5 other fieldsHigh correlation
용기·포장류 제조업 등 기타 is highly overall correlated with 식품제조가공업High correlation
식품보존업 is highly imbalanced (74.2%)Imbalance
용기·포장류 제조업 등 기타 is highly imbalanced (74.2%)Imbalance
행정동 has unique valuesUnique
일반음식점 has unique valuesUnique
단란주점 has 8 (34.8%) zerosZeros
위탁급식영업 has 9 (39.1%) zerosZeros
식품제조가공업 has 3 (13.0%) zerosZeros

Reproduction

Analysis started2023-12-12 06:35:11.553307
Analysis finished2023-12-12 06:35:23.795265
Duration12.24 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기준연도
Categorical

CONSTANT 

Distinct1
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size316.0 B
2021
23 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021
2nd row2021
3rd row2021
4th row2021
5th row2021

Common Values

ValueCountFrequency (%)
2021 23
100.0%

Length

2023-12-12T15:35:23.859655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:35:23.948664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021 23
100.0%

행정동
Text

UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size316.0 B
2023-12-12T15:35:24.126918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.4782609
Min length2

Characters and Unicode

Total characters80
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

Unique23 ?
Unique (%)100.0%

Sample

1st row복수동
2nd row도마1동
3rd row도마2동
4th row정림동
5th row변동
ValueCountFrequency (%)
복수동 1
 
4.3%
내동 1
 
4.3%
관저2동 1
 
4.3%
관저1동 1
 
4.3%
가수원동 1
 
4.3%
만년동 1
 
4.3%
월평3동 1
 
4.3%
월평2동 1
 
4.3%
월평1동 1
 
4.3%
갈마2동 1
 
4.3%
Other values (13) 13
56.5%
2023-12-12T15:35:24.507858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
23
28.7%
1 5
 
6.2%
2 5
 
6.2%
4
 
5.0%
3
 
3.8%
3
 
3.8%
3
 
3.8%
3
 
3.8%
2
 
2.5%
2
 
2.5%
Other values (21) 27
33.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 68
85.0%
Decimal Number 12
 
15.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
23
33.8%
4
 
5.9%
3
 
4.4%
3
 
4.4%
3
 
4.4%
3
 
4.4%
2
 
2.9%
2
 
2.9%
2
 
2.9%
2
 
2.9%
Other values (18) 21
30.9%
Decimal Number
ValueCountFrequency (%)
1 5
41.7%
2 5
41.7%
3 2
 
16.7%

Most occurring scripts

ValueCountFrequency (%)
Hangul 68
85.0%
Common 12
 
15.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
23
33.8%
4
 
5.9%
3
 
4.4%
3
 
4.4%
3
 
4.4%
3
 
4.4%
2
 
2.9%
2
 
2.9%
2
 
2.9%
2
 
2.9%
Other values (18) 21
30.9%
Common
ValueCountFrequency (%)
1 5
41.7%
2 5
41.7%
3 2
 
16.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 68
85.0%
ASCII 12
 
15.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
23
33.8%
4
 
5.9%
3
 
4.4%
3
 
4.4%
3
 
4.4%
3
 
4.4%
2
 
2.9%
2
 
2.9%
2
 
2.9%
2
 
2.9%
Other values (18) 21
30.9%
ASCII
ValueCountFrequency (%)
1 5
41.7%
2 5
41.7%
3 2
 
16.7%

휴게음식점
Real number (ℝ)

HIGH CORRELATION 

Distinct21
Distinct (%)91.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.913043
Minimum4
Maximum177
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-12T15:35:24.632381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile9.7
Q134.5
median53
Q393.5
95-th percentile171
Maximum177
Range173
Interquartile range (IQR)59

Descriptive statistics

Standard deviation50.757814
Coefficient of variation (CV)0.72601352
Kurtosis-0.050345407
Mean69.913043
Median Absolute Deviation (MAD)26
Skewness0.91718324
Sum1608
Variance2576.3557
MonotonicityNot monotonic
2023-12-12T15:35:24.775717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
37 2
 
8.7%
25 2
 
8.7%
53 1
 
4.3%
75 1
 
4.3%
8 1
 
4.3%
128 1
 
4.3%
69 1
 
4.3%
162 1
 
4.3%
48 1
 
4.3%
4 1
 
4.3%
Other values (11) 11
47.8%
ValueCountFrequency (%)
4 1
4.3%
8 1
4.3%
25 2
8.7%
27 1
4.3%
33 1
4.3%
36 1
4.3%
37 2
8.7%
48 1
4.3%
50 1
4.3%
53 1
4.3%
ValueCountFrequency (%)
177 1
4.3%
172 1
4.3%
162 1
4.3%
128 1
4.3%
108 1
4.3%
107 1
4.3%
80 1
4.3%
79 1
4.3%
75 1
4.3%
69 1
4.3%

일반음식점
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean264.34783
Minimum22
Maximum558
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-12T15:35:24.942513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum22
5-th percentile58.5
Q1153.5
median206
Q3367.5
95-th percentile546.8
Maximum558
Range536
Interquartile range (IQR)214

Descriptive statistics

Standard deviation160.51384
Coefficient of variation (CV)0.60720695
Kurtosis-0.87962635
Mean264.34783
Median Absolute Deviation (MAD)107
Skewness0.46095812
Sum6080
Variance25764.692
MonotonicityNot monotonic
2023-12-12T15:35:25.095511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
175 1
 
4.3%
317 1
 
4.3%
54 1
 
4.3%
354 1
 
4.3%
280 1
 
4.3%
527 1
 
4.3%
206 1
 
4.3%
22 1
 
4.3%
162 1
 
4.3%
404 1
 
4.3%
Other values (13) 13
56.5%
ValueCountFrequency (%)
22 1
4.3%
54 1
4.3%
99 1
4.3%
104 1
4.3%
130 1
4.3%
150 1
4.3%
157 1
4.3%
159 1
4.3%
162 1
4.3%
174 1
4.3%
ValueCountFrequency (%)
558 1
4.3%
549 1
4.3%
527 1
4.3%
457 1
4.3%
404 1
4.3%
369 1
4.3%
366 1
4.3%
354 1
4.3%
317 1
4.3%
307 1
4.3%

제과점
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)65.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.3043478
Minimum1
Maximum28
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-12T15:35:25.277337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.1
Q14
median6
Q311
95-th percentile22.5
Maximum28
Range27
Interquartile range (IQR)7

Descriptive statistics

Standard deviation7.0931131
Coefficient of variation (CV)0.85414451
Kurtosis1.7620587
Mean8.3043478
Median Absolute Deviation (MAD)3
Skewness1.472807
Sum191
Variance50.312253
MonotonicityNot monotonic
2023-12-12T15:35:25.411952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
6 3
13.0%
4 3
13.0%
5 2
 
8.7%
2 2
 
8.7%
1 2
 
8.7%
7 2
 
8.7%
3 1
 
4.3%
18 1
 
4.3%
13 1
 
4.3%
23 1
 
4.3%
Other values (5) 5
21.7%
ValueCountFrequency (%)
1 2
8.7%
2 2
8.7%
3 1
 
4.3%
4 3
13.0%
5 2
8.7%
6 3
13.0%
7 2
8.7%
8 1
 
4.3%
10 1
 
4.3%
12 1
 
4.3%
ValueCountFrequency (%)
28 1
 
4.3%
23 1
 
4.3%
18 1
 
4.3%
16 1
 
4.3%
13 1
 
4.3%
12 1
 
4.3%
10 1
 
4.3%
8 1
 
4.3%
7 2
8.7%
6 3
13.0%

단란주점
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10
Distinct (%)43.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.7826087
Minimum0
Maximum38
Zeros8
Zeros (%)34.8%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-12T15:35:25.569553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q34
95-th percentile19.3
Maximum38
Range38
Interquartile range (IQR)4

Descriptive statistics

Standard deviation8.7019147
Coefficient of variation (CV)1.8194913
Kurtosis9.8868151
Mean4.7826087
Median Absolute Deviation (MAD)2
Skewness3.0023823
Sum110
Variance75.72332
MonotonicityNot monotonic
2023-12-12T15:35:25.706795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 8
34.8%
2 3
 
13.0%
3 3
 
13.0%
4 2
 
8.7%
1 2
 
8.7%
5 1
 
4.3%
38 1
 
4.3%
20 1
 
4.3%
9 1
 
4.3%
13 1
 
4.3%
ValueCountFrequency (%)
0 8
34.8%
1 2
 
8.7%
2 3
 
13.0%
3 3
 
13.0%
4 2
 
8.7%
5 1
 
4.3%
9 1
 
4.3%
13 1
 
4.3%
20 1
 
4.3%
38 1
 
4.3%
ValueCountFrequency (%)
38 1
 
4.3%
20 1
 
4.3%
13 1
 
4.3%
9 1
 
4.3%
5 1
 
4.3%
4 2
 
8.7%
3 3
 
13.0%
2 3
 
13.0%
1 2
 
8.7%
0 8
34.8%

유흥주점
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)21.7%
Missing0
Missing (%)0.0%
Memory size316.0 B
0
18 
7
2
 
1
1
 
1
4
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique3 ?
Unique (%)13.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row2

Common Values

ValueCountFrequency (%)
0 18
78.3%
7 2
 
8.7%
2 1
 
4.3%
1 1
 
4.3%
4 1
 
4.3%

Length

2023-12-12T15:35:25.843277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:35:25.977150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 18
78.3%
7 2
 
8.7%
2 1
 
4.3%
1 1
 
4.3%
4 1
 
4.3%

위탁급식영업
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)30.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.173913
Minimum0
Maximum17
Zeros9
Zeros (%)39.1%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-12T15:35:26.109855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile7.8
Maximum17
Range17
Interquartile range (IQR)2

Descriptive statistics

Standard deviation3.8097026
Coefficient of variation (CV)1.7524632
Kurtosis10.69313
Mean2.173913
Median Absolute Deviation (MAD)1
Skewness3.0731605
Sum50
Variance14.513834
MonotonicityNot monotonic
2023-12-12T15:35:26.248477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 9
39.1%
1 5
21.7%
2 4
17.4%
3 2
 
8.7%
8 1
 
4.3%
6 1
 
4.3%
17 1
 
4.3%
ValueCountFrequency (%)
0 9
39.1%
1 5
21.7%
2 4
17.4%
3 2
 
8.7%
6 1
 
4.3%
8 1
 
4.3%
17 1
 
4.3%
ValueCountFrequency (%)
17 1
 
4.3%
8 1
 
4.3%
6 1
 
4.3%
3 2
 
8.7%
2 4
17.4%
1 5
21.7%
0 9
39.1%

집단급식소
Real number (ℝ)

HIGH CORRELATION 

Distinct18
Distinct (%)78.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.913043
Minimum3
Maximum35
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-12T15:35:26.406235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile4.2
Q19
median13
Q319.5
95-th percentile27.7
Maximum35
Range32
Interquartile range (IQR)10.5

Descriptive statistics

Standard deviation8.0674038
Coefficient of variation (CV)0.54096294
Kurtosis0.29018215
Mean14.913043
Median Absolute Deviation (MAD)4
Skewness0.75773035
Sum343
Variance65.083004
MonotonicityNot monotonic
2023-12-12T15:35:26.549634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
13 4
17.4%
9 2
 
8.7%
15 2
 
8.7%
22 1
 
4.3%
6 1
 
4.3%
14 1
 
4.3%
25 1
 
4.3%
24 1
 
4.3%
3 1
 
4.3%
8 1
 
4.3%
Other values (8) 8
34.8%
ValueCountFrequency (%)
3 1
 
4.3%
4 1
 
4.3%
6 1
 
4.3%
7 1
 
4.3%
8 1
 
4.3%
9 2
8.7%
11 1
 
4.3%
13 4
17.4%
14 1
 
4.3%
15 2
8.7%
ValueCountFrequency (%)
35 1
4.3%
28 1
4.3%
25 1
4.3%
24 1
4.3%
23 1
4.3%
22 1
4.3%
17 1
4.3%
16 1
4.3%
15 2
8.7%
14 1
4.3%

식품제조가공업
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)47.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3478261
Minimum0
Maximum11
Zeros3
Zeros (%)13.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-12T15:35:26.694918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34.5
95-th percentile8.9
Maximum11
Range11
Interquartile range (IQR)3.5

Descriptive statistics

Standard deviation3.024276
Coefficient of variation (CV)0.90335516
Kurtosis0.59054392
Mean3.3478261
Median Absolute Deviation (MAD)1
Skewness1.1249872
Sum77
Variance9.1462451
MonotonicityNot monotonic
2023-12-12T15:35:26.840629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
2 5
21.7%
1 4
17.4%
0 3
13.0%
3 3
13.0%
4 2
 
8.7%
9 1
 
4.3%
6 1
 
4.3%
5 1
 
4.3%
8 1
 
4.3%
7 1
 
4.3%
ValueCountFrequency (%)
0 3
13.0%
1 4
17.4%
2 5
21.7%
3 3
13.0%
4 2
 
8.7%
5 1
 
4.3%
6 1
 
4.3%
7 1
 
4.3%
8 1
 
4.3%
9 1
 
4.3%
ValueCountFrequency (%)
11 1
 
4.3%
9 1
 
4.3%
8 1
 
4.3%
7 1
 
4.3%
6 1
 
4.3%
5 1
 
4.3%
4 2
 
8.7%
3 3
13.0%
2 5
21.7%
1 4
17.4%

즉석판매 제조가공업
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)82.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.521739
Minimum4
Maximum84
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-12T15:35:26.952333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile8.3
Q118.5
median23
Q348
95-th percentile65.6
Maximum84
Range80
Interquartile range (IQR)29.5

Descriptive statistics

Standard deviation21.41552
Coefficient of variation (CV)0.65849862
Kurtosis-0.12348436
Mean32.521739
Median Absolute Deviation (MAD)11
Skewness0.87683161
Sum748
Variance458.62451
MonotonicityNot monotonic
2023-12-12T15:35:27.066562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
22 3
 
13.0%
18 2
 
8.7%
23 2
 
8.7%
8 1
 
4.3%
11 1
 
4.3%
51 1
 
4.3%
84 1
 
4.3%
4 1
 
4.3%
24 1
 
4.3%
45 1
 
4.3%
Other values (9) 9
39.1%
ValueCountFrequency (%)
4 1
 
4.3%
8 1
 
4.3%
11 1
 
4.3%
12 1
 
4.3%
18 2
8.7%
19 1
 
4.3%
22 3
13.0%
23 2
8.7%
24 1
 
4.3%
26 1
 
4.3%
ValueCountFrequency (%)
84 1
4.3%
66 1
4.3%
62 1
4.3%
61 1
4.3%
56 1
4.3%
51 1
4.3%
45 1
4.3%
40 1
4.3%
31 1
4.3%
26 1
4.3%

식품첨가물 제조업
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size316.0 B
0
20 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 20
87.0%
1 3
 
13.0%

Length

2023-12-12T15:35:27.220587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:35:27.317415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 20
87.0%
1 3
 
13.0%

식품운반업
Categorical

Distinct2
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size316.0 B
0
19 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 19
82.6%
1 4
 
17.4%

Length

2023-12-12T15:35:27.404672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:35:27.491618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 19
82.6%
1 4
 
17.4%

식품소분·판매업
Real number (ℝ)

HIGH CORRELATION 

Distinct18
Distinct (%)78.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.130435
Minimum9
Maximum65
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-12T15:35:27.570909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile10.1
Q115
median26
Q330.5
95-th percentile58.2
Maximum65
Range56
Interquartile range (IQR)15.5

Descriptive statistics

Standard deviation15.846721
Coefficient of variation (CV)0.5840939
Kurtosis0.31623521
Mean27.130435
Median Absolute Deviation (MAD)11
Skewness0.99863623
Sum624
Variance251.11858
MonotonicityNot monotonic
2023-12-12T15:35:27.663462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
30 4
17.4%
15 2
 
8.7%
12 2
 
8.7%
31 1
 
4.3%
42 1
 
4.3%
9 1
 
4.3%
26 1
 
4.3%
59 1
 
4.3%
17 1
 
4.3%
20 1
 
4.3%
Other values (8) 8
34.8%
ValueCountFrequency (%)
9 1
4.3%
10 1
4.3%
11 1
4.3%
12 2
8.7%
15 2
8.7%
16 1
4.3%
17 1
4.3%
20 1
4.3%
21 1
4.3%
26 1
4.3%
ValueCountFrequency (%)
65 1
 
4.3%
59 1
 
4.3%
51 1
 
4.3%
44 1
 
4.3%
42 1
 
4.3%
31 1
 
4.3%
30 4
17.4%
28 1
 
4.3%
26 1
 
4.3%
21 1
 
4.3%

식품보존업
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size316.0 B
0
22 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)4.3%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 22
95.7%
1 1
 
4.3%

Length

2023-12-12T15:35:27.775067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:35:27.859253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 22
95.7%
1 1
 
4.3%

용기·포장류 제조업 등 기타
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size316.0 B
0
22 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)4.3%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 22
95.7%
1 1
 
4.3%

Length

2023-12-12T15:35:27.952231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:35:28.034088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 22
95.7%
1 1
 
4.3%

건강기능식품제조업
Categorical

CONSTANT 

Distinct1
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size316.0 B
0
23 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 23
100.0%

Length

2023-12-12T15:35:28.112416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:35:28.192817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 23
100.0%

건강기능식품판매업
Real number (ℝ)

HIGH CORRELATION 

Distinct21
Distinct (%)91.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.913043
Minimum5
Maximum147
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-12T15:35:28.272921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile17.3
Q129.5
median42
Q362.5
95-th percentile140.2
Maximum147
Range142
Interquartile range (IQR)33

Descriptive statistics

Standard deviation37.564385
Coefficient of variation (CV)0.69675875
Kurtosis1.4154857
Mean53.913043
Median Absolute Deviation (MAD)18
Skewness1.352554
Sum1240
Variance1411.083
MonotonicityNot monotonic
2023-12-12T15:35:28.383049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
40 2
 
8.7%
45 2
 
8.7%
35 1
 
4.3%
42 1
 
4.3%
5 1
 
4.3%
91 1
 
4.3%
106 1
 
4.3%
17 1
 
4.3%
61 1
 
4.3%
60 1
 
4.3%
Other values (11) 11
47.8%
ValueCountFrequency (%)
5 1
4.3%
17 1
4.3%
20 1
4.3%
22 1
4.3%
25 1
4.3%
29 1
4.3%
30 1
4.3%
35 1
4.3%
38 1
4.3%
40 2
8.7%
ValueCountFrequency (%)
147 1
4.3%
144 1
4.3%
106 1
4.3%
91 1
4.3%
81 1
4.3%
64 1
4.3%
61 1
4.3%
60 1
4.3%
53 1
4.3%
45 2
8.7%

Interactions

2023-12-12T15:35:22.050810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:12.357557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:13.491699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:14.334703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:15.015999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:15.970898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:17.509224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:18.671049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:19.803625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:20.971401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:22.146285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:12.487111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:13.584352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:14.409992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:15.087780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:16.084278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:17.627751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:18.790419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:19.922532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:21.064970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:22.236624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:12.612551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:13.671591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:14.477668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:15.159778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:16.177601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:17.751670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:18.885036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:20.047945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:21.157334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:22.329040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:12.728184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:13.771165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:14.540103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:15.231403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:16.284472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:17.858380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:18.990722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:20.145767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:21.255239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:22.439411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:12.837750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:13.882142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:14.607417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:15.311779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:16.404193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:17.968285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:19.107471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:20.263196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:21.365979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:22.554096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:12.933812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:13.955782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:14.683975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:15.427695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:16.864987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:18.108957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:19.244528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:20.376359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:21.474536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:22.672075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:13.037117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:14.040345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:14.752301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:15.553581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:16.995250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:18.213236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:19.357141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:20.494749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:21.568230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:22.776866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:13.148014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:14.105750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:14.817841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:15.637966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:17.131049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:18.317152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:19.463574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:20.616351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:21.704762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:22.904897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:13.280590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:14.184319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:14.886508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:15.734039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:17.267559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:18.426637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:19.574584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:20.744197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:21.831106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:23.000668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:13.382625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:14.261300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:14.946993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:15.839059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:17.382122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:18.546582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:19.684268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:20.846573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:35:21.928883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T15:35:28.477984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동휴게음식점일반음식점제과점단란주점유흥주점위탁급식영업집단급식소식품제조가공업즉석판매 제조가공업식품첨가물 제조업식품운반업식품소분·판매업식품보존업용기·포장류 제조업 등 기타건강기능식품판매업
행정동1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
휴게음식점1.0001.0000.8880.8710.8320.0000.6160.6950.4410.8430.0000.0000.6831.0000.4450.982
일반음식점1.0000.8881.0000.0000.7600.0000.0000.0000.6330.7120.0000.0000.0001.0000.4880.793
제과점1.0000.8710.0001.0000.7980.0000.8430.6310.7750.8990.1250.0000.9590.4450.0000.962
단란주점1.0000.8320.7600.7981.0000.6810.8190.7230.8410.6940.0000.3550.7641.0000.0000.859
유흥주점1.0000.0000.0000.0000.6811.0000.3830.0000.4260.0000.0000.2370.0000.0000.0000.000
위탁급식영업1.0000.6160.0000.8430.8190.3831.0000.8640.9620.0000.0000.2110.8300.0000.0000.733
집단급식소1.0000.6950.0000.6310.7230.0000.8641.0000.6560.4210.3230.0000.5790.0000.0000.577
식품제조가공업1.0000.4410.6330.7750.8410.4260.9620.6561.0000.5280.9600.2650.7950.0001.0000.624
즉석판매 제조가공업1.0000.8430.7120.8990.6940.0000.0000.4210.5281.0000.4090.0000.9081.0000.0000.929
식품첨가물 제조업1.0000.0000.0000.1250.0000.0000.0000.3230.9600.4091.0000.0000.1610.0000.1700.449
식품운반업1.0000.0000.0000.0000.3550.2370.2110.0000.2650.0000.0001.0000.2130.0000.0000.000
식품소분·판매업1.0000.6830.0000.9590.7640.0000.8300.5790.7950.9080.1610.2131.0001.0000.0000.874
식품보존업1.0001.0001.0000.4451.0000.0000.0000.0000.0001.0000.0000.0001.0001.0000.0001.000
용기·포장류 제조업 등 기타1.0000.4450.4880.0000.0000.0000.0000.0001.0000.0000.1700.0000.0000.0001.0000.445
건강기능식품판매업1.0000.9820.7930.9620.8590.0000.7330.5770.6240.9290.4490.0000.8741.0000.4451.000
2023-12-12T15:35:28.623410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
식품보존업식품운반업유흥주점용기·포장류 제조업 등 기타식품첨가물 제조업
식품보존업1.0000.0000.0000.0000.000
식품운반업0.0001.0000.2540.0000.000
유흥주점0.0000.2541.0000.0000.000
용기·포장류 제조업 등 기타0.0000.0000.0001.0000.099
식품첨가물 제조업0.0000.0000.0000.0991.000
2023-12-12T15:35:28.737193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
휴게음식점일반음식점제과점단란주점위탁급식영업집단급식소식품제조가공업즉석판매 제조가공업식품소분·판매업건강기능식품판매업유흥주점식품첨가물 제조업식품운반업식품보존업용기·포장류 제조업 등 기타
휴게음식점1.0000.9580.8410.5290.5950.7180.4000.7580.8020.9200.0000.0000.0000.8160.345
일반음식점0.9581.0000.7780.6290.6450.7290.4740.7980.8480.8620.0000.0000.0000.7870.267
제과점0.8410.7781.0000.4190.5060.6190.2080.6930.7780.8790.0000.0000.0000.3450.000
단란주점0.5290.6290.4191.0000.3730.3410.2320.6150.3920.5310.5200.0000.2080.9000.000
위탁급식영업0.5950.6450.5060.3731.0000.7230.3340.3250.6090.5220.1160.0000.2220.0000.000
집단급식소0.7180.7290.6190.3410.7231.0000.6560.4910.7040.6770.0000.2340.0000.0000.000
식품제조가공업0.4000.4740.2080.2320.3340.6561.0000.3970.4550.3120.0720.6470.0870.0000.787
즉석판매 제조가공업0.7580.7980.6930.6150.3250.4910.3971.0000.6710.7820.0000.3130.0000.8160.000
식품소분·판매업0.8020.8480.7780.3920.6090.7040.4550.6711.0000.7810.0000.0340.1200.8160.000
건강기능식품판매업0.9200.8620.8790.5310.5220.6770.3120.7820.7811.0000.0000.3490.0000.8160.345
유흥주점0.0000.0000.0000.5200.1160.0000.0720.0000.0000.0001.0000.0000.2540.0000.000
식품첨가물 제조업0.0000.0000.0000.0000.0000.2340.6470.3130.0340.3490.0001.0000.0000.0000.099
식품운반업0.0000.0000.0000.2080.2220.0000.0870.0000.1200.0000.2540.0001.0000.0000.000
식품보존업0.8160.7870.3450.9000.0000.0000.0000.8160.8160.8160.0000.0000.0001.0000.000
용기·포장류 제조업 등 기타0.3450.2670.0000.0000.0000.0000.7870.0000.0000.3450.0000.0990.0000.0001.000

Missing values

2023-12-12T15:35:23.444443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T15:35:23.708282image/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

기준연도행정동휴게음식점일반음식점제과점단란주점유흥주점위탁급식영업집단급식소식품제조가공업즉석판매 제조가공업식품첨가물 제조업식품운반업식품소분·판매업식품보존업용기·포장류 제조업 등 기타건강기능식품제조업건강기능식품판매업
02021복수동53175500322418113100035
12021도마1동68317640016966003000045
22021도마2동37174420211022001000020
32021정림동2713022007119011500025
42021변동25159232213326002100029
52021용문동3315031709112001200040
62021탄방동17254918508236610165000144
72021둔산1동10736613387628423002800064
82021둔산2동1775582320017355620051000147
92021둔산3동3615742004231001100030
기준연도행정동휴게음식점일반음식점제과점단란주점유흥주점위탁급식영업집단급식소식품제조가공업즉석판매 제조가공업식품첨가물 제조업식품운반업식품소분·판매업식품보존업용기·포장류 제조업 등 기타건강기능식품제조업건강기능식품판매업
132021갈마1동793691030117340004200053
142021갈마2동7530743008222001600038
152021월평1동804047131213845003000060
162021월평2동50162844113024012000061
172021월평3동4227000304001700017
182021만년동48206500213222003000040
192021가수원동16252728101247841059000106
202021관저1동69280600325318002600045
212021관저2동1283541600114251003000091
222021기성동85410001511111090105