Overview

Dataset statistics

Number of variables7
Number of observations31
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.0 KiB
Average record size in memory65.3 B

Variable types

Categorical1
Text1
Numeric5

Dataset

Description식품 안전보호 구역정보 집계 현황
Author경기도
URLhttps://data.gg.go.kr/portal/data/service/selectServicePage.do?&infId=UJ4150G4JO0AZE86357411841089&infSeq=1

Alerts

집계년월 has constant value ""Constant
전체학교수 is highly overall correlated with 지정학교수 and 3 other fieldsHigh correlation
지정학교수 is highly overall correlated with 전체학교수 and 3 other fieldsHigh correlation
식품안전보호구역수 is highly overall correlated with 전체학교수 and 3 other fieldsHigh correlation
전담관리원지정수 is highly overall correlated with 전체학교수 and 2 other fieldsHigh correlation
우수판패업소수 is highly overall correlated with 전체학교수 and 2 other fieldsHigh correlation
시군명 has unique valuesUnique
우수판패업소수 has 1 (3.2%) zerosZeros

Reproduction

Analysis started2023-12-10 22:12:38.669996
Analysis finished2023-12-10 22:12:41.293343
Duration2.62 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

집계년월
Categorical

CONSTANT 

Distinct1
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size380.0 B
2023-04
31 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-04
2nd row2023-04
3rd row2023-04
4th row2023-04
5th row2023-04

Common Values

ValueCountFrequency (%)
2023-04 31
100.0%

Length

2023-12-11T07:12:41.345781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:12:41.423080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-04 31
100.0%

시군명
Text

UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size380.0 B
2023-12-11T07:12:41.584254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.0967742
Min length3

Characters and Unicode

Total characters96
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
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 row가평군
2nd row고양시
3rd row과천시
4th row광명시
5th row광주시
ValueCountFrequency (%)
가평군 1
 
3.2%
안양시 1
 
3.2%
하남시 1
 
3.2%
포천시 1
 
3.2%
평택시 1
 
3.2%
파주시 1
 
3.2%
이천시 1
 
3.2%
의정부시 1
 
3.2%
의왕시 1
 
3.2%
용인시 1
 
3.2%
Other values (21) 21
67.7%
2023-12-11T07:12:41.874542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 29
30.2%
천 6
 
6.2%
양 5
 
5.2%
주 5
 
5.2%
군 4
 
4.2%
성 3
 
3.1%
포 3
 
3.1%
안 3
 
3.1%
평 3
 
3.1%
남 3
 
3.1%
Other values (28) 32
33.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 96
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 29
30.2%
천 6
 
6.2%
양 5
 
5.2%
주 5
 
5.2%
군 4
 
4.2%
성 3
 
3.1%
포 3
 
3.1%
안 3
 
3.1%
평 3
 
3.1%
남 3
 
3.1%
Other values (28) 32
33.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 96
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 29
30.2%
천 6
 
6.2%
양 5
 
5.2%
주 5
 
5.2%
군 4
 
4.2%
성 3
 
3.1%
포 3
 
3.1%
안 3
 
3.1%
평 3
 
3.1%
남 3
 
3.1%
Other values (28) 32
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 96
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 29
30.2%
천 6
 
6.2%
양 5
 
5.2%
주 5
 
5.2%
군 4
 
4.2%
성 3
 
3.1%
포 3
 
3.1%
안 3
 
3.1%
평 3
 
3.1%
남 3
 
3.1%
Other values (28) 32
33.3%

전체학교수
Real number (ℝ)

HIGH CORRELATION 

Distinct27
Distinct (%)87.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean82.032258
Minimum11
Maximum215
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-11T07:12:41.985598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile22.5
Q143
median58
Q3114
95-th percentile193.5
Maximum215
Range204
Interquartile range (IQR)71

Descriptive statistics

Standard deviation56.954651
Coefficient of variation (CV)0.69429578
Kurtosis-0.040537259
Mean82.032258
Median Absolute Deviation (MAD)32
Skewness0.96025399
Sum2543
Variance3243.8323
MonotonicityNot monotonic
2023-12-11T07:12:42.095133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
47 2
 
6.5%
57 2
 
6.5%
127 2
 
6.5%
43 2
 
6.5%
24 1
 
3.2%
45 1
 
3.2%
183 1
 
3.2%
50 1
 
3.2%
117 1
 
3.2%
108 1
 
3.2%
Other values (17) 17
54.8%
ValueCountFrequency (%)
11 1
3.2%
22 1
3.2%
23 1
3.2%
24 1
3.2%
26 1
3.2%
32 1
3.2%
38 1
3.2%
43 2
6.5%
45 1
3.2%
47 2
6.5%
ValueCountFrequency (%)
215 1
3.2%
204 1
3.2%
183 1
3.2%
175 1
3.2%
157 1
3.2%
127 2
6.5%
117 1
3.2%
111 1
3.2%
108 1
3.2%
91 1
3.2%

지정학교수
Real number (ℝ)

HIGH CORRELATION 

Distinct28
Distinct (%)90.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean77.419355
Minimum11
Maximum203
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-11T07:12:42.212375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile19
Q135.5
median58
Q3109
95-th percentile182.5
Maximum203
Range192
Interquartile range (IQR)73.5

Descriptive statistics

Standard deviation55.466972
Coefficient of variation (CV)0.71644838
Kurtosis-0.236319
Mean77.419355
Median Absolute Deviation (MAD)28
Skewness0.92258901
Sum2400
Variance3076.5849
MonotonicityNot monotonic
2023-12-11T07:12:42.329122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
47 2
 
6.5%
58 2
 
6.5%
86 2
 
6.5%
20 1
 
3.2%
38 1
 
3.2%
183 1
 
3.2%
43 1
 
3.2%
50 1
 
3.2%
107 1
 
3.2%
79 1
 
3.2%
Other values (18) 18
58.1%
ValueCountFrequency (%)
11 1
3.2%
18 1
3.2%
20 1
3.2%
21 1
3.2%
23 1
3.2%
26 1
3.2%
32 1
3.2%
33 1
3.2%
38 1
3.2%
43 1
3.2%
ValueCountFrequency (%)
203 1
3.2%
183 1
3.2%
182 1
3.2%
174 1
3.2%
157 1
3.2%
127 1
3.2%
126 1
3.2%
111 1
3.2%
107 1
3.2%
86 2
6.5%

식품안전보호구역수
Real number (ℝ)

HIGH CORRELATION 

Distinct27
Distinct (%)87.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57.677419
Minimum6
Maximum135
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-11T07:12:42.443976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile16
Q125.5
median48
Q378.5
95-th percentile132.5
Maximum135
Range129
Interquartile range (IQR)53

Descriptive statistics

Standard deviation39.498006
Coefficient of variation (CV)0.68480883
Kurtosis-0.56138396
Mean57.677419
Median Absolute Deviation (MAD)25
Skewness0.79438026
Sum1788
Variance1560.0925
MonotonicityNot monotonic
2023-12-11T07:12:42.561409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
16 2
 
6.5%
131 2
 
6.5%
23 2
 
6.5%
26 2
 
6.5%
39 1
 
3.2%
97 1
 
3.2%
80 1
 
3.2%
49 1
 
3.2%
54 1
 
3.2%
134 1
 
3.2%
Other values (17) 17
54.8%
ValueCountFrequency (%)
6 1
3.2%
16 2
6.5%
20 1
3.2%
21 1
3.2%
23 2
6.5%
25 1
3.2%
26 2
6.5%
31 1
3.2%
32 1
3.2%
37 1
3.2%
ValueCountFrequency (%)
135 1
3.2%
134 1
3.2%
131 2
6.5%
109 1
3.2%
101 1
3.2%
97 1
3.2%
80 1
3.2%
77 1
3.2%
76 1
3.2%
63 1
3.2%

전담관리원지정수
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)41.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.129032
Minimum2
Maximum32
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-11T07:12:42.689335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q16
median10
Q318.5
95-th percentile22
Maximum32
Range30
Interquartile range (IQR)12.5

Descriptive statistics

Standard deviation7.3518339
Coefficient of variation (CV)0.60613525
Kurtosis0.026835701
Mean12.129032
Median Absolute Deviation (MAD)5
Skewness0.69246833
Sum376
Variance54.049462
MonotonicityNot monotonic
2023-12-11T07:12:42.780525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
20 6
19.4%
6 5
16.1%
8 4
12.9%
14 2
 
6.5%
16 2
 
6.5%
10 2
 
6.5%
12 2
 
6.5%
4 2
 
6.5%
2 2
 
6.5%
5 1
 
3.2%
Other values (3) 3
9.7%
ValueCountFrequency (%)
2 2
 
6.5%
4 2
 
6.5%
5 1
 
3.2%
6 5
16.1%
8 4
12.9%
10 2
 
6.5%
12 2
 
6.5%
14 2
 
6.5%
16 2
 
6.5%
17 1
 
3.2%
ValueCountFrequency (%)
32 1
 
3.2%
24 1
 
3.2%
20 6
19.4%
17 1
 
3.2%
16 2
 
6.5%
14 2
 
6.5%
12 2
 
6.5%
10 2
 
6.5%
8 4
12.9%
6 5
16.1%

우수판패업소수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct18
Distinct (%)58.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.935484
Minimum0
Maximum43
Zeros1
Zeros (%)3.2%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-11T07:12:42.877712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median9
Q316.5
95-th percentile27.5
Maximum43
Range43
Interquartile range (IQR)13.5

Descriptive statistics

Standard deviation9.9194606
Coefficient of variation (CV)0.90708932
Kurtosis2.9378257
Mean10.935484
Median Absolute Deviation (MAD)7
Skewness1.5313059
Sum339
Variance98.395699
MonotonicityNot monotonic
2023-12-11T07:12:42.978818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
2 4
12.9%
12 3
 
9.7%
19 3
 
9.7%
9 2
 
6.5%
1 2
 
6.5%
7 2
 
6.5%
3 2
 
6.5%
6 2
 
6.5%
14 2
 
6.5%
4 1
 
3.2%
Other values (8) 8
25.8%
ValueCountFrequency (%)
0 1
 
3.2%
1 2
6.5%
2 4
12.9%
3 2
6.5%
4 1
 
3.2%
5 1
 
3.2%
6 2
6.5%
7 2
6.5%
9 2
6.5%
12 3
9.7%
ValueCountFrequency (%)
43 1
 
3.2%
35 1
 
3.2%
20 1
 
3.2%
19 3
9.7%
18 1
 
3.2%
17 1
 
3.2%
16 1
 
3.2%
14 2
6.5%
12 3
9.7%
9 2
6.5%

Interactions

2023-12-11T07:12:40.762012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:12:38.856876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:12:39.287844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:12:39.707646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:12:40.118222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:12:40.855630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:12:38.940485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:12:39.369110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:12:39.790787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:12:40.196405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:12:40.934186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:12:39.035435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:12:39.448412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:12:39.864713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:12:40.286706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:12:41.004039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:12:39.132225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:12:39.529002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:12:39.944776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:12:40.370045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:12:41.070872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:12:39.207577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:12:39.622433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:12:40.033148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:12:40.454703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T07:12:43.050924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명전체학교수지정학교수식품안전보호구역수전담관리원지정수우수판패업소수
시군명1.0001.0001.0001.0001.0001.000
전체학교수1.0001.0000.9690.8520.7270.800
지정학교수1.0000.9691.0000.7950.6380.822
식품안전보호구역수1.0000.8520.7951.0000.4800.658
전담관리원지정수1.0000.7270.6380.4801.0000.470
우수판패업소수1.0000.8000.8220.6580.4701.000
2023-12-11T07:12:43.145401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
전체학교수지정학교수식품안전보호구역수전담관리원지정수우수판패업소수
전체학교수1.0000.9880.9390.6920.723
지정학교수0.9881.0000.9350.6850.715
식품안전보호구역수0.9390.9351.0000.6630.744
전담관리원지정수0.6920.6850.6631.0000.477
우수판패업소수0.7230.7150.7440.4771.000

Missing values

2023-12-11T07:12:41.156127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T07:12:41.254814image/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

집계년월시군명전체학교수지정학교수식품안전보호구역수전담관리원지정수우수판패업소수
02023-04가평군24201662
12023-04고양시1751741312017
22023-04과천시1111669
32023-04광명시474721201
42023-04광주시575543612
52023-04구리시58582587
62023-04군포시47473263
72023-04김포시868462820
82023-04남양주시1271271091412
92023-04동두천시23232052
집계년월시군명전체학교수지정학교수식품안전보호구역수전담관리원지정수우수판패업소수
212023-04오산시382123169
222023-04용인시2151821342414
232023-04의왕시26262625
242023-04의정부시72705487
252023-04이천시605849144
262023-04파주시1087980818
272023-04평택시117107973214
282023-04포천시505039122
292023-04하남시43432320
302023-04화성시1831831312016