Overview

Dataset statistics

Number of variables16
Number of observations29
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.2 KiB
Average record size in memory147.6 B

Variable types

Text1
Numeric7
Categorical8

Dataset

Description광주광역시보건환경연구원 2022년 식품유형별 검사현황으로 시청, 5개 구청, 자체수거, 민원의뢰 등 의뢰기관 및 의뢰건수, 부적합 건수 등의 항목을 제공합니다.
URLhttps://www.data.go.kr/data/15069105/fileData.do

Alerts

남구부적합(건) has constant value ""Constant
북구부적합(건) has constant value ""Constant
동구검사의뢰(건) is highly overall correlated with 서구검사의뢰(건) and 8 other fieldsHigh correlation
서구검사의뢰(건) is highly overall correlated with 동구검사의뢰(건) and 4 other fieldsHigh correlation
남구검사의뢰(건) is highly overall correlated with 동구검사의뢰(건) and 9 other fieldsHigh correlation
북구검사의뢰(건) is highly overall correlated with 동구검사의뢰(건) and 8 other fieldsHigh correlation
광산구검사의뢰(건) is highly overall correlated with 동구검사의뢰(건) and 8 other fieldsHigh correlation
시청교육청검사의뢰(건) is highly overall correlated with 동구검사의뢰(건) and 5 other fieldsHigh correlation
기타(민원)의뢰건 is highly overall correlated with 동구검사의뢰(건) and 6 other fieldsHigh correlation
농수산물검사소등검사의뢰(건) is highly overall correlated with 시청교육청검사의뢰(건) and 1 other fieldsHigh correlation
동구부적합(건) is highly overall correlated with 동구검사의뢰(건) and 4 other fieldsHigh correlation
서구부적합(건) is highly overall correlated with 농수산물검사소등검사의뢰(건)High correlation
광산구부적합(건) is highly overall correlated with 동구검사의뢰(건) and 3 other fieldsHigh correlation
시합동부적합(건) is highly overall correlated with 남구검사의뢰(건) and 2 other fieldsHigh correlation
기타(민원)부적합건 is highly overall correlated with 동구검사의뢰(건) and 3 other fieldsHigh correlation
농수산물검사소등검사의뢰(건) is highly imbalanced (72.8%)Imbalance
동구부적합(건) is highly imbalanced (78.4%)Imbalance
서구부적합(건) is highly imbalanced (63.8%)Imbalance
광산구부적합(건) is highly imbalanced (63.8%)Imbalance
시합동부적합(건) is highly imbalanced (72.8%)Imbalance
기타(민원)부적합건 is highly imbalanced (63.8%)Imbalance
식품유형 has unique valuesUnique
동구검사의뢰(건) has 9 (31.0%) zerosZeros
서구검사의뢰(건) has 13 (44.8%) zerosZeros
남구검사의뢰(건) has 9 (31.0%) zerosZeros
북구검사의뢰(건) has 13 (44.8%) zerosZeros
광산구검사의뢰(건) has 8 (27.6%) zerosZeros
시청교육청검사의뢰(건) has 8 (27.6%) zerosZeros
기타(민원)의뢰건 has 13 (44.8%) zerosZeros

Reproduction

Analysis started2023-12-12 21:15:09.590478
Analysis finished2023-12-12 21:15:15.535602
Duration5.95 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

식품유형
Text

UNIQUE 

Distinct29
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size364.0 B
2023-12-13T06:15:15.683134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length10
Mean length5.6896552
Min length2

Characters and Unicode

Total characters165
Distinct characters64
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique29 ?
Unique (%)100.0%

Sample

1st row과자류, 빵류 또는 떡류
2nd row빙과류
3rd row코코아가공품류 또는 초콜릿류
4th row당류
5th row잼류
ValueCountFrequency (%)
또는 4
 
9.5%
및 2
 
4.8%
과자류 1
 
2.4%
화분가공품류 1
 
2.4%
포장육 1
 
2.4%
알가공품 1
 
2.4%
유가공품 1
 
2.4%
수산가공식품류 1
 
2.4%
동물성가공식품류 1
 
2.4%
벌꿀 1
 
2.4%
Other values (28) 28
66.7%
2023-12-13T06:15:16.086547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
류 23
 
13.9%
품 15
 
9.1%
13
 
7.9%
식 12
 
7.3%
가 9
 
5.5%
공 8
 
4.8%
물 4
 
2.4%
산 4
 
2.4%
또 4
 
2.4%
는 4
 
2.4%
Other values (54) 69
41.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 151
91.5%
Space Separator 13
 
7.9%
Other Punctuation 1
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
류 23
 
15.2%
품 15
 
9.9%
식 12
 
7.9%
가 9
 
6.0%
공 8
 
5.3%
물 4
 
2.6%
산 4
 
2.6%
또 4
 
2.6%
는 4
 
2.6%
기 3
 
2.0%
Other values (52) 65
43.0%
Space Separator
ValueCountFrequency (%)
13
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 151
91.5%
Common 14
 
8.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
류 23
 
15.2%
품 15
 
9.9%
식 12
 
7.9%
가 9
 
6.0%
공 8
 
5.3%
물 4
 
2.6%
산 4
 
2.6%
또 4
 
2.6%
는 4
 
2.6%
기 3
 
2.0%
Other values (52) 65
43.0%
Common
ValueCountFrequency (%)
13
92.9%
, 1
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 151
91.5%
ASCII 14
 
8.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
류 23
 
15.2%
품 15
 
9.9%
식 12
 
7.9%
가 9
 
6.0%
공 8
 
5.3%
물 4
 
2.6%
산 4
 
2.6%
또 4
 
2.6%
는 4
 
2.6%
기 3
 
2.0%
Other values (52) 65
43.0%
ASCII
ValueCountFrequency (%)
13
92.9%
, 1
 
7.1%

동구검사의뢰(건)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)51.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.137931
Minimum0
Maximum133
Zeros9
Zeros (%)31.0%
Negative0
Negative (%)0.0%
Memory size393.0 B
2023-12-13T06:15:16.251480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median8
Q319
95-th percentile54.2
Maximum133
Range133
Interquartile range (IQR)19

Descriptive statistics

Standard deviation27.995821
Coefficient of variation (CV)1.633559
Kurtosis10.099148
Mean17.137931
Median Absolute Deviation (MAD)8
Skewness2.8761136
Sum497
Variance783.76601
MonotonicityNot monotonic
2023-12-13T06:15:16.398845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 9
31.0%
2 3
 
10.3%
14 3
 
10.3%
8 2
 
6.9%
44 2
 
6.9%
133 1
 
3.4%
20 1
 
3.4%
3 1
 
3.4%
9 1
 
3.4%
41 1
 
3.4%
Other values (5) 5
17.2%
ValueCountFrequency (%)
0 9
31.0%
2 3
 
10.3%
3 1
 
3.4%
6 1
 
3.4%
8 2
 
6.9%
9 1
 
3.4%
11 1
 
3.4%
14 3
 
10.3%
19 1
 
3.4%
20 1
 
3.4%
ValueCountFrequency (%)
133 1
 
3.4%
61 1
 
3.4%
44 2
6.9%
42 1
 
3.4%
41 1
 
3.4%
20 1
 
3.4%
19 1
 
3.4%
14 3
10.3%
11 1
 
3.4%
9 1
 
3.4%

서구검사의뢰(건)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)51.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.241379
Minimum0
Maximum168
Zeros13
Zeros (%)44.8%
Negative0
Negative (%)0.0%
Memory size393.0 B
2023-12-13T06:15:16.534480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q313
95-th percentile60
Maximum168
Range168
Interquartile range (IQR)13

Descriptive statistics

Standard deviation34.192371
Coefficient of variation (CV)1.9831575
Kurtosis13.675682
Mean17.241379
Median Absolute Deviation (MAD)3
Skewness3.3997879
Sum500
Variance1169.1182
MonotonicityNot monotonic
2023-12-13T06:15:16.648253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 13
44.8%
3 2
 
6.9%
10 2
 
6.9%
35 1
 
3.4%
38 1
 
3.4%
66 1
 
3.4%
5 1
 
3.4%
51 1
 
3.4%
168 1
 
3.4%
8 1
 
3.4%
Other values (5) 5
 
17.2%
ValueCountFrequency (%)
0 13
44.8%
3 2
 
6.9%
5 1
 
3.4%
7 1
 
3.4%
8 1
 
3.4%
9 1
 
3.4%
10 2
 
6.9%
13 1
 
3.4%
28 1
 
3.4%
35 1
 
3.4%
ValueCountFrequency (%)
168 1
3.4%
66 1
3.4%
51 1
3.4%
46 1
3.4%
38 1
3.4%
35 1
3.4%
28 1
3.4%
13 1
3.4%
10 2
6.9%
9 1
3.4%

남구검사의뢰(건)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct18
Distinct (%)62.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.965517
Minimum0
Maximum127
Zeros9
Zeros (%)31.0%
Negative0
Negative (%)0.0%
Memory size393.0 B
2023-12-13T06:15:16.783390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median7
Q320
95-th percentile71.6
Maximum127
Range127
Interquartile range (IQR)20

Descriptive statistics

Standard deviation28.526282
Coefficient of variation (CV)1.6814272
Kurtosis8.0301139
Mean16.965517
Median Absolute Deviation (MAD)7
Skewness2.7212925
Sum492
Variance813.74877
MonotonicityNot monotonic
2023-12-13T06:15:16.955544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0 9
31.0%
3 2
 
6.9%
21 2
 
6.9%
20 2
 
6.9%
127 1
 
3.4%
6 1
 
3.4%
9 1
 
3.4%
10 1
 
3.4%
2 1
 
3.4%
7 1
 
3.4%
Other values (8) 8
27.6%
ValueCountFrequency (%)
0 9
31.0%
1 1
 
3.4%
2 1
 
3.4%
3 2
 
6.9%
6 1
 
3.4%
7 1
 
3.4%
8 1
 
3.4%
9 1
 
3.4%
10 1
 
3.4%
11 1
 
3.4%
ValueCountFrequency (%)
127 1
3.4%
84 1
3.4%
53 1
3.4%
47 1
3.4%
22 1
3.4%
21 2
6.9%
20 2
6.9%
17 1
3.4%
11 1
3.4%
10 1
3.4%

북구검사의뢰(건)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)51.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.034483
Minimum0
Maximum131
Zeros13
Zeros (%)44.8%
Negative0
Negative (%)0.0%
Memory size393.0 B
2023-12-13T06:15:17.117037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q315
95-th percentile99.8
Maximum131
Range131
Interquartile range (IQR)15

Descriptive statistics

Standard deviation33.495717
Coefficient of variation (CV)1.9663478
Kurtosis6.3238697
Mean17.034483
Median Absolute Deviation (MAD)1
Skewness2.6179217
Sum494
Variance1121.9631
MonotonicityNot monotonic
2023-12-13T06:15:17.242396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 13
44.8%
1 2
 
6.9%
9 2
 
6.9%
131 1
 
3.4%
15 1
 
3.4%
7 1
 
3.4%
2 1
 
3.4%
16 1
 
3.4%
77 1
 
3.4%
26 1
 
3.4%
Other values (5) 5
 
17.2%
ValueCountFrequency (%)
0 13
44.8%
1 2
 
6.9%
2 1
 
3.4%
7 1
 
3.4%
9 2
 
6.9%
12 1
 
3.4%
14 1
 
3.4%
15 1
 
3.4%
16 1
 
3.4%
24 1
 
3.4%
ValueCountFrequency (%)
131 1
3.4%
115 1
3.4%
77 1
3.4%
35 1
3.4%
26 1
3.4%
24 1
3.4%
16 1
3.4%
15 1
3.4%
14 1
3.4%
12 1
3.4%

광산구검사의뢰(건)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct18
Distinct (%)62.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.310345
Minimum0
Maximum108
Zeros8
Zeros (%)27.6%
Negative0
Negative (%)0.0%
Memory size393.0 B
2023-12-13T06:15:17.365425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q314
95-th percentile80.2
Maximum108
Range108
Interquartile range (IQR)14

Descriptive statistics

Standard deviation28.324779
Coefficient of variation (CV)1.636292
Kurtosis3.5690755
Mean17.310345
Median Absolute Deviation (MAD)4
Skewness2.0371607
Sum502
Variance802.2931
MonotonicityNot monotonic
2023-12-13T06:15:17.472681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0 8
27.6%
3 3
 
10.3%
1 2
 
6.9%
4 2
 
6.9%
108 1
 
3.4%
33 1
 
3.4%
9 1
 
3.4%
7 1
 
3.4%
6 1
 
3.4%
42 1
 
3.4%
Other values (8) 8
27.6%
ValueCountFrequency (%)
0 8
27.6%
1 2
 
6.9%
2 1
 
3.4%
3 3
 
10.3%
4 2
 
6.9%
6 1
 
3.4%
7 1
 
3.4%
9 1
 
3.4%
10 1
 
3.4%
12 1
 
3.4%
ValueCountFrequency (%)
108 1
3.4%
85 1
3.4%
73 1
3.4%
46 1
3.4%
42 1
3.4%
36 1
3.4%
33 1
3.4%
14 1
3.4%
12 1
3.4%
10 1
3.4%

시청교육청검사의뢰(건)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct14
Distinct (%)48.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.965517
Minimum0
Maximum60
Zeros8
Zeros (%)27.6%
Negative0
Negative (%)0.0%
Memory size393.0 B
2023-12-13T06:15:17.622409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median6
Q314
95-th percentile42.8
Maximum60
Range60
Interquartile range (IQR)14

Descriptive statistics

Standard deviation15.27716
Coefficient of variation (CV)1.2767656
Kurtosis2.789259
Mean11.965517
Median Absolute Deviation (MAD)6
Skewness1.7623901
Sum347
Variance233.39163
MonotonicityNot monotonic
2023-12-13T06:15:17.723469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 8
27.6%
5 4
13.8%
6 3
 
10.3%
8 2
 
6.9%
14 2
 
6.9%
10 2
 
6.9%
1 1
 
3.4%
20 1
 
3.4%
33 1
 
3.4%
60 1
 
3.4%
Other values (4) 4
13.8%
ValueCountFrequency (%)
0 8
27.6%
1 1
 
3.4%
5 4
13.8%
6 3
 
10.3%
8 2
 
6.9%
10 2
 
6.9%
14 2
 
6.9%
20 1
 
3.4%
22 1
 
3.4%
25 1
 
3.4%
ValueCountFrequency (%)
60 1
3.4%
46 1
3.4%
38 1
3.4%
33 1
3.4%
25 1
3.4%
22 1
3.4%
20 1
3.4%
14 2
6.9%
10 2
6.9%
8 2
6.9%

농수산물검사소등검사의뢰(건)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)10.3%
Missing0
Missing (%)0.0%
Memory size364.0 B
0
27 
41
 
1
132
 
1

Length

Max length3
Median length1
Mean length1.1034483
Min length1

Unique

Unique2 ?
Unique (%)6.9%

Sample

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

Common Values

ValueCountFrequency (%)
0 27
93.1%
41 1
 
3.4%
132 1
 
3.4%

Length

2023-12-13T06:15:17.849895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:15:17.955134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 27
93.1%
41 1
 
3.4%
132 1
 
3.4%

기타(민원)의뢰건
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)41.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.8965517
Minimum0
Maximum51
Zeros13
Zeros (%)44.8%
Negative0
Negative (%)0.0%
Memory size393.0 B
2023-12-13T06:15:18.048438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q36
95-th percentile26.4
Maximum51
Range51
Interquartile range (IQR)6

Descriptive statistics

Standard deviation11.449719
Coefficient of variation (CV)1.9417652
Kurtosis8.6574707
Mean5.8965517
Median Absolute Deviation (MAD)1
Skewness2.8120781
Sum171
Variance131.09606
MonotonicityNot monotonic
2023-12-13T06:15:18.166227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 13
44.8%
2 4
 
13.8%
1 3
 
10.3%
17 1
 
3.4%
6 1
 
3.4%
32 1
 
3.4%
18 1
 
3.4%
51 1
 
3.4%
9 1
 
3.4%
14 1
 
3.4%
Other values (2) 2
 
6.9%
ValueCountFrequency (%)
0 13
44.8%
1 3
 
10.3%
2 4
 
13.8%
3 1
 
3.4%
6 1
 
3.4%
9 1
 
3.4%
10 1
 
3.4%
14 1
 
3.4%
17 1
 
3.4%
18 1
 
3.4%
ValueCountFrequency (%)
51 1
 
3.4%
32 1
 
3.4%
18 1
 
3.4%
17 1
 
3.4%
14 1
 
3.4%
10 1
 
3.4%
9 1
 
3.4%
6 1
 
3.4%
3 1
 
3.4%
2 4
13.8%

동구부적합(건)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)6.9%
Missing0
Missing (%)0.0%
Memory size364.0 B
0
28 
2
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)3.4%

Sample

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

Common Values

ValueCountFrequency (%)
0 28
96.6%
2 1
 
3.4%

Length

2023-12-13T06:15:18.284806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:15:18.374662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 28
96.6%
2 1
 
3.4%

서구부적합(건)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)6.9%
Missing0
Missing (%)0.0%
Memory size364.0 B
0
27 
1
 
2

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 27
93.1%
1 2
 
6.9%

Length

2023-12-13T06:15:18.482388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:15:18.569338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 27
93.1%
1 2
 
6.9%

남구부적합(건)
Categorical

CONSTANT 

Distinct1
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size364.0 B
0
29 

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 29
100.0%

Length

2023-12-13T06:15:18.657166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:15:18.742621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 29
100.0%

북구부적합(건)
Categorical

CONSTANT 

Distinct1
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size364.0 B
0
29 

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 29
100.0%

Length

2023-12-13T06:15:18.837791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:15:18.931570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 29
100.0%

광산구부적합(건)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)6.9%
Missing0
Missing (%)0.0%
Memory size364.0 B
0
27 
1
 
2

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 27
93.1%
1 2
 
6.9%

Length

2023-12-13T06:15:19.028446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:15:19.111653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 27
93.1%
1 2
 
6.9%

시합동부적합(건)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)10.3%
Missing0
Missing (%)0.0%
Memory size364.0 B
0
27 
1
 
1
2
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique2 ?
Unique (%)6.9%

Sample

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

Common Values

ValueCountFrequency (%)
0 27
93.1%
1 1
 
3.4%
2 1
 
3.4%

Length

2023-12-13T06:15:19.196280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:15:19.285795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 27
93.1%
1 1
 
3.4%
2 1
 
3.4%

기타(민원)부적합건
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)6.9%
Missing0
Missing (%)0.0%
Memory size364.0 B
0
27 
1
 
2

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 27
93.1%
1 2
 
6.9%

Length

2023-12-13T06:15:19.373785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:15:19.465711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 27
93.1%
1 2
 
6.9%

Interactions

2023-12-13T06:15:14.559155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:10.327109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:11.074030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:11.840287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:12.453175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:13.066398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:13.890921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:14.642029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:10.416840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:11.190662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:11.931971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:12.543845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:13.143389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:13.973060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:14.736548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:10.517985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:11.314336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:12.018816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:12.651975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:13.234747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:14.095815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:14.827855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:10.619447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:11.405473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:12.096020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:12.745836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:13.311512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:14.204509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:14.900262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:10.704293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:11.518919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:12.175578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:12.830660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:13.392342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:14.285700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:14.981333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:10.803964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:11.610112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:12.259596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:12.907611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:13.471218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:14.375123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:15.077201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:10.947502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:11.731364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:12.353958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:12.987338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:13.559176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:15:14.473281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T06:15:19.526266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
식품유형동구검사의뢰(건)서구검사의뢰(건)남구검사의뢰(건)북구검사의뢰(건)광산구검사의뢰(건)시청교육청검사의뢰(건)농수산물검사소등검사의뢰(건)기타(민원)의뢰건동구부적합(건)서구부적합(건)광산구부적합(건)시합동부적합(건)기타(민원)부적합건
식품유형1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
동구검사의뢰(건)1.0001.0000.7270.8810.7500.9140.6470.0000.7641.0000.0000.5290.3990.529
서구검사의뢰(건)1.0000.7271.0000.7130.8120.8340.8620.0000.6940.3660.0000.0000.2060.000
남구검사의뢰(건)1.0000.8810.7131.0000.9450.9350.8110.0000.9491.0000.0000.8160.9350.816
북구검사의뢰(건)1.0000.7500.8120.9451.0000.9030.7560.0000.9091.0000.0000.8810.0000.815
광산구검사의뢰(건)1.0000.9140.8340.9350.9031.0000.7420.0000.8461.0000.0000.5560.4920.556
시청교육청검사의뢰(건)1.0000.6470.8620.8110.7560.7421.0001.0000.8530.0000.5590.0000.9730.000
농수산물검사소등검사의뢰(건)1.0000.0000.0000.0000.0000.0001.0001.0000.0000.0000.4270.0000.0000.000
기타(민원)의뢰건1.0000.7640.6940.9490.9090.8460.8530.0001.0000.8070.0000.3330.9070.333
동구부적합(건)1.0001.0000.3661.0001.0001.0000.0000.0000.8071.0000.0000.4070.0000.407
서구부적합(건)1.0000.0000.0000.0000.0000.0000.5590.4270.0000.0001.0000.0000.0000.000
광산구부적합(건)1.0000.5290.0000.8160.8810.5560.0000.0000.3330.4070.0001.0000.0000.092
시합동부적합(건)1.0000.3990.2060.9350.0000.4920.9730.0000.9070.0000.0000.0001.0000.000
기타(민원)부적합건1.0000.5290.0000.8160.8150.5560.0000.0000.3330.4070.0000.0920.0001.000
2023-12-13T06:15:19.670827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시합동부적합(건)동구부적합(건)농수산물검사소등검사의뢰(건)기타(민원)부적합건광산구부적합(건)서구부적합(건)
시합동부적합(건)1.0000.0000.0000.0000.0000.000
동구부적합(건)0.0001.0000.0000.2650.2650.000
농수산물검사소등검사의뢰(건)0.0000.0001.0000.0000.0000.653
기타(민원)부적합건0.0000.2650.0001.0000.0470.000
광산구부적합(건)0.0000.2650.0000.0471.0000.000
서구부적합(건)0.0000.0000.6530.0000.0001.000
2023-12-13T06:15:19.783001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
동구검사의뢰(건)서구검사의뢰(건)남구검사의뢰(건)북구검사의뢰(건)광산구검사의뢰(건)시청교육청검사의뢰(건)기타(민원)의뢰건농수산물검사소등검사의뢰(건)동구부적합(건)서구부적합(건)광산구부적합(건)시합동부적합(건)기타(민원)부적합건
동구검사의뢰(건)1.0000.7560.8260.8440.8550.5690.6330.0000.9430.0000.6020.3090.602
서구검사의뢰(건)0.7561.0000.7940.8040.8180.4350.5910.0000.4160.0000.0000.1330.000
남구검사의뢰(건)0.8260.7941.0000.7920.8980.6050.5040.0000.9230.0000.5690.6480.569
북구검사의뢰(건)0.8440.8040.7921.0000.8500.5050.5120.0000.9230.0000.6370.0000.568
광산구검사의뢰(건)0.8550.8180.8980.8501.0000.5720.5730.0000.9030.0000.5350.3390.535
시청교육청검사의뢰(건)0.5690.4350.6050.5050.5721.0000.2660.8770.0000.4680.0000.7000.000
기타(민원)의뢰건0.6330.5910.5040.5120.5730.2661.0000.0000.5610.0000.2070.5940.207
농수산물검사소등검사의뢰(건)0.0000.0000.0000.0000.0000.8770.0001.0000.0000.6530.0000.0000.000
동구부적합(건)0.9430.4160.9230.9230.9030.0000.5610.0001.0000.0000.2650.0000.265
서구부적합(건)0.0000.0000.0000.0000.0000.4680.0000.6530.0001.0000.0000.0000.000
광산구부적합(건)0.6020.0000.5690.6370.5350.0000.2070.0000.2650.0001.0000.0000.047
시합동부적합(건)0.3090.1330.6480.0000.3390.7000.5940.0000.0000.0000.0001.0000.000
기타(민원)부적합건0.6020.0000.5690.5680.5350.0000.2070.0000.2650.0000.0470.0001.000

Missing values

2023-12-13T06:15:15.196395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T06:15:15.429902image/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

식품유형동구검사의뢰(건)서구검사의뢰(건)남구검사의뢰(건)북구검사의뢰(건)광산구검사의뢰(건)시청교육청검사의뢰(건)농수산물검사소등검사의뢰(건)기타(민원)의뢰건동구부적합(건)서구부적합(건)남구부적합(건)북구부적합(건)광산구부적합(건)시합동부적합(건)기타(민원)부적합건
0과자류, 빵류 또는 떡류1333512713110850172000101
1빙과류000000000000000
2코코아가공품류 또는 초콜릿류2032215128000000000
3당류83837105020000000
4잼류31011230010000000
5두부류 또는 묵류900111060000000
6식용유지류2331646000000100
7면류41662177466010000000
8음료류615472685200320000000
9특수용도식품008045020000001
식품유형동구검사의뢰(건)서구검사의뢰(건)남구검사의뢰(건)북구검사의뢰(건)광산구검사의뢰(건)시청교육청검사의뢰(건)농수산물검사소등검사의뢰(건)기타(민원)의뢰건동구부적합(건)서구부적합(건)남구부적합(건)북구부적합(건)광산구부적합(건)시합동부적합(건)기타(민원)부적합건
19동물성가공식품류076030000100000
20벌꿀 및 화분가공품류200000000000000
21즉석식품류441353244222000000020
22기타식품류141079680100000000
23장기보존식품000000000000000
24식품접객업소0020014000000000
25식품첨가물800010020000000
26농산물601007464100000000
27수산물14991493813210100000
28기타000000000000000