Overview

Dataset statistics

Number of variables14
Number of observations60
Missing cells22
Missing cells (%)2.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.2 KiB
Average record size in memory122.2 B

Variable types

Categorical4
Numeric8
Text1
DateTime1

Dataset

Description제주특별자치도 악취농가에 대한 자료로 읍면동, 리 단위의 농가 수와 인구수 등의 정보와 악취농가 수에 대한 현황 데이터를 제공합니다.
Author제주특별자치도
URLhttps://www.data.go.kr/data/15097045/fileData.do

Alerts

시도명 has constant value ""Constant
농가 has constant value ""Constant
데이터기준일자 has constant value ""Constant
읍면동코드 is highly overall correlated with 리코드 and 2 other fieldsHigh correlation
리코드 is highly overall correlated with 읍면동코드 and 2 other fieldsHigh correlation
농가수 is highly overall correlated with 사육두수 and 2 other fieldsHigh correlation
사육두수 is highly overall correlated with 농가수 and 2 other fieldsHigh correlation
시설면적 is highly overall correlated with 농가수 and 2 other fieldsHigh correlation
세대수_2020 is highly overall correlated with 인구수_2020 and 1 other fieldsHigh correlation
인구수_2020 is highly overall correlated with 세대수_2020 and 1 other fieldsHigh correlation
악취농가수_202106 is highly overall correlated with 농가수 and 2 other fieldsHigh correlation
시군구명 is highly overall correlated with 읍면동코드 and 2 other fieldsHigh correlation
읍면동명 is highly overall correlated with 읍면동코드 and 4 other fieldsHigh correlation
리명 has 11 (18.3%) missing valuesMissing
리코드 has 11 (18.3%) missing valuesMissing
시설면적 has unique valuesUnique
세대수_2020 has unique valuesUnique
인구수_2020 has unique valuesUnique
악취농가수_202106 has 42 (70.0%) zerosZeros

Reproduction

Analysis started2023-12-12 22:01:12.460064
Analysis finished2023-12-12 22:01:19.620892
Duration7.16 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size612.0 B
제주특별자치도
60 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
제주특별자치도 60
100.0%

Length

2023-12-13T07:01:19.682350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:01:19.774087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
제주특별자치도 60
100.0%

시군구명
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size612.0 B
제주시
31 
서귀포시
29 

Length

Max length4
Median length3
Mean length3.4833333
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
제주시 31
51.7%
서귀포시 29
48.3%

Length

2023-12-13T07:01:20.197701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:01:20.312339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
제주시 31
51.7%
서귀포시 29
48.3%

읍면동명
Categorical

HIGH CORRELATION 

Distinct21
Distinct (%)35.0%
Missing0
Missing (%)0.0%
Memory size612.0 B
한경면
8 
한림읍
7 
애월읍
6 
대정읍
6 
남원읍
5 
Other values (16)
28 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique11 ?
Unique (%)18.3%

Sample

1st row구좌읍
2nd row구좌읍
3rd row구좌읍
4th row구좌읍
5th row노형동

Common Values

ValueCountFrequency (%)
한경면 8
13.3%
한림읍 7
11.7%
애월읍 6
10.0%
대정읍 6
10.0%
남원읍 5
8.3%
구좌읍 4
 
6.7%
성산읍 4
 
6.7%
조천읍 3
 
5.0%
표선면 3
 
5.0%
안덕면 3
 
5.0%
Other values (11) 11
18.3%

Length

2023-12-13T07:01:20.419902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
한경면 8
13.3%
한림읍 7
11.7%
애월읍 6
10.0%
대정읍 6
10.0%
남원읍 5
8.3%
구좌읍 4
 
6.7%
성산읍 4
 
6.7%
조천읍 3
 
5.0%
표선면 3
 
5.0%
안덕면 3
 
5.0%
Other values (11) 11
18.3%

읍면동코드
Real number (ℝ)

HIGH CORRELATION 

Distinct21
Distinct (%)35.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50119909
Minimum50110122
Maximum50130320
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size672.0 B
2023-12-13T07:01:20.553751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum50110122
5-th percentile50110244
Q150110253
median50110310
Q350130251
95-th percentile50130310
Maximum50130320
Range20198
Interquartile range (IQR)19997.75

Descriptive statistics

Standard deviation10064.479
Coefficient of variation (CV)0.00020080801
Kurtosis-2.0651567
Mean50119909
Median Absolute Deviation (MAD)182
Skewness0.068472991
Sum3.0071945 × 109
Variance1.0129374 × 108
MonotonicityNot monotonic
2023-12-13T07:01:20.696586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
50110310 8
13.3%
50110250 7
11.7%
50110253 6
10.0%
50130250 6
10.0%
50130253 5
8.3%
50110256 4
 
6.7%
50130259 4
 
6.7%
50110259 3
 
5.0%
50130320 3
 
5.0%
50130310 3
 
5.0%
Other values (11) 11
18.3%
ValueCountFrequency (%)
50110122 1
 
1.7%
50110134 1
 
1.7%
50110139 1
 
1.7%
50110250 7
11.7%
50110253 6
10.0%
50110256 4
6.7%
50110259 3
 
5.0%
50110310 8
13.3%
50130105 1
 
1.7%
50130106 1
 
1.7%
ValueCountFrequency (%)
50130320 3
5.0%
50130310 3
5.0%
50130259 4
6.7%
50130253 5
8.3%
50130250 6
10.0%
50130120 1
 
1.7%
50130119 1
 
1.7%
50130118 1
 
1.7%
50130114 1
 
1.7%
50130113 1
 
1.7%

리명
Text

MISSING 

Distinct48
Distinct (%)98.0%
Missing11
Missing (%)18.3%
Memory size612.0 B
2023-12-13T07:01:20.946027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.0204082
Min length3

Characters and Unicode

Total characters148
Distinct characters69
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

Unique47 ?
Unique (%)95.9%

Sample

1st row김녕리
2nd row동복리
3rd row세화리
4th row한동리
5th row고성리
ValueCountFrequency (%)
세화리 2
 
4.1%
금악리 1
 
2.0%
신도리 1
 
2.0%
김녕리 1
 
2.0%
안성리 1
 
2.0%
협재리 1
 
2.0%
신례리 1
 
2.0%
신흥리 1
 
2.0%
위미리 1
 
2.0%
의귀리 1
 
2.0%
Other values (38) 38
77.6%
2023-12-13T07:01:21.358593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
리 49
33.1%
수 6
 
4.1%
신 5
 
3.4%
성 5
 
3.4%
상 3
 
2.0%
동 3
 
2.0%
산 2
 
1.4%
화 2
 
1.4%
월 2
 
1.4%
명 2
 
1.4%
Other values (59) 69
46.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 148
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 49
33.1%
수 6
 
4.1%
신 5
 
3.4%
성 5
 
3.4%
상 3
 
2.0%
동 3
 
2.0%
산 2
 
1.4%
화 2
 
1.4%
월 2
 
1.4%
명 2
 
1.4%
Other values (59) 69
46.6%

Most occurring scripts

ValueCountFrequency (%)
Hangul 148
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 49
33.1%
수 6
 
4.1%
신 5
 
3.4%
성 5
 
3.4%
상 3
 
2.0%
동 3
 
2.0%
산 2
 
1.4%
화 2
 
1.4%
월 2
 
1.4%
명 2
 
1.4%
Other values (59) 69
46.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 148
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
리 49
33.1%
수 6
 
4.1%
신 5
 
3.4%
성 5
 
3.4%
상 3
 
2.0%
동 3
 
2.0%
산 2
 
1.4%
화 2
 
1.4%
월 2
 
1.4%
명 2
 
1.4%
Other values (59) 69
46.6%

리코드
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct49
Distinct (%)100.0%
Missing11
Missing (%)18.3%
Infinite0
Infinite (%)0.0%
Mean5.0118434 × 109
Minimum5.011025 × 109
Maximum5.013032 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size672.0 B
2023-12-13T07:01:21.557178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5.011025 × 109
5-th percentile5.011025 × 109
Q15.0110253 × 109
median5.011031 × 109
Q35.0130253 × 109
95-th percentile5.0130316 × 109
Maximum5.013032 × 109
Range2007000
Interquartile range (IQR)1999986

Descriptive statistics

Standard deviation993304.04
Coefficient of variation (CV)0.00019819136
Kurtosis-1.9318947
Mean5.0118434 × 109
Median Absolute Deviation (MAD)5993
Skewness0.38560942
Sum2.4558032 × 1011
Variance9.8665292 × 1011
MonotonicityNot monotonic
2023-12-13T07:01:21.744215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
5013025024 1
 
1.7%
5011025031 1
 
1.7%
5013025329 1
 
1.7%
5013025326 1
 
1.7%
5013025327 1
 
1.7%
5013025325 1
 
1.7%
5013025323 1
 
1.7%
5013025028 1
 
1.7%
5013025033 1
 
1.7%
5013025027 1
 
1.7%
Other values (39) 39
65.0%
(Missing) 11
 
18.3%
ValueCountFrequency (%)
5011025025 1
1.7%
5011025027 1
1.7%
5011025029 1
1.7%
5011025030 1
1.7%
5011025031 1
1.7%
5011025033 1
1.7%
5011025034 1
1.7%
5011025321 1
1.7%
5011025325 1
1.7%
5011025327 1
1.7%
ValueCountFrequency (%)
5013032025 1
1.7%
5013032024 1
1.7%
5013032023 1
1.7%
5013031026 1
1.7%
5013031025 1
1.7%
5013031024 1
1.7%
5013025929 1
1.7%
5013025928 1
1.7%
5013025926 1
1.7%
5013025329 1
1.7%

농가
Categorical

CONSTANT 

Distinct1
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size612.0 B
양돈농가
60 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
양돈농가 60
100.0%

Length

2023-12-13T07:01:21.910850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:01:22.000308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
양돈농가 60
100.0%

농가수
Real number (ℝ)

HIGH CORRELATION 

Distinct14
Distinct (%)23.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4
Minimum1
Maximum56
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size672.0 B
2023-12-13T07:01:22.096034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33.25
95-th percentile18.1
Maximum56
Range55
Interquartile range (IQR)2.25

Descriptive statistics

Standard deviation8.3263926
Coefficient of variation (CV)1.892362
Kurtosis25.64847
Mean4.4
Median Absolute Deviation (MAD)1
Skewness4.6363235
Sum264
Variance69.328814
MonotonicityNot monotonic
2023-12-13T07:01:22.196942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
1 27
45.0%
2 12
20.0%
3 6
 
10.0%
5 4
 
6.7%
4 2
 
3.3%
7 1
 
1.7%
9 1
 
1.7%
14 1
 
1.7%
56 1
 
1.7%
18 1
 
1.7%
Other values (4) 4
 
6.7%
ValueCountFrequency (%)
1 27
45.0%
2 12
20.0%
3 6
 
10.0%
4 2
 
3.3%
5 4
 
6.7%
6 1
 
1.7%
7 1
 
1.7%
9 1
 
1.7%
12 1
 
1.7%
14 1
 
1.7%
ValueCountFrequency (%)
56 1
 
1.7%
25 1
 
1.7%
20 1
 
1.7%
18 1
 
1.7%
14 1
 
1.7%
12 1
 
1.7%
9 1
 
1.7%
7 1
 
1.7%
6 1
 
1.7%
5 4
6.7%

사육두수
Real number (ℝ)

HIGH CORRELATION 

Distinct58
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8203.7833
Minimum150
Maximum107514
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size672.0 B
2023-12-13T07:01:22.370483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum150
5-th percentile224.3
Q11273.25
median2575
Q36612.75
95-th percentile31832.15
Maximum107514
Range107364
Interquartile range (IQR)5339.5

Descriptive statistics

Standard deviation16054.922
Coefficient of variation (CV)1.9570144
Kurtosis25.000946
Mean8203.7833
Median Absolute Deviation (MAD)1880
Skewness4.4817091
Sum492227
Variance2.5776051 × 108
MonotonicityNot monotonic
2023-12-13T07:01:22.529424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
150 2
 
3.3%
2000 2
 
3.3%
2834 1
 
1.7%
2415 1
 
1.7%
38021 1
 
1.7%
2048 1
 
1.7%
9949 1
 
1.7%
3229 1
 
1.7%
1462 1
 
1.7%
5276 1
 
1.7%
Other values (48) 48
80.0%
ValueCountFrequency (%)
150 2
3.3%
211 1
1.7%
225 1
1.7%
300 1
1.7%
420 1
1.7%
443 1
1.7%
523 1
1.7%
540 1
1.7%
850 1
1.7%
1010 1
1.7%
ValueCountFrequency (%)
107514 1
1.7%
38021 1
1.7%
37972 1
1.7%
31509 1
1.7%
31198 1
1.7%
28264 1
1.7%
20904 1
1.7%
20100 1
1.7%
14434 1
1.7%
12311 1
1.7%

시설면적
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct60
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9892.4167
Minimum340
Maximum128435
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size672.0 B
2023-12-13T07:01:22.677806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum340
5-th percentile704.05
Q11667.25
median3293
Q38111.75
95-th percentile42114.45
Maximum128435
Range128095
Interquartile range (IQR)6444.5

Descriptive statistics

Standard deviation19379.156
Coefficient of variation (CV)1.9589911
Kurtosis23.92895
Mean9892.4167
Median Absolute Deviation (MAD)1941.5
Skewness4.3991678
Sum593545
Variance3.7555169 × 108
MonotonicityNot monotonic
2023-12-13T07:01:22.869448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3434 1
 
1.7%
2753 1
 
1.7%
8189 1
 
1.7%
2063 1
 
1.7%
2381 1
 
1.7%
42013 1
 
1.7%
3068 1
 
1.7%
12081 1
 
1.7%
4783 1
 
1.7%
1211 1
 
1.7%
Other values (50) 50
83.3%
ValueCountFrequency (%)
340 1
1.7%
403 1
1.7%
458 1
1.7%
717 1
1.7%
746 1
1.7%
1211 1
1.7%
1273 1
1.7%
1279 1
1.7%
1327 1
1.7%
1350 1
1.7%
ValueCountFrequency (%)
128435 1
1.7%
49101 1
1.7%
44042 1
1.7%
42013 1
1.7%
39135 1
1.7%
34661 1
1.7%
24270 1
1.7%
24085 1
1.7%
14326 1
1.7%
13966 1
1.7%

세대수_2020
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct60
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean985.21667
Minimum4
Maximum21524
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size672.0 B
2023-12-13T07:01:23.105309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile143.4
Q1300.75
median536
Q3777
95-th percentile2059
Maximum21524
Range21520
Interquartile range (IQR)476.25

Descriptive statistics

Standard deviation2738.484
Coefficient of variation (CV)2.7795754
Kurtosis56.218296
Mean985.21667
Median Absolute Deviation (MAD)241.5
Skewness7.3923529
Sum59113
Variance7499294.9
MonotonicityNot monotonic
2023-12-13T07:01:23.257628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1368 1
 
1.7%
786 1
 
1.7%
2053 1
 
1.7%
496 1
 
1.7%
240 1
 
1.7%
358 1
 
1.7%
443 1
 
1.7%
216 1
 
1.7%
274 1
 
1.7%
242 1
 
1.7%
Other values (50) 50
83.3%
ValueCountFrequency (%)
4 1
1.7%
78 1
1.7%
132 1
1.7%
144 1
1.7%
188 1
1.7%
203 1
1.7%
216 1
1.7%
232 1
1.7%
240 1
1.7%
242 1
1.7%
ValueCountFrequency (%)
21524 1
1.7%
2302 1
1.7%
2173 1
1.7%
2053 1
1.7%
1368 1
1.7%
1265 1
1.7%
1219 1
1.7%
1165 1
1.7%
1127 1
1.7%
1101 1
1.7%

인구수_2020
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct60
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2179.35
Minimum7
Maximum52374
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size672.0 B
2023-12-13T07:01:23.409129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile317.6
Q1599.5
median1126.5
Q31653.5
95-th percentile4535.9
Maximum52374
Range52367
Interquartile range (IQR)1054

Descriptive statistics

Standard deviation6674.0608
Coefficient of variation (CV)3.0624089
Kurtosis56.878476
Mean2179.35
Median Absolute Deviation (MAD)538.5
Skewness7.454416
Sum130761
Variance44543087
MonotonicityNot monotonic
2023-12-13T07:01:23.569932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2795 1
 
1.7%
1788 1
 
1.7%
4519 1
 
1.7%
1125 1
 
1.7%
486 1
 
1.7%
719 1
 
1.7%
831 1
 
1.7%
444 1
 
1.7%
565 1
 
1.7%
500 1
 
1.7%
Other values (50) 50
83.3%
ValueCountFrequency (%)
7 1
1.7%
116 1
1.7%
253 1
1.7%
321 1
1.7%
351 1
1.7%
411 1
1.7%
418 1
1.7%
444 1
1.7%
485 1
1.7%
486 1
1.7%
ValueCountFrequency (%)
52374 1
1.7%
5118 1
1.7%
4857 1
1.7%
4519 1
1.7%
2817 1
1.7%
2795 1
1.7%
2713 1
1.7%
2432 1
1.7%
2300 1
1.7%
2275 1
1.7%

악취농가수_202106
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7166667
Minimum0
Maximum41
Zeros42
Zeros (%)70.0%
Negative0
Negative (%)0.0%
Memory size672.0 B
2023-12-13T07:01:23.704663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31.25
95-th percentile5.25
Maximum41
Range41
Interquartile range (IQR)1.25

Descriptive statistics

Standard deviation5.6691005
Coefficient of variation (CV)3.3023886
Kurtosis40.493194
Mean1.7166667
Median Absolute Deviation (MAD)0
Skewness6.0136775
Sum103
Variance32.138701
MonotonicityNot monotonic
2023-12-13T07:01:23.818996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 42
70.0%
2 5
 
8.3%
1 3
 
5.0%
4 3
 
5.0%
3 3
 
5.0%
41 1
 
1.7%
5 1
 
1.7%
13 1
 
1.7%
10 1
 
1.7%
ValueCountFrequency (%)
0 42
70.0%
1 3
 
5.0%
2 5
 
8.3%
3 3
 
5.0%
4 3
 
5.0%
5 1
 
1.7%
10 1
 
1.7%
13 1
 
1.7%
41 1
 
1.7%
ValueCountFrequency (%)
41 1
 
1.7%
13 1
 
1.7%
10 1
 
1.7%
5 1
 
1.7%
4 3
 
5.0%
3 3
 
5.0%
2 5
 
8.3%
1 3
 
5.0%
0 42
70.0%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size612.0 B
Minimum2021-12-21 00:00:00
Maximum2021-12-21 00:00:00
2023-12-13T07:01:23.925218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:01:24.010626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-13T07:01:18.448262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:01:12.908567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:01:13.703843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:01:14.857226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:01:15.627663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:01:16.380734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:01:17.166820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:01:17.813444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:01:18.632441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:01:13.014154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:01:13.819074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:01:14.954522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:01:15.743467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:01:16.476711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:01:17.296038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:01:17.896336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:01:18.745285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:01:13.111145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:01:14.254764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:01:15.053019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:01:15.859657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:01:16.594915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:01:17.382381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:01:17.989694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:01:18.819888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:01:13.193317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:01:14.351050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:01:15.123904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:01:15.962397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:01:16.676231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:01:17.463901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:01:18.062949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:01:18.891835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:01:13.291836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:01:14.459953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:01:15.226277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:01:16.044210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:01:16.762730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:01:17.538902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:01:18.136089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:01:18.968609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:01:13.407728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:01:14.557894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:01:15.362331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:01:16.127155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:01:16.863196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:01:17.609368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:01:18.219260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:01:19.041922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:01:13.507603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:01:14.652216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:01:15.445966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:01:16.216830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:01:16.977359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:01:17.670812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:01:18.295838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:01:19.155894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:01:13.599105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:01:14.753425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:01:15.541430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:01:16.307420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:01:17.059406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:01:17.739231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:01:18.371902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T07:01:24.104205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구명읍면동명읍면동코드리명리코드농가수사육두수시설면적세대수_2020인구수_2020악취농가수_202106
시군구명1.0001.0000.9990.0000.9910.2090.0950.0000.0770.0000.000
읍면동명1.0001.0001.0000.8250.9980.0000.0000.0000.8801.0000.000
읍면동코드0.9991.0001.0000.0000.9910.2300.1100.0000.000NaN0.005
리명0.0000.8250.0001.0000.0001.0000.7240.0001.000NaN1.000
리코드0.9910.9980.9910.0001.0000.0000.1260.0000.000NaN0.000
농가수0.2090.0000.2301.0000.0001.0000.8650.8410.0000.0000.904
사육두수0.0950.0000.1100.7240.1260.8651.0000.9890.0000.0000.967
시설면적0.0000.0000.0000.0000.0000.8410.9891.0000.0000.0000.928
세대수_20200.0770.8800.0001.0000.0000.0000.0000.0001.0001.0000.000
인구수_20200.0001.000NaNNaNNaN0.0000.0000.0001.0001.0000.000
악취농가수_2021060.0000.0000.0051.0000.0000.9040.9670.9280.0000.0001.000
2023-12-13T07:01:24.260201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구명읍면동명
시군구명1.0000.820
읍면동명0.8201.000
2023-12-13T07:01:24.375433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
읍면동코드리코드농가수사육두수시설면적세대수_2020인구수_2020악취농가수_202106시군구명읍면동명
읍면동코드1.0000.937-0.221-0.165-0.211-0.068-0.068-0.3600.9660.820
리코드0.9371.000-0.296-0.254-0.2510.0540.072-0.4160.9150.875
농가수-0.221-0.2961.0000.8250.8530.0170.0160.7400.1400.000
사육두수-0.165-0.2540.8251.0000.9560.1490.1510.6440.0660.000
시설면적-0.211-0.2510.8530.9561.0000.1760.1800.6690.0000.000
세대수_2020-0.0680.0540.0170.1490.1761.0000.995-0.0260.1250.531
인구수_2020-0.0680.0720.0160.1510.1800.9951.000-0.0230.0000.820
악취농가수_202106-0.360-0.4160.7400.6440.669-0.026-0.0231.0000.0000.000
시군구명0.9660.9150.1400.0660.0000.1250.0000.0001.0000.820
읍면동명0.8200.8750.0000.0000.0000.5310.8200.0000.8201.000

Missing values

2023-12-13T07:01:19.263992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T07:01:19.455064image/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.
2023-12-13T07:01:19.572848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

시도명시군구명읍면동명읍면동코드리명리코드농가농가수사육두수시설면적세대수_2020인구수_2020악취농가수_202106데이터기준일자
0제주특별자치도제주시구좌읍50110256김녕리5011025634양돈농가3283434341368279502021-12-21
1제주특별자치도제주시구좌읍50110256동복리5011025621양돈농가11918191039479812021-12-21
2제주특별자치도제주시구좌읍50110256세화리5011025628양돈농가220100240851265230002021-12-21
3제주특별자치도제주시구좌읍50110256한동리5011025630양돈농가51117712928588121602021-12-21
4제주특별자치도제주시노형동50110122<NA><NA>양돈농가13001670215245237402021-12-21
5제주특별자치도제주시애월읍50110253고성리5011025329양돈농가71083211545794175142021-12-21
6제주특별자치도제주시애월읍50110253광령리5011025321양돈농가520904242702173485742021-12-21
7제주특별자치도제주시애월읍50110253봉성리5011025339양돈농가115001375550119702021-12-21
8제주특별자치도제주시애월읍50110253어음리5011025336양돈농가11440135029154602021-12-21
9제주특별자치도제주시애월읍50110253유수암리5011025342양돈농가120001659952199602021-12-21
시도명시군구명읍면동명읍면동코드리명리코드농가농가수사육두수시설면적세대수_2020인구수_2020악취농가수_202106데이터기준일자
50제주특별자치도서귀포시성산읍50130259온평리5013025926양돈농가121272114740146302021-12-21
51제주특별자치도서귀포시안덕면50130310덕수리5013031026양돈농가112352304595125002021-12-21
52제주특별자치도서귀포시안덕면50130310사계리5013031025양돈농가1173217981219243202021-12-21
53제주특별자치도서귀포시안덕면50130310상창리5013031024양돈농가11580145727352802021-12-21
54제주특별자치도서귀포시토평동50130111<NA><NA>양돈농가12117467811602021-12-21
55제주특별자치도서귀포시표선면50130320가시리5013032024양돈농가51443414326648135302021-12-21
56제주특별자치도서귀포시표선면50130320성읍리5013032023양돈농가238533418717152402021-12-21
57제주특별자치도서귀포시표선면50130320세화리5013032025양돈농가161175008773162102021-12-21
58제주특별자치도서귀포시하원동50130118<NA><NA>양돈농가1443403647142402021-12-21
59제주특별자치도서귀포시회수동50130113<NA><NA>양돈농가43907425230464102021-12-21