Overview

Dataset statistics

Number of variables7
Number of observations273
Missing cells268
Missing cells (%)14.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory15.6 KiB
Average record size in memory58.5 B

Variable types

Numeric2
Text3
Categorical2

Dataset

Description전라남도 완도군 가금류 현황(농장명, 축종-품종, 사육두수, 전화번호, 주소)
Author전라남도 완도군
URLhttps://www.data.go.kr/data/15077074/fileData.do

Alerts

번호 is highly overall correlated with 읍면High correlation
읍면 is highly overall correlated with 번호High correlation
축종-품종 is highly imbalanced (76.0%)Imbalance
전화번호 has 267 (97.8%) missing valuesMissing
번호 has unique valuesUnique

Reproduction

Analysis started2023-12-12 08:04:22.945790
Analysis finished2023-12-12 08:04:24.162159
Duration1.22 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct273
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean137
Minimum1
Maximum273
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-12-12T17:04:24.317274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile14.6
Q169
median137
Q3205
95-th percentile259.4
Maximum273
Range272
Interquartile range (IQR)136

Descriptive statistics

Standard deviation78.952517
Coefficient of variation (CV)0.57629575
Kurtosis-1.2
Mean137
Median Absolute Deviation (MAD)68
Skewness0
Sum37401
Variance6233.5
MonotonicityStrictly increasing
2023-12-12T17:04:24.520754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.4%
181 1
 
0.4%
187 1
 
0.4%
186 1
 
0.4%
185 1
 
0.4%
184 1
 
0.4%
183 1
 
0.4%
182 1
 
0.4%
180 1
 
0.4%
206 1
 
0.4%
Other values (263) 263
96.3%
ValueCountFrequency (%)
1 1
0.4%
2 1
0.4%
3 1
0.4%
4 1
0.4%
5 1
0.4%
6 1
0.4%
7 1
0.4%
8 1
0.4%
9 1
0.4%
10 1
0.4%
ValueCountFrequency (%)
273 1
0.4%
272 1
0.4%
271 1
0.4%
270 1
0.4%
269 1
0.4%
268 1
0.4%
267 1
0.4%
266 1
0.4%
265 1
0.4%
264 1
0.4%

농장명(농장주)
Text

Distinct207
Distinct (%)75.8%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
2023-12-12T17:04:25.052572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.992674
Min length2

Characters and Unicode

Total characters817
Distinct characters111
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

Unique163 ?
Unique (%)59.7%

Sample

1st row고*오
2nd row우*은
3rd row우*은
4th row임*용
5th row소*조
ValueCountFrequency (%)
이*석 6
 
2.2%
김*남 5
 
1.8%
김*호 5
 
1.8%
천*호 4
 
1.5%
박*순 4
 
1.5%
서*수 4
 
1.5%
김*수 3
 
1.1%
김*천 3
 
1.1%
최*선 3
 
1.1%
조*봉 3
 
1.1%
Other values (196) 233
85.3%
2023-12-12T17:04:25.715316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
* 273
33.4%
김 55
 
6.7%
박 35
 
4.3%
이 33
 
4.0%
정 16
 
2.0%
수 16
 
2.0%
호 15
 
1.8%
주 15
 
1.8%
남 13
 
1.6%
조 12
 
1.5%
Other values (101) 334
40.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 543
66.5%
Other Punctuation 273
33.4%
Space Separator 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
김 55
 
10.1%
박 35
 
6.4%
이 33
 
6.1%
정 16
 
2.9%
수 16
 
2.9%
호 15
 
2.8%
주 15
 
2.8%
남 13
 
2.4%
조 12
 
2.2%
임 12
 
2.2%
Other values (99) 321
59.1%
Other Punctuation
ValueCountFrequency (%)
* 273
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 543
66.5%
Common 274
33.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
김 55
 
10.1%
박 35
 
6.4%
이 33
 
6.1%
정 16
 
2.9%
수 16
 
2.9%
호 15
 
2.8%
주 15
 
2.8%
남 13
 
2.4%
조 12
 
2.2%
임 12
 
2.2%
Other values (99) 321
59.1%
Common
ValueCountFrequency (%)
* 273
99.6%
1
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 543
66.5%
ASCII 274
33.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 273
99.6%
1
 
0.4%
Hangul
ValueCountFrequency (%)
김 55
 
10.1%
박 35
 
6.4%
이 33
 
6.1%
정 16
 
2.9%
수 16
 
2.9%
호 15
 
2.8%
주 15
 
2.8%
남 13
 
2.4%
조 12
 
2.2%
임 12
 
2.2%
Other values (99) 321
59.1%

축종-품종
Categorical

IMBALANCE 

Distinct12
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
닭-토종닭
242 
거위
 
7
오골계
 
5
청계
 
5
칠면조
 
5
Other values (7)
 
9

Length

Max length10
Median length5
Mean length4.7765568
Min length2

Unique

Unique5 ?
Unique (%)1.8%

Sample

1st row닭-토종닭
2nd row닭-토종닭
3rd row기러기
4th row닭-토종닭
5th row닭-토종닭

Common Values

ValueCountFrequency (%)
닭-토종닭 242
88.6%
거위 7
 
2.6%
오골계 5
 
1.8%
청계 5
 
1.8%
칠면조 5
 
1.8%
기러기 2
 
0.7%
타조 2
 
0.7%
화이트 아메리카우나 1
 
0.4%
백봉 1
 
0.4%
오리-육용오리 1
 
0.4%
Other values (2) 2
 
0.7%

Length

2023-12-12T17:04:25.940688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
닭-토종닭 242
88.3%
거위 7
 
2.6%
오골계 5
 
1.8%
청계 5
 
1.8%
칠면조 5
 
1.8%
기러기 2
 
0.7%
타조 2
 
0.7%
화이트 1
 
0.4%
아메리카우나 1
 
0.4%
백봉 1
 
0.4%
Other values (3) 3
 
1.1%

사육두수
Real number (ℝ)

Distinct36
Distinct (%)13.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.065934
Minimum1
Maximum370
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-12-12T17:04:26.101404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q15
median9
Q318
95-th percentile50
Maximum370
Range369
Interquartile range (IQR)13

Descriptive statistics

Standard deviation28.772486
Coefficient of variation (CV)1.7909003
Kurtosis90.366797
Mean16.065934
Median Absolute Deviation (MAD)5
Skewness8.2537982
Sum4386
Variance827.85593
MonotonicityNot monotonic
2023-12-12T17:04:26.275161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
5 43
15.8%
10 22
 
8.1%
3 20
 
7.3%
20 17
 
6.2%
30 17
 
6.2%
15 16
 
5.9%
2 15
 
5.5%
7 13
 
4.8%
8 12
 
4.4%
6 11
 
4.0%
Other values (26) 87
31.9%
ValueCountFrequency (%)
1 4
 
1.5%
2 15
 
5.5%
3 20
7.3%
4 11
 
4.0%
5 43
15.8%
6 11
 
4.0%
7 13
 
4.8%
8 12
 
4.4%
9 8
 
2.9%
10 22
8.1%
ValueCountFrequency (%)
370 1
 
0.4%
200 1
 
0.4%
100 3
 
1.1%
80 1
 
0.4%
70 1
 
0.4%
60 1
 
0.4%
50 9
3.3%
40 3
 
1.1%
35 1
 
0.4%
34 1
 
0.4%

전화번호
Text

MISSING 

Distinct6
Distinct (%)100.0%
Missing267
Missing (%)97.8%
Memory size2.3 KiB
2023-12-12T17:04:26.510026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters72
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)100.0%

Sample

1st row061-553-2410
2nd row061-552-5718
3rd row061-554-8674
4th row061-553-0432
5th row061-553-9787
ValueCountFrequency (%)
061-553-2410 1
16.7%
061-552-5718 1
16.7%
061-554-8674 1
16.7%
061-553-0432 1
16.7%
061-553-9787 1
16.7%
061-554-3317 1
16.7%
2023-12-12T17:04:26.893679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 13
18.1%
- 12
16.7%
1 9
12.5%
0 8
11.1%
6 7
9.7%
3 6
8.3%
4 5
 
6.9%
7 5
 
6.9%
2 3
 
4.2%
8 3
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 60
83.3%
Dash Punctuation 12
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 13
21.7%
1 9
15.0%
0 8
13.3%
6 7
11.7%
3 6
10.0%
4 5
 
8.3%
7 5
 
8.3%
2 3
 
5.0%
8 3
 
5.0%
9 1
 
1.7%
Dash Punctuation
ValueCountFrequency (%)
- 12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 72
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 13
18.1%
- 12
16.7%
1 9
12.5%
0 8
11.1%
6 7
9.7%
3 6
8.3%
4 5
 
6.9%
7 5
 
6.9%
2 3
 
4.2%
8 3
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 72
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 13
18.1%
- 12
16.7%
1 9
12.5%
0 8
11.1%
6 7
9.7%
3 6
8.3%
4 5
 
6.9%
7 5
 
6.9%
2 3
 
4.2%
8 3
 
4.2%

읍면
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
완도읍
51 
고금면
51 
청산면
51 
군외면
29 
약산면
27 
Other values (4)
64 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
완도읍 51
18.7%
고금면 51
18.7%
청산면 51
18.7%
군외면 29
10.6%
약산면 27
9.9%
신지면 21
7.7%
생일면 17
 
6.2%
금당면 16
 
5.9%
금일읍 10
 
3.7%

Length

2023-12-12T17:04:27.094073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T17:04:27.269622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
완도읍 51
18.7%
고금면 51
18.7%
청산면 51
18.7%
군외면 29
10.6%
약산면 27
9.9%
신지면 21
7.7%
생일면 17
 
6.2%
금당면 16
 
5.9%
금일읍 10
 
3.7%

주소
Text

Distinct245
Distinct (%)90.1%
Missing1
Missing (%)0.4%
Memory size2.3 KiB
2023-12-12T17:04:27.662774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length32
Median length21.5
Mean length9.9632353
Min length4

Characters and Unicode

Total characters2710
Distinct characters113
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique224 ?
Unique (%)82.4%

Sample

1st row가용리 199
2nd row가용리 288-1
3rd row가용리 288-1
4th row가용리 312-89
5th row가용리 381
ValueCountFrequency (%)
생일면 17
 
2.8%
산 14
 
2.3%
우두리 13
 
2.2%
중도리 12
 
2.0%
대야리 10
 
1.7%
가용리 10
 
1.7%
황진리 7
 
1.2%
망석리 7
 
1.2%
신학리 6
 
1.0%
청산로 6
 
1.0%
Other values (352) 496
82.9%
2023-12-12T17:04:28.230512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
326
 
12.0%
1 239
 
8.8%
- 157
 
5.8%
리 143
 
5.3%
3 128
 
4.7%
2 120
 
4.4%
5 113
 
4.2%
길 112
 
4.1%
4 109
 
4.0%
7 84
 
3.1%
Other values (103) 1179
43.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1081
39.9%
Other Letter 1064
39.3%
Space Separator 326
 
12.0%
Dash Punctuation 157
 
5.8%
Close Punctuation 41
 
1.5%
Open Punctuation 41
 
1.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 143
 
13.4%
길 112
 
10.5%
로 56
 
5.3%
산 44
 
4.1%
청 37
 
3.5%
번 33
 
3.1%
일 30
 
2.8%
신 29
 
2.7%
생 26
 
2.4%
동 25
 
2.3%
Other values (89) 529
49.7%
Decimal Number
ValueCountFrequency (%)
1 239
22.1%
3 128
11.8%
2 120
11.1%
5 113
10.5%
4 109
10.1%
7 84
 
7.8%
6 80
 
7.4%
8 78
 
7.2%
9 70
 
6.5%
0 60
 
5.6%
Space Separator
ValueCountFrequency (%)
326
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 157
100.0%
Close Punctuation
ValueCountFrequency (%)
) 41
100.0%
Open Punctuation
ValueCountFrequency (%)
( 41
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1646
60.7%
Hangul 1064
39.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 143
 
13.4%
길 112
 
10.5%
로 56
 
5.3%
산 44
 
4.1%
청 37
 
3.5%
번 33
 
3.1%
일 30
 
2.8%
신 29
 
2.7%
생 26
 
2.4%
동 25
 
2.3%
Other values (89) 529
49.7%
Common
ValueCountFrequency (%)
326
19.8%
1 239
14.5%
- 157
9.5%
3 128
 
7.8%
2 120
 
7.3%
5 113
 
6.9%
4 109
 
6.6%
7 84
 
5.1%
6 80
 
4.9%
8 78
 
4.7%
Other values (4) 212
12.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1646
60.7%
Hangul 1064
39.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
326
19.8%
1 239
14.5%
- 157
9.5%
3 128
 
7.8%
2 120
 
7.3%
5 113
 
6.9%
4 109
 
6.6%
7 84
 
5.1%
6 80
 
4.9%
8 78
 
4.7%
Other values (4) 212
12.9%
Hangul
ValueCountFrequency (%)
리 143
 
13.4%
길 112
 
10.5%
로 56
 
5.3%
산 44
 
4.1%
청 37
 
3.5%
번 33
 
3.1%
일 30
 
2.8%
신 29
 
2.7%
생 26
 
2.4%
동 25
 
2.3%
Other values (89) 529
49.7%

Interactions

2023-12-12T17:04:23.517926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:04:23.275532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:04:23.652123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:04:23.374093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T17:04:28.341784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호축종-품종사육두수전화번호읍면
번호1.0000.3290.0001.0000.935
축종-품종0.3291.0000.000NaN0.303
사육두수0.0000.0001.000NaN0.000
전화번호1.000NaNNaN1.0001.000
읍면0.9350.3030.0001.0001.000
2023-12-12T17:04:28.459831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
축종-품종읍면
축종-품종1.0000.132
읍면0.1321.000
2023-12-12T17:04:28.559156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호사육두수축종-품종읍면
번호1.000-0.1440.1430.783
사육두수-0.1441.0000.0000.000
축종-품종0.1430.0001.0000.132
읍면0.7830.0000.1321.000

Missing values

2023-12-12T17:04:23.815834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T17:04:23.976203image/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.
2023-12-12T17:04:24.085239image/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

번호농장명(농장주)축종-품종사육두수전화번호읍면주소
01고*오닭-토종닭8<NA>완도읍가용리 199
12우*은닭-토종닭3<NA>완도읍가용리 288-1
23우*은기러기2<NA>완도읍가용리 288-1
34임*용닭-토종닭10<NA>완도읍가용리 312-89
45소*조닭-토종닭25<NA>완도읍가용리 381
56박*규화이트 아메리카우나25<NA>완도읍가용리 389-5
67추*현닭-토종닭5<NA>완도읍가용리 409-1
78추*룡닭-토종닭40<NA>완도읍가용리 487-3
89고*주닭-토종닭16<NA>완도읍가용리 733-2
910고*주기러기5<NA>완도읍가용리 733-2
번호농장명(농장주)축종-품종사육두수전화번호읍면주소
263264김*순닭-토종닭3<NA>생일면생일면 생일로613번길 12
264265이*석닭-토종닭2<NA>생일면생일면 생일로613번길35
265266김*우닭-토종닭30<NA>생일면생일면 생일로741-14
266267주*기닭-토종닭15<NA>생일면생일면 생일로795-86
267268주*기칠면조4<NA>생일면생일면 생일로795-86
268269정*학닭-토종닭30<NA>생일면생일면 생일로826
269270윤*옥닭-토종닭2<NA>생일면생일면 용출2길26
270271류*숙닭-토종닭9<NA>생일면생일면 용출길 34-12
271272신*근닭-토종닭22<NA>생일면생일면 유촌1길2-10
272273김*은닭-토종닭8<NA>생일면생일면 유촌길5-1