Overview

Dataset statistics

Number of variables8
Number of observations57
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.0 KiB
Average record size in memory72.3 B

Variable types

Categorical6
Text1
Numeric1

Dataset

Description강원도 영월군 가금농가 현황 및 사육두수 데이터를 공개합니다. 계, 육계, 산란계 지역별 현황 데이터가 포함됩니다.
Author강원도 영월군
URLhttps://www.data.go.kr/data/15093684/fileData.do

Alerts

두수(산란계) is highly overall correlated with 두수(계) and 3 other fieldsHigh correlation
농가수(산란계) is highly overall correlated with 두수(계) and 2 other fieldsHigh correlation
두수(계) is highly overall correlated with 농가수(계) and 3 other fieldsHigh correlation
농가수(계) is highly overall correlated with 두수(계) and 4 other fieldsHigh correlation
농가수(육계) is highly overall correlated with 농가수(계) and 1 other fieldsHigh correlation
두수(육계) is highly overall correlated with 두수(계) and 3 other fieldsHigh correlation
농가수(계) is highly imbalanced (55.9%)Imbalance
농가수(육계) is highly imbalanced (51.5%)Imbalance
두수(육계) is highly imbalanced (78.2%)Imbalance
농가수(산란계) is highly imbalanced (78.2%)Imbalance
두수(산란계) is highly imbalanced (81.0%)Imbalance
리 has unique valuesUnique
두수(계) has 51 (89.5%) zerosZeros

Reproduction

Analysis started2023-12-12 12:53:05.419076
Analysis finished2023-12-12 12:53:06.131419
Duration0.71 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

읍면
Categorical

Distinct9
Distinct (%)15.8%
Missing0
Missing (%)0.0%
Memory size588.0 B
영월읍
11 
김삿갓면
9 
중동면
6 
남면
6 
주천면
6 
Other values (4)
19 

Length

Max length5
Median length4
Mean length3.2280702
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
영월읍 11
19.3%
김삿갓면 9
15.8%
중동면 6
10.5%
남면 6
10.5%
주천면 6
10.5%
북면 5
8.8%
한반도면 5
8.8%
무릉도원면 5
8.8%
상동읍 4
 
7.0%

Length

2023-12-12T21:53:06.192978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:53:06.300118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
영월읍 11
19.3%
김삿갓면 9
15.8%
중동면 6
10.5%
남면 6
10.5%
주천면 6
10.5%
북면 5
8.8%
한반도면 5
8.8%
무릉도원면 5
8.8%
상동읍 4
 
7.0%

리
Text

UNIQUE 

Distinct57
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size588.0 B
2023-12-12T21:53:06.552599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.9824561
Min length2

Characters and Unicode

Total characters170
Distinct characters75
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

Unique57 ?
Unique (%)100.0%

Sample

1st row거운리
2nd row덕포리
3rd row문산리
4th row방절리
5th row삼옥리
ValueCountFrequency (%)
거운리 1
 
1.8%
진별리 1
 
1.8%
덕상리 1
 
1.8%
마차리 1
 
1.8%
문곡리 1
 
1.8%
연덕리 1
 
1.8%
광천리 1
 
1.8%
북쌍리 1
 
1.8%
연당리 1
 
1.8%
조전리 1
 
1.8%
Other values (47) 47
82.5%
2023-12-12T21:53:06.888796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
리 57
33.5%
덕 5
 
2.9%
천 5
 
2.9%
연 4
 
2.4%
전 3
 
1.8%
흥 3
 
1.8%
원 3
 
1.8%
운 3
 
1.8%
석 3
 
1.8%
동 3
 
1.8%
Other values (65) 81
47.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 170
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 57
33.5%
덕 5
 
2.9%
천 5
 
2.9%
연 4
 
2.4%
전 3
 
1.8%
흥 3
 
1.8%
원 3
 
1.8%
운 3
 
1.8%
석 3
 
1.8%
동 3
 
1.8%
Other values (65) 81
47.6%

Most occurring scripts

ValueCountFrequency (%)
Hangul 170
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 57
33.5%
덕 5
 
2.9%
천 5
 
2.9%
연 4
 
2.4%
전 3
 
1.8%
흥 3
 
1.8%
원 3
 
1.8%
운 3
 
1.8%
석 3
 
1.8%
동 3
 
1.8%
Other values (65) 81
47.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 170
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
리 57
33.5%
덕 5
 
2.9%
천 5
 
2.9%
연 4
 
2.4%
전 3
 
1.8%
흥 3
 
1.8%
원 3
 
1.8%
운 3
 
1.8%
석 3
 
1.8%
동 3
 
1.8%
Other values (65) 81
47.6%

농가수(계)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Memory size588.0 B
0
49 
1
6 
2
 
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 49
86.0%
1 6
 
10.5%
2 2
 
3.5%

Length

2023-12-12T21:53:06.999280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:53:07.078042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 49
86.0%
1 6
 
10.5%
2 2
 
3.5%

두수(계)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)12.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4304.7895
Minimum0
Maximum200200
Zeros51
Zeros (%)89.5%
Negative0
Negative (%)0.0%
Memory size645.0 B
2023-12-12T21:53:07.153836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile8100
Maximum200200
Range200200
Interquartile range (IQR)0

Descriptive statistics

Standard deviation26672.044
Coefficient of variation (CV)6.1958997
Kurtosis54.678233
Mean4304.7895
Median Absolute Deviation (MAD)0
Skewness7.335606
Sum245373
Variance7.1139793 × 108
MonotonicityNot monotonic
2023-12-12T21:53:07.517809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 51
89.5%
150 1
 
1.8%
22500 1
 
1.8%
6000 1
 
1.8%
200200 1
 
1.8%
23 1
 
1.8%
16500 1
 
1.8%
ValueCountFrequency (%)
0 51
89.5%
23 1
 
1.8%
150 1
 
1.8%
6000 1
 
1.8%
16500 1
 
1.8%
22500 1
 
1.8%
200200 1
 
1.8%
ValueCountFrequency (%)
200200 1
 
1.8%
22500 1
 
1.8%
16500 1
 
1.8%
6000 1
 
1.8%
150 1
 
1.8%
23 1
 
1.8%
0 51
89.5%

농가수(육계)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Memory size588.0 B
0
51 
1
6 

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 51
89.5%
1 6
 
10.5%

Length

2023-12-12T21:53:07.623721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:53:07.711818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 51
89.5%
1 6
 
10.5%

두수(육계)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)8.8%
Missing0
Missing (%)0.0%
Memory size588.0 B
0
53 
150
 
1
6000
 
1
200000
 
1
23
 
1

Length

Max length6
Median length1
Mean length1.1929825
Min length1

Unique

Unique4 ?
Unique (%)7.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 53
93.0%
150 1
 
1.8%
6000 1
 
1.8%
200000 1
 
1.8%
23 1
 
1.8%

Length

2023-12-12T21:53:07.806585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:53:07.937860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 53
93.0%
150 1
 
1.8%
6000 1
 
1.8%
200000 1
 
1.8%
23 1
 
1.8%

농가수(산란계)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Memory size588.0 B
0
54 
1
 
2
2
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)1.8%

Sample

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

Common Values

ValueCountFrequency (%)
0 54
94.7%
1 2
 
3.5%
2 1
 
1.8%

Length

2023-12-12T21:53:08.055231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:53:08.141172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 54
94.7%
1 2
 
3.5%
2 1
 
1.8%

두수(산란계)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Memory size588.0 B
0
54 
22500
 
1
200
 
1
16500
 
1

Length

Max length5
Median length1
Mean length1.1754386
Min length1

Unique

Unique3 ?
Unique (%)5.3%

Sample

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

Common Values

ValueCountFrequency (%)
0 54
94.7%
22500 1
 
1.8%
200 1
 
1.8%
16500 1
 
1.8%

Length

2023-12-12T21:53:08.240991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:53:08.355584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 54
94.7%
22500 1
 
1.8%
200 1
 
1.8%
16500 1
 
1.8%

Interactions

2023-12-12T21:53:05.838146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T21:53:08.440805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
읍면리농가수(계)두수(계)농가수(육계)두수(육계)농가수(산란계)두수(산란계)
읍면1.0001.0000.4050.2520.2710.0690.5970.190
리1.0001.0001.0001.0001.0001.0001.0001.000
농가수(계)0.4051.0001.0000.9380.5920.6870.9040.705
두수(계)0.2521.0000.9381.0000.2140.6980.9871.000
농가수(육계)0.2711.0000.5920.2141.0000.6540.1000.485
두수(육계)0.0691.0000.6870.6980.6541.0000.5000.605
농가수(산란계)0.5971.0000.9040.9870.1000.5001.0001.000
두수(산란계)0.1901.0000.7051.0000.4850.6051.0001.000
2023-12-12T21:53:08.568714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
두수(산란계)두수(육계)농가수(산란계)읍면농가수(육계)농가수(계)
두수(산란계)1.0000.5270.9910.1050.3210.736
두수(육계)0.5271.0000.4260.0000.7620.652
농가수(산란계)0.9910.4261.0000.3010.1630.622
읍면0.1050.0000.3011.0000.2490.177
농가수(육계)0.3210.7620.1630.2491.0000.854
농가수(계)0.7360.6520.6220.1770.8541.000
2023-12-12T21:53:08.660952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
두수(계)읍면농가수(계)농가수(육계)두수(육계)농가수(산란계)두수(산란계)
두수(계)1.0000.0950.6940.3460.6670.8580.991
읍면0.0951.0000.1770.2490.0000.3010.105
농가수(계)0.6940.1771.0000.8540.6520.6220.736
농가수(육계)0.3460.2490.8541.0000.7620.1630.321
두수(육계)0.6670.0000.6520.7621.0000.4260.527
농가수(산란계)0.8580.3010.6220.1630.4261.0000.991
두수(산란계)0.9910.1050.7360.3210.5270.9911.000

Missing values

2023-12-12T21:53:05.962688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T21:53:06.086824image/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

읍면리농가수(계)두수(계)농가수(육계)두수(육계)농가수(산란계)두수(산란계)
0영월읍거운리000000
1영월읍덕포리000000
2영월읍문산리000000
3영월읍방절리000000
4영월읍삼옥리000000
5영월읍연하리000000
6영월읍영흥리000000
7영월읍정양리000000
8영월읍팔괴리1150115000
9영월읍하송리000000
읍면리농가수(계)두수(계)농가수(육계)두수(육계)농가수(산란계)두수(산란계)
47주천면도천리000000
48주천면신일리12312300
49주천면용석리000000
50주천면주천리000000
51주천면판운리11650000116500
52무릉도원면도원리000000
53무릉도원면두산리000000
54무릉도원면무릉리000000
55무릉도원면법흥리000000
56무릉도원면운학리000000