Overview

Dataset statistics

Number of variables14
Number of observations5326
Missing cells250
Missing cells (%)0.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory629.5 KiB
Average record size in memory121.0 B

Variable types

Categorical6
Numeric7
Text1

Dataset

Description시도별 축산업 허가, 등록현황
Author농림축산식품부
URLhttps://data.mafra.go.kr/opendata/data/indexOpenDataDetail.do?data_id=20220216000000001971

Alerts

RGSDE has constant value ""Constant
INDUTY_NM is highly overall correlated with INDUTY_CD and 1 other fieldsHigh correlation
INDUTY_CD is highly overall correlated with INDUTY_NM and 1 other fieldsHigh correlation
LVSTCKSPC_NM is highly overall correlated with LVSTCKSPC_CD and 2 other fieldsHigh correlation
ADMINIST_ATPT_CD is highly overall correlated with ADMINIST_SIGNGU_CD and 1 other fieldsHigh correlation
ADMINIST_SIGNGU_CD is highly overall correlated with ADMINIST_ATPT_CD and 1 other fieldsHigh correlation
LVSTCKSPC_CD is highly overall correlated with LVSTCKSPC_NMHigh correlation
BSN_STTUS_CD is highly overall correlated with BSN_STTUS_NMHigh correlation
PLACE is highly overall correlated with SCALE and 1 other fieldsHigh correlation
SCALE is highly overall correlated with PLACE and 1 other fieldsHigh correlation
DONGSU is highly overall correlated with PLACE and 1 other fieldsHigh correlation
ADMINIST_ATPT_NM is highly overall correlated with ADMINIST_ATPT_CD and 1 other fieldsHigh correlation
BSN_STTUS_NM is highly overall correlated with BSN_STTUS_CDHigh correlation
INDUTY_NM is highly imbalanced (63.2%)Imbalance
INDUTY_CD is highly imbalanced (63.2%)Imbalance
LVSTCKSPC_CD has 250 (4.7%) missing valuesMissing
LVSTCKSPC_CD is highly skewed (γ1 = 31.06226113)Skewed
LVSTCKSPC_CD has 188 (3.5%) zerosZeros
BSN_STTUS_CD has 2076 (39.0%) zerosZeros
SCALE has 191 (3.6%) zerosZeros
DONGSU has 249 (4.7%) zerosZeros

Reproduction

Analysis started2023-12-11 03:07:27.453351
Analysis finished2023-12-11 03:07:36.186556
Duration8.73 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

RGSDE
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size41.7 KiB
20220201
5326 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20220201
2nd row20220201
3rd row20220201
4th row20220201
5th row20220201

Common Values

ValueCountFrequency (%)
20220201 5326
100.0%

Length

2023-12-11T12:07:36.252430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T12:07:36.337595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20220201 5326
100.0%

ADMINIST_ATPT_NM
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size41.7 KiB
경기도
820 
경상북도
733 
전라남도
699 
충청남도
581 
경상남도
545 
Other values (12)
1948 

Length

Max length7
Median length4
Mean length3.9106271
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row강원도
2nd row경상남도
3rd row대구광역시
4th row충청북도
5th row강원도

Common Values

ValueCountFrequency (%)
경기도 820
15.4%
경상북도 733
13.8%
전라남도 699
13.1%
충청남도 581
10.9%
경상남도 545
10.2%
전라북도 502
9.4%
강원도 474
8.9%
충청북도 400
7.5%
인천광역시 123
 
2.3%
대구광역시 84
 
1.6%
Other values (7) 365
6.9%

Length

2023-12-11T12:07:36.450061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기도 820
15.4%
경상북도 733
13.8%
전라남도 699
13.1%
충청남도 581
10.9%
경상남도 545
10.2%
전라북도 502
9.4%
강원도 474
8.9%
충청북도 400
7.5%
인천광역시 123
 
2.3%
대구광역시 84
 
1.6%
Other values (7) 365
6.9%

ADMINIST_ATPT_CD
Real number (ℝ)

HIGH CORRELATION 

Distinct17
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6371554.6
Minimum0
Maximum6500000
Zeros50
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size46.9 KiB
2023-12-11T12:07:36.594504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6280000
Q16420000
median6440000
Q36470000
95-th percentile6480000
Maximum6500000
Range6500000
Interquartile range (IQR)50000

Descriptive statistics

Standard deviation622514.62
Coefficient of variation (CV)0.097702156
Kurtosis100.16151
Mean6371554.6
Median Absolute Deviation (MAD)30000
Skewness-10.069961
Sum3.39349 × 1010
Variance3.8752446 × 1011
MonotonicityNot monotonic
2023-12-11T12:07:36.714935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
6410000 820
15.4%
6470000 733
13.8%
6460000 699
13.1%
6440000 581
10.9%
6480000 545
10.2%
6450000 502
9.4%
6420000 474
8.9%
6430000 400
7.5%
6280000 123
 
2.3%
6270000 84
 
1.6%
Other values (7) 365
6.9%
ValueCountFrequency (%)
0 50
 
0.9%
6110000 7
 
0.1%
6260000 63
 
1.2%
6270000 84
 
1.6%
6280000 123
 
2.3%
6290000 60
 
1.1%
6300000 46
 
0.9%
6310000 66
 
1.2%
6410000 820
15.4%
6420000 474
8.9%
ValueCountFrequency (%)
6500000 73
 
1.4%
6480000 545
10.2%
6470000 733
13.8%
6460000 699
13.1%
6450000 502
9.4%
6440000 581
10.9%
6430000 400
7.5%
6420000 474
8.9%
6410000 820
15.4%
6310000 66
 
1.2%

ADMINIST_SIGNGU_NM
Text

Distinct193
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Memory size41.7 KiB
2023-12-11T12:07:37.119368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length8
Mean length7.8623733
Min length7

Characters and Unicode

Total characters41875
Distinct characters130
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)0.1%

Sample

1st row강원도 양양군
2nd row경상남도 통영시
3rd row대구광역시 달성군
4th row충청북도 단양군
5th row강원도 정선군
ValueCountFrequency (%)
경기도 820
 
7.7%
경상북도 733
 
6.9%
전라남도 699
 
6.6%
충청남도 581
 
5.5%
경상남도 545
 
5.1%
전라북도 502
 
4.7%
강원도 474
 
4.5%
충청북도 400
 
3.8%
인천광역시 123
 
1.2%
대구광역시 84
 
0.8%
Other values (187) 5641
53.2%
2023-12-11T12:07:37.705782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5276
 
12.6%
도 4897
 
11.7%
시 2885
 
6.9%
군 2670
 
6.4%
경 2210
 
5.3%
남 2009
 
4.8%
북 1683
 
4.0%
상 1314
 
3.1%
전 1263
 
3.0%
라 1201
 
2.9%
Other values (120) 16467
39.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 36599
87.4%
Space Separator 5276
 
12.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 4897
 
13.4%
시 2885
 
7.9%
군 2670
 
7.3%
경 2210
 
6.0%
남 2009
 
5.5%
북 1683
 
4.6%
상 1314
 
3.6%
전 1263
 
3.5%
라 1201
 
3.3%
청 1157
 
3.2%
Other values (119) 15310
41.8%
Space Separator
ValueCountFrequency (%)
5276
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 36599
87.4%
Common 5276
 
12.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 4897
 
13.4%
시 2885
 
7.9%
군 2670
 
7.3%
경 2210
 
6.0%
남 2009
 
5.5%
북 1683
 
4.6%
상 1314
 
3.6%
전 1263
 
3.5%
라 1201
 
3.3%
청 1157
 
3.2%
Other values (119) 15310
41.8%
Common
ValueCountFrequency (%)
5276
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 36599
87.4%
ASCII 5276
 
12.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5276
100.0%
Hangul
ValueCountFrequency (%)
도 4897
 
13.4%
시 2885
 
7.9%
군 2670
 
7.3%
경 2210
 
6.0%
남 2009
 
5.5%
북 1683
 
4.6%
상 1314
 
3.6%
전 1263
 
3.5%
라 1201
 
3.3%
청 1157
 
3.2%
Other values (119) 15310
41.8%

ADMINIST_SIGNGU_CD
Real number (ℝ)

HIGH CORRELATION 

Distinct193
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4706838.2
Minimum3000000
Maximum6520000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.9 KiB
2023-12-11T12:07:37.865131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3000000
5-th percentile3572500
Q14260000
median4730000
Q35140000
95-th percentile5670000
Maximum6520000
Range3520000
Interquartile range (IQR)880000

Descriptive statistics

Standard deviation621944.36
Coefficient of variation (CV)0.13213634
Kurtosis-0.10760277
Mean4706838.2
Median Absolute Deviation (MAD)440000
Skewness0.005766237
Sum2.506862 × 1010
Variance3.8681479 × 1011
MonotonicityNot monotonic
2023-12-11T12:07:38.058888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5710000 57
 
1.1%
4830000 53
 
1.0%
5690000 50
 
0.9%
4690000 48
 
0.9%
4610000 48
 
0.9%
4530000 46
 
0.9%
4490000 46
 
0.9%
4470000 46
 
0.9%
5530000 46
 
0.9%
4070000 46
 
0.9%
Other values (183) 4840
90.9%
ValueCountFrequency (%)
3000000 1
 
< 0.1%
3150000 1
 
< 0.1%
3160000 1
 
< 0.1%
3210000 3
 
0.1%
3220000 1
 
< 0.1%
3330000 13
0.2%
3340000 2
 
< 0.1%
3350000 8
 
0.2%
3360000 19
0.4%
3400000 21
0.4%
ValueCountFrequency (%)
6520000 31
0.6%
6510000 42
0.8%
5710000 57
1.1%
5700000 44
0.8%
5690000 50
0.9%
5680000 41
0.8%
5670000 31
0.6%
5600000 42
0.8%
5590000 34
0.6%
5580000 8
 
0.2%

INDUTY_NM
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size41.7 KiB
가축사육업
4608 
종축업
468 
부화업
 
178
정액등처리업
 
72

Length

Max length27
Median length25
Mean length25.229065
Min length24

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
가축사육업 4608
86.5%
종축업 468
 
8.8%
부화업 178
 
3.3%
정액등처리업 72
 
1.4%

Length

2023-12-11T12:07:38.247087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T12:07:38.356441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
가축사육업 4608
86.5%
종축업 468
 
8.8%
부화업 178
 
3.3%
정액등처리업 72
 
1.4%

INDUTY_CD
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size41.7 KiB
33
4608 
2
468 
1
 
178
41
 
72

Length

Max length2
Median length2
Mean length1.8787082
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row33
2nd row33
3rd row33
4th row33
5th row33

Common Values

ValueCountFrequency (%)
33 4608
86.5%
2 468
 
8.8%
1 178
 
3.3%
41 72
 
1.4%

Length

2023-12-11T12:07:38.476114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T12:07:38.589766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
33 4608
86.5%
2 468
 
8.8%
1 178
 
3.3%
41 72
 
1.4%

LVSTCKSPC_NM
Categorical

HIGH CORRELATION 

Distinct26
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size41.7 KiB
한우
683 
돼지
575 
육계
517 
젖소
472 
산란계
464 
Other values (21)
2615 

Length

Max length6
Median length2
Mean length2.3409688
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row육계
2nd row산란계
3rd row염소
4th row염소
5th row면양

Common Values

ValueCountFrequency (%)
한우 683
12.8%
돼지 575
10.8%
육계 517
9.7%
젖소 472
8.9%
산란계 464
8.7%
육우 391
 
7.3%
산양 322
 
6.0%
염소 301
 
5.7%
오리 293
 
5.5%
<NA> 278
 
5.2%
Other values (16) 1030
19.3%

Length

2023-12-11T12:07:38.704614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
한우 683
12.8%
돼지 575
10.8%
육계 517
9.7%
젖소 472
8.9%
산란계 464
8.7%
육우 391
 
7.3%
산양 322
 
6.0%
염소 301
 
5.7%
오리 293
 
5.5%
na 278
 
5.2%
Other values (16) 1030
19.3%

LVSTCKSPC_CD
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct29
Distinct (%)0.6%
Missing250
Missing (%)4.7%
Infinite0
Infinite (%)0.0%
Mean47.194641
Minimum0
Maximum9999
Zeros188
Zeros (%)3.5%
Negative0
Negative (%)0.0%
Memory size46.9 KiB
2023-12-11T12:07:38.846941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q111
median20
Q350
95-th percentile150
Maximum9999
Range9999
Interquartile range (IQR)39

Descriptive statistics

Standard deviation315.06636
Coefficient of variation (CV)6.6758926
Kurtosis978.85294
Mean47.194641
Median Absolute Deviation (MAD)10
Skewness31.062261
Sum239560
Variance99266.811
MonotonicityNot monotonic
2023-12-11T12:07:38.970309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
10 683
12.8%
20 575
10.8%
31 517
9.7%
12 472
8.9%
30 464
8.7%
11 391
7.3%
60 322
6.0%
150 301
 
5.7%
50 293
 
5.5%
80 255
 
4.8%
Other values (19) 803
15.1%
(Missing) 250
 
4.7%
ValueCountFrequency (%)
0 188
 
3.5%
1 192
 
3.6%
2 88
 
1.7%
10 683
12.8%
11 391
7.3%
12 472
8.9%
20 575
10.8%
30 464
8.7%
31 517
9.7%
32 45
 
0.8%
ValueCountFrequency (%)
9999 5
 
0.1%
200 4
 
0.1%
190 14
 
0.3%
180 2
 
< 0.1%
170 1
 
< 0.1%
150 301
5.7%
140 4
 
0.1%
130 33
 
0.6%
120 47
 
0.9%
110 70
 
1.3%

BSN_STTUS_NM
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size41.7 KiB
정상
2076 
폐업
1771 
휴업
739 
말소
672 
행정처분
 
61

Length

Max length4
Median length2
Mean length2.0255351
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row정상
2nd row폐업
3rd row폐업
4th row정상
5th row폐업

Common Values

ValueCountFrequency (%)
정상 2076
39.0%
폐업 1771
33.3%
휴업 739
 
13.9%
말소 672
 
12.6%
행정처분 61
 
1.1%
<NA> 7
 
0.1%

Length

2023-12-11T12:07:39.167438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T12:07:39.328581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
정상 2076
39.0%
폐업 1771
33.3%
휴업 739
 
13.9%
말소 672
 
12.6%
행정처분 61
 
1.1%
na 7
 
0.1%

BSN_STTUS_CD
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3494179
Minimum0
Maximum5
Zeros2076
Zeros (%)39.0%
Negative0
Negative (%)0.0%
Memory size46.9 KiB
2023-12-11T12:07:39.453998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile4
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3427414
Coefficient of variation (CV)0.99505231
Kurtosis-0.56811602
Mean1.3494179
Median Absolute Deviation (MAD)1
Skewness0.65834827
Sum7187
Variance1.8029546
MonotonicityNot monotonic
2023-12-11T12:07:39.573185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 2076
39.0%
2 1771
33.3%
1 739
 
13.9%
4 672
 
12.6%
3 61
 
1.1%
5 7
 
0.1%
ValueCountFrequency (%)
0 2076
39.0%
1 739
 
13.9%
2 1771
33.3%
3 61
 
1.1%
4 672
 
12.6%
5 7
 
0.1%
ValueCountFrequency (%)
5 7
 
0.1%
4 672
 
12.6%
3 61
 
1.1%
2 1771
33.3%
1 739
 
13.9%
0 2076
39.0%

PLACE
Real number (ℝ)

HIGH CORRELATION 

Distinct361
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.263612
Minimum1
Maximum3840
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.9 KiB
2023-12-11T12:07:40.014676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median4
Q314
95-th percentile147
Maximum3840
Range3839
Interquartile range (IQR)13

Descriptive statistics

Standard deviation146.28091
Coefficient of variation (CV)4.0338207
Kurtosis143.02279
Mean36.263612
Median Absolute Deviation (MAD)3
Skewness9.7192231
Sum193140
Variance21398.104
MonotonicityNot monotonic
2023-12-11T12:07:40.207923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1459
27.4%
2 684
12.8%
3 435
 
8.2%
4 292
 
5.5%
5 204
 
3.8%
6 188
 
3.5%
7 148
 
2.8%
8 118
 
2.2%
10 97
 
1.8%
9 93
 
1.7%
Other values (351) 1608
30.2%
ValueCountFrequency (%)
1 1459
27.4%
2 684
12.8%
3 435
 
8.2%
4 292
 
5.5%
5 204
 
3.8%
6 188
 
3.5%
7 148
 
2.8%
8 118
 
2.2%
9 93
 
1.7%
10 97
 
1.8%
ValueCountFrequency (%)
3840 1
< 0.1%
2129 1
< 0.1%
1902 1
< 0.1%
1771 1
< 0.1%
1725 1
< 0.1%
1708 1
< 0.1%
1671 1
< 0.1%
1624 1
< 0.1%
1595 1
< 0.1%
1594 1
< 0.1%

SCALE
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct4654
Distinct (%)87.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29630.083
Minimum0
Maximum1721100.2
Zeros191
Zeros (%)3.6%
Negative0
Negative (%)0.0%
Memory size46.9 KiB
2023-12-11T12:07:40.363057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile39.025
Q1764.1
median3560.2
Q315902.375
95-th percentile140351.78
Maximum1721100.2
Range1721100.2
Interquartile range (IQR)15138.275

Descriptive statistics

Standard deviation95319.373
Coefficient of variation (CV)3.2169796
Kurtosis75.725482
Mean29630.083
Median Absolute Deviation (MAD)3328.79
Skewness7.5093952
Sum1.5780982 × 108
Variance9.0857829 × 109
MonotonicityNot monotonic
2023-12-11T12:07:40.525866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 191
 
3.6%
330.0 14
 
0.3%
150.0 11
 
0.2%
300.0 11
 
0.2%
165.0 10
 
0.2%
50.0 10
 
0.2%
198.0 10
 
0.2%
400.0 9
 
0.2%
396.0 9
 
0.2%
60.0 8
 
0.2%
Other values (4644) 5043
94.7%
ValueCountFrequency (%)
0.0 191
3.6%
1.0 1
 
< 0.1%
3.06 1
 
< 0.1%
5.0 1
 
< 0.1%
8.45 1
 
< 0.1%
10.0 8
 
0.2%
10.85 1
 
< 0.1%
12.0 2
 
< 0.1%
13.22 1
 
< 0.1%
15.0 6
 
0.1%
ValueCountFrequency (%)
1721100.16 1
< 0.1%
1402369.69 1
< 0.1%
1249479.31 1
< 0.1%
1219037.8 1
< 0.1%
1172416.75 1
< 0.1%
1120168.42 1
< 0.1%
1092941.06 1
< 0.1%
1044038.37 1
< 0.1%
1036824.45 1
< 0.1%
1022320.34 1
< 0.1%

DONGSU
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct530
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75.82163
Minimum0
Maximum5623
Zeros249
Zeros (%)4.7%
Negative0
Negative (%)0.0%
Memory size46.9 KiB
2023-12-11T12:07:40.687302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median10
Q339
95-th percentile375.5
Maximum5623
Range5623
Interquartile range (IQR)36

Descriptive statistics

Standard deviation241.07691
Coefficient of variation (CV)3.1795268
Kurtosis101.37856
Mean75.82163
Median Absolute Deviation (MAD)9
Skewness8.0810889
Sum403826
Variance58118.075
MonotonicityNot monotonic
2023-12-11T12:07:40.854642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 523
 
9.8%
2 408
 
7.7%
3 301
 
5.7%
4 250
 
4.7%
0 249
 
4.7%
5 238
 
4.5%
6 196
 
3.7%
7 157
 
2.9%
8 138
 
2.6%
9 119
 
2.2%
Other values (520) 2747
51.6%
ValueCountFrequency (%)
0 249
4.7%
1 523
9.8%
2 408
7.7%
3 301
5.7%
4 250
4.7%
5 238
4.5%
6 196
 
3.7%
7 157
 
2.9%
8 138
 
2.6%
9 119
 
2.2%
ValueCountFrequency (%)
5623 1
< 0.1%
3580 1
< 0.1%
3175 1
< 0.1%
2987 1
< 0.1%
2833 1
< 0.1%
2674 1
< 0.1%
2593 1
< 0.1%
2547 1
< 0.1%
2402 1
< 0.1%
2328 1
< 0.1%

Interactions

2023-12-11T12:07:34.761259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:29.176207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:30.155621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:31.085069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:32.007970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:32.942770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:33.789578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:34.932286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:29.303663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:30.310349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:31.223329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:32.108779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:33.050979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:33.897212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:35.083071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:29.447418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:30.451953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:31.374581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:32.452529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:33.172594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:34.010923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:35.231677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:29.588766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:30.562067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:31.511485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:32.560937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:33.310008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:34.168916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:35.367275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:29.742910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:30.668819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:31.644956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:32.644950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:33.435776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:34.332094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:35.527187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:29.883872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:30.813363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:31.790088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:32.739677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:33.545918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:34.484044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:35.665672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:30.023561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:30.956207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:31.901396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:32.841678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:33.683686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:34.633179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T12:07:41.000186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ADMINIST_ATPT_NMADMINIST_ATPT_CDADMINIST_SIGNGU_CDINDUTY_NMINDUTY_CDLVSTCKSPC_NMLVSTCKSPC_CDBSN_STTUS_NMBSN_STTUS_CDPLACESCALEDONGSU
ADMINIST_ATPT_NM1.0001.0000.9560.1300.1300.1090.0000.1100.1080.0000.0000.000
ADMINIST_ATPT_CD1.0001.0000.3110.0000.0000.0000.0000.0800.1340.0000.0000.000
ADMINIST_SIGNGU_CD0.9560.3111.0000.1080.1080.1260.0000.0730.0790.0000.0180.010
INDUTY_NM0.1300.0000.1081.0001.0001.0000.0000.1100.1380.0000.0220.039
INDUTY_CD0.1300.0000.1081.0001.0001.0000.0000.1100.1380.0000.0220.039
LVSTCKSPC_NM0.1090.0000.1261.0001.0001.000NaN0.3850.3330.3100.2000.276
LVSTCKSPC_CD0.0000.0000.0000.0000.000NaN1.0000.0230.0340.0000.0000.000
BSN_STTUS_NM0.1100.0800.0730.1100.1100.3850.0231.0001.0000.0860.2310.111
BSN_STTUS_CD0.1080.1340.0790.1380.1380.3330.0341.0001.0000.0780.1620.105
PLACE0.0000.0000.0000.0000.0000.3100.0000.0860.0781.0000.8440.868
SCALE0.0000.0000.0180.0220.0220.2000.0000.2310.1620.8441.0000.879
DONGSU0.0000.0000.0100.0390.0390.2760.0000.1110.1050.8680.8791.000
2023-12-11T12:07:41.180546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
BSN_STTUS_NMINDUTY_NMINDUTY_CDLVSTCKSPC_NMADMINIST_ATPT_NM
BSN_STTUS_NM1.0000.0900.0900.1760.057
INDUTY_NM0.0901.0001.0000.9980.073
INDUTY_CD0.0901.0001.0000.9980.073
LVSTCKSPC_NM0.1760.9980.9981.0000.032
ADMINIST_ATPT_NM0.0570.0730.0730.0321.000
2023-12-11T12:07:41.305691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ADMINIST_ATPT_CDADMINIST_SIGNGU_CDLVSTCKSPC_CDBSN_STTUS_CDPLACESCALEDONGSUADMINIST_ATPT_NMINDUTY_NMINDUTY_CDLVSTCKSPC_NMBSN_STTUS_NM
ADMINIST_ATPT_CD1.0000.749-0.004-0.0270.0240.0910.0490.9990.0000.0000.0000.097
ADMINIST_SIGNGU_CD0.7491.000-0.013-0.0050.0570.1160.0770.8050.0690.0690.0480.043
LVSTCKSPC_CD-0.004-0.0131.000-0.158-0.214-0.365-0.3330.0000.0000.0001.0000.028
BSN_STTUS_CD-0.027-0.005-0.1581.000-0.099-0.129-0.0740.0510.0890.0890.1561.000
PLACE0.0240.057-0.214-0.0991.0000.7990.8890.0000.0000.0000.1370.055
SCALE0.0910.116-0.365-0.1290.7991.0000.9290.0000.0130.0130.0720.098
DONGSU0.0490.077-0.333-0.0740.8890.9291.0000.0000.0170.0170.1130.068
ADMINIST_ATPT_NM0.9990.8050.0000.0510.0000.0000.0001.0000.0730.0730.0320.057
INDUTY_NM0.0000.0690.0000.0890.0000.0130.0170.0731.0001.0000.9980.090
INDUTY_CD0.0000.0690.0000.0890.0000.0130.0170.0731.0001.0000.9980.090
LVSTCKSPC_NM0.0000.0481.0000.1560.1370.0720.1130.0320.9980.9981.0000.176
BSN_STTUS_NM0.0970.0430.0281.0000.0550.0980.0680.0570.0900.0900.1761.000

Missing values

2023-12-11T12:07:35.850645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T12:07:36.098780image/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

RGSDEADMINIST_ATPT_NMADMINIST_ATPT_CDADMINIST_SIGNGU_NMADMINIST_SIGNGU_CDINDUTY_NMINDUTY_CDLVSTCKSPC_NMLVSTCKSPC_CDBSN_STTUS_NMBSN_STTUS_CDPLACESCALEDONGSU
020220201강원도6420000강원도 양양군4350000가축사육업33육계31정상08814.8910
120220201경상남도6480000경상남도 통영시5330000가축사육업33산란계30폐업2171474.5120
220220201대구광역시6270000대구광역시 달성군3480000가축사육업33염소150폐업23728.010
320220201충청북도6430000충청북도 단양군4480000가축사육업33염소150정상061146.07
420220201강원도6420000강원도 정선군4290000가축사육업33면양70폐업212983.01
520220201전라북도6450000전라북도 남원시4700000가축사육업33염소150정상03414831.0339
620220201충청남도6440000충청남도 보령시4510000가축사육업33염소150정상03112048.6254
720220201충청남도6440000충청남도 보령시4510000가축사육업33염소150폐업26625.08
820220201경상북도6470000경상북도 예천군5230000가축사육업33염소150정상07513595.6898
920220201경상남도6480000경상남도 밀양시5360000가축사육업33사슴80정상01270.01
RGSDEADMINIST_ATPT_NMADMINIST_ATPT_CDADMINIST_SIGNGU_NMADMINIST_SIGNGU_CDINDUTY_NMINDUTY_CDLVSTCKSPC_NMLVSTCKSPC_CDBSN_STTUS_NMBSN_STTUS_CDPLACESCALEDONGSU
531620220201전라남도6460000전라남도 영광군4970000가축사육업33돼지20말소4149.01
531720220201부산광역시6260000부산광역시 기장군3400000가축사육업33산란계30폐업221500.06
531820220201강원도6420000강원도 동해시4210000가축사육업33돼지20폐업231756.529
531920220201경상남도6480000경상남도 거창군5470000가축사육업33육우11폐업211422.04
532020220201경상북도6470000경상북도 봉화군5240000가축사육업33산란계30말소42567.168
532120220201경상북도6470000경상북도 청송군5160000가축사육업33산란계30말소434452.05
532220220201경상남도6480000경상남도 남해군5430000가축사육업33오리50폐업21578.04
532320220201경기도6410000경기도 고양시3940000가축사육업33산란계30말소431240.05
532420220201경상북도6470000경상북도 청송군5160000가축사육업33산양60정상02726.03
532520220201경기도6410000경기도 이천시4070000가축사육업33면양70정상01104.282