Overview

Dataset statistics

Number of variables14
Number of observations5364
Missing cells250
Missing cells (%)0.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory634.0 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 (63.3%)Imbalance
INDUTY_CD is highly imbalanced (63.3%)Imbalance
LVSTCKSPC_CD has 250 (4.7%) missing valuesMissing
LVSTCKSPC_CD is highly skewed (γ1 = 31.16829926)Skewed
LVSTCKSPC_CD has 188 (3.5%) zerosZeros
BSN_STTUS_CD has 2067 (38.5%) zerosZeros
SCALE has 193 (3.6%) zerosZeros
DONGSU has 251 (4.7%) zerosZeros

Reproduction

Analysis started2023-12-11 03:07:44.047713
Analysis finished2023-12-11 03:07:50.820985
Duration6.77 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

RGSDE
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size42.0 KiB
20220601
5364 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20220601
2nd row20220601
3rd row20220601
4th row20220601
5th row20220601

Common Values

ValueCountFrequency (%)
20220601 5364
100.0%

Length

2023-12-11T12:07:50.892314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T12:07:50.983995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20220601 5364
100.0%

ADMINIST_ATPT_NM
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size42.0 KiB
경기도
828 
경상북도
734 
전라남도
707 
충청남도
581 
경상남도
546 
Other values (12)
1968 

Length

Max length7
Median length4
Mean length3.9101417
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row전라남도
2nd row대구광역시
3rd row경상북도
4th row경상북도
5th row경기도

Common Values

ValueCountFrequency (%)
경기도 828
15.4%
경상북도 734
13.7%
전라남도 707
13.2%
충청남도 581
10.8%
경상남도 546
10.2%
전라북도 504
9.4%
강원도 478
8.9%
충청북도 410
7.6%
인천광역시 124
 
2.3%
대구광역시 85
 
1.6%
Other values (7) 367
6.8%

Length

2023-12-11T12:07:51.084992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기도 828
15.4%
경상북도 734
13.7%
전라남도 707
13.2%
충청남도 581
10.8%
경상남도 546
10.2%
전라북도 504
9.4%
강원도 478
8.9%
충청북도 410
7.6%
인천광역시 124
 
2.3%
대구광역시 85
 
1.6%
Other values (7) 367
6.8%

ADMINIST_ATPT_CD
Real number (ℝ)

HIGH CORRELATION 

Distinct17
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6371923.9
Minimum0
Maximum6500000
Zeros50
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size47.3 KiB
2023-12-11T12:07:51.198340image/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 deviation620335.11
Coefficient of variation (CV)0.097354444
Kurtosis100.90096
Mean6371923.9
Median Absolute Deviation (MAD)30000
Skewness-10.10612
Sum3.4179 × 1010
Variance3.8481565 × 1011
MonotonicityNot monotonic
2023-12-11T12:07:51.329828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
6410000 828
15.4%
6470000 734
13.7%
6460000 707
13.2%
6440000 581
10.8%
6480000 546
10.2%
6450000 504
9.4%
6420000 478
8.9%
6430000 410
7.6%
6280000 124
 
2.3%
6270000 85
 
1.6%
Other values (7) 367
6.8%
ValueCountFrequency (%)
0 50
 
0.9%
6110000 7
 
0.1%
6260000 64
 
1.2%
6270000 85
 
1.6%
6280000 124
 
2.3%
6290000 60
 
1.1%
6300000 46
 
0.9%
6310000 66
 
1.2%
6410000 828
15.4%
6420000 478
8.9%
ValueCountFrequency (%)
6500000 74
 
1.4%
6480000 546
10.2%
6470000 734
13.7%
6460000 707
13.2%
6450000 504
9.4%
6440000 581
10.8%
6430000 410
7.6%
6420000 478
8.9%
6410000 828
15.4%
6310000 66
 
1.2%

ADMINIST_SIGNGU_NM
Text

Distinct193
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Memory size42.0 KiB
2023-12-11T12:07:51.810141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length8
Mean length7.8620433
Min length7

Characters and Unicode

Total characters42172
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 row전라남도 곡성군
2nd row대구광역시 달성군
3rd row경상북도 경산시
4th row경상북도 경주시
5th row경기도 고양시
ValueCountFrequency (%)
경기도 828
 
7.8%
경상북도 734
 
6.9%
전라남도 707
 
6.6%
충청남도 581
 
5.4%
경상남도 546
 
5.1%
전라북도 504
 
4.7%
강원도 478
 
4.5%
충청북도 410
 
3.8%
인천광역시 124
 
1.2%
대구광역시 85
 
0.8%
Other values (187) 5681
53.2%
2023-12-11T12:07:52.442086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5314
 
12.6%
도 4935
 
11.7%
시 2906
 
6.9%
군 2690
 
6.4%
경 2221
 
5.3%
남 2020
 
4.8%
북 1696
 
4.0%
상 1316
 
3.1%
전 1273
 
3.0%
라 1211
 
2.9%
Other values (120) 16590
39.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 36858
87.4%
Space Separator 5314
 
12.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 4935
 
13.4%
시 2906
 
7.9%
군 2690
 
7.3%
경 2221
 
6.0%
남 2020
 
5.5%
북 1696
 
4.6%
상 1316
 
3.6%
전 1273
 
3.5%
라 1211
 
3.3%
청 1169
 
3.2%
Other values (119) 15421
41.8%
Space Separator
ValueCountFrequency (%)
5314
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 36858
87.4%
Common 5314
 
12.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 4935
 
13.4%
시 2906
 
7.9%
군 2690
 
7.3%
경 2221
 
6.0%
남 2020
 
5.5%
북 1696
 
4.6%
상 1316
 
3.6%
전 1273
 
3.5%
라 1211
 
3.3%
청 1169
 
3.2%
Other values (119) 15421
41.8%
Common
ValueCountFrequency (%)
5314
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 36858
87.4%
ASCII 5314
 
12.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5314
100.0%
Hangul
ValueCountFrequency (%)
도 4935
 
13.4%
시 2906
 
7.9%
군 2690
 
7.3%
경 2221
 
6.0%
남 2020
 
5.5%
북 1696
 
4.6%
상 1316
 
3.6%
전 1273
 
3.5%
라 1211
 
3.3%
청 1169
 
3.2%
Other values (119) 15421
41.8%

ADMINIST_SIGNGU_CD
Real number (ℝ)

HIGH CORRELATION 

Distinct193
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4707479.5
Minimum3000000
Maximum6520000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size47.3 KiB
2023-12-11T12:07:52.616077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3000000
5-th percentile3571500
Q14267500
median4730000
Q35140000
95-th percentile5670000
Maximum6520000
Range3520000
Interquartile range (IQR)872500

Descriptive statistics

Standard deviation622175.29
Coefficient of variation (CV)0.13216739
Kurtosis-0.10306741
Mean4707479.5
Median Absolute Deviation (MAD)440000
Skewness0.0072021925
Sum2.525092 × 1010
Variance3.8710209 × 1011
MonotonicityNot monotonic
2023-12-11T12:07:52.791719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5710000 57
 
1.1%
4830000 55
 
1.0%
5690000 50
 
0.9%
4610000 48
 
0.9%
4470000 48
 
0.9%
4450000 47
 
0.9%
4690000 47
 
0.9%
4490000 46
 
0.9%
4530000 46
 
0.9%
5530000 46
 
0.9%
Other values (183) 4874
90.9%
ValueCountFrequency (%)
3000000 1
 
< 0.1%
3150000 1
 
< 0.1%
3160000 1
 
< 0.1%
3210000 3
 
0.1%
3220000 1
 
< 0.1%
3330000 14
0.3%
3340000 2
 
< 0.1%
3350000 8
 
0.1%
3360000 19
0.4%
3400000 21
0.4%
ValueCountFrequency (%)
6520000 31
0.6%
6510000 43
0.8%
5710000 57
1.1%
5700000 46
0.9%
5690000 50
0.9%
5680000 41
0.8%
5670000 31
0.6%
5600000 43
0.8%
5590000 38
0.7%
5580000 8
 
0.1%

INDUTY_NM
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size42.0 KiB
가축사육업
4645 
종축업
469 
부화업
 
178
정액등처리업
 
72

Length

Max length27
Median length25
Mean length25.227815
Min length24

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
가축사육업 4645
86.6%
종축업 469
 
8.7%
부화업 178
 
3.3%
정액등처리업 72
 
1.3%

Length

2023-12-11T12:07:52.966613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T12:07:53.104868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
가축사육업 4645
86.6%
종축업 469
 
8.7%
부화업 178
 
3.3%
정액등처리업 72
 
1.3%

INDUTY_CD
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size42.0 KiB
33
4645 
2
469 
1
 
178
41
 
72

Length

Max length2
Median length2
Mean length1.8793811
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
33 4645
86.6%
2 469
 
8.7%
1 178
 
3.3%
41 72
 
1.3%

Length

2023-12-11T12:07:53.249202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T12:07:53.390883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
33 4645
86.6%
2 469
 
8.7%
1 178
 
3.3%
41 72
 
1.3%

LVSTCKSPC_NM
Categorical

HIGH CORRELATION 

Distinct26
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size42.0 KiB
한우
684 
돼지
576 
육계
524 
젖소
472 
산란계
462 
Other values (21)
2646 

Length

Max length6
Median length2
Mean length2.3413497
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row돼지
2nd row젖소
3rd row돼지
4th row육우
5th row젖소

Common Values

ValueCountFrequency (%)
한우 684
12.8%
돼지 576
10.7%
육계 524
9.8%
젖소 472
8.8%
산란계 462
8.6%
육우 391
 
7.3%
산양 332
 
6.2%
염소 307
 
5.7%
오리 292
 
5.4%
<NA> 280
 
5.2%
Other values (16) 1044
19.5%

Length

2023-12-11T12:07:53.518943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
한우 684
12.8%
돼지 576
10.7%
육계 524
9.8%
젖소 472
8.8%
산란계 462
8.6%
육우 391
 
7.3%
산양 332
 
6.2%
염소 307
 
5.7%
오리 292
 
5.4%
na 280
 
5.2%
Other values (16) 1044
19.5%

LVSTCKSPC_CD
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct29
Distinct (%)0.6%
Missing250
Missing (%)4.7%
Infinite0
Infinite (%)0.0%
Mean47.366641
Minimum0
Maximum9999
Zeros188
Zeros (%)3.5%
Negative0
Negative (%)0.0%
Memory size47.3 KiB
2023-12-11T12:07:53.738765image/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 deviation313.9214
Coefficient of variation (CV)6.6274787
Kurtosis985.77461
Mean47.366641
Median Absolute Deviation (MAD)10
Skewness31.168299
Sum242233
Variance98546.647
MonotonicityNot monotonic
2023-12-11T12:07:53.914725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
10 684
12.8%
20 576
10.7%
31 524
9.8%
12 472
8.8%
30 462
8.6%
11 391
7.3%
60 332
6.2%
150 307
 
5.7%
50 292
 
5.4%
80 261
 
4.9%
Other values (19) 813
15.2%
(Missing) 250
 
4.7%
ValueCountFrequency (%)
0 188
 
3.5%
1 192
 
3.6%
2 89
 
1.7%
10 684
12.8%
11 391
7.3%
12 472
8.8%
20 576
10.7%
30 462
8.6%
31 524
9.8%
32 50
 
0.9%
ValueCountFrequency (%)
9999 5
 
0.1%
200 4
 
0.1%
190 14
 
0.3%
180 2
 
< 0.1%
170 1
 
< 0.1%
150 307
5.7%
140 4
 
0.1%
130 33
 
0.6%
120 48
 
0.9%
110 71
 
1.3%

BSN_STTUS_NM
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size42.0 KiB
정상
2067 
폐업
1797 
휴업
747 
말소
686 
행정처분
 
60

Length

Max length4
Median length2
Mean length2.0249814
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row행정처분
2nd row말소
3rd row폐업
4th row휴업
5th row정상

Common Values

ValueCountFrequency (%)
정상 2067
38.5%
폐업 1797
33.5%
휴업 747
 
13.9%
말소 686
 
12.8%
행정처분 60
 
1.1%
<NA> 7
 
0.1%

Length

2023-12-11T12:07:54.090514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T12:07:54.231048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
정상 2067
38.5%
폐업 1797
33.5%
휴업 747
 
13.9%
말소 686
 
12.8%
행정처분 60
 
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%
Mean1.3609247
Minimum0
Maximum5
Zeros2067
Zeros (%)38.5%
Negative0
Negative (%)0.0%
Memory size47.3 KiB
2023-12-11T12:07:54.348578image/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 deviation1.3442684
Coefficient of variation (CV)0.98776107
Kurtosis-0.5813135
Mean1.3609247
Median Absolute Deviation (MAD)1
Skewness0.64766261
Sum7300
Variance1.8070576
MonotonicityNot monotonic
2023-12-11T12:07:54.730026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 2067
38.5%
2 1797
33.5%
1 747
 
13.9%
4 686
 
12.8%
3 60
 
1.1%
5 7
 
0.1%
ValueCountFrequency (%)
0 2067
38.5%
1 747
 
13.9%
2 1797
33.5%
3 60
 
1.1%
4 686
 
12.8%
5 7
 
0.1%
ValueCountFrequency (%)
5 7
 
0.1%
4 686
 
12.8%
3 60
 
1.1%
2 1797
33.5%
1 747
 
13.9%
0 2067
38.5%

PLACE
Real number (ℝ)

HIGH CORRELATION 

Distinct352
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.217189
Minimum1
Maximum3844
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size47.3 KiB
2023-12-11T12:07:54.885529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median4
Q314
95-th percentile147.85
Maximum3844
Range3843
Interquartile range (IQR)13

Descriptive statistics

Standard deviation144.94984
Coefficient of variation (CV)4.002239
Kurtosis145.10547
Mean36.217189
Median Absolute Deviation (MAD)3
Skewness9.7214413
Sum194269
Variance21010.457
MonotonicityNot monotonic
2023-12-11T12:07:55.080507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1466
27.3%
2 685
12.8%
3 454
 
8.5%
4 289
 
5.4%
5 203
 
3.8%
6 185
 
3.4%
7 147
 
2.7%
8 121
 
2.3%
9 110
 
2.1%
10 89
 
1.7%
Other values (342) 1615
30.1%
ValueCountFrequency (%)
1 1466
27.3%
2 685
12.8%
3 454
 
8.5%
4 289
 
5.4%
5 203
 
3.8%
6 185
 
3.4%
7 147
 
2.7%
8 121
 
2.3%
9 110
 
2.1%
10 89
 
1.7%
ValueCountFrequency (%)
3844 1
< 0.1%
2139 1
< 0.1%
1869 1
< 0.1%
1761 1
< 0.1%
1730 1
< 0.1%
1717 1
< 0.1%
1642 1
< 0.1%
1613 1
< 0.1%
1599 1
< 0.1%
1502 1
< 0.1%

SCALE
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct4678
Distinct (%)87.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29703.541
Minimum0
Maximum1711377.4
Zeros193
Zeros (%)3.6%
Negative0
Negative (%)0.0%
Memory size47.3 KiB
2023-12-11T12:07:55.265357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile36
Q1761.935
median3513.97
Q315847.305
95-th percentile140881.97
Maximum1711377.4
Range1711377.4
Interquartile range (IQR)15085.37

Descriptive statistics

Standard deviation95739.174
Coefficient of variation (CV)3.223157
Kurtosis75.629759
Mean29703.541
Median Absolute Deviation (MAD)3289.47
Skewness7.5172472
Sum1.5932979 × 108
Variance9.1659895 × 109
MonotonicityNot monotonic
2023-12-11T12:07:55.444388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 193
 
3.6%
150.0 13
 
0.2%
330.0 13
 
0.2%
300.0 10
 
0.2%
165.0 10
 
0.2%
198.0 9
 
0.2%
100.0 9
 
0.2%
400.0 9
 
0.2%
50.0 9
 
0.2%
396.0 9
 
0.2%
Other values (4668) 5080
94.7%
ValueCountFrequency (%)
0.0 193
3.6%
1.0 1
 
< 0.1%
3.06 1
 
< 0.1%
5.0 2
 
< 0.1%
8.45 1
 
< 0.1%
10.0 8
 
0.1%
10.85 1
 
< 0.1%
12.0 2
 
< 0.1%
13.22 1
 
< 0.1%
15.0 6
 
0.1%
ValueCountFrequency (%)
1711377.44 1
< 0.1%
1415583.7 1
< 0.1%
1271861.18 1
< 0.1%
1227692.18 1
< 0.1%
1157891.75 1
< 0.1%
1142080.73 1
< 0.1%
1132330.16 1
< 0.1%
1061851.86 1
< 0.1%
1047895.98 1
< 0.1%
1023046.14 1
< 0.1%

DONGSU
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct538
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75.662752
Minimum0
Maximum5623
Zeros251
Zeros (%)4.7%
Negative0
Negative (%)0.0%
Memory size47.3 KiB
2023-12-11T12:07:55.604542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median10
Q339
95-th percentile378.85
Maximum5623
Range5623
Interquartile range (IQR)36

Descriptive statistics

Standard deviation239.17659
Coefficient of variation (CV)3.1610877
Kurtosis102.12022
Mean75.662752
Median Absolute Deviation (MAD)9
Skewness8.0594132
Sum405855
Variance57205.441
MonotonicityNot monotonic
2023-12-11T12:07:55.734943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 521
 
9.7%
2 417
 
7.8%
3 307
 
5.7%
4 256
 
4.8%
0 251
 
4.7%
5 228
 
4.3%
6 189
 
3.5%
7 163
 
3.0%
8 141
 
2.6%
9 127
 
2.4%
Other values (528) 2764
51.5%
ValueCountFrequency (%)
0 251
4.7%
1 521
9.7%
2 417
7.8%
3 307
5.7%
4 256
4.8%
5 228
4.3%
6 189
 
3.5%
7 163
 
3.0%
8 141
 
2.6%
9 127
 
2.4%
ValueCountFrequency (%)
5623 1
< 0.1%
3577 1
< 0.1%
3187 1
< 0.1%
2954 1
< 0.1%
2773 1
< 0.1%
2690 1
< 0.1%
2648 1
< 0.1%
2551 1
< 0.1%
2390 1
< 0.1%
2280 1
< 0.1%

Interactions

2023-12-11T12:07:49.797000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:45.346118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:46.041450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:46.837327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:47.779822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:48.422102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:49.136013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:49.891513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:45.449071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:46.146553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:46.916177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:47.872467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:48.514816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:49.228820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:49.997504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:45.551658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:46.262895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:46.999663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:47.960529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:48.611825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:49.318763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:50.106324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:45.641074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:46.370748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:47.075217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:48.058893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:48.759102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:49.399469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:50.199196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:45.744876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:46.481520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:47.438553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:48.140090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:48.843334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:49.480063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:50.297156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:45.849585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:46.614902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:47.558603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:48.242541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:48.941482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:49.581483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:50.402688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:45.951122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:46.719621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:47.692770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:48.332930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:49.049403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:07:49.678972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T12:07:55.849217image/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_NM1.0001.0000.9560.1320.1320.1110.0000.1120.1100.0000.0000.000
ADMINIST_ATPT_CD1.0001.0000.3080.0000.0000.0000.0000.0800.1360.0000.0000.000
ADMINIST_SIGNGU_CD0.9560.3081.0000.1080.1080.1230.0000.0770.0830.0130.0240.000
INDUTY_NM0.1320.0000.1081.0001.0001.0000.0000.1090.1370.0020.0230.039
INDUTY_CD0.1320.0000.1081.0001.0001.0000.0000.1090.1370.0020.0230.039
LVSTCKSPC_NM0.1110.0000.1231.0001.0001.000NaN0.3760.3250.3150.2030.273
LVSTCKSPC_CD0.0000.0000.0000.0000.000NaN1.0000.0230.0350.0000.0000.000
BSN_STTUS_NM0.1120.0800.0770.1090.1090.3760.0231.0001.0000.0850.2300.111
BSN_STTUS_CD0.1100.1360.0830.1370.1370.3250.0351.0001.0000.0780.1620.106
PLACE0.0000.0000.0130.0020.0020.3150.0000.0850.0781.0000.8550.882
SCALE0.0000.0000.0240.0230.0230.2030.0000.2300.1620.8551.0000.870
DONGSU0.0000.0000.0000.0390.0390.2730.0000.1110.1060.8820.8701.000
2023-12-11T12:07:56.011088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
BSN_STTUS_NMINDUTY_NMINDUTY_CDLVSTCKSPC_NMADMINIST_ATPT_NM
BSN_STTUS_NM1.0000.0890.0890.1710.058
INDUTY_NM0.0891.0001.0000.9980.073
INDUTY_CD0.0891.0001.0000.9980.073
LVSTCKSPC_NM0.1710.9980.9981.0000.032
ADMINIST_ATPT_NM0.0580.0730.0730.0321.000
2023-12-11T12:07:56.160824image/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_CD1.0000.745-0.002-0.0310.0270.0900.0500.9990.0000.0000.0000.098
ADMINIST_SIGNGU_CD0.7451.000-0.010-0.0060.0560.1130.0760.8050.0690.0690.0470.045
LVSTCKSPC_CD-0.002-0.0101.000-0.148-0.219-0.371-0.3390.0000.0000.0001.0000.029
BSN_STTUS_CD-0.031-0.006-0.1481.000-0.095-0.130-0.0740.0520.0880.0880.1521.000
PLACE0.0270.056-0.219-0.0951.0000.7980.8880.0000.0010.0010.1390.054
SCALE0.0900.113-0.371-0.1300.7981.0000.9290.0000.0140.0140.0730.097
DONGSU0.0500.076-0.339-0.0740.8880.9291.0000.0000.0170.0170.1110.068
ADMINIST_ATPT_NM0.9990.8050.0000.0520.0000.0000.0001.0000.0730.0730.0320.058
INDUTY_NM0.0000.0690.0000.0880.0010.0140.0170.0731.0001.0000.9980.089
INDUTY_CD0.0000.0690.0000.0880.0010.0140.0170.0731.0001.0000.9980.089
LVSTCKSPC_NM0.0000.0471.0000.1520.1390.0730.1110.0320.9980.9981.0000.171
BSN_STTUS_NM0.0980.0450.0291.0000.0540.0970.0680.0580.0890.0890.1711.000

Missing values

2023-12-11T12:07:50.548640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T12:07:50.735710image/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
020220601전라남도6460000전라남도 곡성군4860000가축사육업33돼지20행정처분321175.44
120220601대구광역시6270000대구광역시 달성군3480000가축사육업33젖소12말소484855.724
220220601경상북도6470000경상북도 경산시5130000가축사육업33돼지20폐업26061193.22248
320220601경상북도6470000경상북도 경주시5050000가축사육업33육우11휴업132884.07
420220601경기도6410000경기도 고양시3940000가축사육업33젖소12정상04568283.99156
520220601경상북도6470000경상북도 성주군5210000가축사육업33한우10휴업194394.1320
620220601경상북도6470000경상북도 김천시5060000가축사육업33육우11말소41282.241
720220601경기도6410000경기도 파주시4060000가축사육업33육우11정상03935558.6887
820220601전라남도6460000전라남도 무안군4950000가축사육업33염소150정상02311459.9147
920220601전라북도6450000전라북도 임실군4760000가축사육업33돼지20말소42646427.1169
RGSDEADMINIST_ATPT_NMADMINIST_ATPT_CDADMINIST_SIGNGU_NMADMINIST_SIGNGU_CDINDUTY_NMINDUTY_CDLVSTCKSPC_NMLVSTCKSPC_CDBSN_STTUS_NMBSN_STTUS_CDPLACESCALEDONGSU
535420220601전라남도6460000전라남도 나주시4830000가축사육업33오리50휴업1514761.619
535520220601경상북도6470000경상북도 영천시5100000가축사육업33메추리110휴업111362.03
535620220601경상북도6470000경상북도 고령군5200000가축사육업33육우11정상037562.7213
535720220601경상북도6470000경상북도 울진군5250000가축사육업33돼지20말소442446.013
535820220601경상북도6470000경상북도 영천시5100000가축사육업33육계31휴업159196.1619
535920220601경기도6410000경기도 이천시4070000가축사육업33오리50폐업2610360.8532
536020220601충청남도6440000충청남도 예산군4610000가축사육업33육계31말소432714.64
536120220601충청남도6440000충청남도 홍성군4600000가축사육업33젖소12휴업112708.037
536220220601경상북도6470000경상북도 경산시5130000가축사육업33꿩120폐업222133.793
536320220601인천광역시6280000인천광역시 남동구3530000가축사육업33사슴80휴업111622.03