Overview

Dataset statistics

Number of variables7
Number of observations1442
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory83.2 KiB
Average record size in memory59.1 B

Variable types

Text1
Categorical3
Numeric3

Dataset

Description구미시의 가축분뇨 처리시설 운영 현황 최근 3개년 자료입니다.항목명 : 설치주소, 대상구분, 준공여부, 배출시설규모, 처리시설용량, 마리수(합산)
Author경상북도 구미시
URLhttps://www.data.go.kr/data/15126854/fileData.do

Alerts

배출시설규모(합산) is highly overall correlated with 처리시설용량(합산) and 1 other fieldsHigh correlation
처리시설용량(합산) is highly overall correlated with 배출시설규모(합산) and 1 other fieldsHigh correlation
마리수(합산) is highly overall correlated with 배출시설규모(합산) and 1 other fieldsHigh correlation
축종 is highly imbalanced (78.2%)Imbalance
배출시설규모(합산) is highly skewed (γ1 = 37.96842814)Skewed
처리시설용량(합산) has 28 (1.9%) zerosZeros
마리수(합산) has 35 (2.4%) zerosZeros

Reproduction

Analysis started2024-03-14 14:43:46.624499
Analysis finished2024-03-14 14:43:49.191256
Duration2.57 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

설치주소(지번)
Text

Distinct1438
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Memory size11.4 KiB
2024-03-14T23:43:50.329953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length59
Median length55
Mean length24.556172
Min length18

Characters and Unicode

Total characters35410
Distinct characters118
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1434 ?
Unique (%)99.4%

Sample

1st row경상북도 구미시 고아읍 봉한리 106
2nd row경상북도 구미시 도개면 다곡리 161 번지 외 17필지
3rd row경상북도 구미시 고아읍 오로리 490-1 .488-1.490-2.490-5.490-6.686
4th row경상북도 구미시 도개면 다곡리 744-2
5th row경상북도 구미시 고아읍 봉한리 102 번지 외 17필지
ValueCountFrequency (%)
경상북도 1443
18.1%
구미시 1443
18.1%
선산읍 298
 
3.7%
해평면 273
 
3.4%
고아읍 262
 
3.3%
도개면 172
 
2.2%
외 156
 
2.0%
옥성면 134
 
1.7%
산동읍 119
 
1.5%
문량리 90
 
1.1%
Other values (1593) 3562
44.8%
2024-03-14T23:43:52.032230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7979
22.5%
도 1647
 
4.7%
상 1499
 
4.2%
북 1472
 
4.2%
구 1445
 
4.1%
경 1443
 
4.1%
미 1443
 
4.1%
시 1443
 
4.1%
리 1441
 
4.1%
1 1419
 
4.0%
Other values (108) 14179
40.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 19450
54.9%
Space Separator 7979
22.5%
Decimal Number 6809
 
19.2%
Dash Punctuation 777
 
2.2%
Other Punctuation 291
 
0.8%
Close Punctuation 52
 
0.1%
Open Punctuation 52
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 1647
 
8.5%
상 1499
 
7.7%
북 1472
 
7.6%
구 1445
 
7.4%
경 1443
 
7.4%
미 1443
 
7.4%
시 1443
 
7.4%
리 1441
 
7.4%
면 753
 
3.9%
읍 679
 
3.5%
Other values (93) 6185
31.8%
Decimal Number
ValueCountFrequency (%)
1 1419
20.8%
2 850
12.5%
4 682
10.0%
3 660
9.7%
5 611
9.0%
6 556
 
8.2%
0 546
 
8.0%
7 518
 
7.6%
8 491
 
7.2%
9 476
 
7.0%
Space Separator
ValueCountFrequency (%)
7979
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 777
100.0%
Other Punctuation
ValueCountFrequency (%)
. 291
100.0%
Close Punctuation
ValueCountFrequency (%)
) 52
100.0%
Open Punctuation
ValueCountFrequency (%)
( 52
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 19450
54.9%
Common 15960
45.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 1647
 
8.5%
상 1499
 
7.7%
북 1472
 
7.6%
구 1445
 
7.4%
경 1443
 
7.4%
미 1443
 
7.4%
시 1443
 
7.4%
리 1441
 
7.4%
면 753
 
3.9%
읍 679
 
3.5%
Other values (93) 6185
31.8%
Common
ValueCountFrequency (%)
7979
50.0%
1 1419
 
8.9%
2 850
 
5.3%
- 777
 
4.9%
4 682
 
4.3%
3 660
 
4.1%
5 611
 
3.8%
6 556
 
3.5%
0 546
 
3.4%
7 518
 
3.2%
Other values (5) 1362
 
8.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 19450
54.9%
ASCII 15960
45.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
7979
50.0%
1 1419
 
8.9%
2 850
 
5.3%
- 777
 
4.9%
4 682
 
4.3%
3 660
 
4.1%
5 611
 
3.8%
6 556
 
3.5%
0 546
 
3.4%
7 518
 
3.2%
Other values (5) 1362
 
8.5%
Hangul
ValueCountFrequency (%)
도 1647
 
8.5%
상 1499
 
7.7%
북 1472
 
7.6%
구 1445
 
7.4%
경 1443
 
7.4%
미 1443
 
7.4%
시 1443
 
7.4%
리 1441
 
7.4%
면 753
 
3.9%
읍 679
 
3.5%
Other values (93) 6185
31.8%

대상구분
Categorical

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size11.4 KiB
신고대상
827 
허가대상
613 
 
2

Length

Max length4
Median length4
Mean length3.9958391
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
신고대상 827
57.4%
허가대상 613
42.5%
2
 
0.1%

Length

2024-03-14T23:43:52.272202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T23:43:52.466963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
신고대상 827
57.4%
허가대상 613
42.6%

준공여부
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.4 KiB
준공
1242 
미준공
200 

Length

Max length3
Median length2
Mean length2.1386963
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row미준공
2nd row준공
3rd row준공
4th row미준공
5th row준공

Common Values

ValueCountFrequency (%)
준공 1242
86.1%
미준공 200
 
13.9%

Length

2024-03-14T23:43:52.659893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T23:43:52.857328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
준공 1242
86.1%
미준공 200
 
13.9%

배출시설규모(합산)
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct994
Distinct (%)68.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17347.788
Minimum0
Maximum22930000
Zeros7
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size12.8 KiB
2024-03-14T23:43:53.112732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile188.1
Q1388.85
median802.2
Q31627.5
95-th percentile3304
Maximum22930000
Range22930000
Interquartile range (IQR)1238.65

Descriptive statistics

Standard deviation603829.14
Coefficient of variation (CV)34.80727
Kurtosis1441.7331
Mean17347.788
Median Absolute Deviation (MAD)502.2
Skewness37.968428
Sum25015511
Variance3.6460963 × 1011
MonotonicityNot monotonic
2024-03-14T23:43:53.409034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
300.0 37
 
2.6%
800.0 19
 
1.3%
200.0 18
 
1.2%
350.0 17
 
1.2%
400.0 16
 
1.1%
600.0 15
 
1.0%
700.0 14
 
1.0%
250.0 14
 
1.0%
1498.0 10
 
0.7%
1650.0 9
 
0.6%
Other values (984) 1273
88.3%
ValueCountFrequency (%)
0.0 7
0.5%
50.0 1
 
0.1%
60.07 1
 
0.1%
70.0 1
 
0.1%
81.4 1
 
0.1%
86.0 1
 
0.1%
92.16 1
 
0.1%
96.0 2
 
0.1%
100.94 1
 
0.1%
105.0 1
 
0.1%
ValueCountFrequency (%)
22930000.0 1
0.1%
172840.0 1
0.1%
115528.0 1
0.1%
49280.0 1
0.1%
30000.0 1
0.1%
29253.0 1
0.1%
20500.0 1
0.1%
11553.22 1
0.1%
11526.2 1
0.1%
10800.0 1
0.1%

처리시설용량(합산)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct909
Distinct (%)63.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean480.49992
Minimum0
Maximum17424
Zeros28
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size12.8 KiB
2024-03-14T23:43:53.654358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile42.026
Q1143.55
median340.83
Q3588
95-th percentile1123.075
Maximum17424
Range17424
Interquartile range (IQR)444.45

Descriptive statistics

Standard deviation821.28493
Coefficient of variation (CV)1.7092301
Kurtosis177.80048
Mean480.49992
Median Absolute Deviation (MAD)212.48
Skewness11.198818
Sum692880.89
Variance674508.93
MonotonicityNot monotonic
2024-03-14T23:43:53.953799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 28
 
1.9%
360.0 24
 
1.7%
100.0 23
 
1.6%
200.0 19
 
1.3%
240.0 17
 
1.2%
480.0 16
 
1.1%
120.0 14
 
1.0%
60.0 13
 
0.9%
50.0 12
 
0.8%
180.0 12
 
0.8%
Other values (899) 1264
87.7%
ValueCountFrequency (%)
0.0 28
1.9%
0.28 2
 
0.1%
0.3 1
 
0.1%
0.52 1
 
0.1%
2.74 1
 
0.1%
4.0 1
 
0.1%
8.0 2
 
0.1%
10.0 1
 
0.1%
12.0 1
 
0.1%
18.9 1
 
0.1%
ValueCountFrequency (%)
17424.0 1
0.1%
10800.0 1
0.1%
10147.51 1
0.1%
9533.4 1
0.1%
9430.71 1
0.1%
5695.72 1
0.1%
5276.96 1
0.1%
5200.54 1
0.1%
4248.3 1
0.1%
3456.0 1
0.1%

축종
Categorical

IMBALANCE 

Distinct11
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size11.4 KiB
소
1298 
젖소
 
33
 
29
돼지
 
23
개
 
18
Other values (6)
 
41

Length

Max length6
Median length1
Mean length1.0728155
Min length1

Unique

Unique2 ?
Unique (%)0.1%

Sample

1st row돼지
2nd row돼지
3rd row소
4th row소
5th row돼지

Common Values

ValueCountFrequency (%)
소 1298
90.0%
젖소 33
 
2.3%
29
 
2.0%
돼지 23
 
1.6%
개 18
 
1.2%
닭 17
 
1.2%
말 11
 
0.8%
양(염소) 8
 
0.6%
소 외 1종 3
 
0.2%
사슴 1
 
0.1%

Length

2024-03-14T23:43:54.246184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
소 1301
91.7%
젖소 33
 
2.3%
돼지 23
 
1.6%
개 18
 
1.3%
닭 17
 
1.2%
말 11
 
0.8%
양(염소 8
 
0.6%
외 3
 
0.2%
1종 3
 
0.2%
사슴 1
 
0.1%

마리수(합산)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct282
Distinct (%)19.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean375.41331
Minimum0
Maximum40000
Zeros35
Zeros (%)2.4%
Negative0
Negative (%)0.0%
Memory size12.8 KiB
2024-03-14T23:43:54.633780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12
Q129
median65
Q3124
95-th percentile279.95
Maximum40000
Range40000
Interquartile range (IQR)95

Descriptive statistics

Standard deviation2685.2246
Coefficient of variation (CV)7.1527155
Kurtosis147.74872
Mean375.41331
Median Absolute Deviation (MAD)41
Skewness11.681751
Sum541346
Variance7210431.3
MonotonicityNot monotonic
2024-03-14T23:43:55.095305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25 50
 
3.5%
66 40
 
2.8%
33 38
 
2.6%
0 35
 
2.4%
20 34
 
2.4%
16 34
 
2.4%
29 29
 
2.0%
124 27
 
1.9%
116 26
 
1.8%
108 25
 
1.7%
Other values (272) 1104
76.6%
ValueCountFrequency (%)
0 35
2.4%
2 1
 
0.1%
5 1
 
0.1%
6 1
 
0.1%
7 2
 
0.1%
8 4
 
0.3%
9 4
 
0.3%
10 6
 
0.4%
11 6
 
0.4%
12 23
1.6%
ValueCountFrequency (%)
40000 3
0.2%
35000 1
 
0.1%
30000 2
0.1%
24000 1
 
0.1%
20000 3
0.2%
15000 2
0.1%
10000 2
0.1%
8000 1
 
0.1%
7074 1
 
0.1%
6500 1
 
0.1%

Interactions

2024-03-14T23:43:48.223938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:43:47.114789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:43:47.684165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:43:48.479160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:43:47.324862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:43:47.857752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:43:48.665549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:43:47.496259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:43:48.035165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T23:43:55.376239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대상구분준공여부배출시설규모(합산)처리시설용량(합산)축종마리수(합산)
대상구분1.0000.0580.0000.1260.3180.000
준공여부0.0581.0000.0000.0000.0900.056
배출시설규모(합산)0.0000.0001.0000.0000.2370.570
처리시설용량(합산)0.1260.0000.0001.0000.7270.451
축종0.3180.0900.2370.7271.0000.646
마리수(합산)0.0000.0560.5700.4510.6461.000
2024-03-14T23:43:55.634113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
축종대상구분준공여부
축종1.0000.1950.086
대상구분0.1951.0000.097
준공여부0.0860.0971.000
2024-03-14T23:43:55.787725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
배출시설규모(합산)처리시설용량(합산)마리수(합산)대상구분준공여부축종
배출시설규모(합산)1.0000.6560.8710.0000.0000.226
처리시설용량(합산)0.6561.0000.6060.0850.0000.471
마리수(합산)0.8710.6061.0000.0000.0560.361
대상구분0.0000.0850.0001.0000.0970.195
준공여부0.0000.0000.0560.0971.0000.086
축종0.2260.4710.3610.1950.0861.000

Missing values

2024-03-14T23:43:48.902706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T23:43:49.106625image/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

설치주소(지번)대상구분준공여부배출시설규모(합산)처리시설용량(합산)축종마리수(합산)
0경상북도 구미시 고아읍 봉한리 106허가대상미준공1373.61760.24돼지980
1경상북도 구미시 도개면 다곡리 161 번지 외 17필지허가대상준공6075.815200.54돼지4332
2경상북도 구미시 고아읍 오로리 490-1 .488-1.490-2.490-5.490-6.686허가대상준공2705.55836.8소225
3경상북도 구미시 도개면 다곡리 744-2허가대상미준공11526.22117.0소300
4경상북도 구미시 고아읍 봉한리 102 번지 외 17필지허가대상준공5885.585695.72돼지4199
5경상북도 구미시 무을면 백자리 40-3허가대상준공2845.092112.78돼지2033
6경상북도 구미시 선산읍 봉곡리 661 외1필지허가대상준공2127.81080.96소170
7경상북도 구미시 고아읍 문성리 399허가대상준공2077.351609.2돼지1481
8경상북도 구미시 고아읍 외예리 62허가대상준공4017.921913.06돼지2866
9경상북도 구미시 장천면 금산리 1072-1허가대상준공920.0302.4소76
설치주소(지번)대상구분준공여부배출시설규모(합산)처리시설용량(합산)축종마리수(합산)
1432경상북도 구미시 도개면 동산리 515-1 번지 외 1필지신고대상준공317.4361.8소23
1433경상북도 구미시 고아읍 예강리 268-3신고대상준공96.051.2개128
1434경상북도 구미시 산동읍 성수리 1020-1허가대상준공1912.0463.32소132
1435경상북도 구미시 산동읍 임천리 51-2신고대상준공198.0152.72소15
1436경상북도 구미시 산동읍 백현리 332-2신고대상준공339.0420.26소28
1437경상북도 구미시 옥성면 초곡리 1080-37신고대상미준공399.0218.5소30
1438경상북도 구미시 무을면 무등리 26신고대상준공384.0396.8소32
1439경상북도 구미시 옥성면 농소리 872신고대상미준공872.0540.0소66
1440경상북도 구미시 선산읍 교리 359신고대상준공173.0458.57소17
1441경상북도 구미시 장천면 묵어리 산 126-2신고대상미준공343.090.65소34