Overview

Dataset statistics

Number of variables9
Number of observations859
Missing cells0
Missing cells (%)0.0%
Duplicate rows47
Duplicate rows (%)5.5%
Total size in memory63.0 KiB
Average record size in memory75.2 B

Variable types

Categorical4
Text1
DateTime1
Numeric3

Dataset

Description전라북도 임실군의 밭직불금 신청정보 데이터 입니다. 데이터 세부내역에는 시군명, 읍면동명, 마을명, 신청일자, 입금은행명, 신청면적, 확인면적, 필지수, 처리상태를 포함하여 제공하고 있습니다.
Author전라북도 임실군
URLhttps://www.data.go.kr/data/15090034/fileData.do

Alerts

시군명 has constant value ""Constant
Dataset has 47 (5.5%) duplicate rowsDuplicates
신청면적 is highly overall correlated with 확인면적High correlation
확인면적 is highly overall correlated with 신청면적High correlation
입금은행명 is highly imbalanced (77.2%)Imbalance
처리상태 is highly imbalanced (53.2%)Imbalance
신청면적 has 266 (31.0%) zerosZeros
확인면적 has 272 (31.7%) zerosZeros
필지수 has 20 (2.3%) zerosZeros

Reproduction

Analysis started2023-12-12 01:12:15.024560
Analysis finished2023-12-12 01:12:17.105163
Duration2.08 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size6.8 KiB
임실군
859 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
임실군 859
100.0%

Length

2023-12-12T10:12:17.173544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T10:12:17.292281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
임실군 859
100.0%

읍면동명
Categorical

Distinct10
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size6.8 KiB
오수면
205 
관촌면
152 
청웅면
117 
강진면
105 
지사면
73 
Other values (5)
207 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row신평면
2nd row관촌면
3rd row오수면
4th row성수면
5th row관촌면

Common Values

ValueCountFrequency (%)
오수면 205
23.9%
관촌면 152
17.7%
청웅면 117
13.6%
강진면 105
12.2%
지사면 73
 
8.5%
신덕면 54
 
6.3%
덕치면 50
 
5.8%
성수면 49
 
5.7%
신평면 44
 
5.1%
운암면 10
 
1.2%

Length

2023-12-12T10:12:17.439240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T10:12:17.600101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
오수면 205
23.9%
관촌면 152
17.7%
청웅면 117
13.6%
강진면 105
12.2%
지사면 73
 
8.5%
신덕면 54
 
6.3%
덕치면 50
 
5.8%
성수면 49
 
5.7%
신평면 44
 
5.1%
운암면 10
 
1.2%

마을명
Text

Distinct160
Distinct (%)18.6%
Missing0
Missing (%)0.0%
Memory size6.8 KiB
2023-12-12T10:12:17.973677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length2
Mean length2.1734575
Min length2

Characters and Unicode

Total characters1867
Distinct characters120
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

Unique30 ?
Unique (%)3.5%

Sample

1st row피암
2nd row용산
3rd row남신
4th row후촌마을
5th row농원
ValueCountFrequency (%)
타읍면 35
 
4.1%
주천 26
 
3.0%
농원 22
 
2.6%
양지 21
 
2.4%
관촌 19
 
2.2%
회문 17
 
2.0%
신기 16
 
1.9%
계산 15
 
1.7%
한암 15
 
1.7%
문방 15
 
1.7%
Other values (150) 658
76.6%
2023-12-12T10:12:18.518589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
산 76
 
4.1%
촌 70
 
3.7%
방 66
 
3.5%
신 66
 
3.5%
천 63
 
3.4%
동 51
 
2.7%
암 51
 
2.7%
마 50
 
2.7%
을 49
 
2.6%
원 43
 
2.3%
Other values (110) 1282
68.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1862
99.7%
Decimal Number 5
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 76
 
4.1%
촌 70
 
3.8%
방 66
 
3.5%
신 66
 
3.5%
천 63
 
3.4%
동 51
 
2.7%
암 51
 
2.7%
마 50
 
2.7%
을 49
 
2.6%
원 43
 
2.3%
Other values (109) 1277
68.6%
Decimal Number
ValueCountFrequency (%)
1 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1862
99.7%
Common 5
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 76
 
4.1%
촌 70
 
3.8%
방 66
 
3.5%
신 66
 
3.5%
천 63
 
3.4%
동 51
 
2.7%
암 51
 
2.7%
마 50
 
2.7%
을 49
 
2.6%
원 43
 
2.3%
Other values (109) 1277
68.6%
Common
ValueCountFrequency (%)
1 5
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1862
99.7%
ASCII 5
 
0.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
산 76
 
4.1%
촌 70
 
3.8%
방 66
 
3.5%
신 66
 
3.5%
천 63
 
3.4%
동 51
 
2.7%
암 51
 
2.7%
마 50
 
2.7%
을 49
 
2.6%
원 43
 
2.3%
Other values (109) 1277
68.6%
ASCII
ValueCountFrequency (%)
1 5
100.0%

신청일자
Date

Distinct70
Distinct (%)8.1%
Missing0
Missing (%)0.0%
Memory size6.8 KiB
Minimum2012-07-18 00:00:00
Maximum2015-07-08 00:00:00
2023-12-12T10:12:18.688993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:12:18.869530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

입금은행명
Categorical

IMBALANCE 

Distinct22
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size6.8 KiB
농협
740 
우체국
 
39
<NA>
 
19
전북은행
 
16
단위농협
 
16
Other values (17)
 
29

Length

Max length6
Median length2
Mean length2.2130384
Min length2

Unique

Unique11 ?
Unique (%)1.3%

Sample

1st row전북은행
2nd row농협
3rd row농협
4th row농협
5th row농협

Common Values

ValueCountFrequency (%)
농협 740
86.1%
우체국 39
 
4.5%
<NA> 19
 
2.2%
전북은행 16
 
1.9%
단위농협 16
 
1.9%
국민은행 7
 
0.8%
새마을금고 3
 
0.3%
국민 2
 
0.2%
하나은행 2
 
0.2%
축협 2
 
0.2%
Other values (12) 13
 
1.5%

Length

2023-12-12T10:12:19.052693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
농협 740
86.1%
우체국 39
 
4.5%
na 19
 
2.2%
전북은행 16
 
1.9%
단위농협 16
 
1.9%
국민은행 7
 
0.8%
새마을금고 3
 
0.3%
국민 2
 
0.2%
하나은행 2
 
0.2%
축협 2
 
0.2%
Other values (12) 13
 
1.5%

신청면적
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct560
Distinct (%)65.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3149.3271
Minimum0
Maximum130710
Zeros266
Zeros (%)31.0%
Negative0
Negative (%)0.0%
Memory size7.7 KiB
2023-12-12T10:12:19.251088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1418
Q33755
95-th percentile11559.9
Maximum130710
Range130710
Interquartile range (IQR)3755

Descriptive statistics

Standard deviation6429.8401
Coefficient of variation (CV)2.0416552
Kurtosis183.58605
Mean3149.3271
Median Absolute Deviation (MAD)1418
Skewness10.389572
Sum2705272
Variance41342844
MonotonicityNot monotonic
2023-12-12T10:12:19.451687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 266
31.0%
2000 4
 
0.5%
1821 3
 
0.3%
1517 3
 
0.3%
1048 3
 
0.3%
1283 2
 
0.2%
1918 2
 
0.2%
694 2
 
0.2%
1507 2
 
0.2%
1187 2
 
0.2%
Other values (550) 570
66.4%
ValueCountFrequency (%)
0 266
31.0%
20 1
 
0.1%
109 1
 
0.1%
150 1
 
0.1%
234 1
 
0.1%
239 1
 
0.1%
248 1
 
0.1%
251 1
 
0.1%
278 1
 
0.1%
294 1
 
0.1%
ValueCountFrequency (%)
130710 1
0.1%
33602 1
0.1%
32933 1
0.1%
32618 1
0.1%
31166 1
0.1%
29924 1
0.1%
28931 1
0.1%
28688 1
0.1%
27620 1
0.1%
26605 1
0.1%

확인면적
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct554
Distinct (%)64.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3117.9627
Minimum0
Maximum130710
Zeros272
Zeros (%)31.7%
Negative0
Negative (%)0.0%
Memory size7.7 KiB
2023-12-12T10:12:19.664398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1400
Q33710
95-th percentile11559.9
Maximum130710
Range130710
Interquartile range (IQR)3710

Descriptive statistics

Standard deviation6428.336
Coefficient of variation (CV)2.0617103
Kurtosis183.96421
Mean3117.9627
Median Absolute Deviation (MAD)1400
Skewness10.409631
Sum2678330
Variance41323504
MonotonicityNot monotonic
2023-12-12T10:12:19.818433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 272
31.7%
2000 4
 
0.5%
1048 3
 
0.3%
1517 3
 
0.3%
1821 3
 
0.3%
694 2
 
0.2%
1507 2
 
0.2%
1918 2
 
0.2%
2522 2
 
0.2%
1349 2
 
0.2%
Other values (544) 564
65.7%
ValueCountFrequency (%)
0 272
31.7%
20 1
 
0.1%
109 1
 
0.1%
150 1
 
0.1%
234 1
 
0.1%
239 1
 
0.1%
248 1
 
0.1%
251 1
 
0.1%
278 1
 
0.1%
294 1
 
0.1%
ValueCountFrequency (%)
130710 1
0.1%
33602 1
0.1%
32933 1
0.1%
32618 1
0.1%
31166 1
0.1%
29924 1
0.1%
28931 1
0.1%
28688 1
0.1%
27620 1
0.1%
26605 1
0.1%

필지수
Real number (ℝ)

ZEROS 

Distinct17
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.548312
Minimum0
Maximum19
Zeros20
Zeros (%)2.3%
Negative0
Negative (%)0.0%
Memory size7.7 KiB
2023-12-12T10:12:19.979558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q33
95-th percentile7
Maximum19
Range19
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.2200467
Coefficient of variation (CV)0.87118324
Kurtosis11.240712
Mean2.548312
Median Absolute Deviation (MAD)1
Skewness2.6313236
Sum2189
Variance4.9286073
MonotonicityNot monotonic
2023-12-12T10:12:20.406619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
1 334
38.9%
2 194
22.6%
3 118
 
13.7%
4 72
 
8.4%
5 51
 
5.9%
6 23
 
2.7%
0 20
 
2.3%
7 17
 
2.0%
8 9
 
1.0%
9 7
 
0.8%
Other values (7) 14
 
1.6%
ValueCountFrequency (%)
0 20
 
2.3%
1 334
38.9%
2 194
22.6%
3 118
 
13.7%
4 72
 
8.4%
5 51
 
5.9%
6 23
 
2.7%
7 17
 
2.0%
8 9
 
1.0%
9 7
 
0.8%
ValueCountFrequency (%)
19 2
 
0.2%
17 1
 
0.1%
14 1
 
0.1%
13 1
 
0.1%
12 1
 
0.1%
11 4
 
0.5%
10 4
 
0.5%
9 7
0.8%
8 9
1.0%
7 17
2.0%

처리상태
Categorical

IMBALANCE 

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size6.8 KiB
접수완료(선정)
584 
접수완료
272 
취소(부적격)
 
2
취소(신청취소)
 
1

Length

Max length8
Median length8
Mean length6.7310827
Min length4

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row접수완료(선정)
2nd row접수완료
3rd row접수완료
4th row접수완료(선정)
5th row접수완료(선정)

Common Values

ValueCountFrequency (%)
접수완료(선정) 584
68.0%
접수완료 272
31.7%
취소(부적격) 2
 
0.2%
취소(신청취소) 1
 
0.1%

Length

2023-12-12T10:12:20.590172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T10:12:20.721484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
접수완료(선정 584
68.0%
접수완료 272
31.7%
취소(부적격 2
 
0.2%
취소(신청취소 1
 
0.1%

Interactions

2023-12-12T10:12:16.294200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:12:15.515589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:12:15.877614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:12:16.461601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:12:15.632516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:12:16.006103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:12:16.632895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:12:15.747085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:12:16.139772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T10:12:20.812421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
읍면동명신청일자입금은행명신청면적확인면적필지수처리상태
읍면동명1.0000.9970.2360.0000.0000.0720.577
신청일자0.9971.0000.5460.8100.8100.0000.583
입금은행명0.2360.5461.0000.0000.0000.0000.000
신청면적0.0000.8100.0001.0001.0000.4910.128
확인면적0.0000.8100.0001.0001.0000.4910.128
필지수0.0720.0000.0000.4910.4911.0000.000
처리상태0.5770.5830.0000.1280.1280.0001.000
2023-12-12T10:12:20.952797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
입금은행명처리상태읍면동명
입금은행명1.0000.0000.088
처리상태0.0001.0000.382
읍면동명0.0880.3821.000
2023-12-12T10:12:21.076799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
신청면적확인면적필지수읍면동명입금은행명처리상태
신청면적1.0000.9900.3130.0000.0000.051
확인면적0.9901.0000.3170.0000.0000.051
필지수0.3130.3171.0000.0220.0000.000
읍면동명0.0000.0000.0221.0000.0880.382
입금은행명0.0000.0000.0000.0881.0000.000
처리상태0.0510.0510.0000.3820.0001.000

Missing values

2023-12-12T10:12:16.835934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T10:12:17.041033image/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

시군명읍면동명마을명신청일자입금은행명신청면적확인면적필지수처리상태
0임실군신평면피암2015-04-23전북은행523252322접수완료(선정)
1임실군관촌면용산2015-05-12농협003접수완료
2임실군오수면남신2015-04-16농협001접수완료
3임실군성수면후촌마을2014-08-12농협211321132접수완료(선정)
4임실군관촌면농원2015-05-12농협251825183접수완료(선정)
5임실군덕치면신촌2015-07-07농협411541152접수완료(선정)
6임실군오수면주천2015-04-16농협104110411접수완료
7임실군지사면계산2015-06-02농협515451542접수완료(선정)
8임실군지사면계산2015-06-02농협001접수완료
9임실군관촌면신전2015-05-12농협001접수완료
시군명읍면동명마을명신청일자입금은행명신청면적확인면적필지수처리상태
849임실군신덕면월성2015-07-05농협183418342접수완료(선정)
850임실군청웅면명동2015-01-09농협002접수완료
851임실군청웅면행촌2015-01-09농협001접수완료
852임실군청웅면암포2015-01-09농협002접수완료
853임실군청웅면수풍2015-01-09농협003접수완료
854임실군관촌면타읍면2015-05-12농협001접수완료
855임실군강진면율치2015-03-03농협345434541접수완료(선정)
856임실군관촌면방동2015-07-02농협145414542접수완료(선정)
857임실군신덕면조월2015-06-30농협422842281접수완료(선정)
858임실군성수면당당마을2014-08-12농협387838781접수완료(선정)

Duplicate rows

Most frequently occurring

시군명읍면동명마을명신청일자입금은행명신청면적확인면적필지수처리상태# duplicates
4임실군관촌면타읍면2015-05-12농협001접수완료12
27임실군청웅면발산2015-01-09농협001접수완료7
20임실군청웅면구고2015-01-09농협001접수완료6
36임실군청웅면양지2015-01-09농협002접수완료5
23임실군청웅면명교2015-01-09농협002접수완료4
30임실군청웅면석두2015-01-09농협002접수완료4
37임실군청웅면양지2015-01-09농협003접수완료4
44임실군청웅면중신2015-01-09농협002접수완료4
0임실군관촌면관촌2015-05-12농협001접수완료3
3임실군관촌면신전2015-05-12농협001접수완료3