Overview

Dataset statistics

Number of variables5
Number of observations34
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.6 KiB
Average record size in memory46.9 B

Variable types

Text1
Categorical1
Numeric3

Dataset

Description농림축산식품부 각 시도별 농경지면적 정보입니다.
Author농림축산식품부
URLhttps://www.data.go.kr/data/15030213/fileData.do

Alerts

2015 is highly overall correlated with 2016 and 1 other fieldsHigh correlation
2016 is highly overall correlated with 2015 and 1 other fieldsHigh correlation
2017 is highly overall correlated with 2015 and 1 other fieldsHigh correlation
2015 has unique valuesUnique
2016 has unique valuesUnique
2017 has unique valuesUnique

Reproduction

Analysis started2023-12-12 08:18:18.745766
Analysis finished2023-12-12 08:18:20.060446
Duration1.31 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도별
Text

Distinct17
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size404.0 B
2023-12-12T17:18:20.518436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length5
Mean length4.4117647
Min length3

Characters and Unicode

Total characters150
Distinct characters31
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

Unique0 ?
Unique (%)0.0%

Sample

1st row서울특별시
2nd row서울특별시
3rd row부산광역시
4th row부산광역시
5th row대구광역시
ValueCountFrequency (%)
서울특별시 2
 
5.9%
강원도 2
 
5.9%
경상남도 2
 
5.9%
경상북도 2
 
5.9%
전라남도 2
 
5.9%
전라북도 2
 
5.9%
충청남도 2
 
5.9%
충청북도 2
 
5.9%
경기도 2
 
5.9%
부산광역시 2
 
5.9%
Other values (7) 14
41.2%
2023-12-12T17:18:20.936035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
도 18
 
12.0%
시 16
 
10.7%
광 14
 
9.3%
역 12
 
8.0%
북 6
 
4.0%
전 6
 
4.0%
경 6
 
4.0%
남 6
 
4.0%
라 4
 
2.7%
청 4
 
2.7%
Other values (21) 58
38.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 150
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 18
 
12.0%
시 16
 
10.7%
광 14
 
9.3%
역 12
 
8.0%
북 6
 
4.0%
전 6
 
4.0%
경 6
 
4.0%
남 6
 
4.0%
라 4
 
2.7%
청 4
 
2.7%
Other values (21) 58
38.7%

Most occurring scripts

ValueCountFrequency (%)
Hangul 150
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 18
 
12.0%
시 16
 
10.7%
광 14
 
9.3%
역 12
 
8.0%
북 6
 
4.0%
전 6
 
4.0%
경 6
 
4.0%
남 6
 
4.0%
라 4
 
2.7%
청 4
 
2.7%
Other values (21) 58
38.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 150
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
도 18
 
12.0%
시 16
 
10.7%
광 14
 
9.3%
역 12
 
8.0%
북 6
 
4.0%
전 6
 
4.0%
경 6
 
4.0%
남 6
 
4.0%
라 4
 
2.7%
청 4
 
2.7%
Other values (21) 58
38.7%

전답별
Categorical

Distinct2
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Memory size404.0 B
논
17 
밭
17 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row논
2nd row밭
3rd row논
4th row밭
5th row논

Common Values

ValueCountFrequency (%)
논 17
50.0%
밭 17
50.0%

Length

2023-12-12T17:18:21.153762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T17:18:21.269960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
논 17
50.0%
밭 17
50.0%

2015
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct34
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49383
Minimum18
Maximum185190
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-12T17:18:21.389575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile256.6
Q13700.75
median26212
Q377097.5
95-th percentile149421.8
Maximum185190
Range185172
Interquartile range (IQR)73396.75

Descriptive statistics

Standard deviation54645.057
Coefficient of variation (CV)1.106556
Kurtosis-0.2676629
Mean49383
Median Absolute Deviation (MAD)25974
Skewness0.90686169
Sum1679022
Variance2.9860823 × 109
MonotonicityNot monotonic
2023-12-12T17:18:21.585271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
176 1
 
2.9%
185190 1
 
2.9%
44541 1
 
2.9%
67027 1
 
2.9%
152677 1
 
2.9%
66110 1
 
2.9%
134380 1
 
2.9%
69179 1
 
2.9%
119609 1
 
2.9%
38979 1
 
2.9%
Other values (24) 24
70.6%
ValueCountFrequency (%)
18 1
2.9%
176 1
2.9%
300 1
2.9%
1606 1
2.9%
2518 1
2.9%
2699 1
2.9%
3475 1
2.9%
3490 1
2.9%
3646 1
2.9%
3865 1
2.9%
ValueCountFrequency (%)
185190 1
2.9%
152677 1
2.9%
147669 1
2.9%
134380 1
2.9%
126818 1
2.9%
119609 1
2.9%
95680 1
2.9%
89803 1
2.9%
79737 1
2.9%
69179 1
2.9%

2016
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct34
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48341.147
Minimum17
Maximum183530
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-12T17:18:21.780249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum17
5-th percentile221.5
Q13789.5
median25241.5
Q374130.75
95-th percentile145700.6
Maximum183530
Range183513
Interquartile range (IQR)70341.25

Descriptive statistics

Standard deviation53690.084
Coefficient of variation (CV)1.1106498
Kurtosis-0.19680202
Mean48341.147
Median Absolute Deviation (MAD)25030.5
Skewness0.92767459
Sum1643599
Variance2.8826251 × 109
MonotonicityNot monotonic
2023-12-12T17:18:21.915050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
176 1
 
2.9%
183530 1
 
2.9%
43807 1
 
2.9%
65354 1
 
2.9%
151431 1
 
2.9%
63669 1
 
2.9%
132854 1
 
2.9%
67866 1
 
2.9%
114565 1
 
2.9%
37763 1
 
2.9%
Other values (24) 24
70.6%
ValueCountFrequency (%)
17 1
2.9%
176 1
2.9%
246 1
2.9%
1544 1
2.9%
2528 1
2.9%
2586 1
2.9%
3348 1
2.9%
3486 1
2.9%
3711 1
2.9%
4025 1
2.9%
ValueCountFrequency (%)
183530 1
2.9%
151431 1
2.9%
142615 1
2.9%
132854 1
2.9%
125846 1
2.9%
114565 1
2.9%
93216 1
2.9%
88753 1
2.9%
76219 1
2.9%
67866 1
2.9%

2017
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct34
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47670.529
Minimum17
Maximum177753
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-12T17:18:22.069489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum17
5-th percentile211.2
Q13634.75
median23952.5
Q374949.75
95-th percentile145800.05
Maximum177753
Range177736
Interquartile range (IQR)71315

Descriptive statistics

Standard deviation52855.186
Coefficient of variation (CV)1.1087602
Kurtosis-0.28570579
Mean47670.529
Median Absolute Deviation (MAD)23751.5
Skewness0.90337575
Sum1620798
Variance2.7936707 × 109
MonotonicityNot monotonic
2023-12-12T17:18:22.212617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
167 1
 
2.9%
177753 1
 
2.9%
40506 1
 
2.9%
66591 1
 
2.9%
148558 1
 
2.9%
64680 1
 
2.9%
130322 1
 
2.9%
68874 1
 
2.9%
116110 1
 
2.9%
35682 1
 
2.9%
Other values (24) 24
70.6%
ValueCountFrequency (%)
17 1
2.9%
167 1
2.9%
235 1
2.9%
1458 1
2.9%
2430 1
2.9%
2500 1
2.9%
3243 1
2.9%
3445 1
2.9%
3515 1
2.9%
3994 1
2.9%
ValueCountFrequency (%)
177753 1
2.9%
148558 1
2.9%
144315 1
2.9%
130322 1
2.9%
121350 1
2.9%
116110 1
2.9%
88733 1
2.9%
84780 1
2.9%
76975 1
2.9%
68874 1
2.9%

Interactions

2023-12-12T17:18:19.572374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:18:18.947883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:18:19.244392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:18:19.684060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:18:19.050355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:18:19.347758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:18:19.789558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:18:19.145892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:18:19.455547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T17:18:22.317664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시도별전답별201520162017
시도별1.0000.0000.4920.4920.685
전답별0.0001.0000.3430.3430.471
20150.4920.3431.0001.0000.976
20160.4920.3431.0001.0000.976
20170.6850.4710.9760.9761.000
2023-12-12T17:18:22.441106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
201520162017전답별
20151.0000.9990.9990.292
20160.9991.0001.0000.292
20170.9991.0001.0000.311
전답별0.2920.2920.3111.000

Missing values

2023-12-12T17:18:19.914701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T17:18:20.017392image/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

시도별전답별201520162017
0서울특별시논176176167
1서울특별시밭300246235
2부산광역시논349033483243
3부산광역시밭251825862500
4대구광역시논386540253994
5대구광역시밭442640774068
6인천광역시논134451272012223
7인천광역시밭666967916781
8광주광역시논661561675931
9광주광역시밭364637113515
시도별전답별201520162017
24전라북도논134380132854130322
25전라북도밭691796786668874
26전라남도논185190183530177753
27전라남도밭119609114565116110
28경상북도논126818125846121350
29경상북도밭147669142615144315
30경상남도논898038875384780
31경상남도밭619666049461986
32제주도논181717
33제주도밭626246212361071