Overview

Dataset statistics

Number of variables6
Number of observations265
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory13.1 KiB
Average record size in memory50.5 B

Variable types

Text1
Numeric2
DateTime3

Dataset

Description산림자원통합정보관련 매각사업의 계약체결정보를 제공
Author산림청
URLhttps://www.data.go.kr/data/15071882/fileData.do

Alerts

매각대금 is highly overall correlated with 계약보증금High correlation
계약보증금 is highly overall correlated with 매각대금High correlation
매각사업번호 has unique valuesUnique
계약보증금 has 27 (10.2%) zerosZeros

Reproduction

Analysis started2023-12-12 17:55:11.997189
Analysis finished2023-12-12 17:55:12.904256
Duration0.91 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

매각사업번호
Text

UNIQUE 

Distinct265
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
2023-12-13T02:55:13.132679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters2650
Distinct characters47
Distinct categories3 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique265 ?
Unique (%)100.0%

Sample

1st row보은2018M028
2nd row민북2018M013
3rd row민북2018M009
4th row춘천2018M030
5th row춘천2018M027
ValueCountFrequency (%)
보은2018m028 1
 
0.4%
보은2015m009 1
 
0.4%
평창2016m035 1
 
0.4%
인제2014m001 1
 
0.4%
영주2014m001 1
 
0.4%
양산2014m014 1
 
0.4%
평창2014m031 1
 
0.4%
평창2014m003 1
 
0.4%
서울2014m001 1
 
0.4%
울진2014m010 1
 
0.4%
Other values (255) 255
96.2%
2023-12-13T02:55:13.668190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 627
23.7%
1 354
13.4%
2 336
12.7%
M 265
10.0%
5 141
 
5.3%
4 100
 
3.8%
6 91
 
3.4%
3 69
 
2.6%
7 62
 
2.3%
8 49
 
1.8%
Other values (37) 556
21.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1855
70.0%
Other Letter 530
 
20.0%
Uppercase Letter 265
 
10.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
보 44
 
8.3%
은 44
 
8.3%
천 43
 
8.1%
춘 37
 
7.0%
양 32
 
6.0%
영 28
 
5.3%
창 28
 
5.3%
평 28
 
5.3%
제 24
 
4.5%
인 24
 
4.5%
Other values (26) 198
37.4%
Decimal Number
ValueCountFrequency (%)
0 627
33.8%
1 354
19.1%
2 336
18.1%
5 141
 
7.6%
4 100
 
5.4%
6 91
 
4.9%
3 69
 
3.7%
7 62
 
3.3%
8 49
 
2.6%
9 26
 
1.4%
Uppercase Letter
ValueCountFrequency (%)
M 265
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1855
70.0%
Hangul 530
 
20.0%
Latin 265
 
10.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
보 44
 
8.3%
은 44
 
8.3%
천 43
 
8.1%
춘 37
 
7.0%
양 32
 
6.0%
영 28
 
5.3%
창 28
 
5.3%
평 28
 
5.3%
제 24
 
4.5%
인 24
 
4.5%
Other values (26) 198
37.4%
Common
ValueCountFrequency (%)
0 627
33.8%
1 354
19.1%
2 336
18.1%
5 141
 
7.6%
4 100
 
5.4%
6 91
 
4.9%
3 69
 
3.7%
7 62
 
3.3%
8 49
 
2.6%
9 26
 
1.4%
Latin
ValueCountFrequency (%)
M 265
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2120
80.0%
Hangul 530
 
20.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 627
29.6%
1 354
16.7%
2 336
15.8%
M 265
12.5%
5 141
 
6.7%
4 100
 
4.7%
6 91
 
4.3%
3 69
 
3.3%
7 62
 
2.9%
8 49
 
2.3%
Hangul
ValueCountFrequency (%)
보 44
 
8.3%
은 44
 
8.3%
천 43
 
8.1%
춘 37
 
7.0%
양 32
 
6.0%
영 28
 
5.3%
창 28
 
5.3%
평 28
 
5.3%
제 24
 
4.5%
인 24
 
4.5%
Other values (26) 198
37.4%

매각대금
Real number (ℝ)

HIGH CORRELATION 

Distinct239
Distinct (%)90.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69996299
Minimum50000
Maximum9.243 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-12-13T02:55:13.890348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum50000
5-th percentile548200
Q110350000
median43700000
Q31 × 108
95-th percentile2.0776 × 108
Maximum9.243 × 108
Range9.2425 × 108
Interquartile range (IQR)89650000

Descriptive statistics

Standard deviation89744125
Coefficient of variation (CV)1.2821267
Kurtosis32.777767
Mean69996299
Median Absolute Deviation (MAD)39700000
Skewness4.2971168
Sum1.8549019 × 1010
Variance8.0540079 × 1015
MonotonicityNot monotonic
2023-12-13T02:55:14.083241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
139890000 3
 
1.1%
102500000 3
 
1.1%
1100000 3
 
1.1%
300000 3
 
1.1%
8410000 2
 
0.8%
21000000 2
 
0.8%
66192000 2
 
0.8%
66900000 2
 
0.8%
2200000 2
 
0.8%
1200000 2
 
0.8%
Other values (229) 241
90.9%
ValueCountFrequency (%)
50000 1
 
0.4%
53000 1
 
0.4%
130000 1
 
0.4%
150000 1
 
0.4%
180000 1
 
0.4%
240000 1
 
0.4%
300000 3
1.1%
350000 1
 
0.4%
375000 1
 
0.4%
400000 2
0.8%
ValueCountFrequency (%)
924300000 1
0.4%
516060000 1
0.4%
382500000 1
0.4%
315000000 1
0.4%
276150000 1
0.4%
269718000 1
0.4%
265000000 1
0.4%
260800000 1
0.4%
255150000 1
0.4%
215999900 1
0.4%

계약보증금
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct220
Distinct (%)83.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6551772.9
Minimum0
Maximum51606000
Zeros27
Zeros (%)10.2%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-12-13T02:55:14.259026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1839460
median4188000
Q39320000
95-th percentile20776000
Maximum51606000
Range51606000
Interquartile range (IQR)8480540

Descriptive statistics

Standard deviation7711859.9
Coefficient of variation (CV)1.1770646
Kurtosis7.617629
Mean6551772.9
Median Absolute Deviation (MAD)3930000
Skewness2.2589081
Sum1.7362198 × 109
Variance5.9472784 × 1013
MonotonicityNot monotonic
2023-12-13T02:55:14.447265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 27
 
10.2%
110000 3
 
1.1%
400000 2
 
0.8%
3560000 2
 
0.8%
841000 2
 
0.8%
1800000 2
 
0.8%
6619200 2
 
0.8%
18155000 2
 
0.8%
13157000 2
 
0.8%
6690000 2
 
0.8%
Other values (210) 219
82.6%
ValueCountFrequency (%)
0 27
10.2%
5000 1
 
0.4%
5300 1
 
0.4%
13000 1
 
0.4%
15000 1
 
0.4%
18000 1
 
0.4%
24000 1
 
0.4%
30000 2
 
0.8%
35000 1
 
0.4%
40000 1
 
0.4%
ValueCountFrequency (%)
51606000 1
0.4%
46989000 1
0.4%
38250000 1
0.4%
31500000 1
0.4%
27615000 1
0.4%
26971800 1
0.4%
26500000 1
0.4%
26080000 1
0.4%
25515000 1
0.4%
21599990 1
0.4%

매각대금납부시작일
Date

Distinct200
Distinct (%)75.5%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
Minimum2014-05-09 00:00:00
Maximum2020-04-01 00:00:00
2023-12-13T02:55:14.666682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:55:14.885443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

매각대금납부종료일
Date

Distinct194
Distinct (%)73.2%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
Minimum2014-05-23 00:00:00
Maximum2020-04-01 00:00:00
2023-12-13T02:55:15.371420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:55:15.567533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

계약일
Date

Distinct198
Distinct (%)74.7%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
Minimum2014-05-09 00:00:00
Maximum2020-04-01 00:00:00
2023-12-13T02:55:15.762572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:55:15.927810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2023-12-13T02:55:12.398398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:55:12.180841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:55:12.519410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:55:12.283744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T02:55:16.043654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
매각대금계약보증금
매각대금1.0000.914
계약보증금0.9141.000
2023-12-13T02:55:16.154647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
매각대금계약보증금
매각대금1.0000.932
계약보증금0.9321.000

Missing values

2023-12-13T02:55:12.701305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T02:55:12.829436image/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

매각사업번호매각대금계약보증금매각대금납부시작일매각대금납부종료일계약일
0보은2018M02883945508394602018-10-022018-10-172018-10-02
1민북2018M013143700000143700002018-09-142018-09-292018-11-12
2민북2018M00924000002400002018-08-102018-10-092018-11-12
3춘천2018M030315000000315000002018-10-302018-11-142018-10-30
4춘천2018M02712570000012570002018-11-022018-11-172018-11-02
5인제2015M05413900000013900002018-11-222018-11-222018-11-22
6인제2015M055123000000123000002018-11-222018-11-222016-01-07
7수원2018M0413355000033550002018-12-132018-12-132018-10-22
8구미2018M0242588000025880002019-07-192019-07-192018-11-26
9무주2020M00110000001000002020-04-012020-04-012020-04-01
매각사업번호매각대금계약보증금매각대금납부시작일매각대금납부종료일계약일
255영월2016M0331129000011290002016-09-132016-09-272016-09-13
256인제2015M052157510000157510002015-12-242016-01-222016-12-24
257춘천2015M0642755000027550002015-12-312016-01-142015-12-31
258춘천2015M0627300000073000002016-01-062016-01-202016-01-06
259춘천2015M0613810000038100002016-01-062016-01-202016-01-06
260순천2018M0307201000072010002019-01-072019-02-222019-02-07
261양양2019M001201770000201770002019-03-042019-03-182019-03-04
262순천2019M0302331000023310002019-09-232019-10-062019-09-23
263충주2020M0033280000032800002020-01-202020-02-042020-01-20
264구미2018M0233050000030500002018-11-282018-12-122018-11-28