Overview

Dataset statistics

Number of variables7
Number of observations3609
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory208.1 KiB
Average record size in memory59.0 B

Variable types

Categorical2
Numeric1
Text4

Dataset

Description공인중개사의 업무 및 부동산 거래신고에 관한 법률 제27조에 의해 신고된 농지(논,밭,과수원)의 실거래 가격 정보(읍.면.동별 평균, 최저,최고가)
Author농림축산식품부
URLhttps://data.mafra.go.kr/opendata/data/indexOpenDataDetail.do?data_id=20220217000000002080

Reproduction

Analysis started2023-12-11 03:25:11.148602
Analysis finished2023-12-11 03:25:11.771611
Duration0.62 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

2010
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
2011
722 
2012
722 
2013
722 
2014
722 
2010
721 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2010
2nd row2010
3rd row2010
4th row2010
5th row2010

Common Values

ValueCountFrequency (%)
2011 722
20.0%
2012 722
20.0%
2013 722
20.0%
2014 722
20.0%
2010 721
20.0%

Length

2023-12-11T12:25:11.861223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T12:25:12.007088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2011 722
20.0%
2012 722
20.0%
2013 722
20.0%
2014 722
20.0%
2010 721
20.0%

1
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
3
1210 
2
1200 
1
1199 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
3 1210
33.5%
2 1200
33.3%
1 1199
33.2%

Length

2023-12-11T12:25:12.142621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T12:25:12.242067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 1210
33.5%
2 1200
33.3%
1 1199
33.2%

1.1
Real number (ℝ)

Distinct242
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean120.86838
Minimum1
Maximum242
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.8 KiB
2023-12-11T12:25:12.363898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile13
Q161
median121
Q3181
95-th percentile229
Maximum242
Range241
Interquartile range (IQR)120

Descriptive statistics

Standard deviation69.467691
Coefficient of variation (CV)0.57473831
Kurtosis-1.1998022
Mean120.86838
Median Absolute Deviation (MAD)60
Skewness0.00014941071
Sum436214
Variance4825.7601
MonotonicityNot monotonic
2023-12-11T12:25:12.537946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 15
 
0.4%
122 15
 
0.4%
154 15
 
0.4%
155 15
 
0.4%
156 15
 
0.4%
157 15
 
0.4%
158 15
 
0.4%
159 15
 
0.4%
160 15
 
0.4%
161 15
 
0.4%
Other values (232) 3459
95.8%
ValueCountFrequency (%)
1 14
0.4%
2 15
0.4%
3 15
0.4%
4 15
0.4%
5 15
0.4%
6 15
0.4%
7 15
0.4%
8 15
0.4%
9 15
0.4%
10 15
0.4%
ValueCountFrequency (%)
242 5
 
0.1%
241 5
 
0.1%
240 15
0.4%
239 15
0.4%
238 15
0.4%
237 15
0.4%
236 15
0.4%
235 15
0.4%
234 15
0.4%
233 15
0.4%

서울특별시 종로구
Text

Distinct242
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
2023-12-11T12:25:13.021977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length8
Mean length8.5552785
Min length7

Characters and Unicode

Total characters30876
Distinct characters144
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

Unique0 ?
Unique (%)0.0%

Sample

1st row서울특별시 중랑구
2nd row서울특별시 성북구
3rd row서울특별시 강북구
4th row서울특별시 도봉구
5th row서울특별시 노원구
ValueCountFrequency (%)
경기도 675
 
8.7%
경상북도 360
 
4.7%
경상남도 330
 
4.3%
전라남도 330
 
4.3%
충청남도 270
 
3.5%
강원도 270
 
3.5%
부산광역시 225
 
2.9%
전라북도 225
 
2.9%
서울특별시 224
 
2.9%
충청북도 205
 
2.7%
Other values (240) 4609
59.7%
2023-12-11T12:25:13.754834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4114
 
13.3%
도 2770
 
9.0%
시 2424
 
7.9%
구 1494
 
4.8%
경 1410
 
4.6%
군 1335
 
4.3%
남 1170
 
3.8%
북 910
 
2.9%
광 825
 
2.7%
상 750
 
2.4%
Other values (134) 13674
44.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 26762
86.7%
Space Separator 4114
 
13.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 2770
 
10.4%
시 2424
 
9.1%
구 1494
 
5.6%
경 1410
 
5.3%
군 1335
 
5.0%
남 1170
 
4.4%
북 910
 
3.4%
광 825
 
3.1%
상 750
 
2.8%
기 720
 
2.7%
Other values (133) 12954
48.4%
Space Separator
ValueCountFrequency (%)
4114
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 26762
86.7%
Common 4114
 
13.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 2770
 
10.4%
시 2424
 
9.1%
구 1494
 
5.6%
경 1410
 
5.3%
군 1335
 
5.0%
남 1170
 
4.4%
북 910
 
3.4%
광 825
 
3.1%
상 750
 
2.8%
기 720
 
2.7%
Other values (133) 12954
48.4%
Common
ValueCountFrequency (%)
4114
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 26762
86.7%
ASCII 4114
 
13.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4114
100.0%
Hangul
ValueCountFrequency (%)
도 2770
 
10.4%
시 2424
 
9.1%
구 1494
 
5.6%
경 1410
 
5.3%
군 1335
 
5.0%
남 1170
 
4.4%
북 910
 
3.4%
광 825
 
3.1%
상 750
 
2.8%
기 720
 
2.7%
Other values (133) 12954
48.4%

7
Text

Distinct1025
Distinct (%)28.4%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
2023-12-11T12:25:14.167881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length3.1911887
Min length1

Characters and Unicode

Total characters11517
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique277 ?
Unique (%)7.7%

Sample

1st row15
2nd row2
3rd row-
4th row5
5th row4
ValueCountFrequency (%)
112
 
3.1%
2 67
 
1.9%
1 58
 
1.6%
0 57
 
1.6%
3 56
 
1.6%
5 40
 
1.1%
4 39
 
1.1%
14 25
 
0.7%
8 23
 
0.6%
9 22
 
0.6%
Other values (1015) 3110
86.2%
2023-12-11T12:25:14.685405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 2238
19.4%
2 1406
12.2%
3 1069
9.3%
4 895
 
7.8%
6 872
 
7.6%
5 868
 
7.5%
7 841
 
7.3%
9 829
 
7.2%
0 806
 
7.0%
, 806
 
7.0%
Other values (2) 887
 
7.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10599
92.0%
Other Punctuation 806
 
7.0%
Dash Punctuation 112
 
1.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2238
21.1%
2 1406
13.3%
3 1069
10.1%
4 895
 
8.4%
6 872
 
8.2%
5 868
 
8.2%
7 841
 
7.9%
9 829
 
7.8%
0 806
 
7.6%
8 775
 
7.3%
Other Punctuation
ValueCountFrequency (%)
, 806
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 112
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 11517
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2238
19.4%
2 1406
12.2%
3 1069
9.3%
4 895
 
7.8%
6 872
 
7.6%
5 868
 
7.5%
7 841
 
7.3%
9 829
 
7.2%
0 806
 
7.0%
, 806
 
7.0%
Other values (2) 887
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11517
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2238
19.4%
2 1406
12.2%
3 1069
9.3%
4 895
 
7.8%
6 872
 
7.6%
5 868
 
7.5%
7 841
 
7.3%
9 829
 
7.2%
0 806
 
7.0%
, 806
 
7.0%
Other values (2) 887
 
7.7%

192,530
Text

Distinct1002
Distinct (%)27.8%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
2023-12-11T12:25:15.039618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length7
Mean length4.5810474
Min length1

Characters and Unicode

Total characters16533
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique244 ?
Unique (%)6.8%

Sample

1st row907,550
2nd row264,710
3rd row-
4th row326,090
5th row173,610
ValueCountFrequency (%)
114
 
3.2%
0 59
 
1.6%
470 27
 
0.7%
220 27
 
0.7%
790 24
 
0.7%
300 24
 
0.7%
380 23
 
0.6%
400 23
 
0.6%
240 23
 
0.6%
480 22
 
0.6%
Other values (992) 3243
89.9%
2023-12-11T12:25:15.511429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 4166
25.2%
, 2294
13.9%
1 1766
10.7%
2 1361
 
8.2%
3 1214
 
7.3%
4 1113
 
6.7%
5 999
 
6.0%
6 965
 
5.8%
7 927
 
5.6%
8 831
 
5.0%
Other values (2) 897
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14125
85.4%
Other Punctuation 2294
 
13.9%
Dash Punctuation 114
 
0.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4166
29.5%
1 1766
12.5%
2 1361
 
9.6%
3 1214
 
8.6%
4 1113
 
7.9%
5 999
 
7.1%
6 965
 
6.8%
7 927
 
6.6%
8 831
 
5.9%
9 783
 
5.5%
Other Punctuation
ValueCountFrequency (%)
, 2294
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 114
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 16533
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4166
25.2%
, 2294
13.9%
1 1766
10.7%
2 1361
 
8.2%
3 1214
 
7.3%
4 1113
 
6.7%
5 999
 
6.0%
6 965
 
5.8%
7 927
 
5.6%
8 831
 
5.0%
Other values (2) 897
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16533
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4166
25.2%
, 2294
13.9%
1 1766
10.7%
2 1361
 
8.2%
3 1214
 
7.3%
4 1113
 
6.7%
5 999
 
6.0%
6 965
 
5.8%
7 927
 
5.6%
8 831
 
5.0%
Other values (2) 897
 
5.4%

939,600
Text

Distinct1437
Distinct (%)39.8%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
2023-12-11T12:25:15.790734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length7
Mean length6.5777224
Min length1

Characters and Unicode

Total characters23739
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique426 ?
Unique (%)11.8%

Sample

1st row1,508,600
2nd row273,110
3rd row-
4th row900,000
5th row465,000
ValueCountFrequency (%)
112
 
3.1%
0 57
 
1.6%
600,000 9
 
0.2%
45,450 9
 
0.2%
303,030 9
 
0.2%
33,330 7
 
0.2%
100,000 7
 
0.2%
151,280 6
 
0.2%
72,360 6
 
0.2%
19,000 6
 
0.2%
Other values (1427) 3381
93.7%
2023-12-11T12:25:16.241079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 4939
20.8%
, 3724
15.7%
1 2129
9.0%
3 1956
 
8.2%
2 1854
 
7.8%
4 1749
 
7.4%
5 1659
 
7.0%
6 1515
 
6.4%
7 1482
 
6.2%
8 1381
 
5.8%
Other values (2) 1351
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 19903
83.8%
Other Punctuation 3724
 
15.7%
Dash Punctuation 112
 
0.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4939
24.8%
1 2129
10.7%
3 1956
 
9.8%
2 1854
 
9.3%
4 1749
 
8.8%
5 1659
 
8.3%
6 1515
 
7.6%
7 1482
 
7.4%
8 1381
 
6.9%
9 1239
 
6.2%
Other Punctuation
ValueCountFrequency (%)
, 3724
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 112
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 23739
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4939
20.8%
, 3724
15.7%
1 2129
9.0%
3 1956
 
8.2%
2 1854
 
7.8%
4 1749
 
7.4%
5 1659
 
7.0%
6 1515
 
6.4%
7 1482
 
6.2%
8 1381
 
5.8%
Other values (2) 1351
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23739
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4939
20.8%
, 3724
15.7%
1 2129
9.0%
3 1956
 
8.2%
2 1854
 
7.8%
4 1749
 
7.4%
5 1659
 
7.0%
6 1515
 
6.4%
7 1482
 
6.2%
8 1381
 
5.8%
Other values (2) 1351
 
5.7%

Interactions

2023-12-11T12:25:11.458463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T12:25:16.365115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
201011.1
20101.0000.0000.000
10.0001.0000.000
1.10.0000.0001.000
2023-12-11T12:25:16.443651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
20101
20101.0000.000
10.0001.000
2023-12-11T12:25:16.517008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
1.120101
1.11.0000.0000.000
20100.0001.0000.000
10.0000.0001.000

Missing values

2023-12-11T12:25:11.592541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T12:25:11.723729image/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

201011.1서울특별시 종로구7192,530939,600
0201012서울특별시 중랑구15907,5501,508,600
1201013서울특별시 성북구2264,710273,110
2201014서울특별시 강북구---
3201015서울특별시 도봉구5326,090900,000
4201016서울특별시 노원구4173,610465,000
5201017서울특별시 은평구11151,350745,450
6201018서울특별시 마포구---
7201019서울특별시 강서구14121,010467,350
82010110서울특별시 구로구2604,890906,830
92010111서울특별시 금천구1338,120338,120
201011.1서울특별시 종로구7192,530939,600
359920143233경상남도 창녕군2,106719128,286
360020143234경상남도 고성군1,4751,856142,857
360120143235경상남도 남해군1,3171,002181,439
360220143236경상남도 하동군1,440279140,187
360320143237경상남도 산청군5711,50298,276
360420143238경상남도 함양군1,17242278,749
360520143239경상남도 거창군1,19530251,560
360620143240경상남도 합천군1,65224043,333
360720143241제주특별자치도 제주시4,069801302,461
360820143242제주특별자치도 서귀포시3,0793,020287,286