Overview

Dataset statistics

Number of variables8
Number of observations1487
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory103.2 KiB
Average record size in memory71.1 B

Variable types

Numeric3
Categorical4
Text1

Dataset

Description경상남도 사천시 개별공시지가(2016년 1월 1일 기준 자료)의 자료입니다.(읍면동,리,토지구분,본번,부번, 결정지가)
Author경상남도 사천시
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15069086

Alerts

법정동 has constant value ""Constant
리 has constant value ""Constant
No is highly overall correlated with 본번 and 1 other fieldsHigh correlation
본번 is highly overall correlated with NoHigh correlation
구분 is highly overall correlated with NoHigh correlation
행정동 is highly imbalanced (87.6%)Imbalance
구분 is highly imbalanced (79.4%)Imbalance
No has unique valuesUnique
부번 has 304 (20.4%) zerosZeros

Reproduction

Analysis started2023-12-10 23:35:21.102244
Analysis finished2023-12-10 23:35:22.677328
Duration1.58 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

No
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct1487
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean744
Minimum1
Maximum1487
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.2 KiB
2023-12-11T08:35:22.746208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile75.3
Q1372.5
median744
Q31115.5
95-th percentile1412.7
Maximum1487
Range1486
Interquartile range (IQR)743

Descriptive statistics

Standard deviation429.40424
Coefficient of variation (CV)0.57715623
Kurtosis-1.2
Mean744
Median Absolute Deviation (MAD)372
Skewness0
Sum1106328
Variance184388
MonotonicityStrictly increasing
2023-12-11T08:35:22.877920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.1%
990 1
 
0.1%
999 1
 
0.1%
998 1
 
0.1%
997 1
 
0.1%
996 1
 
0.1%
995 1
 
0.1%
994 1
 
0.1%
993 1
 
0.1%
992 1
 
0.1%
Other values (1477) 1477
99.3%
ValueCountFrequency (%)
1 1
0.1%
2 1
0.1%
3 1
0.1%
4 1
0.1%
5 1
0.1%
6 1
0.1%
7 1
0.1%
8 1
0.1%
9 1
0.1%
10 1
0.1%
ValueCountFrequency (%)
1487 1
0.1%
1486 1
0.1%
1485 1
0.1%
1484 1
0.1%
1483 1
0.1%
1482 1
0.1%
1481 1
0.1%
1480 1
0.1%
1479 1
0.1%
1478 1
0.1%

법정동
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.7 KiB
101
1487 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row101
2nd row101
3rd row101
4th row101
5th row101

Common Values

ValueCountFrequency (%)
101 1487
100.0%

Length

2023-12-11T08:35:23.025625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:35:23.126128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
101 1487
100.0%

행정동
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size11.7 KiB
510
1443 
101
 
43
0
 
1

Length

Max length3
Median length3
Mean length2.998655
Min length1

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row510
2nd row510
3rd row510
4th row101
5th row510

Common Values

ValueCountFrequency (%)
510 1443
97.0%
101 43
 
2.9%
0 1
 
0.1%

Length

2023-12-11T08:35:23.225537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:35:23.333195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
510 1443
97.0%
101 43
 
2.9%
0 1
 
0.1%

리
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.7 KiB
0
1487 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1487
100.0%

Length

2023-12-11T08:35:23.441850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:35:23.530429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1487
100.0%

구분
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.7 KiB
1
1439 
2
 
48

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 (%)
1 1439
96.8%
2 48
 
3.2%

Length

2023-12-11T08:35:23.617370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:35:23.705893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 1439
96.8%
2 48
 
3.2%

본번
Real number (ℝ)

HIGH CORRELATION 

Distinct445
Distinct (%)29.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean251.45192
Minimum1
Maximum490
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.2 KiB
2023-12-11T08:35:23.820027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile11
Q1156
median295
Q3342
95-th percentile481
Maximum490
Range489
Interquartile range (IQR)186

Descriptive statistics

Standard deviation142.81728
Coefficient of variation (CV)0.56797052
Kurtosis-0.97688357
Mean251.45192
Median Absolute Deviation (MAD)108
Skewness-0.26467063
Sum373909
Variance20396.774
MonotonicityNot monotonic
2023-12-11T08:35:23.966231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
184 53
 
3.6%
206 44
 
3.0%
156 35
 
2.4%
481 35
 
2.4%
173 30
 
2.0%
304 25
 
1.7%
307 24
 
1.6%
314 19
 
1.3%
9 19
 
1.3%
331 18
 
1.2%
Other values (435) 1185
79.7%
ValueCountFrequency (%)
1 4
 
0.3%
2 2
 
0.1%
3 5
 
0.3%
4 5
 
0.3%
5 8
0.5%
6 12
0.8%
7 3
 
0.2%
8 6
 
0.4%
9 19
1.3%
10 5
 
0.3%
ValueCountFrequency (%)
490 1
 
0.1%
489 1
 
0.1%
488 1
 
0.1%
487 1
 
0.1%
486 1
 
0.1%
485 4
 
0.3%
484 17
1.1%
483 12
 
0.8%
482 5
 
0.3%
481 35
2.4%

부번
Real number (ℝ)

ZEROS 

Distinct64
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.7646268
Minimum0
Maximum66
Zeros304
Zeros (%)20.4%
Negative0
Negative (%)0.0%
Memory size13.2 KiB
2023-12-11T08:35:24.090144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q38.5
95-th percentile29
Maximum66
Range66
Interquartile range (IQR)7.5

Descriptive statistics

Standard deviation10.004562
Coefficient of variation (CV)1.4789526
Kurtosis8.967463
Mean6.7646268
Median Absolute Deviation (MAD)3
Skewness2.7515402
Sum10059
Variance100.09126
MonotonicityNot monotonic
2023-12-11T08:35:24.231016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 304
20.4%
1 209
14.1%
2 169
11.4%
3 123
 
8.3%
4 86
 
5.8%
5 66
 
4.4%
6 63
 
4.2%
7 52
 
3.5%
8 43
 
2.9%
9 42
 
2.8%
Other values (54) 330
22.2%
ValueCountFrequency (%)
0 304
20.4%
1 209
14.1%
2 169
11.4%
3 123
8.3%
4 86
 
5.8%
5 66
 
4.4%
6 63
 
4.2%
7 52
 
3.5%
8 43
 
2.9%
9 42
 
2.8%
ValueCountFrequency (%)
66 1
0.1%
65 1
0.1%
64 1
0.1%
63 1
0.1%
62 1
0.1%
61 1
0.1%
60 1
0.1%
59 1
0.1%
58 1
0.1%
57 1
0.1%

결정지가
Text

Distinct382
Distinct (%)25.7%
Missing0
Missing (%)0.0%
Memory size11.7 KiB
2023-12-11T08:35:24.532899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length7
Mean length6.7955615
Min length3

Characters and Unicode

Total characters10105
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique174 ?
Unique (%)11.7%

Sample

1st row110,000
2nd row34,800
3rd row148,500
4th row105,500
5th row109,700
ValueCountFrequency (%)
230,000 44
 
3.0%
56,100 35
 
2.4%
29,000 33
 
2.2%
75,900 30
 
2.0%
170,000 29
 
2.0%
1,130,000 25
 
1.7%
156,700 23
 
1.5%
15,600 22
 
1.5%
165,000 20
 
1.3%
168,300 19
 
1.3%
Other values (372) 1207
81.2%
2023-12-11T08:35:24.963675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 3737
37.0%
, 1548
15.3%
1 1032
 
10.2%
2 764
 
7.6%
5 566
 
5.6%
6 560
 
5.5%
7 474
 
4.7%
4 445
 
4.4%
3 416
 
4.1%
9 296
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8557
84.7%
Other Punctuation 1548
 
15.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3737
43.7%
1 1032
 
12.1%
2 764
 
8.9%
5 566
 
6.6%
6 560
 
6.5%
7 474
 
5.5%
4 445
 
5.2%
3 416
 
4.9%
9 296
 
3.5%
8 267
 
3.1%
Other Punctuation
ValueCountFrequency (%)
, 1548
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10105
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3737
37.0%
, 1548
15.3%
1 1032
 
10.2%
2 764
 
7.6%
5 566
 
5.6%
6 560
 
5.5%
7 474
 
4.7%
4 445
 
4.4%
3 416
 
4.1%
9 296
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10105
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3737
37.0%
, 1548
15.3%
1 1032
 
10.2%
2 764
 
7.6%
5 566
 
5.6%
6 560
 
5.5%
7 474
 
4.7%
4 445
 
4.4%
3 416
 
4.1%
9 296
 
2.9%

Interactions

2023-12-11T08:35:22.197037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:35:21.350185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:35:21.931468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:35:22.290691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:35:21.439024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:35:22.014850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:35:22.375110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:35:21.518385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:35:22.099551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T08:35:25.054051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
No행정동구분본번부번
No1.0000.0500.6960.9810.571
행정동0.0501.0000.0000.0700.000
구분0.6960.0001.0000.5500.038
본번0.9810.0700.5501.0000.529
부번0.5710.0000.0380.5291.000
2023-12-11T08:35:25.140733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분행정동
구분1.0000.000
행정동0.0001.000
2023-12-11T08:35:25.220924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
No본번부번행정동구분
No1.0000.832-0.0390.0300.542
본번0.8321.0000.0240.0410.423
부번-0.0390.0241.0000.0000.029
행정동0.0300.0410.0001.0000.000
구분0.5420.4230.0290.0001.000

Missing values

2023-12-11T08:35:22.502580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T08:35:22.632636image/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

No법정동행정동리구분본번부번결정지가
011015100110110,000
12101510011134,800
231015100112148,500
341011010120105,500
451015100130109,700
561015100131157,900
671015100132106,300
781015100133139,600
891015100140162,700
9101015100141141,800
No법정동행정동리구분본번부번결정지가
14771478101510022641,440
14781479101510022651,440
14791480101510022661,440
14801481101510022671,440
14811482101510022681,440
14821483101510022691,440
148314841015100226101,440
148414851015100226111,400
148514861015100226121,440
14861487101510022701,440