Overview

Dataset statistics

Number of variables15
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows33
Duplicate rows (%)0.3%
Total size in memory1.3 MiB
Average record size in memory139.0 B

Variable types

Categorical6
Numeric8
Text1

Dataset

Description일반건축물에 대한 지방세 부과기준인 시가표준액을 제공하며 자치단체명, 자치단체코드, 물건지, 시가표준금액, 연면적, 기준일자로 구성되어 있음
Author경상남도 함양군
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15079879

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
과세년도 has constant value ""Constant
기준일자 has constant value ""Constant
Dataset has 33 (0.3%) duplicate rowsDuplicates
시가표준액 is highly overall correlated with 연면적High correlation
연면적 is highly overall correlated with 시가표준액High correlation
특수지 is highly imbalanced (81.6%)Imbalance
is highly skewed (γ1 = 81.54573018)Skewed
시가표준액 is highly skewed (γ1 = 39.40828648)Skewed
연면적 is highly skewed (γ1 = 28.26505892)Skewed
부번 has 3968 (39.7%) zerosZeros
has 102 (1.0%) zerosZeros

Reproduction

Analysis started2024-04-06 08:01:44.460891
Analysis finished2024-04-06 08:02:04.152614
Duration19.69 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
경상남도
10000 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경상남도 10000
100.0%

Length

2024-04-06T17:02:04.283259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T17:02:04.463023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경상남도 10000
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
함양군
10000 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
함양군 10000
100.0%

Length

2024-04-06T17:02:04.637609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T17:02:04.803998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
함양군 10000
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
48870
10000 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row48870
2nd row48870
3rd row48870
4th row48870
5th row48870

Common Values

ValueCountFrequency (%)
48870 10000
100.0%

Length

2024-04-06T17:02:04.990084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T17:02:05.150373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
48870 10000
100.0%

과세년도
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2021
10000 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021
2nd row2021
3rd row2021
4th row2021
5th row2021

Common Values

ValueCountFrequency (%)
2021 10000
100.0%

Length

2024-04-06T17:02:05.349168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T17:02:05.501025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021 10000
100.0%

법정동
Real number (ℝ)

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean320.94
Minimum250
Maximum400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-06T17:02:05.672055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum250
5-th percentile250
Q1250
median330
Q3360
95-th percentile390
Maximum400
Range150
Interquartile range (IQR)110

Descriptive statistics

Standard deviation51.452708
Coefficient of variation (CV)0.16031878
Kurtosis-1.3222869
Mean320.94
Median Absolute Deviation (MAD)30
Skewness-0.29820288
Sum3209400
Variance2647.3811
MonotonicityNot monotonic
2024-04-06T17:02:05.927958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
250 2985
29.8%
360 1530
15.3%
340 947
 
9.5%
310 925
 
9.2%
320 644
 
6.4%
380 596
 
6.0%
350 583
 
5.8%
390 487
 
4.9%
330 455
 
4.5%
400 439
 
4.4%
ValueCountFrequency (%)
250 2985
29.8%
310 925
 
9.2%
320 644
 
6.4%
330 455
 
4.5%
340 947
 
9.5%
350 583
 
5.8%
360 1530
15.3%
370 409
 
4.1%
380 596
 
6.0%
390 487
 
4.9%
ValueCountFrequency (%)
400 439
 
4.4%
390 487
 
4.9%
380 596
 
6.0%
370 409
 
4.1%
360 1530
15.3%
350 583
 
5.8%
340 947
9.5%
330 455
 
4.5%
320 644
6.4%
310 925
9.2%

법정리
Real number (ℝ)

Distinct15
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.4355
Minimum21
Maximum35
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-06T17:02:06.136545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile21
Q122
median25
Q328
95-th percentile33
Maximum35
Range14
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.6623737
Coefficient of variation (CV)0.1439867
Kurtosis-0.15845844
Mean25.4355
Median Absolute Deviation (MAD)3
Skewness0.75565147
Sum254355
Variance13.412981
MonotonicityNot monotonic
2024-04-06T17:02:06.350834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
22 1524
15.2%
24 1386
13.9%
21 1360
13.6%
25 871
8.7%
27 848
8.5%
26 708
7.1%
29 700
7.0%
23 611
6.1%
28 589
 
5.9%
30 387
 
3.9%
Other values (5) 1016
10.2%
ValueCountFrequency (%)
21 1360
13.6%
22 1524
15.2%
23 611
6.1%
24 1386
13.9%
25 871
8.7%
26 708
7.1%
27 848
8.5%
28 589
 
5.9%
29 700
7.0%
30 387
 
3.9%
ValueCountFrequency (%)
35 241
 
2.4%
34 149
 
1.5%
33 210
 
2.1%
32 142
 
1.4%
31 274
 
2.7%
30 387
3.9%
29 700
7.0%
28 589
5.9%
27 848
8.5%
26 708
7.1%

특수지
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
9720 
2
 
280

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 9720
97.2%
2 280
 
2.8%

Length

2024-04-06T17:02:06.703521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T17:02:06.883900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9720
97.2%
2 280
 
2.8%

본번
Real number (ℝ)

Distinct1395
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean573.6158
Minimum1
Maximum2417
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-06T17:02:07.065954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile34
Q1226
median521.5
Q3815
95-th percentile1329
Maximum2417
Range2416
Interquartile range (IQR)589

Descriptive statistics

Standard deviation423.60697
Coefficient of variation (CV)0.73848553
Kurtosis0.81915609
Mean573.6158
Median Absolute Deviation (MAD)295.5
Skewness0.91178483
Sum5736158
Variance179442.86
MonotonicityNot monotonic
2024-04-06T17:02:07.378824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1250 71
 
0.7%
607 70
 
0.7%
723 50
 
0.5%
181 44
 
0.4%
701 43
 
0.4%
691 41
 
0.4%
1782 36
 
0.4%
31 32
 
0.3%
480 32
 
0.3%
285 32
 
0.3%
Other values (1385) 9549
95.5%
ValueCountFrequency (%)
1 29
0.3%
2 14
0.1%
3 19
0.2%
4 15
0.1%
5 14
0.1%
6 12
0.1%
7 17
0.2%
8 6
 
0.1%
9 16
0.2%
10 19
0.2%
ValueCountFrequency (%)
2417 1
 
< 0.1%
2331 1
 
< 0.1%
2263 2
 
< 0.1%
2206 2
 
< 0.1%
2205 1
 
< 0.1%
2204 3
< 0.1%
2203 2
 
< 0.1%
2202 1
 
< 0.1%
2201 3
< 0.1%
2200 5
0.1%

부번
Real number (ℝ)

ZEROS 

Distinct83
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.7083
Minimum0
Maximum202
Zeros3968
Zeros (%)39.7%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-06T17:02:07.650061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q34
95-th percentile15
Maximum202
Range202
Interquartile range (IQR)4

Descriptive statistics

Standard deviation10.050309
Coefficient of variation (CV)2.7102201
Kurtosis129.62095
Mean3.7083
Median Absolute Deviation (MAD)1
Skewness9.4117124
Sum37083
Variance101.00871
MonotonicityNot monotonic
2024-04-06T17:02:08.000256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3968
39.7%
1 1882
18.8%
2 898
 
9.0%
3 738
 
7.4%
4 404
 
4.0%
5 380
 
3.8%
6 324
 
3.2%
7 204
 
2.0%
8 175
 
1.8%
9 125
 
1.2%
Other values (73) 902
 
9.0%
ValueCountFrequency (%)
0 3968
39.7%
1 1882
18.8%
2 898
 
9.0%
3 738
 
7.4%
4 404
 
4.0%
5 380
 
3.8%
6 324
 
3.2%
7 204
 
2.0%
8 175
 
1.8%
9 125
 
1.2%
ValueCountFrequency (%)
202 3
< 0.1%
191 2
< 0.1%
189 1
 
< 0.1%
149 1
 
< 0.1%
140 1
 
< 0.1%
139 1
 
< 0.1%
134 2
< 0.1%
123 1
 
< 0.1%
122 3
< 0.1%
119 1
 
< 0.1%


Real number (ℝ)

SKEWED  ZEROS 

Distinct19
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.208
Minimum0
Maximum715
Zeros102
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-06T17:02:08.251176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum715
Range715
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7.6984844
Coefficient of variation (CV)6.3729176
Kurtosis7417.1386
Mean1.208
Median Absolute Deviation (MAD)0
Skewness81.54573
Sum12080
Variance59.266663
MonotonicityNot monotonic
2024-04-06T17:02:08.452241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
1 9505
95.0%
2 299
 
3.0%
0 102
 
1.0%
3 34
 
0.3%
4 15
 
0.1%
5 10
 
0.1%
6 5
 
0.1%
8 5
 
0.1%
107 4
 
< 0.1%
7 4
 
< 0.1%
Other values (9) 17
 
0.2%
ValueCountFrequency (%)
0 102
 
1.0%
1 9505
95.0%
2 299
 
3.0%
3 34
 
0.3%
4 15
 
0.1%
5 10
 
0.1%
6 5
 
0.1%
7 4
 
< 0.1%
8 5
 
0.1%
9 2
 
< 0.1%
ValueCountFrequency (%)
715 1
 
< 0.1%
108 2
< 0.1%
107 4
< 0.1%
101 1
 
< 0.1%
33 3
< 0.1%
14 1
 
< 0.1%
13 1
 
< 0.1%
11 3
< 0.1%
10 3
< 0.1%
9 2
< 0.1%


Real number (ℝ)

Distinct90
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean156.7671
Minimum0
Maximum8201
Zeros12
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-06T17:02:08.689726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median3
Q3101
95-th percentile203
Maximum8201
Range8201
Interquartile range (IQR)100

Descriptive statistics

Standard deviation897.95426
Coefficient of variation (CV)5.727951
Kurtosis73.639621
Mean156.7671
Median Absolute Deviation (MAD)2
Skewness8.6563471
Sum1567671
Variance806321.86
MonotonicityNot monotonic
2024-04-06T17:02:08.928382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 3582
35.8%
101 1411
 
14.1%
2 1367
 
13.7%
201 861
 
8.6%
3 646
 
6.5%
102 499
 
5.0%
4 289
 
2.9%
103 162
 
1.6%
301 151
 
1.5%
5 147
 
1.5%
Other values (80) 885
 
8.8%
ValueCountFrequency (%)
0 12
 
0.1%
1 3582
35.8%
2 1367
 
13.7%
3 646
 
6.5%
4 289
 
2.9%
5 147
 
1.5%
6 80
 
0.8%
7 50
 
0.5%
8 33
 
0.3%
9 21
 
0.2%
ValueCountFrequency (%)
8201 1
 
< 0.1%
8103 3
 
< 0.1%
8102 18
 
0.2%
8101 102
1.0%
8001 1
 
< 0.1%
907 1
 
< 0.1%
801 3
 
< 0.1%
707 1
 
< 0.1%
704 1
 
< 0.1%
701 2
 
< 0.1%
Distinct9419
Distinct (%)94.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-06T17:02:09.504148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length34
Median length31
Mean length26.9019
Min length20

Characters and Unicode

Total characters269019
Distinct characters238
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9075 ?
Unique (%)90.8%

Sample

1st row경상남도 함양군 함양읍 백천리 1239-2 1동 2호
2nd row경상남도 함양군 마천면 강청리 458 1동 201호
3rd row[ 하림강변길 43-30 ] 0001동 0001호
4th row[ 천왕봉로 3097 ] 0001동 0001호
5th row경상남도 함양군 휴천면 송전리 611-1 2동 101호
ValueCountFrequency (%)
경상남도 7180
 
10.7%
함양군 7180
 
10.7%
1동 6814
 
10.1%
5640
 
8.4%
0001동 2691
 
4.0%
1호 2510
 
3.7%
함양읍 1952
 
2.9%
안의면 1132
 
1.7%
2호 1097
 
1.6%
0001호 1071
 
1.6%
Other values (4276) 30113
44.7%
2024-04-06T17:02:10.640257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
57380
21.3%
1 25649
 
9.5%
0 20507
 
7.6%
11040
 
4.1%
10089
 
3.8%
9591
 
3.6%
9457
 
3.5%
8006
 
3.0%
2 7973
 
3.0%
7618
 
2.8%
Other values (228) 101709
37.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 123829
46.0%
Decimal Number 77003
28.6%
Space Separator 57380
21.3%
Dash Punctuation 5158
 
1.9%
Close Punctuation 2820
 
1.0%
Open Punctuation 2820
 
1.0%
Uppercase Letter 9
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
11040
 
8.9%
10089
 
8.1%
9591
 
7.7%
9457
 
7.6%
8006
 
6.5%
7618
 
6.2%
7531
 
6.1%
7245
 
5.9%
7207
 
5.8%
7201
 
5.8%
Other values (213) 38844
31.4%
Decimal Number
ValueCountFrequency (%)
1 25649
33.3%
0 20507
26.6%
2 7973
 
10.4%
3 5031
 
6.5%
4 3435
 
4.5%
5 3144
 
4.1%
7 3088
 
4.0%
6 2887
 
3.7%
8 2862
 
3.7%
9 2427
 
3.2%
Space Separator
ValueCountFrequency (%)
57380
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5158
100.0%
Close Punctuation
ValueCountFrequency (%)
] 2820
100.0%
Open Punctuation
ValueCountFrequency (%)
[ 2820
100.0%
Uppercase Letter
ValueCountFrequency (%)
B 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 145181
54.0%
Hangul 123829
46.0%
Latin 9
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
11040
 
8.9%
10089
 
8.1%
9591
 
7.7%
9457
 
7.6%
8006
 
6.5%
7618
 
6.2%
7531
 
6.1%
7245
 
5.9%
7207
 
5.8%
7201
 
5.8%
Other values (213) 38844
31.4%
Common
ValueCountFrequency (%)
57380
39.5%
1 25649
17.7%
0 20507
 
14.1%
2 7973
 
5.5%
- 5158
 
3.6%
3 5031
 
3.5%
4 3435
 
2.4%
5 3144
 
2.2%
7 3088
 
2.1%
6 2887
 
2.0%
Other values (4) 10929
 
7.5%
Latin
ValueCountFrequency (%)
B 9
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 145190
54.0%
Hangul 123829
46.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
57380
39.5%
1 25649
17.7%
0 20507
 
14.1%
2 7973
 
5.5%
- 5158
 
3.6%
3 5031
 
3.5%
4 3435
 
2.4%
5 3144
 
2.2%
7 3088
 
2.1%
6 2887
 
2.0%
Other values (5) 10938
 
7.5%
Hangul
ValueCountFrequency (%)
11040
 
8.9%
10089
 
8.1%
9591
 
7.7%
9457
 
7.6%
8006
 
6.5%
7618
 
6.2%
7531
 
6.1%
7245
 
5.9%
7207
 
5.8%
7201
 
5.8%
Other values (213) 38844
31.4%

시가표준액
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct8096
Distinct (%)81.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33989911
Minimum13200
Maximum9.1726435 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-06T17:02:10.913688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum13200
5-th percentile347070
Q11762852.5
median7598255
Q330942550
95-th percentile1.4097719 × 108
Maximum9.1726435 × 109
Range9.1726303 × 109
Interquartile range (IQR)29179698

Descriptive statistics

Standard deviation1.323509 × 108
Coefficient of variation (CV)3.8938289
Kurtosis2419.3818
Mean33989911
Median Absolute Deviation (MAD)6938255
Skewness39.408286
Sum3.3989911 × 1011
Variance1.7516761 × 1016
MonotonicityNot monotonic
2024-04-06T17:02:11.158770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126000 19
 
0.2%
950400 18
 
0.2%
660000 17
 
0.2%
462000 16
 
0.2%
252000 16
 
0.2%
528000 16
 
0.2%
396000 14
 
0.1%
924000 13
 
0.1%
1900800 13
 
0.1%
540000 13
 
0.1%
Other values (8086) 9845
98.5%
ValueCountFrequency (%)
13200 1
< 0.1%
19000 1
< 0.1%
26000 1
< 0.1%
36000 1
< 0.1%
37000 1
< 0.1%
40000 2
< 0.1%
42000 2
< 0.1%
42500 1
< 0.1%
46000 1
< 0.1%
46800 1
< 0.1%
ValueCountFrequency (%)
9172643510 1
< 0.1%
4173749150 1
< 0.1%
3077982800 1
< 0.1%
2314651900 1
< 0.1%
2068992210 1
< 0.1%
1755094950 1
< 0.1%
1342224800 1
< 0.1%
1256993200 1
< 0.1%
1157352850 1
< 0.1%
1052774140 1
< 0.1%

연면적
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct4949
Distinct (%)49.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean141.25818
Minimum0.81
Maximum20906.31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-06T17:02:11.417204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.81
5-th percentile11.25
Q132
median69.22
Q3146.3475
95-th percentile483.623
Maximum20906.31
Range20905.5
Interquartile range (IQR)114.3475

Descriptive statistics

Standard deviation348.15367
Coefficient of variation (CV)2.464662
Kurtosis1411.4358
Mean141.25818
Median Absolute Deviation (MAD)46.12
Skewness28.265059
Sum1412581.8
Variance121210.98
MonotonicityNot monotonic
2024-04-06T17:02:11.667742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.0 390
 
3.9%
33.0 76
 
0.8%
50.0 72
 
0.7%
66.0 63
 
0.6%
36.0 59
 
0.6%
24.0 55
 
0.5%
60.0 54
 
0.5%
40.0 53
 
0.5%
30.0 48
 
0.5%
27.0 44
 
0.4%
Other values (4939) 9086
90.9%
ValueCountFrequency (%)
0.81 2
 
< 0.1%
0.86 1
 
< 0.1%
0.9 1
 
< 0.1%
0.92 1
 
< 0.1%
1.0 5
0.1%
1.05 1
 
< 0.1%
1.3 1
 
< 0.1%
1.4 1
 
< 0.1%
1.44 2
 
< 0.1%
1.5 1
 
< 0.1%
ValueCountFrequency (%)
20906.31 1
< 0.1%
10433.84 1
< 0.1%
8195.07 1
< 0.1%
7145.09 1
< 0.1%
7007.0 1
< 0.1%
4935.65 1
< 0.1%
3923.23 1
< 0.1%
3278.91 1
< 0.1%
3141.33 1
< 0.1%
3117.0 1
< 0.1%

기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2021-06-01
10000 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021-06-01
2nd row2021-06-01
3rd row2021-06-01
4th row2021-06-01
5th row2021-06-01

Common Values

ValueCountFrequency (%)
2021-06-01 10000
100.0%

Length

2024-04-06T17:02:11.908325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T17:02:12.074611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021-06-01 10000
100.0%

Interactions

2024-04-06T17:02:01.720807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:01:49.481724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:01:51.295131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:01:53.046089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:01:54.920361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:01:56.622226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:01:58.231005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:01:59.560885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:02.009637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:01:49.665032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:01:51.548606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:01:53.306725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:01:55.141580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:01:56.844134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:01:58.424517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:01:59.747420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:02.219345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:01:49.910533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:01:51.849180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:01:53.534998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:01:55.347278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:01:57.061090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:01:58.600822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:01:59.985238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:02.379091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:01:50.105768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:01:52.053196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:01:53.766940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:01:55.538226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:01:57.332916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:01:58.770079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:00.229732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:02.604268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:01:50.363569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:01:52.273603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:01:54.005962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:01:55.802084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:01:57.529071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:01:58.966329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:00.513739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:02.795026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:01:50.643041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:01:52.526947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:01:54.241074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:01:55.991078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:01:57.717955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:01:59.136994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:00.718470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:02.964020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:01:50.817830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:01:52.697015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:01:54.439992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:01:56.182861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:01:57.869601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:01:59.285023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:00.924959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:03.139795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:01:51.008439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:01:52.870807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:01:54.620831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:01:56.411033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:01:58.054324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:01:59.424371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:02:01.106048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-06T17:02:12.613614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동법정리특수지본번부번시가표준액연면적
법정동1.0000.4890.0630.3640.1450.0090.0460.0550.011
법정리0.4891.0000.1680.3810.1130.0430.0110.0000.045
특수지0.0630.1681.0000.3590.0000.0000.0120.0000.000
본번0.3640.3810.3591.0000.0850.0660.0000.0950.064
부번0.1450.1130.0000.0851.0000.0000.0000.0000.000
0.0090.0430.0000.0660.0001.0000.0000.0000.000
0.0460.0110.0120.0000.0000.0001.0000.0920.000
시가표준액0.0550.0000.0000.0950.0000.0000.0921.0000.967
연면적0.0110.0450.0000.0640.0000.0000.0000.9671.000
2024-04-06T17:02:12.896592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동법정리본번부번시가표준액연면적특수지
법정동1.000-0.0780.037-0.187-0.044-0.201-0.218-0.0650.102
법정리-0.0781.0000.037-0.136-0.006-0.152-0.091-0.0040.128
본번0.0370.0371.0000.000-0.022-0.0080.0040.0270.276
부번-0.187-0.1360.0001.000-0.0020.1260.1550.0270.000
-0.044-0.006-0.022-0.0021.0000.0490.0470.0090.000
-0.201-0.152-0.0080.1260.0491.0000.3420.0630.020
시가표준액-0.218-0.0910.0040.1550.0470.3421.0000.6390.000
연면적-0.065-0.0040.0270.0270.0090.0630.6391.0000.000
특수지0.1020.1280.2760.0000.0000.0200.0000.0001.000

Missing values

2024-04-06T17:02:03.495910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-06T17:02:03.974120image/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

시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번물건지시가표준액연면적기준일자
4375경상남도함양군4887020212502711239212경상남도 함양군 함양읍 백천리 1239-2 1동 2호35055020192.612021-06-01
7868경상남도함양군48870202131025145801201경상남도 함양군 마천면 강청리 458 1동 201호3336565084.472021-06-01
2343경상남도함양군488702021250221475111[ 하림강변길 43-30 ] 0001동 0001호5819000105.82021-06-01
5640경상남도함양군488702021330261456311[ 천왕봉로 3097 ] 0001동 0001호10704280100.042021-06-01
5588경상남도함양군48870202132030161112101경상남도 함양군 휴천면 송전리 611-1 2동 101호48000024.02021-06-01
939경상남도함양군48870202125027130901201경상남도 함양군 함양읍 백천리 309 1동 201호101471000349.92021-06-01
6673경상남도함양군488702021310231232011경상남도 함양군 마천면 덕전리 232 1동 1호493695016.22021-06-01
10008경상남도함양군4887020213602311111101[ 약초시장길 37 ] 0001동 0101호141894900212.12021-06-01
3769경상남도함양군4887020212502711593012경상남도 함양군 함양읍 백천리 1593 1동 2호297920060.82021-06-01
1747경상남도함양군48870202125022160531301[ 용평중앙길 32 ] 0001동 0301호3964457084.532021-06-01
시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번물건지시가표준액연면적기준일자
17172경상남도함양군488702021400231376211경상남도 함양군 병곡면 송평리 376-2 1동 1호756162066.332021-06-01
1131경상남도함양군48870202125027130901201경상남도 함양군 함양읍 백천리 309 1동 201호1749300073.52021-06-01
12642경상남도함양군4887020213802411017411경상남도 함양군 서상면 금당리 1017-4 1동 1호84564000270.02021-06-01
12866경상남도함양군488702021380261552013경상남도 함양군 서상면 상남리 552 1동 3호68764809.882021-06-01
14623경상남도함양군488702021390221529212[ 동백길 8-10 ] 0001동 0002호232200018.02021-06-01
12825경상남도함양군488702021370231204419경상남도 함양군 서하면 다곡리 204-4 1동 9호82500075.02021-06-01
220경상남도함양군488702021250351104681103[ 삼봉로 292-10 ] 0001동 0103호2388808062.162021-06-01
9149경상남도함양군488702021360251396011경상남도 함양군 안의면 황곡리 396 1동 1호147600036.02021-06-01
17048경상남도함양군48870202140023139111201[ 함양남서로 1736 ] 0001동 0201호1664272070.522021-06-01
11836경상남도함양군488702021340261158011경상남도 함양군 수동면 내백리 158 1동 1호3663000333.02021-06-01

Duplicate rows

Most frequently occurring

시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번물건지시가표준액연면적기준일자# duplicates
8경상남도함양군488702021310251788011경상남도 함양군 마천면 강청리 788 1동 1호1801800024.752021-06-015
7경상남도함양군488702021310242161011경상남도 함양군 마천면 삼정리 산 161 1동 1호492100025.92021-06-014
9경상남도함양군488702021310251788011경상남도 함양군 마천면 강청리 788 1동 1호2063880022.682021-06-014
25경상남도함양군4887020213603211358011경상남도 함양군 안의면 하원리 1358 1동 1호387504024.842021-06-014
28경상남도함양군4887020213802611782011경상남도 함양군 서상면 상남리 1782 1동 1호12040004.02021-06-014
10경상남도함양군488702021310251788012경상남도 함양군 마천면 강청리 788 1동 2호1146854015.132021-06-013
11경상남도함양군488702021310251788012경상남도 함양군 마천면 강청리 788 1동 2호1706400018.02021-06-013
15경상남도함양군488702021330211113801101경상남도 함양군 유림면 손곡리 1138 1동 101호38903760390.62021-06-013
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