Overview

Dataset statistics

Number of variables15
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows35
Duplicate rows (%)0.4%
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://www.data.go.kr/data/15079879/fileData.do

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 35 (0.4%) duplicate rowsDuplicates
시가표준액 is highly overall correlated with 연면적High correlation
연면적 is highly overall correlated with 시가표준액High correlation
특수지 is highly imbalanced (82.3%)Imbalance
is highly skewed (γ1 = 42.26988113)Skewed
시가표준액 is highly skewed (γ1 = 34.31017415)Skewed
연면적 is highly skewed (γ1 = 27.87859683)Skewed
부번 has 4026 (40.3%) zerosZeros
has 107 (1.1%) zerosZeros

Reproduction

Analysis started2024-04-06 08:24:56.937189
Analysis finished2024-04-06 08:25:16.869293
Duration19.93 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

2024-04-06T17:25:17.040016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T17:25:17.310997image/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

2024-04-06T17:25:17.558278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T17:25:17.767832image/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

2024-04-06T17:25:17.959474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T17:25:18.186889image/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

2024-04-06T17:25:18.463382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T17:25:18.681833image/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%
Mean320.458
Minimum250
Maximum400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-06T17:25:18.891286image/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 deviation51.370752
Coefficient of variation (CV)0.16030416
Kurtosis-1.3304341
Mean320.458
Median Absolute Deviation (MAD)30
Skewness-0.29476359
Sum3204580
Variance2638.9541
MonotonicityNot monotonic
2024-04-06T17:25:19.149413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
250 3020
30.2%
360 1522
15.2%
340 988
 
9.9%
310 887
 
8.9%
320 637
 
6.4%
350 597
 
6.0%
380 557
 
5.6%
390 479
 
4.8%
330 471
 
4.7%
370 424
 
4.2%
ValueCountFrequency (%)
250 3020
30.2%
310 887
 
8.9%
320 637
 
6.4%
330 471
 
4.7%
340 988
 
9.9%
350 597
 
6.0%
360 1522
15.2%
370 424
 
4.2%
380 557
 
5.6%
390 479
 
4.8%
ValueCountFrequency (%)
400 418
 
4.2%
390 479
 
4.8%
380 557
 
5.6%
370 424
 
4.2%
360 1522
15.2%
350 597
 
6.0%
340 988
9.9%
330 471
 
4.7%
320 637
6.4%
310 887
8.9%

법정리
Real number (ℝ)

Distinct15
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.4161
Minimum21
Maximum35
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-06T17:25:19.424509image/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 deviation3.6485466
Coefficient of variation (CV)0.14355257
Kurtosis-0.08920702
Mean25.4161
Median Absolute Deviation (MAD)3
Skewness0.7737716
Sum254161
Variance13.311892
MonotonicityNot monotonic
2024-04-06T17:25:19.748791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
22 1499
15.0%
24 1390
13.9%
21 1370
13.7%
27 863
8.6%
25 863
8.6%
26 743
7.4%
29 714
7.1%
23 628
6.3%
28 575
 
5.8%
30 374
 
3.7%
Other values (5) 981
9.8%
ValueCountFrequency (%)
21 1370
13.7%
22 1499
15.0%
23 628
6.3%
24 1390
13.9%
25 863
8.6%
26 743
7.4%
27 863
8.6%
28 575
 
5.8%
29 714
7.1%
30 374
 
3.7%
ValueCountFrequency (%)
35 259
 
2.6%
34 143
 
1.4%
33 192
 
1.9%
32 129
 
1.3%
31 258
 
2.6%
30 374
3.7%
29 714
7.1%
28 575
5.8%
27 863
8.6%
26 743
7.4%

특수지
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
9735 
2
 
265

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 9735
97.4%
2 265
 
2.6%

Length

2024-04-06T17:25:20.159862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T17:25:20.394845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9735
97.4%
2 265
 
2.6%

본번
Real number (ℝ)

Distinct1399
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean576.6734
Minimum1
Maximum2417
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-06T17:25:20.749637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile33
Q1224
median522
Q3817
95-th percentile1355
Maximum2417
Range2416
Interquartile range (IQR)593

Descriptive statistics

Standard deviation429.31231
Coefficient of variation (CV)0.74446352
Kurtosis0.89065243
Mean576.6734
Median Absolute Deviation (MAD)297
Skewness0.94072855
Sum5766734
Variance184309.06
MonotonicityNot monotonic
2024-04-06T17:25:21.111673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
607 78
 
0.8%
1250 70
 
0.7%
701 42
 
0.4%
691 42
 
0.4%
181 41
 
0.4%
1782 40
 
0.4%
723 35
 
0.4%
480 35
 
0.4%
611 34
 
0.3%
21 34
 
0.3%
Other values (1389) 9549
95.5%
ValueCountFrequency (%)
1 33
0.3%
2 13
 
0.1%
3 20
0.2%
4 8
 
0.1%
5 18
0.2%
6 6
 
0.1%
7 22
0.2%
8 7
 
0.1%
9 20
0.2%
10 17
0.2%
ValueCountFrequency (%)
2417 1
 
< 0.1%
2331 1
 
< 0.1%
2263 1
 
< 0.1%
2206 2
 
< 0.1%
2205 1
 
< 0.1%
2204 3
< 0.1%
2203 3
< 0.1%
2202 7
0.1%
2201 4
< 0.1%
2200 6
0.1%

부번
Real number (ℝ)

ZEROS 

Distinct78
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6483
Minimum0
Maximum248
Zeros4026
Zeros (%)40.3%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-06T17:25:21.461108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile15
Maximum248
Range248
Interquartile range (IQR)3

Descriptive statistics

Standard deviation9.7158349
Coefficient of variation (CV)2.6631129
Kurtosis133.73503
Mean3.6483
Median Absolute Deviation (MAD)1
Skewness9.2266238
Sum36483
Variance94.397447
MonotonicityNot monotonic
2024-04-06T17:25:21.781693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4026
40.3%
1 1929
19.3%
2 839
 
8.4%
3 752
 
7.5%
5 370
 
3.7%
4 360
 
3.6%
6 313
 
3.1%
7 209
 
2.1%
8 180
 
1.8%
9 130
 
1.3%
Other values (68) 892
 
8.9%
ValueCountFrequency (%)
0 4026
40.3%
1 1929
19.3%
2 839
 
8.4%
3 752
 
7.5%
4 360
 
3.6%
5 370
 
3.7%
6 313
 
3.1%
7 209
 
2.1%
8 180
 
1.8%
9 130
 
1.3%
ValueCountFrequency (%)
248 1
 
< 0.1%
202 1
 
< 0.1%
191 2
< 0.1%
149 1
 
< 0.1%
134 2
< 0.1%
123 1
 
< 0.1%
122 1
 
< 0.1%
118 2
< 0.1%
112 3
< 0.1%
107 3
< 0.1%


Real number (ℝ)

SKEWED  ZEROS 

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1085
Minimum0
Maximum108
Zeros107
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-06T17:25:22.072045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum108
Range108
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.2729814
Coefficient of variation (CV)2.0505019
Kurtosis1934.7085
Mean1.1085
Median Absolute Deviation (MAD)0
Skewness42.269881
Sum11085
Variance5.1664444
MonotonicityNot monotonic
2024-04-06T17:25:22.392534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
1 9485
94.8%
2 317
 
3.2%
0 107
 
1.1%
3 39
 
0.4%
5 12
 
0.1%
4 12
 
0.1%
6 5
 
0.1%
7 4
 
< 0.1%
33 4
 
< 0.1%
11 4
 
< 0.1%
Other values (6) 11
 
0.1%
ValueCountFrequency (%)
0 107
 
1.1%
1 9485
94.8%
2 317
 
3.2%
3 39
 
0.4%
4 12
 
0.1%
5 12
 
0.1%
6 5
 
0.1%
7 4
 
< 0.1%
8 3
 
< 0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
108 2
 
< 0.1%
107 2
 
< 0.1%
33 4
< 0.1%
15 1
 
< 0.1%
14 2
 
< 0.1%
11 4
< 0.1%
10 1
 
< 0.1%
8 3
< 0.1%
7 4
< 0.1%
6 5
0.1%


Real number (ℝ)

Distinct89
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean160.0132
Minimum0
Maximum8201
Zeros5
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-06T17:25:22.798396image/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 deviation908.49946
Coefficient of variation (CV)5.6776532
Kurtosis71.776408
Mean160.0132
Median Absolute Deviation (MAD)2
Skewness8.5485556
Sum1600132
Variance825371.28
MonotonicityNot monotonic
2024-04-06T17:25:23.074709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 3562
35.6%
101 1431
14.3%
2 1339
 
13.4%
201 862
 
8.6%
3 650
 
6.5%
102 470
 
4.7%
4 292
 
2.9%
103 165
 
1.7%
301 160
 
1.6%
5 159
 
1.6%
Other values (79) 910
 
9.1%
ValueCountFrequency (%)
0 5
 
0.1%
1 3562
35.6%
2 1339
 
13.4%
3 650
 
6.5%
4 292
 
2.9%
5 159
 
1.6%
6 88
 
0.9%
7 50
 
0.5%
8 33
 
0.3%
9 21
 
0.2%
ValueCountFrequency (%)
8201 1
 
< 0.1%
8104 1
 
< 0.1%
8103 3
 
< 0.1%
8102 17
 
0.2%
8101 105
1.1%
8001 1
 
< 0.1%
1107 1
 
< 0.1%
907 1
 
< 0.1%
801 4
 
< 0.1%
707 1
 
< 0.1%
Distinct9430
Distinct (%)94.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-06T17:25:24.013639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length34
Median length32
Mean length26.903
Min length21

Characters and Unicode

Total characters269030
Distinct characters234
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 row[ 함양남서로 996-48 ] 0007동 0101호
2nd row[ 한들로 179 ] 0001동 0102호
3rd row경상남도 함양군 휴천면 남호리 762 1동 101호
4th row경상남도 함양군 함양읍 백연리 67-1 1동 201호
5th row경상남도 함양군 수동면 내백리 89-29 2동 201호
ValueCountFrequency (%)
경상남도 7239
 
10.7%
함양군 7239
 
10.7%
1동 6869
 
10.2%
5522
 
8.2%
0001동 2616
 
3.9%
1호 2545
 
3.8%
함양읍 1997
 
3.0%
안의면 1130
 
1.7%
2호 1090
 
1.6%
0001호 1015
 
1.5%
Other values (4237) 30155
44.7%
2024-04-06T17:25:24.967936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
57417
21.3%
1 25706
 
9.6%
0 20182
 
7.5%
11065
 
4.1%
10103
 
3.8%
9680
 
3.6%
9564
 
3.6%
8022
 
3.0%
2 7957
 
3.0%
7679
 
2.9%
Other values (224) 101655
37.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 124343
46.2%
Decimal Number 76618
28.5%
Space Separator 57417
21.3%
Dash Punctuation 5120
 
1.9%
Open Punctuation 2761
 
1.0%
Close Punctuation 2761
 
1.0%
Uppercase Letter 10
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
11065
 
8.9%
10103
 
8.1%
9680
 
7.8%
9564
 
7.7%
8022
 
6.5%
7679
 
6.2%
7592
 
6.1%
7309
 
5.9%
7268
 
5.8%
7256
 
5.8%
Other values (209) 38805
31.2%
Decimal Number
ValueCountFrequency (%)
1 25706
33.6%
0 20182
26.3%
2 7957
 
10.4%
3 5012
 
6.5%
4 3433
 
4.5%
5 3155
 
4.1%
7 3107
 
4.1%
8 2818
 
3.7%
6 2814
 
3.7%
9 2434
 
3.2%
Space Separator
ValueCountFrequency (%)
57417
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5120
100.0%
Open Punctuation
ValueCountFrequency (%)
[ 2761
100.0%
Close Punctuation
ValueCountFrequency (%)
] 2761
100.0%
Uppercase Letter
ValueCountFrequency (%)
B 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 144677
53.8%
Hangul 124343
46.2%
Latin 10
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
11065
 
8.9%
10103
 
8.1%
9680
 
7.8%
9564
 
7.7%
8022
 
6.5%
7679
 
6.2%
7592
 
6.1%
7309
 
5.9%
7268
 
5.8%
7256
 
5.8%
Other values (209) 38805
31.2%
Common
ValueCountFrequency (%)
57417
39.7%
1 25706
17.8%
0 20182
 
13.9%
2 7957
 
5.5%
- 5120
 
3.5%
3 5012
 
3.5%
4 3433
 
2.4%
5 3155
 
2.2%
7 3107
 
2.1%
8 2818
 
1.9%
Other values (4) 10770
 
7.4%
Latin
ValueCountFrequency (%)
B 10
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 144687
53.8%
Hangul 124343
46.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
57417
39.7%
1 25706
17.8%
0 20182
 
13.9%
2 7957
 
5.5%
- 5120
 
3.5%
3 5012
 
3.5%
4 3433
 
2.4%
5 3155
 
2.2%
7 3107
 
2.1%
8 2818
 
1.9%
Other values (5) 10780
 
7.5%
Hangul
ValueCountFrequency (%)
11065
 
8.9%
10103
 
8.1%
9680
 
7.8%
9564
 
7.7%
8022
 
6.5%
7679
 
6.2%
7592
 
6.1%
7309
 
5.9%
7268
 
5.8%
7256
 
5.8%
Other values (209) 38805
31.2%

시가표준액
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct8151
Distinct (%)81.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36477054
Minimum5000
Maximum9.1726435 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-06T17:25:25.356328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5000
5-th percentile350000
Q11808095
median7902100
Q332346720
95-th percentile1.4505913 × 108
Maximum9.1726435 × 109
Range9.1726385 × 109
Interquartile range (IQR)30538625

Descriptive statistics

Standard deviation1.4903589 × 108
Coefficient of variation (CV)4.0857437
Kurtosis1749.5444
Mean36477054
Median Absolute Deviation (MAD)7232450
Skewness34.310174
Sum3.6477054 × 1011
Variance2.2211697 × 1016
MonotonicityNot monotonic
2024-04-06T17:25:25.776600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126000 21
 
0.2%
396000 17
 
0.2%
1900800 17
 
0.2%
252000 16
 
0.2%
950400 15
 
0.1%
840000 14
 
0.1%
462000 14
 
0.1%
540000 13
 
0.1%
660000 13
 
0.1%
594000 12
 
0.1%
Other values (8141) 9848
98.5%
ValueCountFrequency (%)
5000 1
< 0.1%
20000 1
< 0.1%
24640 1
< 0.1%
26000 1
< 0.1%
27450 1
< 0.1%
36000 1
< 0.1%
37000 1
< 0.1%
40000 1
< 0.1%
42000 1
< 0.1%
42500 1
< 0.1%
ValueCountFrequency (%)
9172643510 1
< 0.1%
5837715400 1
< 0.1%
4173749150 1
< 0.1%
3287984620 1
< 0.1%
3077982800 1
< 0.1%
2068992210 1
< 0.1%
1755094950 1
< 0.1%
1342224800 1
< 0.1%
1256993200 1
< 0.1%
1157352850 1
< 0.1%

연면적
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct5004
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean147.81596
Minimum0.81
Maximum20906.31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-06T17:25:26.470947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.81
5-th percentile11
Q132
median71.22
Q3153
95-th percentile493.924
Maximum20906.31
Range20905.5
Interquartile range (IQR)121

Descriptive statistics

Standard deviation390.43519
Coefficient of variation (CV)2.6413602
Kurtosis1237.2872
Mean147.81596
Median Absolute Deviation (MAD)48.72
Skewness27.878597
Sum1478159.6
Variance152439.64
MonotonicityNot monotonic
2024-04-06T17:25:27.119404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.0 381
 
3.8%
50.0 65
 
0.7%
33.0 64
 
0.6%
36.0 61
 
0.6%
66.0 57
 
0.6%
40.0 46
 
0.5%
12.0 44
 
0.4%
27.0 44
 
0.4%
24.0 43
 
0.4%
60.0 42
 
0.4%
Other values (4994) 9153
91.5%
ValueCountFrequency (%)
0.81 4
< 0.1%
0.86 1
 
< 0.1%
0.9 1
 
< 0.1%
1.0 4
< 0.1%
1.2 1
 
< 0.1%
1.44 1
 
< 0.1%
1.5 1
 
< 0.1%
1.54 4
< 0.1%
1.62 1
 
< 0.1%
1.95 1
 
< 0.1%
ValueCountFrequency (%)
20906.31 1
< 0.1%
17119.4 1
< 0.1%
10433.84 1
< 0.1%
8195.07 1
< 0.1%
6669.34 1
< 0.1%
4935.65 1
< 0.1%
3923.23 1
< 0.1%
3713.0 1
< 0.1%
3298.24 1
< 0.1%
3278.91 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

2024-04-06T17:25:27.766194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T17:25:28.018482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021-06-01 10000
100.0%

Interactions

2024-04-06T17:25:14.569167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:25:02.000499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:25:03.834874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:25:05.702535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:25:07.354597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:25:09.281717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:25:10.890108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:25:12.505745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:25:14.760695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:25:02.254248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:25:04.129697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:25:06.018457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:25:07.572891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:25:09.480098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:25:11.087743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:25:12.883175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:25:14.965814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:25:02.535852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:25:04.340689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:25:06.250029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:25:07.783407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:25:09.763124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:25:11.273203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:25:13.225975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:25:15.150459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:25:02.725982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:25:04.565645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:25:06.427954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:25:07.954421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:25:09.988617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:25:11.442652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:25:13.431993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:25:15.374362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:25:02.924633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:25:04.800104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:25:06.613767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:25:08.117880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:25:10.212790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:25:11.617333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:25:13.668656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:25:15.548634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:25:03.127692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:25:04.999762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:25:06.775849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:25:08.281138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:25:10.382054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:25:11.802720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:25:13.929224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:25:15.723974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:25:03.339833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:25:05.227545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:25:06.964933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:25:08.518074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:25:10.549276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:25:11.993827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:25:14.117807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:25:15.909378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:25:03.556356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:25:05.528843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:25:07.190859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:25:09.106310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:25:10.729397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:25:12.171685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:25:14.285973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-06T17:25:28.239027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동법정리특수지본번부번시가표준액연면적
법정동1.0000.4870.0800.3750.0870.0660.0120.0820.022
법정리0.4871.0000.1840.3920.1440.0670.0440.0000.033
특수지0.0800.1841.0000.3460.0000.1810.0000.0360.000
본번0.3750.3920.3461.0000.1440.0420.0250.0700.066
부번0.0870.1440.0000.1441.0000.0000.0000.0000.000
0.0660.0670.1810.0420.0001.0000.0260.0800.060
0.0120.0440.0000.0250.0000.0261.0000.0860.000
시가표준액0.0820.0000.0360.0700.0000.0800.0861.0000.977
연면적0.0220.0330.0000.0660.0000.0600.0000.9771.000
2024-04-06T17:25:28.507861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동법정리본번부번시가표준액연면적특수지
법정동1.000-0.0790.047-0.184-0.027-0.215-0.214-0.0670.103
법정리-0.0791.0000.049-0.140-0.006-0.160-0.103-0.0120.139
본번0.0470.0491.000-0.004-0.031-0.0120.0100.0400.265
부번-0.184-0.140-0.0041.000-0.0050.1300.1400.0150.000
-0.027-0.006-0.031-0.0051.0000.0410.0350.0090.120
-0.215-0.160-0.0120.1300.0411.0000.3410.0610.000
시가표준액-0.214-0.1030.0100.1400.0350.3411.0000.6450.039
연면적-0.067-0.0120.0400.0150.0090.0610.6451.0000.000
특수지0.1030.1390.2650.0000.1200.0000.0390.0001.000

Missing values

2024-04-06T17:25:16.187961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-06T17:25:16.656044image/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
950경상남도함양군4887020212502916407101[ 함양남서로 996-48 ] 0007동 0101호74179800130.142021-06-01
3681경상남도함양군48870202125024113681102[ 한들로 179 ] 0001동 0102호1395479029.242021-06-01
8661경상남도함양군48870202132027176201101경상남도 함양군 휴천면 남호리 762 1동 101호51421690128.492021-06-01
2388경상남도함양군4887020212502816711201경상남도 함양군 함양읍 백연리 67-1 1동 201호78201530132.772021-06-01
11994경상남도함양군48870202134026189292201경상남도 함양군 수동면 내백리 89-29 2동 201호6772700144.12021-06-01
6840경상남도함양군488702021330271720112경상남도 함양군 유림면 대궁리 720-1 1동 2호653140057.82021-06-01
10656경상남도함양군4887020213503011376111경상남도 함양군 지곡면 보산리 1376-1 1동 1호997047023.912021-06-01
5339경상남도함양군488702021320241192101104경상남도 함양군 휴천면 목현리 192-10 1동 104호31998420522.852021-06-01
9506경상남도함양군48870202135030127861213경상남도 함양군 지곡면 보산리 278-6 1동 213호1715852040.662021-06-01
6916경상남도함양군48870202131022193113경상남도 함양군 마천면 군자리 93-1 1동 3호320760044.552021-06-01
시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번물건지시가표준액연면적기준일자
15942경상남도함양군4887020213802111764111[ 칠형정길 23 ] 0001동 0001호24750016.52021-06-01
16874경상남도함양군48870202136031148051401경상남도 함양군 안의면 신안리 480-5 1동 401호193815360498.242021-06-01
9600경상남도함양군488702021360291627012경상남도 함양군 안의면 귀곡리 627 1동 2호39600019.82021-06-01
7789경상남도함양군488702021310231266112[ 덕전길 327 ] 0001동 0002호41580019.82021-06-01
2450경상남도함양군4887020212502112711101경상남도 함양군 함양읍 운림리 27-1 1동 101호321688800758.72021-06-01
7024경상남도함양군488702021320241219013경상남도 함양군 휴천면 목현리 219 1동 3호23905800139.82021-06-01
2878경상남도함양군488702021250241956011경상남도 함양군 함양읍 교산리 956 1동 1호967500045.02021-06-01
17135경상남도함양군488702021400261382711경상남도 함양군 병곡면 광평리 382-7 1동 1호501811017.282021-06-01
14271경상남도함양군4887020213702511492013경상남도 함양군 서하면 봉전리 1492 1동 3호1014507066.72021-06-01
101경상남도함양군488702021250241626303[ 뇌계길 100 ] 0000동 0003호20069280114.032021-06-01

Duplicate rows

Most frequently occurring

시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번물건지시가표준액연면적기준일자# duplicates
30경상남도함양군4887020213802611782011경상남도 함양군 서상면 상남리 1782 1동 1호12040004.02021-06-016
12경상남도함양군488702021310251788011경상남도 함양군 마천면 강청리 788 1동 1호1801800024.752021-06-015
13경상남도함양군488702021310251788011경상남도 함양군 마천면 강청리 788 1동 1호2063880022.682021-06-015
23경상남도함양군488702021350261523011경상남도 함양군 지곡면 남효리 523 1동 1호214200018.02021-06-014
15경상남도함양군488702021310251788012경상남도 함양군 마천면 강청리 788 1동 2호1706400018.02021-06-013
18경상남도함양군488702021330211113801101경상남도 함양군 유림면 손곡리 1138 1동 101호38246400384.02021-06-013
19경상남도함양군488702021330241436012경상남도 함양군 유림면 화촌리 436 1동 2호31447900278.32021-06-013
28경상남도함양군4887020213603211358011경상남도 함양군 안의면 하원리 1358 1동 1호387504024.842021-06-013
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