Overview

Dataset statistics

Number of variables15
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows14
Duplicate rows (%)0.1%
Total size in memory1.3 MiB
Average record size in memory139.0 B

Variable types

Categorical6
Numeric8
Text1

Dataset

Description상기 데이터는 연도별 일반건축물에 대한 지방세 부과기준인 시가표준액을 제공하여 물건별 재산가액 확인이 가능하도록 함
Author충청남도
URLhttps://alldam.chungnam.go.kr/index.chungnam?menuCd=DOM_000000201001001001&st=&cds=&orgCd=&apiType=&isOpen=Y&pageIndex=348&beforeMenuCd=DOM_000000201001001000&publicdatapk=15079984

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
Dataset has 14 (0.1%) duplicate rowsDuplicates
과세년도 is highly overall correlated with 기준일자High correlation
기준일자 is highly overall correlated with 과세년도High correlation
시가표준액 is highly overall correlated with 연면적High correlation
연면적 is highly overall correlated with 시가표준액 High correlation
특수지 is highly imbalanced (86.3%)Imbalance
is highly skewed (γ1 = 50.24096082)Skewed
시가표준액 is highly skewed (γ1 = 22.84282816)Skewed
부번 has 3084 (30.8%) zerosZeros
has 7721 (77.2%) zerosZeros

Reproduction

Analysis started2024-01-09 22:09:32.927467
Analysis finished2024-01-09 22:09:40.182591
Duration7.26 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
충청남도
10000 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
충청남도 10000
100.0%

Length

2024-01-10T07:09:40.228826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T07:09:40.296281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
충청남도 10000
100.0%

시군구명
Categorical

CONSTANT 

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

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
부여군 10000
100.0%

Length

2024-01-10T07:09:40.365316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T07:09:40.436897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
부여군 10000
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
44760
10000 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row44760
2nd row44760
3rd row44760
4th row44760
5th row44760

Common Values

ValueCountFrequency (%)
44760 10000
100.0%

Length

2024-01-10T07:09:40.508015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T07:09:40.573146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
44760 10000
100.0%

과세년도
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2019
3506 
2018
3312 
2017
3182 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2018
2nd row2017
3rd row2018
4th row2017
5th row2018

Common Values

ValueCountFrequency (%)
2019 3506
35.1%
2018 3312
33.1%
2017 3182
31.8%

Length

2024-01-10T07:09:40.648814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T07:09:40.746737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2019 3506
35.1%
2018 3312
33.1%
2017 3182
31.8%

법정동
Real number (ℝ)

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean342.878
Minimum250
Maximum450
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T07:09:40.844495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum250
5-th percentile250
Q1310
median340
Q3410
95-th percentile440
Maximum450
Range200
Interquartile range (IQR)100

Descriptive statistics

Standard deviation68.000732
Coefficient of variation (CV)0.1983234
Kurtosis-1.2883771
Mean342.878
Median Absolute Deviation (MAD)70
Skewness-0.014470343
Sum3428780
Variance4624.0995
MonotonicityNot monotonic
2024-01-10T07:09:40.965054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
250 2480
24.8%
310 1087
10.9%
320 899
 
9.0%
440 639
 
6.4%
420 587
 
5.9%
360 581
 
5.8%
410 558
 
5.6%
430 496
 
5.0%
330 473
 
4.7%
450 431
 
4.3%
Other values (6) 1769
17.7%
ValueCountFrequency (%)
250 2480
24.8%
310 1087
10.9%
320 899
 
9.0%
330 473
 
4.7%
340 304
 
3.0%
350 398
 
4.0%
360 581
 
5.8%
370 232
 
2.3%
380 258
 
2.6%
390 217
 
2.2%
ValueCountFrequency (%)
450 431
4.3%
440 639
6.4%
430 496
5.0%
420 587
5.9%
410 558
5.6%
400 360
3.6%
390 217
 
2.2%
380 258
2.6%
370 232
 
2.3%
360 581
5.8%

법정리
Real number (ℝ)

Distinct22
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.2065
Minimum21
Maximum42
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T07:09:41.086858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile21
Q124
median27
Q330
95-th percentile35
Maximum42
Range21
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.2721052
Coefficient of variation (CV)0.15702517
Kurtosis0.20837827
Mean27.2065
Median Absolute Deviation (MAD)3
Skewness0.59959231
Sum272065
Variance18.250883
MonotonicityNot monotonic
2024-01-10T07:09:41.411804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
24 1244
12.4%
29 1172
11.7%
28 1145
11.5%
21 962
9.6%
30 686
 
6.9%
26 672
 
6.7%
23 611
 
6.1%
27 546
 
5.5%
25 533
 
5.3%
31 532
 
5.3%
Other values (12) 1897
19.0%
ValueCountFrequency (%)
21 962
9.6%
22 514
5.1%
23 611
6.1%
24 1244
12.4%
25 533
5.3%
26 672
6.7%
27 546
5.5%
28 1145
11.5%
29 1172
11.7%
30 686
6.9%
ValueCountFrequency (%)
42 30
 
0.3%
41 31
 
0.3%
40 42
 
0.4%
39 28
 
0.3%
38 59
 
0.6%
37 99
 
1.0%
36 162
1.6%
35 188
1.9%
34 121
 
1.2%
33 317
3.2%

특수지
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
9808 
2
 
192

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row2

Common Values

ValueCountFrequency (%)
1 9808
98.1%
2 192
 
1.9%

Length

2024-01-10T07:09:41.501264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T07:09:41.570485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9808
98.1%
2 192
 
1.9%

본번
Real number (ℝ)

Distinct930
Distinct (%)9.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean322.625
Minimum1
Maximum1497
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T07:09:41.649187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile20
Q1123
median252
Q3451
95-th percentile865.05
Maximum1497
Range1496
Interquartile range (IQR)328

Descriptive statistics

Standard deviation269.92532
Coefficient of variation (CV)0.83665344
Kurtosis1.9981725
Mean322.625
Median Absolute Deviation (MAD)152
Skewness1.368343
Sum3226250
Variance72859.676
MonotonicityNot monotonic
2024-01-10T07:09:41.760531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
430 68
 
0.7%
2 67
 
0.7%
199 62
 
0.6%
17 57
 
0.6%
1005 56
 
0.6%
40 54
 
0.5%
200 51
 
0.5%
1 50
 
0.5%
591 48
 
0.5%
124 47
 
0.5%
Other values (920) 9440
94.4%
ValueCountFrequency (%)
1 50
0.5%
2 67
0.7%
3 17
 
0.2%
4 17
 
0.2%
5 13
 
0.1%
6 27
0.3%
7 20
 
0.2%
8 24
 
0.2%
9 8
 
0.1%
10 12
 
0.1%
ValueCountFrequency (%)
1497 2
 
< 0.1%
1456 2
 
< 0.1%
1454 1
 
< 0.1%
1446 2
 
< 0.1%
1320 8
0.1%
1316 7
0.1%
1308 7
0.1%
1305 1
 
< 0.1%
1303 12
0.1%
1293 3
 
< 0.1%

부번
Real number (ℝ)

ZEROS 

Distinct77
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1923
Minimum0
Maximum214
Zeros3084
Zeros (%)30.8%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T07:09:41.879149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q34
95-th percentile16
Maximum214
Range214
Interquartile range (IQR)4

Descriptive statistics

Standard deviation9.8766834
Coefficient of variation (CV)2.3559105
Kurtosis156.47755
Mean4.1923
Median Absolute Deviation (MAD)1
Skewness9.84363
Sum41923
Variance97.548876
MonotonicityNot monotonic
2024-01-10T07:09:41.990275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3084
30.8%
1 1941
19.4%
2 1132
 
11.3%
3 754
 
7.5%
4 653
 
6.5%
5 413
 
4.1%
6 329
 
3.3%
7 248
 
2.5%
8 206
 
2.1%
9 154
 
1.5%
Other values (67) 1086
 
10.9%
ValueCountFrequency (%)
0 3084
30.8%
1 1941
19.4%
2 1132
 
11.3%
3 754
 
7.5%
4 653
 
6.5%
5 413
 
4.1%
6 329
 
3.3%
7 248
 
2.5%
8 206
 
2.1%
9 154
 
1.5%
ValueCountFrequency (%)
214 5
0.1%
213 1
 
< 0.1%
137 1
 
< 0.1%
131 1
 
< 0.1%
128 3
< 0.1%
122 1
 
< 0.1%
121 4
< 0.1%
119 1
 
< 0.1%
97 3
< 0.1%
92 2
 
< 0.1%


Real number (ℝ)

SKEWED  ZEROS 

Distinct40
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0722
Minimum0
Maximum6000
Zeros7721
Zeros (%)77.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T07:09:42.089275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum6000
Range6000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation80.755418
Coefficient of variation (CV)26.28586
Kurtosis3248.6149
Mean3.0722
Median Absolute Deviation (MAD)0
Skewness50.240961
Sum30722
Variance6521.4375
MonotonicityNot monotonic
2024-01-10T07:09:42.184554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
0 7721
77.2%
1 1722
 
17.2%
2 239
 
2.4%
3 109
 
1.1%
4 44
 
0.4%
6 30
 
0.3%
7 17
 
0.2%
5 15
 
0.1%
8 11
 
0.1%
9 9
 
0.1%
Other values (30) 83
 
0.8%
ValueCountFrequency (%)
0 7721
77.2%
1 1722
 
17.2%
2 239
 
2.4%
3 109
 
1.1%
4 44
 
0.4%
5 15
 
0.1%
6 30
 
0.3%
7 17
 
0.2%
8 11
 
0.1%
9 9
 
0.1%
ValueCountFrequency (%)
6000 1
 
< 0.1%
2001 5
0.1%
1000 9
0.1%
218 4
< 0.1%
201 1
 
< 0.1%
99 1
 
< 0.1%
39 5
0.1%
38 2
 
< 0.1%
36 2
 
< 0.1%
35 1
 
< 0.1%


Real number (ℝ)

Distinct129
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean237.4894
Minimum0
Maximum8109
Zeros5
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T07:09:42.288625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile101
Q1101
median102
Q3104
95-th percentile301
Maximum8109
Range8109
Interquartile range (IQR)3

Descriptive statistics

Standard deviation962.31583
Coefficient of variation (CV)4.052037
Kurtosis62.595452
Mean237.4894
Median Absolute Deviation (MAD)1
Skewness8.0219451
Sum2374894
Variance926051.75
MonotonicityNot monotonic
2024-01-10T07:09:42.395752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
101 4445
44.5%
102 1875
18.8%
103 937
 
9.4%
201 678
 
6.8%
104 425
 
4.2%
105 232
 
2.3%
301 197
 
2.0%
106 169
 
1.7%
202 127
 
1.3%
8101 119
 
1.2%
Other values (119) 796
 
8.0%
ValueCountFrequency (%)
0 5
 
0.1%
1 35
0.4%
2 6
 
0.1%
4 2
 
< 0.1%
5 3
 
< 0.1%
6 1
 
< 0.1%
8 1
 
< 0.1%
9 2
 
< 0.1%
10 2
 
< 0.1%
12 2
 
< 0.1%
ValueCountFrequency (%)
8109 1
 
< 0.1%
8104 1
 
< 0.1%
8103 2
 
< 0.1%
8102 24
 
0.2%
8101 119
1.2%
1101 1
 
< 0.1%
1022 2
 
< 0.1%
1017 1
 
< 0.1%
901 1
 
< 0.1%
801 3
 
< 0.1%
Distinct8094
Distinct (%)80.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-01-10T07:09:42.624697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length34
Median length32
Mean length26.3794
Min length20

Characters and Unicode

Total characters263794
Distinct characters208
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6593 ?
Unique (%)65.9%

Sample

1st row[ 삽티로 340-24 ] 0000동 0101호
2nd row충청남도 부여군 은산면 은산리 128-4 116호
3rd row[ 사비로72번길 7 ] 0000동 0101호
4th row[ 증산로 40 ] 0001동 0201호
5th row[ 회동로47번길 42 ] 0000동 0101호
ValueCountFrequency (%)
충청남도 7301
 
11.8%
부여군 7301
 
11.8%
5398
 
8.7%
101호 3144
 
5.1%
0000동 2350
 
3.8%
102호 1474
 
2.4%
1동 1421
 
2.3%
0101호 1301
 
2.1%
부여읍 1301
 
2.1%
규암면 800
 
1.3%
Other values (4059) 30318
48.8%
2024-01-10T07:09:42.960513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
52111
19.8%
0 25516
 
9.7%
1 23413
 
8.9%
10063
 
3.8%
8758
 
3.3%
8676
 
3.3%
2 8240
 
3.1%
7956
 
3.0%
7739
 
2.9%
7738
 
2.9%
Other values (198) 103584
39.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 119411
45.3%
Decimal Number 80984
30.7%
Space Separator 52111
19.8%
Dash Punctuation 5890
 
2.2%
Close Punctuation 2699
 
1.0%
Open Punctuation 2699
 
1.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
10063
 
8.4%
8758
 
7.3%
8676
 
7.3%
7956
 
6.7%
7739
 
6.5%
7738
 
6.5%
7444
 
6.2%
7415
 
6.2%
7304
 
6.1%
6002
 
5.0%
Other values (184) 40316
33.8%
Decimal Number
ValueCountFrequency (%)
0 25516
31.5%
1 23413
28.9%
2 8240
 
10.2%
3 5359
 
6.6%
4 4181
 
5.2%
5 3485
 
4.3%
6 3222
 
4.0%
7 2700
 
3.3%
8 2548
 
3.1%
9 2320
 
2.9%
Space Separator
ValueCountFrequency (%)
52111
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5890
100.0%
Close Punctuation
ValueCountFrequency (%)
] 2699
100.0%
Open Punctuation
ValueCountFrequency (%)
[ 2699
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 144383
54.7%
Hangul 119411
45.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
10063
 
8.4%
8758
 
7.3%
8676
 
7.3%
7956
 
6.7%
7739
 
6.5%
7738
 
6.5%
7444
 
6.2%
7415
 
6.2%
7304
 
6.1%
6002
 
5.0%
Other values (184) 40316
33.8%
Common
ValueCountFrequency (%)
52111
36.1%
0 25516
17.7%
1 23413
16.2%
2 8240
 
5.7%
- 5890
 
4.1%
3 5359
 
3.7%
4 4181
 
2.9%
5 3485
 
2.4%
6 3222
 
2.2%
7 2700
 
1.9%
Other values (4) 10266
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 144383
54.7%
Hangul 119411
45.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
52111
36.1%
0 25516
17.7%
1 23413
16.2%
2 8240
 
5.7%
- 5890
 
4.1%
3 5359
 
3.7%
4 4181
 
2.9%
5 3485
 
2.4%
6 3222
 
2.2%
7 2700
 
1.9%
Other values (4) 10266
 
7.1%
Hangul
ValueCountFrequency (%)
10063
 
8.4%
8758
 
7.3%
8676
 
7.3%
7956
 
6.7%
7739
 
6.5%
7738
 
6.5%
7444
 
6.2%
7415
 
6.2%
7304
 
6.1%
6002
 
5.0%
Other values (184) 40316
33.8%

시가표준액
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct8431
Distinct (%)84.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39172343
Minimum14610
Maximum7.9203663 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T07:09:43.076035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum14610
5-th percentile293039
Q11544640
median6792240
Q330542122
95-th percentile1.5582799 × 108
Maximum7.9203663 × 109
Range7.9203517 × 109
Interquartile range (IQR)28997482

Descriptive statistics

Standard deviation1.5730308 × 108
Coefficient of variation (CV)4.0156669
Kurtosis845.70921
Mean39172343
Median Absolute Deviation (MAD)6237840
Skewness22.842828
Sum3.9172343 × 1011
Variance2.4744259 × 1016
MonotonicityNot monotonic
2024-01-10T07:09:43.185935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5443200 18
 
0.2%
720000 15
 
0.1%
5459400 15
 
0.1%
5394600 15
 
0.1%
936000 14
 
0.1%
828000 13
 
0.1%
594000 12
 
0.1%
547200 12
 
0.1%
759000 11
 
0.1%
1260000 11
 
0.1%
Other values (8421) 9864
98.6%
ValueCountFrequency (%)
14610 1
< 0.1%
27000 1
< 0.1%
28960 1
< 0.1%
31200 1
< 0.1%
34200 1
< 0.1%
36000 1
< 0.1%
36560 1
< 0.1%
37020 1
< 0.1%
37800 1
< 0.1%
38880 2
< 0.1%
ValueCountFrequency (%)
7920366300 1
< 0.1%
4407581520 1
< 0.1%
4383196560 1
< 0.1%
4233271900 1
< 0.1%
2931713510 1
< 0.1%
2666482960 1
< 0.1%
2456808240 1
< 0.1%
2319946970 1
< 0.1%
2249989500 1
< 0.1%
1866096370 1
< 0.1%

연면적
Real number (ℝ)

HIGH CORRELATION 

Distinct4777
Distinct (%)47.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean190.25038
Minimum0.7
Maximum19707
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T07:09:43.295158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.7
5-th percentile12.999
Q139.05
median94.255
Q3194.005
95-th percentile705.62
Maximum19707
Range19706.3
Interquartile range (IQR)154.955

Descriptive statistics

Standard deviation410.03978
Coefficient of variation (CV)2.155264
Kurtosis596.38721
Mean190.25038
Median Absolute Deviation (MAD)66.155
Skewness16.74782
Sum1902503.8
Variance168132.62
MonotonicityNot monotonic
2024-01-10T07:09:43.405441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.0 215
 
2.1%
16.5 120
 
1.2%
50.0 56
 
0.6%
16.2 54
 
0.5%
198.0 49
 
0.5%
99.22 44
 
0.4%
165.0 38
 
0.4%
36.0 35
 
0.4%
96.0 35
 
0.4%
40.0 34
 
0.3%
Other values (4767) 9320
93.2%
ValueCountFrequency (%)
0.7 2
 
< 0.1%
1.37 1
 
< 0.1%
1.44 8
0.1%
1.65 2
 
< 0.1%
1.87 1
 
< 0.1%
1.92 1
 
< 0.1%
1.95 1
 
< 0.1%
1.96 1
 
< 0.1%
2.0 2
 
< 0.1%
2.16 1
 
< 0.1%
ValueCountFrequency (%)
19707.0 1
< 0.1%
10353.42 1
< 0.1%
7091.71 1
< 0.1%
6982.0 1
< 0.1%
6096.24 2
< 0.1%
5568.2 1
< 0.1%
5379.0 2
< 0.1%
5338.3 1
< 0.1%
4859.28 2
< 0.1%
4078.91 2
< 0.1%

기준일자
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2019-06-01
3506 
2018-06-01
3312 
2017-06-01
3182 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2018-06-01
2nd row2017-06-01
3rd row2018-06-01
4th row2017-06-01
5th row2018-06-01

Common Values

ValueCountFrequency (%)
2019-06-01 3506
35.1%
2018-06-01 3312
33.1%
2017-06-01 3182
31.8%

Length

2024-01-10T07:09:43.506608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T07:09:43.585531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2019-06-01 3506
35.1%
2018-06-01 3312
33.1%
2017-06-01 3182
31.8%

Interactions

2024-01-10T07:09:39.277658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:09:34.577322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:09:35.213228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:09:35.801678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:09:36.578097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:09:37.366325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:09:37.985304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:09:38.633762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:09:39.363804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:09:34.659293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:09:35.290461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:09:35.882190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:09:36.653776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:09:37.448284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:09:38.069201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:09:38.718588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:09:39.436974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:09:34.731200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:09:35.354097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:09:35.954854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:09:36.717846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:09:37.516727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:09:38.140237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:09:38.791597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:09:39.519777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:09:34.811845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:09:35.427950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:09:36.059344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:09:37.002839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:09:37.593409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:09:38.221038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:09:38.872934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:09:39.593021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:09:34.882371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:09:35.490612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:09:36.158051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:09:37.063862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:09:37.661744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:09:38.291260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:09:38.944340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:09:39.674116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:09:34.959479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:09:35.557890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:09:36.258933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:09:37.131852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:09:37.739796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:09:38.380050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:09:39.022760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:09:39.756488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:09:35.045985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:09:35.640103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:09:36.370234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:09:37.206714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:09:37.823797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:09:38.465073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:09:39.104049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:09:39.850281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:09:35.132505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:09:35.721650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:09:36.479971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:09:37.289134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:09:37.905792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:09:38.550973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:09:39.190596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-10T07:09:43.645102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도법정동법정리특수지본번부번시가표준액연면적기준일자
과세년도1.0000.0000.0000.0000.0000.0000.0130.0000.0000.0001.000
법정동0.0001.0000.4830.0920.3960.1260.0730.0760.0700.0500.000
법정리0.0000.4831.0000.0860.4130.0700.0380.1010.0510.0410.000
특수지0.0000.0920.0861.0000.2560.0000.0000.0000.0000.0000.000
본번0.0000.3960.4130.2561.0000.1930.0710.0480.0570.0400.000
부번0.0000.1260.0700.0000.1931.0000.0000.0000.0000.0000.000
0.0130.0730.0380.0000.0710.0001.0000.0000.0490.0000.013
0.0000.0760.1010.0000.0480.0000.0001.0000.3630.1750.000
시가표준액0.0000.0700.0510.0000.0570.0000.0490.3631.0000.9300.000
연면적0.0000.0500.0410.0000.0400.0000.0000.1750.9301.0000.000
기준일자1.0000.0000.0000.0000.0000.0000.0130.0000.0000.0001.000
2024-01-10T07:09:43.740113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도특수지기준일자
과세년도1.0000.0001.000
특수지0.0001.0000.000
기준일자1.0000.0001.000
2024-01-10T07:09:43.813843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동법정리본번부번시가표준액연면적과세년도특수지기준일자
법정동1.000-0.4550.111-0.0040.021-0.127-0.1860.0630.0060.0770.006
법정리-0.4551.000-0.036-0.0010.0020.0360.085-0.0060.0000.0660.000
본번0.111-0.0361.000-0.0730.0180.0010.0460.0450.0000.1960.000
부번-0.004-0.001-0.0731.0000.001-0.0470.0590.0110.0000.0000.000
0.0210.0020.0180.0011.000-0.0870.1620.0630.0120.0000.012
-0.1270.0360.001-0.047-0.0871.0000.090-0.0460.0000.0000.000
시가표준액-0.1860.0850.0460.0590.1620.0901.0000.5660.0000.0000.000
연면적0.063-0.0060.0450.0110.063-0.0460.5661.0000.0000.0000.000
과세년도0.0060.0000.0000.0000.0120.0000.0000.0001.0000.0001.000
특수지0.0770.0660.1960.0000.0000.0000.0000.0000.0001.0000.000
기준일자0.0060.0000.0000.0000.0120.0000.0000.0001.0000.0001.000

Missing values

2024-01-10T07:09:39.965077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T07:09:40.113034image/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
29685충청남도부여군447602018360291517140101[ 삽티로 340-24 ] 0000동 0101호75900033.02018-06-01
15884충청남도부여군44760201732024112840116충청남도 부여군 은산면 은산리 128-4 116호77756027.772017-06-01
34767충청남도부여군44760201825029127600101[ 사비로72번길 7 ] 0000동 0101호50258000193.32018-06-01
12275충청남도부여군4476020174402411218141201[ 증산로 40 ] 0001동 0201호56482920126.362017-06-01
30833충청남도부여군4476020183802523300101[ 회동로47번길 42 ] 0000동 0101호266798070.212018-06-01
37655충청남도부여군44760201825030112140301[ 성왕로 242 ] 0000동 0301호984592033.042018-06-01
42648충청남도부여군4476020194102517211103충청남도 부여군 임천면 비정리 72-1 1동 103호320400018.02019-06-01
37080충청남도부여군44760201825042136700101충청남도 부여군 부여읍 현북리 367 101호1980000198.02018-06-01
22037충청남도부여군44760201845021147301101[ 신암로 206-5 ] 0001동 0101호50188600325.92018-06-01
24175충청남도부여군44760201841027126100101충청남도 부여군 임천면 만사리 261 101호73800018.02018-06-01
시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번물건지시가표준액연면적기준일자
50699충청남도부여군4476020193502214710101[ 망해로54번길 62-6 ] 0000동 0101호1238400096.02019-06-01
33421충청남도부여군44760201832024112560301[ 충의로 681 ] 0000동 0301호810466026.062018-06-01
19038충청남도부여군4476020173502513510103충청남도 부여군 구룡면 논티리 35-1 103호345129039.672017-06-01
36057충청남도부여군44760201831022155920101충청남도 부여군 규암면 나복리 559-2 101호1162326096.062018-06-01
59709충청남도부여군44760201937030139400101충청남도 부여군 옥산면 내대리 394 101호323080134.622019-06-01
13367충청남도부여군44760201745024112310105[ 초촌로 23 ] 0000동 0105호65287800381.82017-06-01
8265충청남도부여군44760201725029117240101충청남도 부여군 부여읍 구아리 172-4 101호599966082.642017-06-01
6234충청남도부여군44760201733026117710201충청남도 부여군 외산면 반교리 177-1 201호82196640228.962017-06-01
19519충청남도부여군44760201736024118220101충청남도 부여군 홍산면 남촌리 182-2 101호270600066.02017-06-01
14769충청남도부여군4476020174202622501101충청남도 부여군 장암면 원문리 산 25 1동 101호104462190581.82017-06-01

Duplicate rows

Most frequently occurring

시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번물건지시가표준액연면적기준일자# duplicates
3충청남도부여군44760201743031159501101충청남도 부여군 세도면 사산리 595 1동 101호46682040319.742017-06-013
7충청남도부여군44760201843031159501101충청남도 부여군 세도면 사산리 595 1동 101호43002650296.572018-06-013
0충청남도부여군447602017250411701101[ 대백제로 2372 ] 0001동 0101호77400018.02017-06-012
1충청남도부여군44760201741028172951102충청남도 부여군 임천면 점리 729-5 1동 102호719670048.32017-06-012
2충청남도부여군44760201743031159501101충청남도 부여군 세도면 사산리 595 1동 101호39914940273.392017-06-012
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