Overview

Dataset statistics

Number of variables15
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows21
Duplicate rows (%)0.2%
Total size in memory1.3 MiB
Average record size in memory137.0 B

Variable types

Categorical6
Numeric6
Text2
DateTime1

Dataset

Description제공범위 : 일반건축물에 대한 지방세 부과기준인 시가표준액을 제공. 관련 법령 : 지방세법. 소관기관 : 지방자치단체. 제공기관 : 시군구. 표준데이터 셋 제공시스템 : 표준지방세시스템. 자료기준일 : 매년 12월 31일.
Author충청남도
URLhttps://alldam.chungnam.go.kr/index.chungnam?menuCd=DOM_000000201001001001&st=&cds=&orgCd=&apiType=&isOpen=Y&pageIndex=348&beforeMenuCd=DOM_000000201001001000&publicdatapk=15080111

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 21 (0.2%) duplicate rowsDuplicates
특수지 is highly imbalanced (95.1%)Imbalance
동 is highly imbalanced (82.2%)Imbalance
부번 is highly skewed (γ1 = 38.12663932)Skewed
연면적 is highly skewed (γ1 = 31.79915312)Skewed
부번 has 3680 (36.8%) zerosZeros

Reproduction

Analysis started2024-01-09 21:23:07.490258
Analysis finished2024-01-09 21:23:12.292377
Duration4.8 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

2024-01-10T06:23:12.339967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T06:23:12.411386image/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
홍성군
10000 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
홍성군 10000
100.0%

Length

2024-01-10T06:23:12.487379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T06:23:12.555776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
홍성군 10000
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
44800
10000 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row44800
2nd row44800
3rd row44800
4th row44800
5th row44800

Common Values

ValueCountFrequency (%)
44800 10000
100.0%

Length

2024-01-10T06:23:12.627783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T06:23:12.694713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
44800 10000
100.0%

과세년도
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2020
10000 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020
2nd row2020
3rd row2020
4th row2020
5th row2020

Common Values

ValueCountFrequency (%)
2020 10000
100.0%

Length

2024-01-10T06:23:12.765126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T06:23:12.833843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020 10000
100.0%

법정동
Real number (ℝ)

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean300.6013
Minimum250
Maximum390
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T06:23:12.892489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum250
5-th percentile250
Q1253
median256
Q3350
95-th percentile390
Maximum390
Range140
Interquartile range (IQR)97

Descriptive statistics

Standard deviation52.983355
Coefficient of variation (CV)0.1762579
Kurtosis-1.5831503
Mean300.6013
Median Absolute Deviation (MAD)6
Skewness0.35110911
Sum3006013
Variance2807.2359
MonotonicityNot monotonic
2024-01-10T06:23:12.972808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
250 2467
24.7%
256 1561
15.6%
253 1219
12.2%
330 750
 
7.5%
350 657
 
6.6%
380 621
 
6.2%
320 576
 
5.8%
340 565
 
5.7%
370 552
 
5.5%
390 521
 
5.2%
ValueCountFrequency (%)
250 2467
24.7%
253 1219
12.2%
256 1561
15.6%
320 576
 
5.8%
330 750
 
7.5%
340 565
 
5.7%
350 657
 
6.6%
360 511
 
5.1%
370 552
 
5.5%
380 621
 
6.2%
ValueCountFrequency (%)
390 521
 
5.2%
380 621
 
6.2%
370 552
 
5.5%
360 511
 
5.1%
350 657
6.6%
340 565
 
5.7%
330 750
7.5%
320 576
 
5.8%
256 1561
15.6%
253 1219
12.2%

법정리
Real number (ℝ)

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.6142
Minimum21
Maximum36
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T06:23:13.055734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile21
Q122
median25
Q329
95-th percentile32
Maximum36
Range15
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.7121067
Coefficient of variation (CV)0.14492378
Kurtosis-0.70404631
Mean25.6142
Median Absolute Deviation (MAD)3
Skewness0.53940514
Sum256142
Variance13.779736
MonotonicityNot monotonic
2024-01-10T06:23:13.143821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
25 1596
16.0%
21 1560
15.6%
22 1213
12.1%
24 832
8.3%
30 727
7.3%
26 647
6.5%
23 622
 
6.2%
31 587
 
5.9%
27 553
 
5.5%
29 493
 
4.9%
Other values (6) 1170
11.7%
ValueCountFrequency (%)
21 1560
15.6%
22 1213
12.1%
23 622
 
6.2%
24 832
8.3%
25 1596
16.0%
26 647
6.5%
27 553
 
5.5%
28 433
 
4.3%
29 493
 
4.9%
30 727
7.3%
ValueCountFrequency (%)
36 32
 
0.3%
35 66
 
0.7%
34 80
 
0.8%
33 271
 
2.7%
32 288
 
2.9%
31 587
5.9%
30 727
7.3%
29 493
4.9%
28 433
4.3%
27 553
5.5%

특수지
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
9913 
2
 
75
5
 
12

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 9913
99.1%
2 75
 
0.8%
5 12
 
0.1%

Length

2024-01-10T06:23:13.239694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T06:23:13.312866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9913
99.1%
2 75
 
0.8%
5 12
 
0.1%

본번
Real number (ℝ)

Distinct1020
Distinct (%)10.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean402.2971
Minimum1
Maximum1377
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T06:23:13.404731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile35
Q1191
median370
Q3571
95-th percentile916.05
Maximum1377
Range1376
Interquartile range (IQR)380

Descriptive statistics

Standard deviation272.05556
Coefficient of variation (CV)0.67625532
Kurtosis0.19906796
Mean402.2971
Median Absolute Deviation (MAD)189
Skewness0.75845807
Sum4022971
Variance74014.225
MonotonicityNot monotonic
2024-01-10T06:23:13.511957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
397 129
 
1.3%
392 93
 
0.9%
893 88
 
0.9%
587 85
 
0.9%
230 83
 
0.8%
589 67
 
0.7%
896 55
 
0.5%
560 50
 
0.5%
588 46
 
0.5%
584 40
 
0.4%
Other values (1010) 9264
92.6%
ValueCountFrequency (%)
1 20
0.2%
2 18
0.2%
3 36
0.4%
4 13
 
0.1%
5 17
0.2%
6 13
 
0.1%
7 17
0.2%
8 11
 
0.1%
9 24
0.2%
10 20
0.2%
ValueCountFrequency (%)
1377 3
< 0.1%
1372 2
 
< 0.1%
1370 2
 
< 0.1%
1369 3
< 0.1%
1364 1
 
< 0.1%
1362 2
 
< 0.1%
1217 2
 
< 0.1%
1205 2
 
< 0.1%
1204 7
0.1%
1201 6
0.1%

부번
Real number (ℝ)

SKEWED  ZEROS 

Distinct91
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.7662
Minimum0
Maximum1101
Zeros3680
Zeros (%)36.8%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T06:23:13.646513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q34
95-th percentile19.05
Maximum1101
Range1101
Interquartile range (IQR)4

Descriptive statistics

Standard deviation22.099914
Coefficient of variation (CV)4.6367994
Kurtosis1831.9548
Mean4.7662
Median Absolute Deviation (MAD)1
Skewness38.126639
Sum47662
Variance488.40618
MonotonicityNot monotonic
2024-01-10T06:23:13.783214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3680
36.8%
1 1806
18.1%
2 919
 
9.2%
3 733
 
7.3%
4 439
 
4.4%
5 368
 
3.7%
6 264
 
2.6%
7 261
 
2.6%
8 179
 
1.8%
9 151
 
1.5%
Other values (81) 1200
 
12.0%
ValueCountFrequency (%)
0 3680
36.8%
1 1806
18.1%
2 919
 
9.2%
3 733
 
7.3%
4 439
 
4.4%
5 368
 
3.7%
6 264
 
2.6%
7 261
 
2.6%
8 179
 
1.8%
9 151
 
1.5%
ValueCountFrequency (%)
1101 3
 
< 0.1%
402 1
 
< 0.1%
301 1
 
< 0.1%
219 1
 
< 0.1%
208 1
 
< 0.1%
155 1
 
< 0.1%
153 2
 
< 0.1%
118 9
0.1%
117 1
 
< 0.1%
108 1
 
< 0.1%

동
Categorical

IMBALANCE 

Distinct35
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
8334 
1
1281 
2
 
148
3
 
45
4
 
42
Other values (30)
 
150

Length

Max length4
Median length1
Mean length1.0182
Min length1

Unique

Unique8 ?
Unique (%)0.1%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row3

Common Values

ValueCountFrequency (%)
0 8334
83.3%
1 1281
 
12.8%
2 148
 
1.5%
3 45
 
0.4%
4 42
 
0.4%
8001 27
 
0.3%
6 19
 
0.2%
7 18
 
0.2%
5 8
 
0.1%
11 7
 
0.1%
Other values (25) 71
 
0.7%

Length

2024-01-10T06:23:13.915224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0 8334
83.3%
1 1281
 
12.8%
2 148
 
1.5%
3 45
 
0.4%
4 42
 
0.4%
8001 27
 
0.3%
6 19
 
0.2%
7 18
 
0.2%
5 8
 
0.1%
8 7
 
0.1%
Other values (25) 71
 
0.7%

호
Text

Distinct386
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-01-10T06:23:14.096639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length3.0237
Min length1

Characters and Unicode

Total characters30237
Distinct characters21
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique254 ?
Unique (%)2.5%

Sample

1st row101
2nd row101
3rd row8101
4th row160
5th row102
ValueCountFrequency (%)
101 4255
42.5%
102 1641
 
16.4%
103 775
 
7.8%
201 691
 
6.9%
104 400
 
4.0%
105 216
 
2.2%
301 212
 
2.1%
8101 152
 
1.5%
106 130
 
1.3%
202 122
 
1.2%
Other values (376) 1406
 
14.1%
2024-01-10T06:23:14.367259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 14035
46.4%
0 9554
31.6%
2 3040
 
10.1%
3 1339
 
4.4%
4 736
 
2.4%
5 468
 
1.5%
6 323
 
1.1%
8 322
 
1.1%
7 210
 
0.7%
9 116
 
0.4%
Other values (11) 94
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30143
99.7%
Dash Punctuation 45
 
0.1%
Uppercase Letter 28
 
0.1%
Other Letter 21
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 14035
46.6%
0 9554
31.7%
2 3040
 
10.1%
3 1339
 
4.4%
4 736
 
2.4%
5 468
 
1.6%
6 323
 
1.1%
8 322
 
1.1%
7 210
 
0.7%
9 116
 
0.4%
Other Letter
ValueCountFrequency (%)
가 6
28.6%
설 5
23.8%
부 5
23.8%
동 2
 
9.5%
화 1
 
4.8%
장 1
 
4.8%
실 1
 
4.8%
Uppercase Letter
ValueCountFrequency (%)
A 23
82.1%
B 4
 
14.3%
C 1
 
3.6%
Dash Punctuation
ValueCountFrequency (%)
- 45
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 30188
99.8%
Latin 28
 
0.1%
Hangul 21
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
1 14035
46.5%
0 9554
31.6%
2 3040
 
10.1%
3 1339
 
4.4%
4 736
 
2.4%
5 468
 
1.6%
6 323
 
1.1%
8 322
 
1.1%
7 210
 
0.7%
9 116
 
0.4%
Hangul
ValueCountFrequency (%)
가 6
28.6%
설 5
23.8%
부 5
23.8%
동 2
 
9.5%
화 1
 
4.8%
장 1
 
4.8%
실 1
 
4.8%
Latin
ValueCountFrequency (%)
A 23
82.1%
B 4
 
14.3%
C 1
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30216
99.9%
Hangul 21
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 14035
46.4%
0 9554
31.6%
2 3040
 
10.1%
3 1339
 
4.4%
4 736
 
2.4%
5 468
 
1.5%
6 323
 
1.1%
8 322
 
1.1%
7 210
 
0.7%
9 116
 
0.4%
Other values (4) 73
 
0.2%
Hangul
ValueCountFrequency (%)
가 6
28.6%
설 5
23.8%
부 5
23.8%
동 2
 
9.5%
화 1
 
4.8%
장 1
 
4.8%
실 1
 
4.8%

물건지
Text

Distinct9330
Distinct (%)93.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-01-10T06:23:14.631032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length35
Median length33
Mean length26.3906
Min length21

Characters and Unicode

Total characters263906
Distinct characters166
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

Unique8883 ?
Unique (%)88.8%

Sample

1st row충청남도 홍성군 결성면 교항리 750-3 101호
2nd row[ 월산로43번길 39-2 ] 0000동 0101호
3rd row[ 충서로 1106-25 ] 0000동 8101호
4th row충청남도 홍성군 광천읍 광천리 230 1동 160호
5th row충청남도 홍성군 결성면 금곡리 98-1 3동 102호
ValueCountFrequency (%)
6840
 
11.1%
충청남도 6580
 
10.7%
홍성군 6580
 
10.7%
0000동 3069
 
5.0%
101호 2973
 
4.8%
0101호 1282
 
2.1%
102호 1267
 
2.1%
홍성읍 1221
 
2.0%
1동 978
 
1.6%
광천읍 913
 
1.5%
Other values (4540) 29679
48.4%
2024-01-10T06:23:15.011971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
51382
19.5%
0 28739
 
10.9%
1 22598
 
8.6%
호 10132
 
3.8%
홍 9614
 
3.6%
성 8591
 
3.3%
2 8330
 
3.2%
남 7395
 
2.8%
청 7095
 
2.7%
충 7063
 
2.7%
Other values (156) 102967
39.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 114358
43.3%
Decimal Number 86010
32.6%
Space Separator 51382
19.5%
Dash Punctuation 5264
 
2.0%
Open Punctuation 3420
 
1.3%
Close Punctuation 3420
 
1.3%
Uppercase Letter 52
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
호 10132
 
8.9%
홍 9614
 
8.4%
성 8591
 
7.5%
남 7395
 
6.5%
청 7095
 
6.2%
충 7063
 
6.2%
도 6654
 
5.8%
리 6580
 
5.8%
군 6580
 
5.8%
동 5554
 
4.9%
Other values (138) 39100
34.2%
Decimal Number
ValueCountFrequency (%)
0 28739
33.4%
1 22598
26.3%
2 8330
 
9.7%
3 6139
 
7.1%
4 4775
 
5.6%
5 3924
 
4.6%
6 3357
 
3.9%
7 2862
 
3.3%
8 2714
 
3.2%
9 2572
 
3.0%
Uppercase Letter
ValueCountFrequency (%)
A 23
44.2%
B 16
30.8%
L 12
23.1%
C 1
 
1.9%
Space Separator
ValueCountFrequency (%)
51382
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5264
100.0%
Open Punctuation
ValueCountFrequency (%)
[ 3420
100.0%
Close Punctuation
ValueCountFrequency (%)
] 3420
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 149496
56.6%
Hangul 114358
43.3%
Latin 52
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
호 10132
 
8.9%
홍 9614
 
8.4%
성 8591
 
7.5%
남 7395
 
6.5%
청 7095
 
6.2%
충 7063
 
6.2%
도 6654
 
5.8%
리 6580
 
5.8%
군 6580
 
5.8%
동 5554
 
4.9%
Other values (138) 39100
34.2%
Common
ValueCountFrequency (%)
51382
34.4%
0 28739
19.2%
1 22598
15.1%
2 8330
 
5.6%
3 6139
 
4.1%
- 5264
 
3.5%
4 4775
 
3.2%
5 3924
 
2.6%
[ 3420
 
2.3%
] 3420
 
2.3%
Other values (4) 11505
 
7.7%
Latin
ValueCountFrequency (%)
A 23
44.2%
B 16
30.8%
L 12
23.1%
C 1
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 149548
56.7%
Hangul 114358
43.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
51382
34.4%
0 28739
19.2%
1 22598
15.1%
2 8330
 
5.6%
3 6139
 
4.1%
- 5264
 
3.5%
4 4775
 
3.2%
5 3924
 
2.6%
[ 3420
 
2.3%
] 3420
 
2.3%
Other values (8) 11557
 
7.7%
Hangul
ValueCountFrequency (%)
호 10132
 
8.9%
홍 9614
 
8.4%
성 8591
 
7.5%
남 7395
 
6.5%
청 7095
 
6.2%
충 7063
 
6.2%
도 6654
 
5.8%
리 6580
 
5.8%
군 6580
 
5.8%
동 5554
 
4.9%
Other values (138) 39100
34.2%

시가표준액
Real number (ℝ)

Distinct8229
Distinct (%)82.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55484860
Minimum6400
Maximum7.226885 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T06:23:15.131316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6400
5-th percentile325796
Q11296840
median8392965
Q351380850
95-th percentile2.2140169 × 108
Maximum7.226885 × 109
Range7.2268786 × 109
Interquartile range (IQR)50084010

Descriptive statistics

Standard deviation1.8748396 × 108
Coefficient of variation (CV)3.3790112
Kurtosis548.3227
Mean55484860
Median Absolute Deviation (MAD)7960965
Skewness18.676569
Sum5.548486 × 1011
Variance3.5150236 × 1016
MonotonicityNot monotonic
2024-01-10T06:23:15.274769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
792000 24
 
0.2%
705600 24
 
0.2%
561600 23
 
0.2%
40741230 23
 
0.2%
37350250 22
 
0.2%
960000 18
 
0.2%
38515190 17
 
0.2%
720000 16
 
0.2%
34453450 15
 
0.1%
1440000 15
 
0.1%
Other values (8219) 9803
98.0%
ValueCountFrequency (%)
6400 1
 
< 0.1%
13200 1
 
< 0.1%
15960 1
 
< 0.1%
17680 1
 
< 0.1%
18000 3
< 0.1%
25520 1
 
< 0.1%
25600 1
 
< 0.1%
25920 1
 
< 0.1%
27000 1
 
< 0.1%
31100 1
 
< 0.1%
ValueCountFrequency (%)
7226884980 1
< 0.1%
7019434320 1
< 0.1%
5432587110 1
< 0.1%
4019989560 1
< 0.1%
3425263920 1
< 0.1%
3261311430 1
< 0.1%
3236376000 1
< 0.1%
3034725460 1
< 0.1%
2886076440 1
< 0.1%
2645136000 1
< 0.1%

연면적
Real number (ℝ)

SKEWED 

Distinct6224
Distinct (%)62.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean221.68813
Minimum0.593
Maximum33912
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T06:23:15.418753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.593
5-th percentile16.74
Q153.1244
median121
Q3235.4
95-th percentile756
Maximum33912
Range33911.407
Interquartile range (IQR)182.2756

Descriptive statistics

Standard deviation525.20776
Coefficient of variation (CV)2.3691289
Kurtosis1794.5441
Mean221.68813
Median Absolute Deviation (MAD)77
Skewness31.799153
Sum2216881.3
Variance275843.19
MonotonicityNot monotonic
2024-01-10T06:23:15.529330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.0 215
 
2.1%
198.0 50
 
0.5%
36.0 34
 
0.3%
66.0 34
 
0.3%
27.0 33
 
0.3%
60.0 31
 
0.3%
192.0 28
 
0.3%
72.0 26
 
0.3%
12.0 25
 
0.2%
200.0 25
 
0.2%
Other values (6214) 9499
95.0%
ValueCountFrequency (%)
0.593 1
< 0.1%
0.598 1
< 0.1%
0.9 1
< 0.1%
1.0 1
< 0.1%
1.233 1
< 0.1%
1.6 1
< 0.1%
1.977 1
< 0.1%
2.16 1
< 0.1%
2.21 1
< 0.1%
2.34 2
< 0.1%
ValueCountFrequency (%)
33912.0 1
< 0.1%
15495.44 1
< 0.1%
9608.09 1
< 0.1%
8063.47 1
< 0.1%
7576.83 1
< 0.1%
6396.0 1
< 0.1%
6162.0 1
< 0.1%
5860.0 1
< 0.1%
5606.68 1
< 0.1%
4980.0 1
< 0.1%

기준일자
Date

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2020-06-01 00:00:00
Maximum2020-06-01 00:00:00
2024-01-10T06:23:15.613764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:23:15.678929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2024-01-10T06:23:11.222305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:23:08.698225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:23:09.171623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:23:09.654156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:23:10.179504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:23:10.755654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:23:11.314922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:23:08.771563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:23:09.255219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:23:09.743845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:23:10.279761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:23:10.831477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:23:11.404493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:23:08.848537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:23:09.335702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:23:09.837893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:23:10.381477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:23:10.908535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:23:11.507347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:23:08.923561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:23:09.413121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:23:09.916162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:23:10.475569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:23:10.986353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:23:11.602777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:23:08.999178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:23:09.487423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:23:09.991416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:23:10.562177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:23:11.058182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:23:11.709242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:23:09.089572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:23:09.568296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:23:10.074139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:23:10.663257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:23:11.141679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-10T06:23:15.737777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동법정리특수지본번부번동시가표준액연면적
법정동1.0000.3920.0620.2890.0000.1730.0000.001
법정리0.3921.0000.0980.5160.1580.1690.0000.000
특수지0.0620.0981.0000.3130.5180.0000.0890.000
본번0.2890.5160.3131.0000.1760.4280.1320.028
부번0.0000.1580.5180.1761.0000.0000.1730.065
동0.1730.1690.0000.4280.0001.0000.0000.000
시가표준액0.0000.0000.0890.1320.1730.0001.0000.823
연면적0.0010.0000.0000.0280.0650.0000.8231.000
2024-01-10T06:23:15.824885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
동특수지
동1.0000.000
특수지0.0001.000
2024-01-10T06:23:15.899442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동법정리본번부번시가표준액연면적특수지동
법정동1.0000.219-0.127-0.068-0.2760.0550.0310.068
법정리0.2191.000-0.001-0.087-0.1090.0790.0840.062
본번-0.127-0.0011.000-0.2080.2440.0220.1970.161
부번-0.068-0.087-0.2081.000-0.036-0.0310.4560.000
시가표준액-0.276-0.1090.244-0.0361.0000.4440.0560.000
연면적0.0550.0790.022-0.0310.4441.0000.0000.000
특수지0.0310.0840.1970.4560.0560.0001.0000.000
동0.0680.0620.1610.0000.0000.0000.0001.000

Missing values

2024-01-10T06:23:12.072851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T06:23:12.220054image/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

시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번동호물건지시가표준액연면적기준일자
17613충청남도홍성군44800202036027175030101충청남도 홍성군 결성면 교항리 750-3 101호1033600258.42020-06-01
30633충청남도홍성군448002020250251431150101[ 월산로43번길 39-2 ] 0000동 0101호95544570192.322020-06-01
30145충청남도홍성군448002020250271535308101[ 충서로 1106-25 ] 0000동 8101호39473070144.592020-06-01
4474충청남도홍성군44800202025322123001160충청남도 홍성군 광천읍 광천리 230 1동 160호172260028.712020-06-01
19541충청남도홍성군4480020203602419813102충청남도 홍성군 결성면 금곡리 98-1 3동 102호40176600230.92020-06-01
7771충청남도홍성군448002020253221199190201[ 광천로299번길 12 ] 0000동 0201호662106040.622020-06-01
21395충청남도홍성군44800202034022178700101충청남도 홍성군 장곡면 신풍리 787 101호3519200879.82020-06-01
22457충청남도홍성군44800202035029113300103충청남도 홍성군 은하면 덕실리 133 103호5120000128.02020-06-01
23420충청남도홍성군44800202035027121172101충청남도 홍성군 은하면 목현리 211-7 2동 101호176400018.02020-06-01
31236충청남도홍성군44800202025031134910103충청남도 홍성군 홍성읍 내법리 349-1 103호1658720103.672020-06-01
시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번동호물건지시가표준액연면적기준일자
5048충청남도홍성군448002020253291360102[ 충서로453번길 16-14 ] 0000동 0102호92070000275.02020-06-01
14982충청남도홍성군44800202035023119300102충청남도 홍성군 은하면 장곡리 193 102호780000195.02020-06-01
14150충청남도홍성군44800202025625189600740[ 상하천로 31 ] 0000동 0740호5107262070.4452020-06-01
10728충청남도홍성군44800202033023110010102충청남도 홍성군 홍동면 원천리 100-1 102호360000090.02020-06-01
13617충청남도홍성군44800202025626115240101충청남도 홍성군 홍북읍 대동리 152-4 101호490000098.02020-06-01
23868충청남도홍성군448002020330341668140101충청남도 홍성군 홍동면 팔괘리 668-14 101호2027086073.182020-06-01
16377충청남도홍성군44800202038036119610103충청남도 홍성군 갈산면 대사리 196-1 103호18600000124.02020-06-01
17354충청남도홍성군44800202036023164600104충청남도 홍성군 결성면 성남리 646 104호27456068.642020-06-01
3606충청남도홍성군44800202025630137540102충청남도 홍성군 홍북읍 신정리 375-4 102호62238056.582020-06-01
14383충청남도홍성군44800202025030156001101충청남도 홍성군 홍성읍 고암리 560 1동 101호393679440607.532020-06-01

Duplicate rows

Most frequently occurring

시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번동호물건지시가표준액연면적기준일자# duplicates
9충청남도홍성군44800202035024126610201충청남도 홍성군 은하면 대율리 266-1 201호19419208.482020-06-013
16충청남도홍성군448002020370221411101충청남도 홍성군 서부면 광리 4-1 1동 101호136224820281.342020-06-013
0충청남도홍성군44800202025025130500127충청남도 홍성군 홍성읍 옥암리 305 127호3817775055.252020-06-012
1충청남도홍성군44800202025027158910101[ 충서로966번길 121 ] 0000동 0101호1458000364.52020-06-012
2충청남도홍성군44800202025321119411101충청남도 홍성군 광천읍 신진리 194-1 1동 101호89934600196.02020-06-012
3충청남도홍성군4480020202562119481101충청남도 홍성군 홍북읍 중계리 94-8 1동 101호14040035.12020-06-012
4충청남도홍성군448002020256221104570101충청남도 홍성군 홍북읍 상하리 104-57 101호4180176055.442020-06-012
5충청남도홍성군44800202025629134550101충청남도 홍성군 홍북읍 갈산리 345-5 101호8880000240.02020-06-012
6충청남도홍성군44800202034027142911101충청남도 홍성군 장곡면 가송리 429-1 1동 101호2016000504.02020-06-012
7충청남도홍성군44800202034033126440101충청남도 홍성군 장곡면 산성리 264-4 101호58608015.842020-06-012