Overview

Dataset statistics

Number of variables15
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows22
Duplicate rows (%)0.2%
Total size in memory1.3 MiB
Average record size in memory137.0 B

Variable types

Categorical6
Numeric7
Text2

Dataset

Description충청북도 옥천군의 일반건축물에 대한 지방세 부과기준인 시가표준액에 대하여 과세년도, 법정동, 법정리, 물건지, 시가표준액, 연멱적을 제공합니다.
Author충청북도 옥천군
URLhttps://www.data.go.kr/data/15079858/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 22 (0.2%) duplicate rowsDuplicates
시가표준액 is highly overall correlated with 연면적High correlation
연면적 is highly overall correlated with 시가표준액High correlation
특수지 is highly imbalanced (88.0%)Imbalance
동 is highly skewed (γ1 = 26.9236771)Skewed
부번 has 3374 (33.7%) zerosZeros

Reproduction

Analysis started2023-12-12 20:54:00.122336
Analysis finished2023-12-12 20:54:10.532890
Duration10.41 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
충청북도
10000 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
충청북도 10000
100.0%

Length

2023-12-13T05:54:10.617185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:54:10.718522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
충청북도 10000
100.0%

시군구명
Categorical

CONSTANT 

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

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
옥천군 10000
100.0%

Length

2023-12-13T05:54:10.811388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:54:10.896107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
옥천군 10000
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
43730
10000 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row43730
2nd row43730
3rd row43730
4th row43730
5th row43730

Common Values

ValueCountFrequency (%)
43730 10000
100.0%

Length

2023-12-13T05:54:10.979631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:54:11.074380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
43730 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

2023-12-13T05:54:11.182388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:54:11.310820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021 10000
100.0%

법정동
Real number (ℝ)

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean306.007
Minimum250
Maximum380
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T05:54:11.393258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum250
5-th percentile250
Q1250
median310
Q3350
95-th percentile380
Maximum380
Range130
Interquartile range (IQR)100

Descriptive statistics

Standard deviation50.356326
Coefficient of variation (CV)0.16455939
Kurtosis-1.6386878
Mean306.007
Median Absolute Deviation (MAD)60
Skewness0.020234431
Sum3060070
Variance2535.7595
MonotonicityNot monotonic
2023-12-13T05:54:11.511261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
250 4106
41.1%
310 1236
 
12.4%
360 947
 
9.5%
370 775
 
7.8%
380 750
 
7.5%
350 714
 
7.1%
330 612
 
6.1%
340 534
 
5.3%
320 326
 
3.3%
ValueCountFrequency (%)
250 4106
41.1%
310 1236
 
12.4%
320 326
 
3.3%
330 612
 
6.1%
340 534
 
5.3%
350 714
 
7.1%
360 947
 
9.5%
370 775
 
7.8%
380 750
 
7.5%
ValueCountFrequency (%)
380 750
 
7.5%
370 775
 
7.8%
360 947
 
9.5%
350 714
 
7.1%
340 534
 
5.3%
330 612
 
6.1%
320 326
 
3.3%
310 1236
 
12.4%
250 4106
41.1%

법정리
Real number (ℝ)

Distinct21
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.1102
Minimum21
Maximum41
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T05:54:11.634029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile21
Q123
median27
Q333
95-th percentile37
Maximum41
Range20
Interquartile range (IQR)10

Descriptive statistics

Standard deviation5.5995349
Coefficient of variation (CV)0.1991994
Kurtosis-1.2815922
Mean28.1102
Median Absolute Deviation (MAD)5
Skewness0.29637265
Sum281102
Variance31.354791
MonotonicityNot monotonic
2023-12-13T05:54:11.768511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
21 1297
13.0%
36 936
 
9.4%
23 929
 
9.3%
22 906
 
9.1%
29 650
 
6.5%
25 511
 
5.1%
26 510
 
5.1%
30 503
 
5.0%
24 491
 
4.9%
35 443
 
4.4%
Other values (11) 2824
28.2%
ValueCountFrequency (%)
21 1297
13.0%
22 906
9.1%
23 929
9.3%
24 491
 
4.9%
25 511
 
5.1%
26 510
 
5.1%
27 372
 
3.7%
28 377
 
3.8%
29 650
6.5%
30 503
 
5.0%
ValueCountFrequency (%)
41 1
 
< 0.1%
40 27
 
0.3%
39 36
 
0.4%
38 371
 
3.7%
37 238
 
2.4%
36 936
9.4%
35 443
4.4%
34 384
3.8%
33 293
 
2.9%
32 377
3.8%

특수지
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
9838 
2
 
162

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 9838
98.4%
2 162
 
1.6%

Length

2023-12-13T05:54:11.911743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:54:12.026483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9838
98.4%
2 162
 
1.6%

본번
Real number (ℝ)

Distinct1070
Distinct (%)10.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean355.4243
Minimum1
Maximum1537
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T05:54:12.156790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile14
Q1113
median253
Q3518
95-th percentile1054
Maximum1537
Range1536
Interquartile range (IQR)405

Descriptive statistics

Standard deviation318.17594
Coefficient of variation (CV)0.89520029
Kurtosis0.79234522
Mean355.4243
Median Absolute Deviation (MAD)181
Skewness1.1745563
Sum3554243
Variance101235.93
MonotonicityNot monotonic
2023-12-13T05:54:12.290321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
222 91
 
0.9%
163 91
 
0.9%
130 89
 
0.9%
11 82
 
0.8%
960 71
 
0.7%
5 68
 
0.7%
161 64
 
0.6%
23 60
 
0.6%
449 59
 
0.6%
1 58
 
0.6%
Other values (1060) 9267
92.7%
ValueCountFrequency (%)
1 58
0.6%
2 40
0.4%
3 20
 
0.2%
4 13
 
0.1%
5 68
0.7%
6 33
0.3%
7 32
0.3%
8 30
0.3%
9 18
 
0.2%
10 24
 
0.2%
ValueCountFrequency (%)
1537 2
 
< 0.1%
1492 3
< 0.1%
1488 1
 
< 0.1%
1483 3
< 0.1%
1481 2
 
< 0.1%
1474 2
 
< 0.1%
1471 1
 
< 0.1%
1470 3
< 0.1%
1457 2
 
< 0.1%
1450 5
0.1%

부번
Real number (ℝ)

ZEROS 

Distinct123
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.2405
Minimum0
Maximum404
Zeros3374
Zeros (%)33.7%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T05:54:12.443977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q34
95-th percentile22
Maximum404
Range404
Interquartile range (IQR)4

Descriptive statistics

Standard deviation22.709052
Coefficient of variation (CV)3.6389795
Kurtosis123.52313
Mean6.2405
Median Absolute Deviation (MAD)1
Skewness9.9025337
Sum62405
Variance515.70103
MonotonicityNot monotonic
2023-12-13T05:54:12.598852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3374
33.7%
1 2010
20.1%
2 1003
 
10.0%
3 676
 
6.8%
4 482
 
4.8%
5 336
 
3.4%
6 285
 
2.9%
7 223
 
2.2%
8 178
 
1.8%
9 153
 
1.5%
Other values (113) 1280
 
12.8%
ValueCountFrequency (%)
0 3374
33.7%
1 2010
20.1%
2 1003
 
10.0%
3 676
 
6.8%
4 482
 
4.8%
5 336
 
3.4%
6 285
 
2.9%
7 223
 
2.2%
8 178
 
1.8%
9 153
 
1.5%
ValueCountFrequency (%)
404 2
< 0.1%
379 2
< 0.1%
375 2
< 0.1%
358 1
 
< 0.1%
355 2
< 0.1%
346 1
 
< 0.1%
345 3
< 0.1%
306 1
 
< 0.1%
301 2
< 0.1%
290 2
< 0.1%

동
Real number (ℝ)

SKEWED 

Distinct59
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.0384
Minimum0
Maximum9999
Zeros71
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T05:54:12.757994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median9
Q310
95-th percentile20
Maximum9999
Range9999
Interquartile range (IQR)9

Descriptive statistics

Standard deviation362.01163
Coefficient of variation (CV)17.207184
Kurtosis727.50486
Mean21.0384
Median Absolute Deviation (MAD)6
Skewness26.923677
Sum210384
Variance131052.42
MonotonicityNot monotonic
2023-12-13T05:54:12.937147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 3985
39.9%
1 3172
31.7%
2 755
 
7.5%
20 539
 
5.4%
3 360
 
3.6%
4 233
 
2.3%
5 169
 
1.7%
30 157
 
1.6%
7 81
 
0.8%
0 71
 
0.7%
Other values (49) 478
 
4.8%
ValueCountFrequency (%)
0 71
 
0.7%
1 3172
31.7%
2 755
 
7.5%
3 360
 
3.6%
4 233
 
2.3%
5 169
 
1.7%
6 69
 
0.7%
7 81
 
0.8%
8 58
 
0.6%
9 41
 
0.4%
ValueCountFrequency (%)
9999 12
0.1%
8001 1
 
< 0.1%
7001 1
 
< 0.1%
715 1
 
< 0.1%
302 1
 
< 0.1%
301 2
 
< 0.1%
112 6
0.1%
111 1
 
< 0.1%
108 1
 
< 0.1%
106 1
 
< 0.1%

호
Text

Distinct143
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T05:54:13.138857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.112
Min length1

Characters and Unicode

Total characters21120
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique64 ?
Unique (%)0.6%

Sample

1st row1
2nd row102
3rd row101
4th row213
5th row1
ValueCountFrequency (%)
101 2470
24.7%
1 2225
22.2%
2 908
 
9.1%
102 840
 
8.4%
3 512
 
5.1%
201 482
 
4.8%
103 386
 
3.9%
4 315
 
3.1%
104 200
 
2.0%
5 195
 
1.9%
Other values (133) 1467
14.7%
2023-12-13T05:54:13.486947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 10163
48.1%
0 5438
25.7%
2 2657
 
12.6%
3 1165
 
5.5%
4 623
 
2.9%
5 377
 
1.8%
8 271
 
1.3%
6 227
 
1.1%
7 126
 
0.6%
9 69
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21116
> 99.9%
Uppercase Letter 4
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 10163
48.1%
0 5438
25.8%
2 2657
 
12.6%
3 1165
 
5.5%
4 623
 
3.0%
5 377
 
1.8%
8 271
 
1.3%
6 227
 
1.1%
7 126
 
0.6%
9 69
 
0.3%
Uppercase Letter
ValueCountFrequency (%)
B 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 21116
> 99.9%
Latin 4
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
1 10163
48.1%
0 5438
25.8%
2 2657
 
12.6%
3 1165
 
5.5%
4 623
 
3.0%
5 377
 
1.8%
8 271
 
1.3%
6 227
 
1.1%
7 126
 
0.6%
9 69
 
0.3%
Latin
ValueCountFrequency (%)
B 4
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 10163
48.1%
0 5438
25.7%
2 2657
 
12.6%
3 1165
 
5.5%
4 623
 
2.9%
5 377
 
1.8%
8 271
 
1.3%
6 227
 
1.1%
7 126
 
0.6%
9 69
 
0.3%

물건지
Text

Distinct9735
Distinct (%)97.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T05:54:13.903355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length34
Median length31
Mean length27.4668
Min length21

Characters and Unicode

Total characters274668
Distinct characters169
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

Unique9543 ?
Unique (%)95.4%

Sample

1st row충청북도 옥천군 군서면 금산리 560-3 10동 1호
2nd row충청북도 옥천군 옥천읍 대천리 411-6 10동 102호
3rd row충청북도 옥천군 옥천읍 장야리 194-1 10동 101호
4th row충청북도 옥천군 이원면 건진리 449 2동 213호
5th row충청북도 옥천군 옥천읍 삼양리 153-7 2동 1호
ValueCountFrequency (%)
충청북도 7156
 
10.6%
옥천군 7156
 
10.6%
5688
 
8.5%
옥천읍 2913
 
4.3%
10동 2557
 
3.8%
1동 2144
 
3.2%
101호 1724
 
2.6%
1호 1500
 
2.2%
0010동 1428
 
2.1%
0001동 1028
 
1.5%
Other values (4004) 33914
50.5%
2023-12-13T05:54:14.585076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
57208
20.8%
1 25464
 
9.3%
0 24765
 
9.0%
동 11703
 
4.3%
천 10414
 
3.8%
옥 10285
 
3.7%
호 9980
 
3.6%
2 8908
 
3.2%
청 8561
 
3.1%
군 8185
 
3.0%
Other values (159) 99195
36.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 122468
44.6%
Decimal Number 83525
30.4%
Space Separator 57208
20.8%
Dash Punctuation 5775
 
2.1%
Open Punctuation 2844
 
1.0%
Close Punctuation 2844
 
1.0%
Uppercase Letter 4
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 11703
 
9.6%
천 10414
 
8.5%
옥 10285
 
8.4%
호 9980
 
8.1%
청 8561
 
7.0%
군 8185
 
6.7%
북 7647
 
6.2%
도 7399
 
6.0%
리 7196
 
5.9%
충 7156
 
5.8%
Other values (144) 33942
27.7%
Decimal Number
ValueCountFrequency (%)
1 25464
30.5%
0 24765
29.6%
2 8908
 
10.7%
3 5918
 
7.1%
4 4463
 
5.3%
5 3616
 
4.3%
6 3162
 
3.8%
7 2592
 
3.1%
8 2373
 
2.8%
9 2264
 
2.7%
Space Separator
ValueCountFrequency (%)
57208
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5775
100.0%
Open Punctuation
ValueCountFrequency (%)
[ 2844
100.0%
Close Punctuation
ValueCountFrequency (%)
] 2844
100.0%
Uppercase Letter
ValueCountFrequency (%)
B 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 152196
55.4%
Hangul 122468
44.6%
Latin 4
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 11703
 
9.6%
천 10414
 
8.5%
옥 10285
 
8.4%
호 9980
 
8.1%
청 8561
 
7.0%
군 8185
 
6.7%
북 7647
 
6.2%
도 7399
 
6.0%
리 7196
 
5.9%
충 7156
 
5.8%
Other values (144) 33942
27.7%
Common
ValueCountFrequency (%)
57208
37.6%
1 25464
16.7%
0 24765
16.3%
2 8908
 
5.9%
3 5918
 
3.9%
- 5775
 
3.8%
4 4463
 
2.9%
5 3616
 
2.4%
6 3162
 
2.1%
[ 2844
 
1.9%
Other values (4) 10073
 
6.6%
Latin
ValueCountFrequency (%)
B 4
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 152200
55.4%
Hangul 122468
44.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
57208
37.6%
1 25464
16.7%
0 24765
16.3%
2 8908
 
5.9%
3 5918
 
3.9%
- 5775
 
3.8%
4 4463
 
2.9%
5 3616
 
2.4%
6 3162
 
2.1%
[ 2844
 
1.9%
Other values (5) 10077
 
6.6%
Hangul
ValueCountFrequency (%)
동 11703
 
9.6%
천 10414
 
8.5%
옥 10285
 
8.4%
호 9980
 
8.1%
청 8561
 
7.0%
군 8185
 
6.7%
북 7647
 
6.2%
도 7399
 
6.0%
리 7196
 
5.9%
충 7156
 
5.8%
Other values (144) 33942
27.7%

시가표준액
Real number (ℝ)

HIGH CORRELATION 

Distinct8044
Distinct (%)80.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38785422
Minimum9180
Maximum3.1632479 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T05:54:14.772466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9180
5-th percentile359708
Q11728000
median7416320
Q332545275
95-th percentile1.7419725 × 108
Maximum3.1632479 × 109
Range3.1632387 × 109
Interquartile range (IQR)30817275

Descriptive statistics

Standard deviation1.0634107 × 108
Coefficient of variation (CV)2.7417793
Kurtosis201.83862
Mean38785422
Median Absolute Deviation (MAD)6776320
Skewness10.617859
Sum3.8785422 × 1011
Variance1.1308423 × 1016
MonotonicityNot monotonic
2023-12-13T05:54:14.976581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22866970 19
 
0.2%
11071500 18
 
0.2%
576000 17
 
0.2%
6554860 17
 
0.2%
480000 17
 
0.2%
960000 15
 
0.1%
8692500 15
 
0.1%
100800 15
 
0.1%
763200 15
 
0.1%
17165070 14
 
0.1%
Other values (8034) 9838
98.4%
ValueCountFrequency (%)
9180 1
< 0.1%
13200 1
< 0.1%
22400 1
< 0.1%
24000 1
< 0.1%
31360 1
< 0.1%
37200 1
< 0.1%
38720 1
< 0.1%
38880 1
< 0.1%
39000 2
< 0.1%
46000 1
< 0.1%
ValueCountFrequency (%)
3163247900 1
< 0.1%
2995200000 1
< 0.1%
2233256360 1
< 0.1%
1834383000 1
< 0.1%
1704660530 1
< 0.1%
1675510200 1
< 0.1%
1651036960 1
< 0.1%
1561600000 1
< 0.1%
1390800000 1
< 0.1%
1287028350 1
< 0.1%

연면적
Real number (ℝ)

HIGH CORRELATION 

Distinct5162
Distinct (%)51.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean176.36525
Minimum0.72
Maximum14400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T05:54:15.142644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.72
5-th percentile11.52
Q135
median84.71
Q3189
95-th percentile567.046
Maximum14400
Range14399.28
Interquartile range (IQR)154

Descriptive statistics

Standard deviation377.24239
Coefficient of variation (CV)2.1389837
Kurtosis332.55012
Mean176.36525
Median Absolute Deviation (MAD)60.71
Skewness13.413006
Sum1763652.5
Variance142311.82
MonotonicityNot monotonic
2023-12-13T05:54:15.311555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.0 374
 
3.7%
32.0 56
 
0.6%
27.0 55
 
0.5%
36.0 55
 
0.5%
50.0 54
 
0.5%
96.0 51
 
0.5%
48.0 45
 
0.4%
64.0 44
 
0.4%
84.0 43
 
0.4%
160.0 42
 
0.4%
Other values (5152) 9181
91.8%
ValueCountFrequency (%)
0.72 1
 
< 0.1%
1.0 3
< 0.1%
1.2 5
0.1%
1.21 1
 
< 0.1%
1.44 3
< 0.1%
1.5 1
 
< 0.1%
1.6 1
 
< 0.1%
1.68 2
 
< 0.1%
1.8 1
 
< 0.1%
1.9 1
 
< 0.1%
ValueCountFrequency (%)
14400.0 1
< 0.1%
9978.7 1
< 0.1%
9152.69 1
< 0.1%
7045.56 1
< 0.1%
6400.0 1
< 0.1%
6065.01 1
< 0.1%
5700.0 1
< 0.1%
4906.91 1
< 0.1%
4692.97 1
< 0.1%
4604.36 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

2023-12-13T05:54:15.481003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:54:15.567210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021-06-01 10000
100.0%

Interactions

2023-12-13T05:54:08.876770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:54:01.736586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:54:02.597369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:54:03.654060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:54:04.659834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:54:05.982429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:54:07.934173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:54:08.983184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:54:01.819581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:54:02.716510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:54:03.775264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:54:04.784506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:54:06.210335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:54:08.062577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:54:09.095921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:54:01.908607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:54:02.855732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:54:03.889628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:54:04.939162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:54:06.519170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:54:08.201738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:54:09.201483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:54:02.002008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:54:02.971162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:54:03.989829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:54:05.062288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:54:06.766586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:54:08.320581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:54:09.318119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:54:02.123687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:54:03.095993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:54:04.115007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:54:05.184368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:54:07.005702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:54:08.439742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:54:09.942404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:54:02.397686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:54:03.412970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:54:04.428690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:54:05.749394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:54:07.403219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:54:08.683900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:54:10.055226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:54:02.494826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:54:03.536824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:54:04.531899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:54:05.867234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:54:07.643816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:54:08.780413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T05:54:15.641065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동법정리특수지본번부번동시가표준액연면적
법정동1.0000.4280.1850.4720.1240.0310.0470.061
법정리0.4281.0000.0890.5120.4000.0400.0220.000
특수지0.1850.0891.0000.2360.0000.0000.0080.000
본번0.4720.5120.2361.0000.1780.0000.0800.021
부번0.1240.4000.0000.1781.0000.1410.0000.000
동0.0310.0400.0000.0000.1411.0000.0000.000
시가표준액0.0470.0220.0080.0800.0000.0001.0000.782
연면적0.0610.0000.0000.0210.0000.0000.7821.000
2023-12-13T05:54:15.792263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동법정리본번부번동시가표준액연면적특수지
법정동1.000-0.4760.185-0.120-0.070-0.173-0.0870.132
법정리-0.4761.000-0.2200.1500.0050.0600.0070.085
본번0.185-0.2201.000-0.228-0.030-0.0600.0620.181
부번-0.1200.150-0.2281.000-0.0400.086-0.0950.000
동-0.0700.005-0.030-0.0401.0000.005-0.0220.275
시가표준액-0.1730.060-0.0600.0860.0051.0000.6340.006
연면적-0.0870.0070.062-0.095-0.0220.6341.0000.000
특수지0.1320.0850.1810.0000.2750.0060.0001.000

Missing values

2023-12-13T05:54:10.218369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T05:54:10.434580image/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
13944충청북도옥천군4373020213702215603101충청북도 옥천군 군서면 금산리 560-3 10동 1호1152500025.02021-06-01
7194충청북도옥천군437302021250331411610102충청북도 옥천군 옥천읍 대천리 411-6 10동 102호734262023.092021-06-01
6054충청북도옥천군437302021250371194110101충청북도 옥천군 옥천읍 장야리 194-1 10동 101호36800020.02021-06-01
18325충청북도옥천군43730202136021144902213충청북도 옥천군 이원면 건진리 449 2동 213호869250028.52021-06-01
10657충청북도옥천군437302021250381153721충청북도 옥천군 옥천읍 삼양리 153-7 2동 1호258192029.342021-06-01
12701충청북도옥천군4373020212503516002201충청북도 옥천군 옥천읍 양수리 60 2동 201호1505520065.62021-06-01
10609충청북도옥천군437302021250391263110101충청북도 옥천군 옥천읍 서정리 263-1 10동 101호107601230198.932021-06-01
16227충청북도옥천군4373020213603012402202충청북도 옥천군 이원면 미동리 240-2 20동 2호72009240216.832021-06-01
16676충청북도옥천군437302021360351693012102충청북도 옥천군 이원면 용방리 693 12동 102호1390675035.752021-06-01
4903충청북도옥천군437302021250371344230201충청북도 옥천군 옥천읍 장야리 344-2 30동 201호52035600101.042021-06-01
시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번동호물건지시가표준액연면적기준일자
6105충청북도옥천군437302021250291160104충청북도 옥천군 옥천읍 구일리 16 10동 4호8400000400.02021-06-01
5025충청북도옥천군437302021310231753112충청북도 옥천군 동이면 적하리 753-1 1동 2호14424800197.62021-06-01
7616충청북도옥천군437302021250211242410103충청북도 옥천군 옥천읍 죽향리 242-4 10동 103호23040018.02021-06-01
12128충청북도옥천군437302021250211111011충청북도 옥천군 옥천읍 죽향리 111 1동 1호534009012.932021-06-01
10917충청북도옥천군4373020213403416571105충청북도 옥천군 청성면 도장리 657-1 10동 5호22866970213.712021-06-01
18660충청북도옥천군437302021350211795101[ 지전길 47-9 ] 0010동 0001호85644000117.02021-06-01
8181충청북도옥천군43730202125023199010101[ 향수길 53 ] 0010동 0101호1755737038.362021-06-01
2471충청북도옥천군437302021310301769115충청북도 옥천군 동이면 우산리 769-1 1동 5호1379000197.02021-06-01
7551충청북도옥천군437302021250361114111102[ 옥천로 1633 ] 0001동 0102호141647590195.932021-06-01
14253충청북도옥천군4373020213802315671105[ 증약길 156 ] 0010동 0005호617040015.68882021-06-01

Duplicate rows

Most frequently occurring

시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번동호물건지시가표준액연면적기준일자# duplicates
5충청북도옥천군437302021250301217011충청북도 옥천군 옥천읍 서대리 217 1동 1호655486029.662021-06-0117
6충청북도옥천군437302021250301217011충청북도 옥천군 옥천읍 서대리 217 1동 1호1716507077.672021-06-0114
8충청북도옥천군4373020212503614011101충청북도 옥천군 옥천읍 금구리 40-1 1동 101호686352012.642021-06-014
16충청북도옥천군437302021350291935011충청북도 옥천군 청산면 인정리 935 1동 1호57750001155.02021-06-013
19충청북도옥천군43730202136021144902200충청북도 옥천군 이원면 건진리 449 2동 200호1525000050.02021-06-013
0충청북도옥천군437302021250211111011충청북도 옥천군 옥천읍 죽향리 111 1동 1호534009012.932021-06-012
1충청북도옥천군437302021250211111011충청북도 옥천군 옥천읍 죽향리 111 1동 1호277287360825.262021-06-012
2충청북도옥천군437302021250221455211충청북도 옥천군 옥천읍 문정리 455-2 1동 1호51765008.72021-06-012
3충청북도옥천군437302021250221455222충청북도 옥천군 옥천읍 문정리 455-2 2동 2호1593270056.72021-06-012
4충청북도옥천군437302021250221455233충청북도 옥천군 옥천읍 문정리 455-2 3동 3호2673000090.02021-06-012