Overview

Dataset statistics

Number of variables15
Number of observations10000
Missing cells548
Missing cells (%)0.4%
Duplicate rows384
Duplicate rows (%)3.8%
Total size in memory1.3 MiB
Average record size in memory138.0 B

Variable types

Categorical5
Numeric7
Text2
DateTime1

Dataset

Description일반건축물에 대한 지방세 부과기준인 시가표준액을 제공한다. 2017년~2020년까지 법정동, 법정리, 본번, 부번 물건지, 시가표준액, 연면적, 기준일자 등의 자료를 제공한다.
URLhttps://www.data.go.kr/data/15080235/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 384 (3.8%) duplicate rowsDuplicates
특수지 is highly imbalanced (86.9%)Imbalance
has 548 (5.5%) missing valuesMissing
is highly skewed (γ1 = 75.57595961)Skewed
연면적 is highly skewed (γ1 = 31.36976723)Skewed
부번 has 2262 (22.6%) zerosZeros
has 8808 (88.1%) zerosZeros
has 416 (4.2%) zerosZeros
연면적 has 1056 (10.6%) zerosZeros

Reproduction

Analysis started2023-12-12 00:15:51.435324
Analysis finished2023-12-12 00:16:00.159288
Duration8.72 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
전라남도
10000 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
전라남도 10000
100.0%

Length

2023-12-12T09:16:00.227347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:16:00.336273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전라남도 10000
100.0%

시군구명
Categorical

CONSTANT 

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

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
장성군 10000
100.0%

Length

2023-12-12T09:16:00.432902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:16:00.532184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
장성군 10000
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
46880
10000 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row46880
2nd row46880
3rd row46880
4th row46880
5th row46880

Common Values

ValueCountFrequency (%)
46880 10000
100.0%

Length

2023-12-12T09:16:00.653200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:16:00.765506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
46880 10000
100.0%

과세년도
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2022
10000 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022
2nd row2022
3rd row2022
4th row2022
5th row2022

Common Values

ValueCountFrequency (%)
2022 10000
100.0%

Length

2023-12-12T09:16:00.872728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:16:00.976137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022 10000
100.0%

법정동
Real number (ℝ)

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean323.777
Minimum250
Maximum400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T09:16:01.076741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum250
5-th percentile250
Q1250
median330
Q3360
95-th percentile400
Maximum400
Range150
Interquartile range (IQR)110

Descriptive statistics

Standard deviation50.5745
Coefficient of variation (CV)0.15620164
Kurtosis-1.1434915
Mean323.777
Median Absolute Deviation (MAD)30
Skewness-0.32552896
Sum3237770
Variance2557.78
MonotonicityNot monotonic
2023-12-12T09:16:01.210315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
250 2669
26.7%
360 1031
 
10.3%
330 965
 
9.7%
350 895
 
8.9%
320 893
 
8.9%
310 766
 
7.7%
340 702
 
7.0%
400 644
 
6.4%
390 643
 
6.4%
380 435
 
4.3%
ValueCountFrequency (%)
250 2669
26.7%
310 766
 
7.7%
320 893
 
8.9%
330 965
 
9.7%
340 702
 
7.0%
350 895
 
8.9%
360 1031
 
10.3%
370 357
 
3.6%
380 435
 
4.3%
390 643
 
6.4%
ValueCountFrequency (%)
400 644
6.4%
390 643
6.4%
380 435
4.3%
370 357
 
3.6%
360 1031
10.3%
350 895
8.9%
340 702
7.0%
330 965
9.7%
320 893
8.9%
310 766
7.7%

법정리
Real number (ℝ)

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.3743
Minimum21
Maximum36
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T09:16:01.347774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile21
Q123
median25
Q328
95-th percentile32
Maximum36
Range15
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.4311772
Coefficient of variation (CV)0.13522254
Kurtosis0.12385198
Mean25.3743
Median Absolute Deviation (MAD)2
Skewness0.77642737
Sum253743
Variance11.772977
MonotonicityNot monotonic
2023-12-12T09:16:01.480194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
24 1810
18.1%
21 1536
15.4%
28 1128
11.3%
25 949
9.5%
26 895
8.9%
23 832
8.3%
27 712
 
7.1%
22 672
 
6.7%
29 339
 
3.4%
32 302
 
3.0%
Other values (6) 825
8.2%
ValueCountFrequency (%)
21 1536
15.4%
22 672
 
6.7%
23 832
8.3%
24 1810
18.1%
25 949
9.5%
26 895
8.9%
27 712
 
7.1%
28 1128
11.3%
29 339
 
3.4%
30 139
 
1.4%
ValueCountFrequency (%)
36 2
 
< 0.1%
35 128
 
1.3%
34 171
 
1.7%
33 179
 
1.8%
32 302
 
3.0%
31 206
 
2.1%
30 139
 
1.4%
29 339
 
3.4%
28 1128
11.3%
27 712
7.1%

특수지
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
9819 
2
 
181

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 9819
98.2%
2 181
 
1.8%

Length

2023-12-12T09:16:01.617236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:16:01.725822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9819
98.2%
2 181
 
1.8%

본번
Real number (ℝ)

Distinct1120
Distinct (%)11.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean570.9972
Minimum1
Maximum1834
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T09:16:01.838508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile38
Q1232
median513
Q3847
95-th percentile1328.05
Maximum1834
Range1833
Interquartile range (IQR)615

Descriptive statistics

Standard deviation399.55171
Coefficient of variation (CV)0.69974373
Kurtosis-0.42611962
Mean570.9972
Median Absolute Deviation (MAD)304
Skewness0.58182878
Sum5709972
Variance159641.57
MonotonicityNot monotonic
2023-12-12T09:16:02.003589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1158 343
 
3.4%
200 104
 
1.0%
1273 99
 
1.0%
830 56
 
0.6%
478 51
 
0.5%
210 50
 
0.5%
333 50
 
0.5%
587 49
 
0.5%
862 48
 
0.5%
524 46
 
0.5%
Other values (1110) 9104
91.0%
ValueCountFrequency (%)
1 30
0.3%
2 7
 
0.1%
3 13
 
0.1%
4 33
0.3%
5 29
0.3%
6 8
 
0.1%
7 14
0.1%
8 32
0.3%
9 4
 
< 0.1%
10 13
 
0.1%
ValueCountFrequency (%)
1834 5
0.1%
1766 7
0.1%
1764 2
 
< 0.1%
1748 11
0.1%
1663 3
 
< 0.1%
1662 5
0.1%
1661 2
 
< 0.1%
1660 1
 
< 0.1%
1654 2
 
< 0.1%
1623 1
 
< 0.1%

부번
Real number (ℝ)

ZEROS 

Distinct136
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.5144
Minimum0
Maximum433
Zeros2262
Zeros (%)22.6%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T09:16:02.156748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q38
95-th percentile30
Maximum433
Range433
Interquartile range (IQR)7

Descriptive statistics

Standard deviation23.962216
Coefficient of variation (CV)2.8143165
Kurtosis143.30025
Mean8.5144
Median Absolute Deviation (MAD)3
Skewness10.2152
Sum85144
Variance574.18781
MonotonicityNot monotonic
2023-12-12T09:16:02.294774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2262
22.6%
1 1849
18.5%
2 831
 
8.3%
3 765
 
7.6%
5 454
 
4.5%
4 447
 
4.5%
17 435
 
4.3%
6 379
 
3.8%
7 317
 
3.2%
8 297
 
3.0%
Other values (126) 1964
19.6%
ValueCountFrequency (%)
0 2262
22.6%
1 1849
18.5%
2 831
 
8.3%
3 765
 
7.6%
4 447
 
4.5%
5 454
 
4.5%
6 379
 
3.8%
7 317
 
3.2%
8 297
 
3.0%
9 193
 
1.9%
ValueCountFrequency (%)
433 9
0.1%
390 3
 
< 0.1%
383 1
 
< 0.1%
341 3
 
< 0.1%
309 1
 
< 0.1%
303 1
 
< 0.1%
250 1
 
< 0.1%
234 1
 
< 0.1%
224 1
 
< 0.1%
220 1
 
< 0.1%


Real number (ℝ)

MISSING  ZEROS 

Distinct24
Distinct (%)0.3%
Missing548
Missing (%)5.5%
Infinite0
Infinite (%)0.0%
Mean0.50031739
Minimum0
Maximum201
Zeros8808
Zeros (%)88.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T09:16:02.429211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum201
Range201
Interquartile range (IQR)0

Descriptive statistics

Standard deviation6.4184572
Coefficient of variation (CV)12.828771
Kurtosis315.75606
Mean0.50031739
Median Absolute Deviation (MAD)0
Skewness17.087489
Sum4729
Variance41.196593
MonotonicityNot monotonic
2023-12-12T09:16:02.553312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 8808
88.1%
1 444
 
4.4%
2 79
 
0.8%
3 26
 
0.3%
4 15
 
0.1%
97 12
 
0.1%
5 11
 
0.1%
6 11
 
0.1%
101 7
 
0.1%
107 6
 
0.1%
Other values (14) 33
 
0.3%
(Missing) 548
 
5.5%
ValueCountFrequency (%)
0 8808
88.1%
1 444
 
4.4%
2 79
 
0.8%
3 26
 
0.3%
4 15
 
0.1%
5 11
 
0.1%
6 11
 
0.1%
7 4
 
< 0.1%
8 3
 
< 0.1%
9 3
 
< 0.1%
ValueCountFrequency (%)
201 1
 
< 0.1%
108 3
 
< 0.1%
107 6
0.1%
103 1
 
< 0.1%
101 7
0.1%
99 1
 
< 0.1%
98 4
 
< 0.1%
97 12
0.1%
16 1
 
< 0.1%
15 2
 
< 0.1%


Real number (ℝ)

SKEWED  ZEROS 

Distinct405
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean708.676
Minimum-101
Maximum500001
Zeros416
Zeros (%)4.2%
Negative1
Negative (%)< 0.1%
Memory size166.0 KiB
2023-12-12T09:16:02.725576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-101
5-th percentile1
Q13
median101
Q3105
95-th percentile8104.05
Maximum500001
Range500102
Interquartile range (IQR)102

Descriptive statistics

Standard deviation5488.3591
Coefficient of variation (CV)7.7445252
Kurtosis6851.5879
Mean708.676
Median Absolute Deviation (MAD)94
Skewness75.57596
Sum7086760
Variance30122086
MonotonicityNot monotonic
2023-12-12T09:16:03.149386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
101 2042
20.4%
1 1463
14.6%
102 1031
10.3%
2 611
 
6.1%
103 604
 
6.0%
201 526
 
5.3%
9999 497
 
5.0%
0 416
 
4.2%
104 343
 
3.4%
3 304
 
3.0%
Other values (395) 2163
21.6%
ValueCountFrequency (%)
-101 1
 
< 0.1%
0 416
 
4.2%
1 1463
14.6%
2 611
6.1%
3 304
 
3.0%
4 189
 
1.9%
5 121
 
1.2%
6 77
 
0.8%
7 49
 
0.5%
8 29
 
0.3%
ValueCountFrequency (%)
500001 1
 
< 0.1%
9999 497
5.0%
8106 1
 
< 0.1%
8105 1
 
< 0.1%
8104 1
 
< 0.1%
8103 3
 
< 0.1%
8102 11
 
0.1%
8101 80
 
0.8%
906 1
 
< 0.1%
806 1
 
< 0.1%
Distinct4299
Distinct (%)43.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T09:16:03.506385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length31
Median length30
Mean length12.8897
Min length9

Characters and Unicode

Total characters128897
Distinct characters191
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

Unique2288 ?
Unique (%)22.9%

Sample

1st row삼서면 홍정리 489
2nd row장성읍 안평리 1158-17
3rd row장성읍 영천리 1055-9
4th row서삼면 용흥리 649-2
5th row삼서면 석마리 861-21
ValueCountFrequency (%)
장성읍 2669
 
8.8%
영천리 1129
 
3.7%
황룡면 1028
 
3.4%
동화면 965
 
3.2%
남면 902
 
3.0%
삼계면 895
 
2.9%
진원면 766
 
2.5%
삼서면 702
 
2.3%
북하면 644
 
2.1%
북이면 640
 
2.1%
Other values (3707) 20102
66.0%
2023-12-12T09:16:03.955974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
20442
 
15.9%
10008
 
7.8%
1 8707
 
6.8%
- 7721
 
6.0%
7331
 
5.7%
2 4502
 
3.5%
3 4272
 
3.3%
5 3741
 
2.9%
8 3556
 
2.8%
7 3548
 
2.8%
Other values (181) 55069
42.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 60116
46.6%
Decimal Number 40596
31.5%
Space Separator 20442
 
15.9%
Dash Punctuation 7721
 
6.0%
Close Punctuation 11
 
< 0.1%
Open Punctuation 11
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
10008
 
16.6%
7331
 
12.2%
3373
 
5.6%
2951
 
4.9%
2669
 
4.4%
2341
 
3.9%
1860
 
3.1%
1719
 
2.9%
1265
 
2.1%
1257
 
2.1%
Other values (167) 25342
42.2%
Decimal Number
ValueCountFrequency (%)
1 8707
21.4%
2 4502
11.1%
3 4272
10.5%
5 3741
9.2%
8 3556
8.8%
7 3548
8.7%
4 3531
8.7%
6 3288
 
8.1%
0 2994
 
7.4%
9 2457
 
6.1%
Space Separator
ValueCountFrequency (%)
20442
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 7721
100.0%
Close Punctuation
ValueCountFrequency (%)
) 11
100.0%
Open Punctuation
ValueCountFrequency (%)
( 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 68781
53.4%
Hangul 60116
46.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
10008
 
16.6%
7331
 
12.2%
3373
 
5.6%
2951
 
4.9%
2669
 
4.4%
2341
 
3.9%
1860
 
3.1%
1719
 
2.9%
1265
 
2.1%
1257
 
2.1%
Other values (167) 25342
42.2%
Common
ValueCountFrequency (%)
20442
29.7%
1 8707
12.7%
- 7721
 
11.2%
2 4502
 
6.5%
3 4272
 
6.2%
5 3741
 
5.4%
8 3556
 
5.2%
7 3548
 
5.2%
4 3531
 
5.1%
6 3288
 
4.8%
Other values (4) 5473
 
8.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 68781
53.4%
Hangul 60116
46.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
20442
29.7%
1 8707
12.7%
- 7721
 
11.2%
2 4502
 
6.5%
3 4272
 
6.2%
5 3741
 
5.4%
8 3556
 
5.2%
7 3548
 
5.2%
4 3531
 
5.1%
6 3288
 
4.8%
Other values (4) 5473
 
8.0%
Hangul
ValueCountFrequency (%)
10008
 
16.6%
7331
 
12.2%
3373
 
5.6%
2951
 
4.9%
2669
 
4.4%
2341
 
3.9%
1860
 
3.1%
1719
 
2.9%
1265
 
2.1%
1257
 
2.1%
Other values (167) 25342
42.2%
Distinct697
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T09:16:04.210892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length7
Mean length6.2011
Min length3

Characters and Unicode

Total characters62011
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique74 ?
Unique (%)0.7%

Sample

1st row151,000
2nd row -
3rd row87,000
4th row5,000
5th row145,000
ValueCountFrequency (%)
1056
 
10.6%
5,000 271
 
2.7%
12,000 134
 
1.3%
107,000 132
 
1.3%
6,000 104
 
1.0%
16,000 98
 
1.0%
48,000 97
 
1.0%
34,000 92
 
0.9%
113,000 90
 
0.9%
47,000 88
 
0.9%
Other values (687) 7838
78.4%
2023-12-12T09:16:04.570797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 28419
45.8%
, 8947
 
14.4%
1 3789
 
6.1%
4 2871
 
4.6%
3 2646
 
4.3%
2 2577
 
4.2%
5 2224
 
3.6%
7 2214
 
3.6%
2112
 
3.4%
6 1871
 
3.0%
Other values (3) 4341
 
7.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 49896
80.5%
Other Punctuation 8947
 
14.4%
Space Separator 2112
 
3.4%
Dash Punctuation 1056
 
1.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 28419
57.0%
1 3789
 
7.6%
4 2871
 
5.8%
3 2646
 
5.3%
2 2577
 
5.2%
5 2224
 
4.5%
7 2214
 
4.4%
6 1871
 
3.7%
8 1744
 
3.5%
9 1541
 
3.1%
Other Punctuation
ValueCountFrequency (%)
, 8947
100.0%
Space Separator
ValueCountFrequency (%)
2112
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1056
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 62011
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 28419
45.8%
, 8947
 
14.4%
1 3789
 
6.1%
4 2871
 
4.6%
3 2646
 
4.3%
2 2577
 
4.2%
5 2224
 
3.6%
7 2214
 
3.6%
2112
 
3.4%
6 1871
 
3.0%
Other values (3) 4341
 
7.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 62011
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 28419
45.8%
, 8947
 
14.4%
1 3789
 
6.1%
4 2871
 
4.6%
3 2646
 
4.3%
2 2577
 
4.2%
5 2224
 
3.6%
7 2214
 
3.6%
2112
 
3.4%
6 1871
 
3.0%
Other values (3) 4341
 
7.0%

연면적
Real number (ℝ)

SKEWED  ZEROS 

Distinct4345
Distinct (%)43.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean182.2511
Minimum0
Maximum33648.87
Zeros1056
Zeros (%)10.6%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T09:16:04.684632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q119.825
median73.795
Q3191.925
95-th percentile677.11
Maximum33648.87
Range33648.87
Interquartile range (IQR)172.1

Descriptive statistics

Standard deviation544.55778
Coefficient of variation (CV)2.9879533
Kurtosis1639.4015
Mean182.2511
Median Absolute Deviation (MAD)61.205
Skewness31.369767
Sum1822511
Variance296543.17
MonotonicityNot monotonic
2023-12-12T09:16:04.822124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 1056
 
10.6%
18.0 374
 
3.7%
10.0 162
 
1.6%
16.5 132
 
1.3%
20.0 73
 
0.7%
198.0 55
 
0.5%
330.0 54
 
0.5%
99.0 46
 
0.5%
96.0 42
 
0.4%
66.0 42
 
0.4%
Other values (4335) 7964
79.6%
ValueCountFrequency (%)
0.0 1056
10.6%
0.81 1
 
< 0.1%
1.0 1
 
< 0.1%
1.1 1
 
< 0.1%
1.2 4
 
< 0.1%
1.36 1
 
< 0.1%
1.39 1
 
< 0.1%
1.44 2
 
< 0.1%
1.5 1
 
< 0.1%
1.62 1
 
< 0.1%
ValueCountFrequency (%)
33648.87 1
< 0.1%
19941.25 1
< 0.1%
10041.0 1
< 0.1%
9866.0 1
< 0.1%
8947.07 1
< 0.1%
6618.0 1
< 0.1%
6431.1 1
< 0.1%
6339.1 1
< 0.1%
5620.0 1
< 0.1%
5492.25 1
< 0.1%

기준일자
Date

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2022-01-01 00:00:00
Maximum2022-01-01 00:00:00
2023-12-12T09:16:04.928514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:16:05.015904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-12T09:15:59.063514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:15:53.449916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:15:54.309898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:15:55.231765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:15:56.191724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:15:57.347040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:15:58.236579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:15:59.183692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:15:53.593919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:15:54.419457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:15:55.359291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:15:56.327663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:15:57.473827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:15:58.338087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:15:59.281372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:15:53.711836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:15:54.533177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:15:55.493644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:15:56.735789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:15:57.590510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:15:58.465779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:15:59.401045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:15:53.845815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:15:54.651022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:15:55.635108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:15:56.848324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:15:57.741355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:15:58.578847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:15:59.491426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:15:53.960672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:15:54.745872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:15:55.760967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:15:56.934680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:15:57.863032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:15:58.691439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:15:59.613323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:15:54.074718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:15:54.960824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:15:55.907688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:15:57.067739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:15:57.998459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:15:58.804518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:15:59.704235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:15:54.196417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:15:55.110224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:15:56.047802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:15:57.225752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:15:58.115796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:15:58.925568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T09:16:05.154506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동법정리특수지본번부번연면적
법정동1.0000.4380.0610.3470.0760.1270.0000.019
법정리0.4381.0000.1570.5960.1400.1840.0000.000
특수지0.0610.1571.0000.3500.0260.0000.0550.000
본번0.3470.5960.3501.0000.2980.1610.0000.185
부번0.0760.1400.0260.2981.0000.0000.0000.000
0.1270.1840.0000.1610.0001.000NaN0.000
0.0000.0000.0550.0000.000NaN1.0000.000
연면적0.0190.0000.0000.1850.0000.0000.0001.000
2023-12-12T09:16:05.261959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동법정리본번부번연면적특수지
법정동1.000-0.090-0.426-0.2220.035-0.1530.0970.072
법정리-0.0901.0000.008-0.0650.049-0.029-0.0250.137
본번-0.4260.0081.0000.225-0.0100.196-0.0380.268
부번-0.222-0.0650.2251.000-0.0450.098-0.0170.020
0.0350.049-0.010-0.0451.0000.014-0.0020.000
-0.153-0.0290.1960.0980.0141.000-0.1720.035
연면적0.097-0.025-0.038-0.017-0.002-0.1721.0000.000
특수지0.0720.1370.2680.0200.0000.0350.0001.000

Missing values

2023-12-12T09:15:59.852220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T09:16:00.064966image/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
9700전라남도장성군46880202234033148900108삼서면 홍정리 489151,00057.422022-01-01
2740전라남도장성군468802022250281115817<NA>67장성읍 안평리 1158-17-0.02022-01-01
3969전라남도장성군468802022250241105590201장성읍 영천리 1055-987,00079.342022-01-01
13496전라남도장성군46880202237027164920109서삼면 용흥리 649-25,00065.72022-01-01
9470전라남도장성군4688020223403018612101삼서면 석마리 861-21145,000198.02022-01-01
2395전라남도장성군468802022250241103000104장성읍 영천리 1030936,0004.862022-01-01
9782전라남도장성군468802022340281555201삼서면 우치리 555-233,000172.02022-01-01
5090전라남도장성군4688020223102711422501진원면 학림리 142-25107,00018.02022-01-01
910전라남도장성군468802022250241105870103장성읍 영천리 1058-7601,00052.42022-01-01
13698전라남도장성군468802022380211944102북일면 신흥리 944-111,00064.02022-01-01
시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번물건지시가표준액연면적기준일자
6695전라남도장성군46880202232028185020103남면 삼태리 850-2588,00010.392022-01-01
11099전라남도장성군46880202235029178001삼계면 수산리 7899,000910.02022-01-01
6607전라남도장성군46880202232028185540201남면 삼태리 855-4789,000287.02022-01-01
4409전라남도장성군4688020223102215810101진원면 산정리 58-1197,000227.052022-01-01
5609전라남도장성군468802022310281213103진원면 상림리 213-113,000100.02022-01-01
3963전라남도장성군468802022250241136000101장성읍 영천리 1360260,00099.02022-01-01
304전라남도장성군468802022250231146670101장성읍 유탕리 1466-7110,00018.02022-01-01
8086전라남도장성군468802022330251106250103동화면 남평리 1062-5502,000193.22022-01-01
3437전라남도장성군46880202225024114752709999장성읍 영천리 1475-27-0.02022-01-01
6161전라남도장성군46880202232027190010101남면 월정리 900-1372,000503.02022-01-01

Duplicate rows

Most frequently occurring

시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번물건지시가표준액연면적기준일자# duplicates
233전라남도장성군468802022330281335119999동화면 구룡리 335-1-0.02022-01-0116
7전라남도장성군4688020222502311545009999장성읍 유탕리 1545-0.02022-01-0112
131전라남도장성군468802022250251524009999장성읍 단광리 524-0.02022-01-0112
116전라남도장성군46880202225024114931000장성읍 영천리 1493-10787,000137.792022-01-019
226전라남도장성군468802022330261874009999동화면 남산리 874-0.02022-01-019
98전라남도장성군46880202225024114752709999장성읍 영천리 1475-27-0.02022-01-018
270전라남도장성군468802022350241492101삼계면 상도리 492-1115,000199.22022-01-018
333전라남도장성군468802022380221158009999북일면 박산리 158-0.02022-01-018
361전라남도장성군468802022390311619109999북이면 오월리 619-1-0.02022-01-018
128전라남도장성군468802022250251483109999장성읍 단광리 483-1-0.02022-01-017