Overview

Dataset statistics

Number of variables7
Number of observations230
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory13.6 KiB
Average record size in memory60.6 B

Variable types

Categorical3
Text1
Numeric3

Dataset

Description- 2022년도 종합부동산세 정기 고지 및 신고분을 기준으로 작성함- 시·군·구별 종합부동산세(주택분, 종합합산토지분, 별도합산토지분) 결정 현황(단위:백만원)
Author국세청
URLhttps://www.data.go.kr/data/15119676/fileData.do

Alerts

귀속연도 has constant value ""Constant
데이터생성일 has constant value ""Constant
주택분 is highly overall correlated with 종합합산토지분 and 1 other fieldsHigh correlation
종합합산토지분 is highly overall correlated with 주택분 and 1 other fieldsHigh correlation
별도합산토지분 is highly overall correlated with 주택분 and 1 other fieldsHigh correlation
별도합산토지분 has 19 (8.3%) zerosZeros

Reproduction

Analysis started2023-12-12 19:08:04.140811
Analysis finished2023-12-12 19:08:06.016384
Duration1.88 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

귀속연도
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
2022
230 

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 230
100.0%

Length

2023-12-13T04:08:06.092442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:08:06.193061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022 230
100.0%

데이터생성일
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
2023-06-30
230 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-06-30
2nd row2023-06-30
3rd row2023-06-30
4th row2023-06-30
5th row2023-06-30

Common Values

ValueCountFrequency (%)
2023-06-30 230
100.0%

Length

2023-12-13T04:08:06.288126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:08:06.399771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-06-30 230
100.0%

구분1
Categorical

Distinct17
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
경기
31 
서울
26 
경북
23 
전남
22 
강원
18 
Other values (12)
110 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique1 ?
Unique (%)0.4%

Sample

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

Common Values

ValueCountFrequency (%)
경기 31
13.5%
서울 26
11.3%
경북 23
10.0%
전남 22
9.6%
강원 18
7.8%
경남 18
7.8%
부산 16
7.0%
충남 15
6.5%
전북 14
 
6.1%
충북 11
 
4.8%
Other values (7) 36
15.7%

Length

2023-12-13T04:08:06.540019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기 31
13.5%
서울 26
11.3%
경북 23
10.0%
전남 22
9.6%
강원 18
7.8%
경남 18
7.8%
부산 16
7.0%
충남 15
6.5%
전북 14
 
6.1%
충북 11
 
4.8%
Other values (7) 36
15.7%

구분2
Text

Distinct208
Distinct (%)90.4%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
2023-12-13T04:08:06.922868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length3
Mean length2.9434783
Min length2

Characters and Unicode

Total characters677
Distinct characters135
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique201 ?
Unique (%)87.4%

Sample

1st row서울
2nd row강남구
3rd row강동구
4th row강북구
5th row강서구
ValueCountFrequency (%)
동구 6
 
2.6%
중구 6
 
2.6%
서구 5
 
2.2%
남구 4
 
1.7%
북구 4
 
1.7%
고성군 2
 
0.9%
강서구 2
 
0.9%
나주시 1
 
0.4%
수성구 1
 
0.4%
목포시 1
 
0.4%
Other values (198) 198
86.1%
2023-12-13T04:08:07.594735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
군 85
 
12.6%
시 78
 
11.5%
구 75
 
11.1%
천 22
 
3.2%
주 20
 
3.0%
성 18
 
2.7%
양 18
 
2.7%
동 17
 
2.5%
산 16
 
2.4%
서 14
 
2.1%
Other values (125) 314
46.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 675
99.7%
Close Punctuation 1
 
0.1%
Open Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
군 85
 
12.6%
시 78
 
11.6%
구 75
 
11.1%
천 22
 
3.3%
주 20
 
3.0%
성 18
 
2.7%
양 18
 
2.7%
동 17
 
2.5%
산 16
 
2.4%
서 14
 
2.1%
Other values (123) 312
46.2%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 675
99.7%
Common 2
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
군 85
 
12.6%
시 78
 
11.6%
구 75
 
11.1%
천 22
 
3.3%
주 20
 
3.0%
성 18
 
2.7%
양 18
 
2.7%
동 17
 
2.5%
산 16
 
2.4%
서 14
 
2.1%
Other values (123) 312
46.2%
Common
ValueCountFrequency (%)
) 1
50.0%
( 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 675
99.7%
ASCII 2
 
0.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
군 85
 
12.6%
시 78
 
11.6%
구 75
 
11.1%
천 22
 
3.3%
주 20
 
3.0%
성 18
 
2.7%
양 18
 
2.7%
동 17
 
2.5%
산 16
 
2.4%
서 14
 
2.1%
Other values (123) 312
46.2%
ASCII
ValueCountFrequency (%)
) 1
50.0%
( 1
50.0%

주택분
Real number (ℝ)

HIGH CORRELATION 

Distinct225
Distinct (%)97.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21588.5
Minimum125
Maximum1668387
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2023-12-13T04:08:07.808408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum125
5-th percentile221.6
Q1720.25
median3468
Q313218.5
95-th percentile56261.05
Maximum1668387
Range1668262
Interquartile range (IQR)12498.25

Descriptive statistics

Standard deviation115605.16
Coefficient of variation (CV)5.3549417
Kurtosis182.30431
Mean21588.5
Median Absolute Deviation (MAD)3034
Skewness12.972831
Sum4965355
Variance1.3364553 × 1010
MonotonicityNot monotonic
2023-12-13T04:08:08.010082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
206 2
 
0.9%
249 2
 
0.9%
730 2
 
0.9%
829 2
 
0.9%
15678 2
 
0.9%
1668387 1
 
0.4%
385 1
 
0.4%
706 1
 
0.4%
2975 1
 
0.4%
3581 1
 
0.4%
Other values (215) 215
93.5%
ValueCountFrequency (%)
125 1
0.4%
162 1
0.4%
163 1
0.4%
169 1
0.4%
171 1
0.4%
177 1
0.4%
188 1
0.4%
206 2
0.9%
207 1
0.4%
209 1
0.4%
ValueCountFrequency (%)
1668387 1
0.4%
425252 1
0.4%
251896 1
0.4%
166081 1
0.4%
139326 1
0.4%
123471 1
0.4%
121058 1
0.4%
63580 1
0.4%
61059 1
0.4%
57942 1
0.4%

종합합산토지분
Real number (ℝ)

HIGH CORRELATION 

Distinct223
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11300.248
Minimum23
Maximum608034
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2023-12-13T04:08:08.223524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum23
5-th percentile74.05
Q1596.75
median3421.5
Q39067.5
95-th percentile37457.5
Maximum608034
Range608011
Interquartile range (IQR)8470.75

Descriptive statistics

Standard deviation43074.644
Coefficient of variation (CV)3.8118318
Kurtosis162.69233
Mean11300.248
Median Absolute Deviation (MAD)3093.5
Skewness11.965438
Sum2599057
Variance1.8554249 × 109
MonotonicityNot monotonic
2023-12-13T04:08:08.441066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
67 2
 
0.9%
389 2
 
0.9%
79 2
 
0.9%
99 2
 
0.9%
123 2
 
0.9%
582 2
 
0.9%
1715 2
 
0.9%
27 1
 
0.4%
82 1
 
0.4%
608034 1
 
0.4%
Other values (213) 213
92.6%
ValueCountFrequency (%)
23 1
0.4%
25 1
0.4%
27 1
0.4%
29 1
0.4%
39 1
0.4%
40 1
0.4%
49 1
0.4%
64 1
0.4%
67 2
0.9%
68 1
0.4%
ValueCountFrequency (%)
608034 1
0.4%
133668 1
0.4%
115941 1
0.4%
97463 1
0.4%
96890 1
0.4%
71806 1
0.4%
51996 1
0.4%
44217 1
0.4%
41749 1
0.4%
41374 1
0.4%

별도합산토지분
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct207
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10520.909
Minimum0
Maximum987987
Zeros19
Zeros (%)8.3%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2023-12-13T04:08:08.646319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1153
median867.5
Q33223.25
95-th percentile26172.9
Maximum987987
Range987987
Interquartile range (IQR)3070.25

Descriptive statistics

Standard deviation69584.543
Coefficient of variation (CV)6.613929
Kurtosis172.6731
Mean10520.909
Median Absolute Deviation (MAD)850
Skewness12.568869
Sum2419809
Variance4.8420086 × 109
MonotonicityNot monotonic
2023-12-13T04:08:08.819076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 19
 
8.3%
148 2
 
0.9%
321 2
 
0.9%
5 2
 
0.9%
169 2
 
0.9%
16 2
 
0.9%
987987 1
 
0.4%
502 1
 
0.4%
630 1
 
0.4%
985 1
 
0.4%
Other values (197) 197
85.7%
ValueCountFrequency (%)
0 19
8.3%
3 1
 
0.4%
5 2
 
0.9%
7 1
 
0.4%
8 1
 
0.4%
14 1
 
0.4%
15 1
 
0.4%
16 2
 
0.9%
17 1
 
0.4%
18 1
 
0.4%
ValueCountFrequency (%)
987987 1
0.4%
258918 1
0.4%
222256 1
0.4%
135887 1
0.4%
95168 1
0.4%
59118 1
0.4%
38569 1
0.4%
38372 1
0.4%
37056 1
0.4%
30981 1
0.4%

Interactions

2023-12-13T04:08:05.271523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:08:04.427650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:08:04.865002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:08:05.415116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:08:04.581843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:08:05.006580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:08:05.551449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:08:04.727705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:08:05.134454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T04:08:08.919657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분1주택분종합합산토지분별도합산토지분
구분11.0000.0000.0000.000
주택분0.0001.0000.9500.996
종합합산토지분0.0000.9501.0000.975
별도합산토지분0.0000.9960.9751.000
2023-12-13T04:08:09.023134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
주택분종합합산토지분별도합산토지분구분1
주택분1.0000.8840.8570.000
종합합산토지분0.8841.0000.9010.000
별도합산토지분0.8570.9011.0000.000
구분10.0000.0000.0001.000

Missing values

2023-12-13T04:08:05.734572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T04:08:05.941246image/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

귀속연도데이터생성일구분1구분2주택분종합합산토지분별도합산토지분
020222023-06-30서울서울1668387608034987987
120222023-06-30서울강남구425252115941258918
220222023-06-30서울강동구41605127597843
320222023-06-30서울강북구86621044960
420222023-06-30서울강서구384072923425855
520222023-06-30서울관악구2420640943582
620222023-06-30서울광진구3810070525646
720222023-06-30서울구로구2167480954274
820222023-06-30서울금천구1191367027352
920222023-06-30서울노원구2604721032671
귀속연도데이터생성일구분1구분2주택분종합합산토지분별도합산토지분
22020222023-06-30경남진주시198113438613149
22120222023-06-30경남창녕군44646385
22220222023-06-30경남창원시16382182102586
22320222023-06-30경남통영시1147642169
22420222023-06-30경남하동군3642910
22520222023-06-30경남함안군588760148
22620222023-06-30경남함양군45258217
22720222023-06-30경남합천군359386170
22820222023-06-30제주제주시9291170826554
22920222023-06-30제주서귀포시16437380474694