Overview

Dataset statistics

Number of variables15
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows10
Duplicate rows (%)0.1%
Total size in memory1.3 MiB
Average record size in memory139.0 B

Variable types

Categorical5
Numeric8
Text1
DateTime1

Dataset

Description본 데이터는 경상남도 합천군의 과세년도별, 일반건축물에 대한 물건지 (법정동, 법정리, 본번, 부번), 시가표준액, 연면적, 기준일자 등을 제공하고 있습니다.
Author경상남도 합천군
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15089290

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
Dataset has 10 (0.1%) duplicate rowsDuplicates
시가표준액 is highly overall correlated with 연면적High correlation
연면적 is highly overall correlated with 시가표준액High correlation
특수지 is highly imbalanced (88.3%)Imbalance
is highly skewed (γ1 = 90.05579628)Skewed
부번 has 4407 (44.1%) zerosZeros
has 207 (2.1%) zerosZeros

Reproduction

Analysis started2023-12-11 00:24:03.810764
Analysis finished2023-12-11 00:24:12.081252
Duration8.27 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
경상남도
10000 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경상남도 10000
100.0%

Length

2023-12-11T09:24:12.130350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:24:12.202926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경상남도 10000
100.0%

시군구명
Categorical

CONSTANT 

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

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
합천군 10000
100.0%

Length

2023-12-11T09:24:12.279013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:24:12.360550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
합천군 10000
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
48890
10000 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row48890
2nd row48890
3rd row48890
4th row48890
5th row48890

Common Values

ValueCountFrequency (%)
48890 10000
100.0%

Length

2023-12-11T09:24:12.433898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:24:12.733477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
48890 10000
100.0%

과세년도
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2019
3090 
2018
2906 
2017
2816 
2020
1188 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2018
2nd row2017
3rd row2018
4th row2020
5th row2018

Common Values

ValueCountFrequency (%)
2019 3090
30.9%
2018 2906
29.1%
2017 2816
28.2%
2020 1188
 
11.9%

Length

2023-12-11T09:24:12.806650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:24:12.883406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2019 3090
30.9%
2018 2906
29.1%
2017 2816
28.2%
2020 1188
 
11.9%

법정동
Real number (ℝ)

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean368.382
Minimum250
Maximum460
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T09:24:12.964680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum250
5-th percentile250
Q1330
median370
Q3430
95-th percentile460
Maximum460
Range210
Interquartile range (IQR)100

Descriptive statistics

Standard deviation63.945844
Coefficient of variation (CV)0.17358569
Kurtosis-0.7646859
Mean368.382
Median Absolute Deviation (MAD)50
Skewness-0.42696602
Sum3683820
Variance4089.071
MonotonicityNot monotonic
2023-12-11T09:24:13.051400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
250 1346
13.5%
430 843
 
8.4%
330 836
 
8.4%
340 786
 
7.9%
460 681
 
6.8%
390 628
 
6.3%
360 569
 
5.7%
350 565
 
5.7%
420 542
 
5.4%
440 540
 
5.4%
Other values (7) 2664
26.6%
ValueCountFrequency (%)
250 1346
13.5%
310 322
 
3.2%
320 442
 
4.4%
330 836
8.4%
340 786
7.9%
350 565
5.7%
360 569
5.7%
370 277
 
2.8%
380 260
 
2.6%
390 628
6.3%
ValueCountFrequency (%)
460 681
6.8%
450 463
4.6%
440 540
5.4%
430 843
8.4%
420 542
5.4%
410 480
4.8%
400 420
4.2%
390 628
6.3%
380 260
 
2.6%
370 277
 
2.8%

법정리
Real number (ℝ)

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.2237
Minimum21
Maximum37
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T09:24:13.132388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile21
Q123
median26
Q329
95-th percentile33
Maximum37
Range16
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.0660437
Coefficient of variation (CV)0.15505225
Kurtosis-0.74462051
Mean26.2237
Median Absolute Deviation (MAD)3
Skewness0.44044023
Sum262237
Variance16.532712
MonotonicityNot monotonic
2023-12-11T09:24:13.222917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
21 1575
15.8%
23 928
9.3%
25 817
8.2%
22 769
7.7%
24 739
7.4%
26 735
7.3%
28 717
7.2%
27 704
 
7.0%
29 624
 
6.2%
30 618
 
6.2%
Other values (7) 1774
17.7%
ValueCountFrequency (%)
21 1575
15.8%
22 769
7.7%
23 928
9.3%
24 739
7.4%
25 817
8.2%
26 735
7.3%
27 704
7.0%
28 717
7.2%
29 624
 
6.2%
30 618
 
6.2%
ValueCountFrequency (%)
37 122
 
1.2%
36 20
 
0.2%
35 10
 
0.1%
34 282
 
2.8%
33 322
3.2%
32 553
5.5%
31 465
4.7%
30 618
6.2%
29 624
6.2%
28 717
7.2%

특수지
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
9843 
2
 
157

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 9843
98.4%
2 157
 
1.6%

Length

2023-12-11T09:24:13.322263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:24:13.393327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9843
98.4%
2 157
 
1.6%

본번
Real number (ℝ)

Distinct1255
Distinct (%)12.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean495.6275
Minimum1
Maximum2087
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T09:24:13.481924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile42
Q1224
median452
Q3698
95-th percentile1110.1
Maximum2087
Range2086
Interquartile range (IQR)474

Descriptive statistics

Standard deviation337.77322
Coefficient of variation (CV)0.68150621
Kurtosis0.4623443
Mean495.6275
Median Absolute Deviation (MAD)234
Skewness0.76816749
Sum4956275
Variance114090.75
MonotonicityNot monotonic
2023-12-11T09:24:13.585478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
473 79
 
0.8%
418 63
 
0.6%
1230 58
 
0.6%
433 52
 
0.5%
502 45
 
0.4%
18 38
 
0.4%
493 38
 
0.4%
413 35
 
0.4%
117 31
 
0.3%
666 30
 
0.3%
Other values (1245) 9531
95.3%
ValueCountFrequency (%)
1 19
0.2%
2 4
 
< 0.1%
3 9
 
0.1%
4 5
 
0.1%
5 5
 
0.1%
6 5
 
0.1%
7 16
0.2%
8 24
0.2%
9 7
 
0.1%
10 19
0.2%
ValueCountFrequency (%)
2087 3
< 0.1%
1910 1
 
< 0.1%
1878 1
 
< 0.1%
1862 1
 
< 0.1%
1853 1
 
< 0.1%
1849 1
 
< 0.1%
1840 2
< 0.1%
1822 2
< 0.1%
1790 1
 
< 0.1%
1787 3
< 0.1%

부번
Real number (ℝ)

ZEROS 

Distinct111
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.0034
Minimum0
Maximum305
Zeros4407
Zeros (%)44.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T09:24:13.700553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile14
Maximum305
Range305
Interquartile range (IQR)3

Descriptive statistics

Standard deviation15.062519
Coefficient of variation (CV)3.7624317
Kurtosis175.80012
Mean4.0034
Median Absolute Deviation (MAD)1
Skewness11.311916
Sum40034
Variance226.87948
MonotonicityNot monotonic
2023-12-11T09:24:13.817586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4407
44.1%
1 1847
18.5%
2 946
 
9.5%
3 663
 
6.6%
4 378
 
3.8%
5 289
 
2.9%
6 232
 
2.3%
7 154
 
1.5%
9 133
 
1.3%
8 128
 
1.3%
Other values (101) 823
 
8.2%
ValueCountFrequency (%)
0 4407
44.1%
1 1847
18.5%
2 946
 
9.5%
3 663
 
6.6%
4 378
 
3.8%
5 289
 
2.9%
6 232
 
2.3%
7 154
 
1.5%
8 128
 
1.3%
9 133
 
1.3%
ValueCountFrequency (%)
305 5
0.1%
302 1
 
< 0.1%
300 2
 
< 0.1%
290 2
 
< 0.1%
210 1
 
< 0.1%
151 3
< 0.1%
137 1
 
< 0.1%
132 1
 
< 0.1%
131 6
0.1%
130 2
 
< 0.1%


Real number (ℝ)

SKEWED  ZEROS 

Distinct21
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2916
Minimum0
Maximum1000
Zeros207
Zeros (%)2.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T09:24:13.914537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum1000
Range1000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation10.367397
Coefficient of variation (CV)8.0267861
Kurtosis8620.5476
Mean1.2916
Median Absolute Deviation (MAD)0
Skewness90.055796
Sum12916
Variance107.48292
MonotonicityNot monotonic
2023-12-11T09:24:14.002145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
1 8972
89.7%
2 626
 
6.3%
0 207
 
2.1%
3 93
 
0.9%
4 30
 
0.3%
5 15
 
0.1%
6 9
 
0.1%
8 9
 
0.1%
7 6
 
0.1%
9 6
 
0.1%
Other values (11) 27
 
0.3%
ValueCountFrequency (%)
0 207
 
2.1%
1 8972
89.7%
2 626
 
6.3%
3 93
 
0.9%
4 30
 
0.3%
5 15
 
0.1%
6 9
 
0.1%
7 6
 
0.1%
8 9
 
0.1%
9 6
 
0.1%
ValueCountFrequency (%)
1000 1
 
< 0.1%
103 1
 
< 0.1%
101 6
0.1%
23 3
< 0.1%
22 1
 
< 0.1%
18 2
 
< 0.1%
16 2
 
< 0.1%
13 5
0.1%
12 1
 
< 0.1%
11 1
 
< 0.1%


Real number (ℝ)

Distinct122
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean222.3234
Minimum0
Maximum9401
Zeros15
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T09:24:14.103087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile101
Q1101
median102
Q3103
95-th percentile201
Maximum9401
Range9401
Interquartile range (IQR)2

Descriptive statistics

Standard deviation952.87162
Coefficient of variation (CV)4.2859709
Kurtosis69.03591
Mean222.3234
Median Absolute Deviation (MAD)1
Skewness8.4036198
Sum2223234
Variance907964.33
MonotonicityNot monotonic
2023-12-11T09:24:14.207968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
101 4597
46.0%
102 1971
19.7%
103 1115
 
11.2%
104 526
 
5.3%
201 427
 
4.3%
105 255
 
2.5%
1 152
 
1.5%
106 133
 
1.3%
301 102
 
1.0%
8101 83
 
0.8%
Other values (112) 639
 
6.4%
ValueCountFrequency (%)
0 15
 
0.1%
1 152
1.5%
2 11
 
0.1%
3 2
 
< 0.1%
5 2
 
< 0.1%
6 1
 
< 0.1%
8 1
 
< 0.1%
10 2
 
< 0.1%
11 5
 
0.1%
12 3
 
< 0.1%
ValueCountFrequency (%)
9401 2
 
< 0.1%
9301 3
 
< 0.1%
9201 1
 
< 0.1%
9103 2
 
< 0.1%
9102 4
 
< 0.1%
9101 15
0.1%
8301 1
 
< 0.1%
8201 7
0.1%
8109 1
 
< 0.1%
8105 1
 
< 0.1%
Distinct8321
Distinct (%)83.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T09:24:14.490826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length34
Median length33
Mean length27.8403
Min length21

Characters and Unicode

Total characters278403
Distinct characters198
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

Unique6896 ?
Unique (%)69.0%

Sample

1st row경상남도 합천군 가야면 치인리 262-2 1동 101호
2nd row경상남도 합천군 합천읍 장계리 479 1동 101호
3rd row[ 용지1길 75 ] 0001동 0101호
4th row[ 강북로 43-47 ] 0001동 0103호
5th row경상남도 합천군 가야면 야천리 196-2 4동 101호
ValueCountFrequency (%)
경상남도 7625
 
11.3%
합천군 7625
 
11.3%
1동 6768
 
10.0%
4750
 
7.0%
101호 3329
 
4.9%
0001동 2204
 
3.3%
102호 1580
 
2.3%
0101호 1268
 
1.9%
103호 922
 
1.4%
합천읍 841
 
1.2%
Other values (4326) 30679
45.4%
2023-12-11T09:24:14.868387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
57591
20.7%
1 30391
 
10.9%
0 21579
 
7.8%
10158
 
3.6%
10154
 
3.6%
9281
 
3.3%
9055
 
3.3%
7804
 
2.8%
7802
 
2.8%
7716
 
2.8%
Other values (188) 106872
38.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 126785
45.5%
Decimal Number 84189
30.2%
Space Separator 57591
20.7%
Dash Punctuation 5088
 
1.8%
Open Punctuation 2375
 
0.9%
Close Punctuation 2375
 
0.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
10158
 
8.0%
10154
 
8.0%
9281
 
7.3%
9055
 
7.1%
7804
 
6.2%
7802
 
6.2%
7716
 
6.1%
7651
 
6.0%
7625
 
6.0%
7625
 
6.0%
Other values (174) 41914
33.1%
Decimal Number
ValueCountFrequency (%)
1 30391
36.1%
0 21579
25.6%
2 7715
 
9.2%
3 5433
 
6.5%
4 4112
 
4.9%
5 3363
 
4.0%
6 3089
 
3.7%
8 2968
 
3.5%
9 2781
 
3.3%
7 2758
 
3.3%
Space Separator
ValueCountFrequency (%)
57591
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5088
100.0%
Open Punctuation
ValueCountFrequency (%)
[ 2375
100.0%
Close Punctuation
ValueCountFrequency (%)
] 2375
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 151618
54.5%
Hangul 126785
45.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
10158
 
8.0%
10154
 
8.0%
9281
 
7.3%
9055
 
7.1%
7804
 
6.2%
7802
 
6.2%
7716
 
6.1%
7651
 
6.0%
7625
 
6.0%
7625
 
6.0%
Other values (174) 41914
33.1%
Common
ValueCountFrequency (%)
57591
38.0%
1 30391
20.0%
0 21579
 
14.2%
2 7715
 
5.1%
3 5433
 
3.6%
- 5088
 
3.4%
4 4112
 
2.7%
5 3363
 
2.2%
6 3089
 
2.0%
8 2968
 
2.0%
Other values (4) 10289
 
6.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 151618
54.5%
Hangul 126785
45.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
57591
38.0%
1 30391
20.0%
0 21579
 
14.2%
2 7715
 
5.1%
3 5433
 
3.6%
- 5088
 
3.4%
4 4112
 
2.7%
5 3363
 
2.2%
6 3089
 
2.0%
8 2968
 
2.0%
Other values (4) 10289
 
6.8%
Hangul
ValueCountFrequency (%)
10158
 
8.0%
10154
 
8.0%
9281
 
7.3%
9055
 
7.1%
7804
 
6.2%
7802
 
6.2%
7716
 
6.1%
7651
 
6.0%
7625
 
6.0%
7625
 
6.0%
Other values (174) 41914
33.1%

시가표준액
Real number (ℝ)

HIGH CORRELATION 

Distinct7382
Distinct (%)73.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23184866
Minimum24000
Maximum2.0606248 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T09:24:14.999446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum24000
5-th percentile247500
Q11155000
median3960000
Q317956700
95-th percentile1.0516266 × 108
Maximum2.0606248 × 109
Range2.0606008 × 109
Interquartile range (IQR)16801700

Descriptive statistics

Standard deviation68225286
Coefficient of variation (CV)2.9426647
Kurtosis200.33362
Mean23184866
Median Absolute Deviation (MAD)3487750
Skewness10.899781
Sum2.3184866 × 1011
Variance4.6546897 × 1015
MonotonicityNot monotonic
2023-12-11T09:24:15.107530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
660000 26
 
0.3%
1800000 22
 
0.2%
2160000 21
 
0.2%
1188000 20
 
0.2%
480000 18
 
0.2%
1440000 18
 
0.2%
1600000 18
 
0.2%
257400 15
 
0.1%
1000000 15
 
0.1%
900000 15
 
0.1%
Other values (7372) 9812
98.1%
ValueCountFrequency (%)
24000 1
< 0.1%
24570 1
< 0.1%
27200 1
< 0.1%
28000 2
< 0.1%
36000 1
< 0.1%
39000 1
< 0.1%
39640 2
< 0.1%
40000 1
< 0.1%
43200 1
< 0.1%
45600 1
< 0.1%
ValueCountFrequency (%)
2060624800 1
< 0.1%
1643365280 1
< 0.1%
1622760320 1
< 0.1%
1325587920 1
< 0.1%
1071581500 1
< 0.1%
1059379420 1
< 0.1%
957282750 1
< 0.1%
953484650 1
< 0.1%
908125930 1
< 0.1%
901533910 1
< 0.1%

연면적
Real number (ℝ)

HIGH CORRELATION 

Distinct3900
Distinct (%)39.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean139.1503
Minimum0.49
Maximum8730
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T09:24:15.217146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.49
5-th percentile12
Q132
median73.8
Q3162.135
95-th percentile434.734
Maximum8730
Range8729.51
Interquartile range (IQR)130.135

Descriptive statistics

Standard deviation236.06238
Coefficient of variation (CV)1.6964561
Kurtosis223.11669
Mean139.1503
Median Absolute Deviation (MAD)51.2
Skewness9.8366013
Sum1391503
Variance55725.446
MonotonicityNot monotonic
2023-12-11T09:24:15.319429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.0 320
 
3.2%
15.0 77
 
0.8%
20.0 70
 
0.7%
66.0 69
 
0.7%
35.0 58
 
0.6%
12.0 57
 
0.6%
198.0 54
 
0.5%
100.0 52
 
0.5%
30.0 52
 
0.5%
24.0 51
 
0.5%
Other values (3890) 9140
91.4%
ValueCountFrequency (%)
0.49 1
< 0.1%
0.66 1
< 0.1%
0.81 2
< 0.1%
0.9 1
< 0.1%
1.0 1
< 0.1%
1.4 1
< 0.1%
1.44 2
< 0.1%
1.5 2
< 0.1%
1.56 2
< 0.1%
1.65 2
< 0.1%
ValueCountFrequency (%)
8730.0 1
< 0.1%
4006.63 1
< 0.1%
3991.18 1
< 0.1%
3608.8 1
< 0.1%
3306.0 1
< 0.1%
3093.48 1
< 0.1%
2982.4 1
< 0.1%
2974.0 1
< 0.1%
2908.72 1
< 0.1%
2896.6 1
< 0.1%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2017-06-01 00:00:00
Maximum2020-06-01 00:00:00
2023-12-11T09:24:15.399605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:15.476563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=4)

Interactions

2023-12-11T09:24:11.080499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:05.914199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:06.802562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:07.531578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:08.274682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:09.018113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:09.721129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:10.381704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:11.159973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:05.988501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:06.894576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:07.612995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:08.358445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:09.122128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:09.818420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:10.453058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:11.242274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:06.077042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:06.984197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:07.718969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:08.465286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:09.232770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:09.914282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:10.553423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:11.320809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:06.153621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:07.074104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:07.799509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:08.551114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:09.313926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:09.989753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:10.666758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:11.419451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:06.229449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:07.159529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:07.886001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:08.637339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:09.396090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:10.065035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:10.742444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:11.498595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:06.544603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:07.248413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:07.977948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:08.719203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:09.471865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:10.138956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:10.825657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:11.573629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:06.617882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:07.336478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:08.084411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:08.802168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:09.552860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:10.223268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:10.916273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:11.657432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:06.719137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:07.449137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:08.189407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:08.919998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:09.633255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:10.303998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:10.998623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T09:24:15.540551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도법정동법정리특수지본번부번시가표준액연면적기준일자
과세년도1.0000.3630.0990.0200.0400.0150.0000.0250.0070.0141.000
법정동0.3631.0000.4380.2030.2510.1320.0560.0630.0360.0510.363
법정리0.0990.4381.0000.2030.3320.2450.0360.0990.0410.0000.099
특수지0.0200.2030.2031.0000.2820.0000.0470.0940.1090.0590.020
본번0.0400.2510.3320.2821.0000.2290.0000.0540.0130.0000.040
부번0.0150.1320.2450.0000.2291.0000.0140.0590.0150.0470.015
0.0000.0560.0360.0470.0000.0141.0000.0170.0000.0000.000
0.0250.0630.0990.0940.0540.0590.0171.0000.0110.0000.025
시가표준액0.0070.0360.0410.1090.0130.0150.0000.0111.0000.7070.007
연면적0.0140.0510.0000.0590.0000.0470.0000.0000.7071.0000.014
기준일자1.0000.3630.0990.0200.0400.0150.0000.0250.0070.0141.000
2023-12-11T09:24:15.662937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
특수지과세년도
특수지1.0000.013
과세년도0.0131.000
2023-12-11T09:24:15.738528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동법정리본번부번시가표준액연면적과세년도특수지
법정동1.0000.1950.052-0.107-0.088-0.092-0.104-0.0160.1830.154
법정리0.1951.000-0.066-0.1000.002-0.053-0.1220.0120.0590.155
본번0.052-0.0661.0000.000-0.045-0.011-0.009-0.0250.0240.216
부번-0.107-0.1000.0001.000-0.0050.0560.1780.0320.0140.000
-0.0880.002-0.045-0.0051.000-0.019-0.011-0.0070.0000.078
-0.092-0.053-0.0110.056-0.0191.0000.017-0.1150.0100.062
시가표준액-0.104-0.122-0.0090.178-0.0110.0171.0000.6130.0040.109
연면적-0.0160.012-0.0250.032-0.007-0.1150.6131.0000.0090.042
과세년도0.1830.0590.0240.0140.0000.0100.0040.0091.0000.013
특수지0.1540.1550.2160.0000.0780.0620.1090.0420.0131.000

Missing values

2023-12-11T09:24:11.776036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T09:24:11.984320image/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
42615경상남도합천군48890201833034126221101경상남도 합천군 가야면 치인리 262-2 1동 101호109456500169.72018-06-01
6997경상남도합천군48890201725028147901101경상남도 합천군 합천읍 장계리 479 1동 101호122400051.02017-06-01
41360경상남도합천군48890201846028140911101[ 용지1길 75 ] 0001동 0101호145442066.112018-06-01
66451경상남도합천군48890202039033166011103[ 강북로 43-47 ] 0001동 0103호22680000150.02020-06-01
24884경상남도합천군48890201833032119624101경상남도 합천군 가야면 야천리 196-2 4동 101호1950000195.02018-06-01
28683경상남도합천군48890201833022111841101경상남도 합천군 가야면 대전리 118-4 1동 101호592800039.02018-06-01
50394경상남도합천군488902019250211669651102[ 충효로3길 7 ] 0001동 0102호1720625050.042019-06-01
28255경상남도합천군48890201832031126421101경상남도 합천군 묘산면 거산리 264-2 1동 101호92400026.42018-06-01
36377경상남도합천군488902018430251104421110경상남도 합천군 삼가면 어전리 1044-2 1동 110호30792300399.92018-06-01
56810경상남도합천군48890201939023165411102경상남도 합천군 청덕면 두곡리 654-1 1동 102호1050000105.02019-06-01
시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번물건지시가표준액연면적기준일자
35222경상남도합천군48890201839023134401101경상남도 합천군 청덕면 두곡리 344 1동 101호31067000330.52018-06-01
7647경상남도합천군488902017250211473141158[ 옥산로 44 ] 0001동 0158호472780015.352017-06-01
27322경상남도합천군488902018360241245019201[ 우회로 72-14 ] 0001동 9201호147193560240.122018-06-01
66028경상남도합천군48890202039032139601101경상남도 합천군 청덕면 초곡리 396 1동 101호1406160234.362020-06-01
22878경상남도합천군48890201825021131501104경상남도 합천군 합천읍 합천리 315 1동 104호57974408.642018-06-01
16547경상남도합천군48890201741030154501102경상남도 합천군 대양면 백암리 545 1동 102호98880082.42017-06-01
2347경상남도합천군488902017340261287231101[ 가야산로 339 ] 0001동 0101호46200000175.02017-06-01
7264경상남도합천군48890201725021163441103[ 동서로 69-13 ] 0001동 0103호109155019.152017-06-01
42471경상남도합천군48890201845027191201101경상남도 합천군 대병면 장단리 912 1동 101호3538080068.042018-06-01
50820경상남도합천군48890201932032146031101[ 봉곡2길 34 ] 0001동 0101호1129911087.592019-06-01

Duplicate rows

Most frequently occurring

시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번물건지시가표준액연면적기준일자# duplicates
1경상남도합천군48890201739032188201101경상남도 합천군 청덕면 초곡리 882 1동 101호218085032.552017-06-013
6경상남도합천군48890201945029150221201경상남도 합천군 대병면 성리 502-2 1동 201호70000020.02019-06-013
0경상남도합천군48890201732030112501101경상남도 합천군 묘산면 화양리 125 1동 101호26392500586.52017-06-012
2경상남도합천군488902017400211119001102경상남도 합천군 적중면 죽고리 1190 1동 102호32472000330.02017-06-012
3경상남도합천군488902018460251205011경상남도 합천군 용주면 평산리 205 1동 1호2470000130.02018-06-012
4경상남도합천군488902018460371118001101경상남도 합천군 용주면 가호리 1180 1동 101호576000036.02018-06-012
5경상남도합천군48890201945029150221101경상남도 합천군 대병면 성리 502-2 1동 101호70000020.02019-06-012
7경상남도합천군488902019460371118001101경상남도 합천군 용주면 가호리 1180 1동 101호561600036.02019-06-012
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