Overview

Dataset statistics

Number of variables15
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows19
Duplicate rows (%)0.2%
Total size in memory1.3 MiB
Average record size in memory138.0 B

Variable types

Categorical6
Numeric8
Text1

Dataset

Description경상북도 문경시의 일반건축물에 대한 지방세 부과기준인 일반건축물 시가표준액 자료를 2019년부터 2021년까지 제공합니다.
Author경상북도 문경시
URLhttps://www.data.go.kr/data/15079947/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 19 (0.2%) duplicate rowsDuplicates
법정동 is highly overall correlated with 법정리High correlation
법정리 is highly overall correlated with 법정동High correlation
특수지 is highly imbalanced (91.1%)Imbalance
동 is highly skewed (γ1 = 27.63616006)Skewed
시가표준액 is highly skewed (γ1 = 35.85448105)Skewed
연면적 is highly skewed (γ1 = 20.42359589)Skewed
법정리 has 3497 (35.0%) zerosZeros
부번 has 3262 (32.6%) zerosZeros

Reproduction

Analysis started2023-12-12 08:59:49.626126
Analysis finished2023-12-12 09:00:12.486367
Duration22.86 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
경상북도
10000 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경상북도 10000
100.0%

Length

2023-12-12T18:00:12.569094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:00:12.691671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경상북도 10000
100.0%

시군구명
Categorical

CONSTANT 

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

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
문경시 10000
100.0%

Length

2023-12-12T18:00:12.833351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:00:12.979462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
문경시 10000
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
47280
10000 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row47280
2nd row47280
3rd row47280
4th row47280
5th row47280

Common Values

ValueCountFrequency (%)
47280 10000
100.0%

Length

2023-12-12T18:00:13.106034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:00:13.221901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
47280 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

2023-12-12T18:00:13.352488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:00:13.484614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021 10000
100.0%

법정동
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean239.5263
Minimum101
Maximum370
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T18:00:13.598701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile101
Q1110
median253
Q3330
95-th percentile370
Maximum370
Range269
Interquartile range (IQR)220

Descriptive statistics

Standard deviation105.11729
Coefficient of variation (CV)0.43885491
Kurtosis-1.5848303
Mean239.5263
Median Absolute Deviation (MAD)107
Skewness-0.28811862
Sum2395263
Variance11049.645
MonotonicityNot monotonic
2023-12-12T18:00:13.775650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
250 1285
12.8%
101 1205
12.0%
110 1045
10.4%
320 859
8.6%
360 845
8.5%
253 818
8.2%
370 733
7.3%
310 576
 
5.8%
103 512
 
5.1%
330 502
 
5.0%
Other values (10) 1620
16.2%
ValueCountFrequency (%)
101 1205
12.0%
102 75
 
0.8%
103 512
5.1%
104 55
 
0.5%
105 16
 
0.2%
106 238
 
2.4%
107 58
 
0.6%
108 83
 
0.8%
109 175
 
1.8%
110 1045
10.4%
ValueCountFrequency (%)
370 733
7.3%
360 845
8.5%
350 385
 
3.9%
340 500
 
5.0%
330 502
 
5.0%
320 859
8.6%
310 576
5.8%
253 818
8.2%
250 1285
12.8%
111 35
 
0.4%

법정리
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct22
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.3954
Minimum0
Maximum41
Zeros3497
Zeros (%)35.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T18:00:13.946678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median23
Q327
95-th percentile34
Maximum41
Range41
Interquartile range (IQR)27

Descriptive statistics

Standard deviation13.252548
Coefficient of variation (CV)0.76184208
Kurtosis-1.5159926
Mean17.3954
Median Absolute Deviation (MAD)7
Skewness-0.39166662
Sum173954
Variance175.63002
MonotonicityNot monotonic
2023-12-12T18:00:14.087524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0 3497
35.0%
21 796
 
8.0%
22 700
 
7.0%
27 631
 
6.3%
25 574
 
5.7%
26 539
 
5.4%
24 485
 
4.9%
28 426
 
4.3%
29 366
 
3.7%
31 334
 
3.3%
Other values (12) 1652
16.5%
ValueCountFrequency (%)
0 3497
35.0%
21 796
 
8.0%
22 700
 
7.0%
23 314
 
3.1%
24 485
 
4.9%
25 574
 
5.7%
26 539
 
5.4%
27 631
 
6.3%
28 426
 
4.3%
29 366
 
3.7%
ValueCountFrequency (%)
41 7
 
0.1%
40 11
 
0.1%
39 12
 
0.1%
38 64
 
0.6%
37 53
 
0.5%
36 171
1.7%
35 159
1.6%
34 134
1.3%
33 170
1.7%
32 247
2.5%

특수지
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
9887 
2
 
113

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 9887
98.9%
2 113
 
1.1%

Length

2023-12-12T18:00:14.252673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:00:14.678950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9887
98.9%
2 113
 
1.1%

본번
Real number (ℝ)

Distinct1037
Distinct (%)10.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean390.6996
Minimum1
Maximum1517
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T18:00:14.786248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile34
Q1164
median309
Q3573
95-th percentile942
Maximum1517
Range1516
Interquartile range (IQR)409

Descriptive statistics

Standard deviation292.29103
Coefficient of variation (CV)0.74812216
Kurtosis0.084065215
Mean390.6996
Median Absolute Deviation (MAD)186
Skewness0.87400378
Sum3906996
Variance85434.046
MonotonicityNot monotonic
2023-12-12T18:00:14.928894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
257 105
 
1.1%
197 75
 
0.8%
288 72
 
0.7%
275 69
 
0.7%
244 56
 
0.6%
188 55
 
0.5%
637 52
 
0.5%
76 47
 
0.5%
630 46
 
0.5%
246 40
 
0.4%
Other values (1027) 9383
93.8%
ValueCountFrequency (%)
1 27
0.3%
2 13
0.1%
3 12
0.1%
4 18
0.2%
5 24
0.2%
6 8
 
0.1%
7 27
0.3%
8 23
0.2%
9 17
0.2%
10 8
 
0.1%
ValueCountFrequency (%)
1517 5
0.1%
1498 1
 
< 0.1%
1345 2
 
< 0.1%
1313 2
 
< 0.1%
1310 1
 
< 0.1%
1307 3
< 0.1%
1303 1
 
< 0.1%
1298 2
 
< 0.1%
1292 1
 
< 0.1%
1291 1
 
< 0.1%

부번
Real number (ℝ)

ZEROS 

Distinct105
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.9319
Minimum0
Maximum190
Zeros3262
Zeros (%)32.6%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T18:00:15.108562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q35
95-th percentile29
Maximum190
Range190
Interquartile range (IQR)5

Descriptive statistics

Standard deviation13.505847
Coefficient of variation (CV)2.2768164
Kurtosis34.992313
Mean5.9319
Median Absolute Deviation (MAD)1
Skewness4.9861219
Sum59319
Variance182.4079
MonotonicityNot monotonic
2023-12-12T18:00:15.298427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3262
32.6%
1 1780
17.8%
2 962
 
9.6%
3 690
 
6.9%
4 471
 
4.7%
5 424
 
4.2%
6 309
 
3.1%
7 238
 
2.4%
9 191
 
1.9%
8 191
 
1.9%
Other values (95) 1482
14.8%
ValueCountFrequency (%)
0 3262
32.6%
1 1780
17.8%
2 962
 
9.6%
3 690
 
6.9%
4 471
 
4.7%
5 424
 
4.2%
6 309
 
3.1%
7 238
 
2.4%
8 191
 
1.9%
9 191
 
1.9%
ValueCountFrequency (%)
190 2
 
< 0.1%
176 1
 
< 0.1%
175 2
 
< 0.1%
173 1
 
< 0.1%
152 1
 
< 0.1%
134 5
0.1%
108 2
 
< 0.1%
105 1
 
< 0.1%
104 1
 
< 0.1%
103 7
0.1%

동
Real number (ℝ)

SKEWED 

Distinct25
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.3224
Minimum0
Maximum9001
Zeros16
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T18:00:15.499119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum9001
Range9001
Interquartile range (IQR)0

Descriptive statistics

Standard deviation324.45418
Coefficient of variation (CV)24.354034
Kurtosis762.71059
Mean13.3224
Median Absolute Deviation (MAD)0
Skewness27.63616
Sum133224
Variance105270.51
MonotonicityNot monotonic
2023-12-12T18:00:15.626054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
1 8827
88.3%
2 730
 
7.3%
3 171
 
1.7%
4 76
 
0.8%
7 35
 
0.4%
5 32
 
0.3%
6 20
 
0.2%
8 19
 
0.2%
101 18
 
0.2%
0 16
 
0.2%
Other values (15) 56
 
0.6%
ValueCountFrequency (%)
0 16
 
0.2%
1 8827
88.3%
2 730
 
7.3%
3 171
 
1.7%
4 76
 
0.8%
5 32
 
0.3%
6 20
 
0.2%
7 35
 
0.4%
8 19
 
0.2%
9 8
 
0.1%
ValueCountFrequency (%)
9001 2
 
< 0.1%
9000 11
0.1%
1000 1
 
< 0.1%
105 6
 
0.1%
104 5
 
0.1%
103 1
 
< 0.1%
101 18
0.2%
28 1
 
< 0.1%
27 1
 
< 0.1%
23 1
 
< 0.1%

호
Real number (ℝ)

Distinct205
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean216.4007
Minimum0
Maximum8203
Zeros11
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T18:00:15.757099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q1101
median101
Q3104
95-th percentile301
Maximum8203
Range8203
Interquartile range (IQR)3

Descriptive statistics

Standard deviation924.8617
Coefficient of variation (CV)4.2738388
Kurtosis67.891411
Mean216.4007
Median Absolute Deviation (MAD)2
Skewness8.3090903
Sum2164007
Variance855369.16
MonotonicityNot monotonic
2023-12-12T18:00:15.897060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
101 3297
33.0%
102 1339
13.4%
1 1108
 
11.1%
201 870
 
8.7%
103 555
 
5.5%
2 474
 
4.7%
104 319
 
3.2%
301 259
 
2.6%
3 241
 
2.4%
202 157
 
1.6%
Other values (195) 1381
13.8%
ValueCountFrequency (%)
0 11
 
0.1%
1 1108
11.1%
2 474
4.7%
3 241
 
2.4%
4 141
 
1.4%
5 69
 
0.7%
6 52
 
0.5%
7 32
 
0.3%
8 25
 
0.2%
9 14
 
0.1%
ValueCountFrequency (%)
8203 1
 
< 0.1%
8202 1
 
< 0.1%
8103 2
 
< 0.1%
8102 9
 
0.1%
8101 121
1.2%
1702 1
 
< 0.1%
1103 1
 
< 0.1%
1003 2
 
< 0.1%
1002 1
 
< 0.1%
917 1
 
< 0.1%

물건지
Text

Distinct9563
Distinct (%)95.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T18:00:16.283134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length33
Median length30
Mean length26.2675
Min length21

Characters and Unicode

Total characters262675
Distinct characters242
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

Unique9283 ?
Unique (%)92.8%

Sample

1st row[ 중앙2길 21 ] 0001동 0102호
2nd row[ 청화로 987 ] 0001동 0001호
3rd row[ 진남1길 183 ] 0001동 0001호
4th row경상북도 문경시 농암면 내서리 336 1동 1호
5th row[ 앗골안길 2 ] 0001동 0103호
ValueCountFrequency (%)
6848
 
10.5%
경상북도 6576
 
10.1%
문경시 6576
 
10.1%
1동 5642
 
8.7%
0001동 3185
 
4.9%
101호 1986
 
3.1%
0101호 1310
 
2.0%
102호 947
 
1.5%
1호 815
 
1.3%
문경읍 797
 
1.2%
Other values (4180) 30402
46.7%
2023-12-12T18:00:16.920831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
55085
21.0%
1 27815
 
10.6%
0 24439
 
9.3%
경 13991
 
5.3%
동 12054
 
4.6%
호 10563
 
4.0%
2 8705
 
3.3%
문 7476
 
2.8%
북 7027
 
2.7%
상 6877
 
2.6%
Other values (232) 88643
33.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 111862
42.6%
Decimal Number 83860
31.9%
Space Separator 55085
21.0%
Dash Punctuation 5019
 
1.9%
Close Punctuation 3424
 
1.3%
Open Punctuation 3424
 
1.3%
Uppercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
경 13991
12.5%
동 12054
 
10.8%
호 10563
 
9.4%
문 7476
 
6.7%
북 7027
 
6.3%
상 6877
 
6.1%
시 6700
 
6.0%
도 6690
 
6.0%
리 5030
 
4.5%
면 3574
 
3.2%
Other values (217) 31880
28.5%
Decimal Number
ValueCountFrequency (%)
1 27815
33.2%
0 24439
29.1%
2 8705
 
10.4%
3 5155
 
6.1%
4 3818
 
4.6%
5 3452
 
4.1%
6 2917
 
3.5%
7 2651
 
3.2%
8 2646
 
3.2%
9 2262
 
2.7%
Space Separator
ValueCountFrequency (%)
55085
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5019
100.0%
Close Punctuation
ValueCountFrequency (%)
] 3424
100.0%
Open Punctuation
ValueCountFrequency (%)
[ 3424
100.0%
Uppercase Letter
ValueCountFrequency (%)
B 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 150812
57.4%
Hangul 111862
42.6%
Latin 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
경 13991
12.5%
동 12054
 
10.8%
호 10563
 
9.4%
문 7476
 
6.7%
북 7027
 
6.3%
상 6877
 
6.1%
시 6700
 
6.0%
도 6690
 
6.0%
리 5030
 
4.5%
면 3574
 
3.2%
Other values (217) 31880
28.5%
Common
ValueCountFrequency (%)
55085
36.5%
1 27815
18.4%
0 24439
16.2%
2 8705
 
5.8%
3 5155
 
3.4%
- 5019
 
3.3%
4 3818
 
2.5%
5 3452
 
2.3%
] 3424
 
2.3%
[ 3424
 
2.3%
Other values (4) 10476
 
6.9%
Latin
ValueCountFrequency (%)
B 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 150813
57.4%
Hangul 111862
42.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
55085
36.5%
1 27815
18.4%
0 24439
16.2%
2 8705
 
5.8%
3 5155
 
3.4%
- 5019
 
3.3%
4 3818
 
2.5%
5 3452
 
2.3%
] 3424
 
2.3%
[ 3424
 
2.3%
Other values (5) 10477
 
6.9%
Hangul
ValueCountFrequency (%)
경 13991
12.5%
동 12054
 
10.8%
호 10563
 
9.4%
문 7476
 
6.7%
북 7027
 
6.3%
상 6877
 
6.1%
시 6700
 
6.0%
도 6690
 
6.0%
리 5030
 
4.5%
면 3574
 
3.2%
Other values (217) 31880
28.5%

시가표준액
Real number (ℝ)

SKEWED 

Distinct8390
Distinct (%)83.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47016983
Minimum12900
Maximum1.5527147 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T18:00:17.268636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum12900
5-th percentile316800
Q11814370
median9912000
Q338541202
95-th percentile1.635888 × 108
Maximum1.5527147 × 1010
Range1.5527134 × 1010
Interquartile range (IQR)36726832

Descriptive statistics

Standard deviation2.5324985 × 108
Coefficient of variation (CV)5.3863484
Kurtosis1776.3334
Mean47016983
Median Absolute Deviation (MAD)9246720
Skewness35.854481
Sum4.7016983 × 1011
Variance6.4135488 × 1016
MonotonicityNot monotonic
2023-12-12T18:00:17.602066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
77145650 38
 
0.4%
115722650 26
 
0.3%
69175040 19
 
0.2%
576000 18
 
0.2%
1152000 17
 
0.2%
960000 16
 
0.2%
1188000 15
 
0.1%
768000 14
 
0.1%
600000 14
 
0.1%
288000 13
 
0.1%
Other values (8380) 9810
98.1%
ValueCountFrequency (%)
12900 1
< 0.1%
19600 1
< 0.1%
25200 1
< 0.1%
32000 1
< 0.1%
33600 2
< 0.1%
38400 1
< 0.1%
42300 1
< 0.1%
44250 1
< 0.1%
44730 1
< 0.1%
45000 1
< 0.1%
ValueCountFrequency (%)
15527147210 1
< 0.1%
9463845840 1
< 0.1%
8385108940 1
< 0.1%
5683426730 1
< 0.1%
5305242000 1
< 0.1%
5020638630 1
< 0.1%
4086542250 1
< 0.1%
2998892000 1
< 0.1%
2588626500 1
< 0.1%
2569136870 1
< 0.1%

연면적
Real number (ℝ)

SKEWED 

Distinct5542
Distinct (%)55.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean175.71456
Minimum0.72
Maximum20403.61
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T18:00:17.865682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.72
5-th percentile12.84
Q142.745
median92.39
Q3184.3475
95-th percentile520
Maximum20403.61
Range20402.89
Interquartile range (IQR)141.6025

Descriptive statistics

Standard deviation442.98252
Coefficient of variation (CV)2.5210347
Kurtosis665.82248
Mean175.71456
Median Absolute Deviation (MAD)58.5125
Skewness20.423596
Sum1757145.6
Variance196233.51
MonotonicityNot monotonic
2023-12-12T18:00:18.037464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.0 181
 
1.8%
66.0 40
 
0.4%
192.0 39
 
0.4%
400.0 38
 
0.4%
92.39 38
 
0.4%
198.0 38
 
0.4%
96.0 36
 
0.4%
36.0 36
 
0.4%
40.0 35
 
0.4%
32.0 34
 
0.3%
Other values (5532) 9485
94.8%
ValueCountFrequency (%)
0.72 1
< 0.1%
0.86 1
< 0.1%
1.0 2
< 0.1%
1.2 1
< 0.1%
1.21 1
< 0.1%
1.29 1
< 0.1%
1.3 1
< 0.1%
1.4 1
< 0.1%
1.44 1
< 0.1%
1.5 1
< 0.1%
ValueCountFrequency (%)
20403.61 1
< 0.1%
13431.35 1
< 0.1%
11200.0 1
< 0.1%
11018.54 1
< 0.1%
10813.97 1
< 0.1%
8101.82 1
< 0.1%
7006.1 1
< 0.1%
6999.0 1
< 0.1%
6601.44 1
< 0.1%
6012.0 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

2023-12-12T18:00:18.170177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:00:18.271811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021-06-01 10000
100.0%

Interactions

2023-12-12T18:00:10.069616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:59:52.008310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:59:53.907636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:59:55.711625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:59:57.586180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:59:59.540438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:00:01.897549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:00:07.840286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:00:10.204741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:59:52.160356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:59:53.999439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:59:55.822952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:59:57.712568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:59:59.704114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:00:02.798852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:00:07.978648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:00:10.330687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:59:52.286636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:59:54.092343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:59:55.919954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:59:57.854198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:59:59.900713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:00:03.320333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:00:08.100466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:00:10.465032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:59:52.418355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:59:54.215238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:59:56.037076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:59:58.054447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:00:00.056920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:00:03.807377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:00:08.260722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:00:10.603897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:59:52.549200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:59:54.328488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:59:56.139849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:59:58.213799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:00:00.171633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:00:04.361194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:00:08.422256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:00:10.753366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:59:52.674245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:59:54.436408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:59:56.233721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:59:58.331051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:00:00.302537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:00:05.122528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:00:08.912734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:00:11.720472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:59:53.664047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:59:55.430015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:59:57.292375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:59:59.270192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:00:01.596283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:00:06.496130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:00:09.787511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:00:11.860282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:59:53.782679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:59:55.591583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:59:57.418588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:59:59.405790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:00:01.748333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:00:07.171483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:00:09.927281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T18:00:18.348446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동법정리특수지본번부번동호시가표준액연면적
법정동1.0000.6980.0550.4500.1640.0430.0610.0000.039
법정리0.6981.0000.0330.2630.1620.0730.0690.0000.000
특수지0.0550.0331.0000.2270.0000.0000.0000.0000.000
본번0.4500.2630.2271.0000.2080.0760.0450.0540.055
부번0.1640.1620.0000.2081.0000.0000.0570.0000.000
동0.0430.0730.0000.0760.0001.0000.0000.0000.000
호0.0610.0690.0000.0450.0570.0001.0000.0000.000
시가표준액0.0000.0000.0000.0540.0000.0000.0001.0000.955
연면적0.0390.0000.0000.0550.0000.0000.0000.9551.000
2023-12-12T18:00:18.489115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동법정리본번부번동호시가표준액연면적특수지
법정동1.0000.677-0.013-0.3010.019-0.098-0.2670.0420.068
법정리0.6771.000-0.030-0.302-0.004-0.105-0.3010.0010.024
본번-0.013-0.0301.000-0.117-0.0170.0150.1120.0710.174
부번-0.301-0.302-0.1171.000-0.0650.0220.120-0.1310.000
동0.019-0.004-0.017-0.0651.000-0.039-0.012-0.0440.000
호-0.098-0.1050.0150.022-0.0391.0000.076-0.0380.000
시가표준액-0.267-0.3010.1120.120-0.0120.0761.0000.4890.000
연면적0.0420.0010.071-0.131-0.044-0.0380.4891.0000.000
특수지0.0680.0240.1740.0000.0000.0000.0000.0001.000

Missing values

2023-12-12T18:00:12.080038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T18:00:12.362410image/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
9042경상북도문경시4728020211010128041102[ 중앙2길 21 ] 0001동 0102호129823021.32021-06-01
7954경상북도문경시472802021370261221011[ 청화로 987 ] 0001동 0001호38775660160.232021-06-01
22713경상북도문경시472802021360251110311[ 진남1길 183 ] 0001동 0001호451429021.932021-06-01
23917경상북도문경시472802021370251336011경상북도 문경시 농암면 내서리 336 1동 1호142080029.62021-06-01
12690경상북도문경시4728020211080120801103[ 앗골안길 2 ] 0001동 0103호940800032.02021-06-01
16625경상북도문경시47280202132024150031103경상북도 문경시 산양면 과곡리 500-3 1동 103호546000182.02021-06-01
1927경상북도문경시47280202125032120402101경상북도 문경시 문경읍 팔영리 204 2동 101호166110079.12021-06-01
17783경상북도문경시4728020213202816011103경상북도 문경시 산양면 연소리 60-1 1동 103호300000100.02021-06-01
22443경상북도문경시472802021360241423021경상북도 문경시 마성면 하내리 423 2동 1호972000324.02021-06-01
1681경상북도문경시47280202125031157401103경상북도 문경시 문경읍 고요리 574 1동 103호268200018.02021-06-01
시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번동호물건지시가표준액연면적기준일자
14234경상북도문경시4728020211100186561301[ 당교로 234 ] 0001동 0301호123826640260.142021-06-01
865경상북도문경시47280202111001866151301[ 당교3길 48 ] 0001동 0301호228639030266.792021-06-01
5310경상북도문경시47280202137028142401102경상북도 문경시 농암면 갈동리 424 1동 102호356400118.82021-06-01
15535경상북도문경시47280202125022136351102[ 온천4길 3 ] 0001동 0102호483840028.82021-06-01
3466경상북도문경시47280202110301261512경상북도 문경시 흥덕동 261-5 1동 2호30887640129.782021-06-01
16511경상북도문경시4728020212532113221511[ 양산개5길 11-5 ] 0001동 0001호270900043.02021-06-01
4613경상북도문경시47280202136026170001301[ 소야2길 2 ] 0001동 0301호5961662088.062021-06-01
3668경상북도문경시472802021253221412414경상북도 문경시 가은읍 갈전리 412-4 1동 4호2090000209.02021-06-01
19203경상북도문경시47280202132030159071103경상북도 문경시 산양면 존도리 590-7 1동 103호29550098.52021-06-01
15259경상북도문경시472802021250221762712[ 청운로 63 ] 0001동 0002호21573600121.22021-06-01

Duplicate rows

Most frequently occurring

시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번동호물건지시가표준액연면적기준일자# duplicates
4경상북도문경시472802021107027111101경상북도 문경시 불정동 산 71-1 1동 101호1418400036.02021-06-016
5경상북도문경시4728020211090196531101경상북도 문경시 공평동 965-3 1동 101호34650000350.02021-06-013
6경상북도문경시472802021250241168111[ 주흘로 241-7 ] 0001동 0001호1104000040.02021-06-013
0경상북도문경시47280202110101411011경상북도 문경시 점촌동 41-10 1동 1호52984800132.02021-06-012
1경상북도문경시47280202110101411012경상북도 문경시 점촌동 41-10 1동 2호52984800132.02021-06-012
2경상북도문경시4728020211020110301101[ 영신영강길 214 ] 0001동 0101호470206019.432021-06-012
3경상북도문경시4728020211020110301101[ 영신영강길 214 ] 0001동 0101호1377462049.022021-06-012
7경상북도문경시47280202125026178901101경상북도 문경시 문경읍 마원리 789 1동 101호2486484043.472021-06-012
8경상북도문경시472802021250291365011경상북도 문경시 문경읍 하초리 365 1동 1호1012936047.782021-06-012
9경상북도문경시47280202125035179611101경상북도 문경시 문경읍 평천리 796-1 1동 101호36920073.842021-06-012