Overview

Dataset statistics

Number of variables14
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows18
Duplicate rows (%)0.2%
Total size in memory1.2 MiB
Average record size in memory128.0 B

Variable types

Categorical6
Numeric5
Unsupported1
Text2

Dataset

Description인천광역시(군, 구 포함) 주택외건축물 시가표준액 정보 데이터 자료 제공하며, 2023년 재산세 과세자료 중 물건지와 연면적 정보를 포함하고 있음
Author인천광역시
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15043085&srcSe=7661IVAWM27C61E190

Alerts

시도 has constant value ""Constant
과세연도 has constant value ""Constant
법정리 has constant value ""Constant
기준일자 has constant value ""Constant
Dataset has 18 (0.2%) duplicate rowsDuplicates
법정동코드 is highly overall correlated with 본번 and 1 other fieldsHigh correlation
본번 is highly overall correlated with 법정동코드High correlation
시가표준액 is highly overall correlated with 연면적High correlation
연면적 is highly overall correlated with 시가표준액High correlation
시군구 is highly overall correlated with 법정동코드High correlation
특수지 is highly imbalanced (98.2%)Imbalance
시가표준액 is highly skewed (γ1 = 65.95005287)Skewed
연면적 is highly skewed (γ1 = 52.60302439)Skewed
동 is an unsupported type, check if it needs cleaning or further analysisUnsupported
부번 has 1890 (18.9%) zerosZeros

Reproduction

Analysis started2024-03-18 05:30:11.752754
Analysis finished2024-03-18 05:30:15.224380
Duration3.47 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도
Categorical

CONSTANT 

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

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
인천광역시 10000
100.0%

Length

2024-03-18T14:30:15.278133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-18T14:30:15.352907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
인천광역시 10000
100.0%

시군구
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
중구
6590 
동구
2513 
미추홀구
897 

Length

Max length4
Median length2
Mean length2.1794
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row중구
2nd row중구
3rd row중구
4th row중구
5th row동구

Common Values

ValueCountFrequency (%)
중구 6590
65.9%
동구 2513
 
25.1%
미추홀구 897
 
9.0%

Length

2024-03-18T14:30:15.438405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-18T14:30:15.528732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
중구 6590
65.9%
동구 2513
 
25.1%
미추홀구 897
 
9.0%

과세연도
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023
10000 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023
2nd row2023
3rd row2023
4th row2023
5th row2023

Common Values

ValueCountFrequency (%)
2023 10000
100.0%

Length

2024-03-18T14:30:15.613358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-18T14:30:15.685681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023 10000
100.0%

법정동코드
Real number (ℝ)

HIGH CORRELATION 

Distinct51
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.6103
Minimum101
Maximum152
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-18T14:30:16.037282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile102
Q1107
median128
Q3145
95-th percentile148
Maximum152
Range51
Interquartile range (IQR)38

Descriptive statistics

Standard deviation18.69823
Coefficient of variation (CV)0.14768332
Kurtosis-1.6980631
Mean126.6103
Median Absolute Deviation (MAD)19
Skewness-0.13201169
Sum1266103
Variance349.6238
MonotonicityNot monotonic
2024-03-18T14:30:16.160879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
147 1561
15.6%
145 1384
13.8%
107 1275
12.8%
103 884
8.8%
118 796
 
8.0%
102 566
 
5.7%
128 405
 
4.0%
101 389
 
3.9%
138 269
 
2.7%
104 262
 
2.6%
Other values (41) 2209
22.1%
ValueCountFrequency (%)
101 389
 
3.9%
102 566
5.7%
103 884
8.8%
104 262
 
2.6%
105 34
 
0.3%
106 92
 
0.9%
107 1275
12.8%
109 8
 
0.1%
110 8
 
0.1%
111 14
 
0.1%
ValueCountFrequency (%)
152 69
 
0.7%
151 57
 
0.6%
150 69
 
0.7%
149 260
 
2.6%
148 186
 
1.9%
147 1561
15.6%
146 248
 
2.5%
145 1384
13.8%
144 14
 
0.1%
143 8
 
0.1%

법정리
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
10000 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 10000
100.0%

Length

2024-03-18T14:30:16.267502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-18T14:30:16.342386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 10000
100.0%

특수지
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
9971 
2
 
28
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 9971
99.7%
2 28
 
0.3%
3 1
 
< 0.1%

Length

2024-03-18T14:30:16.421936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-18T14:30:16.504143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9971
99.7%
2 28
 
0.3%
3 1
 
< 0.1%

본번
Real number (ℝ)

HIGH CORRELATION 

Distinct897
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean861.8208
Minimum1
Maximum3246
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-18T14:30:16.600692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q151
median287
Q31873
95-th percentile3090
Maximum3246
Range3245
Interquartile range (IQR)1822

Descriptive statistics

Standard deviation1083.7172
Coefficient of variation (CV)1.257474
Kurtosis-0.57135338
Mean861.8208
Median Absolute Deviation (MAD)274
Skewness1.013228
Sum8618208
Variance1174443
MonotonicityNot monotonic
2024-03-18T14:30:16.730411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
129 406
 
4.1%
294 398
 
4.0%
1873 362
 
3.6%
1886 295
 
2.9%
295 285
 
2.9%
2850 284
 
2.8%
27 261
 
2.6%
1 254
 
2.5%
7 199
 
2.0%
2 182
 
1.8%
Other values (887) 7074
70.7%
ValueCountFrequency (%)
1 254
2.5%
2 182
1.8%
3 94
 
0.9%
4 86
 
0.9%
5 34
 
0.3%
6 106
1.1%
7 199
2.0%
8 66
 
0.7%
9 45
 
0.4%
10 35
 
0.4%
ValueCountFrequency (%)
3246 4
 
< 0.1%
3243 10
0.1%
3238 3
 
< 0.1%
3234 6
0.1%
3233 8
0.1%
3231 8
0.1%
3215 1
 
< 0.1%
3212 1
 
< 0.1%
3209 1
 
< 0.1%
3202 8
0.1%

부번
Real number (ℝ)

ZEROS 

Distinct373
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.278
Minimum0
Maximum888
Zeros1890
Zeros (%)18.9%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-18T14:30:16.881220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median5
Q320
95-th percentile173
Maximum888
Range888
Interquartile range (IQR)19

Descriptive statistics

Standard deviation92.927093
Coefficient of variation (CV)2.7924483
Kurtosis30.852379
Mean33.278
Median Absolute Deviation (MAD)5
Skewness5.1257133
Sum332780
Variance8635.4447
MonotonicityNot monotonic
2024-03-18T14:30:17.012719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1890
18.9%
1 1153
 
11.5%
2 690
 
6.9%
4 634
 
6.3%
3 525
 
5.2%
5 446
 
4.5%
6 342
 
3.4%
7 314
 
3.1%
8 208
 
2.1%
11 201
 
2.0%
Other values (363) 3597
36.0%
ValueCountFrequency (%)
0 1890
18.9%
1 1153
11.5%
2 690
 
6.9%
3 525
 
5.2%
4 634
 
6.3%
5 446
 
4.5%
6 342
 
3.4%
7 314
 
3.1%
8 208
 
2.1%
9 128
 
1.3%
ValueCountFrequency (%)
888 1
< 0.1%
883 1
< 0.1%
881 1
< 0.1%
875 1
< 0.1%
866 1
< 0.1%
860 1
< 0.1%
849 2
< 0.1%
847 1
< 0.1%
846 1
< 0.1%
824 2
< 0.1%

동
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size156.2 KiB

호수
Text

Distinct1048
Distinct (%)10.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-03-18T14:30:17.325501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length7
Mean length2.3814
Min length1

Characters and Unicode

Total characters23814
Distinct characters22
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique411 ?
Unique (%)4.1%

Sample

1st row1602
2nd row1218
3rd row514
4th row509
5th row219
ValueCountFrequency (%)
1 1725
 
17.2%
2 672
 
6.7%
101 396
 
4.0%
3 392
 
3.9%
0 333
 
3.3%
201 230
 
2.3%
4 227
 
2.3%
102 168
 
1.7%
5 156
 
1.6%
301 137
 
1.4%
Other values (1040) 5575
55.7%
2024-03-18T14:30:17.729955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 7488
31.4%
0 4573
19.2%
2 3511
14.7%
3 2303
 
9.7%
4 1304
 
5.5%
5 1065
 
4.5%
8 980
 
4.1%
6 969
 
4.1%
7 843
 
3.5%
9 666
 
2.8%
Other values (12) 112
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23702
99.5%
Dash Punctuation 67
 
0.3%
Other Letter 25
 
0.1%
Space Separator 11
 
< 0.1%
Uppercase Letter 7
 
< 0.1%
Lowercase Letter 2
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 7488
31.6%
0 4573
19.3%
2 3511
14.8%
3 2303
 
9.7%
4 1304
 
5.5%
5 1065
 
4.5%
8 980
 
4.1%
6 969
 
4.1%
7 843
 
3.6%
9 666
 
2.8%
Other Letter
ValueCountFrequency (%)
일 11
44.0%
월 11
44.0%
비 1
 
4.0%
주 1
 
4.0%
층 1
 
4.0%
Uppercase Letter
ValueCountFrequency (%)
B 5
71.4%
J 1
 
14.3%
A 1
 
14.3%
Lowercase Letter
ValueCountFrequency (%)
a 1
50.0%
n 1
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 67
100.0%
Space Separator
ValueCountFrequency (%)
11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 23780
99.9%
Hangul 25
 
0.1%
Latin 9
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
1 7488
31.5%
0 4573
19.2%
2 3511
14.8%
3 2303
 
9.7%
4 1304
 
5.5%
5 1065
 
4.5%
8 980
 
4.1%
6 969
 
4.1%
7 843
 
3.5%
9 666
 
2.8%
Other values (2) 78
 
0.3%
Hangul
ValueCountFrequency (%)
일 11
44.0%
월 11
44.0%
비 1
 
4.0%
주 1
 
4.0%
층 1
 
4.0%
Latin
ValueCountFrequency (%)
B 5
55.6%
J 1
 
11.1%
a 1
 
11.1%
n 1
 
11.1%
A 1
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23789
99.9%
Hangul 25
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 7488
31.5%
0 4573
19.2%
2 3511
14.8%
3 2303
 
9.7%
4 1304
 
5.5%
5 1065
 
4.5%
8 980
 
4.1%
6 969
 
4.1%
7 843
 
3.5%
9 666
 
2.8%
Other values (7) 87
 
0.4%
Hangul
ValueCountFrequency (%)
일 11
44.0%
월 11
44.0%
비 1
 
4.0%
주 1
 
4.0%
층 1
 
4.0%

물건지
Text

Distinct9600
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-03-18T14:30:17.965628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length33
Median length30
Mean length25.313
Min length16

Characters and Unicode

Total characters253130
Distinct characters193
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

Unique9317 ?
Unique (%)93.2%

Sample

1st row[ 영종대로 881 ] 0000동 1602호
2nd row[ 영종대로196번길 15-30 ] 0000동 1218호
3rd row[ 은하수로29번길 47 ] 0001동 0514호
4th row[ 인중로 290 ] 0001동 0509호
5th row인천광역시 동구 송림동 294 16동 219호
ValueCountFrequency (%)
12114
20.4%
인천광역시 3943
 
6.6%
0001동 3074
 
5.2%
0000동 2338
 
3.9%
중구 2086
 
3.5%
동구 1704
 
2.9%
1동 1330
 
2.2%
송림동 936
 
1.6%
0001호 916
 
1.5%
1호 809
 
1.4%
Other values (4140) 30162
50.8%
2024-03-18T14:30:18.381995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
49412
19.5%
0 36914
14.6%
1 20009
 
7.9%
동 15230
 
6.0%
2 10177
 
4.0%
호 9860
 
3.9%
3 6481
 
2.6%
[ 6057
 
2.4%
] 6057
 
2.4%
로 5973
 
2.4%
Other values (183) 86960
34.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 99259
39.2%
Other Letter 88650
35.0%
Space Separator 49412
19.5%
Open Punctuation 6057
 
2.4%
Close Punctuation 6057
 
2.4%
Dash Punctuation 3689
 
1.5%
Uppercase Letter 6
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 15230
17.2%
호 9860
 
11.1%
로 5973
 
6.7%
인 4499
 
5.1%
시 4149
 
4.7%
광 4029
 
4.5%
천 3967
 
4.5%
역 3948
 
4.5%
구 3945
 
4.5%
길 3006
 
3.4%
Other values (167) 30044
33.9%
Decimal Number
ValueCountFrequency (%)
0 36914
37.2%
1 20009
20.2%
2 10177
 
10.3%
3 6481
 
6.5%
9 5000
 
5.0%
4 4855
 
4.9%
5 4338
 
4.4%
7 3937
 
4.0%
6 3911
 
3.9%
8 3637
 
3.7%
Uppercase Letter
ValueCountFrequency (%)
B 5
83.3%
A 1
 
16.7%
Space Separator
ValueCountFrequency (%)
49412
100.0%
Open Punctuation
ValueCountFrequency (%)
[ 6057
100.0%
Close Punctuation
ValueCountFrequency (%)
] 6057
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3689
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 164474
65.0%
Hangul 88650
35.0%
Latin 6
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 15230
17.2%
호 9860
 
11.1%
로 5973
 
6.7%
인 4499
 
5.1%
시 4149
 
4.7%
광 4029
 
4.5%
천 3967
 
4.5%
역 3948
 
4.5%
구 3945
 
4.5%
길 3006
 
3.4%
Other values (167) 30044
33.9%
Common
ValueCountFrequency (%)
49412
30.0%
0 36914
22.4%
1 20009
12.2%
2 10177
 
6.2%
3 6481
 
3.9%
[ 6057
 
3.7%
] 6057
 
3.7%
9 5000
 
3.0%
4 4855
 
3.0%
5 4338
 
2.6%
Other values (4) 15174
 
9.2%
Latin
ValueCountFrequency (%)
B 5
83.3%
A 1
 
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 164480
65.0%
Hangul 88650
35.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
49412
30.0%
0 36914
22.4%
1 20009
12.2%
2 10177
 
6.2%
3 6481
 
3.9%
[ 6057
 
3.7%
] 6057
 
3.7%
9 5000
 
3.0%
4 4855
 
3.0%
5 4338
 
2.6%
Other values (6) 15180
 
9.2%
Hangul
ValueCountFrequency (%)
동 15230
17.2%
호 9860
 
11.1%
로 5973
 
6.7%
인 4499
 
5.1%
시 4149
 
4.7%
광 4029
 
4.5%
천 3967
 
4.5%
역 3948
 
4.5%
구 3945
 
4.5%
길 3006
 
3.4%
Other values (167) 30044
33.9%

시가표준액
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct6518
Distinct (%)65.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1613251 × 108
Minimum4400
Maximum1.16 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-18T14:30:18.506492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4400
5-th percentile1240862.5
Q16216047.5
median29077370
Q361691190
95-th percentile2.4289258 × 108
Maximum1.16 × 1011
Range1.16 × 1011
Interquartile range (IQR)55475142

Descriptive statistics

Standard deviation1.3669177 × 109
Coefficient of variation (CV)11.770328
Kurtosis5301.6985
Mean1.1613251 × 108
Median Absolute Deviation (MAD)24803300
Skewness65.950053
Sum1.1613251 × 1012
Variance1.8684641 × 1018
MonotonicityNot monotonic
2024-03-18T14:30:18.630603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13791820 238
 
2.4%
2050860 203
 
2.0%
2361830 105
 
1.1%
18568530 104
 
1.0%
46161960 54
 
0.5%
2104830 48
 
0.5%
14165340 44
 
0.4%
49009390 44
 
0.4%
39994020 43
 
0.4%
46195110 41
 
0.4%
Other values (6508) 9076
90.8%
ValueCountFrequency (%)
4400 1
 
< 0.1%
9400 1
 
< 0.1%
18800 4
< 0.1%
26320 2
 
< 0.1%
26460 1
 
< 0.1%
28200 1
 
< 0.1%
29100 1
 
< 0.1%
29400 1
 
< 0.1%
31950 6
0.1%
37240 1
 
< 0.1%
ValueCountFrequency (%)
116000000000 1
< 0.1%
45580478940 1
< 0.1%
19849834620 1
< 0.1%
15689235620 1
< 0.1%
14435489520 2
< 0.1%
13089964710 1
< 0.1%
12503688460 1
< 0.1%
12075844480 1
< 0.1%
11041153200 1
< 0.1%
10551914510 1
< 0.1%

연면적
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct5167
Distinct (%)51.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean207.23909
Minimum0.1
Maximum127968.28
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-18T14:30:18.747103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile7.98
Q133.0615
median51.14455
Q3106.5646
95-th percentile568.75662
Maximum127968.28
Range127968.18
Interquartile range (IQR)73.5031

Descriptive statistics

Standard deviation1646.9672
Coefficient of variation (CV)7.9471838
Kurtosis3750.1573
Mean207.23909
Median Absolute Deviation (MAD)31.14455
Skewness52.603024
Sum2072390.9
Variance2712500.8
MonotonicityNot monotonic
2024-03-18T14:30:18.866917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33.97 238
 
2.4%
18.0 237
 
2.4%
7.98 203
 
2.0%
9.19 107
 
1.1%
39.09 104
 
1.0%
27.0 74
 
0.7%
48.3878 54
 
0.5%
8.19 48
 
0.5%
57.9722 47
 
0.5%
34.89 44
 
0.4%
Other values (5157) 8844
88.4%
ValueCountFrequency (%)
0.1 1
 
< 0.1%
0.1102 1
 
< 0.1%
0.2 4
 
< 0.1%
0.27 1
 
< 0.1%
0.28 2
 
< 0.1%
0.3 23
0.2%
0.38 1
 
< 0.1%
0.4 2
 
< 0.1%
0.42 1
 
< 0.1%
0.52 1
 
< 0.1%
ValueCountFrequency (%)
127968.28 1
< 0.1%
50348.48 1
< 0.1%
34618.79 1
< 0.1%
26039.4 1
< 0.1%
24036.31 1
< 0.1%
21436.905 1
< 0.1%
20767.5004 2
< 0.1%
16462.11 1
< 0.1%
15450.95 1
< 0.1%
15269.15 1
< 0.1%

기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-06-01
10000 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-06-01
2nd row2023-06-01
3rd row2023-06-01
4th row2023-06-01
5th row2023-06-01

Common Values

ValueCountFrequency (%)
2023-06-01 10000
100.0%

Length

2024-03-18T14:30:19.008799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-18T14:30:19.121480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-06-01 10000
100.0%

Interactions

2024-03-18T14:30:14.486568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:30:12.819160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:30:13.221319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:30:13.652233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:30:14.072128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:30:14.568932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:30:12.897542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:30:13.300912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:30:13.749919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:30:14.153050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:30:14.672709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:30:12.978662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:30:13.381055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:30:13.842846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:30:14.233179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:30:14.766697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:30:13.051570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:30:13.464467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:30:13.915049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:30:14.306508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:30:14.860685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:30:13.138242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:30:13.569968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:30:13.993794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:30:14.395227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-18T14:30:19.176563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구법정동코드특수지본번부번시가표준액연면적
시군구1.0000.8450.0460.6300.2040.0000.000
법정동코드0.8451.0000.0660.8590.3940.0000.000
특수지0.0460.0661.0000.0470.0000.0000.000
본번0.6300.8590.0471.0000.2150.0210.053
부번0.2040.3940.0000.2151.0000.0000.000
시가표준액0.0000.0000.0000.0210.0001.0000.921
연면적0.0000.0000.0000.0530.0000.9211.000
2024-03-18T14:30:19.270938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
특수지시군구
특수지1.0000.014
시군구0.0141.000
2024-03-18T14:30:19.346421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동코드본번부번시가표준액연면적시군구특수지
법정동코드1.0000.569-0.0520.2470.1180.7590.039
본번0.5691.000-0.2930.3110.0300.4750.028
부번-0.052-0.2931.0000.0770.1530.1240.000
시가표준액0.2470.3110.0771.0000.8060.0000.000
연면적0.1180.0300.1530.8061.0000.0000.000
시군구0.7590.4750.1240.0000.0001.0000.014
특수지0.0390.0280.0000.0000.0000.0141.000

Missing values

2024-03-18T14:30:14.994110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-18T14:30:15.150424image/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

시도시군구과세연도법정동코드법정리특수지본번부번동호수물건지시가표준액연면적기준일자
35527인천광역시중구20231450118732001602[ 영종대로 881 ] 0000동 1602호3712078040.9272023-06-01
49658인천광역시중구2023147013098401218[ 영종대로196번길 15-30 ] 0000동 1218호4994880052.32023-06-01
43974인천광역시중구202314501195321514[ 은하수로29번길 47 ] 0001동 0514호3406473035.012023-06-01
19353인천광역시중구2023141015611509[ 인중로 290 ] 0001동 0509호4616196048.38782023-06-01
84944인천광역시동구202310701294016219인천광역시 동구 송림동 294 16동 219호1379182033.972023-06-01
61399인천광역시중구20231280164014인천광역시 중구 신흥동3가 64 1동 4호581175011.072023-06-01
50825인천광역시중구2023145011946180118[ 영종진광장로 52 ] 0000동 0118호7061432065.462023-06-01
63410인천광역시동구202310701295022322인천광역시 동구 송림동 295 22동 322호1379182033.972023-06-01
66095인천광역시동구202310401457014[ 송화로 5-3 ] 0001동 0004호112800012.02023-06-01
16823인천광역시중구20231360137011[ 우현로 79-10 ] 0001동 0001호403115036.31672023-06-01
시도시군구과세연도법정동코드법정리특수지본번부번동호수물건지시가표준액연면적기준일자
9068인천광역시중구20231250111208인천광역시 중구 답동 11-2 8호15191540166.942023-06-01
52816인천광역시중구2023118011042190014인천광역시 중구 항동7가 104-21 9001동 4호439200018.02023-06-01
32597인천광역시중구202311801931321인천광역시 중구 항동7가 93-13 2동 1호139672000316.02023-06-01
60012인천광역시중구2023148017894200[ 백운로 565 ] 0000동 0000호209547000300.02023-06-01
15499인천광역시중구2023134012312611[ 개항로 68-2 ] 0001동 0001호2957150059.52023-06-01
54199인천광역시중구202311801853517[ 연안부두로81번길 18 ] 0001동 0007호448245071.152023-06-01
34580인천광역시중구2023145011873811814[ 자연대로 29 ] 0001동 1814호3958251035.312023-06-01
39783인천광역시중구2023145011886110624[ 자연대로 8 ] 0000동 0624호4687722040.0662023-06-01
46401인천광역시중구202314701308721132[ 영종대로 166 ] 0001동 0132호14121732090.512023-06-01
13269인천광역시중구2023130012714인천광역시 중구 유동 2-7 1동 4호19363470129.08982023-06-01

Duplicate rows

Most frequently occurring

시도시군구과세연도법정동코드법정리특수지본번부번호수물건지시가표준액연면적기준일자# duplicates
0인천광역시동구20231070129627101인천광역시 동구 송림동 296-27 2동 101호1432620037.82023-06-012
1인천광역시중구2023118016586인천광역시 중구 항동7가 65-8 4동 6호5449604.162023-06-012
2인천광역시중구20231180110471[ 서해대로 143 ] 0000동 0001호43634401906193.232023-06-012
3인천광역시중구20231180110472[ 서해대로 143 ] 0000동 0002호41102302505219.342023-06-012
4인천광역시중구202312701741[ 서해대로454번길 5-1 ] 0001동 0001호179874240221.522023-06-012
5인천광역시중구202312701545112인천광역시 중구 신흥동2가 54-5 112호498400.32023-06-012
6인천광역시중구202312701545211인천광역시 중구 신흥동2가 54-5 211호426000.32023-06-012
7인천광역시중구202312701545227인천광역시 중구 신흥동2가 54-5 227호426000.32023-06-012
8인천광역시중구20231280142321[ 서해대로180번길 33 ] 0001동 0001호49128300110.52023-06-012
9인천광역시중구20231280150191[ 서해대로180번길 11-1 ] 0001동 0001호44932450100.052023-06-012