Overview

Dataset statistics

Number of variables14
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows9
Duplicate rows (%)0.1%
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 9 (0.1%) 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 (98.1%)Imbalance
시가표준액 is highly skewed (γ1 = 71.45516214)Skewed
연면적 is highly skewed (γ1 = 55.46980873)Skewed
동 is an unsupported type, check if it needs cleaning or further analysisUnsupported
부번 has 1816 (18.2%) zerosZeros

Reproduction

Analysis started2024-03-18 05:29:48.051828
Analysis finished2024-03-18 05:29:53.138478
Duration5.09 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

2024-03-18T14:29:53.204742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-18T14:29:53.281049image/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
6638 
동구
2483 
미추홀구
879 

Length

Max length4
Median length2
Mean length2.1758
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
중구 6638
66.4%
동구 2483
 
24.8%
미추홀구 879
 
8.8%

Length

2024-03-18T14:29:53.372776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-18T14:29:53.464290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
중구 6638
66.4%
동구 2483
 
24.8%
미추홀구 879
 
8.8%

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

2024-03-18T14:29:53.551986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-18T14:29:53.626227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023 10000
100.0%

법정동코드
Real number (ℝ)

HIGH CORRELATION 

Distinct52
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.8374
Minimum101
Maximum152
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-18T14:29:53.730393image/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 deviation18.658676
Coefficient of variation (CV)0.14710705
Kurtosis-1.6913488
Mean126.8374
Median Absolute Deviation (MAD)19
Skewness-0.15327772
Sum1268374
Variance348.14618
MonotonicityNot monotonic
2024-03-18T14:29:53.857169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
147 1563
15.6%
145 1425
14.2%
107 1276
12.8%
103 902
9.0%
118 772
 
7.7%
102 533
 
5.3%
128 408
 
4.1%
101 374
 
3.7%
146 297
 
3.0%
149 253
 
2.5%
Other values (42) 2197
22.0%
ValueCountFrequency (%)
101 374
 
3.7%
102 533
5.3%
103 902
9.0%
104 236
 
2.4%
105 30
 
0.3%
106 102
 
1.0%
107 1276
12.8%
108 1
 
< 0.1%
109 12
 
0.1%
110 7
 
0.1%
ValueCountFrequency (%)
152 73
 
0.7%
151 56
 
0.6%
150 59
 
0.6%
149 253
 
2.5%
148 169
 
1.7%
147 1563
15.6%
146 297
 
3.0%
145 1425
14.2%
144 15
 
0.1%
143 7
 
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

2024-03-18T14:29:53.963270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-18T14:29:54.034295image/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
9970 
2
 
29
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 9970
99.7%
2 29
 
0.3%
3 1
 
< 0.1%

Length

2024-03-18T14:29:54.110035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-18T14:29:54.466955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9970
99.7%
2 29
 
0.3%
3 1
 
< 0.1%

본번
Real number (ℝ)

HIGH CORRELATION 

Distinct922
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean870.7183
Minimum1
Maximum3246
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-18T14:29:54.560274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q151
median294
Q31874
95-th percentile3088.1
Maximum3246
Range3245
Interquartile range (IQR)1823

Descriptive statistics

Standard deviation1084.5859
Coefficient of variation (CV)1.245622
Kurtosis-0.62073858
Mean870.7183
Median Absolute Deviation (MAD)282
Skewness0.98634329
Sum8707183
Variance1176326.5
MonotonicityNot monotonic
2024-03-18T14:29:54.673763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
294 378
 
3.8%
129 375
 
3.8%
1886 355
 
3.5%
1873 342
 
3.4%
2850 279
 
2.8%
295 276
 
2.8%
27 266
 
2.7%
1 250
 
2.5%
2 194
 
1.9%
7 194
 
1.9%
Other values (912) 7091
70.9%
ValueCountFrequency (%)
1 250
2.5%
2 194
1.9%
3 80
 
0.8%
4 87
 
0.9%
5 38
 
0.4%
6 93
 
0.9%
7 194
1.9%
8 56
 
0.6%
9 49
 
0.5%
10 41
 
0.4%
ValueCountFrequency (%)
3246 7
0.1%
3244 1
 
< 0.1%
3243 9
0.1%
3238 4
< 0.1%
3234 7
0.1%
3233 9
0.1%
3231 9
0.1%
3215 1
 
< 0.1%
3209 1
 
< 0.1%
3202 4
< 0.1%

부번
Real number (ℝ)

ZEROS 

Distinct368
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.6007
Minimum0
Maximum888
Zeros1816
Zeros (%)18.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-18T14:29:54.796785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median5
Q320
95-th percentile177
Maximum888
Range888
Interquartile range (IQR)19

Descriptive statistics

Standard deviation93.677593
Coefficient of variation (CV)2.7879655
Kurtosis32.504528
Mean33.6007
Median Absolute Deviation (MAD)5
Skewness5.2523114
Sum336007
Variance8775.4914
MonotonicityNot monotonic
2024-03-18T14:29:54.916938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1816
18.2%
1 1148
 
11.5%
4 680
 
6.8%
2 676
 
6.8%
3 515
 
5.1%
5 445
 
4.5%
7 332
 
3.3%
6 309
 
3.1%
8 230
 
2.3%
11 209
 
2.1%
Other values (358) 3640
36.4%
ValueCountFrequency (%)
0 1816
18.2%
1 1148
11.5%
2 676
 
6.8%
3 515
 
5.1%
4 680
 
6.8%
5 445
 
4.5%
6 309
 
3.1%
7 332
 
3.3%
8 230
 
2.3%
9 141
 
1.4%
ValueCountFrequency (%)
888 1
< 0.1%
884 1
< 0.1%
882 1
< 0.1%
871 1
< 0.1%
868 1
< 0.1%
863 1
< 0.1%
862 1
< 0.1%
861 1
< 0.1%
860 1
< 0.1%
858 1
< 0.1%

동
Unsupported

REJECTED  UNSUPPORTED 

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

호수
Text

Distinct1051
Distinct (%)10.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-03-18T14:29:55.242728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length7
Mean length2.3873
Min length1

Characters and Unicode

Total characters23873
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

Unique399 ?
Unique (%)4.0%

Sample

1st row102
2nd row8101
3rd row106
4th row101
5th row8002
ValueCountFrequency (%)
1 1662
 
16.6%
2 667
 
6.7%
3 409
 
4.1%
101 369
 
3.7%
0 326
 
3.3%
4 254
 
2.5%
201 230
 
2.3%
5 188
 
1.9%
102 157
 
1.6%
301 150
 
1.5%
Other values (1044) 5599
55.9%
2024-03-18T14:29:55.651773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 7329
30.7%
0 4631
19.4%
2 3566
14.9%
3 2353
 
9.9%
4 1376
 
5.8%
5 1109
 
4.6%
8 994
 
4.2%
6 949
 
4.0%
7 825
 
3.5%
9 628
 
2.6%
Other values (12) 113
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23760
99.5%
Dash Punctuation 64
 
0.3%
Other Letter 25
 
0.1%
Space Separator 11
 
< 0.1%
Uppercase Letter 9
 
< 0.1%
Lowercase Letter 4
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 7329
30.8%
0 4631
19.5%
2 3566
15.0%
3 2353
 
9.9%
4 1376
 
5.8%
5 1109
 
4.7%
8 994
 
4.2%
6 949
 
4.0%
7 825
 
3.5%
9 628
 
2.6%
Other Letter
ValueCountFrequency (%)
일 11
44.0%
월 11
44.0%
부 1
 
4.0%
층 1
 
4.0%
지 1
 
4.0%
Uppercase Letter
ValueCountFrequency (%)
B 5
55.6%
T 2
 
22.2%
F 2
 
22.2%
Lowercase Letter
ValueCountFrequency (%)
b 2
50.0%
e 2
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 64
100.0%
Space Separator
ValueCountFrequency (%)
11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 23835
99.8%
Hangul 25
 
0.1%
Latin 13
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
1 7329
30.7%
0 4631
19.4%
2 3566
15.0%
3 2353
 
9.9%
4 1376
 
5.8%
5 1109
 
4.7%
8 994
 
4.2%
6 949
 
4.0%
7 825
 
3.5%
9 628
 
2.6%
Other values (2) 75
 
0.3%
Hangul
ValueCountFrequency (%)
일 11
44.0%
월 11
44.0%
부 1
 
4.0%
층 1
 
4.0%
지 1
 
4.0%
Latin
ValueCountFrequency (%)
B 5
38.5%
T 2
 
15.4%
F 2
 
15.4%
b 2
 
15.4%
e 2
 
15.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23848
99.9%
Hangul 25
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 7329
30.7%
0 4631
19.4%
2 3566
15.0%
3 2353
 
9.9%
4 1376
 
5.8%
5 1109
 
4.7%
8 994
 
4.2%
6 949
 
4.0%
7 825
 
3.5%
9 628
 
2.6%
Other values (7) 88
 
0.4%
Hangul
ValueCountFrequency (%)
일 11
44.0%
월 11
44.0%
부 1
 
4.0%
층 1
 
4.0%
지 1
 
4.0%

물건지
Text

Distinct9641
Distinct (%)96.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-03-18T14:29:55.918401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length32
Median length29
Mean length25.3133
Min length16

Characters and Unicode

Total characters253133
Distinct characters195
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

Unique9378 ?
Unique (%)93.8%

Sample

1st row[ 방축로 16 ] 0000동 0102호
2nd row[ 도원로 38 ] 0001동 8101호
3rd row인천광역시 중구 운남동 1778 3동 106호
4th row[ 소성로 236 ] 0001동 0101호
5th row인천광역시 미추홀구 용현동 611-4 8002호
ValueCountFrequency (%)
12184
20.5%
인천광역시 3908
 
6.6%
0001동 3012
 
5.1%
0000동 2400
 
4.0%
중구 2063
 
3.5%
동구 1693
 
2.8%
1동 1355
 
2.3%
송림동 962
 
1.6%
0001호 843
 
1.4%
1호 815
 
1.4%
Other values (4172) 30190
50.8%
2024-03-18T14:29:56.335654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
49425
19.5%
0 37062
14.6%
1 19693
 
7.8%
동 15181
 
6.0%
2 10436
 
4.1%
호 9872
 
3.9%
3 6558
 
2.6%
[ 6092
 
2.4%
] 6092
 
2.4%
로 6013
 
2.4%
Other values (185) 86709
34.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 99348
39.2%
Other Letter 88498
35.0%
Space Separator 49425
19.5%
Open Punctuation 6092
 
2.4%
Close Punctuation 6092
 
2.4%
Dash Punctuation 3671
 
1.5%
Uppercase Letter 7
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 15181
17.2%
호 9872
 
11.2%
로 6013
 
6.8%
인 4488
 
5.1%
시 4140
 
4.7%
광 3999
 
4.5%
천 3930
 
4.4%
역 3916
 
4.4%
구 3911
 
4.4%
길 3010
 
3.4%
Other values (169) 30038
33.9%
Decimal Number
ValueCountFrequency (%)
0 37062
37.3%
1 19693
19.8%
2 10436
 
10.5%
3 6558
 
6.6%
4 5033
 
5.1%
9 4920
 
5.0%
5 4280
 
4.3%
6 3865
 
3.9%
7 3858
 
3.9%
8 3643
 
3.7%
Uppercase Letter
ValueCountFrequency (%)
B 5
71.4%
T 2
 
28.6%
Space Separator
ValueCountFrequency (%)
49425
100.0%
Open Punctuation
ValueCountFrequency (%)
[ 6092
100.0%
Close Punctuation
ValueCountFrequency (%)
] 6092
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3671
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 164628
65.0%
Hangul 88498
35.0%
Latin 7
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 15181
17.2%
호 9872
 
11.2%
로 6013
 
6.8%
인 4488
 
5.1%
시 4140
 
4.7%
광 3999
 
4.5%
천 3930
 
4.4%
역 3916
 
4.4%
구 3911
 
4.4%
길 3010
 
3.4%
Other values (169) 30038
33.9%
Common
ValueCountFrequency (%)
49425
30.0%
0 37062
22.5%
1 19693
 
12.0%
2 10436
 
6.3%
3 6558
 
4.0%
[ 6092
 
3.7%
] 6092
 
3.7%
4 5033
 
3.1%
9 4920
 
3.0%
5 4280
 
2.6%
Other values (4) 15037
 
9.1%
Latin
ValueCountFrequency (%)
B 5
71.4%
T 2
 
28.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 164635
65.0%
Hangul 88498
35.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
49425
30.0%
0 37062
22.5%
1 19693
 
12.0%
2 10436
 
6.3%
3 6558
 
4.0%
[ 6092
 
3.7%
] 6092
 
3.7%
4 5033
 
3.1%
9 4920
 
3.0%
5 4280
 
2.6%
Other values (6) 15044
 
9.1%
Hangul
ValueCountFrequency (%)
동 15181
17.2%
호 9872
 
11.2%
로 6013
 
6.8%
인 4488
 
5.1%
시 4140
 
4.7%
광 3999
 
4.5%
천 3930
 
4.4%
역 3916
 
4.4%
구 3911
 
4.4%
길 3010
 
3.4%
Other values (169) 30038
33.9%

시가표준액
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct6534
Distinct (%)65.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1126101 × 108
Minimum4400
Maximum1.16 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-18T14:29:56.497430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4400
5-th percentile1109370
Q15975895
median30340860
Q362106330
95-th percentile2.5851107 × 108
Maximum1.16 × 1011
Range1.16 × 1011
Interquartile range (IQR)56130435

Descriptive statistics

Standard deviation1.3168563 × 109
Coefficient of variation (CV)11.83574
Kurtosis6073.9519
Mean1.1126101 × 108
Median Absolute Deviation (MAD)25884510
Skewness71.455162
Sum1.1126101 × 1012
Variance1.7341106 × 1018
MonotonicityNot monotonic
2024-03-18T14:29:56.627343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2050860 235
 
2.4%
13791820 215
 
2.1%
18568530 96
 
1.0%
2361830 91
 
0.9%
46161960 71
 
0.7%
39951740 56
 
0.6%
46195110 56
 
0.6%
39994020 53
 
0.5%
49009390 52
 
0.5%
14165340 52
 
0.5%
Other values (6524) 9023
90.2%
ValueCountFrequency (%)
4400 1
 
< 0.1%
28200 2
 
< 0.1%
29100 1
 
< 0.1%
31950 4
< 0.1%
42600 8
0.1%
44000 1
 
< 0.1%
48000 1
 
< 0.1%
49840 5
0.1%
53100 1
 
< 0.1%
53400 1
 
< 0.1%
ValueCountFrequency (%)
116000000000 1
< 0.1%
37799162900 1
< 0.1%
15829183390 1
< 0.1%
13234907230 1
< 0.1%
11894611000 1
< 0.1%
11075040980 1
< 0.1%
11041153200 1
< 0.1%
10551914510 1
< 0.1%
10461391240 1
< 0.1%
10238523200 1
< 0.1%

연면적
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct5184
Distinct (%)51.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean204.48857
Minimum0.1102
Maximum127968.28
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-18T14:29:56.762404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.1102
5-th percentile7.98
Q133.0615
median51.265
Q3107.8225
95-th percentile551.383
Maximum127968.28
Range127968.17
Interquartile range (IQR)74.761

Descriptive statistics

Standard deviation1597.7111
Coefficient of variation (CV)7.8132052
Kurtosis4165.4169
Mean204.48857
Median Absolute Deviation (MAD)31.535
Skewness55.469809
Sum2044885.7
Variance2552680.9
MonotonicityNot monotonic
2024-03-18T14:29:56.892545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.98 235
 
2.4%
18.0 229
 
2.3%
33.97 215
 
2.1%
39.09 97
 
1.0%
9.19 91
 
0.9%
27.0 87
 
0.9%
48.3878 71
 
0.7%
40.3553 56
 
0.6%
39.483 56
 
0.6%
36.4909 53
 
0.5%
Other values (5174) 8810
88.1%
ValueCountFrequency (%)
0.1102 1
 
< 0.1%
0.3 20
0.2%
0.44 1
 
< 0.1%
0.56 1
 
< 0.1%
0.65 1
 
< 0.1%
0.67 1
 
< 0.1%
0.68 1
 
< 0.1%
0.7 10
0.1%
0.8 1
 
< 0.1%
0.84 1
 
< 0.1%
ValueCountFrequency (%)
127968.28 1
< 0.1%
41753.19 1
< 0.1%
30421.0 1
< 0.1%
20610.61 1
< 0.1%
20372.1794 1
< 0.1%
19055.37 1
< 0.1%
18194.89 1
< 0.1%
18186.93 1
< 0.1%
17883.16 1
< 0.1%
14484.0 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

2024-03-18T14:29:57.003053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-18T14:29:57.076296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-06-01 10000
100.0%

Interactions

2024-03-18T14:29:52.412215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:29:50.622025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:29:51.136345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:29:51.550379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:29:51.959247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:29:52.491844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:29:50.772095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:29:51.221807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:29:51.626329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:29:52.055736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:29:52.580116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:29:50.868203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:29:51.310087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:29:51.726632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:29:52.157032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:29:52.668480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:29:50.962057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:29:51.384594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:29:51.803686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:29:52.242224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:29:52.758552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:29:51.048395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:29:51.466525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:29:51.883545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:29:52.328716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-18T14:29:57.128268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구법정동코드특수지본번부번시가표준액연면적
시군구1.0000.8440.0460.6290.2330.0000.000
법정동코드0.8441.0000.0860.8570.3750.0000.000
특수지0.0460.0861.0000.0320.0000.0000.000
본번0.6290.8570.0321.0000.2300.0220.015
부번0.2330.3750.0000.2301.0000.0000.080
시가표준액0.0000.0000.0000.0220.0001.0000.903
연면적0.0000.0000.0000.0150.0800.9031.000
2024-03-18T14:29:57.239450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
특수지시군구
특수지1.0000.013
시군구0.0131.000
2024-03-18T14:29:57.333124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동코드본번부번시가표준액연면적시군구특수지
법정동코드1.0000.583-0.0840.2390.1090.7580.051
본번0.5831.000-0.3060.2990.0270.4750.019
부번-0.084-0.3061.0000.0590.1390.1430.000
시가표준액0.2390.2990.0591.0000.8070.0000.000
연면적0.1090.0270.1390.8071.0000.0000.000
시군구0.7580.4750.1430.0000.0001.0000.013
특수지0.0510.0190.0000.0000.0000.0131.000

Missing values

2024-03-18T14:29:52.877827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-18T14:29:53.060282image/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
62472인천광역시동구2023103012540102[ 방축로 16 ] 0000동 0102호847704034.322023-06-01
14052인천광역시중구202313201245918101[ 도원로 38 ] 0001동 8101호1134540079.22023-06-01
50497인천광역시중구202314601177803106인천광역시 중구 운남동 1778 3동 106호3030606033.342023-06-01
92664인천광역시미추홀구20231030167241101[ 소성로 236 ] 0001동 0101호81452210193.922023-06-01
90809인천광역시미추홀구202310201611408002인천광역시 미추홀구 용현동 611-4 8002호520636018.222023-06-01
13715인천광역시중구2023131012311511인천광역시 중구 율목동 231-15 1동 1호111280017.122023-06-01
5151인천광역시중구202311801600173인천광역시 중구 항동7가 60 1동 73호560266034.72023-06-01
48016인천광역시중구202314701309370629[ 흰바위로 103 ] 0000동 0629호5005034054.282023-06-01
47086인천광역시중구2023147013090311032[ 영종대로162번길 26 ] 0001동 1032호5181603049.492023-06-01
1129인천광역시중구20231110141311[ 신포로31번길 18 ] 0001동 0001호1002104044.392023-06-01
시도시군구과세연도법정동코드법정리특수지본번부번동호수물건지시가표준액연면적기준일자
42143인천광역시중구202314501194710603[ 영종진광장로 32 ] 0000동 0603호4564773044.88472023-06-01
72481인천광역시동구20231060123213인천광역시 동구 금곡동 2-32 1동 3호1405800099.02023-06-01
72866인천광역시동구202310701856811인천광역시 동구 송림동 8-568 1동 1호230880059.22023-06-01
38771인천광역시중구2023145011886401314[ 하늘별빛로65번길 7-11 ] 0000동 1314호4900939044.88042023-06-01
9802인천광역시중구20231260134341703[ 인중로 108 ] 0001동 0703호126564930134.772023-06-01
93030인천광역시미추홀구20231040124101601인천광역시 미추홀구 도화동 241 1동 601호1246605092.152023-06-01
43899인천광역시중구202314501195321202[ 은하수로29번길 47 ] 0001동 0202호3328204032.892023-06-01
64630인천광역시동구202310301180811인천광역시 동구 송현동 1-808 1동 1호190500015.02023-06-01
17829인천광역시중구20231380198121108[ 월미문화로 71 ] 0001동 0108호2316658094.9452023-06-01
23877인천광역시중구202314701280311121[ 신도시남로142번길 6 ] 0001동 0121호8267202076.47172023-06-01

Duplicate rows

Most frequently occurring

시도시군구과세연도법정동코드법정리특수지본번부번호수물건지시가표준액연면적기준일자# duplicates
0인천광역시동구2023101011240인천광역시 동구 만석동 1-24 1동8871560444.692023-06-013
1인천광역시동구20231030116701인천광역시 동구 송현동 1-670 1동 1호14787508.752023-06-012
2인천광역시동구202310701373811인천광역시 동구 송림동 37-381 1동 1호419162720653.922023-06-012
3인천광역시중구2023111011112[ 신포로31번길 6 ] 0002동 0002호41299600185.22023-06-012
4인천광역시중구20231180110471[ 서해대로 143 ] 0000동 0001호43634401906193.232023-06-012
5인천광역시중구202312701545207인천광역시 중구 신흥동2가 54-5 207호426000.32023-06-012
6인천광역시중구202314701280630[ 영종대로 124 ] 0001동 0000호587678040811.712023-06-012
7인천광역시중구202314701287711인천광역시 중구 운서동 2877-1 1동 1호17580794901991.032023-06-012
8인천광역시중구202314901810921[ 용유서로423번길 37-1 ] 0001동 0001호110292900129.32023-06-012