Overview

Dataset statistics

Number of variables18
Number of observations7810
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.2 MiB
Average record size in memory156.0 B

Variable types

Categorical7
Numeric7
Text2
Boolean1
DateTime1

Dataset

Description대구광역시_중구_개별주택가격정보_20190430
Author대구광역시 중구
URLhttp://data.daegu.go.kr/open/data/dataView.do?dataSetId=15029187&dataSetDetailId=150291872e80361f58f50_201909301520&provdMethod=FILE

Alerts

고유번호 has constant value ""Constant
특수지구분코드 has constant value ""Constant
특수지구분명 has constant value ""Constant
건축물대장고유번호 has constant value ""Constant
기준년도 has constant value ""Constant
기준월 has constant value ""Constant
표준지여부 has constant value ""Constant
데이터기준일자 has constant value ""Constant
동코드 is highly overall correlated with 동명High correlation
토지대장면적 is highly overall correlated with 산정대지면적 and 3 other fieldsHigh correlation
산정대지면적 is highly overall correlated with 토지대장면적 and 2 other fieldsHigh correlation
건물전체연면적 is highly overall correlated with 토지대장면적 and 2 other fieldsHigh correlation
건물산정연면적 is highly overall correlated with 토지대장면적 and 3 other fieldsHigh correlation
주택가격 is highly overall correlated with 토지대장면적 and 3 other fieldsHigh correlation
동명 is highly overall correlated with 동코드High correlation
동명 is highly imbalanced (93.7%)Imbalance
산정대지면적 has 99 (1.3%) zerosZeros

Reproduction

Analysis started2024-04-21 10:06:30.076574
Analysis finished2024-04-21 10:06:41.597346
Duration11.52 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

고유번호
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size61.1 KiB
2710000000000000000
7810 

Length

Max length19
Median length19
Mean length19
Min length19

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2710000000000000000
2nd row2710000000000000000
3rd row2710000000000000000
4th row2710000000000000000
5th row2710000000000000000

Common Values

ValueCountFrequency (%)
2710000000000000000 7810
100.0%

Length

2024-04-21T19:06:41.700161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T19:06:41.862536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2710000000000000000 7810
100.0%

법정동코드
Real number (ℝ)

Distinct56
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7110138 × 109
Minimum2.7110101 × 109
Maximum2.7110157 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size68.8 KiB
2024-04-21T19:06:42.045904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.7110101 × 109
5-th percentile2.7110103 × 109
Q12.7110107 × 109
median2.7110155 × 109
Q32.7110156 × 109
95-th percentile2.7110157 × 109
Maximum2.7110157 × 109
Range5600
Interquartile range (IQR)4900

Descriptive statistics

Standard deviation2288.4455
Coefficient of variation (CV)8.4412906 × 10-7
Kurtosis-1.4105519
Mean2.7110138 × 109
Median Absolute Deviation (MAD)200
Skewness-0.66274342
Sum2.1173018 × 1013
Variance5236983
MonotonicityIncreasing
2024-04-21T19:06:42.289355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2711015600 2634
33.7%
2711015700 1003
 
12.8%
2711010400 560
 
7.2%
2711010300 545
 
7.0%
2711015400 449
 
5.7%
2711015500 417
 
5.3%
2711010700 400
 
5.1%
2711010800 298
 
3.8%
2711010600 190
 
2.4%
2711010100 178
 
2.3%
Other values (46) 1136
14.5%
ValueCountFrequency (%)
2711010100 178
 
2.3%
2711010200 70
 
0.9%
2711010300 545
7.0%
2711010400 560
7.2%
2711010500 12
 
0.2%
2711010600 190
 
2.4%
2711010700 400
5.1%
2711010800 298
3.8%
2711010900 20
 
0.3%
2711011000 1
 
< 0.1%
ValueCountFrequency (%)
2711015700 1003
 
12.8%
2711015600 2634
33.7%
2711015500 417
 
5.3%
2711015400 449
 
5.7%
2711015300 24
 
0.3%
2711015200 17
 
0.2%
2711015100 4
 
0.1%
2711015000 13
 
0.2%
2711014900 15
 
0.2%
2711014800 2
 
< 0.1%
Distinct56
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size61.1 KiB
2024-04-21T19:06:42.874611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length3.5668374
Min length2

Characters and Unicode

Total characters27857
Distinct characters47
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st row동인동1가
2nd row동인동1가
3rd row동인동1가
4th row동인동1가
5th row동인동1가
ValueCountFrequency (%)
남산동 2634
33.7%
대봉동 1003
 
12.8%
동인동4가 560
 
7.2%
동인동3가 545
 
7.0%
대신동 449
 
5.7%
달성동 417
 
5.3%
삼덕동3가 400
 
5.1%
봉산동 298
 
3.8%
삼덕동2가 190
 
2.4%
동인동1가 178
 
2.3%
Other values (46) 1136
14.5%
2024-04-21T19:06:43.665602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9076
32.6%
3152
 
11.3%
2654
 
9.5%
2244
 
8.1%
1494
 
5.4%
1447
 
5.2%
1301
 
4.7%
3 984
 
3.5%
610
 
2.2%
602
 
2.2%
Other values (37) 4293
15.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 25613
91.9%
Decimal Number 2244
 
8.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
9076
35.4%
3152
 
12.3%
2654
 
10.4%
2244
 
8.8%
1494
 
5.8%
1447
 
5.6%
1301
 
5.1%
610
 
2.4%
602
 
2.4%
508
 
2.0%
Other values (33) 2525
 
9.9%
Decimal Number
ValueCountFrequency (%)
3 984
43.9%
4 560
25.0%
2 363
 
16.2%
1 337
 
15.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 25613
91.9%
Common 2244
 
8.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
9076
35.4%
3152
 
12.3%
2654
 
10.4%
2244
 
8.8%
1494
 
5.8%
1447
 
5.6%
1301
 
5.1%
610
 
2.4%
602
 
2.4%
508
 
2.0%
Other values (33) 2525
 
9.9%
Common
ValueCountFrequency (%)
3 984
43.9%
4 560
25.0%
2 363
 
16.2%
1 337
 
15.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 25613
91.9%
ASCII 2244
 
8.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
9076
35.4%
3152
 
12.3%
2654
 
10.4%
2244
 
8.8%
1494
 
5.8%
1447
 
5.6%
1301
 
5.1%
610
 
2.4%
602
 
2.4%
508
 
2.0%
Other values (33) 2525
 
9.9%
ASCII
ValueCountFrequency (%)
3 984
43.9%
4 560
25.0%
2 363
 
16.2%
1 337
 
15.0%

특수지구분코드
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size61.1 KiB
1
7810 

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 7810
100.0%

Length

2024-04-21T19:06:43.877092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T19:06:44.037900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 7810
100.0%

특수지구분명
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size61.1 KiB
일반
7810 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
일반 7810
100.0%

Length

2024-04-21T19:06:44.204511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T19:06:44.365195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반 7810
100.0%

지번
Text

Distinct6569
Distinct (%)84.1%
Missing0
Missing (%)0.0%
Memory size61.1 KiB
2024-04-21T19:06:45.271568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters70290
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5729 ?
Unique (%)73.4%

Sample

1st row0010-0000
2nd row0010-0005
3rd row0010-0006
4th row0010-0012
5th row0010-0014
ValueCountFrequency (%)
0082-0000 16
 
0.2%
0006-0021 9
 
0.1%
0033-0000 8
 
0.1%
2114-0001 7
 
0.1%
0006-0019 7
 
0.1%
0020-0000 7
 
0.1%
0010-0003 7
 
0.1%
0039-0000 7
 
0.1%
0010-0007 7
 
0.1%
0613-0032 6
 
0.1%
Other values (6559) 7729
99.0%
2024-04-21T19:06:46.474961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 30827
43.9%
- 7810
 
11.1%
1 6455
 
9.2%
2 6321
 
9.0%
3 3505
 
5.0%
6 2949
 
4.2%
7 2703
 
3.8%
5 2691
 
3.8%
4 2685
 
3.8%
9 2284
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 62480
88.9%
Dash Punctuation 7810
 
11.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 30827
49.3%
1 6455
 
10.3%
2 6321
 
10.1%
3 3505
 
5.6%
6 2949
 
4.7%
7 2703
 
4.3%
5 2691
 
4.3%
4 2685
 
4.3%
9 2284
 
3.7%
8 2060
 
3.3%
Dash Punctuation
ValueCountFrequency (%)
- 7810
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 70290
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 30827
43.9%
- 7810
 
11.1%
1 6455
 
9.2%
2 6321
 
9.0%
3 3505
 
5.0%
6 2949
 
4.2%
7 2703
 
3.8%
5 2691
 
3.8%
4 2685
 
3.8%
9 2284
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 70290
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 30827
43.9%
- 7810
 
11.1%
1 6455
 
9.2%
2 6321
 
9.0%
3 3505
 
5.0%
6 2949
 
4.2%
7 2703
 
3.8%
5 2691
 
3.8%
4 2685
 
3.8%
9 2284
 
3.2%

건축물대장고유번호
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size61.1 KiB
2710000000000000000
7810 

Length

Max length19
Median length19
Mean length19
Min length19

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2710000000000000000
2nd row2710000000000000000
3rd row2710000000000000000
4th row2710000000000000000
5th row2710000000000000000

Common Values

ValueCountFrequency (%)
2710000000000000000 7810
100.0%

Length

2024-04-21T19:06:46.875167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T19:06:47.156885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2710000000000000000 7810
100.0%

기준년도
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size61.1 KiB
2019
7810 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2019
2nd row2019
3rd row2019
4th row2019
5th row2019

Common Values

ValueCountFrequency (%)
2019 7810
100.0%

Length

2024-04-21T19:06:47.335256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T19:06:47.494703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2019 7810
100.0%

기준월
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size61.1 KiB
1
7810 

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 7810
100.0%

Length

2024-04-21T19:06:47.660867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T19:06:47.898786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 7810
100.0%

동코드
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0504481
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size68.8 KiB
2024-04-21T19:06:48.052404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum12
Range11
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.40834606
Coefficient of variation (CV)0.38873509
Kurtosis252.08629
Mean1.0504481
Median Absolute Deviation (MAD)0
Skewness13.935658
Sum8204
Variance0.1667465
MonotonicityNot monotonic
2024-04-21T19:06:48.244716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 7585
97.1%
2 165
 
2.1%
3 23
 
0.3%
4 11
 
0.1%
5 9
 
0.1%
6 6
 
0.1%
7 4
 
0.1%
8 2
 
< 0.1%
9 2
 
< 0.1%
10 1
 
< 0.1%
Other values (2) 2
 
< 0.1%
ValueCountFrequency (%)
1 7585
97.1%
2 165
 
2.1%
3 23
 
0.3%
4 11
 
0.1%
5 9
 
0.1%
6 6
 
0.1%
7 4
 
0.1%
8 2
 
< 0.1%
9 2
 
< 0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
12 1
 
< 0.1%
11 1
 
< 0.1%
10 1
 
< 0.1%
9 2
 
< 0.1%
8 2
 
< 0.1%
7 4
 
0.1%
6 6
 
0.1%
5 9
 
0.1%
4 11
0.1%
3 23
0.3%

동명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct13
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size61.1 KiB
1동
7584 
2동
 
165
3동
 
23
4동
 
11
5동
 
9
Other values (8)
 
18

Length

Max length3
Median length2
Mean length2.0005122
Min length2

Unique

Unique4 ?
Unique (%)0.1%

Sample

1st row1동
2nd row1동
3rd row1동
4th row1동
5th row1동

Common Values

ValueCountFrequency (%)
1동 7584
97.1%
2동 165
 
2.1%
3동 23
 
0.3%
4동 11
 
0.1%
5동 9
 
0.1%
6동 6
 
0.1%
7동 4
 
0.1%
8동 2
 
< 0.1%
9동 2
 
< 0.1%
01동 1
 
< 0.1%
Other values (3) 3
 
< 0.1%

Length

2024-04-21T19:06:48.459788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1동 7584
97.1%
2동 165
 
2.1%
3동 23
 
0.3%
4동 11
 
0.1%
5동 9
 
0.1%
6동 6
 
0.1%
7동 4
 
0.1%
8동 2
 
< 0.1%
9동 2
 
< 0.1%
01동 1
 
< 0.1%
Other values (3) 3
 
< 0.1%

토지대장면적
Real number (ℝ)

HIGH CORRELATION 

Distinct1866
Distinct (%)23.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean150.07249
Minimum0.1
Maximum1680
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size68.8 KiB
2024-04-21T19:06:48.673457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile46
Q187
median126
Q3183.65
95-th percentile326.6
Maximum1680
Range1679.9
Interquartile range (IQR)96.65

Descriptive statistics

Standard deviation104.55144
Coefficient of variation (CV)0.6966729
Kurtosis22.040777
Mean150.07249
Median Absolute Deviation (MAD)44.7
Skewness3.3188799
Sum1172066.2
Variance10931.003
MonotonicityNot monotonic
2024-04-21T19:06:48.925781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
86.0 87
 
1.1%
99.0 85
 
1.1%
119.0 85
 
1.1%
76.0 79
 
1.0%
89.0 75
 
1.0%
66.0 67
 
0.9%
109.0 65
 
0.8%
83.0 64
 
0.8%
96.0 63
 
0.8%
116.0 62
 
0.8%
Other values (1856) 7078
90.6%
ValueCountFrequency (%)
0.1 1
 
< 0.1%
7.0 1
 
< 0.1%
7.7 1
 
< 0.1%
10.0 3
< 0.1%
10.9 1
 
< 0.1%
11.5 1
 
< 0.1%
12.5 1
 
< 0.1%
12.9 2
< 0.1%
13.0 2
< 0.1%
13.2 2
< 0.1%
ValueCountFrequency (%)
1680.0 1
 
< 0.1%
1245.2 1
 
< 0.1%
1183.0 3
< 0.1%
1167.0 1
 
< 0.1%
1040.0 1
 
< 0.1%
1038.1 2
< 0.1%
1006.7 1
 
< 0.1%
993.0 1
 
< 0.1%
986.9 1
 
< 0.1%
979.8 1
 
< 0.1%

산정대지면적
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct3571
Distinct (%)45.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean109.37175
Minimum0
Maximum986.9
Zeros99
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size68.8 KiB
2024-04-21T19:06:49.391044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile26.519
Q160
median95.175
Q3139.93
95-th percentile242.82
Maximum986.9
Range986.9
Interquartile range (IQR)79.93

Descriptive statistics

Standard deviation72.84572
Coefficient of variation (CV)0.66603779
Kurtosis11.612833
Mean109.37175
Median Absolute Deviation (MAD)39.175
Skewness2.180223
Sum854193.38
Variance5306.4989
MonotonicityNot monotonic
2024-04-21T19:06:49.642735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 99
 
1.3%
86.0 71
 
0.9%
99.0 68
 
0.9%
76.0 67
 
0.9%
119.0 67
 
0.9%
89.0 64
 
0.8%
66.0 61
 
0.8%
96.0 56
 
0.7%
83.0 55
 
0.7%
116.0 54
 
0.7%
Other values (3561) 7148
91.5%
ValueCountFrequency (%)
0.0 99
1.3%
0.1 1
 
< 0.1%
5.43 1
 
< 0.1%
6.53 1
 
< 0.1%
7.0 1
 
< 0.1%
7.03 1
 
< 0.1%
7.61 1
 
< 0.1%
7.7 1
 
< 0.1%
8.43 1
 
< 0.1%
9.12 1
 
< 0.1%
ValueCountFrequency (%)
986.9 1
< 0.1%
899.0 1
< 0.1%
869.1 1
< 0.1%
798.93 1
< 0.1%
735.5 1
< 0.1%
732.1 1
< 0.1%
657.9 1
< 0.1%
619.8 1
< 0.1%
618.2 1
< 0.1%
575.9 1
< 0.1%

건물전체연면적
Real number (ℝ)

HIGH CORRELATION 

Distinct6010
Distinct (%)77.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean158.07749
Minimum3.31
Maximum7205.64
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size68.8 KiB
2024-04-21T19:06:49.886602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.31
5-th percentile25.2245
Q146.58
median85.56
Q3192.35
95-th percentile494.452
Maximum7205.64
Range7202.33
Interquartile range (IQR)145.77

Descriptive statistics

Standard deviation204.21662
Coefficient of variation (CV)1.2918767
Kurtosis237.05055
Mean158.07749
Median Absolute Deviation (MAD)49.2
Skewness9.3516178
Sum1234585.2
Variance41704.428
MonotonicityNot monotonic
2024-04-21T19:06:50.134725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41.65 15
 
0.2%
26.45 15
 
0.2%
39.67 13
 
0.2%
19.83 13
 
0.2%
29.75 11
 
0.1%
33.06 10
 
0.1%
35.7 10
 
0.1%
29.09 10
 
0.1%
44.63 10
 
0.1%
23.8 10
 
0.1%
Other values (6000) 7693
98.5%
ValueCountFrequency (%)
3.31 2
< 0.1%
6.68 1
 
< 0.1%
8.93 1
 
< 0.1%
9.52 3
< 0.1%
9.9 1
 
< 0.1%
9.92 4
0.1%
10.1 1
 
< 0.1%
10.12 1
 
< 0.1%
10.31 1
 
< 0.1%
10.41 1
 
< 0.1%
ValueCountFrequency (%)
7205.64 1
< 0.1%
4712.84 1
< 0.1%
4100.25 1
< 0.1%
2283.53 1
< 0.1%
1985.66 1
< 0.1%
1811.48 1
< 0.1%
1735.22 1
< 0.1%
1646.41 1
< 0.1%
1589.26 1
< 0.1%
1546.16 1
< 0.1%

건물산정연면적
Real number (ℝ)

HIGH CORRELATION 

Distinct5622
Distinct (%)72.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean116.31798
Minimum0
Maximum875.71
Zeros74
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size68.8 KiB
2024-04-21T19:06:50.390321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile22.61
Q142.48
median67.38
Q3131.1575
95-th percentile442.982
Maximum875.71
Range875.71
Interquartile range (IQR)88.6775

Descriptive statistics

Standard deviation133.52501
Coefficient of variation (CV)1.147931
Kurtosis8.3848105
Mean116.31798
Median Absolute Deviation (MAD)32.495
Skewness2.7543166
Sum908443.44
Variance17828.929
MonotonicityNot monotonic
2024-04-21T19:06:50.634618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 74
 
0.9%
26.45 16
 
0.2%
41.65 16
 
0.2%
19.83 15
 
0.2%
23.8 13
 
0.2%
39.67 12
 
0.2%
29.75 11
 
0.1%
33.06 11
 
0.1%
35.7 10
 
0.1%
36.36 10
 
0.1%
Other values (5612) 7622
97.6%
ValueCountFrequency (%)
0.0 74
0.9%
3.31 2
 
< 0.1%
6.68 1
 
< 0.1%
6.81 1
 
< 0.1%
7.29 1
 
< 0.1%
8.36 1
 
< 0.1%
8.93 1
 
< 0.1%
9.52 2
 
< 0.1%
9.9 1
 
< 0.1%
9.92 4
 
0.1%
ValueCountFrequency (%)
875.71 1
< 0.1%
872.62 1
< 0.1%
872.1 1
< 0.1%
869.36 1
< 0.1%
864.21 1
< 0.1%
863.64 1
< 0.1%
862.04 1
< 0.1%
861.12 1
< 0.1%
860.64 1
< 0.1%
859.87 1
< 0.1%

주택가격
Real number (ℝ)

HIGH CORRELATION 

Distinct1410
Distinct (%)18.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2898525 × 108
Minimum0
Maximum1.5 × 109
Zeros74
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size68.8 KiB
2024-04-21T19:06:50.879711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile28145000
Q158800000
median88900000
Q31.4 × 108
95-th percentile4.26 × 108
Maximum1.5 × 109
Range1.5 × 109
Interquartile range (IQR)81200000

Descriptive statistics

Standard deviation1.2862194 × 108
Coefficient of variation (CV)0.99718331
Kurtosis10.09413
Mean1.2898525 × 108
Median Absolute Deviation (MAD)36150000
Skewness2.8097428
Sum1.0073748 × 1012
Variance1.6543603 × 1016
MonotonicityNot monotonic
2024-04-21T19:06:51.134579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 74
 
0.9%
105000000 50
 
0.6%
110000000 49
 
0.6%
108000000 46
 
0.6%
101000000 45
 
0.6%
121000000 44
 
0.6%
122000000 44
 
0.6%
104000000 43
 
0.6%
106000000 43
 
0.6%
107000000 42
 
0.5%
Other values (1400) 7330
93.9%
ValueCountFrequency (%)
0 74
0.9%
560000 1
 
< 0.1%
594000 1
 
< 0.1%
801000 1
 
< 0.1%
940000 1
 
< 0.1%
1070000 1
 
< 0.1%
1110000 1
 
< 0.1%
1280000 1
 
< 0.1%
1550000 2
 
< 0.1%
1880000 1
 
< 0.1%
ValueCountFrequency (%)
1500000000 1
< 0.1%
1260000000 1
< 0.1%
997000000 1
< 0.1%
981000000 1
< 0.1%
959000000 1
< 0.1%
950000000 1
< 0.1%
930000000 1
< 0.1%
907000000 1
< 0.1%
891000000 1
< 0.1%
872000000 1
< 0.1%

표준지여부
Boolean

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.8 KiB
False
7810 
ValueCountFrequency (%)
False 7810
100.0%
2024-04-21T19:06:51.339801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size61.1 KiB
Minimum2019-04-30 00:00:00
Maximum2019-04-30 00:00:00
2024-04-21T19:06:51.476866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T19:06:51.630902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2024-04-21T19:06:39.731088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T19:06:32.261499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T19:06:33.774459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T19:06:34.907334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T19:06:36.093519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T19:06:37.234854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T19:06:38.617656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T19:06:39.902649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T19:06:32.521270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T19:06:33.935097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T19:06:35.076864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T19:06:36.256622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T19:06:37.402988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T19:06:38.775307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T19:06:40.069432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T19:06:32.783788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T19:06:34.091837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T19:06:35.240941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T19:06:36.413682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T19:06:37.570622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T19:06:38.928967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T19:06:40.247571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T19:06:33.059072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T19:06:34.259473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T19:06:35.415786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T19:06:36.583454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T19:06:37.746166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T19:06:39.096972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T19:06:40.412666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T19:06:33.271715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T19:06:34.416919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T19:06:35.577760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T19:06:36.741764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T19:06:37.912404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T19:06:39.248900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T19:06:40.593722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T19:06:33.444220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T19:06:34.584352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T19:06:35.756789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T19:06:36.911553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T19:06:38.086411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T19:06:39.417497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T19:06:40.753729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T19:06:33.597743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T19:06:34.735664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T19:06:35.912704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T19:06:37.059648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T19:06:38.436834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T19:06:39.561565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-21T19:06:51.766478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동코드법정동명동코드동명토지대장면적산정대지면적건물전체연면적건물산정연면적주택가격
법정동코드1.0001.0000.2060.1750.1380.2200.0750.2500.211
법정동명1.0001.0000.2700.2860.2700.3040.2150.3510.430
동코드0.2060.2701.0001.0000.2780.0000.0000.0000.000
동명0.1750.2861.0001.0000.3160.0000.0000.0260.000
토지대장면적0.1380.2700.2780.3161.0000.6730.7690.4480.628
산정대지면적0.2200.3040.0000.0000.6731.0000.0000.7130.686
건물전체연면적0.0750.2150.0000.0000.7690.0001.0000.1850.180
건물산정연면적0.2500.3510.0000.0260.4480.7130.1851.0000.753
주택가격0.2110.4300.0000.0000.6280.6860.1800.7531.000
2024-04-21T19:06:51.975751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동코드동코드토지대장면적산정대지면적건물전체연면적건물산정연면적주택가격동명
법정동코드1.000-0.016-0.140-0.051-0.166-0.136-0.0470.073
동코드-0.0161.0000.166-0.142-0.051-0.105-0.1101.000
토지대장면적-0.1400.1661.0000.5930.7070.6600.7380.141
산정대지면적-0.051-0.1420.5931.0000.2870.5590.7960.000
건물전체연면적-0.166-0.0510.7070.2871.0000.8880.6600.000
건물산정연면적-0.136-0.1050.6600.5590.8881.0000.8150.011
주택가격-0.047-0.1100.7380.7960.6600.8151.0000.000
동명0.0731.0000.1410.0000.0000.0110.0001.000

Missing values

2024-04-21T19:06:41.017226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T19:06:41.431468image/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

고유번호법정동코드법정동명특수지구분코드특수지구분명지번건축물대장고유번호기준년도기준월동코드동명토지대장면적산정대지면적건물전체연면적건물산정연면적주택가격표준지여부데이터기준일자
027100000000000000002711010100동인동1가1일반0010-000027100000000000000002019111동187.7187.791.691.6121000000N2019-04-30
127100000000000000002711010100동인동1가1일반0010-000527100000000000000002019111동72.172.144.6344.6350200000N2019-04-30
227100000000000000002711010100동인동1가1일반0010-000627100000000000000002019111동85.685.644.6244.6259200000N2019-04-30
327100000000000000002711010100동인동1가1일반0010-001227100000000000000002019111동47.347.324.7924.7931700000N2019-04-30
427100000000000000002711010100동인동1가1일반0010-001427100000000000000002019111동51.651.622.3122.3134400000N2019-04-30
527100000000000000002711010100동인동1가1일반0010-001527100000000000000002019111동81.725.6342.1413.2217200000N2019-04-30
627100000000000000002711010100동인동1가1일반0010-001627100000000000000002019111동46.946.944.6344.6331800000N2019-04-30
727100000000000000002711010100동인동1가1일반0010-001927100000000000000002019111동187.5167.46460.65411.41405000000N2019-04-30
827100000000000000002711010100동인동1가1일반0010-002227100000000000000002019111동42.042.037.1937.1929700000N2019-04-30
927100000000000000002711010100동인동1가1일반0014-000127100000000000000002019111동161.026.26106.4517.3658100000N2019-04-30
고유번호법정동코드법정동명특수지구분코드특수지구분명지번건축물대장고유번호기준년도기준월동코드동명토지대장면적산정대지면적건물전체연면적건물산정연면적주택가격표준지여부데이터기준일자
780027100000000000000002711015700대봉동1일반0745-001427100000000000000002019111동59.559.527.7727.7752800000N2019-04-30
780127100000000000000002711015700대봉동1일반0745-002127100000000000000002019111동76.476.426.4126.4191200000N2019-04-30
780227100000000000000002711015700대봉동1일반0747-000927100000000000000002019111동236.3236.384.0884.08277000000N2019-04-30
780327100000000000000002711015700대봉동1일반0747-001527100000000000000002019111동120.024.85203.5857.0551900000N2019-04-30
780427100000000000000002711015700대봉동1일반0748-000327100000000000000002019111동137.273.084.7945.1292600000N2019-04-30
780527100000000000000002711015700대봉동1일반0748-000427100000000000000002019111동68.968.937.2937.2957200000N2019-04-30
780627100000000000000002711015700대봉동1일반0748-000727100000000000000002019111동60.860.827.6727.6771700000N2019-04-30
780727100000000000000002711015700대봉동1일반0748-000927100000000000000002019111동70.770.743.1443.1483800000N2019-04-30
780827100000000000000002711015700대봉동1일반0748-001627100000000000000002019111동110.151.19121.9656.754000000N2019-04-30
780927100000000000000002711015700대봉동1일반0748-002227100000000000000002019111동116.454.85130.2761.3865500000N2019-04-30