Overview

Dataset statistics

Number of variables18
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.6 MiB
Average record size in memory164.0 B

Variable types

Numeric7
Categorical9
Text1
Boolean1

Dataset

Description대구광역시_남구_개별주택가격정보_20190703
Author대구광역시 남구
URLhttp://data.daegu.go.kr/open/data/dataView.do?dataSetId=15013463&dataSetDetailId=150134631d90ad0fc45f3_201906281326&provdMethod=FILE

Alerts

동코드 has constant value ""Constant
데이터기준일자 has constant value ""Constant
법정동명 is highly overall correlated with 고유번호 and 2 other fieldsHigh correlation
법정동코드 is highly overall correlated with 고유번호 and 2 other fieldsHigh correlation
고유번호 is highly overall correlated with 건축물대장고유번호 and 2 other fieldsHigh correlation
건축물대장고유번호 is highly overall correlated with 고유번호 and 2 other fieldsHigh 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
특수지구분코드 is highly imbalanced (99.9%)Imbalance
특수지구분명 is highly imbalanced (99.9%)Imbalance
기준월 is highly imbalanced (99.1%)Imbalance
동명 is highly imbalanced (86.2%)Imbalance
표준지여부 is highly imbalanced (72.3%)Imbalance
건물산정연면적 has 288 (2.9%) zerosZeros

Reproduction

Analysis started2023-12-10 18:49:50.123283
Analysis finished2023-12-10 18:50:05.940271
Duration15.82 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

고유번호
Real number (ℝ)

HIGH CORRELATION 

Distinct8428
Distinct (%)84.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7200103 × 1018
Minimum2.7200101 × 1018
Maximum2.7200103 × 1018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T03:50:06.048074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.7200101 × 1018
5-th percentile2.7200101 × 1018
Q12.7200102 × 1018
median2.7200103 × 1018
Q32.7200103 × 1018
95-th percentile2.7200103 × 1018
Maximum2.7200103 × 1018
Range2.0002935 × 1011
Interquartile range (IQR)1.0000865 × 1011

Descriptive statistics

Standard deviation6.2939251 × 1010
Coefficient of variation (CV)2.3139343 × 10-8
Kurtosis0.66328925
Mean2.7200103 × 1018
Median Absolute Deviation (MAD)10584576
Skewness-1.3582844
Sum-8.8449001 × 1018
Variance3.9613493 × 1021
MonotonicityNot monotonic
2023-12-11T03:50:06.278330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2720010100102030005 10
 
0.1%
2720010300104730001 9
 
0.1%
2720010100102030006 7
 
0.1%
2720010200112830114 6
 
0.1%
2720010300120140059 5
 
0.1%
2720010100102030008 5
 
0.1%
2720010100105160020 4
 
< 0.1%
2720010100105840009 4
 
< 0.1%
2720010300115590006 3
 
< 0.1%
2720010100104680009 3
 
< 0.1%
Other values (8418) 9944
99.4%
ValueCountFrequency (%)
2720010100101210139 1
 
< 0.1%
2720010100101590009 1
 
< 0.1%
2720010100101590010 3
< 0.1%
2720010100101610016 1
 
< 0.1%
2720010100101610020 1
 
< 0.1%
2720010100101840009 1
 
< 0.1%
2720010100101930012 1
 
< 0.1%
2720010100101930019 2
< 0.1%
2720010100101930024 1
 
< 0.1%
2720010100101930026 2
< 0.1%
ValueCountFrequency (%)
2720010300130560023 1
< 0.1%
2720010300130550089 1
< 0.1%
2720010300130550087 1
< 0.1%
2720010300130550085 1
< 0.1%
2720010300130550069 1
< 0.1%
2720010300130550068 1
< 0.1%
2720010300130550022 1
< 0.1%
2720010300130550021 1
< 0.1%
2720010300130540072 1
< 0.1%
2720010300130540070 1
< 0.1%

법정동코드
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2720010300
6863 
2720010200
2349 
2720010100
788 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2720010300
2nd row2720010300
3rd row2720010300
4th row2720010300
5th row2720010200

Common Values

ValueCountFrequency (%)
2720010300 6863
68.6%
2720010200 2349
 
23.5%
2720010100 788
 
7.9%

Length

2023-12-11T03:50:06.492766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T03:50:06.626064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2720010300 6863
68.6%
2720010200 2349
 
23.5%
2720010100 788
 
7.9%

법정동명
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
대구광역시 남구 대명동
6863 
대구광역시 남구 봉덕동
2349 
대구광역시 남구 이천동
788 

Length

Max length12
Median length12
Mean length12
Min length12

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row대구광역시 남구 대명동
2nd row대구광역시 남구 대명동
3rd row대구광역시 남구 대명동
4th row대구광역시 남구 대명동
5th row대구광역시 남구 봉덕동

Common Values

ValueCountFrequency (%)
대구광역시 남구 대명동 6863
68.6%
대구광역시 남구 봉덕동 2349
 
23.5%
대구광역시 남구 이천동 788
 
7.9%

Length

2023-12-11T03:50:06.764997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T03:50:06.919122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대구광역시 10000
33.3%
남구 10000
33.3%
대명동 6863
22.9%
봉덕동 2349
 
7.8%
이천동 788
 
2.6%

특수지구분코드
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
9999 
2
 
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 9999
> 99.9%
2 1
 
< 0.1%

Length

2023-12-11T03:50:07.100370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T03:50:07.233353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9999
> 99.9%
2 1
 
< 0.1%

특수지구분명
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
일반
9999 
산
 
1

Length

Max length2
Median length2
Mean length1.9999
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
일반 9999
> 99.9%
산 1
 
< 0.1%

Length

2023-12-11T03:50:07.382966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T03:50:07.505129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반 9999
> 99.9%
산 1
 
< 0.1%

지번
Text

Distinct8306
Distinct (%)83.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T03:50:07.966621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length6.1919
Min length3

Characters and Unicode

Total characters61919
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

Unique6784 ?
Unique (%)67.8%

Sample

1st row2153-26
2nd row3021-12
3rd row601-3
4th row890-1
5th row995-12
ValueCountFrequency (%)
203-5 10
 
0.1%
473-1 9
 
0.1%
203-6 7
 
0.1%
1283-114 6
 
0.1%
939-13 5
 
< 0.1%
203-8 5
 
< 0.1%
2014-59 5
 
< 0.1%
516-20 4
 
< 0.1%
559-1 4
 
< 0.1%
487-7 4
 
< 0.1%
Other values (8296) 9941
99.4%
2023-12-11T03:50:08.525568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 10661
17.2%
- 9906
16.0%
2 6847
11.1%
3 5691
9.2%
0 4892
7.9%
5 4746
7.7%
4 4377
7.1%
6 4259
 
6.9%
9 3688
 
6.0%
7 3528
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 52013
84.0%
Dash Punctuation 9906
 
16.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 10661
20.5%
2 6847
13.2%
3 5691
10.9%
0 4892
9.4%
5 4746
9.1%
4 4377
8.4%
6 4259
 
8.2%
9 3688
 
7.1%
7 3528
 
6.8%
8 3324
 
6.4%
Dash Punctuation
ValueCountFrequency (%)
- 9906
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 61919
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 10661
17.2%
- 9906
16.0%
2 6847
11.1%
3 5691
9.2%
0 4892
7.9%
5 4746
7.7%
4 4377
7.1%
6 4259
 
6.9%
9 3688
 
6.0%
7 3528
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 61919
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 10661
17.2%
- 9906
16.0%
2 6847
11.1%
3 5691
9.2%
0 4892
7.9%
5 4746
7.7%
4 4377
7.1%
6 4259
 
6.9%
9 3688
 
6.0%
7 3528
 
5.7%

건축물대장고유번호
Real number (ℝ)

HIGH CORRELATION 

Distinct8428
Distinct (%)84.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7200103 × 1018
Minimum2.7200101 × 1018
Maximum2.7200103 × 1018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T03:50:08.727407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.7200101 × 1018
5-th percentile2.7200101 × 1018
Q12.7200102 × 1018
median2.7200103 × 1018
Q32.7200103 × 1018
95-th percentile2.7200103 × 1018
Maximum2.7200103 × 1018
Range2.0002935 × 1011
Interquartile range (IQR)1.0000865 × 1011

Descriptive statistics

Standard deviation6.2939251 × 1010
Coefficient of variation (CV)2.3139343 × 10-8
Kurtosis0.66328925
Mean2.7200103 × 1018
Median Absolute Deviation (MAD)10584576
Skewness-1.3582844
Sum-8.8449001 × 1018
Variance3.9613493 × 1021
MonotonicityNot monotonic
2023-12-11T03:50:08.909212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2720010100102030005 10
 
0.1%
2720010300104730001 9
 
0.1%
2720010100102030006 7
 
0.1%
2720010200112830114 6
 
0.1%
2720010300120140059 5
 
0.1%
2720010100102030008 5
 
0.1%
2720010100105160020 4
 
< 0.1%
2720010100105840009 4
 
< 0.1%
2720010300115590006 3
 
< 0.1%
2720010100104680009 3
 
< 0.1%
Other values (8418) 9944
99.4%
ValueCountFrequency (%)
2720010100101210139 1
 
< 0.1%
2720010100101590009 1
 
< 0.1%
2720010100101590010 3
< 0.1%
2720010100101610016 1
 
< 0.1%
2720010100101610020 1
 
< 0.1%
2720010100101840009 1
 
< 0.1%
2720010100101930012 1
 
< 0.1%
2720010100101930019 2
< 0.1%
2720010100101930024 1
 
< 0.1%
2720010100101930026 2
< 0.1%
ValueCountFrequency (%)
2720010300130560023 1
< 0.1%
2720010300130550089 1
< 0.1%
2720010300130550087 1
< 0.1%
2720010300130550085 1
< 0.1%
2720010300130550069 1
< 0.1%
2720010300130550068 1
< 0.1%
2720010300130550022 1
< 0.1%
2720010300130550021 1
< 0.1%
2720010300130540072 1
< 0.1%
2720010300130540070 1
< 0.1%

기준년도
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2015
3183 
2017
3135 
2016
3113 
2018
569 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2016
2nd row2017
3rd row2017
4th row2017
5th row2015

Common Values

ValueCountFrequency (%)
2015 3183
31.8%
2017 3135
31.4%
2016 3113
31.1%
2018 569
 
5.7%

Length

2023-12-11T03:50:09.064609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T03:50:09.215358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2015 3183
31.8%
2017 3135
31.4%
2016 3113
31.1%
2018 569
 
5.7%

기준월
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
9992 
6
 
8

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 9992
99.9%
6 8
 
0.1%

Length

2023-12-11T03:50:09.368180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T03:50:09.493919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9992
99.9%
6 8
 
0.1%

동코드
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

2023-12-11T03:50:09.611277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T03:50:09.734855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 10000
100.0%

동명
Categorical

IMBALANCE 

Distinct21
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1동
9102 
10동
 
482
2동
 
274
11동
 
65
3동
 
23
Other values (16)
 
54

Length

Max length3
Median length2
Mean length2.0576
Min length2

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
1동 9102
91.0%
10동 482
 
4.8%
2동 274
 
2.7%
11동 65
 
0.7%
3동 23
 
0.2%
20동 9
 
0.1%
6동 6
 
0.1%
12동 5
 
0.1%
4동 5
 
0.1%
9동 5
 
0.1%
Other values (11) 24
 
0.2%

Length

2023-12-11T03:50:09.875227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1동 9102
91.0%
10동 482
 
4.8%
2동 274
 
2.7%
11동 65
 
0.7%
3동 23
 
0.2%
20동 9
 
0.1%
6동 6
 
0.1%
12동 5
 
< 0.1%
4동 5
 
< 0.1%
9동 5
 
< 0.1%
Other values (11) 24
 
0.2%

토지대장면적
Real number (ℝ)

HIGH CORRELATION 

Distinct1940
Distinct (%)19.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean176.55821
Minimum13
Maximum3147.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T03:50:10.028867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile78
Q1119
median153.9
Q3202
95-th percentile337
Maximum3147.2
Range3134.2
Interquartile range (IQR)83

Descriptive statistics

Standard deviation141.20381
Coefficient of variation (CV)0.79975781
Kurtosis241.19984
Mean176.55821
Median Absolute Deviation (MAD)39.5
Skewness12.722832
Sum1765582.1
Variance19938.516
MonotonicityNot monotonic
2023-12-11T03:50:10.564354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
119.0 92
 
0.9%
116.0 78
 
0.8%
165.0 78
 
0.8%
126.0 75
 
0.8%
99.0 71
 
0.7%
129.0 62
 
0.6%
132.0 59
 
0.6%
139.0 59
 
0.6%
162.0 57
 
0.6%
122.0 56
 
0.6%
Other values (1930) 9313
93.1%
ValueCountFrequency (%)
13.0 2
< 0.1%
16.9 1
 
< 0.1%
17.0 3
< 0.1%
18.9 1
 
< 0.1%
19.0 1
 
< 0.1%
20.0 3
< 0.1%
23.0 3
< 0.1%
23.5 1
 
< 0.1%
23.8 2
< 0.1%
25.1 1
 
< 0.1%
ValueCountFrequency (%)
3147.2 9
0.1%
2750.0 5
0.1%
2284.3 1
 
< 0.1%
2108.7 1
 
< 0.1%
1758.0 1
 
< 0.1%
1183.5 2
 
< 0.1%
1145.8 1
 
< 0.1%
1111.3 1
 
< 0.1%
1031.1 1
 
< 0.1%
992.4 1
 
< 0.1%

산정대지면적
Real number (ℝ)

HIGH CORRELATION 

Distinct3841
Distinct (%)38.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean151.91517
Minimum0
Maximum3147.2
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T03:50:10.788060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile49.230833
Q198.8
median135.5
Q3183.5
95-th percentile303.01
Maximum3147.2
Range3147.2
Interquartile range (IQR)84.7

Descriptive statistics

Standard deviation118.22584
Coefficient of variation (CV)0.7782359
Kurtosis319.65476
Mean151.91517
Median Absolute Deviation (MAD)41.5
Skewness13.682049
Sum1519151.7
Variance13977.348
MonotonicityNot monotonic
2023-12-11T03:50:10.995730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
119.0 88
 
0.9%
116.0 70
 
0.7%
165.0 67
 
0.7%
126.0 63
 
0.6%
99.0 59
 
0.6%
129.0 57
 
0.6%
93.0 49
 
0.5%
132.0 49
 
0.5%
122.0 48
 
0.5%
139.0 48
 
0.5%
Other values (3831) 9402
94.0%
ValueCountFrequency (%)
0.0 2
 
< 0.1%
6.78 2
 
< 0.1%
8.11 1
 
< 0.1%
8.45 1
 
< 0.1%
10.0 2
 
< 0.1%
10.7 3
< 0.1%
11.5 2
 
< 0.1%
11.8 1
 
< 0.1%
12.43 1
 
< 0.1%
12.85 7
0.1%
ValueCountFrequency (%)
3147.2 6
0.1%
2750.0 3
< 0.1%
1758.0 1
 
< 0.1%
992.4 1
 
< 0.1%
978.8 1
 
< 0.1%
671.0 1
 
< 0.1%
668.8 1
 
< 0.1%
665.0 2
 
< 0.1%
621.5 1
 
< 0.1%
620.5 1
 
< 0.1%

건물전체연면적
Real number (ℝ)

HIGH CORRELATION 

Distinct7002
Distinct (%)70.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean195.48983
Minimum5.78
Maximum5582.74
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T03:50:11.199336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5.78
5-th percentile38.9975
Q172.1075
median141.105
Q3220.43
95-th percentile609.34
Maximum5582.74
Range5576.96
Interquartile range (IQR)148.3225

Descriptive statistics

Standard deviation200.0413
Coefficient of variation (CV)1.0232824
Kurtosis76.298445
Mean195.48983
Median Absolute Deviation (MAD)71.29
Skewness4.8979026
Sum1954898.3
Variance40016.523
MonotonicityNot monotonic
2023-12-11T03:50:11.400459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41.56 35
 
0.4%
33.06 13
 
0.1%
62.08 11
 
0.1%
25.12 10
 
0.1%
105.39 10
 
0.1%
29.75 9
 
0.1%
30.42 9
 
0.1%
59.5 9
 
0.1%
65.82 8
 
0.1%
145.92 8
 
0.1%
Other values (6992) 9878
98.8%
ValueCountFrequency (%)
5.78 1
 
< 0.1%
6.28 5
0.1%
7.27 2
 
< 0.1%
9.88 1
 
< 0.1%
12.03 1
 
< 0.1%
12.17 1
 
< 0.1%
13.25 1
 
< 0.1%
13.3 1
 
< 0.1%
15.53 2
 
< 0.1%
15.54 1
 
< 0.1%
ValueCountFrequency (%)
5582.74 1
< 0.1%
4052.68 1
< 0.1%
2756.19 1
< 0.1%
2631.87 1
< 0.1%
2178.11 1
< 0.1%
1938.81 1
< 0.1%
1875.18 1
< 0.1%
1818.66 1
< 0.1%
1708.23 2
< 0.1%
1346.04 1
< 0.1%

건물산정연면적
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6660
Distinct (%)66.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean156.8366
Minimum0
Maximum2178.11
Zeros288
Zeros (%)2.9%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T03:50:11.715355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile27.497
Q162.29
median110.78
Q3176.145
95-th percentile520.35
Maximum2178.11
Range2178.11
Interquartile range (IQR)113.855

Descriptive statistics

Standard deviation158.80801
Coefficient of variation (CV)1.0125699
Kurtosis9.0153088
Mean156.8366
Median Absolute Deviation (MAD)53.235
Skewness2.6020996
Sum1568366
Variance25219.985
MonotonicityNot monotonic
2023-12-11T03:50:11.964317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 288
 
2.9%
41.56 34
 
0.3%
25.12 10
 
0.1%
65.82 10
 
0.1%
29.75 9
 
0.1%
15.21 9
 
0.1%
33.06 9
 
0.1%
59.5 9
 
0.1%
62.08 9
 
0.1%
105.39 9
 
0.1%
Other values (6650) 9604
96.0%
ValueCountFrequency (%)
0.0 288
2.9%
5.78 1
 
< 0.1%
6.28 5
 
0.1%
7.15 1
 
< 0.1%
7.27 2
 
< 0.1%
7.57 1
 
< 0.1%
8.11 1
 
< 0.1%
8.55 1
 
< 0.1%
9.88 1
 
< 0.1%
12.03 1
 
< 0.1%
ValueCountFrequency (%)
2178.11 1
< 0.1%
1422.19 1
< 0.1%
894.27 1
< 0.1%
889.76 2
< 0.1%
888.29 2
< 0.1%
878.66 1
< 0.1%
876.39 1
< 0.1%
874.44 2
< 0.1%
874.25 1
< 0.1%
874.18 1
< 0.1%

주택가격
Real number (ℝ)

HIGH CORRELATION 

Distinct1468
Distinct (%)14.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2386178 × 108
Minimum0
Maximum1.5 × 109
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T03:50:12.147681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile33900000
Q158900000
median85600000
Q31.26 × 108
95-th percentile4.06 × 108
Maximum1.5 × 109
Range1.5 × 109
Interquartile range (IQR)67100000

Descriptive statistics

Standard deviation1.2372466 × 108
Coefficient of variation (CV)0.99889298
Kurtosis10.000723
Mean1.2386178 × 108
Median Absolute Deviation (MAD)30700000
Skewness2.9022501
Sum1.2386178 × 1012
Variance1.5307791 × 1016
MonotonicityNot monotonic
2023-12-11T03:50:12.371269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
111000000 75
 
0.8%
100000000 73
 
0.7%
104000000 73
 
0.7%
110000000 69
 
0.7%
105000000 66
 
0.7%
103000000 66
 
0.7%
108000000 61
 
0.6%
106000000 59
 
0.6%
101000000 58
 
0.6%
115000000 56
 
0.6%
Other values (1458) 9344
93.4%
ValueCountFrequency (%)
0 2
< 0.1%
1020000 1
< 0.1%
2360000 1
< 0.1%
2610000 1
< 0.1%
3670000 1
< 0.1%
4210000 1
< 0.1%
4520000 1
< 0.1%
5420000 2
< 0.1%
5550000 1
< 0.1%
5650000 1
< 0.1%
ValueCountFrequency (%)
1500000000 1
< 0.1%
1130000000 1
< 0.1%
831000000 1
< 0.1%
824000000 1
< 0.1%
818000000 1
< 0.1%
813000000 1
< 0.1%
807000000 1
< 0.1%
802000000 2
< 0.1%
801000000 1
< 0.1%
800000000 1
< 0.1%

표준지여부
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size87.9 KiB
False
9523 
True
 
477
ValueCountFrequency (%)
False 9523
95.2%
True 477
 
4.8%
2023-12-11T03:50:12.540834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2019-07-03
10000 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2019-07-03
2nd row2019-07-03
3rd row2019-07-03
4th row2019-07-03
5th row2019-07-03

Common Values

ValueCountFrequency (%)
2019-07-03 10000
100.0%

Length

2023-12-11T03:50:12.713189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T03:50:12.851690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2019-07-03 10000
100.0%

Interactions

2023-12-11T03:50:04.145184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:49:55.176958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:49:56.610147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:49:58.251839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:49:59.865427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:50:01.628227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:50:02.927312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:50:04.333796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:49:55.368332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:49:56.844074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:49:58.621569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:50:00.091823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:50:01.806835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:50:03.143786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:50:04.521564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:49:55.548012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:49:57.093102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:49:58.909873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:50:00.296282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:50:02.033000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:50:03.342736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:50:04.695827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:49:55.761186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:49:57.325625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:49:59.167880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:50:00.859912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:50:02.244340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:50:03.514612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:50:04.867414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:49:55.935968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:49:57.513154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:49:59.387104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:50:01.092102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:50:02.439925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:50:03.700376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:50:05.030318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:49:56.153352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:49:57.779652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:49:59.543749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:50:01.279223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:50:02.604434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:50:03.857583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:50:05.156763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:49:56.412558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:49:58.007088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:49:59.685381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:50:01.429891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:50:02.765425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:50:03.997171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T03:50:12.937581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
고유번호법정동코드법정동명특수지구분코드특수지구분명건축물대장고유번호기준년도기준월동명토지대장면적산정대지면적건물전체연면적건물산정연면적주택가격표준지여부
고유번호1.0001.0001.0000.3520.3521.0000.5060.0000.1150.0000.0000.0000.0590.1050.013
법정동코드1.0001.0001.0000.0070.0071.0000.2760.0000.1740.0000.0170.0020.0770.0960.008
법정동명1.0001.0001.0000.0070.0071.0000.2760.0000.1740.0000.0170.0020.0770.0960.008
특수지구분코드0.3520.0070.0071.0000.7070.3520.0000.0000.0000.0000.0000.0000.0000.0190.000
특수지구분명0.3520.0070.0070.7071.0000.3520.0000.0000.0000.0000.0000.0000.0000.0190.000
건축물대장고유번호1.0001.0001.0000.3520.3521.0000.5060.0000.1150.0000.0000.0000.0590.1050.013
기준년도0.5060.2760.2760.0000.0000.5061.0000.0580.0260.0200.0230.0000.0300.0830.000
기준월0.0000.0000.0000.0000.0000.0000.0581.0000.1340.1140.0000.0000.0100.0940.000
동명0.1150.1740.1740.0000.0000.1150.0260.1341.0000.6400.7270.1720.2780.2500.062
토지대장면적0.0000.0000.0000.0000.0000.0000.0200.1140.6401.0000.8860.7380.7080.7140.000
산정대지면적0.0000.0170.0170.0000.0000.0000.0230.0000.7270.8861.0000.6040.8670.7210.000
건물전체연면적0.0000.0020.0020.0000.0000.0000.0000.0000.1720.7380.6041.0000.8480.6840.022
건물산정연면적0.0590.0770.0770.0000.0000.0590.0300.0100.2780.7080.8670.8481.0000.9070.048
주택가격0.1050.0960.0960.0190.0190.1050.0830.0940.2500.7140.7210.6840.9071.0000.006
표준지여부0.0130.0080.0080.0000.0000.0130.0000.0000.0620.0000.0000.0220.0480.0061.000
2023-12-11T03:50:13.161924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
동명특수지구분명기준월표준지여부법정동명기준년도특수지구분코드법정동코드
동명1.0000.0000.1170.0540.0800.0140.0000.080
특수지구분명0.0001.0000.0000.0000.0110.0000.5000.011
기준월0.1170.0001.0000.0000.0000.0380.0000.000
표준지여부0.0540.0000.0001.0000.0130.0000.0000.013
법정동명0.0800.0110.0000.0131.0000.2650.0111.000
기준년도0.0140.0000.0380.0000.2651.0000.0000.265
특수지구분코드0.0000.5000.0000.0000.0110.0001.0000.011
법정동코드0.0800.0110.0000.0131.0000.2650.0111.000
2023-12-11T03:50:13.349616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
고유번호건축물대장고유번호토지대장면적산정대지면적건물전체연면적건물산정연면적주택가격법정동코드법정동명특수지구분코드특수지구분명기준년도기준월동명표준지여부
고유번호1.0001.000-0.074-0.0700.0490.038-0.0261.0001.0000.2350.2350.2170.0000.0620.009
건축물대장고유번호1.0001.000-0.074-0.0700.0490.038-0.0261.0001.0000.2350.2350.2170.0000.0620.009
토지대장면적-0.074-0.0741.0000.7450.6150.5590.7210.0000.0000.0000.0000.0130.1140.3080.000
산정대지면적-0.070-0.0700.7451.0000.3240.5190.7160.0110.0110.0000.0000.0160.0000.3660.000
건물전체연면적0.0490.0490.6150.3241.0000.8560.7430.0010.0010.0000.0000.0000.0000.0650.023
건물산정연면적0.0380.0380.5590.5190.8561.0000.8070.0510.0510.0000.0000.0210.0100.1080.052
주택가격-0.026-0.0260.7210.7160.7430.8071.0000.0610.0610.0140.0140.0380.0700.1050.004
법정동코드1.0001.0000.0000.0110.0010.0510.0611.0001.0000.0110.0110.2650.0000.0800.013
법정동명1.0001.0000.0000.0110.0010.0510.0611.0001.0000.0110.0110.2650.0000.0800.013
특수지구분코드0.2350.2350.0000.0000.0000.0000.0140.0110.0111.0000.5000.0000.0000.0000.000
특수지구분명0.2350.2350.0000.0000.0000.0000.0140.0110.0110.5001.0000.0000.0000.0000.000
기준년도0.2170.2170.0130.0160.0000.0210.0380.2650.2650.0000.0001.0000.0380.0140.000
기준월0.0000.0000.1140.0000.0000.0100.0700.0000.0000.0000.0000.0381.0000.1170.000
동명0.0620.0620.3080.3660.0650.1080.1050.0800.0800.0000.0000.0140.1171.0000.054
표준지여부0.0090.0090.0000.0000.0230.0520.0040.0130.0130.0000.0000.0000.0000.0541.000

Missing values

2023-12-11T03:50:05.410048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T03:50:05.769781image/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
3858527200103001215300262720010300대구광역시 남구 대명동1일반2153-2627200103001215300262016101동135.2135.2153.1153.186200000N2019-07-03
6172327200103001302100122720010300대구광역시 남구 대명동1일반3021-1227200103001302100122017101동149.4149.481.4681.4689200000N2019-07-03
5051727200103001060100032720010300대구광역시 남구 대명동1일반601-327200103001060100032017101동209.1209.1180.01180.01135000000N2019-07-03
5163027200103001089000012720010300대구광역시 남구 대명동1일반890-127200103001089000012017101동265.7265.7174.21174.21216000000N2019-07-03
407227200102001099500122720010200대구광역시 남구 봉덕동1일반995-1227200102001099500122015101동201.0201.0121.75121.75107000000N2019-07-03
6389327200101001042300012720010100대구광역시 남구 이천동1일반423-1272001010010423000120181010동149.0149.0204.02204.02217000000N2019-07-03
1836827200103001229600032720010300대구광역시 남구 대명동1일반2296-327200103001229600032015101동143.5143.5147.01147.0198700000N2019-07-03
1755527200103001215300252720010300대구광역시 남구 대명동1일반2153-2527200103001215300252015101동126.6126.6126.31126.3155800000N2019-07-03
6508227200102001055700072720010200대구광역시 남구 봉덕동1일반557-727200102001055700072018101동234.0234.0199.8199.8122000000N2019-07-03
282527200102001067900052720010200대구광역시 남구 봉덕동1일반679-527200102001067900052015101동151.0151.0211.46211.4677100000N2019-07-03
고유번호법정동코드법정동명특수지구분코드특수지구분명지번건축물대장고유번호기준년도기준월동코드동명토지대장면적산정대지면적건물전체연면적건물산정연면적주택가격표준지여부데이터기준일자
730627200103001037700312720010300대구광역시 남구 대명동1일반377-3127200103001037700312015101동151.4151.496.796.790000000N2019-07-03
3767327200103001199800152720010300대구광역시 남구 대명동1일반1998-1527200103001199800152016101동139.566.9879187.0489.8272200000N2019-07-03
3112327200103001094500162720010300대구광역시 남구 대명동1일반945-1627200103001094500162016101동230.6230.6193.74193.74153000000N2019-07-03
6521827200102001056200182720010200대구광역시 남구 봉덕동1일반562-1827200102001056200182018101동179.8179.863.9463.9488900000N2019-07-03
1483627200103001172300182720010300대구광역시 남구 대명동1일반1723-1827200103001172300182015101동192.7192.7111.97111.9775700000N2019-07-03
2302227200102001054100042720010200대구광역시 남구 봉덕동1일반541-427200102001054100042016101동174.1159.07517192.54175.93125000000N2019-07-03
3964227200103001230800092720010300대구광역시 남구 대명동1일반2308-927200103001230800092016101동84.684.641.5841.5856600000N2019-07-03
3505327200103001165500302720010300대구광역시 남구 대명동1일반1655-3027200103001165500302016101동97.997.961.8861.8869100000N2019-07-03
4247827200101001027100362720010100대구광역시 남구 이천동1일반271-3627200101001027100362017101동190.0190.0377.0377.0301000000N2019-07-03
2385327200102001062300042720010200대구광역시 남구 봉덕동1일반623-427200102001062300042016102동132.2132.2158.4158.4181000000N2019-07-03