Overview

Dataset statistics

Number of variables11
Number of observations500
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory48.0 KiB
Average record size in memory98.3 B

Variable types

Numeric7
Categorical3
Text1

Dataset

Description샘플 데이터
Author서울시
URLhttps://bigdata.seoul.go.kr/data/selectSampleData.do?sample_data_seq=22

Alerts

기준월(BASE_MON) has constant value ""Constant
표준지여부(PYO_YN) is highly imbalanced (79.6%)Imbalance
필지정보(PILGI) is highly imbalanced (91.4%)Imbalance
토지코드(LAND_CD) has unique valuesUnique
부번(BUBEON) has 27 (5.4%) zerosZeros

Reproduction

Analysis started2023-12-10 14:54:24.000888
Analysis finished2023-12-10 14:54:30.872377
Duration6.87 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

토지코드(LAND_CD)
Real number (ℝ)

UNIQUE 

Distinct500
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1405013 × 1018
Minimum1.1110101 × 1018
Maximum1.1740109 × 1018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:54:30.941886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.1110101 × 1018
5-th percentile1.1110184 × 1018
Q11.1260101 × 1018
median1.1410112 × 1018
Q31.1560119 × 1018
95-th percentile1.1710108 × 1018
Maximum1.1740109 × 1018
Range6.30008 × 1016
Interquartile range (IQR)3.000175 × 1016

Descriptive statistics

Standard deviation1.8261631 × 1016
Coefficient of variation (CV)0.016011933
Kurtosis-1.1076729
Mean1.1405013 × 1018
Median Absolute Deviation (MAD)1.500105 × 1016
Skewness0.098486927
Sum-1.5984105 × 1018
Variance3.3348715 × 1032
MonotonicityNot monotonic
2023-12-10T23:54:31.121568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1117013600100130099 1
 
0.2%
1150010600106600020 1
 
0.2%
1144010200100120153 1
 
0.2%
1150010900105970035 1
 
0.2%
1162010200115550004 1
 
0.2%
1114013900100180008 1
 
0.2%
1144012200103670030 1
 
0.2%
1174010700102710001 1
 
0.2%
1154510100105690012 1
 
0.2%
1138010700106260046 1
 
0.2%
Other values (490) 490
98.0%
ValueCountFrequency (%)
1111010100100530003 1
0.2%
1111010800100530000 1
0.2%
1111010800101350005 1
0.2%
1111011100100100001 1
0.2%
1111011100100470336 1
0.2%
1111011500102620036 1
0.2%
1111011500103290001 1
0.2%
1111011900100820012 1
0.2%
1111013500100430012 1
0.2%
1111013900100270005 1
0.2%
ValueCountFrequency (%)
1174010900104100241 1
0.2%
1174010900104100105 1
0.2%
1174010900104020004 1
0.2%
1174010900103380015 1
0.2%
1174010900102170019 1
0.2%
1174010900101670177 1
0.2%
1174010900100290036 1
0.2%
1174010700104890092 1
0.2%
1174010700104510020 1
0.2%
1174010700104210132 1
0.2%

년도(YEAR)
Real number (ℝ)

Distinct22
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2010.374
Minimum2000
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:54:31.291821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2000
5-th percentile2001
Q12006
median2010
Q32015
95-th percentile2020
Maximum2021
Range21
Interquartile range (IQR)9

Descriptive statistics

Standard deviation5.8496633
Coefficient of variation (CV)0.0029097389
Kurtosis-1.1037662
Mean2010.374
Median Absolute Deviation (MAD)5
Skewness-0.03481346
Sum1005187
Variance34.218561
MonotonicityNot monotonic
2023-12-10T23:54:31.450904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
2008 39
 
7.8%
2017 34
 
6.8%
2012 30
 
6.0%
2014 30
 
6.0%
2011 29
 
5.8%
2020 28
 
5.6%
2007 28
 
5.6%
2018 26
 
5.2%
2015 25
 
5.0%
2005 23
 
4.6%
Other values (12) 208
41.6%
ValueCountFrequency (%)
2000 20
4.0%
2001 15
 
3.0%
2002 23
4.6%
2003 18
3.6%
2004 21
4.2%
2005 23
4.6%
2006 23
4.6%
2007 28
5.6%
2008 39
7.8%
2009 20
4.0%
ValueCountFrequency (%)
2021 1
 
0.2%
2020 28
5.6%
2019 15
3.0%
2018 26
5.2%
2017 34
6.8%
2016 17
3.4%
2015 25
5.0%
2014 30
6.0%
2013 13
 
2.6%
2012 30
6.0%

기준월(BASE_MON)
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
500 

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 (%)
1 500
100.0%

Length

2023-12-10T23:54:31.632371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:54:31.776752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 500
100.0%

지가(JIGA)
Real number (ℝ)

Distinct475
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4777192.9
Minimum6930
Maximum58410000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:54:31.924809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6930
5-th percentile419400
Q12462500
median3713500
Q35436000
95-th percentile12067500
Maximum58410000
Range58403070
Interquartile range (IQR)2973500

Descriptive statistics

Standard deviation5114977.3
Coefficient of variation (CV)1.0707077
Kurtosis36.193221
Mean4777192.9
Median Absolute Deviation (MAD)1476500
Skewness4.8732763
Sum2.3885964 × 109
Variance2.6162993 × 1013
MonotonicityNot monotonic
2023-12-10T23:54:32.169135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4840000 2
 
0.4%
3345000 2
 
0.4%
8000000 2
 
0.4%
3375000 2
 
0.4%
4975000 2
 
0.4%
64000 2
 
0.4%
1056000 2
 
0.4%
4992000 2
 
0.4%
3720000 2
 
0.4%
2640000 2
 
0.4%
Other values (465) 480
96.0%
ValueCountFrequency (%)
6930 1
0.2%
56200 1
0.2%
64000 2
0.4%
74300 1
0.2%
123700 1
0.2%
132000 1
0.2%
152200 1
0.2%
158800 1
0.2%
166300 1
0.2%
166600 1
0.2%
ValueCountFrequency (%)
58410000 1
0.2%
43890000 1
0.2%
35360000 1
0.2%
32390000 1
0.2%
29250000 1
0.2%
26750000 1
0.2%
22340000 1
0.2%
22230000 1
0.2%
20650000 1
0.2%
19100000 1
0.2%

표준지여부(PYO_YN)
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0
484 
1
 
16

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 (%)
0 484
96.8%
1 16
 
3.2%

Length

2023-12-10T23:54:32.351576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:54:32.479097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 484
96.8%
1 16
 
3.2%

임시_시군구코드(OSGG)
Real number (ℝ)

Distinct25
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11421.91
Minimum11110
Maximum11740
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:54:32.598410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11110
5-th percentile11140
Q111260
median11440
Q311560
95-th percentile11710
Maximum11740
Range630
Interquartile range (IQR)300

Descriptive statistics

Standard deviation183.91016
Coefficient of variation (CV)0.016101524
Kurtosis-1.1972266
Mean11421.91
Median Absolute Deviation (MAD)150
Skewness-0.034369848
Sum5710955
Variance33822.948
MonotonicityNot monotonic
2023-12-10T23:54:32.789834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
11620 30
 
6.0%
11290 30
 
6.0%
11440 30
 
6.0%
11560 26
 
5.2%
11530 26
 
5.2%
11710 25
 
5.0%
11380 24
 
4.8%
11110 23
 
4.6%
11500 23
 
4.6%
11230 23
 
4.6%
Other values (15) 240
48.0%
ValueCountFrequency (%)
11110 23
4.6%
11140 18
3.6%
11170 17
3.4%
11200 18
3.6%
11215 15
3.0%
11230 23
4.6%
11260 20
4.0%
11290 30
6.0%
11305 15
3.0%
11320 12
 
2.4%
ValueCountFrequency (%)
11740 12
 
2.4%
11710 25
5.0%
11680 17
3.4%
11650 18
3.6%
11620 30
6.0%
11590 18
3.6%
11560 26
5.2%
11545 17
3.4%
11530 26
5.2%
11500 23
4.6%

시군구(SIGUNGU_CD)
Real number (ℝ)

Distinct25
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11411.65
Minimum11110
Maximum11740
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:54:32.992801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11110
5-th percentile11110
Q111260
median11410
Q311590
95-th percentile11710
Maximum11740
Range630
Interquartile range (IQR)330

Descriptive statistics

Standard deviation193.93801
Coefficient of variation (CV)0.016994739
Kurtosis-1.2404818
Mean11411.65
Median Absolute Deviation (MAD)180
Skewness0.070319477
Sum5705825
Variance37611.951
MonotonicityNot monotonic
2023-12-10T23:54:33.170684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
11620 36
 
7.2%
11110 35
 
7.0%
11290 29
 
5.8%
11440 26
 
5.2%
11170 25
 
5.0%
11260 23
 
4.6%
11410 23
 
4.6%
11740 21
 
4.2%
11380 21
 
4.2%
11140 21
 
4.2%
Other values (15) 240
48.0%
ValueCountFrequency (%)
11110 35
7.0%
11140 21
4.2%
11170 25
5.0%
11200 9
 
1.8%
11215 13
 
2.6%
11230 20
4.0%
11260 23
4.6%
11290 29
5.8%
11305 16
3.2%
11320 18
3.6%
ValueCountFrequency (%)
11740 21
4.2%
11710 20
4.0%
11680 20
4.0%
11650 20
4.0%
11620 36
7.2%
11590 14
 
2.8%
11560 18
3.6%
11545 8
 
1.6%
11530 19
3.8%
11500 19
3.8%

법정동(BJDONG_CD)
Real number (ℝ)

Distinct56
Distinct (%)11.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11345.2
Minimum10100
Maximum18400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:54:33.350131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10100
5-th percentile10100
Q110200
median10700
Q311625
95-th percentile15615
Maximum18400
Range8300
Interquartile range (IQR)1425

Descriptive statistics

Standard deviation1728.4247
Coefficient of variation (CV)0.15234854
Kurtosis4.3351129
Mean11345.2
Median Absolute Deviation (MAD)500
Skewness2.1388767
Sum5672600
Variance2987451.9
MonotonicityNot monotonic
2023-12-10T23:54:33.541958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10100 66
 
13.2%
10200 65
 
13.0%
10300 48
 
9.6%
10700 31
 
6.2%
10800 24
 
4.8%
10500 24
 
4.8%
11000 24
 
4.8%
10900 20
 
4.0%
11100 16
 
3.2%
10600 15
 
3.0%
Other values (46) 167
33.4%
ValueCountFrequency (%)
10100 66
13.2%
10200 65
13.0%
10300 48
9.6%
10400 15
 
3.0%
10500 24
 
4.8%
10600 15
 
3.0%
10700 31
6.2%
10800 24
 
4.8%
10900 20
 
4.0%
11000 24
 
4.8%
ValueCountFrequency (%)
18400 1
 
0.2%
18200 1
 
0.2%
18100 1
 
0.2%
17500 3
0.6%
17400 7
1.4%
17300 1
 
0.2%
17100 1
 
0.2%
16700 4
0.8%
16300 1
 
0.2%
16200 3
0.6%

필지정보(PILGI)
Categorical

IMBALANCE 

Distinct5
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
488 
2
 
7
3
 
3
8
 
1
5
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique2 ?
Unique (%)0.4%

Sample

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

Common Values

ValueCountFrequency (%)
1 488
97.6%
2 7
 
1.4%
3 3
 
0.6%
8 1
 
0.2%
5 1
 
0.2%

Length

2023-12-10T23:54:33.720045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:54:33.849426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 488
97.6%
2 7
 
1.4%
3 3
 
0.6%
8 1
 
0.2%
5 1
 
0.2%

본번(BONBEON)
Text

Distinct352
Distinct (%)70.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-10T23:54:34.234382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters2000
Distinct characters12
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

Unique251 ?
Unique (%)50.2%

Sample

1st row0926
2nd row0073
3rd row0302
4th row0339
5th row0289
ValueCountFrequency (%)
0001 9
 
1.8%
0149 5
 
1.0%
0057 5
 
1.0%
0047 4
 
0.8%
0028 4
 
0.8%
0121 4
 
0.8%
0248 4
 
0.8%
0035 4
 
0.8%
0020 3
 
0.6%
0331 3
 
0.6%
Other values (342) 455
91.0%
2023-12-10T23:54:34.852415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 725
36.2%
1 231
 
11.6%
2 172
 
8.6%
3 148
 
7.4%
4 147
 
7.3%
7 137
 
6.9%
5 133
 
6.7%
6 113
 
5.7%
8 100
 
5.0%
9 92
 
4.6%
Other values (2) 2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1998
99.9%
Other Letter 2
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 725
36.3%
1 231
 
11.6%
2 172
 
8.6%
3 148
 
7.4%
4 147
 
7.4%
7 137
 
6.9%
5 133
 
6.7%
6 113
 
5.7%
8 100
 
5.0%
9 92
 
4.6%
Other Letter
ValueCountFrequency (%)
자 1
50.0%
족 1
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1998
99.9%
Hangul 2
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 725
36.3%
1 231
 
11.6%
2 172
 
8.6%
3 148
 
7.4%
4 147
 
7.4%
7 137
 
6.9%
5 133
 
6.7%
6 113
 
5.7%
8 100
 
5.0%
9 92
 
4.6%
Hangul
ValueCountFrequency (%)
자 1
50.0%
족 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1998
99.9%
Hangul 2
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 725
36.3%
1 231
 
11.6%
2 172
 
8.6%
3 148
 
7.4%
4 147
 
7.4%
7 137
 
6.9%
5 133
 
6.7%
6 113
 
5.7%
8 100
 
5.0%
9 92
 
4.6%
Hangul
ValueCountFrequency (%)
자 1
50.0%
족 1
50.0%

부번(BUBEON)
Real number (ℝ)

ZEROS 

Distinct150
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean78.096
Minimum0
Maximum2496
Zeros27
Zeros (%)5.4%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:54:35.085925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16
median17
Q350
95-th percentile332.45
Maximum2496
Range2496
Interquartile range (IQR)44

Descriptive statistics

Standard deviation220.36069
Coefficient of variation (CV)2.8216642
Kurtosis61.538425
Mean78.096
Median Absolute Deviation (MAD)15
Skewness6.9632742
Sum39048
Variance48558.832
MonotonicityNot monotonic
2023-12-10T23:54:35.302721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 28
 
5.6%
0 27
 
5.4%
1 26
 
5.2%
9 19
 
3.8%
3 18
 
3.6%
8 16
 
3.2%
7 15
 
3.0%
4 14
 
2.8%
17 13
 
2.6%
10 12
 
2.4%
Other values (140) 312
62.4%
ValueCountFrequency (%)
0 27
5.4%
1 26
5.2%
2 28
5.6%
3 18
3.6%
4 14
2.8%
5 10
 
2.0%
6 9
 
1.8%
7 15
3.0%
8 16
3.2%
9 19
3.8%
ValueCountFrequency (%)
2496 1
0.2%
2373 1
0.2%
1755 1
0.2%
1131 1
0.2%
1090 1
0.2%
1043 1
0.2%
961 1
0.2%
943 1
0.2%
816 1
0.2%
725 1
0.2%

Interactions

2023-12-10T23:54:29.753921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:24.430227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:25.224510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:25.942850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:26.734925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:27.812043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:28.962348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:29.871844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:24.536588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:25.345028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:26.052316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:26.835684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:27.921328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:29.076139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:29.981032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:24.638707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:25.426633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:26.146814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:26.949624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:28.022597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:29.188398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:30.155486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:24.766071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:25.526118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:26.264842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:27.240445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:28.146520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:29.306966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:30.260338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:24.892935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:25.618416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:26.381205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:27.469109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:28.270151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:29.463300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:30.378152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:25.004233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:25.715120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:26.524699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:27.606623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:28.376167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:29.551599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:30.469898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:25.121443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:25.813032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:26.636492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:27.711547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:28.490483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:54:29.654214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:54:35.430598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
토지코드(LAND_CD)년도(YEAR)지가(JIGA)표준지여부(PYO_YN)임시_시군구코드(OSGG)시군구(SIGUNGU_CD)법정동(BJDONG_CD)필지정보(PILGI)부번(BUBEON)
토지코드(LAND_CD)1.0000.2770.0000.0000.0000.1120.0000.0910.060
년도(YEAR)0.2771.0000.0630.0000.0000.0000.0840.0000.000
지가(JIGA)0.0000.0631.0000.2170.1030.0000.3690.0000.000
표준지여부(PYO_YN)0.0000.0000.2171.0000.0000.0140.0000.0000.000
임시_시군구코드(OSGG)0.0000.0000.1030.0001.0000.0840.2000.0000.000
시군구(SIGUNGU_CD)0.1120.0000.0000.0140.0841.0000.1540.0000.000
법정동(BJDONG_CD)0.0000.0840.3690.0000.2000.1541.0000.1570.180
필지정보(PILGI)0.0910.0000.0000.0000.0000.0000.1571.0000.118
부번(BUBEON)0.0600.0000.0000.0000.0000.0000.1800.1181.000
2023-12-10T23:54:35.580781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
표준지여부(PYO_YN)필지정보(PILGI)
표준지여부(PYO_YN)1.0000.000
필지정보(PILGI)0.0001.000
2023-12-10T23:54:35.683776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
토지코드(LAND_CD)년도(YEAR)지가(JIGA)임시_시군구코드(OSGG)시군구(SIGUNGU_CD)법정동(BJDONG_CD)부번(BUBEON)표준지여부(PYO_YN)필지정보(PILGI)
토지코드(LAND_CD)1.000-0.0200.041-0.015-0.0410.0180.0140.0000.036
년도(YEAR)-0.0201.000-0.0190.004-0.014-0.050-0.0270.0000.000
지가(JIGA)0.041-0.0191.000-0.0330.003-0.018-0.0250.2160.000
임시_시군구코드(OSGG)-0.0150.004-0.0331.0000.0050.0590.0790.0000.000
시군구(SIGUNGU_CD)-0.041-0.0140.0030.0051.0000.0560.0190.0380.000
법정동(BJDONG_CD)0.018-0.050-0.0180.0590.0561.0000.0290.0000.082
부번(BUBEON)0.014-0.027-0.0250.0790.0190.0291.0000.0000.075
표준지여부(PYO_YN)0.0000.0000.2160.0000.0380.0000.0001.0000.000
필지정보(PILGI)0.0360.0000.0000.0000.0000.0820.0750.0001.000

Missing values

2023-12-10T23:54:30.626864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:54:30.807608image/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

토지코드(LAND_CD)년도(YEAR)기준월(BASE_MON)지가(JIGA)표준지여부(PYO_YN)임시_시군구코드(OSGG)시군구(SIGUNGU_CD)법정동(BJDONG_CD)필지정보(PILGI)본번(BONBEON)부번(BUBEON)
011170136001001300992019131970000114701156010300109269
111305102001041700362000125990000115451123010100100730
2113201050010315041520001523200001114011740107001030260
311230105001096100102002178200000111101162010100103397
41121510500100010001200512849000011740113801090010289121
511140173001006201362016115460000113201123015900101421
611350102001087300142008148670000115301130510200103050
711560133001106700052005192070001121511350107001038040
811410111001015800822009118310001117101138011000104455
9112601050071009002620041115000000113801168010700103091
토지코드(LAND_CD)년도(YEAR)기준월(BASE_MON)지가(JIGA)표준지여부(PYO_YN)임시_시군구코드(OSGG)시군구(SIGUNGU_CD)법정동(BJDONG_CD)필지정보(PILGI)본번(BONBEON)부번(BUBEON)
49011305101001010300952011113890000112901150012300107897
491115901020010134035320021747400001144011530108001013519
4921168011400106780005200711666000111101159013500100121755
493116201020010503002020041474200001135011710105001007531
494115001030010836000720131563500001162011530105001031219
4951150010500100420001200915027000011290112001080010389317
49611290134001128400182015157420000114401129013200100284
49711260101001019300572009133490001126011410110001007125
498112601010010582000720171715800001123011230126001015414
499115301070010312004720091220700001165011410104001087312