Overview

Dataset statistics

Number of variables20
Number of observations150
Missing cells155
Missing cells (%)5.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory25.5 KiB
Average record size in memory173.9 B

Variable types

Numeric10
Categorical8
Boolean1
Text1

Dataset

DescriptionSample
Author고려대학교 세종산학협력단
URLhttps://www.bigdata-telecom.kr/invoke/SOKBP2603/?goodsCode=KRUBULD0000000000001

Alerts

LNDPCL_ESNTL_NO has constant value ""Constant
LEGALDONG_CODE has constant value ""Constant
LEGALDONG_NM has constant value ""Constant
BILD_STRCTU_NM is highly overall correlated with BILD_STRCTU_CODE and 2 other fieldsHigh correlation
SPCL_LAD_CODE is highly overall correlated with USE_CONFM_DT and 7 other fieldsHigh correlation
BILD_PRPOS_NM is highly overall correlated with BILD_AR and 5 other fieldsHigh correlation
SPCL_LAD_SE is highly overall correlated with USE_CONFM_DT and 7 other fieldsHigh correlation
VIOLT_BILD_AT is highly overall correlated with SPCL_LAD_CODE and 1 other fieldsHigh correlation
BILD_PRPOS_CODE is highly overall correlated with BILD_AR and 5 other fieldsHigh correlation
GIS_BULD_UNITY_NO is highly overall correlated with BILD_AR and 6 other fieldsHigh correlation
BILD_AR is highly overall correlated with GIS_BULD_UNITY_NO and 8 other fieldsHigh correlation
USE_CONFM_DT is highly overall correlated with GIS_BULD_UNITY_NO and 7 other fieldsHigh correlation
TOTAR is highly overall correlated with GIS_BULD_UNITY_NO and 5 other fieldsHigh correlation
PLOT_AR is highly overall correlated with GIS_BULD_UNITY_NO and 8 other fieldsHigh correlation
HG is highly overall correlated with GIS_BULD_UNITY_NO and 6 other fieldsHigh correlation
BDTLDR is highly overall correlated with GIS_BULD_UNITY_NO and 4 other fieldsHigh correlation
FLOR_AREA_RAT is highly overall correlated with GIS_BULD_UNITY_NO and 6 other fieldsHigh correlation
BILD_ID is highly overall correlated with SPCL_LAD_CODE and 3 other fieldsHigh correlation
BILD_STRCTU_CODE is highly overall correlated with SPCL_LAD_CODE and 2 other fieldsHigh correlation
VIOLT_BILD_AT is highly imbalanced (82.8%)Imbalance
SPCL_LAD_CODE is highly imbalanced (70.0%)Imbalance
SPCL_LAD_SE is highly imbalanced (70.0%)Imbalance
USE_CONFM_DT has 46 (30.7%) missing valuesMissing
BILD_ID has 33 (22.0%) missing valuesMissing
VIOLT_BILD_AT has 33 (22.0%) missing valuesMissing
REFRN_SYSTM_CNTC_CODE has 10 (6.7%) missing valuesMissing
BILD_STRCTU_CODE has 33 (22.0%) missing valuesMissing
BILD_AR has 80 (53.3%) zerosZeros
TOTAR has 33 (22.0%) zerosZeros
PLOT_AR has 101 (67.3%) zerosZeros
HG has 100 (66.7%) zerosZeros
BDTLDR has 87 (58.0%) zerosZeros
FLOR_AREA_RAT has 87 (58.0%) zerosZeros

Reproduction

Analysis started2023-12-10 06:41:46.481165
Analysis finished2023-12-10 06:42:04.806914
Duration18.33 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

GIS_BULD_UNITY_NO
Real number (ℝ)

HIGH CORRELATION 

Distinct96
Distinct (%)64.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3744725 × 1027
Minimum1.96897 × 1023
Maximum2.0122 × 1027
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-10T15:42:04.954633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.96897 × 1023
5-th percentile1.9703735 × 1023
Q11.9740875 × 1023
median1.9672 × 1027
Q31.99295 × 1027
95-th percentile2.0072 × 1027
Maximum2.0122 × 1027
Range2.0120031 × 1027
Interquartile range (IQR)1.9927526 × 1027

Descriptive statistics

Standard deviation9.1718416 × 1026
Coefficient of variation (CV)0.66729904
Kurtosis-1.3002706
Mean1.3744725 × 1027
Median Absolute Deviation (MAD)3.1 × 1025
Skewness-0.8460238
Sum2.0617087 × 1029
Variance8.4122679 × 1053
MonotonicityNot monotonic
2023-12-10T15:42:05.200344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.0011999999999997e+27 6
 
4.0%
1.9652e+27 4
 
2.7%
1.9661999999999997e+27 4
 
2.7%
1.9911999999999998e+27 4
 
2.7%
1.9601999999999998e+27 4
 
2.7%
1.9951999999999998e+27 4
 
2.7%
1.9741999999999998e+27 4
 
2.7%
2.0002e+27 3
 
2.0%
2.0021999999999998e+27 3
 
2.0%
1.9671999999999998e+27 3
 
2.0%
Other values (86) 111
74.0%
ValueCountFrequency (%)
1.96897e+23 1
0.7%
1.96903e+23 1
0.7%
1.96925e+23 1
0.7%
1.96972e+23 1
0.7%
1.96974e+23 2
1.3%
1.97004e+23 1
0.7%
1.97036e+23 1
0.7%
1.97039e+23 1
0.7%
1.97042e+23 1
0.7%
1.97114e+23 1
0.7%
ValueCountFrequency (%)
2.0121999999999998e+27 1
 
0.7%
2.0111999999999997e+27 1
 
0.7%
2.0102e+27 2
1.3%
2.0091999999999998e+27 2
1.3%
2.0081999999999997e+27 1
 
0.7%
2.0072e+27 2
1.3%
2.0061999999999999e+27 1
 
0.7%
2.0051999999999998e+27 2
1.3%
2.0041999999999997e+27 1
 
0.7%
2.0032e+27 3
2.0%

LNDPCL_ESNTL_NO
Categorical

CONSTANT 

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
1111010000000000000
150 

Length

Max length19
Median length19
Mean length19
Min length19

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1111010000000000000
2nd row1111010000000000000
3rd row1111010000000000000
4th row1111010000000000000
5th row1111010000000000000

Common Values

ValueCountFrequency (%)
1111010000000000000 150
100.0%

Length

2023-12-10T15:42:05.448328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:42:05.630163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1111010000000000000 150
100.0%

BILD_AR
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct71
Distinct (%)47.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean113.6188
Minimum0
Maximum2749.71
Zeros80
Zeros (%)53.3%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-10T15:42:05.802127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3124.6
95-th percentile432.4865
Maximum2749.71
Range2749.71
Interquartile range (IQR)124.6

Descriptive statistics

Standard deviation303.54282
Coefficient of variation (CV)2.6715897
Kurtosis43.755067
Mean113.6188
Median Absolute Deviation (MAD)0
Skewness6.0168192
Sum17042.82
Variance92138.243
MonotonicityNot monotonic
2023-12-10T15:42:06.040180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 80
53.3%
175.66 1
 
0.7%
124.21 1
 
0.7%
247.93 1
 
0.7%
59.0 1
 
0.7%
158.68 1
 
0.7%
135.47 1
 
0.7%
176.06 1
 
0.7%
130.07 1
 
0.7%
195.97 1
 
0.7%
Other values (61) 61
40.7%
ValueCountFrequency (%)
0.0 80
53.3%
32.99 1
 
0.7%
36.8 1
 
0.7%
43.66 1
 
0.7%
46.13 1
 
0.7%
48.61 1
 
0.7%
52.89 1
 
0.7%
53.98 1
 
0.7%
56.53 1
 
0.7%
57.67 1
 
0.7%
ValueCountFrequency (%)
2749.71 1
0.7%
1622.25 1
0.7%
1395.19 1
0.7%
982.32 1
0.7%
572.4 1
0.7%
548.74 1
0.7%
499.6 1
0.7%
440.78 1
0.7%
422.35 1
0.7%
382.09 1
0.7%

USE_CONFM_DT
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct101
Distinct (%)97.1%
Missing46
Missing (%)30.7%
Infinite0
Infinite (%)0.0%
Mean19822054
Minimum19360611
Maximum20120214
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-10T15:42:06.257923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19360611
5-th percentile19491874
Q119661171
median19866074
Q320000947
95-th percentile20089446
Maximum20120214
Range759603
Interquartile range (IQR)339776

Descriptive statistics

Standard deviation195245.76
Coefficient of variation (CV)0.0098499254
Kurtosis-0.6953689
Mean19822054
Median Absolute Deviation (MAD)154701
Skewness-0.4343216
Sum2.0614937 × 109
Variance3.8120906 × 1010
MonotonicityNot monotonic
2023-12-10T15:42:06.547140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19881111 2
 
1.3%
20071211 2
 
1.3%
19911202 2
 
1.3%
19790207 1
 
0.7%
19641125 1
 
0.7%
19920718 1
 
0.7%
19740826 1
 
0.7%
19901123 1
 
0.7%
19540908 1
 
0.7%
19480411 1
 
0.7%
Other values (91) 91
60.7%
(Missing) 46
30.7%
ValueCountFrequency (%)
19360611 1
0.7%
19360728 1
0.7%
19371119 1
0.7%
19381116 1
0.7%
19480411 1
0.7%
19490330 1
0.7%
19500625 1
0.7%
19540908 1
0.7%
19550510 1
0.7%
19551101 1
0.7%
ValueCountFrequency (%)
20120214 1
0.7%
20111206 1
0.7%
20100430 1
0.7%
20100105 1
0.7%
20091130 1
0.7%
20091023 1
0.7%
20080507 1
0.7%
20071211 2
1.3%
20060224 1
0.7%
20050430 1
0.7%

TOTAR
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct116
Distinct (%)77.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean513.41893
Minimum0
Maximum12957.62
Zeros33
Zeros (%)22.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-10T15:42:06.799702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q146.28
median179.16
Q3375.2325
95-th percentile2099.2445
Maximum12957.62
Range12957.62
Interquartile range (IQR)328.9525

Descriptive statistics

Standard deviation1316.8304
Coefficient of variation (CV)2.5648264
Kurtosis55.554948
Mean513.41893
Median Absolute Deviation (MAD)155.585
Skewness6.6112282
Sum77012.84
Variance1734042.3
MonotonicityNot monotonic
2023-12-10T15:42:07.008827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 33
 
22.0%
46.28 2
 
1.3%
39.67 2
 
1.3%
328.04 1
 
0.7%
199.29 1
 
0.7%
563.64 1
 
0.7%
208.27 1
 
0.7%
254.78 1
 
0.7%
1461.19 1
 
0.7%
182.02 1
 
0.7%
Other values (106) 106
70.7%
ValueCountFrequency (%)
0.0 33
22.0%
23.14 1
 
0.7%
29.75 1
 
0.7%
39.67 2
 
1.3%
46.28 2
 
1.3%
47.27 1
 
0.7%
56.2 1
 
0.7%
59.5 1
 
0.7%
60.51 1
 
0.7%
62.81 1
 
0.7%
ValueCountFrequency (%)
12957.62 1
0.7%
5653.83 1
0.7%
4686.15 1
0.7%
3886.43 1
0.7%
3214.08 1
0.7%
2662.33 1
0.7%
2283.68 1
0.7%
2172.32 1
0.7%
2009.93 1
0.7%
1915.44 1
0.7%

PLOT_AR
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct49
Distinct (%)32.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean298.60687
Minimum0
Maximum9585.5
Zeros101
Zeros (%)67.3%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-10T15:42:07.228023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3133.58
95-th percentile910.77
Maximum9585.5
Range9585.5
Interquartile range (IQR)133.58

Descriptive statistics

Standard deviation1177.7285
Coefficient of variation (CV)3.9440771
Kurtosis37.788404
Mean298.60687
Median Absolute Deviation (MAD)0
Skewness5.9844365
Sum44791.03
Variance1387044.4
MonotonicityNot monotonic
2023-12-10T15:42:07.473096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
0.0 101
67.3%
6036.7 2
 
1.3%
148.4 1
 
0.7%
104.8 1
 
0.7%
689.32 1
 
0.7%
137.7 1
 
0.7%
243.03 1
 
0.7%
165.12 1
 
0.7%
150.9 1
 
0.7%
6659.0 1
 
0.7%
Other values (39) 39
 
26.0%
ValueCountFrequency (%)
0.0 101
67.3%
78.91 1
 
0.7%
80.12 1
 
0.7%
83.2 1
 
0.7%
92.42 1
 
0.7%
100.14 1
 
0.7%
104.8 1
 
0.7%
111.5 1
 
0.7%
113.4 1
 
0.7%
113.7 1
 
0.7%
ValueCountFrequency (%)
9585.5 1
0.7%
6659.0 1
0.7%
6036.7 2
1.3%
1479.65 1
0.7%
1286.0 1
0.7%
1104.1 1
0.7%
1057.2 1
0.7%
731.8 1
0.7%
689.32 1
0.7%
655.2 1
0.7%

HG
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct47
Distinct (%)31.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.7360667
Minimum0
Maximum19.9
Zeros100
Zeros (%)66.7%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-10T15:42:07.698025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q37.9875
95-th percentile15.265
Maximum19.9
Range19.9
Interquartile range (IQR)7.9875

Descriptive statistics

Standard deviation5.7479308
Coefficient of variation (CV)1.5384979
Kurtosis0.081195705
Mean3.7360667
Median Absolute Deviation (MAD)0
Skewness1.1983961
Sum560.41
Variance33.038709
MonotonicityNot monotonic
2023-12-10T15:42:07.971227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
0.0 100
66.7%
7.8 3
 
2.0%
15.4 2
 
1.3%
7.95 2
 
1.3%
12.4 1
 
0.7%
19.9 1
 
0.7%
6.5 1
 
0.7%
7.6 1
 
0.7%
18.7 1
 
0.7%
10.0 1
 
0.7%
Other values (37) 37
 
24.7%
ValueCountFrequency (%)
0.0 100
66.7%
4.0 1
 
0.7%
4.25 1
 
0.7%
5.85 1
 
0.7%
6.5 1
 
0.7%
6.6 1
 
0.7%
6.9 1
 
0.7%
7.6 1
 
0.7%
7.8 3
 
2.0%
7.95 2
 
1.3%
ValueCountFrequency (%)
19.9 1
0.7%
19.7 1
0.7%
19.5 1
0.7%
18.7 1
0.7%
15.97 1
0.7%
15.9 1
0.7%
15.4 2
1.3%
15.1 1
0.7%
15.0 1
0.7%
14.9 1
0.7%

BDTLDR
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct63
Distinct (%)42.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.181867
Minimum0
Maximum67.37
Zeros87
Zeros (%)58.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-10T15:42:08.211848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q336.3625
95-th percentile57.967
Maximum67.37
Range67.37
Interquartile range (IQR)36.3625

Descriptive statistics

Standard deviation21.95678
Coefficient of variation (CV)1.2779042
Kurtosis-1.0414539
Mean17.181867
Median Absolute Deviation (MAD)0
Skewness0.74048675
Sum2577.28
Variance482.1002
MonotonicityNot monotonic
2023-12-10T15:42:08.426031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 87
58.0%
29.86 2
 
1.3%
36.85 1
 
0.7%
33.41 1
 
0.7%
59.35 1
 
0.7%
43.9 1
 
0.7%
63.52 1
 
0.7%
57.01 1
 
0.7%
56.32 1
 
0.7%
29.59 1
 
0.7%
Other values (53) 53
35.3%
ValueCountFrequency (%)
0.0 87
58.0%
5.83 1
 
0.7%
16.2 1
 
0.7%
23.1 1
 
0.7%
23.11 1
 
0.7%
25.65 1
 
0.7%
26.55 1
 
0.7%
26.92 1
 
0.7%
27.7 1
 
0.7%
28.29 1
 
0.7%
ValueCountFrequency (%)
67.37 1
0.7%
63.52 1
0.7%
60.03 1
0.7%
59.93 1
0.7%
59.71 1
0.7%
59.69 1
0.7%
59.35 1
0.7%
58.75 1
0.7%
57.01 1
0.7%
56.45 1
0.7%

FLOR_AREA_RAT
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct64
Distinct (%)42.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.148733
Minimum0
Maximum303.36
Zeros87
Zeros (%)58.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-10T15:42:08.642936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q374.655
95-th percentile188.0645
Maximum303.36
Range303.36
Interquartile range (IQR)74.655

Descriptive statistics

Standard deviation67.644786
Coefficient of variation (CV)1.567712
Kurtosis3.6461402
Mean43.148733
Median Absolute Deviation (MAD)0
Skewness1.9062631
Sum6472.31
Variance4575.817
MonotonicityNot monotonic
2023-12-10T15:42:08.866641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 87
58.0%
30.7 1
 
0.7%
42.92 1
 
0.7%
59.03 1
 
0.7%
294.46 1
 
0.7%
87.81 1
 
0.7%
127.05 1
 
0.7%
181.69 1
 
0.7%
268.86 1
 
0.7%
85.11 1
 
0.7%
Other values (54) 54
36.0%
ValueCountFrequency (%)
0.0 87
58.0%
11.52 1
 
0.7%
17.42 1
 
0.7%
23.11 1
 
0.7%
25.65 1
 
0.7%
28.29 1
 
0.7%
30.7 1
 
0.7%
36.85 1
 
0.7%
41.36 1
 
0.7%
42.82 1
 
0.7%
ValueCountFrequency (%)
303.36 1
0.7%
298.55 1
0.7%
294.46 1
0.7%
268.86 1
0.7%
223.57 1
0.7%
208.99 1
0.7%
197.2 1
0.7%
193.28 1
0.7%
181.69 1
0.7%
165.56 1
0.7%

BILD_ID
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct117
Distinct (%)100.0%
Missing33
Missing (%)22.0%
Infinite0
Infinite (%)0.0%
Mean20539124
Minimum1216
Maximum8.0000001 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-10T15:42:09.091990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1216
5-th percentile1453.4
Q18393
median15906
Q323357
95-th percentile1.0018717 × 108
Maximum8.0000001 × 108
Range7.9999879 × 108
Interquartile range (IQR)14964

Descriptive statistics

Standard deviation1.0630726 × 108
Coefficient of variation (CV)5.1758419
Kurtosis49.421309
Mean20539124
Median Absolute Deviation (MAD)7513
Skewness6.9371516
Sum2.4030775 × 109
Variance1.1301233 × 1016
MonotonicityNot monotonic
2023-12-10T15:42:09.334425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16154 1
 
0.7%
17030 1
 
0.7%
26840 1
 
0.7%
24928 1
 
0.7%
13383 1
 
0.7%
13251 1
 
0.7%
21113 1
 
0.7%
22424 1
 
0.7%
1294 1
 
0.7%
5644 1
 
0.7%
Other values (107) 107
71.3%
(Missing) 33
 
22.0%
ValueCountFrequency (%)
1216 1
0.7%
1291 1
0.7%
1294 1
0.7%
1295 1
0.7%
1300 1
0.7%
1303 1
0.7%
1491 1
0.7%
2380 1
0.7%
2381 1
0.7%
2386 1
0.7%
ValueCountFrequency (%)
800000008 1
0.7%
800000007 1
0.7%
100191587 1
0.7%
100190942 1
0.7%
100189020 1
0.7%
100187963 1
0.7%
100186968 1
0.7%
100186823 1
0.7%
100186671 1
0.7%
100179249 1
0.7%

VIOLT_BILD_AT
Boolean

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)1.7%
Missing33
Missing (%)22.0%
Memory size432.0 B
False
114 
True
 
3
(Missing)
33 
ValueCountFrequency (%)
False 114
76.0%
True 3
 
2.0%
(Missing) 33
 
22.0%
2023-12-10T15:42:09.524213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

REFRN_SYSTM_CNTC_CODE
Text

MISSING 

Distinct138
Distinct (%)98.6%
Missing10
Missing (%)6.7%
Memory size1.3 KiB
2023-12-10T15:42:09.805825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters2380
Distinct characters36
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique136 ?
Unique (%)97.1%

Sample

1st rowB00100000000TSAAJ
2nd rowB00100000000TSJK3
3rd rowB00100000000TSJ3L
4th rowB0010000000BKBU4I
5th rowB00100000000TSYLJ
ValueCountFrequency (%)
b0010000000bkdl29 2
 
1.4%
b0010000000bkdqam 2
 
1.4%
b00100000000tsdlx 1
 
0.7%
b00100000000ts7x4 1
 
0.7%
b0010000000bq7yau 1
 
0.7%
b00100000000tsizh 1
 
0.7%
b00100000000tsl1l 1
 
0.7%
b00100000000ts3z2 1
 
0.7%
b00100000000tsldx 1
 
0.7%
b00100000000tsl2m 1
 
0.7%
Other values (128) 128
91.4%
2023-12-10T15:42:10.368522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1370
57.6%
B 195
 
8.2%
1 155
 
6.5%
T 121
 
5.1%
S 99
 
4.2%
K 58
 
2.4%
D 42
 
1.8%
Q 21
 
0.9%
4 20
 
0.8%
L 19
 
0.8%
Other values (26) 280
 
11.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1620
68.1%
Uppercase Letter 760
31.9%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B 195
25.7%
T 121
15.9%
S 99
13.0%
K 58
 
7.6%
D 42
 
5.5%
Q 21
 
2.8%
L 19
 
2.5%
A 17
 
2.2%
J 17
 
2.2%
F 16
 
2.1%
Other values (16) 155
20.4%
Decimal Number
ValueCountFrequency (%)
0 1370
84.6%
1 155
 
9.6%
4 20
 
1.2%
2 15
 
0.9%
3 14
 
0.9%
7 13
 
0.8%
5 9
 
0.6%
8 9
 
0.6%
6 8
 
0.5%
9 7
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Common 1620
68.1%
Latin 760
31.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
B 195
25.7%
T 121
15.9%
S 99
13.0%
K 58
 
7.6%
D 42
 
5.5%
Q 21
 
2.8%
L 19
 
2.5%
A 17
 
2.2%
J 17
 
2.2%
F 16
 
2.1%
Other values (16) 155
20.4%
Common
ValueCountFrequency (%)
0 1370
84.6%
1 155
 
9.6%
4 20
 
1.2%
2 15
 
0.9%
3 14
 
0.9%
7 13
 
0.8%
5 9
 
0.6%
8 9
 
0.6%
6 8
 
0.5%
9 7
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2380
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1370
57.6%
B 195
 
8.2%
1 155
 
6.5%
T 121
 
5.1%
S 99
 
4.2%
K 58
 
2.4%
D 42
 
1.8%
Q 21
 
0.9%
4 20
 
0.8%
L 19
 
0.8%
Other values (26) 280
 
11.8%

LEGALDONG_CODE
Categorical

CONSTANT 

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
1111010100
150 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1111010100
2nd row1111010100
3rd row1111010100
4th row1111010100
5th row1111010100

Common Values

ValueCountFrequency (%)
1111010100 150
100.0%

Length

2023-12-10T15:42:10.932983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:42:11.083113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1111010100 150
100.0%

LEGALDONG_NM
Categorical

CONSTANT 

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
서울특별시 종로구 청운동
150 

Length

Max length13
Median length13
Mean length13
Min length13

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row서울특별시 종로구 청운동
2nd row서울특별시 종로구 청운동
3rd row서울특별시 종로구 청운동
4th row서울특별시 종로구 청운동
5th row서울특별시 종로구 청운동

Common Values

ValueCountFrequency (%)
서울특별시 종로구 청운동 150
100.0%

Length

2023-12-10T15:42:11.246291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:42:11.406663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서울특별시 150
33.3%
종로구 150
33.3%
청운동 150
33.3%

SPCL_LAD_CODE
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
1
142 
2
 
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 (%)
1 142
94.7%
2 8
 
5.3%

Length

2023-12-10T15:42:11.566378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:42:11.722910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 142
94.7%
2 8
 
5.3%

SPCL_LAD_SE
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
일반
142 
 
8

Length

Max length2
Median length2
Mean length1.9466667
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
일반 142
94.7%
8
 
5.3%

Length

2023-12-10T15:42:11.874444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:42:12.042411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반 142
94.7%
8
 
5.3%

BILD_PRPOS_CODE
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)7.3%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
1000
72 
<NA>
33 
2000
16 
4000
13 
10000
 
5
Other values (6)
11 

Length

Max length5
Median length4
Mean length4.0666667
Min length4

Unique

Unique3 ?
Unique (%)2.0%

Sample

1st row<NA>
2nd row1000
3rd row1000
4th row1000
5th row1000

Common Values

ValueCountFrequency (%)
1000 72
48.0%
<NA> 33
22.0%
2000 16
 
10.7%
4000 13
 
8.7%
10000 5
 
3.3%
3000 4
 
2.7%
11000 2
 
1.3%
13000 2
 
1.3%
Z8000 1
 
0.7%
5000 1
 
0.7%

Length

2023-12-10T15:42:12.203291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1000 72
48.0%
na 33
22.0%
2000 16
 
10.7%
4000 13
 
8.7%
10000 5
 
3.3%
3000 4
 
2.7%
11000 2
 
1.3%
13000 2
 
1.3%
z8000 1
 
0.7%
5000 1
 
0.7%

BILD_PRPOS_NM
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)7.3%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
단독주택
72 
<NA>
33 
공동주택
16 
제2종근린생활시설
13 
교육연구시설
 
5
Other values (6)
11 

Length

Max length9
Median length4
Mean length4.7
Min length4

Unique

Unique3 ?
Unique (%)2.0%

Sample

1st row<NA>
2nd row단독주택
3rd row단독주택
4th row단독주택
5th row단독주택

Common Values

ValueCountFrequency (%)
단독주택 72
48.0%
<NA> 33
22.0%
공동주택 16
 
10.7%
제2종근린생활시설 13
 
8.7%
교육연구시설 5
 
3.3%
제1종근린생활시설 4
 
2.7%
노유자시설 2
 
1.3%
운동시설 2
 
1.3%
교육연구및복지시설 1
 
0.7%
문화및집회시설 1
 
0.7%

Length

2023-12-10T15:42:12.417326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
단독주택 72
48.0%
na 33
22.0%
공동주택 16
 
10.7%
제2종근린생활시설 13
 
8.7%
교육연구시설 5
 
3.3%
제1종근린생활시설 4
 
2.7%
노유자시설 2
 
1.3%
운동시설 2
 
1.3%
교육연구및복지시설 1
 
0.7%
문화및집회시설 1
 
0.7%

BILD_STRCTU_CODE
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct7
Distinct (%)6.0%
Missing33
Missing (%)22.0%
Infinite0
Infinite (%)0.0%
Mean22.74359
Minimum11
Maximum51
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-10T15:42:12.596773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile11
Q111
median21
Q321
95-th percentile51
Maximum51
Range40
Interquartile range (IQR)10

Descriptive statistics

Standard deviation13.837202
Coefficient of variation (CV)0.6084001
Kurtosis0.2809714
Mean22.74359
Median Absolute Deviation (MAD)10
Skewness1.2721767
Sum2661
Variance191.46817
MonotonicityNot monotonic
2023-12-10T15:42:12.785648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
21 51
34.0%
11 41
27.3%
51 20
 
13.3%
31 2
 
1.3%
32 1
 
0.7%
13 1
 
0.7%
12 1
 
0.7%
(Missing) 33
22.0%
ValueCountFrequency (%)
11 41
27.3%
12 1
 
0.7%
13 1
 
0.7%
21 51
34.0%
31 2
 
1.3%
32 1
 
0.7%
51 20
 
13.3%
ValueCountFrequency (%)
51 20
 
13.3%
32 1
 
0.7%
31 2
 
1.3%
21 51
34.0%
13 1
 
0.7%
12 1
 
0.7%
11 41
27.3%

BILD_STRCTU_NM
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
철근콘크리트구조
51 
벽돌구조
41 
<NA>
33 
일반목구조
20 
일반철골구조
 
2
Other values (3)
 
3

Length

Max length8
Median length7
Mean length5.5266667
Min length3

Unique

Unique3 ?
Unique (%)2.0%

Sample

1st row<NA>
2nd row벽돌구조
3rd row벽돌구조
4th row철근콘크리트구조
5th row경량철골구조

Common Values

ValueCountFrequency (%)
철근콘크리트구조 51
34.0%
벽돌구조 41
27.3%
<NA> 33
22.0%
일반목구조 20
 
13.3%
일반철골구조 2
 
1.3%
경량철골구조 1
 
0.7%
석구조 1
 
0.7%
블록구조 1
 
0.7%

Length

2023-12-10T15:42:12.971298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:42:13.126034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
철근콘크리트구조 51
34.0%
벽돌구조 41
27.3%
na 33
22.0%
일반목구조 20
 
13.3%
일반철골구조 2
 
1.3%
경량철골구조 1
 
0.7%
석구조 1
 
0.7%
블록구조 1
 
0.7%

Interactions

2023-12-10T15:42:01.974963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:48.070437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:49.693189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:50.967830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:52.542800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:53.997354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:55.529913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:57.426386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:58.891648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:00.447839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:02.102215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:48.225198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:49.846471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:51.131610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:52.706752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:54.144522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:55.698850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:57.582542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:59.033639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:00.607389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:02.233304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:48.365755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:49.942543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:51.299286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:52.853729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:54.279952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:56.218026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:57.715655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:59.169522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:00.756229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:02.389560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:48.527389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:50.056749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:51.445631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:52.991567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:54.445277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:56.367696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:57.897446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:59.308149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:00.896883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:02.546448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:48.794271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:50.177987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:51.580662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:53.123046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:54.600852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:56.511385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:58.040408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:59.438785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:01.051394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:02.680589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:48.946573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:50.297032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:51.715498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:53.254479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:54.735877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:56.652279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:58.190298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:59.568774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:01.194298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:02.861452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:49.120846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:50.440910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:51.903605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:53.410301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:54.909383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:56.801540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:58.346087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:59.728284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:01.356250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:03.019714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:49.249237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:50.584249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:52.048240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:53.561130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:55.064018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:56.963602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:58.498571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:59.887909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:01.514631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:03.152283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:49.384176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:50.711659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:52.205416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:53.708585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:55.212071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:57.118086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:58.620204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:00.039762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:01.663491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:03.322947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:49.536449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:50.838411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:52.366740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:53.873833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:55.361249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:57.268692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:41:58.748127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:00.232990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:42:01.825222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T15:42:13.292037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
GIS_BULD_UNITY_NOBILD_ARUSE_CONFM_DTTOTARPLOT_ARHGBDTLDRFLOR_AREA_RATBILD_IDVIOLT_BILD_ATSPCL_LAD_CODESPCL_LAD_SEBILD_PRPOS_CODEBILD_PRPOS_NMBILD_STRCTU_CODEBILD_STRCTU_NM
GIS_BULD_UNITY_NO1.0000.093NaN0.1920.1010.3940.3620.4090.0000.0000.4440.4440.0000.0000.6670.419
BILD_AR0.0931.0000.0000.9600.8370.6360.0000.0000.7920.0000.0000.0000.8200.8200.1280.000
USE_CONFM_DTNaN0.0001.0000.0820.2400.5200.3400.3050.7070.000NaNNaN0.5660.5660.8210.694
TOTAR0.1920.9600.0821.0000.8670.5120.0000.0000.6200.0000.0000.0000.6040.6040.2500.000
PLOT_AR0.1010.8370.2400.8671.0000.4950.4590.1650.5540.0000.0000.0000.8220.8220.1970.000
HG0.3940.6360.5200.5120.4951.0000.7640.6750.6740.0000.0000.0000.6100.6100.5970.484
BDTLDR0.3620.0000.3400.0000.4590.7641.0000.7850.1310.0000.0000.0000.6710.6710.4220.383
FLOR_AREA_RAT0.4090.0000.3050.0000.1650.6750.7851.0000.1710.2500.0000.0000.5340.5340.4290.305
BILD_ID0.0000.7920.7070.6200.5540.6740.1310.1711.0000.000NaNNaN0.8550.8550.2030.156
VIOLT_BILD_AT0.0000.0000.0000.0000.0000.0000.0000.2500.0001.000NaNNaN0.0000.0000.0000.000
SPCL_LAD_CODE0.4440.000NaN0.0000.0000.0000.0000.000NaNNaN1.0000.995NaNNaNNaNNaN
SPCL_LAD_SE0.4440.000NaN0.0000.0000.0000.0000.000NaNNaN0.9951.000NaNNaNNaNNaN
BILD_PRPOS_CODE0.0000.8200.5660.6040.8220.6100.6710.5340.8550.000NaNNaN1.0001.0000.4400.000
BILD_PRPOS_NM0.0000.8200.5660.6040.8220.6100.6710.5340.8550.000NaNNaN1.0001.0000.4400.000
BILD_STRCTU_CODE0.6670.1280.8210.2500.1970.5970.4220.4290.2030.000NaNNaN0.4400.4401.0001.000
BILD_STRCTU_NM0.4190.0000.6940.0000.0000.4840.3830.3050.1560.000NaNNaN0.0000.0001.0001.000
2023-12-10T15:42:13.531779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
BILD_STRCTU_NMSPCL_LAD_CODEBILD_PRPOS_NMSPCL_LAD_SEVIOLT_BILD_ATBILD_PRPOS_CODE
BILD_STRCTU_NM1.0001.0000.0001.0000.0000.000
SPCL_LAD_CODE1.0001.0001.0000.9341.0001.000
BILD_PRPOS_NM0.0001.0001.0001.0000.0001.000
SPCL_LAD_SE1.0000.9341.0001.0001.0001.000
VIOLT_BILD_AT0.0001.0000.0001.0001.0000.000
BILD_PRPOS_CODE0.0001.0001.0001.0000.0001.000
2023-12-10T15:42:13.713403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
GIS_BULD_UNITY_NOBILD_ARUSE_CONFM_DTTOTARPLOT_ARHGBDTLDRFLOR_AREA_RATBILD_IDBILD_STRCTU_CODEVIOLT_BILD_ATSPCL_LAD_CODESPCL_LAD_SEBILD_PRPOS_CODEBILD_PRPOS_NMBILD_STRCTU_NM
GIS_BULD_UNITY_NO1.0000.6201.0000.7720.6450.7490.5190.5960.1810.0000.0000.3150.3150.0000.0000.438
BILD_AR0.6201.0000.5300.7200.6270.7160.7120.7280.1290.0570.0000.0000.0000.6040.6040.000
USE_CONFM_DT1.0000.5301.0000.5810.5820.7750.4090.5280.2550.1800.0001.0001.0000.2060.2060.442
TOTAR0.7720.7200.5811.0000.5700.6560.4780.5250.068-0.1290.0000.0000.0000.3650.3650.000
PLOT_AR0.6450.6270.5820.5701.0000.7430.6610.7390.1270.1130.0000.0000.0000.6370.6370.000
HG0.7490.7160.7750.6560.7431.0000.5970.6770.1760.2020.0000.0000.0000.3300.3300.277
BDTLDR0.5190.7120.4090.4780.6610.5971.0000.9790.1390.0590.0000.0000.0000.3820.3820.210
FLOR_AREA_RAT0.5960.7280.5280.5250.7390.6770.9791.0000.1410.0860.1830.0000.0000.1870.1870.155
BILD_ID0.1810.1290.2550.0680.1270.1760.1390.1411.0000.0560.0001.0001.0000.7320.7320.094
BILD_STRCTU_CODE0.0000.0570.180-0.1290.1130.2020.0590.0860.0561.0000.0001.0001.0000.1860.1860.991
VIOLT_BILD_AT0.0000.0000.0000.0000.0000.0000.0000.1830.0000.0001.0001.0001.0000.0000.0000.000
SPCL_LAD_CODE0.3150.0001.0000.0000.0000.0000.0000.0001.0001.0001.0001.0000.9341.0001.0001.000
SPCL_LAD_SE0.3150.0001.0000.0000.0000.0000.0000.0001.0001.0001.0000.9341.0001.0001.0001.000
BILD_PRPOS_CODE0.0000.6040.2060.3650.6370.3300.3820.1870.7320.1860.0001.0001.0001.0001.0000.000
BILD_PRPOS_NM0.0000.6040.2060.3650.6370.3300.3820.1870.7320.1860.0001.0001.0001.0001.0000.000
BILD_STRCTU_NM0.4380.0000.4420.0000.0000.2770.2100.1550.0940.9910.0001.0001.0000.0000.0001.000

Missing values

2023-12-10T15:42:03.931668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T15:42:04.348325image/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.
2023-12-10T15:42:04.640317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

GIS_BULD_UNITY_NOLNDPCL_ESNTL_NOBILD_ARUSE_CONFM_DTTOTARPLOT_ARHGBDTLDRFLOR_AREA_RATBILD_IDVIOLT_BILD_ATREFRN_SYSTM_CNTC_CODELEGALDONG_CODELEGALDONG_NMSPCL_LAD_CODESPCL_LAD_SEBILD_PRPOS_CODEBILD_PRPOS_NMBILD_STRCTU_CODEBILD_STRCTU_NM
0197265000000000006029312.011110100000000000000.0<NA>0.00.00.00.00.0<NA><NA><NA>1111010100서울특별시 종로구 청운동1일반<NA><NA><NA><NA>
11978199999999999845800607744.011110100000000000000.019781206196.130.00.00.00.019396NB00100000000TSAAJ1111010100서울특별시 종로구 청운동1일반1000단독주택11벽돌구조
21982199999999999886535688192.011110100000000000000.019820602260.860.00.00.00.04568NB00100000000TSJK31111010100서울특별시 종로구 청운동1일반1000단독주택11벽돌구조
32008199999999999738996850688.01111010000000000000175.6620080507634.53634.111.727.764.84100179249N<NA>1111010100서울특별시 종로구 청운동1일반1000단독주택21철근콘크리트구조
42003199999999999894236430336.01111010000000000000142.2220030924327.16480.011.6229.6368.1615906NB00100000000TSJ3L1111010100서울특별시 종로구 청운동1일반1000단독주택32경량철골구조
5197390999999999985582080.011110100000000000000.0<NA>81.520.00.00.00.07139NB0010000000BKBU4I1111010100서울특별시 종로구 청운동1일반1000단독주택51일반목구조
6197348000000000015728640.011110100000000000000.0<NA>0.00.00.00.00.0<NA><NA>B00100000000TSYLJ1111010100서울특별시 종로구 청운동2<NA><NA><NA><NA>
71960199999999999799931699200.011110100000000000000.019601010194.970.00.00.00.027753NB0010000000BKDDTS1111010100서울특별시 종로구 청운동1일반1000단독주택11벽돌구조
81975199999999999883968774144.011110100000000000000.01975031399.240.00.00.00.016553NB00100000000TSWUQ1111010100서울특별시 종로구 청운동1일반1000단독주택11벽돌구조
91988199999999999810199355392.011110100000000000000.0198811114686.150.00.00.00.02381NB00100000000TT1VW1111010100서울특별시 종로구 청운동1일반2000공동주택21철근콘크리트구조
GIS_BULD_UNITY_NOLNDPCL_ESNTL_NOBILD_ARUSE_CONFM_DTTOTARPLOT_ARHGBDTLDRFLOR_AREA_RATBILD_IDVIOLT_BILD_ATREFRN_SYSTM_CNTC_CODELEGALDONG_CODELEGALDONG_NMSPCL_LAD_CODESPCL_LAD_SEBILD_PRPOS_CODEBILD_PRPOS_NMBILD_STRCTU_CODEBILD_STRCTU_NM
1401987199999999999731296108544.011110100000000000000.019870917285.390.00.00.00.026486NB00100000000TS7GM1111010100서울특별시 종로구 청운동1일반4000제2종근린생활시설11벽돌구조
141197537000000000001835008.011110100000000000000.0<NA>0.00.00.00.00.0<NA><NA>B00100000000TS2RS1111010100서울특별시 종로구 청운동1일반<NA><NA><NA><NA>
1421984199999999999769464274944.0111101000000000000096.9319840817228.870.00.034.959.333637NB00100000000TSFO31111010100서울특별시 종로구 청운동1일반1000단독주택11벽돌구조
143197039000000000010747904.011110100000000000000.0<NA>0.00.00.00.00.0<NA><NA>B0010000000BKDL291111010100서울특별시 종로구 청운동1일반<NA><NA><NA><NA>
1441967199999999999802498613248.01111010000000000000178.5419671220352.5416.20.042.972.2416877NB0010000000BKDVGX1111010100서울특별시 종로구 청운동1일반1000단독주택11벽돌구조
1451991199999999999772031188992.01111010000000000000352.22199112021561.46036.712.05.8317.42800000007NB00100000000TSFIW1111010100서울특별시 종로구 청운동1일반13000운동시설21철근콘크리트구조
1462000199999999999932404596736.01111010000000000000102.6120000907486.3171.912.759.69223.5714868NB0010000000BQ854V1111010100서울특별시 종로구 청운동1일반2000공동주택21철근콘크리트구조
1471992199999999999850934435840.01111010000000000000422.35199208312172.321057.214.639.95133.3910133NB0010000000BKD7MF1111010100서울특별시 종로구 청운동1일반6000종교시설21철근콘크리트구조
1481979199999999999924703854592.01111010000000000000130.9119791012177.850.00.028.2928.2923299NB00100000000TSWIE1111010100서울특별시 종로구 청운동1일반1000단독주택11벽돌구조
1491997199999999999695694856192.01111010000000000000100.3919970912357.84167.510.259.93153.723719NB00100000000TSX1X1111010100서울특별시 종로구 청운동1일반1000단독주택21철근콘크리트구조