Overview

Dataset statistics

Number of variables18
Number of observations500
Missing cells610
Missing cells (%)6.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory76.3 KiB
Average record size in memory156.3 B

Variable types

Numeric11
Text6
Categorical1

Dataset

Description샘플 데이터
Author빅밸류
URLhttps://bigdata.seoul.go.kr/data/selectSampleData.do?sample_data_seq=325

Alerts

대지구분(DAEJI) has constant value ""Constant
건물이름(BLDNAME) has 246 (49.2%) missing valuesMissing
건물(동)이름(DONGNAME) has 364 (72.8%) missing valuesMissing
전유부_키코드(PKCODE2) has unique valuesUnique
부번(BUNJI2) has 29 (5.8%) zerosZeros
본건_사례수(PRED_BON_CNT) has 187 (37.4%) zerosZeros
주변건_사례수(PRED_SARE_CNT) has 7 (1.4%) zerosZeros

Reproduction

Analysis started2023-12-10 15:06:54.032320
Analysis finished2023-12-10 15:07:22.260154
Duration28.23 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

PNU코드(PNU)
Real number (ℝ)

Distinct499
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1465818 × 1018
Minimum1.1110105 × 1018
Maximum1.1740109 × 1018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:07:22.373068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.1110105 × 1018
5-th percentile1.1200115 × 1018
Q11.1305103 × 1018
median1.1470103 × 1018
Q31.1620102 × 1018
95-th percentile1.1740105 × 1018
Maximum1.1740109 × 1018
Range6.30004 × 1016
Interquartile range (IQR)3.14999 × 1016

Descriptive statistics

Standard deviation1.7714361 × 1016
Coefficient of variation (CV)0.015449714
Kurtosis-1.1505522
Mean1.1465818 × 1018
Median Absolute Deviation (MAD)1.65 × 1016
Skewness-0.063628651
Sum1.4418473 × 1018
Variance3.1379859 × 1032
MonotonicityNot monotonic
2023-12-11T00:07:22.649937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1130510100102580601 2
 
0.4%
1153010900102810007 1
 
0.2%
1138010500100270024 1
 
0.2%
1126010300103300022 1
 
0.2%
1135010300103660012 1
 
0.2%
1130510300102520097 1
 
0.2%
1174010800105250006 1
 
0.2%
1171010800100650010 1
 
0.2%
1150010300100510044 1
 
0.2%
1123010900102860165 1
 
0.2%
Other values (489) 489
97.8%
ValueCountFrequency (%)
1111010500100210001 1
0.2%
1111010900100790001 1
0.2%
1111010900101140002 1
0.2%
1111016800101920002 1
0.2%
1111016900100050045 1
0.2%
1111017000100160004 1
0.2%
1111017100100840001 1
0.2%
1111018400101850008 1
0.2%
1111018600101360001 1
0.2%
1111018600102350014 1
0.2%
ValueCountFrequency (%)
1174010900103970419 1
0.2%
1174010900103630014 1
0.2%
1174010900102890043 1
0.2%
1174010900102280001 1
0.2%
1174010900102190001 1
0.2%
1174010900101670002 1
0.2%
1174010900101370013 1
0.2%
1174010900100360006 1
0.2%
1174010900100340004 1
0.2%
1174010800105550010 1
0.2%

기준년월(KEYMONTH)
Real number (ℝ)

Distinct18
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean202033.39
Minimum201912
Maximum202105
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:07:22.899173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum201912
5-th percentile202001
Q1202005
median202009
Q3202101
95-th percentile202105
Maximum202105
Range193
Interquartile range (IQR)96

Descriptive statistics

Standard deviation43.804617
Coefficient of variation (CV)0.00021681869
Kurtosis-0.96258283
Mean202033.39
Median Absolute Deviation (MAD)4
Skewness0.91934465
Sum1.010167 × 108
Variance1918.8445
MonotonicityNot monotonic
2023-12-11T00:07:23.157742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
202008 41
 
8.2%
202009 38
 
7.6%
202005 37
 
7.4%
202006 35
 
7.0%
202003 34
 
6.8%
202105 33
 
6.6%
202103 32
 
6.4%
202101 29
 
5.8%
202012 28
 
5.6%
202010 27
 
5.4%
Other values (8) 166
33.2%
ValueCountFrequency (%)
201912 1
 
0.2%
202001 27
5.4%
202002 21
4.2%
202003 34
6.8%
202004 26
5.2%
202005 37
7.4%
202006 35
7.0%
202007 22
4.4%
202008 41
8.2%
202009 38
7.6%
ValueCountFrequency (%)
202105 33
6.6%
202104 25
5.0%
202103 32
6.4%
202102 21
4.2%
202101 29
5.8%
202012 28
5.6%
202011 23
4.6%
202010 27
5.4%
202009 38
7.6%
202008 41
8.2%
Distinct495
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-11T00:07:23.576850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length11
Mean length12.776
Min length10

Characters and Unicode

Total characters6388
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

Unique490 ?
Unique (%)98.0%

Sample

1st row11305-13083
2nd row11590-21381
3rd row11500-100181744
4th row11470-100235488
5th row11500-1747
ValueCountFrequency (%)
11710-100447819 2
 
0.4%
11500-100257712 2
 
0.4%
11260-100246330 2
 
0.4%
11590-16810 2
 
0.4%
11710-100464122 2
 
0.4%
11500-100286136 1
 
0.2%
11500-15552 1
 
0.2%
11680-10388 1
 
0.2%
11320-4174 1
 
0.2%
11320-13438 1
 
0.2%
Other values (485) 485
97.0%
2023-12-11T00:07:24.206546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 1740
27.2%
0 1215
19.0%
2 593
 
9.3%
- 500
 
7.8%
3 404
 
6.3%
4 394
 
6.2%
5 378
 
5.9%
6 310
 
4.9%
7 295
 
4.6%
8 282
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5888
92.2%
Dash Punctuation 500
 
7.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1740
29.6%
0 1215
20.6%
2 593
 
10.1%
3 404
 
6.9%
4 394
 
6.7%
5 378
 
6.4%
6 310
 
5.3%
7 295
 
5.0%
8 282
 
4.8%
9 277
 
4.7%
Dash Punctuation
ValueCountFrequency (%)
- 500
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6388
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1740
27.2%
0 1215
19.0%
2 593
 
9.3%
- 500
 
7.8%
3 404
 
6.3%
4 394
 
6.2%
5 378
 
5.9%
6 310
 
4.9%
7 295
 
4.6%
8 282
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6388
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1740
27.2%
0 1215
19.0%
2 593
 
9.3%
- 500
 
7.8%
3 404
 
6.3%
4 394
 
6.2%
5 378
 
5.9%
6 310
 
4.9%
7 295
 
4.6%
8 282
 
4.4%
Distinct500
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-11T00:07:24.608833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length12
Mean length12.954
Min length11

Characters and Unicode

Total characters6477
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

Unique500 ?
Unique (%)100.0%

Sample

1st row11410-70002
2nd row11620-117005
3rd row11290-80418
4th row11215-100233544
5th row11290-117521
ValueCountFrequency (%)
11410-70002 1
 
0.2%
11260-52976 1
 
0.2%
11470-100197540 1
 
0.2%
11440-99912 1
 
0.2%
11305-38367 1
 
0.2%
11530-51787 1
 
0.2%
11710-65091 1
 
0.2%
11350-69416 1
 
0.2%
11530-134960 1
 
0.2%
11440-100223018 1
 
0.2%
Other values (490) 490
98.0%
2023-12-11T00:07:25.212970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 1692
26.1%
0 1225
18.9%
2 536
 
8.3%
- 500
 
7.7%
5 446
 
6.9%
4 415
 
6.4%
3 380
 
5.9%
7 340
 
5.2%
6 335
 
5.2%
8 321
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5977
92.3%
Dash Punctuation 500
 
7.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1692
28.3%
0 1225
20.5%
2 536
 
9.0%
5 446
 
7.5%
4 415
 
6.9%
3 380
 
6.4%
7 340
 
5.7%
6 335
 
5.6%
8 321
 
5.4%
9 287
 
4.8%
Dash Punctuation
ValueCountFrequency (%)
- 500
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6477
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1692
26.1%
0 1225
18.9%
2 536
 
8.3%
- 500
 
7.7%
5 446
 
6.9%
4 415
 
6.4%
3 380
 
5.9%
7 340
 
5.2%
6 335
 
5.2%
8 321
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6477
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1692
26.1%
0 1225
18.9%
2 536
 
8.3%
- 500
 
7.7%
5 446
 
6.9%
4 415
 
6.4%
3 380
 
5.9%
7 340
 
5.2%
6 335
 
5.2%
8 321
 
5.0%
Distinct429
Distinct (%)85.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-11T00:07:25.586086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length24
Mean length22.764
Min length18

Characters and Unicode

Total characters11382
Distinct characters52
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique369 ?
Unique (%)73.8%

Sample

1st row서**별**은** **동**1**2**지**
2nd row서**별**구** ** **3**1**
3rd row서**별**마** **산**1**1**번**
4th row서**별**양** ** **7**2**
5th row서**별**동** **동**0**-**번**
ValueCountFrequency (%)
서**별**강 115
 
11.4%
서**별**송 39
 
3.9%
서**별**은 32
 
3.2%
29
 
2.9%
서**별**구 28
 
2.8%
서**별**광 26
 
2.6%
서**별**마 26
 
2.6%
서**별**중 25
 
2.5%
서**별**동 25
 
2.5%
서**별**관 23
 
2.3%
Other values (271) 639
63.5%
2023-12-11T00:07:26.241745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
* 7588
66.7%
532
 
4.7%
507
 
4.5%
500
 
4.4%
452
 
4.0%
228
 
2.0%
1 194
 
1.7%
115
 
1.0%
3 110
 
1.0%
108
 
0.9%
Other values (42) 1048
 
9.2%

Most occurring categories

ValueCountFrequency (%)
Other Punctuation 7588
66.7%
Other Letter 2347
 
20.6%
Decimal Number 871
 
7.7%
Space Separator 507
 
4.5%
Dash Punctuation 69
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
532
22.7%
500
21.3%
452
19.3%
228
9.7%
115
 
4.9%
108
 
4.6%
68
 
2.9%
39
 
1.7%
35
 
1.5%
28
 
1.2%
Other values (29) 242
10.3%
Decimal Number
ValueCountFrequency (%)
1 194
22.3%
3 110
12.6%
2 105
12.1%
4 96
11.0%
5 65
 
7.5%
9 65
 
7.5%
0 64
 
7.3%
8 62
 
7.1%
7 57
 
6.5%
6 53
 
6.1%
Other Punctuation
ValueCountFrequency (%)
* 7588
100.0%
Space Separator
ValueCountFrequency (%)
507
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 69
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 9035
79.4%
Hangul 2347
 
20.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
532
22.7%
500
21.3%
452
19.3%
228
9.7%
115
 
4.9%
108
 
4.6%
68
 
2.9%
39
 
1.7%
35
 
1.5%
28
 
1.2%
Other values (29) 242
10.3%
Common
ValueCountFrequency (%)
* 7588
84.0%
507
 
5.6%
1 194
 
2.1%
3 110
 
1.2%
2 105
 
1.2%
4 96
 
1.1%
- 69
 
0.8%
5 65
 
0.7%
9 65
 
0.7%
0 64
 
0.7%
Other values (3) 172
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9035
79.4%
Hangul 2347
 
20.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 7588
84.0%
507
 
5.6%
1 194
 
2.1%
3 110
 
1.2%
2 105
 
1.2%
4 96
 
1.1%
- 69
 
0.8%
5 65
 
0.7%
9 65
 
0.7%
0 64
 
0.7%
Other values (3) 172
 
1.9%
Hangul
ValueCountFrequency (%)
532
22.7%
500
21.3%
452
19.3%
228
9.7%
115
 
4.9%
108
 
4.6%
68
 
2.9%
39
 
1.7%
35
 
1.5%
28
 
1.2%
Other values (29) 242
10.3%

자치구코드(SREG)
Real number (ℝ)

Distinct25
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11462.2
Minimum11110
Maximum11740
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:07:26.479022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11110
5-th percentile11170
Q111305
median11470
Q311620
95-th percentile11710
Maximum11740
Range630
Interquartile range (IQR)315

Descriptive statistics

Standard deviation172.2725
Coefficient of variation (CV)0.015029619
Kurtosis-1.0393627
Mean11462.2
Median Absolute Deviation (MAD)150
Skewness-0.081228359
Sum5731100
Variance29677.816
MonotonicityNot monotonic
2023-12-11T00:07:26.703797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
11380 53
 
10.6%
11500 44
 
8.8%
11710 37
 
7.4%
11215 27
 
5.4%
11440 25
 
5.0%
11590 25
 
5.0%
11305 24
 
4.8%
11740 24
 
4.8%
11680 24
 
4.8%
11620 22
 
4.4%
Other values (15) 195
39.0%
ValueCountFrequency (%)
11110 6
 
1.2%
11140 5
 
1.0%
11170 16
3.2%
11200 3
 
0.6%
11215 27
5.4%
11230 12
2.4%
11260 17
3.4%
11290 16
3.2%
11305 24
4.8%
11320 8
 
1.6%
ValueCountFrequency (%)
11740 24
4.8%
11710 37
7.4%
11680 24
4.8%
11650 19
3.8%
11620 22
4.4%
11590 25
5.0%
11560 9
 
1.8%
11545 15
 
3.0%
11530 20
4.0%
11500 44
8.8%

법정동코드(SEUB)
Real number (ℝ)

Distinct40
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10873
Minimum10100
Maximum18400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:07:26.957512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10100
5-th percentile10100
Q110200
median10500
Q310825
95-th percentile13200
Maximum18400
Range8300
Interquartile range (IQR)625

Descriptive statistics

Standard deviation1259.1194
Coefficient of variation (CV)0.11580239
Kurtosis15.99951
Mean10873
Median Absolute Deviation (MAD)300
Skewness3.6760312
Sum5436500
Variance1585381.8
MonotonicityNot monotonic
2023-12-11T00:07:27.200917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
10300 83
16.6%
10200 69
13.8%
10100 58
11.6%
10500 43
8.6%
10700 36
7.2%
10600 35
7.0%
10800 29
 
5.8%
10900 27
 
5.4%
10400 22
 
4.4%
11800 11
 
2.2%
Other values (30) 87
17.4%
ValueCountFrequency (%)
10100 58
11.6%
10200 69
13.8%
10300 83
16.6%
10400 22
 
4.4%
10500 43
8.6%
10600 35
7.0%
10700 36
7.2%
10800 29
 
5.8%
10900 27
 
5.4%
11000 2
 
0.4%
ValueCountFrequency (%)
18400 1
 
0.2%
18300 2
0.4%
18100 1
 
0.2%
17500 1
 
0.2%
17400 3
0.6%
17100 1
 
0.2%
16200 2
0.4%
13900 3
0.6%
13800 2
0.4%
13600 1
 
0.2%

대지구분(DAEJI)
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

2023-12-11T00:07:27.454745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T00:07:27.624517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 500
100.0%

본번(BUNJI1)
Real number (ℝ)

Distinct366
Distinct (%)73.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean373.9
Minimum1
Maximum4427
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:07:27.860322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile13
Q1123
median292
Q3521.75
95-th percentile976.5
Maximum4427
Range4426
Interquartile range (IQR)398.75

Descriptive statistics

Standard deviation368.19142
Coefficient of variation (CV)0.98473234
Kurtosis29.879721
Mean373.9
Median Absolute Deviation (MAD)189
Skewness3.5880527
Sum186950
Variance135564.92
MonotonicityNot monotonic
2023-12-11T00:07:28.124785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 6
 
1.2%
494 4
 
0.8%
300 4
 
0.8%
164 4
 
0.8%
1 4
 
0.8%
13 4
 
0.8%
227 4
 
0.8%
4 3
 
0.6%
270 3
 
0.6%
221 3
 
0.6%
Other values (356) 461
92.2%
ValueCountFrequency (%)
1 4
0.8%
2 2
 
0.4%
3 1
 
0.2%
4 3
0.6%
5 6
1.2%
6 1
 
0.2%
8 2
 
0.4%
9 1
 
0.2%
10 3
0.6%
12 1
 
0.2%
ValueCountFrequency (%)
4427 1
0.2%
1709 1
0.2%
1664 1
0.2%
1608 1
0.2%
1592 1
0.2%
1518 1
0.2%
1507 1
0.2%
1502 1
0.2%
1496 1
0.2%
1480 1
0.2%

부번(BUNJI2)
Real number (ℝ)

ZEROS 

Distinct145
Distinct (%)29.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59.15
Minimum0
Maximum1990
Zeros29
Zeros (%)5.8%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:07:28.405961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16
median18
Q347
95-th percentile230.45
Maximum1990
Range1990
Interquartile range (IQR)41

Descriptive statistics

Standard deviation160.67858
Coefficient of variation (CV)2.7164595
Kurtosis90.687323
Mean59.15
Median Absolute Deviation (MAD)15
Skewness8.4940121
Sum29575
Variance25817.607
MonotonicityNot monotonic
2023-12-11T00:07:28.719527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 29
 
5.8%
1 21
 
4.2%
3 18
 
3.6%
4 17
 
3.4%
16 16
 
3.2%
2 16
 
3.2%
9 15
 
3.0%
6 14
 
2.8%
7 14
 
2.8%
12 12
 
2.4%
Other values (135) 328
65.6%
ValueCountFrequency (%)
0 29
5.8%
1 21
4.2%
2 16
3.2%
3 18
3.6%
4 17
3.4%
5 11
 
2.2%
6 14
2.8%
7 14
2.8%
8 10
 
2.0%
9 15
3.0%
ValueCountFrequency (%)
1990 1
0.2%
1981 1
0.2%
1339 1
0.2%
804 1
0.2%
557 1
0.2%
476 1
0.2%
468 1
0.2%
457 1
0.2%
419 1
0.2%
412 1
0.2%

건물이름(BLDNAME)
Text

MISSING 

Distinct245
Distinct (%)96.5%
Missing246
Missing (%)49.2%
Memory size4.0 KiB
2023-12-11T00:07:29.446995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length12
Mean length5.2322835
Min length1

Characters and Unicode

Total characters1329
Distinct characters208
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique238 ?
Unique (%)93.7%

Sample

1st row아* *2*3
2nd row아*떼*
3rd row대*스*이*빌
4th row창* *원*힐*리*5*
5th row복*연*주*
ValueCountFrequency (%)
대*아*빌 4
 
1.5%
신*빌 3
 
1.1%
3
 
1.1%
드*빌 2
 
0.7%
라*빌 2
 
0.7%
성*하*츠 2
 
0.7%
반*빌 2
 
0.7%
스*이 2
 
0.7%
2
 
0.7%
유*빌*지 2
 
0.7%
Other values (241) 247
91.1%
2023-12-11T00:07:30.371527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
* 594
44.7%
103
 
7.8%
42
 
3.2%
28
 
2.1%
22
 
1.7%
21
 
1.6%
17
 
1.3%
15
 
1.1%
14
 
1.1%
12
 
0.9%
Other values (198) 461
34.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 657
49.4%
Other Punctuation 596
44.8%
Decimal Number 27
 
2.0%
Uppercase Letter 20
 
1.5%
Space Separator 17
 
1.3%
Lowercase Letter 9
 
0.7%
Open Punctuation 2
 
0.2%
Close Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
103
 
15.7%
42
 
6.4%
28
 
4.3%
22
 
3.3%
21
 
3.2%
15
 
2.3%
14
 
2.1%
12
 
1.8%
11
 
1.7%
10
 
1.5%
Other values (163) 379
57.7%
Uppercase Letter
ValueCountFrequency (%)
S 5
25.0%
H 2
 
10.0%
U 2
 
10.0%
R 2
 
10.0%
B 1
 
5.0%
O 1
 
5.0%
T 1
 
5.0%
C 1
 
5.0%
M 1
 
5.0%
L 1
 
5.0%
Other values (3) 3
15.0%
Decimal Number
ValueCountFrequency (%)
1 6
22.2%
5 5
18.5%
2 5
18.5%
3 4
14.8%
4 2
 
7.4%
0 2
 
7.4%
6 1
 
3.7%
8 1
 
3.7%
9 1
 
3.7%
Lowercase Letter
ValueCountFrequency (%)
e 3
33.3%
l 1
 
11.1%
v 1
 
11.1%
a 1
 
11.1%
d 1
 
11.1%
n 1
 
11.1%
m 1
 
11.1%
Other Punctuation
ValueCountFrequency (%)
* 594
99.7%
, 1
 
0.2%
. 1
 
0.2%
Space Separator
ValueCountFrequency (%)
17
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 657
49.4%
Common 643
48.4%
Latin 29
 
2.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
103
 
15.7%
42
 
6.4%
28
 
4.3%
22
 
3.3%
21
 
3.2%
15
 
2.3%
14
 
2.1%
12
 
1.8%
11
 
1.7%
10
 
1.5%
Other values (163) 379
57.7%
Latin
ValueCountFrequency (%)
S 5
17.2%
e 3
 
10.3%
H 2
 
6.9%
U 2
 
6.9%
R 2
 
6.9%
l 1
 
3.4%
B 1
 
3.4%
v 1
 
3.4%
a 1
 
3.4%
O 1
 
3.4%
Other values (10) 10
34.5%
Common
ValueCountFrequency (%)
* 594
92.4%
17
 
2.6%
1 6
 
0.9%
5 5
 
0.8%
2 5
 
0.8%
3 4
 
0.6%
4 2
 
0.3%
0 2
 
0.3%
( 2
 
0.3%
6 1
 
0.2%
Other values (5) 5
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 672
50.6%
Hangul 657
49.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 594
88.4%
17
 
2.5%
1 6
 
0.9%
5 5
 
0.7%
S 5
 
0.7%
2 5
 
0.7%
3 4
 
0.6%
e 3
 
0.4%
H 2
 
0.3%
U 2
 
0.3%
Other values (25) 29
 
4.3%
Hangul
ValueCountFrequency (%)
103
 
15.7%
42
 
6.4%
28
 
4.3%
22
 
3.3%
21
 
3.2%
15
 
2.3%
14
 
2.1%
12
 
1.8%
11
 
1.7%
10
 
1.5%
Other values (163) 379
57.7%
Distinct74
Distinct (%)54.4%
Missing364
Missing (%)72.8%
Memory size4.0 KiB
2023-12-11T00:07:30.888401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length8
Mean length3.4926471
Min length1

Characters and Unicode

Total characters475
Distinct characters124
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique63 ?
Unique (%)46.3%

Sample

1st row102동
2nd row나동
3rd row용민스위트빌
4th row에이동
5th row토미하우스
ValueCountFrequency (%)
나동 11
 
8.1%
a동 10
 
7.4%
101동 10
 
7.4%
b동 9
 
6.6%
102동 8
 
5.9%
가동 8
 
5.9%
주건축물제1동 4
 
2.9%
2동 4
 
2.9%
104동 4
 
2.9%
1동 3
 
2.2%
Other values (64) 65
47.8%
2023-12-11T00:07:31.613416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
92
19.4%
1 49
 
10.3%
0 27
 
5.7%
25
 
5.3%
2 14
 
2.9%
13
 
2.7%
A 13
 
2.7%
B 12
 
2.5%
10
 
2.1%
9
 
1.9%
Other values (114) 211
44.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 340
71.6%
Decimal Number 105
 
22.1%
Uppercase Letter 27
 
5.7%
Other Punctuation 1
 
0.2%
Close Punctuation 1
 
0.2%
Open Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
92
27.1%
25
 
7.4%
13
 
3.8%
10
 
2.9%
9
 
2.6%
8
 
2.4%
7
 
2.1%
6
 
1.8%
6
 
1.8%
6
 
1.8%
Other values (99) 158
46.5%
Decimal Number
ValueCountFrequency (%)
1 49
46.7%
0 27
25.7%
2 14
 
13.3%
4 5
 
4.8%
3 4
 
3.8%
8 2
 
1.9%
5 2
 
1.9%
6 1
 
1.0%
7 1
 
1.0%
Uppercase Letter
ValueCountFrequency (%)
A 13
48.1%
B 12
44.4%
C 2
 
7.4%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 340
71.6%
Common 108
 
22.7%
Latin 27
 
5.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
92
27.1%
25
 
7.4%
13
 
3.8%
10
 
2.9%
9
 
2.6%
8
 
2.4%
7
 
2.1%
6
 
1.8%
6
 
1.8%
6
 
1.8%
Other values (99) 158
46.5%
Common
ValueCountFrequency (%)
1 49
45.4%
0 27
25.0%
2 14
 
13.0%
4 5
 
4.6%
3 4
 
3.7%
8 2
 
1.9%
5 2
 
1.9%
6 1
 
0.9%
7 1
 
0.9%
. 1
 
0.9%
Other values (2) 2
 
1.9%
Latin
ValueCountFrequency (%)
A 13
48.1%
B 12
44.4%
C 2
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 340
71.6%
ASCII 135
 
28.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
92
27.1%
25
 
7.4%
13
 
3.8%
10
 
2.9%
9
 
2.6%
8
 
2.4%
7
 
2.1%
6
 
1.8%
6
 
1.8%
6
 
1.8%
Other values (99) 158
46.5%
ASCII
ValueCountFrequency (%)
1 49
36.3%
0 27
20.0%
2 14
 
10.4%
A 13
 
9.6%
B 12
 
8.9%
4 5
 
3.7%
3 4
 
3.0%
8 2
 
1.5%
5 2
 
1.5%
C 2
 
1.5%
Other values (5) 5
 
3.7%
Distinct106
Distinct (%)21.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-11T00:07:32.145945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length3
Mean length3.558
Min length2

Characters and Unicode

Total characters1779
Distinct characters22
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique57 ?
Unique (%)11.4%

Sample

1st row301
2nd row301
3rd row401호
4th row203호
5th row4층401호
ValueCountFrequency (%)
302 38
 
7.6%
301 34
 
6.8%
201 30
 
6.0%
401 29
 
5.8%
202 25
 
5.0%
501 25
 
5.0%
402 21
 
4.2%
301호 21
 
4.2%
502 14
 
2.8%
201호 12
 
2.4%
Other values (97) 253
50.4%
2023-12-11T00:07:33.027781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 482
27.1%
2 311
17.5%
1 297
16.7%
3 215
12.1%
183
 
10.3%
4 114
 
6.4%
5 74
 
4.2%
56
 
3.1%
6 14
 
0.8%
7 7
 
0.4%
Other values (12) 26
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1520
85.4%
Other Letter 252
 
14.2%
Uppercase Letter 3
 
0.2%
Space Separator 2
 
0.1%
Lowercase Letter 1
 
0.1%
Dash Punctuation 1
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 482
31.7%
2 311
20.5%
1 297
19.5%
3 215
14.1%
4 114
 
7.5%
5 74
 
4.9%
6 14
 
0.9%
7 7
 
0.5%
8 4
 
0.3%
9 2
 
0.1%
Other Letter
ValueCountFrequency (%)
183
72.6%
56
 
22.2%
3
 
1.2%
3
 
1.2%
3
 
1.2%
2
 
0.8%
2
 
0.8%
Uppercase Letter
ValueCountFrequency (%)
A 2
66.7%
B 1
33.3%
Space Separator
ValueCountFrequency (%)
2
100.0%
Lowercase Letter
ValueCountFrequency (%)
b 1
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1523
85.6%
Hangul 252
 
14.2%
Latin 4
 
0.2%

Most frequent character per script

Common
ValueCountFrequency (%)
0 482
31.6%
2 311
20.4%
1 297
19.5%
3 215
14.1%
4 114
 
7.5%
5 74
 
4.9%
6 14
 
0.9%
7 7
 
0.5%
8 4
 
0.3%
9 2
 
0.1%
Other values (2) 3
 
0.2%
Hangul
ValueCountFrequency (%)
183
72.6%
56
 
22.2%
3
 
1.2%
3
 
1.2%
3
 
1.2%
2
 
0.8%
2
 
0.8%
Latin
ValueCountFrequency (%)
A 2
50.0%
b 1
25.0%
B 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1527
85.8%
Hangul 252
 
14.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 482
31.6%
2 311
20.4%
1 297
19.4%
3 215
14.1%
4 114
 
7.5%
5 74
 
4.8%
6 14
 
0.9%
7 7
 
0.5%
8 4
 
0.3%
9 2
 
0.1%
Other values (5) 7
 
0.5%
Hangul
ValueCountFrequency (%)
183
72.6%
56
 
22.2%
3
 
1.2%
3
 
1.2%
3
 
1.2%
2
 
0.8%
2
 
0.8%

건축연도(BLDCONYEAR)
Real number (ℝ)

Distinct42
Distinct (%)8.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2005.018
Minimum1976
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:07:33.366196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1976
5-th percentile1987
Q11997
median2003.5
Q32014
95-th percentile2019
Maximum2021
Range45
Interquartile range (IQR)17

Descriptive statistics

Standard deviation10.281103
Coefficient of variation (CV)0.0051276861
Kurtosis-0.73790097
Mean2005.018
Median Absolute Deviation (MAD)9.5
Skewness-0.38780391
Sum1002509
Variance105.70108
MonotonicityNot monotonic
2023-12-11T00:07:33.620860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
2002 53
 
10.6%
2016 32
 
6.4%
2003 30
 
6.0%
2013 27
 
5.4%
2001 26
 
5.2%
2012 23
 
4.6%
2017 20
 
4.0%
2014 19
 
3.8%
2018 19
 
3.8%
2015 18
 
3.6%
Other values (32) 233
46.6%
ValueCountFrequency (%)
1976 1
 
0.2%
1977 1
 
0.2%
1979 2
 
0.4%
1980 3
 
0.6%
1981 1
 
0.2%
1983 2
 
0.4%
1985 5
1.0%
1986 9
1.8%
1987 3
 
0.6%
1988 3
 
0.6%
ValueCountFrequency (%)
2021 5
 
1.0%
2020 13
2.6%
2019 13
2.6%
2018 19
3.8%
2017 20
4.0%
2016 32
6.4%
2015 18
3.6%
2014 19
3.8%
2013 27
5.4%
2012 23
4.6%

본건_사례수(PRED_BON_CNT)
Real number (ℝ)

ZEROS 

Distinct32
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.066
Minimum0
Maximum274
Zeros187
Zeros (%)37.4%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:07:33.878026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q34
95-th percentile17
Maximum274
Range274
Interquartile range (IQR)4

Descriptive statistics

Standard deviation13.541358
Coefficient of variation (CV)3.3303881
Kurtosis317.80457
Mean4.066
Median Absolute Deviation (MAD)1
Skewness16.194735
Sum2033
Variance183.36838
MonotonicityNot monotonic
2023-12-11T00:07:34.092948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
0 187
37.4%
1 80
16.0%
2 62
 
12.4%
3 29
 
5.8%
4 29
 
5.8%
5 21
 
4.2%
7 13
 
2.6%
6 11
 
2.2%
8 9
 
1.8%
12 6
 
1.2%
Other values (22) 53
 
10.6%
ValueCountFrequency (%)
0 187
37.4%
1 80
16.0%
2 62
 
12.4%
3 29
 
5.8%
4 29
 
5.8%
5 21
 
4.2%
6 11
 
2.2%
7 13
 
2.6%
8 9
 
1.8%
9 3
 
0.6%
ValueCountFrequency (%)
274 1
0.2%
42 1
0.2%
41 1
0.2%
40 1
0.2%
33 1
0.2%
30 2
0.4%
28 1
0.2%
26 2
0.4%
25 1
0.2%
22 1
0.2%

주변건_사례수(PRED_SARE_CNT)
Real number (ℝ)

ZEROS 

Distinct109
Distinct (%)21.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.434
Minimum0
Maximum285
Zeros7
Zeros (%)1.4%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:07:34.355412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q19
median18
Q336
95-th percentile98.05
Maximum285
Range285
Interquartile range (IQR)27

Descriptive statistics

Standard deviation38.479852
Coefficient of variation (CV)1.2643705
Kurtosis15.447144
Mean30.434
Median Absolute Deviation (MAD)11
Skewness3.4606033
Sum15217
Variance1480.699
MonotonicityNot monotonic
2023-12-11T00:07:34.633307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7 31
 
6.2%
5 21
 
4.2%
13 18
 
3.6%
4 17
 
3.4%
11 16
 
3.2%
17 15
 
3.0%
9 15
 
3.0%
10 15
 
3.0%
18 14
 
2.8%
12 14
 
2.8%
Other values (99) 324
64.8%
ValueCountFrequency (%)
0 7
 
1.4%
1 6
 
1.2%
2 6
 
1.2%
3 7
 
1.4%
4 17
3.4%
5 21
4.2%
6 13
2.6%
7 31
6.2%
8 13
2.6%
9 15
3.0%
ValueCountFrequency (%)
285 1
0.2%
263 1
0.2%
262 1
0.2%
257 1
0.2%
249 1
0.2%
222 1
0.2%
211 1
0.2%
186 1
0.2%
162 1
0.2%
158 1
0.2%
Distinct495
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean475.95602
Minimum170.38
Maximum1652.78
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:07:34.902025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum170.38
5-th percentile243.535
Q1305.9825
median426.845
Q3589.21
95-th percentile857.1985
Maximum1652.78
Range1482.4
Interquartile range (IQR)283.2275

Descriptive statistics

Standard deviation211.85004
Coefficient of variation (CV)0.44510423
Kurtosis2.5784023
Mean475.95602
Median Absolute Deviation (MAD)132.35
Skewness1.3210175
Sum237978.01
Variance44880.439
MonotonicityNot monotonic
2023-12-11T00:07:35.579566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
316.2 2
 
0.4%
310.3 2
 
0.4%
236.23 2
 
0.4%
280.83 2
 
0.4%
255.27 2
 
0.4%
785.56 1
 
0.2%
779.46 1
 
0.2%
462.99 1
 
0.2%
283.59 1
 
0.2%
528.42 1
 
0.2%
Other values (485) 485
97.0%
ValueCountFrequency (%)
170.38 1
0.2%
197.4 1
0.2%
200.49 1
0.2%
202.01 1
0.2%
202.95 1
0.2%
208.49 1
0.2%
208.56 1
0.2%
214.71 1
0.2%
218.45 1
0.2%
219.18 1
0.2%
ValueCountFrequency (%)
1652.78 1
0.2%
1303.13 1
0.2%
1298.9 1
0.2%
1225.63 1
0.2%
1153.32 1
0.2%
1147.8 1
0.2%
1099.48 1
0.2%
1095.24 1
0.2%
1088.74 1
0.2%
1071.4 1
0.2%
Distinct485
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20706.654
Minimum4814
Maximum144245
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:07:35.835310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4814
5-th percentile10030.35
Q115329
median19315.5
Q324036
95-th percentile32481.25
Maximum144245
Range139431
Interquartile range (IQR)8707

Descriptive statistics

Standard deviation10382.594
Coefficient of variation (CV)0.50141341
Kurtosis53.418398
Mean20706.654
Median Absolute Deviation (MAD)4430.5
Skewness5.4799136
Sum10353327
Variance1.0779826 × 108
MonotonicityNot monotonic
2023-12-11T00:07:36.069797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18791 3
 
0.6%
13475 2
 
0.4%
20704 2
 
0.4%
11516 2
 
0.4%
25483 2
 
0.4%
18766 2
 
0.4%
16087 2
 
0.4%
23334 2
 
0.4%
15475 2
 
0.4%
14912 2
 
0.4%
Other values (475) 479
95.8%
ValueCountFrequency (%)
4814 1
0.2%
5513 1
0.2%
6829 1
0.2%
7035 1
0.2%
7157 1
0.2%
7779 1
0.2%
7834 1
0.2%
8451 1
0.2%
8480 1
0.2%
8528 1
0.2%
ValueCountFrequency (%)
144245 1
0.2%
109698 1
0.2%
85866 1
0.2%
69294 1
0.2%
47849 1
0.2%
45453 1
0.2%
42380 1
0.2%
42155 1
0.2%
41957 1
0.2%
40195 1
0.2%

Interactions

2023-12-11T00:07:18.984447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:06:55.377834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:06:58.466026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:07:01.241992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:07:03.418946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:07:05.469132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:07:07.156316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:07:10.053295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:07:12.181017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:07:14.547718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:07:16.838924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:07:19.135526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:06:55.586287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:06:58.768434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:07:01.508191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:07:03.614619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:07:05.661169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:07:07.332088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:07:10.329525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:07:12.384113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:07:14.782810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:07:17.034323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:07:19.316873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:06:55.795857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:06:59.067036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:07:01.713253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:07:03.820455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:07:05.824723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:07:07.530176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:07:10.497594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:07:12.563496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:07:15.020722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:07:17.261712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:07:19.495387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:06:56.006356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:06:59.343545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:07:01.920419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:07:03.973117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:07:05.982828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:07:07.760384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:07:10.678086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:07:12.840157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:07:15.220174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:07:17.442168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:07:19.645320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:06:56.208622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:06:59.549574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:07:02.094198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:07:04.117089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:07:06.114870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:07:07.989955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:07:10.817866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:07:13.061673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:07:15.433362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:07:17.579625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:07:19.810476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:06:56.440778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:06:59.764845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:07:02.263901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:07:04.297385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:07:06.238402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:07:08.279669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:07:10.988964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:07:13.254134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:07:15.750870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:07:17.761533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:07:19.962823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:06:57.147424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:06:59.983452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:07:02.471783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:07:04.496943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:07:06.422064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:07:08.554541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:07:11.191849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:07:13.469080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:07:15.969455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:07:17.930416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:07:20.114868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:06:57.369271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:07:00.253815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:07:02.669257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:07:04.665652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:07:06.610154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:07:08.793932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:07:11.364826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:07:13.673939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:07:16.134923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:07:18.094653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:07:20.283842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:06:57.603878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:07:00.468781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:07:02.856544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:07:04.843173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:07:06.763595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:07:09.026442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:07:11.553787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:07:13.883193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:07:16.286216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:07:18.313872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:07:20.550919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:06:57.864853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:07:00.739547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:07:03.037195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:07:05.031407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:07:06.884401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:07:09.208328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:07:11.750694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:07:14.065976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:07:16.442469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:07:18.574324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:07:20.786880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:06:58.120971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:07:00.996313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:07:03.231633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:07:05.265735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:07:07.010515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:07:09.859102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:07:11.933475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:07:14.251511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:07:16.641740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:07:18.775554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T00:07:36.239614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
PNU코드(PNU)기준년월(KEYMONTH)자치구코드(SREG)법정동코드(SEUB)본번(BUNJI1)부번(BUNJI2)건물(동)이름(DONGNAME)건축연도(BLDCONYEAR)본건_사례수(PRED_BON_CNT)주변건_사례수(PRED_SARE_CNT)면적대비_전세시세(DEPO_AREA_PRED)예측_전세시세(DEPO_PRED)
PNU코드(PNU)1.0000.1510.0000.0000.0000.0000.3650.0000.1100.0000.1310.000
기준년월(KEYMONTH)0.1511.0000.0000.0800.0540.0000.0000.1810.0000.0000.0000.095
자치구코드(SREG)0.0000.0001.0000.0510.2020.0000.4960.1300.0000.0000.0000.000
법정동코드(SEUB)0.0000.0800.0511.0000.0000.0000.9420.0000.2920.3210.0000.000
본번(BUNJI1)0.0000.0540.2020.0001.0000.3430.0000.2010.0000.2790.1580.000
부번(BUNJI2)0.0000.0000.0000.0000.3431.0000.0000.0360.0000.0000.1850.000
건물(동)이름(DONGNAME)0.3650.0000.4960.9420.0000.0001.0000.0001.0000.4440.0000.873
건축연도(BLDCONYEAR)0.0000.1810.1300.0000.2010.0360.0001.0000.0000.0860.0000.215
본건_사례수(PRED_BON_CNT)0.1100.0000.0000.2920.0000.0001.0000.0001.0000.0000.0000.357
주변건_사례수(PRED_SARE_CNT)0.0000.0000.0000.3210.2790.0000.4440.0860.0001.0000.0000.000
면적대비_전세시세(DEPO_AREA_PRED)0.1310.0000.0000.0000.1580.1850.0000.0000.0000.0001.0000.028
예측_전세시세(DEPO_PRED)0.0000.0950.0000.0000.0000.0000.8730.2150.3570.0000.0281.000
2023-12-11T00:07:36.523700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
PNU코드(PNU)기준년월(KEYMONTH)자치구코드(SREG)법정동코드(SEUB)본번(BUNJI1)부번(BUNJI2)건축연도(BLDCONYEAR)본건_사례수(PRED_BON_CNT)주변건_사례수(PRED_SARE_CNT)면적대비_전세시세(DEPO_AREA_PRED)예측_전세시세(DEPO_PRED)
PNU코드(PNU)1.0000.053-0.0220.0620.004-0.0120.0640.0390.0460.056-0.062
기준년월(KEYMONTH)0.0531.000-0.0240.0130.037-0.0260.027-0.0080.014-0.123-0.026
자치구코드(SREG)-0.022-0.0241.0000.0750.0960.0020.0050.024-0.013-0.0540.057
법정동코드(SEUB)0.0620.0130.0751.000-0.0450.0090.017-0.004-0.006-0.032-0.011
본번(BUNJI1)0.0040.0370.096-0.0451.0000.115-0.046-0.0070.031-0.059-0.006
부번(BUNJI2)-0.012-0.0260.0020.0090.1151.000-0.0480.0260.002-0.0180.007
건축연도(BLDCONYEAR)0.0640.0270.0050.017-0.046-0.0481.0000.0340.0260.0110.017
본건_사례수(PRED_BON_CNT)0.039-0.0080.024-0.004-0.0070.0260.0341.0000.054-0.031-0.076
주변건_사례수(PRED_SARE_CNT)0.0460.014-0.013-0.0060.0310.0020.0260.0541.000-0.063-0.005
면적대비_전세시세(DEPO_AREA_PRED)0.056-0.123-0.054-0.032-0.059-0.0180.011-0.031-0.0631.000-0.083
예측_전세시세(DEPO_PRED)-0.062-0.0260.057-0.011-0.0060.0070.017-0.076-0.005-0.0831.000

Missing values

2023-12-11T00:07:21.410896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T00:07:21.881599image/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.
2023-12-11T00:07:22.171975image/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

PNU코드(PNU)기준년월(KEYMONTH)표제부_키코드(PKCODE1)전유부_키코드(PKCODE2)대상지주소(ADDRESS)자치구코드(SREG)법정동코드(SEUB)대지구분(DAEJI)본번(BUNJI1)부번(BUNJI2)건물이름(BLDNAME)건물(동)이름(DONGNAME)호_이름(HONAME)건축연도(BLDCONYEAR)본건_사례수(PRED_BON_CNT)주변건_사례수(PRED_SARE_CNT)면적대비_전세시세(DEPO_AREA_PRED)예측_전세시세(DEPO_PRED)
0115301090010281000720210511305-1308311410-70002서**별**은** **동**1**2**지**112151070011343<NA><NA>3012015341914.9821788
1112301080010103015020201211590-2138111620-117005서**별**구** ** **3**1**11545103001667<NA><NA>3012015091052.5442380
2114701020010554000220200911500-10018174411290-80418서**별**마** **산**1**1**번**11140107001498<NA><NA>401호202005603.7917569
3115001090010606004520201111470-10023548811215-100233544서**별**양** ** **7**2**1144012900190109아* *2*3<NA>203호1979118282.2315769
4116801050010124001120200111500-174711290-117521서**별**동** **동**0**-**번**1171010800124531아*떼*<NA>4층401호2020138642.4924657
5115001060010717002320200411650-10023775411710-100242469서**별**은** **동**3**1**117101080017226<NA>102동3022002029236.758480
6116501010010456002520200811320-10020903411140-100224030서**별**강** **산**6**-**번**1156010800147232<NA>나동401199119506.3916141
7117401070010464001320200711440-2760111680-100264179서**별**강** **동**7**지**1138010700115262대*스*이*빌<NA>3012010175280.221943
8113051030010527006120200511530-618111380-66947서**별**서** **동**0**2**1162011800133711<NA><NA>403200206533.2328311
9111701230010008001520200811290-10021971211530-59834서**별**성** **동**-**8**1159010900148201<NA><NA>402201945247.119113
PNU코드(PNU)기준년월(KEYMONTH)표제부_키코드(PKCODE1)전유부_키코드(PKCODE2)대상지주소(ADDRESS)자치구코드(SREG)법정동코드(SEUB)대지구분(DAEJI)본번(BUNJI1)부번(BUNJI2)건물이름(BLDNAME)건물(동)이름(DONGNAME)호_이름(HONAME)건축연도(BLDCONYEAR)본건_사례수(PRED_BON_CNT)주변건_사례수(PRED_SARE_CNT)면적대비_전세시세(DEPO_AREA_PRED)예측_전세시세(DEPO_PRED)
490113801080010022000920210511215-1011711560-100224420서**별**강** **동**9**4**113801230014707라*하*츠*라<NA>302호1994017347.9814535
491115451020010194001320210111500-10018447111590-100236206서**별**구** **동**1**1**지**111701030011518274대*파*빌A동2021996153299.8813928
492116201020010610012120200311140-2169211710-149410서**별**은** **동**3**5**117401010012684역*동*자*의*<NA>302201837560.5920204
493113051020010148030920210211350-10018899311320-102388서**별**관** **동**9**9**1174010600152683<NA><NA>402호20031133484.8721370
494112601040010121008420200811590-10020849611305-73838서**별**중** **동**7**8**지**1154510800129710차*빌<NA>202198008236.2322985
495111101860010235001420200811710-2387411620-102201서**별**송** **동**7**지**115451110011592115<NA><NA>2층202호1983724308.1212706
496116501010010541015220200611500-2676611590-58859서**별**강** **동**5**4**지**114701020015783베*트*제3동505201624241.9924894
497115451020010957000220200811710-1694811320-100206968서**별**용** **원**1**-**번**113501090018630정*아*빌삼보월드빌501호201706316.5520866
498115001030010430001020200911200-429311380-106510서**별**광** **동**1**번**1144010700117843체*빌101동30219871711336.7524357
499114101200010005019320200311320-472311710-100234747서**별**광** **동**4**4**지**1126012100125617아*스*<NA>5012015224504.9934987