Overview

Dataset statistics

Number of variables15
Number of observations500
Missing cells658
Missing cells (%)8.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory63.1 KiB
Average record size in memory129.3 B

Variable types

Numeric8
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 248 (49.6%) missing valuesMissing
건물(동)이름(DONGNAME) has 379 (75.8%) missing valuesMissing
호_이름(HONAME) has 31 (6.2%) missing valuesMissing
전유부_키코드(PKCODE2) has unique valuesUnique
부번(BUNJI2) has 24 (4.8%) zerosZeros

Reproduction

Analysis started2023-12-10 15:05:16.058367
Analysis finished2023-12-10 15:05:32.111487
Duration16.05 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

PNU코드(PNU)
Real number (ℝ)

Distinct498
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1465548 × 1018
Minimum1.1110109 × 1018
Maximum1.1740109 × 1018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:05:32.251155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.1110109 × 1018
5-th percentile1.117013 × 1018
Q11.1305103 × 1018
median1.1500102 × 1018
Q31.1620101 × 1018
95-th percentile1.1710114 × 1018
Maximum1.1740109 × 1018
Range6.3 × 1016
Interquartile range (IQR)3.14998 × 1016

Descriptive statistics

Standard deviation1.7663572 × 1016
Coefficient of variation (CV)0.015405781
Kurtosis-1.0758781
Mean1.1465548 × 1018
Median Absolute Deviation (MAD)1.499975 × 1016
Skewness-0.14383761
Sum1.4283498 × 1018
Variance3.1200179 × 1032
MonotonicityNot monotonic
2023-12-11T00:05:32.551296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1165010200103670000 2
 
0.4%
1123010600103210010 2
 
0.4%
1126010300104560000 1
 
0.2%
1171010500101720016 1
 
0.2%
1138010400104010009 1
 
0.2%
1150010300100610034 1
 
0.2%
1150010600107000015 1
 
0.2%
1171011200101160013 1
 
0.2%
1129013800102190257 1
 
0.2%
1171011300102920005 1
 
0.2%
Other values (488) 488
97.6%
ValueCountFrequency (%)
1111010900100170000 1
0.2%
1111010900101000001 1
0.2%
1111010900101660037 1
0.2%
1111010900101660251 1
0.2%
1111011500102620022 1
0.2%
1111017300100230000 1
0.2%
1111018200101100018 1
0.2%
1111018200101390021 1
0.2%
1111018300102930046 1
0.2%
1111018600101360013 1
0.2%
ValueCountFrequency (%)
1174010900105610000 1
0.2%
1174010900103620038 1
0.2%
1174010900103310002 1
0.2%
1174010900103170010 1
0.2%
1174010900103150008 1
0.2%
1174010900103100017 1
0.2%
1174010900103090016 1
0.2%
1174010900101850035 1
0.2%
1174010900101670117 1
0.2%
1174010900100900002 1
0.2%

기준년월(KEYMONTH)
Real number (ℝ)

Distinct18
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean202034.46
Minimum201912
Maximum202105
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:05:32.815806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum201912
5-th percentile202001
Q1202005
median202009
Q3202101
95-th percentile202104.05
Maximum202105
Range193
Interquartile range (IQR)96

Descriptive statistics

Standard deviation44.245084
Coefficient of variation (CV)0.00021899771
Kurtosis-1.0839686
Mean202034.46
Median Absolute Deviation (MAD)5
Skewness0.85494202
Sum1.0101723 × 108
Variance1957.6275
MonotonicityNot monotonic
2023-12-11T00:05:33.030005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
202101 39
 
7.8%
202009 38
 
7.6%
202012 32
 
6.4%
202006 31
 
6.2%
202002 31
 
6.2%
202010 31
 
6.2%
202001 30
 
6.0%
202005 30
 
6.0%
202103 30
 
6.0%
202008 29
 
5.8%
Other values (8) 179
35.8%
ValueCountFrequency (%)
201912 1
 
0.2%
202001 30
6.0%
202002 31
6.2%
202003 27
5.4%
202004 28
5.6%
202005 30
6.0%
202006 31
6.2%
202007 22
4.4%
202008 29
5.8%
202009 38
7.6%
ValueCountFrequency (%)
202105 25
5.0%
202104 26
5.2%
202103 30
6.0%
202102 26
5.2%
202101 39
7.8%
202012 32
6.4%
202011 24
4.8%
202010 31
6.2%
202009 38
7.6%
202008 29
5.8%

표제부_키코드(PKCODE1)
Text

Distinct496
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-11T00:05:33.475638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length11
Mean length12.386
Min length9

Characters and Unicode

Total characters6193
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

Unique492 ?
Unique (%)98.4%

Sample

1st row11215-100200522
2nd row11290-100256230
3rd row11290-100245949
4th row11680-7902
5th row11500-17348
ValueCountFrequency (%)
11305-30648 2
 
0.4%
11470-14507 2
 
0.4%
11680-100272874 2
 
0.4%
11410-100223282 2
 
0.4%
11650-7460 1
 
0.2%
11260-100195287 1
 
0.2%
11305-13023 1
 
0.2%
11710-21903 1
 
0.2%
11470-100224148 1
 
0.2%
11710-15374 1
 
0.2%
Other values (486) 486
97.2%
2023-12-11T00:05:34.206887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 1675
27.0%
0 1120
18.1%
2 591
 
9.5%
- 500
 
8.1%
5 407
 
6.6%
4 371
 
6.0%
3 368
 
5.9%
6 311
 
5.0%
7 302
 
4.9%
8 277
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5693
91.9%
Dash Punctuation 500
 
8.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1675
29.4%
0 1120
19.7%
2 591
 
10.4%
5 407
 
7.1%
4 371
 
6.5%
3 368
 
6.5%
6 311
 
5.5%
7 302
 
5.3%
8 277
 
4.9%
9 271
 
4.8%
Dash Punctuation
ValueCountFrequency (%)
- 500
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6193
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1675
27.0%
0 1120
18.1%
2 591
 
9.5%
- 500
 
8.1%
5 407
 
6.6%
4 371
 
6.0%
3 368
 
5.9%
6 311
 
5.0%
7 302
 
4.9%
8 277
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6193
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1675
27.0%
0 1120
18.1%
2 591
 
9.5%
- 500
 
8.1%
5 407
 
6.6%
4 371
 
6.0%
3 368
 
5.9%
6 311
 
5.0%
7 302
 
4.9%
8 277
 
4.5%

전유부_키코드(PKCODE2)
Text

UNIQUE 

Distinct500
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-11T00:05:34.616914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length12
Mean length12.982
Min length11

Characters and Unicode

Total characters6491
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 row11710-76789
2nd row11590-78834
3rd row11305-90089
4th row11620-100200173
5th row11380-100190494
ValueCountFrequency (%)
11710-76789 1
 
0.2%
11215-100252583 1
 
0.2%
11215-41449 1
 
0.2%
11545-100232153 1
 
0.2%
11440-100219159 1
 
0.2%
11140-69399 1
 
0.2%
11500-100281280 1
 
0.2%
11380-100254980 1
 
0.2%
11650-130940 1
 
0.2%
11380-69734 1
 
0.2%
Other values (490) 490
98.0%
2023-12-11T00:05:35.259304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 1749
26.9%
0 1205
18.6%
2 548
 
8.4%
- 500
 
7.7%
5 444
 
6.8%
4 388
 
6.0%
3 370
 
5.7%
7 359
 
5.5%
6 327
 
5.0%
8 309
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5991
92.3%
Dash Punctuation 500
 
7.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1749
29.2%
0 1205
20.1%
2 548
 
9.1%
5 444
 
7.4%
4 388
 
6.5%
3 370
 
6.2%
7 359
 
6.0%
6 327
 
5.5%
8 309
 
5.2%
9 292
 
4.9%
Dash Punctuation
ValueCountFrequency (%)
- 500
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6491
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1749
26.9%
0 1205
18.6%
2 548
 
8.4%
- 500
 
7.7%
5 444
 
6.8%
4 388
 
6.0%
3 370
 
5.7%
7 359
 
5.5%
6 327
 
5.0%
8 309
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6491
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1749
26.9%
0 1205
18.6%
2 548
 
8.4%
- 500
 
7.7%
5 444
 
6.8%
4 388
 
6.0%
3 370
 
5.7%
7 359
 
5.5%
6 327
 
5.0%
8 309
 
4.8%

대상지주소(ADDRESS)
Text

Distinct429
Distinct (%)85.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-11T00:05:35.653986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length24
Mean length22.65
Min length18

Characters and Unicode

Total characters11325
Distinct characters54
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

Unique376 ?
Unique (%)75.2%

Sample

1st row서**별**동**구**기**1**-**번**
2nd row서**별**송** **동**5**3**
3rd row서**별**강** **동**8**1**지**
4th row서**별**강** **동**7**2**지**
5th row서**별**서** **동**9**2**지**
ValueCountFrequency (%)
서**별**강 118
 
11.5%
서**별**송 46
 
4.5%
서**별**은 44
 
4.3%
34
 
3.3%
서**별**서 31
 
3.0%
서**별**마 26
 
2.5%
서**별**성 24
 
2.3%
서**별**관 23
 
2.2%
서**별**동 22
 
2.1%
서**별**도 21
 
2.1%
Other values (275) 635
62.0%
2023-12-11T00:05:36.227407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
* 7550
66.7%
서 548
 
4.8%
524
 
4.6%
별 500
 
4.4%
동 453
 
4.0%
지 208
 
1.8%
1 167
 
1.5%
2 122
 
1.1%
강 118
 
1.0%
번 107
 
0.9%
Other values (44) 1028
 
9.1%

Most occurring categories

ValueCountFrequency (%)
Other Punctuation 7550
66.7%
Other Letter 2310
 
20.4%
Decimal Number 875
 
7.7%
Space Separator 524
 
4.6%
Dash Punctuation 66
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 548
23.7%
별 500
21.6%
동 453
19.6%
지 208
 
9.0%
강 118
 
5.1%
번 107
 
4.6%
구 51
 
2.2%
은 47
 
2.0%
송 46
 
2.0%
중 26
 
1.1%
Other values (31) 206
 
8.9%
Decimal Number
ValueCountFrequency (%)
1 167
19.1%
2 122
13.9%
3 94
10.7%
4 76
8.7%
7 74
8.5%
5 74
8.5%
8 73
8.3%
6 72
8.2%
9 63
 
7.2%
0 60
 
6.9%
Other Punctuation
ValueCountFrequency (%)
* 7550
100.0%
Space Separator
ValueCountFrequency (%)
524
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 66
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 9015
79.6%
Hangul 2310
 
20.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 548
23.7%
별 500
21.6%
동 453
19.6%
지 208
 
9.0%
강 118
 
5.1%
번 107
 
4.6%
구 51
 
2.2%
은 47
 
2.0%
송 46
 
2.0%
중 26
 
1.1%
Other values (31) 206
 
8.9%
Common
ValueCountFrequency (%)
* 7550
83.7%
524
 
5.8%
1 167
 
1.9%
2 122
 
1.4%
3 94
 
1.0%
4 76
 
0.8%
7 74
 
0.8%
5 74
 
0.8%
8 73
 
0.8%
6 72
 
0.8%
Other values (3) 189
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9015
79.6%
Hangul 2310
 
20.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 7550
83.7%
524
 
5.8%
1 167
 
1.9%
2 122
 
1.4%
3 94
 
1.0%
4 76
 
0.8%
7 74
 
0.8%
5 74
 
0.8%
8 73
 
0.8%
6 72
 
0.8%
Other values (3) 189
 
2.1%
Hangul
ValueCountFrequency (%)
서 548
23.7%
별 500
21.6%
동 453
19.6%
지 208
 
9.0%
강 118
 
5.1%
번 107
 
4.6%
구 51
 
2.2%
은 47
 
2.0%
송 46
 
2.0%
중 26
 
1.1%
Other values (31) 206
 
8.9%

자치구코드(SREG)
Real number (ℝ)

Distinct25
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11469.1
Minimum11110
Maximum11740
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:05:36.471452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11110
5-th percentile11215
Q111305
median11485
Q311620
95-th percentile11710
Maximum11740
Range630
Interquartile range (IQR)315

Descriptive statistics

Standard deviation170.93691
Coefficient of variation (CV)0.014904126
Kurtosis-1.0928544
Mean11469.1
Median Absolute Deviation (MAD)165
Skewness-0.11650486
Sum5734550
Variance29219.429
MonotonicityNot monotonic
2023-12-11T00:05:36.715877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
11710 44
 
8.8%
11500 38
 
7.6%
11380 34
 
6.8%
11590 31
 
6.2%
11470 31
 
6.2%
11215 26
 
5.2%
11290 25
 
5.0%
11620 24
 
4.8%
11305 24
 
4.8%
11545 22
 
4.4%
Other values (15) 201
40.2%
ValueCountFrequency (%)
11110 6
 
1.2%
11140 5
 
1.0%
11170 7
 
1.4%
11200 3
 
0.6%
11215 26
5.2%
11230 8
 
1.6%
11260 22
4.4%
11290 25
5.0%
11305 24
4.8%
11320 21
4.2%
ValueCountFrequency (%)
11740 22
4.4%
11710 44
8.8%
11680 20
4.0%
11650 18
3.6%
11620 24
4.8%
11590 31
6.2%
11560 12
 
2.4%
11545 22
4.4%
11530 19
3.8%
11500 38
7.6%

법정동코드(SEUB)
Real number (ℝ)

Distinct41
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10900.4
Minimum10100
Maximum18400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:05:37.035204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10100
5-th percentile10100
Q110300
median10500
Q310900
95-th percentile13300
Maximum18400
Range8300
Interquartile range (IQR)600

Descriptive statistics

Standard deviation1288.6742
Coefficient of variation (CV)0.11822265
Kurtosis13.488245
Mean10900.4
Median Absolute Deviation (MAD)300
Skewness3.40714
Sum5450200
Variance1660681.2
MonotonicityNot monotonic
2023-12-11T00:05:37.298440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
10300 98
19.6%
10100 72
14.4%
10200 52
10.4%
10800 33
 
6.6%
10600 33
 
6.6%
10700 33
 
6.6%
10500 32
 
6.4%
10900 22
 
4.4%
11100 13
 
2.6%
10400 13
 
2.6%
Other values (31) 99
19.8%
ValueCountFrequency (%)
10100 72
14.4%
10200 52
10.4%
10300 98
19.6%
10400 13
 
2.6%
10500 32
 
6.4%
10600 33
 
6.6%
10700 33
 
6.6%
10800 33
 
6.6%
10900 22
 
4.4%
11000 5
 
1.0%
ValueCountFrequency (%)
18400 2
0.4%
18300 1
 
0.2%
17500 1
 
0.2%
17000 4
0.8%
16800 1
 
0.2%
16600 1
 
0.2%
16200 3
0.6%
13900 2
0.4%
13800 1
 
0.2%
13600 4
0.8%

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

2023-12-11T00:05:37.550252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T00:05:37.837023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 500
100.0%

본번(BUNJI1)
Real number (ℝ)

Distinct377
Distinct (%)75.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean395.376
Minimum1
Maximum1692
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:05:38.051487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile19.95
Q1131.75
median290.5
Q3588.5
95-th percentile1043.15
Maximum1692
Range1691
Interquartile range (IQR)456.75

Descriptive statistics

Standard deviation347.25856
Coefficient of variation (CV)0.87829954
Kurtosis1.3529337
Mean395.376
Median Absolute Deviation (MAD)198
Skewness1.2296115
Sum197688
Variance120588.51
MonotonicityNot monotonic
2023-12-11T00:05:38.346043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 6
 
1.2%
5 5
 
1.0%
432 5
 
1.0%
127 4
 
0.8%
145 4
 
0.8%
158 4
 
0.8%
227 4
 
0.8%
56 4
 
0.8%
460 3
 
0.6%
206 3
 
0.6%
Other values (367) 458
91.6%
ValueCountFrequency (%)
1 6
1.2%
3 2
 
0.4%
4 1
 
0.2%
5 5
1.0%
7 3
0.6%
8 1
 
0.2%
10 1
 
0.2%
11 1
 
0.2%
18 3
0.6%
19 2
 
0.4%
ValueCountFrequency (%)
1692 1
0.2%
1678 1
0.2%
1640 1
0.2%
1581 1
0.2%
1564 1
0.2%
1556 1
0.2%
1549 1
0.2%
1507 2
0.4%
1479 1
0.2%
1432 1
0.2%

부번(BUNJI2)
Real number (ℝ)

ZEROS 

Distinct143
Distinct (%)28.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.784
Minimum0
Maximum1842
Zeros24
Zeros (%)4.8%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:05:38.638618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median18
Q340.25
95-th percentile281.2
Maximum1842
Range1842
Interquartile range (IQR)34.25

Descriptive statistics

Standard deviation177.15941
Coefficient of variation (CV)2.6930471
Kurtosis56.795975
Mean65.784
Median Absolute Deviation (MAD)14
Skewness6.7631115
Sum32892
Variance31385.456
MonotonicityNot monotonic
2023-12-11T00:05:38.932824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 24
 
4.8%
6 22
 
4.4%
2 21
 
4.2%
4 19
 
3.8%
1 18
 
3.6%
8 16
 
3.2%
11 16
 
3.2%
3 15
 
3.0%
5 15
 
3.0%
13 14
 
2.8%
Other values (133) 320
64.0%
ValueCountFrequency (%)
0 24
4.8%
1 18
3.6%
2 21
4.2%
3 15
3.0%
4 19
3.8%
5 15
3.0%
6 22
4.4%
7 14
2.8%
8 16
3.2%
9 11
2.2%
ValueCountFrequency (%)
1842 1
0.2%
1825 1
0.2%
1689 1
0.2%
1103 1
0.2%
949 1
0.2%
737 1
0.2%
700 1
0.2%
624 1
0.2%
613 1
0.2%
546 1
0.2%

건물이름(BLDNAME)
Text

MISSING 

Distinct236
Distinct (%)93.7%
Missing248
Missing (%)49.6%
Memory size4.0 KiB
2023-12-11T00:05:39.551119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length18
Mean length5.3849206
Min length2

Characters and Unicode

Total characters1357
Distinct characters204
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

Unique222 ?
Unique (%)88.1%

Sample

1st row태*팰*스
2nd rowp*r* *i*l*g*
3rd row리*팰*스
4th row대*맨*
5th rowp*r*-*i*l
ValueCountFrequency (%)
우*빌 3
 
1.1%
리*팰*스 3
 
1.1%
동*택 3
 
1.1%
한*빌 3
 
1.1%
월*빌 2
 
0.7%
2
 
0.7%
동*하*츠 2
 
0.7%
다*네*트 2
 
0.7%
효*주 2
 
0.7%
청 2
 
0.7%
Other values (242) 249
91.2%
2023-12-11T00:05:40.484988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
* 604
44.5%
빌 102
 
7.5%
스 31
 
2.3%
하 30
 
2.2%
리 23
 
1.7%
21
 
1.5%
아 21
 
1.5%
동 20
 
1.5%
주 13
 
1.0%
다 12
 
0.9%
Other values (194) 480
35.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 663
48.9%
Other Punctuation 605
44.6%
Decimal Number 23
 
1.7%
Space Separator 21
 
1.5%
Lowercase Letter 21
 
1.5%
Uppercase Letter 19
 
1.4%
Dash Punctuation 4
 
0.3%
Open Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
빌 102
 
15.4%
스 31
 
4.7%
하 30
 
4.5%
리 23
 
3.5%
아 21
 
3.2%
동 20
 
3.0%
주 13
 
2.0%
다 12
 
1.8%
트 12
 
1.8%
그 12
 
1.8%
Other values (160) 387
58.4%
Uppercase Letter
ValueCountFrequency (%)
C 3
15.8%
H 3
15.8%
N 2
10.5%
L 2
10.5%
I 2
10.5%
J 1
 
5.3%
R 1
 
5.3%
W 1
 
5.3%
T 1
 
5.3%
K 1
 
5.3%
Other values (2) 2
10.5%
Lowercase Letter
ValueCountFrequency (%)
l 5
23.8%
i 4
19.0%
r 4
19.0%
p 2
 
9.5%
e 1
 
4.8%
t 1
 
4.8%
a 1
 
4.8%
g 1
 
4.8%
w 1
 
4.8%
h 1
 
4.8%
Decimal Number
ValueCountFrequency (%)
1 7
30.4%
2 6
26.1%
5 4
17.4%
8 2
 
8.7%
3 2
 
8.7%
0 1
 
4.3%
7 1
 
4.3%
Other Punctuation
ValueCountFrequency (%)
* 604
99.8%
. 1
 
0.2%
Space Separator
ValueCountFrequency (%)
21
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 663
48.9%
Common 654
48.2%
Latin 40
 
2.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
빌 102
 
15.4%
스 31
 
4.7%
하 30
 
4.5%
리 23
 
3.5%
아 21
 
3.2%
동 20
 
3.0%
주 13
 
2.0%
다 12
 
1.8%
트 12
 
1.8%
그 12
 
1.8%
Other values (160) 387
58.4%
Latin
ValueCountFrequency (%)
l 5
12.5%
i 4
 
10.0%
r 4
 
10.0%
C 3
 
7.5%
H 3
 
7.5%
N 2
 
5.0%
p 2
 
5.0%
L 2
 
5.0%
I 2
 
5.0%
J 1
 
2.5%
Other values (12) 12
30.0%
Common
ValueCountFrequency (%)
* 604
92.4%
21
 
3.2%
1 7
 
1.1%
2 6
 
0.9%
- 4
 
0.6%
5 4
 
0.6%
8 2
 
0.3%
3 2
 
0.3%
0 1
 
0.2%
7 1
 
0.2%
Other values (2) 2
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 694
51.1%
Hangul 663
48.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 604
87.0%
21
 
3.0%
1 7
 
1.0%
2 6
 
0.9%
l 5
 
0.7%
- 4
 
0.6%
i 4
 
0.6%
r 4
 
0.6%
5 4
 
0.6%
C 3
 
0.4%
Other values (24) 32
 
4.6%
Hangul
ValueCountFrequency (%)
빌 102
 
15.4%
스 31
 
4.7%
하 30
 
4.5%
리 23
 
3.5%
아 21
 
3.2%
동 20
 
3.0%
주 13
 
2.0%
다 12
 
1.8%
트 12
 
1.8%
그 12
 
1.8%
Other values (160) 387
58.4%

건물(동)이름(DONGNAME)
Text

MISSING 

Distinct76
Distinct (%)62.8%
Missing379
Missing (%)75.8%
Memory size4.0 KiB
2023-12-11T00:05:41.012500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length12
Mean length4.0826446
Min length1

Characters and Unicode

Total characters494
Distinct characters139
Distinct categories7 ?
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 (%)52.1%

Sample

1st row주건축물제1동
2nd row우경주택
3rd row금강하이츠빌라
4th row태원빌라
5th row주건축물제1동
ValueCountFrequency (%)
a동 10
 
8.3%
주건축물제1동 9
 
7.4%
b동 8
 
6.6%
에이동 5
 
4.1%
101동 4
 
3.3%
102동 4
 
3.3%
나동 4
 
3.3%
비동 3
 
2.5%
가 3
 
2.5%
1동 2
 
1.7%
Other values (66) 69
57.0%
2023-12-11T00:05:41.748872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 72
 
14.6%
1 32
 
6.5%
빌 27
 
5.5%
0 15
 
3.0%
이 14
 
2.8%
A 13
 
2.6%
제 11
 
2.2%
라 11
 
2.2%
건 11
 
2.2%
2 11
 
2.2%
Other values (129) 277
56.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 385
77.9%
Decimal Number 67
 
13.6%
Uppercase Letter 24
 
4.9%
Lowercase Letter 15
 
3.0%
Close Punctuation 1
 
0.2%
Open Punctuation 1
 
0.2%
Other Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 72
 
18.7%
빌 27
 
7.0%
이 14
 
3.6%
제 11
 
2.9%
라 11
 
2.9%
건 11
 
2.9%
주 11
 
2.9%
물 10
 
2.6%
우 10
 
2.6%
하 9
 
2.3%
Other values (104) 199
51.7%
Lowercase Letter
ValueCountFrequency (%)
l 4
26.7%
r 2
13.3%
e 2
13.3%
h 1
 
6.7%
t 1
 
6.7%
o 1
 
6.7%
n 1
 
6.7%
i 1
 
6.7%
w 1
 
6.7%
s 1
 
6.7%
Decimal Number
ValueCountFrequency (%)
1 32
47.8%
0 15
22.4%
2 11
 
16.4%
3 2
 
3.0%
5 2
 
3.0%
6 2
 
3.0%
8 2
 
3.0%
4 1
 
1.5%
Uppercase Letter
ValueCountFrequency (%)
A 13
54.2%
B 9
37.5%
N 1
 
4.2%
H 1
 
4.2%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 385
77.9%
Common 70
 
14.2%
Latin 39
 
7.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 72
 
18.7%
빌 27
 
7.0%
이 14
 
3.6%
제 11
 
2.9%
라 11
 
2.9%
건 11
 
2.9%
주 11
 
2.9%
물 10
 
2.6%
우 10
 
2.6%
하 9
 
2.3%
Other values (104) 199
51.7%
Latin
ValueCountFrequency (%)
A 13
33.3%
B 9
23.1%
l 4
 
10.3%
r 2
 
5.1%
e 2
 
5.1%
h 1
 
2.6%
t 1
 
2.6%
o 1
 
2.6%
N 1
 
2.6%
n 1
 
2.6%
Other values (4) 4
 
10.3%
Common
ValueCountFrequency (%)
1 32
45.7%
0 15
21.4%
2 11
 
15.7%
3 2
 
2.9%
5 2
 
2.9%
6 2
 
2.9%
8 2
 
2.9%
) 1
 
1.4%
( 1
 
1.4%
. 1
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 385
77.9%
ASCII 109
 
22.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 72
 
18.7%
빌 27
 
7.0%
이 14
 
3.6%
제 11
 
2.9%
라 11
 
2.9%
건 11
 
2.9%
주 11
 
2.9%
물 10
 
2.6%
우 10
 
2.6%
하 9
 
2.3%
Other values (104) 199
51.7%
ASCII
ValueCountFrequency (%)
1 32
29.4%
0 15
13.8%
A 13
11.9%
2 11
 
10.1%
B 9
 
8.3%
l 4
 
3.7%
r 2
 
1.8%
3 2
 
1.8%
e 2
 
1.8%
5 2
 
1.8%
Other values (15) 17
15.6%

호_이름(HONAME)
Text

MISSING 

Distinct94
Distinct (%)20.0%
Missing31
Missing (%)6.2%
Memory size4.0 KiB
2023-12-11T00:05:42.292178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length3.5863539
Min length2

Characters and Unicode

Total characters1682
Distinct characters21
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique49 ?
Unique (%)10.4%

Sample

1st row501
2nd row201
3rd row201호
4th row502
5th row3층1호
ValueCountFrequency (%)
201 30
 
6.4%
301 28
 
5.9%
501 27
 
5.7%
401 22
 
4.7%
202 21
 
4.4%
401호 19
 
4.0%
302 19
 
4.0%
402 18
 
3.8%
201호 17
 
3.6%
101 16
 
3.4%
Other values (84) 255
54.0%
2023-12-11T00:05:43.167784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 455
27.1%
1 299
17.8%
2 272
16.2%
3 190
11.3%
호 181
 
10.8%
4 123
 
7.3%
5 68
 
4.0%
층 56
 
3.3%
6 19
 
1.1%
이 3
 
0.2%
Other values (11) 16
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1431
85.1%
Other Letter 245
 
14.6%
Space Separator 3
 
0.2%
Dash Punctuation 2
 
0.1%
Uppercase Letter 1
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 455
31.8%
1 299
20.9%
2 272
19.0%
3 190
13.3%
4 123
 
8.6%
5 68
 
4.8%
6 19
 
1.3%
8 2
 
0.1%
7 2
 
0.1%
9 1
 
0.1%
Other Letter
ValueCountFrequency (%)
호 181
73.9%
층 56
 
22.9%
이 3
 
1.2%
사 1
 
0.4%
삼 1
 
0.4%
가 1
 
0.4%
다 1
 
0.4%
동 1
 
0.4%
Space Separator
ValueCountFrequency (%)
3
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%
Uppercase Letter
ValueCountFrequency (%)
B 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1436
85.4%
Hangul 245
 
14.6%
Latin 1
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 455
31.7%
1 299
20.8%
2 272
18.9%
3 190
13.2%
4 123
 
8.6%
5 68
 
4.7%
6 19
 
1.3%
3
 
0.2%
8 2
 
0.1%
- 2
 
0.1%
Other values (2) 3
 
0.2%
Hangul
ValueCountFrequency (%)
호 181
73.9%
층 56
 
22.9%
이 3
 
1.2%
사 1
 
0.4%
삼 1
 
0.4%
가 1
 
0.4%
다 1
 
0.4%
동 1
 
0.4%
Latin
ValueCountFrequency (%)
B 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1437
85.4%
Hangul 245
 
14.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 455
31.7%
1 299
20.8%
2 272
18.9%
3 190
13.2%
4 123
 
8.6%
5 68
 
4.7%
6 19
 
1.3%
3
 
0.2%
8 2
 
0.1%
- 2
 
0.1%
Other values (3) 4
 
0.3%
Hangul
ValueCountFrequency (%)
호 181
73.9%
층 56
 
22.9%
이 3
 
1.2%
사 1
 
0.4%
삼 1
 
0.4%
가 1
 
0.4%
다 1
 
0.4%
동 1
 
0.4%

면적대비_월세시세(AREA_PRED_RENT)
Real number (ℝ)

Distinct494
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4295726
Minimum0.4742
Maximum4.1436
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:05:43.903569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.4742
5-th percentile0.803385
Q11.0066
median1.2547
Q31.68725
95-th percentile2.709155
Maximum4.1436
Range3.6694
Interquartile range (IQR)0.68065

Descriptive statistics

Standard deviation0.61340251
Coefficient of variation (CV)0.42908105
Kurtosis2.6206296
Mean1.4295726
Median Absolute Deviation (MAD)0.3015
Skewness1.4937528
Sum714.7863
Variance0.37626264
MonotonicityNot monotonic
2023-12-11T00:05:44.236118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.0503 2
 
0.4%
1.9029 2
 
0.4%
1.1315 2
 
0.4%
1.0045 2
 
0.4%
0.864 2
 
0.4%
1.0417 2
 
0.4%
2.0178 1
 
0.2%
0.4837 1
 
0.2%
2.0059 1
 
0.2%
1.1171 1
 
0.2%
Other values (484) 484
96.8%
ValueCountFrequency (%)
0.4742 1
0.2%
0.477 1
0.2%
0.4837 1
0.2%
0.4974 1
0.2%
0.5124 1
0.2%
0.5244 1
0.2%
0.5747 1
0.2%
0.6072 1
0.2%
0.659 1
0.2%
0.6645 1
0.2%
ValueCountFrequency (%)
4.1436 1
0.2%
4.1005 1
0.2%
3.8527 1
0.2%
3.684 1
0.2%
3.6801 1
0.2%
3.5602 1
0.2%
3.4242 1
0.2%
3.314 1
0.2%
3.2799 1
0.2%
3.2189 1
0.2%

예측_월세시세(PRED_RENT)
Real number (ℝ)

Distinct83
Distinct (%)16.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean61.492
Minimum26
Maximum403
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:05:44.574976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum26
5-th percentile37
Q148.75
median58
Q366
95-th percentile93
Maximum403
Range377
Interquartile range (IQR)17.25

Descriptive statistics

Standard deviation29.914607
Coefficient of variation (CV)0.48647965
Kurtosis59.585497
Mean61.492
Median Absolute Deviation (MAD)9
Skewness6.536421
Sum30746
Variance894.8837
MonotonicityNot monotonic
2023-12-11T00:05:44.834940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
62 19
 
3.8%
53 19
 
3.8%
61 18
 
3.6%
60 18
 
3.6%
58 17
 
3.4%
66 15
 
3.0%
51 15
 
3.0%
57 15
 
3.0%
52 14
 
2.8%
56 14
 
2.8%
Other values (73) 336
67.2%
ValueCountFrequency (%)
26 1
 
0.2%
29 1
 
0.2%
30 3
0.6%
31 1
 
0.2%
32 2
 
0.4%
33 2
 
0.4%
34 4
0.8%
35 6
1.2%
36 2
 
0.4%
37 4
0.8%
ValueCountFrequency (%)
403 1
0.2%
339 1
0.2%
304 1
0.2%
227 1
0.2%
222 1
0.2%
164 1
0.2%
147 1
0.2%
138 1
0.2%
130 1
0.2%
122 1
0.2%

Interactions

2023-12-11T00:05:29.746727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:05:17.519789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:05:19.545147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:05:21.174535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:05:22.853948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:05:24.381056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:05:26.089094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:05:27.705101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:05:29.921292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:05:18.109579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:05:19.754448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:05:21.394593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:05:23.050246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:05:24.590100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:05:26.270577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:05:27.928378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:05:30.083222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:05:18.312546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:05:19.951536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:05:21.617245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:05:23.246915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:05:24.827022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:05:26.483976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:05:28.174368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:05:30.268656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:05:18.523970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:05:20.140367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:05:21.826135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:05:23.439247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:05:25.057239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:05:26.683195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:05:28.379430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:05:30.423877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:05:18.703624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:05:20.317640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:05:22.029601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:05:23.659197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:05:25.252507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:05:26.881218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:05:28.566856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:05:30.621385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:05:18.926671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:05:20.531015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:05:22.250398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:05:23.856400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:05:25.443970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:05:27.093452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:05:28.785727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:05:30.831038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:05:19.125709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:05:20.739961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:05:22.458328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:05:24.035108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:05:25.664447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:05:27.279716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:05:28.978843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:05:31.047868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:05:19.341589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:05:20.943396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:05:22.669544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:05:24.201087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:05:25.894896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:05:27.487016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:05:29.547031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T00:05:45.084310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
PNU코드(PNU)기준년월(KEYMONTH)자치구코드(SREG)법정동코드(SEUB)본번(BUNJI1)부번(BUNJI2)건물(동)이름(DONGNAME)호_이름(HONAME)면적대비_월세시세(AREA_PRED_RENT)예측_월세시세(PRED_RENT)
PNU코드(PNU)1.0000.0000.0000.0000.2120.0740.0680.1490.0710.155
기준년월(KEYMONTH)0.0001.0000.1440.0000.0990.0000.2830.0000.2350.128
자치구코드(SREG)0.0000.1441.0000.0000.0000.0250.5380.0000.0000.150
법정동코드(SEUB)0.0000.0000.0001.0000.0000.2070.8880.6960.0000.000
본번(BUNJI1)0.2120.0990.0000.0001.0000.1310.8180.0000.0000.000
부번(BUNJI2)0.0740.0000.0250.2070.1311.0000.0000.7120.2780.000
건물(동)이름(DONGNAME)0.0680.2830.5380.8880.8180.0001.0000.9500.0000.000
호_이름(HONAME)0.1490.0000.0000.6960.0000.7120.9501.0000.3850.000
면적대비_월세시세(AREA_PRED_RENT)0.0710.2350.0000.0000.0000.2780.0000.3851.0000.000
예측_월세시세(PRED_RENT)0.1550.1280.1500.0000.0000.0000.0000.0000.0001.000
2023-12-11T00:05:45.380085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
PNU코드(PNU)기준년월(KEYMONTH)자치구코드(SREG)법정동코드(SEUB)본번(BUNJI1)부번(BUNJI2)면적대비_월세시세(AREA_PRED_RENT)예측_월세시세(PRED_RENT)
PNU코드(PNU)1.000-0.032-0.046-0.041-0.035-0.0210.0560.097
기준년월(KEYMONTH)-0.0321.000-0.061-0.0750.013-0.0200.0190.109
자치구코드(SREG)-0.046-0.0611.000-0.031-0.027-0.0100.044-0.165
법정동코드(SEUB)-0.041-0.075-0.0311.0000.1250.040-0.001-0.029
본번(BUNJI1)-0.0350.013-0.0270.1251.0000.004-0.1050.012
부번(BUNJI2)-0.021-0.020-0.0100.0400.0041.0000.0040.025
면적대비_월세시세(AREA_PRED_RENT)0.0560.0190.044-0.001-0.1050.0041.0000.053
예측_월세시세(PRED_RENT)0.0970.109-0.165-0.0290.0120.0250.0531.000

Missing values

2023-12-11T00:05:31.320437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T00:05:31.717406image/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.
2023-12-11T00:05:32.002005image/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)면적대비_월세시세(AREA_PRED_RENT)예측_월세시세(PRED_RENT)
0112601030010456000020200711215-10020052211710-76789서**별**동**구**기**1**-**번**114401030017626<NA><NA>5011.231959
1115001030010099001520210111290-10025623011590-78834서**별**송** **동**5**3**1147010300146612<NA><NA>2011.600941
2113801030010105000820200411290-10024594911305-90089서**별**강** **동**8**1**지**115901030019416<NA><NA>201호1.253259
3115001030010366009620201111680-790211620-100200173서**별**강** **동**7**2**지**1132010300123728태*팰*스<NA>5021.19851
4113801040010009013220200111500-1734811380-100190494서**별**서** **동**9**2**지**1171010300128114p*r* *i*l*g*<NA>3층1호1.617691
5117101060010042001020201211440-10023969511170-47440서**별**동** **동**7**4**번**112301620012099리*팰*스<NA>204호1.306554
6117401080010403000020200511710-1302711680-81414서**별**강** **동**1**2**지**1120010100118465대*맨*<NA>103호0.743765
7112001150010573000520200411710-2342811620-125917서**별**송** **동**3**4**1130510800133820p*r*-*i*l<NA>3021.359660
8115901020010211011520210211740-10025826211710-165475서**별**중** ** **9**7**115451010014862명*그*빌<NA>2011.867941
9115451020010882002220200611710-1872011740-65546서**별**성** **동**2**7**지**112901020013077<NA><NA>3011.265860
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)면적대비_월세시세(AREA_PRED_RENT)예측_월세시세(PRED_RENT)
490114101110010261000520200511305-1353711470-94681서**별**성** **동**6**4**지**115301140012088반*그*빌*(*차*<NA>203호0.96738
491113801010010310000220200611305-2819411740-79999서**별**동** **동**1**1**지**11545108001827281아*캐*<NA>2021.164448
492115451030010837000520200311215-2062611545-100204361서**별**양** ** **4**5**117101120013410<NA>비동4013.680156
493113051030010035001020200811200-743111620-77744서**별**성** **동** **2**번**1168010700114515대*빌*5*<NA>501호2.502460
494115451020010378032620200211320-422111215-100195590서**별**금** **동**3**1**지**11650106001734씨*스*<NA>6011.894551
495115301090010213002120200911650-696611380-100195744서**별**관** **동**0**7**지**11380117001928<NA><NA>5011.178642
496114701030010442001020200511470-10022660111380-100182005서**별**양** **동**0**번**1150011100126032<NA><NA>401호0.819179
497113051030010348000120200311680-10022220911590-100212959서**별**도** ** **2**8**1159010100158012<NA><NA>3011.423246
498115301080010166000420200611260-1361611260-100214358서**별**송** **동**1**1**지**11590102001116<NA><NA><NA>1.1117222
499112301080010060008920210111305-1402311500-100236602서**별**양** **동**5**1**지**1156010500111580<NA><NA>101호2.021562