Overview

Dataset statistics

Number of variables9
Number of observations5065
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory366.2 KiB
Average record size in memory74.0 B

Variable types

Numeric2
Categorical2
Text5

Alerts

TNSHP_NM is highly overall correlated with HOUSE_CPRSS_PRTNR_RISK_INFO_NO and 1 other fieldsHigh correlation
SIDO_NM is highly overall correlated with HOUSE_CPRSS_PRTNR_RISK_INFO_NO and 1 other fieldsHigh correlation
HOUSE_CPRSS_PRTNR_RISK_INFO_NO is highly overall correlated with SIDO_NM and 1 other fieldsHigh correlation
TNSHP_NM is highly imbalanced (75.3%)Imbalance
HOUSE_CPRSS_PRTNR_RISK_INFO_NO has unique valuesUnique
HOUSE_AUCT_CASCNT has 1752 (34.6%) zerosZeros

Reproduction

Analysis started2023-12-11 22:32:14.652203
Analysis finished2023-12-11 22:32:17.036185
Duration2.38 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

HOUSE_CPRSS_PRTNR_RISK_INFO_NO
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct5065
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2533
Minimum1
Maximum5065
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.6 KiB
2023-12-12T07:32:17.152535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile254.2
Q11267
median2533
Q33799
95-th percentile4811.8
Maximum5065
Range5064
Interquartile range (IQR)2532

Descriptive statistics

Standard deviation1462.2839
Coefficient of variation (CV)0.57729328
Kurtosis-1.2
Mean2533
Median Absolute Deviation (MAD)1266
Skewness0
Sum12829645
Variance2138274.2
MonotonicityStrictly increasing
2023-12-12T07:32:17.319684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
3375 1
 
< 0.1%
3382 1
 
< 0.1%
3381 1
 
< 0.1%
3380 1
 
< 0.1%
3379 1
 
< 0.1%
3378 1
 
< 0.1%
3377 1
 
< 0.1%
3376 1
 
< 0.1%
3374 1
 
< 0.1%
Other values (5055) 5055
99.8%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
5065 1
< 0.1%
5064 1
< 0.1%
5063 1
< 0.1%
5062 1
< 0.1%
5061 1
< 0.1%
5060 1
< 0.1%
5059 1
< 0.1%
5058 1
< 0.1%
5057 1
< 0.1%
5056 1
< 0.1%

SIDO_NM
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size39.7 KiB
경기도
747 
경상남도
547 
경상북도
533 
서울특별시
467 
전라남도
422 
Other values (12)
2349 

Length

Max length7
Median length5
Mean length4.1472853
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 747
14.7%
경상남도 547
10.8%
경상북도 533
10.5%
서울특별시 467
9.2%
전라남도 422
8.3%
전라북도 410
8.1%
강원도 298
 
5.9%
충청남도 285
 
5.6%
충청북도 238
 
4.7%
대구광역시 204
 
4.0%
Other values (7) 914
18.0%

Length

2023-12-12T07:32:17.455712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기도 747
14.7%
경상남도 547
10.8%
경상북도 533
10.5%
서울특별시 467
9.2%
전라남도 422
8.3%
전라북도 410
8.1%
강원도 298
 
5.9%
충청남도 285
 
5.6%
충청북도 238
 
4.7%
대구광역시 204
 
4.0%
Other values (7) 914
18.0%

SIGNGU_NM
Text

Distinct207
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Memory size39.7 KiB
2023-12-12T07:32:17.762854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.899309
Min length2

Characters and Unicode

Total characters14685
Distinct characters133
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row춘천시
2nd row춘천시
3rd row춘천시
4th row춘천시
5th row춘천시
ValueCountFrequency (%)
중구 268
 
5.3%
창원시 204
 
4.0%
동구 143
 
2.8%
북구 104
 
2.1%
서구 99
 
2.0%
청주시 95
 
1.9%
종로구 87
 
1.7%
전주시 83
 
1.6%
광산구 79
 
1.6%
목포시 64
 
1.3%
Other values (197) 3839
75.8%
2023-12-12T07:32:18.158839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 2830
19.3%
구 1504
 
10.2%
군 910
 
6.2%
주 734
 
5.0%
산 478
 
3.3%
천 451
 
3.1%
성 382
 
2.6%
원 342
 
2.3%
동 336
 
2.3%
중 274
 
1.9%
Other values (123) 6444
43.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 14685
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 2830
19.3%
구 1504
 
10.2%
군 910
 
6.2%
주 734
 
5.0%
산 478
 
3.3%
천 451
 
3.1%
성 382
 
2.6%
원 342
 
2.3%
동 336
 
2.3%
중 274
 
1.9%
Other values (123) 6444
43.9%

Most occurring scripts

ValueCountFrequency (%)
Hangul 14685
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 2830
19.3%
구 1504
 
10.2%
군 910
 
6.2%
주 734
 
5.0%
산 478
 
3.3%
천 451
 
3.1%
성 382
 
2.6%
원 342
 
2.3%
동 336
 
2.3%
중 274
 
1.9%
Other values (123) 6444
43.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 14685
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 2830
19.3%
구 1504
 
10.2%
군 910
 
6.2%
주 734
 
5.0%
산 478
 
3.3%
천 451
 
3.1%
성 382
 
2.6%
원 342
 
2.3%
동 336
 
2.3%
중 274
 
1.9%
Other values (123) 6444
43.9%

TNSHP_NM
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct33
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size39.7 KiB
<NA>
4353 
진해구
 
65
마산합포구
 
64
완산구
 
46
성산구
 
41
Other values (28)
496 

Length

Max length5
Median length4
Mean length3.88154
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 4353
85.9%
진해구 65
 
1.3%
마산합포구 64
 
1.3%
완산구 46
 
0.9%
성산구 41
 
0.8%
덕진구 37
 
0.7%
흥덕구 34
 
0.7%
덕양구 32
 
0.6%
상당구 31
 
0.6%
북구 31
 
0.6%
Other values (23) 331
 
6.5%

Length

2023-12-12T07:32:18.280393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 4353
85.9%
진해구 65
 
1.3%
마산합포구 64
 
1.3%
완산구 46
 
0.9%
성산구 41
 
0.8%
덕진구 37
 
0.7%
흥덕구 34
 
0.7%
덕양구 32
 
0.6%
북구 31
 
0.6%
상당구 31
 
0.6%
Other values (23) 331
 
6.5%

EMD_NM
Text

Distinct3975
Distinct (%)78.5%
Missing0
Missing (%)0.0%
Memory size39.7 KiB
2023-12-12T07:32:18.565624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length3.1461007
Min length2

Characters and Unicode

Total characters15935
Distinct characters370
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3385 ?
Unique (%)66.8%

Sample

1st row봉의동
2nd row요선동
3rd row낙원동
4th row중앙로1가
5th row중앙로2가
ValueCountFrequency (%)
교동 18
 
0.4%
송정동 14
 
0.3%
중동 13
 
0.3%
남면 12
 
0.2%
중앙동 11
 
0.2%
금곡동 10
 
0.2%
신흥동 9
 
0.2%
내동 9
 
0.2%
서면 9
 
0.2%
신동 9
 
0.2%
Other values (3965) 4951
97.7%
2023-12-12T07:32:18.973728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 3760
23.6%
면 1183
 
7.4%
가 510
 
3.2%
산 376
 
2.4%
읍 246
 
1.5%
남 221
 
1.4%
성 214
 
1.3%
신 214
 
1.3%
대 199
 
1.2%
천 186
 
1.2%
Other values (360) 8826
55.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 15500
97.3%
Decimal Number 435
 
2.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 3760
24.3%
면 1183
 
7.6%
가 510
 
3.3%
산 376
 
2.4%
읍 246
 
1.6%
남 221
 
1.4%
성 214
 
1.4%
신 214
 
1.4%
대 199
 
1.3%
천 186
 
1.2%
Other values (352) 8391
54.1%
Decimal Number
ValueCountFrequency (%)
1 133
30.6%
2 132
30.3%
3 87
20.0%
4 40
 
9.2%
5 24
 
5.5%
6 12
 
2.8%
7 6
 
1.4%
8 1
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Hangul 15500
97.3%
Common 435
 
2.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 3760
24.3%
면 1183
 
7.6%
가 510
 
3.3%
산 376
 
2.4%
읍 246
 
1.6%
남 221
 
1.4%
성 214
 
1.4%
신 214
 
1.4%
대 199
 
1.3%
천 186
 
1.2%
Other values (352) 8391
54.1%
Common
ValueCountFrequency (%)
1 133
30.6%
2 132
30.3%
3 87
20.0%
4 40
 
9.2%
5 24
 
5.5%
6 12
 
2.8%
7 6
 
1.4%
8 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 15500
97.3%
ASCII 435
 
2.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 3760
24.3%
면 1183
 
7.6%
가 510
 
3.3%
산 376
 
2.4%
읍 246
 
1.6%
남 221
 
1.4%
성 214
 
1.4%
신 214
 
1.4%
대 199
 
1.3%
천 186
 
1.2%
Other values (352) 8391
54.1%
ASCII
ValueCountFrequency (%)
1 133
30.6%
2 132
30.3%
3 87
20.0%
4 40
 
9.2%
5 24
 
5.5%
6 12
 
2.8%
7 6
 
1.4%
8 1
 
0.2%

MTRNT_CNTRCT_CASCNT
Text

Distinct905
Distinct (%)17.9%
Missing0
Missing (%)0.0%
Memory size39.7 KiB
2023-12-12T07:32:19.271524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length1
Mean length1.8764067
Min length1

Characters and Unicode

Total characters9504
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

Unique493 ?
Unique (%)9.7%

Sample

1st row6
2nd row20
3rd row9
4th row2
5th row1
ValueCountFrequency (%)
0 1472
29.1%
1 355
 
7.0%
2 211
 
4.2%
3 154
 
3.0%
4 110
 
2.2%
5 74
 
1.5%
6 58
 
1.1%
7 52
 
1.0%
8 50
 
1.0%
9 46
 
0.9%
Other values (895) 2483
49.0%
2023-12-12T07:32:19.716833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1943
20.4%
1 1647
17.3%
2 1126
11.8%
3 907
9.5%
4 768
 
8.1%
5 631
 
6.6%
7 585
 
6.2%
6 574
 
6.0%
8 554
 
5.8%
9 509
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9244
97.3%
Other Punctuation 260
 
2.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1943
21.0%
1 1647
17.8%
2 1126
12.2%
3 907
9.8%
4 768
 
8.3%
5 631
 
6.8%
7 585
 
6.3%
6 574
 
6.2%
8 554
 
6.0%
9 509
 
5.5%
Other Punctuation
ValueCountFrequency (%)
, 260
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 9504
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1943
20.4%
1 1647
17.3%
2 1126
11.8%
3 907
9.5%
4 768
 
8.1%
5 631
 
6.6%
7 585
 
6.2%
6 574
 
6.0%
8 554
 
5.8%
9 509
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9504
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1943
20.4%
1 1647
17.3%
2 1126
11.8%
3 907
9.5%
4 768
 
8.1%
5 631
 
6.6%
7 585
 
6.2%
6 574
 
6.0%
8 554
 
5.8%
9 509
 
5.4%

HOUSE_AUCT_CASCNT
Real number (ℝ)

ZEROS 

Distinct127
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.5214215
Minimum0
Maximum880
Zeros1752
Zeros (%)34.6%
Negative0
Negative (%)0.0%
Memory size44.6 KiB
2023-12-12T07:32:19.845696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q310
95-th percentile38
Maximum880
Range880
Interquartile range (IQR)10

Descriptive statistics

Standard deviation23.300004
Coefficient of variation (CV)2.447114
Kurtosis410.80331
Mean9.5214215
Median Absolute Deviation (MAD)3
Skewness13.929651
Sum48226
Variance542.89019
MonotonicityNot monotonic
2023-12-12T07:32:19.955273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1752
34.6%
1 347
 
6.9%
2 346
 
6.8%
3 269
 
5.3%
4 231
 
4.6%
5 191
 
3.8%
6 180
 
3.6%
7 150
 
3.0%
10 120
 
2.4%
8 115
 
2.3%
Other values (117) 1364
26.9%
ValueCountFrequency (%)
0 1752
34.6%
1 347
 
6.9%
2 346
 
6.8%
3 269
 
5.3%
4 231
 
4.6%
5 191
 
3.8%
6 180
 
3.6%
7 150
 
3.0%
8 115
 
2.3%
9 104
 
2.1%
ValueCountFrequency (%)
880 1
< 0.1%
294 1
< 0.1%
276 1
< 0.1%
265 1
< 0.1%
256 1
< 0.1%
229 1
< 0.1%
228 1
< 0.1%
222 1
< 0.1%
217 1
< 0.1%
194 1
< 0.1%

AUCT_RATE
Text

Distinct1201
Distinct (%)23.7%
Missing0
Missing (%)0.0%
Memory size39.7 KiB
2023-12-12T07:32:20.279314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length3.6965449
Min length1

Characters and Unicode

Total characters18723
Distinct characters15
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique585 ?
Unique (%)11.5%

Sample

1st row -
2nd row0.42
3rd row -
4th row -
5th row -
ValueCountFrequency (%)
1723
34.0%
nan 29
 
0.6%
0.85 23
 
0.5%
0.56 21
 
0.4%
0.95 20
 
0.4%
0.33 19
 
0.4%
0.53 17
 
0.3%
1.41 16
 
0.3%
11.11 16
 
0.3%
0.88 16
 
0.3%
Other values (1191) 3165
62.5%
2023-12-12T07:32:20.714433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3504
18.7%
. 3244
17.3%
1 1728
9.2%
- 1723
9.2%
2 1261
 
6.7%
3 1108
 
5.9%
5 963
 
5.1%
0 921
 
4.9%
4 908
 
4.8%
6 880
 
4.7%
Other values (5) 2483
13.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10165
54.3%
Space Separator 3504
 
18.7%
Other Punctuation 3244
 
17.3%
Dash Punctuation 1723
 
9.2%
Lowercase Letter 87
 
0.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1728
17.0%
2 1261
12.4%
3 1108
10.9%
5 963
9.5%
0 921
9.1%
4 908
8.9%
6 880
8.7%
8 839
8.3%
7 808
7.9%
9 749
7.4%
Lowercase Letter
ValueCountFrequency (%)
n 58
66.7%
a 29
33.3%
Space Separator
ValueCountFrequency (%)
3504
100.0%
Other Punctuation
ValueCountFrequency (%)
. 3244
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1723
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 18636
99.5%
Latin 87
 
0.5%

Most frequent character per script

Common
ValueCountFrequency (%)
3504
18.8%
. 3244
17.4%
1 1728
9.3%
- 1723
9.2%
2 1261
 
6.8%
3 1108
 
5.9%
5 963
 
5.2%
0 921
 
4.9%
4 908
 
4.9%
6 880
 
4.7%
Other values (3) 2396
12.9%
Latin
ValueCountFrequency (%)
n 58
66.7%
a 29
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 18723
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3504
18.7%
. 3244
17.3%
1 1728
9.2%
- 1723
9.2%
2 1261
 
6.7%
3 1108
 
5.9%
5 963
 
5.1%
0 921
 
4.9%
4 908
 
4.8%
6 880
 
4.7%
Other values (5) 2483
13.3%

RISK_IDEX
Text

Distinct843
Distinct (%)16.6%
Missing0
Missing (%)0.0%
Memory size39.7 KiB
2023-12-12T07:32:21.048153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length3
Mean length3.2108588
Min length1

Characters and Unicode

Total characters16263
Distinct characters14
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique597 ?
Unique (%)11.8%

Sample

1st row -
2nd row0.2
3rd row -
4th row -
5th row -
ValueCountFrequency (%)
2387
47.1%
0.1 169
 
3.3%
0.2 118
 
2.3%
0 112
 
2.2%
0.3 78
 
1.5%
0.4 72
 
1.4%
0.5 66
 
1.3%
0.6 51
 
1.0%
0.8 51
 
1.0%
1 42
 
0.8%
Other values (833) 1919
37.9%
2023-12-12T07:32:21.694350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4774
29.4%
- 2387
14.7%
. 2372
14.6%
1 1199
 
7.4%
0 1102
 
6.8%
2 839
 
5.2%
3 668
 
4.1%
4 589
 
3.6%
5 507
 
3.1%
6 482
 
3.0%
Other values (4) 1344
 
8.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6632
40.8%
Space Separator 4774
29.4%
Other Punctuation 2470
 
15.2%
Dash Punctuation 2387
 
14.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1199
18.1%
0 1102
16.6%
2 839
12.7%
3 668
10.1%
4 589
8.9%
5 507
7.6%
6 482
7.3%
7 448
 
6.8%
8 439
 
6.6%
9 359
 
5.4%
Other Punctuation
ValueCountFrequency (%)
. 2372
96.0%
, 98
 
4.0%
Space Separator
ValueCountFrequency (%)
4774
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2387
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 16263
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4774
29.4%
- 2387
14.7%
. 2372
14.6%
1 1199
 
7.4%
0 1102
 
6.8%
2 839
 
5.2%
3 668
 
4.1%
4 589
 
3.6%
5 507
 
3.1%
6 482
 
3.0%
Other values (4) 1344
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16263
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4774
29.4%
- 2387
14.7%
. 2372
14.6%
1 1199
 
7.4%
0 1102
 
6.8%
2 839
 
5.2%
3 668
 
4.1%
4 589
 
3.6%
5 507
 
3.1%
6 482
 
3.0%
Other values (4) 1344
 
8.3%

Interactions

2023-12-12T07:32:16.647182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:16.397892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:16.725836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:16.549661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T07:32:21.788149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
HOUSE_CPRSS_PRTNR_RISK_INFO_NOSIDO_NMTNSHP_NMHOUSE_AUCT_CASCNT
HOUSE_CPRSS_PRTNR_RISK_INFO_NO1.0000.9770.9950.089
SIDO_NM0.9771.0001.0000.087
TNSHP_NM0.9951.0001.0000.291
HOUSE_AUCT_CASCNT0.0890.0870.2911.000
2023-12-12T07:32:21.877329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
TNSHP_NMSIDO_NM
TNSHP_NM1.0000.981
SIDO_NM0.9811.000
2023-12-12T07:32:21.969696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
HOUSE_CPRSS_PRTNR_RISK_INFO_NOHOUSE_AUCT_CASCNTSIDO_NMTNSHP_NM
HOUSE_CPRSS_PRTNR_RISK_INFO_NO1.0000.0080.8890.954
HOUSE_AUCT_CASCNT0.0081.0000.0450.149
SIDO_NM0.8890.0451.0000.981
TNSHP_NM0.9540.1490.9811.000

Missing values

2023-12-12T07:32:16.828815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T07:32:16.966043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

HOUSE_CPRSS_PRTNR_RISK_INFO_NOSIDO_NMSIGNGU_NMTNSHP_NMEMD_NMMTRNT_CNTRCT_CASCNTHOUSE_AUCT_CASCNTAUCT_RATERISK_IDEX
01강원도춘천시<NA>봉의동60--
12강원도춘천시<NA>요선동2010.420.2
23강원도춘천시<NA>낙원동90--
34강원도춘천시<NA>중앙로1가20--
45강원도춘천시<NA>중앙로2가10--
56강원도춘천시<NA>중앙로3가170--
67강원도춘천시<NA>옥천동140--
78강원도춘천시<NA>조양동230--
89강원도춘천시<NA>죽림동40--
910강원도춘천시<NA>운교동2920.850.6
HOUSE_CPRSS_PRTNR_RISK_INFO_NOSIDO_NMSIGNGU_NMTNSHP_NMEMD_NMMTRNT_CNTRCT_CASCNTHOUSE_AUCT_CASCNTAUCT_RATERISK_IDEX
50555056충청북도보은군<NA>회남면10--
50565057충청북도진천군<NA>백곡면0108.85-
50575058충청북도보은군<NA>회인면022.9-
50585059충청북도보은군<NA>내북면0811.59-
50595060충청북도보은군<NA>산외면111.4514.5
50605061충청북도옥천군<NA>옥천읍1171920.4333.2
50615062충청북도옥천군<NA>동이면477.53131.7
50625063충청북도옥천군<NA>안남면077.53-
50635064충청북도괴산군<NA>장연면110.777.7
50645065충청북도진천군<NA>덕산읍1,0422118.583.7