Overview

Dataset statistics

Number of variables20
Number of observations10000
Missing cells9461
Missing cells (%)4.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.7 MiB
Average record size in memory178.0 B

Variable types

Numeric9
Categorical5
Text6

Alerts

SIDO_NM is highly overall correlated with RHMH_HOUSE_MTHT_CONV_RATE_INFO_NO and 1 other fieldsHigh correlation
TNSHP_NM is highly overall correlated with RHMH_HOUSE_MTHT_CONV_RATE_INFO_NO and 3 other fieldsHigh correlation
SUSAR_NM is highly overall correlated with TNSHP_NMHigh correlation
RHMH_HOUSE_MTHT_CONV_RATE_INFO_NO is highly overall correlated with SIDO_NM and 1 other fieldsHigh correlation
SMOEU is highly overall correlated with MNTH_RENTCGHigh correlation
MNTH_RENTCG is highly overall correlated with SMOEUHigh correlation
NMHSH is highly overall correlated with TNSHP_NMHigh correlation
MTHT_CONV_RT_MINM_VL is highly overall correlated with MTHT_CONV_RT_MAX_VLHigh correlation
MTHT_CONV_RT_MAX_VL is highly overall correlated with MTHT_CONV_RT_MINM_VLHigh correlation
TNSHP_NM is highly imbalanced (73.6%)Imbalance
SUSAR_NM is highly imbalanced (90.2%)Imbalance
NMHSH has 9461 (94.6%) missing valuesMissing
MTHT_CONV_RT_MINM_VL is highly skewed (γ1 = -62.69063969)Skewed
MTHT_CONV_RT_MAX_VL is highly skewed (γ1 = 97.67879052)Skewed
RHMH_HOUSE_MTHT_CONV_RATE_INFO_NO has unique valuesUnique

Reproduction

Analysis started2023-12-11 22:32:40.945365
Analysis finished2023-12-11 22:32:53.671592
Duration12.73 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

RHMH_HOUSE_MTHT_CONV_RATE_INFO_NO
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8802.7573
Minimum1
Maximum17676
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T07:32:53.731041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile837.95
Q14410.75
median8739
Q313245.25
95-th percentile16823.05
Maximum17676
Range17675
Interquartile range (IQR)8834.5

Descriptive statistics

Standard deviation5113.8882
Coefficient of variation (CV)0.58094163
Kurtosis-1.1997693
Mean8802.7573
Median Absolute Deviation (MAD)4421.5
Skewness0.011711492
Sum88027573
Variance26151852
MonotonicityNot monotonic
2023-12-12T07:32:54.116528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15769 1
 
< 0.1%
12120 1
 
< 0.1%
8084 1
 
< 0.1%
17670 1
 
< 0.1%
1586 1
 
< 0.1%
2571 1
 
< 0.1%
17470 1
 
< 0.1%
13429 1
 
< 0.1%
2747 1
 
< 0.1%
3620 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 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%
11 1
< 0.1%
13 1
< 0.1%
ValueCountFrequency (%)
17676 1
< 0.1%
17675 1
< 0.1%
17674 1
< 0.1%
17672 1
< 0.1%
17671 1
< 0.1%
17670 1
< 0.1%
17669 1
< 0.1%
17667 1
< 0.1%
17663 1
< 0.1%
17662 1
< 0.1%

SIDO_NM
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
서울특별시
5831 
경기도
3356 
인천광역시
813 

Length

Max length5
Median length5
Mean length4.3288
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row경기도
2nd row서울특별시
3rd row서울특별시
4th row서울특별시
5th row서울특별시

Common Values

ValueCountFrequency (%)
서울특별시 5831
58.3%
경기도 3356
33.6%
인천광역시 813
 
8.1%

Length

2023-12-12T07:32:54.238864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T07:32:54.339608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서울특별시 5831
58.3%
경기도 3356
33.6%
인천광역시 813
 
8.1%

SIGNGU_NM
Text

Distinct65
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T07:32:54.532004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.0514
Min length2

Characters and Unicode

Total characters30514
Distinct characters66
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 row평택시
2nd row강서구
3rd row중랑구
4th row서대문구
5th row송파구
ValueCountFrequency (%)
송파구 1022
 
10.2%
강남구 424
 
4.2%
마포구 401
 
4.0%
수원시 397
 
4.0%
안산시 380
 
3.8%
서초구 351
 
3.5%
강동구 345
 
3.5%
강서구 319
 
3.2%
부천시 313
 
3.1%
광진구 309
 
3.1%
Other values (55) 5739
57.4%
2023-12-12T07:32:54.864591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
구 6780
22.2%
시 3449
 
11.3%
강 1252
 
4.1%
파 1108
 
3.6%
서 1049
 
3.4%
송 1022
 
3.3%
동 966
 
3.2%
남 844
 
2.8%
천 792
 
2.6%
평 788
 
2.6%
Other values (56) 12464
40.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 30514
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 6780
22.2%
시 3449
 
11.3%
강 1252
 
4.1%
파 1108
 
3.6%
서 1049
 
3.4%
송 1022
 
3.3%
동 966
 
3.2%
남 844
 
2.8%
천 792
 
2.6%
평 788
 
2.6%
Other values (56) 12464
40.8%

Most occurring scripts

ValueCountFrequency (%)
Hangul 30514
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 6780
22.2%
시 3449
 
11.3%
강 1252
 
4.1%
파 1108
 
3.6%
서 1049
 
3.4%
송 1022
 
3.3%
동 966
 
3.2%
남 844
 
2.8%
천 792
 
2.6%
평 788
 
2.6%
Other values (56) 12464
40.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 30514
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
구 6780
22.2%
시 3449
 
11.3%
강 1252
 
4.1%
파 1108
 
3.6%
서 1049
 
3.4%
송 1022
 
3.3%
동 966
 
3.2%
남 844
 
2.8%
천 792
 
2.6%
평 788
 
2.6%
Other values (56) 12464
40.8%

TNSHP_NM
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct18
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
8610 
상록구
 
269
권선구
 
146
팔달구
 
123
처인구
 
114
Other values (13)
 
738

Length

Max length4
Median length4
Mean length3.8647
Min length3

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> 8610
86.1%
상록구 269
 
2.7%
권선구 146
 
1.5%
팔달구 123
 
1.2%
처인구 114
 
1.1%
단원구 111
 
1.1%
덕양구 106
 
1.1%
장안구 97
 
1.0%
기흥구 72
 
0.7%
만안구 65
 
0.7%
Other values (8) 287
 
2.9%

Length

2023-12-12T07:32:54.982218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 8610
86.1%
상록구 269
 
2.7%
권선구 146
 
1.5%
팔달구 123
 
1.2%
처인구 114
 
1.1%
단원구 111
 
1.1%
덕양구 106
 
1.1%
장안구 97
 
1.0%
기흥구 72
 
0.7%
만안구 65
 
0.7%
Other values (8) 287
 
2.9%

EMD_NM
Text

Distinct689
Distinct (%)6.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T07:32:55.264021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length3.0193
Min length2

Characters and Unicode

Total characters30193
Distinct characters251
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

Unique123 ?
Unique (%)1.2%

Sample

1st row합정동
2nd row화곡동
3rd row면목동
4th row홍제동
5th row오금동
ValueCountFrequency (%)
화곡동 203
 
2.0%
삼전동 189
 
1.9%
송파동 166
 
1.7%
석촌동 159
 
1.6%
잠실동 143
 
1.4%
신림동 135
 
1.4%
방배동 133
 
1.3%
봉천동 125
 
1.2%
정왕동 123
 
1.2%
방이동 120
 
1.2%
Other values (679) 8504
85.0%
2023-12-12T07:32:55.717815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 9410
31.2%
신 565
 
1.9%
정 544
 
1.8%
곡 519
 
1.7%
가 479
 
1.6%
읍 458
 
1.5%
화 439
 
1.5%
양 406
 
1.3%
산 405
 
1.3%
삼 388
 
1.3%
Other values (241) 16580
54.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 29933
99.1%
Decimal Number 260
 
0.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 9410
31.4%
신 565
 
1.9%
정 544
 
1.8%
곡 519
 
1.7%
가 479
 
1.6%
읍 458
 
1.5%
화 439
 
1.5%
양 406
 
1.4%
산 405
 
1.4%
삼 388
 
1.3%
Other values (233) 16320
54.5%
Decimal Number
ValueCountFrequency (%)
1 93
35.8%
2 80
30.8%
3 39
15.0%
4 23
 
8.8%
6 9
 
3.5%
7 7
 
2.7%
5 6
 
2.3%
8 3
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
Hangul 29933
99.1%
Common 260
 
0.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 9410
31.4%
신 565
 
1.9%
정 544
 
1.8%
곡 519
 
1.7%
가 479
 
1.6%
읍 458
 
1.5%
화 439
 
1.5%
양 406
 
1.4%
산 405
 
1.4%
삼 388
 
1.3%
Other values (233) 16320
54.5%
Common
ValueCountFrequency (%)
1 93
35.8%
2 80
30.8%
3 39
15.0%
4 23
 
8.8%
6 9
 
3.5%
7 7
 
2.7%
5 6
 
2.3%
8 3
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 29933
99.1%
ASCII 260
 
0.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 9410
31.4%
신 565
 
1.9%
정 544
 
1.8%
곡 519
 
1.7%
가 479
 
1.6%
읍 458
 
1.5%
화 439
 
1.5%
양 406
 
1.4%
산 405
 
1.4%
삼 388
 
1.3%
Other values (233) 16320
54.5%
ASCII
ValueCountFrequency (%)
1 93
35.8%
2 80
30.8%
3 39
15.0%
4 23
 
8.8%
6 9
 
3.5%
7 7
 
2.7%
5 6
 
2.3%
8 3
 
1.2%

HONO_NM
Text

Distinct7486
Distinct (%)74.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T07:32:56.066569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length6.3543
Min length2

Characters and Unicode

Total characters63543
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5852 ?
Unique (%)58.5%

Sample

1st row 737-32
2nd row 362-107
3rd row 137-5
4th row 136-9
5th row 133-23
ValueCountFrequency (%)
1152 29
 
0.3%
25-14 16
 
0.2%
1198-2 12
 
0.1%
316-5 12
 
0.1%
33-14 11
 
0.1%
152-4 9
 
0.1%
90-13 8
 
0.1%
607-18 8
 
0.1%
333-10 8
 
0.1%
926-3 8
 
0.1%
Other values (7476) 9879
98.8%
2023-12-12T07:32:56.525767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
10000
15.7%
- 9261
14.6%
1 8793
13.8%
2 5803
9.1%
3 5023
7.9%
4 4523
7.1%
5 3939
 
6.2%
7 3594
 
5.7%
6 3575
 
5.6%
9 3157
 
5.0%
Other values (2) 5875
9.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 44282
69.7%
Control 10000
 
15.7%
Dash Punctuation 9261
 
14.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 8793
19.9%
2 5803
13.1%
3 5023
11.3%
4 4523
10.2%
5 3939
8.9%
7 3594
8.1%
6 3575
8.1%
9 3157
 
7.1%
8 2973
 
6.7%
0 2902
 
6.6%
Control
ValueCountFrequency (%)
10000
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 9261
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 63543
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
10000
15.7%
- 9261
14.6%
1 8793
13.8%
2 5803
9.1%
3 5023
7.9%
4 4523
7.1%
5 3939
 
6.2%
7 3594
 
5.7%
6 3575
 
5.6%
9 3157
 
5.0%
Other values (2) 5875
9.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 63543
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
10000
15.7%
- 9261
14.6%
1 8793
13.8%
2 5803
9.1%
3 5023
7.9%
4 4523
7.1%
5 3939
 
6.2%
7 3594
 
5.7%
6 3575
 
5.6%
9 3157
 
5.0%
Other values (2) 5875
9.2%

SMOEU
Real number (ℝ)

HIGH CORRELATION 

Distinct4903
Distinct (%)49.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.515388
Minimum9.52
Maximum244.53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T07:32:56.663827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9.52
5-th percentile14.4885
Q124.5
median36.78
Q350.73125
95-th percentile73.5015
Maximum244.53
Range235.01
Interquartile range (IQR)26.23125

Descriptive statistics

Standard deviation20.474011
Coefficient of variation (CV)0.51812755
Kurtosis10.541214
Mean39.515388
Median Absolute Deviation (MAD)13.1
Skewness1.9815993
Sum395153.88
Variance419.18514
MonotonicityNot monotonic
2023-12-12T07:32:56.803049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29.96 21
 
0.2%
29.98 17
 
0.2%
29.99 16
 
0.2%
29.91 16
 
0.2%
29.97 15
 
0.1%
29.89 15
 
0.1%
19.8 14
 
0.1%
29.88 14
 
0.1%
29.95 14
 
0.1%
35.64 13
 
0.1%
Other values (4893) 9845
98.5%
ValueCountFrequency (%)
9.52 1
< 0.1%
9.97 1
< 0.1%
10.31 1
< 0.1%
10.44 1
< 0.1%
10.5 1
< 0.1%
10.6 1
< 0.1%
10.78 1
< 0.1%
10.89 1
< 0.1%
11.03 1
< 0.1%
11.12 2
< 0.1%
ValueCountFrequency (%)
244.53 1
< 0.1%
243.994 1
< 0.1%
229.56 1
< 0.1%
225.343 1
< 0.1%
224.9 1
< 0.1%
208.04 2
< 0.1%
205.42 1
< 0.1%
201.12 1
< 0.1%
195.84 1
< 0.1%
194.91 2
< 0.1%

CNTRCT_YM
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
202207
3505 
202208
3345 
202209
3150 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row202208
2nd row202208
3rd row202207
4th row202208
5th row202207

Common Values

ValueCountFrequency (%)
202207 3505
35.0%
202208 3345
33.5%
202209 3150
31.5%

Length

2023-12-12T07:32:56.932146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T07:32:57.011309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
202207 3505
35.0%
202208 3345
33.5%
202209 3150
31.5%

CNTRCT_DE
Real number (ℝ)

Distinct31
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.1749
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T07:32:57.098679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median17
Q324
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)16

Descriptive statistics

Standard deviation8.8301476
Coefficient of variation (CV)0.54591668
Kurtosis-1.1740257
Mean16.1749
Median Absolute Deviation (MAD)8
Skewness-0.10387316
Sum161749
Variance77.971507
MonotonicityNot monotonic
2023-12-12T07:32:57.211548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
20 455
 
4.5%
16 424
 
4.2%
19 421
 
4.2%
29 405
 
4.0%
27 387
 
3.9%
6 383
 
3.8%
23 382
 
3.8%
5 380
 
3.8%
22 379
 
3.8%
13 377
 
3.8%
Other values (21) 6007
60.1%
ValueCountFrequency (%)
1 374
3.7%
2 320
3.2%
3 273
2.7%
4 285
2.9%
5 380
3.8%
6 383
3.8%
7 261
2.6%
8 318
3.2%
9 244
2.4%
10 158
1.6%
ValueCountFrequency (%)
31 131
 
1.3%
30 368
3.7%
29 405
4.0%
28 283
2.8%
27 387
3.9%
26 358
3.6%
25 280
2.8%
24 297
3.0%
23 382
3.8%
22 379
3.8%

MTHT_GTN
Text

Distinct694
Distinct (%)6.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T07:32:57.565678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length2
Mean length2.2355
Min length1

Characters and Unicode

Total characters22355
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

Unique183 ?
Unique (%)1.8%

Sample

1st row15
2nd row68
3rd row155
4th row219
5th row33
ValueCountFrequency (%)
14 216
 
2.2%
13 188
 
1.9%
17 187
 
1.9%
25 183
 
1.8%
12 163
 
1.6%
18 158
 
1.6%
19 151
 
1.5%
34 148
 
1.5%
22 147
 
1.5%
20 143
 
1.4%
Other values (684) 8316
83.2%
2023-12-12T07:32:58.128761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 4066
18.2%
2 3315
14.8%
3 2779
12.4%
4 2328
10.4%
5 2038
9.1%
6 1747
7.8%
7 1703
7.6%
8 1567
 
7.0%
0 1472
 
6.6%
9 1319
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 22334
99.9%
Other Punctuation 21
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 4066
18.2%
2 3315
14.8%
3 2779
12.4%
4 2328
10.4%
5 2038
9.1%
6 1747
7.8%
7 1703
7.6%
8 1567
 
7.0%
0 1472
 
6.6%
9 1319
 
5.9%
Other Punctuation
ValueCountFrequency (%)
, 21
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 22355
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 4066
18.2%
2 3315
14.8%
3 2779
12.4%
4 2328
10.4%
5 2038
9.1%
6 1747
7.8%
7 1703
7.6%
8 1567
 
7.0%
0 1472
 
6.6%
9 1319
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 22355
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 4066
18.2%
2 3315
14.8%
3 2779
12.4%
4 2328
10.4%
5 2038
9.1%
6 1747
7.8%
7 1703
7.6%
8 1567
 
7.0%
0 1472
 
6.6%
9 1319
 
5.9%

MNTH_RENTCG
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7352
Minimum1
Maximum11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T07:32:58.286766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile4
Maximum11
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.9962832
Coefficient of variation (CV)0.57416044
Kurtosis4.7554117
Mean1.7352
Median Absolute Deviation (MAD)0
Skewness1.7695776
Sum17352
Variance0.99258022
MonotonicityNot monotonic
2023-12-12T07:32:58.380058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 5336
53.4%
2 2837
28.4%
3 1237
 
12.4%
4 422
 
4.2%
5 107
 
1.1%
6 35
 
0.4%
7 15
 
0.1%
8 8
 
0.1%
9 2
 
< 0.1%
11 1
 
< 0.1%
ValueCountFrequency (%)
1 5336
53.4%
2 2837
28.4%
3 1237
 
12.4%
4 422
 
4.2%
5 107
 
1.1%
6 35
 
0.4%
7 15
 
0.1%
8 8
 
0.1%
9 2
 
< 0.1%
11 1
 
< 0.1%
ValueCountFrequency (%)
11 1
 
< 0.1%
9 2
 
< 0.1%
8 8
 
0.1%
7 15
 
0.1%
6 35
 
0.4%
5 107
 
1.1%
4 422
 
4.2%
3 1237
 
12.4%
2 2837
28.4%
1 5336
53.4%

FLR
Real number (ℝ)

Distinct20
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0307
Minimum-1
Maximum19
Zeros0
Zeros (%)0.0%
Negative282
Negative (%)2.8%
Memory size166.0 KiB
2023-12-12T07:32:58.501827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum19
Range20
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.808061
Coefficient of variation (CV)0.59658196
Kurtosis8.8614851
Mean3.0307
Median Absolute Deviation (MAD)1
Skewness1.6731315
Sum30307
Variance3.2690844
MonotonicityNot monotonic
2023-12-12T07:32:58.628817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
2 2707
27.1%
3 2558
25.6%
4 1899
19.0%
1 1082
 
10.8%
5 982
 
9.8%
-1 282
 
2.8%
6 188
 
1.9%
7 91
 
0.9%
8 66
 
0.7%
9 47
 
0.5%
Other values (10) 98
 
1.0%
ValueCountFrequency (%)
-1 282
 
2.8%
1 1082
 
10.8%
2 2707
27.1%
3 2558
25.6%
4 1899
19.0%
5 982
 
9.8%
6 188
 
1.9%
7 91
 
0.9%
8 66
 
0.7%
9 47
 
0.5%
ValueCountFrequency (%)
19 1
 
< 0.1%
18 3
 
< 0.1%
17 5
 
0.1%
16 4
 
< 0.1%
15 1
 
< 0.1%
14 6
 
0.1%
13 12
0.1%
12 21
0.2%
11 18
0.2%
10 27
0.3%

BILDNG_YR
Real number (ℝ)

Distinct52
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2007.3882
Minimum1965
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T07:32:58.783676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1965
5-th percentile1988
Q12001
median2011
Q32016
95-th percentile2021
Maximum2023
Range58
Interquartile range (IQR)15

Descriptive statistics

Standard deviation10.524285
Coefficient of variation (CV)0.0052427752
Kurtosis-0.62520016
Mean2007.3882
Median Absolute Deviation (MAD)8
Skewness-0.5967455
Sum20073882
Variance110.76058
MonotonicityNot monotonic
2023-12-12T07:32:58.897629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2002 784
 
7.8%
2012 726
 
7.3%
2013 628
 
6.3%
2016 489
 
4.9%
2017 440
 
4.4%
2003 427
 
4.3%
2018 421
 
4.2%
2014 416
 
4.2%
2021 414
 
4.1%
2011 392
 
3.9%
Other values (42) 4863
48.6%
ValueCountFrequency (%)
1965 1
 
< 0.1%
1968 1
 
< 0.1%
1971 3
 
< 0.1%
1974 1
 
< 0.1%
1976 5
 
0.1%
1977 6
 
0.1%
1978 13
0.1%
1979 5
 
0.1%
1980 9
0.1%
1981 21
0.2%
ValueCountFrequency (%)
2023 1
 
< 0.1%
2022 329
3.3%
2021 414
4.1%
2020 306
3.1%
2019 285
2.9%
2018 421
4.2%
2017 440
4.4%
2016 489
4.9%
2015 389
3.9%
2014 416
4.2%

CNTRCT_CLSF_NM
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
신규
7199 
<NA>
1825 
갱신
976 

Length

Max length4
Median length2
Mean length2.365
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row신규
2nd row신규
3rd row갱신
4th row갱신
5th row신규

Common Values

ValueCountFrequency (%)
신규 7199
72.0%
<NA> 1825
 
18.2%
갱신 976
 
9.8%

Length

2023-12-12T07:32:59.040379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T07:32:59.183861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
신규 7199
72.0%
na 1825
 
18.2%
갱신 976
 
9.8%

SUSAR_NM
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9744 
주거지역
 
204
녹지지역
 
36
상업지역
 
16

Length

Max length4
Median length4
Mean length4
Min length4

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> 9744
97.4%
주거지역 204
 
2.0%
녹지지역 36
 
0.4%
상업지역 16
 
0.2%

Length

2023-12-12T07:32:59.326815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T07:32:59.430474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9744
97.4%
주거지역 204
 
2.0%
녹지지역 36
 
0.4%
상업지역 16
 
0.2%

NMHSH
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct50
Distinct (%)9.3%
Missing9461
Missing (%)94.6%
Infinite0
Infinite (%)0.0%
Mean20.998145
Minimum2
Maximum284
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T07:32:59.543928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile6
Q18
median12
Q319
95-th percentile40.2
Maximum284
Range282
Interquartile range (IQR)11

Descriptive statistics

Standard deviation36.284322
Coefficient of variation (CV)1.7279775
Kurtosis28.696713
Mean20.998145
Median Absolute Deviation (MAD)4
Skewness5.2504545
Sum11318
Variance1316.552
MonotonicityNot monotonic
2023-12-12T07:32:59.653792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8 121
 
1.2%
16 48
 
0.5%
12 44
 
0.4%
24 30
 
0.3%
15 26
 
0.3%
11 23
 
0.2%
10 23
 
0.2%
19 22
 
0.2%
7 18
 
0.2%
6 18
 
0.2%
Other values (40) 166
 
1.7%
(Missing) 9461
94.6%
ValueCountFrequency (%)
2 3
 
< 0.1%
4 8
 
0.1%
5 4
 
< 0.1%
6 18
 
0.2%
7 18
 
0.2%
8 121
1.2%
9 15
 
0.1%
10 23
 
0.2%
11 23
 
0.2%
12 44
 
0.4%
ValueCountFrequency (%)
284 2
 
< 0.1%
233 8
0.1%
200 2
 
< 0.1%
196 1
 
< 0.1%
138 1
 
< 0.1%
126 1
 
< 0.1%
112 1
 
< 0.1%
108 1
 
< 0.1%
89 1
 
< 0.1%
88 1
 
< 0.1%

AVE_LFSTS_PC_MINM_AMT
Text

Distinct438
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T07:32:59.996335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length2.9991
Min length2

Characters and Unicode

Total characters29991
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

Unique47 ?
Unique (%)0.5%

Sample

1st row217
2nd row561
3rd row663
4th row421
5th row578
ValueCountFrequency (%)
561 309
 
3.1%
620 273
 
2.7%
649 185
 
1.8%
721 166
 
1.7%
272 160
 
1.6%
622 156
 
1.6%
715 143
 
1.4%
612 136
 
1.4%
868 133
 
1.3%
568 125
 
1.2%
Other values (428) 8214
82.1%
2023-12-12T07:33:00.447420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 4624
15.4%
6 3836
12.8%
1 3424
11.4%
5 3166
10.6%
3 3039
10.1%
4 2882
9.6%
7 2546
8.5%
8 2444
8.1%
9 2118
7.1%
0 1903
6.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 29982
> 99.9%
Other Punctuation 9
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 4624
15.4%
6 3836
12.8%
1 3424
11.4%
5 3166
10.6%
3 3039
10.1%
4 2882
9.6%
7 2546
8.5%
8 2444
8.2%
9 2118
7.1%
0 1903
6.3%
Other Punctuation
ValueCountFrequency (%)
, 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 29991
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 4624
15.4%
6 3836
12.8%
1 3424
11.4%
5 3166
10.6%
3 3039
10.1%
4 2882
9.6%
7 2546
8.5%
8 2444
8.1%
9 2118
7.1%
0 1903
6.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 29991
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 4624
15.4%
6 3836
12.8%
1 3424
11.4%
5 3166
10.6%
3 3039
10.1%
4 2882
9.6%
7 2546
8.5%
8 2444
8.1%
9 2118
7.1%
0 1903
6.3%

AVE_LFSTS_PC_MAX_AMT
Text

Distinct464
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T07:33:00.766124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length3.1201
Min length2

Characters and Unicode

Total characters31201
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

Unique46 ?
Unique (%)0.5%

Sample

1st row255
2nd row658
3rd row778
4th row494
5th row678
ValueCountFrequency (%)
658 299
 
3.0%
728 273
 
2.7%
762 185
 
1.8%
846 166
 
1.7%
319 160
 
1.6%
730 156
 
1.6%
839 143
 
1.4%
265 141
 
1.4%
718 135
 
1.4%
1,019 133
 
1.3%
Other values (454) 8209
82.1%
2023-12-12T07:33:01.190953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 3871
12.4%
7 3596
11.5%
4 3182
10.2%
5 3157
10.1%
6 3076
9.9%
1 3013
9.7%
8 2875
9.2%
3 2874
9.2%
9 2522
8.1%
0 2432
7.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30598
98.1%
Other Punctuation 603
 
1.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 3871
12.7%
7 3596
11.8%
4 3182
10.4%
5 3157
10.3%
6 3076
10.1%
1 3013
9.8%
8 2875
9.4%
3 2874
9.4%
9 2522
8.2%
0 2432
7.9%
Other Punctuation
ValueCountFrequency (%)
, 603
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 31201
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 3871
12.4%
7 3596
11.5%
4 3182
10.2%
5 3157
10.1%
6 3076
9.9%
1 3013
9.7%
8 2875
9.2%
3 2874
9.2%
9 2522
8.1%
0 2432
7.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 31201
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 3871
12.4%
7 3596
11.5%
4 3182
10.2%
5 3157
10.1%
6 3076
9.9%
1 3013
9.7%
8 2875
9.2%
3 2874
9.2%
9 2522
8.1%
0 2432
7.8%

MTHT_CONV_RT_MINM_VL
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct155
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.8423
Minimum-4434
Maximum1101
Zeros0
Zeros (%)0.0%
Negative207
Negative (%)2.1%
Memory size166.0 KiB
2023-12-12T07:33:01.305018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-4434
5-th percentile2
Q14
median6
Q38
95-th percentile14
Maximum1101
Range5535
Interquartile range (IQR)4

Descriptive statistics

Standard deviation53.077025
Coefficient of variation (CV)9.0849536
Kurtosis5078.3609
Mean5.8423
Median Absolute Deviation (MAD)2
Skewness-62.69064
Sum58423
Variance2817.1705
MonotonicityNot monotonic
2023-12-12T07:33:01.440510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 1330
13.3%
5 1279
12.8%
6 1194
11.9%
3 1152
11.5%
7 997
10.0%
2 805
8.1%
8 757
7.6%
9 536
5.4%
10 357
 
3.6%
11 301
 
3.0%
Other values (145) 1292
12.9%
ValueCountFrequency (%)
-4434 1
< 0.1%
-1695 1
< 0.1%
-1443 1
< 0.1%
-283 1
< 0.1%
-187 1
< 0.1%
-186 1
< 0.1%
-175 1
< 0.1%
-168 1
< 0.1%
-110 1
< 0.1%
-109 2
< 0.1%
ValueCountFrequency (%)
1101 1
< 0.1%
459 1
< 0.1%
456 1
< 0.1%
454 1
< 0.1%
408 1
< 0.1%
361 1
< 0.1%
341 1
< 0.1%
299 1
< 0.1%
273 1
< 0.1%
218 1
< 0.1%

MTHT_CONV_RT_MAX_VL
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct120
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.4309
Minimum-2507
Maximum31870
Zeros0
Zeros (%)0.0%
Negative104
Negative (%)1.0%
Memory size166.0 KiB
2023-12-12T07:33:01.757369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-2507
5-th percentile1
Q13
median5
Q37
95-th percentile11
Maximum31870
Range34377
Interquartile range (IQR)4

Descriptive statistics

Standard deviation321.11293
Coefficient of variation (CV)38.087621
Kurtosis9696.7908
Mean8.4309
Median Absolute Deviation (MAD)2
Skewness97.678791
Sum84309
Variance103113.51
MonotonicityNot monotonic
2023-12-12T07:33:01.867907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 1607
16.1%
3 1548
15.5%
5 1456
14.6%
2 1146
11.5%
6 1114
11.1%
7 836
8.4%
8 515
 
5.1%
1 431
 
4.3%
9 375
 
3.8%
10 252
 
2.5%
Other values (110) 720
7.2%
ValueCountFrequency (%)
-2507 1
 
< 0.1%
-885 3
< 0.1%
-586 1
 
< 0.1%
-271 1
 
< 0.1%
-251 1
 
< 0.1%
-240 1
 
< 0.1%
-171 1
 
< 0.1%
-163 1
 
< 0.1%
-145 1
 
< 0.1%
-132 1
 
< 0.1%
ValueCountFrequency (%)
31870 1
 
< 0.1%
1222 3
< 0.1%
939 1
 
< 0.1%
683 1
 
< 0.1%
389 1
 
< 0.1%
257 1
 
< 0.1%
162 1
 
< 0.1%
136 1
 
< 0.1%
134 1
 
< 0.1%
127 1
 
< 0.1%

Interactions

2023-12-12T07:32:52.433983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:45.274627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:46.167802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:46.971305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:47.847719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:48.645610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:49.679123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:50.498785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:51.537906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:52.539184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:45.412780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:46.255113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:47.070276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:47.939096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:48.750338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:49.766771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:50.606715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:51.627215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:52.630294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:45.501228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:46.362579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:47.165614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:48.014955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:49.026961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:49.847143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:50.780462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:51.713383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:52.722434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:45.600271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:46.476562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:47.265216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:48.111719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:49.120531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:49.937255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:50.921213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:51.808967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:52.814496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:45.682502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:46.563732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:47.343454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:48.206479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:49.221832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:50.027370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:51.099291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:51.892287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:52.911063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:45.781246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:46.661888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:47.448760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:48.314109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:49.321872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:50.132057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:51.204332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:52.007332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:52.993930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:45.879961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:46.737066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:47.581624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:48.393981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:49.406296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:50.229083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:51.280806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:52.131324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:53.107848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:45.974687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:46.812701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:47.660089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:48.483850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:49.491137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:50.314845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:51.360867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:52.218368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:53.206154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:46.075812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:46.889146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:47.745307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:48.563058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:49.592671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:50.405574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:51.446486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:52.308165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T07:33:01.972110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
RHMH_HOUSE_MTHT_CONV_RATE_INFO_NOSIDO_NMSIGNGU_NMTNSHP_NMSMOEUCNTRCT_YMCNTRCT_DEMNTH_RENTCGFLRBILDNG_YRCNTRCT_CLSF_NMSUSAR_NMNMHSHMTHT_CONV_RT_MINM_VLMTHT_CONV_RT_MAX_VL
RHMH_HOUSE_MTHT_CONV_RATE_INFO_NO1.0000.9380.9970.9360.2850.0460.0360.3570.2110.3480.2370.1880.2260.0460.042
SIDO_NM0.9381.0000.998NaN0.2700.0690.0200.3690.1540.3210.0800.0000.0000.0280.052
SIGNGU_NM0.9970.9981.0001.0000.3980.1360.1080.3940.2930.5100.2770.3920.5220.0000.045
TNSHP_NM0.936NaN1.0001.0000.4850.0000.0000.3380.2830.4760.230NaNNaN0.0800.116
SMOEU0.2850.2700.3980.4851.0000.0740.0680.5650.2360.4750.0880.3070.3960.0600.000
CNTRCT_YM0.0460.0690.1360.0000.0741.0000.2500.0380.0550.0640.0000.1240.2270.0000.000
CNTRCT_DE0.0360.0200.1080.0000.0680.2501.0000.0280.0180.0430.0510.0000.0000.0000.014
MNTH_RENTCG0.3570.3690.3940.3380.5650.0380.0281.0000.3520.4550.1520.2500.0000.0290.000
FLR0.2110.1540.2930.2830.2360.0550.0180.3521.0000.4100.0510.5340.0000.0570.000
BILDNG_YR0.3480.3210.5100.4760.4750.0640.0430.4550.4101.0000.0870.1930.2970.1230.000
CNTRCT_CLSF_NM0.2370.0800.2770.2300.0880.0000.0510.1520.0510.0871.0000.0390.0000.0210.000
SUSAR_NM0.1880.0000.392NaN0.3070.1240.0000.2500.5340.1930.0391.0000.2750.0000.000
NMHSH0.2260.0000.522NaN0.3960.2270.0000.0000.0000.2970.0000.2751.0000.0000.000
MTHT_CONV_RT_MINM_VL0.0460.0280.0000.0800.0600.0000.0000.0290.0570.1230.0210.0000.0001.0000.164
MTHT_CONV_RT_MAX_VL0.0420.0520.0450.1160.0000.0000.0140.0000.0000.0000.0000.0000.0000.1641.000
2023-12-12T07:33:02.122994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
CNTRCT_YMSIDO_NMTNSHP_NMSUSAR_NMCNTRCT_CLSF_NM
CNTRCT_YM1.0000.0200.0000.0370.000
SIDO_NM0.0201.0001.0000.0000.132
TNSHP_NM0.0001.0001.0001.0000.205
SUSAR_NM0.0370.0001.0001.0000.064
CNTRCT_CLSF_NM0.0000.1320.2050.0641.000
2023-12-12T07:33:02.253561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
RHMH_HOUSE_MTHT_CONV_RATE_INFO_NOSMOEUCNTRCT_DEMNTH_RENTCGFLRBILDNG_YRNMHSHMTHT_CONV_RT_MINM_VLMTHT_CONV_RT_MAX_VLSIDO_NMTNSHP_NMCNTRCT_YMCNTRCT_CLSF_NMSUSAR_NM
RHMH_HOUSE_MTHT_CONV_RATE_INFO_NO1.0000.130-0.013-0.279-0.120-0.1260.0840.3790.3900.9200.8190.0270.1810.142
SMOEU0.1301.0000.025-0.599-0.197-0.307-0.343-0.383-0.4040.1670.2110.0440.0670.243
CNTRCT_DE-0.0130.0251.000-0.0010.0020.006-0.022-0.011-0.0160.0290.0120.1290.0420.000
MNTH_RENTCG-0.279-0.599-0.0011.0000.2380.3830.3340.3990.4120.2020.1680.0160.0750.163
FLR-0.120-0.1970.0020.2381.0000.3590.1310.1300.1390.1010.1370.0250.0440.278
BILDNG_YR-0.126-0.3070.0060.3830.3591.0000.1070.2320.2530.2030.2210.0370.0670.130
NMHSH0.084-0.343-0.0220.3340.1310.1071.0000.1930.1990.0001.0000.1010.0000.117
MTHT_CONV_RT_MINM_VL0.379-0.383-0.0110.3990.1300.2320.1931.0000.9180.0200.0440.0000.0380.000
MTHT_CONV_RT_MAX_VL0.390-0.404-0.0160.4120.1390.2530.1990.9181.0000.0150.1040.0000.0000.000
SIDO_NM0.9200.1670.0290.2020.1010.2030.0000.0200.0151.0001.0000.0200.1320.000
TNSHP_NM0.8190.2110.0120.1680.1370.2211.0000.0440.1041.0001.0000.0000.2051.000
CNTRCT_YM0.0270.0440.1290.0160.0250.0370.1010.0000.0000.0200.0001.0000.0000.037
CNTRCT_CLSF_NM0.1810.0670.0420.0750.0440.0670.0000.0380.0000.1320.2050.0001.0000.064
SUSAR_NM0.1420.2430.0000.1630.2780.1300.1170.0000.0000.0001.0000.0370.0641.000

Missing values

2023-12-12T07:32:53.346393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T07:32:53.571944image/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

RHMH_HOUSE_MTHT_CONV_RATE_INFO_NOSIDO_NMSIGNGU_NMTNSHP_NMEMD_NMHONO_NMSMOEUCNTRCT_YMCNTRCT_DEMTHT_GTNMNTH_RENTCGFLRBILDNG_YRCNTRCT_CLSF_NMSUSAR_NMNMHSHAVE_LFSTS_PC_MINM_AMTAVE_LFSTS_PC_MAX_AMTMTHT_CONV_RT_MINM_VLMTHT_CONV_RT_MAX_VL
1576815769경기도평택시<NA>합정동737-3247.15202208315122013신규<NA><NA>21725597
19351936서울특별시강서구<NA>화곡동362-10729.372022081968232016신규<NA><NA>56165865
99919992서울특별시중랑구<NA>면목동137-538.722022071155162013갱신<NA><NA>66377833
55685569서울특별시서대문구<NA>홍제동136-945.7220220816219142016갱신<NA><NA>42149486
80158016서울특별시송파구<NA>오금동133-2314.982022072833331990신규<NA><NA>57867876
75917592서울특별시송파구<NA>석촌동228-1147.622022091921222004신규<NA><NA>64976233
1399113992경기도안양시동안구관양동1481-423.4452022072343232015신규<NA><NA>37443987
32063207서울특별시구로구<NA>가리봉동134-7239.22202207376141994신규<NA><NA>55665332
1154211543경기도부천시<NA>고강동404-933.75202208242222019신규<NA><NA>26431097
1393213933경기도안산시상록구일동595-929.6420220728101-11997<NA><NA><NA>23027043
RHMH_HOUSE_MTHT_CONV_RATE_INFO_NOSIDO_NMSIGNGU_NMTNSHP_NMEMD_NMHONO_NMSMOEUCNTRCT_YMCNTRCT_DEMTHT_GTNMNTH_RENTCGFLRBILDNG_YRCNTRCT_CLSF_NMSUSAR_NMNMHSHAVE_LFSTS_PC_MINM_AMTAVE_LFSTS_PC_MAX_AMTMTHT_CONV_RT_MINM_VLMTHT_CONV_RT_MAX_VL
84378438서울특별시양천구<NA>목동783-2028.822022071810242018신규<NA><NA>56165843
1365613657경기도안산시상록구부곡동605-447.252022071513132021<NA><NA><NA>22226133
696697서울특별시강남구<NA>청담동13-2135.2820220818198221996갱신<NA><NA>9831,15422
1502415025경기도의정부시<NA>의정부동555-451.272022071839121996신규<NA><NA>24228454
1026110262경기도가평군<NA>설악면34-483.322022082312232022신규<NA>242042401210
18321833서울특별시강서구<NA>화곡동379-5055.32022082490222010신규<NA><NA>56165843
1126811269경기도남양주시<NA>오남읍597-1373.042022082714142019갱신주거지역1628633543
1744317444인천광역시서구<NA>석남동529-337.62022071913111990신규<NA><NA>19723187
1741317414인천광역시서구<NA>당하동1083-132.92022072061242014<NA><NA><NA>277325119
34043405서울특별시구로구<NA>오류동152-1357.122022091735121987신규<NA><NA>36142443