Overview

Dataset statistics

Number of variables28
Number of observations500
Missing cells764
Missing cells (%)5.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory119.8 KiB
Average record size in memory245.3 B

Variable types

Numeric17
Text5
Categorical6

Dataset

Description샘플 데이터
Author빅밸류
URLhttps://bigdata.seoul.go.kr/data/selectSampleData.do?sample_data_seq=47

Alerts

법정동_구코드(SREG) has constant value ""Constant
대지구분(DAEJI) has constant value ""Constant
사례_대지구분(SAMPLE_DAEJI) has constant value ""Constant
사례_법정동_구코드(SAMPLE_SREG) is highly imbalanced (85.9%)Imbalance
건물이름(BLDGNAME) has 321 (64.2%) missing valuesMissing
동이름(DONGNAME) has 443 (88.6%) missing valuesMissing
번지2(BUNJI2) has 22 (4.4%) zerosZeros
사례_번지2(SAMPLE_BUNJI2) has 29 (5.8%) zerosZeros
거리(SAMPLE_DISTANCE) has 30 (6.0%) zerosZeros

Reproduction

Analysis started2023-12-10 14:56:38.963612
Analysis finished2023-12-10 14:56:39.671932
Duration0.71 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

년월(KEYMONTH)
Real number (ℝ)

Distinct24
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean201956.46
Minimum201901
Maximum202012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:56:39.790353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum201901
5-th percentile201902
Q1201907
median201912
Q3202007
95-th percentile202011.05
Maximum202012
Range111
Interquartile range (IQR)100

Descriptive statistics

Standard deviation50.046272
Coefficient of variation (CV)0.00024780724
Kurtosis-1.9891941
Mean201956.46
Median Absolute Deviation (MAD)11
Skewness0.015231634
Sum1.0097823 × 108
Variance2504.6293
MonotonicityNot monotonic
2023-12-10T23:56:40.049978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
201910 29
 
5.8%
202004 29
 
5.8%
201912 26
 
5.2%
202008 26
 
5.2%
202012 25
 
5.0%
202007 25
 
5.0%
201907 25
 
5.0%
201911 24
 
4.8%
201908 23
 
4.6%
202005 23
 
4.6%
Other values (14) 245
49.0%
ValueCountFrequency (%)
201901 17
3.4%
201902 17
3.4%
201903 21
4.2%
201904 20
4.0%
201905 19
3.8%
201906 12
2.4%
201907 25
5.0%
201908 23
4.6%
201909 19
3.8%
201910 29
5.8%
ValueCountFrequency (%)
202012 25
5.0%
202011 18
3.6%
202010 22
4.4%
202009 14
2.8%
202008 26
5.2%
202007 25
5.0%
202006 19
3.8%
202005 23
4.6%
202004 29
5.8%
202003 19
3.8%

지번주소+동코드(KEY_DONG)
Text

Distinct480
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-10T23:56:40.476903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length24
Mean length24
Min length24

Characters and Unicode

Total characters12000
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique461 ?
Unique (%)92.2%

Sample

1st rowBV1171010600101160015000
2nd rowBV1171010600100650004000
3rd rowBV1171010500102730016000
4th rowBV1171010500102570014000
5th rowBV1171010100102310011000
ValueCountFrequency (%)
bv1171010800100070007000 3
 
0.6%
bv1171010800100270011000 2
 
0.4%
bv1171011100101660007000 2
 
0.4%
bv1171011200100900005000 2
 
0.4%
bv1171010400100360000000 2
 
0.4%
bv1171010100102430022000 2
 
0.4%
bv1171011400103700009000 2
 
0.4%
bv1171010700100130003000 2
 
0.4%
bv1171010700101820010000 2
 
0.4%
bv1171010100102170007000 2
 
0.4%
Other values (470) 479
95.8%
2023-12-10T23:56:41.060859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 5420
45.2%
1 3348
27.9%
7 688
 
5.7%
B 500
 
4.2%
V 500
 
4.2%
2 339
 
2.8%
4 254
 
2.1%
3 227
 
1.9%
5 213
 
1.8%
6 196
 
1.6%
Other values (2) 315
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11000
91.7%
Uppercase Letter 1000
 
8.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5420
49.3%
1 3348
30.4%
7 688
 
6.3%
2 339
 
3.1%
4 254
 
2.3%
3 227
 
2.1%
5 213
 
1.9%
6 196
 
1.8%
8 185
 
1.7%
9 130
 
1.2%
Uppercase Letter
ValueCountFrequency (%)
B 500
50.0%
V 500
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 11000
91.7%
Latin 1000
 
8.3%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5420
49.3%
1 3348
30.4%
7 688
 
6.3%
2 339
 
3.1%
4 254
 
2.3%
3 227
 
2.1%
5 213
 
1.9%
6 196
 
1.8%
8 185
 
1.7%
9 130
 
1.2%
Latin
ValueCountFrequency (%)
B 500
50.0%
V 500
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5420
45.2%
1 3348
27.9%
7 688
 
5.7%
B 500
 
4.2%
V 500
 
4.2%
2 339
 
2.8%
4 254
 
2.1%
3 227
 
1.9%
5 213
 
1.8%
6 196
 
1.6%
Other values (2) 315
 
2.6%

호코드(KEY_HO)
Categorical

Distinct21
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
HO004
73 
HO001
64 
HO003
55 
HO006
52 
HO002
52 
Other values (16)
204 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique2 ?
Unique (%)0.4%

Sample

1st rowHO004
2nd rowHO013
3rd rowHO000
4th rowHO002
5th rowHO011

Common Values

ValueCountFrequency (%)
HO004 73
14.6%
HO001 64
12.8%
HO003 55
11.0%
HO006 52
10.4%
HO002 52
10.4%
HO005 49
9.8%
HO000 43
8.6%
HO007 38
7.6%
HO008 26
 
5.2%
HO009 12
 
2.4%
Other values (11) 36
7.2%

Length

2023-12-10T23:56:41.294084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ho004 73
14.6%
ho001 64
12.8%
ho003 55
11.0%
ho006 52
10.4%
ho002 52
10.4%
ho005 49
9.8%
ho000 43
8.6%
ho007 38
7.6%
ho008 26
 
5.2%
ho009 12
 
2.4%
Other values (11) 36
7.2%

사례주소_연번(KEY_SAMPLE)
Real number (ℝ)

Distinct30
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.58
Minimum1
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:56:41.495497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q17
median15
Q322
95-th percentile29
Maximum30
Range29
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.5494905
Coefficient of variation (CV)0.58638481
Kurtosis-1.1695497
Mean14.58
Median Absolute Deviation (MAD)7.5
Skewness0.11804827
Sum7290
Variance73.093788
MonotonicityNot monotonic
2023-12-10T23:56:41.708889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
17 26
 
5.2%
11 22
 
4.4%
4 21
 
4.2%
6 21
 
4.2%
1 21
 
4.2%
16 21
 
4.2%
7 20
 
4.0%
9 20
 
4.0%
25 19
 
3.8%
5 19
 
3.8%
Other values (20) 290
58.0%
ValueCountFrequency (%)
1 21
4.2%
2 17
3.4%
3 16
3.2%
4 21
4.2%
5 19
3.8%
6 21
4.2%
7 20
4.0%
8 14
2.8%
9 20
4.0%
10 17
3.4%
ValueCountFrequency (%)
30 12
2.4%
29 15
3.0%
28 11
2.2%
27 16
3.2%
26 12
2.4%
25 19
3.8%
24 16
3.2%
23 14
2.8%
22 16
3.2%
21 15
3.0%

주소(ADDRESS)
Text

Distinct373
Distinct (%)74.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-10T23:56:42.151345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length20
Mean length19.468
Min length16

Characters and Unicode

Total characters9734
Distinct characters42
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

Unique292 ?
Unique (%)58.4%

Sample

1st row서울특별시 송파구 석촌동 2*3*1*
2nd row서울특별시 송파구 가락동 1*5*1*
3rd row서울특별시 송파구 잠실동 2*4*2*
4th row서울특별시 송파구 마천동 3*-*
5th row서울특별시 송파구 방이동 1*3*8*
ValueCountFrequency (%)
서울특별시 500
25.0%
송파구 500
25.0%
석촌동 79
 
4.0%
삼전동 72
 
3.6%
방이동 63
 
3.1%
송파동 59
 
2.9%
잠실동 54
 
2.7%
가락동 45
 
2.2%
문정동 38
 
1.9%
오금동 31
 
1.6%
Other values (213) 559
28.0%
2023-12-10T23:56:42.840230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1500
15.4%
* 1367
14.0%
송 559
 
5.7%
파 559
 
5.7%
서 500
 
5.1%
특 500
 
5.1%
별 500
 
5.1%
시 500
 
5.1%
구 500
 
5.1%
울 500
 
5.1%
Other values (32) 2749
28.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5500
56.5%
Space Separator 1500
 
15.4%
Other Punctuation 1367
 
14.0%
Decimal Number 1216
 
12.5%
Dash Punctuation 151
 
1.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
송 559
10.2%
파 559
10.2%
서 500
9.1%
특 500
9.1%
별 500
9.1%
시 500
9.1%
구 500
9.1%
울 500
9.1%
동 500
9.1%
석 79
 
1.4%
Other values (19) 803
14.6%
Decimal Number
ValueCountFrequency (%)
1 400
32.9%
2 199
16.4%
3 114
 
9.4%
5 81
 
6.7%
4 79
 
6.5%
6 78
 
6.4%
9 74
 
6.1%
8 73
 
6.0%
7 62
 
5.1%
0 56
 
4.6%
Space Separator
ValueCountFrequency (%)
1500
100.0%
Other Punctuation
ValueCountFrequency (%)
* 1367
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 151
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5500
56.5%
Common 4234
43.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
송 559
10.2%
파 559
10.2%
서 500
9.1%
특 500
9.1%
별 500
9.1%
시 500
9.1%
구 500
9.1%
울 500
9.1%
동 500
9.1%
석 79
 
1.4%
Other values (19) 803
14.6%
Common
ValueCountFrequency (%)
1500
35.4%
* 1367
32.3%
1 400
 
9.4%
2 199
 
4.7%
- 151
 
3.6%
3 114
 
2.7%
5 81
 
1.9%
4 79
 
1.9%
6 78
 
1.8%
9 74
 
1.7%
Other values (3) 191
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5500
56.5%
ASCII 4234
43.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1500
35.4%
* 1367
32.3%
1 400
 
9.4%
2 199
 
4.7%
- 151
 
3.6%
3 114
 
2.7%
5 81
 
1.9%
4 79
 
1.9%
6 78
 
1.8%
9 74
 
1.7%
Other values (3) 191
 
4.5%
Hangul
ValueCountFrequency (%)
송 559
10.2%
파 559
10.2%
서 500
9.1%
특 500
9.1%
별 500
9.1%
시 500
9.1%
구 500
9.1%
울 500
9.1%
동 500
9.1%
석 79
 
1.4%
Other values (19) 803
14.6%

법정동_구코드(SREG)
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
11710
500 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row11710
2nd row11710
3rd row11710
4th row11710
5th row11710

Common Values

ValueCountFrequency (%)
11710 500
100.0%

Length

2023-12-10T23:56:43.109987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:56:43.290629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
11710 500
100.0%

법정동_동코드(SEUB)
Real number (ℝ)

Distinct11
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10731
Minimum10100
Maximum11400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:56:43.471198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10100
5-th percentile10100
Q110500
median10600
Q311100
95-th percentile11400
Maximum11400
Range1300
Interquartile range (IQR)600

Descriptive statistics

Standard deviation378.45139
Coefficient of variation (CV)0.035267113
Kurtosis-0.97610012
Mean10731
Median Absolute Deviation (MAD)200
Skewness0.25581138
Sum5365500
Variance143225.45
MonotonicityNot monotonic
2023-12-10T23:56:43.682162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
10500 82
16.4%
11100 75
15.0%
10600 71
14.2%
10400 60
12.0%
10100 43
8.6%
11400 40
8.0%
10700 37
7.4%
11200 34
6.8%
10800 34
6.8%
11300 14
 
2.8%
ValueCountFrequency (%)
10100 43
8.6%
10300 10
 
2.0%
10400 60
12.0%
10500 82
16.4%
10600 71
14.2%
10700 37
7.4%
10800 34
6.8%
11100 75
15.0%
11200 34
6.8%
11300 14
 
2.8%
ValueCountFrequency (%)
11400 40
8.0%
11300 14
 
2.8%
11200 34
6.8%
11100 75
15.0%
10800 34
6.8%
10700 37
7.4%
10600 71
14.2%
10500 82
16.4%
10400 60
12.0%
10300 10
 
2.0%

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

2023-12-10T23:56:43.912001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:56:44.060853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 500
100.0%

번지1(BUNJI1)
Real number (ℝ)

Distinct229
Distinct (%)45.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean133.784
Minimum1
Maximum552
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:56:44.676216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile11
Q154
median124
Q3181
95-th percentile297.15
Maximum552
Range551
Interquartile range (IQR)127

Descriptive statistics

Standard deviation92.765555
Coefficient of variation (CV)0.69339798
Kurtosis1.0436347
Mean133.784
Median Absolute Deviation (MAD)67
Skewness0.85281577
Sum66892
Variance8605.4482
MonotonicityNot monotonic
2023-12-10T23:56:44.948721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
150 7
 
1.4%
54 7
 
1.4%
113 6
 
1.2%
50 6
 
1.2%
100 5
 
1.0%
8 5
 
1.0%
11 5
 
1.0%
168 5
 
1.0%
13 5
 
1.0%
117 5
 
1.0%
Other values (219) 444
88.8%
ValueCountFrequency (%)
1 3
0.6%
2 1
 
0.2%
4 2
 
0.4%
5 3
0.6%
6 1
 
0.2%
7 4
0.8%
8 5
1.0%
9 1
 
0.2%
10 2
 
0.4%
11 5
1.0%
ValueCountFrequency (%)
552 2
0.4%
408 1
 
0.2%
400 1
 
0.2%
398 2
0.4%
393 1
 
0.2%
370 1
 
0.2%
366 1
 
0.2%
341 1
 
0.2%
330 1
 
0.2%
329 3
0.6%

번지2(BUNJI2)
Real number (ℝ)

ZEROS 

Distinct49
Distinct (%)9.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.062
Minimum0
Maximum217
Zeros22
Zeros (%)4.4%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:56:45.233876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q14
median9
Q315
95-th percentile31
Maximum217
Range217
Interquartile range (IQR)11

Descriptive statistics

Standard deviation17.038862
Coefficient of variation (CV)1.4126067
Kurtosis83.561399
Mean12.062
Median Absolute Deviation (MAD)5
Skewness7.6627402
Sum6031
Variance290.3228
MonotonicityNot monotonic
2023-12-10T23:56:45.482709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
5 35
 
7.0%
3 32
 
6.4%
6 32
 
6.4%
4 30
 
6.0%
10 28
 
5.6%
1 28
 
5.6%
11 27
 
5.4%
12 26
 
5.2%
13 24
 
4.8%
15 23
 
4.6%
Other values (39) 215
43.0%
ValueCountFrequency (%)
0 22
4.4%
1 28
5.6%
2 19
3.8%
3 32
6.4%
4 30
6.0%
5 35
7.0%
6 32
6.4%
7 22
4.4%
8 17
3.4%
9 18
3.6%
ValueCountFrequency (%)
217 1
0.2%
216 1
0.2%
87 1
0.2%
78 1
0.2%
73 1
0.2%
67 1
0.2%
60 1
0.2%
59 1
0.2%
58 1
0.2%
56 1
0.2%

건물이름(BLDGNAME)
Text

MISSING 

Distinct155
Distinct (%)86.6%
Missing321
Missing (%)64.2%
Memory size4.0 KiB
2023-12-10T23:56:46.059558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length8
Mean length5.3296089
Min length2

Characters and Unicode

Total characters954
Distinct characters134
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique136 ?
Unique (%)76.0%

Sample

1st row와*앤*비*브*
2nd row1*
3rd row다*
4th row그*빌*
5th row한* *
ValueCountFrequency (%)
리*빌 4
 
2.0%
동 4
 
2.0%
1 4
 
2.0%
청*빌 3
 
1.5%
제*동 3
 
1.5%
세*빌 3
 
1.5%
삼*빌 2
 
1.0%
라 2
 
1.0%
트 2
 
1.0%
엘*시 2
 
1.0%
Other values (153) 167
85.2%
2023-12-10T23:56:46.833901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
* 477
50.0%
빌 75
 
7.9%
스 24
 
2.5%
동 18
 
1.9%
17
 
1.8%
하 17
 
1.8%
트 15
 
1.6%
리 14
 
1.5%
파 13
 
1.4%
1 10
 
1.0%
Other values (124) 274
28.7%

Most occurring categories

ValueCountFrequency (%)
Other Punctuation 478
50.1%
Other Letter 437
45.8%
Space Separator 17
 
1.8%
Decimal Number 17
 
1.8%
Uppercase Letter 4
 
0.4%
Letter Number 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
빌 75
 
17.2%
스 24
 
5.5%
동 18
 
4.1%
하 17
 
3.9%
트 15
 
3.4%
리 14
 
3.2%
파 13
 
3.0%
아 9
 
2.1%
주 9
 
2.1%
삼 9
 
2.1%
Other values (113) 234
53.5%
Decimal Number
ValueCountFrequency (%)
1 10
58.8%
2 3
 
17.6%
0 2
 
11.8%
3 1
 
5.9%
5 1
 
5.9%
Other Punctuation
ValueCountFrequency (%)
* 477
99.8%
. 1
 
0.2%
Uppercase Letter
ValueCountFrequency (%)
A 3
75.0%
D 1
 
25.0%
Space Separator
ValueCountFrequency (%)
17
100.0%
Letter Number
ValueCountFrequency (%)
Ⅱ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 512
53.7%
Hangul 437
45.8%
Latin 5
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
빌 75
 
17.2%
스 24
 
5.5%
동 18
 
4.1%
하 17
 
3.9%
트 15
 
3.4%
리 14
 
3.2%
파 13
 
3.0%
아 9
 
2.1%
주 9
 
2.1%
삼 9
 
2.1%
Other values (113) 234
53.5%
Common
ValueCountFrequency (%)
* 477
93.2%
17
 
3.3%
1 10
 
2.0%
2 3
 
0.6%
0 2
 
0.4%
3 1
 
0.2%
. 1
 
0.2%
5 1
 
0.2%
Latin
ValueCountFrequency (%)
A 3
60.0%
Ⅱ 1
 
20.0%
D 1
 
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 516
54.1%
Hangul 437
45.8%
Number Forms 1
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 477
92.4%
17
 
3.3%
1 10
 
1.9%
2 3
 
0.6%
A 3
 
0.6%
0 2
 
0.4%
D 1
 
0.2%
3 1
 
0.2%
. 1
 
0.2%
5 1
 
0.2%
Hangul
ValueCountFrequency (%)
빌 75
 
17.2%
스 24
 
5.5%
동 18
 
4.1%
하 17
 
3.9%
트 15
 
3.4%
리 14
 
3.2%
파 13
 
3.0%
아 9
 
2.1%
주 9
 
2.1%
삼 9
 
2.1%
Other values (113) 234
53.5%
Number Forms
ValueCountFrequency (%)
Ⅱ 1
100.0%

동이름(DONGNAME)
Text

MISSING 

Distinct42
Distinct (%)73.7%
Missing443
Missing (%)88.6%
Memory size4.0 KiB
2023-12-10T23:56:47.202057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length4.3157895
Min length2

Characters and Unicode

Total characters246
Distinct characters56
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique34 ?
Unique (%)59.6%

Sample

1st row1*8*
2nd row1*1*
3rd row1*1*
4th row아*리*
5th row한*
ValueCountFrequency (%)
1 5
 
8.8%
1*1 5
 
8.8%
가 3
 
5.3%
오*다*빌 2
 
3.5%
나 2
 
3.5%
1*3 2
 
3.5%
b 2
 
3.5%
아*리 2
 
3.5%
보*파*빌 1
 
1.8%
지*홈*운 1
 
1.8%
Other values (32) 32
56.1%
2023-12-10T23:56:47.850211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
* 123
50.0%
1 21
 
8.5%
빌 19
 
7.7%
가 4
 
1.6%
다 4
 
1.6%
2 3
 
1.2%
3 3
 
1.2%
리 3
 
1.2%
스 3
 
1.2%
파 3
 
1.2%
Other values (46) 60
24.4%

Most occurring categories

ValueCountFrequency (%)
Other Punctuation 123
50.0%
Other Letter 85
34.6%
Decimal Number 28
 
11.4%
Uppercase Letter 10
 
4.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
빌 19
22.4%
가 4
 
4.7%
다 4
 
4.7%
리 3
 
3.5%
스 3
 
3.5%
파 3
 
3.5%
지 3
 
3.5%
트 2
 
2.4%
정 2
 
2.4%
크 2
 
2.4%
Other values (34) 40
47.1%
Uppercase Letter
ValueCountFrequency (%)
O 2
20.0%
A 2
20.0%
B 2
20.0%
C 1
10.0%
G 1
10.0%
P 1
10.0%
L 1
10.0%
Decimal Number
ValueCountFrequency (%)
1 21
75.0%
2 3
 
10.7%
3 3
 
10.7%
8 1
 
3.6%
Other Punctuation
ValueCountFrequency (%)
* 123
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 151
61.4%
Hangul 85
34.6%
Latin 10
 
4.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
빌 19
22.4%
가 4
 
4.7%
다 4
 
4.7%
리 3
 
3.5%
스 3
 
3.5%
파 3
 
3.5%
지 3
 
3.5%
트 2
 
2.4%
정 2
 
2.4%
크 2
 
2.4%
Other values (34) 40
47.1%
Latin
ValueCountFrequency (%)
O 2
20.0%
A 2
20.0%
B 2
20.0%
C 1
10.0%
G 1
10.0%
P 1
10.0%
L 1
10.0%
Common
ValueCountFrequency (%)
* 123
81.5%
1 21
 
13.9%
2 3
 
2.0%
3 3
 
2.0%
8 1
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 161
65.4%
Hangul 85
34.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 123
76.4%
1 21
 
13.0%
2 3
 
1.9%
3 3
 
1.9%
O 2
 
1.2%
A 2
 
1.2%
B 2
 
1.2%
C 1
 
0.6%
8 1
 
0.6%
G 1
 
0.6%
Other values (2) 2
 
1.2%
Hangul
ValueCountFrequency (%)
빌 19
22.4%
가 4
 
4.7%
다 4
 
4.7%
리 3
 
3.5%
스 3
 
3.5%
파 3
 
3.5%
지 3
 
3.5%
트 2
 
2.4%
정 2
 
2.4%
크 2
 
2.4%
Other values (34) 40
47.1%

호이름(HONAME)
Categorical

Distinct30
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
202
59 
301
56 
401
55 
201
55 
501
48 
Other values (25)
227 

Length

Max length4
Median length3
Mean length3.002
Min length2

Unique

Unique8 ?
Unique (%)1.6%

Sample

1st row401
2nd row501
3rd row501
4th row401
5th row201

Common Values

ValueCountFrequency (%)
202 59
11.8%
301 56
11.2%
401 55
11.0%
201 55
11.0%
501 48
9.6%
402 44
8.8%
302 43
8.6%
203 26
 
5.2%
303 21
 
4.2%
502 18
 
3.6%
Other values (20) 75
15.0%

Length

2023-12-10T23:56:48.211180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
202 59
11.8%
301 56
11.2%
401 55
11.0%
201 55
11.0%
501 48
9.6%
402 44
8.8%
302 43
8.6%
203 26
 
5.2%
303 21
 
4.2%
502 18
 
3.6%
Other values (20) 75
15.0%

예측년월(MONTH)
Real number (ℝ)

Distinct24
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean201955.46
Minimum201900
Maximum202011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:56:48.476896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum201900
5-th percentile201901
Q1201906
median201911
Q3202006
95-th percentile202010.05
Maximum202011
Range111
Interquartile range (IQR)100

Descriptive statistics

Standard deviation50.046272
Coefficient of variation (CV)0.00024780847
Kurtosis-1.9891941
Mean201955.46
Median Absolute Deviation (MAD)11
Skewness0.015231634
Sum1.0097773 × 108
Variance2504.6293
MonotonicityNot monotonic
2023-12-10T23:56:48.726643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
201909 29
 
5.8%
202003 29
 
5.8%
201911 26
 
5.2%
202007 26
 
5.2%
202011 25
 
5.0%
202006 25
 
5.0%
201906 25
 
5.0%
201910 24
 
4.8%
201907 23
 
4.6%
202004 23
 
4.6%
Other values (14) 245
49.0%
ValueCountFrequency (%)
201900 17
3.4%
201901 17
3.4%
201902 21
4.2%
201903 20
4.0%
201904 19
3.8%
201905 12
2.4%
201906 25
5.0%
201907 23
4.6%
201908 19
3.8%
201909 29
5.8%
ValueCountFrequency (%)
202011 25
5.0%
202010 18
3.6%
202009 22
4.4%
202008 14
2.8%
202007 26
5.2%
202006 25
5.0%
202005 19
3.8%
202004 23
4.6%
202003 29
5.8%
202002 19
3.8%

사례주소(SAMPLE_ADDRESS)
Text

Distinct322
Distinct (%)64.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-10T23:56:49.137975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length20
Mean length19.468
Min length14

Characters and Unicode

Total characters9734
Distinct characters45
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

Unique218 ?
Unique (%)43.6%

Sample

1st row서울특별시 송파구 오금동 7*-*1*
2nd row서울특별시 송파구 삼전동 6*-*8*
3rd row서울특별시 송파구 풍납동 3*8*1*
4th row서울특별시 송파구 석촌동 2*7*1*
5th row서울특별시 송파구 문정동 2*-*
ValueCountFrequency (%)
서울특별시 500
25.0%
송파구 491
24.6%
삼전동 92
 
4.6%
석촌동 76
 
3.8%
송파동 59
 
3.0%
방이동 54
 
2.7%
오금동 45
 
2.3%
문정동 44
 
2.2%
잠실동 39
 
2.0%
가락동 33
 
1.7%
Other values (212) 566
28.3%
2023-12-10T23:56:49.844277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1500
15.4%
* 1367
14.0%
송 550
 
5.7%
파 550
 
5.7%
동 509
 
5.2%
서 500
 
5.1%
특 500
 
5.1%
별 500
 
5.1%
시 500
 
5.1%
구 500
 
5.1%
Other values (35) 2758
28.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5500
56.5%
Space Separator 1500
 
15.4%
Other Punctuation 1367
 
14.0%
Decimal Number 1204
 
12.4%
Dash Punctuation 163
 
1.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
송 550
10.0%
파 550
10.0%
동 509
9.3%
서 500
9.1%
특 500
9.1%
별 500
9.1%
시 500
9.1%
구 500
9.1%
울 500
9.1%
삼 92
 
1.7%
Other values (22) 799
14.5%
Decimal Number
ValueCountFrequency (%)
1 394
32.7%
2 201
16.7%
3 102
 
8.5%
5 85
 
7.1%
9 85
 
7.1%
4 82
 
6.8%
7 79
 
6.6%
6 67
 
5.6%
8 65
 
5.4%
0 44
 
3.7%
Space Separator
ValueCountFrequency (%)
1500
100.0%
Other Punctuation
ValueCountFrequency (%)
* 1367
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 163
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5500
56.5%
Common 4234
43.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
송 550
10.0%
파 550
10.0%
동 509
9.3%
서 500
9.1%
특 500
9.1%
별 500
9.1%
시 500
9.1%
구 500
9.1%
울 500
9.1%
삼 92
 
1.7%
Other values (22) 799
14.5%
Common
ValueCountFrequency (%)
1500
35.4%
* 1367
32.3%
1 394
 
9.3%
2 201
 
4.7%
- 163
 
3.8%
3 102
 
2.4%
5 85
 
2.0%
9 85
 
2.0%
4 82
 
1.9%
7 79
 
1.9%
Other values (3) 176
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5500
56.5%
ASCII 4234
43.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1500
35.4%
* 1367
32.3%
1 394
 
9.3%
2 201
 
4.7%
- 163
 
3.8%
3 102
 
2.4%
5 85
 
2.0%
9 85
 
2.0%
4 82
 
1.9%
7 79
 
1.9%
Other values (3) 176
 
4.2%
Hangul
ValueCountFrequency (%)
송 550
10.0%
파 550
10.0%
동 509
9.3%
서 500
9.1%
특 500
9.1%
별 500
9.1%
시 500
9.1%
구 500
9.1%
울 500
9.1%
삼 92
 
1.7%
Other values (22) 799
14.5%

사례_법정동_구코드(SAMPLE_SREG)
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
11710
490 
11740
 
10

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row11710
2nd row11710
3rd row11710
4th row11710
5th row11710

Common Values

ValueCountFrequency (%)
11710 490
98.0%
11740 10
 
2.0%

Length

2023-12-10T23:56:50.103103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:56:50.297073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
11710 490
98.0%
11740 10
 
2.0%

사례_법정동_동코드(SAMPLE_SEUB)
Real number (ℝ)

Distinct11
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10727.4
Minimum10100
Maximum11400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:56:50.464317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10100
5-th percentile10100
Q110500
median10600
Q311100
95-th percentile11300
Maximum11400
Range1300
Interquartile range (IQR)600

Descriptive statistics

Standard deviation356.20641
Coefficient of variation (CV)0.033205288
Kurtosis-0.84827839
Mean10727.4
Median Absolute Deviation (MAD)200
Skewness0.2077319
Sum5363700
Variance126883.01
MonotonicityNot monotonic
2023-12-10T23:56:50.679034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
10500 81
16.2%
11100 81
16.2%
10600 76
15.2%
10400 56
11.2%
10700 45
9.0%
10800 43
8.6%
10100 41
8.2%
11200 34
6.8%
11400 24
 
4.8%
11300 16
 
3.2%
ValueCountFrequency (%)
10100 41
8.2%
10300 3
 
0.6%
10400 56
11.2%
10500 81
16.2%
10600 76
15.2%
10700 45
9.0%
10800 43
8.6%
11100 81
16.2%
11200 34
6.8%
11300 16
 
3.2%
ValueCountFrequency (%)
11400 24
 
4.8%
11300 16
 
3.2%
11200 34
6.8%
11100 81
16.2%
10800 43
8.6%
10700 45
9.0%
10600 76
15.2%
10500 81
16.2%
10400 56
11.2%
10300 3
 
0.6%

사례_대지구분(SAMPLE_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

2023-12-10T23:56:50.909239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:56:51.077335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 500
100.0%

사례_번지1(SAMPLE_BUNJI1)
Real number (ℝ)

Distinct216
Distinct (%)43.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean141.072
Minimum1
Maximum625
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:56:51.284227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q160.75
median124
Q3183
95-th percentile312.2
Maximum625
Range624
Interquartile range (IQR)122.25

Descriptive statistics

Standard deviation103.77457
Coefficient of variation (CV)0.73561422
Kurtosis3.2089651
Mean141.072
Median Absolute Deviation (MAD)62.5
Skewness1.3687311
Sum70536
Variance10769.161
MonotonicityNot monotonic
2023-12-10T23:56:51.546220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
116 9
 
1.8%
100 9
 
1.8%
123 8
 
1.6%
111 7
 
1.4%
166 7
 
1.4%
130 7
 
1.4%
5 6
 
1.2%
124 6
 
1.2%
115 6
 
1.2%
57 6
 
1.2%
Other values (206) 429
85.8%
ValueCountFrequency (%)
1 3
0.6%
2 2
 
0.4%
3 1
 
0.2%
4 1
 
0.2%
5 6
1.2%
6 6
1.2%
7 2
 
0.4%
8 5
1.0%
10 1
 
0.2%
11 1
 
0.2%
ValueCountFrequency (%)
625 1
 
0.2%
560 1
 
0.2%
559 2
0.4%
550 1
 
0.2%
546 3
0.6%
544 1
 
0.2%
408 1
 
0.2%
398 1
 
0.2%
370 1
 
0.2%
366 2
0.4%

사례_번지2(SAMPLE_BUNJI2)
Real number (ℝ)

ZEROS 

Distinct47
Distinct (%)9.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.784
Minimum0
Maximum217
Zeros29
Zeros (%)5.8%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:56:51.826986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median8
Q313
95-th percentile30
Maximum217
Range217
Interquartile range (IQR)10

Descriptive statistics

Standard deviation16.919512
Coefficient of variation (CV)1.5689458
Kurtosis92.561518
Mean10.784
Median Absolute Deviation (MAD)5
Skewness8.3363889
Sum5392
Variance286.26988
MonotonicityNot monotonic
2023-12-10T23:56:52.099424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
2 44
 
8.8%
3 32
 
6.4%
7 32
 
6.4%
1 32
 
6.4%
5 30
 
6.0%
0 29
 
5.8%
8 29
 
5.8%
12 26
 
5.2%
9 26
 
5.2%
4 23
 
4.6%
Other values (37) 197
39.4%
ValueCountFrequency (%)
0 29
5.8%
1 32
6.4%
2 44
8.8%
3 32
6.4%
4 23
4.6%
5 30
6.0%
6 23
4.6%
7 32
6.4%
8 29
5.8%
9 26
5.2%
ValueCountFrequency (%)
217 1
 
0.2%
216 1
 
0.2%
136 1
 
0.2%
76 1
 
0.2%
49 1
 
0.2%
46 2
0.4%
41 3
0.6%
40 1
 
0.2%
39 2
0.4%
38 1
 
0.2%

유사도(SAMPLE_SCORE)
Real number (ℝ)

Distinct443
Distinct (%)88.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1352614
Minimum0.0006
Maximum0.2991
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:56:52.361598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.0006
5-th percentile0.04186
Q10.083075
median0.12105
Q30.180425
95-th percentile0.264115
Maximum0.2991
Range0.2985
Interquartile range (IQR)0.09735

Descriptive statistics

Standard deviation0.068171425
Coefficient of variation (CV)0.50399763
Kurtosis-0.57501425
Mean0.1352614
Median Absolute Deviation (MAD)0.04375
Skewness0.5417404
Sum67.6307
Variance0.0046473432
MonotonicityNot monotonic
2023-12-10T23:56:52.648506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0984 3
 
0.6%
0.1427 3
 
0.6%
0.0774 3
 
0.6%
0.1196 2
 
0.4%
0.0851 2
 
0.4%
0.2324 2
 
0.4%
0.2058 2
 
0.4%
0.1878 2
 
0.4%
0.1086 2
 
0.4%
0.0976 2
 
0.4%
Other values (433) 477
95.4%
ValueCountFrequency (%)
0.0006 1
0.2%
0.0218 1
0.2%
0.0229 1
0.2%
0.027 1
0.2%
0.0279 1
0.2%
0.0283 1
0.2%
0.0285 1
0.2%
0.0292 1
0.2%
0.0303 1
0.2%
0.0304 1
0.2%
ValueCountFrequency (%)
0.2991 1
0.2%
0.2989 2
0.4%
0.2979 1
0.2%
0.2937 1
0.2%
0.2931 1
0.2%
0.2927 1
0.2%
0.2903 1
0.2%
0.29 1
0.2%
0.2877 1
0.2%
0.2871 1
0.2%

거리(SAMPLE_DISTANCE)
Real number (ℝ)

ZEROS 

Distinct102
Distinct (%)20.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean528.54
Minimum0
Maximum1050
Zeros30
Zeros (%)6.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:56:52.933527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1310
median550
Q3782.5
95-th percentile940
Maximum1050
Range1050
Interquartile range (IQR)472.5

Descriptive statistics

Standard deviation288.0688
Coefficient of variation (CV)0.54502744
Kurtosis-1.0013447
Mean528.54
Median Absolute Deviation (MAD)240
Skewness-0.24936916
Sum264270
Variance82983.636
MonotonicityNot monotonic
2023-12-10T23:56:53.216540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 30
 
6.0%
880 10
 
2.0%
800 10
 
2.0%
790 10
 
2.0%
630 10
 
2.0%
760 9
 
1.8%
530 9
 
1.8%
600 9
 
1.8%
750 9
 
1.8%
870 9
 
1.8%
Other values (92) 385
77.0%
ValueCountFrequency (%)
0 30
6.0%
10 3
 
0.6%
20 3
 
0.6%
30 1
 
0.2%
40 1
 
0.2%
50 3
 
0.6%
60 2
 
0.4%
70 1
 
0.2%
80 3
 
0.6%
90 4
 
0.8%
ValueCountFrequency (%)
1050 1
 
0.2%
1020 1
 
0.2%
1010 3
0.6%
1000 2
 
0.4%
990 1
 
0.2%
980 5
1.0%
970 4
0.8%
960 3
0.6%
950 4
0.8%
940 2
 
0.4%

거래가(SAMPLE_MEMEVALUE)
Real number (ℝ)

Distinct178
Distinct (%)35.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32459.45
Minimum12850
Maximum84000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:56:53.471882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum12850
5-th percentile17485
Q125500
median30000
Q337800
95-th percentile51025
Maximum84000
Range71150
Interquartile range (IQR)12300

Descriptive statistics

Standard deviation10793.114
Coefficient of variation (CV)0.33251067
Kurtosis2.4712351
Mean32459.45
Median Absolute Deviation (MAD)5700
Skewness1.1876954
Sum16229725
Variance1.164913 × 108
MonotonicityNot monotonic
2023-12-10T23:56:53.698395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29000 17
 
3.4%
35000 12
 
2.4%
28000 12
 
2.4%
30000 12
 
2.4%
34000 11
 
2.2%
29500 11
 
2.2%
25000 11
 
2.2%
41500 10
 
2.0%
32000 10
 
2.0%
42000 9
 
1.8%
Other values (168) 385
77.0%
ValueCountFrequency (%)
12850 1
 
0.2%
13500 2
0.4%
14300 1
 
0.2%
14950 2
0.4%
15000 4
0.8%
15500 4
0.8%
16000 2
0.4%
16450 1
 
0.2%
16500 3
0.6%
16600 1
 
0.2%
ValueCountFrequency (%)
84000 1
0.2%
80000 1
0.2%
75000 1
0.2%
72000 1
0.2%
71500 1
0.2%
69000 1
0.2%
65000 1
0.2%
63000 1
0.2%
62000 2
0.4%
61000 1
0.2%

거래월(SAMPLE_MEMEMONTH)
Real number (ℝ)

Distinct36
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean201949.82
Minimum201512
Maximum202011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:56:53.952189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum201512
5-th percentile201901
Q1201907
median202001
Q3202007
95-th percentile202010
Maximum202011
Range499
Interquartile range (IQR)100

Descriptive statistics

Standard deviation72.973863
Coefficient of variation (CV)0.00036134651
Kurtosis8.7573922
Mean201949.82
Median Absolute Deviation (MAD)10
Skewness-2.2174745
Sum1.0097491 × 108
Variance5325.1847
MonotonicityNot monotonic
2023-12-10T23:56:54.212958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
202007 39
 
7.8%
202006 33
 
6.6%
201911 29
 
5.8%
201912 28
 
5.6%
202010 25
 
5.0%
202009 24
 
4.8%
202003 24
 
4.8%
202002 23
 
4.6%
202008 22
 
4.4%
201909 21
 
4.2%
Other values (26) 232
46.4%
ValueCountFrequency (%)
201512 2
0.4%
201602 1
0.2%
201603 2
0.4%
201606 1
0.2%
201609 1
0.2%
201702 1
0.2%
201703 2
0.4%
201802 1
0.2%
201803 1
0.2%
201805 1
0.2%
ValueCountFrequency (%)
202011 20
4.0%
202010 25
5.0%
202009 24
4.8%
202008 22
4.4%
202007 39
7.8%
202006 33
6.6%
202005 17
3.4%
202004 13
 
2.6%
202003 24
4.8%
202002 23
4.6%

건축년도(SAMPLE_BUILDYEAR)
Real number (ℝ)

Distinct36
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2004.442
Minimum1981
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:56:54.467656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1981
5-th percentile1990
Q12001
median2003
Q32013
95-th percentile2018
Maximum2020
Range39
Interquartile range (IQR)12

Descriptive statistics

Standard deviation8.8443814
Coefficient of variation (CV)0.0044123908
Kurtosis-0.69438629
Mean2004.442
Median Absolute Deviation (MAD)7
Skewness-0.080002278
Sum1002221
Variance78.223082
MonotonicityNot monotonic
2023-12-10T23:56:54.730707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
2002 85
17.0%
2003 50
 
10.0%
2004 40
 
8.0%
2016 32
 
6.4%
2001 29
 
5.8%
2015 27
 
5.4%
2017 19
 
3.8%
1991 17
 
3.4%
2018 17
 
3.4%
2019 15
 
3.0%
Other values (26) 169
33.8%
ValueCountFrequency (%)
1981 2
 
0.4%
1983 1
 
0.2%
1986 7
1.4%
1987 1
 
0.2%
1988 3
 
0.6%
1989 5
 
1.0%
1990 11
2.2%
1991 17
3.4%
1992 13
2.6%
1993 13
2.6%
ValueCountFrequency (%)
2020 3
 
0.6%
2019 15
3.0%
2018 17
3.4%
2017 19
3.8%
2016 32
6.4%
2015 27
5.4%
2014 9
 
1.8%
2013 7
 
1.4%
2012 5
 
1.0%
2011 6
 
1.2%

층(SAMPLE_FLOOR)
Real number (ℝ)

Distinct7
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.028
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:56:54.966692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum7
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.241894
Coefficient of variation (CV)0.41013671
Kurtosis-0.057998034
Mean3.028
Median Absolute Deviation (MAD)1
Skewness0.5140425
Sum1514
Variance1.5423006
MonotonicityNot monotonic
2023-12-10T23:56:55.179964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2 159
31.8%
3 130
26.0%
4 112
22.4%
5 45
 
9.0%
1 39
 
7.8%
6 11
 
2.2%
7 4
 
0.8%
ValueCountFrequency (%)
1 39
 
7.8%
2 159
31.8%
3 130
26.0%
4 112
22.4%
5 45
 
9.0%
6 11
 
2.2%
7 4
 
0.8%
ValueCountFrequency (%)
7 4
 
0.8%
6 11
 
2.2%
5 45
 
9.0%
4 112
22.4%
3 130
26.0%
2 159
31.8%
1 39
 
7.8%

전용면적(SAMPLE_JYAREA)
Real number (ℝ)

Distinct435
Distinct (%)87.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48.36908
Minimum16.38
Maximum101.28
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:56:55.454861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum16.38
5-th percentile25.1365
Q137.185
median47.445
Q359.1075
95-th percentile75.572
Maximum101.28
Range84.9
Interquartile range (IQR)21.9225

Descriptive statistics

Standard deviation15.755511
Coefficient of variation (CV)0.32573518
Kurtosis-0.2171352
Mean48.36908
Median Absolute Deviation (MAD)10.82
Skewness0.37044212
Sum24184.54
Variance248.23613
MonotonicityNot monotonic
2023-12-10T23:56:56.171118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29.85 4
 
0.8%
46.56 3
 
0.6%
29.93 3
 
0.6%
35.38 3
 
0.6%
54.28 3
 
0.6%
69.13 3
 
0.6%
44.46 3
 
0.6%
59.5 3
 
0.6%
62.16 2
 
0.4%
64.47 2
 
0.4%
Other values (425) 471
94.2%
ValueCountFrequency (%)
16.38 1
0.2%
18.0 1
0.2%
18.45 1
0.2%
18.51 1
0.2%
19.76 1
0.2%
19.92 1
0.2%
20.0 1
0.2%
20.64 1
0.2%
20.91 1
0.2%
21.56 2
0.4%
ValueCountFrequency (%)
101.28 1
0.2%
99.08 1
0.2%
91.33 1
0.2%
87.98 1
0.2%
86.04 1
0.2%
85.0 1
0.2%
84.84 1
0.2%
84.66 1
0.2%
84.46 1
0.2%
84.43 1
0.2%

대지권면적(SAMPLE_DJAREA)
Real number (ℝ)

Distinct401
Distinct (%)80.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.56574
Minimum10.39
Maximum176.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:56:56.414735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10.39
5-th percentile15.99
Q120.645
median26.17
Q331.53
95-th percentile43.6015
Maximum176.6
Range166.21
Interquartile range (IQR)10.885

Descriptive statistics

Standard deviation11.2518
Coefficient of variation (CV)0.40818059
Kurtosis61.973604
Mean27.56574
Median Absolute Deviation (MAD)5.44
Skewness5.3007388
Sum13782.87
Variance126.603
MonotonicityNot monotonic
2023-12-10T23:56:56.656025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17.3 7
 
1.4%
17.9 7
 
1.4%
23.14 4
 
0.8%
26.22 4
 
0.8%
20.16 4
 
0.8%
29.63 4
 
0.8%
25.1 4
 
0.8%
33.38 4
 
0.8%
15.0 3
 
0.6%
17.52 3
 
0.6%
Other values (391) 456
91.2%
ValueCountFrequency (%)
10.39 1
0.2%
11.08 1
0.2%
11.09 1
0.2%
11.19 1
0.2%
11.6 2
0.4%
12.28 1
0.2%
12.77 1
0.2%
12.9 1
0.2%
12.94 1
0.2%
13.05 2
0.4%
ValueCountFrequency (%)
176.6 1
0.2%
74.53 1
0.2%
71.01 1
0.2%
65.27 1
0.2%
58.97 1
0.2%
58.53 1
0.2%
58.0 1
0.2%
56.88 1
0.2%
53.94 1
0.2%
52.8 1
0.2%

Sample

년월(KEYMONTH)지번주소+동코드(KEY_DONG)호코드(KEY_HO)사례주소_연번(KEY_SAMPLE)주소(ADDRESS)법정동_구코드(SREG)법정동_동코드(SEUB)대지구분(DAEJI)번지1(BUNJI1)번지2(BUNJI2)건물이름(BLDGNAME)동이름(DONGNAME)호이름(HONAME)예측년월(MONTH)사례주소(SAMPLE_ADDRESS)사례_법정동_구코드(SAMPLE_SREG)사례_법정동_동코드(SAMPLE_SEUB)사례_대지구분(SAMPLE_DAEJI)사례_번지1(SAMPLE_BUNJI1)사례_번지2(SAMPLE_BUNJI2)유사도(SAMPLE_SCORE)거리(SAMPLE_DISTANCE)거래가(SAMPLE_MEMEVALUE)거래월(SAMPLE_MEMEMONTH)건축년도(SAMPLE_BUILDYEAR)층(SAMPLE_FLOOR)전용면적(SAMPLE_JYAREA)대지권면적(SAMPLE_DJAREA)
0202011BV1171010600101160015000HO0044서울특별시 송파구 석촌동 2*3*1*117101060013293<NA><NA>401202010서울특별시 송파구 오금동 7*-*1*1171011400142110.1081670345002020052006329.233.49
1201910BV1171010600100650004000HO01325서울특별시 송파구 가락동 1*5*1*117101040012025와*앤*비*브*<NA>501201909서울특별시 송파구 삼전동 6*-*8*11710104001268120.1394600300002020032003372.7765.27
2201902BV1171010500102730016000HO00017서울특별시 송파구 잠실동 2*4*2*117101050013113<NA>1*8*501201901서울특별시 송파구 풍납동 3*8*1*1171010500128050.2341220230002019032002252.3226.91
3202008BV1171010500102570014000HO0029서울특별시 송파구 마천동 3*-*1171011200112718<NA><NA>401202007서울특별시 송파구 석촌동 2*7*1*11710106001155150.143510300002020081989441.1126.22
4201911BV1171010100102310011000HO0112서울특별시 송파구 방이동 1*3*8*11710108001125<NA><NA>201201910서울특별시 송파구 문정동 2*-*11710106001123370.0882720210002020032005320.033.12
5202012BV1171010500102910001000HO00017서울특별시 송파구 잠실동 2*3*2*1171011100115912<NA><NA>401202011서울특별시 송파구 삼전동 9*-*11710107001366150.2584590270002020022016442.8430.91
6202007BV1171010400100950003000HO0002서울특별시 송파구 송파동 1*-*6*11710104001282<NA><NA>302202006서울특별시 송파구 송파동 1*9*3*11710114001750.2123880650002019022002350.7729.96
7201907BV1171010100102980008000HO00915서울특별시 송파구 잠실동 3*6*7*11710106001674<NA><NA>402201906서울특별시 송파구 잠실동 2*1*2*117101110015120.2811320240002020111992338.027.27
8201906BV1171010400101010009000HO00627서울특별시 송파구 가락동 4*1*117101110011307<NA><NA>501201905서울특별시 송파구 잠실동 3*1*1*1171010600113640.1646280184002020082016367.1123.46
9201911BV1171010600101760007000HO00111서울특별시 송파구 삼전동 1*3*8*1171010400124213<NA><NA>501201910서울특별시 송파구 오금동 7*-*1*117101110019130.0941260347002020092001242.5916.1
년월(KEYMONTH)지번주소+동코드(KEY_DONG)호코드(KEY_HO)사례주소_연번(KEY_SAMPLE)주소(ADDRESS)법정동_구코드(SREG)법정동_동코드(SEUB)대지구분(DAEJI)번지1(BUNJI1)번지2(BUNJI2)건물이름(BLDGNAME)동이름(DONGNAME)호이름(HONAME)예측년월(MONTH)사례주소(SAMPLE_ADDRESS)사례_법정동_구코드(SAMPLE_SREG)사례_법정동_동코드(SAMPLE_SEUB)사례_대지구분(SAMPLE_DAEJI)사례_번지1(SAMPLE_BUNJI1)사례_번지2(SAMPLE_BUNJI2)유사도(SAMPLE_SCORE)거리(SAMPLE_DISTANCE)거래가(SAMPLE_MEMEVALUE)거래월(SAMPLE_MEMEMONTH)건축년도(SAMPLE_BUILDYEAR)층(SAMPLE_FLOOR)전용면적(SAMPLE_JYAREA)대지권면적(SAMPLE_DJAREA)
490202006BV1171011400100880018000HO0029서울특별시 송파구 송파동 9*117101040011318<NA><NA>202202005서울특별시 송파구 문정동 1*8*3*117101050014660.1332810279002018061991528.8126.8
491202005BV1171011100101480017000HO00324서울특별시 송파구 삼전동 1*4*1*117101050011113에*빌*A*<NA>501202004서울특별시 송파구 삼전동 1*5*7*1171010600116600.0619560170002019112014535.3827.69
492201901BV1171011100101270023000HO0096서울특별시 송파구 송파동 1*0*8*117101060011176<NA><NA>203201900서울특별시 송파구 삼전동 7*-*1*11710104001225230.0851130208002020012013450.2836.25
493201909BV1171010500100540006000HO00430서울특별시 송파구 방이동 2*0*1*1171011100111011성*하*스*<NA>301201908서울특별시 송파구 문정동 1*-*117101040015740.1582470283002019072002259.425.89
494202007BV1171010500102330005000HO00523서울특별시 송파구 가락동 1*8*2*117101110011053<NA><NA>302202006서울특별시 송파구 방이동 1*3*2*11710112001177330.2836410280002019102004273.6219.52
495201911BV1171010700100060001000HO00514서울특별시 송파구 방이동 1*7*1*1171010300123124<NA><NA>203201910서울특별시 송파구 송파동 1*0*2*1171010500129970.1097870292002019031994445.8117.2
496201908BV1171011100101190018000HO00023서울특별시 송파구 잠실동 3*0*7*117101140012504<NA><NA>401201907서울특별시 강동구 성내동 4*4*5*117101110012600.12660177002020102015350.1916.9
497202011BV1171010400101350011000HO00110서울특별시 송파구 삼전동 5*-*117101110011474정*하*스*<NA>302202010서울특별시 송파구 문정동 7*-*11710108001111130.1355950306002020062003436.8735.23
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499202011BV1171010800100360004000HO00711서울특별시 송파구 가락동 1*4*1*11710111001956<NA>1*302202010서울특별시 송파구 잠실동 2*1*9*1174010700156030.1244570690002019082018552.4422.15