Overview

Dataset statistics

Number of variables11
Number of observations10000
Missing cells69
Missing cells (%)0.1%
Duplicate rows90
Duplicate rows (%)0.9%
Total size in memory996.1 KiB
Average record size in memory102.0 B

Variable types

Numeric6
Categorical2
Text3

Alerts

Dataset has 90 (0.9%) duplicate rowsDuplicates
시군구명 is highly overall correlated with 시군명High correlation
시군명 is highly overall correlated with 시군구명High correlation
거래금액(만원) is highly overall correlated with 전용면적(㎡)High correlation
전용면적(㎡) is highly overall correlated with 거래금액(만원)High correlation

Reproduction

Analysis started2023-12-10 21:46:20.413085
Analysis finished2023-12-10 21:46:25.191660
Duration4.78 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기준년도
Real number (ℝ)

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2018.5298
Minimum2015
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T06:46:25.239147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2015
5-th percentile2015
Q12016
median2019
Q32021
95-th percentile2022
Maximum2022
Range7
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.3739578
Coefficient of variation (CV)0.0011760826
Kurtosis-1.4704036
Mean2018.5298
Median Absolute Deviation (MAD)2
Skewness-0.15051807
Sum20185298
Variance5.6356755
MonotonicityNot monotonic
2023-12-11T06:46:25.325722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
2021 2510
25.1%
2015 1461
14.6%
2020 1407
14.1%
2016 1356
13.6%
2017 1145
11.5%
2018 934
 
9.3%
2022 669
 
6.7%
2019 518
 
5.2%
ValueCountFrequency (%)
2015 1461
14.6%
2016 1356
13.6%
2017 1145
11.5%
2018 934
 
9.3%
2019 518
 
5.2%
2020 1407
14.1%
2021 2510
25.1%
2022 669
 
6.7%
ValueCountFrequency (%)
2022 669
 
6.7%
2021 2510
25.1%
2020 1407
14.1%
2019 518
 
5.2%
2018 934
 
9.3%
2017 1145
11.5%
2016 1356
13.6%
2015 1461
14.6%

기준월
Real number (ℝ)

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.3486
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T06:46:25.414426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.461138
Coefficient of variation (CV)0.54518129
Kurtosis-1.2269042
Mean6.3486
Median Absolute Deviation (MAD)3
Skewness0.09374517
Sum63486
Variance11.979476
MonotonicityNot monotonic
2023-12-11T06:46:25.506050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
3 1056
10.6%
4 939
9.4%
6 895
8.9%
10 867
8.7%
12 839
8.4%
1 837
8.4%
7 811
8.1%
2 806
8.1%
11 763
7.6%
5 751
7.5%
Other values (2) 1436
14.4%
ValueCountFrequency (%)
1 837
8.4%
2 806
8.1%
3 1056
10.6%
4 939
9.4%
5 751
7.5%
6 895
8.9%
7 811
8.1%
8 744
7.4%
9 692
6.9%
10 867
8.7%
ValueCountFrequency (%)
12 839
8.4%
11 763
7.6%
10 867
8.7%
9 692
6.9%
8 744
7.4%
7 811
8.1%
6 895
8.9%
5 751
7.5%
4 939
9.4%
3 1056
10.6%

기준일
Real number (ℝ)

Distinct31
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.9121
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T06:46:25.605732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q16
median11
Q325
95-th percentile31
Maximum31
Range30
Interquartile range (IQR)19

Descriptive statistics

Standard deviation10.723715
Coefficient of variation (CV)0.71912846
Kurtosis-1.2854855
Mean14.9121
Median Absolute Deviation (MAD)10
Skewness0.25097155
Sum149121
Variance114.99807
MonotonicityNot monotonic
2023-12-11T06:46:25.725374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
11 1873
18.7%
1 1841
18.4%
31 1738
17.4%
18 209
 
2.1%
13 193
 
1.9%
21 191
 
1.9%
8 187
 
1.9%
17 185
 
1.8%
15 179
 
1.8%
7 173
 
1.7%
Other values (21) 3231
32.3%
ValueCountFrequency (%)
1 1841
18.4%
2 168
 
1.7%
3 151
 
1.5%
4 146
 
1.5%
5 169
 
1.7%
6 135
 
1.4%
7 173
 
1.7%
8 187
 
1.9%
9 144
 
1.4%
10 161
 
1.6%
ValueCountFrequency (%)
31 1738
17.4%
30 153
 
1.5%
29 150
 
1.5%
28 146
 
1.5%
27 123
 
1.2%
26 147
 
1.5%
25 160
 
1.6%
24 159
 
1.6%
23 168
 
1.7%
22 138
 
1.4%

시군명
Categorical

HIGH CORRELATION 

Distinct30
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
성남시
1697 
고양시
1409 
수원시
1296 
부천시
1047 
화성시
699 
Other values (25)
3852 

Length

Max length4
Median length3
Mean length3.0391
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row수원시
2nd row고양시
3rd row용인시
4th row수원시
5th row군포시

Common Values

ValueCountFrequency (%)
성남시 1697
17.0%
고양시 1409
14.1%
수원시 1296
13.0%
부천시 1047
10.5%
화성시 699
7.0%
용인시 620
 
6.2%
안양시 590
 
5.9%
안산시 528
 
5.3%
하남시 336
 
3.4%
의정부시 241
 
2.4%
Other values (20) 1537
15.4%

Length

2023-12-11T06:46:25.833107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
성남시 1697
17.0%
고양시 1409
14.1%
수원시 1296
13.0%
부천시 1047
10.5%
화성시 699
7.0%
용인시 620
 
6.2%
안양시 590
 
5.9%
안산시 528
 
5.3%
하남시 336
 
3.4%
의정부시 241
 
2.4%
Other values (20) 1537
15.4%

시군구명
Categorical

HIGH CORRELATION 

Distinct41
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
성남분당구
1401 
부천시
1047 
고양일산동구
1027 
화성시
699 
수원영통구
575 
Other values (36)
5251 

Length

Max length6
Median length5
Mean length4.3866
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row수원권선구
2nd row고양일산동구
3rd row용인기흥구
4th row수원권선구
5th row군포시

Common Values

ValueCountFrequency (%)
성남분당구 1401
14.0%
부천시 1047
 
10.5%
고양일산동구 1027
 
10.3%
화성시 699
 
7.0%
수원영통구 575
 
5.8%
안양동안구 417
 
4.2%
수원팔달구 417
 
4.2%
안산단원구 370
 
3.7%
하남시 336
 
3.4%
용인수지구 267
 
2.7%
Other values (31) 3444
34.4%

Length

2023-12-11T06:46:25.933041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
성남분당구 1401
14.0%
부천시 1047
 
10.5%
고양일산동구 1027
 
10.3%
화성시 699
 
7.0%
수원영통구 575
 
5.8%
안양동안구 417
 
4.2%
수원팔달구 417
 
4.2%
안산단원구 370
 
3.7%
하남시 336
 
3.4%
용인수지구 267
 
2.7%
Other values (31) 3444
34.4%

법정동명
Text

Distinct410
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T06:46:26.208471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length3.5617
Min length2

Characters and Unicode

Total characters35617
Distinct characters177
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique54 ?
Unique (%)0.5%

Sample

1st row 금곡동
2nd row 장항동
3rd row 중동
4th row권선동
5th row 금정동
ValueCountFrequency (%)
중동 673
 
6.5%
장항동 559
 
5.4%
정자동 439
 
4.2%
백석동 429
 
4.2%
이의동 392
 
3.8%
관양동 328
 
3.2%
인계동 324
 
3.1%
고잔동 260
 
2.5%
수내동 216
 
2.1%
반송동 215
 
2.1%
Other values (301) 6499
62.9%
2023-12-11T06:46:26.648785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 9741
27.3%
5490
 
15.4%
정 875
 
2.5%
중 737
 
2.1%
양 703
 
2.0%
장 693
 
1.9%
항 559
 
1.6%
석 525
 
1.5%
의 510
 
1.4%
곡 465
 
1.3%
Other values (167) 15319
43.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 30071
84.4%
Space Separator 5490
 
15.4%
Decimal Number 56
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 9741
32.4%
정 875
 
2.9%
중 737
 
2.5%
양 703
 
2.3%
장 693
 
2.3%
항 559
 
1.9%
석 525
 
1.7%
의 510
 
1.7%
곡 465
 
1.5%
산 456
 
1.5%
Other values (164) 14807
49.2%
Decimal Number
ValueCountFrequency (%)
2 33
58.9%
1 23
41.1%
Space Separator
ValueCountFrequency (%)
5490
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 30071
84.4%
Common 5546
 
15.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 9741
32.4%
정 875
 
2.9%
중 737
 
2.5%
양 703
 
2.3%
장 693
 
2.3%
항 559
 
1.9%
석 525
 
1.7%
의 510
 
1.7%
곡 465
 
1.5%
산 456
 
1.5%
Other values (164) 14807
49.2%
Common
ValueCountFrequency (%)
5490
99.0%
2 33
 
0.6%
1 23
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 30071
84.4%
ASCII 5546
 
15.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 9741
32.4%
정 875
 
2.9%
중 737
 
2.5%
양 703
 
2.3%
장 693
 
2.3%
항 559
 
1.9%
석 525
 
1.7%
의 510
 
1.7%
곡 465
 
1.5%
산 456
 
1.5%
Other values (164) 14807
49.2%
ASCII
ValueCountFrequency (%)
5490
99.0%
2 33
 
0.6%
1 23
 
0.4%

번지
Text

Distinct1531
Distinct (%)15.4%
Missing69
Missing (%)0.7%
Memory size156.2 KiB
2023-12-11T06:46:26.987082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length4.6382036
Min length1

Characters and Unicode

Total characters46062
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

Unique479 ?
Unique (%)4.8%

Sample

1st row1115-3
2nd row854-1
3rd row1111-9
4th row1013-4
5th row47-24
ValueCountFrequency (%)
255-1 114
 
1.1%
1352 107
 
1.1%
162-2 85
 
0.9%
7 82
 
0.8%
1338 60
 
0.6%
1591 58
 
0.6%
1330 56
 
0.6%
1336-1 51
 
0.5%
1123-2 50
 
0.5%
24 48
 
0.5%
Other values (1521) 9220
92.8%
2023-12-11T06:46:27.437150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 10747
23.3%
- 6269
13.6%
2 4926
10.7%
3 4184
 
9.1%
5 3540
 
7.7%
4 3365
 
7.3%
6 2994
 
6.5%
7 2910
 
6.3%
0 2613
 
5.7%
9 2319
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 39793
86.4%
Dash Punctuation 6269
 
13.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 10747
27.0%
2 4926
12.4%
3 4184
 
10.5%
5 3540
 
8.9%
4 3365
 
8.5%
6 2994
 
7.5%
7 2910
 
7.3%
0 2613
 
6.6%
9 2319
 
5.8%
8 2195
 
5.5%
Dash Punctuation
ValueCountFrequency (%)
- 6269
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 46062
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 10747
23.3%
- 6269
13.6%
2 4926
10.7%
3 4184
 
9.1%
5 3540
 
7.7%
4 3365
 
7.3%
6 2994
 
6.5%
7 2910
 
6.3%
0 2613
 
5.7%
9 2319
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 46062
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 10747
23.3%
- 6269
13.6%
2 4926
10.7%
3 4184
 
9.1%
5 3540
 
7.7%
4 3365
 
7.3%
6 2994
 
6.5%
7 2910
 
6.3%
0 2613
 
5.7%
9 2319
 
5.0%

단지명
Text

Distinct1555
Distinct (%)15.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T06:46:27.672122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length20
Mean length8.476
Min length1

Characters and Unicode

Total characters84760
Distinct characters470
Distinct categories12 ?
Distinct scripts3 ?
Distinct blocks6 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique466 ?
Unique (%)4.7%

Sample

1st row로얄팰리스3차
2nd row양우드라마시티
3rd row하우스타
4th row한라비발디파크
5th row우진아트리움
ValueCountFrequency (%)
오피스텔 654
 
4.3%
시티 232
 
1.5%
푸르지오 207
 
1.4%
광교 191
 
1.2%
동탄 186
 
1.2%
타워 143
 
0.9%
분당 133
 
0.9%
분당풍림아이원플러스오피스텔 113
 
0.7%
힐스테이트 113
 
0.7%
에듀하임1309오피스텔 107
 
0.7%
Other values (1734) 13229
86.4%
2023-12-11T06:46:28.062877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5315
 
6.3%
스 5053
 
6.0%
오 2700
 
3.2%
텔 2198
 
2.6%
이 1960
 
2.3%
피 1943
 
2.3%
트 1743
 
2.1%
리 1679
 
2.0%
빌 1438
 
1.7%
동 1388
 
1.6%
Other values (460) 59343
70.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 68145
80.4%
Space Separator 5315
 
6.3%
Decimal Number 5299
 
6.3%
Uppercase Letter 2618
 
3.1%
Dash Punctuation 861
 
1.0%
Open Punctuation 761
 
0.9%
Close Punctuation 761
 
0.9%
Lowercase Letter 538
 
0.6%
Letter Number 258
 
0.3%
Control 138
 
0.2%
Other values (2) 66
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
스 5053
 
7.4%
오 2700
 
4.0%
텔 2198
 
3.2%
이 1960
 
2.9%
피 1943
 
2.9%
트 1743
 
2.6%
리 1679
 
2.5%
빌 1438
 
2.1%
동 1388
 
2.0%
아 1225
 
1.8%
Other values (393) 46818
68.7%
Uppercase Letter
ValueCountFrequency (%)
I 473
18.1%
A 222
 
8.5%
K 177
 
6.8%
C 172
 
6.6%
E 163
 
6.2%
B 139
 
5.3%
S 132
 
5.0%
T 127
 
4.9%
R 120
 
4.6%
O 111
 
4.2%
Other values (16) 782
29.9%
Lowercase Letter
ValueCountFrequency (%)
e 129
24.0%
l 116
21.6%
i 44
 
8.2%
t 44
 
8.2%
r 40
 
7.4%
h 28
 
5.2%
s 27
 
5.0%
a 23
 
4.3%
w 21
 
3.9%
o 21
 
3.9%
Other values (8) 45
 
8.4%
Decimal Number
ValueCountFrequency (%)
1 1370
25.9%
2 853
16.1%
3 782
14.8%
0 768
14.5%
9 294
 
5.5%
4 289
 
5.5%
6 276
 
5.2%
7 240
 
4.5%
8 229
 
4.3%
5 198
 
3.7%
Letter Number
ValueCountFrequency (%)
Ⅱ 128
49.6%
Ⅰ 93
36.0%
Ⅲ 37
 
14.3%
Other Punctuation
ValueCountFrequency (%)
. 30
61.2%
′ 18
36.7%
: 1
 
2.0%
Math Symbol
ValueCountFrequency (%)
∥ 11
64.7%
｜ 6
35.3%
Space Separator
ValueCountFrequency (%)
5315
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 861
100.0%
Open Punctuation
ValueCountFrequency (%)
( 761
100.0%
Close Punctuation
ValueCountFrequency (%)
) 761
100.0%
Control
ValueCountFrequency (%)
138
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 68145
80.4%
Common 13201
 
15.6%
Latin 3414
 
4.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
스 5053
 
7.4%
오 2700
 
4.0%
텔 2198
 
3.2%
이 1960
 
2.9%
피 1943
 
2.9%
트 1743
 
2.6%
리 1679
 
2.5%
빌 1438
 
2.1%
동 1388
 
2.0%
아 1225
 
1.8%
Other values (393) 46818
68.7%
Latin
ValueCountFrequency (%)
I 473
 
13.9%
A 222
 
6.5%
K 177
 
5.2%
C 172
 
5.0%
E 163
 
4.8%
B 139
 
4.1%
S 132
 
3.9%
e 129
 
3.8%
Ⅱ 128
 
3.7%
T 127
 
3.7%
Other values (37) 1552
45.5%
Common
ValueCountFrequency (%)
5315
40.3%
1 1370
 
10.4%
- 861
 
6.5%
2 853
 
6.5%
3 782
 
5.9%
0 768
 
5.8%
( 761
 
5.8%
) 761
 
5.8%
9 294
 
2.2%
4 289
 
2.2%
Other values (10) 1147
 
8.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 68145
80.4%
ASCII 16322
 
19.3%
Number Forms 258
 
0.3%
Punctuation 18
 
< 0.1%
Math Operators 11
 
< 0.1%
None 6
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5315
32.6%
1 1370
 
8.4%
- 861
 
5.3%
2 853
 
5.2%
3 782
 
4.8%
0 768
 
4.7%
( 761
 
4.7%
) 761
 
4.7%
I 473
 
2.9%
9 294
 
1.8%
Other values (51) 4084
25.0%
Hangul
ValueCountFrequency (%)
스 5053
 
7.4%
오 2700
 
4.0%
텔 2198
 
3.2%
이 1960
 
2.9%
피 1943
 
2.9%
트 1743
 
2.6%
리 1679
 
2.5%
빌 1438
 
2.1%
동 1388
 
2.0%
아 1225
 
1.8%
Other values (393) 46818
68.7%
Number Forms
ValueCountFrequency (%)
Ⅱ 128
49.6%
Ⅰ 93
36.0%
Ⅲ 37
 
14.3%
Punctuation
ValueCountFrequency (%)
′ 18
100.0%
Math Operators
ValueCountFrequency (%)
∥ 11
100.0%
None
ValueCountFrequency (%)
｜ 6
100.0%

층수
Real number (ℝ)

Distinct46
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.8934
Minimum1
Maximum49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T06:46:28.189394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q15
median8
Q311
95-th percentile20
Maximum49
Range48
Interquartile range (IQR)6

Descriptive statistics

Standard deviation5.8243308
Coefficient of variation (CV)0.65490485
Kurtosis4.2379578
Mean8.8934
Median Absolute Deviation (MAD)3
Skewness1.7324961
Sum88934
Variance33.922829
MonotonicityNot monotonic
2023-12-11T06:46:28.309399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
6 971
 
9.7%
5 959
 
9.6%
4 940
 
9.4%
7 881
 
8.8%
8 839
 
8.4%
3 729
 
7.3%
9 675
 
6.8%
10 592
 
5.9%
2 472
 
4.7%
11 464
 
4.6%
Other values (36) 2478
24.8%
ValueCountFrequency (%)
1 37
 
0.4%
2 472
4.7%
3 729
7.3%
4 940
9.4%
5 959
9.6%
6 971
9.7%
7 881
8.8%
8 839
8.4%
9 675
6.8%
10 592
5.9%
ValueCountFrequency (%)
49 1
 
< 0.1%
48 1
 
< 0.1%
47 1
 
< 0.1%
43 2
 
< 0.1%
42 1
 
< 0.1%
41 1
 
< 0.1%
40 1
 
< 0.1%
39 2
 
< 0.1%
38 3
< 0.1%
37 5
0.1%

거래금액(만원)
Real number (ℝ)

HIGH CORRELATION 

Distinct1520
Distinct (%)15.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19356.433
Minimum1000
Maximum180000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T06:46:28.426855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile6000
Q110300
median15393.5
Q323700
95-th percentile46000
Maximum180000
Range179000
Interquartile range (IQR)13400

Descriptive statistics

Standard deviation14603.203
Coefficient of variation (CV)0.75443668
Kurtosis14.059407
Mean19356.433
Median Absolute Deviation (MAD)5993.5
Skewness2.9470316
Sum1.9356433 × 108
Variance2.1325355 × 108
MonotonicityNot monotonic
2023-12-11T06:46:28.544929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13000 140
 
1.4%
15000 137
 
1.4%
10000 135
 
1.4%
14000 132
 
1.3%
12000 119
 
1.2%
11000 113
 
1.1%
17000 111
 
1.1%
16000 110
 
1.1%
9000 108
 
1.1%
18000 102
 
1.0%
Other values (1510) 8793
87.9%
ValueCountFrequency (%)
1000 3
< 0.1%
1400 1
 
< 0.1%
1500 1
 
< 0.1%
1600 2
< 0.1%
1700 1
 
< 0.1%
1750 1
 
< 0.1%
1900 2
< 0.1%
1950 1
 
< 0.1%
2000 2
< 0.1%
2300 1
 
< 0.1%
ValueCountFrequency (%)
180000 1
< 0.1%
155000 2
< 0.1%
149000 1
< 0.1%
145000 1
< 0.1%
140000 1
< 0.1%
139000 1
< 0.1%
136500 1
< 0.1%
134000 1
< 0.1%
130000 1
< 0.1%
129000 2
< 0.1%

전용면적(㎡)
Real number (ℝ)

HIGH CORRELATION 

Distinct2874
Distinct (%)28.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.847493
Minimum10.58
Maximum227.58
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T06:46:28.677757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10.58
5-th percentile19.98
Q126.65
median35.185
Q357.63
95-th percentile91.7625
Maximum227.58
Range217
Interquartile range (IQR)30.98

Descriptive statistics

Standard deviation26.166562
Coefficient of variation (CV)0.58345651
Kurtosis4.5452707
Mean44.847493
Median Absolute Deviation (MAD)11.065
Skewness1.82782
Sum448474.93
Variance684.68895
MonotonicityNot monotonic
2023-12-11T06:46:28.838682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40.0 63
 
0.6%
62.99 46
 
0.5%
42.12 41
 
0.4%
66.57 37
 
0.4%
31.59 34
 
0.3%
24.97 34
 
0.3%
23.42 32
 
0.3%
24.2 31
 
0.3%
30.43 28
 
0.3%
33.97 28
 
0.3%
Other values (2864) 9626
96.3%
ValueCountFrequency (%)
10.58 1
 
< 0.1%
10.78 4
 
< 0.1%
10.8 2
 
< 0.1%
10.83 1
 
< 0.1%
11.16 12
0.1%
11.61 1
 
< 0.1%
12.15 1
 
< 0.1%
12.48 1
 
< 0.1%
12.5 1
 
< 0.1%
12.63 2
 
< 0.1%
ValueCountFrequency (%)
227.58 1
 
< 0.1%
213.84 1
 
< 0.1%
203.06 1
 
< 0.1%
200.51 3
< 0.1%
199.92 2
< 0.1%
183.92 1
 
< 0.1%
182.62 2
< 0.1%
181.1 4
< 0.1%
179.91 1
 
< 0.1%
178.54 2
< 0.1%

Interactions

2023-12-11T06:46:24.326475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:46:21.770297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:46:22.316348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:46:22.829639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:46:23.338556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:46:23.840686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:46:24.401882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:46:21.848464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:46:22.403961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:46:22.916479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:46:23.426632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:46:23.932152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:46:24.674431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:46:21.932190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:46:22.487904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:46:22.998644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:46:23.508321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:46:24.014684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:46:24.764817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:46:22.013202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:46:22.577468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:46:23.078621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:46:23.591841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:46:24.097298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:46:24.835834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:46:22.102367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:46:22.657763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:46:23.169705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:46:23.676239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:46:24.172701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:46:24.914721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:46:22.211690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:46:22.749102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:46:23.259632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:46:23.764892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:46:24.254039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T06:46:28.922984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준년도기준월기준일시군명시군구명층수거래금액(만원)전용면적(㎡)
기준년도1.0000.3460.5000.2470.3200.0720.1830.147
기준월0.3461.0000.1240.1700.1860.0000.0940.081
기준일0.5000.1241.0000.1990.2440.0890.2020.104
시군명0.2470.1700.1991.0001.0000.2830.3720.444
시군구명0.3200.1860.2441.0001.0000.3540.4120.465
층수0.0720.0000.0890.2830.3541.0000.4440.257
거래금액(만원)0.1830.0940.2020.3720.4120.4441.0000.733
전용면적(㎡)0.1470.0810.1040.4440.4650.2570.7331.000
2023-12-11T06:46:29.026312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구명시군명
시군구명1.0000.999
시군명0.9991.000
2023-12-11T06:46:29.096547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준년도기준월기준일층수거래금액(만원)전용면적(㎡)시군명시군구명
기준년도1.000-0.0890.0920.0220.215-0.0320.1050.132
기준월-0.0891.0000.011-0.0070.0510.0450.0550.065
기준일0.0920.0111.0000.0010.0390.0160.0580.079
층수0.022-0.0070.0011.0000.1950.0670.0940.129
거래금액(만원)0.2150.0510.0390.1951.0000.7180.1270.154
전용면적(㎡)-0.0320.0450.0160.0670.7181.0000.1560.178
시군명0.1050.0550.0580.0940.1270.1561.0000.999
시군구명0.1320.0650.0790.1290.1540.1780.9991.000

Missing values

2023-12-11T06:46:25.013120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T06:46:25.136138image/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

기준년도기준월기준일시군명시군구명법정동명번지단지명층수거래금액(만원)전용면적(㎡)
368862019523수원시수원권선구금곡동1115-3로얄팰리스3차42185047.37
31024202023고양시고양일산동구장항동854-1양우드라마시티31000033.97
39655201911용인시용인기흥구중동1111-9하우스타4960023.61
78250201511수원시수원권선구권선동1013-4한라비발디파크61900071.27
166762021314군포시군포시금정동47-24우진아트리움122450039.54
532352017211부천시부천시심곡본동784-7다빈치아파트22300084.07
17120202136용인시용인수지구상현동1116광교2차 푸르지오시티 A동71160025.55
626482016311용인시용인수지구상현동1116광교2차 푸르지오시티 A동91430025.55
73966201531성남시성남분당구구미동18시그마2 오피스텔31600050.3
4501920181211성남시성남분당구정자동168-1대림아크로텔102440041.09
기준년도기준월기준일시군명시군구명법정동명번지단지명층수거래금액(만원)전용면적(㎡)
2168220211029성남시성남분당구수내동16-7한솔인피니티오피스텔31800034.28
604772016531용인시용인기흥구신갈동52-3한도베스트빌61730069.4
457232018111평택시평택시신장동243-3평택 송탄역 클래시아61313317.6
18611202125용인시용인수지구동천동899분당 수지 유타워121560024.96
62982021913하남시하남시학암동661위례 효성해링턴 타워61550024.26
228142021108성남시성남분당구운중동943큐브타워102000032.36
110192021619부천시부천시원종동317-4공간블리체23400084.81
523212017311수원시수원장안구정자동40-5평산시티텔7420022.27
712762015511성남시성남분당구야탑동342-3엔즈 빌 오피스텔61530031.96
5377620171231성남시성남수정구신흥동2463-4한신프라자13925031.05

Duplicate rows

Most frequently occurring

기준년도기준월기준일시군명시군구명법정동명번지단지명층수거래금액(만원)전용면적(㎡)# duplicates
162016531안산시안산단원구원시동774안산 드림타운4848223.425
602021413수원시수원권선구권선동1011-15DK-ECO 권선21595025.034
612021413수원시수원권선구권선동1011-15DK-ECO 권선31615025.034
22015311화성시화성시능동1065-2동탄 퍼스트빌스타31025920.153
192016531안산시안산단원구원시동774안산 드림타운10866923.423
212016531안산시안산단원구원시동774안산 드림타운12866923.423
242017331용인시용인기흥구중동1112-17하우스타 100-3841400048.753
4520191011시흥시시흥시조남동652-2시흥목감레이크타운101739942.463
5120201228수원시수원권선구금곡동1113-1수원 호매실 동광뷰웰21630027.823
58202147안산시안산단원구원시동774안산 드림타운7970023.423