Overview

Dataset statistics

Number of variables7
Number of observations500
Missing cells13
Missing cells (%)0.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory29.4 KiB
Average record size in memory60.3 B

Variable types

Text3
Numeric4

Dataset

Description샘플 데이터
Author오픈메이트
URLhttps://bigdata.seoul.go.kr/data/selectSampleData.do?sample_data_seq=6

Alerts

아파트_호_면적 has 8 (1.6%) missing valuesMissing
아파트_호_코드 has unique valuesUnique

Reproduction

Analysis started2023-12-10 14:58:37.665425
Analysis finished2023-12-10 14:58:42.750197
Duration5.08 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

아파트_호_코드
Text

UNIQUE 

Distinct500
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-10T23:58:43.084864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters5000
Distinct characters13
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

Unique500 ?
Unique (%)100.0%

Sample

1st rowA001436927
2nd rowA001363316
3rd rowA010613853
4th rowA001087303
5th rowA001791540
ValueCountFrequency (%)
a001436927 1
 
0.2%
a001077679 1
 
0.2%
a000176615 1
 
0.2%
a000805616 1
 
0.2%
a000922421 1
 
0.2%
u000353551 1
 
0.2%
a000753490 1
 
0.2%
a010238037 1
 
0.2%
a010561315 1
 
0.2%
a001640354 1
 
0.2%
Other values (490) 490
98.0%
2023-12-10T23:58:43.813692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1509
30.2%
1 576
 
11.5%
A 394
 
7.9%
3 332
 
6.6%
9 330
 
6.6%
6 322
 
6.4%
2 316
 
6.3%
5 283
 
5.7%
8 282
 
5.6%
4 275
 
5.5%
Other values (3) 381
 
7.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4500
90.0%
Uppercase Letter 500
 
10.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1509
33.5%
1 576
 
12.8%
3 332
 
7.4%
9 330
 
7.3%
6 322
 
7.2%
2 316
 
7.0%
5 283
 
6.3%
8 282
 
6.3%
4 275
 
6.1%
7 275
 
6.1%
Uppercase Letter
ValueCountFrequency (%)
A 394
78.8%
B 75
 
15.0%
U 31
 
6.2%

Most occurring scripts

ValueCountFrequency (%)
Common 4500
90.0%
Latin 500
 
10.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1509
33.5%
1 576
 
12.8%
3 332
 
7.4%
9 330
 
7.3%
6 322
 
7.2%
2 316
 
7.0%
5 283
 
6.3%
8 282
 
6.3%
4 275
 
6.1%
7 275
 
6.1%
Latin
ValueCountFrequency (%)
A 394
78.8%
B 75
 
15.0%
U 31
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1509
30.2%
1 576
 
11.5%
A 394
 
7.9%
3 332
 
6.6%
9 330
 
6.6%
6 322
 
6.4%
2 316
 
6.3%
5 283
 
5.7%
8 282
 
5.6%
4 275
 
5.5%
Other values (3) 381
 
7.6%

아파트_동_코드
Text

Distinct492
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-10T23:58:44.291592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters5000
Distinct characters14
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

Unique484 ?
Unique (%)96.8%

Sample

1st rowA000083037
2nd rowA002008669
3rd rowA000031474
4th rowB000035571
5th rowB000072631
ValueCountFrequency (%)
u000016708 2
 
0.4%
a002008669 2
 
0.4%
a000044940 2
 
0.4%
a000065121 2
 
0.4%
a000030488 2
 
0.4%
a000059449 2
 
0.4%
a000059000 2
 
0.4%
a000064797 2
 
0.4%
a000018695 1
 
0.2%
a000012456 1
 
0.2%
Other values (482) 482
96.4%
2023-12-10T23:58:45.072622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 2229
44.6%
A 350
 
7.0%
1 313
 
6.3%
2 290
 
5.8%
4 276
 
5.5%
7 249
 
5.0%
5 240
 
4.8%
8 238
 
4.8%
3 232
 
4.6%
6 217
 
4.3%
Other values (4) 366
 
7.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4500
90.0%
Uppercase Letter 500
 
10.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2229
49.5%
1 313
 
7.0%
2 290
 
6.4%
4 276
 
6.1%
7 249
 
5.5%
5 240
 
5.3%
8 238
 
5.3%
3 232
 
5.2%
6 217
 
4.8%
9 216
 
4.8%
Uppercase Letter
ValueCountFrequency (%)
A 350
70.0%
B 106
 
21.2%
U 34
 
6.8%
X 10
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4500
90.0%
Latin 500
 
10.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2229
49.5%
1 313
 
7.0%
2 290
 
6.4%
4 276
 
6.1%
7 249
 
5.5%
5 240
 
5.3%
8 238
 
5.3%
3 232
 
5.2%
6 217
 
4.8%
9 216
 
4.8%
Latin
ValueCountFrequency (%)
A 350
70.0%
B 106
 
21.2%
U 34
 
6.8%
X 10
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2229
44.6%
A 350
 
7.0%
1 313
 
6.3%
2 290
 
5.8%
4 276
 
5.5%
7 249
 
5.0%
5 240
 
4.8%
8 238
 
4.8%
3 232
 
4.6%
6 217
 
4.3%
Other values (4) 366
 
7.3%

아파트_호_명
Text

Distinct166
Distinct (%)33.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-10T23:58:45.742125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.27
Min length1

Characters and Unicode

Total characters1635
Distinct characters11
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

Unique78 ?
Unique (%)15.6%

Sample

1st row505
2nd row105
3rd row1506
4th rowB02
5th row202
ValueCountFrequency (%)
301 24
 
4.8%
501 22
 
4.4%
201 21
 
4.2%
302 18
 
3.6%
402 17
 
3.4%
202 16
 
3.2%
102 15
 
3.0%
502 14
 
2.8%
101 13
 
2.6%
401 11
 
2.2%
Other values (156) 329
65.8%
2023-12-10T23:58:46.640977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 494
30.2%
1 365
22.3%
2 230
14.1%
3 145
 
8.9%
4 117
 
7.2%
5 104
 
6.4%
6 51
 
3.1%
7 50
 
3.1%
8 38
 
2.3%
9 28
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1622
99.2%
Uppercase Letter 13
 
0.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 494
30.5%
1 365
22.5%
2 230
14.2%
3 145
 
8.9%
4 117
 
7.2%
5 104
 
6.4%
6 51
 
3.1%
7 50
 
3.1%
8 38
 
2.3%
9 28
 
1.7%
Uppercase Letter
ValueCountFrequency (%)
B 13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1622
99.2%
Latin 13
 
0.8%

Most frequent character per script

Common
ValueCountFrequency (%)
0 494
30.5%
1 365
22.5%
2 230
14.2%
3 145
 
8.9%
4 117
 
7.2%
5 104
 
6.4%
6 51
 
3.1%
7 50
 
3.1%
8 38
 
2.3%
9 28
 
1.7%
Latin
ValueCountFrequency (%)
B 13
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1635
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 494
30.2%
1 365
22.3%
2 230
14.1%
3 145
 
8.9%
4 117
 
7.2%
5 104
 
6.4%
6 51
 
3.1%
7 50
 
3.1%
8 38
 
2.3%
9 28
 
1.7%

아파트_호_층_수
Real number (ℝ)

Distinct26
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.492
Minimum-1
Maximum33
Zeros0
Zeros (%)0.0%
Negative16
Negative (%)3.2%
Memory size4.5 KiB
2023-12-10T23:58:46.884606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile1
Q12
median5
Q310
95-th percentile17
Maximum33
Range34
Interquartile range (IQR)8

Descriptive statistics

Standard deviation5.5036821
Coefficient of variation (CV)0.84776373
Kurtosis1.4209135
Mean6.492
Median Absolute Deviation (MAD)3
Skewness1.1981713
Sum3246
Variance30.290517
MonotonicityNot monotonic
2023-12-10T23:58:47.131416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
2 69
13.8%
3 62
12.4%
4 53
10.6%
1 48
 
9.6%
5 39
 
7.8%
6 24
 
4.8%
8 23
 
4.6%
12 20
 
4.0%
11 19
 
3.8%
7 18
 
3.6%
Other values (16) 125
25.0%
ValueCountFrequency (%)
-1 16
 
3.2%
1 48
9.6%
2 69
13.8%
3 62
12.4%
4 53
10.6%
5 39
7.8%
6 24
 
4.8%
7 18
 
3.6%
8 23
 
4.6%
9 16
 
3.2%
ValueCountFrequency (%)
33 1
 
0.2%
29 1
 
0.2%
25 1
 
0.2%
23 3
 
0.6%
22 3
 
0.6%
20 3
 
0.6%
19 3
 
0.6%
18 3
 
0.6%
17 12
2.4%
16 12
2.4%

아파트_호_면적
Real number (ℝ)

MISSING 

Distinct383
Distinct (%)77.8%
Missing8
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean69.678984
Minimum0
Maximum200.1
Zeros1
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:58:47.405816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile25.038
Q149.5
median64.45
Q384.9025
95-th percentile122.5295
Maximum200.1
Range200.1
Interquartile range (IQR)35.4025

Descriptive statistics

Standard deviation30.695798
Coefficient of variation (CV)0.44053165
Kurtosis1.6664448
Mean69.678984
Median Absolute Deviation (MAD)20.33
Skewness0.88661854
Sum34282.06
Variance942.23199
MonotonicityNot monotonic
2023-12-10T23:58:48.066960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
84.98 13
 
2.6%
84.99 7
 
1.4%
84.87 7
 
1.4%
84.96 6
 
1.2%
59.94 6
 
1.2%
59.82 5
 
1.0%
84.93 5
 
1.0%
84.94 5
 
1.0%
84.92 5
 
1.0%
49.5 4
 
0.8%
Other values (373) 429
85.8%
(Missing) 8
 
1.6%
ValueCountFrequency (%)
0.0 1
0.2%
12.01 1
0.2%
12.02 1
0.2%
12.21 1
0.2%
13.72 1
0.2%
14.24 1
0.2%
14.32 1
0.2%
14.52 1
0.2%
14.95 1
0.2%
15.04 1
0.2%
ValueCountFrequency (%)
200.1 1
0.2%
188.94 1
0.2%
175.8 1
0.2%
171.04 1
0.2%
169.48 1
0.2%
168.15 1
0.2%
167.14 1
0.2%
160.31 1
0.2%
160.19 1
0.2%
157.37 1
0.2%

발표_일자
Real number (ℝ)

Distinct13
Distinct (%)2.6%
Missing2
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean20128196
Minimum20120501
Maximum20160620
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:58:48.362164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20120501
5-th percentile20120501
Q120120501
median20120501
Q320120501
95-th percentile20160101
Maximum20160620
Range40119
Interquartile range (IQR)0

Descriptive statistics

Standard deviation14930.038
Coefficient of variation (CV)0.00074174745
Kurtosis0.53790183
Mean20128196
Median Absolute Deviation (MAD)0
Skewness1.5469274
Sum1.0023842 × 1010
Variance2.2290604 × 108
MonotonicityNot monotonic
2023-12-10T23:58:48.610635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
20120501 377
75.4%
20160101 78
 
15.6%
20140101 10
 
2.0%
20130101 8
 
1.6%
20120803 7
 
1.4%
20150101 7
 
1.4%
20140630 4
 
0.8%
20130601 2
 
0.4%
20160620 1
 
0.2%
20140930 1
 
0.2%
Other values (3) 3
 
0.6%
(Missing) 2
 
0.4%
ValueCountFrequency (%)
20120501 377
75.4%
20120803 7
 
1.4%
20130101 8
 
1.6%
20130601 2
 
0.4%
20140101 10
 
2.0%
20140630 4
 
0.8%
20140930 1
 
0.2%
20150101 7
 
1.4%
20150403 1
 
0.2%
20150831 1
 
0.2%
ValueCountFrequency (%)
20160620 1
 
0.2%
20160225 1
 
0.2%
20160101 78
15.6%
20150831 1
 
0.2%
20150403 1
 
0.2%
20150101 7
 
1.4%
20140930 1
 
0.2%
20140630 4
 
0.8%
20140101 10
 
2.0%
20130601 2
 
0.4%

아파트_기준시가_금액
Real number (ℝ)

Distinct291
Distinct (%)58.6%
Missing3
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean2.6692555 × 108
Minimum0
Maximum1.864 × 109
Zeros5
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:58:48.854531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile64000000
Q11.12 × 108
median2.08 × 108
Q33.44 × 108
95-th percentile6.792 × 108
Maximum1.864 × 109
Range1.864 × 109
Interquartile range (IQR)2.32 × 108

Descriptive statistics

Standard deviation2.1916476 × 108
Coefficient of variation (CV)0.82107072
Kurtosis8.6648619
Mean2.6692555 × 108
Median Absolute Deviation (MAD)1.04 × 108
Skewness2.2806938
Sum1.32662 × 1011
Variance4.803319 × 1016
MonotonicityNot monotonic
2023-12-10T23:58:49.116614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
136000000 8
 
1.6%
128000000 5
 
1.0%
176000000 5
 
1.0%
76000000 5
 
1.0%
300000000 5
 
1.0%
96000000 5
 
1.0%
167000000 5
 
1.0%
0 5
 
1.0%
93000000 4
 
0.8%
209000000 4
 
0.8%
Other values (281) 446
89.2%
ValueCountFrequency (%)
0 5
1.0%
29000000 1
 
0.2%
39000000 1
 
0.2%
42000000 1
 
0.2%
48000000 2
 
0.4%
51000000 1
 
0.2%
54000000 1
 
0.2%
56000000 2
 
0.4%
59000000 2
 
0.4%
60000000 1
 
0.2%
ValueCountFrequency (%)
1864000000 1
0.2%
1504000000 1
0.2%
1152000000 1
0.2%
1016000000 1
0.2%
1008000000 1
0.2%
992000000 2
0.4%
976000000 2
0.4%
944000000 1
0.2%
920000000 1
0.2%
896000000 1
0.2%

Interactions

2023-12-10T23:58:41.379592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:38.859220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:39.638499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:40.481817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:41.588471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:39.035780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:39.835371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:40.692475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:41.803449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:39.228502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:40.038239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:40.930009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:42.015995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:39.428672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:40.265749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:41.160019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:58:49.308560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
아파트_호_층_수아파트_호_면적발표_일자아파트_기준시가_금액
아파트_호_층_수1.0000.0480.0000.206
아파트_호_면적0.0481.0000.0000.077
발표_일자0.0000.0001.0000.000
아파트_기준시가_금액0.2060.0770.0001.000
2023-12-10T23:58:49.500391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
아파트_호_층_수아파트_호_면적발표_일자아파트_기준시가_금액
아파트_호_층_수1.000-0.0140.100-0.005
아파트_호_면적-0.0141.0000.031-0.014
발표_일자0.1000.0311.000-0.035
아파트_기준시가_금액-0.005-0.014-0.0351.000

Missing values

2023-12-10T23:58:42.244524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:58:42.471904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-12-10T23:58:42.654157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

아파트_호_코드아파트_동_코드아파트_호_명아파트_호_층_수아파트_호_면적발표_일자아파트_기준시가_금액
0A001436927A000083037505384.9820120501253000000
1A001363316A00200866910515114.7520120501305000000
2A010613853A0000314741506228.1920160101198000000
3A001087303B000035571B02661.2620120501234000000
4A001791540B0000726312021498.3520130601108000000
5A000531281A0000840321109316.1220120501611000000
6B001774677A000043620501180.3320120501144000000
7A010839807A000041537101369.5420120501168000000
8A001009323U00000265319029160.3120120501278000000
9A009355534A000018979601684.9320120501461000000
아파트_호_코드아파트_동_코드아파트_호_명아파트_호_층_수아파트_호_면적발표_일자아파트_기준시가_금액
490A000952905B0000570283062059.9720120501411000000
491A009708033X000001881301684.2820120501102000000
492A009304259A000091430301584.5720120501380000000
493A001449091A000084418110211121.2320160101394000000
494A001500542B0000885514061175.6820120501208000000
495A001553233A0010537233082127.6520120501521000000
496A001015654A0000836089041784.9920120501632000000
497A000502940A0000928371202647.8120120501552000000
498A001426293A0000914557012044.120160101493000000
499U001023130A0000506215021393.1320120501263000000