Overview

Dataset statistics

Number of variables12
Number of observations7008
Missing cells4863
Missing cells (%)5.8%
Duplicate rows2
Duplicate rows (%)< 0.1%
Total size in memory698.2 KiB
Average record size in memory102.0 B

Variable types

Text5
Numeric6
DateTime1

Dataset

Description제주특별자치도 제주시 관내 공동주택 현황에 대한 데이터로 주소, 위·경도, 세대수, 연면적, 지상층수, 지하층수, 동명, 사용승인일자, 허가일자 등의 정보를 제공합니다.
Author제주특별자치도 제주시
URLhttps://www.data.go.kr/data/15127459/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
Dataset has 2 (< 0.1%) duplicate rowsDuplicates
위도 is highly overall correlated with 경도High correlation
경도 is highly overall correlated with 위도High correlation
세대수 is highly overall correlated with 연면적(제곱미터) and 1 other fieldsHigh correlation
연면적(제곱미터) is highly overall correlated with 세대수 and 1 other fieldsHigh correlation
지상층수 is highly overall correlated with 세대수 and 1 other fieldsHigh correlation
위도 has 455 (6.5%) missing valuesMissing
경도 has 455 (6.5%) missing valuesMissing
지하층수 has 743 (10.6%) missing valuesMissing
동명 has 3115 (44.4%) missing valuesMissing
연면적(제곱미터) is highly skewed (γ1 = 77.93653424)Skewed
지하층수 has 2799 (39.9%) zerosZeros

Reproduction

Analysis started2024-04-17 18:21:37.739016
Analysis finished2024-04-17 18:21:43.056026
Duration5.32 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Distinct4756
Distinct (%)68.5%
Missing63
Missing (%)0.9%
Memory size54.9 KiB
2024-04-18T03:21:43.312709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length29
Median length27
Mean length20.561555
Min length17

Characters and Unicode

Total characters142800
Distinct characters206
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3977 ?
Unique (%)57.3%

Sample

1st row제주특별자치도 제주시 연삼로 612-1
2nd row제주특별자치도 제주시 연삼로 612-2
3rd row제주특별자치도 제주시 천수동로 29
4th row제주특별자치도 제주시 천수동로 29
5th row제주특별자치도 제주시 천수동로 29
ValueCountFrequency (%)
제주특별자치도 6945
24.1%
제주시 6945
24.1%
애월읍 392
 
1.4%
조천읍 330
 
1.1%
한림읍 160
 
0.6%
14 119
 
0.4%
12 116
 
0.4%
20 112
 
0.4%
10 109
 
0.4%
아연로 109
 
0.4%
Other values (2129) 13480
46.8%
2024-04-18T03:21:43.737408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
21872
15.3%
14043
 
9.8%
13924
 
9.8%
7304
 
5.1%
6963
 
4.9%
6945
 
4.9%
6945
 
4.9%
6945
 
4.9%
6945
 
4.9%
1 5211
 
3.6%
Other values (196) 45703
32.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 97237
68.1%
Space Separator 21872
 
15.3%
Decimal Number 21762
 
15.2%
Dash Punctuation 1929
 
1.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
14043
14.4%
13924
14.3%
7304
 
7.5%
6963
 
7.2%
6945
 
7.1%
6945
 
7.1%
6945
 
7.1%
6945
 
7.1%
4763
 
4.9%
4035
 
4.1%
Other values (184) 18425
18.9%
Decimal Number
ValueCountFrequency (%)
1 5211
23.9%
2 3392
15.6%
3 2324
10.7%
4 2045
 
9.4%
6 1806
 
8.3%
5 1624
 
7.5%
7 1526
 
7.0%
8 1394
 
6.4%
9 1248
 
5.7%
0 1192
 
5.5%
Space Separator
ValueCountFrequency (%)
21872
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1929
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 97237
68.1%
Common 45563
31.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
14043
14.4%
13924
14.3%
7304
 
7.5%
6963
 
7.2%
6945
 
7.1%
6945
 
7.1%
6945
 
7.1%
6945
 
7.1%
4763
 
4.9%
4035
 
4.1%
Other values (184) 18425
18.9%
Common
ValueCountFrequency (%)
21872
48.0%
1 5211
 
11.4%
2 3392
 
7.4%
3 2324
 
5.1%
4 2045
 
4.5%
- 1929
 
4.2%
6 1806
 
4.0%
5 1624
 
3.6%
7 1526
 
3.3%
8 1394
 
3.1%
Other values (2) 2440
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 97237
68.1%
ASCII 45563
31.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
21872
48.0%
1 5211
 
11.4%
2 3392
 
7.4%
3 2324
 
5.1%
4 2045
 
4.5%
- 1929
 
4.2%
6 1806
 
4.0%
5 1624
 
3.6%
7 1526
 
3.3%
8 1394
 
3.1%
Other values (2) 2440
 
5.4%
Hangul
ValueCountFrequency (%)
14043
14.4%
13924
14.3%
7304
 
7.5%
6963
 
7.2%
6945
 
7.1%
6945
 
7.1%
6945
 
7.1%
6945
 
7.1%
4763
 
4.9%
4035
 
4.1%
Other values (184) 18425
18.9%
Distinct4757
Distinct (%)67.9%
Missing0
Missing (%)0.0%
Memory size54.9 KiB
2024-04-18T03:21:44.062462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length30
Median length28
Mean length22.394692
Min length17

Characters and Unicode

Total characters156942
Distinct characters114
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3957 ?
Unique (%)56.5%

Sample

1st row제주특별자치도 제주시 건입동 24-2
2nd row제주특별자치도 제주시 건입동 25
3rd row제주특별자치도 제주시 건입동 111-1
4th row제주특별자치도 제주시 건입동 111-1
5th row제주특별자치도 제주시 건입동 111-1
ValueCountFrequency (%)
제주특별자치도 7008
24.1%
제주시 7008
24.1%
노형동 795
 
2.7%
연동 790
 
2.7%
이도이동 492
 
1.7%
애월읍 396
 
1.4%
일도이동 353
 
1.2%
아라일동 342
 
1.2%
조천읍 335
 
1.2%
도남동 295
 
1.0%
Other values (4659) 11282
38.8%
2024-04-18T03:21:44.489138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
22188
 
14.1%
14016
 
8.9%
14016
 
8.9%
8987
 
5.7%
7011
 
4.5%
7008
 
4.5%
7008
 
4.5%
7008
 
4.5%
7008
 
4.5%
1 6847
 
4.4%
Other values (104) 55845
35.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 96915
61.8%
Decimal Number 32473
 
20.7%
Space Separator 22188
 
14.1%
Dash Punctuation 5366
 
3.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
14016
14.5%
14016
14.5%
8987
9.3%
7011
7.2%
7008
7.2%
7008
7.2%
7008
7.2%
7008
7.2%
5971
 
6.2%
2554
 
2.6%
Other values (92) 16328
16.8%
Decimal Number
ValueCountFrequency (%)
1 6847
21.1%
2 4535
14.0%
3 3625
11.2%
0 3200
9.9%
5 2652
 
8.2%
4 2630
 
8.1%
9 2416
 
7.4%
6 2359
 
7.3%
7 2128
 
6.6%
8 2081
 
6.4%
Space Separator
ValueCountFrequency (%)
22188
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5366
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 96915
61.8%
Common 60027
38.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
14016
14.5%
14016
14.5%
8987
9.3%
7011
7.2%
7008
7.2%
7008
7.2%
7008
7.2%
7008
7.2%
5971
 
6.2%
2554
 
2.6%
Other values (92) 16328
16.8%
Common
ValueCountFrequency (%)
22188
37.0%
1 6847
 
11.4%
- 5366
 
8.9%
2 4535
 
7.6%
3 3625
 
6.0%
0 3200
 
5.3%
5 2652
 
4.4%
4 2630
 
4.4%
9 2416
 
4.0%
6 2359
 
3.9%
Other values (2) 4209
 
7.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 96915
61.8%
ASCII 60027
38.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
22188
37.0%
1 6847
 
11.4%
- 5366
 
8.9%
2 4535
 
7.6%
3 3625
 
6.0%
0 3200
 
5.3%
5 2652
 
4.4%
4 2630
 
4.4%
9 2416
 
4.0%
6 2359
 
3.9%
Other values (2) 4209
 
7.0%
Hangul
ValueCountFrequency (%)
14016
14.5%
14016
14.5%
8987
9.3%
7011
7.2%
7008
7.2%
7008
7.2%
7008
7.2%
7008
7.2%
5971
 
6.2%
2554
 
2.6%
Other values (92) 16328
16.8%

위도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct4592
Distinct (%)70.1%
Missing455
Missing (%)6.5%
Infinite0
Infinite (%)0.0%
Mean33.491925
Minimum33.299858
Maximum33.959073
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.7 KiB
2024-04-18T03:21:44.600235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33.299858
5-th percentile33.454164
Q133.48327
median33.491823
Q333.506276
95-th percentile33.523618
Maximum33.959073
Range0.65921489
Interquartile range (IQR)0.02300565

Descriptive statistics

Standard deviation0.027017988
Coefficient of variation (CV)0.00080670156
Kurtosis47.845271
Mean33.491925
Median Absolute Deviation (MAD)0.01095543
Skewness0.62375156
Sum219472.59
Variance0.00072997168
MonotonicityNot monotonic
2024-04-18T03:21:44.701498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33.48711576 21
 
0.3%
33.5165344 21
 
0.3%
33.51800241 21
 
0.3%
33.50313939 17
 
0.2%
33.45416423 17
 
0.2%
33.52090761 16
 
0.2%
33.4932458 16
 
0.2%
33.51928849 15
 
0.2%
33.45140562 15
 
0.2%
33.49234625 14
 
0.2%
Other values (4582) 6380
91.0%
(Missing) 455
 
6.5%
ValueCountFrequency (%)
33.29985817 3
 
< 0.1%
33.30252617 1
 
< 0.1%
33.30351838 1
 
< 0.1%
33.30605105 1
 
< 0.1%
33.30707461 1
 
< 0.1%
33.30713792 1
 
< 0.1%
33.30841933 3
 
< 0.1%
33.31543342 1
 
< 0.1%
33.32256572 8
0.1%
33.32469372 3
 
< 0.1%
ValueCountFrequency (%)
33.95907306 1
< 0.1%
33.95497659 1
< 0.1%
33.9545685 1
< 0.1%
33.55644337 2
< 0.1%
33.55604081 1
< 0.1%
33.55590459 1
< 0.1%
33.55557636 2
< 0.1%
33.55554722 1
< 0.1%
33.55551005 1
< 0.1%
33.55539465 1
< 0.1%

경도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct4585
Distinct (%)70.0%
Missing455
Missing (%)6.5%
Infinite0
Infinite (%)0.0%
Mean126.51287
Minimum126.16496
Maximum126.95327
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.7 KiB
2024-04-18T03:21:44.806944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.16496
5-th percentile126.37952
Q1126.48224
median126.51943
Q3126.5468
95-th percentile126.62236
Maximum126.95327
Range0.7883161
Interquartile range (IQR)0.064556

Descriptive statistics

Standard deviation0.078826225
Coefficient of variation (CV)0.00062306883
Kurtosis5.1237008
Mean126.51287
Median Absolute Deviation (MAD)0.0325818
Skewness-0.23407734
Sum829038.82
Variance0.0062135738
MonotonicityNot monotonic
2024-04-18T03:21:44.926387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.5761162 21
 
0.3%
126.4732127 21
 
0.3%
126.5739458 21
 
0.3%
126.575733 17
 
0.2%
126.551619 17
 
0.2%
126.5773132 16
 
0.2%
126.6658519 16
 
0.2%
126.5764772 15
 
0.2%
126.5860325 15
 
0.2%
126.5342455 14
 
0.2%
Other values (4575) 6380
91.0%
(Missing) 455
 
6.5%
ValueCountFrequency (%)
126.164956 3
< 0.1%
126.1762743 7
0.1%
126.1772674 3
< 0.1%
126.1789486 1
 
< 0.1%
126.1791524 1
 
< 0.1%
126.1792343 1
 
< 0.1%
126.1792857 1
 
< 0.1%
126.1813534 1
 
< 0.1%
126.1837531 3
< 0.1%
126.2259025 1
 
< 0.1%
ValueCountFrequency (%)
126.9532721 1
 
< 0.1%
126.9439215 1
 
< 0.1%
126.9419184 1
 
< 0.1%
126.9014761 1
 
< 0.1%
126.9008103 2
< 0.1%
126.8683434 3
< 0.1%
126.8678481 2
< 0.1%
126.8666213 3
< 0.1%
126.8605527 3
< 0.1%
126.8580418 1
 
< 0.1%

세대수
Real number (ℝ)

HIGH CORRELATION 

Distinct122
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.244292
Minimum1
Maximum220
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.7 KiB
2024-04-18T03:21:45.039375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q18
median8
Q317
95-th percentile52
Maximum220
Range219
Interquartile range (IQR)9

Descriptive statistics

Standard deviation18.27925
Coefficient of variation (CV)1.1252722
Kurtosis17.016361
Mean16.244292
Median Absolute Deviation (MAD)2
Skewness3.3302772
Sum113840
Variance334.13098
MonotonicityNot monotonic
2024-04-18T03:21:45.139930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8 2340
33.4%
6 527
 
7.5%
7 453
 
6.5%
16 445
 
6.3%
4 289
 
4.1%
12 251
 
3.6%
3 196
 
2.8%
9 164
 
2.3%
19 146
 
2.1%
5 140
 
2.0%
Other values (112) 2057
29.4%
ValueCountFrequency (%)
1 1
 
< 0.1%
2 84
 
1.2%
3 196
 
2.8%
4 289
 
4.1%
5 140
 
2.0%
6 527
 
7.5%
7 453
 
6.5%
8 2340
33.4%
9 164
 
2.3%
10 83
 
1.2%
ValueCountFrequency (%)
220 1
< 0.1%
216 1
< 0.1%
207 1
< 0.1%
198 1
< 0.1%
189 1
< 0.1%
160 1
< 0.1%
158 1
< 0.1%
154 1
< 0.1%
151 1
< 0.1%
148 1
< 0.1%

연면적(제곱미터)
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct5181
Distinct (%)73.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1643.8053
Minimum86.26
Maximum739052
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.7 KiB
2024-04-18T03:21:45.237359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum86.26
5-th percentile323.121
Q1594.6925
median659.86
Q31706.435
95-th percentile5361.51
Maximum739052
Range738965.74
Interquartile range (IQR)1111.7425

Descriptive statistics

Standard deviation9023.8323
Coefficient of variation (CV)5.4895992
Kurtosis6366.5858
Mean1643.8053
Median Absolute Deviation (MAD)272.075
Skewness77.936534
Sum11519788
Variance81429549
MonotonicityNot monotonic
2024-04-18T03:21:45.338905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
659.84 40
 
0.6%
658.64 20
 
0.3%
659.28 19
 
0.3%
659.68 19
 
0.3%
659.76 18
 
0.3%
657.84 17
 
0.2%
659.88 17
 
0.2%
659.52 17
 
0.2%
659.6 17
 
0.2%
659.36 17
 
0.2%
Other values (5171) 6807
97.1%
ValueCountFrequency (%)
86.26 1
 
< 0.1%
99.6 1
 
< 0.1%
110.02 2
 
< 0.1%
110.28 1
 
< 0.1%
110.45 2
 
< 0.1%
112.55 1
 
< 0.1%
115.11 5
0.1%
115.24 1
 
< 0.1%
115.7 1
 
< 0.1%
118.8 1
 
< 0.1%
ValueCountFrequency (%)
739052.0 1
< 0.1%
28838.83 1
< 0.1%
27958.12 1
< 0.1%
19276.45 1
< 0.1%
18801.84 1
< 0.1%
18611.87 1
< 0.1%
18498.37 1
< 0.1%
17455.75 1
< 0.1%
17324.2 1
< 0.1%
16710.28 1
< 0.1%

지상층수
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.9307934
Minimum1
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.7 KiB
2024-04-18T03:21:45.430681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q14
median4
Q35
95-th percentile10
Maximum20
Range19
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.4150897
Coefficient of variation (CV)0.48979737
Kurtosis4.8780498
Mean4.9307934
Median Absolute Deviation (MAD)0
Skewness2.1840612
Sum34555
Variance5.832658
MonotonicityNot monotonic
2024-04-18T03:21:45.515059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
4 3900
55.7%
5 833
 
11.9%
3 694
 
9.9%
7 346
 
4.9%
10 278
 
4.0%
2 215
 
3.1%
6 198
 
2.8%
12 133
 
1.9%
9 97
 
1.4%
8 87
 
1.2%
Other values (9) 227
 
3.2%
ValueCountFrequency (%)
1 23
 
0.3%
2 215
 
3.1%
3 694
 
9.9%
4 3900
55.7%
5 833
 
11.9%
6 198
 
2.8%
7 346
 
4.9%
8 87
 
1.2%
9 97
 
1.4%
10 278
 
4.0%
ValueCountFrequency (%)
20 1
 
< 0.1%
18 5
 
0.1%
17 1
 
< 0.1%
16 2
 
< 0.1%
15 53
 
0.8%
14 32
 
0.5%
13 44
 
0.6%
12 133
1.9%
11 66
 
0.9%
10 278
4.0%

지하층수
Real number (ℝ)

MISSING  ZEROS 

Distinct6
Distinct (%)0.1%
Missing743
Missing (%)10.6%
Infinite0
Infinite (%)0.0%
Mean0.58371907
Minimum0
Maximum5
Zeros2799
Zeros (%)39.9%
Negative0
Negative (%)0.0%
Memory size61.7 KiB
2024-04-18T03:21:45.598067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile1
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.56141631
Coefficient of variation (CV)0.96179196
Kurtosis1.4221493
Mean0.58371907
Median Absolute Deviation (MAD)0
Skewness0.53929619
Sum3657
Variance0.31518827
MonotonicityNot monotonic
2024-04-18T03:21:45.688994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 3299
47.1%
0 2799
39.9%
2 152
 
2.2%
3 8
 
0.1%
4 5
 
0.1%
5 2
 
< 0.1%
(Missing) 743
 
10.6%
ValueCountFrequency (%)
0 2799
39.9%
1 3299
47.1%
2 152
 
2.2%
3 8
 
0.1%
4 5
 
0.1%
5 2
 
< 0.1%
ValueCountFrequency (%)
5 2
 
< 0.1%
4 5
 
0.1%
3 8
 
0.1%
2 152
 
2.2%
1 3299
47.1%
0 2799
39.9%

동명
Text

MISSING 

Distinct1083
Distinct (%)27.8%
Missing3115
Missing (%)44.4%
Memory size54.9 KiB
2024-04-18T03:21:45.947256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length4
Mean length4.3609042
Min length1

Characters and Unicode

Total characters16977
Distinct characters305
Distinct categories9 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique915 ?
Unique (%)23.5%

Sample

1st row그린스페이스(休)II
2nd row106동
3rd row107동
4th row108동
5th row101동
ValueCountFrequency (%)
101동 390
 
9.4%
102동 380
 
9.1%
103동 293
 
7.0%
104동 188
 
4.5%
가동 168
 
4.0%
나동 162
 
3.9%
105동 147
 
3.5%
106동 104
 
2.5%
에이동 80
 
1.9%
비동 72
 
1.7%
Other values (935) 2186
52.4%
2024-04-18T03:21:46.309763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3286
19.4%
1 2626
15.5%
0 2186
 
12.9%
2 871
 
5.1%
3 553
 
3.3%
356
 
2.1%
352
 
2.1%
332
 
2.0%
313
 
1.8%
4 304
 
1.8%
Other values (295) 5798
34.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 9267
54.6%
Decimal Number 7233
42.6%
Space Separator 285
 
1.7%
Uppercase Letter 138
 
0.8%
Dash Punctuation 17
 
0.1%
Lowercase Letter 15
 
0.1%
Close Punctuation 10
 
0.1%
Open Punctuation 10
 
0.1%
Letter Number 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
3286
35.5%
356
 
3.8%
352
 
3.8%
332
 
3.6%
313
 
3.4%
278
 
3.0%
188
 
2.0%
186
 
2.0%
185
 
2.0%
170
 
1.8%
Other values (270) 3621
39.1%
Decimal Number
ValueCountFrequency (%)
1 2626
36.3%
0 2186
30.2%
2 871
 
12.0%
3 553
 
7.6%
4 304
 
4.2%
5 255
 
3.5%
6 170
 
2.4%
7 110
 
1.5%
8 95
 
1.3%
9 63
 
0.9%
Uppercase Letter
ValueCountFrequency (%)
A 52
37.7%
B 44
31.9%
C 19
 
13.8%
D 12
 
8.7%
E 3
 
2.2%
I 3
 
2.2%
G 2
 
1.4%
F 2
 
1.4%
H 1
 
0.7%
Space Separator
ValueCountFrequency (%)
285
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 17
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 15
100.0%
Close Punctuation
ValueCountFrequency (%)
) 10
100.0%
Open Punctuation
ValueCountFrequency (%)
( 10
100.0%
Letter Number
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 9266
54.6%
Common 7555
44.5%
Latin 155
 
0.9%
Han 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
3286
35.5%
356
 
3.8%
352
 
3.8%
332
 
3.6%
313
 
3.4%
278
 
3.0%
188
 
2.0%
186
 
2.0%
185
 
2.0%
170
 
1.8%
Other values (269) 3620
39.1%
Common
ValueCountFrequency (%)
1 2626
34.8%
0 2186
28.9%
2 871
 
11.5%
3 553
 
7.3%
4 304
 
4.0%
285
 
3.8%
5 255
 
3.4%
6 170
 
2.3%
7 110
 
1.5%
8 95
 
1.3%
Other values (4) 100
 
1.3%
Latin
ValueCountFrequency (%)
A 52
33.5%
B 44
28.4%
C 19
 
12.3%
e 15
 
9.7%
D 12
 
7.7%
E 3
 
1.9%
I 3
 
1.9%
G 2
 
1.3%
F 2
 
1.3%
2
 
1.3%
Han
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 9266
54.6%
ASCII 7708
45.4%
Number Forms 2
 
< 0.1%
CJK 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
3286
35.5%
356
 
3.8%
352
 
3.8%
332
 
3.6%
313
 
3.4%
278
 
3.0%
188
 
2.0%
186
 
2.0%
185
 
2.0%
170
 
1.8%
Other values (269) 3620
39.1%
ASCII
ValueCountFrequency (%)
1 2626
34.1%
0 2186
28.4%
2 871
 
11.3%
3 553
 
7.2%
4 304
 
3.9%
285
 
3.7%
5 255
 
3.3%
6 170
 
2.2%
7 110
 
1.4%
8 95
 
1.2%
Other values (14) 253
 
3.3%
Number Forms
ValueCountFrequency (%)
2
100.0%
CJK
ValueCountFrequency (%)
1
100.0%
Distinct3108
Distinct (%)44.3%
Missing0
Missing (%)0.0%
Memory size54.9 KiB
2024-04-18T03:21:46.568781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.9982877
Min length6

Characters and Unicode

Total characters70068
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

Unique1707 ?
Unique (%)24.4%

Sample

1st row2007-01-03
2nd row2007-01-12
3rd row2017-02-22
4th row2017-02-22
5th row2017-02-22
ValueCountFrequency (%)
2024-01-10 25
 
0.4%
2006-01-23 23
 
0.3%
2013-01-28 23
 
0.3%
2010-12-28 23
 
0.3%
2023-09-22 23
 
0.3%
1998-01-15 22
 
0.3%
1998-01-10 21
 
0.3%
2001-01-20 20
 
0.3%
2020-08-14 20
 
0.3%
2020-12-17 18
 
0.3%
Other values (3098) 6790
96.9%
2024-04-18T03:21:46.939595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 15266
21.8%
- 14010
20.0%
1 12009
17.1%
2 11532
16.5%
9 5244
 
7.5%
3 2860
 
4.1%
8 2165
 
3.1%
4 1893
 
2.7%
7 1847
 
2.6%
6 1671
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 56058
80.0%
Dash Punctuation 14010
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15266
27.2%
1 12009
21.4%
2 11532
20.6%
9 5244
 
9.4%
3 2860
 
5.1%
8 2165
 
3.9%
4 1893
 
3.4%
7 1847
 
3.3%
6 1671
 
3.0%
5 1571
 
2.8%
Dash Punctuation
ValueCountFrequency (%)
- 14010
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 70068
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15266
21.8%
- 14010
20.0%
1 12009
17.1%
2 11532
16.5%
9 5244
 
7.5%
3 2860
 
4.1%
8 2165
 
3.1%
4 1893
 
2.7%
7 1847
 
2.6%
6 1671
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 70068
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15266
21.8%
- 14010
20.0%
1 12009
17.1%
2 11532
16.5%
9 5244
 
7.5%
3 2860
 
4.1%
8 2165
 
3.1%
4 1893
 
2.7%
7 1847
 
2.6%
6 1671
 
2.4%
Distinct3069
Distinct (%)44.0%
Missing32
Missing (%)0.5%
Memory size54.9 KiB
2024-04-18T03:21:47.190276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.9969897
Min length6

Characters and Unicode

Total characters69739
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

Unique1696 ?
Unique (%)24.3%

Sample

1st row2005-10-10
2nd row2005-09-15
3rd row2016-01-07
4th row2016-01-07
5th row2016-01-07
ValueCountFrequency (%)
1995-12-20 43
 
0.6%
2011-12-01 41
 
0.6%
2015-05-28 37
 
0.5%
2002-11-09 33
 
0.5%
2019-07-04 22
 
0.3%
2012-06-07 21
 
0.3%
2006-12-15 21
 
0.3%
2017-04-01 20
 
0.3%
2012-04-26 18
 
0.3%
1995-05-17 18
 
0.3%
Other values (3059) 6702
96.1%
2024-04-18T03:21:47.773852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 15329
22.0%
- 13940
20.0%
1 12068
17.3%
2 10428
15.0%
9 5582
 
8.0%
5 2391
 
3.4%
3 2181
 
3.1%
6 2143
 
3.1%
8 2129
 
3.1%
4 1819
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 55799
80.0%
Dash Punctuation 13940
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15329
27.5%
1 12068
21.6%
2 10428
18.7%
9 5582
 
10.0%
5 2391
 
4.3%
3 2181
 
3.9%
6 2143
 
3.8%
8 2129
 
3.8%
4 1819
 
3.3%
7 1729
 
3.1%
Dash Punctuation
ValueCountFrequency (%)
- 13940
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 69739
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15329
22.0%
- 13940
20.0%
1 12068
17.3%
2 10428
15.0%
9 5582
 
8.0%
5 2391
 
3.4%
3 2181
 
3.1%
6 2143
 
3.1%
8 2129
 
3.1%
4 1819
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69739
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15329
22.0%
- 13940
20.0%
1 12068
17.3%
2 10428
15.0%
9 5582
 
8.0%
5 2391
 
3.4%
3 2181
 
3.1%
6 2143
 
3.1%
8 2129
 
3.1%
4 1819
 
2.6%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size54.9 KiB
Minimum2024-02-29 00:00:00
Maximum2024-02-29 00:00:00
2024-04-18T03:21:47.866395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:21:47.932202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2024-04-18T03:21:42.307054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:21:39.740983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:21:40.229738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:21:40.705226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:21:41.119743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:21:41.847702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:21:42.383282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:21:39.855836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:21:40.312488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:21:40.776338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:21:41.196264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:21:41.922384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:21:42.457886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:21:39.936416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:21:40.400600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:21:40.848772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:21:41.275089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:21:41.999777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:21:42.521457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:21:40.004903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:21:40.473488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:21:40.910888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:21:41.342705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:21:42.067804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:21:42.594553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:21:40.081488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:21:40.553527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:21:40.981078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:21:41.420198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:21:42.161483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:21:42.666868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:21:40.156394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:21:40.632893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:21:41.053277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:21:41.520810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:21:42.237125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-18T03:21:47.990988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도세대수연면적(제곱미터)지상층수지하층수
위도1.0000.9140.0740.0000.2800.113
경도0.9141.0000.1240.0000.3590.154
세대수0.0740.1241.0000.0000.7100.628
연면적(제곱미터)0.0000.0000.0001.0000.0000.000
지상층수0.2800.3590.7100.0001.0000.534
지하층수0.1130.1540.6280.0000.5341.000
2024-04-18T03:21:48.091510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도세대수연면적(제곱미터)지상층수지하층수
위도1.0000.591-0.016-0.0360.0980.075
경도0.5911.0000.0310.0360.0550.013
세대수-0.0160.0311.0000.8430.6980.268
연면적(제곱미터)-0.0360.0360.8431.0000.7070.348
지상층수0.0980.0550.6980.7071.0000.157
지하층수0.0750.0130.2680.3480.1571.000

Missing values

2024-04-18T03:21:42.761863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-18T03:21:42.889020image/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.
2024-04-18T03:21:42.994604image/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

도로명주소지번주소위도경도세대수연면적(제곱미터)지상층수지하층수동명사용승인일자허가일자데이터기준일자
0제주특별자치도 제주시 연삼로 612-1제주특별자치도 제주시 건입동 24-233.50469126.553326196223.8441<NA>2007-01-032005-10-102024-02-29
1제주특별자치도 제주시 연삼로 612-2제주특별자치도 제주시 건입동 2533.505108126.554146196097.7941그린스페이스(休)II2007-01-122005-09-152024-02-29
2제주특별자치도 제주시 천수동로 29제주특별자치도 제주시 건입동 111-133.507875126.5463618835.1541106동2017-02-222016-01-072024-02-29
3제주특별자치도 제주시 천수동로 29제주특별자치도 제주시 건입동 111-133.507875126.5463618942.741107동2017-02-222016-01-072024-02-29
4제주특별자치도 제주시 천수동로 29제주특별자치도 제주시 건입동 111-133.507875126.5463618942.7341108동2017-02-222016-01-072024-02-29
5제주특별자치도 제주시 천수동로 29제주특별자치도 제주시 건입동 111-133.507875126.5463618801.084<NA>101동2017-02-222016-01-072024-02-29
6제주특별자치도 제주시 천수동로 29제주특별자치도 제주시 건입동 111-133.507875126.5463618801.084<NA>102동2017-02-222016-01-072024-02-29
7제주특별자치도 제주시 천수동로 29제주특별자치도 제주시 건입동 111-133.507875126.5463618801.084<NA>103동2017-02-222016-01-072024-02-29
8제주특별자치도 제주시 천수동로 29제주특별자치도 제주시 건입동 111-133.507875126.5463618801.084<NA>104동2017-02-222016-01-072024-02-29
9제주특별자치도 제주시 천수동로 29제주특별자치도 제주시 건입동 111-133.507875126.5463618801.084<NA>105동2017-02-222016-01-072024-02-29
도로명주소지번주소위도경도세대수연면적(제곱미터)지상층수지하층수동명사용승인일자허가일자데이터기준일자
6998제주특별자치도 제주시 중산간동로 151제주특별자치도 제주시 회천동 258533.499634126.607112850.084<NA>105동2017-04-042015-10-292024-02-29
6999제주특별자치도 제주시 중산간동로 151제주특별자치도 제주시 회천동 258533.499634126.607112774.04<NA>106동2017-04-042015-10-292024-02-29
7000제주특별자치도 제주시 서회천서1길 11-1제주특별자치도 제주시 회천동 284733.498394126.604898645.040101동2017-01-312016-01-282024-02-29
7001제주특별자치도 제주시 서회천서1길 11-1제주특별자치도 제주시 회천동 284733.498394126.604898645.040102동2017-01-312016-01-282024-02-29
7002제주특별자치도 제주시 서회천서1길 11-1제주특별자치도 제주시 회천동 284733.498394126.604898645.040103동2017-01-312016-01-282024-02-29
7003제주특별자치도 제주시 서회천서1길 11-1제주특별자치도 제주시 회천동 284733.498394126.604898637.6440104동2017-01-312016-01-282024-02-29
7004제주특별자치도 제주시 서회천서1길 11-1제주특별자치도 제주시 회천동 284733.498394126.604898645.040105동2017-01-312016-01-282024-02-29
7005제주특별자치도 제주시 서회천서1길 11-1제주특별자치도 제주시 회천동 284733.498394126.604898645.040106동2017-01-312016-01-282024-02-29
7006제주특별자치도 제주시 서회천서1길 11제주특별자치도 제주시 회천동 2896-333.498458126.60554981407.9841101동2017-07-252016-09-012024-02-29
7007제주특별자치도 제주시 서회천서1길 11제주특별자치도 제주시 회천동 2896-333.498458126.6055498653.2341102동2017-07-252016-09-012024-02-29

Duplicate rows

Most frequently occurring

도로명주소지번주소위도경도세대수연면적(제곱미터)지상층수지하층수동명사용승인일자허가일자데이터기준일자# duplicates
0제주특별자치도 제주시 애월읍 신엄8길 25제주특별자치도 제주시 애월읍 신엄리 1391-433.469015126.3681778490.3240<NA>1999-12-151999-07-012024-02-292
1제주특별자치도 제주시 애월읍 하귀로15길 14제주특별자치도 제주시 애월읍 하귀1리 1358-333.482438126.4084248659.6840<NA>1998-08-281997-10-232024-02-292