Overview

Dataset statistics

Number of variables11
Number of observations91
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.3 KiB
Average record size in memory93.5 B

Variable types

Text4
Categorical1
Numeric4
DateTime2

Dataset

Description도봉구에서 시행하고 있는 의무관리 공동주택 현황 데이터(시설명, 주소, 구분등)
Author서울특별시 도봉구
URLhttps://www.data.go.kr/data/15028125/fileData.do

Alerts

데이터기준일 has constant value ""Constant
위도 is highly overall correlated with 동구분High correlation
동수 is highly overall correlated with 세대수High correlation
세대수 is highly overall correlated with 동수High correlation
동구분 is highly overall correlated with 위도High correlation
시설명 has unique valuesUnique
주소 has unique valuesUnique
전화번호 has unique valuesUnique
위도 has unique valuesUnique
경도 has unique valuesUnique
구주소 has unique valuesUnique

Reproduction

Analysis started2023-12-12 18:55:46.721058
Analysis finished2023-12-12 18:55:50.797167
Duration4.08 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시설명
Text

UNIQUE 

Distinct91
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size860.0 B
2023-12-13T03:55:51.083611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length7.2637363
Min length5

Characters and Unicode

Total characters661
Distinct characters113
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique91 ?
Unique (%)100.0%

Sample

1st row방학 청구
2nd row도봉 동아에코빌
3rd row방학 한화 성원
4th row방학 신동아4차
5th row방학 삼성래미안 2단지
ValueCountFrequency (%)
창동 33
 
17.6%
방학 21
 
11.2%
쌍문 19
 
10.1%
도봉 13
 
6.9%
성원 4
 
2.1%
삼성래미안 4
 
2.1%
극동 3
 
1.6%
2단지 2
 
1.1%
1단지 2
 
1.1%
현대2차 2
 
1.1%
Other values (81) 85
45.2%
2023-12-13T03:55:51.701193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
98
 
14.8%
동 49
 
7.4%
창 34
 
5.1%
차 24
 
3.6%
학 22
 
3.3%
방 21
 
3.2%
쌍 20
 
3.0%
문 19
 
2.9%
대 16
 
2.4%
도 15
 
2.3%
Other values (103) 343
51.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 514
77.8%
Space Separator 98
 
14.8%
Decimal Number 41
 
6.2%
Uppercase Letter 6
 
0.9%
Other Punctuation 2
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 49
 
9.5%
창 34
 
6.6%
차 24
 
4.7%
학 22
 
4.3%
방 21
 
4.1%
쌍 20
 
3.9%
문 19
 
3.7%
대 16
 
3.1%
도 15
 
2.9%
봉 13
 
2.5%
Other values (89) 281
54.7%
Decimal Number
ValueCountFrequency (%)
1 14
34.1%
2 11
26.8%
3 5
 
12.2%
4 4
 
9.8%
7 2
 
4.9%
5 2
 
4.9%
9 1
 
2.4%
6 1
 
2.4%
8 1
 
2.4%
Uppercase Letter
ValueCountFrequency (%)
A 2
33.3%
S 2
33.3%
E 2
33.3%
Space Separator
ValueCountFrequency (%)
98
100.0%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 514
77.8%
Common 141
 
21.3%
Latin 6
 
0.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 49
 
9.5%
창 34
 
6.6%
차 24
 
4.7%
학 22
 
4.3%
방 21
 
4.1%
쌍 20
 
3.9%
문 19
 
3.7%
대 16
 
3.1%
도 15
 
2.9%
봉 13
 
2.5%
Other values (89) 281
54.7%
Common
ValueCountFrequency (%)
98
69.5%
1 14
 
9.9%
2 11
 
7.8%
3 5
 
3.5%
4 4
 
2.8%
7 2
 
1.4%
5 2
 
1.4%
, 2
 
1.4%
9 1
 
0.7%
6 1
 
0.7%
Latin
ValueCountFrequency (%)
A 2
33.3%
S 2
33.3%
E 2
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 514
77.8%
ASCII 147
 
22.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
98
66.7%
1 14
 
9.5%
2 11
 
7.5%
3 5
 
3.4%
4 4
 
2.7%
A 2
 
1.4%
S 2
 
1.4%
E 2
 
1.4%
7 2
 
1.4%
5 2
 
1.4%
Other values (4) 5
 
3.4%
Hangul
ValueCountFrequency (%)
동 49
 
9.5%
창 34
 
6.6%
차 24
 
4.7%
학 22
 
4.3%
방 21
 
4.1%
쌍 20
 
3.9%
문 19
 
3.7%
대 16
 
3.1%
도 15
 
2.9%
봉 13
 
2.5%
Other values (89) 281
54.7%

동구분
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)15.4%
Missing0
Missing (%)0.0%
Memory size860.0 B
방학3동
15 
창5동
12 
도봉2동
11 
쌍문4동
10 
방학1동
9 
Other values (9)
34 

Length

Max length4
Median length4
Mean length3.6263736
Min length3

Unique

Unique1 ?
Unique (%)1.1%

Sample

1st row방학3동
2nd row도봉2동
3rd row방학3동
4th row방학3동
5th row방학1동

Common Values

ValueCountFrequency (%)
방학3동 15
16.5%
창5동 12
13.2%
도봉2동 11
12.1%
쌍문4동 10
11.0%
방학1동 9
9.9%
창4동 9
9.9%
창2동 5
 
5.5%
창1동 5
 
5.5%
쌍문1동 4
 
4.4%
쌍문2동 3
 
3.3%
Other values (4) 8
8.8%

Length

2023-12-13T03:55:51.931538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
방학3동 15
16.5%
창5동 12
13.2%
도봉2동 11
12.1%
쌍문4동 10
11.0%
방학1동 9
9.9%
창4동 9
9.9%
창2동 5
 
5.5%
창1동 5
 
5.5%
쌍문1동 4
 
4.4%
쌍문2동 3
 
3.3%
Other values (4) 8
8.8%

주소
Text

UNIQUE 

Distinct91
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size860.0 B
2023-12-13T03:55:52.312564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length16
Mean length12.197802
Min length9

Characters and Unicode

Total characters1110
Distinct characters39
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

Unique91 ?
Unique (%)100.0%

Sample

1st row도봉구 시루봉로71
2nd row도봉구 도봉로180길 6-23
3rd row도봉구 방학로11길10
4th row도봉구 방학로200
5th row도봉구 도봉로150길42
ValueCountFrequency (%)
도봉구 91
46.4%
도봉로180길 3
 
1.5%
도봉로136가길 2
 
1.0%
22-8 1
 
0.5%
시루봉로166 1
 
0.5%
40 1
 
0.5%
도봉로136나길 1
 
0.5%
해등로25길41 1
 
0.5%
시루봉로58 1
 
0.5%
우이천로367 1
 
0.5%
Other values (93) 93
47.4%
2023-12-13T03:55:53.405884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
봉 119
 
10.7%
도 116
 
10.5%
105
 
9.5%
구 91
 
8.2%
로 90
 
8.1%
1 82
 
7.4%
길 53
 
4.8%
6 47
 
4.2%
3 42
 
3.8%
2 40
 
3.6%
Other values (29) 325
29.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 625
56.3%
Decimal Number 361
32.5%
Space Separator 105
 
9.5%
Dash Punctuation 19
 
1.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
봉 119
19.0%
도 116
18.6%
구 91
14.6%
로 90
14.4%
길 53
8.5%
해 26
 
4.2%
등 15
 
2.4%
노 11
 
1.8%
마 10
 
1.6%
들 10
 
1.6%
Other values (17) 84
13.4%
Decimal Number
ValueCountFrequency (%)
1 82
22.7%
6 47
13.0%
3 42
11.6%
2 40
11.1%
0 32
 
8.9%
5 32
 
8.9%
4 29
 
8.0%
7 24
 
6.6%
8 23
 
6.4%
9 10
 
2.8%
Space Separator
ValueCountFrequency (%)
105
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 19
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 625
56.3%
Common 485
43.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
봉 119
19.0%
도 116
18.6%
구 91
14.6%
로 90
14.4%
길 53
8.5%
해 26
 
4.2%
등 15
 
2.4%
노 11
 
1.8%
마 10
 
1.6%
들 10
 
1.6%
Other values (17) 84
13.4%
Common
ValueCountFrequency (%)
105
21.6%
1 82
16.9%
6 47
9.7%
3 42
 
8.7%
2 40
 
8.2%
0 32
 
6.6%
5 32
 
6.6%
4 29
 
6.0%
7 24
 
4.9%
8 23
 
4.7%
Other values (2) 29
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 625
56.3%
ASCII 485
43.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
봉 119
19.0%
도 116
18.6%
구 91
14.6%
로 90
14.4%
길 53
8.5%
해 26
 
4.2%
등 15
 
2.4%
노 11
 
1.8%
마 10
 
1.6%
들 10
 
1.6%
Other values (17) 84
13.4%
ASCII
ValueCountFrequency (%)
105
21.6%
1 82
16.9%
6 47
9.7%
3 42
 
8.7%
2 40
 
8.2%
0 32
 
6.6%
5 32
 
6.6%
4 29
 
6.0%
7 24
 
4.9%
8 23
 
4.7%
Other values (2) 29
 
6.0%

전화번호
Text

UNIQUE 

Distinct91
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size860.0 B
2023-12-13T03:55:53.825007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length11
Mean length11.230769
Min length11

Characters and Unicode

Total characters1022
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

Unique91 ?
Unique (%)100.0%

Sample

1st row02-3491-0208
2nd row02-3491-0252
3rd row02-3491-0486
4th row02-3491-3438
5th row02-3491-4739
ValueCountFrequency (%)
02-3491-0208 1
 
1.1%
02-955-0125 1
 
1.1%
02-991-9881 1
 
1.1%
02-991-9713 1
 
1.1%
02-991-7809 1
 
1.1%
02-991-5566 1
 
1.1%
02-991-1907 1
 
1.1%
02-990-1871 1
 
1.1%
02-956-9245 1
 
1.1%
02-956-8617 1
 
1.1%
Other values (81) 81
89.0%
2023-12-13T03:55:54.474938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 182
17.8%
9 156
15.3%
0 149
14.6%
2 142
13.9%
5 71
 
6.9%
4 65
 
6.4%
6 60
 
5.9%
1 57
 
5.6%
8 52
 
5.1%
3 48
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 840
82.2%
Dash Punctuation 182
 
17.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 156
18.6%
0 149
17.7%
2 142
16.9%
5 71
8.5%
4 65
7.7%
6 60
 
7.1%
1 57
 
6.8%
8 52
 
6.2%
3 48
 
5.7%
7 40
 
4.8%
Dash Punctuation
ValueCountFrequency (%)
- 182
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1022
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 182
17.8%
9 156
15.3%
0 149
14.6%
2 142
13.9%
5 71
 
6.9%
4 65
 
6.4%
6 60
 
5.9%
1 57
 
5.6%
8 52
 
5.1%
3 48
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1022
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 182
17.8%
9 156
15.3%
0 149
14.6%
2 142
13.9%
5 71
 
6.9%
4 65
 
6.4%
6 60
 
5.9%
1 57
 
5.6%
8 52
 
5.1%
3 48
 
4.7%

위도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct91
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.658954
Minimum37.633352
Maximum37.687636
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size951.0 B
2023-12-13T03:55:54.701077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.633352
5-th percentile37.64261
Q137.65168
median37.657843
Q337.663432
95-th percentile37.682184
Maximum37.687636
Range0.0542836
Interquartile range (IQR)0.0117521

Descriptive statistics

Standard deviation0.011487107
Coefficient of variation (CV)0.00030502989
Kurtosis0.37566205
Mean37.658954
Median Absolute Deviation (MAD)0.0057642
Skewness0.65044213
Sum3426.9648
Variance0.00013195362
MonotonicityNot monotonic
2023-12-13T03:55:54.952649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.6575265 1
 
1.1%
37.6578986 1
 
1.1%
37.6408568 1
 
1.1%
37.6560862 1
 
1.1%
37.6560041 1
 
1.1%
37.6557483 1
 
1.1%
37.6479714 1
 
1.1%
37.6512992 1
 
1.1%
37.6588125 1
 
1.1%
37.660255 1
 
1.1%
Other values (81) 81
89.0%
ValueCountFrequency (%)
37.6333523 1
1.1%
37.6377532 1
1.1%
37.6408568 1
1.1%
37.6412989 1
1.1%
37.6423565 1
1.1%
37.6428627 1
1.1%
37.6439505 1
1.1%
37.6453302 1
1.1%
37.645691 1
1.1%
37.6457729 1
1.1%
ValueCountFrequency (%)
37.6876359 1
1.1%
37.6864633 1
1.1%
37.6857879 1
1.1%
37.6829494 1
1.1%
37.6824746 1
1.1%
37.6818929 1
1.1%
37.6814584 1
1.1%
37.6809076 1
1.1%
37.6795941 1
1.1%
37.6794041 1
1.1%

경도
Real number (ℝ)

UNIQUE 

Distinct91
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.03832
Minimum127.01364
Maximum127.05332
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size951.0 B
2023-12-13T03:55:55.183813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum127.01364
5-th percentile127.02371
Q1127.03027
median127.03964
Q3127.04645
95-th percentile127.0501
Maximum127.05332
Range0.0396763
Interquartile range (IQR)0.0161752

Descriptive statistics

Standard deviation0.009265113
Coefficient of variation (CV)7.2931638 × 10-5
Kurtosis-0.97511782
Mean127.03832
Median Absolute Deviation (MAD)0.0079991
Skewness-0.31604483
Sum11560.487
Variance8.5842318 × 10-5
MonotonicityNot monotonic
2023-12-13T03:55:55.463390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.0260117 1
 
1.1%
127.023934 1
 
1.1%
127.0418875 1
 
1.1%
127.0453524 1
 
1.1%
127.0338322 1
 
1.1%
127.0292388 1
 
1.1%
127.0239732 1
 
1.1%
127.0272796 1
 
1.1%
127.0462219 1
 
1.1%
127.0232072 1
 
1.1%
Other values (81) 81
89.0%
ValueCountFrequency (%)
127.0136444 1
1.1%
127.0216044 1
1.1%
127.0219908 1
1.1%
127.0232072 1
1.1%
127.0234943 1
1.1%
127.023934 1
1.1%
127.0239732 1
1.1%
127.0247059 1
1.1%
127.0254295 1
1.1%
127.0260117 1
1.1%
ValueCountFrequency (%)
127.0533207 1
1.1%
127.0525489 1
1.1%
127.0520542 1
1.1%
127.0507188 1
1.1%
127.050345 1
1.1%
127.0498458 1
1.1%
127.0497224 1
1.1%
127.0496053 1
1.1%
127.0493723 1
1.1%
127.0492941 1
1.1%

동수
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)22.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.6593407
Minimum1
Maximum32
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size951.0 B
2023-12-13T03:55:55.669142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median4
Q38
95-th percentile18
Maximum32
Range31
Interquartile range (IQR)5

Descriptive statistics

Standard deviation6.0538689
Coefficient of variation (CV)0.90907932
Kurtosis5.7931062
Mean6.6593407
Median Absolute Deviation (MAD)2
Skewness2.2824768
Sum606
Variance36.649328
MonotonicityNot monotonic
2023-12-13T03:55:55.859033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
4 16
17.6%
3 15
16.5%
2 11
12.1%
5 10
11.0%
7 7
7.7%
1 4
 
4.4%
6 4
 
4.4%
10 3
 
3.3%
8 3
 
3.3%
9 3
 
3.3%
Other values (10) 15
16.5%
ValueCountFrequency (%)
1 4
 
4.4%
2 11
12.1%
3 15
16.5%
4 16
17.6%
5 10
11.0%
6 4
 
4.4%
7 7
7.7%
8 3
 
3.3%
9 3
 
3.3%
10 3
 
3.3%
ValueCountFrequency (%)
32 1
1.1%
30 1
1.1%
25 2
2.2%
18 2
2.2%
16 1
1.1%
15 2
2.2%
14 1
1.1%
13 1
1.1%
12 2
2.2%
11 2
2.2%

세대수
Real number (ℝ)

HIGH CORRELATION 

Distinct84
Distinct (%)92.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean641
Minimum153
Maximum3169
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size951.0 B
2023-12-13T03:55:56.059231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum153
5-th percentile163
Q1216.5
median407
Q3699.5
95-th percentile1980.5
Maximum3169
Range3016
Interquartile range (IQR)483

Descriptive statistics

Standard deviation638.51762
Coefficient of variation (CV)0.99612734
Kurtosis4.143919
Mean641
Median Absolute Deviation (MAD)205
Skewness2.0678291
Sum58331
Variance407704.76
MonotonicityNot monotonic
2023-12-13T03:55:56.266888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
205 3
 
3.3%
660 2
 
2.2%
194 2
 
2.2%
202 2
 
2.2%
190 2
 
2.2%
258 2
 
2.2%
448 1
 
1.1%
408 1
 
1.1%
582 1
 
1.1%
293 1
 
1.1%
Other values (74) 74
81.3%
ValueCountFrequency (%)
153 1
1.1%
154 1
1.1%
155 1
1.1%
158 1
1.1%
159 1
1.1%
167 1
1.1%
174 1
1.1%
183 1
1.1%
188 1
1.1%
190 2
2.2%
ValueCountFrequency (%)
3169 1
1.1%
2856 1
1.1%
2678 1
1.1%
2061 1
1.1%
1981 1
1.1%
1980 1
1.1%
1764 1
1.1%
1716 1
1.1%
1710 1
1.1%
1668 1
1.1%

사용승인일
Date

Distinct85
Distinct (%)93.4%
Missing0
Missing (%)0.0%
Memory size860.0 B
Minimum1902-08-19 00:00:00
Maximum2007-07-31 00:00:00
2023-12-13T03:55:56.474395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:55:56.753415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

구주소
Text

UNIQUE 

Distinct91
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size860.0 B
2023-12-13T03:55:57.266320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length15
Mean length12.021978
Min length10

Characters and Unicode

Total characters1094
Distinct characters24
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

Unique91 ?
Unique (%)100.0%

Sample

1st row도봉구 방학3동 734
2nd row도봉구 도봉2동 654
3rd row도봉구 방학3동 731
4th row도봉구 방학3동 731,733
5th row도봉구 방학1동 715-12
ValueCountFrequency (%)
도봉구 91
33.3%
방학3동 15
 
5.5%
창5동 11
 
4.0%
도봉2동 11
 
4.0%
쌍문4동 10
 
3.7%
창4동 9
 
3.3%
방학1동 9
 
3.3%
창2동 5
 
1.8%
쌍문1동 4
 
1.5%
창1동 4
 
1.5%
Other values (98) 104
38.1%
2023-12-13T03:55:57.980295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
182
16.6%
도 104
 
9.5%
봉 104
 
9.5%
동 91
 
8.3%
구 91
 
8.3%
1 58
 
5.3%
3 56
 
5.1%
5 49
 
4.5%
2 45
 
4.1%
4 40
 
3.7%
Other values (14) 274
25.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 516
47.2%
Decimal Number 375
34.3%
Space Separator 182
 
16.6%
Dash Punctuation 20
 
1.8%
Other Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 104
20.2%
봉 104
20.2%
동 91
17.6%
구 91
17.6%
창 33
 
6.4%
방 24
 
4.7%
학 24
 
4.7%
쌍 21
 
4.1%
문 21
 
4.1%
산 2
 
0.4%
Decimal Number
ValueCountFrequency (%)
1 58
15.5%
3 56
14.9%
5 49
13.1%
2 45
12.0%
4 40
10.7%
8 36
9.6%
7 31
8.3%
0 23
 
6.1%
6 19
 
5.1%
9 18
 
4.8%
Space Separator
ValueCountFrequency (%)
182
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 20
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 578
52.8%
Hangul 516
47.2%

Most frequent character per script

Common
ValueCountFrequency (%)
182
31.5%
1 58
 
10.0%
3 56
 
9.7%
5 49
 
8.5%
2 45
 
7.8%
4 40
 
6.9%
8 36
 
6.2%
7 31
 
5.4%
0 23
 
4.0%
- 20
 
3.5%
Other values (3) 38
 
6.6%
Hangul
ValueCountFrequency (%)
도 104
20.2%
봉 104
20.2%
동 91
17.6%
구 91
17.6%
창 33
 
6.4%
방 24
 
4.7%
학 24
 
4.7%
쌍 21
 
4.1%
문 21
 
4.1%
산 2
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 578
52.8%
Hangul 516
47.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
182
31.5%
1 58
 
10.0%
3 56
 
9.7%
5 49
 
8.5%
2 45
 
7.8%
4 40
 
6.9%
8 36
 
6.2%
7 31
 
5.4%
0 23
 
4.0%
- 20
 
3.5%
Other values (3) 38
 
6.6%
Hangul
ValueCountFrequency (%)
도 104
20.2%
봉 104
20.2%
동 91
17.6%
구 91
17.6%
창 33
 
6.4%
방 24
 
4.7%
학 24
 
4.7%
쌍 21
 
4.1%
문 21
 
4.1%
산 2
 
0.4%

데이터기준일
Date

CONSTANT 

Distinct1
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size860.0 B
Minimum2018-01-01 00:00:00
Maximum2018-01-01 00:00:00
2023-12-13T03:55:58.199723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:55:58.362028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-13T03:55:49.721964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:55:47.645002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:55:48.299273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:55:49.029214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:55:49.874030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:55:47.785836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:55:48.474453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:55:49.206498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:55:50.051679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:55:47.979999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:55:48.664787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:55:49.407925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:55:50.211977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:55:48.144383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:55:48.849519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:55:49.568863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T03:55:58.512913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시설명동구분주소전화번호위도경도동수세대수사용승인일구주소
시설명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
동구분1.0001.0001.0001.0000.8480.8030.1900.0000.9901.000
주소1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
전화번호1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
위도1.0000.8481.0001.0001.0000.1170.1390.0000.9641.000
경도1.0000.8031.0001.0000.1171.0000.0000.0000.9511.000
동수1.0000.1901.0001.0000.1390.0001.0000.8240.9251.000
세대수1.0000.0001.0001.0000.0000.0000.8241.0000.9161.000
사용승인일1.0000.9901.0001.0000.9640.9510.9250.9161.0001.000
구주소1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
2023-12-13T03:55:58.744978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도동수세대수동구분
위도1.0000.218-0.157-0.1590.542
경도0.2181.0000.1440.1630.489
동수-0.1570.1441.0000.8730.070
세대수-0.1590.1630.8731.0000.000
동구분0.5420.4890.0700.0001.000

Missing values

2023-12-13T03:55:50.426444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T03:55:50.689527image/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

시설명동구분주소전화번호위도경도동수세대수사용승인일구주소데이터기준일
0방학 청구방학3동도봉구 시루봉로7102-3491-020837.657527127.026012109781995-06-23도봉구 방학3동 7342018-01-01
1도봉 동아에코빌도봉2동도봉구 도봉로180길 6-2302-3491-025237.681458127.04683575262003-03-15도봉구 도봉2동 6542018-01-01
2방학 한화 성원방학3동도봉구 방학로11길1002-3491-048637.661223127.03160843361996-04-02도봉구 방학3동 7312018-01-01
3방학 신동아4차방학3동도봉구 방학로20002-3491-343837.66262127.02991333611995-05-27도봉구 방학3동 731,7332018-01-01
4방학 삼성래미안 2단지방학1동도봉구 도봉로150길4202-3491-473937.665064127.04580552882002-10-19도봉구 방학1동 715-122018-01-01
5방학 삼성래미안 1단지방학1동도봉구 마들로64602-3491-486937.666898127.04739116032002-10-19도봉구 방학1동 720-182018-01-01
6도봉 서광도봉2동도봉구 마들로77002-3491-846837.678334127.04984631591999-08-24도봉구 도봉2동 6432018-01-01
7도봉 현대성우도봉2동도봉구 마들로735-802-3491-895437.676302127.04763711901998-12-01도봉구 도봉2동 6422018-01-01
8방학 벽산1차방학3동도봉구 방학로11길3602-3491-933137.660298127.03218144891991-09-02도봉구 방학3동 2752018-01-01
9방학 거성학마을방학1동도봉구 도당로11202-3493-411537.666467127.03963833471994-12-13도봉구 방학1동 701-152018-01-01
시설명동구분주소전화번호위도경도동수세대수사용승인일구주소데이터기준일
81창동 동아그린창5동도봉구 노해로63다길3402-997-817137.654475127.04640834491996-11-29도봉구 창5동 8052018-01-01
82쌍문 한양5차쌍문4동도봉구 해등로25길3602-997-853637.655935127.03193234141991-05-28도봉구 쌍문4동 59-32018-01-01
83창동 현대아파트창5동도봉구 해등로16길1202-997-968337.655844127.04069922052000-05-09도봉구 창5동 8132018-01-01
84창동 금용창2동도봉구 우이천로20002-998-285037.637753127.03574711831997-05-09도봉구 창2동 8092018-01-01
85쌍문 삼성래미안쌍문3동도봉구 우이천로32802-998-661537.646768127.027929104072002-07-26도봉구 쌍문3동 7252018-01-01
86창동 대우창2동도봉구 덕릉로63가길4302-998-697337.642356127.03750279521995-11-24도봉구 창2동 8042018-01-01
87창동 대우그린창1동도봉구 덕릉로37102-998-857237.646309127.04972253662000-11-30도봉구 창1동 8142018-01-01
88창동 주공4단지창1동도봉구 덕릉로355-802-998-864237.64533127.0475671017101991-11-17도봉구 창1동 3732018-01-01
89창동 삼성창1동도봉구 노해로 66길2102-999-505837.64888127.0484781816681992-07-31도봉구 창1동 452018-01-01
90쌍문 대우파크힐쌍문4동도봉구 시루봉로8070-8899-155537.651554127.0291241902004-04-01도봉구 쌍문4동 7302018-01-01