Overview

Dataset statistics

Number of variables12
Number of observations55
Missing cells25
Missing cells (%)3.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.5 KiB
Average record size in memory102.4 B

Variable types

Text4
Categorical3
Numeric4
DateTime1

Dataset

Description도봉구에서 시행하고 있는 임의관리 단지 현황 데이터(시설명, 주소, 구분등)
Author서울특별시 도봉구
URLhttps://www.data.go.kr/data/15028126/fileData.do

Alerts

데이터기준일 has constant value ""Constant
위도 is highly overall correlated with 동구분High correlation
경도 is highly overall correlated with 동구분High correlation
동구분 is highly overall correlated with 위도 and 1 other fieldsHigh correlation
전화번호 has 23 (41.8%) missing valuesMissing
사업승인 has 2 (3.6%) missing valuesMissing

Reproduction

Analysis started2023-12-12 20:26:17.283478
Analysis finished2023-12-12 20:26:20.505259
Duration3.22 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시설명
Text

Distinct53
Distinct (%)96.4%
Missing0
Missing (%)0.0%
Memory size572.0 B
2023-12-13T05:26:20.725946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length11
Mean length5.7272727
Min length4

Characters and Unicode

Total characters315
Distinct characters109
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique51 ?
Unique (%)92.7%

Sample

1st row한양 수자인(도봉시장정비사업)
2nd row완성연립
3rd row삼익그린빌라
4th row청한빌라
5th row방학동효성
ValueCountFrequency (%)
중흥s클레스 2
 
3.6%
청호빌라 2
 
3.6%
백조아파트 1
 
1.8%
쌍문동신일라이프 1
 
1.8%
한양 1
 
1.8%
수자인(도봉시장정비사업 1
 
1.8%
에벤에셀(신원1차 1
 
1.8%
쌍문e편한세상 1
 
1.8%
대웅아파트 1
 
1.8%
미래빌라 1
 
1.8%
Other values (44) 44
78.6%
2023-12-13T05:26:21.202572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
빌 21
 
6.7%
라 20
 
6.3%
파 15
 
4.8%
트 15
 
4.8%
아 14
 
4.4%
신 10
 
3.2%
우 10
 
3.2%
동 9
 
2.9%
스 7
 
2.2%
성 7
 
2.2%
Other values (99) 187
59.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 299
94.9%
Decimal Number 6
 
1.9%
Close Punctuation 3
 
1.0%
Open Punctuation 3
 
1.0%
Uppercase Letter 2
 
0.6%
Lowercase Letter 1
 
0.3%
Space Separator 1
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
빌 21
 
7.0%
라 20
 
6.7%
파 15
 
5.0%
트 15
 
5.0%
아 14
 
4.7%
신 10
 
3.3%
우 10
 
3.3%
동 9
 
3.0%
스 7
 
2.3%
성 7
 
2.3%
Other values (91) 171
57.2%
Decimal Number
ValueCountFrequency (%)
2 3
50.0%
1 2
33.3%
3 1
 
16.7%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Uppercase Letter
ValueCountFrequency (%)
S 2
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 1
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 299
94.9%
Common 13
 
4.1%
Latin 3
 
1.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
빌 21
 
7.0%
라 20
 
6.7%
파 15
 
5.0%
트 15
 
5.0%
아 14
 
4.7%
신 10
 
3.3%
우 10
 
3.3%
동 9
 
3.0%
스 7
 
2.3%
성 7
 
2.3%
Other values (91) 171
57.2%
Common
ValueCountFrequency (%)
) 3
23.1%
2 3
23.1%
( 3
23.1%
1 2
15.4%
3 1
 
7.7%
1
 
7.7%
Latin
ValueCountFrequency (%)
S 2
66.7%
e 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 299
94.9%
ASCII 16
 
5.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
빌 21
 
7.0%
라 20
 
6.7%
파 15
 
5.0%
트 15
 
5.0%
아 14
 
4.7%
신 10
 
3.3%
우 10
 
3.3%
동 9
 
3.0%
스 7
 
2.3%
성 7
 
2.3%
Other values (91) 171
57.2%
ASCII
ValueCountFrequency (%)
) 3
18.8%
2 3
18.8%
( 3
18.8%
S 2
12.5%
1 2
12.5%
e 1
 
6.2%
3 1
 
6.2%
1
 
6.2%

동구분
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)25.5%
Missing0
Missing (%)0.0%
Memory size572.0 B
쌍문1동
8 
창1동
7 
방학1동
6 
도봉1동
5 
쌍문2동
5 
Other values (9)
24 

Length

Max length4
Median length4
Mean length3.7272727
Min length3

Unique

Unique1 ?
Unique (%)1.8%

Sample

1st row도봉동
2nd row쌍문1동
3rd row쌍문1동
4th row쌍문1동
5th row방학3동

Common Values

ValueCountFrequency (%)
쌍문1동 8
14.5%
창1동 7
12.7%
방학1동 6
10.9%
도봉1동 5
9.1%
쌍문2동 5
9.1%
방학2동 5
9.1%
창3동 3
 
5.5%
도봉2동 3
 
5.5%
쌍문3동 3
 
5.5%
쌍문4동 3
 
5.5%
Other values (4) 7
12.7%

Length

2023-12-13T05:26:21.364957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
쌍문1동 8
14.5%
창1동 7
12.7%
방학1동 6
10.9%
도봉1동 5
9.1%
쌍문2동 5
9.1%
방학2동 5
9.1%
창3동 3
 
5.5%
도봉2동 3
 
5.5%
쌍문3동 3
 
5.5%
쌍문4동 3
 
5.5%
Other values (4) 7
12.7%

주소
Text

Distinct54
Distinct (%)98.2%
Missing0
Missing (%)0.0%
Memory size572.0 B
2023-12-13T05:26:21.595984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length15
Mean length12.381818
Min length9

Characters and Unicode

Total characters681
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

Unique53 ?
Unique (%)96.4%

Sample

1st row도봉구 마들로724
2nd row도봉구 우이천로44길44
3rd row도봉구 우이천로48길67
4th row도봉구 해등로373
5th row도봉구 방학로11길33
ValueCountFrequency (%)
도봉구 55
47.4%
방학로4길32 2
 
1.7%
시루봉로15자길7 1
 
0.9%
해등로190 1
 
0.9%
169다길 1
 
0.9%
30 1
 
0.9%
우이천로48길21 1
 
0.9%
우이천로38길17 1
 
0.9%
방학로210 1
 
0.9%
해등로255 1
 
0.9%
Other values (51) 51
44.0%
2023-12-13T05:26:22.016955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
봉 74
 
10.9%
도 72
 
10.6%
62
 
9.1%
구 55
 
8.1%
로 55
 
8.1%
1 49
 
7.2%
길 40
 
5.9%
3 28
 
4.1%
4 24
 
3.5%
5 22
 
3.2%
Other values (29) 200
29.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 395
58.0%
Decimal Number 217
31.9%
Space Separator 62
 
9.1%
Dash Punctuation 7
 
1.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
봉 74
18.7%
도 72
18.2%
구 55
13.9%
로 55
13.9%
길 40
10.1%
해 15
 
3.8%
등 9
 
2.3%
천 7
 
1.8%
이 7
 
1.8%
우 7
 
1.8%
Other values (17) 54
13.7%
Decimal Number
ValueCountFrequency (%)
1 49
22.6%
3 28
12.9%
4 24
11.1%
5 22
10.1%
2 20
9.2%
7 18
 
8.3%
0 17
 
7.8%
6 16
 
7.4%
8 15
 
6.9%
9 8
 
3.7%
Space Separator
ValueCountFrequency (%)
62
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 395
58.0%
Common 286
42.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
봉 74
18.7%
도 72
18.2%
구 55
13.9%
로 55
13.9%
길 40
10.1%
해 15
 
3.8%
등 9
 
2.3%
천 7
 
1.8%
이 7
 
1.8%
우 7
 
1.8%
Other values (17) 54
13.7%
Common
ValueCountFrequency (%)
62
21.7%
1 49
17.1%
3 28
9.8%
4 24
 
8.4%
5 22
 
7.7%
2 20
 
7.0%
7 18
 
6.3%
0 17
 
5.9%
6 16
 
5.6%
8 15
 
5.2%
Other values (2) 15
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 395
58.0%
ASCII 286
42.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
봉 74
18.7%
도 72
18.2%
구 55
13.9%
로 55
13.9%
길 40
10.1%
해 15
 
3.8%
등 9
 
2.3%
천 7
 
1.8%
이 7
 
1.8%
우 7
 
1.8%
Other values (17) 54
13.7%
ASCII
ValueCountFrequency (%)
62
21.7%
1 49
17.1%
3 28
9.8%
4 24
 
8.4%
5 22
 
7.7%
2 20
 
7.0%
7 18
 
6.3%
0 17
 
5.9%
6 16
 
5.6%
8 15
 
5.2%
Other values (2) 15
 
5.2%

전화번호
Text

MISSING 

Distinct32
Distinct (%)100.0%
Missing23
Missing (%)41.8%
Memory size572.0 B
2023-12-13T05:26:22.251015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length11
Mean length11.28125
Min length11

Characters and Unicode

Total characters361
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)100.0%

Sample

1st row02-954-0016
2nd row02-908-5309
3rd row02-955-7583
4th row02-907-2475
5th row02-998-3536
ValueCountFrequency (%)
02-3492-6109 1
 
3.0%
02-3493-7808 1
 
3.0%
02-906-6991 1
 
3.0%
02-997-0001 1
 
3.0%
02-908-8365 1
 
3.0%
02-993-1489 1
 
3.0%
02-906-5405 1
 
3.0%
02-900-2247 1
 
3.0%
02-954-0016 1
 
3.0%
02-906-0390 1
 
3.0%
Other values (23) 23
69.7%
2023-12-13T05:26:22.618470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 68
18.8%
- 64
17.7%
2 55
15.2%
9 52
14.4%
3 21
 
5.8%
4 20
 
5.5%
1 20
 
5.5%
6 17
 
4.7%
5 16
 
4.4%
8 14
 
3.9%
Other values (3) 14
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 295
81.7%
Dash Punctuation 64
 
17.7%
Other Punctuation 1
 
0.3%
Space Separator 1
 
0.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 68
23.1%
2 55
18.6%
9 52
17.6%
3 21
 
7.1%
4 20
 
6.8%
1 20
 
6.8%
6 17
 
5.8%
5 16
 
5.4%
8 14
 
4.7%
7 12
 
4.1%
Dash Punctuation
ValueCountFrequency (%)
- 64
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 361
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 68
18.8%
- 64
17.7%
2 55
15.2%
9 52
14.4%
3 21
 
5.8%
4 20
 
5.5%
1 20
 
5.5%
6 17
 
4.7%
5 16
 
4.4%
8 14
 
3.9%
Other values (3) 14
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 361
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 68
18.8%
- 64
17.7%
2 55
15.2%
9 52
14.4%
3 21
 
5.8%
4 20
 
5.5%
1 20
 
5.5%
6 17
 
4.7%
5 16
 
4.4%
8 14
 
3.9%
Other values (3) 14
 
3.9%

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct53
Distinct (%)96.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.659569
Minimum37.633053
Maximum37.685611
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size627.0 B
2023-12-13T05:26:22.774769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.633053
5-th percentile37.63999
Q137.650101
median37.658323
Q337.664992
95-th percentile37.683588
Maximum37.685611
Range0.0525577
Interquartile range (IQR)0.01489145

Descriptive statistics

Standard deviation0.012666615
Coefficient of variation (CV)0.00033634518
Kurtosis-0.21964275
Mean37.659569
Median Absolute Deviation (MAD)0.0078161
Skewness0.36065433
Sum2071.2763
Variance0.00016044313
MonotonicityNot monotonic
2023-12-13T05:26:22.923620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.6703415 2
 
3.6%
37.6642607 2
 
3.6%
37.6744994 1
 
1.8%
37.6558434 1
 
1.8%
37.6598202 1
 
1.8%
37.6549402 1
 
1.8%
37.6543515 1
 
1.8%
37.6604107 1
 
1.8%
37.6583229 1
 
1.8%
37.6492235 1
 
1.8%
Other values (43) 43
78.2%
ValueCountFrequency (%)
37.6330531 1
1.8%
37.6372226 1
1.8%
37.6389718 1
1.8%
37.6404265 1
1.8%
37.6458608 1
1.8%
37.6460275 1
1.8%
37.6463034 1
1.8%
37.6464735 1
1.8%
37.6478368 1
1.8%
37.6480461 1
1.8%
ValueCountFrequency (%)
37.6856108 1
1.8%
37.6848884 1
1.8%
37.6841853 1
1.8%
37.6833323 1
1.8%
37.6831018 1
1.8%
37.6821957 1
1.8%
37.6792393 1
1.8%
37.6744994 1
1.8%
37.6704361 1
1.8%
37.6703415 2
3.6%

경도
Real number (ℝ)

HIGH CORRELATION 

Distinct53
Distinct (%)96.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.03598
Minimum127.01484
Maximum127.05068
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size627.0 B
2023-12-13T05:26:23.059517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum127.01484
5-th percentile127.01821
Q1127.02995
median127.03901
Q3127.0428
95-th percentile127.0473
Maximum127.05068
Range0.035841
Interquartile range (IQR)0.01285265

Descriptive statistics

Standard deviation0.0089392228
Coefficient of variation (CV)7.0367647 × 10-5
Kurtosis-0.35156121
Mean127.03598
Median Absolute Deviation (MAD)0.0060605
Skewness-0.63188165
Sum6986.9787
Variance7.9909705 × 10-5
MonotonicityNot monotonic
2023-12-13T05:26:23.217686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.0407797 2
 
3.6%
127.0395171 2
 
3.6%
127.0476796 1
 
1.8%
127.0276577 1
 
1.8%
127.0356267 1
 
1.8%
127.0362758 1
 
1.8%
127.0344776 1
 
1.8%
127.0396392 1
 
1.8%
127.0358637 1
 
1.8%
127.0279696 1
 
1.8%
Other values (43) 43
78.2%
ValueCountFrequency (%)
127.0148371 1
1.8%
127.0172885 1
1.8%
127.0173606 1
1.8%
127.0185778 1
1.8%
127.0191896 1
1.8%
127.0222102 1
1.8%
127.0262163 1
1.8%
127.0263108 1
1.8%
127.027271 1
1.8%
127.0276577 1
1.8%
ValueCountFrequency (%)
127.0506781 1
1.8%
127.0493739 1
1.8%
127.0476796 1
1.8%
127.0471375 1
1.8%
127.046112 1
1.8%
127.0456082 1
1.8%
127.0452774 1
1.8%
127.0450668 1
1.8%
127.0449351 1
1.8%
127.0445084 1
1.8%

구분
Categorical

Distinct4
Distinct (%)7.3%
Missing0
Missing (%)0.0%
Memory size572.0 B
아파트
28 
연립
25 
다세대 (도시형생활)
 
1
연립 (도시형생활)
 
1

Length

Max length11
Median length3
Mean length2.8181818
Min length2

Unique

Unique2 ?
Unique (%)3.6%

Sample

1st row아파트
2nd row연립
3rd row연립
4th row연립
5th row아파트

Common Values

ValueCountFrequency (%)
아파트 28
50.9%
연립 25
45.5%
다세대 (도시형생활) 1
 
1.8%
연립 (도시형생활) 1
 
1.8%

Length

2023-12-13T05:26:23.397541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:26:23.502551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
아파트 28
49.1%
연립 26
45.6%
도시형생활 2
 
3.5%
다세대 1
 
1.8%

동수
Real number (ℝ)

Distinct9
Distinct (%)16.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size627.0 B
2023-12-13T05:26:23.601656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile6.6
Maximum12
Range11
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.2244433
Coefficient of variation (CV)0.85555513
Kurtosis6.0053471
Mean2.6
Median Absolute Deviation (MAD)1
Skewness2.2455567
Sum143
Variance4.9481481
MonotonicityNot monotonic
2023-12-13T05:26:23.700290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 22
40.0%
2 13
23.6%
3 9
16.4%
5 3
 
5.5%
4 3
 
5.5%
6 2
 
3.6%
9 1
 
1.8%
8 1
 
1.8%
12 1
 
1.8%
ValueCountFrequency (%)
1 22
40.0%
2 13
23.6%
3 9
16.4%
4 3
 
5.5%
5 3
 
5.5%
6 2
 
3.6%
8 1
 
1.8%
9 1
 
1.8%
12 1
 
1.8%
ValueCountFrequency (%)
12 1
 
1.8%
9 1
 
1.8%
8 1
 
1.8%
6 2
 
3.6%
5 3
 
5.5%
4 3
 
5.5%
3 9
16.4%
2 13
23.6%
1 22
40.0%

세대수
Real number (ℝ)

Distinct41
Distinct (%)74.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean82.527273
Minimum24
Maximum168
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size627.0 B
2023-12-13T05:26:23.812280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum24
5-th percentile26.1
Q148.5
median82
Q3110.5
95-th percentile150.9
Maximum168
Range144
Interquartile range (IQR)62

Descriptive statistics

Standard deviation40.737122
Coefficient of variation (CV)0.49362012
Kurtosis-0.89755699
Mean82.527273
Median Absolute Deviation (MAD)33
Skewness0.37035756
Sum4539
Variance1659.5131
MonotonicityNot monotonic
2023-12-13T05:26:23.971006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
54 3
 
5.5%
48 3
 
5.5%
24 3
 
5.5%
83 2
 
3.6%
96 2
 
3.6%
87 2
 
3.6%
141 2
 
3.6%
160 2
 
3.6%
49 2
 
3.6%
30 2
 
3.6%
Other values (31) 32
58.2%
ValueCountFrequency (%)
24 3
5.5%
27 1
 
1.8%
30 2
3.6%
32 1
 
1.8%
33 1
 
1.8%
39 1
 
1.8%
42 1
 
1.8%
46 1
 
1.8%
48 3
5.5%
49 2
3.6%
ValueCountFrequency (%)
168 1
1.8%
160 2
3.6%
147 1
1.8%
142 1
1.8%
141 2
3.6%
139 1
1.8%
136 1
1.8%
135 1
1.8%
122 1
1.8%
121 1
1.8%

사업승인
Text

MISSING 

Distinct50
Distinct (%)94.3%
Missing2
Missing (%)3.6%
Memory size572.0 B
2023-12-13T05:26:24.224880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters530
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

Unique48 ?
Unique (%)90.6%

Sample

1st row2007-08-20
2nd row1988-02-23
3rd row1988-03-30
4th row1997-04-02
5th row1991-07-25
ValueCountFrequency (%)
1991-07-25 3
 
5.7%
2012-03-28 2
 
3.8%
1993-01-13 1
 
1.9%
1996-10-07 1
 
1.9%
2007-08-20 1
 
1.9%
1988-02-08 1
 
1.9%
1988-10-25 1
 
1.9%
1988-03-04 1
 
1.9%
1998-05-09 1
 
1.9%
1995-06-09 1
 
1.9%
Other values (40) 40
75.5%
2023-12-13T05:26:24.690079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 106
20.0%
0 94
17.7%
1 88
16.6%
9 74
14.0%
2 51
9.6%
8 39
 
7.4%
3 24
 
4.5%
5 19
 
3.6%
7 16
 
3.0%
6 10
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 424
80.0%
Dash Punctuation 106
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 94
22.2%
1 88
20.8%
9 74
17.5%
2 51
12.0%
8 39
9.2%
3 24
 
5.7%
5 19
 
4.5%
7 16
 
3.8%
6 10
 
2.4%
4 9
 
2.1%
Dash Punctuation
ValueCountFrequency (%)
- 106
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 530
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 106
20.0%
0 94
17.7%
1 88
16.6%
9 74
14.0%
2 51
9.6%
8 39
 
7.4%
3 24
 
4.5%
5 19
 
3.6%
7 16
 
3.0%
6 10
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 530
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 106
20.0%
0 94
17.7%
1 88
16.6%
9 74
14.0%
2 51
9.6%
8 39
 
7.4%
3 24
 
4.5%
5 19
 
3.6%
7 16
 
3.0%
6 10
 
1.9%

준공일
Date

Distinct52
Distinct (%)94.5%
Missing0
Missing (%)0.0%
Memory size572.0 B
Minimum1981-12-19 00:00:00
Maximum2012-09-20 00:00:00
2023-12-13T05:26:24.858039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:25.040522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

데이터기준일
Categorical

CONSTANT 

Distinct1
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size572.0 B
2018-01-01
55 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2018-01-01
2nd row2018-01-01
3rd row2018-01-01
4th row2018-01-01
5th row2018-01-01

Common Values

ValueCountFrequency (%)
2018-01-01 55
100.0%

Length

2023-12-13T05:26:25.197707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:26:25.295705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2018-01-01 55
100.0%

Interactions

2023-12-13T05:26:19.682830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:18.049088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:18.565425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:18.985820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:19.782971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:18.182849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:18.655393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:19.071552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:19.874697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:18.322919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:18.783957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:19.168205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:19.979185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:18.445910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:18.879704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:26:19.586757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T05:26:25.373542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시설명동구분주소전화번호위도경도구분동수세대수사업승인준공일
시설명1.0000.9811.0001.0000.9770.9451.0000.9880.9190.9720.974
동구분0.9811.0001.0001.0000.8660.8400.6360.0000.4600.9790.954
주소1.0001.0001.0001.0001.0001.0001.0000.9790.9710.9890.990
전화번호1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
위도0.9770.8661.0001.0001.0000.7330.4060.0000.5180.9850.985
경도0.9450.8401.0001.0000.7331.0000.0000.4680.5250.9940.952
구분1.0000.6361.0001.0000.4060.0001.0000.1760.4170.0000.000
동수0.9880.0000.9791.0000.0000.4680.1761.0000.3910.0000.900
세대수0.9190.4600.9711.0000.5180.5250.4170.3911.0000.8360.926
사업승인0.9720.9790.9891.0000.9850.9940.0000.0000.8361.0000.999
준공일0.9740.9540.9901.0000.9850.9520.0000.9000.9260.9991.000
2023-12-13T05:26:25.540073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분동구분
구분1.0000.369
동구분0.3691.000
2023-12-13T05:26:25.643938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도동수세대수동구분구분
위도1.0000.322-0.096-0.1250.5590.232
경도0.3221.000-0.145-0.0160.5150.000
동수-0.096-0.1451.0000.0980.0000.058
세대수-0.125-0.0160.0981.0000.1530.238
동구분0.5590.5150.0000.1531.0000.369
구분0.2320.0000.0580.2380.3691.000

Missing values

2023-12-13T05:26:20.113254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T05:26:20.323552image/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.
2023-12-13T05:26:20.450996image/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

시설명동구분주소전화번호위도경도구분동수세대수사업승인준공일데이터기준일
0한양 수자인(도봉시장정비사업)도봉동도봉구 마들로72402-954-001637.674499127.04768아파트1802007-08-202010-12-272018-01-01
1완성연립쌍문1동도봉구 우이천로44길44<NA>37.654798127.018578연립3651988-02-231988-12-302018-01-01
2삼익그린빌라쌍문1동도봉구 우이천로48길67<NA>37.654988127.01919연립1271988-03-301988-12-272018-01-01
3청한빌라쌍문1동도봉구 해등로37302-908-530937.65692127.014837연립966<NA>1984-08-032018-01-01
4방학동효성방학3동도봉구 방학로11길3302-955-758337.661322127.032909아파트11221997-04-022002-05-162018-01-01
5동진빌리지창1동도봉구 해등로8102-907-247537.650662127.04085연립2541991-07-251992-05-082018-01-01
6금호빌라창1동도봉구 해등로71<NA>37.649659127.04132연립2461993-04-271994-07-282018-01-01
7삼익빌라창1동도봉구 해등로3길63<NA>37.646473127.040843연립2321991-08-311994-07-272018-01-01
8삼환빌라창1동도봉구 해등로3길41<NA>37.646303127.042314연립5481991-07-251992-05-082018-01-01
9신화연립창1동도봉구 해등로21<NA>37.645861127.043618연립2241992-07-281993-10-282018-01-01
시설명동구분주소전화번호위도경도구분동수세대수사업승인준공일데이터기준일
45우암센스뷰방학1동도봉구 도봉로146길4802-3494-524237.663725127.045608아파트1832001-12-112003-10-212018-01-01
46금광포란재방학1동도봉구 도봉로150바길2602-956-123637.664583127.045067아파트21052001-08-102005-06-142018-01-01
47오성연립방학1동도봉구 도봉로150라길17<NA>37.66522127.044935연립1241986-07-041986-12-222018-01-01
48신우2차빌라방학2동도봉구 도당로13다길4502-3493-780837.664765127.032745연립121681985-11-151989-06-202018-01-01
49신우1차빌라방학2동도봉구 방학로10길39<NA>37.664098127.033318연립6871985-10-221986-08-072018-01-01
50성심빌라방학2동도봉구 시루봉로13가길3<NA>37.666711127.031219연립3601987-12-281988-11-102018-01-01
51우일빌라방학2동도봉구 시루봉로15라길55<NA>37.670436127.032545연립1241985-08-211987-10-132018-01-01
52우방연립방학2동도봉구 시루봉로15자길7<NA>37.669505127.030576연립3481996-10-071998-03-312018-01-01
53신동아타워방학3동도봉구 방학로21002-3493-059137.66217127.028794아파트11041994-12-241997-08-252018-01-01
54우이그린빌라쌍문1동도봉구 우이천로48길43<NA>37.6558127.017289연립61141985-09-031986-08-272018-01-01