Overview

Dataset statistics

Number of variables11
Number of observations332
Missing cells34
Missing cells (%)0.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory29.3 KiB
Average record size in memory90.4 B

Variable types

Numeric2
Text3
DateTime1
Categorical5

Dataset

Description이 데이터는 서울특별시 동작구 에 소재한 어린이놀이시설 현황에 관한 것입니다. 이 데이터에는 시설명, 주소, 설치일자, 설치 장소 등의 정보가 포함되어 있습니다.
Author서울특별시 동작구
URLhttps://www.data.go.kr/data/15077350/fileData.do

Alerts

연번 is highly overall correlated with 시설번호High correlation
시설번호 is highly overall correlated with 연번High correlation
설치장소 is highly overall correlated with 민공구분 and 1 other fieldsHigh correlation
민공구분 is highly overall correlated with 설치장소High correlation
실내외구분 is highly overall correlated with 설치장소High correlation
운영구분 is highly imbalanced (92.6%)Imbalance
실내외구분 is highly imbalanced (69.6%)Imbalance
전화번호 has 34 (10.2%) missing valuesMissing
연번 has unique valuesUnique
시설번호 has unique valuesUnique

Reproduction

Analysis started2024-03-14 20:48:43.982592
Analysis finished2024-03-14 20:48:46.497046
Duration2.51 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct332
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean166.5
Minimum1
Maximum332
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2024-03-15T05:48:46.628452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile17.55
Q183.75
median166.5
Q3249.25
95-th percentile315.45
Maximum332
Range331
Interquartile range (IQR)165.5

Descriptive statistics

Standard deviation95.984374
Coefficient of variation (CV)0.57648273
Kurtosis-1.2
Mean166.5
Median Absolute Deviation (MAD)83
Skewness0
Sum55278
Variance9213
MonotonicityStrictly increasing
2024-03-15T05:48:47.025000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.3%
230 1
 
0.3%
228 1
 
0.3%
227 1
 
0.3%
226 1
 
0.3%
225 1
 
0.3%
224 1
 
0.3%
223 1
 
0.3%
222 1
 
0.3%
221 1
 
0.3%
Other values (322) 322
97.0%
ValueCountFrequency (%)
1 1
0.3%
2 1
0.3%
3 1
0.3%
4 1
0.3%
5 1
0.3%
6 1
0.3%
7 1
0.3%
8 1
0.3%
9 1
0.3%
10 1
0.3%
ValueCountFrequency (%)
332 1
0.3%
331 1
0.3%
330 1
0.3%
329 1
0.3%
328 1
0.3%
327 1
0.3%
326 1
0.3%
325 1
0.3%
324 1
0.3%
323 1
0.3%

시설번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct332
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean406330.54
Minimum229
Maximum576971
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2024-03-15T05:48:47.459468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum229
5-th percentile2298.55
Q139733.75
median532735
Q3549499.75
95-th percentile570008
Maximum576971
Range576742
Interquartile range (IQR)509766

Descriptive statistics

Standard deviation227394.25
Coefficient of variation (CV)0.55962876
Kurtosis-0.82033473
Mean406330.54
Median Absolute Deviation (MAD)19708.5
Skewness-1.0746481
Sum1.3490174 × 108
Variance5.1708146 × 1010
MonotonicityStrictly increasing
2024-03-15T05:48:47.852372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
229 1
 
0.3%
544426 1
 
0.3%
543734 1
 
0.3%
543402 1
 
0.3%
543236 1
 
0.3%
542348 1
 
0.3%
542341 1
 
0.3%
542277 1
 
0.3%
541480 1
 
0.3%
540716 1
 
0.3%
Other values (322) 322
97.0%
ValueCountFrequency (%)
229 1
0.3%
484 1
0.3%
487 1
0.3%
724 1
0.3%
725 1
0.3%
793 1
0.3%
794 1
0.3%
1322 1
0.3%
1323 1
0.3%
1639 1
0.3%
ValueCountFrequency (%)
576971 1
0.3%
576138 1
0.3%
576137 1
0.3%
575767 1
0.3%
575766 1
0.3%
575765 1
0.3%
575236 1
0.3%
573647 1
0.3%
571653 1
0.3%
571621 1
0.3%

놀이시설명
Text

Distinct331
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
2024-03-15T05:48:48.676634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length33
Median length26
Mean length15.792169
Min length4

Characters and Unicode

Total characters5243
Distinct characters296
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique330 ?
Unique (%)99.4%

Sample

1st row한강현대아파트 관리소앞
2nd row신한토탈아파트 어린이놀이터1
3rd row신한토탈아파트 어린이놀이터2
4th row상도 삼호아파트 1동 놀이터
5th row상도 삼호아파트 2동 놀이터
ValueCountFrequency (%)
놀이터 144
 
18.1%
앞 34
 
4.3%
어린이놀이터 26
 
3.3%
뒤 12
 
1.5%
101동 11
 
1.4%
놀이시설 9
 
1.1%
102동 8
 
1.0%
105동 6
 
0.8%
106동 6
 
0.8%
어린이놀이터1 6
 
0.8%
Other values (387) 533
67.0%
2024-03-15T05:48:50.026863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
이 487
 
9.3%
463
 
8.8%
놀 298
 
5.7%
터 282
 
5.4%
동 210
 
4.0%
아 210
 
4.0%
1 201
 
3.8%
트 195
 
3.7%
파 194
 
3.7%
린 155
 
3.0%
Other values (286) 2548
48.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4085
77.9%
Decimal Number 581
 
11.1%
Space Separator 463
 
8.8%
Close Punctuation 38
 
0.7%
Open Punctuation 38
 
0.7%
Uppercase Letter 19
 
0.4%
Lowercase Letter 11
 
0.2%
Dash Punctuation 4
 
0.1%
Math Symbol 3
 
0.1%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
이 487
 
11.9%
놀 298
 
7.3%
터 282
 
6.9%
동 210
 
5.1%
아 210
 
5.1%
트 195
 
4.8%
파 194
 
4.7%
린 155
 
3.8%
어 153
 
3.7%
상 67
 
1.6%
Other values (261) 1834
44.9%
Decimal Number
ValueCountFrequency (%)
1 201
34.6%
0 145
25.0%
2 88
15.1%
3 50
 
8.6%
4 25
 
4.3%
6 21
 
3.6%
8 18
 
3.1%
5 16
 
2.8%
7 11
 
1.9%
9 6
 
1.0%
Uppercase Letter
ValueCountFrequency (%)
C 6
31.6%
S 4
21.1%
K 4
21.1%
H 3
15.8%
D 1
 
5.3%
I 1
 
5.3%
Lowercase Letter
ValueCountFrequency (%)
e 9
81.8%
t 1
 
9.1%
h 1
 
9.1%
Space Separator
ValueCountFrequency (%)
463
100.0%
Close Punctuation
ValueCountFrequency (%)
) 38
100.0%
Open Punctuation
ValueCountFrequency (%)
( 38
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%
Math Symbol
ValueCountFrequency (%)
~ 3
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4085
77.9%
Common 1128
 
21.5%
Latin 30
 
0.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
이 487
 
11.9%
놀 298
 
7.3%
터 282
 
6.9%
동 210
 
5.1%
아 210
 
5.1%
트 195
 
4.8%
파 194
 
4.7%
린 155
 
3.8%
어 153
 
3.7%
상 67
 
1.6%
Other values (261) 1834
44.9%
Common
ValueCountFrequency (%)
463
41.0%
1 201
17.8%
0 145
 
12.9%
2 88
 
7.8%
3 50
 
4.4%
) 38
 
3.4%
( 38
 
3.4%
4 25
 
2.2%
6 21
 
1.9%
8 18
 
1.6%
Other values (6) 41
 
3.6%
Latin
ValueCountFrequency (%)
e 9
30.0%
C 6
20.0%
S 4
13.3%
K 4
13.3%
H 3
 
10.0%
t 1
 
3.3%
D 1
 
3.3%
I 1
 
3.3%
h 1
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4085
77.9%
ASCII 1158
 
22.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
이 487
 
11.9%
놀 298
 
7.3%
터 282
 
6.9%
동 210
 
5.1%
아 210
 
5.1%
트 195
 
4.8%
파 194
 
4.7%
린 155
 
3.8%
어 153
 
3.7%
상 67
 
1.6%
Other values (261) 1834
44.9%
ASCII
ValueCountFrequency (%)
463
40.0%
1 201
17.4%
0 145
 
12.5%
2 88
 
7.6%
3 50
 
4.3%
) 38
 
3.3%
( 38
 
3.3%
4 25
 
2.2%
6 21
 
1.8%
8 18
 
1.6%
Other values (15) 71
 
6.1%

주소
Text

Distinct248
Distinct (%)74.7%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
2024-03-15T05:48:51.176284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length62
Median length43
Mean length23.704819
Min length8

Characters and Unicode

Total characters7870
Distinct characters261
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique200 ?
Unique (%)60.2%

Sample

1st row 현충로 151 (흑석동, 한강현대아파트)
2nd row 등용로14길 34 (대방동, 신한토탈아파트)
3rd row 등용로14길 34 (대방동, 신한토탈아파트)
4th row 상도로 407 (상도동, 삼호아파트)
5th row 상도로 407 (상도동, 삼호아파트)
ValueCountFrequency (%)
상도동 70
 
5.7%
사당동 69
 
5.6%
신대방동 33
 
2.7%
대방동 23
 
1.9%
흑석동 21
 
1.7%
동작대로29길 18
 
1.5%
상도로 18
 
1.5%
노량진동 15
 
1.2%
본동 15
 
1.2%
만양로 12
 
1.0%
Other values (482) 930
76.0%
2024-03-15T05:48:53.275716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1222
 
15.5%
동 458
 
5.8%
( 318
 
4.0%
) 318
 
4.0%
1 307
 
3.9%
로 290
 
3.7%
2 241
 
3.1%
길 217
 
2.8%
상 204
 
2.6%
대 200
 
2.5%
Other values (251) 4095
52.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4402
55.9%
Decimal Number 1380
 
17.5%
Space Separator 1222
 
15.5%
Open Punctuation 318
 
4.0%
Close Punctuation 318
 
4.0%
Other Punctuation 144
 
1.8%
Dash Punctuation 70
 
0.9%
Lowercase Letter 8
 
0.1%
Letter Number 3
 
< 0.1%
Uppercase Letter 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 458
 
10.4%
로 290
 
6.6%
길 217
 
4.9%
상 204
 
4.6%
대 200
 
4.5%
도 191
 
4.3%
사 162
 
3.7%
아 161
 
3.7%
당 154
 
3.5%
파 152
 
3.5%
Other values (229) 2213
50.3%
Decimal Number
ValueCountFrequency (%)
1 307
22.2%
2 241
17.5%
3 145
10.5%
5 125
9.1%
0 121
 
8.8%
4 114
 
8.3%
9 109
 
7.9%
7 84
 
6.1%
6 78
 
5.7%
8 56
 
4.1%
Other Punctuation
ValueCountFrequency (%)
, 136
94.4%
， 6
 
4.2%
. 2
 
1.4%
Uppercase Letter
ValueCountFrequency (%)
C 2
66.7%
K 1
33.3%
Space Separator
ValueCountFrequency (%)
1222
100.0%
Open Punctuation
ValueCountFrequency (%)
( 318
100.0%
Close Punctuation
ValueCountFrequency (%)
) 318
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 70
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 8
100.0%
Letter Number
ValueCountFrequency (%)
Ⅱ 3
100.0%
Math Symbol
ValueCountFrequency (%)
~ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4402
55.9%
Common 3454
43.9%
Latin 14
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 458
 
10.4%
로 290
 
6.6%
길 217
 
4.9%
상 204
 
4.6%
대 200
 
4.5%
도 191
 
4.3%
사 162
 
3.7%
아 161
 
3.7%
당 154
 
3.5%
파 152
 
3.5%
Other values (229) 2213
50.3%
Common
ValueCountFrequency (%)
1222
35.4%
( 318
 
9.2%
) 318
 
9.2%
1 307
 
8.9%
2 241
 
7.0%
3 145
 
4.2%
, 136
 
3.9%
5 125
 
3.6%
0 121
 
3.5%
4 114
 
3.3%
Other values (8) 407
 
11.8%
Latin
ValueCountFrequency (%)
e 8
57.1%
Ⅱ 3
 
21.4%
C 2
 
14.3%
K 1
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4402
55.9%
ASCII 3459
44.0%
None 6
 
0.1%
Number Forms 3
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1222
35.3%
( 318
 
9.2%
) 318
 
9.2%
1 307
 
8.9%
2 241
 
7.0%
3 145
 
4.2%
, 136
 
3.9%
5 125
 
3.6%
0 121
 
3.5%
4 114
 
3.3%
Other values (10) 412
 
11.9%
Hangul
ValueCountFrequency (%)
동 458
 
10.4%
로 290
 
6.6%
길 217
 
4.9%
상 204
 
4.6%
대 200
 
4.5%
도 191
 
4.3%
사 162
 
3.7%
아 161
 
3.7%
당 154
 
3.5%
파 152
 
3.5%
Other values (229) 2213
50.3%
None
ValueCountFrequency (%)
， 6
100.0%
Number Forms
ValueCountFrequency (%)
Ⅱ 3
100.0%

설치일자
Date

Distinct215
Distinct (%)64.8%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
Minimum1990-08-07 00:00:00
Maximum2020-12-21 00:00:00
2024-03-15T05:48:53.700661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:48:54.184954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

설치장소
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
주택단지
222 
어린이집
47 
도시공원
47 
아동복지시설
 
5
놀이제공영업소
 
5
Other values (3)
 
6

Length

Max length7
Median length4
Mean length4.0903614
Min length4

Unique

Unique1 ?
Unique (%)0.3%

Sample

1st row주택단지
2nd row주택단지
3rd row주택단지
4th row주택단지
5th row주택단지

Common Values

ValueCountFrequency (%)
주택단지 222
66.9%
어린이집 47
 
14.2%
도시공원 47
 
14.2%
아동복지시설 5
 
1.5%
놀이제공영업소 5
 
1.5%
종교시설 3
 
0.9%
식품접객업소 2
 
0.6%
목욕장업소 1
 
0.3%

Length

2024-03-15T05:48:54.638279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T05:48:55.046516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
주택단지 222
66.9%
어린이집 47
 
14.2%
도시공원 47
 
14.2%
아동복지시설 5
 
1.5%
놀이제공영업소 5
 
1.5%
종교시설 3
 
0.9%
식품접객업소 2
 
0.6%
목욕장업소 1
 
0.3%

운영구분
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
운영
329 
이용금지
 
3

Length

Max length4
Median length2
Mean length2.0180723
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row운영
2nd row운영
3rd row운영
4th row운영
5th row운영

Common Values

ValueCountFrequency (%)
운영 329
99.1%
이용금지 3
 
0.9%

Length

2024-03-15T05:48:55.579696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T05:48:55.966092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
운영 329
99.1%
이용금지 3
 
0.9%

지역분류
Categorical

Distinct9
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
상도동
92 
사당동
80 
신대방동
44 
흑석동
39 
대방동
27 
Other values (4)
50 

Length

Max length5
Median length4
Mean length4.1536145
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row 흑석동
2nd row 대방동
3rd row 대방동
4th row 상도동
5th row 상도동

Common Values

ValueCountFrequency (%)
상도동 92
27.7%
사당동 80
24.1%
신대방동 44
13.3%
흑석동 39
11.7%
대방동 27
 
8.1%
노량진동 18
 
5.4%
본동 18
 
5.4%
동작동 7
 
2.1%
상도1동 7
 
2.1%

Length

2024-03-15T05:48:56.405816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T05:48:56.810617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
상도동 92
27.7%
사당동 80
24.1%
신대방동 44
13.3%
흑석동 39
11.7%
대방동 27
 
8.1%
노량진동 18
 
5.4%
본동 18
 
5.4%
동작동 7
 
2.1%
상도1동 7
 
2.1%

민공구분
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
민간
257 
공공
75 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row민간
2nd row민간
3rd row민간
4th row민간
5th row민간

Common Values

ValueCountFrequency (%)
민간 257
77.4%
공공 75
 
22.6%

Length

2024-03-15T05:48:57.274251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T05:48:57.638960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
민간 257
77.4%
공공 75
 
22.6%

실내외구분
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
실외
314 
실내
 
18

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row실외
2nd row실외
3rd row실외
4th row실외
5th row실외

Common Values

ValueCountFrequency (%)
실외 314
94.6%
실내 18
 
5.4%

Length

2024-03-15T05:48:58.003558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T05:48:58.336673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
실외 314
94.6%
실내 18
 
5.4%

전화번호
Text

MISSING 

Distinct180
Distinct (%)60.4%
Missing34
Missing (%)10.2%
Memory size2.7 KiB
2024-03-15T05:48:59.412161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length11
Mean length11.167785
Min length11

Characters and Unicode

Total characters3328
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

Unique126 ?
Unique (%)42.3%

Sample

1st row02-812-6812
2nd row02-824-8315
3rd row02-824-8315
4th row02-825-6525
5th row02-825-6525
ValueCountFrequency (%)
02-820-9845 28
 
9.4%
02-820-1395 6
 
2.0%
02-812-2936 6
 
2.0%
02-812-0322 5
 
1.7%
02-862-0312 4
 
1.3%
031-766-4111 4
 
1.3%
02-823-6884 4
 
1.3%
02-824-5436 3
 
1.0%
02-848-4912 3
 
1.0%
02-593-3465 3
 
1.0%
Other values (170) 232
77.9%
2024-03-15T05:49:00.939210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 596
17.9%
2 588
17.7%
0 491
14.8%
8 348
10.5%
5 234
 
7.0%
1 225
 
6.8%
3 201
 
6.0%
4 191
 
5.7%
9 160
 
4.8%
6 156
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2732
82.1%
Dash Punctuation 596
 
17.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 588
21.5%
0 491
18.0%
8 348
12.7%
5 234
 
8.6%
1 225
 
8.2%
3 201
 
7.4%
4 191
 
7.0%
9 160
 
5.9%
6 156
 
5.7%
7 138
 
5.1%
Dash Punctuation
ValueCountFrequency (%)
- 596
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3328
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 596
17.9%
2 588
17.7%
0 491
14.8%
8 348
10.5%
5 234
 
7.0%
1 225
 
6.8%
3 201
 
6.0%
4 191
 
5.7%
9 160
 
4.8%
6 156
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3328
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 596
17.9%
2 588
17.7%
0 491
14.8%
8 348
10.5%
5 234
 
7.0%
1 225
 
6.8%
3 201
 
6.0%
4 191
 
5.7%
9 160
 
4.8%
6 156
 
4.7%

Interactions

2024-03-15T05:48:45.575079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:48:44.964242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:48:45.844959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:48:45.215493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T05:49:01.221112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번시설번호설치장소운영구분지역분류민공구분실내외구분
연번1.0000.8530.3640.1610.3270.4900.422
시설번호0.8531.0000.3210.0000.1660.1340.069
설치장소0.3640.3211.0000.0000.2500.9780.859
운영구분0.1610.0000.0001.0000.0000.0000.000
지역분류0.3270.1660.2500.0001.0000.1050.129
민공구분0.4900.1340.9780.0000.1051.0000.000
실내외구분0.4220.0690.8590.0000.1290.0001.000
2024-03-15T05:49:01.511116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지역분류설치장소운영구분실내외구분민공구분
지역분류1.0000.1250.0000.1270.104
설치장소0.1251.0000.0000.6740.862
운영구분0.0000.0001.0000.0000.000
실내외구분0.1270.6740.0001.0000.000
민공구분0.1040.8620.0000.0001.000
2024-03-15T05:49:01.823652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번시설번호설치장소운영구분지역분류민공구분실내외구분
연번1.0001.0000.1820.1210.1540.3720.320
시설번호1.0001.0000.2140.0000.0530.2190.115
설치장소0.1820.2141.0000.0000.1250.8620.674
운영구분0.1210.0000.0001.0000.0000.0000.000
지역분류0.1540.0530.1250.0001.0000.1040.127
민공구분0.3720.2190.8620.0000.1041.0000.000
실내외구분0.3200.1150.6740.0000.1270.0001.000

Missing values

2024-03-15T05:48:46.096101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T05:48:46.390763image/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

연번시설번호놀이시설명주소설치일자설치장소운영구분지역분류민공구분실내외구분전화번호
01229한강현대아파트 관리소앞현충로 151 (흑석동, 한강현대아파트)2008-10-28주택단지운영흑석동민간실외02-812-6812
12484신한토탈아파트 어린이놀이터1등용로14길 34 (대방동, 신한토탈아파트)2009-02-04주택단지운영대방동민간실외02-824-8315
23487신한토탈아파트 어린이놀이터2등용로14길 34 (대방동, 신한토탈아파트)2009-02-05주택단지운영대방동민간실외02-824-8315
34724상도 삼호아파트 1동 놀이터상도로 407 (상도동, 삼호아파트)2009-06-17주택단지운영상도동민간실외02-825-6525
45725상도 삼호아파트 2동 놀이터상도로 407 (상도동, 삼호아파트)2009-06-17주택단지운영상도동민간실외02-825-6525
56793사당 극동아파트 103동앞 놀이터동작대로29길 119 (사당동, 극동아파트)2009-06-30주택단지운영사당동민간실외02-595-7010
67794사당 극동아파트 111동앞 놀이터동작대로29길 119 (사당동, 극동아파트)2009-06-30주택단지운영사당동민간실외02-595-7010
781322대방주공1단지아파트 101동앞 놀이터여의대방로44길 47 (대방동, 주공아파트)2009-11-02주택단지운영대방동민간실외02-824-9974
891323대방주공1단지아파트 103동앞 놀이터여의대방로44길 47 (대방동, 주공아파트)2009-11-02주택단지운영대방동민간실외02-824-9974
9101639상도동 두산위브아파트 104동앞 놀이터상도로30길 39 (상도동, 상도두산위브아파트)2010-02-16주택단지운영상도동민간실외02-813-0041
연번시설번호놀이시설명주소설치일자설치장소운영구분지역분류민공구분실내외구분전화번호
322323571621롯데캐슬골든포레 어린이놀이터2사당동181-55 롯데캐슬골든포레2019-11-25주택단지운영사당동민간실외031-766-4111
323324571653구립한울어린이집 실외놀이터보라매로9다길 17 (신대방동)구립한울어린이집2019-11-28어린이집운영신대방동공공실외02-821-4645
324325573647장은해그린아파트 어린이놀이터사당로2길 40 (사당동, 장은해그린 아파트)2020-04-20주택단지운영사당동민간실외031-766-4111
325326575236엘리스앤안나흑석한강로 27 (흑석동, 흑석한강푸르지오)제상가에이동 제지2층 203호2020-08-17식품접객업소운영흑석동민간실내02-820-1602
326327575765상도역세권롯데캐슬 어린이놀이터1(108동)상도동159-52019-11-16주택단지운영상도동민간실외02-827-0847
327328575766상도역세권롯데캐슬 어린이놀이터2(110동)상도동159-52019-11-16주택단지운영상도동민간실외02-827-0847
328329575767상도역세권롯데캐슬 어린이놀이터4(112동)상도동159-52019-11-16주택단지운영상도동민간실외02-827-0847
329330576137상도역롯데캐슬아파트 109동 어린이놀이터상도동 상도동 159-250 상도역롯데캐슬2020-11-25주택단지운영상도동민간실외02-415-5260
330331576138상도역롯데캐슬아파트 105동 유아놀이터상도동 상도동 159-250 상도역롯데캐슬아파트2020-11-15주택단지운영상도동민간실외02-415-5260
331332576971현대아파트 201동 놀이터사당로2가길 10-5 (상도동, 현대아파트)2020-12-21주택단지운영상도동민간실외02-539-8344