Overview

Dataset statistics

Number of variables14
Number of observations144
Missing cells446
Missing cells (%)22.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory17.1 KiB
Average record size in memory121.9 B

Variable types

Text4
Categorical7
Numeric3

Dataset

Description부산광역시 수영구 내 어린이놀이시설의 데이터로어린이놀이시설명, 실내외 구분, 주소, 부대시설 현황을 포함합니다.
Author부산광역시 수영구
URLhttps://www.data.go.kr/data/15088642/fileData.do

Alerts

시설면적 is highly overall correlated with 파고라(정자) and 1 other fieldsHigh correlation
조명등 is highly overall correlated with 실내외구분 and 1 other fieldsHigh correlation
벤치(의자) is highly overall correlated with 파고라(정자)High correlation
실내외구분 is highly overall correlated with 조명등High correlation
안전이용수칙 is highly overall correlated with 음용수시설High correlation
CCTV is highly overall correlated with 조명등High correlation
파고라(정자) is highly overall correlated with 시설면적 and 1 other fieldsHigh correlation
음용수시설 is highly overall correlated with 안전이용수칙High correlation
쓰레기통 is highly overall correlated with 시설면적High correlation
실내외구분 is highly imbalanced (69.0%)Imbalance
지번주소 has 91 (63.2%) missing valuesMissing
도로명주소 has 34 (23.6%) missing valuesMissing
시설면적 has 44 (30.6%) missing valuesMissing
조명등 has 81 (56.2%) missing valuesMissing
벤치(의자) has 72 (50.0%) missing valuesMissing
부대시설(기타) has 124 (86.1%) missing valuesMissing
놀이시설명 has unique valuesUnique
조명등 has 12 (8.3%) zerosZeros
벤치(의자) has 10 (6.9%) zerosZeros

Reproduction

Analysis started2024-03-14 20:04:45.698190
Analysis finished2024-03-14 20:04:51.704918
Duration6.01 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

놀이시설명
Text

UNIQUE 

Distinct144
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2024-03-15T05:04:52.573080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length29
Median length23
Mean length18.111111
Min length6

Characters and Unicode

Total characters2608
Distinct characters202
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

Unique144 ?
Unique (%)100.0%

Sample

1st row민락동협성휴포레아파트 어린이놀이터
2nd row삼둥이네 무인키즈카페 부산광안비치점
3rd row수영구 육아종합지원센터 분소 랑랑키즈카페 놀이시설
4th row남천자이 105동 옆 어린이놀이터2
5th row남천자이 104동 옆 어린이놀이터1
ValueCountFrequency (%)
놀이터 44
 
9.8%
어린이놀이터 27
 
6.0%
어린이 10
 
2.2%
앞 10
 
2.2%
어린이놀이시설 9
 
2.0%
e-편한세상 9
 
2.0%
아파트 8
 
1.8%
삼익비치타운아파트 7
 
1.6%
104동 7
 
1.6%
어린이놀이터2 7
 
1.6%
Other values (175) 311
69.3%
2024-03-15T05:04:54.097657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
305
 
11.7%
이 266
 
10.2%
놀 137
 
5.3%
터 122
 
4.7%
린 114
 
4.4%
어 109
 
4.2%
1 96
 
3.7%
동 81
 
3.1%
아 65
 
2.5%
0 58
 
2.2%
Other values (192) 1255
48.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1902
72.9%
Space Separator 305
 
11.7%
Decimal Number 247
 
9.5%
Uppercase Letter 41
 
1.6%
Close Punctuation 31
 
1.2%
Open Punctuation 31
 
1.2%
Dash Punctuation 23
 
0.9%
Lowercase Letter 19
 
0.7%
Other Punctuation 5
 
0.2%
Math Symbol 4
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
이 266
 
14.0%
놀 137
 
7.2%
터 122
 
6.4%
린 114
 
6.0%
어 109
 
5.7%
동 81
 
4.3%
아 65
 
3.4%
파 57
 
3.0%
트 51
 
2.7%
영 29
 
1.5%
Other values (165) 871
45.8%
Decimal Number
ValueCountFrequency (%)
1 96
38.9%
0 58
23.5%
2 37
 
15.0%
3 16
 
6.5%
4 14
 
5.7%
5 9
 
3.6%
6 5
 
2.0%
8 5
 
2.0%
7 4
 
1.6%
9 3
 
1.2%
Uppercase Letter
ValueCountFrequency (%)
K 6
14.6%
V 6
14.6%
I 6
14.6%
E 6
14.6%
W 6
14.6%
S 6
14.6%
T 3
7.3%
F 2
 
4.9%
Lowercase Letter
ValueCountFrequency (%)
e 16
84.2%
h 3
 
15.8%
Other Punctuation
ValueCountFrequency (%)
# 3
60.0%
& 2
40.0%
Space Separator
ValueCountFrequency (%)
305
100.0%
Close Punctuation
ValueCountFrequency (%)
) 31
100.0%
Open Punctuation
ValueCountFrequency (%)
( 31
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 23
100.0%
Math Symbol
ValueCountFrequency (%)
~ 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1902
72.9%
Common 646
 
24.8%
Latin 60
 
2.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
이 266
 
14.0%
놀 137
 
7.2%
터 122
 
6.4%
린 114
 
6.0%
어 109
 
5.7%
동 81
 
4.3%
아 65
 
3.4%
파 57
 
3.0%
트 51
 
2.7%
영 29
 
1.5%
Other values (165) 871
45.8%
Common
ValueCountFrequency (%)
305
47.2%
1 96
 
14.9%
0 58
 
9.0%
2 37
 
5.7%
) 31
 
4.8%
( 31
 
4.8%
- 23
 
3.6%
3 16
 
2.5%
4 14
 
2.2%
5 9
 
1.4%
Other values (7) 26
 
4.0%
Latin
ValueCountFrequency (%)
e 16
26.7%
K 6
 
10.0%
V 6
 
10.0%
I 6
 
10.0%
E 6
 
10.0%
W 6
 
10.0%
S 6
 
10.0%
h 3
 
5.0%
T 3
 
5.0%
F 2
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1902
72.9%
ASCII 706
 
27.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
305
43.2%
1 96
 
13.6%
0 58
 
8.2%
2 37
 
5.2%
) 31
 
4.4%
( 31
 
4.4%
- 23
 
3.3%
3 16
 
2.3%
e 16
 
2.3%
4 14
 
2.0%
Other values (17) 79
 
11.2%
Hangul
ValueCountFrequency (%)
이 266
 
14.0%
놀 137
 
7.2%
터 122
 
6.4%
린 114
 
6.0%
어 109
 
5.7%
동 81
 
4.3%
아 65
 
3.4%
파 57
 
3.0%
트 51
 
2.7%
영 29
 
1.5%
Other values (165) 871
45.8%

실내외구분
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
실외
136 
실내
 
8

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
실외 136
94.4%
실내 8
 
5.6%

Length

2024-03-15T05:04:54.323565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T05:04:54.594103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
실외 136
94.4%
실내 8
 
5.6%

지번주소
Text

MISSING 

Distinct45
Distinct (%)84.9%
Missing91
Missing (%)63.2%
Memory size1.2 KiB
2024-03-15T05:04:55.350616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length21
Mean length16.943396
Min length11

Characters and Unicode

Total characters898
Distinct characters36
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique40 ?
Unique (%)75.5%

Sample

1st row부산광역시 수영구 민락동 114-10
2nd row부산광역시 수영구 남천동 501
3rd row부산광역시 수영구 남천동 501
4th row부산광역시 수영구 남천동 501
5th row부산광역시 수영구 남천동 501
ValueCountFrequency (%)
수영구 53
26.4%
부산 22
 
10.9%
부산광역시 18
 
9.0%
민락동 17
 
8.5%
망미동 10
 
5.0%
남천동 9
 
4.5%
광안동 7
 
3.5%
501 4
 
2.0%
광안1동 3
 
1.5%
474-11 3
 
1.5%
Other values (50) 55
27.4%
2024-03-15T05:04:56.673085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
148
16.5%
1 74
 
8.2%
수 55
 
6.1%
영 55
 
6.1%
동 53
 
5.9%
구 53
 
5.9%
부 40
 
4.5%
- 40
 
4.5%
산 40
 
4.5%
광 32
 
3.6%
Other values (26) 308
34.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 479
53.3%
Decimal Number 229
25.5%
Space Separator 148
 
16.5%
Dash Punctuation 40
 
4.5%
Open Punctuation 1
 
0.1%
Close Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
수 55
11.5%
영 55
11.5%
동 53
11.1%
구 53
11.1%
부 40
8.4%
산 40
8.4%
광 32
 
6.7%
시 18
 
3.8%
역 18
 
3.8%
락 17
 
3.5%
Other values (12) 98
20.5%
Decimal Number
ValueCountFrequency (%)
1 74
32.3%
4 28
 
12.2%
7 24
 
10.5%
0 21
 
9.2%
5 18
 
7.9%
2 18
 
7.9%
3 14
 
6.1%
8 12
 
5.2%
6 12
 
5.2%
9 8
 
3.5%
Space Separator
ValueCountFrequency (%)
148
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 40
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 479
53.3%
Common 419
46.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
수 55
11.5%
영 55
11.5%
동 53
11.1%
구 53
11.1%
부 40
8.4%
산 40
8.4%
광 32
 
6.7%
시 18
 
3.8%
역 18
 
3.8%
락 17
 
3.5%
Other values (12) 98
20.5%
Common
ValueCountFrequency (%)
148
35.3%
1 74
17.7%
- 40
 
9.5%
4 28
 
6.7%
7 24
 
5.7%
0 21
 
5.0%
5 18
 
4.3%
2 18
 
4.3%
3 14
 
3.3%
8 12
 
2.9%
Other values (4) 22
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 479
53.3%
ASCII 419
46.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
148
35.3%
1 74
17.7%
- 40
 
9.5%
4 28
 
6.7%
7 24
 
5.7%
0 21
 
5.0%
5 18
 
4.3%
2 18
 
4.3%
3 14
 
3.3%
8 12
 
2.9%
Other values (4) 22
 
5.3%
Hangul
ValueCountFrequency (%)
수 55
11.5%
영 55
11.5%
동 53
11.1%
구 53
11.1%
부 40
8.4%
산 40
8.4%
광 32
 
6.7%
시 18
 
3.8%
역 18
 
3.8%
락 17
 
3.5%
Other values (12) 98
20.5%

도로명주소
Text

MISSING 

Distinct68
Distinct (%)61.8%
Missing34
Missing (%)23.6%
Memory size1.2 KiB
2024-03-15T05:04:57.903775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length52
Median length37
Mean length32.027273
Min length21

Characters and Unicode

Total characters3523
Distinct characters159
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique45 ?
Unique (%)40.9%

Sample

1st row부산광역시 수영구 남천동로108번길 38 (남천동) 골든비치 6층
2nd row부산광역시 수영구 수미로50번길 37-15 (수영동)
3rd row부산광역시 수영구 남천동로 46 (남천동) 남천자이아파트
4th row부산광역시 수영구 남천동로 46 (남천동) 남천자이아파트
5th row부산광역시 수영구 남천동로 46 (남천동) 남천자이아파트
ValueCountFrequency (%)
수영구 111
 
17.2%
부산광역시 110
 
17.1%
광안동 30
 
4.7%
남천동 29
 
4.5%
망미동 27
 
4.2%
광안해변로 16
 
2.5%
민락동 13
 
2.0%
좌수영로 9
 
1.4%
수영동 9
 
1.4%
비치아파트 7
 
1.1%
Other values (152) 284
44.0%
2024-03-15T05:04:59.514224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
535
 
15.2%
광 184
 
5.2%
수 169
 
4.8%
영 166
 
4.7%
동 127
 
3.6%
산 126
 
3.6%
부 114
 
3.2%
로 114
 
3.2%
시 112
 
3.2%
구 111
 
3.2%
Other values (149) 1765
50.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2282
64.8%
Space Separator 535
 
15.2%
Decimal Number 385
 
10.9%
Close Punctuation 111
 
3.2%
Open Punctuation 111
 
3.2%
Other Punctuation 63
 
1.8%
Uppercase Letter 24
 
0.7%
Lowercase Letter 9
 
0.3%
Dash Punctuation 3
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
광 184
 
8.1%
수 169
 
7.4%
영 166
 
7.3%
동 127
 
5.6%
산 126
 
5.5%
부 114
 
5.0%
로 114
 
5.0%
시 112
 
4.9%
구 111
 
4.9%
역 110
 
4.8%
Other values (123) 949
41.6%
Decimal Number
ValueCountFrequency (%)
1 67
17.4%
4 53
13.8%
2 42
10.9%
0 39
10.1%
6 38
9.9%
3 38
9.9%
5 38
9.9%
7 32
8.3%
8 21
 
5.5%
9 17
 
4.4%
Uppercase Letter
ValueCountFrequency (%)
W 4
16.7%
E 4
16.7%
I 4
16.7%
V 4
16.7%
K 4
16.7%
S 4
16.7%
Other Punctuation
ValueCountFrequency (%)
, 60
95.2%
* 2
 
3.2%
， 1
 
1.6%
Close Punctuation
ValueCountFrequency (%)
) 109
98.2%
] 2
 
1.8%
Open Punctuation
ValueCountFrequency (%)
( 109
98.2%
[ 2
 
1.8%
Space Separator
ValueCountFrequency (%)
535
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 9
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2282
64.8%
Common 1208
34.3%
Latin 33
 
0.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
광 184
 
8.1%
수 169
 
7.4%
영 166
 
7.3%
동 127
 
5.6%
산 126
 
5.5%
부 114
 
5.0%
로 114
 
5.0%
시 112
 
4.9%
구 111
 
4.9%
역 110
 
4.8%
Other values (123) 949
41.6%
Common
ValueCountFrequency (%)
535
44.3%
) 109
 
9.0%
( 109
 
9.0%
1 67
 
5.5%
, 60
 
5.0%
4 53
 
4.4%
2 42
 
3.5%
0 39
 
3.2%
6 38
 
3.1%
3 38
 
3.1%
Other values (9) 118
 
9.8%
Latin
ValueCountFrequency (%)
e 9
27.3%
W 4
12.1%
E 4
12.1%
I 4
12.1%
V 4
12.1%
K 4
12.1%
S 4
12.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2282
64.8%
ASCII 1240
35.2%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
535
43.1%
) 109
 
8.8%
( 109
 
8.8%
1 67
 
5.4%
, 60
 
4.8%
4 53
 
4.3%
2 42
 
3.4%
0 39
 
3.1%
6 38
 
3.1%
3 38
 
3.1%
Other values (15) 150
 
12.1%
Hangul
ValueCountFrequency (%)
광 184
 
8.1%
수 169
 
7.4%
영 166
 
7.3%
동 127
 
5.6%
산 126
 
5.5%
부 114
 
5.0%
로 114
 
5.0%
시 112
 
4.9%
구 111
 
4.9%
역 110
 
4.8%
Other values (123) 949
41.6%
None
ValueCountFrequency (%)
， 1
100.0%

시설면적
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct86
Distinct (%)86.0%
Missing44
Missing (%)30.6%
Infinite0
Infinite (%)0.0%
Mean543.12124
Minimum9
Maximum2395
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-03-15T05:04:59.762444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile48.474
Q1183.9075
median410.1
Q3595.58
95-th percentile2395
Maximum2395
Range2386
Interquartile range (IQR)411.6725

Descriptive statistics

Standard deviation565.60808
Coefficient of variation (CV)1.041403
Kurtosis4.6397035
Mean543.12124
Median Absolute Deviation (MAD)205.4
Skewness2.1738656
Sum54312.124
Variance319912.5
MonotonicityNot monotonic
2024-03-15T05:05:00.043989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2395.0 6
 
4.2%
1192.0 3
 
2.1%
755.07 2
 
1.4%
507.6 2
 
1.4%
1325.78 2
 
1.4%
295.5 2
 
1.4%
595.58 2
 
1.4%
433.0 2
 
1.4%
150.0 2
 
1.4%
591.1 1
 
0.7%
Other values (76) 76
52.8%
(Missing) 44
30.6%
ValueCountFrequency (%)
9.0 1
0.7%
11.0 1
0.7%
28.0 1
0.7%
39.82 1
0.7%
40.0 1
0.7%
48.92 1
0.7%
50.0 1
0.7%
64.8 1
0.7%
87.4 1
0.7%
92.0 1
0.7%
ValueCountFrequency (%)
2395.0 6
4.2%
1325.78 2
 
1.4%
1316.0 1
 
0.7%
1269.0 1
 
0.7%
1192.0 3
2.1%
991.1 1
 
0.7%
868.95 1
 
0.7%
816.0 1
 
0.7%
789.0 1
 
0.7%
755.07 2
 
1.4%

안전이용수칙
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
<NA>
74 
1
56 
2
 
7
0
 
6
3
 
1

Length

Max length4
Median length4
Mean length2.5416667
Min length1

Unique

Unique1 ?
Unique (%)0.7%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 74
51.4%
1 56
38.9%
2 7
 
4.9%
0 6
 
4.2%
3 1
 
0.7%

Length

2024-03-15T05:05:00.403755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T05:05:00.770966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 74
51.4%
1 56
38.9%
2 7
 
4.9%
0 6
 
4.2%
3 1
 
0.7%

비상벨
Categorical

Distinct4
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
<NA>
104 
0
32 
1
 
6
2
 
2

Length

Max length4
Median length4
Mean length3.1666667
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 104
72.2%
0 32
 
22.2%
1 6
 
4.2%
2 2
 
1.4%

Length

2024-03-15T05:05:01.168783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T05:05:01.534947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 104
72.2%
0 32
 
22.2%
1 6
 
4.2%
2 2
 
1.4%

CCTV
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
<NA>
80 
1
32 
2
26 
3
 
5
5
 
1

Length

Max length4
Median length4
Mean length2.6666667
Min length1

Unique

Unique1 ?
Unique (%)0.7%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 80
55.6%
1 32
 
22.2%
2 26
 
18.1%
3 5
 
3.5%
5 1
 
0.7%

Length

2024-03-15T05:05:01.879565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T05:05:02.077523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 80
55.6%
1 32
 
22.2%
2 26
 
18.1%
3 5
 
3.5%
5 1
 
0.7%

조명등
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct10
Distinct (%)15.9%
Missing81
Missing (%)56.2%
Infinite0
Infinite (%)0.0%
Mean2.7936508
Minimum0
Maximum30
Zeros12
Zeros (%)8.3%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-03-15T05:05:02.333405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33.5
95-th percentile6
Maximum30
Range30
Interquartile range (IQR)2.5

Descriptive statistics

Standard deviation4.2697689
Coefficient of variation (CV)1.5283832
Kurtosis27.419266
Mean2.7936508
Median Absolute Deviation (MAD)1
Skewness4.6795118
Sum176
Variance18.230927
MonotonicityNot monotonic
2024-03-15T05:05:02.685558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
2 16
 
11.1%
1 15
 
10.4%
0 12
 
8.3%
5 5
 
3.5%
6 4
 
2.8%
3 4
 
2.8%
4 4
 
2.8%
15 1
 
0.7%
30 1
 
0.7%
7 1
 
0.7%
(Missing) 81
56.2%
ValueCountFrequency (%)
0 12
8.3%
1 15
10.4%
2 16
11.1%
3 4
 
2.8%
4 4
 
2.8%
5 5
 
3.5%
6 4
 
2.8%
7 1
 
0.7%
15 1
 
0.7%
30 1
 
0.7%
ValueCountFrequency (%)
30 1
 
0.7%
15 1
 
0.7%
7 1
 
0.7%
6 4
 
2.8%
5 5
 
3.5%
4 4
 
2.8%
3 4
 
2.8%
2 16
11.1%
1 15
10.4%
0 12
8.3%

파고라(정자)
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
<NA>
82 
1
35 
0
19 
2
 
5
3
 
2

Length

Max length4
Median length4
Mean length2.7083333
Min length1

Unique

Unique1 ?
Unique (%)0.7%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 82
56.9%
1 35
24.3%
0 19
 
13.2%
2 5
 
3.5%
3 2
 
1.4%
6 1
 
0.7%

Length

2024-03-15T05:05:03.135523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T05:05:03.436280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 82
56.9%
1 35
24.3%
0 19
 
13.2%
2 5
 
3.5%
3 2
 
1.4%
6 1
 
0.7%

벤치(의자)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct14
Distinct (%)19.4%
Missing72
Missing (%)50.0%
Infinite0
Infinite (%)0.0%
Mean4.1944444
Minimum0
Maximum37
Zeros10
Zeros (%)6.9%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-03-15T05:05:03.777763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3.5
Q35
95-th percentile11.9
Maximum37
Range37
Interquartile range (IQR)3

Descriptive statistics

Standard deviation5.1584442
Coefficient of variation (CV)1.2298277
Kurtosis23.421075
Mean4.1944444
Median Absolute Deviation (MAD)1.5
Skewness4.194967
Sum302
Variance26.609546
MonotonicityNot monotonic
2024-03-15T05:05:04.016408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
2 17
 
11.8%
4 17
 
11.8%
0 10
 
6.9%
3 6
 
4.2%
6 6
 
4.2%
5 5
 
3.5%
1 3
 
2.1%
7 2
 
1.4%
16 1
 
0.7%
13 1
 
0.7%
Other values (4) 4
 
2.8%
(Missing) 72
50.0%
ValueCountFrequency (%)
0 10
6.9%
1 3
 
2.1%
2 17
11.8%
3 6
 
4.2%
4 17
11.8%
5 5
 
3.5%
6 6
 
4.2%
7 2
 
1.4%
9 1
 
0.7%
11 1
 
0.7%
ValueCountFrequency (%)
37 1
 
0.7%
18 1
 
0.7%
16 1
 
0.7%
13 1
 
0.7%
11 1
 
0.7%
9 1
 
0.7%
7 2
 
1.4%
6 6
 
4.2%
5 5
 
3.5%
4 17
11.8%

음용수시설
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
<NA>
106 
0
35 
1
 
3

Length

Max length4
Median length4
Mean length3.2083333
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 106
73.6%
0 35
 
24.3%
1 3
 
2.1%

Length

2024-03-15T05:05:04.446199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T05:05:04.782406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 106
73.6%
0 35
 
24.3%
1 3
 
2.1%

쓰레기통
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
<NA>
99 
0
28 
1
14 
2
 
3

Length

Max length4
Median length4
Mean length3.0625
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 99
68.8%
0 28
 
19.4%
1 14
 
9.7%
2 3
 
2.1%

Length

2024-03-15T05:05:05.158792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T05:05:05.508407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 99
68.8%
0 28
 
19.4%
1 14
 
9.7%
2 3
 
2.1%

부대시설(기타)
Text

MISSING 

Distinct11
Distinct (%)55.0%
Missing124
Missing (%)86.1%
Memory size1.2 KiB
2024-03-15T05:05:06.229246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length44
Median length30
Mean length8.65
Min length1

Characters and Unicode

Total characters173
Distinct characters60
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

Unique9 ?
Unique (%)45.0%

Sample

1st row흔들의자 1
2nd row0
3rd row1(운동시설)
4th row오르기, 기어서 통과하기용 놀이기구 각 1종
5th row종합놀이기구 2ea, 그네 1ea, 흔들놀이기구 2ea, 충격흡수용표면재 1ea
ValueCountFrequency (%)
없음 6
16.2%
0 5
 
13.5%
충격흡수용표면재 3
 
8.1%
2ea 2
 
5.4%
조합놀이대 2
 
5.4%
1ea 2
 
5.4%
충격흡수용표면재(고무매트 1
 
2.7%
흔들놀이기구1 1
 
2.7%
폐쇄형놀이기구 1
 
2.7%
야외테이블3 1
 
2.7%
Other values (13) 13
35.1%
2024-03-15T05:05:07.356846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
17
 
9.8%
, 8
 
4.6%
기 8
 
4.6%
이 8
 
4.6%
놀 7
 
4.0%
1 7
 
4.0%
음 6
 
3.5%
없 6
 
3.5%
용 5
 
2.9%
구 5
 
2.9%
Other values (50) 96
55.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 119
68.8%
Space Separator 17
 
9.8%
Decimal Number 15
 
8.7%
Other Punctuation 8
 
4.6%
Lowercase Letter 8
 
4.6%
Open Punctuation 3
 
1.7%
Close Punctuation 3
 
1.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
기 8
 
6.7%
이 8
 
6.7%
놀 7
 
5.9%
음 6
 
5.0%
없 6
 
5.0%
용 5
 
4.2%
구 5
 
4.2%
수 4
 
3.4%
흡 4
 
3.4%
충 4
 
3.4%
Other values (40) 62
52.1%
Decimal Number
ValueCountFrequency (%)
1 7
46.7%
0 5
33.3%
2 2
 
13.3%
3 1
 
6.7%
Lowercase Letter
ValueCountFrequency (%)
a 4
50.0%
e 4
50.0%
Space Separator
ValueCountFrequency (%)
17
100.0%
Other Punctuation
ValueCountFrequency (%)
, 8
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 119
68.8%
Common 46
 
26.6%
Latin 8
 
4.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
기 8
 
6.7%
이 8
 
6.7%
놀 7
 
5.9%
음 6
 
5.0%
없 6
 
5.0%
용 5
 
4.2%
구 5
 
4.2%
수 4
 
3.4%
흡 4
 
3.4%
충 4
 
3.4%
Other values (40) 62
52.1%
Common
ValueCountFrequency (%)
17
37.0%
, 8
17.4%
1 7
15.2%
0 5
 
10.9%
( 3
 
6.5%
) 3
 
6.5%
2 2
 
4.3%
3 1
 
2.2%
Latin
ValueCountFrequency (%)
a 4
50.0%
e 4
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 119
68.8%
ASCII 54
31.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
17
31.5%
, 8
14.8%
1 7
13.0%
0 5
 
9.3%
a 4
 
7.4%
e 4
 
7.4%
( 3
 
5.6%
) 3
 
5.6%
2 2
 
3.7%
3 1
 
1.9%
Hangul
ValueCountFrequency (%)
기 8
 
6.7%
이 8
 
6.7%
놀 7
 
5.9%
음 6
 
5.0%
없 6
 
5.0%
용 5
 
4.2%
구 5
 
4.2%
수 4
 
3.4%
흡 4
 
3.4%
충 4
 
3.4%
Other values (40) 62
52.1%

Interactions

2024-03-15T05:04:48.760596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:04:47.211788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:04:47.992605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:04:49.142518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:04:47.449365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:04:48.244901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:04:49.435482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:04:47.699758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:04:48.495937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T05:05:07.584353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
실내외구분지번주소도로명주소시설면적안전이용수칙비상벨CCTV조명등파고라(정자)벤치(의자)음용수시설쓰레기통부대시설(기타)
실내외구분1.0000.0000.9050.2090.0000.1930.0001.0000.0000.0000.0000.0471.000
지번주소0.0001.0001.0000.9481.0001.0000.9371.0000.9941.0001.0001.0001.000
도로명주소0.9051.0001.0000.9551.0000.9950.7380.6000.9640.9341.0001.0000.000
시설면적0.2090.9480.9551.0000.3840.0000.4370.0000.6380.0000.0000.9460.990
안전이용수칙0.0001.0001.0000.3841.0000.0000.2830.4780.2850.1630.8620.3280.665
비상벨0.1931.0000.9950.0000.0001.0000.6710.5520.0000.0000.0000.1250.442
CCTV0.0000.9370.7380.4370.2830.6711.0000.5750.3290.3540.0000.0000.798
조명등1.0001.0000.6000.0000.4780.5520.5751.0000.6920.7580.3670.3590.880
파고라(정자)0.0000.9940.9640.6380.2850.0000.3290.6921.0000.8000.0710.7660.000
벤치(의자)0.0001.0000.9340.0000.1630.0000.3540.7580.8001.0000.1410.0000.000
음용수시설0.0001.0001.0000.0000.8620.0000.0000.3670.0710.1411.0000.2470.000
쓰레기통0.0471.0001.0000.9460.3280.1250.0000.3590.7660.0000.2471.0000.788
부대시설(기타)1.0001.0000.0000.9900.6650.4420.7980.8800.0000.0000.0000.7881.000
2024-03-15T05:05:07.989301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
안전이용수칙비상벨CCTV쓰레기통음용수시설파고라(정자)실내외구분
안전이용수칙1.0000.0000.2670.3100.6420.2310.000
비상벨0.0001.0000.3280.1990.0000.0000.311
CCTV0.2670.3281.0000.0000.0000.2560.000
쓰레기통0.3100.1990.0001.0000.3950.4240.071
음용수시설0.6420.0000.0000.3951.0000.1100.000
파고라(정자)0.2310.0000.2560.4240.1101.0000.000
실내외구분0.0000.3110.0000.0710.0000.0001.000
2024-03-15T05:05:08.210626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시설면적조명등벤치(의자)실내외구분안전이용수칙비상벨CCTV파고라(정자)음용수시설쓰레기통
시설면적1.0000.1860.3420.2160.2500.0000.2880.5600.0000.687
조명등0.1861.0000.2530.9750.2660.3830.6940.2700.2850.372
벤치(의자)0.3420.2531.0000.0000.0970.0000.2910.6800.2270.000
실내외구분0.2160.9750.0001.0000.0000.3110.0000.0000.0000.071
안전이용수칙0.2500.2660.0970.0001.0000.0000.2670.2310.6420.310
비상벨0.0000.3830.0000.3110.0001.0000.3280.0000.0000.199
CCTV0.2880.6940.2910.0000.2670.3281.0000.2560.0000.000
파고라(정자)0.5600.2700.6800.0000.2310.0000.2561.0000.1100.424
음용수시설0.0000.2850.2270.0000.6420.0000.0000.1101.0000.395
쓰레기통0.6870.3720.0000.0710.3100.1990.0000.4240.3951.000

Missing values

2024-03-15T05:04:49.889601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T05:04:50.801810image/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.
2024-03-15T05:04:51.290092image/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

놀이시설명실내외구분지번주소도로명주소시설면적안전이용수칙비상벨CCTV조명등파고라(정자)벤치(의자)음용수시설쓰레기통부대시설(기타)
0민락동협성휴포레아파트 어린이놀이터실외부산광역시 수영구 민락동 114-10<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
1삼둥이네 무인키즈카페 부산광안비치점실내<NA>부산광역시 수영구 남천동로108번길 38 (남천동) 골든비치 6층<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
2수영구 육아종합지원센터 분소 랑랑키즈카페 놀이시설실내<NA>부산광역시 수영구 수미로50번길 37-15 (수영동)<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
3남천자이 105동 옆 어린이놀이터2실외<NA>부산광역시 수영구 남천동로 46 (남천동) 남천자이아파트<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
4남천자이 104동 옆 어린이놀이터1실외<NA>부산광역시 수영구 남천동로 46 (남천동) 남천자이아파트<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
5남천자이아파트 유아놀이터(어린이집 옆)실외<NA>부산광역시 수영구 남천동로 46 (남천동) 남천자이아파트<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
6포스코건설 남천더샵 커뮤니티 키즈룸(실내)실내부산광역시 수영구 남천동 501<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
7남천더샵프레스티지아파트 유아놀이터 (201동앞)실외부산광역시 수영구 남천동 501<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
8남천더샵프레스티지아파트 어린이놀이터-2 (104동앞)실외부산광역시 수영구 남천동 501<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
9남천더샵프레스티지아파트 어린이놀이터-1 (103동앞)실외부산광역시 수영구 남천동 501<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
놀이시설명실내외구분지번주소도로명주소시설면적안전이용수칙비상벨CCTV조명등파고라(정자)벤치(의자)음용수시설쓰레기통부대시설(기타)
134수영중앙어린이집 놀이터실외수영구 수영동 478-1부산광역시 수영구 망미번영로74번길 88 (수영동)105.8410100000없음
135사랑공원 어린이 놀이터실외부산광역시 수영구 광안1동 693-15<NA>717.01<NA><NA>11<NA><NA><NA><NA>
136부산중앙어린이집 놀이터실외수영구 남천1동 39-30부산광역시 수영구 황령산로 42 (남천동)245.010110301없음
137수아&영이 어린이집 놀이터실외수영구 광안2동 150-31부산광역시 수영구 광안로35번길 9 (광안동)50.01<NA><NA><NA><NA>2<NA><NA><NA>
138바람개비어린이집 놀이터실외<NA>부산광역시 수영구 장대골로27번길 10 (광안동) 바람개비어린이집139.2<NA><NA><NA><NA><NA><NA><NA><NA><NA>
139망미 어린이집 놀이터실외수영구 망미동 451-6부산광역시 수영구 망미배산로24번길 27 (망미동)112.0<NA><NA><NA><NA><NA><NA><NA><NA><NA>
140동성큰사랑어린이집 놀이터실외수영구 남천동 48-16부산광역시 수영구 황령산로8번길 6 (남천동)144.011220101없음
141도산어린이집 놀이터실외수영구 민락동 149-6부산광역시 수영구 무학로63번길 5 (민락동)524.0<NA><NA>151611<NA>
142광안어린이집 놀이터실외수영구 광안1동 526-3부산광역시 수영구 광일로29번가길 66 (광안동)388.87102013000
143늘푸른어린이공원 놀이터실외부산광역시 수영구 남천동 152<NA>284.431<NA><NA><NA>19<NA><NA><NA>