Overview

Dataset statistics

Number of variables18
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory14.6 KiB
Average record size in memory149.3 B

Variable types

Numeric4
Text3
Categorical10
DateTime1

Alerts

park_hold_fclty_cltr has constant value ""Constant
mgc_nm is highly overall correlated with skey and 6 other fieldsHigh correlation
telno is highly overall correlated with skey and 7 other fieldsHigh correlation
data_stdde is highly overall correlated with skey and 6 other fieldsHigh correlation
skey is highly overall correlated with la and 5 other fieldsHigh correlation
la is highly overall correlated with skey and 4 other fieldsHigh correlation
lo is highly overall correlated with skey and 4 other fieldsHigh correlation
park_ar is highly overall correlated with park_se and 1 other fieldsHigh correlation
park_se is highly overall correlated with park_arHigh correlation
park_hold_fclty_mvm is highly overall correlated with park_hold_fclty_amsmt and 1 other fieldsHigh correlation
park_hold_fclty_amsmt is highly overall correlated with park_hold_fclty_mvmHigh correlation
park_hold_fclty_cnvnnc is highly overall correlated with la and 6 other fieldsHigh correlation
park_hold_fclty_etc is highly overall correlated with skey and 4 other fieldsHigh correlation
rdnmadr is highly imbalanced (50.4%)Imbalance
park_hold_fclty_mvm is highly imbalanced (76.0%)Imbalance
park_hold_fclty_amsmt is highly imbalanced (55.6%)Imbalance
park_hold_fclty_cnvnnc is highly imbalanced (59.0%)Imbalance
park_hold_fclty_etc is highly imbalanced (54.6%)Imbalance
skey has unique valuesUnique
manage_no has unique valuesUnique
lnm_adres has unique valuesUnique
la has unique valuesUnique
lo has unique valuesUnique
park_ar has unique valuesUnique

Reproduction

Analysis started2023-12-10 09:56:48.618614
Analysis finished2023-12-10 09:56:55.587655
Duration6.97 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

skey
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68.5
Minimum1
Maximum615
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:56:55.761646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6.95
Q127.75
median53.5
Q378.25
95-th percentile98.05
Maximum615
Range614
Interquartile range (IQR)50.5

Descriptive statistics

Standard deviation100.45467
Coefficient of variation (CV)1.4664916
Kurtosis24.931743
Mean68.5
Median Absolute Deviation (MAD)25.5
Skewness4.905555
Sum6850
Variance10091.141
MonotonicityNot monotonic
2023-12-10T18:56:56.052758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.0%
65 1
 
1.0%
75 1
 
1.0%
74 1
 
1.0%
73 1
 
1.0%
72 1
 
1.0%
71 1
 
1.0%
70 1
 
1.0%
69 1
 
1.0%
68 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
1 1
1.0%
3 1
1.0%
4 1
1.0%
5 1
1.0%
6 1
1.0%
7 1
1.0%
9 1
1.0%
10 1
1.0%
11 1
1.0%
12 1
1.0%
ValueCountFrequency (%)
615 1
1.0%
614 1
1.0%
613 1
1.0%
100 1
1.0%
99 1
1.0%
98 1
1.0%
97 1
1.0%
96 1
1.0%
95 1
1.0%
94 1
1.0%

manage_no
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:56:56.560823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters1100
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

Unique100 ?
Unique (%)100.0%

Sample

1st row26290-00003
2nd row26530-00030
3rd row26290-00004
4th row26290-00005
5th row26290-00006
ValueCountFrequency (%)
26290-00003 1
 
1.0%
26230-00002 1
 
1.0%
26230-00013 1
 
1.0%
26230-00012 1
 
1.0%
26230-00011 1
 
1.0%
26230-00010 1
 
1.0%
26230-00009 1
 
1.0%
26230-00008 1
 
1.0%
26230-00007 1
 
1.0%
26230-00006 1
 
1.0%
Other values (90) 90
90.0%
2023-12-10T18:56:57.433442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 435
39.5%
2 209
19.0%
6 116
 
10.5%
- 100
 
9.1%
3 63
 
5.7%
9 47
 
4.3%
1 41
 
3.7%
4 33
 
3.0%
7 33
 
3.0%
5 13
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1000
90.9%
Dash Punctuation 100
 
9.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 435
43.5%
2 209
20.9%
6 116
 
11.6%
3 63
 
6.3%
9 47
 
4.7%
1 41
 
4.1%
4 33
 
3.3%
7 33
 
3.3%
5 13
 
1.3%
8 10
 
1.0%
Dash Punctuation
ValueCountFrequency (%)
- 100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1100
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 435
39.5%
2 209
19.0%
6 116
 
10.5%
- 100
 
9.1%
3 63
 
5.7%
9 47
 
4.3%
1 41
 
3.7%
4 33
 
3.0%
7 33
 
3.0%
5 13
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1100
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 435
39.5%
2 209
19.0%
6 116
 
10.5%
- 100
 
9.1%
3 63
 
5.7%
9 47
 
4.3%
1 41
 
3.7%
4 33
 
3.0%
7 33
 
3.0%
5 13
 
1.2%

park_nm
Text

Distinct82
Distinct (%)82.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:56:58.273973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length10
Mean length5.71
Min length3

Characters and Unicode

Total characters571
Distinct characters141
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique78 ?
Unique (%)78.0%

Sample

1st row당곡공원
2nd row모라벤처타워공원
3rd row대일어린이공원
4th row못골어린이공원
5th row솔밭어린이공원
ValueCountFrequency (%)
어린이공원 13
 
12.7%
소공원 7
 
6.9%
문화공원 2
 
2.0%
개나리어린이공원 2
 
2.0%
당곡공원 1
 
1.0%
백양가족공원 1
 
1.0%
까치어린이공원 1
 
1.0%
화목어린이공원 1
 
1.0%
솔바람어린이공원 1
 
1.0%
백양어린이공원 1
 
1.0%
Other values (72) 72
70.6%
2023-12-10T18:56:59.131351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
공 96
16.8%
원 96
16.8%
린 40
 
7.0%
이 37
 
6.5%
어 35
 
6.1%
화 13
 
2.3%
소 11
 
1.9%
연 9
 
1.6%
지 8
 
1.4%
동 7
 
1.2%
Other values (131) 219
38.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 545
95.4%
Decimal Number 18
 
3.2%
Uppercase Letter 4
 
0.7%
Space Separator 2
 
0.4%
Close Punctuation 1
 
0.2%
Open Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
공 96
17.6%
원 96
17.6%
린 40
 
7.3%
이 37
 
6.8%
어 35
 
6.4%
화 13
 
2.4%
소 11
 
2.0%
연 9
 
1.7%
지 8
 
1.5%
동 7
 
1.3%
Other values (116) 193
35.4%
Decimal Number
ValueCountFrequency (%)
1 6
33.3%
0 4
22.2%
2 3
16.7%
6 1
 
5.6%
3 1
 
5.6%
8 1
 
5.6%
9 1
 
5.6%
5 1
 
5.6%
Uppercase Letter
ValueCountFrequency (%)
A 1
25.0%
U 1
25.0%
N 1
25.0%
B 1
25.0%
Space Separator
ValueCountFrequency (%)
2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 545
95.4%
Common 22
 
3.9%
Latin 4
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
공 96
17.6%
원 96
17.6%
린 40
 
7.3%
이 37
 
6.8%
어 35
 
6.4%
화 13
 
2.4%
소 11
 
2.0%
연 9
 
1.7%
지 8
 
1.5%
동 7
 
1.3%
Other values (116) 193
35.4%
Common
ValueCountFrequency (%)
1 6
27.3%
0 4
18.2%
2 3
13.6%
2
 
9.1%
6 1
 
4.5%
) 1
 
4.5%
( 1
 
4.5%
3 1
 
4.5%
8 1
 
4.5%
9 1
 
4.5%
Latin
ValueCountFrequency (%)
A 1
25.0%
U 1
25.0%
N 1
25.0%
B 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 545
95.4%
ASCII 26
 
4.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
공 96
17.6%
원 96
17.6%
린 40
 
7.3%
이 37
 
6.8%
어 35
 
6.4%
화 13
 
2.4%
소 11
 
2.0%
연 9
 
1.7%
지 8
 
1.5%
동 7
 
1.3%
Other values (116) 193
35.4%
ASCII
ValueCountFrequency (%)
1 6
23.1%
0 4
15.4%
2 3
11.5%
2
 
7.7%
6 1
 
3.8%
) 1
 
3.8%
( 1
 
3.8%
3 1
 
3.8%
8 1
 
3.8%
9 1
 
3.8%
Other values (5) 5
19.2%

park_se
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
어린이공원
48 
근린공원
22 
소공원
20 
문화공원
6 
역사공원
 
1
Other values (3)
 
3

Length

Max length5
Median length4
Mean length4.28
Min length3

Unique

Unique4 ?
Unique (%)4.0%

Sample

1st row역사공원
2nd row소공원
3rd row어린이공원
4th row어린이공원
5th row어린이공원

Common Values

ValueCountFrequency (%)
어린이공원 48
48.0%
근린공원 22
22.0%
소공원 20
20.0%
문화공원 6
 
6.0%
역사공원 1
 
1.0%
묘지공원 1
 
1.0%
수변공원 1
 
1.0%
체육공원 1
 
1.0%

Length

2023-12-10T18:56:59.466809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:56:59.755751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
어린이공원 48
48.0%
근린공원 22
22.0%
소공원 20
20.0%
문화공원 6
 
6.0%
역사공원 1
 
1.0%
묘지공원 1
 
1.0%
수변공원 1
 
1.0%
체육공원 1
 
1.0%

rdnmadr
Categorical

IMBALANCE 

Distinct33
Distinct (%)33.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
-
68 
부산광역시 남구 유엔평화로13번길 63(대연동)
 
1
부산광역시 남구 진남로 46번길 37
 
1
부산광역시 남구 동명로 114
 
1
부산광역시 남구 진남로188번가길 49-6
 
1
Other values (28)
28 

Length

Max length29
Median length1
Mean length8.06
Min length1

Unique

Unique32 ?
Unique (%)32.0%

Sample

1st row-
2nd row-
3rd row부산광역시 남구 유엔평화로13번길 63(대연동)
4th row부산광역시 남구 진남로 46번길 37
5th row부산광역시 남구 동명로 114

Common Values

ValueCountFrequency (%)
- 68
68.0%
부산광역시 남구 유엔평화로13번길 63(대연동) 1
 
1.0%
부산광역시 남구 진남로 46번길 37 1
 
1.0%
부산광역시 남구 동명로 114 1
 
1.0%
부산광역시 남구 진남로188번가길 49-6 1
 
1.0%
부산광역시 남구 분포로111일원(용호동) 1
 
1.0%
부산광역시 남구 고동골로69번가길 50 1
 
1.0%
부산광역시 남구 유엔로27번길 33-17 1
 
1.0%
부산광역시 남구 우암양달로89번길 25 1
 
1.0%
부산광역시 남구 용주로14번길 5 1
 
1.0%
Other values (23) 23
 
23.0%

Length

2023-12-10T18:57:00.019745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
68
34.7%
부산광역시 32
16.3%
남구 18
 
9.2%
부산진구 13
 
6.6%
일원 5
 
2.6%
황령대로492번길 1
 
0.5%
백양대로208번길 1
 
0.5%
새싹로296(초읍동 1
 
0.5%
성지로113번나길27-8(초읍동 1
 
0.5%
15 1
 
0.5%
Other values (55) 55
28.1%

lnm_adres
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:57:00.565921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length21
Mean length18.97
Min length16

Characters and Unicode

Total characters1897
Distinct characters59
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

Unique100 ?
Unique (%)100.0%

Sample

1st row부산광역시 남구 대연동 산205-2
2nd row부산광역시 사상구 모라동 1375-3
3rd row부산광역시 남구 대연동 1727-1
4th row부산광역시 남구 대연동 1475-2
5th row부산광역시 남구 용호동 409-5
ValueCountFrequency (%)
부산광역시 100
24.8%
남구 38
 
9.4%
부산진구 30
 
7.4%
연제구 20
 
5.0%
연산동 17
 
4.2%
대연동 14
 
3.5%
북구 9
 
2.2%
화명동 7
 
1.7%
문현동 7
 
1.7%
용호동 6
 
1.5%
Other values (122) 155
38.5%
2023-12-10T18:57:01.474195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
307
16.2%
산 156
 
8.2%
부 135
 
7.1%
구 101
 
5.3%
동 100
 
5.3%
시 100
 
5.3%
역 100
 
5.3%
광 100
 
5.3%
1 85
 
4.5%
2 67
 
3.5%
Other values (49) 646
34.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1117
58.9%
Decimal Number 407
 
21.5%
Space Separator 307
 
16.2%
Dash Punctuation 66
 
3.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 156
14.0%
부 135
12.1%
구 101
9.0%
동 100
9.0%
시 100
9.0%
역 100
9.0%
광 100
9.0%
연 53
 
4.7%
남 38
 
3.4%
진 30
 
2.7%
Other values (37) 204
18.3%
Decimal Number
ValueCountFrequency (%)
1 85
20.9%
2 67
16.5%
3 38
9.3%
7 38
9.3%
4 37
9.1%
6 34
 
8.4%
0 29
 
7.1%
8 28
 
6.9%
5 27
 
6.6%
9 24
 
5.9%
Space Separator
ValueCountFrequency (%)
307
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 66
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1117
58.9%
Common 780
41.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 156
14.0%
부 135
12.1%
구 101
9.0%
동 100
9.0%
시 100
9.0%
역 100
9.0%
광 100
9.0%
연 53
 
4.7%
남 38
 
3.4%
진 30
 
2.7%
Other values (37) 204
18.3%
Common
ValueCountFrequency (%)
307
39.4%
1 85
 
10.9%
2 67
 
8.6%
- 66
 
8.5%
3 38
 
4.9%
7 38
 
4.9%
4 37
 
4.7%
6 34
 
4.4%
0 29
 
3.7%
8 28
 
3.6%
Other values (2) 51
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1117
58.9%
ASCII 780
41.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
307
39.4%
1 85
 
10.9%
2 67
 
8.6%
- 66
 
8.5%
3 38
 
4.9%
7 38
 
4.9%
4 37
 
4.7%
6 34
 
4.4%
0 29
 
3.7%
8 28
 
3.6%
Other values (2) 51
 
6.5%
Hangul
ValueCountFrequency (%)
산 156
14.0%
부 135
12.1%
구 101
9.0%
동 100
9.0%
시 100
9.0%
역 100
9.0%
광 100
9.0%
연 53
 
4.7%
남 38
 
3.4%
진 30
 
2.7%
Other values (37) 204
18.3%

la
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.162288
Minimum35.102539
Maximum35.244866
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:57:01.826671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.102539
5-th percentile35.117459
Q135.136698
median35.167531
Q335.181941
95-th percentile35.224651
Maximum35.244866
Range0.142327
Interquartile range (IQR)0.0452431

Descriptive statistics

Standard deviation0.031756904
Coefficient of variation (CV)0.00090315238
Kurtosis-0.19766118
Mean35.162288
Median Absolute Deviation (MAD)0.020661
Skewness0.37217105
Sum3516.2288
Variance0.0010085009
MonotonicityNot monotonic
2023-12-10T18:57:02.149593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.125023 1
 
1.0%
35.1477989 1
 
1.0%
35.1625589 1
 
1.0%
35.1611801 1
 
1.0%
35.1608739 1
 
1.0%
35.1812528 1
 
1.0%
35.1494392 1
 
1.0%
35.1671061 1
 
1.0%
35.1731395 1
 
1.0%
35.1522506 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
35.102539 1
1.0%
35.107143 1
1.0%
35.112435 1
1.0%
35.112869 1
1.0%
35.112918 1
1.0%
35.117698 1
1.0%
35.120074 1
1.0%
35.121729 1
1.0%
35.121847 1
1.0%
35.121966 1
1.0%
ValueCountFrequency (%)
35.244866 1
1.0%
35.238961 1
1.0%
35.233152 1
1.0%
35.229995 1
1.0%
35.229211 1
1.0%
35.224411 1
1.0%
35.223098 1
1.0%
35.215768 1
1.0%
35.201017 1
1.0%
35.195288 1
1.0%

lo
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean129.06746
Minimum128.98569
Maximum129.12235
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:57:02.542506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum128.98569
5-th percentile129.00781
Q1129.03538
median129.07498
Q3129.09132
95-th percentile129.11366
Maximum129.12235
Range0.13666
Interquartile range (IQR)0.05594185

Descriptive statistics

Standard deviation0.034238161
Coefficient of variation (CV)0.00026527338
Kurtosis-0.79342386
Mean129.06746
Median Absolute Deviation (MAD)0.0246354
Skewness-0.48566909
Sum12906.746
Variance0.0011722517
MonotonicityNot monotonic
2023-12-10T18:57:02.965070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
129.091385 1
 
1.0%
129.0273201 1
 
1.0%
129.0229822 1
 
1.0%
129.0314583 1
 
1.0%
129.0289772 1
 
1.0%
129.0521228 1
 
1.0%
129.0721831 1
 
1.0%
129.0546641 1
 
1.0%
129.0286135 1
 
1.0%
129.0419191 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
128.985689 1
1.0%
128.998979 1
1.0%
128.999502 1
1.0%
129.000791 1
1.0%
129.006387 1
1.0%
129.007882 1
1.0%
129.008101 1
1.0%
129.009497 1
1.0%
129.011566 1
1.0%
129.014814 1
1.0%
ValueCountFrequency (%)
129.122349 1
1.0%
129.119861 1
1.0%
129.115151 1
1.0%
129.114843 1
1.0%
129.114819 1
1.0%
129.1136 1
1.0%
129.113548 1
1.0%
129.11194 1
1.0%
129.111521 1
1.0%
129.109518 1
1.0%

park_ar
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean87419.779
Minimum196.8
Maximum4947290
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:57:03.471683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum196.8
5-th percentile421.295
Q11549.725
median2283.05
Q310001.25
95-th percentile97698.7
Maximum4947290
Range4947093.2
Interquartile range (IQR)8451.525

Descriptive statistics

Standard deviation530464.69
Coefficient of variation (CV)6.0680168
Kurtosis74.063631
Mean87419.779
Median Absolute Deviation (MAD)1505.45
Skewness8.3565467
Sum8741977.9
Variance2.8139278 × 1011
MonotonicityNot monotonic
2023-12-10T18:57:03.782063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
75465.0 1
 
1.0%
14292.0 1
 
1.0%
3920.3 1
 
1.0%
3351.8 1
 
1.0%
1904.2 1
 
1.0%
640.3 1
 
1.0%
41446.0 1
 
1.0%
470758.0 1
 
1.0%
16375.0 1
 
1.0%
55526.0 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
196.8 1
1.0%
283.0 1
1.0%
349.0 1
1.0%
370.0 1
1.0%
406.0 1
1.0%
422.1 1
1.0%
437.0 1
1.0%
479.0 1
1.0%
499.8 1
1.0%
619.8 1
1.0%
ValueCountFrequency (%)
4947290.0 1
1.0%
1924935.0 1
1.0%
470758.0 1
1.0%
391299.0 1
1.0%
176486.0 1
1.0%
93552.0 1
1.0%
75465.0 1
1.0%
70396.0 1
1.0%
60490.0 1
1.0%
55526.0 1
1.0%

park_hold_fclty_mvm
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct9
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
-
90 
게이트볼장
 
2
7
 
2
풋살운동장
 
1
배드민턴장
 
1
Other values (4)
 
4

Length

Max length6
Median length1
Mean length1.22
Min length1

Unique

Unique6 ?
Unique (%)6.0%

Sample

1st row-
2nd row-
3rd row-
4th row-
5th row-

Common Values

ValueCountFrequency (%)
- 90
90.0%
게이트볼장 2
 
2.0%
7 2
 
2.0%
풋살운동장 1
 
1.0%
배드민턴장 1
 
1.0%
5 1
 
1.0%
3 1
 
1.0%
12 1
 
1.0%
10조12종 1
 
1.0%

Length

2023-12-10T18:57:04.053555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:57:04.293847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
90
90.0%
게이트볼장 2
 
2.0%
7 2
 
2.0%
풋살운동장 1
 
1.0%
배드민턴장 1
 
1.0%
5 1
 
1.0%
3 1
 
1.0%
12 1
 
1.0%
10조12종 1
 
1.0%

park_hold_fclty_amsmt
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct15
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
-
72 
조합놀이대 등
10 
조합놀이대
 
5
미끄럼틀, 시소
 
2
목마시소
 
1
Other values (10)
10 

Length

Max length17
Median length1
Mean length3.01
Min length1

Unique

Unique11 ?
Unique (%)11.0%

Sample

1st row-
2nd row-
3rd row-
4th row-
5th row-

Common Values

ValueCountFrequency (%)
- 72
72.0%
조합놀이대 등 10
 
10.0%
조합놀이대 5
 
5.0%
미끄럼틀, 시소 2
 
2.0%
목마시소 1
 
1.0%
조합놀이대, 그네, 시소 1
 
1.0%
조합놀이대, 흔들놀이 1
 
1.0%
조합놀이대, 그네 1
 
1.0%
조합놀이대, 흔들놀이대 1
 
1.0%
조합놀이대, 건너는기구 1
 
1.0%
Other values (5) 5
 
5.0%

Length

2023-12-10T18:57:04.592827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
72
59.0%
조합놀이대 22
 
18.0%
등 10
 
8.2%
시소 5
 
4.1%
흔들놀이기구 3
 
2.5%
미끄럼틀 2
 
1.6%
그네 2
 
1.6%
흔들놀이 2
 
1.6%
목마시소 1
 
0.8%
흔들놀이대 1
 
0.8%
Other values (2) 2
 
1.6%

park_hold_fclty_cnvnnc
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
-
88 
화장실
 
6
파고라
 
6

Length

Max length3
Median length1
Mean length1.24
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-
2nd row-
3rd row-
4th row-
5th row-

Common Values

ValueCountFrequency (%)
- 88
88.0%
화장실 6
 
6.0%
파고라 6
 
6.0%

Length

2023-12-10T18:57:04.912106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:57:05.119034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
88
88.0%
화장실 6
 
6.0%
파고라 6
 
6.0%

park_hold_fclty_cltr
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
-
100 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-
2nd row-
3rd row-
4th row-
5th row-

Common Values

ValueCountFrequency (%)
- 100
100.0%

Length

2023-12-10T18:57:05.448126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:57:05.658633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
100
100.0%

park_hold_fclty_etc
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct10
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
-
72 
파고라 및 체력단련시설 등
14 
파고라 및 벤치 등
 
4
화장실
 
4
평의자 외 2종 12점
 
1
Other values (5)
 
5

Length

Max length14
Median length1
Mean length3.75
Min length1

Unique

Unique6 ?
Unique (%)6.0%

Sample

1st row-
2nd row평의자 외 2종 12점
3rd row-
4th row-
5th row-

Common Values

ValueCountFrequency (%)
- 72
72.0%
파고라 및 체력단련시설 등 14
 
14.0%
파고라 및 벤치 등 4
 
4.0%
화장실 4
 
4.0%
평의자 외 2종 12점 1
 
1.0%
파고라 외 4종 7점 1
 
1.0%
벤치 등 1
 
1.0%
체력단련시설 및 벤치 등 1
 
1.0%
세미나실, 공연장 1
 
1.0%
야외 공연장 1
 
1.0%

Length

2023-12-10T18:57:05.905399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:57:06.176688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
72
43.4%
등 20
 
12.0%
파고라 19
 
11.4%
및 19
 
11.4%
체력단련시설 15
 
9.0%
벤치 6
 
3.6%
화장실 4
 
2.4%
외 2
 
1.2%
공연장 2
 
1.2%
평의자 1
 
0.6%
Other values (6) 6
 
3.6%

appn_ntfc_de
Date

Distinct71
Distinct (%)71.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
Minimum1944-01-03 00:00:00
Maximum2017-07-19 00:00:00
2023-12-10T18:57:06.517949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:06.848045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

mgc_nm
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
-
38 
부산광역시 부산진구청
28 
연제구청 경제진흥과
20 
부산광역시 북구청 청정녹지과
9 
부산광역시 사상구청
 
3

Length

Max length15
Median length11
Mean length7.23
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-
2nd row부산광역시 사상구청
3rd row-
4th row-
5th row-

Common Values

ValueCountFrequency (%)
- 38
38.0%
부산광역시 부산진구청 28
28.0%
연제구청 경제진흥과 20
20.0%
부산광역시 북구청 청정녹지과 9
 
9.0%
부산광역시 사상구청 3
 
3.0%
부산시설공단 2
 
2.0%

Length

2023-12-10T18:57:07.175268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:57:07.488028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
부산광역시 40
23.7%
38
22.5%
부산진구청 28
16.6%
연제구청 20
11.8%
경제진흥과 20
11.8%
북구청 9
 
5.3%
청정녹지과 9
 
5.3%
사상구청 3
 
1.8%
부산시설공단 2
 
1.2%

telno
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
-
38 
051-605-4544
28 
051-665-4534
20 
051-309-2054
9 
051-310-4521
 
3
Other values (2)
 
2

Length

Max length12
Median length12
Mean length7.82
Min length1

Unique

Unique2 ?
Unique (%)2.0%

Sample

1st row-
2nd row051-310-4521
3rd row-
4th row-
5th row-

Common Values

ValueCountFrequency (%)
- 38
38.0%
051-605-4544 28
28.0%
051-665-4534 20
20.0%
051-309-2054 9
 
9.0%
051-310-4521 3
 
3.0%
051-860-7848 1
 
1.0%
051-850-6000 1
 
1.0%

Length

2023-12-10T18:57:07.767147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:57:08.027969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
38
38.0%
051-605-4544 28
28.0%
051-665-4534 20
20.0%
051-309-2054 9
 
9.0%
051-310-4521 3
 
3.0%
051-860-7848 1
 
1.0%
051-850-6000 1
 
1.0%

data_stdde
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2018-08-14
38 
2018-04-05
30 
2018-04-04
20 
2018-08-30
9 
2019-03-21
 
3

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2018-08-14
2nd row2019-03-21
3rd row2018-08-14
4th row2018-08-14
5th row2018-08-14

Common Values

ValueCountFrequency (%)
2018-08-14 38
38.0%
2018-04-05 30
30.0%
2018-04-04 20
20.0%
2018-08-30 9
 
9.0%
2019-03-21 3
 
3.0%

Length

2023-12-10T18:57:08.327102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:57:08.552088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2018-08-14 38
38.0%
2018-04-05 30
30.0%
2018-04-04 20
20.0%
2018-08-30 9
 
9.0%
2019-03-21 3
 
3.0%

Interactions

2023-12-10T18:56:53.792582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:56:51.331650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:56:52.076907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:56:52.976771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:56:53.970090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:56:51.519865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:56:52.278340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:56:53.188443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:56:54.202017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:56:51.708409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:56:52.491435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:56:53.393414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:56:54.508366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:56:51.904937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:56:52.767686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:56:53.616649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T18:57:08.754925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
skeymanage_nopark_nmpark_serdnmadrlnm_adreslalopark_arpark_hold_fclty_mvmpark_hold_fclty_amsmtpark_hold_fclty_cnvnncpark_hold_fclty_etcappn_ntfc_demgc_nmtelnodata_stdde
skey1.0001.0000.7220.4560.1941.0000.6600.8520.0000.0000.4930.5810.7560.9911.0000.9650.944
manage_no1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
park_nm0.7221.0001.0001.0000.0001.0000.8910.9301.0001.0000.9941.0001.0000.8990.9620.9750.937
park_se0.4561.0001.0001.0000.0001.0000.3190.3720.7790.4300.0000.3710.0000.9620.4520.3900.463
rdnmadr0.1941.0000.0000.0001.0001.0000.0000.0000.7740.0000.0000.0000.0000.9700.6180.8110.000
lnm_adres1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
la0.6601.0000.8910.3190.0001.0001.0000.7330.0000.6760.4320.7880.7180.9650.8310.7980.960
lo0.8521.0000.9300.3720.0001.0000.7331.0000.0000.4740.5450.7000.8290.9850.8000.7820.932
park_ar0.0001.0001.0000.7790.7741.0000.0000.0001.0000.0000.0000.0000.0001.0000.7850.7570.000
park_hold_fclty_mvm0.0001.0001.0000.4300.0001.0000.6760.4740.0001.0000.8980.9600.4070.0000.5150.4110.541
park_hold_fclty_amsmt0.4931.0000.9940.0000.0001.0000.4320.5450.0000.8981.0000.7110.0000.6020.5700.4590.706
park_hold_fclty_cnvnnc0.5811.0001.0000.3710.0001.0000.7880.7000.0000.9600.7111.0000.7800.7500.8850.7110.624
park_hold_fclty_etc0.7561.0001.0000.0000.0001.0000.7180.8290.0000.4070.0000.7801.0000.9660.8620.8670.950
appn_ntfc_de0.9911.0000.8990.9620.9701.0000.9650.9851.0000.0000.6020.7500.9661.0000.9980.9990.998
mgc_nm1.0001.0000.9620.4520.6181.0000.8310.8000.7850.5150.5700.8850.8620.9981.0001.0001.000
telno0.9651.0000.9750.3900.8111.0000.7980.7820.7570.4110.4590.7110.8670.9991.0001.0001.000
data_stdde0.9441.0000.9370.4630.0001.0000.9600.9320.0000.5410.7060.6240.9500.9981.0001.0001.000
2023-12-10T18:57:09.096930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
rdnmadrpark_hold_fclty_amsmtmgc_nmpark_hold_fclty_mvmtelnodata_stddepark_hold_fclty_etcpark_separk_hold_fclty_cnvnnc
rdnmadr1.0000.0000.2660.0000.4240.0000.0000.0000.000
park_hold_fclty_amsmt0.0001.0000.2900.6300.2140.3590.0000.0000.409
mgc_nm0.2660.2901.0000.2800.9950.9950.6700.2670.587
park_hold_fclty_mvm0.0000.6300.2801.0000.2270.3410.1940.2240.739
telno0.4240.2140.9950.2271.0000.9890.6720.2180.616
data_stdde0.0000.3590.9950.3410.9891.0000.6750.3000.577
park_hold_fclty_etc0.0000.0000.6700.1940.6720.6751.0000.0000.638
park_se0.0000.0000.2670.2240.2180.3000.0001.0000.247
park_hold_fclty_cnvnnc0.0000.4090.5870.7390.6160.5770.6380.2471.000
2023-12-10T18:57:09.378590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
skeylalopark_arpark_serdnmadrpark_hold_fclty_mvmpark_hold_fclty_amsmtpark_hold_fclty_cnvnncpark_hold_fclty_etcmgc_nmtelnodata_stdde
skey1.0000.690-0.7440.1320.3160.0490.0000.2410.2580.6060.9740.9680.979
la0.6901.000-0.4290.0330.1530.0000.3860.1670.6480.2930.6190.5620.704
lo-0.744-0.4291.000-0.1660.1870.0000.2370.2310.5370.3080.6000.5630.661
park_ar0.1320.033-0.1661.0000.6760.4280.0000.0000.0000.0000.4580.6760.000
park_se0.3160.1530.1870.6761.0000.0000.2240.0000.2470.0000.2670.2180.300
rdnmadr0.0490.0000.0000.4280.0001.0000.0000.0000.0000.0000.2660.4240.000
park_hold_fclty_mvm0.0000.3860.2370.0000.2240.0001.0000.6300.7390.1940.2800.2270.341
park_hold_fclty_amsmt0.2410.1670.2310.0000.0000.0000.6301.0000.4090.0000.2900.2140.359
park_hold_fclty_cnvnnc0.2580.6480.5370.0000.2470.0000.7390.4091.0000.6380.5870.6160.577
park_hold_fclty_etc0.6060.2930.3080.0000.0000.0000.1940.0000.6381.0000.6700.6720.675
mgc_nm0.9740.6190.6000.4580.2670.2660.2800.2900.5870.6701.0000.9950.995
telno0.9680.5620.5630.6760.2180.4240.2270.2140.6160.6720.9951.0000.989
data_stdde0.9790.7040.6610.0000.3000.0000.3410.3590.5770.6750.9950.9891.000

Missing values

2023-12-10T18:56:54.795873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T18:56:55.392722image/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

skeymanage_nopark_nmpark_serdnmadrlnm_adreslalopark_arpark_hold_fclty_mvmpark_hold_fclty_amsmtpark_hold_fclty_cnvnncpark_hold_fclty_cltrpark_hold_fclty_etcappn_ntfc_demgc_nmtelnodata_stdde
0126290-00003당곡공원역사공원-부산광역시 남구 대연동 산205-235.125023129.09138575465.0-----1944-01-08--2018-08-14
161326530-00030모라벤처타워공원소공원-부산광역시 사상구 모라동 1375-335.184372128.999502735.8----평의자 외 2종 12점2012-04-11부산광역시 사상구청051-310-45212019-03-21
2326290-00004대일어린이공원어린이공원부산광역시 남구 유엔평화로13번길 63(대연동)부산광역시 남구 대연동 1727-135.134079129.095471422.1-----1965-11-10--2018-08-14
3426290-00005못골어린이공원어린이공원부산광역시 남구 진남로 46번길 37부산광역시 남구 대연동 1475-235.139799129.090081499.8-----1968-10-25--2018-08-14
4526290-00006솔밭어린이공원어린이공원부산광역시 남구 동명로 114부산광역시 남구 용호동 409-535.120074129.1095181500.2-----1970-08-07--2018-08-14
5626290-00007UN공원묘지공원-부산광역시 남구 대연동 80035.127677129.098688176486.0-----1971-04-06--2018-08-14
6726290-00008지게골어린이공원어린이공원부산광역시 남구 진남로188번가길 49-6부산광역시 남구 문현동 96-335.141946129.0793591654.0-----1971-10-22--2018-08-14
761426530-00031주감공원소공원-부산광역시 사상구 주례동 1288-135.153963128.9989791250.0----파고라 외 4종 7점2013-01-09부산광역시 사상구청051-310-45212019-03-21
8926290-00010개나리어린이공원어린이공원-부산광역시 남구 문현동 189-2235.138072129.073376370.0-----1971-10-23--2018-08-14
91026290-00011통일동산어린이공원어린이공원-부산광역시 남구 문현동 189-1635.139757129.073124479.0-----1971-10-23--2018-08-14
skeymanage_nopark_nmpark_serdnmadrlnm_adreslalopark_arpark_hold_fclty_mvmpark_hold_fclty_amsmtpark_hold_fclty_cnvnncpark_hold_fclty_cltrpark_hold_fclty_etcappn_ntfc_demgc_nmtelnodata_stdde
909126230-00030어린이공원어린이공원부산광역시 부산진구 동평로131번나길 20부산광역시 부산진구 부암동 320-4435.167956129.0446713110.0-----2008-08-06부산광역시 부산진구청051-605-45442018-04-05
919226320-00072화명중앙공원문화공원-부산광역시 북구 화명동 2292-135.233152129.00810111977.55-파고라-화장실2012-08-01부산광역시 북구청 청정녹지과051-309-20542018-08-30
929326320-00071구포문화공원문화공원-부산광역시 북구 구포동 782-4335.201017129.00079193552.0-----2006-04-26부산광역시 북구청 청정녹지과051-309-20542018-08-30
939426320-00070화정근린공원근린공원-부산광역시 북구 화명동 232835.229995129.01156610051.13흔들놀이파고라-화장실2012-07-04부산광역시 북구청 청정녹지과051-309-20542018-08-30
949526320-00069만덕사지당간지주역사공원근린공원-부산광역시 북구 만덕동 78435.215768129.043395349.0-----2016-08-16부산광역시 북구청 청정녹지과051-309-20542018-08-30
959626320-00068수정근린공원근린공원-부산광역시 북구 화명동 232135.223098129.00638710016.07-파고라--2001-10-12부산광역시 북구청 청정녹지과051-309-20542018-08-30
969726320-00067현충근린공원근린공원-부산광역시 북구 화명동 230435.229211129.00788210002.07-파고라-화장실2001-10-12부산광역시 북구청 청정녹지과051-309-20542018-08-30
979826320-00066명진근린공원근린공원-부산광역시 북구 화명동 226035.238961129.00949710001.012-파고라--2001-10-12부산광역시 북구청 청정녹지과051-309-20542018-08-30
989926320-00065화명3택지지구(양달숲공원)근린공원-부산광역시 북구 화명동 18735.244866129.01505322525.0-----1995-12-16부산광역시 북구청 청정녹지과051-309-20542018-08-30
9910026320-00064화명근린공원근린공원-부산광역시 북구 화명동 918-235.224411129.01481470396.010조12종조합놀이대,그네,흔들놀이파고라-화장실2002-08-02부산광역시 북구청 청정녹지과051-309-20542018-08-30