Overview

Dataset statistics

Number of variables10
Number of observations112
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.4 KiB
Average record size in memory86.2 B

Variable types

Text3
Categorical2
Numeric5

Dataset

Description대전광역시 서구 도시공원 조명시설 현황을(화장실, 건물 내 조명 제외) 공원 내 공원등, 잔디등, 투광기, LED, CDM, NH등 조명의 종류와 개수를 제공합니다.
URLhttps://www.data.go.kr/data/15081729/fileData.do

Alerts

계 is highly overall correlated with 씨디엠(CDM)High correlation
씨디엠(CDM) is highly overall correlated with 계High correlation
공원종류 is highly imbalanced (54.5%)Imbalance
번호 has unique valuesUnique
소 재 지 has unique valuesUnique
계 has 2 (1.8%) zerosZeros
발광다이오드(LED) has 95 (84.8%) zerosZeros
씨디엠(CDM) has 29 (25.9%) zerosZeros
나트륨 has 87 (77.7%) zerosZeros
기타 has 104 (92.9%) zerosZeros

Reproduction

Analysis started2023-12-12 21:59:32.820408
Analysis finished2023-12-12 21:59:36.161774
Duration3.34 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

번호
Text

UNIQUE 

Distinct112
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
2023-12-13T06:59:36.406125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4.0089286
Min length4

Characters and Unicode

Total characters449
Distinct characters19
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique112 ?
Unique (%)100.0%

Sample

1st row근-01
2nd row근-02
3rd row근-03
4th row근-04
5th row근-05
ValueCountFrequency (%)
근-01 1
 
0.9%
근-02 1
 
0.9%
어-53 1
 
0.9%
어-52 1
 
0.9%
어-51 1
 
0.9%
어-50 1
 
0.9%
어-49 1
 
0.9%
어-48 1
 
0.9%
어-47 1
 
0.9%
어-46 1
 
0.9%
Other values (102) 102
91.1%
2023-12-13T06:59:36.848644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 112
24.9%
어 80
17.8%
0 36
 
8.0%
1 36
 
8.0%
2 27
 
6.0%
근 23
 
5.1%
3 22
 
4.9%
5 22
 
4.9%
4 21
 
4.7%
8 19
 
4.2%
Other values (9) 51
11.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 224
49.9%
Other Letter 113
25.2%
Dash Punctuation 112
24.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 36
16.1%
1 36
16.1%
2 27
12.1%
3 22
9.8%
5 22
9.8%
4 21
9.4%
8 19
8.5%
6 15
6.7%
7 13
 
5.8%
9 13
 
5.8%
Other Letter
ValueCountFrequency (%)
어 80
70.8%
근 23
 
20.4%
쌈 3
 
2.7%
체 3
 
2.7%
문 1
 
0.9%
수 1
 
0.9%
가 1
 
0.9%
로 1
 
0.9%
Dash Punctuation
ValueCountFrequency (%)
- 112
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 336
74.8%
Hangul 113
 
25.2%

Most frequent character per script

Common
ValueCountFrequency (%)
- 112
33.3%
0 36
 
10.7%
1 36
 
10.7%
2 27
 
8.0%
3 22
 
6.5%
5 22
 
6.5%
4 21
 
6.2%
8 19
 
5.7%
6 15
 
4.5%
7 13
 
3.9%
Hangul
ValueCountFrequency (%)
어 80
70.8%
근 23
 
20.4%
쌈 3
 
2.7%
체 3
 
2.7%
문 1
 
0.9%
수 1
 
0.9%
가 1
 
0.9%
로 1
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 336
74.8%
Hangul 113
 
25.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 112
33.3%
0 36
 
10.7%
1 36
 
10.7%
2 27
 
8.0%
3 22
 
6.5%
5 22
 
6.5%
4 21
 
6.2%
8 19
 
5.7%
6 15
 
4.5%
7 13
 
3.9%
Hangul
ValueCountFrequency (%)
어 80
70.8%
근 23
 
20.4%
쌈 3
 
2.7%
체 3
 
2.7%
문 1
 
0.9%
수 1
 
0.9%
가 1
 
0.9%
로 1
 
0.9%

공원명
Text

Distinct107
Distinct (%)95.5%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
2023-12-13T06:59:37.174461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length2
Mean length2.4821429
Min length2

Characters and Unicode

Total characters278
Distinct characters133
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique102 ?
Unique (%)91.1%

Sample

1st row도안
2nd row소태
3rd row봉우재
4th row월평
5th row남선
ValueCountFrequency (%)
봉우재 2
 
1.8%
느리울 2
 
1.8%
용소 2
 
1.8%
월평 2
 
1.8%
구봉 2
 
1.8%
문정 1
 
0.9%
도안 1
 
0.9%
수밋들 1
 
0.9%
느티나무 1
 
0.9%
천변 1
 
0.9%
Other values (97) 97
86.6%
2023-12-13T06:59:37.630603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 7
 
2.5%
샘 7
 
2.5%
도 6
 
2.2%
정 6
 
2.2%
리 6
 
2.2%
봉 5
 
1.8%
평 5
 
1.8%
미 5
 
1.8%
지 5
 
1.8%
마 5
 
1.8%
Other values (123) 221
79.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 267
96.0%
Decimal Number 11
 
4.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 7
 
2.6%
샘 7
 
2.6%
도 6
 
2.2%
정 6
 
2.2%
리 6
 
2.2%
봉 5
 
1.9%
평 5
 
1.9%
미 5
 
1.9%
지 5
 
1.9%
마 5
 
1.9%
Other values (120) 210
78.7%
Decimal Number
ValueCountFrequency (%)
2 4
36.4%
1 4
36.4%
3 3
27.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 267
96.0%
Common 11
 
4.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 7
 
2.6%
샘 7
 
2.6%
도 6
 
2.2%
정 6
 
2.2%
리 6
 
2.2%
봉 5
 
1.9%
평 5
 
1.9%
미 5
 
1.9%
지 5
 
1.9%
마 5
 
1.9%
Other values (120) 210
78.7%
Common
ValueCountFrequency (%)
2 4
36.4%
1 4
36.4%
3 3
27.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 267
96.0%
ASCII 11
 
4.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 7
 
2.6%
샘 7
 
2.6%
도 6
 
2.2%
정 6
 
2.2%
리 6
 
2.2%
봉 5
 
1.9%
평 5
 
1.9%
미 5
 
1.9%
지 5
 
1.9%
마 5
 
1.9%
Other values (120) 210
78.7%
ASCII
ValueCountFrequency (%)
2 4
36.4%
1 4
36.4%
3 3
27.3%

공원종류
Categorical

IMBALANCE 

Distinct7
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
어린이공원
80 
근린공원
23 
체육공원
 
3
쌈지공원
 
3
문화공원
 
1
Other values (2)
 
2

Length

Max length5
Median length5
Mean length4.7142857
Min length4

Unique

Unique3 ?
Unique (%)2.7%

Sample

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

Common Values

ValueCountFrequency (%)
어린이공원 80
71.4%
근린공원 23
 
20.5%
체육공원 3
 
2.7%
쌈지공원 3
 
2.7%
문화공원 1
 
0.9%
수변공원 1
 
0.9%
가로공원 1
 
0.9%

Length

2023-12-13T06:59:37.773826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:59:37.910159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
어린이공원 80
71.4%
근린공원 23
 
20.5%
체육공원 3
 
2.7%
쌈지공원 3
 
2.7%
문화공원 1
 
0.9%
수변공원 1
 
0.9%
가로공원 1
 
0.9%

행정동
Categorical

Distinct17
Distinct (%)15.2%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
관저동
26 
둔산동
12 
복수동
11 
가수원동
9 
갈마동
8 
Other values (12)
46 

Length

Max length4
Median length3
Mean length3.0178571
Min length2

Unique

Unique2 ?
Unique (%)1.8%

Sample

1st row도안동
2nd row도안동
3rd row관저동
4th row도마동
5th row탄방동

Common Values

ValueCountFrequency (%)
관저동 26
23.2%
둔산동 12
10.7%
복수동 11
9.8%
가수원동 9
 
8.0%
갈마동 8
 
7.1%
도마동 8
 
7.1%
월평동 6
 
5.4%
도안동 5
 
4.5%
괴정동 5
 
4.5%
내동 4
 
3.6%
Other values (7) 18
16.1%

Length

2023-12-13T06:59:38.030124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
관저동 26
23.2%
둔산동 12
10.7%
복수동 11
9.8%
가수원동 9
 
8.0%
갈마동 8
 
7.1%
도마동 8
 
7.1%
월평동 6
 
5.4%
괴정동 5
 
4.5%
도안동 5
 
4.5%
내동 4
 
3.6%
Other values (7) 18
16.1%

소 재 지
Text

UNIQUE 

Distinct112
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
2023-12-13T06:59:38.314899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length14
Mean length8.4107143
Min length5

Characters and Unicode

Total characters942
Distinct characters45
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

Unique112 ?
Unique (%)100.0%

Sample

1st row도안동 967 외 12필지
2nd row도안동 1368 외 1필지
3rd row관저동 1662
4th row도마2동 산7-9 외 7필지
5th row탄방동 1084
ValueCountFrequency (%)
관저동 26
 
11.1%
복수동 11
 
4.7%
둔산동 11
 
4.7%
가수원동 9
 
3.8%
도안동 5
 
2.1%
탄방동 5
 
2.1%
외 5
 
2.1%
갈마1동 5
 
2.1%
월평1동 5
 
2.1%
도마1동 5
 
2.1%
Other values (126) 148
63.0%
2023-12-13T06:59:38.707629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
123
 
13.1%
1 120
 
12.7%
동 112
 
11.9%
3 43
 
4.6%
5 40
 
4.2%
2 39
 
4.1%
4 35
 
3.7%
- 34
 
3.6%
8 34
 
3.6%
6 33
 
3.5%
Other values (35) 329
34.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 427
45.3%
Other Letter 358
38.0%
Space Separator 123
 
13.1%
Dash Punctuation 34
 
3.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 112
31.3%
관 26
 
7.3%
저 26
 
7.3%
수 20
 
5.6%
마 16
 
4.5%
산 16
 
4.5%
도 13
 
3.6%
둔 11
 
3.1%
가 11
 
3.1%
복 11
 
3.1%
Other values (23) 96
26.8%
Decimal Number
ValueCountFrequency (%)
1 120
28.1%
3 43
 
10.1%
5 40
 
9.4%
2 39
 
9.1%
4 35
 
8.2%
8 34
 
8.0%
6 33
 
7.7%
9 31
 
7.3%
0 29
 
6.8%
7 23
 
5.4%
Space Separator
ValueCountFrequency (%)
123
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 34
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 584
62.0%
Hangul 358
38.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 112
31.3%
관 26
 
7.3%
저 26
 
7.3%
수 20
 
5.6%
마 16
 
4.5%
산 16
 
4.5%
도 13
 
3.6%
둔 11
 
3.1%
가 11
 
3.1%
복 11
 
3.1%
Other values (23) 96
26.8%
Common
ValueCountFrequency (%)
123
21.1%
1 120
20.5%
3 43
 
7.4%
5 40
 
6.8%
2 39
 
6.7%
4 35
 
6.0%
- 34
 
5.8%
8 34
 
5.8%
6 33
 
5.7%
9 31
 
5.3%
Other values (2) 52
8.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 584
62.0%
Hangul 358
38.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
123
21.1%
1 120
20.5%
3 43
 
7.4%
5 40
 
6.8%
2 39
 
6.7%
4 35
 
6.0%
- 34
 
5.8%
8 34
 
5.8%
6 33
 
5.7%
9 31
 
5.3%
Other values (2) 52
8.9%
Hangul
ValueCountFrequency (%)
동 112
31.3%
관 26
 
7.3%
저 26
 
7.3%
수 20
 
5.6%
마 16
 
4.5%
산 16
 
4.5%
도 13
 
3.6%
둔 11
 
3.1%
가 11
 
3.1%
복 11
 
3.1%
Other values (23) 96
26.8%

계
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct41
Distinct (%)36.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.875
Minimum0
Maximum392
Zeros2
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-13T06:59:38.835547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.55
Q15
median8
Q315.25
95-th percentile102.8
Maximum392
Range392
Interquartile range (IQR)10.25

Descriptive statistics

Standard deviation52.016997
Coefficient of variation (CV)2.1787224
Kurtosis26.263916
Mean23.875
Median Absolute Deviation (MAD)4
Skewness4.6930649
Sum2674
Variance2705.768
MonotonicityNot monotonic
2023-12-13T06:59:38.967077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
4 14
 
12.5%
6 14
 
12.5%
8 13
 
11.6%
5 10
 
8.9%
9 6
 
5.4%
10 6
 
5.4%
7 5
 
4.5%
19 3
 
2.7%
12 3
 
2.7%
2 3
 
2.7%
Other values (31) 35
31.2%
ValueCountFrequency (%)
0 2
 
1.8%
1 1
 
0.9%
2 3
 
2.7%
3 3
 
2.7%
4 14
12.5%
5 10
8.9%
6 14
12.5%
7 5
 
4.5%
8 13
11.6%
9 6
5.4%
ValueCountFrequency (%)
392 1
0.9%
255 1
0.9%
180 1
0.9%
168 1
0.9%
147 1
0.9%
105 1
0.9%
101 1
0.9%
92 1
0.9%
70 1
0.9%
65 1
0.9%

발광다이오드(LED)
Real number (ℝ)

ZEROS 

Distinct15
Distinct (%)13.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.2946429
Minimum0
Maximum147
Zeros95
Zeros (%)84.8%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-13T06:59:39.092848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile12.9
Maximum147
Range147
Interquartile range (IQR)0

Descriptive statistics

Standard deviation20.094658
Coefficient of variation (CV)4.6790056
Kurtosis38.585505
Mean4.2946429
Median Absolute Deviation (MAD)0
Skewness6.0838564
Sum481
Variance403.79529
MonotonicityNot monotonic
2023-12-13T06:59:39.204025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 95
84.8%
1 2
 
1.8%
2 2
 
1.8%
6 2
 
1.8%
65 1
 
0.9%
12 1
 
0.9%
147 1
 
0.9%
134 1
 
0.9%
46 1
 
0.9%
14 1
 
0.9%
Other values (5) 5
 
4.5%
ValueCountFrequency (%)
0 95
84.8%
1 2
 
1.8%
2 2
 
1.8%
3 1
 
0.9%
5 1
 
0.9%
6 2
 
1.8%
8 1
 
0.9%
10 1
 
0.9%
12 1
 
0.9%
14 1
 
0.9%
ValueCountFrequency (%)
147 1
0.9%
134 1
0.9%
65 1
0.9%
46 1
0.9%
19 1
0.9%
14 1
0.9%
12 1
0.9%
10 1
0.9%
8 1
0.9%
6 2
1.8%

씨디엠(CDM)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct31
Distinct (%)27.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.428571
Minimum0
Maximum391
Zeros29
Zeros (%)25.9%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-13T06:59:39.307585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5
Q39
95-th percentile49.6
Maximum391
Range391
Interquartile range (IQR)9

Descriptive statistics

Standard deviation41.268777
Coefficient of variation (CV)2.8602123
Kurtosis64.052209
Mean14.428571
Median Absolute Deviation (MAD)4.5
Skewness7.397189
Sum1616
Variance1703.112
MonotonicityNot monotonic
2023-12-13T06:59:39.413676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0 29
25.9%
4 14
12.5%
8 11
 
9.8%
6 9
 
8.0%
5 9
 
8.0%
10 4
 
3.6%
7 4
 
3.6%
9 3
 
2.7%
2 3
 
2.7%
21 2
 
1.8%
Other values (21) 24
21.4%
ValueCountFrequency (%)
0 29
25.9%
1 1
 
0.9%
2 3
 
2.7%
3 2
 
1.8%
4 14
12.5%
5 9
 
8.0%
6 9
 
8.0%
7 4
 
3.6%
8 11
 
9.8%
9 3
 
2.7%
ValueCountFrequency (%)
391 1
0.9%
136 1
0.9%
122 1
0.9%
62 1
0.9%
59 1
0.9%
54 1
0.9%
46 2
1.8%
44 1
0.9%
36 1
0.9%
33 1
0.9%

나트륨
Real number (ℝ)

ZEROS 

Distinct17
Distinct (%)15.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.7589286
Minimum0
Maximum65
Zeros87
Zeros (%)77.7%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-13T06:59:39.520737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile25.45
Maximum65
Range65
Interquartile range (IQR)0

Descriptive statistics

Standard deviation10.403781
Coefficient of variation (CV)2.7677517
Kurtosis16.292795
Mean3.7589286
Median Absolute Deviation (MAD)0
Skewness3.8355006
Sum421
Variance108.23866
MonotonicityNot monotonic
2023-12-13T06:59:39.692540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 87
77.7%
6 4
 
3.6%
9 3
 
2.7%
34 2
 
1.8%
3 2
 
1.8%
10 2
 
1.8%
4 2
 
1.8%
7 1
 
0.9%
12 1
 
0.9%
16 1
 
0.9%
Other values (7) 7
 
6.2%
ValueCountFrequency (%)
0 87
77.7%
3 2
 
1.8%
4 2
 
1.8%
6 4
 
3.6%
7 1
 
0.9%
8 1
 
0.9%
9 3
 
2.7%
10 2
 
1.8%
12 1
 
0.9%
16 1
 
0.9%
ValueCountFrequency (%)
65 1
0.9%
54 1
0.9%
37 1
0.9%
34 2
1.8%
26 1
0.9%
25 1
0.9%
18 1
0.9%
16 1
0.9%
12 1
0.9%
10 2
1.8%

기타
Real number (ℝ)

ZEROS 

Distinct9
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3928571
Minimum0
Maximum62
Zeros104
Zeros (%)92.9%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-13T06:59:39.828207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4.45
Maximum62
Range62
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7.3403208
Coefficient of variation (CV)5.2699739
Kurtosis49.036165
Mean1.3928571
Median Absolute Deviation (MAD)0
Skewness6.7337796
Sum156
Variance53.880309
MonotonicityNot monotonic
2023-12-13T06:59:39.918892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 104
92.9%
62 1
 
0.9%
2 1
 
0.9%
40 1
 
0.9%
12 1
 
0.9%
22 1
 
0.9%
9 1
 
0.9%
4 1
 
0.9%
5 1
 
0.9%
ValueCountFrequency (%)
0 104
92.9%
2 1
 
0.9%
4 1
 
0.9%
5 1
 
0.9%
9 1
 
0.9%
12 1
 
0.9%
22 1
 
0.9%
40 1
 
0.9%
62 1
 
0.9%
ValueCountFrequency (%)
62 1
 
0.9%
40 1
 
0.9%
22 1
 
0.9%
12 1
 
0.9%
9 1
 
0.9%
5 1
 
0.9%
4 1
 
0.9%
2 1
 
0.9%
0 104
92.9%

Interactions

2023-12-13T06:59:35.545446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:33.206745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:33.726372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:34.591244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:35.102756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:35.624384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:33.319089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:33.834729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:34.702507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:35.195337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:35.697536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:33.430518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:33.924510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:34.823508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:35.276098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:35.771838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:33.556098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:34.415577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:34.914843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:35.359456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:35.843064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:33.643021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:34.501128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:34.995475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:59:35.454147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T06:59:39.992594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
공원종류행정동계발광다이오드(LED)씨디엠(CDM)나트륨기타
공원종류1.0000.4860.6930.0000.4170.4990.582
행정동0.4861.0000.0000.3180.3190.0000.000
계0.6930.0001.0000.8130.9040.7470.553
발광다이오드(LED)0.0000.3180.8131.0000.5360.6260.000
씨디엠(CDM)0.4170.3190.9040.5361.0000.7670.000
나트륨0.4990.0000.7470.6260.7671.0000.835
기타0.5820.0000.5530.0000.0000.8351.000
2023-12-13T06:59:40.087346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
공원종류행정동
공원종류1.0000.229
행정동0.2291.000
2023-12-13T06:59:40.172655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
계발광다이오드(LED)씨디엠(CDM)나트륨기타공원종류행정동
계1.0000.4400.5180.2300.2650.3040.000
발광다이오드(LED)0.4401.0000.1160.0000.0850.0000.155
씨디엠(CDM)0.5180.1161.000-0.407-0.0380.2940.167
나트륨0.2300.000-0.4071.0000.1460.3090.000
기타0.2650.085-0.0380.1461.0000.4190.000
공원종류0.3040.0000.2940.3090.4191.0000.229
행정동0.0000.1550.1670.0000.0000.2291.000

Missing values

2023-12-13T06:59:35.955109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T06:59:36.102858image/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

번호공원명공원종류행정동소 재 지계발광다이오드(LED)씨디엠(CDM)나트륨기타
0근-01도안근린공원도안동도안동 967 외 12필지392139100
1근-02소태근린공원도안동도안동 1368 외 1필지2912800
2근-03봉우재근린공원관저동관저동 16621921700
3근-04월평근린공원도마동도마2동 산7-9 외 7필지25565136540
4근-05남선근린공원탄방동탄방동 108416812122340
5근-06둔지미1근린공원둔산동둔산동 9561901900
6근-07둔지미2근린공원둔산동둔산동 953 외 1필지1701700
7근-08둔지미3근린공원둔산동둔산동 13752402400
8근-09샘머리1근린공원둔산동둔산동 13794604600
9근-10샘머리2근린공원둔산동둔산동 13815405400
번호공원명공원종류행정동소 재 지계발광다이오드(LED)씨디엠(CDM)나트륨기타
102어-82나비어린이공원도안동도안동 140140400
103어-83바다어린이공원가수원동가수원동 109160600
104어-84미나리어린이공원가수원동가수원동 123740400
105어-85윗용소어린이공원가수원동가수원동 92590900
106어-87용소어린이공원가수원동가수원동 98960600
107어-88햇살아래어린이공원관저동관저동 15741000100
108어-89느리울어린이공원관저동관저동 349-290090
109어-90창골어린이공원관저동관저동 160370070
110어-91동방어린이공원관저동관저동 161660060
111어-92불티어린이공원복수동복수동 10051010000