Overview

Dataset statistics

Number of variables7
Number of observations64
Missing cells36
Missing cells (%)8.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.9 KiB
Average record size in memory62.1 B

Variable types

Numeric4
Text3

Dataset

Description인천광역시 계양구 공원현황에 대한 데이터입니다. 공원의 명칭, 도로명주소, 지번주소, 위도, 경도, 면적등의 데이터를 포함하고 있습니다.
URLhttps://www.data.go.kr/data/15113730/fileData.do

Alerts

도로명주소 has 36 (56.2%) missing valuesMissing
연번 has unique valuesUnique
공원명 has unique valuesUnique
지번주소 has unique valuesUnique
위도 has unique valuesUnique
경도 has unique valuesUnique
면적 has unique valuesUnique

Reproduction

Analysis started2023-12-12 14:13:52.588019
Analysis finished2023-12-12 14:13:54.914071
Duration2.33 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct64
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.5
Minimum1
Maximum64
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size708.0 B
2023-12-12T23:13:54.997684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4.15
Q116.75
median32.5
Q348.25
95-th percentile60.85
Maximum64
Range63
Interquartile range (IQR)31.5

Descriptive statistics

Standard deviation18.618987
Coefficient of variation (CV)0.5728919
Kurtosis-1.2
Mean32.5
Median Absolute Deviation (MAD)16
Skewness0
Sum2080
Variance346.66667
MonotonicityStrictly increasing
2023-12-12T23:13:55.145372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.6%
34 1
 
1.6%
36 1
 
1.6%
37 1
 
1.6%
38 1
 
1.6%
39 1
 
1.6%
40 1
 
1.6%
41 1
 
1.6%
42 1
 
1.6%
43 1
 
1.6%
Other values (54) 54
84.4%
ValueCountFrequency (%)
1 1
1.6%
2 1
1.6%
3 1
1.6%
4 1
1.6%
5 1
1.6%
6 1
1.6%
7 1
1.6%
8 1
1.6%
9 1
1.6%
10 1
1.6%
ValueCountFrequency (%)
64 1
1.6%
63 1
1.6%
62 1
1.6%
61 1
1.6%
60 1
1.6%
59 1
1.6%
58 1
1.6%
57 1
1.6%
56 1
1.6%
55 1
1.6%

공원명
Text

UNIQUE 

Distinct64
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size644.0 B
2023-12-12T23:13:55.414664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length8
Mean length7.046875
Min length5

Characters and Unicode

Total characters451
Distinct characters97
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

Unique64 ?
Unique (%)100.0%

Sample

1st row하마리소공원
2nd row동양소공원
3rd row용종5소공원
4th row살나리소공원
5th row하야소공원
ValueCountFrequency (%)
하마리소공원 1
 
1.6%
동양소공원 1
 
1.6%
장터어린이공원 1
 
1.6%
초정어린이공원 1
 
1.6%
은행어린이공원 1
 
1.6%
도두리어린이공원 1
 
1.6%
개나리어린이공원 1
 
1.6%
계산4어린이공원 1
 
1.6%
샘터어린이공원 1
 
1.6%
안골어린이공원 1
 
1.6%
Other values (54) 54
84.4%
2023-12-12T23:13:55.836443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
원 65
14.4%
공 64
14.2%
린 52
 
11.5%
이 43
 
9.5%
어 40
 
8.9%
근 12
 
2.7%
소 8
 
1.8%
양 8
 
1.8%
리 7
 
1.6%
동 5
 
1.1%
Other values (87) 147
32.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 442
98.0%
Decimal Number 9
 
2.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
원 65
14.7%
공 64
14.5%
린 52
 
11.8%
이 43
 
9.7%
어 40
 
9.0%
근 12
 
2.7%
소 8
 
1.8%
양 8
 
1.8%
리 7
 
1.6%
동 5
 
1.1%
Other values (82) 138
31.2%
Decimal Number
ValueCountFrequency (%)
1 3
33.3%
2 3
33.3%
3 1
 
11.1%
4 1
 
11.1%
5 1
 
11.1%

Most occurring scripts

ValueCountFrequency (%)
Hangul 442
98.0%
Common 9
 
2.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
원 65
14.7%
공 64
14.5%
린 52
 
11.8%
이 43
 
9.7%
어 40
 
9.0%
근 12
 
2.7%
소 8
 
1.8%
양 8
 
1.8%
리 7
 
1.6%
동 5
 
1.1%
Other values (82) 138
31.2%
Common
ValueCountFrequency (%)
1 3
33.3%
2 3
33.3%
3 1
 
11.1%
4 1
 
11.1%
5 1
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 442
98.0%
ASCII 9
 
2.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
원 65
14.7%
공 64
14.5%
린 52
 
11.8%
이 43
 
9.7%
어 40
 
9.0%
근 12
 
2.7%
소 8
 
1.8%
양 8
 
1.8%
리 7
 
1.6%
동 5
 
1.1%
Other values (82) 138
31.2%
ASCII
ValueCountFrequency (%)
1 3
33.3%
2 3
33.3%
3 1
 
11.1%
4 1
 
11.1%
5 1
 
11.1%

도로명주소
Text

MISSING 

Distinct28
Distinct (%)100.0%
Missing36
Missing (%)56.2%
Memory size644.0 B
2023-12-12T23:13:56.096907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length30
Median length27
Mean length24.535714
Min length16

Characters and Unicode

Total characters687
Distinct characters75
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique28 ?
Unique (%)100.0%

Sample

1st row인천광역시 갈현동 황어로 66
2nd row인천광역시 계양구 주부토로 570 (계산동)
3rd row인천광역시 계양구 다남로165번길 71 (다남동)
4th row인천광역시 계양구 서운산단로 30 (서운동)
5th row인천광역시 계양구 계양문화로 1 (작전동)
ValueCountFrequency (%)
인천광역시 28
20.0%
계양구 27
19.3%
작전동 6
 
4.3%
서운동 4
 
2.9%
병방동 3
 
2.1%
계산동 3
 
2.1%
효성동 3
 
2.1%
귤현동 2
 
1.4%
오조산로 2
 
1.4%
봉오대로894번길 2
 
1.4%
Other values (52) 60
42.9%
2023-12-12T23:13:56.509306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
112
 
16.3%
계 32
 
4.7%
양 31
 
4.5%
동 29
 
4.2%
시 29
 
4.2%
천 28
 
4.1%
인 28
 
4.1%
광 28
 
4.1%
역 28
 
4.1%
) 27
 
3.9%
Other values (65) 315
45.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 435
63.3%
Space Separator 112
 
16.3%
Decimal Number 84
 
12.2%
Close Punctuation 27
 
3.9%
Open Punctuation 27
 
3.9%
Dash Punctuation 1
 
0.1%
Other Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
계 32
 
7.4%
양 31
 
7.1%
동 29
 
6.7%
시 29
 
6.7%
천 28
 
6.4%
인 28
 
6.4%
광 28
 
6.4%
역 28
 
6.4%
구 27
 
6.2%
로 26
 
6.0%
Other values (50) 149
34.3%
Decimal Number
ValueCountFrequency (%)
1 15
17.9%
5 10
11.9%
3 10
11.9%
4 9
10.7%
6 8
9.5%
0 8
9.5%
9 6
 
7.1%
8 6
 
7.1%
2 6
 
7.1%
7 6
 
7.1%
Space Separator
ValueCountFrequency (%)
112
100.0%
Close Punctuation
ValueCountFrequency (%)
) 27
100.0%
Open Punctuation
ValueCountFrequency (%)
( 27
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 435
63.3%
Common 252
36.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
계 32
 
7.4%
양 31
 
7.1%
동 29
 
6.7%
시 29
 
6.7%
천 28
 
6.4%
인 28
 
6.4%
광 28
 
6.4%
역 28
 
6.4%
구 27
 
6.2%
로 26
 
6.0%
Other values (50) 149
34.3%
Common
ValueCountFrequency (%)
112
44.4%
) 27
 
10.7%
( 27
 
10.7%
1 15
 
6.0%
5 10
 
4.0%
3 10
 
4.0%
4 9
 
3.6%
6 8
 
3.2%
0 8
 
3.2%
9 6
 
2.4%
Other values (5) 20
 
7.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 435
63.3%
ASCII 252
36.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
112
44.4%
) 27
 
10.7%
( 27
 
10.7%
1 15
 
6.0%
5 10
 
4.0%
3 10
 
4.0%
4 9
 
3.6%
6 8
 
3.2%
0 8
 
3.2%
9 6
 
2.4%
Other values (5) 20
 
7.9%
Hangul
ValueCountFrequency (%)
계 32
 
7.4%
양 31
 
7.1%
동 29
 
6.7%
시 29
 
6.7%
천 28
 
6.4%
인 28
 
6.4%
광 28
 
6.4%
역 28
 
6.4%
구 27
 
6.2%
로 26
 
6.0%
Other values (50) 149
34.3%

지번주소
Text

UNIQUE 

Distinct64
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size644.0 B
2023-12-12T23:13:56.835594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length19
Mean length18.109375
Min length16

Characters and Unicode

Total characters1159
Distinct characters47
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

Unique64 ?
Unique (%)100.0%

Sample

1st row인천광역시 계양구 서운동 204
2nd row인천광역시 계양구 동양동 615-3
3rd row인천광역시 계양구 용종동 224
4th row인천광역시 계양구 서운동 93-2
5th row인천광역시 계양구 하야동 64-29
ValueCountFrequency (%)
인천광역시 64
24.6%
계양구 64
24.6%
서운동 10
 
3.8%
계산동 9
 
3.5%
귤현동 8
 
3.1%
동양동 7
 
2.7%
작전동 7
 
2.7%
산 4
 
1.5%
장기동 4
 
1.5%
병방동 3
 
1.2%
Other values (73) 80
30.8%
2023-12-12T23:13:57.324369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
196
16.9%
계 73
 
6.3%
동 71
 
6.1%
양 71
 
6.1%
인 64
 
5.5%
구 64
 
5.5%
천 64
 
5.5%
시 64
 
5.5%
역 64
 
5.5%
광 64
 
5.5%
Other values (37) 364
31.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 709
61.2%
Decimal Number 219
 
18.9%
Space Separator 196
 
16.9%
Dash Punctuation 35
 
3.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
계 73
10.3%
동 71
10.0%
양 71
10.0%
인 64
9.0%
구 64
9.0%
천 64
9.0%
시 64
9.0%
역 64
9.0%
광 64
9.0%
산 14
 
2.0%
Other values (25) 96
13.5%
Decimal Number
ValueCountFrequency (%)
2 46
21.0%
1 41
18.7%
9 23
10.5%
3 19
8.7%
4 18
 
8.2%
0 17
 
7.8%
5 17
 
7.8%
8 16
 
7.3%
6 13
 
5.9%
7 9
 
4.1%
Space Separator
ValueCountFrequency (%)
196
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 35
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 709
61.2%
Common 450
38.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
계 73
10.3%
동 71
10.0%
양 71
10.0%
인 64
9.0%
구 64
9.0%
천 64
9.0%
시 64
9.0%
역 64
9.0%
광 64
9.0%
산 14
 
2.0%
Other values (25) 96
13.5%
Common
ValueCountFrequency (%)
196
43.6%
2 46
 
10.2%
1 41
 
9.1%
- 35
 
7.8%
9 23
 
5.1%
3 19
 
4.2%
4 18
 
4.0%
0 17
 
3.8%
5 17
 
3.8%
8 16
 
3.6%
Other values (2) 22
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 709
61.2%
ASCII 450
38.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
196
43.6%
2 46
 
10.2%
1 41
 
9.1%
- 35
 
7.8%
9 23
 
5.1%
3 19
 
4.2%
4 18
 
4.0%
0 17
 
3.8%
5 17
 
3.8%
8 16
 
3.6%
Other values (2) 22
 
4.9%
Hangul
ValueCountFrequency (%)
계 73
10.3%
동 71
10.0%
양 71
10.0%
인 64
9.0%
구 64
9.0%
천 64
9.0%
시 64
9.0%
역 64
9.0%
광 64
9.0%
산 14
 
2.0%
Other values (25) 96
13.5%

위도
Real number (ℝ)

UNIQUE 

Distinct64
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.548943
Minimum37.525675
Maximum37.583303
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size708.0 B
2023-12-12T23:13:57.510301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.525675
5-th percentile37.527689
Q137.534551
median37.54446
Q337.564006
95-th percentile37.578288
Maximum37.583303
Range0.057628
Interquartile range (IQR)0.02945525

Descriptive statistics

Standard deviation0.01705999
Coefficient of variation (CV)0.00045434009
Kurtosis-1.1312596
Mean37.548943
Median Absolute Deviation (MAD)0.014251
Skewness0.41753848
Sum2403.1323
Variance0.00029104325
MonotonicityNot monotonic
2023-12-12T23:13:57.716026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.527552 1
 
1.6%
37.544113 1
 
1.6%
37.537556 1
 
1.6%
37.53136 1
 
1.6%
37.538508 1
 
1.6%
37.537896 1
 
1.6%
37.567828 1
 
1.6%
37.569792 1
 
1.6%
37.567896 1
 
1.6%
37.559026 1
 
1.6%
Other values (54) 54
84.4%
ValueCountFrequency (%)
37.525675 1
1.6%
37.526738 1
1.6%
37.527552 1
1.6%
37.527673 1
1.6%
37.527778 1
1.6%
37.528287 1
1.6%
37.528747 1
1.6%
37.529492 1
1.6%
37.529516 1
1.6%
37.530156 1
1.6%
ValueCountFrequency (%)
37.583303 1
1.6%
37.580616 1
1.6%
37.579411 1
1.6%
37.578391 1
1.6%
37.577702 1
1.6%
37.577654 1
1.6%
37.576707 1
1.6%
37.569792 1
1.6%
37.568555 1
1.6%
37.567896 1
1.6%

경도
Real number (ℝ)

UNIQUE 

Distinct64
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.74089
Minimum126.7115
Maximum126.78108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size708.0 B
2023-12-12T23:13:57.907025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.7115
5-th percentile126.71621
Q1126.73234
median126.74175
Q3126.74922
95-th percentile126.75825
Maximum126.78108
Range0.069582
Interquartile range (IQR)0.016871

Descriptive statistics

Standard deviation0.014082389
Coefficient of variation (CV)0.00011111164
Kurtosis0.65726216
Mean126.74089
Median Absolute Deviation (MAD)0.008262
Skewness0.15606511
Sum8111.4172
Variance0.00019831367
MonotonicityNot monotonic
2023-12-12T23:13:58.089358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.742481 1
 
1.6%
126.74203 1
 
1.6%
126.739279 1
 
1.6%
126.738458 1
 
1.6%
126.732649 1
 
1.6%
126.73356 1
 
1.6%
126.74514 1
 
1.6%
126.746669 1
 
1.6%
126.749049 1
 
1.6%
126.753403 1
 
1.6%
Other values (54) 54
84.4%
ValueCountFrequency (%)
126.711498 1
1.6%
126.711992 1
1.6%
126.715116 1
1.6%
126.715627 1
1.6%
126.719517 1
1.6%
126.719853 1
1.6%
126.720286 1
1.6%
126.723238 1
1.6%
126.723728 1
1.6%
126.725359 1
1.6%
ValueCountFrequency (%)
126.78108 1
1.6%
126.778863 1
1.6%
126.75937 1
1.6%
126.758308 1
1.6%
126.757915 1
1.6%
126.757755 1
1.6%
126.756463 1
1.6%
126.754944 1
1.6%
126.754554 1
1.6%
126.754115 1
1.6%

면적
Real number (ℝ)

UNIQUE 

Distinct64
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8266.3078
Minimum291
Maximum95416
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size708.0 B
2023-12-12T23:13:58.653627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum291
5-th percentile632.195
Q11528.325
median2461.8
Q39572.45
95-th percentile24792.855
Maximum95416
Range95125
Interquartile range (IQR)8044.125

Descriptive statistics

Standard deviation14085.708
Coefficient of variation (CV)1.7039903
Kurtosis23.615276
Mean8266.3078
Median Absolute Deviation (MAD)1708.5
Skewness4.306456
Sum529043.7
Variance1.9840718 × 108
MonotonicityNot monotonic
2023-12-12T23:13:58.800812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
291.0 1
 
1.6%
6634.0 1
 
1.6%
2500.6 1
 
1.6%
3000.2 1
 
1.6%
625.7 1
 
1.6%
2143.4 1
 
1.6%
1599.9 1
 
1.6%
2089.8 1
 
1.6%
1600.4 1
 
1.6%
2368.0 1
 
1.6%
Other values (54) 54
84.4%
ValueCountFrequency (%)
291.0 1
1.6%
359.3 1
1.6%
360.3 1
1.6%
625.7 1
1.6%
669.0 1
1.6%
837.6 1
1.6%
900.8 1
1.6%
1171.3 1
1.6%
1197.4 1
1.6%
1449.2 1
1.6%
ValueCountFrequency (%)
95416.0 1
1.6%
45895.0 1
1.6%
33001.5 1
1.6%
25236.3 1
1.6%
22280.0 1
1.6%
21091.5 1
1.6%
20527.9 1
1.6%
18827.0 1
1.6%
18314.4 1
1.6%
17043.9 1
1.6%

Interactions

2023-12-12T23:13:54.243117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:13:52.934811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:13:53.367941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:13:53.790474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:13:54.351998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:13:53.056240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:13:53.462269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:13:53.893449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:13:54.471834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:13:53.152742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:13:53.561961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:13:54.007099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:13:54.578024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:13:53.269855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:13:53.686378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:13:54.140712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T23:13:58.906448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번공원명도로명주소지번주소위도경도면적
연번1.0001.0001.0001.0000.7440.5180.552
공원명1.0001.0001.0001.0001.0001.0001.000
도로명주소1.0001.0001.0001.0001.0001.0001.000
지번주소1.0001.0001.0001.0001.0001.0001.000
위도0.7441.0001.0001.0001.0000.6120.674
경도0.5181.0001.0001.0000.6121.0000.197
면적0.5521.0001.0001.0000.6740.1971.000
2023-12-12T23:13:59.034315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번위도경도면적
연번1.0000.3030.058-0.247
위도0.3031.0000.303-0.277
경도0.0580.3031.000-0.151
면적-0.247-0.277-0.1511.000

Missing values

2023-12-12T23:13:54.715743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T23:13:54.862961image/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

연번공원명도로명주소지번주소위도경도면적
01하마리소공원<NA>인천광역시 계양구 서운동 20437.527552126.742481291.0
12동양소공원<NA>인천광역시 계양구 동양동 615-337.56162126.757915837.6
23용종5소공원<NA>인천광역시 계양구 용종동 22437.542711126.742719360.3
34살나리소공원<NA>인천광역시 계양구 서운동 93-237.537926126.745134669.0
45하야소공원<NA>인천광역시 계양구 하야동 64-2937.583303126.78108359.3
56길머리소공원<NA>인천광역시 계양구 서운동 21037.536114126.7534584999.5
67목숙교소공원<NA>인천광역시 계양구 서운동 221-737.530909126.7549442919.1
78갈현체육공원인천광역시 갈현동 황어로 66인천광역시 계양구 갈현동산 52-937.576707126.7300345895.0
89계산체육공원인천광역시 계양구 주부토로 570 (계산동)인천광역시 계양구 계산동 907-137.545044126.72899822280.0
910다남체육공원인천광역시 계양구 다남로165번길 71 (다남동)인천광역시 계양구 다남동 42-837.565632126.72028618827.0
연번공원명도로명주소지번주소위도경도면적
5455귤현2어린이공원<NA>인천광역시 계양구 귤현동 22837.566852126.7497141507.0
5556귤현3어린이공원<NA>인천광역시 계양구 귤현동 산 937.564482126.7486981506.0
5657들놀이어린이공원인천광역시 계양구 양지로 109 (귤현동)인천광역시 계양구 귤현동 23137.565833126.751776285.0
5758소양어린이공원인천광역시 계양구 박촌로 83 (박촌동)인천광역시 계양구 박촌동 3837.558197126.7415992423.0
5859양촌어린이공원인천광역시 계양구 장제로948번길 31 (병방동)인천광역시 계양구 병방동 33-237.548758126.7458854285.0
5960박촌어린이공원<NA>인천광역시 계양구 박촌동 149-337.557142126.7476074019.0
6061상야2어린이공원<NA>인천광역시 계양구 상야동 12-937.580616126.7788631500.5
6162쑥쑥어린이공원인천광역시 계양구 봉오대로600번길 20 (효성동)인천광역시 계양구 효성동 268-937.528287126.7151161532.0
6263꿈나무어린이공원<NA>인천광역시 계양구 효성1동 247-237.529516126.7119924741.0
6364서운어린이공원인천광역시 계양구 봉오대로894번길 20 (서운동)인천광역시 계양구 서운동 127-3037.528747126.7481873191.1