Overview

Dataset statistics

Number of variables7
Number of observations40
Missing cells26
Missing cells (%)9.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.5 KiB
Average record size in memory63.3 B

Variable types

Numeric4
Text3

Dataset

Description인천광역시 계양구 관내 어린이공원에 대한 데이터파일로서 연번, 공원명, 도로명주소, 지번주소, 위도, 경도, 면적을 포함하고 있습니다.
Author인천광역시 계양구
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15117164&srcSe=7661IVAWM27C61E190

Alerts

위도 is highly overall correlated with 경도High correlation
경도 is highly overall correlated with 위도High correlation
도로명주소 has 26 (65.0%) 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 started2024-01-28 08:37:18.854051
Analysis finished2024-01-28 08:37:20.832977
Duration1.98 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct40
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.5
Minimum1
Maximum40
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size492.0 B
2024-01-28T17:37:20.894770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.95
Q110.75
median20.5
Q330.25
95-th percentile38.05
Maximum40
Range39
Interquartile range (IQR)19.5

Descriptive statistics

Standard deviation11.690452
Coefficient of variation (CV)0.57026595
Kurtosis-1.2
Mean20.5
Median Absolute Deviation (MAD)10
Skewness0
Sum820
Variance136.66667
MonotonicityStrictly increasing
2024-01-28T17:37:21.025165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
1 1
 
2.5%
22 1
 
2.5%
24 1
 
2.5%
25 1
 
2.5%
26 1
 
2.5%
27 1
 
2.5%
28 1
 
2.5%
29 1
 
2.5%
30 1
 
2.5%
31 1
 
2.5%
Other values (30) 30
75.0%
ValueCountFrequency (%)
1 1
2.5%
2 1
2.5%
3 1
2.5%
4 1
2.5%
5 1
2.5%
6 1
2.5%
7 1
2.5%
8 1
2.5%
9 1
2.5%
10 1
2.5%
ValueCountFrequency (%)
40 1
2.5%
39 1
2.5%
38 1
2.5%
37 1
2.5%
36 1
2.5%
35 1
2.5%
34 1
2.5%
33 1
2.5%
32 1
2.5%
31 1
2.5%

공원명
Text

UNIQUE 

Distinct40
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size452.0 B
2024-01-28T17:37:21.264821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length7
Mean length7.525
Min length7

Characters and Unicode

Total characters301
Distinct characters70
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

Unique40 ?
Unique (%)100.0%

Sample

1st row효성어린이공원
2nd row작전어린이공원
3rd row부일어린이공원
4th row안남어린이공원
5th row낙원어린이공원
ValueCountFrequency (%)
효성어린이공원 1
 
2.5%
작전어린이공원 1
 
2.5%
귤현1어린이공원 1
 
2.5%
중앙어린이공원 1
 
2.5%
장터어린이공원 1
 
2.5%
양지말어린이공원 1
 
2.5%
모텡이어린이공원 1
 
2.5%
서녘어린이공원 1
 
2.5%
살나리어린이공원 1
 
2.5%
계산1어린이공원 1
 
2.5%
Other values (30) 30
75.0%
2024-01-28T17:37:21.570434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
이 42
14.0%
원 41
13.6%
어 40
13.3%
린 40
13.3%
공 40
13.3%
양 5
 
1.7%
리 3
 
1.0%
동 3
 
1.0%
산 3
 
1.0%
나 3
 
1.0%
Other values (60) 81
26.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 293
97.3%
Decimal Number 8
 
2.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
이 42
14.3%
원 41
14.0%
어 40
13.7%
린 40
13.7%
공 40
13.7%
양 5
 
1.7%
리 3
 
1.0%
동 3
 
1.0%
산 3
 
1.0%
나 3
 
1.0%
Other values (56) 73
24.9%
Decimal Number
ValueCountFrequency (%)
1 3
37.5%
2 3
37.5%
3 1
 
12.5%
4 1
 
12.5%

Most occurring scripts

ValueCountFrequency (%)
Hangul 293
97.3%
Common 8
 
2.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
이 42
14.3%
원 41
14.0%
어 40
13.7%
린 40
13.7%
공 40
13.7%
양 5
 
1.7%
리 3
 
1.0%
동 3
 
1.0%
산 3
 
1.0%
나 3
 
1.0%
Other values (56) 73
24.9%
Common
ValueCountFrequency (%)
1 3
37.5%
2 3
37.5%
3 1
 
12.5%
4 1
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 293
97.3%
ASCII 8
 
2.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
이 42
14.3%
원 41
14.0%
어 40
13.7%
린 40
13.7%
공 40
13.7%
양 5
 
1.7%
리 3
 
1.0%
동 3
 
1.0%
산 3
 
1.0%
나 3
 
1.0%
Other values (56) 73
24.9%
ASCII
ValueCountFrequency (%)
1 3
37.5%
2 3
37.5%
3 1
 
12.5%
4 1
 
12.5%

도로명주소
Text

MISSING 

Distinct14
Distinct (%)100.0%
Missing26
Missing (%)65.0%
Memory size452.0 B
2024-01-28T17:37:21.750720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length30
Median length27
Mean length25.142857
Min length21

Characters and Unicode

Total characters352
Distinct characters62
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

Unique14 ?
Unique (%)100.0%

Sample

1st row인천광역시 계양구 새벌로111번길 7 (효성동)
2nd row인천광역시 계양구 아나지로307번길 1 (작전동)
3rd row인천광역시 계양구 병방시장로55번길 4 (병방동)
4th row인천광역시 계양구 화산로 3 (임학동)
5th row인천광역시 계양구 용종로 115 (병방동)
ValueCountFrequency (%)
인천광역시 14
19.7%
계양구 14
19.7%
병방동 3
 
4.2%
작전동 2
 
2.8%
20 2
 
2.8%
귤현동 2
 
2.8%
오조산로 2
 
2.8%
효성동 2
 
2.8%
1 1
 
1.4%
만봉길 1
 
1.4%
Other values (28) 28
39.4%
2024-01-28T17:37:22.033215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
57
 
16.2%
계 16
 
4.5%
양 16
 
4.5%
시 15
 
4.3%
인 14
 
4.0%
( 14
 
4.0%
광 14
 
4.0%
역 14
 
4.0%
구 14
 
4.0%
) 14
 
4.0%
Other values (52) 164
46.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 221
62.8%
Space Separator 57
 
16.2%
Decimal Number 44
 
12.5%
Open Punctuation 14
 
4.0%
Close Punctuation 14
 
4.0%
Other Punctuation 1
 
0.3%
Dash Punctuation 1
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
계 16
 
7.2%
양 16
 
7.2%
시 15
 
6.8%
인 14
 
6.3%
광 14
 
6.3%
역 14
 
6.3%
구 14
 
6.3%
동 14
 
6.3%
천 14
 
6.3%
로 13
 
5.9%
Other values (37) 77
34.8%
Decimal Number
ValueCountFrequency (%)
1 9
20.5%
0 6
13.6%
3 5
11.4%
4 4
9.1%
8 4
9.1%
5 4
9.1%
2 3
 
6.8%
6 3
 
6.8%
7 3
 
6.8%
9 3
 
6.8%
Space Separator
ValueCountFrequency (%)
57
100.0%
Open Punctuation
ValueCountFrequency (%)
( 14
100.0%
Close Punctuation
ValueCountFrequency (%)
) 14
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 221
62.8%
Common 131
37.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
계 16
 
7.2%
양 16
 
7.2%
시 15
 
6.8%
인 14
 
6.3%
광 14
 
6.3%
역 14
 
6.3%
구 14
 
6.3%
동 14
 
6.3%
천 14
 
6.3%
로 13
 
5.9%
Other values (37) 77
34.8%
Common
ValueCountFrequency (%)
57
43.5%
( 14
 
10.7%
) 14
 
10.7%
1 9
 
6.9%
0 6
 
4.6%
3 5
 
3.8%
4 4
 
3.1%
8 4
 
3.1%
5 4
 
3.1%
2 3
 
2.3%
Other values (5) 11
 
8.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 221
62.8%
ASCII 131
37.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
57
43.5%
( 14
 
10.7%
) 14
 
10.7%
1 9
 
6.9%
0 6
 
4.6%
3 5
 
3.8%
4 4
 
3.1%
8 4
 
3.1%
5 4
 
3.1%
2 3
 
2.3%
Other values (5) 11
 
8.4%
Hangul
ValueCountFrequency (%)
계 16
 
7.2%
양 16
 
7.2%
시 15
 
6.8%
인 14
 
6.3%
광 14
 
6.3%
역 14
 
6.3%
구 14
 
6.3%
동 14
 
6.3%
천 14
 
6.3%
로 13
 
5.9%
Other values (37) 77
34.8%

지번주소
Text

UNIQUE 

Distinct40
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size452.0 B
2024-01-28T17:37:22.217686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length19
Mean length18.15
Min length16

Characters and Unicode

Total characters726
Distinct characters43
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

Unique40 ?
Unique (%)100.0%

Sample

1st row인천광역시 계양구 효성동 223
2nd row인천광역시 계양구 작전동 414
3rd row인천광역시 계양구 계산동 930-1
4th row인천광역시 계양구 계산동 949
5th row인천광역시 계양구 임학동 25
ValueCountFrequency (%)
인천광역시 40
24.8%
계양구 40
24.8%
계산동 7
 
4.3%
귤현동 7
 
4.3%
동양동 5
 
3.1%
장기동 4
 
2.5%
병방동 3
 
1.9%
효성동 2
 
1.2%
작전동 2
 
1.2%
서운동 2
 
1.2%
Other values (47) 49
30.4%
2024-01-28T17:37:22.530313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
121
16.7%
계 47
 
6.5%
동 45
 
6.2%
양 45
 
6.2%
인 40
 
5.5%
구 40
 
5.5%
천 40
 
5.5%
시 40
 
5.5%
역 40
 
5.5%
광 40
 
5.5%
Other values (33) 228
31.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 441
60.7%
Decimal Number 142
 
19.6%
Space Separator 121
 
16.7%
Dash Punctuation 22
 
3.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
계 47
10.7%
동 45
10.2%
양 45
10.2%
인 40
9.1%
구 40
9.1%
천 40
9.1%
시 40
9.1%
역 40
9.1%
광 40
9.1%
산 8
 
1.8%
Other values (21) 56
12.7%
Decimal Number
ValueCountFrequency (%)
1 29
20.4%
2 27
19.0%
3 14
9.9%
4 14
9.9%
9 14
9.9%
6 10
 
7.0%
5 9
 
6.3%
0 9
 
6.3%
8 9
 
6.3%
7 7
 
4.9%
Space Separator
ValueCountFrequency (%)
121
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 22
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 441
60.7%
Common 285
39.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
계 47
10.7%
동 45
10.2%
양 45
10.2%
인 40
9.1%
구 40
9.1%
천 40
9.1%
시 40
9.1%
역 40
9.1%
광 40
9.1%
산 8
 
1.8%
Other values (21) 56
12.7%
Common
ValueCountFrequency (%)
121
42.5%
1 29
 
10.2%
2 27
 
9.5%
- 22
 
7.7%
3 14
 
4.9%
4 14
 
4.9%
9 14
 
4.9%
6 10
 
3.5%
5 9
 
3.2%
0 9
 
3.2%
Other values (2) 16
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 441
60.7%
ASCII 285
39.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
121
42.5%
1 29
 
10.2%
2 27
 
9.5%
- 22
 
7.7%
3 14
 
4.9%
4 14
 
4.9%
9 14
 
4.9%
6 10
 
3.5%
5 9
 
3.2%
0 9
 
3.2%
Other values (2) 16
 
5.6%
Hangul
ValueCountFrequency (%)
계 47
10.7%
동 45
10.2%
양 45
10.2%
인 40
9.1%
구 40
9.1%
천 40
9.1%
시 40
9.1%
역 40
9.1%
광 40
9.1%
산 8
 
1.8%
Other values (21) 56
12.7%

위도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct40
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.552539
Minimum37.525675
Maximum37.580616
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size492.0 B
2024-01-28T17:37:22.634541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.525675
5-th percentile37.52821
Q137.540033
median37.549643
Q337.566088
95-th percentile37.578442
Maximum37.580616
Range0.054941
Interquartile range (IQR)0.02605425

Descriptive statistics

Standard deviation0.016445781
Coefficient of variation (CV)0.00043794059
Kurtosis-1.1309798
Mean37.552539
Median Absolute Deviation (MAD)0.012717
Skewness0.065793709
Sum1502.1016
Variance0.00027046372
MonotonicityNot monotonic
2024-01-28T17:37:22.750426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
37.526738 1
 
2.5%
37.577702 1
 
2.5%
37.578391 1
 
2.5%
37.563848 1
 
2.5%
37.561596 1
 
2.5%
37.560911 1
 
2.5%
37.536296 1
 
2.5%
37.542765 1
 
2.5%
37.568555 1
 
2.5%
37.566852 1
 
2.5%
Other values (30) 30
75.0%
ValueCountFrequency (%)
37.525675 1
2.5%
37.526738 1
2.5%
37.528287 1
2.5%
37.528747 1
2.5%
37.529516 1
2.5%
37.53136 1
2.5%
37.536296 1
2.5%
37.537556 1
2.5%
37.537896 1
2.5%
37.538508 1
2.5%
ValueCountFrequency (%)
37.580616 1
2.5%
37.579411 1
2.5%
37.578391 1
2.5%
37.577702 1
2.5%
37.577654 1
2.5%
37.569792 1
2.5%
37.568555 1
2.5%
37.567896 1
2.5%
37.567828 1
2.5%
37.566852 1
2.5%

경도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct40
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.74
Minimum126.71199
Maximum126.77886
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size492.0 B
2024-01-28T17:37:22.865700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.71199
5-th percentile126.7156
Q1126.73234
median126.74099
Q3126.74879
95-th percentile126.75778
Maximum126.77886
Range0.066871
Interquartile range (IQR)0.0164415

Descriptive statistics

Standard deviation0.013605057
Coefficient of variation (CV)0.0001073462
Kurtosis0.63398201
Mean126.74
Median Absolute Deviation (MAD)0.0082
Skewness0.090355586
Sum5069.5998
Variance0.00018509759
MonotonicityNot monotonic
2024-01-28T17:37:22.980660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
126.715627 1
 
2.5%
126.73143 1
 
2.5%
126.736773 1
 
2.5%
126.754115 1
 
2.5%
126.754554 1
 
2.5%
126.757755 1
 
2.5%
126.746173 1
 
2.5%
126.723728 1
 
2.5%
126.750261 1
 
2.5%
126.749714 1
 
2.5%
Other values (30) 30
75.0%
ValueCountFrequency (%)
126.711992 1
2.5%
126.715116 1
2.5%
126.715627 1
2.5%
126.719517 1
2.5%
126.723238 1
2.5%
126.723728 1
2.5%
126.725359 1
2.5%
126.727323 1
2.5%
126.73063 1
2.5%
126.73143 1
2.5%
ValueCountFrequency (%)
126.778863 1
2.5%
126.758308 1
2.5%
126.757755 1
2.5%
126.754554 1
2.5%
126.754115 1
2.5%
126.753403 1
2.5%
126.75177 1
2.5%
126.750261 1
2.5%
126.749714 1
2.5%
126.749049 1
2.5%

면적
Real number (ℝ)

UNIQUE 

Distinct40
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3074.165
Minimum625.7
Maximum10877
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size492.0 B
2024-01-28T17:37:23.093139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum625.7
5-th percentile1157.775
Q11515
median2071.65
Q33398.075
95-th percentile8981.17
Maximum10877
Range10251.3
Interquartile range (IQR)1883.075

Descriptive statistics

Standard deviation2540.0786
Coefficient of variation (CV)0.82626618
Kurtosis2.3249909
Mean3074.165
Median Absolute Deviation (MAD)565.15
Skewness1.777347
Sum122966.6
Variance6451999.1
MonotonicityNot monotonic
2024-01-28T17:37:23.211305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
10877.0 1
 
2.5%
1683.8 1
 
2.5%
2202.8 1
 
2.5%
8957.7 1
 
2.5%
1637.5 1
 
2.5%
1517.3 1
 
2.5%
2053.5 1
 
2.5%
1197.4 1
 
2.5%
1508.1 1
 
2.5%
1507.0 1
 
2.5%
Other values (30) 30
75.0%
ValueCountFrequency (%)
625.7 1
2.5%
900.8 1
2.5%
1171.3 1
2.5%
1197.4 1
2.5%
1449.2 1
2.5%
1485.4 1
2.5%
1500.5 1
2.5%
1506.0 1
2.5%
1507.0 1
2.5%
1508.1 1
2.5%
ValueCountFrequency (%)
10877.0 1
2.5%
9427.1 1
2.5%
8957.7 1
2.5%
8339.9 1
2.5%
6634.0 1
2.5%
6285.0 1
2.5%
4845.9 1
2.5%
4741.0 1
2.5%
4285.0 1
2.5%
4019.0 1
2.5%

Interactions

2024-01-28T17:37:20.031129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T17:37:19.148151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T17:37:19.451472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T17:37:19.739874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T17:37:20.407835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T17:37:19.236095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T17:37:19.535624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T17:37:19.816270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T17:37:20.472826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T17:37:19.309440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T17:37:19.605888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T17:37:19.887421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T17:37:20.555232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T17:37:19.386747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T17:37:19.681967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T17:37:19.969312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-28T17:37:23.280979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번공원명도로명주소지번주소위도경도면적
연번1.0001.0001.0001.0000.8940.7370.487
공원명1.0001.0001.0001.0001.0001.0001.000
도로명주소1.0001.0001.0001.0001.0001.0001.000
지번주소1.0001.0001.0001.0001.0001.0001.000
위도0.8941.0001.0001.0001.0000.7470.283
경도0.7371.0001.0001.0000.7471.0000.138
면적0.4871.0001.0001.0000.2830.1381.000
2024-01-28T17:37:23.370567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번위도경도면적
연번1.0000.2800.4070.040
위도0.2801.0000.504-0.156
경도0.4070.5041.000-0.009
면적0.040-0.156-0.0091.000

Missing values

2024-01-28T17:37:20.662934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-28T17:37:20.794571image/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
01효성어린이공원인천광역시 계양구 새벌로111번길 7 (효성동)인천광역시 계양구 효성동 22337.526738126.71562710877.0
12작전어린이공원인천광역시 계양구 아나지로307번길 1 (작전동)인천광역시 계양구 작전동 41437.525675126.7253599427.1
23부일어린이공원<NA>인천광역시 계양구 계산동 930-137.544808126.7232381171.3
34안남어린이공원<NA>인천광역시 계양구 계산동 94937.54123126.7273231449.2
45낙원어린이공원<NA>인천광역시 계양구 임학동 2537.546426126.737535900.8
56고향골어린이공원<NA>인천광역시 계양구 계산동 98237.540542126.7195171485.4
67방축어린이공원<NA>인천광역시 계양구 방축동 21137.550528126.7403731810.0
78병방어린이공원인천광역시 계양구 병방시장로55번길 4 (병방동)인천광역시 계양구 병방동 36737.54814126.7380341667.5
89임학어린이공원인천광역시 계양구 화산로 3 (임학동)인천광역시 계양구 임학동 13537.548332126.7334138339.9
910학마을어린이공원인천광역시 계양구 용종로 115 (병방동)인천광역시 계양구 병방동 432-437.544113126.742036634.0
연번공원명도로명주소지번주소위도경도면적
3031귤현2어린이공원<NA>인천광역시 계양구 귤현동 22837.566852126.7497141507.0
3132귤현3어린이공원<NA>인천광역시 계양구 귤현동 산 937.564482126.7486981506.0
3233들놀이어린이공원인천광역시 계양구 양지로 109 (귤현동)인천광역시 계양구 귤현동 23137.565833126.751776285.0
3334소양어린이공원인천광역시 계양구 박촌로 83 (박촌동)인천광역시 계양구 박촌동 3837.558197126.7415992423.0
3435양촌어린이공원인천광역시 계양구 장제로948번길 31 (병방동)인천광역시 계양구 병방동 33-237.548758126.7458854285.0
3536박촌어린이공원<NA>인천광역시 계양구 박촌동 149-337.557142126.7476074019.0
3637상야2어린이공원<NA>인천광역시 계양구 상야동 12-937.580616126.7788631500.5
3738쑥쑥어린이공원인천광역시 계양구 봉오대로600번길 20 (효성동)인천광역시 계양구 효성동 268-937.528287126.7151161532.0
3839꿈나무어린이공원<NA>인천광역시 계양구 효성1동 247-237.529516126.7119924741.0
3940서운어린이공원인천광역시 계양구 봉오대로894번길 20 (서운동)인천광역시 계양구 서운동 127-3037.528747126.7481873191.1