Overview

Dataset statistics

Number of variables8
Number of observations24
Missing cells3
Missing cells (%)1.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.8 KiB
Average record size in memory75.3 B

Variable types

Numeric6
Text2

Dataset

Description부산광역시남구_쌈지공원현황_20230911
Author부산광역시 남구
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=15022374

Alerts

면적(제곱미터) is highly overall correlated with 합계 and 2 other fieldsHigh correlation
합계 is highly overall correlated with 면적(제곱미터) and 2 other fieldsHigh correlation
관목 is highly overall correlated with 면적(제곱미터) and 2 other fieldsHigh correlation
시설물 is highly overall correlated with 면적(제곱미터) and 2 other fieldsHigh correlation
교목 has 1 (4.2%) missing valuesMissing
시설물 has 2 (8.3%) missing valuesMissing
연번 has unique valuesUnique
명칭 has unique valuesUnique
위치(도로명 주소) has unique valuesUnique
합계 has unique valuesUnique
관목 has unique valuesUnique

Reproduction

Analysis started2024-04-21 11:10:28.307978
Analysis finished2024-04-21 11:10:36.542567
Duration8.23 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.5
Minimum1
Maximum24
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size344.0 B
2024-04-21T20:10:36.646171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.15
Q16.75
median12.5
Q318.25
95-th percentile22.85
Maximum24
Range23
Interquartile range (IQR)11.5

Descriptive statistics

Standard deviation7.0710678
Coefficient of variation (CV)0.56568542
Kurtosis-1.2
Mean12.5
Median Absolute Deviation (MAD)6
Skewness0
Sum300
Variance50
MonotonicityStrictly increasing
2024-04-21T20:10:36.858438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1 1
 
4.2%
14 1
 
4.2%
24 1
 
4.2%
23 1
 
4.2%
22 1
 
4.2%
21 1
 
4.2%
20 1
 
4.2%
19 1
 
4.2%
18 1
 
4.2%
17 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
1 1
4.2%
2 1
4.2%
3 1
4.2%
4 1
4.2%
5 1
4.2%
6 1
4.2%
7 1
4.2%
8 1
4.2%
9 1
4.2%
10 1
4.2%
ValueCountFrequency (%)
24 1
4.2%
23 1
4.2%
22 1
4.2%
21 1
4.2%
20 1
4.2%
19 1
4.2%
18 1
4.2%
17 1
4.2%
16 1
4.2%
15 1
4.2%

명칭
Text

UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size320.0 B
2024-04-21T20:10:37.451328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length20
Mean length17.083333
Min length13

Characters and Unicode

Total characters410
Distinct characters64
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique24 ?
Unique (%)100.0%

Sample

1st row부산광역시 남구 문현번영로 쌈지공원
2nd row부산광역시 남구 문현교통광장
3rd row부산광역시 남구 문현2동 쌈지공원
4th row부산광역시 남구 우암1동 쌈지공원
5th row부산광역시 남구 양달행복마을
ValueCountFrequency (%)
부산광역시 24
26.4%
남구 24
26.4%
쌈지공원 13
14.3%
공원 1
 
1.1%
연포하늘 1
 
1.1%
우암양달로 1
 
1.1%
양지골 1
 
1.1%
감만 1
 
1.1%
문현동쉼터 1
 
1.1%
문현교통광장(녹지쉼터 1
 
1.1%
Other values (23) 23
25.3%
2024-04-21T20:10:38.253063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
67
16.3%
광 26
 
6.3%
산 25
 
6.1%
부 24
 
5.9%
역 24
 
5.9%
시 24
 
5.9%
남 24
 
5.9%
구 24
 
5.9%
공 16
 
3.9%
원 15
 
3.7%
Other values (54) 141
34.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 335
81.7%
Space Separator 67
 
16.3%
Decimal Number 6
 
1.5%
Close Punctuation 1
 
0.2%
Open Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
광 26
 
7.8%
산 25
 
7.5%
부 24
 
7.2%
역 24
 
7.2%
시 24
 
7.2%
남 24
 
7.2%
구 24
 
7.2%
공 16
 
4.8%
원 15
 
4.5%
지 15
 
4.5%
Other values (47) 118
35.2%
Decimal Number
ValueCountFrequency (%)
1 2
33.3%
2 2
33.3%
4 1
16.7%
3 1
16.7%
Space Separator
ValueCountFrequency (%)
67
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 335
81.7%
Common 75
 
18.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
광 26
 
7.8%
산 25
 
7.5%
부 24
 
7.2%
역 24
 
7.2%
시 24
 
7.2%
남 24
 
7.2%
구 24
 
7.2%
공 16
 
4.8%
원 15
 
4.5%
지 15
 
4.5%
Other values (47) 118
35.2%
Common
ValueCountFrequency (%)
67
89.3%
1 2
 
2.7%
2 2
 
2.7%
4 1
 
1.3%
) 1
 
1.3%
( 1
 
1.3%
3 1
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 335
81.7%
ASCII 75
 
18.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
67
89.3%
1 2
 
2.7%
2 2
 
2.7%
4 1
 
1.3%
) 1
 
1.3%
( 1
 
1.3%
3 1
 
1.3%
Hangul
ValueCountFrequency (%)
광 26
 
7.8%
산 25
 
7.5%
부 24
 
7.2%
역 24
 
7.2%
시 24
 
7.2%
남 24
 
7.2%
구 24
 
7.2%
공 16
 
4.8%
원 15
 
4.5%
지 15
 
4.5%
Other values (47) 118
35.2%

위치(도로명 주소)
Text

UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size320.0 B
2024-04-21T20:10:38.837003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length21
Mean length19.25
Min length16

Characters and Unicode

Total characters462
Distinct characters30
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

Unique24 ?
Unique (%)100.0%

Sample

1st row부산광역시 남구 문현3동 655-96
2nd row부산광역시 남구 문현동 373-20
3rd row부산광역시 남구 문현동 805-2
4th row부산광역시 남구 우암동 89-14
5th row부산광역시 남구 우암동 174-164
ValueCountFrequency (%)
부산광역시 24
25.8%
남구 24
25.8%
우암동 5
 
5.4%
대연동 4
 
4.3%
문현동 4
 
4.3%
대연3동 3
 
3.2%
감만동 3
 
3.2%
1115-56 1
 
1.1%
34-45번지 1
 
1.1%
746-5번지 1
 
1.1%
Other values (23) 23
24.7%
2024-04-21T20:10:39.845293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
69
 
14.9%
부 24
 
5.2%
산 24
 
5.2%
광 24
 
5.2%
역 24
 
5.2%
시 24
 
5.2%
남 24
 
5.2%
구 24
 
5.2%
동 24
 
5.2%
- 22
 
4.8%
Other values (20) 179
38.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 256
55.4%
Decimal Number 115
24.9%
Space Separator 69
 
14.9%
Dash Punctuation 22
 
4.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
부 24
9.4%
산 24
9.4%
광 24
9.4%
역 24
9.4%
시 24
9.4%
남 24
9.4%
구 24
9.4%
동 24
9.4%
번 8
 
3.1%
지 8
 
3.1%
Other values (8) 48
18.8%
Decimal Number
ValueCountFrequency (%)
4 19
16.5%
1 19
16.5%
5 14
12.2%
2 13
11.3%
3 11
9.6%
8 10
8.7%
7 8
7.0%
0 7
 
6.1%
6 7
 
6.1%
9 7
 
6.1%
Space Separator
ValueCountFrequency (%)
69
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 22
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 256
55.4%
Common 206
44.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
부 24
9.4%
산 24
9.4%
광 24
9.4%
역 24
9.4%
시 24
9.4%
남 24
9.4%
구 24
9.4%
동 24
9.4%
번 8
 
3.1%
지 8
 
3.1%
Other values (8) 48
18.8%
Common
ValueCountFrequency (%)
69
33.5%
- 22
 
10.7%
4 19
 
9.2%
1 19
 
9.2%
5 14
 
6.8%
2 13
 
6.3%
3 11
 
5.3%
8 10
 
4.9%
7 8
 
3.9%
0 7
 
3.4%
Other values (2) 14
 
6.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 256
55.4%
ASCII 206
44.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
69
33.5%
- 22
 
10.7%
4 19
 
9.2%
1 19
 
9.2%
5 14
 
6.8%
2 13
 
6.3%
3 11
 
5.3%
8 10
 
4.9%
7 8
 
3.9%
0 7
 
3.4%
Other values (2) 14
 
6.8%
Hangul
ValueCountFrequency (%)
부 24
9.4%
산 24
9.4%
광 24
9.4%
역 24
9.4%
시 24
9.4%
남 24
9.4%
구 24
9.4%
동 24
9.4%
번 8
 
3.1%
지 8
 
3.1%
Other values (8) 48
18.8%

면적(제곱미터)
Real number (ℝ)

HIGH CORRELATION 

Distinct23
Distinct (%)95.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean529.95833
Minimum50
Maximum3775
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size344.0 B
2024-04-21T20:10:40.047811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile75.8
Q1150.75
median248.5
Q3380
95-th percentile2116.95
Maximum3775
Range3725
Interquartile range (IQR)229.25

Descriptive statistics

Standard deviation843.63765
Coefficient of variation (CV)1.5918943
Kurtosis9.9937804
Mean529.95833
Median Absolute Deviation (MAD)126.5
Skewness3.0849306
Sum12719
Variance711724.48
MonotonicityNot monotonic
2024-04-21T20:10:40.257614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
380 2
 
8.3%
250 1
 
4.2%
313 1
 
4.2%
470 1
 
4.2%
2253 1
 
4.2%
100 1
 
4.2%
197 1
 
4.2%
174 1
 
4.2%
151 1
 
4.2%
3775 1
 
4.2%
Other values (13) 13
54.2%
ValueCountFrequency (%)
50 1
4.2%
74 1
4.2%
86 1
4.2%
93 1
4.2%
100 1
4.2%
150 1
4.2%
151 1
4.2%
174 1
4.2%
197 1
4.2%
202 1
4.2%
ValueCountFrequency (%)
3775 1
4.2%
2253 1
4.2%
1346 1
4.2%
820 1
4.2%
470 1
4.2%
380 2
8.3%
370 1
4.2%
330 1
4.2%
313 1
4.2%
300 1
4.2%

합계
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1796.2083
Minimum150
Maximum11726
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size344.0 B
2024-04-21T20:10:40.468050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum150
5-th percentile197.1
Q1439.75
median809
Q31683
95-th percentile8581.65
Maximum11726
Range11576
Interquartile range (IQR)1243.25

Descriptive statistics

Standard deviation2853.8863
Coefficient of variation (CV)1.5888393
Kurtosis7.8924243
Mean1796.2083
Median Absolute Deviation (MAD)507.5
Skewness2.8686558
Sum43109
Variance8144667
MonotonicityNot monotonic
2024-04-21T20:10:40.679700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
772 1
 
4.2%
482 1
 
4.2%
1815 1
 
4.2%
11726 1
 
4.2%
192 1
 
4.2%
1639 1
 
4.2%
246 1
 
4.2%
856 1
 
4.2%
9504 1
 
4.2%
580 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
150 1
4.2%
192 1
4.2%
226 1
4.2%
246 1
4.2%
357 1
4.2%
364 1
4.2%
465 1
4.2%
482 1
4.2%
580 1
4.2%
594 1
4.2%
ValueCountFrequency (%)
11726 1
4.2%
9504 1
4.2%
3355 1
4.2%
2460 1
4.2%
2336 1
4.2%
1815 1
4.2%
1639 1
4.2%
1402 1
4.2%
1165 1
4.2%
861 1
4.2%

교목
Real number (ℝ)

MISSING 

Distinct17
Distinct (%)73.9%
Missing1
Missing (%)4.2%
Infinite0
Infinite (%)0.0%
Mean27.73913
Minimum1
Maximum304
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size344.0 B
2024-04-21T20:10:40.877503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median12
Q318
95-th percentile93.5
Maximum304
Range303
Interquartile range (IQR)14

Descriptive statistics

Standard deviation63.595467
Coefficient of variation (CV)2.2926265
Kurtosis17.930135
Mean27.73913
Median Absolute Deviation (MAD)8
Skewness4.1221212
Sum638
Variance4044.3834
MonotonicityNot monotonic
2024-04-21T20:10:41.075713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
1 3
12.5%
16 2
 
8.3%
6 2
 
8.3%
4 2
 
8.3%
12 2
 
8.3%
11 1
 
4.2%
15 1
 
4.2%
304 1
 
4.2%
100 1
 
4.2%
20 1
 
4.2%
Other values (7) 7
29.2%
ValueCountFrequency (%)
1 3
12.5%
2 1
 
4.2%
3 1
 
4.2%
4 2
8.3%
6 2
8.3%
9 1
 
4.2%
11 1
 
4.2%
12 2
8.3%
13 1
 
4.2%
15 1
 
4.2%
ValueCountFrequency (%)
304 1
4.2%
100 1
4.2%
35 1
4.2%
26 1
4.2%
21 1
4.2%
20 1
4.2%
16 2
8.3%
15 1
4.2%
13 1
4.2%
12 2
8.3%

관목
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1769.625
Minimum150
Maximum11422
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size344.0 B
2024-04-21T20:10:41.290965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum150
5-th percentile190.65
Q1437.25
median795.5
Q31676.25
95-th percentile8496.2
Maximum11422
Range11272
Interquartile range (IQR)1239

Descriptive statistics

Standard deviation2798.9486
Coefficient of variation (CV)1.581662
Kurtosis7.7258237
Mean1769.625
Median Absolute Deviation (MAD)502.5
Skewness2.8437748
Sum42471
Variance7834113.4
MonotonicityNot monotonic
2024-04-21T20:10:41.493407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
771 1
 
4.2%
470 1
 
4.2%
1800 1
 
4.2%
11422 1
 
4.2%
186 1
 
4.2%
1635 1
 
4.2%
230 1
 
4.2%
844 1
 
4.2%
9404 1
 
4.2%
560 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
150 1
4.2%
186 1
4.2%
217 1
4.2%
230 1
4.2%
356 1
4.2%
360 1
4.2%
463 1
4.2%
470 1
4.2%
560 1
4.2%
593 1
4.2%
ValueCountFrequency (%)
11422 1
4.2%
9404 1
4.2%
3352 1
4.2%
2444 1
4.2%
2330 1
4.2%
1800 1
4.2%
1635 1
4.2%
1381 1
4.2%
1130 1
4.2%
850 1
4.2%

시설물
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct13
Distinct (%)59.1%
Missing2
Missing (%)8.3%
Infinite0
Infinite (%)0.0%
Mean8.4090909
Minimum1
Maximum28
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size344.0 B
2024-04-21T20:10:41.690046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median7
Q310.75
95-th percentile21.75
Maximum28
Range27
Interquartile range (IQR)7.75

Descriptive statistics

Standard deviation7.0282362
Coefficient of variation (CV)0.83579025
Kurtosis1.8037194
Mean8.4090909
Median Absolute Deviation (MAD)4
Skewness1.3018642
Sum185
Variance49.396104
MonotonicityNot monotonic
2024-04-21T20:10:41.891161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
1 4
16.7%
6 3
12.5%
13 2
8.3%
3 2
8.3%
10 2
8.3%
7 2
8.3%
2 1
 
4.2%
17 1
 
4.2%
8 1
 
4.2%
28 1
 
4.2%
Other values (3) 3
12.5%
(Missing) 2
8.3%
ValueCountFrequency (%)
1 4
16.7%
2 1
 
4.2%
3 2
8.3%
6 3
12.5%
7 2
8.3%
8 1
 
4.2%
9 1
 
4.2%
10 2
8.3%
11 1
 
4.2%
13 2
8.3%
ValueCountFrequency (%)
28 1
 
4.2%
22 1
 
4.2%
17 1
 
4.2%
13 2
8.3%
11 1
 
4.2%
10 2
8.3%
9 1
 
4.2%
8 1
 
4.2%
7 2
8.3%
6 3
12.5%

Interactions

2024-04-21T20:10:34.760544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:10:28.663428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:10:30.106861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:10:31.611254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:10:33.027388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:10:33.909832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:10:34.893547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:10:28.897945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:10:30.354800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:10:31.850672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:10:33.180921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:10:34.045703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:10:35.123057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:10:29.147149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:10:30.613451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:10:32.106772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:10:33.336548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:10:34.199670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:10:35.364559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:10:29.391718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:10:30.864938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:10:32.350985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:10:33.481946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:10:34.343878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:10:35.609464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:10:29.635536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:10:31.119402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:10:32.599882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:10:33.627678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:10:34.486449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:10:35.843370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:10:29.873350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:10:31.364318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:10:32.835551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:10:33.766764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:10:34.620268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-21T20:10:42.043013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번명칭위치(도로명 주소)면적(제곱미터)합계교목관목시설물
연번1.0001.0001.0000.0510.4060.0000.6070.000
명칭1.0001.0001.0001.0001.0001.0001.0001.000
위치(도로명 주소)1.0001.0001.0001.0001.0001.0001.0001.000
면적(제곱미터)0.0511.0001.0001.0000.9081.0000.8520.838
합계0.4061.0001.0000.9081.0000.8130.9960.987
교목0.0001.0001.0001.0000.8131.0000.8130.989
관목0.6071.0001.0000.8520.9960.8131.0000.857
시설물0.0001.0001.0000.8380.9870.9890.8571.000
2024-04-21T20:10:42.240190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번면적(제곱미터)합계교목관목시설물
연번1.000-0.0140.1380.3170.1380.239
면적(제곱미터)-0.0141.0000.7740.2950.7740.505
합계0.1380.7741.0000.4031.0000.658
교목0.3170.2950.4031.0000.4030.393
관목0.1380.7741.0000.4031.0000.658
시설물0.2390.5050.6580.3930.6581.000

Missing values

2024-04-21T20:10:36.081686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T20:10:36.297039image/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.
2024-04-21T20:10:36.463517image/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

연번명칭위치(도로명 주소)면적(제곱미터)합계교목관목시설물
01부산광역시 남구 문현번영로 쌈지공원부산광역시 남구 문현3동 655-9625077217716
12부산광역시 남구 문현교통광장부산광역시 남구 문현동 373-201346116535113013
23부산광역시 남구 문현2동 쌈지공원부산광역시 남구 문현동 805-2300716137033
34부산광역시 남구 우암1동 쌈지공원부산광역시 남구 우암동 89-1486150<NA>1501
45부산광역시 남구 양달행복마을부산광역시 남구 우암동 174-16433024601624446
56부산광역시 남구 보호수 주변 쌈지공원부산광역시 남구 대연3동 561-12208465246310
67부산광역시 남구 창의문화촌 쌈지공원부산광역시 남구 감만1동 78820335533352<NA>
78부산광역시 남구 우암동 노인쉼터부산광역시 남구 우암동 189-9487422692177
89부산광역시 남구 우영쉼터부산광역시 남구 대연3동 207-437023366233010
910부산광역시 남구 대동골 문화센터 쌈지공원부산광역시 남구 대연3동 245-17024759415932
연번명칭위치(도로명 주소)면적(제곱미터)합계교목관목시설물
1415부산광역시 남구 대연4동 주민쉼터부산광역시 남구 대연동 1115-56313861118508
1516부산광역시 남구 감만2동 쌈지공원부산광역시 남구 감만동 34-45번지202846268201
1617부산광역시 남구 유엔 쌈지공원부산광역시 남구 대연동 746-5번지150580205601
1718부산광역시 남구 문현교통광장(녹지쉼터)부산광역시 남구 문현동 842-3번지37759504100940428
1819부산광역시 남구 우암 쌈지공원부산광역시 남구 우암동 91-5번지1518561284413
1920부산광역시 남구 감만 쌈지공원부산광역시 남구 감만동 3-473번지174246162307
2021부산광역시 남구 양지골 쌈지공원부산광역시 남구 감만동 205-1번지19716394163511
2122부산광역시 남구 우암양달로 쌈지공원부산광역시 남구 우암동 181-429번지10019261863
2223부산광역시 남구 연포하늘 공원부산광역시 남구 대연동 1584-142253117263041142222
2324부산광역시 남구 대연3동 쌈지공원부산광역시 남구 대연동 243-2647018151518009