Overview

Dataset statistics

Number of variables8
Number of observations22
Missing cells3
Missing cells (%)1.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.6 KiB
Average record size in memory75.8 B

Variable types

Numeric6
Text2

Dataset

Description부산광역시남구_쌈지공원현황_20220922
Author부산광역시 남구
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=15022374

Alerts

면적(제곱미터) is highly overall correlated with 합계 and 1 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 합계 and 1 other fieldsHigh correlation
교목 has 1 (4.5%) missing valuesMissing
시설물 has 2 (9.1%) 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 11:10:44.133777
Analysis finished2024-04-21 11:10:54.067248
Duration9.93 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.5
Minimum1
Maximum22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size326.0 B
2024-04-21T20:10:54.265927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.05
Q16.25
median11.5
Q316.75
95-th percentile20.95
Maximum22
Range21
Interquartile range (IQR)10.5

Descriptive statistics

Standard deviation6.4935866
Coefficient of variation (CV)0.5646597
Kurtosis-1.2
Mean11.5
Median Absolute Deviation (MAD)5.5
Skewness0
Sum253
Variance42.166667
MonotonicityStrictly increasing
2024-04-21T20:10:54.629270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
1 1
 
4.5%
13 1
 
4.5%
22 1
 
4.5%
21 1
 
4.5%
20 1
 
4.5%
19 1
 
4.5%
18 1
 
4.5%
17 1
 
4.5%
16 1
 
4.5%
15 1
 
4.5%
Other values (12) 12
54.5%
ValueCountFrequency (%)
1 1
4.5%
2 1
4.5%
3 1
4.5%
4 1
4.5%
5 1
4.5%
6 1
4.5%
7 1
4.5%
8 1
4.5%
9 1
4.5%
10 1
4.5%
ValueCountFrequency (%)
22 1
4.5%
21 1
4.5%
20 1
4.5%
19 1
4.5%
18 1
4.5%
17 1
4.5%
16 1
4.5%
15 1
4.5%
14 1
4.5%
13 1
4.5%

명칭
Text

UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size304.0 B
2024-04-21T20:10:55.292255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length19.5
Mean length17.090909
Min length13

Characters and Unicode

Total characters376
Distinct characters60
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

Unique22 ?
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 22
26.5%
남구 22
26.5%
쌈지공원 12
14.5%
산수쉼터 1
 
1.2%
양지골 1
 
1.2%
감만 1
 
1.2%
우암 1
 
1.2%
문현교통광장(녹지쉼터 1
 
1.2%
유엔 1
 
1.2%
감만2동 1
 
1.2%
Other values (20) 20
24.1%
2024-04-21T20:10:56.351654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
61
16.2%
광 24
 
6.4%
산 23
 
6.1%
부 22
 
5.9%
역 22
 
5.9%
시 22
 
5.9%
남 22
 
5.9%
구 22
 
5.9%
지 14
 
3.7%
공 14
 
3.7%
Other values (50) 130
34.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 308
81.9%
Space Separator 61
 
16.2%
Decimal Number 5
 
1.3%
Open Punctuation 1
 
0.3%
Close Punctuation 1
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
광 24
 
7.8%
산 23
 
7.5%
부 22
 
7.1%
역 22
 
7.1%
시 22
 
7.1%
남 22
 
7.1%
구 22
 
7.1%
지 14
 
4.5%
공 14
 
4.5%
원 13
 
4.2%
Other values (44) 110
35.7%
Decimal Number
ValueCountFrequency (%)
2 2
40.0%
1 2
40.0%
4 1
20.0%
Space Separator
ValueCountFrequency (%)
61
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 308
81.9%
Common 68
 
18.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
광 24
 
7.8%
산 23
 
7.5%
부 22
 
7.1%
역 22
 
7.1%
시 22
 
7.1%
남 22
 
7.1%
구 22
 
7.1%
지 14
 
4.5%
공 14
 
4.5%
원 13
 
4.2%
Other values (44) 110
35.7%
Common
ValueCountFrequency (%)
61
89.7%
2 2
 
2.9%
1 2
 
2.9%
( 1
 
1.5%
4 1
 
1.5%
) 1
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 308
81.9%
ASCII 68
 
18.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
61
89.7%
2 2
 
2.9%
1 2
 
2.9%
( 1
 
1.5%
4 1
 
1.5%
) 1
 
1.5%
Hangul
ValueCountFrequency (%)
광 24
 
7.8%
산 23
 
7.5%
부 22
 
7.1%
역 22
 
7.1%
시 22
 
7.1%
남 22
 
7.1%
구 22
 
7.1%
지 14
 
4.5%
공 14
 
4.5%
원 13
 
4.2%
Other values (44) 110
35.7%

위치(도로명 주소)
Text

UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size304.0 B
2024-04-21T20:10:57.020396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length21
Mean length19.227273
Min length16

Characters and Unicode

Total characters423
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

Unique22 ?
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 22
25.9%
남구 22
25.9%
우암동 5
 
5.9%
문현동 4
 
4.7%
대연3동 3
 
3.5%
감만동 3
 
3.5%
대연동 2
 
2.4%
54-27 1
 
1.2%
1115-56 1
 
1.2%
746-5번지 1
 
1.2%
Other values (21) 21
24.7%
2024-04-21T20:10:58.096293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
63
 
14.9%
부 22
 
5.2%
산 22
 
5.2%
광 22
 
5.2%
역 22
 
5.2%
시 22
 
5.2%
남 22
 
5.2%
구 22
 
5.2%
동 22
 
5.2%
- 20
 
4.7%
Other values (20) 164
38.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 236
55.8%
Decimal Number 104
24.6%
Space Separator 63
 
14.9%
Dash Punctuation 20
 
4.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
부 22
9.3%
산 22
9.3%
광 22
9.3%
역 22
9.3%
시 22
9.3%
남 22
9.3%
구 22
9.3%
동 22
9.3%
번 8
 
3.4%
지 8
 
3.4%
Other values (8) 44
18.6%
Decimal Number
ValueCountFrequency (%)
1 17
16.3%
4 16
15.4%
5 13
12.5%
2 11
10.6%
3 10
9.6%
8 9
8.7%
7 8
7.7%
0 7
6.7%
9 7
6.7%
6 6
 
5.8%
Space Separator
ValueCountFrequency (%)
63
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 20
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 236
55.8%
Common 187
44.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
부 22
9.3%
산 22
9.3%
광 22
9.3%
역 22
9.3%
시 22
9.3%
남 22
9.3%
구 22
9.3%
동 22
9.3%
번 8
 
3.4%
지 8
 
3.4%
Other values (8) 44
18.6%
Common
ValueCountFrequency (%)
63
33.7%
- 20
 
10.7%
1 17
 
9.1%
4 16
 
8.6%
5 13
 
7.0%
2 11
 
5.9%
3 10
 
5.3%
8 9
 
4.8%
7 8
 
4.3%
0 7
 
3.7%
Other values (2) 13
 
7.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 236
55.8%
ASCII 187
44.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
63
33.7%
- 20
 
10.7%
1 17
 
9.1%
4 16
 
8.6%
5 13
 
7.0%
2 11
 
5.9%
3 10
 
5.3%
8 9
 
4.8%
7 8
 
4.3%
0 7
 
3.7%
Other values (2) 13
 
7.0%
Hangul
ValueCountFrequency (%)
부 22
9.3%
산 22
9.3%
광 22
9.3%
역 22
9.3%
시 22
9.3%
남 22
9.3%
구 22
9.3%
동 22
9.3%
번 8
 
3.4%
지 8
 
3.4%
Other values (8) 44
18.6%

면적(제곱미터)
Real number (ℝ)

HIGH CORRELATION 

Distinct21
Distinct (%)95.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean454.36364
Minimum50
Maximum3775
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size326.0 B
2024-04-21T20:10:58.453558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile74.6
Q1150.25
median227.5
Q3360
95-th percentile1319.7
Maximum3775
Range3725
Interquartile range (IQR)209.75

Descriptive statistics

Standard deviation794.96856
Coefficient of variation (CV)1.7496307
Kurtosis15.947067
Mean454.36364
Median Absolute Deviation (MAD)115
Skewness3.8531695
Sum9996
Variance631975
MonotonicityNot monotonic
2024-04-21T20:10:58.830733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
380 2
 
9.1%
1346 1
 
4.5%
100 1
 
4.5%
197 1
 
4.5%
174 1
 
4.5%
151 1
 
4.5%
3775 1
 
4.5%
150 1
 
4.5%
202 1
 
4.5%
313 1
 
4.5%
Other values (11) 11
50.0%
ValueCountFrequency (%)
50 1
4.5%
74 1
4.5%
86 1
4.5%
93 1
4.5%
100 1
4.5%
150 1
4.5%
151 1
4.5%
174 1
4.5%
197 1
4.5%
202 1
4.5%
ValueCountFrequency (%)
3775 1
4.5%
1346 1
4.5%
820 1
4.5%
380 2
9.1%
370 1
4.5%
330 1
4.5%
313 1
4.5%
300 1
4.5%
250 1
4.5%
247 1
4.5%

합계
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1344
Minimum150
Maximum9504
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size326.0 B
2024-04-21T20:10:59.198332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum150
5-th percentile193.7
Q1389.25
median744
Q31342.75
95-th percentile3310.25
Maximum9504
Range9354
Interquartile range (IQR)953.5

Descriptive statistics

Standard deviation2002.7052
Coefficient of variation (CV)1.4901081
Kurtosis14.111827
Mean1344
Median Absolute Deviation (MAD)404
Skewness3.5411508
Sum29568
Variance4010828.3
MonotonicityNot monotonic
2024-04-21T20:10:59.547263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
772 1
 
4.5%
357 1
 
4.5%
192 1
 
4.5%
1639 1
 
4.5%
246 1
 
4.5%
856 1
 
4.5%
9504 1
 
4.5%
580 1
 
4.5%
846 1
 
4.5%
861 1
 
4.5%
Other values (12) 12
54.5%
ValueCountFrequency (%)
150 1
4.5%
192 1
4.5%
226 1
4.5%
246 1
4.5%
357 1
4.5%
364 1
4.5%
465 1
4.5%
482 1
4.5%
580 1
4.5%
594 1
4.5%
ValueCountFrequency (%)
9504 1
4.5%
3355 1
4.5%
2460 1
4.5%
2336 1
4.5%
1639 1
4.5%
1402 1
4.5%
1165 1
4.5%
861 1
4.5%
856 1
4.5%
846 1
4.5%

교목
Real number (ℝ)

MISSING 

Distinct15
Distinct (%)71.4%
Missing1
Missing (%)4.5%
Infinite0
Infinite (%)0.0%
Mean15.190476
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size326.0 B
2024-04-21T20:10:59.891742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median11
Q316
95-th percentile35
Maximum100
Range99
Interquartile range (IQR)12

Descriptive statistics

Standard deviation21.43273
Coefficient of variation (CV)1.4109321
Kurtosis13.176147
Mean15.190476
Median Absolute Deviation (MAD)7
Skewness3.3786968
Sum319
Variance459.3619
MonotonicityNot monotonic
2024-04-21T20:11:00.247693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
1 3
13.6%
16 2
 
9.1%
6 2
 
9.1%
4 2
 
9.1%
12 2
 
9.1%
35 1
 
4.5%
13 1
 
4.5%
2 1
 
4.5%
3 1
 
4.5%
9 1
 
4.5%
Other values (5) 5
22.7%
ValueCountFrequency (%)
1 3
13.6%
2 1
 
4.5%
3 1
 
4.5%
4 2
9.1%
6 2
9.1%
9 1
 
4.5%
11 1
 
4.5%
12 2
9.1%
13 1
 
4.5%
16 2
9.1%
ValueCountFrequency (%)
100 1
4.5%
35 1
4.5%
26 1
4.5%
21 1
4.5%
20 1
4.5%
16 2
9.1%
13 1
4.5%
12 2
9.1%
11 1
4.5%
9 1
4.5%

관목
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1329.5
Minimum150
Maximum9404
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size326.0 B
2024-04-21T20:11:00.590211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum150
5-th percentile187.55
Q1385.75
median737
Q31318.25
95-th percentile3306.6
Maximum9404
Range9254
Interquartile range (IQR)932.5

Descriptive statistics

Standard deviation1985.1079
Coefficient of variation (CV)1.4931237
Kurtosis13.997428
Mean1329.5
Median Absolute Deviation (MAD)387
Skewness3.5256458
Sum29249
Variance3940653.4
MonotonicityNot monotonic
2024-04-21T20:11:00.939347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
771 1
 
4.5%
356 1
 
4.5%
186 1
 
4.5%
1635 1
 
4.5%
230 1
 
4.5%
844 1
 
4.5%
9404 1
 
4.5%
560 1
 
4.5%
820 1
 
4.5%
850 1
 
4.5%
Other values (12) 12
54.5%
ValueCountFrequency (%)
150 1
4.5%
186 1
4.5%
217 1
4.5%
230 1
4.5%
356 1
4.5%
360 1
4.5%
463 1
4.5%
470 1
4.5%
560 1
4.5%
593 1
4.5%
ValueCountFrequency (%)
9404 1
4.5%
3352 1
4.5%
2444 1
4.5%
2330 1
4.5%
1635 1
4.5%
1381 1
4.5%
1130 1
4.5%
850 1
4.5%
844 1
4.5%
820 1
4.5%

시설물
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct11
Distinct (%)55.0%
Missing2
Missing (%)9.1%
Infinite0
Infinite (%)0.0%
Mean7.7
Minimum1
Maximum28
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size326.0 B
2024-04-21T20:11:01.271163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12.75
median6.5
Q310.25
95-th percentile17.55
Maximum28
Range27
Interquartile range (IQR)7.5

Descriptive statistics

Standard deviation6.6578011
Coefficient of variation (CV)0.8646495
Kurtosis3.4158409
Mean7.7
Median Absolute Deviation (MAD)4
Skewness1.5687211
Sum154
Variance44.326316
MonotonicityNot monotonic
2024-04-21T20:11:01.633998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1 4
18.2%
6 3
13.6%
13 2
9.1%
3 2
9.1%
10 2
9.1%
7 2
9.1%
2 1
 
4.5%
17 1
 
4.5%
8 1
 
4.5%
28 1
 
4.5%
(Missing) 2
9.1%
ValueCountFrequency (%)
1 4
18.2%
2 1
 
4.5%
3 2
9.1%
6 3
13.6%
7 2
9.1%
8 1
 
4.5%
10 2
9.1%
11 1
 
4.5%
13 2
9.1%
17 1
 
4.5%
ValueCountFrequency (%)
28 1
 
4.5%
17 1
 
4.5%
13 2
9.1%
11 1
 
4.5%
10 2
9.1%
8 1
 
4.5%
7 2
9.1%
6 3
13.6%
3 2
9.1%
2 1
 
4.5%

Interactions

2024-04-21T20:10:51.519979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:10:44.484951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:10:45.870330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:10:47.333148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:10:48.716178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:10:50.126004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:10:51.750693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:10:44.707896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:10:46.107390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:10:47.558532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:10:48.947438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:10:50.354561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:10:52.000226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:10:44.954829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:10:46.365313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:10:47.806930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:10:49.196721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:10:50.600131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:10:52.234079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:10:45.179702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:10:46.602546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:10:48.028784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:10:49.428187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:10:50.827986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:10:52.663432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:10:45.413251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:10:46.849253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:10:48.261641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:10:49.660688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:10:51.060961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:10:52.893704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:10:45.638030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:10:47.086550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:10:48.484819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:10:49.891611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:10:51.287481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-21T20:11:01.878531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번명칭위치(도로명 주소)면적(제곱미터)합계교목관목시설물
연번1.0001.0001.0000.1690.5920.4660.5920.000
명칭1.0001.0001.0001.0001.0001.0001.0001.000
위치(도로명 주소)1.0001.0001.0001.0001.0001.0001.0001.000
면적(제곱미터)0.1691.0001.0001.0000.8600.8150.8600.825
합계0.5921.0001.0000.8601.0000.8451.0000.762
교목0.4661.0001.0000.8150.8451.0000.8450.740
관목0.5921.0001.0000.8601.0000.8451.0000.762
시설물0.0001.0001.0000.8250.7620.7400.7621.000
2024-04-21T20:11:02.172814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번면적(제곱미터)합계교목관목시설물
연번1.000-0.260-0.0540.204-0.0540.107
면적(제곱미터)-0.2601.0000.7220.1470.7220.399
합계-0.0540.7221.0000.3041.0000.603
교목0.2040.1470.3041.0000.3040.306
관목-0.0540.7221.0000.3041.0000.603
시설물0.1070.3990.6030.3060.6031.000

Missing values

2024-04-21T20:10:53.224392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T20:10:53.635128image/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.
2024-04-21T20:10:53.932602image/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
1213부산광역시 남구 문현동쉼터부산광역시 남구 문현동80-14번지38035713561
1314부산광역시 남구 문현1동 방재공원부산광역시 남구 문현동 54-275048212470<NA>
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