Overview

Dataset statistics

Number of variables13
Number of observations46
Missing cells6
Missing cells (%)1.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.2 KiB
Average record size in memory115.9 B

Variable types

Numeric9
Categorical1
Text3

Dataset

Description부산광역시연제구_화단및쌈지쉼터현황_20230409
Author부산광역시 연제구
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=15037646

Alerts

기관명 has constant value ""Constant
연번 is highly overall correlated with 시설물(종) and 1 other fieldsHigh correlation
면적 is highly overall correlated with 수목(합계) and 3 other fieldsHigh correlation
수목(합계) is highly overall correlated with 면적 and 2 other fieldsHigh correlation
수목(종) is highly overall correlated with 면적 and 1 other fieldsHigh correlation
수목(교목) is highly overall correlated with 면적 and 2 other fieldsHigh correlation
수목(관목) is highly overall correlated with 면적 and 1 other fieldsHigh correlation
시설물(종) is highly overall correlated with 연번 and 1 other fieldsHigh correlation
시설물(점) is highly overall correlated with 시설물(종)High correlation
조성년도 is highly overall correlated with 연번High correlation
소재지 has 6 (13.0%) missing valuesMissing
연번 has unique valuesUnique
명칭 has unique valuesUnique
수목(종) has 1 (2.2%) zerosZeros
수목(교목) has 10 (21.7%) zerosZeros
수목(관목) has 3 (6.5%) zerosZeros
시설물(종) has 30 (65.2%) zerosZeros
시설물(점) has 30 (65.2%) zerosZeros

Reproduction

Analysis started2023-12-10 16:45:56.098473
Analysis finished2023-12-10 16:46:05.495545
Duration9.4 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct46
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.5
Minimum1
Maximum46
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size546.0 B
2023-12-11T01:46:05.616944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.25
Q112.25
median23.5
Q334.75
95-th percentile43.75
Maximum46
Range45
Interquartile range (IQR)22.5

Descriptive statistics

Standard deviation13.422618
Coefficient of variation (CV)0.57117522
Kurtosis-1.2
Mean23.5
Median Absolute Deviation (MAD)11.5
Skewness0
Sum1081
Variance180.16667
MonotonicityStrictly increasing
2023-12-11T01:46:05.827663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
1 1
 
2.2%
36 1
 
2.2%
27 1
 
2.2%
28 1
 
2.2%
29 1
 
2.2%
30 1
 
2.2%
31 1
 
2.2%
32 1
 
2.2%
33 1
 
2.2%
34 1
 
2.2%
Other values (36) 36
78.3%
ValueCountFrequency (%)
1 1
2.2%
2 1
2.2%
3 1
2.2%
4 1
2.2%
5 1
2.2%
6 1
2.2%
7 1
2.2%
8 1
2.2%
9 1
2.2%
10 1
2.2%
ValueCountFrequency (%)
46 1
2.2%
45 1
2.2%
44 1
2.2%
43 1
2.2%
42 1
2.2%
41 1
2.2%
40 1
2.2%
39 1
2.2%
38 1
2.2%
37 1
2.2%

기관명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size500.0 B
연제구
46 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
연제구 46
100.0%

Length

2023-12-11T01:46:06.089313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:46:06.223250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
연제구 46
100.0%

명칭
Text

UNIQUE 

Distinct46
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size500.0 B
2023-12-11T01:46:06.504527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length11
Mean length8.4782609
Min length4

Characters and Unicode

Total characters390
Distinct characters118
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique46 ?
Unique (%)100.0%

Sample

1st row한양동산
2nd row해맞이쉼터
3rd row연제쉼터
4th row밤골쉼터
5th row수련쉼터
ValueCountFrequency (%)
화단 23
23.5%
옆 5
 
5.1%
앞 4
 
4.1%
쉼터 3
 
3.1%
입구 2
 
2.0%
도로개설지 2
 
2.0%
통학로 2
 
2.0%
울타리 1
 
1.0%
교대로 1
 
1.0%
온천천공원길 1
 
1.0%
Other values (54) 54
55.1%
2023-12-11T01:46:07.114527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
52
 
13.3%
화 27
 
6.9%
단 26
 
6.7%
로 19
 
4.9%
쉼 12
 
3.1%
터 12
 
3.1%
교 8
 
2.1%
산 8
 
2.1%
거 8
 
2.1%
제 7
 
1.8%
Other values (108) 211
54.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 331
84.9%
Space Separator 52
 
13.3%
Decimal Number 2
 
0.5%
Uppercase Letter 2
 
0.5%
Open Punctuation 1
 
0.3%
Lowercase Letter 1
 
0.3%
Close Punctuation 1
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
화 27
 
8.2%
단 26
 
7.9%
로 19
 
5.7%
쉼 12
 
3.6%
터 12
 
3.6%
교 8
 
2.4%
산 8
 
2.4%
거 8
 
2.4%
제 7
 
2.1%
아 7
 
2.1%
Other values (100) 197
59.5%
Decimal Number
ValueCountFrequency (%)
3 1
50.0%
1 1
50.0%
Uppercase Letter
ValueCountFrequency (%)
K 1
50.0%
S 1
50.0%
Space Separator
ValueCountFrequency (%)
52
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 331
84.9%
Common 56
 
14.4%
Latin 3
 
0.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
화 27
 
8.2%
단 26
 
7.9%
로 19
 
5.7%
쉼 12
 
3.6%
터 12
 
3.6%
교 8
 
2.4%
산 8
 
2.4%
거 8
 
2.4%
제 7
 
2.1%
아 7
 
2.1%
Other values (100) 197
59.5%
Common
ValueCountFrequency (%)
52
92.9%
( 1
 
1.8%
3 1
 
1.8%
1 1
 
1.8%
) 1
 
1.8%
Latin
ValueCountFrequency (%)
e 1
33.3%
K 1
33.3%
S 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 331
84.9%
ASCII 59
 
15.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
52
88.1%
( 1
 
1.7%
3 1
 
1.7%
1 1
 
1.7%
e 1
 
1.7%
K 1
 
1.7%
S 1
 
1.7%
) 1
 
1.7%
Hangul
ValueCountFrequency (%)
화 27
 
8.2%
단 26
 
7.9%
로 19
 
5.7%
쉼 12
 
3.6%
터 12
 
3.6%
교 8
 
2.4%
산 8
 
2.4%
거 8
 
2.4%
제 7
 
2.1%
아 7
 
2.1%
Other values (100) 197
59.5%

소재지
Text

MISSING 

Distinct40
Distinct (%)100.0%
Missing6
Missing (%)13.0%
Memory size500.0 B
2023-12-11T01:46:07.514016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length13
Mean length9.975
Min length3

Characters and Unicode

Total characters399
Distinct characters83
Distinct categories6 ?
Distinct scripts3 ?
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 row거제1동 129-1
2nd row거제4동 681-25
3rd row연산2동 1607-1
4th row연산4동 641-6
5th row연산4동 700-2
ValueCountFrequency (%)
연산동 5
 
6.2%
거제동 4
 
4.9%
연산4동 4
 
4.9%
옆 4
 
4.9%
거제1동 3
 
3.7%
연산3동 2
 
2.5%
거제4동 2
 
2.5%
반도보라 1
 
1.2%
아파트 1
 
1.2%
557-1 1
 
1.2%
Other values (54) 54
66.7%
2023-12-11T01:46:08.124354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
41
 
10.3%
1 34
 
8.5%
동 29
 
7.3%
- 24
 
6.0%
2 20
 
5.0%
산 20
 
5.0%
연 18
 
4.5%
3 16
 
4.0%
4 15
 
3.8%
0 15
 
3.8%
Other values (73) 167
41.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 187
46.9%
Decimal Number 144
36.1%
Space Separator 41
 
10.3%
Dash Punctuation 24
 
6.0%
Uppercase Letter 2
 
0.5%
Other Punctuation 1
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 29
15.5%
산 20
 
10.7%
연 18
 
9.6%
거 12
 
6.4%
제 12
 
6.4%
로 6
 
3.2%
옆 5
 
2.7%
교 5
 
2.7%
아 4
 
2.1%
파 4
 
2.1%
Other values (58) 72
38.5%
Decimal Number
ValueCountFrequency (%)
1 34
23.6%
2 20
13.9%
3 16
11.1%
4 15
10.4%
0 15
10.4%
6 12
 
8.3%
5 11
 
7.6%
8 8
 
5.6%
9 7
 
4.9%
7 6
 
4.2%
Uppercase Letter
ValueCountFrequency (%)
K 1
50.0%
S 1
50.0%
Space Separator
ValueCountFrequency (%)
41
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 24
100.0%
Other Punctuation
ValueCountFrequency (%)
@ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 210
52.6%
Hangul 187
46.9%
Latin 2
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 29
15.5%
산 20
 
10.7%
연 18
 
9.6%
거 12
 
6.4%
제 12
 
6.4%
로 6
 
3.2%
옆 5
 
2.7%
교 5
 
2.7%
아 4
 
2.1%
파 4
 
2.1%
Other values (58) 72
38.5%
Common
ValueCountFrequency (%)
41
19.5%
1 34
16.2%
- 24
11.4%
2 20
9.5%
3 16
 
7.6%
4 15
 
7.1%
0 15
 
7.1%
6 12
 
5.7%
5 11
 
5.2%
8 8
 
3.8%
Other values (3) 14
 
6.7%
Latin
ValueCountFrequency (%)
K 1
50.0%
S 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 212
53.1%
Hangul 187
46.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
41
19.3%
1 34
16.0%
- 24
11.3%
2 20
9.4%
3 16
 
7.5%
4 15
 
7.1%
0 15
 
7.1%
6 12
 
5.7%
5 11
 
5.2%
8 8
 
3.8%
Other values (5) 16
 
7.5%
Hangul
ValueCountFrequency (%)
동 29
15.5%
산 20
 
10.7%
연 18
 
9.6%
거 12
 
6.4%
제 12
 
6.4%
로 6
 
3.2%
옆 5
 
2.7%
교 5
 
2.7%
아 4
 
2.1%
파 4
 
2.1%
Other values (58) 72
38.5%

면적
Real number (ℝ)

HIGH CORRELATION 

Distinct39
Distinct (%)84.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean504.63043
Minimum12
Maximum6020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size546.0 B
2023-12-11T01:46:08.432311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile18
Q147
median82.5
Q3291.25
95-th percentile2528.25
Maximum6020
Range6008
Interquartile range (IQR)244.25

Descriptive statistics

Standard deviation1176.2563
Coefficient of variation (CV)2.3309262
Kurtosis12.924216
Mean504.63043
Median Absolute Deviation (MAD)57.5
Skewness3.5224181
Sum23213
Variance1383578.9
MonotonicityNot monotonic
2023-12-11T01:46:08.695438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
50 4
 
8.7%
18 2
 
4.3%
40 2
 
4.3%
45 2
 
4.3%
80 2
 
4.3%
140 1
 
2.2%
110 1
 
2.2%
305 1
 
2.2%
62 1
 
2.2%
320 1
 
2.2%
Other values (29) 29
63.0%
ValueCountFrequency (%)
12 1
 
2.2%
15 1
 
2.2%
18 2
4.3%
25 1
 
2.2%
35 1
 
2.2%
37 1
 
2.2%
40 2
4.3%
45 2
4.3%
46 1
 
2.2%
50 4
8.7%
ValueCountFrequency (%)
6020 1
2.2%
4500 1
2.2%
2530 1
2.2%
2523 1
2.2%
1620 1
2.2%
820 1
2.2%
579 1
2.2%
550 1
2.2%
375 1
2.2%
345 1
2.2%

수목(합계)
Real number (ℝ)

HIGH CORRELATION 

Distinct45
Distinct (%)97.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1264.1739
Minimum1
Maximum9081
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size546.0 B
2023-12-11T01:46:09.308118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6.5
Q1129.75
median624
Q31329
95-th percentile4771.25
Maximum9081
Range9080
Interquartile range (IQR)1199.25

Descriptive statistics

Standard deviation1797.0609
Coefficient of variation (CV)1.4215298
Kurtosis8.2380355
Mean1264.1739
Median Absolute Deviation (MAD)546.5
Skewness2.6764088
Sum58152
Variance3229427.7
MonotonicityNot monotonic
2023-12-11T01:46:09.527595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
1329 2
 
4.3%
1397 1
 
2.2%
65 1
 
2.2%
380 1
 
2.2%
235 1
 
2.2%
456 1
 
2.2%
1081 1
 
2.2%
129 1
 
2.2%
2701 1
 
2.2%
993 1
 
2.2%
Other values (35) 35
76.1%
ValueCountFrequency (%)
1 1
2.2%
3 1
2.2%
5 1
2.2%
11 1
2.2%
51 1
2.2%
64 1
2.2%
65 1
2.2%
74 1
2.2%
81 1
2.2%
99 1
2.2%
ValueCountFrequency (%)
9081 1
2.2%
6293 1
2.2%
5056 1
2.2%
3917 1
2.2%
3664 1
2.2%
2701 1
2.2%
2326 1
2.2%
2314 1
2.2%
1974 1
2.2%
1658 1
2.2%

수목(수종)
Text

Distinct24
Distinct (%)52.2%
Missing0
Missing (%)0.0%
Memory size500.0 B
2023-12-11T01:46:09.783263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length6.1086957
Min length3

Characters and Unicode

Total characters281
Distinct characters51
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

Unique16 ?
Unique (%)34.8%

Sample

1st row 홍가시나무
2nd row 느티나무
3rd row 느티나무
4th row 느티나무
5th row 느티나무
ValueCountFrequency (%)
느티나무 10
20.0%
금목서 5
10.0%
배롱나무 5
10.0%
홍가시나무 4
 
8.0%
등 4
 
8.0%
꽃댕강나무 3
 
6.0%
이팝나무 3
 
6.0%
가이즈까향나무 2
 
4.0%
왕벚나무 2
 
4.0%
살구나무 1
 
2.0%
Other values (11) 11
22.0%
2023-12-11T01:46:10.237446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
90
32.0%
나 32
 
11.4%
무 32
 
11.4%
느 10
 
3.6%
티 10
 
3.6%
홍 7
 
2.5%
가 7
 
2.5%
이 6
 
2.1%
배 5
 
1.8%
롱 5
 
1.8%
Other values (41) 77
27.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 191
68.0%
Space Separator 90
32.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
나 32
16.8%
무 32
16.8%
느 10
 
5.2%
티 10
 
5.2%
홍 7
 
3.7%
가 7
 
3.7%
이 6
 
3.1%
배 5
 
2.6%
롱 5
 
2.6%
시 5
 
2.6%
Other values (40) 72
37.7%
Space Separator
ValueCountFrequency (%)
90
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 191
68.0%
Common 90
32.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
나 32
16.8%
무 32
16.8%
느 10
 
5.2%
티 10
 
5.2%
홍 7
 
3.7%
가 7
 
3.7%
이 6
 
3.1%
배 5
 
2.6%
롱 5
 
2.6%
시 5
 
2.6%
Other values (40) 72
37.7%
Common
ValueCountFrequency (%)
90
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 191
68.0%
ASCII 90
32.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
90
100.0%
Hangul
ValueCountFrequency (%)
나 32
16.8%
무 32
16.8%
느 10
 
5.2%
티 10
 
5.2%
홍 7
 
3.7%
가 7
 
3.7%
이 6
 
3.1%
배 5
 
2.6%
롱 5
 
2.6%
시 5
 
2.6%
Other values (40) 72
37.7%

수목(종)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct17
Distinct (%)37.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.4565217
Minimum0
Maximum31
Zeros1
Zeros (%)2.2%
Negative0
Negative (%)0.0%
Memory size546.0 B
2023-12-11T01:46:10.456034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12.25
median5
Q38
95-th percentile15
Maximum31
Range31
Interquartile range (IQR)5.75

Descriptive statistics

Standard deviation5.7337462
Coefficient of variation (CV)0.88805497
Kurtosis6.2067762
Mean6.4565217
Median Absolute Deviation (MAD)3
Skewness2.0321587
Sum297
Variance32.875845
MonotonicityNot monotonic
2023-12-11T01:46:10.698103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
2 7
15.2%
3 7
15.2%
8 5
10.9%
6 4
8.7%
1 4
8.7%
7 3
6.5%
4 3
6.5%
14 2
 
4.3%
5 2
 
4.3%
15 2
 
4.3%
Other values (7) 7
15.2%
ValueCountFrequency (%)
0 1
 
2.2%
1 4
8.7%
2 7
15.2%
3 7
15.2%
4 3
6.5%
5 2
 
4.3%
6 4
8.7%
7 3
6.5%
8 5
10.9%
10 1
 
2.2%
ValueCountFrequency (%)
31 1
 
2.2%
16 1
 
2.2%
15 2
 
4.3%
14 2
 
4.3%
13 1
 
2.2%
12 1
 
2.2%
11 1
 
2.2%
10 1
 
2.2%
8 5
10.9%
7 3
6.5%

수목(교목)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct23
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.76087
Minimum0
Maximum648
Zeros10
Zeros (%)21.7%
Negative0
Negative (%)0.0%
Memory size546.0 B
2023-12-11T01:46:10.894495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.5
median7
Q321
95-th percentile56
Maximum648
Range648
Interquartile range (IQR)19.5

Descriptive statistics

Standard deviation99.007785
Coefficient of variation (CV)3.2186276
Kurtosis35.498155
Mean30.76087
Median Absolute Deviation (MAD)7
Skewness5.7801502
Sum1415
Variance9802.5415
MonotonicityNot monotonic
2023-12-11T01:46:11.094493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 10
21.7%
15 5
 
10.9%
6 4
 
8.7%
3 3
 
6.5%
4 2
 
4.3%
1 2
 
4.3%
7 2
 
4.3%
23 2
 
4.3%
14 2
 
4.3%
26 1
 
2.2%
Other values (13) 13
28.3%
ValueCountFrequency (%)
0 10
21.7%
1 2
 
4.3%
3 3
 
6.5%
4 2
 
4.3%
5 1
 
2.2%
6 4
 
8.7%
7 2
 
4.3%
8 1
 
2.2%
10 1
 
2.2%
11 1
 
2.2%
ValueCountFrequency (%)
648 1
2.2%
221 1
2.2%
58 1
2.2%
50 1
2.2%
46 1
2.2%
39 1
2.2%
31 1
2.2%
29 1
2.2%
27 1
2.2%
26 1
2.2%

수목(관목)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct43
Distinct (%)93.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1207.0217
Minimum0
Maximum8860
Zeros3
Zeros (%)6.5%
Negative0
Negative (%)0.0%
Memory size546.0 B
2023-12-11T01:46:11.299688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.25
Q1119
median615
Q31318.5
95-th percentile4941.5
Maximum8860
Range8860
Interquartile range (IQR)1199.5

Descriptive statistics

Standard deviation1746.8592
Coefficient of variation (CV)1.4472475
Kurtosis8.9492126
Mean1207.0217
Median Absolute Deviation (MAD)541
Skewness2.816606
Sum55523
Variance3051517.2
MonotonicityNot monotonic
2023-12-11T01:46:11.486552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
0 3
 
6.5%
74 2
 
4.3%
1371 1
 
2.2%
65 1
 
2.2%
235 1
 
2.2%
450 1
 
2.2%
1075 1
 
2.2%
125 1
 
2.2%
2695 1
 
2.2%
993 1
 
2.2%
Other values (33) 33
71.7%
ValueCountFrequency (%)
0 3
6.5%
1 1
 
2.2%
41 1
 
2.2%
64 1
 
2.2%
65 1
 
2.2%
74 2
4.3%
98 1
 
2.2%
112 1
 
2.2%
117 1
 
2.2%
125 1
 
2.2%
ValueCountFrequency (%)
8860 1
2.2%
6270 1
2.2%
5372 1
2.2%
3650 1
2.2%
2695 1
2.2%
2314 1
2.2%
2303 1
2.2%
1924 1
2.2%
1710 1
2.2%
1600 1
2.2%

시설물(종)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.73913043
Minimum0
Maximum6
Zeros30
Zeros (%)65.2%
Negative0
Negative (%)0.0%
Memory size546.0 B
2023-12-11T01:46:11.627677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3.75
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.3405601
Coefficient of variation (CV)1.813699
Kurtosis5.131252
Mean0.73913043
Median Absolute Deviation (MAD)0
Skewness2.238308
Sum34
Variance1.7971014
MonotonicityNot monotonic
2023-12-11T01:46:11.766400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 30
65.2%
1 8
 
17.4%
2 3
 
6.5%
4 2
 
4.3%
3 2
 
4.3%
6 1
 
2.2%
ValueCountFrequency (%)
0 30
65.2%
1 8
 
17.4%
2 3
 
6.5%
3 2
 
4.3%
4 2
 
4.3%
6 1
 
2.2%
ValueCountFrequency (%)
6 1
 
2.2%
4 2
 
4.3%
3 2
 
4.3%
2 3
 
6.5%
1 8
 
17.4%
0 30
65.2%

시설물(점)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)17.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8695652
Minimum0
Maximum17
Zeros30
Zeros (%)65.2%
Negative0
Negative (%)0.0%
Memory size546.0 B
2023-12-11T01:46:11.911427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile12
Maximum17
Range17
Interquartile range (IQR)1

Descriptive statistics

Standard deviation3.9362063
Coefficient of variation (CV)2.1054127
Kurtosis5.9951203
Mean1.8695652
Median Absolute Deviation (MAD)0
Skewness2.5244759
Sum86
Variance15.49372
MonotonicityNot monotonic
2023-12-11T01:46:12.050807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 30
65.2%
1 5
 
10.9%
2 3
 
6.5%
6 3
 
6.5%
13 2
 
4.3%
5 1
 
2.2%
17 1
 
2.2%
9 1
 
2.2%
ValueCountFrequency (%)
0 30
65.2%
1 5
 
10.9%
2 3
 
6.5%
5 1
 
2.2%
6 3
 
6.5%
9 1
 
2.2%
13 2
 
4.3%
17 1
 
2.2%
ValueCountFrequency (%)
17 1
 
2.2%
13 2
 
4.3%
9 1
 
2.2%
6 3
 
6.5%
5 1
 
2.2%
2 3
 
6.5%
1 5
 
10.9%
0 30
65.2%

조성년도
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)43.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2009.8043
Minimum1978
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size546.0 B
2023-12-11T01:46:12.211512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1978
5-th percentile1997
Q12005
median2012.5
Q32015
95-th percentile2021
Maximum2021
Range43
Interquartile range (IQR)10

Descriptive statistics

Standard deviation9.4483851
Coefficient of variation (CV)0.0047011467
Kurtosis2.5033831
Mean2009.8043
Median Absolute Deviation (MAD)4.5
Skewness-1.3720739
Sum92451
Variance89.271981
MonotonicityNot monotonic
2023-12-11T01:46:12.420199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
2014 5
10.9%
2021 5
10.9%
2013 5
10.9%
2005 4
 
8.7%
2008 4
 
8.7%
1997 4
 
8.7%
2020 3
 
6.5%
2010 2
 
4.3%
2011 2
 
4.3%
2015 2
 
4.3%
Other values (10) 10
21.7%
ValueCountFrequency (%)
1978 1
 
2.2%
1983 1
 
2.2%
1997 4
8.7%
1999 1
 
2.2%
2002 1
 
2.2%
2004 1
 
2.2%
2005 4
8.7%
2008 4
8.7%
2009 1
 
2.2%
2010 2
4.3%
ValueCountFrequency (%)
2021 5
10.9%
2020 3
6.5%
2019 1
 
2.2%
2017 1
 
2.2%
2016 1
 
2.2%
2015 2
 
4.3%
2014 5
10.9%
2013 5
10.9%
2012 1
 
2.2%
2011 2
 
4.3%

Interactions

2023-12-11T01:46:03.998201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:56.517367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:57.267503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:58.276537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:59.164593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:59.996130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:01.167718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:02.036110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:02.912589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:04.095313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:56.587298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:57.370108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:58.356663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:59.246123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:00.106156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:01.266531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:02.123889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:03.020058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:04.185738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:56.676708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:57.471378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:58.447222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:59.338530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:00.191941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:01.355955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:02.216086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:03.145844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:04.289772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:56.763458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:57.566247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:58.527735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:59.451754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:00.569789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:01.452766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:02.295958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:03.241150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:04.394636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:56.853550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:57.672090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:58.626762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:59.531936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:00.683521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:01.566805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:02.405999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:03.368930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:04.508296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:56.953420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:57.784119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:58.720989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:59.620888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:00.803593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:01.671352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:02.506714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:03.490743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:04.616606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:57.026964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:57.925645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:58.822687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:59.695194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:00.896608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:01.761205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:02.588362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:03.610981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:04.739264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:57.110178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:58.064165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:58.933866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:59.782439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:00.987137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:01.849107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:02.680240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:03.739865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:04.881540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:57.192949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:58.181875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:59.063625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:45:59.870623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:01.079484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:01.944599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:02.800885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:46:03.866282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T01:46:12.607579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번명칭소재지면적수목(합계)수목(수종)수목(종)수목(교목)수목(관목)시설물(종)시설물(점)조성년도
연번1.0001.0001.0000.0000.1350.8510.4050.3380.1820.4340.1590.774
명칭1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
소재지1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
면적0.0001.0001.0001.0000.7980.9690.5160.9840.7790.7660.6050.641
수목(합계)0.1351.0001.0000.7981.0000.7640.6940.8590.9590.3870.5970.516
수목(수종)0.8511.0001.0000.9690.7641.0000.0000.0000.5050.0000.0000.639
수목(종)0.4051.0001.0000.5160.6940.0001.0000.1230.4120.5900.8310.555
수목(교목)0.3381.0001.0000.9840.8590.0000.1231.0001.0000.5210.4790.752
수목(관목)0.1821.0001.0000.7790.9590.5050.4121.0001.0000.4900.5070.565
시설물(종)0.4341.0001.0000.7660.3870.0000.5900.5210.4901.0000.8140.343
시설물(점)0.1591.0001.0000.6050.5970.0000.8310.4790.5070.8141.0000.000
조성년도0.7741.0001.0000.6410.5160.6390.5550.7520.5650.3430.0001.000
2023-12-11T01:46:12.821855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번면적수목(합계)수목(종)수목(교목)수목(관목)시설물(종)시설물(점)조성년도
연번1.000-0.1440.050-0.412-0.2100.074-0.506-0.4790.711
면적-0.1441.0000.6860.5210.6490.6760.3360.3220.020
수목(합계)0.0500.6861.0000.4840.5050.9960.2620.2300.354
수목(종)-0.4120.5210.4841.0000.7560.4600.4570.422-0.206
수목(교목)-0.2100.6490.5050.7561.0000.4740.3510.343-0.145
수목(관목)0.0740.6760.9960.4600.4741.0000.2660.2330.361
시설물(종)-0.5060.3360.2620.4570.3510.2661.0000.987-0.083
시설물(점)-0.4790.3220.2300.4220.3430.2330.9871.000-0.079
조성년도0.7110.0200.354-0.206-0.1450.361-0.083-0.0791.000

Missing values

2023-12-11T01:46:05.067997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T01:46:05.362027image/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

연번기관명명칭소재지면적수목(합계)수목(수종)수목(종)수목(교목)수목(관목)시설물(종)시설물(점)조성년도
01연제구한양동산거제1동 129-11401397홍가시나무14261371112009
12연제구해맞이쉼터거제4동 681-252502326느티나무8232303222008
23연제구연제쉼터연산2동 1607-158284느티나무54280162008
34연제구밤골쉼터연산4동 641-6186120느티나무781124131999
45연제구수련쉼터연산4동 700-212643느티나무63640112008
56연제구우물쉼터연산4동 1230-124985느티나무150152008
67연제구양지쌈지공원연산5동 1289-101591244느티나무8151229362005
78연제구연수쉼터연산3동 2005-564664영산홍6064112010
89연제구거울바위공원연산9동 산70-45791974느티나무165019244172004
910연제구금련쉼터연산3동 1811-39276587배롱나무615572112010
연번기관명명칭소재지면적수목(합계)수목(수종)수목(종)수목(교목)수목(관목)시설물(종)시설물(점)조성년도
3637연제구신리교차로변 화단연산동681-51565애기동백3065002016
3738연제구거제삼거리 화단<NA>501조형홍가시101002015
3839연제구연산홈플러스 앞 화단연산홈플러스75980홍가시나무20980002017
3940연제구종합운동장로 화단<NA>25239081이팝나무622188602132019
4041연제구중앙천로 도로개설지 화단<NA>37374배롱나무814360122020
4142연제구거성교차로 화단<NA>45001658팽나무10581600392020
4243연제구경상대학교 앞 화단<NA>168605금목서 등515590002020
4344연제구시청 옆 도로개설 화단<NA>35535금목서 등315520002021
4445연제구벽산 e메트로폴리스 화단거제동35-35506293메타세쿼이어 등8236270002021
4546연제구국세청(별관)화단연산동 243-1385803금목서 등23800002021