Overview

Dataset statistics

Number of variables13
Number of observations23
Missing cells6
Missing cells (%)2.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.6 KiB
Average record size in memory117.7 B

Variable types

Categorical3
Text3
Numeric7

Alerts

규모(㎡) is highly overall correlated with 종수 and 5 other fieldsHigh correlation
종수 is highly overall correlated with 규모(㎡) and 2 other fieldsHigh correlation
개체수 is highly overall correlated with 규모(㎡) and 2 other fieldsHigh correlation
사육사수 is highly overall correlated with 규모(㎡) and 3 other fieldsHigh correlation
정제우편번호 is highly overall correlated with 정제WGS84위도 and 2 other fieldsHigh correlation
정제WGS84위도 is highly overall correlated with 정제우편번호 and 2 other fieldsHigh correlation
정제WGS84경도 is highly overall correlated with 시군명High correlation
시군명 is highly overall correlated with 규모(㎡) and 5 other fieldsHigh correlation
운영구분 is highly overall correlated with 규모(㎡) and 2 other fieldsHigh correlation
수의사수 is highly overall correlated with 규모(㎡) and 5 other fieldsHigh correlation
수의사수 is highly imbalanced (61.7%)Imbalance
정제도로명주소 has 3 (13.0%) missing valuesMissing
종수 has 1 (4.3%) missing valuesMissing
개체수 has 1 (4.3%) missing valuesMissing
사육사수 has 1 (4.3%) missing valuesMissing
정제지번주소 has unique valuesUnique
규모(㎡) has unique valuesUnique
정제WGS84위도 has unique valuesUnique
정제WGS84경도 has unique valuesUnique

Reproduction

Analysis started2023-12-10 23:03:38.782809
Analysis finished2023-12-10 23:03:43.346855
Duration4.56 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군명
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)47.8%
Missing0
Missing (%)0.0%
Memory size316.0 B
고양시
4 
화성시
3 
평택시
3 
파주시
3 
용인시
2 
Other values (6)
8 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique4 ?
Unique (%)17.4%

Sample

1st row과천시
2nd row고양시
3rd row고양시
4th row고양시
5th row고양시

Common Values

ValueCountFrequency (%)
고양시 4
17.4%
화성시 3
13.0%
평택시 3
13.0%
파주시 3
13.0%
용인시 2
8.7%
가평군 2
8.7%
하남시 2
8.7%
과천시 1
 
4.3%
부천시 1
 
4.3%
오산시 1
 
4.3%

Length

2023-12-11T08:03:43.423663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
고양시 4
17.4%
화성시 3
13.0%
평택시 3
13.0%
파주시 3
13.0%
용인시 2
8.7%
가평군 2
8.7%
하남시 2
8.7%
과천시 1
 
4.3%
부천시 1
 
4.3%
오산시 1
 
4.3%

동물원명
Text

Distinct22
Distinct (%)95.7%
Missing0
Missing (%)0.0%
Memory size316.0 B
2023-12-11T08:03:43.590798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length14
Mean length7.6956522
Min length3

Characters and Unicode

Total characters177
Distinct characters92
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)91.3%

Sample

1st row서울대공원
2nd row배다골테마파크
3rd row해피쥬
4th row테마동물원쥬쥬
5th row수피아
ValueCountFrequency (%)
쥬라리움 2
 
6.9%
주렁주렁 2
 
6.9%
에버랜드동물원 1
 
3.4%
관광농원 1
 
3.4%
천원화조 1
 
3.4%
아프리카쥬 1
 
3.4%
테이블에이 1
 
3.4%
테마파크 1
 
3.4%
사파리체험 1
 
3.4%
아침고요가족동물원 1
 
3.4%
Other values (17) 17
58.6%
2023-12-11T08:03:43.923076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
쥬 6
 
3.4%
파 6
 
3.4%
아 6
 
3.4%
원 6
 
3.4%
6
 
3.4%
크 5
 
2.8%
리 5
 
2.8%
테 5
 
2.8%
동 5
 
2.8%
렁 4
 
2.3%
Other values (82) 123
69.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 168
94.9%
Space Separator 6
 
3.4%
Other Symbol 1
 
0.6%
Open Punctuation 1
 
0.6%
Close Punctuation 1
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
쥬 6
 
3.6%
파 6
 
3.6%
아 6
 
3.6%
원 6
 
3.6%
크 5
 
3.0%
리 5
 
3.0%
테 5
 
3.0%
동 5
 
3.0%
렁 4
 
2.4%
주 4
 
2.4%
Other values (78) 116
69.0%
Space Separator
ValueCountFrequency (%)
6
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 169
95.5%
Common 8
 
4.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
쥬 6
 
3.6%
파 6
 
3.6%
아 6
 
3.6%
원 6
 
3.6%
크 5
 
3.0%
리 5
 
3.0%
테 5
 
3.0%
동 5
 
3.0%
렁 4
 
2.4%
주 4
 
2.4%
Other values (79) 117
69.2%
Common
ValueCountFrequency (%)
6
75.0%
( 1
 
12.5%
) 1
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 168
94.9%
ASCII 8
 
4.5%
None 1
 
0.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
쥬 6
 
3.6%
파 6
 
3.6%
아 6
 
3.6%
원 6
 
3.6%
크 5
 
3.0%
리 5
 
3.0%
테 5
 
3.0%
동 5
 
3.0%
렁 4
 
2.4%
주 4
 
2.4%
Other values (78) 116
69.0%
ASCII
ValueCountFrequency (%)
6
75.0%
( 1
 
12.5%
) 1
 
12.5%
None
ValueCountFrequency (%)
㈜ 1
100.0%

정제도로명주소
Text

MISSING 

Distinct20
Distinct (%)100.0%
Missing3
Missing (%)13.0%
Memory size316.0 B
2023-12-11T08:03:44.130886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length20.5
Mean length19.55
Min length14

Characters and Unicode

Total characters391
Distinct characters96
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

Unique20 ?
Unique (%)100.0%

Sample

1st row경기도 과천시 대공원광장로 102
2nd row경기도 고양시 덕양구 원당로458번길 7-42
3rd row경기도 고양시 일산동구 강촌로26번길 7-4
4th row경기도 부천시 조마루로 2
5th row경기도 오산시 성호대로 141
ValueCountFrequency (%)
경기도 20
 
21.7%
평택시 3
 
3.3%
파주시 3
 
3.3%
화성시 3
 
3.3%
하남시 2
 
2.2%
탄현면 2
 
2.2%
10 2
 
2.2%
가평군 2
 
2.2%
고양시 2
 
2.2%
102 1
 
1.1%
Other values (52) 52
56.5%
2023-12-11T08:03:44.465253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
72
 
18.4%
도 21
 
5.4%
경 20
 
5.1%
기 20
 
5.1%
시 18
 
4.6%
로 17
 
4.3%
1 12
 
3.1%
2 11
 
2.8%
4 9
 
2.3%
길 8
 
2.0%
Other values (86) 183
46.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 248
63.4%
Space Separator 72
 
18.4%
Decimal Number 66
 
16.9%
Dash Punctuation 5
 
1.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 21
 
8.5%
경 20
 
8.1%
기 20
 
8.1%
시 18
 
7.3%
로 17
 
6.9%
길 8
 
3.2%
대 7
 
2.8%
면 6
 
2.4%
성 5
 
2.0%
평 5
 
2.0%
Other values (74) 121
48.8%
Decimal Number
ValueCountFrequency (%)
1 12
18.2%
2 11
16.7%
4 9
13.6%
5 6
9.1%
9 6
9.1%
0 6
9.1%
3 5
7.6%
6 4
 
6.1%
8 4
 
6.1%
7 3
 
4.5%
Space Separator
ValueCountFrequency (%)
72
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 248
63.4%
Common 143
36.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 21
 
8.5%
경 20
 
8.1%
기 20
 
8.1%
시 18
 
7.3%
로 17
 
6.9%
길 8
 
3.2%
대 7
 
2.8%
면 6
 
2.4%
성 5
 
2.0%
평 5
 
2.0%
Other values (74) 121
48.8%
Common
ValueCountFrequency (%)
72
50.3%
1 12
 
8.4%
2 11
 
7.7%
4 9
 
6.3%
5 6
 
4.2%
9 6
 
4.2%
0 6
 
4.2%
3 5
 
3.5%
- 5
 
3.5%
6 4
 
2.8%
Other values (2) 7
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 248
63.4%
ASCII 143
36.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
72
50.3%
1 12
 
8.4%
2 11
 
7.7%
4 9
 
6.3%
5 6
 
4.2%
9 6
 
4.2%
0 6
 
4.2%
3 5
 
3.5%
- 5
 
3.5%
6 4
 
2.8%
Other values (2) 7
 
4.9%
Hangul
ValueCountFrequency (%)
도 21
 
8.5%
경 20
 
8.1%
기 20
 
8.1%
시 18
 
7.3%
로 17
 
6.9%
길 8
 
3.2%
대 7
 
2.8%
면 6
 
2.4%
성 5
 
2.0%
평 5
 
2.0%
Other values (74) 121
48.8%

정제지번주소
Text

UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size316.0 B
2023-12-11T08:03:44.674697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length39
Median length28
Mean length23.478261
Min length17

Characters and Unicode

Total characters540
Distinct characters103
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique23 ?
Unique (%)100.0%

Sample

1st row경기도 과천시 막계동 159-1번지
2nd row경기도 고양시 덕양구 화정동 7-7
3rd row경기도 고양시 덕양구 화정동 6-5
4th row경기도 고양시 덕양구 관산동 290번지
5th row경기도 고양시 일산동구 백석동 1413-6번지 지층
ValueCountFrequency (%)
경기도 23
 
19.5%
고양시 4
 
3.4%
화성시 3
 
2.5%
덕양구 3
 
2.5%
파주시 3
 
2.5%
평택시 3
 
2.5%
하남시 2
 
1.7%
용인시 2
 
1.7%
탄현면 2
 
1.7%
가평군 2
 
1.7%
Other values (69) 71
60.2%
2023-12-11T08:03:45.024393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
95
 
17.6%
도 25
 
4.6%
기 25
 
4.6%
1 24
 
4.4%
경 23
 
4.3%
지 23
 
4.3%
시 22
 
4.1%
번 21
 
3.9%
동 16
 
3.0%
2 14
 
2.6%
Other values (93) 252
46.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 332
61.5%
Decimal Number 98
 
18.1%
Space Separator 95
 
17.6%
Dash Punctuation 13
 
2.4%
Math Symbol 1
 
0.2%
Other Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 25
 
7.5%
기 25
 
7.5%
경 23
 
6.9%
지 23
 
6.9%
시 22
 
6.6%
번 21
 
6.3%
동 16
 
4.8%
리 10
 
3.0%
산 7
 
2.1%
구 7
 
2.1%
Other values (79) 153
46.1%
Decimal Number
ValueCountFrequency (%)
1 24
24.5%
2 14
14.3%
0 10
10.2%
6 10
10.2%
4 9
 
9.2%
7 8
 
8.2%
5 8
 
8.2%
3 7
 
7.1%
9 7
 
7.1%
8 1
 
1.0%
Space Separator
ValueCountFrequency (%)
95
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 13
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 332
61.5%
Common 208
38.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 25
 
7.5%
기 25
 
7.5%
경 23
 
6.9%
지 23
 
6.9%
시 22
 
6.6%
번 21
 
6.3%
동 16
 
4.8%
리 10
 
3.0%
산 7
 
2.1%
구 7
 
2.1%
Other values (79) 153
46.1%
Common
ValueCountFrequency (%)
95
45.7%
1 24
 
11.5%
2 14
 
6.7%
- 13
 
6.2%
0 10
 
4.8%
6 10
 
4.8%
4 9
 
4.3%
7 8
 
3.8%
5 8
 
3.8%
3 7
 
3.4%
Other values (4) 10
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 332
61.5%
ASCII 208
38.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
95
45.7%
1 24
 
11.5%
2 14
 
6.7%
- 13
 
6.2%
0 10
 
4.8%
6 10
 
4.8%
4 9
 
4.3%
7 8
 
3.8%
5 8
 
3.8%
3 7
 
3.4%
Other values (4) 10
 
4.8%
Hangul
ValueCountFrequency (%)
도 25
 
7.5%
기 25
 
7.5%
경 23
 
6.9%
지 23
 
6.9%
시 22
 
6.6%
번 21
 
6.3%
동 16
 
4.8%
리 10
 
3.0%
산 7
 
2.1%
구 7
 
2.1%
Other values (79) 153
46.1%

운영구분
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)30.4%
Missing0
Missing (%)0.0%
Memory size316.0 B
민간
12 
민간(법인)
4 
법인
2 
개인
2 
공영
 
1
Other values (2)
2 

Length

Max length6
Median length2
Mean length2.8695652
Min length2

Unique

Unique3 ?
Unique (%)13.0%

Sample

1st row공영
2nd row민간
3rd row민간
4th row민간
5th row민간

Common Values

ValueCountFrequency (%)
민간 12
52.2%
민간(법인) 4
 
17.4%
법인 2
 
8.7%
개인 2
 
8.7%
공영 1
 
4.3%
협동조합 1
 
4.3%
민간법인 1
 
4.3%

Length

2023-12-11T08:03:45.168258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:03:45.278593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
민간 12
52.2%
민간(법인 4
 
17.4%
법인 2
 
8.7%
개인 2
 
8.7%
공영 1
 
4.3%
협동조합 1
 
4.3%
민간법인 1
 
4.3%

규모(㎡)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean168737.52
Minimum99
Maximum2420000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-11T08:03:45.376668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum99
5-th percentile125.4
Q1345
median1465
Q33419
95-th percentile1172494.5
Maximum2420000
Range2419901
Interquartile range (IQR)3074

Descriptive statistics

Standard deviation559145.4
Coefficient of variation (CV)3.3136993
Kurtosis13.207171
Mean168737.52
Median Absolute Deviation (MAD)1238
Skewness3.6209339
Sum3880963
Variance3.1264358 × 1011
MonotonicityNot monotonic
2023-12-11T08:03:45.483996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
2420000 1
 
4.3%
1653 1
 
4.3%
576 1
 
4.3%
3474 1
 
4.3%
695 1
 
4.3%
414 1
 
4.3%
123 1
 
4.3%
99 1
 
4.3%
300 1
 
4.3%
3319 1
 
4.3%
Other values (13) 13
56.5%
ValueCountFrequency (%)
99 1
4.3%
123 1
4.3%
147 1
4.3%
227 1
4.3%
300 1
4.3%
340 1
4.3%
350 1
4.3%
414 1
4.3%
465 1
4.3%
576 1
4.3%
ValueCountFrequency (%)
2420000 1
4.3%
1287000 1
4.3%
141945 1
4.3%
5551 1
4.3%
4727 1
4.3%
3474 1
4.3%
3364 1
4.3%
3319 1
4.3%
2698 1
4.3%
2031 1
4.3%

종수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct21
Distinct (%)95.5%
Missing1
Missing (%)4.3%
Infinite0
Infinite (%)0.0%
Mean66.772727
Minimum1
Maximum258
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-11T08:03:45.584037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.3
Q120.25
median42
Q3106.25
95-th percentile231.5
Maximum258
Range257
Interquartile range (IQR)86

Descriptive statistics

Standard deviation70.508987
Coefficient of variation (CV)1.0559549
Kurtosis2.3756025
Mean66.772727
Median Absolute Deviation (MAD)27.5
Skewness1.6676669
Sum1469
Variance4971.5173
MonotonicityNot monotonic
2023-12-11T08:03:45.683685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
11 2
 
8.7%
237 1
 
4.3%
50 1
 
4.3%
44 1
 
4.3%
110 1
 
4.3%
1 1
 
4.3%
24 1
 
4.3%
26 1
 
4.3%
29 1
 
4.3%
111 1
 
4.3%
Other values (11) 11
47.8%
ValueCountFrequency (%)
1 1
4.3%
5 1
4.3%
11 2
8.7%
18 1
4.3%
19 1
4.3%
24 1
4.3%
26 1
4.3%
29 1
4.3%
39 1
4.3%
41 1
4.3%
ValueCountFrequency (%)
258 1
4.3%
237 1
4.3%
127 1
4.3%
125 1
4.3%
111 1
4.3%
110 1
4.3%
95 1
4.3%
50 1
4.3%
45 1
4.3%
44 1
4.3%

개체수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct22
Distinct (%)100.0%
Missing1
Missing (%)4.3%
Infinite0
Infinite (%)0.0%
Mean1499.2273
Minimum6
Maximum22358
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-11T08:03:45.777883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile11.35
Q148
median184.5
Q3990
95-th percentile2083.55
Maximum22358
Range22352
Interquartile range (IQR)942

Descriptive statistics

Standard deviation4699.4856
Coefficient of variation (CV)3.1346052
Kurtosis21.134901
Mean1499.2273
Median Absolute Deviation (MAD)164.5
Skewness4.5607606
Sum32983
Variance22085165
MonotonicityNot monotonic
2023-12-11T08:03:45.886584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
2112 1
 
4.3%
266 1
 
4.3%
219 1
 
4.3%
22358 1
 
4.3%
40 1
 
4.3%
11 1
 
4.3%
44 1
 
4.3%
60 1
 
4.3%
112 1
 
4.3%
770 1
 
4.3%
Other values (12) 12
52.2%
ValueCountFrequency (%)
6 1
4.3%
11 1
4.3%
18 1
4.3%
37 1
4.3%
40 1
4.3%
44 1
4.3%
60 1
4.3%
110 1
4.3%
112 1
4.3%
119 1
4.3%
ValueCountFrequency (%)
22358 1
4.3%
2112 1
4.3%
1543 1
4.3%
1383 1
4.3%
1364 1
4.3%
1023 1
4.3%
891 1
4.3%
770 1
4.3%
347 1
4.3%
266 1
4.3%

수의사수
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)17.4%
Missing0
Missing (%)0.0%
Memory size316.0 B
1
20 
9
 
1
<NA>
 
1
5
 
1

Length

Max length4
Median length1
Mean length1.1304348
Min length1

Unique

Unique3 ?
Unique (%)13.0%

Sample

1st row9
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 20
87.0%
9 1
 
4.3%
<NA> 1
 
4.3%
5 1
 
4.3%

Length

2023-12-11T08:03:46.006876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:03:46.099268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 20
87.0%
9 1
 
4.3%
na 1
 
4.3%
5 1
 
4.3%

사육사수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct11
Distinct (%)50.0%
Missing1
Missing (%)4.3%
Infinite0
Infinite (%)0.0%
Mean9.5909091
Minimum1
Maximum59
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-11T08:03:46.173144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median4.5
Q39.5
95-th percentile53.95
Maximum59
Range58
Interquartile range (IQR)8.5

Descriptive statistics

Standard deviation16.055437
Coefficient of variation (CV)1.6740266
Kurtosis6.6829883
Mean9.5909091
Median Absolute Deviation (MAD)3.5
Skewness2.7133366
Sum211
Variance257.77706
MonotonicityNot monotonic
2023-12-11T08:03:46.267161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1 8
34.8%
8 2
 
8.7%
10 2
 
8.7%
6 2
 
8.7%
2 2
 
8.7%
56 1
 
4.3%
15 1
 
4.3%
59 1
 
4.3%
11 1
 
4.3%
7 1
 
4.3%
ValueCountFrequency (%)
1 8
34.8%
2 2
 
8.7%
3 1
 
4.3%
6 2
 
8.7%
7 1
 
4.3%
8 2
 
8.7%
10 2
 
8.7%
11 1
 
4.3%
15 1
 
4.3%
56 1
 
4.3%
ValueCountFrequency (%)
59 1
4.3%
56 1
4.3%
15 1
4.3%
11 1
4.3%
10 2
8.7%
8 2
8.7%
7 1
4.3%
6 2
8.7%
3 1
4.3%
2 2
8.7%

정제우편번호
Real number (ℝ)

HIGH CORRELATION 

Distinct22
Distinct (%)95.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14445.826
Minimum10288
Maximum18516
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-11T08:03:46.391127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10288
5-th percentile10429.8
Q110883.5
median13829
Q317785.5
95-th percentile18495.1
Maximum18516
Range8228
Interquartile range (IQR)6902

Descriptive statistics

Standard deviation3279.129
Coefficient of variation (CV)0.22699491
Kurtosis-1.824953
Mean14445.826
Median Absolute Deviation (MAD)3347
Skewness0.014865855
Sum332254
Variance10752687
MonotonicityNot monotonic
2023-12-11T08:03:46.495064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
10482 2
 
8.7%
13829 1
 
4.3%
17854 1
 
4.3%
12912 1
 
4.3%
12942 1
 
4.3%
12461 1
 
4.3%
17930 1
 
4.3%
10908 1
 
4.3%
10859 1
 
4.3%
10858 1
 
4.3%
Other values (12) 12
52.2%
ValueCountFrequency (%)
10288 1
4.3%
10424 1
4.3%
10482 2
8.7%
10858 1
4.3%
10859 1
4.3%
10908 1
4.3%
12447 1
4.3%
12461 1
4.3%
12912 1
4.3%
12942 1
4.3%
ValueCountFrequency (%)
18516 1
4.3%
18497 1
4.3%
18478 1
4.3%
18132 1
4.3%
17930 1
4.3%
17854 1
4.3%
17717 1
4.3%
17558 1
4.3%
17065 1
4.3%
17023 1
4.3%

정제WGS84위도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.421891
Minimum36.989812
Maximum37.791436
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-11T08:03:46.602262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.989812
5-th percentile36.9928
Q137.17761
median37.499869
Q337.665479
95-th percentile37.786156
Maximum37.791436
Range0.80162419
Interquartile range (IQR)0.48786987

Descriptive statistics

Standard deviation0.28082874
Coefficient of variation (CV)0.0075043973
Kurtosis-1.5052979
Mean37.421891
Median Absolute Deviation (MAD)0.22484469
Skewness-0.24431407
Sum860.7035
Variance0.078864781
MonotonicityNot monotonic
2023-12-11T08:03:46.727433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
37.4364431708 1
 
4.3%
37.6309654088 1
 
4.3%
37.5669131698 1
 
4.3%
37.5436331659 1
 
4.3%
37.6845728831 1
 
4.3%
36.9898118847 1
 
4.3%
37.7163820015 1
 
4.3%
37.7914360764 1
 
4.3%
37.7904129369 1
 
4.3%
37.747848038 1
 
4.3%
Other values (13) 13
56.5%
ValueCountFrequency (%)
36.9898118847 1
4.3%
36.9920854519 1
4.3%
36.9992291365 1
4.3%
37.0814485091 1
4.3%
37.1490352115 1
4.3%
37.1669505985 1
4.3%
37.1882686427 1
4.3%
37.1971953981 1
4.3%
37.2750244068 1
4.3%
37.2899600847 1
4.3%
ValueCountFrequency (%)
37.7914360764 1
4.3%
37.7904129369 1
4.3%
37.747848038 1
4.3%
37.7163820015 1
4.3%
37.689409561 1
4.3%
37.6845728831 1
4.3%
37.6463860953 1
4.3%
37.6309654088 1
4.3%
37.6302175294 1
4.3%
37.5669131698 1
4.3%

정제WGS84경도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.02066
Minimum126.68506
Maximum127.52726
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-11T08:03:46.829594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.68506
5-th percentile126.70104
Q1126.84756
median127.07766
Q3127.15904
95-th percentile127.34668
Maximum127.52726
Range0.84220131
Interquartile range (IQR)0.31148234

Descriptive statistics

Standard deviation0.22133781
Coefficient of variation (CV)0.0017425339
Kurtosis-0.35233253
Mean127.02066
Median Absolute Deviation (MAD)0.14598575
Skewness0.27208502
Sum2921.4751
Variance0.048990425
MonotonicityNot monotonic
2023-12-11T08:03:46.925894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
127.0141028429 1
 
4.3%
126.8475893325 1
 
4.3%
127.1971054253 1
 
4.3%
127.2236432499 1
 
4.3%
127.5272649073 1
 
4.3%
126.9132829131 1
 
4.3%
126.7620412209 1
 
4.3%
126.6962394881 1
 
4.3%
126.6850635963 1
 
4.3%
127.3603473317 1
 
4.3%
Other values (13) 13
56.5%
ValueCountFrequency (%)
126.6850635963 1
4.3%
126.6962394881 1
4.3%
126.7442661118 1
4.3%
126.7620412209 1
4.3%
126.7796354328 1
4.3%
126.8475342462 1
4.3%
126.8475893325 1
4.3%
126.8549386073 1
4.3%
126.9132829131 1
4.3%
126.9769963722 1
4.3%
ValueCountFrequency (%)
127.5272649073 1
4.3%
127.3603473317 1
4.3%
127.2236432499 1
4.3%
127.216586166 1
4.3%
127.1971054253 1
4.3%
127.1944218309 1
4.3%
127.1236664337 1
4.3%
127.11741481 1
4.3%
127.112518542 1
4.3%
127.1050522109 1
4.3%

Interactions

2023-12-11T08:03:42.158759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:03:39.287261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:03:39.755300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:03:40.216638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:03:40.772508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:03:41.215365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:03:41.657128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:03:42.223978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:03:39.346378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:03:39.822829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:03:40.292903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:03:40.841131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:03:41.277445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:03:41.724037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:03:42.593513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:03:39.405120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:03:39.889176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:03:40.376519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:03:40.902128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:03:41.342133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:03:41.790244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:03:42.650237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:03:39.471896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:03:39.949719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:03:40.453433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:03:40.958512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:03:41.401326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:03:41.855586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:03:42.726608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:03:39.555840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:03:40.016389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:03:40.560826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:03:41.021386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:03:41.462729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:03:41.931700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:03:42.805722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:03:39.626258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:03:40.076257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:03:40.632474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:03:41.083082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:03:41.531267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:03:42.006001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:03:42.889333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:03:39.697214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:03:40.157243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:03:40.713096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:03:41.155271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:03:41.599194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:03:42.083526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T08:03:47.007615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명동물원명정제도로명주소정제지번주소운영구분규모(㎡)종수개체수수의사수사육사수정제우편번호정제WGS84위도정제WGS84경도
시군명1.0000.9721.0001.0000.8691.0000.7050.4390.8360.6701.0000.9200.830
동물원명0.9721.0001.0001.0000.8211.0001.0001.0001.0001.0000.9180.9450.957
정제도로명주소1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
정제지번주소1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
운영구분0.8690.8211.0001.0001.0001.0000.0000.1860.6740.0000.8190.7420.242
규모(㎡)1.0001.0001.0001.0001.0001.0000.4290.0000.9280.4420.7780.9250.000
종수0.7051.0001.0001.0000.0000.4291.0000.1630.5090.3940.5560.4930.544
개체수0.4391.0001.0001.0000.1860.0000.1631.0000.0000.0000.7501.0000.000
수의사수0.8361.0001.0001.0000.6740.9280.5090.0001.0000.6290.9520.9620.000
사육사수0.6701.0001.0001.0000.0000.4420.3940.0000.6291.0000.6570.6080.000
정제우편번호1.0000.9181.0001.0000.8190.7780.5560.7500.9520.6571.0000.8180.780
정제WGS84위도0.9200.9451.0001.0000.7420.9250.4931.0000.9620.6080.8181.0000.732
정제WGS84경도0.8300.9571.0001.0000.2420.0000.5440.0000.0000.0000.7800.7321.000
2023-12-11T08:03:47.126380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
수의사수운영구분시군명
수의사수1.0000.5080.584
운영구분0.5081.0000.574
시군명0.5840.5741.000
2023-12-11T08:03:47.211641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
규모(㎡)종수개체수사육사수정제우편번호정제WGS84위도정제WGS84경도시군명운영구분수의사수
규모(㎡)1.0000.6790.7360.6320.273-0.3700.4290.7750.8940.670
종수0.6791.0000.9310.7420.323-0.1500.3800.2740.0000.412
개체수0.7360.9311.0000.7010.354-0.2000.4270.2240.1290.000
사육사수0.6320.7420.7011.0000.317-0.3190.3510.3610.0000.628
정제우편번호0.2730.3230.3540.3171.000-0.8430.3560.8660.4040.675
정제WGS84위도-0.370-0.150-0.200-0.319-0.8431.000-0.3290.6910.4670.628
정제WGS84경도0.4290.3800.4270.3510.356-0.3291.0000.5120.0000.000
시군명0.7750.2740.2240.3610.8660.6910.5121.0000.5740.584
운영구분0.8940.0000.1290.0000.4040.4670.0000.5741.0000.508
수의사수0.6700.4120.0000.6280.6750.6280.0000.5840.5081.000

Missing values

2023-12-11T08:03:43.010165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T08:03:43.176713image/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.
2023-12-11T08:03:43.284462image/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

시군명동물원명정제도로명주소정제지번주소운영구분규모(㎡)종수개체수수의사수사육사수정제우편번호정제WGS84위도정제WGS84경도
0과천시서울대공원경기도 과천시 대공원광장로 102경기도 과천시 막계동 159-1번지공영242000023721129561382937.436443127.014103
1고양시배다골테마파크<NA>경기도 고양시 덕양구 화정동 7-7민간165319119111048237.630965126.847589
2고양시해피쥬<NA>경기도 고양시 덕양구 화정동 6-5민간3501118111048237.630218126.847534
3고양시테마동물원쥬쥬경기도 고양시 덕양구 원당로458번길 7-42경기도 고양시 덕양구 관산동 290번지민간269895347181028837.68941126.854939
4고양시수피아경기도 고양시 일산동구 강촌로26번길 7-4경기도 고양시 일산동구 백석동 1413-6번지 지층민간14756111042437.646386126.779635
5부천시신라애니멀그룹 부천점 플레이아쿠아리움경기도 부천시 조마루로 2경기도 부천시 상동 572-1번지 웅진플레이도시민간(법인)5551<NA><NA><NA><NA>1459237.499869126.744266
6용인시㈜하이파크<NA>경기도 용인시 기흥구 구갈동 660번지 힐스테이트기흥민간465391101151706537.275024127.117415
7오산시오산버드파크(오산자연생태체험관)경기도 오산시 성호대로 141경기도 오산시 오산동 915번지민간472725813831101813237.149035127.077658
8화성시센트럴곤충생태농장경기도 화성시 정남면 세자로 225경기도 화성시 정남면 보통리 194-2번지민간203143150111851637.188269126.976996
9화성시주렁주렁 동탄점경기도 화성시 동탄대로5길 21경기도 화성시 송동 726-1번지민간1465411023181849737.166951127.105052
시군명동물원명정제도로명주소정제지번주소운영구분규모(㎡)종수개체수수의사수사육사수정제우편번호정제WGS84위도정제WGS84경도
13평택시브룩스월드 평택점경기도 평택시 비전5로 10경기도 평택시 비전동 1104번지 3층민간(법인)3364502661111785436.999229127.112519
14안성시안성팜랜드경기도 안성시 공도읍 대신두길 28경기도 안성시 공도읍 신두리 451번지민간법인1287000458911101755836.992085127.194422
15가평군아침고요가족동물원경기도 가평군 상면 임초밤안골로 301경기도 가평군 상면 임초리 622-13번지민간3319111770171244737.747848127.360347
16파주시쥬라리움경기도 파주시 탄현면 새오리로 69경기도 파주시 탄현면 성동리 84번지 지하1층법인30029112131085837.790413126.685064
17파주시사파리체험 테마파크경기도 파주시 탄현면 헤이리마을길 93-75경기도 파주시 탄현면 법흥리 1652-23번지개인992660111085937.791436126.696239
18파주시테이블에이경기도 파주시 소리천로 39경기도 파주시 야당동 1079-1번지 파크뷰터라스 2003호~2007호법인1232444121090837.716382126.762041
19평택시아프리카쥬경기도 평택시 안중읍 서동대로 1464경기도 평택시 안중읍 현화리 640-4번지개인414111111793036.989812126.913283
20가평군천원화조 관광농원경기도 가평군 설악면 미사리로286번길 45-32경기도 가평군 설악면 송산리 257번지민간6951140111246137.684573127.527265
21하남시주렁주렁경기도 하남시 하남유니온로 120경기도 하남시 신장동 612번지 2층민간(법인)347411022358161294237.543633127.223643
22하남시쥬라리움경기도 하남시 미사대로 410경기도 하남시 망월동 1143-1번지 미사강변오벨리스크 1,2층민간(법인)57644219111291237.566913127.197105