Overview

Dataset statistics

Number of variables5
Number of observations97
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.2 KiB
Average record size in memory44.3 B

Variable types

Categorical1
Text1
Numeric3

Dataset

Description광주광역시 우치근린공원 내 우치동물원에서 사육 및 관리하고 있는 동물에 대한 암, 수, 개체수로 구분하여 관리하고 있는 공공데이터 입니다.
Author광주광역시
URLhttps://www.data.go.kr/data/3076331/fileData.do

Alerts

동물명 has unique valuesUnique
수 has 17 (17.5%) zerosZeros
암 has 27 (27.8%) zerosZeros
미상 has 86 (88.7%) zerosZeros

Reproduction

Analysis started2024-03-14 20:56:39.189728
Analysis finished2024-03-14 20:56:42.493848
Duration3.3 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Categorical

Distinct3
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size904.0 B
포유류
43 
조류
39 
파충류
15 

Length

Max length3
Median length3
Mean length2.5979381
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
포유류 43
44.3%
조류 39
40.2%
파충류 15
 
15.5%

Length

2024-03-15T05:56:42.756219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T05:56:42.986945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
포유류 43
44.3%
조류 39
40.2%
파충류 15
 
15.5%

동물명
Text

UNIQUE 

Distinct97
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size904.0 B
2024-03-15T05:56:44.512740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length7
Mean length4.4948454
Min length2

Characters and Unicode

Total characters436
Distinct characters177
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

Unique97 ?
Unique (%)100.0%

Sample

1st row들소
2nd row자넨
3rd row면양
4th row무플론
5th row단봉낙타
ValueCountFrequency (%)
들소 1
 
1.0%
회색앵무 1
 
1.0%
청금강 1
 
1.0%
푸른이마아마존앵무 1
 
1.0%
왕관앵무 1
 
1.0%
솔로몬유황앵무 1
 
1.0%
검은머리흰따오기 1
 
1.0%
펠리칸 1
 
1.0%
두루미 1
 
1.0%
회색관두루미 1
 
1.0%
Other values (89) 89
89.9%
2024-03-15T05:56:46.406181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
이 15
 
3.4%
리 13
 
3.0%
무 12
 
2.8%
앵 9
 
2.1%
마 9
 
2.1%
아 9
 
2.1%
거 7
 
1.6%
기 7
 
1.6%
은 7
 
1.6%
코 6
 
1.4%
Other values (167) 342
78.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 427
97.9%
Open Punctuation 3
 
0.7%
Close Punctuation 3
 
0.7%
Space Separator 3
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
이 15
 
3.5%
리 13
 
3.0%
무 12
 
2.8%
앵 9
 
2.1%
마 9
 
2.1%
아 9
 
2.1%
거 7
 
1.6%
기 7
 
1.6%
은 7
 
1.6%
코 6
 
1.4%
Other values (164) 333
78.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Space Separator
ValueCountFrequency (%)
3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 427
97.9%
Common 9
 
2.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
이 15
 
3.5%
리 13
 
3.0%
무 12
 
2.8%
앵 9
 
2.1%
마 9
 
2.1%
아 9
 
2.1%
거 7
 
1.6%
기 7
 
1.6%
은 7
 
1.6%
코 6
 
1.4%
Other values (164) 333
78.0%
Common
ValueCountFrequency (%)
( 3
33.3%
) 3
33.3%
3
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 427
97.9%
ASCII 9
 
2.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
이 15
 
3.5%
리 13
 
3.0%
무 12
 
2.8%
앵 9
 
2.1%
마 9
 
2.1%
아 9
 
2.1%
거 7
 
1.6%
기 7
 
1.6%
은 7
 
1.6%
코 6
 
1.4%
Other values (164) 333
78.0%
ASCII
ValueCountFrequency (%)
( 3
33.3%
) 3
33.3%
3
33.3%

수
Real number (ℝ)

ZEROS 

Distinct12
Distinct (%)12.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9072165
Minimum0
Maximum53
Zeros17
Zeros (%)17.5%
Negative0
Negative (%)0.0%
Memory size1001.0 B
2024-03-15T05:56:46.853007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q33
95-th percentile7.2
Maximum53
Range53
Interquartile range (IQR)2

Descriptive statistics

Standard deviation6.9042295
Coefficient of variation (CV)2.3748591
Kurtosis36.45672
Mean2.9072165
Median Absolute Deviation (MAD)1
Skewness5.7673868
Sum282
Variance47.668385
MonotonicityNot monotonic
2024-03-15T05:56:47.213120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 43
44.3%
0 17
 
17.5%
2 12
 
12.4%
4 7
 
7.2%
3 6
 
6.2%
5 5
 
5.2%
7 2
 
2.1%
9 1
 
1.0%
21 1
 
1.0%
39 1
 
1.0%
Other values (2) 2
 
2.1%
ValueCountFrequency (%)
0 17
 
17.5%
1 43
44.3%
2 12
 
12.4%
3 6
 
6.2%
4 7
 
7.2%
5 5
 
5.2%
7 2
 
2.1%
8 1
 
1.0%
9 1
 
1.0%
21 1
 
1.0%
ValueCountFrequency (%)
53 1
 
1.0%
39 1
 
1.0%
21 1
 
1.0%
9 1
 
1.0%
8 1
 
1.0%
7 2
 
2.1%
5 5
5.2%
4 7
7.2%
3 6
6.2%
2 12
12.4%

암
Real number (ℝ)

ZEROS 

Distinct12
Distinct (%)12.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2164948
Minimum0
Maximum94
Zeros27
Zeros (%)27.8%
Negative0
Negative (%)0.0%
Memory size1001.0 B
2024-03-15T05:56:47.569605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile6.2
Maximum94
Range94
Interquartile range (IQR)2

Descriptive statistics

Standard deviation10.722083
Coefficient of variation (CV)3.333468
Kurtosis56.053555
Mean3.2164948
Median Absolute Deviation (MAD)1
Skewness7.0909688
Sum312
Variance114.96306
MonotonicityNot monotonic
2024-03-15T05:56:47.813860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 35
36.1%
0 27
27.8%
2 14
 
14.4%
3 8
 
8.2%
4 4
 
4.1%
5 2
 
2.1%
6 2
 
2.1%
7 1
 
1.0%
41 1
 
1.0%
30 1
 
1.0%
Other values (2) 2
 
2.1%
ValueCountFrequency (%)
0 27
27.8%
1 35
36.1%
2 14
 
14.4%
3 8
 
8.2%
4 4
 
4.1%
5 2
 
2.1%
6 2
 
2.1%
7 1
 
1.0%
15 1
 
1.0%
30 1
 
1.0%
ValueCountFrequency (%)
94 1
 
1.0%
41 1
 
1.0%
30 1
 
1.0%
15 1
 
1.0%
7 1
 
1.0%
6 2
 
2.1%
5 2
 
2.1%
4 4
 
4.1%
3 8
8.2%
2 14
14.4%

미상
Real number (ℝ)

ZEROS 

Distinct8
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.89690722
Minimum0
Maximum20
Zeros86
Zeros (%)88.7%
Negative0
Negative (%)0.0%
Memory size1001.0 B
2024-03-15T05:56:47.989411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile7
Maximum20
Range20
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.1737351
Coefficient of variation (CV)3.5385322
Kurtosis20.431736
Mean0.89690722
Median Absolute Deviation (MAD)0
Skewness4.3362558
Sum87
Variance10.072595
MonotonicityNot monotonic
2024-03-15T05:56:48.186054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 86
88.7%
5 3
 
3.1%
7 2
 
2.1%
1 2
 
2.1%
10 1
 
1.0%
9 1
 
1.0%
17 1
 
1.0%
20 1
 
1.0%
ValueCountFrequency (%)
0 86
88.7%
1 2
 
2.1%
5 3
 
3.1%
7 2
 
2.1%
9 1
 
1.0%
10 1
 
1.0%
17 1
 
1.0%
20 1
 
1.0%
ValueCountFrequency (%)
20 1
 
1.0%
17 1
 
1.0%
10 1
 
1.0%
9 1
 
1.0%
7 2
 
2.1%
5 3
 
3.1%
1 2
 
2.1%
0 86
88.7%

Interactions

2024-03-15T05:56:41.061938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:56:39.483379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:56:40.316081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:56:41.347005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:56:39.747876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:56:40.555673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:56:41.633544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:56:39.994084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:56:40.792635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T05:56:48.339132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분동물명수암미상
구분1.0001.0000.0000.0000.000
동물명1.0001.0001.0001.0001.000
수0.0001.0001.0000.9950.802
암0.0001.0000.9951.0000.802
미상0.0001.0000.8020.8021.000
2024-03-15T05:56:48.617759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
수암미상구분
수1.0000.3400.1610.000
암0.3401.0000.2120.000
미상0.1610.2121.0000.000
구분0.0000.0000.0001.000

Missing values

2024-03-15T05:56:42.006457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T05:56:42.363037image/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

구분동물명수암미상
0포유류들소110
1포유류자넨100
2포유류면양147
3포유류무플론470
4포유류단봉낙타010
5포유류과나코110
6포유류알파카210
7포유류붉은사슴100
8포유류꽃사슴200
9포유류다마사슴(백사슴)040
구분동물명수암미상
87파충류푸른혀도마뱀110
88파충류테구110
89파충류아나콘다100
90파충류볼 파이톤210
91파충류그물무늬왕뱀000
92파충류뱀목거북100
93파충류견목거북330
94파충류레드풋육지거북100
95파충류설카타육지거북105
96파충류레오파트육지거북200