Overview

Dataset statistics

Number of variables7
Number of observations188
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory11.3 KiB
Average record size in memory61.7 B

Variable types

Categorical1
Text1
Numeric5

Dataset

Description창원시 관내 읍면동별로 등록되어있는 반려견 수의 통계입니다. 지역구를 첫행에 표시해두었으니 잘 참고하시길 바랍니다.
URLhttps://www.data.go.kr/data/15115585/fileData.do

Alerts

내장형 is highly overall correlated with 외장형 and 2 other fieldsHigh correlation
외장형 is highly overall correlated with 내장형 and 2 other fieldsHigh correlation
인식표 is highly overall correlated with 내장형 and 2 other fieldsHigh correlation
동물소유자수 is highly overall correlated with 내장형 and 2 other fieldsHigh correlation
내장형 has 5 (2.7%) zerosZeros
외장형 has 17 (9.0%) zerosZeros
인식표 has 11 (5.9%) zerosZeros

Reproduction

Analysis started2023-12-12 08:04:06.281233
Analysis finished2023-12-12 08:04:10.059287
Duration3.78 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

지역구
Categorical

Distinct5
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
마산합포구
64 
진해구
60 
성산구
34 
의창구
21 
마산회원구
9 

Length

Max length5
Median length3
Mean length3.7765957
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
마산합포구 64
34.0%
진해구 60
31.9%
성산구 34
18.1%
의창구 21
 
11.2%
마산회원구 9
 
4.8%

Length

2023-12-12T17:04:10.166488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T17:04:10.335092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
마산합포구 64
34.0%
진해구 60
31.9%
성산구 34
18.1%
의창구 21
 
11.2%
마산회원구 9
 
4.8%

읍면동(법정동)
Text

Distinct179
Distinct (%)95.2%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2023-12-12T17:04:10.724311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length3.1914894
Min length2

Characters and Unicode

Total characters600
Distinct characters118
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

Unique171 ?
Unique (%)91.0%

Sample

1st row내동
2nd row외동
3rd row가음동
4th row귀곡동
5th row귀산동
ValueCountFrequency (%)
신월동 3
 
1.6%
용호동 2
 
1.1%
남양동 2
 
1.1%
대원동 2
 
1.1%
신흥동 2
 
1.1%
상남동 2
 
1.1%
중앙동 2
 
1.1%
현동 2
 
1.1%
두척동 1
 
0.5%
양덕동 1
 
0.5%
Other values (169) 169
89.9%
2023-12-12T17:04:11.245443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 188
31.3%
가 27
 
4.5%
월 16
 
2.7%
대 15
 
2.5%
남 14
 
2.3%
성 13
 
2.2%
산 12
 
2.0%
신 10
 
1.7%
중 9
 
1.5%
포 8
 
1.3%
Other values (108) 288
48.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 577
96.2%
Decimal Number 23
 
3.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 188
32.6%
가 27
 
4.7%
월 16
 
2.8%
대 15
 
2.6%
남 14
 
2.4%
성 13
 
2.3%
산 12
 
2.1%
신 10
 
1.7%
중 9
 
1.6%
포 8
 
1.4%
Other values (103) 265
45.9%
Decimal Number
ValueCountFrequency (%)
2 7
30.4%
1 7
30.4%
3 5
21.7%
4 2
 
8.7%
5 2
 
8.7%

Most occurring scripts

ValueCountFrequency (%)
Hangul 577
96.2%
Common 23
 
3.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 188
32.6%
가 27
 
4.7%
월 16
 
2.8%
대 15
 
2.6%
남 14
 
2.4%
성 13
 
2.3%
산 12
 
2.1%
신 10
 
1.7%
중 9
 
1.6%
포 8
 
1.4%
Other values (103) 265
45.9%
Common
ValueCountFrequency (%)
2 7
30.4%
1 7
30.4%
3 5
21.7%
4 2
 
8.7%
5 2
 
8.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 577
96.2%
ASCII 23
 
3.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 188
32.6%
가 27
 
4.7%
월 16
 
2.8%
대 15
 
2.6%
남 14
 
2.4%
성 13
 
2.3%
산 12
 
2.1%
신 10
 
1.7%
중 9
 
1.6%
포 8
 
1.4%
Other values (103) 265
45.9%
ASCII
ValueCountFrequency (%)
2 7
30.4%
1 7
30.4%
3 5
21.7%
4 2
 
8.7%
5 2
 
8.7%

내장형
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct112
Distinct (%)59.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean144.32979
Minimum0
Maximum1300
Zeros5
Zeros (%)2.7%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-12T17:04:11.462187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.35
Q18.75
median29
Q3163.5
95-th percentile622.45
Maximum1300
Range1300
Interquartile range (IQR)154.75

Descriptive statistics

Standard deviation229.93119
Coefficient of variation (CV)1.5930959
Kurtosis5.4947227
Mean144.32979
Median Absolute Deviation (MAD)26
Skewness2.2598018
Sum27134
Variance52868.351
MonotonicityNot monotonic
2023-12-12T17:04:11.665238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 8
 
4.3%
10 6
 
3.2%
2 6
 
3.2%
4 6
 
3.2%
5 6
 
3.2%
29 5
 
2.7%
3 5
 
2.7%
0 5
 
2.7%
1 5
 
2.7%
17 5
 
2.7%
Other values (102) 131
69.7%
ValueCountFrequency (%)
0 5
2.7%
1 5
2.7%
2 6
3.2%
3 5
2.7%
4 6
3.2%
5 6
3.2%
6 8
4.3%
7 3
 
1.6%
8 3
 
1.6%
9 1
 
0.5%
ValueCountFrequency (%)
1300 1
0.5%
1040 1
0.5%
957 1
0.5%
904 1
0.5%
895 1
0.5%
787 1
0.5%
689 1
0.5%
683 1
0.5%
642 1
0.5%
627 1
0.5%

외장형
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct93
Distinct (%)49.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean78.765957
Minimum0
Maximum897
Zeros17
Zeros (%)9.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-12T17:04:11.825846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median16.5
Q399.5
95-th percentile330.05
Maximum897
Range897
Interquartile range (IQR)94.5

Descriptive statistics

Standard deviation131.91359
Coefficient of variation (CV)1.6747539
Kurtosis9.4613533
Mean78.765957
Median Absolute Deviation (MAD)15.5
Skewness2.6909612
Sum14808
Variance17401.196
MonotonicityNot monotonic
2023-12-12T17:04:11.999384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 17
 
9.0%
1 12
 
6.4%
3 10
 
5.3%
6 8
 
4.3%
7 7
 
3.7%
10 7
 
3.7%
12 6
 
3.2%
8 5
 
2.7%
5 4
 
2.1%
13 4
 
2.1%
Other values (83) 108
57.4%
ValueCountFrequency (%)
0 17
9.0%
1 12
6.4%
2 3
 
1.6%
3 10
5.3%
4 4
 
2.1%
5 4
 
2.1%
6 8
4.3%
7 7
3.7%
8 5
 
2.7%
9 2
 
1.1%
ValueCountFrequency (%)
897 1
0.5%
593 1
0.5%
520 1
0.5%
517 1
0.5%
466 1
0.5%
420 1
0.5%
366 1
0.5%
356 1
0.5%
343 1
0.5%
336 1
0.5%

인식표
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct97
Distinct (%)51.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76.744681
Minimum0
Maximum765
Zeros11
Zeros (%)5.9%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-12T17:04:12.182519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median17.5
Q392
95-th percentile306.95
Maximum765
Range765
Interquartile range (IQR)87

Descriptive statistics

Standard deviation119.51729
Coefficient of variation (CV)1.5573365
Kurtosis6.6069674
Mean76.744681
Median Absolute Deviation (MAD)16.5
Skewness2.2868453
Sum14428
Variance14284.384
MonotonicityNot monotonic
2023-12-12T17:04:12.344543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 12
 
6.4%
2 12
 
6.4%
0 11
 
5.9%
5 8
 
4.3%
9 7
 
3.7%
6 7
 
3.7%
3 6
 
3.2%
10 5
 
2.7%
7 5
 
2.7%
16 4
 
2.1%
Other values (87) 111
59.0%
ValueCountFrequency (%)
0 11
5.9%
1 12
6.4%
2 12
6.4%
3 6
3.2%
4 3
 
1.6%
5 8
4.3%
6 7
3.7%
7 5
2.7%
8 3
 
1.6%
9 7
3.7%
ValueCountFrequency (%)
765 1
0.5%
474 1
0.5%
455 1
0.5%
440 1
0.5%
426 1
0.5%
425 1
0.5%
340 1
0.5%
323 1
0.5%
318 1
0.5%
308 1
0.5%

동물소유자수
Real number (ℝ)

HIGH CORRELATION 

Distinct120
Distinct (%)63.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean226.7766
Minimum1
Maximum2317
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-12T17:04:12.516711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q113
median44
Q3266.25
95-th percentile941.2
Maximum2317
Range2316
Interquartile range (IQR)253.25

Descriptive statistics

Standard deviation363.59221
Coefficient of variation (CV)1.6033057
Kurtosis6.9492407
Mean226.7766
Median Absolute Deviation (MAD)40
Skewness2.3752282
Sum42634
Variance132199.3
MonotonicityNot monotonic
2023-12-12T17:04:12.666167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 7
 
3.7%
1 7
 
3.7%
8 5
 
2.7%
12 5
 
2.7%
26 4
 
2.1%
4 4
 
2.1%
19 4
 
2.1%
9 4
 
2.1%
10 3
 
1.6%
25 3
 
1.6%
Other values (110) 142
75.5%
ValueCountFrequency (%)
1 7
3.7%
2 7
3.7%
3 3
1.6%
4 4
2.1%
5 1
 
0.5%
6 3
1.6%
7 2
 
1.1%
8 5
2.7%
9 4
2.1%
10 3
1.6%
ValueCountFrequency (%)
2317 1
0.5%
1540 1
0.5%
1439 1
0.5%
1389 1
0.5%
1194 1
0.5%
1180 1
0.5%
1078 1
0.5%
1013 1
0.5%
962 1
0.5%
944 1
0.5%

동물소유자당 동물등록수
Real number (ℝ)

Distinct61
Distinct (%)32.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4143085
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-12T17:04:12.862981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.084
Q11.25
median1.33
Q31.4825
95-th percentile1.852
Maximum5
Range4
Interquartile range (IQR)0.2325

Descriptive statistics

Standard deviation0.383916
Coefficient of variation (CV)0.27145138
Kurtosis43.232713
Mean1.4143085
Median Absolute Deviation (MAD)0.1
Skewness5.4162461
Sum265.89
Variance0.1473915
MonotonicityNot monotonic
2023-12-12T17:04:13.030438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.28 12
 
6.4%
1.0 9
 
4.8%
1.25 9
 
4.8%
1.27 8
 
4.3%
1.5 8
 
4.3%
1.33 7
 
3.7%
1.21 6
 
3.2%
1.17 5
 
2.7%
1.26 5
 
2.7%
1.4 5
 
2.7%
Other values (51) 114
60.6%
ValueCountFrequency (%)
1.0 9
4.8%
1.07 1
 
0.5%
1.11 2
 
1.1%
1.13 1
 
0.5%
1.14 1
 
0.5%
1.15 1
 
0.5%
1.16 1
 
0.5%
1.17 5
2.7%
1.2 1
 
0.5%
1.21 6
3.2%
ValueCountFrequency (%)
5.0 1
 
0.5%
3.08 1
 
0.5%
3.0 1
 
0.5%
2.38 1
 
0.5%
2.37 1
 
0.5%
2.0 4
2.1%
1.88 1
 
0.5%
1.8 1
 
0.5%
1.75 4
2.1%
1.74 1
 
0.5%

Interactions

2023-12-12T17:04:09.128841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:04:06.589995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:04:07.454558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:04:07.943326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:04:08.615081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:04:09.262279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:04:06.693127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:04:07.571116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:04:08.125558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:04:08.710076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:04:09.372004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:04:06.786906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:04:07.655702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:04:08.261033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:04:08.792971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:04:09.507469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:04:06.904255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:04:07.762677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:04:08.386138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:04:08.889302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:04:09.613964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:04:07.332625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:04:07.840117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:04:08.489946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:04:09.000708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T17:04:13.130597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지역구내장형외장형인식표동물소유자수동물소유자당 동물등록수
지역구1.0000.4190.3890.4180.3940.102
내장형0.4191.0000.9040.8490.9550.000
외장형0.3890.9041.0000.9390.9790.000
인식표0.4180.8490.9391.0000.9450.000
동물소유자수0.3940.9550.9790.9451.0000.000
동물소유자당 동물등록수0.1020.0000.0000.0000.0001.000
2023-12-12T17:04:13.230248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
내장형외장형인식표동물소유자수동물소유자당 동물등록수지역구
내장형1.0000.9500.9270.983-0.0870.254
외장형0.9501.0000.9240.976-0.1180.249
인식표0.9270.9241.0000.961-0.0410.270
동물소유자수0.9830.9760.9611.000-0.1370.252
동물소유자당 동물등록수-0.087-0.118-0.041-0.1371.0000.089
지역구0.2540.2490.2700.2520.0891.000

Missing values

2023-12-12T17:04:09.807917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T17:04:09.994081image/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

지역구읍면동(법정동)내장형외장형인식표동물소유자수동물소유자당 동물등록수
0성산구내동10343501501.31
1성산구외동114321671322.37
2성산구가음동3281561325001.23
3성산구귀곡동30122.0
4성산구귀산동6521221.45
5성산구귀현동01011.0
6성산구남산동9353551581.27
7성산구남양동5473102028601.23
8성산구남지동60041.5
9성산구대방동5503192478641.29
지역구읍면동(법정동)내장형외장형인식표동물소유자수동물소유자당 동물등록수
178진해구충의동251222341.74
179진해구태백동7723761211.45
180진해구태평동311847641.5
181진해구통신동00111.0
182진해구평안동50031.67
183진해구풍호동4422432247261.25
184진해구행암동8617211.48
185진해구화천동151012321.16
186진해구회현동55191.22
187진해구제황산동8442791301.58