Overview

Dataset statistics

Number of variables3
Number of observations83
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.2 KiB
Average record size in memory26.6 B

Variable types

Categorical1
Text1
Numeric1

Dataset

Description창원시 읍면동별 공영주차장 개소수의 데이터로 로 창원시 관내의 읍면동 구별한 공영주차장 현황자료제공 파일입니다.
Author공공데이터포털
URLhttps://www.data.go.kr/data/15119566/fileData.do

Reproduction

Analysis started2024-04-17 09:10:54.216419
Analysis finished2024-04-17 09:10:54.495757
Duration0.28 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구
Categorical

Distinct5
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Memory size796.0 B
진해구
29 
마산합포구
19 
성산구
13 
마산회원구
12 
의창구
10 

Length

Max length5
Median length3
Mean length3.746988
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
진해구 29
34.9%
마산합포구 19
22.9%
성산구 13
15.7%
마산회원구 12
14.5%
의창구 10
 
12.0%

Length

2024-04-17T18:10:54.552893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T18:10:54.663333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
진해구 29
34.9%
마산합포구 19
22.9%
성산구 13
15.7%
마산회원구 12
14.5%
의창구 10
 
12.0%

읍면동
Text

Distinct80
Distinct (%)96.4%
Missing0
Missing (%)0.0%
Memory size796.0 B
2024-04-17T18:10:54.877395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length3.0481928
Min length2

Characters and Unicode

Total characters253
Distinct characters82
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

Unique78 ?
Unique (%)94.0%

Sample

1st row대산면
2nd row도계동
3rd row동읍
4th row명서동
5th row명곡동
ValueCountFrequency (%)
중앙동 3
 
3.6%
상남동 2
 
2.4%
경화동 1
 
1.2%
서중동 1
 
1.2%
병암동 1
 
1.2%
명동 1
 
1.2%
덕산동 1
 
1.2%
대흥동 1
 
1.2%
남문동 1
 
1.2%
봉암동 1
 
1.2%
Other values (70) 70
84.3%
2024-04-17T18:10:55.229755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 80
31.6%
중 7
 
2.8%
성 7
 
2.8%
면 5
 
2.0%
산 5
 
2.0%
2 5
 
2.0%
1 5
 
2.0%
월 4
 
1.6%
화 4
 
1.6%
앙 4
 
1.6%
Other values (72) 127
50.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 243
96.0%
Decimal Number 10
 
4.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 80
32.9%
중 7
 
2.9%
성 7
 
2.9%
면 5
 
2.1%
산 5
 
2.1%
월 4
 
1.6%
화 4
 
1.6%
앙 4
 
1.6%
웅 4
 
1.6%
암 4
 
1.6%
Other values (70) 119
49.0%
Decimal Number
ValueCountFrequency (%)
2 5
50.0%
1 5
50.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 243
96.0%
Common 10
 
4.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 80
32.9%
중 7
 
2.9%
성 7
 
2.9%
면 5
 
2.1%
산 5
 
2.1%
월 4
 
1.6%
화 4
 
1.6%
앙 4
 
1.6%
웅 4
 
1.6%
암 4
 
1.6%
Other values (70) 119
49.0%
Common
ValueCountFrequency (%)
2 5
50.0%
1 5
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 243
96.0%
ASCII 10
 
4.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 80
32.9%
중 7
 
2.9%
성 7
 
2.9%
면 5
 
2.1%
산 5
 
2.1%
월 4
 
1.6%
화 4
 
1.6%
앙 4
 
1.6%
웅 4
 
1.6%
암 4
 
1.6%
Other values (70) 119
49.0%
ASCII
ValueCountFrequency (%)
2 5
50.0%
1 5
50.0%

공영주차장(개소수)
Real number (ℝ)

Distinct25
Distinct (%)30.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.3493976
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size879.0 B
2024-04-17T18:10:55.358232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median5
Q312.5
95-th percentile25
Maximum31
Range30
Interquartile range (IQR)10.5

Descriptive statistics

Standard deviation8.3028554
Coefficient of variation (CV)0.99442569
Kurtosis-0.091727496
Mean8.3493976
Median Absolute Deviation (MAD)4
Skewness1.0321847
Sum693
Variance68.937408
MonotonicityNot monotonic
2024-04-17T18:10:55.473134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
1 20
24.1%
2 13
15.7%
18 5
 
6.0%
12 4
 
4.8%
7 4
 
4.8%
4 4
 
4.8%
10 3
 
3.6%
3 3
 
3.6%
5 3
 
3.6%
6 3
 
3.6%
Other values (15) 21
25.3%
ValueCountFrequency (%)
1 20
24.1%
2 13
15.7%
3 3
 
3.6%
4 4
 
4.8%
5 3
 
3.6%
6 3
 
3.6%
7 4
 
4.8%
8 3
 
3.6%
9 1
 
1.2%
10 3
 
3.6%
ValueCountFrequency (%)
31 1
 
1.2%
29 1
 
1.2%
26 1
 
1.2%
25 3
3.6%
24 1
 
1.2%
22 1
 
1.2%
21 2
 
2.4%
20 1
 
1.2%
19 2
 
2.4%
18 5
6.0%

Interactions

2024-04-17T18:10:54.321455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-17T18:10:55.555391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구읍면동공영주차장(개소수)
구1.0000.6050.573
읍면동0.6051.0000.944
공영주차장(개소수)0.5730.9441.000
2024-04-17T18:10:55.640113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
공영주차장(개소수)구
공영주차장(개소수)1.0000.249
구0.2491.000

Missing values

2024-04-17T18:10:54.409995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-17T18:10:54.470761image/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

구읍면동공영주차장(개소수)
0의창구대산면18
1의창구도계동3
2의창구동읍12
3의창구명서동1
4의창구명곡동25
5의창구봉림동18
6의창구봉곡동2
7의창구북면8
8의창구의창동24
9의창구팔용동25
구읍면동공영주차장(개소수)
73진해구자은동8
74진해구장천동1
75진해구중앙동5
76진해구중평동1
77진해구청안동1
78진해구충무동13
79진해구태백동26
80진해구태평동7
81진해구풍호동4
82진해구화천동1