Overview

Dataset statistics

Number of variables8
Number of observations54
Missing cells53
Missing cells (%)12.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.8 KiB
Average record size in memory71.4 B

Variable types

Numeric4
Text2
Categorical1
DateTime1

Dataset

Description인천광역시 미추홀구시설관리공단의 주차장 중, 추첨주차장 경쟁률에 대한 데이터로 추첨주차장명, 배정면, 신청인원, 경쟁률에 대해 나타낸 정보를 제공합니다.
Author인천광역시미추홀구시설관리공단
URLhttps://www.data.go.kr/data/15125447/fileData.do

Alerts

참고 has constant value ""Constant
참고 has 53 (98.1%) missing valuesMissing
연번 has unique valuesUnique
주차장명 has unique valuesUnique
신청 인원 has 1 (1.9%) zerosZeros
경쟁률 has 1 (1.9%) zerosZeros

Reproduction

Analysis started2023-12-16 16:00:42.621532
Analysis finished2023-12-16 16:00:50.115172
Duration7.49 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct54
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.5
Minimum1
Maximum54
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size618.0 B
2023-12-16T16:00:50.463089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.65
Q114.25
median27.5
Q340.75
95-th percentile51.35
Maximum54
Range53
Interquartile range (IQR)26.5

Descriptive statistics

Standard deviation15.732133
Coefficient of variation (CV)0.57207755
Kurtosis-1.2
Mean27.5
Median Absolute Deviation (MAD)13.5
Skewness0
Sum1485
Variance247.5
MonotonicityStrictly increasing
2023-12-16T16:00:51.484971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.9%
42 1
 
1.9%
31 1
 
1.9%
32 1
 
1.9%
33 1
 
1.9%
34 1
 
1.9%
35 1
 
1.9%
36 1
 
1.9%
37 1
 
1.9%
38 1
 
1.9%
Other values (44) 44
81.5%
ValueCountFrequency (%)
1 1
1.9%
2 1
1.9%
3 1
1.9%
4 1
1.9%
5 1
1.9%
6 1
1.9%
7 1
1.9%
8 1
1.9%
9 1
1.9%
10 1
1.9%
ValueCountFrequency (%)
54 1
1.9%
53 1
1.9%
52 1
1.9%
51 1
1.9%
50 1
1.9%
49 1
1.9%
48 1
1.9%
47 1
1.9%
46 1
1.9%
45 1
1.9%

주차장명
Text

UNIQUE 

Distinct54
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size564.0 B
2023-12-16T16:00:52.100716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length12
Mean length12.185185
Min length10

Characters and Unicode

Total characters658
Distinct characters52
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

Unique54 ?
Unique (%)100.0%

Sample

1st row관교동 제1노외주차장
2nd row관교동 제3노외주차장
3rd row도화1동 제1노외주차장
4th row도화2,3동 제4노외주차장
5th row도화2,3동 제5노외주차장
ValueCountFrequency (%)
제1노외주차장 8
 
7.4%
제4노외주차장 7
 
6.5%
제5노외주차장 6
 
5.6%
제2노외주차장 6
 
5.6%
공영주차장 5
 
4.6%
제3노외주차장 5
 
4.6%
주안2동 5
 
4.6%
용현1,4동 4
 
3.7%
문학동 4
 
3.7%
학익2동 4
 
3.7%
Other values (29) 54
50.0%
2023-12-16T16:00:54.224523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 70
 
10.6%
장 62
 
9.4%
54
 
8.2%
차 54
 
8.2%
제 49
 
7.4%
동 44
 
6.7%
노 44
 
6.7%
외 44
 
6.7%
2 22
 
3.3%
1 20
 
3.0%
Other values (42) 195
29.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 502
76.3%
Decimal Number 95
 
14.4%
Space Separator 54
 
8.2%
Other Punctuation 7
 
1.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 70
13.9%
장 62
12.4%
차 54
10.8%
제 49
9.8%
동 44
8.8%
노 44
8.8%
외 44
8.8%
안 16
 
3.2%
학 12
 
2.4%
공 12
 
2.4%
Other values (31) 95
18.9%
Decimal Number
ValueCountFrequency (%)
2 22
23.2%
1 20
21.1%
4 17
17.9%
3 13
13.7%
7 7
 
7.4%
5 7
 
7.4%
6 4
 
4.2%
8 3
 
3.2%
9 2
 
2.1%
Space Separator
ValueCountFrequency (%)
54
100.0%
Other Punctuation
ValueCountFrequency (%)
, 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 502
76.3%
Common 156
 
23.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 70
13.9%
장 62
12.4%
차 54
10.8%
제 49
9.8%
동 44
8.8%
노 44
8.8%
외 44
8.8%
안 16
 
3.2%
학 12
 
2.4%
공 12
 
2.4%
Other values (31) 95
18.9%
Common
ValueCountFrequency (%)
54
34.6%
2 22
14.1%
1 20
 
12.8%
4 17
 
10.9%
3 13
 
8.3%
, 7
 
4.5%
7 7
 
4.5%
5 7
 
4.5%
6 4
 
2.6%
8 3
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 502
76.3%
ASCII 156
 
23.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 70
13.9%
장 62
12.4%
차 54
10.8%
제 49
9.8%
동 44
8.8%
노 44
8.8%
외 44
8.8%
안 16
 
3.2%
학 12
 
2.4%
공 12
 
2.4%
Other values (31) 95
18.9%
ASCII
ValueCountFrequency (%)
54
34.6%
2 22
14.1%
1 20
 
12.8%
4 17
 
10.9%
3 13
 
8.3%
, 7
 
4.5%
7 7
 
4.5%
5 7
 
4.5%
6 4
 
2.6%
8 3
 
1.9%

급지
Categorical

Distinct3
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Memory size564.0 B
3
38 
2
14 
4
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row4
5th row3

Common Values

ValueCountFrequency (%)
3 38
70.4%
2 14
 
25.9%
4 2
 
3.7%

Length

2023-12-16T16:00:56.040874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-16T16:00:57.470774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 38
70.4%
2 14
 
25.9%
4 2
 
3.7%

배정면
Real number (ℝ)

Distinct27
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.037037
Minimum5
Maximum150
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size618.0 B
2023-12-16T16:00:58.298264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile5
Q110
median15
Q321
95-th percentile61.7
Maximum150
Range145
Interquartile range (IQR)11

Descriptive statistics

Standard deviation23.50268
Coefficient of variation (CV)1.0665082
Kurtosis16.336067
Mean22.037037
Median Absolute Deviation (MAD)5.5
Skewness3.5577907
Sum1190
Variance552.37596
MonotonicityNot monotonic
2023-12-16T16:00:58.930531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
15 6
 
11.1%
10 6
 
11.1%
5 4
 
7.4%
21 4
 
7.4%
11 3
 
5.6%
13 3
 
5.6%
8 3
 
5.6%
9 2
 
3.7%
19 2
 
3.7%
14 2
 
3.7%
Other values (17) 19
35.2%
ValueCountFrequency (%)
5 4
7.4%
6 1
 
1.9%
8 3
5.6%
9 2
 
3.7%
10 6
11.1%
11 3
5.6%
12 1
 
1.9%
13 3
5.6%
14 2
 
3.7%
15 6
11.1%
ValueCountFrequency (%)
150 1
1.9%
70 1
1.9%
63 1
1.9%
61 1
1.9%
56 1
1.9%
50 1
1.9%
48 1
1.9%
35 1
1.9%
30 2
3.7%
26 1
1.9%

23년 추첨 일자
Date

Distinct25
Distinct (%)46.3%
Missing0
Missing (%)0.0%
Memory size564.0 B
Minimum2023-06-20 00:00:00
Maximum2023-12-11 00:00:00
2023-12-16T16:00:59.497029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T16:00:59.892286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)

신청 인원
Real number (ℝ)

ZEROS 

Distinct30
Distinct (%)55.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.333333
Minimum0
Maximum296
Zeros1
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size618.0 B
2023-12-16T16:01:00.336822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7.95
Q116.5
median23
Q331.5
95-th percentile81.3
Maximum296
Range296
Interquartile range (IQR)15

Descriptive statistics

Standard deviation41.929861
Coefficient of variation (CV)1.2967998
Kurtosis30.285375
Mean32.333333
Median Absolute Deviation (MAD)7
Skewness5.0558647
Sum1746
Variance1758.1132
MonotonicityNot monotonic
2023-12-16T16:01:00.956823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
23 6
 
11.1%
16 4
 
7.4%
21 4
 
7.4%
25 3
 
5.6%
18 3
 
5.6%
30 3
 
5.6%
35 3
 
5.6%
24 2
 
3.7%
32 2
 
3.7%
33 2
 
3.7%
Other values (20) 22
40.7%
ValueCountFrequency (%)
0 1
 
1.9%
1 1
 
1.9%
6 1
 
1.9%
9 1
 
1.9%
10 1
 
1.9%
11 1
 
1.9%
12 1
 
1.9%
13 2
3.7%
14 1
 
1.9%
16 4
7.4%
ValueCountFrequency (%)
296 1
 
1.9%
110 1
 
1.9%
93 1
 
1.9%
75 1
 
1.9%
71 1
 
1.9%
70 1
 
1.9%
36 1
 
1.9%
35 3
5.6%
33 2
3.7%
32 2
3.7%

경쟁률
Real number (ℝ)

ZEROS 

Distinct45
Distinct (%)83.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7585185
Minimum0
Maximum7
Zeros1
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size618.0 B
2023-12-16T16:01:01.507955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.5315
Q11.1025
median1.42
Q32
95-th percentile4.83
Maximum7
Range7
Interquartile range (IQR)0.8975

Descriptive statistics

Standard deviation1.3451372
Coefficient of variation (CV)0.76492636
Kurtosis6.3479008
Mean1.7585185
Median Absolute Deviation (MAD)0.37
Skewness2.391694
Sum94.96
Variance1.809394
MonotonicityNot monotonic
2023-12-16T16:01:02.261696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
1.57 3
 
5.6%
6.0 2
 
3.7%
1.15 2
 
3.7%
2.0 2
 
3.7%
1.53 2
 
3.7%
0.93 2
 
3.7%
1.42 2
 
3.7%
1.25 2
 
3.7%
2.09 1
 
1.9%
1.19 1
 
1.9%
Other values (35) 35
64.8%
ValueCountFrequency (%)
0.0 1
1.9%
0.2 1
1.9%
0.46 1
1.9%
0.57 1
1.9%
0.64 1
1.9%
0.66 1
1.9%
0.92 1
1.9%
0.93 2
3.7%
0.94 1
1.9%
1.04 1
1.9%
ValueCountFrequency (%)
7.0 1
1.9%
6.0 2
3.7%
4.2 1
1.9%
3.5 1
1.9%
2.62 1
1.9%
2.6 1
1.9%
2.5 1
1.9%
2.4 1
1.9%
2.3 1
1.9%
2.27 1
1.9%

참고
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing53
Missing (%)98.1%
Memory size564.0 B
2023-12-16T16:01:02.629946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row예정
ValueCountFrequency (%)
예정 1
100.0%
2023-12-16T16:01:03.784016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
예 1
50.0%
정 1
50.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
예 1
50.0%
정 1
50.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
예 1
50.0%
정 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
예 1
50.0%
정 1
50.0%

Interactions

2023-12-16T16:00:47.848592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T16:00:43.157757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T16:00:44.702851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T16:00:46.432377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T16:00:48.102221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T16:00:43.397703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T16:00:45.058772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T16:00:46.720106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T16:00:48.384619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T16:00:43.714431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T16:00:45.408879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T16:00:47.198195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T16:00:48.720002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T16:00:44.168353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T16:00:45.825717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T16:00:47.528521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-16T16:01:04.159478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번주차장명급지배정면23년 추첨 일자신청 인원경쟁률
연번1.0001.0000.0000.0000.6290.0000.032
주차장명1.0001.0001.0001.0001.0001.0001.000
급지0.0001.0001.0000.3370.7000.3050.000
배정면0.0001.0000.3371.0000.7890.8700.000
23년 추첨 일자0.6291.0000.7000.7891.0000.6060.687
신청 인원0.0001.0000.3050.8700.6061.0000.000
경쟁률0.0321.0000.0000.0000.6870.0001.000
2023-12-16T16:01:04.652597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번배정면신청 인원경쟁률급지
연번1.000-0.1420.0510.1600.000
배정면-0.1421.0000.476-0.4170.045
신청 인원0.0510.4761.0000.4590.234
경쟁률0.160-0.4170.4591.0000.000
급지0.0000.0450.2340.0001.000

Missing values

2023-12-16T16:00:49.346251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-16T16:00:49.816084image/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

연번주차장명급지배정면23년 추첨 일자신청 인원경쟁률참고
01관교동 제1노외주차장3112023-08-14232.09<NA>
12관교동 제3노외주차장3702023-09-041101.57<NA>
23도화1동 제1노외주차장3122023-09-19131.08<NA>
34도화2,3동 제4노외주차장4142023-09-0490.64<NA>
45도화2,3동 제5노외주차장392023-09-04131.44<NA>
56도화2,3동 제6노외주차장3142023-11-13161.14<NA>
67문학동 제1노외주차장3102023-08-21232.3<NA>
78문학동 제3노외주차장3152023-11-13211.4<NA>
89문학동 제4노외주차장3152023-09-04231.53<NA>
910문학동 제5노외주차장3132023-09-05120.92<NA>
연번주차장명급지배정면23년 추첨 일자신청 인원경쟁률참고
4445주안7동 제2노외주차장262023-09-12366.0<NA>
4546주안7동 제5노외주차장3102023-11-20262.6<NA>
4647주안8동 제1노외주차장2182023-09-12201.11<NA>
4748학익1동 제2노외주차장3352023-12-1100.0예정
4849학익1동 제5노외주차장3132023-09-0560.46<NA>
4950학익2동 제3노외주차장3112023-10-02222.0<NA>
5051학익2동 제4노외주차장482023-07-18101.25<NA>
5152학익2동 제5노외주차장3112023-10-04252.27<NA>
5253학익2동 제6노외주차장3252023-10-23321.28<NA>
5354학익시장 공영주차장2152023-09-12231.53<NA>