Overview

Dataset statistics

Number of variables6
Number of observations66
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.5 KiB
Average record size in memory54.0 B

Variable types

Categorical3
Numeric3

Dataset

Description서울특별시 강남구의 불법 주정차 단속 현황(년도, 동, 견인건수, 단속원금, 데이터기준일자) 입니다.......
Author서울특별시 강남구
URLhttps://www.data.go.kr/data/15048827/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
부과건수 is highly overall correlated with 견인건수 and 1 other fieldsHigh correlation
견인건수 is highly overall correlated with 부과건수 and 1 other fieldsHigh correlation
단속원금(원) is highly overall correlated with 부과건수 and 1 other fieldsHigh correlation
부과건수 has unique valuesUnique
단속원금(원) has unique valuesUnique

Reproduction

Analysis started2023-12-12 05:36:02.415068
Analysis finished2023-12-12 05:36:03.953709
Duration1.54 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Categorical

Distinct5
Distinct (%)7.6%
Missing0
Missing (%)0.0%
Memory size660.0 B
2019
14 
2017
13 
2018
13 
2020
13 
2021
13 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2017
2nd row2017
3rd row2017
4th row2017
5th row2017

Common Values

ValueCountFrequency (%)
2019 14
21.2%
2017 13
19.7%
2018 13
19.7%
2020 13
19.7%
2021 13
19.7%

Length

2023-12-12T14:36:04.053815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:36:04.197330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2019 14
21.2%
2017 13
19.7%
2018 13
19.7%
2020 13
19.7%
2021 13
19.7%

동명
Categorical

Distinct16
Distinct (%)24.2%
Missing0
Missing (%)0.0%
Memory size660.0 B
개포동
5 
논현동
5 
대치동
5 
도곡동
5 
삼성동
5 
Other values (11)
41 

Length

Max length4
Median length3
Mean length3.1666667
Min length3

Unique

Unique1 ?
Unique (%)1.5%

Sample

1st row개포동
2nd row논현동
3rd row대치동
4th row도곡동
5th row삼성동

Common Values

ValueCountFrequency (%)
개포동 5
 
7.6%
논현동 5
 
7.6%
대치동 5
 
7.6%
도곡동 5
 
7.6%
삼성동 5
 
7.6%
세곡동 5
 
7.6%
수서동 5
 
7.6%
신사동 5
 
7.6%
압구정동 5
 
7.6%
역삼동 5
 
7.6%
Other values (6) 16
24.2%

Length

2023-12-12T14:36:04.356532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
개포동 5
 
7.6%
논현동 5
 
7.6%
대치동 5
 
7.6%
도곡동 5
 
7.6%
삼성동 5
 
7.6%
세곡동 5
 
7.6%
수서동 5
 
7.6%
신사동 5
 
7.6%
압구정동 5
 
7.6%
역삼동 5
 
7.6%
Other values (4) 16
24.2%

부과건수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct66
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20413.97
Minimum1071
Maximum90385
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size726.0 B
2023-12-12T14:36:04.525546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1071
5-th percentile2614.5
Q15851
median10217
Q329005
95-th percentile64512.25
Maximum90385
Range89314
Interquartile range (IQR)23154

Descriptive statistics

Standard deviation20107.746
Coefficient of variation (CV)0.98499931
Kurtosis1.7752797
Mean20413.97
Median Absolute Deviation (MAD)7550.5
Skewness1.4521353
Sum1347322
Variance4.0432145 × 108
MonotonicityNot monotonic
2023-12-12T14:36:04.688768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9409 1
 
1.5%
5809 1
 
1.5%
1268 1
 
1.5%
7467 1
 
1.5%
2664 1
 
1.5%
28516 1
 
1.5%
4581 1
 
1.5%
27407 1
 
1.5%
24356 1
 
1.5%
3986 1
 
1.5%
Other values (56) 56
84.8%
ValueCountFrequency (%)
1071 1
1.5%
1268 1
1.5%
1967 1
1.5%
2598 1
1.5%
2664 1
1.5%
2669 1
1.5%
3986 1
1.5%
4338 1
1.5%
4412 1
1.5%
4461 1
1.5%
ValueCountFrequency (%)
90385 1
1.5%
71771 1
1.5%
68450 1
1.5%
67609 1
1.5%
55222 1
1.5%
52528 1
1.5%
48776 1
1.5%
48210 1
1.5%
43306 1
1.5%
41600 1
1.5%

견인건수
Real number (ℝ)

HIGH CORRELATION 

Distinct65
Distinct (%)98.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1277.8485
Minimum2
Maximum12394
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size726.0 B
2023-12-12T14:36:04.886863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile15.75
Q1191.75
median435.5
Q31201
95-th percentile4884.25
Maximum12394
Range12392
Interquartile range (IQR)1009.25

Descriptive statistics

Standard deviation2221.9391
Coefficient of variation (CV)1.7388126
Kurtosis11.793155
Mean1277.8485
Median Absolute Deviation (MAD)314
Skewness3.2356145
Sum84338
Variance4937013.2
MonotonicityNot monotonic
2023-12-12T14:36:05.072587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
179 2
 
3.0%
284 1
 
1.5%
1165 1
 
1.5%
322 1
 
1.5%
271 1
 
1.5%
4 1
 
1.5%
722 1
 
1.5%
150 1
 
1.5%
1241 1
 
1.5%
245 1
 
1.5%
Other values (55) 55
83.3%
ValueCountFrequency (%)
2 1
1.5%
3 1
1.5%
4 1
1.5%
8 1
1.5%
39 1
1.5%
40 1
1.5%
83 1
1.5%
104 1
1.5%
115 1
1.5%
128 1
1.5%
ValueCountFrequency (%)
12394 1
1.5%
9557 1
1.5%
6964 1
1.5%
5002 1
1.5%
4531 1
1.5%
4400 1
1.5%
4214 1
1.5%
3720 1
1.5%
2624 1
1.5%
2054 1
1.5%

단속원금(원)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct66
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.5661182 × 108
Minimum51500000
Maximum3.6154 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size726.0 B
2023-12-12T14:36:05.298954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum51500000
5-th percentile1.06445 × 108
Q12.6511375 × 108
median4.1098 × 108
Q31.3044175 × 109
95-th percentile2.5856262 × 109
Maximum3.6154 × 109
Range3.5639 × 109
Interquartile range (IQR)1.0393038 × 109

Descriptive statistics

Standard deviation8.2148087 × 108
Coefficient of variation (CV)0.95898849
Kurtosis1.2641291
Mean8.5661182 × 108
Median Absolute Deviation (MAD)3.02305 × 108
Skewness1.3350555
Sum5.653638 × 1010
Variance6.7483082 × 1017
MonotonicityNot monotonic
2023-12-12T14:36:05.527740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
376360000 1
 
1.5%
301820000 1
 
1.5%
51500000 1
 
1.5%
353840000 1
 
1.5%
110590000 1
 
1.5%
1162725000 1
 
1.5%
224330000 1
 
1.5%
2229425000 1
 
1.5%
1013160000 1
 
1.5%
164190000 1
 
1.5%
Other values (56) 56
84.8%
ValueCountFrequency (%)
51500000 1
1.5%
52500000 1
1.5%
95680000 1
1.5%
106340000 1
1.5%
106760000 1
1.5%
110590000 1
1.5%
164190000 1
1.5%
176480000 1
1.5%
176705000 1
1.5%
186930000 1
1.5%
ValueCountFrequency (%)
3615400000 1
1.5%
2870840000 1
1.5%
2759485000 1
1.5%
2704360000 1
1.5%
2229425000 1
1.5%
2208880000 1
1.5%
2101120000 1
1.5%
1991535000 1
1.5%
1951040000 1
1.5%
1732240000 1
1.5%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size660.0 B
2022-02-07
66 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022-02-07
2nd row2022-02-07
3rd row2022-02-07
4th row2022-02-07
5th row2022-02-07

Common Values

ValueCountFrequency (%)
2022-02-07 66
100.0%

Length

2023-12-12T14:36:05.711024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:36:05.818293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022-02-07 66
100.0%

Interactions

2023-12-12T14:36:03.376529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:36:02.670250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:36:03.024194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:36:03.477871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:36:02.798229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:36:03.137698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:36:03.591465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:36:02.906263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:36:03.247454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T14:36:05.887400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도동명부과건수견인건수단속원금(원)
연도1.0000.0000.0000.0040.000
동명0.0001.0000.6450.0000.688
부과건수0.0000.6451.0000.7810.999
견인건수0.0040.0000.7811.0000.709
단속원금(원)0.0000.6880.9990.7091.000
2023-12-12T14:36:06.021804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도동명
연도1.0000.000
동명0.0001.000
2023-12-12T14:36:06.115644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
부과건수견인건수단속원금(원)연도동명
부과건수1.0000.7340.9840.0000.311
견인건수0.7341.0000.7460.0000.000
단속원금(원)0.9840.7461.0000.0000.317
연도0.0000.0000.0001.0000.000
동명0.3110.0000.3170.0001.000

Missing values

2023-12-12T14:36:03.773928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T14:36:03.896612image/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

연도동명부과건수견인건수단속원금(원)데이터기준일자
02017개포동94092843763600002022-02-07
12017논현동71771205428708400002022-02-07
22017대치동55222262422088800002022-02-07
32017도곡동107631304305200002022-02-07
42017삼성동36472150714588800002022-02-07
52017세곡동8006393202400002022-02-07
62017수서동97864343914400002022-02-07
72017신사동5252861021011200002022-02-07
82017압구정동65613502624400002022-02-07
92017역삼동90385421436154000002022-02-07
연도동명부과건수견인건수단속원금(원)데이터기준일자
562021도곡동50901282160000002022-02-07
572021삼성동181283677581450002022-02-07
582021세곡동446121869300002022-02-07
592021수서동50981692731350002022-02-07
602021신사동186101798008900002022-02-07
612021압구정동81062263463250002022-02-07
622021역삼동3798293315994750002022-02-07
632021일원동60281793752700002022-02-07
642021자곡동19673956800002022-02-07
652021청담동210383029141250002022-02-07