Overview

Dataset statistics

Number of variables5
Number of observations80
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.6 KiB
Average record size in memory45.6 B

Variable types

Categorical3
Numeric2

Dataset

Description서울특별시 강북구 동별 주정차 단속현황 (년도 자료, 동명 자료,견인건수 자료,단속건수 자료,단속원금 자료)에 관한 자료입니다.
Author서울특별시 강북구
URLhttps://www.data.go.kr/data/15034473/fileData.do

Alerts

통계기준 is highly overall correlated with 견인건수High correlation
견인건수 is highly overall correlated with 단속건수 and 3 other fieldsHigh correlation
연도 is highly overall correlated with 견인건수High correlation
단속건수 is highly overall correlated with 단속원금(원) and 1 other fieldsHigh correlation
단속원금(원) is highly overall correlated with 단속건수 and 1 other fieldsHigh correlation
견인건수 is highly imbalanced (81.3%)Imbalance

Reproduction

Analysis started2023-12-12 14:00:33.913901
Analysis finished2023-12-12 14:00:34.776127
Duration0.86 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Memory size772.0 B
2020
17 
2017
17 
2018
16 
2021
15 
2019
15 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021
2nd row2021
3rd row2021
4th row2021
5th row2021

Common Values

ValueCountFrequency (%)
2020 17
21.2%
2017 17
21.2%
2018 16
20.0%
2021 15
18.8%
2019 15
18.8%

Length

2023-12-12T23:00:34.857709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:00:34.996503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020 17
21.2%
2017 17
21.2%
2018 16
20.0%
2021 15
18.8%
2019 15
18.8%

통계기준
Categorical

HIGH CORRELATION 

Distinct18
Distinct (%)22.5%
Missing0
Missing (%)0.0%
Memory size772.0 B
강북구
 
5
송중동
 
5
우이동
 
5
수유동
 
5
수유3동
 
5
Other values (13)
55 

Length

Max length5
Median length3
Mean length3.225
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row강북구
2nd row미아동
3rd row번1동
4th row번2동
5th row번3동

Common Values

ValueCountFrequency (%)
강북구 5
 
6.2%
송중동 5
 
6.2%
우이동 5
 
6.2%
수유동 5
 
6.2%
수유3동 5
 
6.2%
인수동 5
 
6.2%
수유1동 5
 
6.2%
미아동 5
 
6.2%
삼양동 5
 
6.2%
번동 5
 
6.2%
Other values (8) 30
37.5%

Length

2023-12-12T23:00:35.159114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
강북구 5
 
6.0%
미아동 5
 
6.0%
번1동 5
 
6.0%
번2동 5
 
6.0%
번3동 5
 
6.0%
송중동 5
 
6.0%
삼양동 5
 
6.0%
번동 5
 
6.0%
수유1동 5
 
6.0%
인수동 5
 
6.0%
Other values (9) 34
40.5%

견인건수
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct6
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Memory size772.0 B
<NA>
75 
2340
 
1
3204
 
1
1186
 
1
1144
 
1

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique5 ?
Unique (%)6.2%

Sample

1st row2340
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 75
93.8%
2340 1
 
1.2%
3204 1
 
1.2%
1186 1
 
1.2%
1144 1
 
1.2%
1652 1
 
1.2%

Length

2023-12-12T23:00:35.290672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:00:35.422440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 75
93.8%
2340 1
 
1.2%
3204 1
 
1.2%
1186 1
 
1.2%
1144 1
 
1.2%
1652 1
 
1.2%

단속건수
Real number (ℝ)

HIGH CORRELATION 

Distinct72
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3459.6
Minimum1
Maximum23719
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size852.0 B
2023-12-12T23:00:35.578303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.95
Q1100.5
median642
Q33427.75
95-th percentile18655.95
Maximum23719
Range23718
Interquartile range (IQR)3327.25

Descriptive statistics

Standard deviation6038.3394
Coefficient of variation (CV)1.7453866
Kurtosis3.1290072
Mean3459.6
Median Absolute Deviation (MAD)636.5
Skewness2.0762363
Sum276768
Variance36461543
MonotonicityNot monotonic
2023-12-12T23:00:35.746887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 3
 
3.8%
1 3
 
3.8%
4 3
 
3.8%
331 2
 
2.5%
6 2
 
2.5%
1139 1
 
1.2%
101 1
 
1.2%
980 1
 
1.2%
17052 1
 
1.2%
5172 1
 
1.2%
Other values (62) 62
77.5%
ValueCountFrequency (%)
1 3
3.8%
2 1
 
1.2%
3 3
3.8%
4 3
3.8%
5 1
 
1.2%
6 2
2.5%
11 1
 
1.2%
68 1
 
1.2%
69 1
 
1.2%
77 1
 
1.2%
ValueCountFrequency (%)
23719 1
1.2%
20695 1
1.2%
20603 1
1.2%
19605 1
1.2%
18606 1
1.2%
18489 1
1.2%
17052 1
1.2%
15763 1
1.2%
15649 1
1.2%
12954 1
1.2%

단속원금(원)
Real number (ℝ)

HIGH CORRELATION 

Distinct75
Distinct (%)93.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5439162 × 108
Minimum40000
Maximum1.08317 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size852.0 B
2023-12-12T23:00:35.947697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum40000
5-th percentile118000
Q17097500
median36422500
Q31.3815 × 108
95-th percentile8.5551825 × 108
Maximum1.08317 × 109
Range1.08313 × 109
Interquartile range (IQR)1.310525 × 108

Descriptive statistics

Standard deviation2.6818783 × 108
Coefficient of variation (CV)1.7370621
Kurtosis3.2873694
Mean1.5439162 × 108
Median Absolute Deviation (MAD)36177500
Skewness2.1055054
Sum1.235133 × 1010
Variance7.1924714 × 1016
MonotonicityNot monotonic
2023-12-12T23:00:36.107093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
160000 4
 
5.0%
40000 3
 
3.8%
45710000 1
 
1.2%
30070000 1
 
1.2%
730495000 1
 
1.2%
206425000 1
 
1.2%
46980000 1
 
1.2%
18560000 1
 
1.2%
101300000 1
 
1.2%
8090000 1
 
1.2%
Other values (65) 65
81.2%
ValueCountFrequency (%)
40000 3
3.8%
80000 1
 
1.2%
120000 1
 
1.2%
130000 1
 
1.2%
160000 4
5.0%
210000 1
 
1.2%
240000 1
 
1.2%
250000 1
 
1.2%
440000 1
 
1.2%
2760000 1
 
1.2%
ValueCountFrequency (%)
1083170000 1
1.2%
912150000 1
1.2%
903720000 1
1.2%
883795000 1
1.2%
854030000 1
1.2%
802135000 1
1.2%
730495000 1
1.2%
704685000 1
1.2%
680505000 1
1.2%
588320000 1
1.2%

Interactions

2023-12-12T23:00:34.355215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:00:34.141611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:00:34.486255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:00:34.232669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T23:00:36.227326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도통계기준견인건수단속건수단속원금(원)
연도1.0000.0001.0000.0000.000
통계기준0.0001.000NaN0.8260.694
견인건수1.000NaN1.000NaNNaN
단속건수0.0000.826NaN1.0000.986
단속원금(원)0.0000.694NaN0.9861.000
2023-12-12T23:00:36.372666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
통계기준견인건수연도
통계기준1.0001.0000.000
견인건수1.0001.0001.000
연도0.0001.0001.000
2023-12-12T23:00:36.473913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
단속건수단속원금(원)연도통계기준견인건수
단속건수1.0000.9930.0000.4001.000
단속원금(원)0.9931.0000.0000.2951.000
연도0.0000.0001.0000.0001.000
통계기준0.4000.2950.0001.0001.000
견인건수1.0001.0001.0001.0001.000

Missing values

2023-12-12T23:00:34.615519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T23:00:34.735196image/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

연도통계기준견인건수단속건수단속원금(원)
02021강북구2340113945710000
12021미아동<NA>18606903720000
22021번1동<NA>3130000
32021번2동<NA>280000
42021번3동<NA>140000
52021번동<NA>7978391330000
62021삼양동<NA>5210000
72021송중동<NA>3160000
82021수유1동<NA>3120000
92021수유2동<NA>4160000
연도통계기준견인건수단속건수단속원금(원)
702017번동<NA>7017305360000
712017삼양동<NA>967720000
722017송중동<NA>2246112380000
732017송천동<NA>52136095000
742017수유1동<NA>125950720000
752017수유3동<NA>4171167615000
762017수유동<NA>20603883795000
772017시민신고웹<NA>6250000
782017우이동<NA>103755220000
792017인수동<NA>54643860000