Overview

Dataset statistics

Number of variables5
Number of observations69
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.0 KiB
Average record size in memory44.9 B

Variable types

Numeric3
DateTime1
Categorical1

Dataset

Description인천광역시 남동구 밤샘주차단속시스템 단속부과처리현황으로 단속연도, 부과일자, 관할지자체, 단속부과건수, 부과금액 항목을 제공합니다.
Author인천광역시 남동구
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15087092&srcSe=7661IVAWM27C61E190

Alerts

단속부과건수 is highly overall correlated with 부과금액High correlation
부과금액 is highly overall correlated with 단속부과건수High correlation
관할지자체 is highly imbalanced (62.7%)Imbalance

Reproduction

Analysis started2024-04-20 18:45:42.863669
Analysis finished2024-04-20 18:45:44.966333
Duration2.1 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

단속연도
Real number (ℝ)

Distinct7
Distinct (%)10.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2020.1449
Minimum2017
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size753.0 B
2024-04-21T03:45:45.016660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2017
5-th percentile2018
Q12019
median2020
Q32021
95-th percentile2023
Maximum2023
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.5650161
Coefficient of variation (CV)0.00077470486
Kurtosis-0.97313836
Mean2020.1449
Median Absolute Deviation (MAD)1
Skewness0.15557764
Sum139390
Variance2.4492754
MonotonicityIncreasing
2024-04-21T03:45:45.123793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2019 17
24.6%
2021 13
18.8%
2020 12
17.4%
2022 11
15.9%
2018 10
14.5%
2023 5
 
7.2%
2017 1
 
1.4%
ValueCountFrequency (%)
2017 1
 
1.4%
2018 10
14.5%
2019 17
24.6%
2020 12
17.4%
2021 13
18.8%
2022 11
15.9%
2023 5
 
7.2%
ValueCountFrequency (%)
2023 5
 
7.2%
2022 11
15.9%
2021 13
18.8%
2020 12
17.4%
2019 17
24.6%
2018 10
14.5%
2017 1
 
1.4%

부과일자
Date

Distinct57
Distinct (%)82.6%
Missing0
Missing (%)0.0%
Memory size684.0 B
Minimum2018-08-07 00:00:00
Maximum2023-04-27 00:00:00
2024-04-21T03:45:45.247519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:45:45.364171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

관할지자체
Categorical

IMBALANCE 

Distinct4
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Memory size684.0 B
인천광역시 남동구
60 
인천광역시 서구
 
5
인천광역시 부평구
 
2
인천광역시 연수구
 
2

Length

Max length9
Median length9
Mean length8.9275362
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row인천광역시 남동구
2nd row인천광역시 남동구
3rd row인천광역시 부평구
4th row인천광역시 서구
5th row인천광역시 남동구

Common Values

ValueCountFrequency (%)
인천광역시 남동구 60
87.0%
인천광역시 서구 5
 
7.2%
인천광역시 부평구 2
 
2.9%
인천광역시 연수구 2
 
2.9%

Length

2024-04-21T03:45:45.471130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T03:45:45.554306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
인천광역시 69
50.0%
남동구 60
43.5%
서구 5
 
3.6%
부평구 2
 
1.4%
연수구 2
 
1.4%

단속부과건수
Real number (ℝ)

HIGH CORRELATION 

Distinct42
Distinct (%)60.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.695652
Minimum1
Maximum255
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size753.0 B
2024-04-21T03:45:45.644851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q19
median39
Q355
95-th percentile89.8
Maximum255
Range254
Interquartile range (IQR)46

Descriptive statistics

Standard deviation38.940869
Coefficient of variation (CV)0.98098576
Kurtosis12.518491
Mean39.695652
Median Absolute Deviation (MAD)27
Skewness2.5470278
Sum2739
Variance1516.3913
MonotonicityNot monotonic
2024-04-21T03:45:45.751432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
1 11
 
15.9%
48 4
 
5.8%
49 4
 
5.8%
9 3
 
4.3%
2 3
 
4.3%
66 2
 
2.9%
3 2
 
2.9%
37 2
 
2.9%
22 2
 
2.9%
72 2
 
2.9%
Other values (32) 34
49.3%
ValueCountFrequency (%)
1 11
15.9%
2 3
 
4.3%
3 2
 
2.9%
5 1
 
1.4%
9 3
 
4.3%
10 1
 
1.4%
11 1
 
1.4%
12 1
 
1.4%
18 1
 
1.4%
22 2
 
2.9%
ValueCountFrequency (%)
255 1
1.4%
101 1
1.4%
98 1
1.4%
93 1
1.4%
85 1
1.4%
80 1
1.4%
78 1
1.4%
76 1
1.4%
74 1
1.4%
73 1
1.4%

부과금액
Real number (ℝ)

HIGH CORRELATION 

Distinct52
Distinct (%)75.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3992391.3
Minimum50000
Maximum35150000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size753.0 B
2024-04-21T03:45:45.862298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum50000
5-th percentile100000
Q1500000
median3400000
Q35300000
95-th percentile8430000
Maximum35150000
Range35100000
Interquartile range (IQR)4800000

Descriptive statistics

Standard deviation4791807.7
Coefficient of variation (CV)1.200235
Kurtosis25.993163
Mean3992391.3
Median Absolute Deviation (MAD)2500000
Skewness4.2053549
Sum2.75475 × 108
Variance2.2961421 × 1013
MonotonicityNot monotonic
2024-04-21T03:45:45.981331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100000 6
 
8.7%
200000 4
 
5.8%
400000 3
 
4.3%
900000 2
 
2.9%
6350000 2
 
2.9%
3100000 2
 
2.9%
4650000 2
 
2.9%
3150000 2
 
2.9%
5300000 2
 
2.9%
50000 2
 
2.9%
Other values (42) 42
60.9%
ValueCountFrequency (%)
50000 2
 
2.9%
100000 6
8.7%
200000 4
5.8%
250000 1
 
1.4%
400000 3
4.3%
450000 1
 
1.4%
500000 1
 
1.4%
900000 2
 
2.9%
1100000 1
 
1.4%
1200000 1
 
1.4%
ValueCountFrequency (%)
35150000 1
1.4%
12800000 1
1.4%
10450000 1
1.4%
8450000 1
1.4%
8400000 1
1.4%
7750000 1
1.4%
7600000 1
1.4%
7550000 1
1.4%
7500000 1
1.4%
7400000 1
1.4%

Interactions

2024-04-21T03:45:44.613318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:45:44.034747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:45:44.404572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:45:44.683567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:45:44.153100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:45:44.479549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:45:44.750396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:45:44.333001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:45:44.541992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-21T03:45:46.058780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
단속연도부과일자관할지자체단속부과건수부과금액
단속연도1.0000.9880.4670.2700.271
부과일자0.9881.0000.0000.0000.000
관할지자체0.4670.0001.0000.1770.000
단속부과건수0.2700.0000.1771.0000.967
부과금액0.2710.0000.0000.9671.000
2024-04-21T03:45:46.139739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
단속연도단속부과건수부과금액관할지자체
단속연도1.0000.3130.2570.289
단속부과건수0.3131.0000.9830.141
부과금액0.2570.9831.0000.000
관할지자체0.2890.1410.0001.000

Missing values

2024-04-21T03:45:44.843736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T03:45:44.922477image/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

단속연도부과일자관할지자체단속부과건수부과금액
020172018-09-28인천광역시 남동구1100000
120182018-08-07인천광역시 남동구25535150000
220182018-08-07인천광역시 부평구1100000
320182018-08-07인천광역시 서구1200000
420182018-09-28인천광역시 남동구9312800000
520182018-09-28인천광역시 부평구150000
620182018-09-28인천광역시 서구2400000
720182018-09-28인천광역시 연수구2200000
820182018-11-30인천광역시 남동구647550000
920182018-11-30인천광역시 연수구1200000
단속연도부과일자관할지자체단속부과건수부과금액
5920222022-09-23인천광역시 남동구494600000
6020222022-10-23인천광역시 남동구666150000
6120222022-11-08인천광역시 남동구7610450000
6220222022-12-05인천광역시 남동구5400000
6320222022-12-07인천광역시 남동구3500000
6420232023-01-30인천광역시 남동구464000000
6520232023-02-21인천광역시 남동구554850000
6620232023-03-27인천광역시 남동구11900000
6720232023-03-23인천광역시 남동구1100000
6820232023-04-27인천광역시 남동구545300000