Overview

Dataset statistics

Number of variables7
Number of observations125
Missing cells1
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.6 KiB
Average record size in memory62.1 B

Variable types

Categorical3
Numeric4

Dataset

Description연수구 주정차위반 단속(단속건수, 단속금액 등)입니다- 주정차위반 단속월 / 단속동 / 단속건수 / 단속금액 등으로 작성함
Author인천광역시 연수구
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15085420&srcSe=7661IVAWM27C61E190

Alerts

단속대상 is highly overall correlated with 단속월 and 5 other fieldsHigh correlation
단속동 is highly overall correlated with 단속건수 and 4 other fieldsHigh correlation
단속년도 is highly overall correlated with 단속월 and 5 other fieldsHigh correlation
단속월 is highly overall correlated with 단속년도 and 1 other fieldsHigh correlation
단속건수 is highly overall correlated with 부과건수 and 4 other fieldsHigh correlation
부과건수 is highly overall correlated with 단속건수 and 4 other fieldsHigh correlation
단속금액 is highly overall correlated with 단속건수 and 4 other fieldsHigh correlation
단속년도 is highly imbalanced (93.3%)Imbalance
단속대상 is highly imbalanced (93.3%)Imbalance

Reproduction

Analysis started2024-01-28 16:42:33.279519
Analysis finished2024-01-28 16:42:35.539690
Duration2.26 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

단속년도
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2023
124 
<NA>
 
1

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique1 ?
Unique (%)0.8%

Sample

1st row2023
2nd row2023
3rd row2023
4th row2023
5th row2023

Common Values

ValueCountFrequency (%)
2023 124
99.2%
<NA> 1
 
0.8%

Length

2024-01-29T01:42:35.613120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-29T01:42:35.744079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023 124
99.2%
na 1
 
0.8%

단속대상
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
주정차
124 
<NA>
 
1

Length

Max length4
Median length3
Mean length3.008
Min length3

Unique

Unique1 ?
Unique (%)0.8%

Sample

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

Common Values

ValueCountFrequency (%)
주정차 124
99.2%
<NA> 1
 
0.8%

Length

2024-01-29T01:42:35.881292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-29T01:42:36.020092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
주정차 124
99.2%
na 1
 
0.8%

단속월
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)4.8%
Missing1
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean3.4919355
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-01-29T01:42:36.144526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3.5
Q35
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7226184
Coefficient of variation (CV)0.49331335
Kurtosis-1.2803388
Mean3.4919355
Median Absolute Deviation (MAD)1.5
Skewness0.0090211892
Sum433
Variance2.9674141
MonotonicityIncreasing
2024-01-29T01:42:36.292401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 21
16.8%
2 21
16.8%
4 21
16.8%
6 21
16.8%
3 20
16.0%
5 20
16.0%
(Missing) 1
 
0.8%
ValueCountFrequency (%)
1 21
16.8%
2 21
16.8%
3 20
16.0%
4 21
16.8%
5 20
16.0%
6 21
16.8%
ValueCountFrequency (%)
6 21
16.8%
5 20
16.0%
4 21
16.8%
3 20
16.0%
2 21
16.8%
1 21
16.8%

단속동
Categorical

HIGH CORRELATION 

Distinct23
Distinct (%)18.4%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
101 옥련동
 
6
102 선학동
 
6
103 연수동
 
6
104 청학동
 
6
105 동춘동
 
6
Other values (18)
95 

Length

Max length8
Median length7
Mean length7.424
Min length2

Unique

Unique2 ?
Unique (%)1.6%

Sample

1st row101 옥련동
2nd row102 선학동
3rd row103 연수동
4th row104 청학동
5th row105 동춘동

Common Values

ValueCountFrequency (%)
101 옥련동 6
 
4.8%
102 선학동 6
 
4.8%
103 연수동 6
 
4.8%
104 청학동 6
 
4.8%
105 동춘동 6
 
4.8%
106 송도동 6
 
4.8%
620 옥련동 6
 
4.8%
640 옥련2동 6
 
4.8%
750 선학동 6
 
4.8%
761 연수1동 6
 
4.8%
Other values (13) 65
52.0%

Length

2024-01-29T01:42:36.452894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
선학동 12
 
4.8%
청학동 12
 
4.8%
동춘동 12
 
4.8%
옥련동 12
 
4.8%
송도동 12
 
4.8%
101 6
 
2.4%
동춘3동 6
 
2.4%
766 6
 
2.4%
770 6
 
2.4%
790 6
 
2.4%
Other values (30) 159
63.9%

단속건수
Real number (ℝ)

HIGH CORRELATION 

Distinct97
Distinct (%)77.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean873.216
Minimum0
Maximum54576
Zeros1
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-01-29T01:42:36.592000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6.2
Q132
median80
Q3314
95-th percentile3488
Maximum54576
Range54576
Interquartile range (IQR)282

Descriptive statistics

Standard deviation4953.7297
Coefficient of variation (CV)5.6729718
Kurtosis113.86009
Mean873.216
Median Absolute Deviation (MAD)65
Skewness10.465211
Sum109152
Variance24539438
MonotonicityNot monotonic
2024-01-29T01:42:36.763092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21 3
 
2.4%
72 3
 
2.4%
50 3
 
2.4%
8 3
 
2.4%
57 2
 
1.6%
32 2
 
1.6%
25 2
 
1.6%
87 2
 
1.6%
61 2
 
1.6%
22 2
 
1.6%
Other values (87) 101
80.8%
ValueCountFrequency (%)
0 1
 
0.8%
1 2
1.6%
2 2
1.6%
3 1
 
0.8%
6 1
 
0.8%
7 1
 
0.8%
8 3
2.4%
10 1
 
0.8%
11 1
 
0.8%
15 1
 
0.8%
ValueCountFrequency (%)
54576 1
0.8%
5530 1
0.8%
5503 1
0.8%
5198 1
0.8%
4644 1
0.8%
4006 1
0.8%
3947 1
0.8%
1652 1
0.8%
1485 1
0.8%
1462 1
0.8%

부과건수
Real number (ℝ)

HIGH CORRELATION 

Distinct98
Distinct (%)78.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean872.992
Minimum0
Maximum54562
Zeros1
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-01-29T01:42:36.929685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6.2
Q132
median80
Q3314
95-th percentile3488
Maximum54562
Range54562
Interquartile range (IQR)282

Descriptive statistics

Standard deviation4952.4362
Coefficient of variation (CV)5.6729457
Kurtosis113.86223
Mean872.992
Median Absolute Deviation (MAD)65
Skewness10.465343
Sum109124
Variance24526624
MonotonicityNot monotonic
2024-01-29T01:42:37.531564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
72 3
 
2.4%
8 3
 
2.4%
50 3
 
2.4%
21 3
 
2.4%
61 2
 
1.6%
81 2
 
1.6%
19 2
 
1.6%
2 2
 
1.6%
30 2
 
1.6%
80 2
 
1.6%
Other values (88) 101
80.8%
ValueCountFrequency (%)
0 1
 
0.8%
1 2
1.6%
2 2
1.6%
3 1
 
0.8%
6 1
 
0.8%
7 1
 
0.8%
8 3
2.4%
10 1
 
0.8%
11 1
 
0.8%
15 1
 
0.8%
ValueCountFrequency (%)
54562 1
0.8%
5525 1
0.8%
5502 1
0.8%
5198 1
0.8%
4641 1
0.8%
4005 1
0.8%
3947 1
0.8%
1652 1
0.8%
1484 1
0.8%
1462 1
0.8%

단속금액
Real number (ℝ)

HIGH CORRELATION 

Distinct117
Distinct (%)93.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40222400
Minimum0
Maximum2.5139 × 109
Zeros1
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-01-29T01:42:37.727901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile320000
Q11420000
median4930000
Q315880000
95-th percentile1.52206 × 108
Maximum2.5139 × 109
Range2.5139 × 109
Interquartile range (IQR)14460000

Descriptive statistics

Standard deviation2.2774807 × 108
Coefficient of variation (CV)5.6622199
Kurtosis114.74622
Mean40222400
Median Absolute Deviation (MAD)4090000
Skewness10.520938
Sum5.0278 × 109
Variance5.1869184 × 1016
MonotonicityNot monotonic
2024-01-29T01:42:37.928569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6000000 3
 
2.4%
320000 2
 
1.6%
840000 2
 
1.6%
760000 2
 
1.6%
120000 2
 
1.6%
1040000 2
 
1.6%
2290000 2
 
1.6%
3530000 1
 
0.8%
40100000 1
 
0.8%
6820000 1
 
0.8%
Other values (107) 107
85.6%
ValueCountFrequency (%)
0 1
0.8%
80000 1
0.8%
120000 2
1.6%
240000 1
0.8%
250000 1
0.8%
320000 2
1.6%
360000 1
0.8%
400000 1
0.8%
450000 1
0.8%
610000 1
0.8%
ValueCountFrequency (%)
2513900000 1
0.8%
244260000 1
0.8%
241650000 1
0.8%
230780000 1
0.8%
203740000 1
0.8%
175420000 1
0.8%
172430000 1
0.8%
71310000 1
0.8%
64780000 1
0.8%
63410000 1
0.8%

Interactions

2024-01-29T01:42:34.872519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:42:33.585215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:42:34.013405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:42:34.452102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:42:34.984338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:42:33.695026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:42:34.122404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:42:34.553173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:42:35.083141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:42:33.802573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:42:34.232381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:42:34.658591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:42:35.192110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:42:33.901418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:42:34.346982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:42:34.762299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-29T01:42:38.042412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
단속월단속동단속건수부과건수단속금액
단속월1.0000.0000.0000.000NaN
단속동0.0001.0000.9020.9021.000
단속건수0.0000.9021.0001.0001.000
부과건수0.0000.9021.0001.0001.000
단속금액NaN1.0001.0001.0001.000
2024-01-29T01:42:38.170714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
단속대상단속동단속년도
단속대상1.0001.0001.000
단속동1.0001.0001.000
단속년도1.0001.0001.000
2024-01-29T01:42:38.286253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
단속월단속건수부과건수단속금액단속년도단속대상단속동
단속월1.0000.0430.0430.0371.0001.0000.000
단속건수0.0431.0001.0000.9781.0001.0000.701
부과건수0.0431.0001.0000.9781.0001.0000.701
단속금액0.0370.9780.9781.0001.0001.0000.911
단속년도1.0001.0001.0001.0001.0001.0001.000
단속대상1.0001.0001.0001.0001.0001.0001.000
단속동0.0000.7010.7010.9111.0001.0001.000

Missing values

2024-01-29T01:42:35.337107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-29T01:42:35.477657image/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

단속년도단속대상단속월단속동단속건수부과건수단속금액
02023주정차1101 옥련동54354323450000
12023주정차1102 선학동95954170000
22023주정차1103 연수동1652165271310000
32023주정차1104 청학동38338316650000
42023주정차1105 동춘동28828812550000
52023주정차1106 송도동39473947172430000
62023주정차1620 옥련동59595250000
72023주정차1630 옥련1동11120000
82023주정차1640 옥련2동26261040000
92023주정차1750 선학동73738150000
단속년도단속대상단속월단속동단속건수부과건수단속금액
1152023주정차6766 청학동2222880000
1162023주정차6770 동춘동1221224970000
1172023주정차6780 동춘1동2280000
1182023주정차6790 동춘2동52522090000
1192023주정차6795 동춘3동57572290000
1202023주정차6810 송도동52052035610000
1212023주정차6820 송도1동63632530000
1222023주정차6840 송도3동2020800000
1232023주정차6850 송도4동45451810000
124<NA><NA><NA>합계54576545622513900000