Overview

Dataset statistics

Number of variables5
Number of observations2686
Missing cells0
Missing cells (%)0.0%
Duplicate rows331
Duplicate rows (%)12.3%
Total size in memory112.9 KiB
Average record size in memory43.0 B

Variable types

Categorical3
Numeric2

Dataset

Description광주광역시 서구의 교통위반과태료고액상습체납의 연도, 체납건수, 체납금액, 시군구명, 과태료명에 대한 정보입니다.
Author광주광역시 서구
URLhttps://www.data.go.kr/data/15090262/fileData.do

Alerts

연도 has constant value ""Constant
시군구명 has constant value ""Constant
과태료명 has constant value ""Constant
Dataset has 331 (12.3%) duplicate rowsDuplicates
체납건수 is highly overall correlated with 체납금액High correlation
체납금액 is highly overall correlated with 체납건수High correlation

Reproduction

Analysis started2024-03-14 18:11:59.697873
Analysis finished2024-03-14 18:12:01.706043
Duration2.01 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size21.1 KiB
2023
2686 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
2023 2686
100.0%

Length

2024-03-15T03:12:01.927469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T03:12:02.263468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023 2686
100.0%

체납건수
Real number (ℝ)

HIGH CORRELATION 

Distinct42
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.4043187
Minimum3
Maximum60
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.7 KiB
2024-03-15T03:12:02.597459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile3
Q13
median4
Q36
95-th percentile13
Maximum60
Range57
Interquartile range (IQR)3

Descriptive statistics

Standard deviation4.551829
Coefficient of variation (CV)0.84225769
Kurtosis37.178459
Mean5.4043187
Median Absolute Deviation (MAD)1
Skewness5.0042817
Sum14516
Variance20.719147
MonotonicityIncreasing
2024-03-15T03:12:03.042302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
3 1060
39.5%
4 570
21.2%
5 302
 
11.2%
6 222
 
8.3%
7 123
 
4.6%
8 91
 
3.4%
9 64
 
2.4%
10 49
 
1.8%
11 46
 
1.7%
13 26
 
1.0%
Other values (32) 133
 
5.0%
ValueCountFrequency (%)
3 1060
39.5%
4 570
21.2%
5 302
 
11.2%
6 222
 
8.3%
7 123
 
4.6%
8 91
 
3.4%
9 64
 
2.4%
10 49
 
1.8%
11 46
 
1.7%
12 21
 
0.8%
ValueCountFrequency (%)
60 1
< 0.1%
56 1
< 0.1%
52 1
< 0.1%
49 1
< 0.1%
48 1
< 0.1%
46 1
< 0.1%
45 1
< 0.1%
44 1
< 0.1%
39 1
< 0.1%
37 1
< 0.1%

체납금액
Real number (ℝ)

HIGH CORRELATION 

Distinct914
Distinct (%)34.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean253121.05
Minimum90000
Maximum4969840
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.7 KiB
2024-03-15T03:12:03.525348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum90000
5-th percentile122160
Q1132240
median177280
Q3269760
95-th percentile608660
Maximum4969840
Range4879840
Interquartile range (IQR)137520

Descriptive statistics

Standard deviation241605.94
Coefficient of variation (CV)0.95450751
Kurtosis100.91399
Mean253121.05
Median Absolute Deviation (MAD)49360
Skewness7.5767997
Sum6.7988314 × 108
Variance5.8373432 × 1010
MonotonicityNot monotonic
2024-03-15T03:12:04.121065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
120000 84
 
3.1%
160000 42
 
1.6%
121680 33
 
1.2%
134160 27
 
1.0%
129360 27
 
1.0%
129840 27
 
1.0%
131760 26
 
1.0%
132240 26
 
1.0%
132720 25
 
0.9%
133200 22
 
0.8%
Other values (904) 2347
87.4%
ValueCountFrequency (%)
90000 3
0.1%
91680 1
 
< 0.1%
92100 1
 
< 0.1%
92160 2
0.1%
93780 1
 
< 0.1%
95460 1
 
< 0.1%
97020 1
 
< 0.1%
98100 1
 
< 0.1%
99180 1
 
< 0.1%
100260 2
0.1%
ValueCountFrequency (%)
4969840 1
< 0.1%
3885740 1
< 0.1%
3720080 1
< 0.1%
2488720 1
< 0.1%
2413100 1
< 0.1%
2200060 1
< 0.1%
2092540 1
< 0.1%
2050240 1
< 0.1%
1856160 1
< 0.1%
1768400 1
< 0.1%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size21.1 KiB
서구
2686 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
서구 2686
100.0%

Length

2024-03-15T03:12:04.779535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T03:12:05.189803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서구 2686
100.0%

과태료명
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size21.1 KiB
주정차위반과태료
2686 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
주정차위반과태료 2686
100.0%

Length

2024-03-15T03:12:05.722367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T03:12:06.112255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
주정차위반과태료 2686
100.0%

Interactions

2024-03-15T03:12:00.406197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:11:59.873305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:12:00.666793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:12:00.136640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T03:12:06.451097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
체납건수체납금액
체납건수1.0000.867
체납금액0.8671.000
2024-03-15T03:12:06.812539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
체납건수체납금액
체납건수1.0000.893
체납금액0.8931.000

Missing values

2024-03-15T03:12:01.234004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T03:12:01.577707image/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

연도체납건수체납금액시군구명과태료명
020233150000서구주정차위반과태료
120233204800서구주정차위반과태료
220233120000서구주정차위반과태료
320233121680서구주정차위반과태료
420233127680서구주정차위반과태료
520233128640서구주정차위반과태료
620233122640서구주정차위반과태료
720233129600서구주정차위반과태료
820233124560서구주정차위반과태료
920233125040서구주정차위반과태료
연도체납건수체납금액시군구명과태료명
26762023371496560서구주정차위반과태료
26772023391644320서구주정차위반과태료
26782023444969840서구주정차위반과태료
26792023451856160서구주정차위반과태료
26802023463720080서구주정차위반과태료
26812023482092540서구주정차위반과태료
26822023492050240서구주정차위반과태료
26832023522200060서구주정차위반과태료
26842023563885740서구주정차위반과태료
26852023602488720서구주정차위반과태료

Duplicate rows

Most frequently occurring

연도체납건수체납금액시군구명과태료명# duplicates
320233120000서구주정차위반과태료84
9020234160000서구주정차위반과태료37
420233121680서구주정차위반과태료33
3320233129360서구주정차위반과태료27
3520233129840서구주정차위반과태료27
4820233134160서구주정차위반과태료27
4320233131760서구주정차위반과태료26
4420233132240서구주정차위반과태료26
4520233132720서구주정차위반과태료25
4120233131280서구주정차위반과태료22