Overview

Dataset statistics

Number of variables4
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows801
Duplicate rows (%)8.0%
Total size in memory410.2 KiB
Average record size in memory42.0 B

Variable types

Categorical2
DateTime1
Numeric1

Dataset

Description질서위반행위 규제법 제55조 및 시행령 제14조 교통과태료(주정차위반과태료, 책임보험과태료 등) 세외수입 영치대상 목록(자치구명, 건수, 금액, 과세년월) 자료 제공
URLhttps://www.data.go.kr/data/15049788/fileData.do

Alerts

자치단체 has constant value ""Constant
체납건수 has constant value ""Constant
Dataset has 801 (8.0%) duplicate rowsDuplicates
체납금액 is highly skewed (γ1 = 47.72387462)Skewed

Reproduction

Analysis started2023-12-12 17:19:54.974473
Analysis finished2023-12-12 17:19:55.424887
Duration0.45 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

자치단체
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
충청북도 청주시
10000 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row충청북도 청주시
2nd row충청북도 청주시
3rd row충청북도 청주시
4th row충청북도 청주시
5th row충청북도 청주시

Common Values

ValueCountFrequency (%)
충청북도 청주시 10000
100.0%

Length

2023-12-13T02:19:55.494470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:19:55.606376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
충청북도 10000
50.0%
청주시 10000
50.0%

부과일자
Date

Distinct858
Distinct (%)8.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum1992-02-27 00:00:00
Maximum2023-07-11 00:00:00
2023-12-13T02:19:55.726473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:19:55.951905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

체납건수
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
10000 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 10000
100.0%

Length

2023-12-13T02:19:56.286413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:19:56.520722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 10000
100.0%

체납금액
Real number (ℝ)

SKEWED 

Distinct1069
Distinct (%)10.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93709.852
Minimum800
Maximum20308000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T02:19:56.767596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum800
5-th percentile17500
Q144080
median52720
Q370800
95-th percentile214332
Maximum20308000
Range20307200
Interquartile range (IQR)26720

Descriptive statistics

Standard deviation262910.08
Coefficient of variation (CV)2.8055756
Kurtosis3517.1274
Mean93709.852
Median Absolute Deviation (MAD)10560
Skewness47.723875
Sum9.3709852 × 108
Variance6.9121708 × 1010
MonotonicityNot monotonic
2023-12-13T02:19:57.470228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
70800 503
 
5.0%
44080 337
 
3.4%
44560 293
 
2.9%
43600 287
 
2.9%
70000 255
 
2.5%
40000 253
 
2.5%
42640 221
 
2.2%
46000 210
 
2.1%
45520 200
 
2.0%
45040 200
 
2.0%
Other values (1059) 7241
72.4%
ValueCountFrequency (%)
800 2
 
< 0.1%
2480 1
 
< 0.1%
2850 1
 
< 0.1%
3040 1
 
< 0.1%
3680 1
 
< 0.1%
4640 1
 
< 0.1%
4840 1
 
< 0.1%
5000 14
0.1%
5440 1
 
< 0.1%
6080 1
 
< 0.1%
ValueCountFrequency (%)
20308000 1
 
< 0.1%
4804000 1
 
< 0.1%
4208000 1
 
< 0.1%
2877310 1
 
< 0.1%
2655000 1
 
< 0.1%
2000000 1
 
< 0.1%
1770000 3
< 0.1%
1556550 1
 
< 0.1%
1531800 1
 
< 0.1%
1477800 1
 
< 0.1%

Interactions

2023-12-13T02:19:55.103773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Missing values

2023-12-13T02:19:55.257545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T02:19:55.377672image/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

자치단체부과일자체납건수체납금액
6398충청북도 청주시2022-10-13156900
5122충청북도 청주시2022-12-09144560
5144충청북도 청주시2022-12-091133680
4495충청북도 청주시2023-01-06144080
14595충청북도 청주시2020-09-091115040
21997충청북도 청주시2012-11-28170800
5379충청북도 청주시2022-12-09144560
10707충청북도 청주시2021-12-09150320
13845충청북도 청주시2020-12-101332150
1772충청북도 청주시2023-04-111127920
자치단체부과일자체납건수체납금액
3384충청북도 청주시2023-02-09143600
21538충청북도 청주시2014-05-121231750
4114충청북도 청주시2023-01-091132240
7984충청북도 청주시2022-08-021115000
19340충청북도 청주시2018-03-12170000
23229충청북도 청주시2007-04-11140000
10213충청북도 청주시2022-01-12149840
14776충청북도 청주시2020-08-12158000
18847충청북도 청주시2018-08-08169520
13358충청북도 청주시2021-03-10117970

Duplicate rows

Most frequently occurring

자치단체부과일자체납건수체납금액# duplicates
713충청북도 청주시2022-12-09144560289
739충청북도 청주시2023-02-09143600285
731충청북도 청주시2023-01-09144080258
766충청북도 청주시2023-04-11142640219
689충청북도 청주시2022-09-13146000210
704충청북도 청주시2022-11-09145040199
699충청북도 청주시2022-10-13145520194
778충청북도 청주시2023-05-12142160183
749충청북도 청주시2023-03-10143120155
684충청북도 청주시2022-08-10146480151