Overview

Dataset statistics

Number of variables7
Number of observations84
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.1 KiB
Average record size in memory62.6 B

Variable types

Numeric5
Categorical1
DateTime1

Dataset

Description대구광역시 남구 체납차량 번호판 영치실적 현황에 대한 데이터로 영치년도, 영치건수, 체납건수, 체납금액의 항목을 제공합니다.
URLhttps://www.data.go.kr/data/15093510/fileData.do

Alerts

영치기관 has constant value ""Constant
데이터기준일자 has constant value ""Constant
영치건수 is highly overall correlated with 체납건수 and 1 other fieldsHigh correlation
체납건수 is highly overall correlated with 영치건수 and 1 other fieldsHigh correlation
체납금액(백만원 단위) is highly overall correlated with 영치건수 and 1 other fieldsHigh correlation
영치건수 has 4 (4.8%) zerosZeros
체납건수 has 4 (4.8%) zerosZeros
체납금액(백만원 단위) has 6 (7.1%) zerosZeros

Reproduction

Analysis started2023-12-12 23:46:54.179881
Analysis finished2023-12-12 23:46:57.097588
Duration2.92 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

영치년도
Real number (ℝ)

Distinct7
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2019
Minimum2016
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size888.0 B
2023-12-13T08:46:57.142236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2016
5-th percentile2016
Q12017
median2019
Q32021
95-th percentile2022
Maximum2022
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.0120121
Coefficient of variation (CV)0.00099653894
Kurtosis-1.2527477
Mean2019
Median Absolute Deviation (MAD)2
Skewness0
Sum169596
Variance4.0481928
MonotonicityIncreasing
2023-12-13T08:46:57.233451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2016 12
14.3%
2017 12
14.3%
2018 12
14.3%
2019 12
14.3%
2020 12
14.3%
2021 12
14.3%
2022 12
14.3%
ValueCountFrequency (%)
2016 12
14.3%
2017 12
14.3%
2018 12
14.3%
2019 12
14.3%
2020 12
14.3%
2021 12
14.3%
2022 12
14.3%
ValueCountFrequency (%)
2022 12
14.3%
2021 12
14.3%
2020 12
14.3%
2019 12
14.3%
2018 12
14.3%
2017 12
14.3%
2016 12
14.3%

영치월
Real number (ℝ)

Distinct12
Distinct (%)14.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size888.0 B
2023-12-13T08:46:57.335481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13.75
median6.5
Q39.25
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)5.5

Descriptive statistics

Standard deviation3.4727858
Coefficient of variation (CV)0.53427473
Kurtosis-1.2174655
Mean6.5
Median Absolute Deviation (MAD)3
Skewness0
Sum546
Variance12.060241
MonotonicityNot monotonic
2023-12-13T08:46:57.433506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 7
8.3%
2 7
8.3%
3 7
8.3%
4 7
8.3%
5 7
8.3%
6 7
8.3%
7 7
8.3%
8 7
8.3%
9 7
8.3%
10 7
8.3%
Other values (2) 14
16.7%
ValueCountFrequency (%)
1 7
8.3%
2 7
8.3%
3 7
8.3%
4 7
8.3%
5 7
8.3%
6 7
8.3%
7 7
8.3%
8 7
8.3%
9 7
8.3%
10 7
8.3%
ValueCountFrequency (%)
12 7
8.3%
11 7
8.3%
10 7
8.3%
9 7
8.3%
8 7
8.3%
7 7
8.3%
6 7
8.3%
5 7
8.3%
4 7
8.3%
3 7
8.3%

영치건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct64
Distinct (%)76.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60.880952
Minimum0
Maximum177
Zeros4
Zeros (%)4.8%
Negative0
Negative (%)0.0%
Memory size888.0 B
2023-12-13T08:46:57.553481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q129.5
median61
Q394
95-th percentile120.25
Maximum177
Range177
Interquartile range (IQR)64.5

Descriptive statistics

Standard deviation40.309766
Coefficient of variation (CV)0.662108
Kurtosis-0.35747738
Mean60.880952
Median Absolute Deviation (MAD)33
Skewness0.27671473
Sum5114
Variance1624.8772
MonotonicityNot monotonic
2023-12-13T08:46:57.693897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
102 4
 
4.8%
0 4
 
4.8%
94 3
 
3.6%
46 3
 
3.6%
69 2
 
2.4%
1 2
 
2.4%
80 2
 
2.4%
10 2
 
2.4%
105 2
 
2.4%
24 2
 
2.4%
Other values (54) 58
69.0%
ValueCountFrequency (%)
0 4
4.8%
1 2
2.4%
2 1
 
1.2%
3 1
 
1.2%
4 1
 
1.2%
5 1
 
1.2%
9 1
 
1.2%
10 2
2.4%
11 1
 
1.2%
13 1
 
1.2%
ValueCountFrequency (%)
177 1
1.2%
154 1
1.2%
141 1
1.2%
126 1
1.2%
121 1
1.2%
116 1
1.2%
115 1
1.2%
105 2
2.4%
104 1
1.2%
103 1
1.2%

체납건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct77
Distinct (%)91.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean186.16667
Minimum0
Maximum527
Zeros4
Zeros (%)4.8%
Negative0
Negative (%)0.0%
Memory size888.0 B
2023-12-13T08:46:57.818935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.3
Q190.5
median191
Q3271.5
95-th percentile372.95
Maximum527
Range527
Interquartile range (IQR)181

Descriptive statistics

Standard deviation123.36017
Coefficient of variation (CV)0.66263295
Kurtosis-0.29011886
Mean186.16667
Median Absolute Deviation (MAD)91
Skewness0.32953364
Sum15638
Variance15217.731
MonotonicityNot monotonic
2023-12-13T08:46:57.950506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4
 
4.8%
282 2
 
2.4%
25 2
 
2.4%
162 2
 
2.4%
257 2
 
2.4%
51 1
 
1.2%
267 1
 
1.2%
311 1
 
1.2%
367 1
 
1.2%
3 1
 
1.2%
Other values (67) 67
79.8%
ValueCountFrequency (%)
0 4
4.8%
1 1
 
1.2%
3 1
 
1.2%
7 1
 
1.2%
8 1
 
1.2%
22 1
 
1.2%
25 2
2.4%
32 1
 
1.2%
34 1
 
1.2%
35 1
 
1.2%
ValueCountFrequency (%)
527 1
1.2%
490 1
1.2%
434 1
1.2%
424 1
1.2%
374 1
1.2%
367 1
1.2%
347 1
1.2%
336 1
1.2%
335 1
1.2%
334 1
1.2%

체납금액(백만원 단위)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct44
Distinct (%)52.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.369048
Minimum0
Maximum70
Zeros6
Zeros (%)7.1%
Negative0
Negative (%)0.0%
Memory size888.0 B
2023-12-13T08:46:58.080225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q111
median26.5
Q338
95-th percentile52.7
Maximum70
Range70
Interquartile range (IQR)27

Descriptive statistics

Standard deviation17.165235
Coefficient of variation (CV)0.67662119
Kurtosis-0.462175
Mean25.369048
Median Absolute Deviation (MAD)12.5
Skewness0.30990218
Sum2131
Variance294.6453
MonotonicityNot monotonic
2023-12-13T08:46:58.186742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
0 6
 
7.1%
5 4
 
4.8%
28 3
 
3.6%
32 3
 
3.6%
10 3
 
3.6%
19 3
 
3.6%
27 3
 
3.6%
43 3
 
3.6%
39 3
 
3.6%
38 3
 
3.6%
Other values (34) 50
59.5%
ValueCountFrequency (%)
0 6
7.1%
1 2
 
2.4%
2 2
 
2.4%
3 1
 
1.2%
4 1
 
1.2%
5 4
4.8%
6 1
 
1.2%
10 3
3.6%
11 2
 
2.4%
12 2
 
2.4%
ValueCountFrequency (%)
70 1
1.2%
69 1
1.2%
57 1
1.2%
56 1
1.2%
53 1
1.2%
51 1
1.2%
48 1
1.2%
47 2
2.4%
46 2
2.4%
45 1
1.2%

영치기관
Categorical

CONSTANT 

Distinct1
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size804.0 B
대구광역시 남구
84 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row대구광역시 남구
2nd row대구광역시 남구
3rd row대구광역시 남구
4th row대구광역시 남구
5th row대구광역시 남구

Common Values

ValueCountFrequency (%)
대구광역시 남구 84
100.0%

Length

2023-12-13T08:46:58.295996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:46:58.375515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대구광역시 84
50.0%
남구 84
50.0%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size804.0 B
Minimum2022-12-31 00:00:00
Maximum2022-12-31 00:00:00
2023-12-13T08:46:58.452266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:46:58.546065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-13T08:46:56.434160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:46:54.338762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:46:54.790027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:46:55.231609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:46:55.684484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:46:56.529856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:46:54.429261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:46:54.878508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:46:55.329304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:46:55.789738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:46:56.630129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:46:54.515553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:46:54.955050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:46:55.421886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:46:56.171831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:46:56.741476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:46:54.613830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:46:55.040151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:46:55.509401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:46:56.269244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:46:56.832969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:46:54.705790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:46:55.142513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:46:55.584677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:46:56.350344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T08:46:58.608290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
영치년도영치월영치건수체납건수체납금액(백만원 단위)
영치년도1.0000.0000.0000.0000.000
영치월0.0001.0000.5570.4180.637
영치건수0.0000.5571.0000.9690.955
체납건수0.0000.4180.9691.0000.982
체납금액(백만원 단위)0.0000.6370.9550.9821.000
2023-12-13T08:46:58.698674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
영치년도영치월영치건수체납건수체납금액(백만원 단위)
영치년도1.0000.0000.0060.0200.039
영치월0.0001.0000.3660.3410.333
영치건수0.0060.3661.0000.9800.974
체납건수0.0200.3410.9801.0000.992
체납금액(백만원 단위)0.0390.3330.9740.9921.000

Missing values

2023-12-13T08:46:56.951373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T08:46:57.052282image/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

영치년도영치월영치건수체납건수체납금액(백만원 단위)영치기관데이터기준일자
02016122516대구광역시 남구2022-12-31
120162218110대구광역시 남구2022-12-31
2201638928241대구광역시 남구2022-12-31
3201647319626대구광역시 남구2022-12-31
4201655316224대구광역시 남구2022-12-31
5201669423331대구광역시 남구2022-12-31
6201674617421대구광역시 남구2022-12-31
7201684821430대구광역시 남구2022-12-31
82016910429543대구광역시 남구2022-12-31
920161012133546대구광역시 남구2022-12-31
영치년도영치월영치건수체납건수체납금액(백만원 단위)영치기관데이터기준일자
7420223371대구광역시 남구2022-12-31
752022411537453대구광역시 남구2022-12-31
762022510534745대구광역시 남구2022-12-31
77202267620132대구광역시 남구2022-12-31
782022715449070대구광역시 남구2022-12-31
79202286218027대구광역시 남구2022-12-31
80202298325737대구광역시 남구2022-12-31
812022106924636대구광역시 남구2022-12-31
8220221110231447대구광역시 남구2022-12-31
8320221213345대구광역시 남구2022-12-31