Overview

Dataset statistics

Number of variables13
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows2
Duplicate rows (%)< 0.1%
Total size in memory1.1 MiB
Average record size in memory116.0 B

Variable types

DateTime3
Text3
Numeric4
Categorical3

Dataset

Description주정차교통행정시스템 내 주정차위반과태료 수납대장에 관련된 데이터들 소인일,고지번호,전자납부번호,단속일,단속장소정보,수납방법,수납일,본세,가산금 등을 포함하고 있습니다.
URLhttps://www.data.go.kr/data/15039451/fileData.do

Alerts

관리기관명 has constant value ""Constant
데이터기준일 has constant value ""Constant
Dataset has 2 (< 0.1%) duplicate rowsDuplicates
전자납부번호 is highly overall correlated with 가산금High correlation
본세 is highly overall correlated with 가산금 and 1 other fieldsHigh correlation
가산금 is highly overall correlated with 전자납부번호 and 2 other fieldsHigh correlation
합계 is highly overall correlated with 본세 and 1 other fieldsHigh correlation
수납방법 is highly imbalanced (62.4%)Imbalance
가산금 has 7493 (74.9%) zerosZeros

Reproduction

Analysis started2023-12-12 00:36:37.485959
Analysis finished2023-12-12 00:36:39.993040
Duration2.51 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

소인일
Date

Distinct396
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2019-09-23 00:00:00
Maximum2021-04-26 00:00:00
2023-12-12T09:36:40.082612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:36:40.226980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

고지번호
Text

Distinct9997
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T09:36:40.392877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length27
Mean length27
Min length27

Characters and Unicode

Total characters270000
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9994 ?
Unique (%)99.9%

Sample

1st row288125 2020 05 3 001 000197
2nd row288125 2019 01 1 001 000805
3rd row288125 2019 09 1 001 000507
4th row288125 2018 02 1 001 008193
5th row288125 2009 06 2 001 000658
ValueCountFrequency (%)
288125 10000
16.7%
001 10000
16.7%
3 6477
 
10.8%
2020 4965
 
8.3%
1 3375
 
5.6%
2019 2501
 
4.2%
2021 1497
 
2.5%
11 1173
 
2.0%
10 1134
 
1.9%
12 1053
 
1.8%
Other values (3663) 17825
29.7%
2023-12-12T09:36:40.681694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 70361
26.1%
50000
18.5%
2 43570
16.1%
1 39782
14.7%
8 23865
 
8.8%
5 13668
 
5.1%
3 11214
 
4.2%
9 6275
 
2.3%
4 3826
 
1.4%
7 3742
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 220000
81.5%
Space Separator 50000
 
18.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 70361
32.0%
2 43570
19.8%
1 39782
18.1%
8 23865
 
10.8%
5 13668
 
6.2%
3 11214
 
5.1%
9 6275
 
2.9%
4 3826
 
1.7%
7 3742
 
1.7%
6 3697
 
1.7%
Space Separator
ValueCountFrequency (%)
50000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 270000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 70361
26.1%
50000
18.5%
2 43570
16.1%
1 39782
14.7%
8 23865
 
8.8%
5 13668
 
5.1%
3 11214
 
4.2%
9 6275
 
2.3%
4 3826
 
1.4%
7 3742
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 270000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 70361
26.1%
50000
18.5%
2 43570
16.1%
1 39782
14.7%
8 23865
 
8.8%
5 13668
 
5.1%
3 11214
 
4.2%
9 6275
 
2.3%
4 3826
 
1.4%
7 3742
 
1.4%

전자납부번호
Real number (ℝ)

HIGH CORRELATION 

Distinct9997
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.163022 × 1018
Minimum4.1630203 × 1018
Maximum4.16303 × 1018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T09:36:40.869338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.1630203 × 1018
5-th percentile4.1630217 × 1018
Q14.163022 × 1018
median4.1630221 × 1018
Q34.1630221 × 1018
95-th percentile4.1630222 × 1018
Maximum4.16303 × 1018
Range9.7 × 1012
Interquartile range (IQR)9.9999913 × 1010

Descriptive statistics

Standard deviation2.0781667 × 1011
Coefficient of variation (CV)4.9919666 × 10-8
Kurtosis230.43049
Mean4.163022 × 1018
Median Absolute Deviation (MAD)9.9999602 × 1010
Skewness2.3875049
Sum-4.0813535 × 1018
Variance4.3187767 × 1022
MonotonicityNot monotonic
2023-12-12T09:36:41.405365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4163021961500501671 2
 
< 0.1%
4163022061500255206 2
 
< 0.1%
4163022061500191476 2
 
< 0.1%
4163022061500097580 1
 
< 0.1%
4163021961500445310 1
 
< 0.1%
4163022061500243902 1
 
< 0.1%
4163022061500246949 1
 
< 0.1%
4163021961500423449 1
 
< 0.1%
4163022161500134817 1
 
< 0.1%
4163021961500364632 1
 
< 0.1%
Other values (9987) 9987
99.9%
ValueCountFrequency (%)
4163020261500003755 1
< 0.1%
4163020261500021691 1
< 0.1%
4163020261500027237 1
< 0.1%
4163020261500049288 1
< 0.1%
4163020361500011997 1
< 0.1%
4163020361500028849 1
< 0.1%
4163020361500031739 1
< 0.1%
4163020461500010649 1
< 0.1%
4163020461500011468 1
< 0.1%
4163020561500000212 1
< 0.1%
ValueCountFrequency (%)
4163029961500007723 1
< 0.1%
4163022161500154220 1
< 0.1%
4163022161500154168 1
< 0.1%
4163022161500153812 1
< 0.1%
4163022161500152842 1
< 0.1%
4163022161500152557 1
< 0.1%
4163022161500151943 1
< 0.1%
4163022161500151596 1
< 0.1%
4163022161500151541 1
< 0.1%
4163022161500151265 1
< 0.1%

단속일
Date

Distinct1661
Distinct (%)16.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum1999-05-12 00:00:00
Maximum2021-04-19 00:00:00
2023-12-12T09:36:41.581968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:36:41.748833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

단속시간
Text

Distinct8220
Distinct (%)82.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T09:36:42.173018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length9
Mean length8.997
Min length7

Characters and Unicode

Total characters89970
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6713 ?
Unique (%)67.1%

Sample

1st row 10:55:31
2nd row 17:30:31
3rd row 13:32:04
4th row 16:40:47
5th row 11:11:31
ValueCountFrequency (%)
19:34:00 7
 
0.1%
19:51:00 6
 
0.1%
14:40:00 5
 
< 0.1%
19:45:00 5
 
< 0.1%
15:00:56 5
 
< 0.1%
15:25:17 5
 
< 0.1%
15:49:40 4
 
< 0.1%
17:17:33 4
 
< 0.1%
17:17:17 4
 
< 0.1%
16:56:00 4
 
< 0.1%
Other values (8210) 9951
99.5%
2023-12-12T09:36:42.937370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 20000
22.2%
1 15075
16.8%
10000
11.1%
0 8665
9.6%
4 7253
 
8.1%
5 6565
 
7.3%
3 5617
 
6.2%
2 5250
 
5.8%
7 3109
 
3.5%
6 3105
 
3.5%
Other values (2) 5331
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 59970
66.7%
Other Punctuation 20000
 
22.2%
Space Separator 10000
 
11.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 15075
25.1%
0 8665
14.4%
4 7253
12.1%
5 6565
10.9%
3 5617
 
9.4%
2 5250
 
8.8%
7 3109
 
5.2%
6 3105
 
5.2%
9 2974
 
5.0%
8 2357
 
3.9%
Other Punctuation
ValueCountFrequency (%)
: 20000
100.0%
Space Separator
ValueCountFrequency (%)
10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 89970
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
: 20000
22.2%
1 15075
16.8%
10000
11.1%
0 8665
9.6%
4 7253
 
8.1%
5 6565
 
7.3%
3 5617
 
6.2%
2 5250
 
5.8%
7 3109
 
3.5%
6 3105
 
3.5%
Other values (2) 5331
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 89970
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
: 20000
22.2%
1 15075
16.8%
10000
11.1%
0 8665
9.6%
4 7253
 
8.1%
5 6565
 
7.3%
3 5617
 
6.2%
2 5250
 
5.8%
7 3109
 
3.5%
6 3105
 
3.5%
Other values (2) 5331
 
5.9%

단속장소정보
Text

Distinct834
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T09:36:43.336285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length17
Mean length10.1453
Min length2

Characters and Unicode

Total characters101453
Distinct characters296
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique437 ?
Unique (%)4.4%

Sample

1st row광사동 고읍파출소 사거리
2nd row덕정동 하모니마트앞
3rd row그린프라자 앞
4th row롯데시네마
5th row남방동 양주시의회
ValueCountFrequency (%)
광사동 2055
 
9.3%
옥정동 1921
 
8.6%
덕정동 1784
 
8.0%
앞 1233
 
5.6%
뒤편 795
 
3.6%
롯데시네마 710
 
3.2%
그랑시떼빌딩 668
 
3.0%
프라임타워 519
 
2.3%
옥정중심상가 488
 
2.2%
사거리 461
 
2.1%
Other values (846) 11580
52.1%
2023-12-12T09:36:43.810598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
12239
 
12.1%
동 7973
 
7.9%
정 6398
 
6.3%
덕 3153
 
3.1%
옥 3132
 
3.1%
사 2845
 
2.8%
광 2607
 
2.6%
로 1922
 
1.9%
라 1841
 
1.8%
프 1837
 
1.8%
Other values (286) 57506
56.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 81150
80.0%
Space Separator 12239
 
12.1%
Decimal Number 6278
 
6.2%
Uppercase Letter 768
 
0.8%
Dash Punctuation 562
 
0.6%
Lowercase Letter 445
 
0.4%
Other Punctuation 11
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 7973
 
9.8%
정 6398
 
7.9%
덕 3153
 
3.9%
옥 3132
 
3.9%
사 2845
 
3.5%
광 2607
 
3.2%
로 1922
 
2.4%
라 1841
 
2.3%
프 1837
 
2.3%
시 1796
 
2.2%
Other values (253) 47646
58.7%
Uppercase Letter
ValueCountFrequency (%)
C 167
21.7%
U 138
18.0%
N 88
11.5%
A 84
10.9%
L 78
10.2%
F 77
10.0%
S 57
 
7.4%
G 29
 
3.8%
V 29
 
3.8%
P 13
 
1.7%
Other values (5) 8
 
1.0%
Decimal Number
ValueCountFrequency (%)
1 1427
22.7%
2 854
13.6%
0 697
11.1%
9 595
9.5%
7 595
9.5%
4 485
 
7.7%
3 482
 
7.7%
5 472
 
7.5%
6 421
 
6.7%
8 250
 
4.0%
Lowercase Letter
ValueCountFrequency (%)
i 138
31.0%
t 138
31.0%
y 138
31.0%
e 31
 
7.0%
Other Punctuation
ValueCountFrequency (%)
. 7
63.6%
@ 4
36.4%
Space Separator
ValueCountFrequency (%)
12239
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 562
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 81150
80.0%
Common 19090
 
18.8%
Latin 1213
 
1.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 7973
 
9.8%
정 6398
 
7.9%
덕 3153
 
3.9%
옥 3132
 
3.9%
사 2845
 
3.5%
광 2607
 
3.2%
로 1922
 
2.4%
라 1841
 
2.3%
프 1837
 
2.3%
시 1796
 
2.2%
Other values (253) 47646
58.7%
Latin
ValueCountFrequency (%)
C 167
13.8%
U 138
11.4%
i 138
11.4%
t 138
11.4%
y 138
11.4%
N 88
7.3%
A 84
6.9%
L 78
6.4%
F 77
6.3%
S 57
 
4.7%
Other values (9) 110
9.1%
Common
ValueCountFrequency (%)
12239
64.1%
1 1427
 
7.5%
2 854
 
4.5%
0 697
 
3.7%
9 595
 
3.1%
7 595
 
3.1%
- 562
 
2.9%
4 485
 
2.5%
3 482
 
2.5%
5 472
 
2.5%
Other values (4) 682
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 81150
80.0%
ASCII 20303
 
20.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
12239
60.3%
1 1427
 
7.0%
2 854
 
4.2%
0 697
 
3.4%
9 595
 
2.9%
7 595
 
2.9%
- 562
 
2.8%
4 485
 
2.4%
3 482
 
2.4%
5 472
 
2.3%
Other values (23) 1895
 
9.3%
Hangul
ValueCountFrequency (%)
동 7973
 
9.8%
정 6398
 
7.9%
덕 3153
 
3.9%
옥 3132
 
3.9%
사 2845
 
3.5%
광 2607
 
3.2%
로 1922
 
2.4%
라 1841
 
2.3%
프 1837
 
2.3%
시 1796
 
2.2%
Other values (253) 47646
58.7%

수납방법
Categorical

IMBALANCE 

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
가상계좌수납
7693 
(간단e)창구수납
 
457
(간단e)링크납부신용카드
 
419
(간단e)CD/ATM
 
297
(간단e)링크납부
 
286
Other values (12)
848 

Length

Max length18
Median length6
Mean length6.9984
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row가상계좌수납
3rd row가상계좌수납
4th row가상계좌수납
5th row가상계좌수납

Common Values

ValueCountFrequency (%)
가상계좌수납 7693
76.9%
(간단e)창구수납 457
 
4.6%
(간단e)링크납부신용카드 419
 
4.2%
(간단e)CD/ATM 297
 
3.0%
(간단e)링크납부 286
 
2.9%
<NA> 238
 
2.4%
(간단e)인터넷지로시스템 128
 
1.3%
(간단e)은행인터넷뱅킹 121
 
1.2%
OCR 완납 104
 
1.0%
(간단e)CD/ATM신용카드 81
 
0.8%
Other values (7) 176
 
1.8%

Length

2023-12-12T09:36:43.997881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
가상계좌수납 7693
76.0%
간단e)창구수납 457
 
4.5%
간단e)링크납부신용카드 419
 
4.1%
간단e)cd/atm 297
 
2.9%
간단e)링크납부 286
 
2.8%
na 238
 
2.4%
간단e)인터넷지로시스템 128
 
1.3%
간단e)은행인터넷뱅킹 121
 
1.2%
완납 110
 
1.1%
ocr 108
 
1.1%
Other values (8) 265
 
2.6%

수납일
Date

Distinct582
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2019-09-11 00:00:00
Maximum2021-04-26 00:00:00
2023-12-12T09:36:44.162750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:36:44.327572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

본세
Real number (ℝ)

HIGH CORRELATION 

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36874.975
Minimum5750
Maximum90000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T09:36:44.483544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5750
5-th percentile32000
Q132000
median32000
Q340000
95-th percentile50000
Maximum90000
Range84250
Interquartile range (IQR)8000

Descriptive statistics

Standard deviation7986.3072
Coefficient of variation (CV)0.21657797
Kurtosis10.558801
Mean36874.975
Median Absolute Deviation (MAD)0
Skewness2.7649636
Sum3.6874975 × 108
Variance63781103
MonotonicityNot monotonic
2023-12-12T09:36:44.608559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
32000 5473
54.7%
40000 3859
38.6%
64000 300
 
3.0%
50000 216
 
2.2%
80000 82
 
0.8%
16000 26
 
0.3%
20000 17
 
0.2%
72000 16
 
0.2%
90000 8
 
0.1%
30000 2
 
< 0.1%
ValueCountFrequency (%)
5750 1
 
< 0.1%
16000 26
 
0.3%
20000 17
 
0.2%
30000 2
 
< 0.1%
32000 5473
54.7%
40000 3859
38.6%
50000 216
 
2.2%
64000 300
 
3.0%
72000 16
 
0.2%
80000 82
 
0.8%
ValueCountFrequency (%)
90000 8
 
0.1%
80000 82
 
0.8%
72000 16
 
0.2%
64000 300
 
3.0%
50000 216
 
2.2%
40000 3859
38.6%
32000 5473
54.7%
30000 2
 
< 0.1%
20000 17
 
0.2%
16000 26
 
0.3%

가산금
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct135
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2588.916
Minimum0
Maximum38500
Zeros7493
Zeros (%)74.9%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T09:36:44.750783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3945
95-th percentile18960
Maximum38500
Range38500
Interquartile range (IQR)945

Descriptive statistics

Standard deviation6749.2898
Coefficient of variation (CV)2.6069945
Kurtosis9.4052125
Mean2588.916
Median Absolute Deviation (MAD)0
Skewness3.136442
Sum25889160
Variance45552913
MonotonicityNot monotonic
2023-12-12T09:36:44.932596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7493
74.9%
1200 340
 
3.4%
30800 247
 
2.5%
1680 237
 
2.4%
2160 115
 
1.1%
3120 75
 
0.8%
2640 66
 
0.7%
3600 62
 
0.6%
5520 54
 
0.5%
4080 51
 
0.5%
Other values (125) 1260
 
12.6%
ValueCountFrequency (%)
0 7493
74.9%
600 4
 
< 0.1%
840 2
 
< 0.1%
900 1
 
< 0.1%
1080 1
 
< 0.1%
1200 340
 
3.4%
1440 1
 
< 0.1%
1500 29
 
0.3%
1680 237
 
2.4%
1800 1
 
< 0.1%
ValueCountFrequency (%)
38500 16
 
0.2%
37300 1
 
< 0.1%
36100 2
 
< 0.1%
35500 1
 
< 0.1%
33100 1
 
< 0.1%
32500 2
 
< 0.1%
31900 1
 
< 0.1%
30800 247
2.5%
30700 1
 
< 0.1%
30320 7
 
0.1%

합계
Real number (ℝ)

HIGH CORRELATION 

Distinct145
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39463.891
Minimum7190
Maximum106740
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T09:36:45.116375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7190
5-th percentile32000
Q132000
median32000
Q341680
95-th percentile64080
Maximum106740
Range99550
Interquartile range (IQR)9680

Descriptive statistics

Standard deviation11425.327
Coefficient of variation (CV)0.28951344
Kurtosis2.9626053
Mean39463.891
Median Absolute Deviation (MAD)0
Skewness1.768338
Sum3.9463891 × 108
Variance1.305381 × 108
MonotonicityNot monotonic
2023-12-12T09:36:45.249218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32000 5472
54.7%
40000 1545
 
15.4%
41200 340
 
3.4%
64000 300
 
3.0%
70800 247
 
2.5%
41680 237
 
2.4%
42160 115
 
1.1%
43120 75
 
0.8%
50000 74
 
0.7%
42640 66
 
0.7%
Other values (135) 1529
 
15.3%
ValueCountFrequency (%)
7190 1
 
< 0.1%
16000 26
 
0.3%
20000 9
 
0.1%
20600 4
 
< 0.1%
20840 2
 
< 0.1%
21080 1
 
< 0.1%
21800 1
 
< 0.1%
30000 1
 
< 0.1%
30900 1
 
< 0.1%
32000 5472
54.7%
ValueCountFrequency (%)
106740 1
 
< 0.1%
97020 1
 
< 0.1%
96800 1
 
< 0.1%
94880 1
 
< 0.1%
92960 1
 
< 0.1%
92700 2
 
< 0.1%
92000 1
 
< 0.1%
90080 1
 
< 0.1%
90000 4
 
< 0.1%
88500 16
0.2%

관리기관명
Categorical

CONSTANT 

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

Length

Max length9
Median length9
Mean length9
Min length9

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row양주시 차량관리과
2nd row양주시 차량관리과
3rd row양주시 차량관리과
4th row양주시 차량관리과
5th row양주시 차량관리과

Common Values

ValueCountFrequency (%)
양주시 차량관리과 10000
100.0%

Length

2023-12-12T09:36:45.366093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:36:45.456866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
양주시 10000
50.0%
차량관리과 10000
50.0%

데이터기준일
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-08-22
10000 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-08-22
2nd row2023-08-22
3rd row2023-08-22
4th row2023-08-22
5th row2023-08-22

Common Values

ValueCountFrequency (%)
2023-08-22 10000
100.0%

Length

2023-12-12T09:36:45.545359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:36:45.639785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-08-22 10000
100.0%

Interactions

2023-12-12T09:36:39.337934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:36:38.380664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:36:38.684640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:36:38.967380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:36:39.448381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:36:38.458902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:36:38.755358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:36:39.046982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:36:39.520497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:36:38.529713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:36:38.819239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:36:39.122936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:36:39.610507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:36:38.612298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:36:38.898779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:36:39.215755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T09:36:45.698251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
전자납부번호수납방법본세가산금합계
전자납부번호1.0000.0000.2500.4110.399
수납방법0.0001.0000.1670.1750.181
본세0.2500.1671.0000.4300.899
가산금0.4110.1750.4301.0000.915
합계0.3990.1810.8990.9151.000
2023-12-12T09:36:45.786675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
전자납부번호본세가산금합계수납방법
전자납부번호1.000-0.316-0.546-0.4320.000
본세-0.3161.0000.5780.9590.064
가산금-0.5460.5781.0000.7540.069
합계-0.4320.9590.7541.0000.072
수납방법0.0000.0640.0690.0721.000

Missing values

2023-12-12T09:36:39.718745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T09:36:39.903571image/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

소인일고지번호전자납부번호단속일단속시간단속장소정보수납방법수납일본세가산금합계관리기관명데이터기준일
204682020-05-11288125 2020 05 3 001 00019741630220615001448512020-05-0110:55:31광사동 고읍파출소 사거리<NA>2020-05-1132000032000양주시 차량관리과2023-08-22
60082019-11-21288125 2019 01 1 001 00080541630219615000080592018-11-0517:30:31덕정동 하모니마트앞가상계좌수납2019-11-2040000552045520양주시 차량관리과2023-08-22
15422019-10-07288125 2019 09 1 001 00050741630219615003624052019-07-0213:32:04그린프라자 앞가상계좌수납2019-10-0450000150051500양주시 차량관리과2023-08-22
286222020-08-21288125 2018 02 1 001 00819341630218615000819342017-12-0316:40:47롯데시네마가상계좌수납2020-08-20400001512055120양주시 차량관리과2023-08-22
441192021-01-28288125 2009 06 2 001 00065841630209615000933532009-06-1011:11:31남방동 양주시의회가상계좌수납2021-01-27400003080070800양주시 차량관리과2023-08-22
122652020-01-22288125 2018 01 1 001 00259741630218615003492992017-11-0916:22:04덕정초교 주변가상계좌수납2020-01-21400001224052240양주시 차량관리과2023-08-22
504092021-04-06288125 2021 03 3 001 00337041630221615001207972021-03-2613:16:00덕정동 123-10가상계좌수납2021-04-0564000064000양주시 차량관리과2023-08-22
387152020-12-04288125 2019 10 1 001 00072441630219615004112432019-08-1813:47:30덕정동 덕정우체국(간단e)링크납부신용카드2020-12-0440000744047440양주시 차량관리과2023-08-22
154902020-03-02288125 2020 02 3 001 00141841630220615000641702020-02-1514:39:20덕정동 그랑시떼빌딩 뒤편가상계좌수납2020-02-2932000032000양주시 차량관리과2023-08-22
198002020-05-04288125 2018 02 1 001 00726741630218615000726772017-12-1714:36:38덕정초교 주변가상계좌수납2020-04-29400001320053200양주시 차량관리과2023-08-22
소인일고지번호전자납부번호단속일단속시간단속장소정보수납방법수납일본세가산금합계관리기관명데이터기준일
303262020-09-08288125 2020 09 1 001 00089441630220615002934752020-07-3116:03:03덕정동 그랑시떼빌딩 뒤편<NA>2020-09-0840000040000양주시 차량관리과2023-08-22
335072020-10-14288125 2020 10 3 001 00011241630220615003382002020-10-0510:28:37회정로109번길가상계좌수납2020-10-1332000032000양주시 차량관리과2023-08-22
260372020-07-17288125 2020 07 3 001 00022841630220615002199422020-07-0515:26:03삼숭동 성우아파트 교차로가상계좌수납2020-07-1632000032000양주시 차량관리과2023-08-22
237912020-06-19288125 2019 01 1 001 00022041630219615000022082018-10-2219:18:43광사동 가보프라자사거리가상계좌수납2020-06-1840000888048880양주시 차량관리과2023-08-22
70252019-11-29288125 2019 11 3 001 00244341630219615004838012019-11-2009:33:31덕정동 덕정우체국가상계좌수납2019-11-2832000032000양주시 차량관리과2023-08-22
300572020-09-04288125 2020 08 3 001 00321241630220615002836112020-08-1514:14:00광적면 가납리 836-1(간단e)창구수납2020-09-0432000032000양주시 차량관리과2023-08-22
374052020-11-23288125 2020 10 1 001 00013641630220615003276182020-08-0317:23:08덕정동 그랑시떼빌딩 뒤편가상계좌수납2020-11-2040000120041200양주시 차량관리과2023-08-22
171402020-03-23288125 2020 03 1 001 00039641630220615000834792020-01-1419:41:15광사동 가보프라자 사거리OCR 완납2020-03-2340000040000양주시 차량관리과2023-08-22
420942021-01-06288125 2020 12 3 001 00166541630220615004394712020-12-1617:33:28옥정동 옥정중심상가 엠타워 앞가상계좌수납2021-01-0532000032000양주시 차량관리과2023-08-22
492172021-03-25288125 2021 03 1 001 00063041630221615000855512021-01-1214:16:08광사동 가보프라자 사거리가상계좌수납2021-03-2440000040000양주시 차량관리과2023-08-22

Duplicate rows

Most frequently occurring

소인일고지번호전자납부번호단속일단속시간단속장소정보수납방법수납일본세가산금합계관리기관명데이터기준일# duplicates
02020-06-22288125 2020 06 3 001 00108241630220615001914762020-04-1810:08:31삼숭동 자이프라자 삼거리가상계좌수납2020-06-1964000064000양주시 차량관리과2023-08-222
12020-09-01288125 2020 08 1 001 00082641630220615002552062020-06-0310:48:51고암길가상계좌수납2020-08-3140000040000양주시 차량관리과2023-08-222