Overview

Dataset statistics

Number of variables12
Number of observations21
Missing cells1
Missing cells (%)0.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.2 KiB
Average record size in memory107.3 B

Variable types

Numeric4
Categorical1
DateTime1
Text6

Dataset

Description대전광역시 서구 관내에서 무등록이륜차과태료를 부과한 현황 정보(행정동, 적발일자, 적발관청, 상세주소 등)를 제공합니다.
URLhttps://www.data.go.kr/data/15104076/fileData.do

Alerts

부과금액(원) has constant value ""Constant
국 is highly overall correlated with 부과일자High correlation
부과일자 is highly overall correlated with 국High correlation
행정동코드 is highly overall correlated with 법정동코드High correlation
법정동코드 is highly overall correlated with 행정동코드High correlation
도로명주소 has 1 (4.8%) missing valuesMissing
국 has unique valuesUnique

Reproduction

Analysis started2023-12-12 14:38:33.690421
Analysis finished2023-12-12 14:38:36.257030
Duration2.57 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

국
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-12T23:38:36.332434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q16
median11
Q316
95-th percentile20
Maximum21
Range20
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.2048368
Coefficient of variation (CV)0.56407607
Kurtosis-1.2
Mean11
Median Absolute Deviation (MAD)5
Skewness0
Sum231
Variance38.5
MonotonicityStrictly increasing
2023-12-12T23:38:36.463393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
1 1
 
4.8%
2 1
 
4.8%
21 1
 
4.8%
20 1
 
4.8%
19 1
 
4.8%
18 1
 
4.8%
17 1
 
4.8%
16 1
 
4.8%
15 1
 
4.8%
14 1
 
4.8%
Other values (11) 11
52.4%
ValueCountFrequency (%)
1 1
4.8%
2 1
4.8%
3 1
4.8%
4 1
4.8%
5 1
4.8%
6 1
4.8%
7 1
4.8%
8 1
4.8%
9 1
4.8%
10 1
4.8%
ValueCountFrequency (%)
21 1
4.8%
20 1
4.8%
19 1
4.8%
18 1
4.8%
17 1
4.8%
16 1
4.8%
15 1
4.8%
14 1
4.8%
13 1
4.8%
12 1
4.8%

부과일자
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)38.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20230479
Minimum20230313
Maximum20230623
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-12T23:38:36.578281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20230313
5-th percentile20230329
Q120230329
median20230515
Q320230623
95-th percentile20230623
Maximum20230623
Range310
Interquartile range (IQR)294

Descriptive statistics

Standard deviation123.98393
Coefficient of variation (CV)6.1285711 × 10-6
Kurtosis-1.6861511
Mean20230479
Median Absolute Deviation (MAD)108
Skewness-0.031263395
Sum4.2484005 × 108
Variance15372.014
MonotonicityIncreasing
2023-12-12T23:38:36.702981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
20230623 7
33.3%
20230329 5
23.8%
20230515 3
14.3%
20230421 2
 
9.5%
20230313 1
 
4.8%
20230407 1
 
4.8%
20230414 1
 
4.8%
20230526 1
 
4.8%
ValueCountFrequency (%)
20230313 1
 
4.8%
20230329 5
23.8%
20230407 1
 
4.8%
20230414 1
 
4.8%
20230421 2
 
9.5%
20230515 3
14.3%
20230526 1
 
4.8%
20230623 7
33.3%
ValueCountFrequency (%)
20230623 7
33.3%
20230526 1
 
4.8%
20230515 3
14.3%
20230421 2
 
9.5%
20230414 1
 
4.8%
20230407 1
 
4.8%
20230329 5
23.8%
20230313 1
 
4.8%

부과금액(원)
Categorical

CONSTANT 

Distinct1
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Memory size300.0 B
500000
21 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row500000
2nd row500000
3rd row500000
4th row500000
5th row500000

Common Values

ValueCountFrequency (%)
500000 21
100.0%

Length

2023-12-12T23:38:36.820050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:38:36.909110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
500000 21
100.0%

적발일자
Date

Distinct20
Distinct (%)95.2%
Missing0
Missing (%)0.0%
Memory size300.0 B
Minimum2022-05-14 00:00:00
Maximum2023-06-01 00:00:00
2023-12-12T23:38:36.996823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:38:37.112102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)

행정동
Text

Distinct15
Distinct (%)71.4%
Missing0
Missing (%)0.0%
Memory size300.0 B
2023-12-12T23:38:37.267130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.6190476
Min length2

Characters and Unicode

Total characters76
Distinct characters27
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)57.1%

Sample

1st row도안동
2nd row가장동
3rd row도마2동
4th row월평1동
5th row관저2동
ValueCountFrequency (%)
월평1동 5
23.8%
도마2동 2
 
9.5%
관저2동 2
 
9.5%
도안동 1
 
4.8%
가장동 1
 
4.8%
전민동 1
 
4.8%
금산읍 1
 
4.8%
문창동 1
 
4.8%
월평2동 1
 
4.8%
괴정동 1
 
4.8%
Other values (5) 5
23.8%
2023-12-12T23:38:37.664693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 20
26.3%
2 7
 
9.2%
월 6
 
7.9%
평 6
 
7.9%
1 5
 
6.6%
도 3
 
3.9%
마 3
 
3.9%
산 3
 
3.9%
관 2
 
2.6%
저 2
 
2.6%
Other values (17) 19
25.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 63
82.9%
Decimal Number 13
 
17.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 20
31.7%
월 6
 
9.5%
평 6
 
9.5%
도 3
 
4.8%
마 3
 
4.8%
산 3
 
4.8%
관 2
 
3.2%
저 2
 
3.2%
가 2
 
3.2%
둔 2
 
3.2%
Other values (14) 14
22.2%
Decimal Number
ValueCountFrequency (%)
2 7
53.8%
1 5
38.5%
3 1
 
7.7%

Most occurring scripts

ValueCountFrequency (%)
Hangul 63
82.9%
Common 13
 
17.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 20
31.7%
월 6
 
9.5%
평 6
 
9.5%
도 3
 
4.8%
마 3
 
4.8%
산 3
 
4.8%
관 2
 
3.2%
저 2
 
3.2%
가 2
 
3.2%
둔 2
 
3.2%
Other values (14) 14
22.2%
Common
ValueCountFrequency (%)
2 7
53.8%
1 5
38.5%
3 1
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 63
82.9%
ASCII 13
 
17.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 20
31.7%
월 6
 
9.5%
평 6
 
9.5%
도 3
 
4.8%
마 3
 
4.8%
산 3
 
4.8%
관 2
 
3.2%
저 2
 
3.2%
가 2
 
3.2%
둔 2
 
3.2%
Other values (14) 14
22.2%
ASCII
ValueCountFrequency (%)
2 7
53.8%
1 5
38.5%
3 1
 
7.7%

행정동코드
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)71.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0862949 × 109
Minimum3.0140605 × 109
Maximum4.471025 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-12T23:38:37.798190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.0140605 × 109
5-th percentile3.017053 × 109
Q13.017057 × 109
median3.0170586 × 109
Q33.0170597 × 109
95-th percentile3.020057 × 109
Maximum4.471025 × 109
Range1.4569645 × 109
Interquartile range (IQR)2700

Descriptive statistics

Standard deviation3.1728294 × 108
Coefficient of variation (CV)0.10280383
Kurtosis20.999566
Mean3.0862949 × 109
Median Absolute Deviation (MAD)1100
Skewness4.5825079
Sum6.4812193 × 1010
Variance1.0066846 × 1017
MonotonicityNot monotonic
2023-12-12T23:38:37.946921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
3017058600 5
23.8%
3017053000 2
 
9.5%
3017059700 2
 
9.5%
3017059300 1
 
4.8%
3017057000 1
 
4.8%
3020057000 1
 
4.8%
4471025000 1
 
4.8%
3014060500 1
 
4.8%
3017058700 1
 
4.8%
3017056000 1
 
4.8%
Other values (5) 5
23.8%
ValueCountFrequency (%)
3014060500 1
 
4.8%
3017053000 2
 
9.5%
3017054000 1
 
4.8%
3017056000 1
 
4.8%
3017057000 1
 
4.8%
3017058200 1
 
4.8%
3017058600 5
23.8%
3017058700 1
 
4.8%
3017059000 1
 
4.8%
3017059300 1
 
4.8%
ValueCountFrequency (%)
4471025000 1
 
4.8%
3020057000 1
 
4.8%
3017066000 1
 
4.8%
3017064000 1
 
4.8%
3017059700 2
 
9.5%
3017059300 1
 
4.8%
3017059000 1
 
4.8%
3017058700 1
 
4.8%
3017058600 5
23.8%
3017058200 1
 
4.8%

법정동
Text

Distinct13
Distinct (%)61.9%
Missing0
Missing (%)0.0%
Memory size300.0 B
2023-12-12T23:38:38.118996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length3.1428571
Min length2

Characters and Unicode

Total characters66
Distinct characters25
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)42.9%

Sample

1st row도안동
2nd row가장동
3rd row도마동
4th row월평동
5th row관저동
ValueCountFrequency (%)
월평동 6
27.3%
도마동 2
 
9.1%
관저동 2
 
9.1%
둔산동 2
 
9.1%
도안동 1
 
4.5%
가장동 1
 
4.5%
문지동 1
 
4.5%
금산읍 1
 
4.5%
중도리 1
 
4.5%
문창동 1
 
4.5%
Other values (4) 4
18.2%
2023-12-12T23:38:38.407134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 20
30.3%
월 6
 
9.1%
평 6
 
9.1%
도 4
 
6.1%
산 3
 
4.5%
마 3
 
4.5%
괴 2
 
3.0%
문 2
 
3.0%
둔 2
 
3.0%
저 2
 
3.0%
Other values (15) 16
24.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 65
98.5%
Space Separator 1
 
1.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 20
30.8%
월 6
 
9.2%
평 6
 
9.2%
도 4
 
6.2%
산 3
 
4.6%
마 3
 
4.6%
괴 2
 
3.1%
문 2
 
3.1%
둔 2
 
3.1%
저 2
 
3.1%
Other values (14) 15
23.1%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 65
98.5%
Common 1
 
1.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 20
30.8%
월 6
 
9.2%
평 6
 
9.2%
도 4
 
6.2%
산 3
 
4.6%
마 3
 
4.6%
괴 2
 
3.1%
문 2
 
3.1%
둔 2
 
3.1%
저 2
 
3.1%
Other values (14) 15
23.1%
Common
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 65
98.5%
ASCII 1
 
1.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 20
30.8%
월 6
 
9.2%
평 6
 
9.2%
도 4
 
6.2%
산 3
 
4.6%
마 3
 
4.6%
괴 2
 
3.1%
문 2
 
3.1%
둔 2
 
3.1%
저 2
 
3.1%
Other values (14) 15
23.1%
ASCII
ValueCountFrequency (%)
1
100.0%

법정동코드
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)61.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.08625 × 109
Minimum3.0140106 × 109
Maximum4.471025 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-12T23:38:38.547408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.0140106 × 109
5-th percentile3.0170102 × 109
Q13.0170109 × 109
median3.0170113 × 109
Q33.0170115 × 109
95-th percentile3.020014 × 109
Maximum4.471025 × 109
Range1.4570144 × 109
Interquartile range (IQR)600

Descriptive statistics

Standard deviation3.1729323 × 108
Coefficient of variation (CV)0.10280866
Kurtosis20.999565
Mean3.08625 × 109
Median Absolute Deviation (MAD)300
Skewness4.5825077
Sum6.4811251 × 1010
Variance1.0067499 × 1017
MonotonicityNot monotonic
2023-12-12T23:38:38.689938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
3017011300 6
28.6%
3017010300 2
 
9.5%
3017011600 2
 
9.5%
3017011200 2
 
9.5%
3017011500 1
 
4.8%
3017010900 1
 
4.8%
3020014000 1
 
4.8%
4471025022 1
 
4.8%
3014010600 1
 
4.8%
3017010800 1
 
4.8%
Other values (3) 3
14.3%
ValueCountFrequency (%)
3014010600 1
 
4.8%
3017010200 1
 
4.8%
3017010300 2
 
9.5%
3017010800 1
 
4.8%
3017010900 1
 
4.8%
3017011100 1
 
4.8%
3017011200 2
 
9.5%
3017011300 6
28.6%
3017011500 1
 
4.8%
3017011600 2
 
9.5%
ValueCountFrequency (%)
4471025022 1
 
4.8%
3020014000 1
 
4.8%
3017012700 1
 
4.8%
3017011600 2
 
9.5%
3017011500 1
 
4.8%
3017011300 6
28.6%
3017011200 2
 
9.5%
3017011100 1
 
4.8%
3017010900 1
 
4.8%
3017010800 1
 
4.8%

지번주소
Text

Distinct19
Distinct (%)90.5%
Missing0
Missing (%)0.0%
Memory size300.0 B
2023-12-12T23:38:38.875564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length8.3333333
Min length6

Characters and Unicode

Total characters175
Distinct characters34
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17 ?
Unique (%)81.0%

Sample

1st row도안동 1564
2nd row가장동 54-1
3rd row도마동 104-4
4th row월평동 1292
5th row관저동 1558-13
ValueCountFrequency (%)
월평동 6
 
14.3%
635 2
 
4.8%
1292 2
 
4.8%
도마동 2
 
4.8%
둔산동 2
 
4.8%
관저동 2
 
4.8%
7-9 1
 
2.4%
437-2 1
 
2.4%
956 1
 
2.4%
변동 1
 
2.4%
Other values (22) 22
52.4%
2023-12-12T23:38:39.256853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
21
 
12.0%
동 20
 
11.4%
1 15
 
8.6%
2 14
 
8.0%
5 13
 
7.4%
- 11
 
6.3%
4 8
 
4.6%
월 6
 
3.4%
평 6
 
3.4%
6 6
 
3.4%
Other values (24) 55
31.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 81
46.3%
Other Letter 62
35.4%
Space Separator 21
 
12.0%
Dash Punctuation 11
 
6.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 20
32.3%
월 6
 
9.7%
평 6
 
9.7%
도 4
 
6.5%
마 3
 
4.8%
둔 2
 
3.2%
문 2
 
3.2%
괴 2
 
3.2%
산 2
 
3.2%
관 2
 
3.2%
Other values (12) 13
21.0%
Decimal Number
ValueCountFrequency (%)
1 15
18.5%
2 14
17.3%
5 13
16.0%
4 8
9.9%
6 6
 
7.4%
3 6
 
7.4%
0 5
 
6.2%
7 5
 
6.2%
9 5
 
6.2%
8 4
 
4.9%
Space Separator
ValueCountFrequency (%)
21
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 113
64.6%
Hangul 62
35.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 20
32.3%
월 6
 
9.7%
평 6
 
9.7%
도 4
 
6.5%
마 3
 
4.8%
둔 2
 
3.2%
문 2
 
3.2%
괴 2
 
3.2%
산 2
 
3.2%
관 2
 
3.2%
Other values (12) 13
21.0%
Common
ValueCountFrequency (%)
21
18.6%
1 15
13.3%
2 14
12.4%
5 13
11.5%
- 11
9.7%
4 8
 
7.1%
6 6
 
5.3%
3 6
 
5.3%
0 5
 
4.4%
7 5
 
4.4%
Other values (2) 9
8.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 113
64.6%
Hangul 62
35.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
21
18.6%
1 15
13.3%
2 14
12.4%
5 13
11.5%
- 11
9.7%
4 8
 
7.1%
6 6
 
5.3%
3 6
 
5.3%
0 5
 
4.4%
7 5
 
4.4%
Other values (2) 9
8.0%
Hangul
ValueCountFrequency (%)
동 20
32.3%
월 6
 
9.7%
평 6
 
9.7%
도 4
 
6.5%
마 3
 
4.8%
둔 2
 
3.2%
문 2
 
3.2%
괴 2
 
3.2%
산 2
 
3.2%
관 2
 
3.2%
Other values (12) 13
21.0%

도로명주소
Text

MISSING 

Distinct18
Distinct (%)90.0%
Missing1
Missing (%)4.8%
Memory size300.0 B
2023-12-12T23:38:39.503047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length21
Mean length16.75
Min length1

Characters and Unicode

Total characters335
Distinct characters55
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)80.0%

Sample

1st row대전광역시 서구 도안동로 77
2nd row대전광역시 서구 도산로 324
3rd row대전광역시 서구 사마7길 37
4th row대전광역시 서구 월평새뜸로4번길 67
5th row대전광역시 서구 관저남로25번길 92
ValueCountFrequency (%)
대전광역시 18
23.1%
서구 16
20.5%
월평새뜸로4번길 2
 
2.6%
67 2
 
2.6%
월평로 2
 
2.6%
75 2
 
2.6%
도산로 2
 
2.6%
관저로 1
 
1.3%
도솔로 1
 
1.3%
378 1
 
1.3%
Other values (31) 31
39.7%
2023-12-12T23:38:39.877043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
58
17.3%
대 19
 
5.7%
광 18
 
5.4%
역 18
 
5.4%
시 18
 
5.4%
구 18
 
5.4%
전 18
 
5.4%
서 17
 
5.1%
로 17
 
5.1%
1 13
 
3.9%
Other values (45) 121
36.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 214
63.9%
Decimal Number 60
 
17.9%
Space Separator 58
 
17.3%
Dash Punctuation 3
 
0.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
대 19
 
8.9%
광 18
 
8.4%
역 18
 
8.4%
시 18
 
8.4%
구 18
 
8.4%
전 18
 
8.4%
서 17
 
7.9%
로 17
 
7.9%
길 7
 
3.3%
도 5
 
2.3%
Other values (33) 59
27.6%
Decimal Number
ValueCountFrequency (%)
1 13
21.7%
2 10
16.7%
7 9
15.0%
5 6
10.0%
3 5
 
8.3%
4 4
 
6.7%
0 4
 
6.7%
9 3
 
5.0%
6 3
 
5.0%
8 3
 
5.0%
Space Separator
ValueCountFrequency (%)
58
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 214
63.9%
Common 121
36.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
대 19
 
8.9%
광 18
 
8.4%
역 18
 
8.4%
시 18
 
8.4%
구 18
 
8.4%
전 18
 
8.4%
서 17
 
7.9%
로 17
 
7.9%
길 7
 
3.3%
도 5
 
2.3%
Other values (33) 59
27.6%
Common
ValueCountFrequency (%)
58
47.9%
1 13
 
10.7%
2 10
 
8.3%
7 9
 
7.4%
5 6
 
5.0%
3 5
 
4.1%
4 4
 
3.3%
0 4
 
3.3%
9 3
 
2.5%
- 3
 
2.5%
Other values (2) 6
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 214
63.9%
ASCII 121
36.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
58
47.9%
1 13
 
10.7%
2 10
 
8.3%
7 9
 
7.4%
5 6
 
5.0%
3 5
 
4.1%
4 4
 
3.3%
0 4
 
3.3%
9 3
 
2.5%
- 3
 
2.5%
Other values (2) 6
 
5.0%
Hangul
ValueCountFrequency (%)
대 19
 
8.9%
광 18
 
8.4%
역 18
 
8.4%
시 18
 
8.4%
구 18
 
8.4%
전 18
 
8.4%
서 17
 
7.9%
로 17
 
7.9%
길 7
 
3.3%
도 5
 
2.3%
Other values (33) 59
27.6%

상세주소
Text

Distinct19
Distinct (%)90.5%
Missing0
Missing (%)0.0%
Memory size300.0 B
2023-12-12T23:38:40.122585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length29
Median length25
Mean length21.666667
Min length8

Characters and Unicode

Total characters455
Distinct characters78
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17 ?
Unique (%)81.0%

Sample

1st row대전광역시 서구 도안동로 77, 도안린풀하우스 주차장
2nd row대전광역시 서구 도산로 324 부근 노상
3rd row도마동 104-4번지 앞 노상
4th row대전광역시 서구 월평새뜸로4번길 67 앞 노상
5th row대전광역시 서구 관저남로25번길 92 앞 노상
ValueCountFrequency (%)
대전광역시 17
14.7%
앞 16
 
13.8%
노상 15
 
12.9%
서구 15
 
12.9%
부근 4
 
3.4%
월평새뜸로4번길 2
 
1.7%
67 2
 
1.7%
월평로 2
 
1.7%
75 2
 
1.7%
동서대로 1
 
0.9%
Other values (40) 40
34.5%
2023-12-12T23:38:40.450631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
95
20.9%
대 19
 
4.2%
로 17
 
3.7%
광 17
 
3.7%
역 17
 
3.7%
시 17
 
3.7%
구 17
 
3.7%
전 17
 
3.7%
서 16
 
3.5%
앞 16
 
3.5%
Other values (68) 207
45.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 289
63.5%
Space Separator 95
 
20.9%
Decimal Number 66
 
14.5%
Dash Punctuation 4
 
0.9%
Other Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
대 19
 
6.6%
로 17
 
5.9%
광 17
 
5.9%
역 17
 
5.9%
시 17
 
5.9%
구 17
 
5.9%
전 17
 
5.9%
서 16
 
5.5%
앞 16
 
5.5%
노 15
 
5.2%
Other values (55) 121
41.9%
Decimal Number
ValueCountFrequency (%)
1 15
22.7%
2 11
16.7%
7 8
12.1%
4 7
10.6%
0 6
 
9.1%
5 5
 
7.6%
3 5
 
7.6%
8 3
 
4.5%
9 3
 
4.5%
6 3
 
4.5%
Space Separator
ValueCountFrequency (%)
95
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 289
63.5%
Common 166
36.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
대 19
 
6.6%
로 17
 
5.9%
광 17
 
5.9%
역 17
 
5.9%
시 17
 
5.9%
구 17
 
5.9%
전 17
 
5.9%
서 16
 
5.5%
앞 16
 
5.5%
노 15
 
5.2%
Other values (55) 121
41.9%
Common
ValueCountFrequency (%)
95
57.2%
1 15
 
9.0%
2 11
 
6.6%
7 8
 
4.8%
4 7
 
4.2%
0 6
 
3.6%
5 5
 
3.0%
3 5
 
3.0%
- 4
 
2.4%
8 3
 
1.8%
Other values (3) 7
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 289
63.5%
ASCII 166
36.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
95
57.2%
1 15
 
9.0%
2 11
 
6.6%
7 8
 
4.8%
4 7
 
4.2%
0 6
 
3.6%
5 5
 
3.0%
3 5
 
3.0%
- 4
 
2.4%
8 3
 
1.8%
Other values (3) 7
 
4.2%
Hangul
ValueCountFrequency (%)
대 19
 
6.6%
로 17
 
5.9%
광 17
 
5.9%
역 17
 
5.9%
시 17
 
5.9%
구 17
 
5.9%
전 17
 
5.9%
서 16
 
5.5%
앞 16
 
5.5%
노 15
 
5.2%
Other values (55) 121
41.9%

적발관청
Text

Distinct15
Distinct (%)71.4%
Missing0
Missing (%)0.0%
Memory size300.0 B
2023-12-12T23:38:40.626911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length13
Mean length11.714286
Min length4

Characters and Unicode

Total characters246
Distinct characters40
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)57.1%

Sample

1st row대전서부경찰서 경비교통과
2nd row합동단속
3rd row대전광역시경찰청 교통과
4th row대전둔산경찰서 수사과
5th row대전서부경찰서 형사과
ValueCountFrequency (%)
대전둔산경찰서 8
20.0%
대전서부경찰서 7
17.5%
경비교통과 7
17.5%
수사과 3
 
7.5%
도룡지구대 1
 
2.5%
가수원지구대 1
 
2.5%
내동지구대 1
 
2.5%
월평지구대 1
 
2.5%
대전중부경찰서 1
 
2.5%
생활안전교통과 1
 
2.5%
Other values (9) 9
22.5%
2023-12-12T23:38:40.924200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
서 26
 
10.6%
경 26
 
10.6%
대 24
 
9.8%
전 20
 
8.1%
찰 19
 
7.7%
19
 
7.7%
과 13
 
5.3%
산 9
 
3.7%
통 9
 
3.7%
교 9
 
3.7%
Other values (30) 72
29.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 227
92.3%
Space Separator 19
 
7.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 26
11.5%
경 26
11.5%
대 24
 
10.6%
전 20
 
8.8%
찰 19
 
8.4%
과 13
 
5.7%
산 9
 
4.0%
통 9
 
4.0%
교 9
 
4.0%
둔 8
 
3.5%
Other values (29) 64
28.2%
Space Separator
ValueCountFrequency (%)
19
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 227
92.3%
Common 19
 
7.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 26
11.5%
경 26
11.5%
대 24
 
10.6%
전 20
 
8.8%
찰 19
 
8.4%
과 13
 
5.7%
산 9
 
4.0%
통 9
 
4.0%
교 9
 
4.0%
둔 8
 
3.5%
Other values (29) 64
28.2%
Common
ValueCountFrequency (%)
19
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 227
92.3%
ASCII 19
 
7.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
서 26
11.5%
경 26
11.5%
대 24
 
10.6%
전 20
 
8.8%
찰 19
 
8.4%
과 13
 
5.7%
산 9
 
4.0%
통 9
 
4.0%
교 9
 
4.0%
둔 8
 
3.5%
Other values (29) 64
28.2%
ASCII
ValueCountFrequency (%)
19
100.0%

Interactions

2023-12-12T23:38:35.553731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:38:34.154424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:38:34.564606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:38:35.030952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:38:35.651042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:38:34.248221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:38:34.684073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:38:35.123122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:38:35.750039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:38:34.347389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:38:34.798665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:38:35.258979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:38:35.849359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:38:34.452735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:38:34.916586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:38:35.434317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T23:38:41.379924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
국부과일자적발일자행정동행정동코드법정동법정동코드지번주소도로명주소상세주소적발관청
국1.0001.0000.9480.7210.2710.5320.2710.8850.8920.8850.774
부과일자1.0001.0001.0000.7060.3660.7670.3660.6620.6420.6620.866
적발일자0.9481.0001.0001.0001.0001.0001.0000.9690.9660.9690.875
행정동0.7210.7061.0001.0001.0001.0001.0001.0001.0001.0000.911
행정동코드0.2710.3661.0001.0001.0001.0000.6231.0001.0001.0001.000
법정동0.5320.7671.0001.0001.0001.0001.0001.0001.0001.0000.762
법정동코드0.2710.3661.0001.0000.6231.0001.0001.0001.0001.0001.000
지번주소0.8850.6620.9691.0001.0001.0001.0001.0001.0001.0000.940
도로명주소0.8920.6420.9661.0001.0001.0001.0001.0001.0001.0000.915
상세주소0.8850.6620.9691.0001.0001.0001.0001.0001.0001.0000.940
적발관청0.7740.8660.8750.9111.0000.7621.0000.9400.9150.9401.000
2023-12-12T23:38:41.506928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
국부과일자행정동코드법정동코드
국1.0000.9730.108-0.049
부과일자0.9731.0000.126-0.090
행정동코드0.1080.1261.0000.824
법정동코드-0.049-0.0900.8241.000

Missing values

2023-12-12T23:38:36.016091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T23:38:36.197966image/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

국부과일자부과금액(원)적발일자행정동행정동코드법정동법정동코드지번주소도로명주소상세주소적발관청
01202303135000002023-01-30도안동3017059300도안동3017011500도안동 1564대전광역시 서구 도안동로 77대전광역시 서구 도안동로 77, 도안린풀하우스 주차장대전서부경찰서 경비교통과
12202303295000002023-03-22가장동3017057000가장동3017010900가장동 54-1대전광역시 서구 도산로 324대전광역시 서구 도산로 324 부근 노상합동단속
23202303295000002023-02-23도마2동3017053000도마동3017010300도마동 104-4대전광역시 서구 사마7길 37도마동 104-4번지 앞 노상대전광역시경찰청 교통과
34202303295000002023-03-05월평1동3017058600월평동3017011300월평동 1292대전광역시 서구 월평새뜸로4번길 67대전광역시 서구 월평새뜸로4번길 67 앞 노상대전둔산경찰서 수사과
45202303295000002023-02-10관저2동3017059700관저동3017011600관저동 1558-13대전광역시 서구 관저남로25번길 92대전광역시 서구 관저남로25번길 92 앞 노상대전서부경찰서 형사과
56202303295000002023-03-08도마2동3017053000도마동3017010300도마동 172-15대전광역시 서구 도산로 51도마네거리 부근대전서부경찰서 경비교통과
67202304075000002023-03-13월평1동3017058600월평동3017011300월평동 635대전광역시 서구 월평로 75대전광역시 서구 월평로 75 앞 노상대전광역시 서구청
78202304145000002023-02-03전민동3020057000문지동3020014000문지동 652-4대전광역시 유성구 문지로299번길 86-11대전광역시 유성구 문지로299번길 86-11 앞 노상대전유성경찰서 도룡지구대
89202304215000002023-03-13월평1동3017058600월평동3017011300월평동 807대전광역시 서구 계룡로325번길 10대전광역시 서구 계룡로325번길 10 앞 노상대전둔산경찰서
910202304215000002023-03-30금산읍4471025000금산읍 중도리4471025022중도리 302-2충청남도 금산군 금산읍 뒷담말2길 20충청남도 금산군 금산읍 뒷담말2길 20 앞 노상금산경찰서 생활안전교통과
국부과일자부과금액(원)적발일자행정동행정동코드법정동법정동코드지번주소도로명주소상세주소적발관청
1112202305155000002023-04-30월평2동3017058700월평동3017011300월평동 218대전광역시 서구 월평북로 11대전광역시 서구 월평북로 11 101동 앞대전둔산경찰서 월평지구대
1213202305155000002022-05-14괴정동3017056000괴정동3017010800괴정동 424-26대전광역시 서구 도솔로 378대전광역시 서구 도솔로 378 앞 노상대전서부경찰서 내동지구대
1314202305265000002023-05-14가수원동3017059000괴곡동3017012700괴곡동 437-2-괴곡동 437-2 부근대전서부경찰서 가수원지구대
1415202306235000002023-04-20둔산2동3017064000둔산동3017011200둔산동 956<NA>대전광역시 서구 한밭대로 둔지미유래비 앞대전둔산경찰서 경비교통과
1516202306235000002023-05-16변동3017054000변동3017010200변동 7-9대전광역시 서구 동서대로 1108대전광역시 서구 동서대로 1108 부근대전서부경찰서 경비교통과
1617202306235000002023-05-11관저2동3017059700관저동3017011600관저동 1515대전광역시 서구 관저로 142대전광역시 서구 관저로 142 앞 노상대전서부경찰서 경비교통과
1718202306235000002023-06-01둔산3동3017066000둔산동3017011200둔산동 1809대전광역시 서구 둔산로 201대전광역시 서구 둔산로 201 앞 노상대전둔산경찰서 청사지구대
1819202306235000002023-05-27월평1동3017058600월평동3017011300월평동 1292대전광역시 서구 월평새뜸로4번길 67대전광역시 서구 월평새뜸로4번길 67 앞 노상대전둔산경찰서 경비교통과
1920202306235000002023-03-14월평1동3017058600월평동3017011300월평동 635대전광역시 서구 월평로 75대전광역시 서구 월평로 75 앞 노상대전둔산경찰서 수사과
2021202306235000002023-03-23갈마2동3017058200갈마동3017011100갈마동 352-1대전광역시 서구 갈마중로 12대전광역시 서구 갈마중로 12 앞 노상대전둔산경찰서 수사과