Overview

Dataset statistics

Number of variables4
Number of observations3025
Missing cells0
Missing cells (%)0.0%
Duplicate rows338
Duplicate rows (%)11.2%
Total size in memory100.6 KiB
Average record size in memory34.0 B

Variable types

DateTime1
Text1
Numeric2

Dataset

Description대전광역시 서구의 장애인전용주차구역 불법주정차 단속현황(발생날짜, 도로명주소, 위도, 경도를 포함) 자료입니다.
Author대전광역시 서구
URLhttps://www.data.go.kr/data/15087165/fileData.do

Alerts

Dataset has 338 (11.2%) duplicate rowsDuplicates
위도 is highly overall correlated with 경도High correlation
경도 is highly overall correlated with 위도High correlation

Reproduction

Analysis started2023-12-12 18:27:43.782866
Analysis finished2023-12-12 18:27:44.638270
Duration0.86 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

발생날짜
Date

Distinct182
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Memory size23.8 KiB
Minimum2020-01-01 00:00:00
Maximum2020-06-30 00:00:00
2023-12-13T03:27:44.741389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:27:44.910055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

도로명 주소
Text

Distinct741
Distinct (%)24.5%
Missing0
Missing (%)0.0%
Memory size23.8 KiB
2023-12-13T03:27:45.260337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length17
Mean length13.715372
Min length9

Characters and Unicode

Total characters41489
Distinct characters71
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

Unique376 ?
Unique (%)12.4%

Sample

1st row대전서구 탄방동 639
2nd row대전서구 탄방동 1449
3rd row대전서구 정림동 716
4th row대전서구 복수동 611
5th row대전서구 복수동 611
ValueCountFrequency (%)
대전서구 3025
33.3%
둔산동 812
 
8.9%
관저동 488
 
5.4%
탄방동 391
 
4.3%
월평동 221
 
2.4%
도안동 162
 
1.8%
갈마동 153
 
1.7%
내동 142
 
1.6%
복수동 108
 
1.2%
도마동 106
 
1.2%
Other values (646) 3488
38.3%
2023-12-13T03:27:45.727373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8953
21.6%
전 3028
 
7.3%
대 3025
 
7.3%
서 3025
 
7.3%
구 3025
 
7.3%
동 3024
 
7.3%
1 2447
 
5.9%
9 1202
 
2.9%
0 1110
 
2.7%
2 1075
 
2.6%
Other values (61) 11575
27.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 21109
50.9%
Decimal Number 10806
26.0%
Space Separator 8953
21.6%
Dash Punctuation 620
 
1.5%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
전 3028
14.3%
대 3025
14.3%
서 3025
14.3%
구 3025
14.3%
동 3024
14.3%
산 818
 
3.9%
둔 812
 
3.8%
관 490
 
2.3%
저 490
 
2.3%
탄 391
 
1.9%
Other values (48) 2981
14.1%
Decimal Number
ValueCountFrequency (%)
1 2447
22.6%
9 1202
11.1%
0 1110
10.3%
2 1075
9.9%
3 1031
9.5%
5 931
 
8.6%
4 914
 
8.5%
6 813
 
7.5%
8 668
 
6.2%
7 615
 
5.7%
Space Separator
ValueCountFrequency (%)
8953
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 620
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 21109
50.9%
Common 20380
49.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
전 3028
14.3%
대 3025
14.3%
서 3025
14.3%
구 3025
14.3%
동 3024
14.3%
산 818
 
3.9%
둔 812
 
3.8%
관 490
 
2.3%
저 490
 
2.3%
탄 391
 
1.9%
Other values (48) 2981
14.1%
Common
ValueCountFrequency (%)
8953
43.9%
1 2447
 
12.0%
9 1202
 
5.9%
0 1110
 
5.4%
2 1075
 
5.3%
3 1031
 
5.1%
5 931
 
4.6%
4 914
 
4.5%
6 813
 
4.0%
8 668
 
3.3%
Other values (3) 1236
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 21109
50.9%
ASCII 20380
49.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8953
43.9%
1 2447
 
12.0%
9 1202
 
5.9%
0 1110
 
5.4%
2 1075
 
5.3%
3 1031
 
5.1%
5 931
 
4.6%
4 914
 
4.5%
6 813
 
4.0%
8 668
 
3.3%
Other values (3) 1236
 
6.1%
Hangul
ValueCountFrequency (%)
전 3028
14.3%
대 3025
14.3%
서 3025
14.3%
구 3025
14.3%
동 3024
14.3%
산 818
 
3.9%
둔 812
 
3.8%
관 490
 
2.3%
저 490
 
2.3%
탄 391
 
1.9%
Other values (48) 2981
14.1%

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct667
Distinct (%)22.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.334573
Minimum36.21798
Maximum36.371458
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.7 KiB
2023-12-13T03:27:45.859729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.21798
5-th percentile36.293243
Q136.310235
median36.344142
Q336.353421
95-th percentile36.361556
Maximum36.371458
Range0.153478
Interquartile range (IQR)0.043186

Descriptive statistics

Standard deviation0.023308809
Coefficient of variation (CV)0.00064150496
Kurtosis-0.94565316
Mean36.334573
Median Absolute Deviation (MAD)0.012421
Skewness-0.6013328
Sum109912.08
Variance0.00054330058
MonotonicityNot monotonic
2023-12-13T03:27:45.991079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36.354757 61
 
2.0%
36.293199 53
 
1.8%
36.336778 48
 
1.6%
36.360968 46
 
1.5%
36.352407 45
 
1.5%
36.355074 42
 
1.4%
36.333443 42
 
1.4%
36.29291 40
 
1.3%
36.358857 40
 
1.3%
36.359036 36
 
1.2%
Other values (657) 2572
85.0%
ValueCountFrequency (%)
36.21798 1
 
< 0.1%
36.291767 2
 
0.1%
36.291821 1
 
< 0.1%
36.291887 4
 
0.1%
36.292031 1
 
< 0.1%
36.292265 1
 
< 0.1%
36.292632 2
 
0.1%
36.292866 8
 
0.3%
36.29291 40
1.3%
36.292972 1
 
< 0.1%
ValueCountFrequency (%)
36.371458 1
 
< 0.1%
36.370781 1
 
< 0.1%
36.370527 1
 
< 0.1%
36.369815 1
 
< 0.1%
36.369789 1
 
< 0.1%
36.369534 1
 
< 0.1%
36.369393 9
0.3%
36.369342 2
 
0.1%
36.368615 1
 
< 0.1%
36.368138 7
0.2%

경도
Real number (ℝ)

HIGH CORRELATION 

Distinct665
Distinct (%)22.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.37143
Minimum127.32238
Maximum127.40091
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.7 KiB
2023-12-13T03:27:46.142780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum127.32238
5-th percentile127.33183
Q1127.36331
median127.3782
Q3127.38724
95-th percentile127.39382
Maximum127.40091
Range0.07853
Interquartile range (IQR)0.023929

Descriptive statistics

Standard deviation0.01979202
Coefficient of variation (CV)0.00015538823
Kurtosis-0.45240005
Mean127.37143
Median Absolute Deviation (MAD)0.010263
Skewness-0.86229958
Sum385298.56
Variance0.00039172405
MonotonicityNot monotonic
2023-12-13T03:27:46.316472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.388638 61
 
2.0%
127.343996 53
 
1.8%
127.368048 48
 
1.6%
127.37163 46
 
1.5%
127.392689 45
 
1.5%
127.388793 42
 
1.4%
127.381983 42
 
1.4%
127.371235 41
 
1.4%
127.325996 40
 
1.3%
127.381141 38
 
1.3%
Other values (655) 2569
84.9%
ValueCountFrequency (%)
127.322383 1
 
< 0.1%
127.323136 8
 
0.3%
127.324218 6
 
0.2%
127.324478 2
 
0.1%
127.324835 1
 
< 0.1%
127.325304 5
 
0.2%
127.325523 1
 
< 0.1%
127.325996 40
1.3%
127.326119 1
 
< 0.1%
127.326274 1
 
< 0.1%
ValueCountFrequency (%)
127.400913 1
 
< 0.1%
127.4007 2
 
0.1%
127.399805 1
 
< 0.1%
127.39884 1
 
< 0.1%
127.398639 8
0.3%
127.398247 5
 
0.2%
127.398206 14
0.5%
127.397985 1
 
< 0.1%
127.397715 1
 
< 0.1%
127.397568 1
 
< 0.1%

Interactions

2023-12-13T03:27:44.184322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:27:43.961604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:27:44.299449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:27:44.072774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T03:27:46.412949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도
위도1.0000.672
경도0.6721.000
2023-12-13T03:27:46.487201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도
위도1.0000.614
경도0.6141.000

Missing values

2023-12-13T03:27:44.463337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T03:27:44.586403image/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

발생날짜도로명 주소위도경도
02020-01-01대전서구 탄방동 63936.343858127.382896
12020-01-01대전서구 탄방동 144936.344797127.384917
22020-01-01대전서구 정림동 71636.30823127.370505
32020-01-01대전서구 복수동 61136.303259127.379809
42020-01-01대전서구 복수동 61136.303259127.379809
52020-01-01대전서구 복수동 28236.309046127.374864
62020-01-01대전서구 둔산동 96136.356355127.378776
72020-01-01대전서구 둔산동 180936.353981127.397368
82020-01-01대전서구 둔산동 1088-136.351447127.374302
92020-01-01대전서구 도안동 149536.323619127.346169
발생날짜도로명 주소위도경도
30152020-06-29대전서구 관저동 115536.295488127.330873
30162020-06-30대전서구 탄방동 59036.348192127.386149
30172020-06-30대전서구 정림동 64036.302748127.364815
30182020-06-30대전서구 정림동 64036.302748127.364815
30192020-06-30대전서구 둔산동 908-136.360234127.391692
30202020-06-30대전서구 도안동 96536.330974127.343045
30212020-06-30대전서구 도마동 185-3436.311735127.378996
30222020-06-30대전서구 관저동 1952-336.296777127.326119
30232020-06-30대전서구 관저동 159936.293199127.343996
30242020-06-30대전서구 관저동 159936.293199127.343996

Duplicate rows

Most frequently occurring

발생날짜도로명 주소위도경도# duplicates
932020-02-15대전서구 탄방동 91-2336.341282127.3901918
952020-02-17대전서구 관저동 159936.293199127.3439965
1062020-02-22대전서구 월평동 30236.360968127.371635
2792020-05-27대전서구 관저동 99936.303383127.3389895
3262020-06-22대전서구 월평동 30236.360968127.371635
132020-01-08대전서구 탄방동 61036.344678127.3811414
432020-01-21대전서구 가장동 52-136.333443127.3887934
662020-02-01대전서구 갈마동 91336.349141127.3745324
802020-02-12대전서구 둔산동 138836.354657127.3920484
1032020-02-22대전서구 관저동 198236.29291127.3259964