Overview

Dataset statistics

Number of variables5
Number of observations23
Missing cells1
Missing cells (%)0.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.1 KiB
Average record size in memory49.7 B

Variable types

Text1
Numeric4

Dataset

Description인천광역시 부평구 동별 자동차 등록현황 데이터입니다.<br/>(승용,승합,화물,특수)<br/>ex) 부평1, 68559, 995, 6114, 96<br/>
Author인천광역시 부평구
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15083454&srcSe=7661IVAWM27C61E190

Alerts

승용 is highly overall correlated with 승합 and 2 other fieldsHigh correlation
승합 is highly overall correlated with 승용 and 2 other fieldsHigh correlation
화물 is highly overall correlated with 승용 and 2 other fieldsHigh correlation
특수 is highly overall correlated with 승용 and 2 other fieldsHigh correlation
특수 has 1 (4.3%) missing valuesMissing
행정동 has unique valuesUnique
승용 has unique valuesUnique
승합 has unique valuesUnique
화물 has unique valuesUnique

Reproduction

Analysis started2024-03-18 05:47:59.191850
Analysis finished2024-03-18 05:48:00.716817
Duration1.52 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

행정동
Text

UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size316.0 B
2024-03-18T14:48:00.834440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4
Min length3

Characters and Unicode

Total characters92
Distinct characters22
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique23 ?
Unique (%)100.0%

Sample

1st row부평1동
2nd row부평2동
3rd row부평3동
4th row부평4동
5th row부평5동
ValueCountFrequency (%)
부평1동 1
 
4.2%
부평2동 1
 
4.2%
구 1
 
4.2%
십정2동 1
 
4.2%
십정1동 1
 
4.2%
일신동 1
 
4.2%
부개3동 1
 
4.2%
부개2동 1
 
4.2%
부개1동 1
 
4.2%
삼산2동 1
 
4.2%
Other values (14) 14
58.3%
2024-03-18T14:48:01.150416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 23
25.0%
부 9
 
9.8%
산 9
 
9.8%
1 7
 
7.6%
2 7
 
7.6%
평 6
 
6.5%
곡 4
 
4.3%
3 3
 
3.3%
개 3
 
3.3%
삼 3
 
3.3%
Other values (12) 18
19.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 70
76.1%
Decimal Number 21
 
22.8%
Space Separator 1
 
1.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 23
32.9%
부 9
 
12.9%
산 9
 
12.9%
평 6
 
8.6%
곡 4
 
5.7%
개 3
 
4.3%
삼 3
 
4.3%
갈 2
 
2.9%
정 2
 
2.9%
십 2
 
2.9%
Other values (5) 7
 
10.0%
Decimal Number
ValueCountFrequency (%)
1 7
33.3%
2 7
33.3%
3 3
14.3%
4 2
 
9.5%
6 1
 
4.8%
5 1
 
4.8%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 70
76.1%
Common 22
 
23.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 23
32.9%
부 9
 
12.9%
산 9
 
12.9%
평 6
 
8.6%
곡 4
 
5.7%
개 3
 
4.3%
삼 3
 
4.3%
갈 2
 
2.9%
정 2
 
2.9%
십 2
 
2.9%
Other values (5) 7
 
10.0%
Common
ValueCountFrequency (%)
1 7
31.8%
2 7
31.8%
3 3
13.6%
4 2
 
9.1%
6 1
 
4.5%
5 1
 
4.5%
1
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 70
76.1%
ASCII 22
 
23.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 23
32.9%
부 9
 
12.9%
산 9
 
12.9%
평 6
 
8.6%
곡 4
 
5.7%
개 3
 
4.3%
삼 3
 
4.3%
갈 2
 
2.9%
정 2
 
2.9%
십 2
 
2.9%
Other values (5) 7
 
10.0%
ASCII
ValueCountFrequency (%)
1 7
31.8%
2 7
31.8%
3 3
13.6%
4 2
 
9.1%
6 1
 
4.5%
5 1
 
4.5%
1
 
4.5%

승용
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10502
Minimum11
Maximum90737
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2024-03-18T14:48:01.255171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile3468.4
Q15062
median6259
Q310015
95-th percentile12499.3
Maximum90737
Range90726
Interquartile range (IQR)4953

Descriptive statistics

Standard deviation17741.14
Coefficient of variation (CV)1.6893106
Kurtosis21.548875
Mean10502
Median Absolute Deviation (MAD)1914
Skewness4.5766934
Sum241546
Variance3.1474804 × 108
MonotonicityNot monotonic
2024-03-18T14:48:01.356547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
90737 1
 
4.3%
4940 1
 
4.3%
11 1
 
4.3%
6321 1
 
4.3%
8693 1
 
4.3%
4527 1
 
4.3%
8968 1
 
4.3%
6205 1
 
4.3%
5573 1
 
4.3%
9814 1
 
4.3%
Other values (13) 13
56.5%
ValueCountFrequency (%)
11 1
4.3%
3371 1
4.3%
4345 1
4.3%
4374 1
4.3%
4527 1
4.3%
4940 1
4.3%
5184 1
4.3%
5495 1
4.3%
5573 1
4.3%
5582 1
4.3%
ValueCountFrequency (%)
90737 1
4.3%
12689 1
4.3%
10792 1
4.3%
10540 1
4.3%
10524 1
4.3%
10216 1
4.3%
9814 1
4.3%
8968 1
4.3%
8693 1
4.3%
6386 1
4.3%

승합
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean263.21739
Minimum5
Maximum891
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2024-03-18T14:48:01.452756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile127.4
Q1161.5
median219
Q3309.5
95-th percentile542.7
Maximum891
Range886
Interquartile range (IQR)148

Descriptive statistics

Standard deviation179.42483
Coefficient of variation (CV)0.68166023
Kurtosis6.3102483
Mean263.21739
Median Absolute Deviation (MAD)75
Skewness2.1474398
Sum6054
Variance32193.269
MonotonicityNot monotonic
2024-03-18T14:48:01.542738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
891 1
 
4.3%
131 1
 
4.3%
5 1
 
4.3%
263 1
 
4.3%
555 1
 
4.3%
175 1
 
4.3%
232 1
 
4.3%
185 1
 
4.3%
192 1
 
4.3%
294 1
 
4.3%
Other values (13) 13
56.5%
ValueCountFrequency (%)
5 1
4.3%
127 1
4.3%
131 1
4.3%
140 1
4.3%
143 1
4.3%
148 1
4.3%
175 1
4.3%
179 1
4.3%
185 1
4.3%
192 1
4.3%
ValueCountFrequency (%)
891 1
4.3%
555 1
4.3%
432 1
4.3%
361 1
4.3%
348 1
4.3%
325 1
4.3%
294 1
4.3%
263 1
4.3%
258 1
4.3%
253 1
4.3%

화물
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1074.6957
Minimum26
Maximum3753
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2024-03-18T14:48:01.643961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum26
5-th percentile518.2
Q1681.5
median948
Q31258.5
95-th percentile1647.8
Maximum3753
Range3727
Interquartile range (IQR)577

Descriptive statistics

Standard deviation704.07634
Coefficient of variation (CV)0.65514022
Kurtosis9.488837
Mean1074.6957
Median Absolute Deviation (MAD)280
Skewness2.5504734
Sum24718
Variance495723.49
MonotonicityNot monotonic
2024-03-18T14:48:01.776616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
3753 1
 
4.3%
592 1
 
4.3%
26 1
 
4.3%
1076 1
 
4.3%
1330 1
 
4.3%
901 1
 
4.3%
973 1
 
4.3%
844 1
 
4.3%
799 1
 
4.3%
948 1
 
4.3%
Other values (13) 13
56.5%
ValueCountFrequency (%)
26 1
4.3%
510 1
4.3%
592 1
4.3%
613 1
4.3%
620 1
4.3%
668 1
4.3%
695 1
4.3%
719 1
4.3%
799 1
4.3%
844 1
4.3%
ValueCountFrequency (%)
3753 1
4.3%
1651 1
4.3%
1619 1
4.3%
1570 1
4.3%
1359 1
4.3%
1330 1
4.3%
1187 1
4.3%
1141 1
4.3%
1124 1
4.3%
1076 1
4.3%

특수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct17
Distinct (%)77.3%
Missing1
Missing (%)4.3%
Infinite0
Infinite (%)0.0%
Mean32.363636
Minimum13
Maximum68
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2024-03-18T14:48:01.882369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile16
Q118
median29.5
Q343.75
95-th percentile62.75
Maximum68
Range55
Interquartile range (IQR)25.75

Descriptive statistics

Standard deviation16.398448
Coefficient of variation (CV)0.50669361
Kurtosis-0.34547721
Mean32.363636
Median Absolute Deviation (MAD)12
Skewness0.80211986
Sum712
Variance268.90909
MonotonicityNot monotonic
2024-03-18T14:48:01.976227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
17 2
 
8.7%
18 2
 
8.7%
45 2
 
8.7%
16 2
 
8.7%
32 2
 
8.7%
63 1
 
4.3%
40 1
 
4.3%
19 1
 
4.3%
26 1
 
4.3%
28 1
 
4.3%
Other values (7) 7
30.4%
ValueCountFrequency (%)
13 1
4.3%
16 2
8.7%
17 2
8.7%
18 2
8.7%
19 1
4.3%
24 1
4.3%
26 1
4.3%
28 1
4.3%
31 1
4.3%
32 2
8.7%
ValueCountFrequency (%)
68 1
4.3%
63 1
4.3%
58 1
4.3%
49 1
4.3%
45 2
8.7%
40 1
4.3%
37 1
4.3%
32 2
8.7%
31 1
4.3%
28 1
4.3%

Interactions

2024-03-18T14:48:00.286800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:47:59.322354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:47:59.610894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:47:59.945032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:48:00.357178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:47:59.380551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:47:59.679657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:48:00.049686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:48:00.442539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:47:59.454651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:47:59.754822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:48:00.129912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:48:00.515160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:47:59.534601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:47:59.867340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:48:00.205062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-18T14:48:02.046896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동승용승합화물특수
행정동1.0001.0001.0001.0001.000
승용1.0001.0000.9180.9900.855
승합1.0000.9181.0000.8990.817
화물1.0000.9900.8991.0000.776
특수1.0000.8550.8170.7761.000
2024-03-18T14:48:02.406735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
승용승합화물특수
승용1.0000.8240.7900.613
승합0.8241.0000.9280.793
화물0.7900.9281.0000.875
특수0.6130.7930.8751.000

Missing values

2024-03-18T14:48:00.605983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-18T14:48:00.685094image/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

행정동승용승합화물특수
0부평1동90737891375363
1부평2동494013159213
2부평3동437419871917
3부평4동10540348157031
4부평5동10216325118737
5부평6동434512762018
6산곡1동518414861318
7산곡2동10524253135945
8산곡3동638617969516
9산곡4동549514351016
행정동승용승합화물특수
13갈산2동558214066817
14삼산1동10792432161968
15삼산2동981429494840
16부개1동557319279932
17부개2동620518584424
18부개3동896823297328
19일신동452717590126
20십정1동8693555133032
21십정2동6321263107619
22구 삼산동11526<NA>