Overview

Dataset statistics

Number of variables7
Number of observations168
Missing cells161
Missing cells (%)13.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.1 KiB
Average record size in memory61.8 B

Variable types

Numeric5
Categorical1
Text1

Dataset

Description순번,시군구코드,자동차전용도로일련번호,자동차전용도로명_한글,자동차전용도로명_영문,X좌표,Y좌표
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-11680/S/1/datasetView.do

Alerts

순번 is highly overall correlated with 시군구코드High correlation
시군구코드 is highly overall correlated with 순번 and 1 other fieldsHigh correlation
X좌표 is highly overall correlated with Y좌표High correlation
Y좌표 is highly overall correlated with X좌표High correlation
자동차전용도로명_한글 is highly overall correlated with 시군구코드High correlation
자동차전용도로명_영문 has 161 (95.8%) missing valuesMissing
순번 has unique valuesUnique
X좌표 has unique valuesUnique
Y좌표 has unique valuesUnique

Reproduction

Analysis started2023-12-11 08:08:27.488265
Analysis finished2023-12-11 08:08:30.726922
Duration3.24 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct168
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84.5
Minimum1
Maximum168
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-11T17:08:30.826439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9.35
Q142.75
median84.5
Q3126.25
95-th percentile159.65
Maximum168
Range167
Interquartile range (IQR)83.5

Descriptive statistics

Standard deviation48.641546
Coefficient of variation (CV)0.5756396
Kurtosis-1.2
Mean84.5
Median Absolute Deviation (MAD)42
Skewness0
Sum14196
Variance2366
MonotonicityStrictly increasing
2023-12-11T17:08:31.018003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.6%
117 1
 
0.6%
109 1
 
0.6%
110 1
 
0.6%
111 1
 
0.6%
112 1
 
0.6%
113 1
 
0.6%
114 1
 
0.6%
115 1
 
0.6%
116 1
 
0.6%
Other values (158) 158
94.0%
ValueCountFrequency (%)
1 1
0.6%
2 1
0.6%
3 1
0.6%
4 1
0.6%
5 1
0.6%
6 1
0.6%
7 1
0.6%
8 1
0.6%
9 1
0.6%
10 1
0.6%
ValueCountFrequency (%)
168 1
0.6%
167 1
0.6%
166 1
0.6%
165 1
0.6%
164 1
0.6%
163 1
0.6%
162 1
0.6%
161 1
0.6%
160 1
0.6%
159 1
0.6%

시군구코드
Real number (ℝ)

HIGH CORRELATION 

Distinct21
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11494.375
Minimum11170
Maximum11740
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-11T17:08:31.176699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11170
5-th percentile11200
Q111440
median11500
Q311680
95-th percentile11740
Maximum11740
Range570
Interquartile range (IQR)240

Descriptive statistics

Standard deviation170.06003
Coefficient of variation (CV)0.014795066
Kurtosis-0.88521626
Mean11494.375
Median Absolute Deviation (MAD)150
Skewness-0.14031095
Sum1931055
Variance28920.415
MonotonicityNot monotonic
2023-12-11T17:08:31.319230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
11440 40
23.8%
11740 23
13.7%
11500 19
11.3%
11680 13
 
7.7%
11560 9
 
5.4%
11350 9
 
5.4%
11215 8
 
4.8%
11710 8
 
4.8%
11200 7
 
4.2%
11545 5
 
3.0%
Other values (11) 27
16.1%
ValueCountFrequency (%)
11170 3
 
1.8%
11200 7
4.2%
11215 8
4.8%
11230 4
2.4%
11260 3
 
1.8%
11290 3
 
1.8%
11320 1
 
0.6%
11350 9
5.4%
11380 1
 
0.6%
11410 1
 
0.6%
ValueCountFrequency (%)
11740 23
13.7%
11710 8
 
4.8%
11680 13
7.7%
11650 3
 
1.8%
11590 2
 
1.2%
11560 9
 
5.4%
11545 5
 
3.0%
11530 4
 
2.4%
11500 19
11.3%
11470 2
 
1.2%

자동차전용도로일련번호
Real number (ℝ)

Distinct40
Distinct (%)23.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.7559524
Minimum1
Maximum40
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-11T17:08:31.469128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q314
95-th percentile31.65
Maximum40
Range39
Interquartile range (IQR)11

Descriptive statistics

Standard deviation9.5048031
Coefficient of variation (CV)0.97425681
Kurtosis1.3872056
Mean9.7559524
Median Absolute Deviation (MAD)4
Skewness1.4362956
Sum1639
Variance90.341282
MonotonicityNot monotonic
2023-12-11T17:08:31.655865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
1 20
 
11.9%
2 18
 
10.7%
3 15
 
8.9%
4 14
 
8.3%
5 10
 
6.0%
7 9
 
5.4%
6 9
 
5.4%
8 8
 
4.8%
9 6
 
3.6%
12 4
 
2.4%
Other values (30) 55
32.7%
ValueCountFrequency (%)
1 20
11.9%
2 18
10.7%
3 15
8.9%
4 14
8.3%
5 10
6.0%
6 9
5.4%
7 9
5.4%
8 8
 
4.8%
9 6
 
3.6%
10 4
 
2.4%
ValueCountFrequency (%)
40 1
0.6%
39 1
0.6%
38 1
0.6%
37 1
0.6%
36 1
0.6%
35 1
0.6%
34 1
0.6%
33 1
0.6%
32 1
0.6%
31 1
0.6%

자동차전용도로명_한글
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
강변북로
49 
올림픽대로
34 
동부간선
32 
판교-구리고속국도
16 
<NA>
13 
Other values (5)
24 

Length

Max length9
Median length4
Mean length4.7559524
Min length4

Unique

Unique3 ?
Unique (%)1.8%

Sample

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

Common Values

ValueCountFrequency (%)
강변북로 49
29.2%
올림픽대로 34
20.2%
동부간선 32
19.0%
판교-구리고속국도 16
 
9.5%
<NA> 13
 
7.7%
서부간선 12
 
7.1%
내부순환로 9
 
5.4%
경인고속국도 1
 
0.6%
북부간선 1
 
0.6%
경부고속국도 1
 
0.6%

Length

2023-12-11T17:08:31.822630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T17:08:31.958607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
강변북로 49
29.2%
올림픽대로 34
20.2%
동부간선 32
19.0%
판교-구리고속국도 16
 
9.5%
na 13
 
7.7%
서부간선 12
 
7.1%
내부순환로 9
 
5.4%
경인고속국도 1
 
0.6%
북부간선 1
 
0.6%
경부고속국도 1
 
0.6%

자동차전용도로명_영문
Text

MISSING 

Distinct4
Distinct (%)57.1%
Missing161
Missing (%)95.8%
Memory size1.4 KiB
2023-12-11T17:08:32.147174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length13
Mean length15.857143
Min length13

Characters and Unicode

Total characters111
Distinct characters27
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)14.3%

Sample

1st rowDongbu Arterial Road
2nd rowNaebu Ringway
3rd rowNaebu Ringway
4th rowDongbu Arterial Road
5th rowGyeongbu Expressway
ValueCountFrequency (%)
dongbu 2
14.3%
arterial 2
14.3%
road 2
14.3%
naebu 2
14.3%
ringway 2
14.3%
ollimpikdaero 2
14.3%
gyeongbu 1
7.1%
expressway 1
7.1%
2023-12-11T17:08:32.494555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 11
 
9.9%
e 8
 
7.2%
i 8
 
7.2%
o 7
 
6.3%
7
 
6.3%
r 7
 
6.3%
l 6
 
5.4%
n 5
 
4.5%
g 5
 
4.5%
b 5
 
4.5%
Other values (17) 42
37.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 90
81.1%
Uppercase Letter 14
 
12.6%
Space Separator 7
 
6.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 11
12.2%
e 8
 
8.9%
i 8
 
8.9%
o 7
 
7.8%
r 7
 
7.8%
l 6
 
6.7%
n 5
 
5.6%
g 5
 
5.6%
b 5
 
5.6%
u 5
 
5.6%
Other values (9) 23
25.6%
Uppercase Letter
ValueCountFrequency (%)
R 4
28.6%
O 2
14.3%
D 2
14.3%
N 2
14.3%
A 2
14.3%
G 1
 
7.1%
E 1
 
7.1%
Space Separator
ValueCountFrequency (%)
7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 104
93.7%
Common 7
 
6.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 11
 
10.6%
e 8
 
7.7%
i 8
 
7.7%
o 7
 
6.7%
r 7
 
6.7%
l 6
 
5.8%
n 5
 
4.8%
g 5
 
4.8%
b 5
 
4.8%
u 5
 
4.8%
Other values (16) 37
35.6%
Common
ValueCountFrequency (%)
7
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 111
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 11
 
9.9%
e 8
 
7.2%
i 8
 
7.2%
o 7
 
6.3%
7
 
6.3%
r 7
 
6.3%
l 6
 
5.4%
n 5
 
4.5%
g 5
 
4.5%
b 5
 
4.5%
Other values (17) 42
37.8%

X좌표
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct168
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32789.818
Minimum17.27322
Maximum462222.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-11T17:08:32.667486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum17.27322
5-th percentile147.8205
Q11431.2714
median8627.2843
Q333076.739
95-th percentile133879.63
Maximum462222.3
Range462205.03
Interquartile range (IQR)31645.468

Descriptive statistics

Standard deviation58813.637
Coefficient of variation (CV)1.7936555
Kurtosis20.484323
Mean32789.818
Median Absolute Deviation (MAD)8311.6957
Skewness3.8575415
Sum5508689.5
Variance3.4590439 × 109
MonotonicityNot monotonic
2023-12-11T17:08:32.853701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5147.92203277 1
 
0.6%
4212.65170517 1
 
0.6%
128540.689434 1
 
0.6%
9366.8517521 1
 
0.6%
7871.70965304 1
 
0.6%
7812.19037382 1
 
0.6%
1239.69575273 1
 
0.6%
1152.6949786 1
 
0.6%
215460.93442 1
 
0.6%
21990.7255154 1
 
0.6%
Other values (158) 158
94.0%
ValueCountFrequency (%)
17.27321997 1
0.6%
53.3873115 1
0.6%
103.37252958 1
0.6%
105.02995196 1
0.6%
110.869441535 1
0.6%
115.892635905 1
0.6%
116.46982756 1
0.6%
129.18615471 1
0.6%
137.4062847 1
0.6%
167.161182095 1
0.6%
ValueCountFrequency (%)
462222.298984 1
0.6%
325384.253345 1
0.6%
215460.93442 1
0.6%
200870.803776 1
0.6%
192450.7109 1
0.6%
182698.091275 1
0.6%
158262.301647 1
0.6%
145930.463749 1
0.6%
136754.436957 1
0.6%
128540.689434 1
0.6%

Y좌표
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct168
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3855.7662
Minimum19.547101
Maximum40331.109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-11T17:08:33.025063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19.547101
5-th percentile57.120234
Q1397.07974
median1372.0684
Q34633.2173
95-th percentile15860.409
Maximum40331.109
Range40311.562
Interquartile range (IQR)4236.1375

Descriptive statistics

Standard deviation6144.3373
Coefficient of variation (CV)1.5935451
Kurtosis11.52826
Mean3855.7662
Median Absolute Deviation (MAD)1144.6758
Skewness3.0238845
Sum647768.73
Variance37752881
MonotonicityNot monotonic
2023-12-11T17:08:33.587090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1411.88022141 1
 
0.6%
671.81085502 1
 
0.6%
11716.7022726 1
 
0.6%
868.360894919 1
 
0.6%
683.461194092 1
 
0.6%
754.367089554 1
 
0.6%
235.599507528 1
 
0.6%
223.024616189 1
 
0.6%
26833.3766213 1
 
0.6%
3589.65037444 1
 
0.6%
Other values (158) 158
94.0%
ValueCountFrequency (%)
19.5471006888 1
0.6%
36.2376186217 1
0.6%
49.6831478818 1
0.6%
50.9233081141 1
0.6%
51.4180937821 1
0.6%
51.7506678074 1
0.6%
52.0013071914 1
0.6%
53.8072665544 1
0.6%
56.5175636395 1
0.6%
58.2394780981 1
0.6%
ValueCountFrequency (%)
40331.1087871 1
0.6%
33731.3068677 1
0.6%
26833.3766213 1
0.6%
25919.3123736 1
0.6%
18855.5649228 1
0.6%
18777.5574392 1
0.6%
17376.467842 1
0.6%
17008.1077486 1
0.6%
16583.0233777 1
0.6%
14518.410463 1
0.6%

Interactions

2023-12-11T17:08:29.888049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:08:27.740905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:08:28.240073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:08:28.749909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:08:29.324713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:08:29.995811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:08:27.844332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:08:28.341319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:08:28.863160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:08:29.448078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:08:30.122563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:08:27.939015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:08:28.443099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:08:28.957899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:08:29.569839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:08:30.236372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:08:28.036920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:08:28.559992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:08:29.062444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:08:29.683585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:08:30.347173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:08:28.128948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:08:28.639762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:08:29.181196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:08:29.768094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T17:08:33.713147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번시군구코드자동차전용도로일련번호자동차전용도로명_한글자동차전용도로명_영문X좌표Y좌표
순번1.0000.9710.7810.7161.0000.2690.374
시군구코드0.9711.0000.4940.7981.0000.4030.373
자동차전용도로일련번호0.7810.4941.0000.086NaN0.0000.000
자동차전용도로명_한글0.7160.7980.0861.0001.0000.6540.640
자동차전용도로명_영문1.0001.000NaN1.0001.0000.5760.974
X좌표0.2690.4030.0000.6540.5761.0000.918
Y좌표0.3740.3730.0000.6400.9740.9181.000
2023-12-11T17:08:33.861406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번시군구코드자동차전용도로일련번호X좌표Y좌표자동차전용도로명_한글
순번1.0000.6930.1030.0990.0650.427
시군구코드0.6931.0000.1250.048-0.0030.523
자동차전용도로일련번호0.1030.1251.000-0.336-0.2980.033
X좌표0.0990.048-0.3361.0000.9780.417
Y좌표0.065-0.003-0.2980.9781.0000.379
자동차전용도로명_한글0.4270.5230.0330.4170.3791.000

Missing values

2023-12-11T17:08:30.525658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T17:08:30.671798image/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

순번시군구코드자동차전용도로일련번호자동차전용도로명_한글자동차전용도로명_영문X좌표Y좌표
011144036강변북로<NA>5147.9220331411.880221
121144037강변북로<NA>69626.4595847542.603916
231144038강변북로<NA>98279.95428417376.467842
341144039강변북로<NA>6052.6954051839.449296
451144040강변북로<NA>122361.7034714162.331652
56114701경인고속국도<NA>30320.8803562590.476237
67114702서부간선<NA>12419.787977895.733578
78115001<NA><NA>1135.160217402.026109
89115002<NA><NA>5916.0281241720.832131
910115003<NA><NA>8372.1470111983.844677
순번시군구코드자동차전용도로일련번호자동차전용도로명_한글자동차전용도로명_영문X좌표Y좌표
1581591174014올림픽대로<NA>1286.85607405.440151
1591601174015판교-구리고속국도<NA>39152.1502613365.119542
1601611174016올림픽대로<NA>28003.3104484593.762761
1611621174017올림픽대로<NA>3213.715786432.229798
1621631174018판교-구리고속국도<NA>6189.717703926.646471
1631641174019판교-구리고속국도<NA>5913.617113891.672595
1641651174020올림픽대로<NA>13734.7141791473.05434
1651661174021판교-구리고속국도<NA>6170.8898351046.575008
1661671174022판교-구리고속국도<NA>5475.698919654.975197
1671681174023올림픽대로<NA>3334.846092963.525553