Overview

Dataset statistics

Number of variables5
Number of observations864
Missing cells0
Missing cells (%)0.0%
Duplicate rows142
Duplicate rows (%)16.4%
Total size in memory36.4 KiB
Average record size in memory43.2 B

Variable types

Numeric3
Text2

Dataset

Description경기도 여주시의 교차로 정보입니다. 교차로번호, 교차로명, 교차로그룹, 그룹명, 검치기채널번호 데이터를 제공합니다.
Author경기도 여주시
URLhttps://www.data.go.kr/data/15109723/fileData.do

Alerts

Dataset has 142 (16.4%) duplicate rowsDuplicates
교차로번호 is highly overall correlated with 교차로그룹High correlation
교차로그룹 is highly overall correlated with 교차로번호High correlation
검지기채널번호 has 808 (93.5%) zerosZeros

Reproduction

Analysis started2023-12-09 14:42:23.467117
Analysis finished2023-12-09 14:42:30.278000
Duration6.81 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

교차로번호
Real number (ℝ)

HIGH CORRELATION 

Distinct144
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean72.520833
Minimum1
Maximum145
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.7 KiB
2023-12-09T14:42:30.913208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q136.75
median72.5
Q3108.25
95-th percentile137
Maximum145
Range144
Interquartile range (IQR)71.5

Descriptive statistics

Standard deviation41.627878
Coefficient of variation (CV)0.57401268
Kurtosis-1.1962848
Mean72.520833
Median Absolute Deviation (MAD)36
Skewness0.0028785876
Sum62658
Variance1732.8802
MonotonicityIncreasing
2023-12-09T14:42:31.665958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 6
 
0.7%
74 6
 
0.7%
94 6
 
0.7%
95 6
 
0.7%
96 6
 
0.7%
97 6
 
0.7%
98 6
 
0.7%
99 6
 
0.7%
100 6
 
0.7%
101 6
 
0.7%
Other values (134) 804
93.1%
ValueCountFrequency (%)
1 6
0.7%
2 6
0.7%
3 6
0.7%
4 6
0.7%
5 6
0.7%
6 6
0.7%
7 6
0.7%
8 6
0.7%
9 6
0.7%
10 6
0.7%
ValueCountFrequency (%)
145 6
0.7%
144 6
0.7%
143 6
0.7%
141 6
0.7%
140 6
0.7%
139 6
0.7%
138 6
0.7%
137 6
0.7%
136 6
0.7%
135 6
0.7%

교차로명
Text

Distinct144
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Memory size6.9 KiB
2023-12-09T14:42:32.943923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length13
Mean length6.9861111
Min length4

Characters and Unicode

Total characters6036
Distinct characters172
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row신해2리 사거리
2nd row신해2리 사거리
3rd row신해2리 사거리
4th row신해2리 사거리
5th row신해2리 사거리
ValueCountFrequency (%)
교차로 252
 
18.0%
삼거리 114
 
8.2%
사거리 60
 
4.3%
입구 42
 
3.0%
앞 24
 
1.7%
버스정류장 18
 
1.3%
태평 12
 
0.9%
중부대로 12
 
0.9%
연양리부대삼거리 6
 
0.4%
소지개1교차로 6
 
0.4%
Other values (142) 852
60.9%
2023-12-09T14:42:35.285060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
534
 
8.8%
교 468
 
7.8%
로 456
 
7.6%
차 420
 
7.0%
리 396
 
6.6%
거 348
 
5.8%
삼 234
 
3.9%
사 162
 
2.7%
2 102
 
1.7%
앞 90
 
1.5%
Other values (162) 2826
46.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5142
85.2%
Space Separator 534
 
8.8%
Decimal Number 246
 
4.1%
Uppercase Letter 102
 
1.7%
Open Punctuation 6
 
0.1%
Close Punctuation 6
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
교 468
 
9.1%
로 456
 
8.9%
차 420
 
8.2%
리 396
 
7.7%
거 348
 
6.8%
삼 234
 
4.6%
사 162
 
3.2%
앞 90
 
1.8%
주 84
 
1.6%
여 78
 
1.5%
Other values (147) 2406
46.8%
Decimal Number
ValueCountFrequency (%)
2 102
41.5%
1 66
26.8%
3 36
 
14.6%
4 24
 
9.8%
6 6
 
2.4%
9 6
 
2.4%
5 6
 
2.4%
Uppercase Letter
ValueCountFrequency (%)
C 54
52.9%
I 30
29.4%
G 6
 
5.9%
S 6
 
5.9%
K 6
 
5.9%
Space Separator
ValueCountFrequency (%)
534
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5142
85.2%
Common 792
 
13.1%
Latin 102
 
1.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
교 468
 
9.1%
로 456
 
8.9%
차 420
 
8.2%
리 396
 
7.7%
거 348
 
6.8%
삼 234
 
4.6%
사 162
 
3.2%
앞 90
 
1.8%
주 84
 
1.6%
여 78
 
1.5%
Other values (147) 2406
46.8%
Common
ValueCountFrequency (%)
534
67.4%
2 102
 
12.9%
1 66
 
8.3%
3 36
 
4.5%
4 24
 
3.0%
( 6
 
0.8%
) 6
 
0.8%
6 6
 
0.8%
9 6
 
0.8%
5 6
 
0.8%
Latin
ValueCountFrequency (%)
C 54
52.9%
I 30
29.4%
G 6
 
5.9%
S 6
 
5.9%
K 6
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5142
85.2%
ASCII 894
 
14.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
534
59.7%
2 102
 
11.4%
1 66
 
7.4%
C 54
 
6.0%
3 36
 
4.0%
I 30
 
3.4%
4 24
 
2.7%
( 6
 
0.7%
) 6
 
0.7%
G 6
 
0.7%
Other values (5) 30
 
3.4%
Hangul
ValueCountFrequency (%)
교 468
 
9.1%
로 456
 
8.9%
차 420
 
8.2%
리 396
 
7.7%
거 348
 
6.8%
삼 234
 
4.6%
사 162
 
3.2%
앞 90
 
1.8%
주 84
 
1.6%
여 78
 
1.5%
Other values (147) 2406
46.8%

교차로그룹
Real number (ℝ)

HIGH CORRELATION 

Distinct55
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.9375
Minimum1
Maximum55
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.7 KiB
2023-12-09T14:42:36.748503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q16
median12
Q333.25
95-th percentile54
Maximum55
Range54
Interquartile range (IQR)27.25

Descriptive statistics

Standard deviation17.734315
Coefficient of variation (CV)0.88949544
Kurtosis-0.82859437
Mean19.9375
Median Absolute Deviation (MAD)10
Skewness0.76556723
Sum17226
Variance314.50594
MonotonicityNot monotonic
2023-12-09T14:42:38.259177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 96
 
11.1%
12 90
 
10.4%
6 54
 
6.2%
10 54
 
6.2%
17 42
 
4.9%
2 36
 
4.2%
3 36
 
4.2%
4 36
 
4.2%
54 30
 
3.5%
8 24
 
2.8%
Other values (45) 366
42.4%
ValueCountFrequency (%)
1 96
11.1%
2 36
 
4.2%
3 36
 
4.2%
4 36
 
4.2%
5 6
 
0.7%
6 54
6.2%
7 6
 
0.7%
8 24
 
2.8%
9 6
 
0.7%
10 54
6.2%
ValueCountFrequency (%)
55 18
2.1%
54 30
3.5%
53 12
 
1.4%
52 24
2.8%
51 6
 
0.7%
50 18
2.1%
49 6
 
0.7%
48 6
 
0.7%
47 12
 
1.4%
46 6
 
0.7%

그룹명
Text

Distinct54
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Memory size6.9 KiB
2023-12-09T14:42:39.272307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length5
Mean length6.8055556
Min length5

Characters and Unicode

Total characters5880
Distinct characters43
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

Unique0 ?
Unique (%)0.0%

Sample

1st row국도3호선
2nd row국도3호선
3rd row국도3호선
4th row국도3호선
5th row국도3호선
ValueCountFrequency (%)
국도3호선 96
 
11.1%
지방도333호선#5 90
 
10.4%
지방도333호선#3 54
 
6.2%
세종로#3 54
 
6.2%
강변북로#1 42
 
4.9%
국도42호선 36
 
4.2%
영릉로#1 36
 
4.2%
세종로#1 36
 
4.2%
국도37호선#-3 30
 
3.5%
국도42호선#-1 24
 
2.8%
Other values (44) 366
42.4%
2023-12-09T14:42:41.369689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 996
16.9%
# 732
12.4%
호 432
 
7.3%
선 432
 
7.3%
도 432
 
7.3%
로 432
 
7.3%
1 294
 
5.0%
국 222
 
3.8%
지 210
 
3.6%
방 210
 
3.6%
Other values (33) 1488
25.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3300
56.1%
Decimal Number 1734
29.5%
Other Punctuation 732
 
12.4%
Dash Punctuation 114
 
1.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
호 432
13.1%
선 432
13.1%
도 432
13.1%
로 432
13.1%
국 222
 
6.7%
지 210
 
6.4%
방 210
 
6.4%
종 96
 
2.9%
세 96
 
2.9%
여 78
 
2.4%
Other values (24) 660
20.0%
Decimal Number
ValueCountFrequency (%)
3 996
57.4%
1 294
 
17.0%
2 162
 
9.3%
4 114
 
6.6%
5 96
 
5.5%
7 66
 
3.8%
6 6
 
0.3%
Other Punctuation
ValueCountFrequency (%)
# 732
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 114
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3300
56.1%
Common 2580
43.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
호 432
13.1%
선 432
13.1%
도 432
13.1%
로 432
13.1%
국 222
 
6.7%
지 210
 
6.4%
방 210
 
6.4%
종 96
 
2.9%
세 96
 
2.9%
여 78
 
2.4%
Other values (24) 660
20.0%
Common
ValueCountFrequency (%)
3 996
38.6%
# 732
28.4%
1 294
 
11.4%
2 162
 
6.3%
4 114
 
4.4%
- 114
 
4.4%
5 96
 
3.7%
7 66
 
2.6%
6 6
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3300
56.1%
ASCII 2580
43.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 996
38.6%
# 732
28.4%
1 294
 
11.4%
2 162
 
6.3%
4 114
 
4.4%
- 114
 
4.4%
5 96
 
3.7%
7 66
 
2.6%
6 6
 
0.2%
Hangul
ValueCountFrequency (%)
호 432
13.1%
선 432
13.1%
도 432
13.1%
로 432
13.1%
국 222
 
6.7%
지 210
 
6.4%
방 210
 
6.4%
종 96
 
2.9%
세 96
 
2.9%
여 78
 
2.4%
Other values (24) 660
20.0%

검지기채널번호
Real number (ℝ)

ZEROS 

Distinct7
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.13657407
Minimum0
Maximum6
Zeros808
Zeros (%)93.5%
Negative0
Negative (%)0.0%
Memory size7.7 KiB
2023-12-09T14:42:42.053241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.60691674
Coefficient of variation (CV)4.4438649
Kurtosis33.331312
Mean0.13657407
Median Absolute Deviation (MAD)0
Skewness5.4423325
Sum118
Variance0.36834792
MonotonicityNot monotonic
2023-12-09T14:42:42.633145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 808
93.5%
1 23
 
2.7%
2 16
 
1.9%
3 9
 
1.0%
4 5
 
0.6%
5 2
 
0.2%
6 1
 
0.1%
ValueCountFrequency (%)
0 808
93.5%
1 23
 
2.7%
2 16
 
1.9%
3 9
 
1.0%
4 5
 
0.6%
5 2
 
0.2%
6 1
 
0.1%
ValueCountFrequency (%)
6 1
 
0.1%
5 2
 
0.2%
4 5
 
0.6%
3 9
 
1.0%
2 16
 
1.9%
1 23
 
2.7%
0 808
93.5%

Interactions

2023-12-09T14:42:27.462576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-09T14:42:24.378755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-09T14:42:26.016935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-09T14:42:28.062754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-09T14:42:24.971686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-09T14:42:26.570323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-09T14:42:28.615149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-09T14:42:25.468794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-09T14:42:26.997124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-09T14:42:42.919307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
교차로번호교차로그룹그룹명검지기채널번호
교차로번호1.0000.9530.9930.378
교차로그룹0.9531.0001.0000.218
그룹명0.9931.0001.0000.075
검지기채널번호0.3780.2180.0751.000
2023-12-09T14:42:43.432801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
교차로번호교차로그룹검지기채널번호
교차로번호1.0000.834-0.384
교차로그룹0.8341.000-0.350
검지기채널번호-0.384-0.3501.000

Missing values

2023-12-09T14:42:29.332347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-09T14:42:29.965890image/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

교차로번호교차로명교차로그룹그룹명검지기채널번호
01신해2리 사거리1국도3호선1
11신해2리 사거리1국도3호선2
21신해2리 사거리1국도3호선0
31신해2리 사거리1국도3호선0
41신해2리 사거리1국도3호선0
51신해2리 사거리1국도3호선0
62동남아파트 교차로1국도3호선1
72동남아파트 교차로1국도3호선2
82동남아파트 교차로1국도3호선3
92동남아파트 교차로1국도3호선4
교차로번호교차로명교차로그룹그룹명검지기채널번호
854144산업단지입구55국도37호선#-40
855144산업단지입구55국도37호선#-40
856144산업단지입구55국도37호선#-40
857144산업단지입구55국도37호선#-40
858145덕평삼거리55국도37호선#-40
859145덕평삼거리55국도37호선#-40
860145덕평삼거리55국도37호선#-40
861145덕평삼거리55국도37호선#-40
862145덕평삼거리55국도37호선#-40
863145덕평삼거리55국도37호선#-40

Duplicate rows

Most frequently occurring

교차로번호교차로명교차로그룹그룹명검지기채널번호# duplicates
1518중부대로 2234번길 입구52국도42호선#-106
1619신근리 삼거리52국도42호선#-106
1821번도 삼거리2국도42호선06
2427영릉 교차로3영릉로#106
2528여주시청앞 사거리4세종로#106
2629한글시장앞4세종로#106
2730홍문 사거리4세종로#106
2831여주경찰서 삼거리4세종로#106
2932홍문현대 교차로4세종로#106
3033해방촌 교차로4세종로#106