Overview

Dataset statistics

Number of variables7
Number of observations26
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.7 KiB
Average record size in memory65.1 B

Variable types

Categorical1
Text2
Numeric4

Dataset

Description충청북도 보은군 군도정보(노선번호, 노선명, 총연장, 개통도, 데이터기준일 등)를 활용할 수 있는 데이터 입니다.
URLhttps://www.data.go.kr/data/15112859/fileData.do

Alerts

도로종별 has constant value ""Constant
포장도 is highly overall correlated with 미포장도High correlation
미포장도 is highly overall correlated with 포장도 and 1 other fieldsHigh correlation
미개통도 is highly overall correlated with 미포장도High correlation
노선번호 has unique valuesUnique
노선명 has unique valuesUnique
포장도 has 3 (11.5%) zerosZeros
미포장도 has 16 (61.5%) zerosZeros
미개통도 has 16 (61.5%) zerosZeros

Reproduction

Analysis started2023-12-11 22:46:37.604270
Analysis finished2023-12-11 22:46:39.580865
Duration1.98 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

도로종별
Categorical

CONSTANT 

Distinct1
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size340.0 B
군 도
26 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row군 도
2nd row군 도
3rd row군 도
4th row군 도
5th row군 도

Common Values

ValueCountFrequency (%)
군 도 26
100.0%

Length

2023-12-12T07:46:39.642616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T07:46:39.722391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
군 26
50.0%
도 26
50.0%

노선번호
Text

UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size340.0 B
2023-12-12T07:46:39.882045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.6538462
Min length3

Characters and Unicode

Total characters95
Distinct characters12
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

Unique26 ?
Unique (%)100.0%

Sample

1st row1호선
2nd row2호선
3rd row3호선
4th row4호선
5th row5호선
ValueCountFrequency (%)
1호선 1
 
3.8%
2호선 1
 
3.8%
26호선 1
 
3.8%
25호선 1
 
3.8%
24호선 1
 
3.8%
23호선 1
 
3.8%
22호선 1
 
3.8%
21호선 1
 
3.8%
20호선 1
 
3.8%
18호선 1
 
3.8%
Other values (16) 16
61.5%
2023-12-12T07:46:40.169951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
호 26
27.4%
선 26
27.4%
1 12
12.6%
2 11
11.6%
3 3
 
3.2%
4 3
 
3.2%
5 3
 
3.2%
6 3
 
3.2%
7 3
 
3.2%
8 2
 
2.1%
Other values (2) 3
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 52
54.7%
Decimal Number 43
45.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 12
27.9%
2 11
25.6%
3 3
 
7.0%
4 3
 
7.0%
5 3
 
7.0%
6 3
 
7.0%
7 3
 
7.0%
8 2
 
4.7%
0 2
 
4.7%
9 1
 
2.3%
Other Letter
ValueCountFrequency (%)
호 26
50.0%
선 26
50.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 52
54.7%
Common 43
45.3%

Most frequent character per script

Common
ValueCountFrequency (%)
1 12
27.9%
2 11
25.6%
3 3
 
7.0%
4 3
 
7.0%
5 3
 
7.0%
6 3
 
7.0%
7 3
 
7.0%
8 2
 
4.7%
0 2
 
4.7%
9 1
 
2.3%
Hangul
ValueCountFrequency (%)
호 26
50.0%
선 26
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 52
54.7%
ASCII 43
45.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
호 26
50.0%
선 26
50.0%
ASCII
ValueCountFrequency (%)
1 12
27.9%
2 11
25.6%
3 3
 
7.0%
4 3
 
7.0%
5 3
 
7.0%
6 3
 
7.0%
7 3
 
7.0%
8 2
 
4.7%
0 2
 
4.7%
9 1
 
2.3%

노선명
Text

UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size340.0 B
2023-12-12T07:46:40.339578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters130
Distinct characters62
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

Unique26 ?
Unique (%)100.0%

Sample

1st row동정-노티
2nd row건천-신궁
3rd row교사-오대
4th row법주-아곡
5th row원평-원평
ValueCountFrequency (%)
동정-노티 1
 
3.8%
건천-신궁 1
 
3.8%
거현-발산 1
 
3.8%
북암-신정 1
 
3.8%
삼가-삼가 1
 
3.8%
금굴-금굴 1
 
3.8%
성암-가고 1
 
3.8%
노성-차정 1
 
3.8%
기대-소여 1
 
3.8%
원정-한중 1
 
3.8%
Other values (16) 16
61.5%
2023-12-12T07:46:40.653170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 26
 
20.0%
정 5
 
3.8%
원 4
 
3.1%
동 3
 
2.3%
가 3
 
2.3%
굴 3
 
2.3%
성 3
 
2.3%
암 3
 
2.3%
현 3
 
2.3%
거 3
 
2.3%
Other values (52) 74
56.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 104
80.0%
Dash Punctuation 26
 
20.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
정 5
 
4.8%
원 4
 
3.8%
동 3
 
2.9%
가 3
 
2.9%
굴 3
 
2.9%
성 3
 
2.9%
암 3
 
2.9%
현 3
 
2.9%
거 3
 
2.9%
남 3
 
2.9%
Other values (51) 71
68.3%
Dash Punctuation
ValueCountFrequency (%)
- 26
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 104
80.0%
Common 26
 
20.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
정 5
 
4.8%
원 4
 
3.8%
동 3
 
2.9%
가 3
 
2.9%
굴 3
 
2.9%
성 3
 
2.9%
암 3
 
2.9%
현 3
 
2.9%
거 3
 
2.9%
남 3
 
2.9%
Other values (51) 71
68.3%
Common
ValueCountFrequency (%)
- 26
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 104
80.0%
ASCII 26
 
20.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 26
100.0%
Hangul
ValueCountFrequency (%)
정 5
 
4.8%
원 4
 
3.8%
동 3
 
2.9%
가 3
 
2.9%
굴 3
 
2.9%
성 3
 
2.9%
암 3
 
2.9%
현 3
 
2.9%
거 3
 
2.9%
남 3
 
2.9%
Other values (51) 71
68.3%

총연장
Real number (ℝ)

Distinct25
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7607.6923
Minimum500
Maximum19600
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-12T07:46:40.793771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum500
5-th percentile1400
Q14725
median6150
Q39725
95-th percentile16300
Maximum19600
Range19100
Interquartile range (IQR)5000

Descriptive statistics

Standard deviation4607.1182
Coefficient of variation (CV)0.60558683
Kurtosis0.79306714
Mean7607.6923
Median Absolute Deviation (MAD)2850
Skewness0.90139729
Sum197800
Variance21225538
MonotonicityNot monotonic
2023-12-12T07:46:40.922158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
5600 2
 
7.7%
4300 1
 
3.8%
9200 1
 
3.8%
4600 1
 
3.8%
7800 1
 
3.8%
5800 1
 
3.8%
1100 1
 
3.8%
5400 1
 
3.8%
6400 1
 
3.8%
5900 1
 
3.8%
Other values (15) 15
57.7%
ValueCountFrequency (%)
500 1
3.8%
1100 1
3.8%
2300 1
3.8%
3500 1
3.8%
4000 1
3.8%
4300 1
3.8%
4600 1
3.8%
5100 1
3.8%
5400 1
3.8%
5600 2
7.7%
ValueCountFrequency (%)
19600 1
3.8%
16700 1
3.8%
15100 1
3.8%
11700 1
3.8%
11000 1
3.8%
10400 1
3.8%
9800 1
3.8%
9500 1
3.8%
9400 1
3.8%
9200 1
3.8%

포장도
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21
Distinct (%)80.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5084.6154
Minimum0
Maximum19600
Zeros3
Zeros (%)11.5%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-12T07:46:41.033862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11325
median4850
Q37450
95-th percentile11175
Maximum19600
Range19600
Interquartile range (IQR)6125

Descriptive statistics

Standard deviation4516.4315
Coefficient of variation (CV)0.88825431
Kurtosis2.8433487
Mean5084.6154
Median Absolute Deviation (MAD)2900
Skewness1.3435712
Sum132200
Variance20398154
MonotonicityNot monotonic
2023-12-12T07:46:41.142423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 3
 
11.5%
500 2
 
7.7%
2800 2
 
7.7%
1100 2
 
7.7%
7300 1
 
3.8%
19600 1
 
3.8%
4600 1
 
3.8%
7800 1
 
3.8%
5800 1
 
3.8%
5400 1
 
3.8%
Other values (11) 11
42.3%
ValueCountFrequency (%)
0 3
11.5%
500 2
7.7%
1100 2
7.7%
2000 1
 
3.8%
2800 2
7.7%
2900 1
 
3.8%
3500 1
 
3.8%
4600 1
 
3.8%
5100 1
 
3.8%
5400 1
 
3.8%
ValueCountFrequency (%)
19600 1
3.8%
11700 1
3.8%
9600 1
3.8%
9400 1
3.8%
9200 1
3.8%
7800 1
3.8%
7500 1
3.8%
7300 1
3.8%
6400 1
3.8%
5800 1
3.8%

미포장도
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)34.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1242.3077
Minimum0
Maximum6200
Zeros16
Zeros (%)61.5%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-12T07:46:41.251685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31275
95-th percentile5650
Maximum6200
Range6200
Interquartile range (IQR)1275

Descriptive statistics

Standard deviation2019.7372
Coefficient of variation (CV)1.6257947
Kurtosis0.99919466
Mean1242.3077
Median Absolute Deviation (MAD)0
Skewness1.5207126
Sum32300
Variance4079338.5
MonotonicityNot monotonic
2023-12-12T07:46:41.345393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 16
61.5%
4600 2
 
7.7%
1200 2
 
7.7%
2900 1
 
3.8%
3400 1
 
3.8%
1300 1
 
3.8%
6200 1
 
3.8%
6000 1
 
3.8%
900 1
 
3.8%
ValueCountFrequency (%)
0 16
61.5%
900 1
 
3.8%
1200 2
 
7.7%
1300 1
 
3.8%
2900 1
 
3.8%
3400 1
 
3.8%
4600 2
 
7.7%
6000 1
 
3.8%
6200 1
 
3.8%
ValueCountFrequency (%)
6200 1
 
3.8%
6000 1
 
3.8%
4600 2
 
7.7%
3400 1
 
3.8%
2900 1
 
3.8%
1300 1
 
3.8%
1200 2
 
7.7%
900 1
 
3.8%
0 16
61.5%

미개통도
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)42.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1280.7692
Minimum0
Maximum10500
Zeros16
Zeros (%)61.5%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-12T07:46:41.440192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32075
95-th percentile5100
Maximum10500
Range10500
Interquartile range (IQR)2075

Descriptive statistics

Standard deviation2362.0363
Coefficient of variation (CV)1.8442325
Kurtosis9.2384791
Mean1280.7692
Median Absolute Deviation (MAD)0
Skewness2.815825
Sum33300
Variance5579215.4
MonotonicityNot monotonic
2023-12-12T07:46:41.543384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 16
61.5%
1400 1
 
3.8%
2100 1
 
3.8%
2700 1
 
3.8%
10500 1
 
3.8%
2500 1
 
3.8%
2000 1
 
3.8%
5900 1
 
3.8%
2400 1
 
3.8%
2200 1
 
3.8%
ValueCountFrequency (%)
0 16
61.5%
1400 1
 
3.8%
1600 1
 
3.8%
2000 1
 
3.8%
2100 1
 
3.8%
2200 1
 
3.8%
2400 1
 
3.8%
2500 1
 
3.8%
2700 1
 
3.8%
5900 1
 
3.8%
ValueCountFrequency (%)
10500 1
3.8%
5900 1
3.8%
2700 1
3.8%
2500 1
3.8%
2400 1
3.8%
2200 1
3.8%
2100 1
3.8%
2000 1
3.8%
1600 1
3.8%
1400 1
3.8%

Interactions

2023-12-12T07:46:39.019655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:46:37.809159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:46:38.168330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:46:38.485129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:46:39.117140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:46:37.898085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:46:38.250874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:46:38.566626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:46:39.202040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:46:38.001941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:46:38.326793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:46:38.881503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:46:39.280499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:46:38.077860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:46:38.406105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:46:38.950364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T07:46:41.630426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
노선번호노선명총연장포장도미포장도미개통도
노선번호1.0001.0001.0001.0001.0001.000
노선명1.0001.0001.0001.0001.0001.000
총연장1.0001.0001.0000.8310.6750.630
포장도1.0001.0000.8311.0000.0000.000
미포장도1.0001.0000.6750.0001.0000.752
미개통도1.0001.0000.6300.0000.7521.000
2023-12-12T07:46:41.785272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
총연장포장도미포장도미개통도
총연장1.0000.4870.2700.379
포장도0.4871.000-0.581-0.480
미포장도0.270-0.5811.0000.824
미개통도0.379-0.4800.8241.000

Missing values

2023-12-12T07:46:39.383127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T07:46:39.526602image/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

도로종별노선번호노선명총연장포장도미포장도미개통도
0군 도1호선동정-노티4300029001400
1군 도2호선건천-신궁9200920000
2군 도3호선교사-오대15100960034002100
3군 도4호선법주-아곡4000013002700
4군 도5호선원평-원평50050000
5군 도6호선고석-오창167000620010500
6군 도7호선천남-죽전9800730002500
7군 도8호선상궁-백석196001960000
8군 도9호선고승-학림9500290046002000
9군 도10호선동정-길상1100050046005900
도로종별노선번호노선명총연장포장도미포장도미개통도
16군 도17호선수문-개안3500350000
17군 도18호선원정-한중5100510000
18군 도20호선기대-소여590028009002200
19군 도21호선노성-차정6400640000
20군 도22호선성암-가고5400540000
21군 도23호선금굴-금굴1100110000
22군 도24호선삼가-삼가5800580000
23군 도25호선북암-신정5600280012001600
24군 도26호선거현-발산7800780000
25군 도27호선교사-봉계4600460000