Overview

Dataset statistics

Number of variables7
Number of observations36
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.3 KiB
Average record size in memory64.7 B

Variable types

Text1
Categorical1
Numeric5

Dataset

Description한국 도로공사 입구영업소별 총주행거리(개방식) 정보를 제공한다. (영업소,대/년,km,1종,2종,3종,4종,5종)
URLhttps://www.data.go.kr/data/15062518/fileData.do

Alerts

1종 is highly overall correlated with 2종 and 3 other fieldsHigh correlation
2종 is highly overall correlated with 1종 and 3 other fieldsHigh correlation
3종 is highly overall correlated with 1종 and 3 other fieldsHigh correlation
4종 is highly overall correlated with 1종 and 3 other fieldsHigh correlation
5종 is highly overall correlated with 1종 and 3 other fieldsHigh correlation
1종 has unique valuesUnique
2종 has unique valuesUnique
3종 has unique valuesUnique
4종 has unique valuesUnique
5종 has unique valuesUnique

Reproduction

Analysis started2023-12-12 04:13:05.667414
Analysis finished2023-12-12 04:13:09.498653
Duration3.83 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

영업소
Text

Distinct18
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size420.0 B
2023-12-12T13:13:09.646842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length2
Mean length3.1111111
Min length2

Characters and Unicode

Total characters112
Distinct characters39
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

Unique0 ?
Unique (%)0.0%

Sample

1st row판교
2nd row판교
3rd row대왕판교
4th row대왕판교
5th row순천만
ValueCountFrequency (%)
판교 2
 
5.6%
대왕판교 2
 
5.6%
인천 2
 
5.6%
남인천 2
 
5.6%
가락(개 2
 
5.6%
김포 2
 
5.6%
시흥 2
 
5.6%
청계 2
 
5.6%
토평 2
 
5.6%
구리남양주 2
 
5.6%
Other values (8) 16
44.4%
2023-12-12T13:13:10.101906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
남 8
 
7.1%
( 6
 
5.4%
) 6
 
5.4%
천 6
 
5.4%
개 6
 
5.4%
서 4
 
3.6%
교 4
 
3.6%
토 4
 
3.6%
판 4
 
3.6%
인 4
 
3.6%
Other values (29) 60
53.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 100
89.3%
Open Punctuation 6
 
5.4%
Close Punctuation 6
 
5.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
남 8
 
8.0%
천 6
 
6.0%
개 6
 
6.0%
서 4
 
4.0%
교 4
 
4.0%
토 4
 
4.0%
판 4
 
4.0%
인 4
 
4.0%
대 4
 
4.0%
락 2
 
2.0%
Other values (27) 54
54.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 100
89.3%
Common 12
 
10.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
남 8
 
8.0%
천 6
 
6.0%
개 6
 
6.0%
서 4
 
4.0%
교 4
 
4.0%
토 4
 
4.0%
판 4
 
4.0%
인 4
 
4.0%
대 4
 
4.0%
락 2
 
2.0%
Other values (27) 54
54.0%
Common
ValueCountFrequency (%)
( 6
50.0%
) 6
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 100
89.3%
ASCII 12
 
10.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
남 8
 
8.0%
천 6
 
6.0%
개 6
 
6.0%
서 4
 
4.0%
교 4
 
4.0%
토 4
 
4.0%
판 4
 
4.0%
인 4
 
4.0%
대 4
 
4.0%
락 2
 
2.0%
Other values (27) 54
54.0%
ASCII
ValueCountFrequency (%)
( 6
50.0%
) 6
50.0%

대_년(km)
Categorical

Distinct2
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Memory size420.0 B
대수
18 
총주행거리
18 

Length

Max length5
Median length3.5
Mean length3.5
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row대수
2nd row총주행거리
3rd row대수
4th row총주행거리
5th row대수

Common Values

ValueCountFrequency (%)
대수 18
50.0%
총주행거리 18
50.0%

Length

2023-12-12T13:13:10.265860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:13:10.415071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대수 18
50.0%
총주행거리 18
50.0%

1종
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct36
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62739337
Minimum649178
Maximum3.1932906 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-12T13:13:10.570034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum649178
5-th percentile901756.16
Q16418779.9
median28817208
Q363628965
95-th percentile2.5243035 × 108
Maximum3.1932906 × 108
Range3.1867988 × 108
Interquartile range (IQR)57210185

Descriptive statistics

Standard deviation84351807
Coefficient of variation (CV)1.3444804
Kurtosis2.7766805
Mean62739337
Median Absolute Deviation (MAD)26672414
Skewness1.8189538
Sum2.2586161 × 109
Variance7.1152273 × 1015
MonotonicityNot monotonic
2023-12-12T13:13:10.760320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
31708288.0 1
 
2.8%
157912771.2 1
 
2.8%
7284309.12 1
 
2.8%
57419320.0 1
 
2.8%
302025623.2 1
 
2.8%
52975573.0 1
 
2.8%
140915024.2 1
 
2.8%
62906291.0 1
 
2.8%
235898591.3 1
 
2.8%
10834691.0 1
 
2.8%
Other values (26) 26
72.2%
ValueCountFrequency (%)
649178.0 1
2.8%
763625.0 1
2.8%
947799.88 1
2.8%
1350598.0 1
2.8%
2128789.0 1
2.8%
2606517.0 1
2.8%
2894898.0 1
2.8%
3787580.0 1
2.8%
3822192.34 1
2.8%
7284309.12 1
2.8%
ValueCountFrequency (%)
319329056.8 1
2.8%
302025623.2 1
2.8%
235898591.3 1
2.8%
166785594.9 1
2.8%
161911002.2 1
2.8%
157912771.2 1
2.8%
140915024.2 1
2.8%
107756919.2 1
2.8%
65796988.0 1
2.8%
62906291.0 1
2.8%

2종
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct36
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1525170.1
Minimum10489
Maximum9355424
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-12T13:13:10.942981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10489
5-th percentile21564.385
Q1146579.64
median504376.1
Q31826339.2
95-th percentile6564548.3
Maximum9355424
Range9344935
Interquartile range (IQR)1679759.5

Descriptive statistics

Standard deviation2229647.3
Coefficient of variation (CV)1.4619007
Kurtosis4.3036054
Mean1525170.1
Median Absolute Deviation (MAD)478907.51
Skewness2.1257323
Sum54906125
Variance4.971327 × 1012
MonotonicityNot monotonic
2023-12-12T13:13:11.126977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
362737.0 1
 
2.8%
4866984.0 1
 
2.8%
224897.31 1
 
2.8%
1349133.0 1
 
2.8%
7096439.58 1
 
2.8%
1341908.0 1
 
2.8%
3569475.28 1
 
2.8%
1703267.0 1
 
2.8%
6387251.25 1
 
2.8%
237604.0 1
 
2.8%
Other values (26) 26
72.2%
ValueCountFrequency (%)
10489.0 1
2.8%
16033.0 1
2.8%
23408.18 1
2.8%
27529.0 1
2.8%
52025.44 1
2.8%
55355.0 1
2.8%
77932.0 1
2.8%
80563.0 1
2.8%
144802.54 1
2.8%
147172.0 1
2.8%
ValueCountFrequency (%)
9355424.0 1
2.8%
7096439.58 1
2.8%
6387251.25 1
2.8%
4866984.0 1
2.8%
3890437.2 1
2.8%
3569475.28 1
2.8%
2180105.6 1
2.8%
2027910.0 1
2.8%
1907996.62 1
2.8%
1799120.0 1
2.8%

3종
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct36
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1735003.6
Minimum2033
Maximum9550372
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-12T13:13:11.317513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2033
5-th percentile14569.145
Q1145964.78
median726407
Q31875062.8
95-th percentile8213390.1
Maximum9550372
Range9548339
Interquartile range (IQR)1729098

Descriptive statistics

Standard deviation2544168.8
Coefficient of variation (CV)1.4663767
Kurtosis3.1161582
Mean1735003.6
Median Absolute Deviation (MAD)687963.47
Skewness1.9609689
Sum62460129
Variance6.4727947 × 1012
MonotonicityNot monotonic
2023-12-12T13:13:11.533897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
1596311.0 1
 
2.8%
4777010.4 1
 
2.8%
151004.7 1
 
2.8%
1549871.0 1
 
2.8%
8152321.46 1
 
2.8%
1363111.0 1
 
2.8%
3625875.26 1
 
2.8%
1551336.0 1
 
2.8%
5817510.0 1
 
2.8%
242514.0 1
 
2.8%
Other values (26) 26
72.2%
ValueCountFrequency (%)
2033.0 1
2.8%
10693.58 1
2.8%
15861.0 1
2.8%
16042.0 1
2.8%
23157.06 1
2.8%
53730.0 1
2.8%
59590.0 1
2.8%
79568.32 1
2.8%
130845.0 1
2.8%
151004.7 1
2.8%
ValueCountFrequency (%)
9550372.0 1
2.8%
8396595.86 1
2.8%
8152321.46 1
2.8%
5817510.0 1
2.8%
4777010.4 1
2.8%
3625875.26 1
2.8%
2835753.8 1
2.8%
2755967.76 1
2.8%
1990421.0 1
2.8%
1836610.0 1
2.8%

4종
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct36
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1070274.5
Minimum329
Maximum7589207.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-12T13:13:11.696606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum329
5-th percentile10271.885
Q1109402
median314976.42
Q31243866.2
95-th percentile4105069.5
Maximum7589207.6
Range7588878.6
Interquartile range (IQR)1134464.2

Descriptive statistics

Standard deviation1625762.3
Coefficient of variation (CV)1.5190144
Kurtosis6.8058466
Mean1070274.5
Median Absolute Deviation (MAD)307551.65
Skewness2.4490834
Sum38529880
Variance2.643103 × 1012
MonotonicityNot monotonic
2023-12-12T13:13:11.874344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
128280.0 1
 
2.8%
4389708.0 1
 
2.8%
171291.78 1
 
2.8%
694264.0 1
 
2.8%
3651828.64 1
 
2.8%
1079215.0 1
 
2.8%
2870711.9 1
 
2.8%
1069384.0 1
 
2.8%
4010190.0 1
 
2.8%
240630.0 1
 
2.8%
Other values (26) 26
72.2%
ValueCountFrequency (%)
329.0 1
2.8%
1730.54 1
2.8%
13119.0 1
2.8%
17952.0 1
2.8%
26209.92 1
2.8%
41845.0 1
2.8%
53168.0 1
2.8%
65070.24 1
2.8%
75544.0 1
2.8%
120688.0 1
2.8%
ValueCountFrequency (%)
7589207.6 1
2.8%
4389708.0 1
2.8%
4010190.0 1
2.8%
3651828.64 1
2.8%
2870711.9 1
2.8%
2363721.36 1
2.8%
1829045.0 1
2.8%
1459463.0 1
2.8%
1279387.8 1
2.8%
1232025.6 1
2.8%

5종
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct36
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1526471
Minimum236
Maximum6922281
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-12T13:13:12.022235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum236
5-th percentile20631.59
Q1140476.37
median566521.57
Q31675330.2
95-th percentile6207551.7
Maximum6922281
Range6922045
Interquartile range (IQR)1534853.9

Descriptive statistics

Standard deviation2074365.6
Coefficient of variation (CV)1.3589289
Kurtosis1.315453
Mean1526471
Median Absolute Deviation (MAD)490350.07
Skewness1.593567
Sum54952956
Variance4.3029925 × 1012
MonotonicityNot monotonic
2023-12-12T13:13:12.221690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
149211.0 1
 
2.8%
3409214.4 1
 
2.8%
199287.0 1
 
2.8%
1008129.0 1
 
2.8%
5302758.54 1
 
2.8%
1221950.0 1
 
2.8%
3250387.0 1
 
2.8%
1626210.0 1
 
2.8%
6098287.5 1
 
2.8%
1352008.0 1
 
2.8%
Other values (26) 26
72.2%
ValueCountFrequency (%)
236.0 1
2.8%
1241.36 1
2.8%
27095.0 1
2.8%
54737.0 1
2.8%
97606.0 1
2.8%
99053.0 1
2.8%
113924.0 1
2.8%
128085.0 1
2.8%
134391.2 1
2.8%
142504.76 1
2.8%
ValueCountFrequency (%)
6922280.96 1
2.8%
6535344.4 1
2.8%
6098287.5 1
2.8%
5686795.92 1
2.8%
5302758.54 1
2.8%
3409214.4 1
2.8%
3250387.0 1
2.8%
2774026.6 1
2.8%
1822691.0 1
2.8%
1626210.0 1
2.8%

Interactions

2023-12-12T13:13:08.584898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:13:05.992792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:13:06.631248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:13:07.239455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:13:07.898553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:13:08.724777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:13:06.137848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:13:06.748013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:13:07.365982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:13:08.047280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:13:08.848822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:13:06.259035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:13:06.864317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:13:07.469682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:13:08.186922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:13:08.969459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:13:06.368067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:13:06.971405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:13:07.599734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:13:08.321037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:13:09.085239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:13:06.486204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:13:07.106962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:13:07.749379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:13:08.438341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T13:13:12.387923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
영업소대_년(km)1종2종3종4종5종
영업소1.0000.0000.0000.2830.1960.0000.297
대_년(km)0.0001.0000.4850.2090.3710.2170.479
1종0.0000.4851.0000.9350.9700.8870.841
2종0.2830.2090.9351.0000.9700.9410.886
3종0.1960.3710.9700.9701.0000.9040.800
4종0.0000.2170.8870.9410.9041.0000.937
5종0.2970.4790.8410.8860.8000.9371.000
2023-12-12T13:13:12.915283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
1종2종3종4종5종대_년(km)
1종1.0000.9750.9520.8710.8370.333
2종0.9751.0000.9640.9360.9020.185
3종0.9520.9641.0000.8970.8640.252
4종0.8710.9360.8971.0000.9330.220
5종0.8370.9020.8640.9331.0000.465
대_년(km)0.3330.1850.2520.2200.4651.000

Missing values

2023-12-12T13:13:09.261193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T13:13:09.439903image/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

영업소대_년(km)1종2종3종4종5종
0판교대수31708288.0362737.01596311.0128280.0149211.0
1판교총주행거리166785594.91907996.628396595.86674752.8784849.86
2대왕판교대수2606517.027529.02033.0329.0236.0
3대왕판교총주행거리13710279.42144802.5410693.581730.541241.36
4순천만대수2128789.055355.059590.041845.0128085.0
5순천만총주행거리9941444.63258507.85278285.3195416.15598156.95
6내서대수2894898.080563.0130845.053168.099053.0
7내서총주행거리15632449.2435040.2706563.0287107.2534886.2
8서영암(개)대수763625.010489.016042.013119.027095.0
9서영암(개)총주행거리3787580.052025.4479568.3265070.24134391.2
영업소대_년(km)1종2종3종4종5종
26김포대수62906291.01703267.01551336.01069384.01626210.0
27김포총주행거리235898591.36387251.255817510.04010190.06098287.5
28가락(개)대수10834691.0237604.0242514.0240630.01352008.0
29가락(개)총주행거리55473617.921216532.481241671.681232025.66922280.96
30남인천대수28357084.0573712.0746251.0336681.0730007.0
31남인천총주행거리107756919.22180105.62835753.81279387.82774026.6
32인천대수51894552.01246935.0883323.0757603.01822691.0
33인천총주행거리161911002.23890437.22755967.762363721.365686795.92
34금토분기점대수11677724.0147172.0179970.075544.054737.0
35금토분기점총주행거리61424828.24774124.72946642.2397361.44287916.62