Overview

Dataset statistics

Number of variables4
Number of observations460
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory15.9 KiB
Average record size in memory35.3 B

Variable types

Text1
Numeric3

Dataset

Description고속도로 영업소 중 폐쇄식 운영형태에 따른 이용차량 정보(2020~2022)를 csv 파일 형식으로 제공한다. (영업소, 연도)
URLhttps://www.data.go.kr/data/15062529/fileData.do

Alerts

2020년 is highly overall correlated with 2021년 and 1 other fieldsHigh correlation
2021년 is highly overall correlated with 2020년 and 1 other fieldsHigh correlation
2022년 is highly overall correlated with 2020년 and 1 other fieldsHigh correlation
영업소 has unique valuesUnique
2020년 has 30 (6.5%) zerosZeros
2021년 has 19 (4.1%) zerosZeros

Reproduction

Analysis started2023-12-12 07:57:51.305594
Analysis finished2023-12-12 07:57:52.767523
Duration1.46 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

영업소
Text

UNIQUE 

Distinct460
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
2023-12-12T16:57:53.054042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length2
Mean length2.6804348
Min length2

Characters and Unicode

Total characters1233
Distinct characters205
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique460 ?
Unique (%)100.0%

Sample

1st row서울
2nd row동수원
3rd row수원신갈
4th row지곡
5th row기흥
ValueCountFrequency (%)
서울 1
 
0.2%
대구 1
 
0.2%
영천jc 1
 
0.2%
남논산상 1
 
0.2%
신녕 1
 
0.2%
동군위 1
 
0.2%
서군위 1
 
0.2%
남풍세 1
 
0.2%
풍세하 1
 
0.2%
도개 1
 
0.2%
Other values (450) 450
97.8%
2023-12-12T16:57:53.559385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
서 56
 
4.5%
동 54
 
4.4%
산 52
 
4.2%
남 48
 
3.9%
주 48
 
3.9%
천 39
 
3.2%
양 36
 
2.9%
북 28
 
2.3%
성 27
 
2.2%
안 24
 
1.9%
Other values (195) 821
66.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1197
97.1%
Uppercase Letter 33
 
2.7%
Close Punctuation 1
 
0.1%
Open Punctuation 1
 
0.1%
Decimal Number 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 56
 
4.7%
동 54
 
4.5%
산 52
 
4.3%
남 48
 
4.0%
주 48
 
4.0%
천 39
 
3.3%
양 36
 
3.0%
북 28
 
2.3%
성 27
 
2.3%
안 24
 
2.0%
Other values (188) 785
65.6%
Uppercase Letter
ValueCountFrequency (%)
C 16
48.5%
J 15
45.5%
K 1
 
3.0%
E 1
 
3.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Decimal Number
ValueCountFrequency (%)
2 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1197
97.1%
Latin 33
 
2.7%
Common 3
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 56
 
4.7%
동 54
 
4.5%
산 52
 
4.3%
남 48
 
4.0%
주 48
 
4.0%
천 39
 
3.3%
양 36
 
3.0%
북 28
 
2.3%
성 27
 
2.3%
안 24
 
2.0%
Other values (188) 785
65.6%
Latin
ValueCountFrequency (%)
C 16
48.5%
J 15
45.5%
K 1
 
3.0%
E 1
 
3.0%
Common
ValueCountFrequency (%)
) 1
33.3%
( 1
33.3%
2 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1197
97.1%
ASCII 36
 
2.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
서 56
 
4.7%
동 54
 
4.5%
산 52
 
4.3%
남 48
 
4.0%
주 48
 
4.0%
천 39
 
3.3%
양 36
 
3.0%
북 28
 
2.3%
성 27
 
2.3%
안 24
 
2.0%
Other values (188) 785
65.6%
ASCII
ValueCountFrequency (%)
C 16
44.4%
J 15
41.7%
K 1
 
2.8%
) 1
 
2.8%
E 1
 
2.8%
( 1
 
2.8%
2 1
 
2.8%

2020년
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct423
Distinct (%)92.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6656.6783
Minimum0
Maximum102212
Zeros30
Zeros (%)6.5%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2023-12-12T16:57:53.739921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11661.5
median3451
Q38350
95-th percentile21340.25
Maximum102212
Range102212
Interquartile range (IQR)6688.5

Descriptive statistics

Standard deviation9892.2544
Coefficient of variation (CV)1.4860647
Kurtosis33.974991
Mean6656.6783
Median Absolute Deviation (MAD)2370
Skewness4.7494227
Sum3062072
Variance97856696
MonotonicityNot monotonic
2023-12-12T16:57:53.921185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 30
 
6.5%
762 2
 
0.4%
6292 2
 
0.4%
1895 2
 
0.4%
2438 2
 
0.4%
3539 2
 
0.4%
2439 2
 
0.4%
2723 2
 
0.4%
834 2
 
0.4%
14201 1
 
0.2%
Other values (413) 413
89.8%
ValueCountFrequency (%)
0 30
6.5%
11 1
 
0.2%
15 1
 
0.2%
41 1
 
0.2%
54 1
 
0.2%
84 1
 
0.2%
96 1
 
0.2%
119 1
 
0.2%
137 1
 
0.2%
147 1
 
0.2%
ValueCountFrequency (%)
102212 1
0.2%
91122 1
0.2%
66306 1
0.2%
47081 1
0.2%
46923 1
0.2%
45606 1
0.2%
38640 1
0.2%
34670 1
0.2%
34020 1
0.2%
33688 1
0.2%

2021년
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct434
Distinct (%)94.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7158.4152
Minimum0
Maximum101122
Zeros19
Zeros (%)4.1%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2023-12-12T16:57:54.061512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile68.45
Q11938.25
median3940
Q39250.75
95-th percentile22197.7
Maximum101122
Range101122
Interquartile range (IQR)7312.5

Descriptive statistics

Standard deviation10118.284
Coefficient of variation (CV)1.413481
Kurtosis32.259832
Mean7158.4152
Median Absolute Deviation (MAD)2591.5
Skewness4.6238758
Sum3292871
Variance1.0237967 × 108
MonotonicityNot monotonic
2023-12-12T16:57:54.225221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 19
 
4.1%
2977 3
 
0.7%
2906 2
 
0.4%
803 2
 
0.4%
2237 2
 
0.4%
2911 2
 
0.4%
2141 2
 
0.4%
755 2
 
0.4%
3475 1
 
0.2%
7452 1
 
0.2%
Other values (424) 424
92.2%
ValueCountFrequency (%)
0 19
4.1%
11 1
 
0.2%
14 1
 
0.2%
45 1
 
0.2%
58 1
 
0.2%
69 1
 
0.2%
74 1
 
0.2%
89 1
 
0.2%
90 1
 
0.2%
147 1
 
0.2%
ValueCountFrequency (%)
101122 1
0.2%
95004 1
0.2%
69857 1
0.2%
50491 1
0.2%
46179 1
0.2%
46062 1
0.2%
37467 1
0.2%
36031 1
0.2%
35601 1
0.2%
34153 1
0.2%

2022년
Real number (ℝ)

HIGH CORRELATION 

Distinct451
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7615.8761
Minimum10
Maximum103880
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 KiB
2023-12-12T16:57:54.383271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile716.95
Q12263.25
median4269
Q39743.5
95-th percentile22750.8
Maximum103880
Range103870
Interquartile range (IQR)7480.25

Descriptive statistics

Standard deviation10194.322
Coefficient of variation (CV)1.3385619
Kurtosis33.387094
Mean7615.8761
Median Absolute Deviation (MAD)2691
Skewness4.6863679
Sum3503303
Variance1.039242 × 108
MonotonicityNot monotonic
2023-12-12T16:57:54.864302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
383 2
 
0.4%
2663 2
 
0.4%
4451 2
 
0.4%
2530 2
 
0.4%
11110 2
 
0.4%
792 2
 
0.4%
3541 2
 
0.4%
12825 2
 
0.4%
4366 2
 
0.4%
18538 1
 
0.2%
Other values (441) 441
95.9%
ValueCountFrequency (%)
10 1
0.2%
18 1
0.2%
46 1
0.2%
62 1
0.2%
105 1
0.2%
125 1
0.2%
159 1
0.2%
166 1
0.2%
186 1
0.2%
190 1
0.2%
ValueCountFrequency (%)
103880 1
0.2%
96347 1
0.2%
70645 1
0.2%
50940 1
0.2%
47287 1
0.2%
46714 1
0.2%
37806 1
0.2%
36410 1
0.2%
34824 1
0.2%
34099 1
0.2%

Interactions

2023-12-12T16:57:52.276137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:51.571074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:51.925055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:52.388535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:51.692096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:52.053709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:52.491330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:51.803206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:57:52.177706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T16:57:54.984787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2020년2021년2022년
2020년1.0000.9660.963
2021년0.9661.0000.999
2022년0.9630.9991.000
2023-12-12T16:57:55.079474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2020년2021년2022년
2020년1.0000.9510.852
2021년0.9511.0000.904
2022년0.8520.9041.000

Missing values

2023-12-12T16:57:52.634873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T16:57:52.727330image/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

영업소2020년2021년2022년
0서울102212101122103880
1동수원386403746737806
2수원신갈470814606246714
3지곡834873871
4기흥157591579015301
5오산268792703525618
6안성197262080722496
7천안290273065232666
8계룡281132393512
9목천724778798282
영업소2020년2021년2022년
450서오창서JC166918111880
451서오창338439284203
452양감554670844
453서영암534956575879
454강진무위사319934473567
455장흥189519451982
456보성445445704636
457벌교301530733089
458고흥435846234754
459남순천108241138011664