Overview

Dataset statistics

Number of variables16
Number of observations31
Missing cells27
Missing cells (%)5.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.2 KiB
Average record size in memory140.1 B

Variable types

Categorical2
Text3
Numeric8
DateTime3

Dataset

Description제주특별자치도에서 제공하는 연도별 도로교통량 조사 관련 노선, 관측지점, 지점번호 등 정보입니다. 담당부서: 건설과
Author제주특별자치도
URLhttps://www.data.go.kr/data/15109152/fileData.do

Alerts

조사시작일 has constant value ""Constant
조사종료일 has constant value ""Constant
데이터기준일자 has constant value ""Constant
2015년조사결과 is highly overall correlated with 2016년조사결과 and 6 other fieldsHigh correlation
2016년조사결과 is highly overall correlated with 2015년조사결과 and 6 other fieldsHigh correlation
2017년조사결과 is highly overall correlated with 2015년조사결과 and 6 other fieldsHigh correlation
2018년조사결과 is highly overall correlated with 2015년조사결과 and 6 other fieldsHigh correlation
2019년조사결과 is highly overall correlated with 2015년조사결과 and 6 other fieldsHigh correlation
2020년조사결과 is highly overall correlated with 2015년조사결과 and 6 other fieldsHigh correlation
2021년조사결과 is highly overall correlated with 2015년조사결과 and 6 other fieldsHigh correlation
2022년조사결과 is highly overall correlated with 2015년조사결과 and 6 other fieldsHigh correlation
도로구분 is highly overall correlated with 노선별High correlation
노선별 is highly overall correlated with 도로구분High correlation
도로구분 is highly imbalanced (79.4%)Imbalance
비고 has 27 (87.1%) missing valuesMissing
지점번호 has unique valuesUnique
2015년조사결과 has unique valuesUnique
2016년조사결과 has unique valuesUnique
2017년조사결과 has unique valuesUnique
2018년조사결과 has unique valuesUnique
2019년조사결과 has unique valuesUnique
2020년조사결과 has unique valuesUnique
2021년조사결과 has unique valuesUnique
2022년조사결과 has unique valuesUnique
2015년조사결과 has 1 (3.2%) zerosZeros
2016년조사결과 has 1 (3.2%) zerosZeros
2017년조사결과 has 1 (3.2%) zerosZeros
2018년조사결과 has 1 (3.2%) zerosZeros
2019년조사결과 has 1 (3.2%) zerosZeros
2020년조사결과 has 1 (3.2%) zerosZeros

Reproduction

Analysis started2024-04-20 17:58:49.826557
Analysis finished2024-04-20 17:59:05.193409
Duration15.37 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

도로구분
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Memory size376.0 B
지방도
30 
국지도
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique1 ?
Unique (%)3.2%

Sample

1st row국지도
2nd row지방도
3rd row지방도
4th row지방도
5th row지방도

Common Values

ValueCountFrequency (%)
지방도 30
96.8%
국지도 1
 
3.2%

Length

2024-04-21T02:59:05.302167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T02:59:05.560503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
지방도 30
96.8%
국지도 1
 
3.2%

노선별
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)35.5%
Missing0
Missing (%)0.0%
Memory size376.0 B
중산간도로(1136호선)
8 
일주도로(1132호선)
7 
1100도로(1139호선)
3 
제2산록도로(1115호선)
2 
남조로(99호선)
2 
Other values (6)
9 

Length

Max length14
Median length12
Mean length12.225806
Min length9

Unique

Unique3 ?
Unique (%)9.7%

Sample

1st row번영로(97호선)
2nd row제2산록도로(1115호선)
3rd row제2산록도로(1115호선)
4th row한창로(1116호선)
5th row남조로(99호선)

Common Values

ValueCountFrequency (%)
중산간도로(1136호선) 8
25.8%
일주도로(1132호선) 7
22.6%
1100도로(1139호선) 3
 
9.7%
제2산록도로(1115호선) 2
 
6.5%
남조로(99호선) 2
 
6.5%
서성로(1119호선) 2
 
6.5%
5.16도로(1131호선) 2
 
6.5%
평화로(1135호선) 2
 
6.5%
번영로(97호선) 1
 
3.2%
한창로(1116호선) 1
 
3.2%

Length

2024-04-21T02:59:05.905851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
중산간도로(1136호선 8
25.8%
일주도로(1132호선 7
22.6%
1100도로(1139호선 3
 
9.7%
제2산록도로(1115호선 2
 
6.5%
남조로(99호선 2
 
6.5%
서성로(1119호선 2
 
6.5%
5.16도로(1131호선 2
 
6.5%
평화로(1135호선 2
 
6.5%
번영로(97호선 1
 
3.2%
한창로(1116호선 1
 
3.2%

관측지점
Text

Distinct26
Distinct (%)83.9%
Missing0
Missing (%)0.0%
Memory size376.0 B
2024-04-21T02:59:06.638278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length2
Mean length2.0322581
Min length2

Characters and Unicode

Total characters63
Distinct characters39
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)67.7%

Sample

1st row표선
2nd row광평
3rd row동홍
4th row동광
5th row남원
ValueCountFrequency (%)
표선 2
 
6.5%
남원 2
 
6.5%
상효 2
 
6.5%
동홍 2
 
6.5%
동광 2
 
6.5%
상창 1
 
3.2%
안성 1
 
3.2%
회수 1
 
3.2%
중문 1
 
3.2%
구억 1
 
3.2%
Other values (16) 16
51.6%
2024-04-21T02:59:07.495112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
수 4
 
6.3%
동 4
 
6.3%
성 4
 
6.3%
상 4
 
6.3%
광 3
 
4.8%
신 3
 
4.8%
표 2
 
3.2%
법 2
 
3.2%
평 2
 
3.2%
홍 2
 
3.2%
Other values (29) 33
52.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 63
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
수 4
 
6.3%
동 4
 
6.3%
성 4
 
6.3%
상 4
 
6.3%
광 3
 
4.8%
신 3
 
4.8%
표 2
 
3.2%
법 2
 
3.2%
평 2
 
3.2%
홍 2
 
3.2%
Other values (29) 33
52.4%

Most occurring scripts

ValueCountFrequency (%)
Hangul 63
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
수 4
 
6.3%
동 4
 
6.3%
성 4
 
6.3%
상 4
 
6.3%
광 3
 
4.8%
신 3
 
4.8%
표 2
 
3.2%
법 2
 
3.2%
평 2
 
3.2%
홍 2
 
3.2%
Other values (29) 33
52.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 63
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
수 4
 
6.3%
동 4
 
6.3%
성 4
 
6.3%
상 4
 
6.3%
광 3
 
4.8%
신 3
 
4.8%
표 2
 
3.2%
법 2
 
3.2%
평 2
 
3.2%
홍 2
 
3.2%
Other values (29) 33
52.4%

지점번호
Text

UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size376.0 B
2024-04-21T02:59:08.383692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length6.9032258
Min length6

Characters and Unicode

Total characters214
Distinct characters19
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique31 ?
Unique (%)100.0%

Sample

1st rowSep-97
2nd row1115-01
3rd row1115-02
4th row1116-05
5th rowJan-99
ValueCountFrequency (%)
sep-97 1
 
3.2%
1132-15 1
 
3.2%
1139-02 1
 
3.2%
1139-01 1
 
3.2%
1136-23 1
 
3.2%
1136-16 1
 
3.2%
1136-14 1
 
3.2%
1136-12 1
 
3.2%
1136-11 1
 
3.2%
1136-09 1
 
3.2%
Other values (21) 21
67.7%
2024-04-21T02:59:09.289123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 78
36.4%
- 31
 
14.5%
3 26
 
12.1%
0 20
 
9.3%
2 16
 
7.5%
9 12
 
5.6%
6 10
 
4.7%
5 7
 
3.3%
7 3
 
1.4%
e 2
 
0.9%
Other values (9) 9
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 174
81.3%
Dash Punctuation 31
 
14.5%
Lowercase Letter 6
 
2.8%
Uppercase Letter 3
 
1.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 78
44.8%
3 26
 
14.9%
0 20
 
11.5%
2 16
 
9.2%
9 12
 
6.9%
6 10
 
5.7%
5 7
 
4.0%
7 3
 
1.7%
8 1
 
0.6%
4 1
 
0.6%
Lowercase Letter
ValueCountFrequency (%)
e 2
33.3%
b 1
16.7%
n 1
16.7%
a 1
16.7%
p 1
16.7%
Uppercase Letter
ValueCountFrequency (%)
F 1
33.3%
S 1
33.3%
J 1
33.3%
Dash Punctuation
ValueCountFrequency (%)
- 31
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 205
95.8%
Latin 9
 
4.2%

Most frequent character per script

Common
ValueCountFrequency (%)
1 78
38.0%
- 31
 
15.1%
3 26
 
12.7%
0 20
 
9.8%
2 16
 
7.8%
9 12
 
5.9%
6 10
 
4.9%
5 7
 
3.4%
7 3
 
1.5%
8 1
 
0.5%
Latin
ValueCountFrequency (%)
e 2
22.2%
F 1
11.1%
b 1
11.1%
S 1
11.1%
n 1
11.1%
a 1
11.1%
J 1
11.1%
p 1
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 214
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 78
36.4%
- 31
 
14.5%
3 26
 
12.1%
0 20
 
9.3%
2 16
 
7.5%
9 12
 
5.6%
6 10
 
4.7%
5 7
 
3.3%
7 3
 
1.4%
e 2
 
0.9%
Other values (9) 9
 
4.2%

2015년조사결과
Real number (ℝ)

HIGH CORRELATION  UNIQUE  ZEROS 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8983.7097
Minimum0
Maximum25996
Zeros1
Zeros (%)3.2%
Negative0
Negative (%)0.0%
Memory size407.0 B
2024-04-21T02:59:09.584494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1639
Q13567.5
median7527
Q311459
95-th percentile22789
Maximum25996
Range25996
Interquartile range (IQR)7891.5

Descriptive statistics

Standard deviation6893.1649
Coefficient of variation (CV)0.76729604
Kurtosis0.49135832
Mean8983.7097
Median Absolute Deviation (MAD)4327
Skewness1.0552117
Sum278495
Variance47515722
MonotonicityNot monotonic
2024-04-21T02:59:09.867354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
10040 1
 
3.2%
7527 1
 
3.2%
1608 1
 
3.2%
3200 1
 
3.2%
3041 1
 
3.2%
0 1
 
3.2%
2703 1
 
3.2%
1865 1
 
3.2%
6495 1
 
3.2%
14097 1
 
3.2%
Other values (21) 21
67.7%
ValueCountFrequency (%)
0 1
3.2%
1608 1
3.2%
1670 1
3.2%
1728 1
3.2%
1865 1
3.2%
2703 1
3.2%
3041 1
3.2%
3200 1
3.2%
3935 1
3.2%
5180 1
3.2%
ValueCountFrequency (%)
25996 1
3.2%
23286 1
3.2%
22292 1
3.2%
22076 1
3.2%
14406 1
3.2%
14201 1
3.2%
14097 1
3.2%
11974 1
3.2%
10944 1
3.2%
10129 1
3.2%

2016년조사결과
Real number (ℝ)

HIGH CORRELATION  UNIQUE  ZEROS 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9481.5484
Minimum0
Maximum30375
Zeros1
Zeros (%)3.2%
Negative0
Negative (%)0.0%
Memory size407.0 B
2024-04-21T02:59:10.134201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1527.5
Q13868.5
median7703
Q312213
95-th percentile23787.5
Maximum30375
Range30375
Interquartile range (IQR)8344.5

Descriptive statistics

Standard deviation7589.317
Coefficient of variation (CV)0.80043013
Kurtosis0.8882045
Mean9481.5484
Median Absolute Deviation (MAD)4231
Skewness1.1764427
Sum293928
Variance57597733
MonotonicityNot monotonic
2024-04-21T02:59:10.512980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
11138 1
 
3.2%
7703 1
 
3.2%
1741 1
 
3.2%
3472 1
 
3.2%
3465 1
 
3.2%
0 1
 
3.2%
3011 1
 
3.2%
1985 1
 
3.2%
6064 1
 
3.2%
15484 1
 
3.2%
Other values (21) 21
67.7%
ValueCountFrequency (%)
0 1
3.2%
1510 1
3.2%
1545 1
3.2%
1741 1
3.2%
1985 1
3.2%
3011 1
3.2%
3465 1
3.2%
3472 1
3.2%
4265 1
3.2%
4593 1
3.2%
ValueCountFrequency (%)
30375 1
3.2%
23788 1
3.2%
23787 1
3.2%
23120 1
3.2%
16430 1
3.2%
15484 1
3.2%
15452 1
3.2%
13288 1
3.2%
11138 1
3.2%
10375 1
3.2%

2017년조사결과
Real number (ℝ)

HIGH CORRELATION  UNIQUE  ZEROS 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9697.3548
Minimum0
Maximum31610
Zeros1
Zeros (%)3.2%
Negative0
Negative (%)0.0%
Memory size407.0 B
2024-04-21T02:59:10.885754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1367.5
Q13811.5
median7902
Q312310
95-th percentile23821.5
Maximum31610
Range31610
Interquartile range (IQR)8498.5

Descriptive statistics

Standard deviation7804.0718
Coefficient of variation (CV)0.80476294
Kurtosis0.87897925
Mean9697.3548
Median Absolute Deviation (MAD)4458
Skewness1.1465296
Sum300618
Variance60903536
MonotonicityNot monotonic
2024-04-21T02:59:11.268949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
11161 1
 
3.2%
7902 1
 
3.2%
1625 1
 
3.2%
3353 1
 
3.2%
3444 1
 
3.2%
0 1
 
3.2%
3313 1
 
3.2%
1769 1
 
3.2%
5844 1
 
3.2%
17630 1
 
3.2%
Other values (21) 21
67.7%
ValueCountFrequency (%)
0 1
3.2%
1320 1
3.2%
1415 1
3.2%
1625 1
3.2%
1769 1
3.2%
3313 1
3.2%
3353 1
3.2%
3444 1
3.2%
4179 1
3.2%
4841 1
3.2%
ValueCountFrequency (%)
31610 1
3.2%
24221 1
3.2%
23422 1
3.2%
22750 1
3.2%
17693 1
3.2%
17630 1
3.2%
15593 1
3.2%
13459 1
3.2%
11161 1
3.2%
10234 1
3.2%

2018년조사결과
Real number (ℝ)

HIGH CORRELATION  UNIQUE  ZEROS 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10231.452
Minimum0
Maximum35128
Zeros1
Zeros (%)3.2%
Negative0
Negative (%)0.0%
Memory size407.0 B
2024-04-21T02:59:11.637072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1706.5
Q14326.5
median7788
Q313928.5
95-th percentile26773
Maximum35128
Range35128
Interquartile range (IQR)9602

Descriptive statistics

Standard deviation8862.4321
Coefficient of variation (CV)0.86619499
Kurtosis1.1512812
Mean10231.452
Median Absolute Deviation (MAD)4044
Skewness1.2925981
Sum317175
Variance78542703
MonotonicityNot monotonic
2024-04-21T02:59:12.036181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
7788 1
 
3.2%
4987 1
 
3.2%
2147 1
 
3.2%
4606 1
 
3.2%
3744 1
 
3.2%
0 1
 
3.2%
5145 1
 
3.2%
1801 1
 
3.2%
2274 1
 
3.2%
22095 1
 
3.2%
Other values (21) 21
67.7%
ValueCountFrequency (%)
0 1
3.2%
1612 1
3.2%
1801 1
3.2%
1914 1
3.2%
2147 1
3.2%
2274 1
3.2%
3744 1
3.2%
4047 1
3.2%
4606 1
3.2%
4895 1
3.2%
ValueCountFrequency (%)
35128 1
3.2%
30762 1
3.2%
22784 1
3.2%
22123 1
3.2%
22095 1
3.2%
19165 1
3.2%
16640 1
3.2%
16023 1
3.2%
11834 1
3.2%
11628 1
3.2%

2019년조사결과
Real number (ℝ)

HIGH CORRELATION  UNIQUE  ZEROS 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10426.774
Minimum0
Maximum41111
Zeros1
Zeros (%)3.2%
Negative0
Negative (%)0.0%
Memory size407.0 B
2024-04-21T02:59:12.425009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1756
Q14443.5
median7863
Q312779
95-th percentile27317.5
Maximum41111
Range41111
Interquartile range (IQR)8335.5

Descriptive statistics

Standard deviation9516.9686
Coefficient of variation (CV)0.91274333
Kurtosis2.6087162
Mean10426.774
Median Absolute Deviation (MAD)3625
Skewness1.5877876
Sum323230
Variance90572691
MonotonicityNot monotonic
2024-04-21T02:59:12.805543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
7863 1
 
3.2%
4923 1
 
3.2%
1859 1
 
3.2%
4468 1
 
3.2%
3652 1
 
3.2%
0 1
 
3.2%
5035 1
 
3.2%
1985 1
 
3.2%
2335 1
 
3.2%
21680 1
 
3.2%
Other values (21) 21
67.7%
ValueCountFrequency (%)
0 1
3.2%
1653 1
3.2%
1859 1
3.2%
1895 1
3.2%
1985 1
3.2%
2335 1
3.2%
3652 1
3.2%
4419 1
3.2%
4468 1
3.2%
4674 1
3.2%
ValueCountFrequency (%)
41111 1
3.2%
31446 1
3.2%
23189 1
3.2%
21680 1
3.2%
20443 1
3.2%
19532 1
3.2%
19509 1
3.2%
14070 1
3.2%
11488 1
3.2%
11440 1
3.2%

2020년조사결과
Real number (ℝ)

HIGH CORRELATION  UNIQUE  ZEROS 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11251.323
Minimum0
Maximum43097
Zeros1
Zeros (%)3.2%
Negative0
Negative (%)0.0%
Memory size407.0 B
2024-04-21T02:59:13.172782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2289.5
Q14654
median7896
Q314682.5
95-th percentile29985.5
Maximum43097
Range43097
Interquartile range (IQR)10028.5

Descriptive statistics

Standard deviation10114.323
Coefficient of variation (CV)0.89894527
Kurtosis2.2579068
Mean11251.323
Median Absolute Deviation (MAD)4648
Skewness1.5354824
Sum348791
Variance1.0229953 × 108
MonotonicityNot monotonic
2024-04-21T02:59:13.552236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
7896 1
 
3.2%
5162 1
 
3.2%
2278 1
 
3.2%
5115 1
 
3.2%
4230 1
 
3.2%
0 1
 
3.2%
5078 1
 
3.2%
2301 1
 
3.2%
2614 1
 
3.2%
23531 1
 
3.2%
Other values (21) 21
67.7%
ValueCountFrequency (%)
0 1
3.2%
2278 1
3.2%
2301 1
3.2%
2595 1
3.2%
2614 1
3.2%
2672 1
3.2%
3248 1
3.2%
4230 1
3.2%
5078 1
3.2%
5115 1
3.2%
ValueCountFrequency (%)
43097 1
3.2%
33311 1
3.2%
26660 1
3.2%
23531 1
3.2%
23154 1
3.2%
19864 1
3.2%
19569 1
3.2%
15931 1
3.2%
13434 1
3.2%
12048 1
3.2%

2021년조사결과
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12586.774
Minimum2104
Maximum48876
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size407.0 B
2024-04-21T02:59:13.920500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2104
5-th percentile2270.5
Q15543
median8685
Q318637.5
95-th percentile30579.5
Maximum48876
Range46772
Interquartile range (IQR)13094.5

Descriptive statistics

Standard deviation10620.1
Coefficient of variation (CV)0.84375074
Kurtosis3.3027496
Mean12586.774
Median Absolute Deviation (MAD)3612
Skewness1.6932717
Sum390190
Variance1.1278653 × 108
MonotonicityNot monotonic
2024-04-21T02:59:14.309686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
8751 1
 
3.2%
5751 1
 
3.2%
2104 1
 
3.2%
5375 1
 
3.2%
4292 1
 
3.2%
5736 1
 
3.2%
5562 1
 
3.2%
2321 1
 
3.2%
21833 1
 
3.2%
21723 1
 
3.2%
Other values (21) 21
67.7%
ValueCountFrequency (%)
2104 1
3.2%
2220 1
3.2%
2321 1
3.2%
4292 1
3.2%
4882 1
3.2%
5073 1
3.2%
5375 1
3.2%
5524 1
3.2%
5562 1
3.2%
5736 1
3.2%
ValueCountFrequency (%)
48876 1
3.2%
32834 1
3.2%
28325 1
3.2%
23932 1
3.2%
21833 1
3.2%
21756 1
3.2%
21723 1
3.2%
20814 1
3.2%
16461 1
3.2%
15931 1
3.2%

2022년조사결과
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20536.194
Minimum2197
Maximum288978
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size407.0 B
2024-04-21T02:59:14.667493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2197
5-th percentile2310
Q15042.5
median9617
Q317794
95-th percentile37689.5
Maximum288978
Range286781
Interquartile range (IQR)12751.5

Descriptive statistics

Standard deviation50751.393
Coefficient of variation (CV)2.4713145
Kurtosis28.558772
Mean20536.194
Median Absolute Deviation (MAD)6124
Skewness5.2592593
Sum636622
Variance2.5757039 × 109
MonotonicityNot monotonic
2024-04-21T02:59:15.050858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
9185 1
 
3.2%
5335 1
 
3.2%
2511 1
 
3.2%
4821 1
 
3.2%
4422 1
 
3.2%
5264 1
 
3.2%
3447 1
 
3.2%
2197 1
 
3.2%
6180 1
 
3.2%
19319 1
 
3.2%
Other values (21) 21
67.7%
ValueCountFrequency (%)
2197 1
3.2%
2208 1
3.2%
2412 1
3.2%
2511 1
3.2%
3447 1
3.2%
3493 1
3.2%
4422 1
3.2%
4821 1
3.2%
5264 1
3.2%
5335 1
3.2%
ValueCountFrequency (%)
288978 1
3.2%
46401 1
3.2%
28978 1
3.2%
25481 1
3.2%
21506 1
3.2%
20224 1
3.2%
19319 1
3.2%
19055 1
3.2%
16533 1
3.2%
15975 1
3.2%

비고
Text

MISSING 

Distinct3
Distinct (%)75.0%
Missing27
Missing (%)87.1%
Memory size376.0 B
2024-04-21T02:59:15.571583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length6
Mean length7.25
Min length6

Characters and Unicode

Total characters29
Distinct characters20
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

Unique2 ?
Unique (%)50.0%

Sample

1st row지점번호변경
2nd row지점번호변경
3rd row조사위치변경
4th row2021년도 신규추가
ValueCountFrequency (%)
지점번호변경 2
40.0%
조사위치변경 1
20.0%
2021년도 1
20.0%
신규추가 1
20.0%
2024-04-21T02:59:16.476108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
변 3
 
10.3%
경 3
 
10.3%
지 2
 
6.9%
점 2
 
6.9%
2 2
 
6.9%
호 2
 
6.9%
번 2
 
6.9%
조 1
 
3.4%
사 1
 
3.4%
위 1
 
3.4%
Other values (10) 10
34.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 24
82.8%
Decimal Number 4
 
13.8%
Space Separator 1
 
3.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
변 3
12.5%
경 3
12.5%
지 2
 
8.3%
점 2
 
8.3%
호 2
 
8.3%
번 2
 
8.3%
조 1
 
4.2%
사 1
 
4.2%
위 1
 
4.2%
치 1
 
4.2%
Other values (6) 6
25.0%
Decimal Number
ValueCountFrequency (%)
2 2
50.0%
0 1
25.0%
1 1
25.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 24
82.8%
Common 5
 
17.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
변 3
12.5%
경 3
12.5%
지 2
 
8.3%
점 2
 
8.3%
호 2
 
8.3%
번 2
 
8.3%
조 1
 
4.2%
사 1
 
4.2%
위 1
 
4.2%
치 1
 
4.2%
Other values (6) 6
25.0%
Common
ValueCountFrequency (%)
2 2
40.0%
0 1
20.0%
1 1
20.0%
1
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 24
82.8%
ASCII 5
 
17.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
변 3
12.5%
경 3
12.5%
지 2
 
8.3%
점 2
 
8.3%
호 2
 
8.3%
번 2
 
8.3%
조 1
 
4.2%
사 1
 
4.2%
위 1
 
4.2%
치 1
 
4.2%
Other values (6) 6
25.0%
ASCII
ValueCountFrequency (%)
2 2
40.0%
0 1
20.0%
1 1
20.0%
1
20.0%

조사시작일
Date

CONSTANT 

Distinct1
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size376.0 B
Minimum2022-10-20 00:00:00
Maximum2022-10-20 00:00:00
2024-04-21T02:59:16.803586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T02:59:17.100643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

조사종료일
Date

CONSTANT 

Distinct1
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size376.0 B
Minimum2022-10-21 00:00:00
Maximum2022-10-21 00:00:00
2024-04-21T02:59:17.385436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T02:59:17.682064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size376.0 B
Minimum2023-04-07 00:00:00
Maximum2023-04-07 00:00:00
2024-04-21T02:59:17.966961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T02:59:18.263867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2024-04-21T02:59:02.195485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T02:58:50.626624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T02:58:52.561414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T02:58:54.522905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T02:58:56.359814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T02:58:57.540569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T02:58:58.631059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T02:59:00.269682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T02:59:02.439532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T02:58:50.864375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T02:58:52.807362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T02:58:54.759517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T02:58:56.505761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T02:58:57.674246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T02:58:58.767903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T02:59:00.509594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T02:59:02.707733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T02:58:51.107110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T02:58:53.058256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T02:58:55.005914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T02:58:56.655055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T02:58:57.812789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T02:58:58.907542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T02:59:00.752839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T02:59:02.958846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T02:58:51.354459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T02:58:53.303500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T02:58:55.248787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T02:58:56.805658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T02:58:57.954541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T02:58:59.098743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T02:59:00.999182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T02:59:03.213812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T02:58:51.604832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T02:58:53.560252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T02:58:55.502620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T02:58:56.965325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T02:58:58.099654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T02:58:59.343045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T02:59:01.250681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T02:59:03.449986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T02:58:51.838038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T02:58:53.796281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T02:58:55.737656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T02:58:57.103092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T02:58:58.229260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T02:58:59.569606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T02:59:01.483614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T02:59:03.684006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T02:58:52.068380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T02:58:54.030234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T02:58:55.873093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T02:58:57.240359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T02:58:58.355170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T02:58:59.793633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T02:59:01.713699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T02:59:03.927833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T02:58:52.304718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T02:58:54.272755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T02:58:56.013223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T02:58:57.386965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T02:58:58.490305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T02:59:00.028340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T02:59:01.953892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-21T02:59:18.491779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
도로구분노선별관측지점지점번호2015년조사결과2016년조사결과2017년조사결과2018년조사결과2019년조사결과2020년조사결과2021년조사결과2022년조사결과비고
도로구분1.0001.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.000NaN
노선별1.0001.0000.0001.0000.0000.0000.0000.4800.3370.3120.5500.6621.000
관측지점0.0000.0001.0001.0000.8070.8160.7810.0000.0000.0000.0000.0001.000
지점번호1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
2015년조사결과0.0000.0000.8071.0001.0000.9860.9820.8840.9340.8070.8750.0001.000
2016년조사결과0.0000.0000.8161.0000.9861.0000.9920.8720.9290.8210.9210.0001.000
2017년조사결과0.0000.0000.7811.0000.9820.9921.0000.9180.9450.8470.9260.0001.000
2018년조사결과0.0000.4800.0001.0000.8840.8720.9181.0000.9420.9820.8550.8821.000
2019년조사결과0.0000.3370.0001.0000.9340.9290.9450.9421.0000.9660.9760.7351.000
2020년조사결과0.0000.3120.0001.0000.8070.8210.8470.9820.9661.0000.8760.8820.000
2021년조사결과0.0000.5500.0001.0000.8750.9210.9260.8550.9760.8761.0000.7650.827
2022년조사결과0.0000.6620.0001.0000.0000.0000.0000.8820.7350.8820.7651.000NaN
비고NaN1.0001.0001.0001.0001.0001.0001.0001.0000.0000.827NaN1.000
2024-04-21T02:59:18.825161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
도로구분노선별
도로구분1.0000.830
노선별0.8301.000
2024-04-21T02:59:19.076161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2015년조사결과2016년조사결과2017년조사결과2018년조사결과2019년조사결과2020년조사결과2021년조사결과2022년조사결과도로구분노선별
2015년조사결과1.0000.9900.9830.9370.9350.9260.8650.8390.0000.000
2016년조사결과0.9901.0000.9930.9530.9440.9400.8740.8610.0000.000
2017년조사결과0.9830.9931.0000.9600.9480.9480.8600.8780.0000.000
2018년조사결과0.9370.9530.9601.0000.9840.9790.8320.8930.0000.197
2019년조사결과0.9350.9440.9480.9841.0000.9630.8150.8680.0000.101
2020년조사결과0.9260.9400.9480.9790.9631.0000.8480.8930.0000.071
2021년조사결과0.8650.8740.8600.8320.8150.8481.0000.8630.0000.285
2022년조사결과0.8390.8610.8780.8930.8680.8930.8631.0000.0000.423
도로구분0.0000.0000.0000.0000.0000.0000.0000.0001.0000.830
노선별0.0000.0000.0000.1970.1010.0710.2850.4230.8301.000

Missing values

2024-04-21T02:59:04.304193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T02:59:04.941404image/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

도로구분노선별관측지점지점번호2015년조사결과2016년조사결과2017년조사결과2018년조사결과2019년조사결과2020년조사결과2021년조사결과2022년조사결과비고조사시작일조사종료일데이터기준일자
0국지도번영로(97호선)표선Sep-9710040111381116177887863789687519185<NA>2022-10-202022-10-212023-04-07
1지방도제2산록도로(1115호선)광평1115-0175277703790249874923516257515335<NA>2022-10-202022-10-212023-04-07
2지방도제2산록도로(1115호선)동홍1115-0239354265417940474419677179268395<NA>2022-10-202022-10-212023-04-07
3지방도한창로(1116호선)동광1116-052328623120234222278423189231542832525481<NA>2022-10-202022-10-212023-04-07
4지방도남조로(99호선)남원Jan-9997378996897681798914841165816323지점번호변경2022-10-202022-10-212023-04-07
5지방도남조로(99호선)수망Feb-99109449996989293441148810912114619617지점번호변경2022-10-202022-10-212023-04-07
6지방도서성로(1119호선)수산1119-0163565653484148954956324848823493<NA>2022-10-202022-10-212023-04-07
7지방도서성로(1119호선)고성1119-0284238812100519356996010384552410079<NA>2022-10-202022-10-212023-04-07
8지방도대한로(1120호선)무릉1120-0116701545141519141895259522202412<NA>2022-10-202022-10-212023-04-07
9지방도5.16도로(1131호선)상효1131-011440616430176931916519509266602393220224<NA>2022-10-202022-10-212023-04-07
도로구분노선별관측지점지점번호2015년조사결과2016년조사결과2017년조사결과2018년조사결과2019년조사결과2020년조사결과2021년조사결과2022년조사결과비고조사시작일조사종료일데이터기준일자
21지방도중산간도로(1136호선)상창1136-0851804593490153514674550062905856<NA>2022-10-202022-10-212023-04-07
22지방도중산간도로(1136호선)색달1136-091197413288134591664019532195692081419055<NA>2022-10-202022-10-212023-04-07
23지방도중산간도로(1136호선)호근1136-111409715484176302209521680235312172319319<NA>2022-10-202022-10-212023-04-07
24지방도중산간도로(1136호선)상효1136-12649560645844227423352614218336180조사위치변경2022-10-202022-10-212023-04-07
25지방도중산간도로(1136호선)신흥1136-1418651985176918011985230123212197<NA>2022-10-202022-10-212023-04-07
26지방도중산간도로(1136호선)성읍1136-1627033011331351455035507855623447<NA>2022-10-202022-10-212023-04-07
27지방도중산간도로(1136호선)구억1136-23000000573652642021년도 신규추가2022-10-202022-10-212023-04-07
28지방도1100도로(1139호선)중문1139-0130413465344437443652423042924422<NA>2022-10-202022-10-212023-04-07
29지방도1100도로(1139호선)회수1139-0232003472335346064468511553754821<NA>2022-10-202022-10-212023-04-07
30지방도1100도로(1139호선)법정사1139-0316081741162521471859227821042511<NA>2022-10-202022-10-212023-04-07