Overview

Dataset statistics

Number of variables11
Number of observations4489
Missing cells202
Missing cells (%)0.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory394.7 KiB
Average record size in memory90.0 B

Variable types

Numeric2
Categorical1
Text6
Boolean1
DateTime1

Dataset

Description경기도 버스노선별 저상버스 유무 현황
Author경기도
URLhttps://data.gg.go.kr/portal/data/service/selectServicePage.do?&infId=Q5K1K45JBTGU1FGJRSQE32504694&infSeq=1

Alerts

순번 is highly overall correlated with 관할시군High correlation
노선ID is highly overall correlated with 관할시군High correlation
관할시군 is highly overall correlated with 순번 and 1 other fieldsHigh correlation
노선ID has 86 (1.9%) missing valuesMissing
기점정류장ID has 58 (1.3%) missing valuesMissing
종점정류장ID has 58 (1.3%) missing valuesMissing
순번 has unique valuesUnique

Reproduction

Analysis started2024-05-03 19:21:58.850480
Analysis finished2024-05-03 19:22:03.101508
Duration4.25 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct4489
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8033
Minimum5789
Maximum10277
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.6 KiB
2024-05-03T19:22:03.327031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5789
5-th percentile6013.4
Q16911
median8033
Q39155
95-th percentile10052.6
Maximum10277
Range4488
Interquartile range (IQR)2244

Descriptive statistics

Standard deviation1296.007
Coefficient of variation (CV)0.16133537
Kurtosis-1.2
Mean8033
Median Absolute Deviation (MAD)1122
Skewness0
Sum36060137
Variance1679634.2
MonotonicityNot monotonic
2024-05-03T19:22:03.773531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5789 1
 
< 0.1%
8762 1
 
< 0.1%
8768 1
 
< 0.1%
8767 1
 
< 0.1%
8766 1
 
< 0.1%
8765 1
 
< 0.1%
8764 1
 
< 0.1%
8763 1
 
< 0.1%
8761 1
 
< 0.1%
8770 1
 
< 0.1%
Other values (4479) 4479
99.8%
ValueCountFrequency (%)
5789 1
< 0.1%
5790 1
< 0.1%
5791 1
< 0.1%
5792 1
< 0.1%
5793 1
< 0.1%
5794 1
< 0.1%
5795 1
< 0.1%
5796 1
< 0.1%
5797 1
< 0.1%
5798 1
< 0.1%
ValueCountFrequency (%)
10277 1
< 0.1%
10276 1
< 0.1%
10275 1
< 0.1%
10274 1
< 0.1%
10273 1
< 0.1%
10272 1
< 0.1%
10271 1
< 0.1%
10270 1
< 0.1%
10269 1
< 0.1%
10268 1
< 0.1%

관할시군
Categorical

HIGH CORRELATION 

Distinct31
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size35.2 KiB
양평군
470 
광주시
360 
고양시
311 
화성시
311 
평택시
 
259
Other values (26)
2778 

Length

Max length4
Median length3
Mean length3.0977946
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row고양시
2nd row고양시
3rd row고양시
4th row고양시
5th row고양시

Common Values

ValueCountFrequency (%)
양평군 470
 
10.5%
광주시 360
 
8.0%
고양시 311
 
6.9%
화성시 311
 
6.9%
평택시 259
 
5.8%
용인시 256
 
5.7%
남양주시 234
 
5.2%
수원시 172
 
3.8%
이천시 171
 
3.8%
파주시 156
 
3.5%
Other values (21) 1789
39.9%

Length

2024-05-03T19:22:04.179306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
양평군 470
 
10.5%
광주시 360
 
8.0%
고양시 311
 
6.9%
화성시 311
 
6.9%
평택시 259
 
5.8%
용인시 256
 
5.7%
남양주시 234
 
5.2%
수원시 172
 
3.8%
이천시 171
 
3.8%
파주시 156
 
3.5%
Other values (21) 1789
39.9%

운행업체명
Text

Distinct245
Distinct (%)5.5%
Missing0
Missing (%)0.0%
Memory size35.2 KiB
2024-05-03T19:22:04.594936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length4
Mean length5.1354422
Min length2

Characters and Unicode

Total characters23053
Distinct characters157
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique23 ?
Unique (%)0.5%

Sample

1st row고양-보은교통
2nd row고양-보은교통
3rd row고양-명보교통
4th row고양-관산운수
5th row고양-관산운수
ValueCountFrequency (%)
주)금강고속 315
 
7.0%
경기고속 292
 
6.5%
대원고속 245
 
5.5%
금강고속 152
 
3.4%
대원운수 143
 
3.2%
백성운수 117
 
2.6%
경남여객 116
 
2.6%
경원여객 115
 
2.6%
경기여객 97
 
2.2%
평택여객 95
 
2.1%
Other values (235) 2802
62.4%
2024-05-03T19:22:05.500628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
고 1381
 
6.0%
운 1123
 
4.9%
수 1107
 
4.8%
- 1106
 
4.8%
속 1106
 
4.8%
여 1068
 
4.6%
객 1057
 
4.6%
통 797
 
3.5%
교 795
 
3.4%
경 762
 
3.3%
Other values (147) 12751
55.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 21250
92.2%
Dash Punctuation 1106
 
4.8%
Close Punctuation 331
 
1.4%
Open Punctuation 331
 
1.4%
Other Symbol 22
 
0.1%
Uppercase Letter 12
 
0.1%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
고 1381
 
6.5%
운 1123
 
5.3%
수 1107
 
5.2%
속 1106
 
5.2%
여 1068
 
5.0%
객 1057
 
5.0%
통 797
 
3.8%
교 795
 
3.7%
경 762
 
3.6%
성 665
 
3.1%
Other values (139) 11389
53.6%
Uppercase Letter
ValueCountFrequency (%)
T 4
33.3%
R 4
33.3%
D 4
33.3%
Dash Punctuation
ValueCountFrequency (%)
- 1106
100.0%
Close Punctuation
ValueCountFrequency (%)
) 331
100.0%
Open Punctuation
ValueCountFrequency (%)
( 331
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 22
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 21272
92.3%
Common 1769
 
7.7%
Latin 12
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
고 1381
 
6.5%
운 1123
 
5.3%
수 1107
 
5.2%
속 1106
 
5.2%
여 1068
 
5.0%
객 1057
 
5.0%
통 797
 
3.7%
교 795
 
3.7%
경 762
 
3.6%
성 665
 
3.1%
Other values (140) 11411
53.6%
Common
ValueCountFrequency (%)
- 1106
62.5%
) 331
 
18.7%
( 331
 
18.7%
/ 1
 
0.1%
Latin
ValueCountFrequency (%)
T 4
33.3%
R 4
33.3%
D 4
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 21250
92.2%
ASCII 1781
 
7.7%
None 22
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
고 1381
 
6.5%
운 1123
 
5.3%
수 1107
 
5.2%
속 1106
 
5.2%
여 1068
 
5.0%
객 1057
 
5.0%
통 797
 
3.8%
교 795
 
3.7%
경 762
 
3.6%
성 665
 
3.1%
Other values (139) 11389
53.6%
ASCII
ValueCountFrequency (%)
- 1106
62.1%
) 331
 
18.6%
( 331
 
18.6%
T 4
 
0.2%
R 4
 
0.2%
D 4
 
0.2%
/ 1
 
0.1%
None
ValueCountFrequency (%)
㈜ 22
100.0%

노선번호
Text

Distinct2053
Distinct (%)45.7%
Missing0
Missing (%)0.0%
Memory size35.2 KiB
2024-05-03T19:22:06.505896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length3.7843618
Min length1

Characters and Unicode

Total characters16988
Distinct characters183
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1312 ?
Unique (%)29.2%

Sample

1st row21
2nd row20
3rd row72
4th row36
5th row37
ValueCountFrequency (%)
3 41
 
0.9%
6 40
 
0.9%
7 39
 
0.9%
5 38
 
0.8%
8 37
 
0.8%
10 36
 
0.8%
1 32
 
0.7%
2 28
 
0.6%
11 28
 
0.6%
9 23
 
0.5%
Other values (2041) 4162
92.4%
2024-05-03T19:22:07.773434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 2286
13.5%
0 2109
12.4%
2 1646
9.7%
4 1536
9.0%
5 1441
8.5%
3 1430
8.4%
9 1105
6.5%
- 997
 
5.9%
8 946
 
5.6%
7 928
 
5.5%
Other values (173) 2564
15.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14296
84.2%
Dash Punctuation 997
 
5.9%
Other Letter 730
 
4.3%
Uppercase Letter 546
 
3.2%
Open Punctuation 165
 
1.0%
Close Punctuation 165
 
1.0%
Other Punctuation 45
 
0.3%
Lowercase Letter 29
 
0.2%
Space Separator 15
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
근 42
 
5.8%
리 24
 
3.3%
큐 23
 
3.2%
출 23
 
3.2%
땡 23
 
3.2%
퇴 22
 
3.0%
공 22
 
3.0%
영 22
 
3.0%
광 16
 
2.2%
대 16
 
2.2%
Other values (137) 497
68.1%
Uppercase Letter
ValueCountFrequency (%)
A 146
26.7%
B 122
22.3%
H 61
11.2%
G 50
 
9.2%
M 50
 
9.2%
N 44
 
8.1%
P 42
 
7.7%
C 11
 
2.0%
J 9
 
1.6%
Y 4
 
0.7%
Other values (4) 7
 
1.3%
Decimal Number
ValueCountFrequency (%)
1 2286
16.0%
0 2109
14.8%
2 1646
11.5%
4 1536
10.7%
5 1441
10.1%
3 1430
10.0%
9 1105
7.7%
8 946
6.6%
7 928
6.5%
6 869
 
6.1%
Lowercase Letter
ValueCountFrequency (%)
a 10
34.5%
n 9
31.0%
b 4
 
13.8%
e 4
 
13.8%
c 1
 
3.4%
r 1
 
3.4%
Other Punctuation
ValueCountFrequency (%)
" 30
66.7%
. 15
33.3%
Dash Punctuation
ValueCountFrequency (%)
- 997
100.0%
Open Punctuation
ValueCountFrequency (%)
( 165
100.0%
Close Punctuation
ValueCountFrequency (%)
) 165
100.0%
Space Separator
ValueCountFrequency (%)
15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 15683
92.3%
Hangul 730
 
4.3%
Latin 575
 
3.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
근 42
 
5.8%
리 24
 
3.3%
큐 23
 
3.2%
출 23
 
3.2%
땡 23
 
3.2%
퇴 22
 
3.0%
공 22
 
3.0%
영 22
 
3.0%
광 16
 
2.2%
대 16
 
2.2%
Other values (137) 497
68.1%
Latin
ValueCountFrequency (%)
A 146
25.4%
B 122
21.2%
H 61
10.6%
G 50
 
8.7%
M 50
 
8.7%
N 44
 
7.7%
P 42
 
7.3%
C 11
 
1.9%
a 10
 
1.7%
J 9
 
1.6%
Other values (10) 30
 
5.2%
Common
ValueCountFrequency (%)
1 2286
14.6%
0 2109
13.4%
2 1646
10.5%
4 1536
9.8%
5 1441
9.2%
3 1430
9.1%
9 1105
7.0%
- 997
6.4%
8 946
6.0%
7 928
5.9%
Other values (6) 1259
8.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16258
95.7%
Hangul 730
 
4.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2286
14.1%
0 2109
13.0%
2 1646
10.1%
4 1536
9.4%
5 1441
8.9%
3 1430
8.8%
9 1105
6.8%
- 997
6.1%
8 946
5.8%
7 928
5.7%
Other values (26) 1834
11.3%
Hangul
ValueCountFrequency (%)
근 42
 
5.8%
리 24
 
3.3%
큐 23
 
3.2%
출 23
 
3.2%
땡 23
 
3.2%
퇴 22
 
3.0%
공 22
 
3.0%
영 22
 
3.0%
광 16
 
2.2%
대 16
 
2.2%
Other values (137) 497
68.1%

노선ID
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct3134
Distinct (%)71.2%
Missing86
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean2.2505301 × 108
Minimum0
Maximum2.4149101 × 108
Zeros33
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size39.6 KiB
2024-05-03T19:22:08.228820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.0000027 × 108
Q12.2100003 × 108
median2.3400101 × 108
Q32.4000021 × 108
95-th percentile2.4143602 × 108
Maximum2.4149101 × 108
Range2.4149101 × 108
Interquartile range (IQR)19000180

Descriptive statistics

Standard deviation34262774
Coefficient of variation (CV)0.15224313
Kurtosis30.473231
Mean2.2505301 × 108
Median Absolute Deviation (MAD)7291994
Skewness-5.3155292
Sum9.9090838 × 1011
Variance1.1739376 × 1015
MonotonicityNot monotonic
2024-05-03T19:22:08.688277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 33
 
0.7%
234000031 4
 
0.1%
241340001 4
 
0.1%
241339005 4
 
0.1%
241339004 4
 
0.1%
241339001 4
 
0.1%
241338003 4
 
0.1%
241338002 4
 
0.1%
241338001 4
 
0.1%
241241001 4
 
0.1%
Other values (3124) 4334
96.5%
(Missing) 86
 
1.9%
ValueCountFrequency (%)
0 33
0.7%
3 3
 
0.1%
10339 1
 
< 0.1%
16087 3
 
0.1%
16136 2
 
< 0.1%
43026 1
 
< 0.1%
43066 1
 
< 0.1%
43214 1
 
< 0.1%
43258 1
 
< 0.1%
43259 1
 
< 0.1%
ValueCountFrequency (%)
241491014 1
< 0.1%
241491012 1
< 0.1%
241491011 1
< 0.1%
241491010 1
< 0.1%
241491009 1
< 0.1%
241491008 1
< 0.1%
241491005 1
< 0.1%
241491004 1
< 0.1%
241491003 1
< 0.1%
241491002 1
< 0.1%

기점
Text

Distinct1116
Distinct (%)24.9%
Missing0
Missing (%)0.0%
Memory size35.2 KiB
2024-05-03T19:22:09.130146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length21
Mean length6.545333
Min length2

Characters and Unicode

Total characters29382
Distinct characters451
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique415 ?
Unique (%)9.2%

Sample

1st row화정역3호선
2nd row화정역3호선
3rd row능곡역.능곡시장
4th row벽제관지
5th row그린씨티동문아파트
ValueCountFrequency (%)
양평터미널 249
 
5.5%
여주역 94
 
2.1%
이천역 82
 
1.8%
문호리종점 69
 
1.5%
용문터미널 68
 
1.5%
전곡재래시장앞 52
 
1.2%
경진여객차고지 38
 
0.8%
임시)용인터미널 37
 
0.8%
기남방송 37
 
0.8%
이천터미널 36
 
0.8%
Other values (1106) 3727
83.0%
2024-05-03T19:22:10.040446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
지 1073
 
3.7%
터 928
 
3.2%
미 825
 
2.8%
고 823
 
2.8%
차 786
 
2.7%
널 782
 
2.7%
역 695
 
2.4%
동 600
 
2.0%
. 566
 
1.9%
리 563
 
1.9%
Other values (441) 21741
74.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 27389
93.2%
Decimal Number 621
 
2.1%
Other Punctuation 566
 
1.9%
Close Punctuation 313
 
1.1%
Open Punctuation 305
 
1.0%
Uppercase Letter 179
 
0.6%
Lowercase Letter 7
 
< 0.1%
Other Symbol 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 1073
 
3.9%
터 928
 
3.4%
미 825
 
3.0%
고 823
 
3.0%
차 786
 
2.9%
널 782
 
2.9%
역 695
 
2.5%
동 600
 
2.2%
리 563
 
2.1%
대 451
 
1.6%
Other values (410) 19863
72.5%
Uppercase Letter
ValueCountFrequency (%)
K 25
14.0%
S 21
11.7%
A 19
10.6%
L 18
10.1%
T 16
8.9%
C 16
8.9%
G 11
6.1%
B 11
6.1%
R 11
6.1%
M 8
 
4.5%
Other values (5) 23
12.8%
Decimal Number
ValueCountFrequency (%)
1 187
30.1%
2 129
20.8%
3 82
13.2%
5 54
 
8.7%
4 50
 
8.1%
6 38
 
6.1%
9 27
 
4.3%
0 19
 
3.1%
7 18
 
2.9%
8 17
 
2.7%
Lowercase Letter
ValueCountFrequency (%)
e 5
71.4%
s 2
 
28.6%
Other Punctuation
ValueCountFrequency (%)
. 566
100.0%
Close Punctuation
ValueCountFrequency (%)
) 313
100.0%
Open Punctuation
ValueCountFrequency (%)
( 305
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 27391
93.2%
Common 1805
 
6.1%
Latin 186
 
0.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 1073
 
3.9%
터 928
 
3.4%
미 825
 
3.0%
고 823
 
3.0%
차 786
 
2.9%
널 782
 
2.9%
역 695
 
2.5%
동 600
 
2.2%
리 563
 
2.1%
대 451
 
1.6%
Other values (411) 19865
72.5%
Latin
ValueCountFrequency (%)
K 25
13.4%
S 21
11.3%
A 19
10.2%
L 18
9.7%
T 16
8.6%
C 16
8.6%
G 11
 
5.9%
B 11
 
5.9%
R 11
 
5.9%
M 8
 
4.3%
Other values (7) 30
16.1%
Common
ValueCountFrequency (%)
. 566
31.4%
) 313
17.3%
( 305
16.9%
1 187
 
10.4%
2 129
 
7.1%
3 82
 
4.5%
5 54
 
3.0%
4 50
 
2.8%
6 38
 
2.1%
9 27
 
1.5%
Other values (3) 54
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 27389
93.2%
ASCII 1991
 
6.8%
None 2
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
지 1073
 
3.9%
터 928
 
3.4%
미 825
 
3.0%
고 823
 
3.0%
차 786
 
2.9%
널 782
 
2.9%
역 695
 
2.5%
동 600
 
2.2%
리 563
 
2.1%
대 451
 
1.6%
Other values (410) 19863
72.5%
ASCII
ValueCountFrequency (%)
. 566
28.4%
) 313
15.7%
( 305
15.3%
1 187
 
9.4%
2 129
 
6.5%
3 82
 
4.1%
5 54
 
2.7%
4 50
 
2.5%
6 38
 
1.9%
9 27
 
1.4%
Other values (20) 240
12.1%
None
ValueCountFrequency (%)
㈜ 2
100.0%

기점정류장ID
Text

MISSING 

Distinct1226
Distinct (%)27.7%
Missing58
Missing (%)1.3%
Memory size35.2 KiB
2024-05-03T19:22:10.578780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length9
Mean length8.8395396
Min length3

Characters and Unicode

Total characters39168
Distinct characters35
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

Unique479 ?
Unique (%)10.8%

Sample

1st row218000974
2nd row218000974
3rd row218000193
4th row218001211
5th row229000473
ValueCountFrequency (%)
240000072 168
 
3.8%
237001007 94
 
2.1%
230001276 82
 
1.9%
240000076 81
 
1.8%
240001309 58
 
1.3%
238000205 41
 
0.9%
233001064 38
 
0.9%
240000203 37
 
0.8%
228001552 37
 
0.8%
214001320 37
 
0.8%
Other values (1216) 3758
84.8%
2024-05-03T19:22:11.441061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 14057
35.9%
2 7670
19.6%
1 3895
 
9.9%
3 3479
 
8.9%
4 2199
 
5.6%
7 2062
 
5.3%
8 1556
 
4.0%
6 1553
 
4.0%
9 1324
 
3.4%
5 1257
 
3.2%
Other values (25) 116
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 39052
99.7%
Other Letter 116
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
역 13
11.2%
곡 12
10.3%
미 12
10.3%
널 12
10.3%
전 12
10.3%
터 12
10.3%
연 11
9.5%
천 11
9.5%
리 3
 
2.6%
신 2
 
1.7%
Other values (15) 16
13.8%
Decimal Number
ValueCountFrequency (%)
0 14057
36.0%
2 7670
19.6%
1 3895
 
10.0%
3 3479
 
8.9%
4 2199
 
5.6%
7 2062
 
5.3%
8 1556
 
4.0%
6 1553
 
4.0%
9 1324
 
3.4%
5 1257
 
3.2%

Most occurring scripts

ValueCountFrequency (%)
Common 39052
99.7%
Hangul 116
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
역 13
11.2%
곡 12
10.3%
미 12
10.3%
널 12
10.3%
전 12
10.3%
터 12
10.3%
연 11
9.5%
천 11
9.5%
리 3
 
2.6%
신 2
 
1.7%
Other values (15) 16
13.8%
Common
ValueCountFrequency (%)
0 14057
36.0%
2 7670
19.6%
1 3895
 
10.0%
3 3479
 
8.9%
4 2199
 
5.6%
7 2062
 
5.3%
8 1556
 
4.0%
6 1553
 
4.0%
9 1324
 
3.4%
5 1257
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 39052
99.7%
Hangul 116
 
0.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14057
36.0%
2 7670
19.6%
1 3895
 
10.0%
3 3479
 
8.9%
4 2199
 
5.6%
7 2062
 
5.3%
8 1556
 
4.0%
6 1553
 
4.0%
9 1324
 
3.4%
5 1257
 
3.2%
Hangul
ValueCountFrequency (%)
역 13
11.2%
곡 12
10.3%
미 12
10.3%
널 12
10.3%
전 12
10.3%
터 12
10.3%
연 11
9.5%
천 11
9.5%
리 3
 
2.6%
신 2
 
1.7%
Other values (15) 16
13.8%

종점
Text

Distinct1740
Distinct (%)38.8%
Missing0
Missing (%)0.0%
Memory size35.2 KiB
2024-05-03T19:22:11.903872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length21
Mean length6.8674538
Min length2

Characters and Unicode

Total characters30828
Distinct characters518
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique794 ?
Unique (%)17.7%

Sample

1st row행신역
2nd row영흥빌라
3rd row산황동입구
4th row오미산주유소
5th row삼송역8번출구
ValueCountFrequency (%)
잠실광역환승센터 78
 
1.7%
양평터미널 50
 
1.1%
연천역 39
 
0.9%
강변역(b 34
 
0.8%
신분당선강남역(중 29
 
0.6%
여주터미널 28
 
0.6%
금정역 25
 
0.6%
화정역3호선 24
 
0.5%
양수역 24
 
0.5%
대신터미널 23
 
0.5%
Other values (1732) 4138
92.1%
2024-05-03T19:22:12.784969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
역 1441
 
4.7%
리 1047
 
3.4%
. 881
 
2.9%
터 597
 
1.9%
지 506
 
1.6%
구 479
 
1.6%
) 463
 
1.5%
마 462
 
1.5%
( 456
 
1.5%
점 454
 
1.5%
Other values (508) 24042
78.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 27582
89.5%
Decimal Number 1154
 
3.7%
Other Punctuation 886
 
2.9%
Close Punctuation 465
 
1.5%
Open Punctuation 458
 
1.5%
Uppercase Letter 269
 
0.9%
Lowercase Letter 11
 
< 0.1%
Space Separator 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
역 1441
 
5.2%
리 1047
 
3.8%
터 597
 
2.2%
지 506
 
1.8%
구 479
 
1.7%
마 462
 
1.7%
점 454
 
1.6%
동 421
 
1.5%
미 404
 
1.5%
종 391
 
1.4%
Other values (471) 21380
77.5%
Uppercase Letter
ValueCountFrequency (%)
A 67
24.9%
B 46
17.1%
K 34
12.6%
C 22
 
8.2%
D 20
 
7.4%
T 19
 
7.1%
L 13
 
4.8%
X 8
 
3.0%
P 8
 
3.0%
M 8
 
3.0%
Other values (7) 24
 
8.9%
Decimal Number
ValueCountFrequency (%)
1 347
30.1%
2 285
24.7%
3 208
18.0%
4 80
 
6.9%
6 64
 
5.5%
5 54
 
4.7%
0 45
 
3.9%
8 43
 
3.7%
7 15
 
1.3%
9 13
 
1.1%
Other Punctuation
ValueCountFrequency (%)
. 881
99.4%
& 3
 
0.3%
· 2
 
0.2%
Close Punctuation
ValueCountFrequency (%)
) 463
99.6%
] 2
 
0.4%
Open Punctuation
ValueCountFrequency (%)
( 456
99.6%
[ 2
 
0.4%
Lowercase Letter
ValueCountFrequency (%)
e 7
63.6%
c 4
36.4%
Space Separator
ValueCountFrequency (%)
3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 27582
89.5%
Common 2966
 
9.6%
Latin 280
 
0.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
역 1441
 
5.2%
리 1047
 
3.8%
터 597
 
2.2%
지 506
 
1.8%
구 479
 
1.7%
마 462
 
1.7%
점 454
 
1.6%
동 421
 
1.5%
미 404
 
1.5%
종 391
 
1.4%
Other values (471) 21380
77.5%
Latin
ValueCountFrequency (%)
A 67
23.9%
B 46
16.4%
K 34
12.1%
C 22
 
7.9%
D 20
 
7.1%
T 19
 
6.8%
L 13
 
4.6%
X 8
 
2.9%
P 8
 
2.9%
M 8
 
2.9%
Other values (9) 35
12.5%
Common
ValueCountFrequency (%)
. 881
29.7%
) 463
15.6%
( 456
15.4%
1 347
 
11.7%
2 285
 
9.6%
3 208
 
7.0%
4 80
 
2.7%
6 64
 
2.2%
5 54
 
1.8%
0 45
 
1.5%
Other values (8) 83
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 27582
89.5%
ASCII 3244
 
10.5%
None 2
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
역 1441
 
5.2%
리 1047
 
3.8%
터 597
 
2.2%
지 506
 
1.8%
구 479
 
1.7%
마 462
 
1.7%
점 454
 
1.6%
동 421
 
1.5%
미 404
 
1.5%
종 391
 
1.4%
Other values (471) 21380
77.5%
ASCII
ValueCountFrequency (%)
. 881
27.2%
) 463
14.3%
( 456
14.1%
1 347
 
10.7%
2 285
 
8.8%
3 208
 
6.4%
4 80
 
2.5%
A 67
 
2.1%
6 64
 
2.0%
5 54
 
1.7%
Other values (26) 339
 
10.5%
None
ValueCountFrequency (%)
· 2
100.0%

종점정류장ID
Text

MISSING 

Distinct1896
Distinct (%)42.8%
Missing58
Missing (%)1.3%
Memory size35.2 KiB
2024-05-03T19:22:13.398216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length9
Mean length8.840668
Min length3

Characters and Unicode

Total characters39173
Distinct characters40
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

Unique940 ?
Unique (%)21.2%

Sample

1st row218000463
2nd row218001006
3rd row219001083
4th row219000101
5th row218001200
ValueCountFrequency (%)
123000611 65
 
1.5%
240000072 48
 
1.1%
121000009 28
 
0.6%
104000280 26
 
0.6%
237000980 25
 
0.6%
121001315 23
 
0.5%
240000277 23
 
0.5%
233000702 21
 
0.5%
230001210 20
 
0.5%
237000813 20
 
0.5%
Other values (1886) 4132
93.3%
2024-05-03T19:22:14.277669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 15037
38.4%
2 6689
17.1%
1 4621
 
11.8%
3 3243
 
8.3%
4 2160
 
5.5%
8 1589
 
4.1%
6 1480
 
3.8%
9 1443
 
3.7%
7 1436
 
3.7%
5 1360
 
3.5%
Other values (30) 115
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 39058
99.7%
Other Letter 115
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
역 14
12.2%
천 11
9.6%
전 10
8.7%
곡 10
8.7%
터 10
8.7%
미 10
8.7%
널 10
8.7%
연 10
8.7%
산 4
 
3.5%
리 3
 
2.6%
Other values (20) 23
20.0%
Decimal Number
ValueCountFrequency (%)
0 15037
38.5%
2 6689
17.1%
1 4621
 
11.8%
3 3243
 
8.3%
4 2160
 
5.5%
8 1589
 
4.1%
6 1480
 
3.8%
9 1443
 
3.7%
7 1436
 
3.7%
5 1360
 
3.5%

Most occurring scripts

ValueCountFrequency (%)
Common 39058
99.7%
Hangul 115
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
역 14
12.2%
천 11
9.6%
전 10
8.7%
곡 10
8.7%
터 10
8.7%
미 10
8.7%
널 10
8.7%
연 10
8.7%
산 4
 
3.5%
리 3
 
2.6%
Other values (20) 23
20.0%
Common
ValueCountFrequency (%)
0 15037
38.5%
2 6689
17.1%
1 4621
 
11.8%
3 3243
 
8.3%
4 2160
 
5.5%
8 1589
 
4.1%
6 1480
 
3.8%
9 1443
 
3.7%
7 1436
 
3.7%
5 1360
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 39058
99.7%
Hangul 115
 
0.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15037
38.5%
2 6689
17.1%
1 4621
 
11.8%
3 3243
 
8.3%
4 2160
 
5.5%
8 1589
 
4.1%
6 1480
 
3.8%
9 1443
 
3.7%
7 1436
 
3.7%
5 1360
 
3.5%
Hangul
ValueCountFrequency (%)
역 14
12.2%
천 11
9.6%
전 10
8.7%
곡 10
8.7%
터 10
8.7%
미 10
8.7%
널 10
8.7%
연 10
8.7%
산 4
 
3.5%
리 3
 
2.6%
Other values (20) 23
20.0%

저상버스운행유무
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.5 KiB
False
3790 
True
699 
ValueCountFrequency (%)
False 3790
84.4%
True 699
 
15.6%
2024-05-03T19:22:14.644033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

데이터기준일자
Date

Distinct32
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size35.2 KiB
Minimum2023-01-10 00:00:00
Maximum2024-04-23 00:00:00
2024-05-03T19:22:14.936609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T19:22:15.198123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)

Interactions

2024-05-03T19:22:01.495143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T19:22:00.954406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T19:22:01.679249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T19:22:01.217126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-03T19:22:15.390789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번관할시군노선ID저상버스운행유무데이터기준일자
순번1.0000.9250.4000.3730.858
관할시군0.9251.0000.8740.4050.929
노선ID0.4000.8741.0000.4400.944
저상버스운행유무0.3730.4050.4401.0000.557
데이터기준일자0.8580.9290.9440.5571.000
2024-05-03T19:22:15.576881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
저상버스운행유무관할시군
저상버스운행유무1.0000.344
관할시군0.3441.000
2024-05-03T19:22:15.731001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번노선ID관할시군저상버스운행유무
순번1.000-0.1040.6560.286
노선ID-0.1041.0000.6630.296
관할시군0.6560.6631.0000.344
저상버스운행유무0.2860.2960.3441.000

Missing values

2024-05-03T19:22:02.007500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-03T19:22:02.438151image/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.
2024-05-03T19:22:02.909792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

순번관할시군운행업체명노선번호노선ID기점기점정류장ID종점종점정류장ID저상버스운행유무데이터기준일자
05789고양시고양-보은교통21241314001화정역3호선218000974행신역218000463N2023-02-21
15790고양시고양-보은교통20241314002화정역3호선218000974영흥빌라218001006N2023-02-21
25791고양시고양-명보교통72241325001능곡역.능곡시장218000193산황동입구219001083N2023-02-21
35792고양시고양-관산운수36241293003벽제관지218001211오미산주유소219000101N2023-02-21
45793고양시고양-관산운수37241293004그린씨티동문아파트229000473삼송역8번출구218001200N2023-02-21
55794고양시고양-관산운수58241293005동문3차아파트219000813빛마루.이비에스219001076N2023-02-21
65795고양시고양-관산운수59241293006마을회관219000293대화역219000366N2023-02-21
75796고양시고양-관산운수88241293007내유동커뮤니티센터218001347삼송역8번출구218001200N2023-02-21
85797고양시고양-한진교통60241294002소만마을1.2.3단지218001032화정역3호선218001046N2023-02-21
95798고양시고양-백마운수38241295703둥지마을.베송쥬쥬219001082화정역3호선218001046N2023-02-21
순번관할시군운행업체명노선번호노선ID기점기점정류장ID종점종점정류장ID저상버스운행유무데이터기준일자
447910265남양주시대원운수1-4222000032차산리222000796미사동차고지227000064Y2023-12-31
448010266남양주시대원운수10222000082진벌리차고지222000652당고개역110000650N2023-12-31
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