Overview

Dataset statistics

Number of variables11
Number of observations3170
Missing cells4
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory288.0 KiB
Average record size in memory93.0 B

Variable types

Numeric5
Text4
Categorical2

Dataset

Description2019년 5월 기준 대구시 시내버스 정류소 전체 목록파일입니다.(정류소명, GPS좌표, 경유노선 등)
Author대구광역시
URLhttps://www.data.go.kr/data/15050944/fileData.do

Alerts

시/도 has constant value ""Constant
정류소ID is highly overall correlated with X좌표 and 2 other fieldsHigh correlation
X좌표 is highly overall correlated with 정류소ID and 1 other fieldsHigh correlation
Y좌표 is highly overall correlated with 정류소IDHigh correlation
구/군 is highly overall correlated with 정류소ID and 1 other fieldsHigh correlation
정류소ID has unique valuesUnique
정류소명 has unique valuesUnique

Reproduction

Analysis started2023-12-12 02:19:29.226673
Analysis finished2023-12-12 02:19:33.297149
Duration4.07 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

정류소ID
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct3170
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.0473595 × 109
Minimum7.0010001 × 109
Maximum7.1110775 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.0 KiB
2023-12-12T11:19:33.682386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7.0010001 × 109
5-th percentile7.011004 × 109
Q17.0210123 × 109
median7.0410178 × 109
Q37.0610263 × 109
95-th percentile7.1110605 × 109
Maximum7.1110775 × 109
Range1.100774 × 108
Interquartile range (IQR)40013950

Descriptive statistics

Standard deviation36083273
Coefficient of variation (CV)0.0051201124
Kurtosis-0.69920331
Mean7.0473595 × 109
Median Absolute Deviation (MAD)20007000
Skewness0.78248969
Sum2.234013 × 1013
Variance1.3020026 × 1015
MonotonicityStrictly increasing
2023-12-12T11:19:33.868759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7001000100 1
 
< 0.1%
7051015900 1
 
< 0.1%
7051016100 1
 
< 0.1%
7051016300 1
 
< 0.1%
7051016500 1
 
< 0.1%
7051016700 1
 
< 0.1%
7051016800 1
 
< 0.1%
7051016900 1
 
< 0.1%
7051017000 1
 
< 0.1%
7051017100 1
 
< 0.1%
Other values (3160) 3160
99.7%
ValueCountFrequency (%)
7001000100 1
< 0.1%
7001000200 1
< 0.1%
7001000300 1
< 0.1%
7001000400 1
< 0.1%
7001000500 1
< 0.1%
7001000600 1
< 0.1%
7001000700 1
< 0.1%
7001000800 1
< 0.1%
7001000900 1
< 0.1%
7001001000 1
< 0.1%
ValueCountFrequency (%)
7111077500 1
< 0.1%
7111077400 1
< 0.1%
7111077300 1
< 0.1%
7111077200 1
< 0.1%
7111077100 1
< 0.1%
7111077000 1
< 0.1%
7111076900 1
< 0.1%
7111076800 1
< 0.1%
7111076700 1
< 0.1%
7111076600 1
< 0.1%

모바일ID
Real number (ℝ)

Distinct3169
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2879.4767
Minimum1
Maximum9277
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.0 KiB
2023-12-12T11:19:34.053840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile219.8
Q11168.25
median2189.5
Q33991.75
95-th percentile9118.55
Maximum9277
Range9276
Interquartile range (IQR)2823.5

Descriptive statistics

Standard deviation2442.2223
Coefficient of variation (CV)0.84814798
Kurtosis1.1568117
Mean2879.4767
Median Absolute Deviation (MAD)1229
Skewness1.3368328
Sum9127941
Variance5964449.8
MonotonicityNot monotonic
2023-12-12T11:19:34.252918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9125 2
 
0.1%
5191 1
 
< 0.1%
3898 1
 
< 0.1%
5082 1
 
< 0.1%
2845 1
 
< 0.1%
2844 1
 
< 0.1%
3885 1
 
< 0.1%
3883 1
 
< 0.1%
3880 1
 
< 0.1%
3886 1
 
< 0.1%
Other values (3159) 3159
99.7%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
12 1
< 0.1%
13 1
< 0.1%
ValueCountFrequency (%)
9277 1
< 0.1%
9275 1
< 0.1%
9274 1
< 0.1%
9273 1
< 0.1%
9272 1
< 0.1%
9271 1
< 0.1%
9270 1
< 0.1%
9269 1
< 0.1%
9268 1
< 0.1%
9267 1
< 0.1%

정류소명
Text

UNIQUE 

Distinct3170
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size24.9 KiB
2023-12-12T11:19:34.564955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length15
Mean length7.4678233
Min length2

Characters and Unicode

Total characters23673
Distinct characters492
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3170 ?
Unique (%)100.0%

Sample

1st row대명시장앞
2nd row대명시장건너
3rd row계명대학교대명캠퍼스앞
4th row계명대학교대명캠퍼스건너
5th row중부소방서건너
ValueCountFrequency (%)
흥사단 2
 
0.1%
건너 2
 
0.1%
범어역(2번출구 1
 
< 0.1%
남구청건너1 1
 
< 0.1%
대명공연거리앞 1
 
< 0.1%
앞산별자리체험학습장2 1
 
< 0.1%
대성유니드앞 1
 
< 0.1%
대덕초등학교건너 1
 
< 0.1%
성명119안전센터건너2 1
 
< 0.1%
봉명네거리1 1
 
< 0.1%
Other values (3163) 3163
99.6%
2023-12-12T11:19:34.996266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
앞 1081
 
4.6%
건 1062
 
4.5%
너 1032
 
4.4%
구 591
 
2.5%
교 548
 
2.3%
대 541
 
2.3%
리 504
 
2.1%
동 478
 
2.0%
1 441
 
1.9%
2 426
 
1.8%
Other values (482) 16969
71.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 21770
92.0%
Decimal Number 1105
 
4.7%
Close Punctuation 297
 
1.3%
Open Punctuation 293
 
1.2%
Uppercase Letter 161
 
0.7%
Other Punctuation 23
 
0.1%
Dash Punctuation 9
 
< 0.1%
Lowercase Letter 8
 
< 0.1%
Space Separator 5
 
< 0.1%
Other Symbol 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
앞 1081
 
5.0%
건 1062
 
4.9%
너 1032
 
4.7%
구 591
 
2.7%
교 548
 
2.5%
대 541
 
2.5%
리 504
 
2.3%
동 478
 
2.2%
학 420
 
1.9%
성 403
 
1.9%
Other values (447) 15110
69.4%
Uppercase Letter
ValueCountFrequency (%)
T 26
16.1%
L 22
13.7%
H 20
12.4%
K 19
11.8%
G 12
7.5%
P 10
 
6.2%
C 8
 
5.0%
A 7
 
4.3%
I 7
 
4.3%
B 7
 
4.3%
Other values (6) 23
14.3%
Decimal Number
ValueCountFrequency (%)
1 441
39.9%
2 426
38.6%
3 96
 
8.7%
4 54
 
4.9%
5 29
 
2.6%
9 17
 
1.5%
6 15
 
1.4%
8 11
 
1.0%
7 9
 
0.8%
0 7
 
0.6%
Other Punctuation
ValueCountFrequency (%)
. 18
78.3%
· 3
 
13.0%
/ 2
 
8.7%
Close Punctuation
ValueCountFrequency (%)
) 297
100.0%
Open Punctuation
ValueCountFrequency (%)
( 293
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 9
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 8
100.0%
Space Separator
ValueCountFrequency (%)
5
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 21772
92.0%
Common 1732
 
7.3%
Latin 169
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
앞 1081
 
5.0%
건 1062
 
4.9%
너 1032
 
4.7%
구 591
 
2.7%
교 548
 
2.5%
대 541
 
2.5%
리 504
 
2.3%
동 478
 
2.2%
학 420
 
1.9%
성 403
 
1.9%
Other values (448) 15112
69.4%
Common
ValueCountFrequency (%)
1 441
25.5%
2 426
24.6%
) 297
17.1%
( 293
16.9%
3 96
 
5.5%
4 54
 
3.1%
5 29
 
1.7%
. 18
 
1.0%
9 17
 
1.0%
6 15
 
0.9%
Other values (7) 46
 
2.7%
Latin
ValueCountFrequency (%)
T 26
15.4%
L 22
13.0%
H 20
11.8%
K 19
11.2%
G 12
 
7.1%
P 10
 
5.9%
e 8
 
4.7%
C 8
 
4.7%
A 7
 
4.1%
I 7
 
4.1%
Other values (7) 30
17.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 21770
92.0%
ASCII 1898
 
8.0%
None 5
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
앞 1081
 
5.0%
건 1062
 
4.9%
너 1032
 
4.7%
구 591
 
2.7%
교 548
 
2.5%
대 541
 
2.5%
리 504
 
2.3%
동 478
 
2.2%
학 420
 
1.9%
성 403
 
1.9%
Other values (447) 15110
69.4%
ASCII
ValueCountFrequency (%)
1 441
23.2%
2 426
22.4%
) 297
15.6%
( 293
15.4%
3 96
 
5.1%
4 54
 
2.8%
5 29
 
1.5%
T 26
 
1.4%
L 22
 
1.2%
H 20
 
1.1%
Other values (23) 194
10.2%
None
ValueCountFrequency (%)
· 3
60.0%
㈜ 2
40.0%

영문명
Text

Distinct1767
Distinct (%)55.8%
Missing2
Missing (%)0.1%
Memory size24.9 KiB
2023-12-12T11:19:35.452680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length50
Median length39
Mean length20.463068
Min length3

Characters and Unicode

Total characters64827
Distinct characters69
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique583 ?
Unique (%)18.4%

Sample

1st rowDaemyeong Market
2nd rowDaemyeong Market
3rd rowKeimyung University Daemyeong Campus
4th rowKeimyung University Daemyeong Campus
5th rowJungbu Fire Station
ValueCountFrequency (%)
school 366
 
4.1%
apts 273
 
3.1%
station 213
 
2.4%
center 207
 
2.3%
elementary 182
 
2.0%
daegu 165
 
1.8%
junction 156
 
1.7%
town 154
 
1.7%
market 126
 
1.4%
high 107
 
1.2%
Other values (1498) 6977
78.2%
2023-12-12T11:19:36.164415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5781
 
8.9%
n 5686
 
8.8%
o 5499
 
8.5%
e 5265
 
8.1%
a 4477
 
6.9%
i 3176
 
4.9%
g 3174
 
4.9%
t 2718
 
4.2%
r 2311
 
3.6%
l 2290
 
3.5%
Other values (59) 24450
37.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 48654
75.1%
Uppercase Letter 8711
 
13.4%
Space Separator 5781
 
8.9%
Dash Punctuation 592
 
0.9%
Decimal Number 557
 
0.9%
Close Punctuation 229
 
0.4%
Open Punctuation 229
 
0.4%
Other Punctuation 74
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 5686
11.7%
o 5499
11.3%
e 5265
10.8%
a 4477
 
9.2%
i 3176
 
6.5%
g 3174
 
6.5%
t 2718
 
5.6%
r 2311
 
4.7%
l 2290
 
4.7%
u 2145
 
4.4%
Other values (16) 11913
24.5%
Uppercase Letter
ValueCountFrequency (%)
S 1401
16.1%
C 874
 
10.0%
D 784
 
9.0%
M 637
 
7.3%
H 554
 
6.4%
B 489
 
5.6%
A 484
 
5.6%
G 428
 
4.9%
J 383
 
4.4%
T 381
 
4.4%
Other values (15) 2296
26.4%
Decimal Number
ValueCountFrequency (%)
1 147
26.4%
2 126
22.6%
4 121
21.7%
3 84
15.1%
5 31
 
5.6%
9 17
 
3.1%
6 13
 
2.3%
8 7
 
1.3%
7 6
 
1.1%
0 5
 
0.9%
Other Punctuation
ValueCountFrequency (%)
' 44
59.5%
. 23
31.1%
& 6
 
8.1%
/ 1
 
1.4%
Space Separator
ValueCountFrequency (%)
5781
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 592
100.0%
Close Punctuation
ValueCountFrequency (%)
) 229
100.0%
Open Punctuation
ValueCountFrequency (%)
( 229
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 57365
88.5%
Common 7462
 
11.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 5686
 
9.9%
o 5499
 
9.6%
e 5265
 
9.2%
a 4477
 
7.8%
i 3176
 
5.5%
g 3174
 
5.5%
t 2718
 
4.7%
r 2311
 
4.0%
l 2290
 
4.0%
u 2145
 
3.7%
Other values (41) 20624
36.0%
Common
ValueCountFrequency (%)
5781
77.5%
- 592
 
7.9%
) 229
 
3.1%
( 229
 
3.1%
1 147
 
2.0%
2 126
 
1.7%
4 121
 
1.6%
3 84
 
1.1%
' 44
 
0.6%
5 31
 
0.4%
Other values (8) 78
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 64827
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5781
 
8.9%
n 5686
 
8.8%
o 5499
 
8.5%
e 5265
 
8.1%
a 4477
 
6.9%
i 3176
 
4.9%
g 3174
 
4.9%
t 2718
 
4.2%
r 2311
 
3.6%
l 2290
 
3.5%
Other values (59) 24450
37.7%

시/도
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size24.9 KiB
대구광역시
3170 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
대구광역시 3170
100.0%

Length

2023-12-12T11:19:36.337439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:19:36.450118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대구광역시 3170
100.0%

구/군
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size24.9 KiB
달성군
637 
북구
557 
달서구
552 
동구
548 
수성구
413 
Other values (3)
463 

Length

Max length3
Median length3
Mean length2.5053628
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row남구
2nd row중구
3rd row남구
4th row중구
5th row중구

Common Values

ValueCountFrequency (%)
달성군 637
20.1%
북구 557
17.6%
달서구 552
17.4%
동구 548
17.3%
수성구 413
13.0%
서구 185
 
5.8%
남구 160
 
5.0%
중구 118
 
3.7%

Length

2023-12-12T11:19:36.562629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:19:36.708098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
달성군 637
20.1%
북구 557
17.6%
달서구 552
17.4%
동구 548
17.3%
수성구 413
13.0%
서구 185
 
5.8%
남구 160
 
5.0%
중구 118
 
3.7%

동
Text

Distinct143
Distinct (%)4.5%
Missing2
Missing (%)0.1%
Memory size24.9 KiB
2023-12-12T11:19:37.032006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length7
Mean length3.665404
Min length2

Characters and Unicode

Total characters11612
Distinct characters97
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 row대명3동
2nd row남산4동
3rd row대명3동
4th row남산4동
5th row대신동
ValueCountFrequency (%)
공산동 133
 
4.2%
안심3.4동 111
 
3.5%
다사읍 104
 
3.3%
가창면 88
 
2.8%
논공읍 80
 
2.5%
동천동 72
 
2.3%
화원읍 71
 
2.2%
하빈면 66
 
2.1%
현풍면 61
 
1.9%
신당동 59
 
1.9%
Other values (133) 2323
73.3%
2023-12-12T11:19:37.576480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 2639
22.7%
2 558
 
4.8%
산 505
 
4.3%
1 503
 
4.3%
면 380
 
3.3%
3 357
 
3.1%
읍 289
 
2.5%
성 245
 
2.1%
4 225
 
1.9%
공 213
 
1.8%
Other values (87) 5698
49.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 9655
83.1%
Decimal Number 1747
 
15.0%
Other Punctuation 210
 
1.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 2639
27.3%
산 505
 
5.2%
면 380
 
3.9%
읍 289
 
3.0%
성 245
 
2.5%
공 213
 
2.2%
안 204
 
2.1%
가 192
 
2.0%
심 185
 
1.9%
천 181
 
1.9%
Other values (77) 4622
47.9%
Decimal Number
ValueCountFrequency (%)
2 558
31.9%
1 503
28.8%
3 357
20.4%
4 225
12.9%
6 30
 
1.7%
5 25
 
1.4%
9 25
 
1.4%
7 15
 
0.9%
0 9
 
0.5%
Other Punctuation
ValueCountFrequency (%)
. 210
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 9655
83.1%
Common 1957
 
16.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 2639
27.3%
산 505
 
5.2%
면 380
 
3.9%
읍 289
 
3.0%
성 245
 
2.5%
공 213
 
2.2%
안 204
 
2.1%
가 192
 
2.0%
심 185
 
1.9%
천 181
 
1.9%
Other values (77) 4622
47.9%
Common
ValueCountFrequency (%)
2 558
28.5%
1 503
25.7%
3 357
18.2%
4 225
11.5%
. 210
 
10.7%
6 30
 
1.5%
5 25
 
1.3%
9 25
 
1.3%
7 15
 
0.8%
0 9
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 9655
83.1%
ASCII 1957
 
16.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 2639
27.3%
산 505
 
5.2%
면 380
 
3.9%
읍 289
 
3.0%
성 245
 
2.5%
공 213
 
2.2%
안 204
 
2.1%
가 192
 
2.0%
심 185
 
1.9%
천 181
 
1.9%
Other values (77) 4622
47.9%
ASCII
ValueCountFrequency (%)
2 558
28.5%
1 503
25.7%
3 357
18.2%
4 225
11.5%
. 210
 
10.7%
6 30
 
1.5%
5 25
 
1.3%
9 25
 
1.3%
7 15
 
0.8%
0 9
 
0.5%

X좌표
Real number (ℝ)

HIGH CORRELATION 

Distinct3121
Distinct (%)98.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12834.437
Minimum12821.486
Maximum12845.545
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.0 KiB
2023-12-12T11:19:37.748298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum12821.486
5-th percentile12826.357
Q112831.425
median12834.358
Q312837.593
95-th percentile12842.525
Maximum12845.545
Range24.058908
Interquartile range (IQR)6.1672697

Descriptive statistics

Standard deviation4.6934547
Coefficient of variation (CV)0.00036569228
Kurtosis-0.48783117
Mean12834.437
Median Absolute Deviation (MAD)3.1445895
Skewness-0.056337364
Sum40685167
Variance22.028517
MonotonicityNot monotonic
2023-12-12T11:19:37.930568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12829.9988 3
 
0.1%
12829.9942 3
 
0.1%
12828.6789 3
 
0.1%
12830.4428 3
 
0.1%
12830.426 2
 
0.1%
12837.9517 2
 
0.1%
12837.9395 2
 
0.1%
12833.3692 2
 
0.1%
12826.1383 2
 
0.1%
12828.688 2
 
0.1%
Other values (3111) 3146
99.2%
ValueCountFrequency (%)
12821.4859 1
< 0.1%
12821.8842 1
< 0.1%
12822.3328 1
< 0.1%
12822.9263 1
< 0.1%
12823.5239 1
< 0.1%
12823.5519 1
< 0.1%
12823.6339 1
< 0.1%
12823.6355 1
< 0.1%
12823.7259 1
< 0.1%
12823.7823 1
< 0.1%
ValueCountFrequency (%)
12845.544808 1
< 0.1%
12845.218406 1
< 0.1%
12845.215133 1
< 0.1%
12845.1514 1
< 0.1%
12845.149271 1
< 0.1%
12845.147394 1
< 0.1%
12845.142812 1
< 0.1%
12845.138961 1
< 0.1%
12845.137032 1
< 0.1%
12845.121301 1
< 0.1%

Y좌표
Real number (ℝ)

HIGH CORRELATION 

Distinct3143
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3551.4371
Minimum3537.0425
Maximum3648.3214
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.0 KiB
2023-12-12T11:19:38.108851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3537.0425
5-th percentile3542.1091
Q13549.8825
median3551.6549
Q33553.2255
95-th percentile3556.8696
Maximum3648.3214
Range111.27886
Interquartile range (IQR)3.343039

Descriptive statistics

Standard deviation6.0858948
Coefficient of variation (CV)0.0017136429
Kurtosis137.5311
Mean3551.4371
Median Absolute Deviation (MAD)1.68085
Skewness8.7765914
Sum11258056
Variance37.038116
MonotonicityNot monotonic
2023-12-12T11:19:38.286275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3551.5373 3
 
0.1%
3550.3502 2
 
0.1%
3559.0525 2
 
0.1%
3554.554 2
 
0.1%
3552.1735 2
 
0.1%
3551.4316 2
 
0.1%
3552.7599 2
 
0.1%
3552.0269 2
 
0.1%
3550.8258 2
 
0.1%
3551.2462 2
 
0.1%
Other values (3133) 3149
99.3%
ValueCountFrequency (%)
3537.0425 1
< 0.1%
3537.0649 1
< 0.1%
3537.0833 1
< 0.1%
3537.3829 1
< 0.1%
3537.4199 1
< 0.1%
3537.4989 1
< 0.1%
3538.0203 1
< 0.1%
3538.0221 1
< 0.1%
3538.0277 1
< 0.1%
3538.0294 1
< 0.1%
ValueCountFrequency (%)
3648.321357 1
< 0.1%
3647.113629 1
< 0.1%
3646.682229 1
< 0.1%
3646.646834 1
< 0.1%
3644.444468 1
< 0.1%
3642.933421 1
< 0.1%
3640.974524 1
< 0.1%
3639.655887 1
< 0.1%
3559.718795 1
< 0.1%
3559.7126 1
< 0.1%

경유노선수
Real number (ℝ)

Distinct20
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6369085
Minimum1
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.0 KiB
2023-12-12T11:19:38.415721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median3
Q35
95-th percentile9
Maximum20
Range19
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.0615379
Coefficient of variation (CV)0.84179678
Kurtosis3.8298673
Mean3.6369085
Median Absolute Deviation (MAD)2
Skewness1.7505503
Sum11529
Variance9.3730141
MonotonicityNot monotonic
2023-12-12T11:19:38.548367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
1 940
29.7%
2 587
18.5%
3 409
12.9%
4 298
 
9.4%
5 237
 
7.5%
6 210
 
6.6%
7 174
 
5.5%
8 100
 
3.2%
9 57
 
1.8%
10 38
 
1.2%
Other values (10) 120
 
3.8%
ValueCountFrequency (%)
1 940
29.7%
2 587
18.5%
3 409
12.9%
4 298
 
9.4%
5 237
 
7.5%
6 210
 
6.6%
7 174
 
5.5%
8 100
 
3.2%
9 57
 
1.8%
10 38
 
1.2%
ValueCountFrequency (%)
20 2
 
0.1%
19 1
 
< 0.1%
18 13
0.4%
17 3
 
0.1%
16 2
 
0.1%
15 12
0.4%
14 21
0.7%
13 21
0.7%
12 22
0.7%
11 23
0.7%

경유노선
Text

Distinct1159
Distinct (%)36.6%
Missing0
Missing (%)0.0%
Memory size24.9 KiB
2023-12-12T11:19:38.941149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length102
Median length87
Mean length16.881703
Min length1

Characters and Unicode

Total characters53515
Distinct characters31
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique570 ?
Unique (%)18.0%

Sample

1st row509, 650, 706, 805, 836, 달서4, 순환2-1
2nd row509, 650, 706, 805, 836, 달서4-1, 순환2
3rd row509, 650, 706, 805, 836, 달서4, 순환2-1
4th row509, 650, 706, 805, 836, 달서4-1, 순환2
5th row250, 323, 909, 성서2
ValueCountFrequency (%)
600 228
 
2.0%
성서2 188
 
1.6%
655 178
 
1.5%
405 174
 
1.5%
524 171
 
1.5%
623 167
 
1.4%
401 158
 
1.4%
653 154
 
1.3%
708 149
 
1.3%
651 149
 
1.3%
Other values (129) 9849
85.2%
2023-12-12T11:19:39.546127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8395
15.7%
, 8359
15.6%
1 4119
 
7.7%
0 4069
 
7.6%
3 3377
 
6.3%
5 2963
 
5.5%
4 2880
 
5.4%
2 2810
 
5.3%
6 2359
 
4.4%
9 1940
 
3.6%
Other values (21) 12244
22.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 27403
51.2%
Space Separator 8395
 
15.7%
Other Punctuation 8359
 
15.6%
Other Letter 8238
 
15.4%
Dash Punctuation 1120
 
2.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 1118
13.6%
성 1104
13.4%
구 1014
12.3%
달 877
10.6%
급 450
 
5.5%
행 450
 
5.5%
동 422
 
5.1%
북 358
 
4.3%
칠 356
 
4.3%
곡 356
 
4.3%
Other values (8) 1733
21.0%
Decimal Number
ValueCountFrequency (%)
1 4119
15.0%
0 4069
14.8%
3 3377
12.3%
5 2963
10.8%
4 2880
10.5%
2 2810
10.3%
6 2359
8.6%
9 1940
7.1%
8 1511
 
5.5%
7 1375
 
5.0%
Space Separator
ValueCountFrequency (%)
8395
100.0%
Other Punctuation
ValueCountFrequency (%)
, 8359
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1120
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 45277
84.6%
Hangul 8238
 
15.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 1118
13.6%
성 1104
13.4%
구 1014
12.3%
달 877
10.6%
급 450
 
5.5%
행 450
 
5.5%
동 422
 
5.1%
북 358
 
4.3%
칠 356
 
4.3%
곡 356
 
4.3%
Other values (8) 1733
21.0%
Common
ValueCountFrequency (%)
8395
18.5%
, 8359
18.5%
1 4119
9.1%
0 4069
9.0%
3 3377
7.5%
5 2963
 
6.5%
4 2880
 
6.4%
2 2810
 
6.2%
6 2359
 
5.2%
9 1940
 
4.3%
Other values (3) 4006
8.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45277
84.6%
Hangul 8238
 
15.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8395
18.5%
, 8359
18.5%
1 4119
9.1%
0 4069
9.0%
3 3377
7.5%
5 2963
 
6.5%
4 2880
 
6.4%
2 2810
 
6.2%
6 2359
 
5.2%
9 1940
 
4.3%
Other values (3) 4006
8.8%
Hangul
ValueCountFrequency (%)
서 1118
13.6%
성 1104
13.4%
구 1014
12.3%
달 877
10.6%
급 450
 
5.5%
행 450
 
5.5%
동 422
 
5.1%
북 358
 
4.3%
칠 356
 
4.3%
곡 356
 
4.3%
Other values (8) 1733
21.0%

Interactions

2023-12-12T11:19:32.283704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:19:30.288293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:19:30.864248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:19:31.291902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:19:31.768530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:19:32.377120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:19:30.392768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:19:30.949860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:19:31.378122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:19:31.880125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:19:32.467630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:19:30.488221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:19:31.041063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:19:31.480538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:19:31.985093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:19:32.565382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:19:30.603323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:19:31.128185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:19:31.561941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:19:32.090087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:19:32.665560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:19:30.746774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:19:31.207867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:19:31.650237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:19:32.180365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T11:19:39.664942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
정류소ID모바일ID구/군X좌표Y좌표경유노선수
정류소ID1.0000.3220.9890.7700.5980.269
모바일ID0.3221.0000.3300.3860.2380.277
구/군0.9890.3301.0000.7760.7940.281
X좌표0.7700.3860.7761.0000.5450.377
Y좌표0.5980.2380.7940.5451.0000.195
경유노선수0.2690.2770.2810.3770.1951.000
2023-12-12T11:19:39.776406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
정류소ID모바일IDX좌표Y좌표경유노선수구/군
정류소ID1.0000.073-0.505-0.702-0.2300.981
모바일ID0.0731.000-0.065-0.0450.0690.168
X좌표-0.505-0.0651.0000.3850.1330.518
Y좌표-0.702-0.0450.3851.0000.0830.461
경유노선수-0.2300.0690.1330.0831.0000.138
구/군0.9810.1680.5180.4610.1381.000

Missing values

2023-12-12T11:19:32.857710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T11:19:33.122748image/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.
2023-12-12T11:19:33.241195image/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

정류소ID모바일ID정류소명영문명시/도구/군동X좌표Y좌표경유노선수경유노선
070010001005191대명시장앞Daemyeong Market대구광역시남구대명3동12834.62783551.52147509, 650, 706, 805, 836, 달서4, 순환2-1
170010002002164대명시장건너Daemyeong Market대구광역시중구남산4동12834.66893551.51847509, 650, 706, 805, 836, 달서4-1, 순환2
270010003002166계명대학교대명캠퍼스앞Keimyung University Daemyeong Campus대구광역시남구대명3동12834.85093551.42677509, 650, 706, 805, 836, 달서4, 순환2-1
370010004002163계명대학교대명캠퍼스건너Keimyung University Daemyeong Campus대구광역시중구남산4동12834.80043551.46057509, 650, 706, 805, 836, 달서4-1, 순환2
470010005002196중부소방서건너Jungbu Fire Station대구광역시중구대신동12834.54033551.78724250, 323, 909, 성서2
570010006002198중부소방서앞Jungbu Fire Station대구광역시중구남산4동12834.60563551.78313250, 323-1, 급행6
670010007002195대신센트럴자이Daesin Central Xi대구광역시중구대신동12834.67873551.82175250, 323, 909, 급행6, 성서2
77001000800445청라언덕(신남)역(1번출구)Cheongna Hill(Sinnam) Station대구광역시중구대신동12834.8710033551.8815134250, 323, 909, 성서2
870010009002199엘디스리젠트호텔건너Eldis Regent Hotel대구광역시중구남산2동12835.20443551.96492405, 609
97001001000643엘디스리젠트호텔앞Eldis Regent Hotel대구광역시중구성내2동12835.11493551.95867405, 609, 840, 909, 990, 991, 북구2
정류소ID모바일ID정류소명영문명시/도구/군동X좌표Y좌표경유노선수경유노선
316071110766004101대구환경공단달성사업소앞Daegu Environmental Corporation대구광역시달성군구지면12824.97793538.39621달성7
316171110767004102대구환경공단달성사업소건너Daegu Environmental Corporation대구광역시달성군구지면12825.00533538.38141달성7
316271110768004110삼산리(범동교)건너Samsan-ri대구광역시달성군가창면12839.59863544.01311가창2
316371110769004117구라3리1Gura 3-ri대구광역시달성군화원읍12829.23143548.87111달성1
316471110770004118구라3리2Gura 3-ri대구광역시달성군화원읍12829.24323548.85641달성1
316571110771004119구라리2Gura-ri대구광역시달성군화원읍12829.72253548.41441달성1
316671110772004123국립대구과학관앞Daegu National Science Museum대구광역시달성군유가면12827.81093541.1961급행4
316771110773004122국립대구과학관건너Daegu National Science Museum대구광역시달성군유가면12827.7913541.18961급행4
316871110774004121구지중학교앞Guji Middle School대구광역시달성군구지면12825.05643539.58963600, 급행8, 달성3
316971110775004120구지중학교건너Guji Middle School대구광역시달성군구지면12825.0333539.62153600, 급행8, 달성3