Overview

Dataset statistics

Number of variables9
Number of observations7007
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory527.0 KiB
Average record size in memory77.0 B

Variable types

Categorical2
Text4
Numeric3

Dataset

Description파일 다운로드
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-12912/S/1/datasetView.do

Alerts

사용일자 has constant value ""Constant
등록일자 has constant value ""Constant
승차총승객수 is highly overall correlated with 하차총승객수High correlation
하차총승객수 is highly overall correlated with 승차총승객수High correlation
승차총승객수 has 435 (6.2%) zerosZeros
하차총승객수 has 311 (4.4%) zerosZeros

Reproduction

Analysis started2024-05-11 06:04:13.924829
Analysis finished2024-05-11 06:04:16.568389
Duration2.64 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

사용일자
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size54.9 KiB
20230401
7007 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20230401
2nd row20230401
3rd row20230401
4th row20230401
5th row20230401

Common Values

ValueCountFrequency (%)
20230401 7007
100.0%

Length

2024-05-11T15:04:16.639146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:04:16.741027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20230401 7007
100.0%

노선번호
Text

Distinct86
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size54.9 KiB
2024-05-11T15:04:17.009901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length4
Mean length3.6019695
Min length3

Characters and Unicode

Total characters25239
Distinct characters20
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row100
2nd row9703
3rd row9703
4th row9703
5th row9703
ValueCountFrequency (%)
n15 249
 
3.6%
n26 247
 
3.5%
n37 205
 
2.9%
542 137
 
2.0%
9701 127
 
1.8%
661 125
 
1.8%
541 123
 
1.8%
441 123
 
1.8%
9408 121
 
1.7%
9403 121
 
1.7%
Other values (76) 5429
77.5%
2024-05-11T15:04:17.426057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7 3900
15.5%
5 3208
12.7%
1 3150
12.5%
0 2839
11.2%
6 2478
9.8%
2 2340
9.3%
4 2308
9.1%
3 2177
8.6%
9 985
 
3.9%
N 701
 
2.8%
Other values (10) 1153
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23818
94.4%
Uppercase Letter 919
 
3.6%
Other Letter 470
 
1.9%
Dash Punctuation 32
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
7 3900
16.4%
5 3208
13.5%
1 3150
13.2%
0 2839
11.9%
6 2478
10.4%
2 2340
9.8%
4 2308
9.7%
3 2177
9.1%
9 985
 
4.1%
8 433
 
1.8%
Other Letter
ValueCountFrequency (%)
노 114
24.3%
원 114
24.3%
동 106
22.6%
강 92
19.6%
성 29
 
6.2%
서 15
 
3.2%
Uppercase Letter
ValueCountFrequency (%)
N 701
76.3%
B 148
 
16.1%
A 70
 
7.6%
Dash Punctuation
ValueCountFrequency (%)
- 32
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 23850
94.5%
Latin 919
 
3.6%
Hangul 470
 
1.9%

Most frequent character per script

Common
ValueCountFrequency (%)
7 3900
16.4%
5 3208
13.5%
1 3150
13.2%
0 2839
11.9%
6 2478
10.4%
2 2340
9.8%
4 2308
9.7%
3 2177
9.1%
9 985
 
4.1%
8 433
 
1.8%
Hangul
ValueCountFrequency (%)
노 114
24.3%
원 114
24.3%
동 106
22.6%
강 92
19.6%
성 29
 
6.2%
서 15
 
3.2%
Latin
ValueCountFrequency (%)
N 701
76.3%
B 148
 
16.1%
A 70
 
7.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 24769
98.1%
Hangul 470
 
1.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
7 3900
15.7%
5 3208
13.0%
1 3150
12.7%
0 2839
11.5%
6 2478
10.0%
2 2340
9.4%
4 2308
9.3%
3 2177
8.8%
9 985
 
4.0%
N 701
 
2.8%
Other values (4) 683
 
2.8%
Hangul
ValueCountFrequency (%)
노 114
24.3%
원 114
24.3%
동 106
22.6%
강 92
19.6%
성 29
 
6.2%
서 15
 
3.2%

노선명
Text

Distinct89
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size54.9 KiB
2024-05-11T15:04:17.712071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length32
Median length21
Mean length17.449122
Min length12

Characters and Unicode

Total characters122266
Distinct characters182
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

Unique1 ?
Unique (%)< 0.1%

Sample

1st row100번(하계동~용산구청)
2nd row9703번(신성교통차고지~서울역)
3rd row9703번(신성교통차고지~서울역)
4th row9703번(신성교통차고지~서울역)
5th row9703번(신성교통차고지~서울역)
ValueCountFrequency (%)
542번(군포버스공영차고지~신사역 137
 
1.9%
n15번(남태령역~우이동도선사입구 131
 
1.8%
9701번(가좌동~서울역 127
 
1.7%
n26번(강서공영차고지~중랑공영차고지 126
 
1.7%
661번(부천상동~영등포역,신세계백화점 125
 
1.7%
541번(군포공영차고지~강남역 123
 
1.7%
441번(월암공영차고지~신사사거리 123
 
1.7%
9403번(구미동차고지~중곡역 121
 
1.7%
9408번(구미동차고지~고속터미널 121
 
1.7%
n26번(중랑공영차고지~강서공영차고지 121
 
1.7%
Other values (82) 6028
82.8%
2024-05-11T15:04:18.144030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
) 7074
 
5.8%
( 7074
 
5.8%
~ 7007
 
5.7%
번 6591
 
5.4%
동 4391
 
3.6%
지 4243
 
3.5%
고 4230
 
3.5%
차 3907
 
3.2%
7 3900
 
3.2%
1 3406
 
2.8%
Other values (172) 70443
57.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 75207
61.5%
Decimal Number 24074
 
19.7%
Close Punctuation 7074
 
5.8%
Open Punctuation 7074
 
5.8%
Math Symbol 7007
 
5.7%
Uppercase Letter 953
 
0.8%
Other Punctuation 569
 
0.5%
Space Separator 276
 
0.2%
Dash Punctuation 32
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
번 6591
 
8.8%
동 4391
 
5.8%
지 4243
 
5.6%
고 4230
 
5.6%
차 3907
 
5.2%
역 3010
 
4.0%
영 2918
 
3.9%
공 2650
 
3.5%
대 1476
 
2.0%
서 1473
 
2.0%
Other values (151) 40318
53.6%
Decimal Number
ValueCountFrequency (%)
7 3900
16.2%
1 3406
14.1%
5 3208
13.3%
0 2839
11.8%
6 2478
10.3%
2 2340
9.7%
4 2308
9.6%
3 2177
9.0%
9 985
 
4.1%
8 433
 
1.8%
Uppercase Letter
ValueCountFrequency (%)
N 701
73.6%
B 148
 
15.5%
A 87
 
9.1%
K 17
 
1.8%
Other Punctuation
ValueCountFrequency (%)
, 434
76.3%
. 135
 
23.7%
Close Punctuation
ValueCountFrequency (%)
) 7074
100.0%
Open Punctuation
ValueCountFrequency (%)
( 7074
100.0%
Math Symbol
ValueCountFrequency (%)
~ 7007
100.0%
Space Separator
ValueCountFrequency (%)
276
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 32
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 75207
61.5%
Common 46106
37.7%
Latin 953
 
0.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
번 6591
 
8.8%
동 4391
 
5.8%
지 4243
 
5.6%
고 4230
 
5.6%
차 3907
 
5.2%
역 3010
 
4.0%
영 2918
 
3.9%
공 2650
 
3.5%
대 1476
 
2.0%
서 1473
 
2.0%
Other values (151) 40318
53.6%
Common
ValueCountFrequency (%)
) 7074
15.3%
( 7074
15.3%
~ 7007
15.2%
7 3900
8.5%
1 3406
7.4%
5 3208
7.0%
0 2839
6.2%
6 2478
 
5.4%
2 2340
 
5.1%
4 2308
 
5.0%
Other values (7) 4472
9.7%
Latin
ValueCountFrequency (%)
N 701
73.6%
B 148
 
15.5%
A 87
 
9.1%
K 17
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 75207
61.5%
ASCII 47059
38.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
) 7074
15.0%
( 7074
15.0%
~ 7007
14.9%
7 3900
8.3%
1 3406
7.2%
5 3208
6.8%
0 2839
6.0%
6 2478
 
5.3%
2 2340
 
5.0%
4 2308
 
4.9%
Other values (11) 5425
11.5%
Hangul
ValueCountFrequency (%)
번 6591
 
8.8%
동 4391
 
5.8%
지 4243
 
5.6%
고 4230
 
5.6%
차 3907
 
5.2%
역 3010
 
4.0%
영 2918
 
3.9%
공 2650
 
3.5%
대 1476
 
2.0%
서 1473
 
2.0%
Other values (151) 40318
53.6%

표준버스정류장ID
Real number (ℝ)

Distinct3739
Distinct (%)53.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4996924 × 108
Minimum1 × 108
Maximum9.998 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.7 KiB
2024-05-11T15:04:18.298927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1 × 108
5-th percentile1.010001 × 108
Q11.12 × 108
median1.1900002 × 108
Q32.0900012 × 108
95-th percentile2.2200149 × 108
Maximum9.998 × 108
Range8.998 × 108
Interquartile range (IQR)97000114

Descriptive statistics

Standard deviation63504226
Coefficient of variation (CV)0.42344835
Kurtosis69.620078
Mean1.4996924 × 108
Median Absolute Deviation (MAD)10000006
Skewness5.6528695
Sum1.0508344 × 1012
Variance4.0327867 × 1015
MonotonicityNot monotonic
2024-05-11T15:04:18.451189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
121000005 12
 
0.2%
121000007 12
 
0.2%
121000006 11
 
0.2%
121000008 11
 
0.2%
121000009 11
 
0.2%
100000384 11
 
0.2%
100000380 11
 
0.2%
112000005 10
 
0.1%
121000010 10
 
0.1%
121000013 10
 
0.1%
Other values (3729) 6898
98.4%
ValueCountFrequency (%)
100000001 3
< 0.1%
100000002 3
< 0.1%
100000003 3
< 0.1%
100000004 3
< 0.1%
100000005 2
< 0.1%
100000006 1
 
< 0.1%
100000007 1
 
< 0.1%
100000008 1
 
< 0.1%
100000015 1
 
< 0.1%
100000016 1
 
< 0.1%
ValueCountFrequency (%)
999800005 2
< 0.1%
999800004 1
 
< 0.1%
999800003 1
 
< 0.1%
999033574 4
0.1%
998501980 1
 
< 0.1%
998501931 1
 
< 0.1%
998001700 1
 
< 0.1%
990070103 2
< 0.1%
990070001 2
< 0.1%
990014944 1
 
< 0.1%

버스정류장ARS번호
Text

Distinct3702
Distinct (%)52.8%
Missing0
Missing (%)0.0%
Memory size54.9 KiB
2024-05-11T15:04:18.829674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length5
Mean length4.9908663
Min length1

Characters and Unicode

Total characters34971
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2085 ?
Unique (%)29.8%

Sample

1st row01002
2nd row35175
3rd row35174
4th row35173
5th row35145
ValueCountFrequency (%)
16
 
0.2%
22005 12
 
0.2%
22007 12
 
0.2%
22006 11
 
0.2%
22008 11
 
0.2%
22009 11
 
0.2%
01007 11
 
0.2%
01009 11
 
0.2%
13005 10
 
0.1%
22010 10
 
0.1%
Other values (3692) 6892
98.4%
2024-05-11T15:04:19.380549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 7099
20.3%
0 6263
17.9%
2 4758
13.6%
3 3766
10.8%
4 2729
 
7.8%
6 2644
 
7.6%
5 2401
 
6.9%
7 2037
 
5.8%
8 1858
 
5.3%
9 1400
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 34955
> 99.9%
Math Symbol 16
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 7099
20.3%
0 6263
17.9%
2 4758
13.6%
3 3766
10.8%
4 2729
 
7.8%
6 2644
 
7.6%
5 2401
 
6.9%
7 2037
 
5.8%
8 1858
 
5.3%
9 1400
 
4.0%
Math Symbol
ValueCountFrequency (%)
~ 16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 34971
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 7099
20.3%
0 6263
17.9%
2 4758
13.6%
3 3766
10.8%
4 2729
 
7.8%
6 2644
 
7.6%
5 2401
 
6.9%
7 2037
 
5.8%
8 1858
 
5.3%
9 1400
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 34971
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 7099
20.3%
0 6263
17.9%
2 4758
13.6%
3 3766
10.8%
4 2729
 
7.8%
6 2644
 
7.6%
5 2401
 
6.9%
7 2037
 
5.8%
8 1858
 
5.3%
9 1400
 
4.0%

역명
Text

Distinct6656
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Memory size54.9 KiB
2024-05-11T15:04:19.637600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length37
Median length30
Mean length15.021122
Min length9

Characters and Unicode

Total characters105253
Distinct characters542
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

Unique6382 ?
Unique (%)91.1%

Sample

1st row창경궁.서울대학교병원(00031)
2nd row큰골입구(00039)
3rd row용사촌입구(00038)
4th row용사촌입구.밥할머니공원앞(00081)
5th row동산마을22단지(00083)
ValueCountFrequency (%)
군포공영차고지(00001 6
 
0.1%
등촌중학교 6
 
0.1%
하안버스공영차고지(00002 5
 
0.1%
광명차고지(00001 5
 
0.1%
군포보건소(00002 5
 
0.1%
덕은교.은평차고지앞(00002 5
 
0.1%
은평공영차고지(00001 5
 
0.1%
하얀마을.그랜드빌.벽산빌라(00005 4
 
0.1%
주공4단지(00004 4
 
0.1%
미금초등학교(00007 4
 
0.1%
Other values (6649) 6966
99.3%
2024-05-11T15:04:20.040847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 22170
 
21.1%
( 7293
 
6.9%
) 7293
 
6.9%
1 2654
 
2.5%
. 2262
 
2.1%
2 1865
 
1.8%
3 1689
 
1.6%
4 1584
 
1.5%
5 1535
 
1.5%
6 1447
 
1.4%
Other values (532) 55461
52.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 51205
48.6%
Decimal Number 36553
34.7%
Open Punctuation 7293
 
6.9%
Close Punctuation 7293
 
6.9%
Other Punctuation 2273
 
2.2%
Uppercase Letter 604
 
0.6%
Lowercase Letter 22
 
< 0.1%
Space Separator 8
 
< 0.1%
Dash Punctuation 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 1412
 
2.8%
역 1395
 
2.7%
교 1246
 
2.4%
지 1111
 
2.2%
구 1011
 
2.0%
파 1008
 
2.0%
아 962
 
1.9%
트 932
 
1.8%
학 892
 
1.7%
서 841
 
1.6%
Other values (490) 40395
78.9%
Uppercase Letter
ValueCountFrequency (%)
C 103
17.1%
M 81
13.4%
K 77
12.7%
D 76
12.6%
T 75
12.4%
G 40
 
6.6%
L 38
 
6.3%
S 25
 
4.1%
B 17
 
2.8%
N 13
 
2.2%
Other values (11) 59
9.8%
Decimal Number
ValueCountFrequency (%)
0 22170
60.7%
1 2654
 
7.3%
2 1865
 
5.1%
3 1689
 
4.6%
4 1584
 
4.3%
5 1535
 
4.2%
6 1447
 
4.0%
7 1316
 
3.6%
8 1196
 
3.3%
9 1097
 
3.0%
Other Punctuation
ValueCountFrequency (%)
. 2262
99.5%
& 7
 
0.3%
, 2
 
0.1%
? 2
 
0.1%
Lowercase Letter
ValueCountFrequency (%)
e 20
90.9%
t 1
 
4.5%
k 1
 
4.5%
Open Punctuation
ValueCountFrequency (%)
( 7293
100.0%
Close Punctuation
ValueCountFrequency (%)
) 7293
100.0%
Space Separator
ValueCountFrequency (%)
8
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 53422
50.8%
Hangul 51205
48.6%
Latin 626
 
0.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 1412
 
2.8%
역 1395
 
2.7%
교 1246
 
2.4%
지 1111
 
2.2%
구 1011
 
2.0%
파 1008
 
2.0%
아 962
 
1.9%
트 932
 
1.8%
학 892
 
1.7%
서 841
 
1.6%
Other values (490) 40395
78.9%
Latin
ValueCountFrequency (%)
C 103
16.5%
M 81
12.9%
K 77
12.3%
D 76
12.1%
T 75
12.0%
G 40
 
6.4%
L 38
 
6.1%
S 25
 
4.0%
e 20
 
3.2%
B 17
 
2.7%
Other values (14) 74
11.8%
Common
ValueCountFrequency (%)
0 22170
41.5%
( 7293
 
13.7%
) 7293
 
13.7%
1 2654
 
5.0%
. 2262
 
4.2%
2 1865
 
3.5%
3 1689
 
3.2%
4 1584
 
3.0%
5 1535
 
2.9%
6 1447
 
2.7%
Other values (8) 3630
 
6.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 54048
51.4%
Hangul 51205
48.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 22170
41.0%
( 7293
 
13.5%
) 7293
 
13.5%
1 2654
 
4.9%
. 2262
 
4.2%
2 1865
 
3.5%
3 1689
 
3.1%
4 1584
 
2.9%
5 1535
 
2.8%
6 1447
 
2.7%
Other values (32) 4256
 
7.9%
Hangul
ValueCountFrequency (%)
동 1412
 
2.8%
역 1395
 
2.7%
교 1246
 
2.4%
지 1111
 
2.2%
구 1011
 
2.0%
파 1008
 
2.0%
아 962
 
1.9%
트 932
 
1.8%
학 892
 
1.7%
서 841
 
1.6%
Other values (490) 40395
78.9%

승차총승객수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct531
Distinct (%)7.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean87.190381
Minimum0
Maximum1418
Zeros435
Zeros (%)6.2%
Negative0
Negative (%)0.0%
Memory size61.7 KiB
2024-05-11T15:04:20.196807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q19
median44
Q3117
95-th percentile319.4
Maximum1418
Range1418
Interquartile range (IQR)108

Descriptive statistics

Standard deviation123.13926
Coefficient of variation (CV)1.4123033
Kurtosis16.492907
Mean87.190381
Median Absolute Deviation (MAD)40
Skewness3.2440912
Sum610943
Variance15163.278
MonotonicityNot monotonic
2024-05-11T15:04:20.345271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 435
 
6.2%
1 305
 
4.4%
2 218
 
3.1%
4 174
 
2.5%
3 173
 
2.5%
5 120
 
1.7%
6 103
 
1.5%
8 99
 
1.4%
9 99
 
1.4%
7 80
 
1.1%
Other values (521) 5201
74.2%
ValueCountFrequency (%)
0 435
6.2%
1 305
4.4%
2 218
3.1%
3 173
 
2.5%
4 174
 
2.5%
5 120
 
1.7%
6 103
 
1.5%
7 80
 
1.1%
8 99
 
1.4%
9 99
 
1.4%
ValueCountFrequency (%)
1418 1
< 0.1%
1379 1
< 0.1%
1234 1
< 0.1%
1180 1
< 0.1%
1149 1
< 0.1%
1091 1
< 0.1%
1087 1
< 0.1%
1078 1
< 0.1%
999 1
< 0.1%
986 1
< 0.1%

하차총승객수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct519
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean85.371771
Minimum0
Maximum1392
Zeros311
Zeros (%)4.4%
Negative0
Negative (%)0.0%
Memory size61.7 KiB
2024-05-11T15:04:20.480961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q110
median45
Q3115
95-th percentile293
Maximum1392
Range1392
Interquartile range (IQR)105

Descriptive statistics

Standard deviation118.42032
Coefficient of variation (CV)1.3871133
Kurtosis20.161582
Mean85.371771
Median Absolute Deviation (MAD)40
Skewness3.4976882
Sum598200
Variance14023.372
MonotonicityNot monotonic
2024-05-11T15:04:20.631060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 311
 
4.4%
1 256
 
3.7%
2 186
 
2.7%
3 174
 
2.5%
4 163
 
2.3%
5 141
 
2.0%
6 125
 
1.8%
8 122
 
1.7%
9 103
 
1.5%
7 103
 
1.5%
Other values (509) 5323
76.0%
ValueCountFrequency (%)
0 311
4.4%
1 256
3.7%
2 186
2.7%
3 174
2.5%
4 163
2.3%
5 141
2.0%
6 125
1.8%
7 103
 
1.5%
8 122
 
1.7%
9 103
 
1.5%
ValueCountFrequency (%)
1392 1
< 0.1%
1368 1
< 0.1%
1334 1
< 0.1%
1305 1
< 0.1%
1240 1
< 0.1%
1190 1
< 0.1%
1109 1
< 0.1%
1050 1
< 0.1%
1027 1
< 0.1%
1008 1
< 0.1%

등록일자
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size54.9 KiB
20230404
7007 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20230404
2nd row20230404
3rd row20230404
4th row20230404
5th row20230404

Common Values

ValueCountFrequency (%)
20230404 7007
100.0%

Length

2024-05-11T15:04:20.799703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:04:20.914079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20230404 7007
100.0%

Interactions

2024-05-11T15:04:15.950431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:04:14.902105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:04:15.595325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:04:16.061619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:04:15.014299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:04:15.713526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:04:16.182185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:04:15.154115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:04:15.820020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T15:04:20.995149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
노선번호노선명표준버스정류장ID승차총승객수하차총승객수
노선번호1.0001.0000.7020.4370.457
노선명1.0001.0000.7020.4360.456
표준버스정류장ID0.7020.7021.0000.1850.192
승차총승객수0.4370.4360.1851.0000.514
하차총승객수0.4570.4560.1920.5141.000
2024-05-11T15:04:21.120589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
표준버스정류장ID승차총승객수하차총승객수
표준버스정류장ID1.000-0.164-0.158
승차총승객수-0.1641.0000.562
하차총승객수-0.1580.5621.000

Missing values

2024-05-11T15:04:16.345664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T15:04:16.503162image/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

사용일자노선번호노선명표준버스정류장ID버스정류장ARS번호역명승차총승객수하차총승객수등록일자
020230401100100번(하계동~용산구청)10000000201002창경궁.서울대학교병원(00031)21813720230404
12023040197039703번(신성교통차고지~서울역)21800062835175큰골입구(00039)10920230404
22023040197039703번(신성교통차고지~서울역)21800055235174용사촌입구(00038)471120230404
32023040197039703번(신성교통차고지~서울역)21800055135173용사촌입구.밥할머니공원앞(00081)234920230404
42023040197039703번(신성교통차고지~서울역)21800055035145동산마을22단지(00083)191920230404
52023040197039703번(신성교통차고지~서울역)21800054935170동산마을22단지(00036)254320230404
62023040197039703번(신성교통차고지~서울역)21800054835123동산고등학교(00084)72320230404
72023040197039703번(신성교통차고지~서울역)21800054735171동산고등학교(00035)161620230404
82023040197039703번(신성교통차고지~서울역)21800054635158삼송교(00085)41820230404
92023040197039703번(신성교통차고지~서울역)21800054535162삼송교(00034)12820230404
사용일자노선번호노선명표준버스정류장ID버스정류장ARS번호역명승차총승객수하차총승객수등록일자
69972023040165156515번(양천차고지~삼막사거리)11900005820151장승배기역(00027)13022720230404
699820230401162162번(정릉~여의도)10000012301219방송통신대.이화장(00064)12120020230404
699920230401602602번(양천공용차고지~시청앞)11400028815405푸른마을1단지.이든채아파트(00083)137120230404
70002023040123112311번(중랑차고지~문정동)10500012906215장안교은석초등학교(00023)1312320230404
700120230401N15N15번(우이동성원아파트~남태령역)12000000221101서울미술고.인헌중고(00081)4120230404
70022023040165156515번(양천차고지~삼막사거리)11900009320186신대방삼거리(00030)25116320230404
70032023040165156515번(양천차고지~삼막사거리)11900009420187신대방삼거리(00086)21229620230404
700420230401N15N15번(우이동성원아파트~남태령역)12000000321102낙성대입구(00082)4020230404
70052023040165156515번(양천차고지~삼막사거리)11900009520188보라매병원입구(00031)10515020230404
70062023040165156515번(양천차고지~삼막사거리)11900009720190동작구청.노량진초등학교앞(00025)898020230404