Overview

Dataset statistics

Number of variables11
Number of observations500
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory47.5 KiB
Average record size in memory97.3 B

Variable types

Categorical2
Numeric7
Text2

Dataset

Description샘플 데이터
Author서울시(스마트카드사)
URLhttps://bigdata.seoul.go.kr/data/selectSampleData.do?sample_data_seq=72

Alerts

시간(HOUR) has 8 (1.6%) zerosZeros
승하차인원(GETON_CNT) has 114 (22.8%) zerosZeros
하차인원(GETOFF_CNT) has 100 (20.0%) zerosZeros

Reproduction

Analysis started2024-01-14 06:50:43.394532
Analysis finished2024-01-14 06:50:51.975993
Duration8.58 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

년(YEAR)
Categorical

Distinct5
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2018
116 
2019
100 
2020
100 
2021
94 
2017
90 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2019
2nd row2019
3rd row2018
4th row2021
5th row2018

Common Values

ValueCountFrequency (%)
2018 116
23.2%
2019 100
20.0%
2020 100
20.0%
2021 94
18.8%
2017 90
18.0%

Length

2024-01-14T15:50:52.054178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-14T15:50:52.176631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2018 116
23.2%
2019 100
20.0%
2020 100
20.0%
2021 94
18.8%
2017 90
18.0%

월(MONTH)
Real number (ℝ)

Distinct12
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.506
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-01-14T15:50:52.307393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median6
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.3753147
Coefficient of variation (CV)0.5188003
Kurtosis-1.2020432
Mean6.506
Median Absolute Deviation (MAD)3
Skewness0.054333844
Sum3253
Variance11.392749
MonotonicityNot monotonic
2024-01-14T15:50:52.430133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
6 50
10.0%
4 50
10.0%
10 47
9.4%
5 46
9.2%
2 44
8.8%
9 43
8.6%
3 41
8.2%
12 40
8.0%
11 39
7.8%
7 37
7.4%
Other values (2) 63
12.6%
ValueCountFrequency (%)
1 31
6.2%
2 44
8.8%
3 41
8.2%
4 50
10.0%
5 46
9.2%
6 50
10.0%
7 37
7.4%
8 32
6.4%
9 43
8.6%
10 47
9.4%
ValueCountFrequency (%)
12 40
8.0%
11 39
7.8%
10 47
9.4%
9 43
8.6%
8 32
6.4%
7 37
7.4%
6 50
10.0%
5 46
9.2%
4 50
10.0%
3 41
8.2%

일(DAY)
Real number (ℝ)

Distinct31
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.762
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-01-14T15:50:52.576790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q110
median17
Q324
95-th percentile30
Maximum31
Range30
Interquartile range (IQR)14

Descriptive statistics

Standard deviation8.6731657
Coefficient of variation (CV)0.51743024
Kurtosis-1.1933993
Mean16.762
Median Absolute Deviation (MAD)7
Skewness-0.10274815
Sum8381
Variance75.223804
MonotonicityNot monotonic
2024-01-14T15:50:52.725644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
13 24
 
4.8%
24 24
 
4.8%
28 22
 
4.4%
26 22
 
4.4%
30 20
 
4.0%
15 20
 
4.0%
22 20
 
4.0%
23 20
 
4.0%
6 19
 
3.8%
12 18
 
3.6%
Other values (21) 291
58.2%
ValueCountFrequency (%)
1 9
1.8%
2 14
2.8%
3 10
2.0%
4 15
3.0%
5 16
3.2%
6 19
3.8%
7 17
3.4%
8 12
2.4%
9 11
2.2%
10 17
3.4%
ValueCountFrequency (%)
31 10
2.0%
30 20
4.0%
29 15
3.0%
28 22
4.4%
27 14
2.8%
26 22
4.4%
25 17
3.4%
24 24
4.8%
23 20
4.0%
22 20
4.0%

시간(HOUR)
Real number (ℝ)

ZEROS 

Distinct23
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.388
Minimum0
Maximum23
Zeros8
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-01-14T15:50:52.913071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q19
median13
Q318
95-th percentile21.05
Maximum23
Range23
Interquartile range (IQR)9

Descriptive statistics

Standard deviation5.4354894
Coefficient of variation (CV)0.40599712
Kurtosis-0.8833422
Mean13.388
Median Absolute Deviation (MAD)5
Skewness-0.16656025
Sum6694
Variance29.544545
MonotonicityNot monotonic
2024-01-14T15:50:53.098207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
20 37
 
7.4%
18 35
 
7.0%
17 34
 
6.8%
12 33
 
6.6%
10 31
 
6.2%
6 29
 
5.8%
19 28
 
5.6%
11 28
 
5.6%
7 27
 
5.4%
8 27
 
5.4%
Other values (13) 191
38.2%
ValueCountFrequency (%)
0 8
 
1.6%
2 1
 
0.2%
3 1
 
0.2%
4 6
 
1.2%
5 13
2.6%
6 29
5.8%
7 27
5.4%
8 27
5.4%
9 27
5.4%
10 31
6.2%
ValueCountFrequency (%)
23 10
 
2.0%
22 15
3.0%
21 19
3.8%
20 37
7.4%
19 28
5.6%
18 35
7.0%
17 34
6.8%
16 24
4.8%
15 23
4.6%
14 24
4.8%

분_30분단위(HALF_HOUR)
Categorical

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0
258 
30
242 

Length

Max length2
Median length1
Mean length1.484
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row30
3rd row30
4th row30
5th row30

Common Values

ValueCountFrequency (%)
0 258
51.6%
30 242
48.4%

Length

2024-01-14T15:50:53.248673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-14T15:50:53.341367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 258
51.6%
30 242
48.4%

노선ID(LINE_ID)
Real number (ℝ)

Distinct302
Distinct (%)60.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15816511
Minimum11110001
Maximum91000034
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-01-14T15:50:53.465556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11110001
5-th percentile11110020
Q111110177
median11110419
Q311110945
95-th percentile41110088
Maximum91000034
Range79890033
Interquartile range (IQR)768

Descriptive statistics

Standard deviation11621153
Coefficient of variation (CV)0.73474822
Kurtosis7.4339244
Mean15816511
Median Absolute Deviation (MAD)334
Skewness2.5475501
Sum7.9082556 × 109
Variance1.3505121 × 1014
MonotonicityNot monotonic
2024-01-14T15:50:53.652314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11410004 6
 
1.2%
11110007 5
 
1.0%
11110194 5
 
1.0%
11110019 5
 
1.0%
11110451 4
 
0.8%
11110226 4
 
0.8%
11110257 4
 
0.8%
11110212 4
 
0.8%
11110059 4
 
0.8%
11111168 4
 
0.8%
Other values (292) 455
91.0%
ValueCountFrequency (%)
11110001 1
 
0.2%
11110005 1
 
0.2%
11110007 5
1.0%
11110009 2
 
0.4%
11110011 1
 
0.2%
11110012 1
 
0.2%
11110013 2
 
0.4%
11110014 2
 
0.4%
11110015 1
 
0.2%
11110017 4
0.8%
ValueCountFrequency (%)
91000034 1
 
0.2%
91000033 1
 
0.2%
41110216 2
0.4%
41110215 2
0.4%
41110214 3
0.6%
41110205 2
0.4%
41110200 2
0.4%
41110147 2
0.4%
41110137 1
 
0.2%
41110128 1
 
0.2%

노선명(LINE_NM)
Text

Distinct311
Distinct (%)62.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2024-01-14T15:50:53.903835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length23
Mean length16.634
Min length12

Characters and Unicode

Total characters8317
Distinct characters291
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique187 ?
Unique (%)37.4%

Sample

1st row4432번(개포동~옛골)
2nd row6613(양천공영차고지~대림역)
3rd row6411번(구로동~개포동)
4th row4211(염곡동~한양대동문앞)
5th row강북09(미아사거리~수유역)
ValueCountFrequency (%)
605번(강서공영차고지~후암동 5
 
1.0%
153번(우이동~당곡사거리 5
 
1.0%
6514번(양천공영차고지~서울대 4
 
0.8%
440번(송파공영차고지~압구정동 4
 
0.8%
141번(도봉산~염곡동 4
 
0.8%
6627번(양천공영차고지~월촌중학교 4
 
0.8%
400번(염곡동~시청 4
 
0.8%
240번(중랑공영차고지~신사역사거리 4
 
0.8%
3214번(마천동~강변역 4
 
0.8%
242번(중랑공영차고지~개포시영아파트 4
 
0.8%
Other values (304) 464
91.7%
2024-01-14T15:50:54.368460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
( 503
 
6.0%
) 503
 
6.0%
~ 501
 
6.0%
번 388
 
4.7%
1 351
 
4.2%
동 346
 
4.2%
0 256
 
3.1%
2 241
 
2.9%
지 221
 
2.7%
역 220
 
2.6%
Other values (281) 4787
57.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5076
61.0%
Decimal Number 1674
 
20.1%
Open Punctuation 503
 
6.0%
Close Punctuation 503
 
6.0%
Math Symbol 501
 
6.0%
Other Punctuation 27
 
0.3%
Uppercase Letter 22
 
0.3%
Space Separator 6
 
0.1%
Dash Punctuation 3
 
< 0.1%
Lowercase Letter 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
번 388
 
7.6%
동 346
 
6.8%
지 221
 
4.4%
역 220
 
4.3%
고 180
 
3.5%
차 169
 
3.3%
영 119
 
2.3%
대 117
 
2.3%
공 113
 
2.2%
서 85
 
1.7%
Other values (257) 3118
61.4%
Decimal Number
ValueCountFrequency (%)
1 351
21.0%
0 256
15.3%
2 241
14.4%
4 169
10.1%
5 161
9.6%
3 152
9.1%
6 149
8.9%
7 147
8.8%
9 29
 
1.7%
8 19
 
1.1%
Uppercase Letter
ValueCountFrequency (%)
A 9
40.9%
B 9
40.9%
H 1
 
4.5%
L 1
 
4.5%
N 1
 
4.5%
T 1
 
4.5%
Other Punctuation
ValueCountFrequency (%)
, 17
63.0%
. 10
37.0%
Open Punctuation
ValueCountFrequency (%)
( 503
100.0%
Close Punctuation
ValueCountFrequency (%)
) 503
100.0%
Math Symbol
ValueCountFrequency (%)
~ 501
100.0%
Space Separator
ValueCountFrequency (%)
6
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5076
61.0%
Common 3217
38.7%
Latin 24
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
번 388
 
7.6%
동 346
 
6.8%
지 221
 
4.4%
역 220
 
4.3%
고 180
 
3.5%
차 169
 
3.3%
영 119
 
2.3%
대 117
 
2.3%
공 113
 
2.2%
서 85
 
1.7%
Other values (257) 3118
61.4%
Common
ValueCountFrequency (%)
( 503
15.6%
) 503
15.6%
~ 501
15.6%
1 351
10.9%
0 256
8.0%
2 241
7.5%
4 169
 
5.3%
5 161
 
5.0%
3 152
 
4.7%
6 149
 
4.6%
Other values (7) 231
7.2%
Latin
ValueCountFrequency (%)
A 9
37.5%
B 9
37.5%
e 2
 
8.3%
H 1
 
4.2%
L 1
 
4.2%
N 1
 
4.2%
T 1
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5076
61.0%
ASCII 3241
39.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
( 503
15.5%
) 503
15.5%
~ 501
15.5%
1 351
10.8%
0 256
7.9%
2 241
7.4%
4 169
 
5.2%
5 161
 
5.0%
3 152
 
4.7%
6 149
 
4.6%
Other values (14) 255
7.9%
Hangul
ValueCountFrequency (%)
번 388
 
7.6%
동 346
 
6.8%
지 221
 
4.4%
역 220
 
4.3%
고 180
 
3.5%
차 169
 
3.3%
영 119
 
2.3%
대 117
 
2.3%
공 113
 
2.2%
서 85
 
1.7%
Other values (257) 3118
61.4%

역ID(STATION_ID)
Real number (ℝ)

Distinct481
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4711448.7
Minimum7197
Maximum9614289
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-01-14T15:50:54.522778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7197
5-th percentile9238.9
Q170804.5
median8001099.5
Q39004429.5
95-th percentile9034658.5
Maximum9614289
Range9607092
Interquartile range (IQR)8933625

Descriptive statistics

Standard deviation4338847.9
Coefficient of variation (CV)0.92091586
Kurtosis-1.9628267
Mean4711448.7
Median Absolute Deviation (MAD)1034866
Skewness-0.13058131
Sum2.3557244 × 109
Variance1.8825601 × 1013
MonotonicityNot monotonic
2024-01-14T15:50:54.713624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
71613 3
 
0.6%
11173 2
 
0.4%
9035023 2
 
0.4%
9006488 2
 
0.4%
72434 2
 
0.4%
75411 2
 
0.4%
9034940 2
 
0.4%
9009184 2
 
0.4%
8502229 2
 
0.4%
8502689 2
 
0.4%
Other values (471) 479
95.8%
ValueCountFrequency (%)
7197 1
0.2%
7522 1
0.2%
7578 1
0.2%
7591 1
0.2%
7620 1
0.2%
7645 1
0.2%
7668 1
0.2%
7708 1
0.2%
7765 1
0.2%
7888 1
0.2%
ValueCountFrequency (%)
9614289 1
0.2%
9501090 1
0.2%
9135942 1
0.2%
9113872 1
0.2%
9112782 1
0.2%
9104829 1
0.2%
9036969 1
0.2%
9036952 1
0.2%
9036729 1
0.2%
9036728 1
0.2%

역명(STATION_NM)
Text

Distinct464
Distinct (%)92.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2024-01-14T15:50:54.946826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length16
Mean length8.252
Min length2

Characters and Unicode

Total characters4126
Distinct characters388
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique433 ?
Unique (%)86.6%

Sample

1st row왕십리광장.왕십리역7번출구
2nd row마장역
3rd row쟁골마을
4th row서대문자연사박물관입구
5th row동교동삼거리연희동방면
ValueCountFrequency (%)
일지아트홀 3
 
0.6%
신촌역 3
 
0.6%
도봉소방서.방학남부역 3
 
0.6%
갈월동 3
 
0.6%
떡전교사거리.동대문노인복지관 3
 
0.6%
장지역.가든파이브 2
 
0.4%
천주교 2
 
0.4%
일산3동주민센터 2
 
0.4%
연희동대우아파트 2
 
0.4%
삼성역7번출구 2
 
0.4%
Other values (455) 476
95.0%
2024-01-14T15:50:55.277530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 165
 
4.0%
동 127
 
3.1%
역 112
 
2.7%
파 99
 
2.4%
아 97
 
2.4%
교 88
 
2.1%
트 86
 
2.1%
지 84
 
2.0%
대 70
 
1.7%
학 69
 
1.7%
Other values (378) 3129
75.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3799
92.1%
Other Punctuation 165
 
4.0%
Decimal Number 113
 
2.7%
Uppercase Letter 36
 
0.9%
Close Punctuation 5
 
0.1%
Open Punctuation 5
 
0.1%
Lowercase Letter 2
 
< 0.1%
Space Separator 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 127
 
3.3%
역 112
 
2.9%
파 99
 
2.6%
아 97
 
2.6%
교 88
 
2.3%
트 86
 
2.3%
지 84
 
2.2%
대 70
 
1.8%
학 69
 
1.8%
구 65
 
1.7%
Other values (348) 2902
76.4%
Uppercase Letter
ValueCountFrequency (%)
K 6
16.7%
T 6
16.7%
S 4
11.1%
G 3
8.3%
C 3
8.3%
M 2
 
5.6%
D 2
 
5.6%
V 2
 
5.6%
L 2
 
5.6%
N 1
 
2.8%
Other values (5) 5
13.9%
Decimal Number
ValueCountFrequency (%)
1 29
25.7%
2 24
21.2%
3 17
15.0%
4 11
 
9.7%
7 9
 
8.0%
5 9
 
8.0%
0 5
 
4.4%
6 4
 
3.5%
9 4
 
3.5%
8 1
 
0.9%
Other Punctuation
ValueCountFrequency (%)
. 165
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 2
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3799
92.1%
Common 289
 
7.0%
Latin 38
 
0.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 127
 
3.3%
역 112
 
2.9%
파 99
 
2.6%
아 97
 
2.6%
교 88
 
2.3%
트 86
 
2.3%
지 84
 
2.2%
대 70
 
1.8%
학 69
 
1.8%
구 65
 
1.7%
Other values (348) 2902
76.4%
Latin
ValueCountFrequency (%)
K 6
15.8%
T 6
15.8%
S 4
10.5%
G 3
7.9%
C 3
7.9%
M 2
 
5.3%
D 2
 
5.3%
V 2
 
5.3%
L 2
 
5.3%
e 2
 
5.3%
Other values (6) 6
15.8%
Common
ValueCountFrequency (%)
. 165
57.1%
1 29
 
10.0%
2 24
 
8.3%
3 17
 
5.9%
4 11
 
3.8%
7 9
 
3.1%
5 9
 
3.1%
0 5
 
1.7%
) 5
 
1.7%
( 5
 
1.7%
Other values (4) 10
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3799
92.1%
ASCII 327
 
7.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 165
50.5%
1 29
 
8.9%
2 24
 
7.3%
3 17
 
5.2%
4 11
 
3.4%
7 9
 
2.8%
5 9
 
2.8%
K 6
 
1.8%
T 6
 
1.8%
0 5
 
1.5%
Other values (20) 46
 
14.1%
Hangul
ValueCountFrequency (%)
동 127
 
3.3%
역 112
 
2.9%
파 99
 
2.6%
아 97
 
2.6%
교 88
 
2.3%
트 86
 
2.3%
지 84
 
2.2%
대 70
 
1.8%
학 69
 
1.8%
구 65
 
1.7%
Other values (348) 2902
76.4%

승하차인원(GETON_CNT)
Real number (ℝ)

ZEROS 

Distinct30
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.044
Minimum0
Maximum49
Zeros114
Zeros (%)22.8%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-01-14T15:50:55.434994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q35
95-th percentile14.05
Maximum49
Range49
Interquartile range (IQR)4

Descriptive statistics

Standard deviation5.7571132
Coefficient of variation (CV)1.4236185
Kurtosis17.310382
Mean4.044
Median Absolute Deviation (MAD)2
Skewness3.4222878
Sum2022
Variance33.144353
MonotonicityNot monotonic
2024-01-14T15:50:55.574797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
0 114
22.8%
1 86
17.2%
2 70
14.0%
3 56
11.2%
4 32
 
6.4%
5 23
 
4.6%
6 21
 
4.2%
7 19
 
3.8%
8 12
 
2.4%
11 12
 
2.4%
Other values (20) 55
11.0%
ValueCountFrequency (%)
0 114
22.8%
1 86
17.2%
2 70
14.0%
3 56
11.2%
4 32
 
6.4%
5 23
 
4.6%
6 21
 
4.2%
7 19
 
3.8%
8 12
 
2.4%
9 10
 
2.0%
ValueCountFrequency (%)
49 1
0.2%
46 1
0.2%
38 1
0.2%
31 1
0.2%
30 1
0.2%
27 1
0.2%
25 2
0.4%
22 1
0.2%
21 2
0.4%
20 1
0.2%

하차인원(GETOFF_CNT)
Real number (ℝ)

ZEROS 

Distinct31
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.13
Minimum0
Maximum63
Zeros100
Zeros (%)20.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-01-14T15:50:55.688041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q35
95-th percentile13
Maximum63
Range63
Interquartile range (IQR)4

Descriptive statistics

Standard deviation6.1296203
Coefficient of variation (CV)1.4841696
Kurtosis28.22466
Mean4.13
Median Absolute Deviation (MAD)2
Skewness4.3319251
Sum2065
Variance37.572244
MonotonicityNot monotonic
2024-01-14T15:50:55.807003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
1 105
21.0%
0 100
20.0%
3 57
11.4%
2 50
10.0%
4 39
 
7.8%
5 28
 
5.6%
6 23
 
4.6%
7 23
 
4.6%
8 15
 
3.0%
9 11
 
2.2%
Other values (21) 49
9.8%
ValueCountFrequency (%)
0 100
20.0%
1 105
21.0%
2 50
10.0%
3 57
11.4%
4 39
 
7.8%
5 28
 
5.6%
6 23
 
4.6%
7 23
 
4.6%
8 15
 
3.0%
9 11
 
2.2%
ValueCountFrequency (%)
63 1
0.2%
49 1
0.2%
41 1
0.2%
39 1
0.2%
31 1
0.2%
30 1
0.2%
27 1
0.2%
26 1
0.2%
24 1
0.2%
23 1
0.2%

Interactions

2024-01-14T15:50:50.556152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:50:45.864795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:50:46.612204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:50:47.275838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:50:48.072330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:50:48.945357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:50:49.818356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:50:50.671518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:50:46.000866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:50:46.697439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:50:47.371027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:50:48.224473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:50:49.044707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:50:49.913334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:50:50.785756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:50:46.114535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:50:46.777584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:50:47.480760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:50:48.322660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:50:49.169065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:50:50.009240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:50:50.903160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:50:46.196887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:50:46.862742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:50:47.579874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:50:48.424343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:50:49.270214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:50:50.104355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:50:51.015872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:50:46.294750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:50:46.976051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:50:47.710184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:50:48.551765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:50:49.413238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:50:50.214558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:50:51.355722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:50:46.403940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:50:47.074054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:50:47.815463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:50:48.690520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:50:49.543976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:50:50.328035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:50:51.483261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:50:46.502074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:50:47.168736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:50:47.926521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:50:48.832661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:50:49.657583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:50:50.442230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-14T15:50:55.925322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년(YEAR)월(MONTH)일(DAY)시간(HOUR)분_30분단위(HALF_HOUR)노선ID(LINE_ID)역ID(STATION_ID)승하차인원(GETON_CNT)하차인원(GETOFF_CNT)
년(YEAR)1.0000.2690.0000.0000.0000.0000.0960.0900.052
월(MONTH)0.2691.0000.0000.1880.2000.0000.0000.0000.083
일(DAY)0.0000.0001.0000.0000.0000.0000.0000.1150.026
시간(HOUR)0.0000.1880.0001.0000.1850.0000.0000.2620.095
분_30분단위(HALF_HOUR)0.0000.2000.0000.1851.0000.0000.0000.0710.051
노선ID(LINE_ID)0.0000.0000.0000.0000.0001.0000.1640.1320.000
역ID(STATION_ID)0.0960.0000.0000.0000.0000.1641.0000.1640.000
승하차인원(GETON_CNT)0.0900.0000.1150.2620.0710.1320.1641.0000.378
하차인원(GETOFF_CNT)0.0520.0830.0260.0950.0510.0000.0000.3781.000
2024-01-14T15:50:56.047658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년(YEAR)분_30분단위(HALF_HOUR)
년(YEAR)1.0000.000
분_30분단위(HALF_HOUR)0.0001.000
2024-01-14T15:50:56.133183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
월(MONTH)일(DAY)시간(HOUR)노선ID(LINE_ID)역ID(STATION_ID)승하차인원(GETON_CNT)하차인원(GETOFF_CNT)년(YEAR)분_30분단위(HALF_HOUR)
월(MONTH)1.0000.056-0.0220.0880.0120.0520.0260.1140.152
일(DAY)0.0561.0000.051-0.047-0.0550.0170.0610.0470.053
시간(HOUR)-0.0220.0511.0000.018-0.001-0.0640.0080.0000.141
노선ID(LINE_ID)0.088-0.0470.0181.000-0.027-0.046-0.0030.0000.000
역ID(STATION_ID)0.012-0.055-0.001-0.0271.000-0.007-0.0280.0370.000
승하차인원(GETON_CNT)0.0520.017-0.064-0.046-0.0071.0000.0000.0510.070
하차인원(GETOFF_CNT)0.0260.0610.008-0.003-0.0280.0001.0000.0310.038
년(YEAR)0.1140.0470.0000.0000.0370.0510.0311.0000.000
분_30분단위(HALF_HOUR)0.1520.0530.1410.0000.0000.0700.0380.0001.000

Missing values

2024-01-14T15:50:51.655061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-14T15:50:51.869912image/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

년(YEAR)월(MONTH)일(DAY)시간(HOUR)분_30분단위(HALF_HOUR)노선ID(LINE_ID)노선명(LINE_NM)역ID(STATION_ID)역명(STATION_NM)승하차인원(GETON_CNT)하차인원(GETOFF_CNT)
0201971360411100244432번(개포동~옛골)7000190왕십리광장.왕십리역7번출구101
120196151730111104206613(양천공영차고지~대림역)9002372마장역14
220181282230111105206411번(구로동~개포동)7591쟁골마을11
320211026930111108904211(염곡동~한양대동문앞)9007898서대문자연사박물관입구96
4201832063041110050강북09(미아사거리~수유역)8000880동교동삼거리연희동방면161
52020524830111101943412번(강동공영차고지~우면동)72529금남시장앞.백범학원터01
62021418120111103852311번(중랑차고지~문정동)7668고려대역.고대앞삼거리12
7201852718011110211노원15(청백1단지아파트~덕성여자대학교)5050290강남구청역21
820209111530111100285528번(가산동~사당역)10741가주초등학교10
9201991130111102146620번(양천공용차고지~당산역)74813동대문역사문화공원171
년(YEAR)월(MONTH)일(DAY)시간(HOUR)분_30분단위(HALF_HOUR)노선ID(LINE_ID)노선명(LINE_NM)역ID(STATION_ID)역명(STATION_NM)승하차인원(GETON_CNT)하차인원(GETOFF_CNT)
49020201115103011110178용산02(한신아파트~용산경찰서)9012073효자촌177
491202162143011110446662번(외발산동~여의나루역)73874효창공원앞역24
49220194717011110249관악01(봉천역~숭실대)72403관문약국53
493201972563011110957110번(B,국민대방향,정릉~정릉)9011001인덕대학교94
49420172315011110798종로09(수성동계곡~숭례문)9036135서울지방경찰청.경복궁역31
4952021712030111109422115번(중랑공영차고지~서경대)8992한뫼도서관.명문자동차학원23
49620182162001111034801A번(서울역환승센터~서울역환승센터)9005269서라벌중학교.우이동대우아파트03
497201938213011110019성동02(마장동현대아파트~논골입구)72504서울준법지원센터.동성빌라07
49820191110630114100043426번(서울버스종점~청담동)9806구파발역2번출구14
499202161363011110619노원09(서울특별시북부기술교육원~삼창아파트)7888송파글마루도서관.버들어린이집23