Overview

Dataset statistics

Number of variables16
Number of observations2638
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory350.5 KiB
Average record size in memory136.1 B

Variable types

Categorical5
Numeric6
Text5

Dataset

Description호선,상/하행선,요일,전철역코드,외부코드,전철역명,첫차시간,첫차출발역코드,첫차도착역코드,첫차출발역명,첫차도착역명,막차시간,막차출발역코드,막차도착역코드,막차출발역명,막차도착역명
Author서울교통공사
URLhttps://data.seoul.go.kr/dataList/OA-15492/S/1/datasetView.do

Alerts

막차출발역명 is highly overall correlated with 전철역코드 and 7 other fieldsHigh correlation
호선 is highly overall correlated with 전철역코드 and 6 other fieldsHigh correlation
상/하행선 is highly overall correlated with 첫차도착역명 and 1 other fieldsHigh correlation
첫차도착역명 is highly overall correlated with 전철역코드 and 7 other fieldsHigh correlation
전철역코드 is highly overall correlated with 첫차출발역코드 and 6 other fieldsHigh correlation
첫차출발역코드 is highly overall correlated with 전철역코드 and 6 other fieldsHigh correlation
첫차도착역코드 is highly overall correlated with 전철역코드 and 6 other fieldsHigh correlation
막차출발역코드 is highly overall correlated with 전철역코드 and 6 other fieldsHigh correlation
막차도착역코드 is highly overall correlated with 전철역코드 and 6 other fieldsHigh correlation

Reproduction

Analysis started2024-05-11 08:32:31.886167
Analysis finished2024-05-11 08:32:37.005140
Duration5.12 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

호선
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size20.7 KiB
01호선
600 
05호선
327 
07호선
312 
02호선
304 
04호선
300 
Other values (4)
795 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row08호선
2nd row05호선
3rd row01호선
4th row09호선
5th row01호선

Common Values

ValueCountFrequency (%)
01호선 600
22.7%
05호선 327
12.4%
07호선 312
11.8%
02호선 304
11.5%
04호선 300
11.4%
03호선 258
9.8%
09호선 222
 
8.4%
06호선 213
 
8.1%
08호선 102
 
3.9%

Length

2024-05-11T17:32:37.059471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T17:32:37.165711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
01호선 600
22.7%
05호선 327
12.4%
07호선 312
11.8%
02호선 304
11.5%
04호선 300
11.4%
03호선 258
9.8%
09호선 222
 
8.4%
06호선 213
 
8.1%
08호선 102
 
3.9%

상/하행선
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size20.7 KiB
2
1321 
1
1317 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row1
4th row1
5th row2

Common Values

ValueCountFrequency (%)
2 1321
50.1%
1 1317
49.9%

Length

2024-05-11T17:32:37.286225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T17:32:37.369175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 1321
50.1%
1 1317
49.9%

요일
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size20.7 KiB
1
880 
2
879 
3
879 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row3
4th row1
5th row3

Common Values

ValueCountFrequency (%)
1 880
33.4%
2 879
33.3%
3 879
33.3%

Length

2024-05-11T17:32:37.454227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T17:32:37.553357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 880
33.4%
2 879
33.3%
3 879
33.3%

전철역코드
Real number (ℝ)

HIGH CORRELATION 

Distinct452
Distinct (%)17.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1868.8082
Minimum150
Maximum4138
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2024-05-11T17:32:37.657091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum150
5-th percentile212
Q1422
median1905
Q32645
95-th percentile4116
Maximum4138
Range3988
Interquartile range (IQR)2223

Descriptive statistics

Standard deviation1204.0194
Coefficient of variation (CV)0.64427123
Kurtosis-0.91564554
Mean1868.8082
Median Absolute Deviation (MAD)830
Skewness0.091248178
Sum4929916
Variance1449662.6
MonotonicityNot monotonic
2024-05-11T17:32:37.799507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1701 9
 
0.3%
234 9
 
0.3%
211 7
 
0.3%
2824 6
 
0.2%
233 6
 
0.2%
3759 6
 
0.2%
2637 6
 
0.2%
2512 6
 
0.2%
1006 6
 
0.2%
1725 6
 
0.2%
Other values (442) 2571
97.5%
ValueCountFrequency (%)
150 6
0.2%
151 6
0.2%
152 6
0.2%
153 6
0.2%
154 6
0.2%
155 6
0.2%
156 6
0.2%
157 6
0.2%
158 6
0.2%
159 6
0.2%
ValueCountFrequency (%)
4138 3
0.1%
4137 6
0.2%
4136 6
0.2%
4135 6
0.2%
4134 6
0.2%
4133 6
0.2%
4132 6
0.2%
4131 6
0.2%
4130 6
0.2%
4129 6
0.2%

외부코드
Text

Distinct452
Distinct (%)17.1%
Missing0
Missing (%)0.0%
Memory size20.7 KiB
2024-05-11T17:32:38.146842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length3.1432904
Min length3

Characters and Unicode

Total characters8292
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
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 row824
2nd row514
3rd rowP162
4th row901
5th rowP158
ValueCountFrequency (%)
141 9
 
0.3%
234 9
 
0.3%
211 7
 
0.3%
745 6
 
0.2%
824 6
 
0.2%
233 6
 
0.2%
757 6
 
0.2%
636 6
 
0.2%
511 6
 
0.2%
139 6
 
0.2%
Other values (442) 2571
97.5%
2024-05-11T17:32:38.597986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 1457
17.6%
2 1117
13.5%
3 1035
12.5%
4 1029
12.4%
5 903
10.9%
7 600
7.2%
6 552
 
6.7%
0 483
 
5.8%
9 462
 
5.6%
8 339
 
4.1%
Other values (2) 315
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7977
96.2%
Uppercase Letter 252
 
3.0%
Dash Punctuation 63
 
0.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1457
18.3%
2 1117
14.0%
3 1035
13.0%
4 1029
12.9%
5 903
11.3%
7 600
7.5%
6 552
 
6.9%
0 483
 
6.1%
9 462
 
5.8%
8 339
 
4.2%
Uppercase Letter
ValueCountFrequency (%)
P 252
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 63
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 8040
97.0%
Latin 252
 
3.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1457
18.1%
2 1117
13.9%
3 1035
12.9%
4 1029
12.8%
5 903
11.2%
7 600
7.5%
6 552
 
6.9%
0 483
 
6.0%
9 462
 
5.7%
8 339
 
4.2%
Latin
ValueCountFrequency (%)
P 252
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8292
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1457
17.6%
2 1117
13.5%
3 1035
12.5%
4 1029
12.4%
5 903
10.9%
7 600
7.2%
6 552
 
6.7%
0 483
 
5.8%
9 462
 
5.6%
8 339
 
4.1%
Other values (2) 315
 
3.8%

전철역명
Text

Distinct399
Distinct (%)15.1%
Missing0
Missing (%)0.0%
Memory size20.7 KiB
2024-05-11T17:32:38.867098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length2
Mean length2.8119788
Min length2

Characters and Unicode

Total characters7418
Distinct characters251
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

Unique0 ?
Unique (%)0.0%

Sample

1st row단대오거리
2nd row마곡
3rd row송탄
4th row개화
5th row세마
ValueCountFrequency (%)
고속터미널 18
 
0.7%
종로3가 18
 
0.7%
동대문역사문화공원 18
 
0.7%
신도림 15
 
0.6%
군자 12
 
0.5%
약수 12
 
0.5%
종합운동장 12
 
0.5%
삼각지 12
 
0.5%
영등포구청 12
 
0.5%
여의도 12
 
0.5%
Other values (389) 2497
94.7%
2024-05-11T17:32:39.286850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
대 249
 
3.4%
산 219
 
3.0%
동 210
 
2.8%
구 198
 
2.7%
신 195
 
2.6%
정 144
 
1.9%
천 135
 
1.8%
원 129
 
1.7%
지 114
 
1.5%
청 114
 
1.5%
Other values (241) 5711
77.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 7370
99.4%
Decimal Number 48
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
대 249
 
3.4%
산 219
 
3.0%
동 210
 
2.8%
구 198
 
2.7%
신 195
 
2.6%
정 144
 
2.0%
천 135
 
1.8%
원 129
 
1.8%
지 114
 
1.5%
청 114
 
1.5%
Other values (238) 5663
76.8%
Decimal Number
ValueCountFrequency (%)
3 30
62.5%
4 12
 
25.0%
5 6
 
12.5%

Most occurring scripts

ValueCountFrequency (%)
Hangul 7370
99.4%
Common 48
 
0.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
대 249
 
3.4%
산 219
 
3.0%
동 210
 
2.8%
구 198
 
2.7%
신 195
 
2.6%
정 144
 
2.0%
천 135
 
1.8%
원 129
 
1.8%
지 114
 
1.5%
청 114
 
1.5%
Other values (238) 5663
76.8%
Common
ValueCountFrequency (%)
3 30
62.5%
4 12
 
25.0%
5 6
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 7370
99.4%
ASCII 48
 
0.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
대 249
 
3.4%
산 219
 
3.0%
동 210
 
2.8%
구 198
 
2.7%
신 195
 
2.6%
정 144
 
2.0%
천 135
 
1.8%
원 129
 
1.8%
지 114
 
1.5%
청 114
 
1.5%
Other values (238) 5663
76.8%
ASCII
ValueCountFrequency (%)
3 30
62.5%
4 12
 
25.0%
5 6
 
12.5%

첫차시간
Real number (ℝ)

Distinct257
Distinct (%)9.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53620.637
Minimum20530
Maximum63000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2024-05-11T17:32:39.633277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20530
5-th percentile51000
Q153000
median53620
Q354150
95-th percentile55530
Maximum63000
Range42470
Interquartile range (IQR)1150

Descriptive statistics

Standard deviation1905.9342
Coefficient of variation (CV)0.035544789
Kurtosis73.69912
Mean53620.637
Median Absolute Deviation (MAD)585
Skewness-3.0478311
Sum1.4145124 × 108
Variance3632585.2
MonotonicityNot monotonic
2024-05-11T17:32:39.746713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
53000 172
 
6.5%
53200 69
 
2.6%
53700 54
 
2.0%
53900 51
 
1.9%
53400 46
 
1.7%
53500 45
 
1.7%
53530 42
 
1.6%
54100 38
 
1.4%
53600 37
 
1.4%
54300 36
 
1.4%
Other values (247) 2048
77.6%
ValueCountFrequency (%)
20530 2
 
0.1%
40530 2
 
0.1%
50000 22
0.8%
50100 3
 
0.1%
50200 3
 
0.1%
50230 9
0.3%
50300 5
 
0.2%
50400 4
 
0.2%
50430 8
 
0.3%
50500 12
0.5%
ValueCountFrequency (%)
63000 3
0.1%
61400 2
 
0.1%
61130 2
 
0.1%
61100 3
0.1%
60930 2
 
0.1%
60900 1
 
< 0.1%
60830 2
 
0.1%
60800 5
0.2%
60700 3
0.1%
60630 3
0.1%

첫차출발역코드
Real number (ℝ)

HIGH CORRELATION 

Distinct87
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1864.0387
Minimum150
Maximum4138
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2024-05-11T17:32:39.855462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum150
5-th percentile211
Q1426
median1812
Q32643
95-th percentile4115
Maximum4138
Range3988
Interquartile range (IQR)2217

Descriptive statistics

Standard deviation1179.9233
Coefficient of variation (CV)0.63299294
Kurtosis-0.8586886
Mean1864.0387
Median Absolute Deviation (MAD)907
Skewness0.099311065
Sum4917334
Variance1392219
MonotonicityNot monotonic
2024-05-11T17:32:39.995807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1701 170
 
6.4%
1019 108
 
4.1%
1716 102
 
3.9%
211 61
 
2.3%
2752 54
 
2.0%
2719 54
 
2.0%
2640 51
 
1.9%
433 51
 
1.9%
219 51
 
1.9%
2545 51
 
1.9%
Other values (77) 1885
71.5%
ValueCountFrequency (%)
150 18
 
0.7%
200 12
 
0.5%
202 45
1.7%
211 61
2.3%
219 51
1.9%
228 45
1.7%
234 45
1.7%
239 33
1.3%
246 12
 
0.5%
310 18
 
0.7%
ValueCountFrequency (%)
4138 21
0.8%
4131 39
1.5%
4125 33
1.3%
4120 36
1.4%
4115 15
 
0.6%
4113 21
0.8%
4110 12
 
0.5%
4107 18
0.7%
4105 12
 
0.5%
4102 6
 
0.2%

첫차도착역코드
Real number (ℝ)

HIGH CORRELATION 

Distinct35
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1970.3685
Minimum158
Maximum4138
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2024-05-11T17:32:40.123723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum158
5-th percentile211
Q11019
median1919
Q32649
95-th percentile4101
Maximum4138
Range3980
Interquartile range (IQR)1630

Descriptive statistics

Standard deviation1206.8372
Coefficient of variation (CV)0.61249317
Kurtosis-0.86861111
Mean1970.3685
Median Absolute Deviation (MAD)792
Skewness0.028899987
Sum5197832
Variance1456456.1
MonotonicityNot monotonic
2024-05-11T17:32:40.253008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
211 268
 
10.2%
1812 174
 
6.6%
2511 165
 
6.3%
3763 156
 
5.9%
342 129
 
4.9%
1958 124
 
4.7%
1762 123
 
4.7%
2711 120
 
4.5%
2566 111
 
4.2%
4101 111
 
4.2%
Other values (25) 1157
43.9%
ValueCountFrequency (%)
158 40
 
1.5%
200 12
 
0.5%
211 268
10.2%
234 12
 
0.5%
246 12
 
0.5%
310 5
 
0.2%
342 129
4.9%
405 47
 
1.8%
409 103
 
3.9%
433 27
 
1.0%
ValueCountFrequency (%)
4138 111
4.2%
4101 111
4.2%
3763 156
5.9%
2827 51
 
1.9%
2811 51
 
1.9%
2712 36
 
1.4%
2711 120
4.5%
2649 105
4.0%
2648 9
 
0.3%
2611 99
3.8%

첫차출발역명
Text

Distinct85
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size20.7 KiB
2024-05-11T17:32:40.514783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length2
Mean length2.6038666
Min length2

Characters and Unicode

Total characters6869
Distinct characters123
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row가락시장
2nd row여의도
3rd row천안
4th row개화
5th row병점
ValueCountFrequency (%)
구로 170
 
6.4%
광운대 108
 
4.1%
병점 102
 
3.9%
서울역 69
 
2.6%
여의도 63
 
2.4%
성수 61
 
2.3%
온수 54
 
2.0%
태릉입구 54
 
2.0%
안암 51
 
1.9%
사당 51
 
1.9%
Other values (75) 1855
70.3%
2024-05-11T17:32:40.924902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
구 491
 
7.1%
대 327
 
4.8%
입 255
 
3.7%
로 215
 
3.1%
수 175
 
2.5%
도 171
 
2.5%
산 168
 
2.4%
신 159
 
2.3%
성 154
 
2.2%
서 132
 
1.9%
Other values (113) 4622
67.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 6869
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 491
 
7.1%
대 327
 
4.8%
입 255
 
3.7%
로 215
 
3.1%
수 175
 
2.5%
도 171
 
2.5%
산 168
 
2.4%
신 159
 
2.3%
성 154
 
2.2%
서 132
 
1.9%
Other values (113) 4622
67.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 6869
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 491
 
7.1%
대 327
 
4.8%
입 255
 
3.7%
로 215
 
3.1%
수 175
 
2.5%
도 171
 
2.5%
산 168
 
2.4%
신 159
 
2.3%
성 154
 
2.2%
서 132
 
1.9%
Other values (113) 4622
67.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 6869
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
구 491
 
7.1%
대 327
 
4.8%
입 255
 
3.7%
로 215
 
3.1%
수 175
 
2.5%
도 171
 
2.5%
산 168
 
2.4%
신 159
 
2.3%
성 154
 
2.2%
서 132
 
1.9%
Other values (113) 4622
67.3%

첫차도착역명
Categorical

HIGH CORRELATION 

Distinct35
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size20.7 KiB
성수
268 
인천
 
174
방화
 
165
석남
 
156
오금
 
129
Other values (30)
1746 

Length

Max length6
Median length2
Mean length2.4992418
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row모란
2nd row방화
3rd row청량리
4th row중앙보훈병원
5th row신창

Common Values

ValueCountFrequency (%)
성수 268
 
10.2%
인천 174
 
6.6%
방화 165
 
6.3%
석남 156
 
5.9%
오금 129
 
4.9%
대화 124
 
4.7%
오이도 123
 
4.7%
장암 120
 
4.5%
중앙보훈병원 111
 
4.2%
하남검단산 111
 
4.2%
Other values (25) 1157
43.9%

Length

2024-05-11T17:32:41.052113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
성수 268
 
10.2%
인천 174
 
6.6%
방화 165
 
6.3%
석남 156
 
5.9%
오금 129
 
4.9%
대화 124
 
4.7%
오이도 123
 
4.7%
장암 120
 
4.5%
개화 111
 
4.2%
중앙보훈병원 111
 
4.2%
Other values (25) 1157
43.9%

막차시간
Text

Distinct454
Distinct (%)17.2%
Missing0
Missing (%)0.0%
Memory size20.7 KiB
2024-05-11T17:32:41.352707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length5.9981046
Min length1

Characters and Unicode

Total characters15823
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

Unique72 ?
Unique (%)2.7%

Sample

1st row245800
2nd row240210
3rd row240330
4th row244300
5th row233400
ValueCountFrequency (%)
235830 46
 
1.7%
235630 37
 
1.4%
235430 33
 
1.3%
235800 32
 
1.2%
235200 32
 
1.2%
235530 32
 
1.2%
234800 28
 
1.1%
235700 28
 
1.1%
235000 26
 
1.0%
235600 25
 
0.9%
Other values (444) 2319
87.9%
2024-05-11T17:32:41.780485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 4205
26.6%
2 3216
20.3%
3 2665
16.8%
4 2369
15.0%
5 1658
 
10.5%
1 612
 
3.9%
8 303
 
1.9%
6 275
 
1.7%
7 267
 
1.7%
9 252
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15822
> 99.9%
Modifier Symbol 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4205
26.6%
2 3216
20.3%
3 2665
16.8%
4 2369
15.0%
5 1658
 
10.5%
1 612
 
3.9%
8 303
 
1.9%
6 275
 
1.7%
7 267
 
1.7%
9 252
 
1.6%
Modifier Symbol
ValueCountFrequency (%)
` 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 15823
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4205
26.6%
2 3216
20.3%
3 2665
16.8%
4 2369
15.0%
5 1658
 
10.5%
1 612
 
3.9%
8 303
 
1.9%
6 275
 
1.7%
7 267
 
1.7%
9 252
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15823
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4205
26.6%
2 3216
20.3%
3 2665
16.8%
4 2369
15.0%
5 1658
 
10.5%
1 612
 
3.9%
8 303
 
1.9%
6 275
 
1.7%
7 267
 
1.7%
9 252
 
1.6%

막차출발역코드
Real number (ℝ)

HIGH CORRELATION 

Distinct36
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1964.6539
Minimum150
Maximum4138
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2024-05-11T17:32:41.924115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum150
5-th percentile211
Q1433
median1919
Q32649
95-th percentile4101
Maximum4138
Range3988
Interquartile range (IQR)2216

Descriptive statistics

Standard deviation1207.7375
Coefficient of variation (CV)0.61473294
Kurtosis-0.86835645
Mean1964.6539
Median Absolute Deviation (MAD)792
Skewness0.025827344
Sum5182757
Variance1458629.8
MonotonicityNot monotonic
2024-05-11T17:32:42.077846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
211 269
 
10.2%
1812 179
 
6.8%
2511 162
 
6.1%
3763 150
 
5.7%
342 129
 
4.9%
1762 127
 
4.8%
1958 121
 
4.6%
2611 114
 
4.3%
2711 112
 
4.2%
4138 111
 
4.2%
Other values (26) 1164
44.1%
ValueCountFrequency (%)
150 6
 
0.2%
158 43
 
1.6%
200 12
 
0.5%
211 269
10.2%
234 11
 
0.4%
246 12
 
0.5%
310 8
 
0.3%
342 129
4.9%
405 67
 
2.5%
409 83
 
3.1%
ValueCountFrequency (%)
4138 111
4.2%
4125 1
 
< 0.1%
4101 110
4.2%
3763 150
5.7%
2827 51
 
1.9%
2811 51
 
1.9%
2752 6
 
0.2%
2712 44
 
1.7%
2711 112
4.2%
2649 45
 
1.7%

막차도착역코드
Real number (ℝ)

HIGH CORRELATION 

Distinct85
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1858.3673
Minimum150
Maximum4138
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2024-05-11T17:32:42.246037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum150
5-th percentile211
Q1426
median1812
Q32648
95-th percentile4115
Maximum4138
Range3988
Interquartile range (IQR)2222

Descriptive statistics

Standard deviation1186.119
Coefficient of variation (CV)0.63825863
Kurtosis-0.87215909
Mean1858.3673
Median Absolute Deviation (MAD)907
Skewness0.094583772
Sum4902373
Variance1406878.2
MonotonicityNot monotonic
2024-05-11T17:32:42.371001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1701 163
 
6.2%
1019 99
 
3.8%
2531 69
 
2.6%
1716 66
 
2.5%
2729 66
 
2.5%
1812 63
 
2.4%
211 63
 
2.4%
2737 57
 
2.2%
2752 54
 
2.0%
1728 54
 
2.0%
Other values (75) 1884
71.4%
ValueCountFrequency (%)
150 26
1.0%
158 3
 
0.1%
159 14
 
0.5%
200 11
 
0.4%
202 42
1.6%
211 63
2.4%
219 51
1.9%
228 45
1.7%
234 45
1.7%
239 34
1.3%
ValueCountFrequency (%)
4138 34
1.3%
4131 27
1.0%
4125 29
1.1%
4120 40
1.5%
4115 15
 
0.6%
4113 18
0.7%
4110 15
 
0.6%
4107 17
0.6%
4105 4
 
0.2%
4102 8
 
0.3%

막차출발역명
Categorical

HIGH CORRELATION 

Distinct36
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size20.7 KiB
성수
269 
인천
179 
방화
 
162
석남
 
150
오금
 
129
Other values (31)
1749 

Length

Max length6
Median length2
Mean length2.509856
Min length2

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row암사
2nd row하남검단산
3rd row신창
4th row개화
5th row광운대

Common Values

ValueCountFrequency (%)
성수 269
 
10.2%
인천 179
 
6.8%
방화 162
 
6.1%
석남 150
 
5.7%
오금 129
 
4.9%
오이도 127
 
4.8%
대화 121
 
4.6%
응암 114
 
4.3%
장암 112
 
4.2%
중앙보훈병원 111
 
4.2%
Other values (26) 1164
44.1%

Length

2024-05-11T17:32:42.508861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
성수 269
 
10.2%
인천 179
 
6.8%
방화 162
 
6.1%
석남 150
 
5.7%
오금 129
 
4.9%
오이도 127
 
4.8%
대화 121
 
4.6%
응암 114
 
4.3%
장암 112
 
4.2%
중앙보훈병원 111
 
4.2%
Other values (26) 1164
44.1%

막차도착역명
Text

Distinct83
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size20.7 KiB
2024-05-11T17:32:42.734237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length2
Mean length2.6376042
Min length2

Characters and Unicode

Total characters6958
Distinct characters123
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row모란
2nd row방화
3rd row병점
4th row가양
5th row천안
ValueCountFrequency (%)
구로 163
 
6.2%
광운대 99
 
3.8%
서울역 77
 
2.9%
애오개 69
 
2.6%
건대입구 66
 
2.5%
병점 66
 
2.5%
성수 63
 
2.4%
인천 63
 
2.4%
내방 57
 
2.2%
천안 54
 
2.0%
Other values (73) 1861
70.5%
2024-05-11T17:32:43.075724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
구 502
 
7.2%
대 331
 
4.8%
입 269
 
3.9%
로 205
 
2.9%
산 182
 
2.6%
수 173
 
2.5%
성 166
 
2.4%
서 155
 
2.2%
화 149
 
2.1%
천 144
 
2.1%
Other values (113) 4682
67.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 6958
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 502
 
7.2%
대 331
 
4.8%
입 269
 
3.9%
로 205
 
2.9%
산 182
 
2.6%
수 173
 
2.5%
성 166
 
2.4%
서 155
 
2.2%
화 149
 
2.1%
천 144
 
2.1%
Other values (113) 4682
67.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 6958
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 502
 
7.2%
대 331
 
4.8%
입 269
 
3.9%
로 205
 
2.9%
산 182
 
2.6%
수 173
 
2.5%
성 166
 
2.4%
서 155
 
2.2%
화 149
 
2.1%
천 144
 
2.1%
Other values (113) 4682
67.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 6958
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
구 502
 
7.2%
대 331
 
4.8%
입 269
 
3.9%
로 205
 
2.9%
산 182
 
2.6%
수 173
 
2.5%
성 166
 
2.4%
서 155
 
2.2%
화 149
 
2.1%
천 144
 
2.1%
Other values (113) 4682
67.3%

Interactions

2024-05-11T17:32:36.107182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T17:32:33.072683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T17:32:33.857038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T17:32:34.370744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T17:32:34.982487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T17:32:35.530438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T17:32:36.225029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T17:32:33.172627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T17:32:33.949472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T17:32:34.493505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T17:32:35.073390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T17:32:35.624389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T17:32:36.325411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T17:32:33.259498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T17:32:34.023687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T17:32:34.589345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T17:32:35.150223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T17:32:35.710498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T17:32:36.426860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T17:32:33.366503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T17:32:34.116378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T17:32:34.704634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T17:32:35.251391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T17:32:35.807086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T17:32:36.528399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T17:32:33.459105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T17:32:34.198508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T17:32:34.797422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T17:32:35.344288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T17:32:35.905665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T17:32:36.623309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T17:32:33.775115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T17:32:34.281303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T17:32:34.896046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T17:32:35.432250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T17:32:36.004028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T17:32:43.165591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
호선상/하행선요일전철역코드첫차시간첫차출발역코드첫차도착역코드첫차출발역명첫차도착역명막차출발역코드막차도착역코드막차출발역명막차도착역명
호선1.0000.0000.0000.8550.3940.8750.8510.9991.0000.8400.8701.0000.999
상/하행선0.0001.0000.0000.0000.0720.0830.3770.7940.9770.3410.1290.9960.805
요일0.0000.0001.0000.0000.0000.0000.0000.0000.0470.0000.0000.2050.000
전철역코드0.8550.0000.0001.0000.2920.9870.9460.9830.9760.9410.9790.9540.979
첫차시간0.3940.0720.0000.2921.0000.2720.2180.6430.6300.2710.2770.6380.737
첫차출발역코드0.8750.0830.0000.9870.2721.0000.9340.9990.9700.9440.9710.9640.987
첫차도착역코드0.8510.3770.0000.9460.2180.9341.0000.9781.0000.9290.9530.9960.978
첫차출발역명0.9990.7940.0000.9830.6430.9990.9781.0000.9950.9790.9860.9920.996
첫차도착역명1.0000.9770.0470.9760.6300.9701.0000.9951.0000.9980.9800.9910.992
막차출발역코드0.8400.3410.0000.9410.2710.9440.9290.9790.9981.0000.9161.0000.979
막차도착역코드0.8700.1290.0000.9790.2770.9710.9530.9860.9800.9161.0000.9721.000
막차출발역명1.0000.9960.2050.9540.6380.9640.9960.9920.9911.0000.9721.0000.994
막차도착역명0.9990.8050.0000.9790.7370.9870.9780.9960.9920.9791.0000.9941.000
2024-05-11T17:32:43.294737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
막차출발역명호선상/하행선첫차도착역명요일
막차출발역명1.0000.9950.9410.7890.095
호선0.9951.0000.0000.9950.000
상/하행선0.9410.0001.0000.9410.000
첫차도착역명0.7890.9950.9411.0000.023
요일0.0950.0000.0000.0231.000
2024-05-11T17:32:43.404602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
전철역코드첫차시간첫차출발역코드첫차도착역코드막차출발역코드막차도착역코드호선상/하행선요일첫차도착역명막차출발역명
전철역코드1.0000.0860.9690.9010.9130.9650.6810.0000.0000.7890.776
첫차시간0.0861.0000.0480.0900.0770.1080.2140.0910.0000.3030.318
첫차출발역코드0.9690.0481.0000.8980.9120.9420.7150.0920.0000.8360.801
첫차도착역코드0.9010.0900.8981.0000.8740.9080.6630.4010.0000.9950.945
막차출발역코드0.9130.0770.9120.8741.0000.9020.6550.3640.0000.9290.994
막차도착역코드0.9650.1080.9420.9080.9021.0000.7040.1370.0000.8000.833
호선0.6810.2140.7150.6630.6550.7041.0000.0000.0000.9950.995
상/하행선0.0000.0910.0920.4010.3640.1370.0001.0000.0000.9410.941
요일0.0000.0000.0000.0000.0000.0000.0000.0001.0000.0230.095
첫차도착역명0.7890.3030.8360.9950.9290.8000.9950.9410.0231.0000.789
막차출발역명0.7760.3180.8010.9450.9940.8330.9950.9410.0950.7891.000

Missing values

2024-05-11T17:32:36.754580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T17:32:36.936540image/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

호선상/하행선요일전철역코드외부코드전철역명첫차시간첫차출발역코드첫차도착역코드첫차출발역명첫차도착역명막차시간막차출발역코드막차도착역코드막차출발역명막차도착역명
008호선212824824단대오거리5395028182827가락시장모란24580028112827암사모란
105호선122515514마곡5523025272511여의도방화24021025662511하남검단산방화
201호선131721P162송탄534001728158천안청량리24033014081716신창병점
309호선114101901개화5300041014138개화중앙보훈병원24430041014107개화가양
401호선231717P158세마5030017161408병점신창23340010191728광운대천안
506호선112617616새절5461026262611대흥응암24384026492611신내응암
604호선22405405진접53230405433진접사당233430405409진접당고개
701호선121018120석계5483017011915구로동두천24280018121019인천광운대
801호선22150133서울역520001501749서울역서동탄24073019161701소요산구로
901호선111916100소요산5570010191919광운대연천24140018121919인천연천
호선상/하행선요일전철역코드외부코드전철역명첫차시간첫차출발역코드첫차도착역코드첫차출발역명첫차도착역명막차시간막차출발역코드막차도착역코드막차출발역명막차도착역명
262804호선12409409당고개53100409405당고개진접234430433405사당진접
262909호선134120920동작5313041204138동작중앙보훈병원23454541014125개화신논현
263006호선222646645태릉입구5405026402649안암신내24031026112648응암봉화산
263102호선23232232구로디지털단지53430234211신도림성수235230211228성수서울대입구
263204호선23421421동대문541304191762한성대입구오이도235200405426진접서울역
263303호선21326336압구정53000326342압구정오금2443001958334대화도곡
263401호선221724P165평택5290017161408병점신창24000010191728광운대천안
263501호선121005137대방5090017011915구로동두천2401301812150인천서울역
263607호선122728726어린이대공원5315027292711건대입구장암23515037632719석남태릉입구
263703호선22341351경찰병원60130326342압구정오금2359301958342대화오금