Overview

Dataset statistics

Number of variables12
Number of observations4941
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory506.8 KiB
Average record size in memory105.0 B

Variable types

Categorical1
Numeric9
Text2

Dataset

Description화성시 2017년 기준 마을버스노선 현황 정보(노선번호, 업체, 기점, 종점, 경유지 등)를 제공합니다.
Author경기도 화성시
URLhttps://www.data.go.kr/data/15052047/fileData.do

Alerts

운수사ID is highly overall correlated with 노선ID and 1 other fieldsHigh correlation
노선ID is highly overall correlated with 운수사ID and 1 other fieldsHigh correlation
위도 is highly overall correlated with TM_YHigh correlation
경도 is highly overall correlated with TM_X and 1 other fieldsHigh correlation
TM_X is highly overall correlated with 경도 and 1 other fieldsHigh correlation
TM_Y is highly overall correlated with 위도High correlation
운수사명 is highly overall correlated with 운수사ID and 3 other fieldsHigh correlation
순번 has 116 (2.3%) zerosZeros
구간거리 has 143 (2.9%) zerosZeros

Reproduction

Analysis started2023-12-12 11:44:42.331999
Analysis finished2023-12-12 11:44:58.396727
Duration16.06 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

운수사명
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size38.7 KiB
(화성)화성순환여객
1425 
(화성)매봉여객
999 
(화성)화성창운여객
826 
(화성)제부마을
543 
(화성)산척마을버스
322 
Other values (5)
826 

Length

Max length10
Median length10
Mean length9.0414896
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row(화성)금오운수
2nd row(화성)금오운수
3rd row(화성)금오운수
4th row(화성)금오운수
5th row(화성)금오운수

Common Values

ValueCountFrequency (%)
(화성)화성순환여객 1425
28.8%
(화성)매봉여객 999
20.2%
(화성)화성창운여객 826
16.7%
(화성)제부마을 543
 
11.0%
(화성)산척마을버스 322
 
6.5%
(화성)명승교통 257
 
5.2%
(화성)부광운수 228
 
4.6%
(화성)금오운수 223
 
4.5%
(화성)소망교통 96
 
1.9%
(화성)청진교통 22
 
0.4%

Length

2023-12-12T20:44:58.581113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:44:59.181912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
화성)화성순환여객 1425
28.8%
화성)매봉여객 999
20.2%
화성)화성창운여객 826
16.7%
화성)제부마을 543
 
11.0%
화성)산척마을버스 322
 
6.5%
화성)명승교통 257
 
5.2%
화성)부광운수 228
 
4.6%
화성)금오운수 223
 
4.5%
화성)소망교통 96
 
1.9%
화성)청진교통 22
 
0.4%

운수사ID
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4137952.8
Minimum4131700
Maximum4149100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size43.6 KiB
2023-12-12T20:44:59.408750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4131700
5-th percentile4131700
Q14131900
median4133500
Q34148300
95-th percentile4148800
Maximum4149100
Range17400
Interquartile range (IQR)16400

Descriptive statistics

Standard deviation7664.1913
Coefficient of variation (CV)0.0018521698
Kurtosis-1.5568618
Mean4137952.8
Median Absolute Deviation (MAD)1600
Skewness0.64613378
Sum2.0445625 × 1010
Variance58739829
MonotonicityNot monotonic
2023-12-12T20:44:59.605602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
4131900 1425
28.8%
4133500 999
20.2%
4148500 826
16.7%
4148300 543
 
11.0%
4131700 322
 
6.5%
4132300 257
 
5.2%
4149100 228
 
4.6%
4132400 223
 
4.5%
4148800 96
 
1.9%
4131800 22
 
0.4%
ValueCountFrequency (%)
4131700 322
 
6.5%
4131800 22
 
0.4%
4131900 1425
28.8%
4132300 257
 
5.2%
4132400 223
 
4.5%
4133500 999
20.2%
4148300 543
 
11.0%
4148500 826
16.7%
4148800 96
 
1.9%
4149100 228
 
4.6%
ValueCountFrequency (%)
4149100 228
 
4.6%
4148800 96
 
1.9%
4148500 826
16.7%
4148300 543
 
11.0%
4133500 999
20.2%
4132400 223
 
4.5%
4132300 257
 
5.2%
4131900 1425
28.8%
4131800 22
 
0.4%
4131700 322
 
6.5%

노선ID
Real number (ℝ)

HIGH CORRELATION 

Distinct116
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41379543
Minimum41317001
Maximum41491007
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size43.6 KiB
2023-12-12T20:44:59.845683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum41317001
5-th percentile41317005
Q141319027
median41335005
Q341483010
95-th percentile41488002
Maximum41491007
Range174006
Interquartile range (IQR)163983

Descriptive statistics

Standard deviation76642.482
Coefficient of variation (CV)0.0018521829
Kurtosis-1.5568673
Mean41379543
Median Absolute Deviation (MAD)15995
Skewness0.64613818
Sum2.0445632 × 1011
Variance5.87407 × 109
MonotonicityNot monotonic
2023-12-12T20:45:00.080851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41483001 120
 
2.4%
41317005 106
 
2.1%
41335024 105
 
2.1%
41485038 95
 
1.9%
41319032 93
 
1.9%
41335001 86
 
1.7%
41485006 83
 
1.7%
41485040 81
 
1.6%
41485018 79
 
1.6%
41483004 72
 
1.5%
Other values (106) 4021
81.4%
ValueCountFrequency (%)
41317001 55
1.1%
41317002 45
0.9%
41317003 32
 
0.6%
41317004 47
1.0%
41317005 106
2.1%
41317006 37
 
0.7%
41318001 22
 
0.4%
41319002 33
 
0.7%
41319003 36
 
0.7%
41319004 36
 
0.7%
ValueCountFrequency (%)
41491007 63
1.3%
41491006 49
1.0%
41491005 24
 
0.5%
41491004 57
1.2%
41491002 35
0.7%
41488002 43
0.9%
41488001 53
1.1%
41485041 52
1.1%
41485040 81
1.6%
41485039 47
1.0%

노선번호
Text

Distinct103
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size38.7 KiB
2023-12-12T20:45:00.506450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length5.1849828
Min length1

Characters and Unicode

Total characters25619
Distinct characters69
Distinct categories7 ?
Distinct scripts3 ?
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 row17
2nd row17
3rd row17
4th row17
5th row17
ValueCountFrequency (%)
20-3(당성왕모대 149
 
3.0%
12 146
 
3.0%
50-1 144
 
2.9%
50-9a 136
 
2.8%
5-1 120
 
2.4%
50-8 106
 
2.1%
50-3 106
 
2.1%
9b 106
 
2.1%
22-4 105
 
2.1%
37 95
 
1.9%
Other values (93) 3728
75.5%
2023-12-12T20:45:01.089775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 2871
 
11.2%
1 2388
 
9.3%
2 2169
 
8.5%
0 1700
 
6.6%
3 1691
 
6.6%
( 1392
 
5.4%
) 1392
 
5.4%
5 1346
 
5.3%
공 854
 
3.3%
영 833
 
3.3%
Other values (59) 8983
35.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11401
44.5%
Other Letter 7306
28.5%
Dash Punctuation 2871
 
11.2%
Open Punctuation 1392
 
5.4%
Close Punctuation 1392
 
5.4%
Uppercase Letter 684
 
2.7%
Other Punctuation 573
 
2.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
공 854
 
11.7%
영 833
 
11.4%
대 301
 
4.1%
리 281
 
3.8%
지 272
 
3.7%
화 248
 
3.4%
포 237
 
3.2%
고 230
 
3.1%
궁 211
 
2.9%
평 211
 
2.9%
Other values (43) 3628
49.7%
Decimal Number
ValueCountFrequency (%)
1 2388
20.9%
2 2169
19.0%
0 1700
14.9%
3 1691
14.8%
5 1346
11.8%
9 623
 
5.5%
7 520
 
4.6%
4 439
 
3.9%
6 278
 
2.4%
8 247
 
2.2%
Uppercase Letter
ValueCountFrequency (%)
A 479
70.0%
B 205
30.0%
Dash Punctuation
ValueCountFrequency (%)
- 2871
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1392
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1392
100.0%
Other Punctuation
ValueCountFrequency (%)
. 573
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 17629
68.8%
Hangul 7306
28.5%
Latin 684
 
2.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
공 854
 
11.7%
영 833
 
11.4%
대 301
 
4.1%
리 281
 
3.8%
지 272
 
3.7%
화 248
 
3.4%
포 237
 
3.2%
고 230
 
3.1%
궁 211
 
2.9%
평 211
 
2.9%
Other values (43) 3628
49.7%
Common
ValueCountFrequency (%)
- 2871
16.3%
1 2388
13.5%
2 2169
12.3%
0 1700
9.6%
3 1691
9.6%
( 1392
7.9%
) 1392
7.9%
5 1346
7.6%
9 623
 
3.5%
. 573
 
3.3%
Other values (4) 1484
8.4%
Latin
ValueCountFrequency (%)
A 479
70.0%
B 205
30.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 18313
71.5%
Hangul 7306
 
28.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 2871
15.7%
1 2388
13.0%
2 2169
11.8%
0 1700
9.3%
3 1691
9.2%
( 1392
7.6%
) 1392
7.6%
5 1346
7.3%
9 623
 
3.4%
. 573
 
3.1%
Other values (6) 2168
11.8%
Hangul
ValueCountFrequency (%)
공 854
 
11.7%
영 833
 
11.4%
대 301
 
4.1%
리 281
 
3.8%
지 272
 
3.7%
화 248
 
3.4%
포 237
 
3.2%
고 230
 
3.1%
궁 211
 
2.9%
평 211
 
2.9%
Other values (43) 3628
49.7%

순번
Real number (ℝ)

ZEROS 

Distinct120
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.990488
Minimum0
Maximum119
Zeros116
Zeros (%)2.3%
Negative0
Negative (%)0.0%
Memory size43.6 KiB
2023-12-12T20:45:01.300184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q110
median21
Q337
95-th percentile68
Maximum119
Range119
Interquartile range (IQR)27

Descriptive statistics

Standard deviation20.877697
Coefficient of variation (CV)0.80328225
Kurtosis1.5766086
Mean25.990488
Median Absolute Deviation (MAD)13
Skewness1.2286314
Sum128419
Variance435.87825
MonotonicityNot monotonic
2023-12-12T20:45:01.499240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 116
 
2.3%
2 116
 
2.3%
3 116
 
2.3%
4 116
 
2.3%
5 116
 
2.3%
6 116
 
2.3%
7 116
 
2.3%
8 116
 
2.3%
9 116
 
2.3%
10 116
 
2.3%
Other values (110) 3781
76.5%
ValueCountFrequency (%)
0 116
2.3%
1 116
2.3%
2 116
2.3%
3 116
2.3%
4 116
2.3%
5 116
2.3%
6 116
2.3%
7 116
2.3%
8 116
2.3%
9 116
2.3%
ValueCountFrequency (%)
119 1
< 0.1%
118 1
< 0.1%
117 1
< 0.1%
116 1
< 0.1%
115 1
< 0.1%
114 1
< 0.1%
113 1
< 0.1%
112 1
< 0.1%
111 1
< 0.1%
110 1
< 0.1%

정류소ID
Real number (ℝ)

Distinct1576
Distinct (%)31.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4142761.2
Minimum4100022
Maximum7100619
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size43.6 KiB
2023-12-12T20:45:01.713835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4100022
5-th percentile4102870
Q14116690
median4120182
Q34170415
95-th percentile4198803
Maximum7100619
Range3000597
Interquartile range (IQR)53725

Descriptive statistics

Standard deviation142874.56
Coefficient of variation (CV)0.034487761
Kurtosis406.52802
Mean4142761.2
Median Absolute Deviation (MAD)12276
Skewness19.766369
Sum2.0469383 × 1010
Variance2.0413139 × 1010
MonotonicityNot monotonic
2023-12-12T20:45:01.908127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4102870 45
 
0.9%
4119839 41
 
0.8%
4119840 41
 
0.8%
4102867 36
 
0.7%
4102868 35
 
0.7%
4102866 35
 
0.7%
4102893 26
 
0.5%
4130189 25
 
0.5%
4130397 25
 
0.5%
4170959 25
 
0.5%
Other values (1566) 4607
93.2%
ValueCountFrequency (%)
4100022 1
 
< 0.1%
4100024 4
 
0.1%
4100027 1
 
< 0.1%
4100028 4
 
0.1%
4100029 1
 
< 0.1%
4100048 18
0.4%
4100049 9
0.2%
4100050 3
 
0.1%
4100051 9
0.2%
4100234 1
 
< 0.1%
ValueCountFrequency (%)
7100619 2
 
< 0.1%
7100618 3
0.1%
7100617 3
0.1%
7100616 3
0.1%
4199954 5
0.1%
4199953 5
0.1%
4199952 1
 
< 0.1%
4199937 3
0.1%
4199936 1
 
< 0.1%
4199933 1
 
< 0.1%

정류소명
Text

Distinct963
Distinct (%)19.5%
Missing0
Missing (%)0.0%
Memory size38.7 KiB
2023-12-12T20:45:02.242573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length16
Mean length6.116778
Min length2

Characters and Unicode

Total characters30223
Distinct characters412
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique163 ?
Unique (%)3.3%

Sample

1st row병점역사거리
2nd row홈플러스.벌말초교
3rd row한신아파트
4th row구봉산근린공원
5th row성호아파트후문
ValueCountFrequency (%)
사강종점 82
 
1.7%
사강복지회관 71
 
1.4%
사강정형외과 55
 
1.1%
남양중고교입구 50
 
1.0%
송산중학교 48
 
1.0%
화성시청 46
 
0.9%
공군부대입구 46
 
0.9%
기업은행 44
 
0.9%
남양성지 44
 
0.9%
남양뉴타운 41
 
0.8%
Other values (953) 4414
89.3%
2023-12-12T20:45:02.779220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
리 1613
 
5.3%
. 910
 
3.0%
트 626
 
2.1%
교 621
 
2.1%
아 598
 
2.0%
지 591
 
2.0%
구 563
 
1.9%
1 563
 
1.9%
마 545
 
1.8%
사 524
 
1.7%
Other values (402) 23069
76.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 27742
91.8%
Decimal Number 1358
 
4.5%
Other Punctuation 910
 
3.0%
Open Punctuation 77
 
0.3%
Close Punctuation 77
 
0.3%
Uppercase Letter 58
 
0.2%
Lowercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 1613
 
5.8%
트 626
 
2.3%
교 621
 
2.2%
아 598
 
2.2%
지 591
 
2.1%
구 563
 
2.0%
마 545
 
2.0%
사 524
 
1.9%
파 523
 
1.9%
남 479
 
1.7%
Other values (379) 21059
75.9%
Decimal Number
ValueCountFrequency (%)
1 563
41.5%
2 384
28.3%
3 171
 
12.6%
4 89
 
6.6%
6 61
 
4.5%
5 42
 
3.1%
8 18
 
1.3%
0 12
 
0.9%
9 10
 
0.7%
7 8
 
0.6%
Uppercase Letter
ValueCountFrequency (%)
A 12
20.7%
K 9
15.5%
S 9
15.5%
M 8
13.8%
D 8
13.8%
G 6
10.3%
T 2
 
3.4%
I 2
 
3.4%
L 2
 
3.4%
Other Punctuation
ValueCountFrequency (%)
. 910
100.0%
Open Punctuation
ValueCountFrequency (%)
( 77
100.0%
Close Punctuation
ValueCountFrequency (%)
) 77
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 27742
91.8%
Common 2422
 
8.0%
Latin 59
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 1613
 
5.8%
트 626
 
2.3%
교 621
 
2.2%
아 598
 
2.2%
지 591
 
2.1%
구 563
 
2.0%
마 545
 
2.0%
사 524
 
1.9%
파 523
 
1.9%
남 479
 
1.7%
Other values (379) 21059
75.9%
Common
ValueCountFrequency (%)
. 910
37.6%
1 563
23.2%
2 384
15.9%
3 171
 
7.1%
4 89
 
3.7%
( 77
 
3.2%
) 77
 
3.2%
6 61
 
2.5%
5 42
 
1.7%
8 18
 
0.7%
Other values (3) 30
 
1.2%
Latin
ValueCountFrequency (%)
A 12
20.3%
K 9
15.3%
S 9
15.3%
M 8
13.6%
D 8
13.6%
G 6
10.2%
T 2
 
3.4%
I 2
 
3.4%
L 2
 
3.4%
e 1
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 27742
91.8%
ASCII 2481
 
8.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
리 1613
 
5.8%
트 626
 
2.3%
교 621
 
2.2%
아 598
 
2.2%
지 591
 
2.1%
구 563
 
2.0%
마 545
 
2.0%
사 524
 
1.9%
파 523
 
1.9%
남 479
 
1.7%
Other values (379) 21059
75.9%
ASCII
ValueCountFrequency (%)
. 910
36.7%
1 563
22.7%
2 384
15.5%
3 171
 
6.9%
4 89
 
3.6%
( 77
 
3.1%
) 77
 
3.1%
6 61
 
2.5%
5 42
 
1.7%
8 18
 
0.7%
Other values (13) 89
 
3.6%

구간거리
Real number (ℝ)

ZEROS 

Distinct915
Distinct (%)18.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean533.77596
Minimum0
Maximum4800
Zeros143
Zeros (%)2.9%
Negative0
Negative (%)0.0%
Memory size43.6 KiB
2023-12-12T20:45:02.967920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile145
Q1279
median421
Q3634
95-th percentile1361
Maximum4800
Range4800
Interquartile range (IQR)355

Descriptive statistics

Standard deviation428.07933
Coefficient of variation (CV)0.80198317
Kurtosis16.651626
Mean533.77596
Median Absolute Deviation (MAD)162
Skewness3.0848641
Sum2637387
Variance183251.92
MonotonicityNot monotonic
2023-12-12T20:45:03.179314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 143
 
2.9%
538 51
 
1.0%
215 41
 
0.8%
247 35
 
0.7%
252 34
 
0.7%
355 34
 
0.7%
412 31
 
0.6%
340 30
 
0.6%
403 30
 
0.6%
358 30
 
0.6%
Other values (905) 4482
90.7%
ValueCountFrequency (%)
0 143
2.9%
35 1
 
< 0.1%
44 1
 
< 0.1%
75 1
 
< 0.1%
79 1
 
< 0.1%
84 1
 
< 0.1%
85 1
 
< 0.1%
87 3
 
0.1%
89 4
 
0.1%
93 2
 
< 0.1%
ValueCountFrequency (%)
4800 1
< 0.1%
4527 2
< 0.1%
4386 2
< 0.1%
4111 1
< 0.1%
3992 1
< 0.1%
3954 1
< 0.1%
3763 1
< 0.1%
3656 1
< 0.1%
3651 1
< 0.1%
3340 2
< 0.1%

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct1404
Distinct (%)28.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3711.6205
Minimum3703.947
Maximum3717.065
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size43.6 KiB
2023-12-12T20:45:03.397415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3703.947
5-th percentile3707.454
Q13710.439
median3712.257
Q33712.888
95-th percentile3714.689
Maximum3717.065
Range13.118
Interquartile range (IQR)2.449

Descriptive statistics

Standard deviation2.2314317
Coefficient of variation (CV)0.00060120147
Kurtosis0.54743153
Mean3711.6205
Median Absolute Deviation (MAD)1.081
Skewness-0.85418352
Sum18339117
Variance4.9792874
MonotonicityNot monotonic
2023-12-12T20:45:03.631207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3712.803 45
 
0.9%
3712.842 42
 
0.9%
3712.845 41
 
0.8%
3712.781 40
 
0.8%
3712.772 36
 
0.7%
3712.762 35
 
0.7%
3712.415 32
 
0.6%
3712.457 31
 
0.6%
3712.49 27
 
0.5%
3712.198 27
 
0.5%
Other values (1394) 4585
92.8%
ValueCountFrequency (%)
3703.947 3
0.1%
3704.006 3
0.1%
3704.281 3
0.1%
3704.291 3
0.1%
3704.523 3
0.1%
3704.584 3
0.1%
3704.749 3
0.1%
3704.756 3
0.1%
3704.914 2
< 0.1%
3704.929 2
< 0.1%
ValueCountFrequency (%)
3717.065 1
< 0.1%
3717.059 1
< 0.1%
3717.052 1
< 0.1%
3716.915 1
< 0.1%
3716.856 1
< 0.1%
3716.772 1
< 0.1%
3716.724 1
< 0.1%
3716.679 1
< 0.1%
3716.593 1
< 0.1%
3716.526 1
< 0.1%

경도
Real number (ℝ)

HIGH CORRELATION 

Distinct1514
Distinct (%)30.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12660.168
Minimum12637.081
Maximum12707.456
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size43.6 KiB
2023-12-12T20:45:03.837917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum12637.081
5-th percentile12641.431
Q112645.015
median12650.692
Q312658.307
95-th percentile12704.495
Maximum12707.456
Range70.375
Interquartile range (IQR)13.292

Descriptive statistics

Standard deviation22.476961
Coefficient of variation (CV)0.0017754077
Kurtosis-0.027173638
Mean12660.168
Median Absolute Deviation (MAD)6.428
Skewness1.303426
Sum62553892
Variance505.21377
MonotonicityNot monotonic
2023-12-12T20:45:04.052418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12644.339 45
 
0.9%
12644.264 41
 
0.8%
12644.254 41
 
0.8%
12643.958 36
 
0.7%
12644.198 35
 
0.7%
12643.959 35
 
0.7%
12642.05 27
 
0.5%
12648.964 26
 
0.5%
12649.329 25
 
0.5%
12649.131 25
 
0.5%
Other values (1504) 4605
93.2%
ValueCountFrequency (%)
12637.081 3
0.1%
12637.126 3
0.1%
12637.147 3
0.1%
12637.23 3
0.1%
12637.321 3
0.1%
12637.335 2
< 0.1%
12637.455 1
 
< 0.1%
12637.514 3
0.1%
12637.545 3
0.1%
12637.607 3
0.1%
ValueCountFrequency (%)
12707.456 2
 
< 0.1%
12707.121 6
0.1%
12707.115 2
 
< 0.1%
12707.07 2
 
< 0.1%
12707.056 2
 
< 0.1%
12707.011 6
0.1%
12706.958 4
0.1%
12706.912 4
0.1%
12706.88 4
0.1%
12706.874 1
 
< 0.1%

TM_X
Real number (ℝ)

HIGH CORRELATION 

Distinct1573
Distinct (%)31.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean188178.25
Minimum166007.84
Maximum210968.38
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size43.6 KiB
2023-12-12T20:45:04.268339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum166007.84
5-th percentile172451.04
Q1177756.54
median186157.25
Q3197427.43
95-th percentile206582.38
Maximum210968.38
Range44960.54
Interquartile range (IQR)19670.89

Descriptive statistics

Standard deviation11395.711
Coefficient of variation (CV)0.060558068
Kurtosis-1.0957245
Mean188178.25
Median Absolute Deviation (MAD)9503.7888
Skewness0.23487384
Sum9.2978872 × 108
Variance1.2986223 × 108
MonotonicityNot monotonic
2023-12-12T20:45:04.467447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
176764.602575079 45
 
0.9%
176653.690338362 41
 
0.8%
176638.158595705 41
 
0.8%
176199.829728872 36
 
0.7%
176555.259977707 35
 
0.7%
176201.570374505 35
 
0.7%
173381.902120259 27
 
0.5%
183604.421205717 26
 
0.5%
183851.783118182 25
 
0.5%
184123.921745625 25
 
0.5%
Other values (1563) 4605
93.2%
ValueCountFrequency (%)
166007.840403453 3
0.1%
166072.778492043 3
0.1%
166102.86149068 3
0.1%
166224.451777073 3
0.1%
166364.999890697 3
0.1%
166379.997696308 2
< 0.1%
166563.710441782 1
 
< 0.1%
166645.828914834 3
0.1%
166696.197975764 3
0.1%
166783.756195592 3
0.1%
ValueCountFrequency (%)
210968.380586751 2
 
< 0.1%
210469.317243427 6
0.1%
210465.0 2
 
< 0.1%
210398.0 2
 
< 0.1%
210376.044533123 2
 
< 0.1%
210306.321974997 6
0.1%
210228.666943819 4
0.1%
210160.49122739 4
0.1%
210112.74575547 4
0.1%
210106.96535591 1
 
< 0.1%

TM_Y
Real number (ℝ)

HIGH CORRELATION 

Distinct1575
Distinct (%)31.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean410220.24
Minimum396013.23
Maximum420278.33
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size43.6 KiB
2023-12-12T20:45:04.675333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum396013.23
5-th percentile402499.84
Q1408060.35
median411392.44
Q3412549.73
95-th percentile415882.28
Maximum420278.33
Range24265.104
Interquartile range (IQR)4489.3847

Descriptive statistics

Standard deviation4129.6204
Coefficient of variation (CV)0.010066837
Kurtosis0.54925177
Mean410220.24
Median Absolute Deviation (MAD)1983.4417
Skewness-0.85543446
Sum2.0268982 × 109
Variance17053764
MonotonicityNot monotonic
2023-12-12T20:45:04.886316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
412424.461768662 45
 
0.9%
412496.742061015 41
 
0.8%
412501.909466114 41
 
0.8%
412367.46700146 36
 
0.7%
412384.036952213 35
 
0.7%
412350.568233439 35
 
0.7%
411768.40296813 26
 
0.5%
411827.919506876 25
 
0.5%
411392.44167859 25
 
0.5%
411414.209085003 25
 
0.5%
Other values (1565) 4607
93.2%
ValueCountFrequency (%)
396013.228314851 3
0.1%
396121.0 3
0.1%
396631.379769205 3
0.1%
396650.09627215 3
0.1%
397080.586593172 3
0.1%
397192.379842228 3
0.1%
397497.306635204 3
0.1%
397510.577308233 3
0.1%
397815.382964122 2
< 0.1%
397843.965599719 2
< 0.1%
ValueCountFrequency (%)
420278.332450489 1
< 0.1%
420268.83175963 1
< 0.1%
420256.359379394 1
< 0.1%
420000.992284797 1
< 0.1%
419893.755099124 1
< 0.1%
419738.252681011 1
< 0.1%
419649.739014815 1
< 0.1%
419567.202732246 1
< 0.1%
419419.637291248 1
< 0.1%
419283.894459751 1
< 0.1%

Interactions

2023-12-12T20:44:56.954991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:45.779303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:47.416981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:48.596919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:49.805532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:51.525811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:52.682784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:53.899876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:55.549801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:57.089378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:46.005866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:47.530745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:48.755977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:49.995062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:51.667184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:52.810782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:54.085539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:55.719051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:57.196087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:46.203631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:47.647521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:48.908703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:50.521350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:51.797494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:52.943960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:54.207194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:55.901708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:57.307945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:46.395686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:47.782673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:49.030090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:50.656637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:51.941771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:53.108820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:54.341591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:56.061228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:57.405501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:46.593756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:47.932675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:49.167104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:50.811590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:52.076869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:53.243052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:54.497295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:56.243255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:57.499288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:46.782964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:48.071099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:49.286567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:50.945807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:52.189509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:53.370902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:54.670567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:56.383641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:57.605314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:46.961427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:48.214592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:49.398884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:51.092578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:52.325459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:53.481188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:54.877581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:56.536404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:57.754880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:47.126931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:48.346034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:49.534688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:51.266289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:52.455195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:53.626411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:55.127015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:56.713568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:57.876085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:47.285866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:48.457006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:49.652729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:51.388872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:52.563867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:53.747794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:55.360128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:44:56.821016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T20:45:05.044451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
운수사명운수사ID노선ID순번정류소ID구간거리위도경도TM_XTM_Y
운수사명1.0001.0001.0000.3040.1260.2750.6850.8290.9310.686
운수사ID1.0001.0001.0000.2020.0370.1170.5200.8160.7610.521
노선ID1.0001.0001.0000.1670.0370.1100.5060.8260.7670.506
순번0.3040.2020.1671.0000.0120.1550.3740.1890.3580.374
정류소ID0.1260.0370.0370.0121.0000.0610.6270.1110.1400.627
구간거리0.2750.1170.1100.1550.0611.0000.2920.2110.3230.290
위도0.6850.5200.5060.3740.6270.2921.0000.5450.7141.000
경도0.8290.8160.8260.1890.1110.2110.5451.0000.9230.543
TM_X0.9310.7610.7670.3580.1400.3230.7140.9231.0000.712
TM_Y0.6860.5210.5060.3740.6270.2901.0000.5430.7121.000
2023-12-12T20:45:05.218478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
운수사ID노선ID순번정류소ID구간거리위도경도TM_XTM_Y운수사명
운수사ID1.0000.9800.0940.0340.005-0.2930.2440.244-0.2950.999
노선ID0.9801.0000.0860.0310.008-0.2810.2620.261-0.2830.999
순번0.0940.0861.0000.0430.016-0.1620.0790.079-0.1630.098
정류소ID0.0340.0310.0431.0000.012-0.2910.1360.136-0.2920.096
구간거리0.0050.0080.0160.0121.000-0.048-0.147-0.147-0.0470.088
위도-0.293-0.281-0.162-0.291-0.0481.000-0.064-0.0641.0000.273
경도0.2440.2620.0790.136-0.147-0.0641.0001.000-0.0690.629
TM_X0.2440.2610.0790.136-0.147-0.0641.0001.000-0.0680.568
TM_Y-0.295-0.283-0.163-0.292-0.0471.000-0.069-0.0681.0000.273
운수사명0.9990.9990.0980.0960.0880.2730.6290.5680.2731.000

Missing values

2023-12-12T20:44:58.069993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T20:44:58.306147image/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

운수사명운수사ID노선ID노선번호순번정류소ID정류소명구간거리위도경도TM_XTM_Y
0(화성)금오운수4132400413240011704151651병점역사거리03712.41512702.138203096.052468411674.092435
1(화성)금오운수4132400413240011714100049홈플러스.벌말초교4633712.19812702.288203318.700559411273.47837
2(화성)금오운수4132400413240011724170352한신아파트4343712.19912702.521203662.781501411274.464307
3(화성)금오운수4132400413240011734199076구봉산근린공원2673712.21712702.653203857.465936411307.455167
4(화성)금오운수4132400413240011744119339성호아파트후문3173712.05112702.672203886.428843411000.371638
5(화성)금오운수4132400413240011754170867우남아파트4343711.93912702.871204181.218366410793.611244
6(화성)금오운수4132400413240011764197436신일해피트리아파트5643712.01812703.198204664.359789410941.360699
7(화성)금오운수4132400413240011774170525두산신일아파트2123712.08512703.262204759.592999411065.272819
8(화성)금오운수4132400413240011784170526모아포스코아파트2993712.0912703.464205058.685442411073.535007
9(화성)금오운수4132400413240011794199432동탄고등학교3893712.22812703.568205212.494419411328.756497
운수사명운수사ID노선ID노선번호순번정류소ID정류소명구간거리위도경도TM_XTM_Y
4931(화성)화성창운여객41485004148504113-1424199622우미린아파트.향일고4793707.7912655.06192615.764158403122.814869
4932(화성)화성창운여객41485004148504113-1434199623신영지웰2차아파트1683707.78312655.173192784.1057403109.006259
4933(화성)화성창운여객41485004148504113-1444199624화성소방서.향남홈플러스2213707.8312655.28192942.776184403196.023376
4934(화성)화성창운여객41485004148504113-1454130350향남읍사무소3103707.90712655.145192742.561839403337.692758
4935(화성)화성창운여객41485004148504113-1464130354화성중앙병원.발안초등학교6773707.87112654.69192068.948955403271.693937
4936(화성)화성창운여객41485004148504113-1474199372바다마트7293708.13512654.654192016.050884403760.35762
4937(화성)화성창운여객41485004148504113-1484170637제로마트4013707.96312654.516191810.699778403441.984542
4938(화성)화성창운여객41485004148504113-1494170540화성시보건소앞5493707.74412654.296191484.179064403038.702287
4939(화성)화성창운여객41485004148504113-1504117352우림아파트앞2813707.67112654.169191295.740678402903.842813
4940(화성)화성창운여객41485004148504113-1514116230우림주공아파트3163707.78712654.11191208.690663403117.381528