Overview

Dataset statistics

Number of variables10
Number of observations10000
Missing cells5358
Missing cells (%)5.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory898.4 KiB
Average record size in memory92.0 B

Variable types

Categorical3
Text3
Numeric4

Alerts

시군명 is highly overall correlated with 정류소id and 4 other fieldsHigh correlation
관할관청 is highly overall correlated with 정류소id and 4 other fieldsHigh correlation
정류소id is highly overall correlated with 정류소번호 and 2 other fieldsHigh correlation
정류소번호 is highly overall correlated with 정류소id and 2 other fieldsHigh correlation
WGS84위도 is highly overall correlated with 시군명 and 1 other fieldsHigh correlation
WGS84경도 is highly overall correlated with 시군명 and 1 other fieldsHigh correlation
중앙차로여부 is highly imbalanced (63.5%)Imbalance
정류소영문명 has 970 (9.7%) missing valuesMissing
정류소번호 has 847 (8.5%) missing valuesMissing
위치 has 3541 (35.4%) missing valuesMissing
정류소id has unique valuesUnique

Reproduction

Analysis started2023-12-10 21:30:33.577956
Analysis finished2023-12-10 21:30:37.141656
Duration3.56 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군명
Categorical

HIGH CORRELATION 

Distinct31
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
용인시
778 
화성시
741 
고양시
 
652
파주시
 
553
평택시
 
539
Other values (26)
6737 

Length

Max length4
Median length3
Mean length3.0805
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row화성시
2nd row성남시
3rd row부천시
4th row용인시
5th row이천시

Common Values

ValueCountFrequency (%)
용인시 778
 
7.8%
화성시 741
 
7.4%
고양시 652
 
6.5%
파주시 553
 
5.5%
평택시 539
 
5.4%
남양주시 491
 
4.9%
수원시 452
 
4.5%
성남시 424
 
4.2%
양주시 415
 
4.2%
여주시 380
 
3.8%
Other values (21) 4575
45.8%

Length

2023-12-11T06:30:37.202744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
용인시 778
 
7.8%
화성시 741
 
7.4%
고양시 652
 
6.5%
파주시 553
 
5.5%
평택시 539
 
5.4%
남양주시 491
 
4.9%
수원시 452
 
4.5%
성남시 424
 
4.2%
양주시 415
 
4.2%
여주시 380
 
3.8%
Other values (21) 4575
45.8%

정류소명
Text

Distinct8355
Distinct (%)83.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T06:30:37.473925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length23
Mean length6.4184
Min length2

Characters and Unicode

Total characters64184
Distinct characters734
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6978 ?
Unique (%)69.8%

Sample

1st row송산고
2nd row스타타워.동방렌탈
3rd row원미1동행정복지센터.원미어울마당
4th row초당역
5th row장천2리
ValueCountFrequency (%)
부대앞 12
 
0.1%
느티나무 9
 
0.1%
홈플러스 9
 
0.1%
현대아파트 8
 
0.1%
쌍용아파트 8
 
0.1%
이마트 8
 
0.1%
대림아파트 8
 
0.1%
삼성아파트 7
 
0.1%
교회앞 6
 
0.1%
보건소 6
 
0.1%
Other values (8345) 9919
99.2%
2023-12-11T06:30:37.929953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
리 2756
 
4.3%
. 1780
 
2.8%
아 1354
 
2.1%
교 1345
 
2.1%
트 1291
 
2.0%
마 1283
 
2.0%
지 1252
 
2.0%
파 1210
 
1.9%
동 1168
 
1.8%
구 1087
 
1.7%
Other values (724) 49658
77.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 58025
90.4%
Decimal Number 2441
 
3.8%
Other Punctuation 1782
 
2.8%
Close Punctuation 637
 
1.0%
Open Punctuation 634
 
1.0%
Uppercase Letter 625
 
1.0%
Lowercase Letter 21
 
< 0.1%
Dash Punctuation 15
 
< 0.1%
Other Symbol 4
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 2756
 
4.7%
아 1354
 
2.3%
교 1345
 
2.3%
트 1291
 
2.2%
마 1283
 
2.2%
지 1252
 
2.2%
파 1210
 
2.1%
동 1168
 
2.0%
구 1087
 
1.9%
을 1030
 
1.8%
Other values (680) 44249
76.3%
Uppercase Letter
ValueCountFrequency (%)
C 157
25.1%
I 105
16.8%
G 71
11.4%
T 55
 
8.8%
K 53
 
8.5%
S 47
 
7.5%
L 34
 
5.4%
A 26
 
4.2%
J 23
 
3.7%
M 10
 
1.6%
Other values (13) 44
 
7.0%
Decimal Number
ValueCountFrequency (%)
1 724
29.7%
2 721
29.5%
3 341
14.0%
4 184
 
7.5%
5 133
 
5.4%
6 107
 
4.4%
7 78
 
3.2%
8 62
 
2.5%
0 51
 
2.1%
9 40
 
1.6%
Lowercase Letter
ValueCountFrequency (%)
e 14
66.7%
s 3
 
14.3%
k 2
 
9.5%
t 1
 
4.8%
n 1
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 1780
99.9%
& 2
 
0.1%
Close Punctuation
ValueCountFrequency (%)
) 637
100.0%
Open Punctuation
ValueCountFrequency (%)
( 634
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 15
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 58029
90.4%
Common 5509
 
8.6%
Latin 646
 
1.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 2756
 
4.7%
아 1354
 
2.3%
교 1345
 
2.3%
트 1291
 
2.2%
마 1283
 
2.2%
지 1252
 
2.2%
파 1210
 
2.1%
동 1168
 
2.0%
구 1087
 
1.9%
을 1030
 
1.8%
Other values (681) 44253
76.3%
Latin
ValueCountFrequency (%)
C 157
24.3%
I 105
16.3%
G 71
11.0%
T 55
 
8.5%
K 53
 
8.2%
S 47
 
7.3%
L 34
 
5.3%
A 26
 
4.0%
J 23
 
3.6%
e 14
 
2.2%
Other values (18) 61
 
9.4%
Common
ValueCountFrequency (%)
. 1780
32.3%
1 724
13.1%
2 721
13.1%
) 637
 
11.6%
( 634
 
11.5%
3 341
 
6.2%
4 184
 
3.3%
5 133
 
2.4%
6 107
 
1.9%
7 78
 
1.4%
Other values (5) 170
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 58025
90.4%
ASCII 6155
 
9.6%
None 4
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
리 2756
 
4.7%
아 1354
 
2.3%
교 1345
 
2.3%
트 1291
 
2.2%
마 1283
 
2.2%
지 1252
 
2.2%
파 1210
 
2.1%
동 1168
 
2.0%
구 1087
 
1.9%
을 1030
 
1.8%
Other values (680) 44249
76.3%
ASCII
ValueCountFrequency (%)
. 1780
28.9%
1 724
11.8%
2 721
11.7%
) 637
 
10.3%
( 634
 
10.3%
3 341
 
5.5%
4 184
 
3.0%
C 157
 
2.6%
5 133
 
2.2%
6 107
 
1.7%
Other values (33) 737
12.0%
None
ValueCountFrequency (%)
㈜ 4
100.0%

정류소영문명
Text

MISSING 

Distinct7639
Distinct (%)84.6%
Missing970
Missing (%)9.7%
Memory size156.2 KiB
2023-12-11T06:30:38.258503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length107
Median length76
Mean length20.8701
Min length2

Characters and Unicode

Total characters188457
Distinct characters74
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6529 ?
Unique (%)72.3%

Sample

1st rowStar Tower, Dongbang Rental
2nd rowWonmi 1-dong Community Service Center, Wonmi Eoulmadang
3rd rowChodang Station
4th rowJangcheon 2-ri
5th rowLuther University
ValueCountFrequency (%)
apartment 1252
 
5.0%
school 830
 
3.3%
maeul 690
 
2.8%
center 669
 
2.7%
community 414
 
1.7%
elementary 398
 
1.6%
2-ri 379
 
1.5%
1-ri 339
 
1.4%
station 332
 
1.3%
high 258
 
1.0%
Other values (5648) 19396
77.7%
2023-12-11T06:30:38.792910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
19031
 
10.1%
n 16353
 
8.7%
a 14487
 
7.7%
e 14336
 
7.6%
o 13808
 
7.3%
i 9826
 
5.2%
g 8738
 
4.6%
r 8673
 
4.6%
t 8033
 
4.3%
u 6299
 
3.3%
Other values (64) 68873
36.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 138698
73.6%
Uppercase Letter 23112
 
12.3%
Space Separator 19031
 
10.1%
Dash Punctuation 3309
 
1.8%
Decimal Number 2238
 
1.2%
Other Punctuation 1967
 
1.0%
Close Punctuation 53
 
< 0.1%
Open Punctuation 48
 
< 0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 16353
11.8%
a 14487
10.4%
e 14336
10.3%
o 13808
10.0%
i 9826
 
7.1%
g 8738
 
6.3%
r 8673
 
6.3%
t 8033
 
5.8%
u 6299
 
4.5%
l 6071
 
4.4%
Other values (16) 32074
23.1%
Uppercase Letter
ValueCountFrequency (%)
S 4074
17.6%
C 2326
10.1%
M 1925
 
8.3%
A 1749
 
7.6%
G 1695
 
7.3%
H 1622
 
7.0%
B 1227
 
5.3%
D 1100
 
4.8%
J 990
 
4.3%
P 918
 
4.0%
Other values (16) 5486
23.7%
Decimal Number
ValueCountFrequency (%)
2 681
30.4%
1 664
29.7%
3 314
14.0%
4 172
 
7.7%
5 119
 
5.3%
6 95
 
4.2%
7 64
 
2.9%
8 56
 
2.5%
0 46
 
2.1%
9 27
 
1.2%
Other Punctuation
ValueCountFrequency (%)
, 1537
78.1%
& 229
 
11.6%
' 92
 
4.7%
. 84
 
4.3%
# 15
 
0.8%
/ 6
 
0.3%
? 4
 
0.2%
Space Separator
ValueCountFrequency (%)
19031
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3309
100.0%
Close Punctuation
ValueCountFrequency (%)
) 53
100.0%
Open Punctuation
ValueCountFrequency (%)
( 48
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 161810
85.9%
Common 26647
 
14.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 16353
 
10.1%
a 14487
 
9.0%
e 14336
 
8.9%
o 13808
 
8.5%
i 9826
 
6.1%
g 8738
 
5.4%
r 8673
 
5.4%
t 8033
 
5.0%
u 6299
 
3.9%
l 6071
 
3.8%
Other values (42) 55186
34.1%
Common
ValueCountFrequency (%)
19031
71.4%
- 3309
 
12.4%
, 1537
 
5.8%
2 681
 
2.6%
1 664
 
2.5%
3 314
 
1.2%
& 229
 
0.9%
4 172
 
0.6%
5 119
 
0.4%
6 95
 
0.4%
Other values (12) 496
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 188457
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
19031
 
10.1%
n 16353
 
8.7%
a 14487
 
7.7%
e 14336
 
7.6%
o 13808
 
7.3%
i 9826
 
5.2%
g 8738
 
4.6%
r 8673
 
4.6%
t 8033
 
4.3%
u 6299
 
3.3%
Other values (64) 68873
36.5%

정류소id
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.267853 × 108
Minimum1.1500058 × 108
Maximum2.771038 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T06:30:38.946819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.1500058 × 108
5-th percentile2.0400029 × 108
Q12.170004 × 108
median2.2800218 × 108
Q32.3400024 × 108
95-th percentile2.400011 × 108
Maximum2.771038 × 108
Range1.6210322 × 108
Interquartile range (IQR)16999846

Descriptive statistics

Standard deviation15219542
Coefficient of variation (CV)0.067109914
Kurtosis3.5586994
Mean2.267853 × 108
Median Absolute Deviation (MAD)7998253.5
Skewness1.085021
Sum2.267853 × 1012
Variance2.3163446 × 1014
MonotonicityNot monotonic
2023-12-11T06:30:39.115944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
233533540 1
 
< 0.1%
231000548 1
 
< 0.1%
225000288 1
 
< 0.1%
216000667 1
 
< 0.1%
233001058 1
 
< 0.1%
224000136 1
 
< 0.1%
207000111 1
 
< 0.1%
204000013 1
 
< 0.1%
228002086 1
 
< 0.1%
208000371 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
115000585 1
< 0.1%
117000907 1
< 0.1%
166000591 1
< 0.1%
176500010 1
< 0.1%
200000004 1
< 0.1%
200000008 1
< 0.1%
200000010 1
< 0.1%
200000011 1
< 0.1%
200000012 1
< 0.1%
200000015 1
< 0.1%
ValueCountFrequency (%)
277103803 1
< 0.1%
277103790 1
< 0.1%
277103788 1
< 0.1%
277103767 1
< 0.1%
277103764 1
< 0.1%
277103761 1
< 0.1%
277103739 1
< 0.1%
277103737 1
< 0.1%
277103734 1
< 0.1%
277103731 1
< 0.1%

정류소번호
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct9153
Distinct (%)100.0%
Missing847
Missing (%)8.5%
Infinite0
Infinite (%)0.0%
Mean31458.26
Minimum1002
Maximum97362
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T06:30:39.264771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1002
5-th percentile5225.4
Q119154
median32628
Q343611
95-th percentile55523.8
Maximum97362
Range96360
Interquartile range (IQR)24457

Descriptive statistics

Standard deviation15308.428
Coefficient of variation (CV)0.48662665
Kurtosis-0.92152841
Mean31458.26
Median Absolute Deviation (MAD)12156
Skewness-0.1834493
Sum2.8793746 × 108
Variance2.3434796 × 108
MonotonicityNot monotonic
2023-12-11T06:30:39.401092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
52255 1
 
< 0.1%
10160 1
 
< 0.1%
16187 1
 
< 0.1%
15451 1
 
< 0.1%
53126 1
 
< 0.1%
7060 1
 
< 0.1%
37213 1
 
< 0.1%
33692 1
 
< 0.1%
31703 1
 
< 0.1%
18380 1
 
< 0.1%
Other values (9143) 9143
91.4%
(Missing) 847
 
8.5%
ValueCountFrequency (%)
1002 1
< 0.1%
1007 1
< 0.1%
1008 1
< 0.1%
1010 1
< 0.1%
1015 1
< 0.1%
1016 1
< 0.1%
1025 1
< 0.1%
1029 1
< 0.1%
1042 1
< 0.1%
1047 1
< 0.1%
ValueCountFrequency (%)
97362 1
< 0.1%
58349 1
< 0.1%
58344 1
< 0.1%
58343 1
< 0.1%
58342 1
< 0.1%
58341 1
< 0.1%
58340 1
< 0.1%
58338 1
< 0.1%
58334 1
< 0.1%
58329 1
< 0.1%

중앙차로여부
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
노변정류장
8701 
<NA>
1269 
중앙차로 정류장
 
30

Length

Max length8
Median length5
Mean length4.8821
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row노변정류장
3rd row노변정류장
4th row노변정류장
5th row노변정류장

Common Values

ValueCountFrequency (%)
노변정류장 8701
87.0%
<NA> 1269
 
12.7%
중앙차로 정류장 30
 
0.3%

Length

2023-12-11T06:30:39.534822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T06:30:39.662657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
노변정류장 8701
86.7%
na 1269
 
12.7%
중앙차로 30
 
0.3%
정류장 30
 
0.3%

관할관청
Categorical

HIGH CORRELATION 

Distinct34
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
969 
경기도 용인시
716 
경기도 화성시
683 
경기도 고양시
 
592
경기도 파주시
 
528
Other values (29)
6512 

Length

Max length11
Median length7
Mean length6.7807
Min length4

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row<NA>
2nd row경기도 성남시
3rd row경기도 부천시
4th row경기도 용인시
5th row경기도 이천시

Common Values

ValueCountFrequency (%)
<NA> 969
 
9.7%
경기도 용인시 716
 
7.2%
경기도 화성시 683
 
6.8%
경기도 고양시 592
 
5.9%
경기도 파주시 528
 
5.3%
경기도 평택시 492
 
4.9%
경기도 남양주시 462
 
4.6%
경기도 수원시 408
 
4.1%
경기도 양주시 396
 
4.0%
경기도 성남시 389
 
3.9%
Other values (24) 4365
43.6%

Length

2023-12-11T06:30:39.844044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기도 9031
47.5%
na 969
 
5.1%
용인시 717
 
3.8%
화성시 683
 
3.6%
고양시 598
 
3.1%
파주시 528
 
2.8%
평택시 492
 
2.6%
남양주시 462
 
2.4%
수원시 408
 
2.1%
양주시 396
 
2.1%
Other values (24) 4748
24.9%

위치
Text

MISSING 

Distinct570
Distinct (%)8.8%
Missing3541
Missing (%)35.4%
Memory size156.2 KiB
2023-12-11T06:30:40.199003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length11
Mean length12.193528
Min length10

Characters and Unicode

Total characters78758
Distinct characters227
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique54 ?
Unique (%)0.8%

Sample

1st row경기도 성남시 중원구 상대원동
2nd row경기도 용인시 기흥구 중동
3rd row경기도 이천시 설성면
4th row경기도 용인시 기흥구 상갈동
5th row경기도 동두천시 상패동
ValueCountFrequency (%)
경기도 6459
30.7%
화성시 486
 
2.3%
용인시 484
 
2.3%
평택시 472
 
2.2%
남양주시 403
 
1.9%
안성시 330
 
1.6%
이천시 318
 
1.5%
처인구 317
 
1.5%
양평군 310
 
1.5%
파주시 305
 
1.5%
Other values (585) 11130
53.0%
2023-12-11T06:30:40.692936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
14555
18.5%
도 6616
 
8.4%
기 6591
 
8.4%
경 6465
 
8.2%
시 6054
 
7.7%
동 3669
 
4.7%
면 2240
 
2.8%
구 1864
 
2.4%
양 1840
 
2.3%
성 1327
 
1.7%
Other values (217) 27537
35.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 64200
81.5%
Space Separator 14555
 
18.5%
Decimal Number 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 6616
 
10.3%
기 6591
 
10.3%
경 6465
 
10.1%
시 6054
 
9.4%
동 3669
 
5.7%
면 2240
 
3.5%
구 1864
 
2.9%
양 1840
 
2.9%
성 1327
 
2.1%
평 1230
 
1.9%
Other values (214) 26304
41.0%
Decimal Number
ValueCountFrequency (%)
1 2
66.7%
3 1
33.3%
Space Separator
ValueCountFrequency (%)
14555
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 64200
81.5%
Common 14558
 
18.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 6616
 
10.3%
기 6591
 
10.3%
경 6465
 
10.1%
시 6054
 
9.4%
동 3669
 
5.7%
면 2240
 
3.5%
구 1864
 
2.9%
양 1840
 
2.9%
성 1327
 
2.1%
평 1230
 
1.9%
Other values (214) 26304
41.0%
Common
ValueCountFrequency (%)
14555
> 99.9%
1 2
 
< 0.1%
3 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 64200
81.5%
ASCII 14558
 
18.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
14555
> 99.9%
1 2
 
< 0.1%
3 1
 
< 0.1%
Hangul
ValueCountFrequency (%)
도 6616
 
10.3%
기 6591
 
10.3%
경 6465
 
10.1%
시 6054
 
9.4%
동 3669
 
5.7%
면 2240
 
3.5%
구 1864
 
2.9%
양 1840
 
2.9%
성 1327
 
2.1%
평 1230
 
1.9%
Other values (214) 26304
41.0%

WGS84위도
Real number (ℝ)

HIGH CORRELATION 

Distinct9096
Distinct (%)91.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.455038
Minimum35.159633
Maximum38.212783
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T06:30:40.827276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.159633
5-th percentile37.019862
Q137.255767
median37.407342
Q337.678775
95-th percentile37.9037
Maximum38.212783
Range3.05315
Interquartile range (IQR)0.42300832

Descriptive statistics

Standard deviation0.27446274
Coefficient of variation (CV)0.0073277924
Kurtosis0.086328315
Mean37.455038
Median Absolute Deviation (MAD)0.21375005
Skewness0.11711074
Sum374550.38
Variance0.075329797
MonotonicityNot monotonic
2023-12-11T06:30:40.997878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.4842333 4
 
< 0.1%
37.4523 4
 
< 0.1%
37.3292667 4
 
< 0.1%
37.16025 4
 
< 0.1%
37.48835 3
 
< 0.1%
37.3476667 3
 
< 0.1%
37.8475167 3
 
< 0.1%
37.2327 3
 
< 0.1%
37.4104833 3
 
< 0.1%
37.4136 3
 
< 0.1%
Other values (9086) 9966
99.7%
ValueCountFrequency (%)
35.1596333 1
< 0.1%
35.2168333 1
< 0.1%
36.91145 1
< 0.1%
36.9183333 1
< 0.1%
36.9187833 1
< 0.1%
36.9197333 1
< 0.1%
36.9199333 1
< 0.1%
36.9208833 1
< 0.1%
36.9258167 1
< 0.1%
36.9294333 1
< 0.1%
ValueCountFrequency (%)
38.2127833 1
< 0.1%
38.2015333 1
< 0.1%
38.2013167 1
< 0.1%
38.2008167 1
< 0.1%
38.1985667 1
< 0.1%
38.18675 1
< 0.1%
38.1835333 1
< 0.1%
38.15945 1
< 0.1%
38.158 1
< 0.1%
38.1578167 1
< 0.1%

WGS84경도
Real number (ℝ)

HIGH CORRELATION 

Distinct9018
Distinct (%)90.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.07109
Minimum126.52932
Maximum127.78622
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T06:30:41.135716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.52932
5-th percentile126.72169
Q1126.86727
median127.05711
Q3127.21468
95-th percentile127.55457
Maximum127.78622
Range1.2569
Interquartile range (IQR)0.3474125

Descriptive statistics

Standard deviation0.25355214
Coefficient of variation (CV)0.0019953567
Kurtosis-0.28452163
Mean127.07109
Median Absolute Deviation (MAD)0.1761417
Skewness0.48815762
Sum1270710.9
Variance0.064288688
MonotonicityNot monotonic
2023-12-11T06:30:41.311620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.1340333 4
 
< 0.1%
127.0514 4
 
< 0.1%
127.0471333 4
 
< 0.1%
127.1092167 4
 
< 0.1%
126.76575 4
 
< 0.1%
127.0747333 4
 
< 0.1%
126.9772 4
 
< 0.1%
127.0587 4
 
< 0.1%
127.0078333 3
 
< 0.1%
126.8163833 3
 
< 0.1%
Other values (9008) 9962
99.6%
ValueCountFrequency (%)
126.5293167 1
< 0.1%
126.5312833 1
< 0.1%
126.5337 1
< 0.1%
126.5355833 1
< 0.1%
126.53565 1
< 0.1%
126.5365 1
< 0.1%
126.5429 1
< 0.1%
126.54465 1
< 0.1%
126.5459333 1
< 0.1%
126.5479167 1
< 0.1%
ValueCountFrequency (%)
127.7862167 1
< 0.1%
127.78595 1
< 0.1%
127.7800167 1
< 0.1%
127.7738667 1
< 0.1%
127.7726333 1
< 0.1%
127.7724333 1
< 0.1%
127.7722667 1
< 0.1%
127.7721333 1
< 0.1%
127.7705333 1
< 0.1%
127.77 1
< 0.1%

Interactions

2023-12-11T06:30:36.042330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:30:34.929599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:30:35.275117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:30:35.607563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:30:36.161933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:30:35.018539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:30:35.363983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:30:35.706819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:30:36.512029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:30:35.095489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:30:35.440087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:30:35.826413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:30:36.603879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:30:35.186139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:30:35.525324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:30:35.946408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T06:30:41.417555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명정류소id정류소번호중앙차로여부관할관청WGS84위도WGS84경도
시군명1.0000.8720.9420.1581.0000.9080.914
정류소id0.8721.0000.8590.0201.0000.6470.426
정류소번호0.9420.8591.0000.0800.9670.3970.555
중앙차로여부0.1580.0200.0801.0000.1580.0070.050
관할관청1.0001.0000.9670.1581.0000.9340.913
WGS84위도0.9080.6470.3970.0070.9341.0000.312
WGS84경도0.9140.4260.5550.0500.9130.3121.000
2023-12-11T06:30:41.534994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명중앙차로여부관할관청
시군명1.0000.1340.999
중앙차로여부0.1341.0000.134
관할관청0.9990.1341.000
2023-12-11T06:30:41.620212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
정류소id정류소번호WGS84위도WGS84경도시군명중앙차로여부관할관청
정류소id1.0000.7110.1420.3440.6150.0340.998
정류소번호0.7111.0000.0750.3220.7590.0570.830
WGS84위도0.1420.0751.000-0.1530.6850.0090.766
WGS84경도0.3440.322-0.1531.0000.6300.0380.630
시군명0.6150.7590.6850.6301.0000.1340.999
중앙차로여부0.0340.0570.0090.0380.1341.0000.134
관할관청0.9980.8300.7660.6300.9990.1341.000

Missing values

2023-12-11T06:30:36.736501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T06:30:36.910242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-12-11T06:30:37.054870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

시군명정류소명정류소영문명정류소id정류소번호중앙차로여부관할관청위치WGS84위도WGS84경도
33255화성시송산고<NA>233533540<NA><NA><NA><NA>37.2743126.827833
10554성남시스타타워.동방렌탈Star Tower, Dongbang Rental2050001336132노변정류장경기도 성남시경기도 성남시 중원구 상대원동37.4345127.176133
9540부천시원미1동행정복지센터.원미어울마당Wonmi 1-dong Community Service Center, Wonmi Eoulmadang21000036011310노변정류장경기도 부천시<NA>37.497383126.7859
23877용인시초당역Chodang Station22800213456003노변정류장경기도 용인시경기도 용인시 기흥구 중동37.2611127.159
26348이천시장천2리Jangcheon 2-ri23000048032935노변정류장경기도 이천시경기도 이천시 설성면37.144133127.513067
12208수원시오목초교삼거리(경유)<NA>277102800<NA><NA><NA><NA>37.2473126.967833
22325용인시루터대학교Luther University22800080029211노변정류장경기도 용인시경기도 용인시 기흥구 상갈동37.265667127.10845
8316동두천시동남아파트Dongnam Apartment21500030316413노변정류장경기도 동두천시경기도 동두천시 상패동37.916083127.05355
687가평군정미소Mill23900009544159노변정류장경기도 가평군경기도 가평군 북면37.876483127.528167
14518안산시일동도서관Ildong Library21600025917275노변정류장경기도 안산시경기도 안산시 상록구 일동37.315517126.872283
시군명정류소명정류소영문명정류소id정류소번호중앙차로여부관할관청위치WGS84위도WGS84경도
18515양평군도장1리무궁화공원Dojang 1-ri Mugunghwa Park24000022046081노변정류장경기도 양평군경기도 양평군 서종면37.6076127.376733
8447동두천시성보주택Seongbo Villa21500004816130노변정류장경기도 동두천시경기도 동두천시 상봉암동37.943783127.059567
4986구리시수산물시장입구<NA>22100036022291<NA><NA><NA>37.608467127.144483
31586하남시미사강변17단지.강일리버7단지Misagangbyeon 17-danji, Ganggil River 7-danji22700000628467노변정류장경기도 하남시경기도 하남시 망월동37.564583127.18015
19034양평군연수리연안상회Yeonsu-ri Yeoan Market24000053245159노변정류장경기도 양평군경기도 양평군 용문면37.525267127.55755
471가평군송산리(경유)<NA>277102499<NA><NA><NA><NA>37.68755127.522967
15786안성시장서리Jangseo-ri23100005834127노변정류장경기도 안성시경기도 안성시 양성면37.108127.215433
7238남양주시부영6단지.금교초등학교Booyoung Apartment 5-danji, Kumkyo Elementary School22200164149521노변정류장경기도 남양주시경기도 남양주시 지금동37.614867127.162117
14155안산시선감마을Seongam Maeul21700041618448노변정류장경기도 안산시경기도 안산시 단원구 선감동37.225417126.633867
21904용인시기흥ICGiheung IC22800166947645노변정류장경기도 용인시경기도 용인시 기흥구 고매동37.221533127.1035