Overview

Dataset statistics

Number of variables8
Number of observations6389
Missing cells1
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory443.1 KiB
Average record size in memory71.0 B

Variable types

Text1
Numeric7

Dataset

Description인천광역시 시내버스 정류장별 이용승객 현황으로정류소명, 정류소아이디, 승차건수, 하차건수 등을 제공합니다.
Author인천광역시
URLhttps://www.data.go.kr/data/15048264/fileData.do

Alerts

총승차건수 is highly overall correlated with 총하차건수 and 4 other fieldsHigh correlation
총하차건수 is highly overall correlated with 총승차건수 and 4 other fieldsHigh correlation
승차건수(카드) is highly overall correlated with 총승차건수 and 4 other fieldsHigh correlation
하차건수(카드) is highly overall correlated with 총승차건수 and 4 other fieldsHigh correlation
승차건수(현금) is highly overall correlated with 총승차건수 and 4 other fieldsHigh correlation
일평균승하차건수 is highly overall correlated with 총승차건수 and 4 other fieldsHigh correlation
총승차건수 has 171 (2.7%) zerosZeros
총하차건수 has 175 (2.7%) zerosZeros
승차건수(카드) has 188 (2.9%) zerosZeros
하차건수(카드) has 175 (2.7%) zerosZeros
승차건수(현금) has 1315 (20.6%) zerosZeros
일평균승하차건수 has 584 (9.1%) zerosZeros

Reproduction

Analysis started2024-04-06 08:41:51.868263
Analysis finished2024-04-06 08:42:13.933490
Duration22.07 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

정류소명
Text

Distinct3729
Distinct (%)58.4%
Missing0
Missing (%)0.0%
Memory size50.0 KiB
2024-04-06T17:42:14.597042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length18
Mean length6.6922836
Min length2

Characters and Unicode

Total characters42757
Distinct characters625
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

Unique1532 ?
Unique (%)24.0%

Sample

1st row(구)국제여객터미널
2nd row(구)국제여객터미널
3rd row(주)경동세라믹스
4th row(주)경인양행앞
5th row(주)경인양행앞
ValueCountFrequency (%)
대동아파트 12
 
0.2%
현대아파트 12
 
0.2%
한국아파트 11
 
0.2%
부대앞 11
 
0.2%
입구 10
 
0.2%
쌍용아파트 9
 
0.1%
광명아파트 9
 
0.1%
신동아아파트 8
 
0.1%
풍림아파트 8
 
0.1%
뉴서울아파트 8
 
0.1%
Other values (3729) 6328
98.5%
2024-04-06T17:42:15.974057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
아 1125
 
2.6%
트 1068
 
2.5%
파 957
 
2.2%
교 877
 
2.1%
리 866
 
2.0%
동 832
 
1.9%
지 801
 
1.9%
구 796
 
1.9%
학 679
 
1.6%
) 525
 
1.2%
Other values (615) 34231
80.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 39721
92.9%
Decimal Number 1236
 
2.9%
Close Punctuation 525
 
1.2%
Open Punctuation 525
 
1.2%
Other Punctuation 338
 
0.8%
Uppercase Letter 333
 
0.8%
Space Separator 37
 
0.1%
Lowercase Letter 37
 
0.1%
Dash Punctuation 5
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
아 1125
 
2.8%
트 1068
 
2.7%
파 957
 
2.4%
교 877
 
2.2%
리 866
 
2.2%
동 832
 
2.1%
지 801
 
2.0%
구 796
 
2.0%
학 679
 
1.7%
장 523
 
1.3%
Other values (566) 31197
78.5%
Uppercase Letter
ValueCountFrequency (%)
K 42
12.6%
S 40
12.0%
T 37
11.1%
C 36
10.8%
G 31
9.3%
L 28
8.4%
H 22
6.6%
I 19
5.7%
A 16
 
4.8%
B 15
 
4.5%
Other values (12) 47
14.1%
Decimal Number
ValueCountFrequency (%)
1 374
30.3%
2 287
23.2%
3 167
13.5%
0 96
 
7.8%
4 92
 
7.4%
5 71
 
5.7%
6 50
 
4.0%
7 45
 
3.6%
8 27
 
2.2%
9 27
 
2.2%
Lowercase Letter
ValueCountFrequency (%)
e 24
64.9%
s 4
 
10.8%
k 2
 
5.4%
t 1
 
2.7%
n 1
 
2.7%
f 1
 
2.7%
i 1
 
2.7%
g 1
 
2.7%
m 1
 
2.7%
y 1
 
2.7%
Other Punctuation
ValueCountFrequency (%)
. 282
83.4%
· 55
 
16.3%
, 1
 
0.3%
Close Punctuation
ValueCountFrequency (%)
) 525
100.0%
Open Punctuation
ValueCountFrequency (%)
( 525
100.0%
Space Separator
ValueCountFrequency (%)
37
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 39721
92.9%
Common 2666
 
6.2%
Latin 370
 
0.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
아 1125
 
2.8%
트 1068
 
2.7%
파 957
 
2.4%
교 877
 
2.2%
리 866
 
2.2%
동 832
 
2.1%
지 801
 
2.0%
구 796
 
2.0%
학 679
 
1.7%
장 523
 
1.3%
Other values (566) 31197
78.5%
Latin
ValueCountFrequency (%)
K 42
11.4%
S 40
10.8%
T 37
10.0%
C 36
9.7%
G 31
8.4%
L 28
 
7.6%
e 24
 
6.5%
H 22
 
5.9%
I 19
 
5.1%
A 16
 
4.3%
Other values (22) 75
20.3%
Common
ValueCountFrequency (%)
) 525
19.7%
( 525
19.7%
1 374
14.0%
2 287
10.8%
. 282
10.6%
3 167
 
6.3%
0 96
 
3.6%
4 92
 
3.5%
5 71
 
2.7%
· 55
 
2.1%
Other values (7) 192
 
7.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 39721
92.9%
ASCII 2981
 
7.0%
None 55
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
아 1125
 
2.8%
트 1068
 
2.7%
파 957
 
2.4%
교 877
 
2.2%
리 866
 
2.2%
동 832
 
2.1%
지 801
 
2.0%
구 796
 
2.0%
학 679
 
1.7%
장 523
 
1.3%
Other values (566) 31197
78.5%
ASCII
ValueCountFrequency (%)
) 525
17.6%
( 525
17.6%
1 374
12.5%
2 287
9.6%
. 282
9.5%
3 167
 
5.6%
0 96
 
3.2%
4 92
 
3.1%
5 71
 
2.4%
6 50
 
1.7%
Other values (38) 512
17.2%
None
ValueCountFrequency (%)
· 55
100.0%

정류소아이디
Real number (ℝ)

Distinct6378
Distinct (%)99.8%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean46725.973
Minimum11002
Maximum92065
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size56.3 KiB
2024-04-06T17:42:16.396110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11002
5-th percentile35311.7
Q138349.75
median40832.5
Q343495.25
95-th percentile90070.65
Maximum92065
Range81063
Interquartile range (IQR)5145.5

Descriptive statistics

Standard deviation17050.145
Coefficient of variation (CV)0.36489653
Kurtosis2.1172612
Mean46725.973
Median Absolute Deviation (MAD)2591
Skewness1.8972986
Sum2.9848551 × 108
Variance2.9070746 × 108
MonotonicityNot monotonic
2024-04-06T17:42:17.498832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35396 2
 
< 0.1%
35610 2
 
< 0.1%
35735 2
 
< 0.1%
35220 2
 
< 0.1%
35097 2
 
< 0.1%
35219 2
 
< 0.1%
35270 2
 
< 0.1%
35214 2
 
< 0.1%
35761 2
 
< 0.1%
35147 2
 
< 0.1%
Other values (6368) 6368
99.7%
ValueCountFrequency (%)
11002 1
< 0.1%
11013 1
< 0.1%
11014 1
< 0.1%
11015 1
< 0.1%
11020 1
< 0.1%
11021 1
< 0.1%
14220 1
< 0.1%
16147 1
< 0.1%
18388 1
< 0.1%
18389 1
< 0.1%
ValueCountFrequency (%)
92065 1
< 0.1%
92064 1
< 0.1%
92063 1
< 0.1%
92062 1
< 0.1%
92061 1
< 0.1%
92060 1
< 0.1%
92059 1
< 0.1%
92056 1
< 0.1%
92055 1
< 0.1%
92053 1
< 0.1%

총승차건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct3325
Distinct (%)52.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3635.2334
Minimum0
Maximum195739
Zeros171
Zeros (%)2.7%
Negative0
Negative (%)0.0%
Memory size56.3 KiB
2024-04-06T17:42:17.932944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q156
median752
Q33797
95-th percentile15334.2
Maximum195739
Range195739
Interquartile range (IQR)3741

Descriptive statistics

Standard deviation8651.356
Coefficient of variation (CV)2.3798626
Kurtosis109.31869
Mean3635.2334
Median Absolute Deviation (MAD)745
Skewness8.0355906
Sum23225506
Variance74845960
MonotonicityNot monotonic
2024-04-06T17:42:18.431988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 171
 
2.7%
1 127
 
2.0%
2 85
 
1.3%
4 63
 
1.0%
8 56
 
0.9%
3 54
 
0.8%
5 50
 
0.8%
7 42
 
0.7%
9 37
 
0.6%
14 36
 
0.6%
Other values (3315) 5668
88.7%
ValueCountFrequency (%)
0 171
2.7%
1 127
2.0%
2 85
1.3%
3 54
 
0.8%
4 63
 
1.0%
5 50
 
0.8%
6 35
 
0.5%
7 42
 
0.7%
8 56
 
0.9%
9 37
 
0.6%
ValueCountFrequency (%)
195739 1
< 0.1%
180955 1
< 0.1%
135122 1
< 0.1%
119689 1
< 0.1%
112909 1
< 0.1%
109300 1
< 0.1%
105030 1
< 0.1%
100379 1
< 0.1%
95356 1
< 0.1%
92608 1
< 0.1%

총하차건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct3398
Distinct (%)53.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3418.9928
Minimum0
Maximum173640
Zeros175
Zeros (%)2.7%
Negative0
Negative (%)0.0%
Memory size56.3 KiB
2024-04-06T17:42:18.874843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q166
median873
Q33730
95-th percentile14015.4
Maximum173640
Range173640
Interquartile range (IQR)3664

Descriptive statistics

Standard deviation7667.1371
Coefficient of variation (CV)2.2425134
Kurtosis92.067391
Mean3418.9928
Median Absolute Deviation (MAD)864
Skewness7.3581941
Sum21843945
Variance58784991
MonotonicityNot monotonic
2024-04-06T17:42:19.306878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 175
 
2.7%
1 88
 
1.4%
2 77
 
1.2%
3 58
 
0.9%
5 54
 
0.8%
8 52
 
0.8%
7 52
 
0.8%
6 47
 
0.7%
4 46
 
0.7%
15 39
 
0.6%
Other values (3388) 5701
89.2%
ValueCountFrequency (%)
0 175
2.7%
1 88
1.4%
2 77
1.2%
3 58
 
0.9%
4 46
 
0.7%
5 54
 
0.8%
6 47
 
0.7%
7 52
 
0.8%
8 52
 
0.8%
9 29
 
0.5%
ValueCountFrequency (%)
173640 1
< 0.1%
134162 1
< 0.1%
117426 1
< 0.1%
99629 1
< 0.1%
98095 1
< 0.1%
95811 1
< 0.1%
91064 1
< 0.1%
84161 1
< 0.1%
81864 1
< 0.1%
81148 1
< 0.1%

승차건수(카드)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct3335
Distinct (%)52.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3576.2498
Minimum0
Maximum192865
Zeros188
Zeros (%)2.9%
Negative0
Negative (%)0.0%
Memory size56.3 KiB
2024-04-06T17:42:19.757112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q151
median738
Q33739
95-th percentile15029
Maximum192865
Range192865
Interquartile range (IQR)3688

Descriptive statistics

Standard deviation8550.8833
Coefficient of variation (CV)2.3910196
Kurtosis109.12139
Mean3576.2498
Median Absolute Deviation (MAD)733
Skewness8.0462789
Sum22848660
Variance73117605
MonotonicityNot monotonic
2024-04-06T17:42:20.190572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 188
 
2.9%
1 143
 
2.2%
2 84
 
1.3%
3 82
 
1.3%
4 61
 
1.0%
8 54
 
0.8%
5 53
 
0.8%
7 50
 
0.8%
18 37
 
0.6%
10 37
 
0.6%
Other values (3325) 5600
87.7%
ValueCountFrequency (%)
0 188
2.9%
1 143
2.2%
2 84
1.3%
3 82
1.3%
4 61
 
1.0%
5 53
 
0.8%
6 35
 
0.5%
7 50
 
0.8%
8 54
 
0.8%
9 31
 
0.5%
ValueCountFrequency (%)
192865 1
< 0.1%
178105 1
< 0.1%
134058 1
< 0.1%
119689 1
< 0.1%
112559 1
< 0.1%
108457 1
< 0.1%
103122 1
< 0.1%
99421 1
< 0.1%
94957 1
< 0.1%
91914 1
< 0.1%

하차건수(카드)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct3398
Distinct (%)53.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3418.9928
Minimum0
Maximum173640
Zeros175
Zeros (%)2.7%
Negative0
Negative (%)0.0%
Memory size56.3 KiB
2024-04-06T17:42:20.755010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q166
median873
Q33730
95-th percentile14015.4
Maximum173640
Range173640
Interquartile range (IQR)3664

Descriptive statistics

Standard deviation7667.1371
Coefficient of variation (CV)2.2425134
Kurtosis92.067391
Mean3418.9928
Median Absolute Deviation (MAD)864
Skewness7.3581941
Sum21843945
Variance58784991
MonotonicityNot monotonic
2024-04-06T17:42:21.154596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 175
 
2.7%
1 88
 
1.4%
2 77
 
1.2%
3 58
 
0.9%
5 54
 
0.8%
8 52
 
0.8%
7 52
 
0.8%
6 47
 
0.7%
4 46
 
0.7%
15 39
 
0.6%
Other values (3388) 5701
89.2%
ValueCountFrequency (%)
0 175
2.7%
1 88
1.4%
2 77
1.2%
3 58
 
0.9%
4 46
 
0.7%
5 54
 
0.8%
6 47
 
0.7%
7 52
 
0.8%
8 52
 
0.8%
9 29
 
0.5%
ValueCountFrequency (%)
173640 1
< 0.1%
134162 1
< 0.1%
117426 1
< 0.1%
99629 1
< 0.1%
98095 1
< 0.1%
95811 1
< 0.1%
91064 1
< 0.1%
84161 1
< 0.1%
81864 1
< 0.1%
81148 1
< 0.1%

승차건수(현금)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct463
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58.983566
Minimum0
Maximum2874
Zeros1315
Zeros (%)20.6%
Negative0
Negative (%)0.0%
Memory size56.3 KiB
2024-04-06T17:42:21.587540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median14
Q367
95-th percentile246.6
Maximum2874
Range2874
Interquartile range (IQR)66

Descriptive statistics

Standard deviation125.77016
Coefficient of variation (CV)2.1322916
Kurtosis135.08814
Mean58.983566
Median Absolute Deviation (MAD)14
Skewness8.2595557
Sum376846
Variance15818.134
MonotonicityNot monotonic
2024-04-06T17:42:22.031423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1315
 
20.6%
1 325
 
5.1%
2 253
 
4.0%
3 212
 
3.3%
4 178
 
2.8%
5 144
 
2.3%
6 121
 
1.9%
7 110
 
1.7%
8 98
 
1.5%
10 89
 
1.4%
Other values (453) 3544
55.5%
ValueCountFrequency (%)
0 1315
20.6%
1 325
 
5.1%
2 253
 
4.0%
3 212
 
3.3%
4 178
 
2.8%
5 144
 
2.3%
6 121
 
1.9%
7 110
 
1.7%
8 98
 
1.5%
9 69
 
1.1%
ValueCountFrequency (%)
2874 1
< 0.1%
2850 1
< 0.1%
2847 1
< 0.1%
1908 1
< 0.1%
1354 1
< 0.1%
1346 1
< 0.1%
1225 1
< 0.1%
1132 1
< 0.1%
1064 1
< 0.1%
1048 1
< 0.1%

일평균승하차건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1034
Distinct (%)16.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean227.07826
Minimum0
Maximum11915
Zeros584
Zeros (%)9.1%
Negative0
Negative (%)0.0%
Memory size56.3 KiB
2024-04-06T17:42:22.446675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16
median70
Q3253
95-th percentile911
Maximum11915
Range11915
Interquartile range (IQR)247

Descriptive statistics

Standard deviation500.03313
Coefficient of variation (CV)2.2020299
Kurtosis113.75282
Mean227.07826
Median Absolute Deviation (MAD)69
Skewness8.056051
Sum1450803
Variance250033.13
MonotonicityNot monotonic
2024-04-06T17:42:22.835835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 584
 
9.1%
1 363
 
5.7%
2 253
 
4.0%
3 163
 
2.6%
4 119
 
1.9%
5 85
 
1.3%
6 75
 
1.2%
7 70
 
1.1%
9 65
 
1.0%
8 56
 
0.9%
Other values (1024) 4556
71.3%
ValueCountFrequency (%)
0 584
9.1%
1 363
5.7%
2 253
4.0%
3 163
 
2.6%
4 119
 
1.9%
5 85
 
1.3%
6 75
 
1.2%
7 70
 
1.1%
8 56
 
0.9%
9 65
 
1.0%
ValueCountFrequency (%)
11915 1
< 0.1%
10165 1
< 0.1%
8146 1
< 0.1%
7025 1
< 0.1%
6616 1
< 0.1%
5515 1
< 0.1%
5482 1
< 0.1%
5468 1
< 0.1%
5388 1
< 0.1%
5315 1
< 0.1%

Interactions

2024-04-06T17:42:10.945139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:41:55.232025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:41:58.464334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:01.282105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:03.652096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:06.278768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:08.510908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:11.256257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:41:55.609958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:41:58.878189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:01.708939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:03.982278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:06.595719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:08.856646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:11.597918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:41:55.980933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:41:59.382064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:02.083661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:04.382504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:06.931657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:09.222677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:11.948642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:41:56.341623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:41:59.814595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:02.401410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:04.727804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:07.228680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:09.610079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:12.346368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:41:56.776919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:00.177555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:02.742792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:05.107253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:07.615346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:10.022471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:12.674667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:41:57.196870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:00.488960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:03.032687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:05.672375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:07.905138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:10.374538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:12.983916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:41:57.573483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:00.878525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:03.339180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:05.958466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:08.219621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:10.635014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-06T17:42:23.111704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
정류소아이디총승차건수총하차건수승차건수(카드)하차건수(카드)승차건수(현금)일평균승하차건수
정류소아이디1.0000.0630.0660.0620.0660.1180.064
총승차건수0.0631.0000.8181.0000.8180.7390.891
총하차건수0.0660.8181.0000.8181.0000.6750.983
승차건수(카드)0.0621.0000.8181.0000.8180.7380.891
하차건수(카드)0.0660.8181.0000.8181.0000.6750.983
승차건수(현금)0.1180.7390.6750.7380.6751.0000.705
일평균승하차건수0.0640.8910.9830.8910.9830.7051.000
2024-04-06T17:42:23.465262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
정류소아이디총승차건수총하차건수승차건수(카드)하차건수(카드)승차건수(현금)일평균승하차건수
정류소아이디1.000-0.388-0.393-0.387-0.393-0.332-0.419
총승차건수-0.3881.0000.7860.9990.7860.8670.937
총하차건수-0.3930.7861.0000.7831.0000.7190.933
승차건수(카드)-0.3870.9990.7831.0000.7830.8530.935
하차건수(카드)-0.3930.7861.0000.7831.0000.7190.933
승차건수(현금)-0.3320.8670.7190.8530.7191.0000.832
일평균승하차건수-0.4190.9370.9330.9350.9330.8321.000

Missing values

2024-04-06T17:42:13.385224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-06T17:42:13.760593image/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

정류소명정류소아이디총승차건수총하차건수승차건수(카드)하차건수(카드)승차건수(현금)일평균승하차건수
0(구)국제여객터미널3505111216992016999258
1(구)국제여객터미널350522421472370475179
2(주)경동세라믹스891464861947719916
3(주)경인양행앞42096105796710409671765
4(주)경인양행앞42097149638191453381943171
5(주)대한특수금속39050119111011801101141
6(주)두남39135160291602906
7(주)세모입구(린나이코리아)375851443157914431579097
8(주)세모입구(린나이코리아)40893319706319706033
9(주)스킨이데아89388191172191172011
정류소명정류소아이디총승차건수총하차건수승차건수(카드)하차건수(카드)승차건수(현금)일평균승하차건수
6379힐스테이트송도더테라스(후문)38714541227949022795190
6380힐스테이트푸르지오주안37293218043174321384317434201727
6381힐스테이트학익370225231396450763964155296
6382힐스테이트학익37023376471313708713156351
6383힐스테이트학익104동앞37669959165393216532784
6384힐스테이트학익104동앞37670170548061683480622210
6385힐캐슬프라자3932911642155361144815536194876
6386힐캐슬프라자39331172329447168519447381860
6387힘찬병원40891553974975467749772420
6388힘찬병원40892442632174353321773246