Overview

Dataset statistics

Number of variables8
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory771.5 KiB
Average record size in memory79.0 B

Variable types

Numeric7
Text1

Dataset

Description제공신청에 따라 2019년도 인천광역시 정류장의 일별 이용객수 통계자료로 일자, 정류장명, 정류장ID, 이용객수(카드, 현금 등) 등을 제공합니다
Author인천광역시
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15104772&srcSe=7661IVAWM27C61E190

Alerts

승차건수 is highly overall correlated with 하차건수 and 3 other fieldsHigh correlation
하차건수 is highly overall correlated with 승차건수 and 3 other fieldsHigh correlation
카드승차건수 is highly overall correlated with 승차건수 and 3 other fieldsHigh correlation
카드하차건수 is highly overall correlated with 승차건수 and 3 other fieldsHigh correlation
현금승차건수 is highly overall correlated with 승차건수 and 3 other fieldsHigh correlation
승차건수 has 748 (7.5%) zerosZeros
하차건수 has 466 (4.7%) zerosZeros
카드승차건수 has 820 (8.2%) zerosZeros
카드하차건수 has 466 (4.7%) zerosZeros
현금승차건수 has 3864 (38.6%) zerosZeros

Reproduction

Analysis started2024-03-18 05:13:34.246371
Analysis finished2024-03-18 05:13:41.577526
Duration7.33 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

운행일자
Real number (ℝ)

Distinct22
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20190111
Minimum20190101
Maximum20190122
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-18T14:13:41.637769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20190101
5-th percentile20190102
Q120190106
median20190111
Q320190117
95-th percentile20190121
Maximum20190122
Range21
Interquartile range (IQR)11

Descriptive statistics

Standard deviation6.180105
Coefficient of variation (CV)3.0609564 × 10-7
Kurtosis-1.1960847
Mean20190111
Median Absolute Deviation (MAD)5
Skewness-0.020098877
Sum2.0190111 × 1011
Variance38.193697
MonotonicityNot monotonic
2024-03-18T14:13:41.742754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
20190120 487
 
4.9%
20190111 487
 
4.9%
20190116 484
 
4.8%
20190112 483
 
4.8%
20190105 482
 
4.8%
20190117 482
 
4.8%
20190121 482
 
4.8%
20190119 475
 
4.8%
20190109 474
 
4.7%
20190115 472
 
4.7%
Other values (12) 5192
51.9%
ValueCountFrequency (%)
20190101 430
4.3%
20190102 472
4.7%
20190103 450
4.5%
20190104 457
4.6%
20190105 482
4.8%
20190106 461
4.6%
20190107 424
4.2%
20190108 449
4.5%
20190109 474
4.7%
20190110 451
4.5%
ValueCountFrequency (%)
20190122 202
2.0%
20190121 482
4.8%
20190120 487
4.9%
20190119 475
4.8%
20190118 461
4.6%
20190117 482
4.8%
20190116 484
4.8%
20190115 472
4.7%
20190114 467
4.7%
20190113 468
4.7%

정류장명
Text

Distinct2663
Distinct (%)26.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-03-18T14:13:41.970723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length18
Mean length6.8284
Min length2

Characters and Unicode

Total characters68284
Distinct characters560
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

Unique429 ?
Unique (%)4.3%

Sample

1st row한양쇼핑
2nd row종합터미널역
3rd row만수주공후문
4th row학익사거리
5th row한국가스공사
ValueCountFrequency (%)
대동아파트 36
 
0.4%
한국아파트 26
 
0.3%
광명아파트 24
 
0.2%
풍림아파트 21
 
0.2%
신동아아파트 20
 
0.2%
삼보아파트 20
 
0.2%
열우물테니스경기장 20
 
0.2%
뉴서울아파트 19
 
0.2%
현대아파트 19
 
0.2%
경남아파트 18
 
0.2%
Other values (2656) 9794
97.8%
2024-03-18T14:13:42.382336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
아 2254
 
3.3%
트 2116
 
3.1%
파 1870
 
2.7%
동 1572
 
2.3%
교 1418
 
2.1%
지 1367
 
2.0%
학 1215
 
1.8%
구 1145
 
1.7%
리 1069
 
1.6%
) 839
 
1.2%
Other values (550) 53419
78.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 63446
92.9%
Decimal Number 2076
 
3.0%
Close Punctuation 839
 
1.2%
Open Punctuation 839
 
1.2%
Other Punctuation 517
 
0.8%
Uppercase Letter 484
 
0.7%
Lowercase Letter 54
 
0.1%
Space Separator 23
 
< 0.1%
Dash Punctuation 6
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
아 2254
 
3.6%
트 2116
 
3.3%
파 1870
 
2.9%
동 1572
 
2.5%
교 1418
 
2.2%
지 1367
 
2.2%
학 1215
 
1.9%
구 1145
 
1.8%
리 1069
 
1.7%
단 830
 
1.3%
Other values (505) 48590
76.6%
Uppercase Letter
ValueCountFrequency (%)
K 76
15.7%
S 69
14.3%
C 66
13.6%
G 49
10.1%
T 43
8.9%
L 37
7.6%
A 32
6.6%
H 22
 
4.5%
B 19
 
3.9%
I 17
 
3.5%
Other values (10) 54
11.2%
Decimal Number
ValueCountFrequency (%)
1 637
30.7%
2 459
22.1%
3 304
14.6%
0 174
 
8.4%
4 153
 
7.4%
5 103
 
5.0%
6 77
 
3.7%
7 75
 
3.6%
8 52
 
2.5%
9 42
 
2.0%
Lowercase Letter
ValueCountFrequency (%)
e 36
66.7%
s 4
 
7.4%
t 2
 
3.7%
n 2
 
3.7%
i 2
 
3.7%
f 2
 
3.7%
g 2
 
3.7%
y 2
 
3.7%
m 2
 
3.7%
Other Punctuation
ValueCountFrequency (%)
. 495
95.7%
· 22
 
4.3%
Close Punctuation
ValueCountFrequency (%)
) 839
100.0%
Open Punctuation
ValueCountFrequency (%)
( 839
100.0%
Space Separator
ValueCountFrequency (%)
23
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 63446
92.9%
Common 4300
 
6.3%
Latin 538
 
0.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
아 2254
 
3.6%
트 2116
 
3.3%
파 1870
 
2.9%
동 1572
 
2.5%
교 1418
 
2.2%
지 1367
 
2.2%
학 1215
 
1.9%
구 1145
 
1.8%
리 1069
 
1.7%
단 830
 
1.3%
Other values (505) 48590
76.6%
Latin
ValueCountFrequency (%)
K 76
14.1%
S 69
12.8%
C 66
12.3%
G 49
9.1%
T 43
8.0%
L 37
6.9%
e 36
6.7%
A 32
 
5.9%
H 22
 
4.1%
B 19
 
3.5%
Other values (19) 89
16.5%
Common
ValueCountFrequency (%)
) 839
19.5%
( 839
19.5%
1 637
14.8%
. 495
11.5%
2 459
10.7%
3 304
 
7.1%
0 174
 
4.0%
4 153
 
3.6%
5 103
 
2.4%
6 77
 
1.8%
Other values (6) 220
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 63446
92.9%
ASCII 4816
 
7.1%
None 22
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
아 2254
 
3.6%
트 2116
 
3.3%
파 1870
 
2.9%
동 1572
 
2.5%
교 1418
 
2.2%
지 1367
 
2.2%
학 1215
 
1.9%
구 1145
 
1.8%
리 1069
 
1.7%
단 830
 
1.3%
Other values (505) 48590
76.6%
ASCII
ValueCountFrequency (%)
) 839
17.4%
( 839
17.4%
1 637
13.2%
. 495
10.3%
2 459
9.5%
3 304
 
6.3%
0 174
 
3.6%
4 153
 
3.2%
5 103
 
2.1%
6 77
 
1.6%
Other values (34) 736
15.3%
None
ValueCountFrequency (%)
· 22
100.0%

정류장ID
Real number (ℝ)

Distinct4245
Distinct (%)42.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43677.403
Minimum11002
Maximum92026
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-18T14:13:42.508202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11002
5-th percentile35163
Q138080
median40045
Q342140
95-th percentile89067
Maximum92026
Range81024
Interquartile range (IQR)4060

Descriptive statistics

Standard deviation13767.094
Coefficient of variation (CV)0.31519947
Kurtosis5.5780481
Mean43677.403
Median Absolute Deviation (MAD)2029
Skewness2.5480981
Sum4.3677403 × 108
Variance1.8953288 × 108
MonotonicityNot monotonic
2024-03-18T14:13:42.859602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37539 9
 
0.1%
39736 7
 
0.1%
92020 7
 
0.1%
38459 7
 
0.1%
39902 7
 
0.1%
40620 7
 
0.1%
80004 7
 
0.1%
70174 7
 
0.1%
35255 7
 
0.1%
35175 7
 
0.1%
Other values (4235) 9928
99.3%
ValueCountFrequency (%)
11002 1
 
< 0.1%
11013 3
< 0.1%
14220 3
< 0.1%
22003 2
 
< 0.1%
22012 5
0.1%
22013 2
 
< 0.1%
22014 3
< 0.1%
22015 1
 
< 0.1%
22057 2
 
< 0.1%
22058 4
< 0.1%
ValueCountFrequency (%)
92026 1
 
< 0.1%
92025 1
 
< 0.1%
92024 2
 
< 0.1%
92021 2
 
< 0.1%
92020 7
0.1%
92019 3
< 0.1%
92018 2
 
< 0.1%
92017 3
< 0.1%
92016 3
< 0.1%
92015 1
 
< 0.1%

승차건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1085
Distinct (%)10.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean179.0506
Minimum0
Maximum9558
Zeros748
Zeros (%)7.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-18T14:13:42.986530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q18
median53
Q3189
95-th percentile712.05
Maximum9558
Range9558
Interquartile range (IQR)181

Descriptive statistics

Standard deviation406.45443
Coefficient of variation (CV)2.2700534
Kurtosis128.29345
Mean179.0506
Median Absolute Deviation (MAD)51
Skewness8.5857818
Sum1790506
Variance165205.2
MonotonicityNot monotonic
2024-03-18T14:13:43.131586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 748
 
7.5%
1 445
 
4.5%
2 320
 
3.2%
3 234
 
2.3%
4 206
 
2.1%
5 198
 
2.0%
6 163
 
1.6%
7 139
 
1.4%
8 131
 
1.3%
9 105
 
1.1%
Other values (1075) 7311
73.1%
ValueCountFrequency (%)
0 748
7.5%
1 445
4.5%
2 320
3.2%
3 234
 
2.3%
4 206
 
2.1%
5 198
 
2.0%
6 163
 
1.6%
7 139
 
1.4%
8 131
 
1.3%
9 105
 
1.1%
ValueCountFrequency (%)
9558 1
< 0.1%
8728 1
< 0.1%
8582 1
< 0.1%
8568 1
< 0.1%
7627 1
< 0.1%
6859 1
< 0.1%
6499 1
< 0.1%
4994 1
< 0.1%
4423 1
< 0.1%
4341 1
< 0.1%

하차건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1035
Distinct (%)10.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean171.8022
Minimum0
Maximum9267
Zeros466
Zeros (%)4.7%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-18T14:13:43.251127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q113
median63
Q3180
95-th percentile663
Maximum9267
Range9267
Interquartile range (IQR)167

Descriptive statistics

Standard deviation366.42045
Coefficient of variation (CV)2.1328042
Kurtosis103.27368
Mean171.8022
Median Absolute Deviation (MAD)58
Skewness7.603047
Sum1718022
Variance134263.94
MonotonicityNot monotonic
2024-03-18T14:13:43.364864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 466
 
4.7%
1 360
 
3.6%
2 287
 
2.9%
3 218
 
2.2%
4 188
 
1.9%
5 147
 
1.5%
6 137
 
1.4%
7 129
 
1.3%
8 114
 
1.1%
9 108
 
1.1%
Other values (1025) 7846
78.5%
ValueCountFrequency (%)
0 466
4.7%
1 360
3.6%
2 287
2.9%
3 218
2.2%
4 188
1.9%
5 147
 
1.5%
6 137
 
1.4%
7 129
 
1.3%
8 114
 
1.1%
9 108
 
1.1%
ValueCountFrequency (%)
9267 1
< 0.1%
8232 1
< 0.1%
5693 1
< 0.1%
5617 1
< 0.1%
5283 1
< 0.1%
4660 1
< 0.1%
4379 1
< 0.1%
4286 1
< 0.1%
4205 1
< 0.1%
4144 1
< 0.1%

카드승차건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1043
Distinct (%)10.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean173.5465
Minimum0
Maximum9408
Zeros820
Zeros (%)8.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-18T14:13:43.487405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17
median51
Q3182
95-th percentile690.1
Maximum9408
Range9408
Interquartile range (IQR)175

Descriptive statistics

Standard deviation397.38338
Coefficient of variation (CV)2.2897804
Kurtosis127.67341
Mean173.5465
Median Absolute Deviation (MAD)49
Skewness8.5867502
Sum1735465
Variance157913.55
MonotonicityNot monotonic
2024-03-18T14:13:43.620404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 820
 
8.2%
1 462
 
4.6%
2 330
 
3.3%
3 235
 
2.4%
4 205
 
2.1%
5 197
 
2.0%
6 153
 
1.5%
7 134
 
1.3%
8 128
 
1.3%
10 108
 
1.1%
Other values (1033) 7228
72.3%
ValueCountFrequency (%)
0 820
8.2%
1 462
4.6%
2 330
3.3%
3 235
 
2.4%
4 205
 
2.1%
5 197
 
2.0%
6 153
 
1.5%
7 134
 
1.3%
8 128
 
1.3%
9 85
 
0.9%
ValueCountFrequency (%)
9408 1
< 0.1%
8445 1
< 0.1%
8343 1
< 0.1%
8301 1
< 0.1%
7338 1
< 0.1%
6797 1
< 0.1%
6412 1
< 0.1%
4972 1
< 0.1%
4289 1
< 0.1%
4279 1
< 0.1%

카드하차건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1035
Distinct (%)10.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean171.8022
Minimum0
Maximum9267
Zeros466
Zeros (%)4.7%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-18T14:13:43.728793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q113
median63
Q3180
95-th percentile663
Maximum9267
Range9267
Interquartile range (IQR)167

Descriptive statistics

Standard deviation366.42045
Coefficient of variation (CV)2.1328042
Kurtosis103.27368
Mean171.8022
Median Absolute Deviation (MAD)58
Skewness7.603047
Sum1718022
Variance134263.94
MonotonicityNot monotonic
2024-03-18T14:13:43.845700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 466
 
4.7%
1 360
 
3.6%
2 287
 
2.9%
3 218
 
2.2%
4 188
 
1.9%
5 147
 
1.5%
6 137
 
1.4%
7 129
 
1.3%
8 114
 
1.1%
9 108
 
1.1%
Other values (1025) 7846
78.5%
ValueCountFrequency (%)
0 466
4.7%
1 360
3.6%
2 287
2.9%
3 218
2.2%
4 188
1.9%
5 147
 
1.5%
6 137
 
1.4%
7 129
 
1.3%
8 114
 
1.1%
9 108
 
1.1%
ValueCountFrequency (%)
9267 1
< 0.1%
8232 1
< 0.1%
5693 1
< 0.1%
5617 1
< 0.1%
5283 1
< 0.1%
4660 1
< 0.1%
4379 1
< 0.1%
4286 1
< 0.1%
4205 1
< 0.1%
4144 1
< 0.1%

현금승차건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct97
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.5041
Minimum0
Maximum289
Zeros3864
Zeros (%)38.6%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-18T14:13:43.956948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q37
95-th percentile23
Maximum289
Range289
Interquartile range (IQR)7

Descriptive statistics

Standard deviation11.173794
Coefficient of variation (CV)2.0300855
Kurtosis146.55162
Mean5.5041
Median Absolute Deviation (MAD)2
Skewness8.3399176
Sum55041
Variance124.85367
MonotonicityNot monotonic
2024-03-18T14:13:44.080568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3864
38.6%
1 973
 
9.7%
2 802
 
8.0%
3 561
 
5.6%
4 525
 
5.2%
5 398
 
4.0%
6 337
 
3.4%
7 267
 
2.7%
8 261
 
2.6%
9 216
 
2.2%
Other values (87) 1796
18.0%
ValueCountFrequency (%)
0 3864
38.6%
1 973
 
9.7%
2 802
 
8.0%
3 561
 
5.6%
4 525
 
5.2%
5 398
 
4.0%
6 337
 
3.4%
7 267
 
2.7%
8 261
 
2.6%
9 216
 
2.2%
ValueCountFrequency (%)
289 1
< 0.1%
283 1
< 0.1%
267 1
< 0.1%
239 1
< 0.1%
150 1
< 0.1%
134 2
< 0.1%
123 1
< 0.1%
119 1
< 0.1%
118 1
< 0.1%
108 1
< 0.1%

Interactions

2024-03-18T14:13:40.763992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:13:37.008408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:13:37.763822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:13:38.401199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:13:38.963204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:13:39.547173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:13:40.166795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:13:40.849837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:13:37.129538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:13:37.864652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:13:38.473063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:13:39.049284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:13:39.631300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:13:40.269594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:13:40.957541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:13:37.217742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:13:37.957181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:13:38.554783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:13:39.152660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:13:39.723218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:13:40.361084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:13:41.052552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:13:37.446136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:13:38.058143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:13:38.628779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:13:39.234337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:13:39.796891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:13:40.442354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:13:41.124672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:13:37.511617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:13:38.146879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:13:38.700247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:13:39.305354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:13:39.879301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:13:40.517525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:13:41.207609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:13:37.582833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:13:38.236127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:13:38.796105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:13:39.378937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:13:39.976626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:13:40.606754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:13:41.290628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:13:37.653225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:13:38.315461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:13:38.878914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:13:39.455573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:13:40.058484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:13:40.676678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-18T14:13:44.161752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
운행일자정류장ID승차건수하차건수카드승차건수카드하차건수현금승차건수
운행일자1.0000.0160.0470.0320.0450.0320.034
정류장ID0.0161.0000.0990.0450.0990.0450.065
승차건수0.0470.0991.0000.8481.0000.8480.810
하차건수0.0320.0450.8481.0000.8761.0000.820
카드승차건수0.0450.0991.0000.8761.0000.8760.843
카드하차건수0.0320.0450.8481.0000.8761.0000.820
현금승차건수0.0340.0650.8100.8200.8430.8201.000
2024-03-18T14:13:44.291157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
운행일자정류장ID승차건수하차건수카드승차건수카드하차건수현금승차건수
운행일자1.0000.002-0.0010.0050.0010.005-0.027
정류장ID0.0021.000-0.152-0.151-0.151-0.151-0.148
승차건수-0.001-0.1521.0000.6750.9990.6750.813
하차건수0.005-0.1510.6751.0000.6731.0000.597
카드승차건수0.001-0.1510.9990.6731.0000.6730.795
카드하차건수0.005-0.1510.6751.0000.6731.0000.597
현금승차건수-0.027-0.1480.8130.5970.7950.5971.000

Missing values

2024-03-18T14:13:41.407565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-18T14:13:41.524785image/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

운행일자정류장명정류장ID승차건수하차건수카드승차건수카드하차건수현금승차건수
8371620190118한양쇼핑403333701743611749
9716520190121종합터미널역372391651650
2921620190107만수주공후문3943560327593271
3666820190108학익사거리3707773220642209
6946820190115한국가스공사39061314831480
766120190102용남파출소3718491282872824
9153920190120안남고(동보1차아파트)410481181081
2786620190107가좌녹지공원4203014658142584
6836220190115왕산교3563311110
4534920190110인천아시아드주경기장(서문)8919805050
운행일자정류장명정류장ID승차건수하차건수카드승차건수카드하차건수현금승차건수
2164020190105올림픽생활기념관3934987245802457
9691320190121인천예술고등학교39332254124411
723220190102신천역.문화의거리701745295290
8719120190119왕길고가425520330330
2965820190107부평역화성파크드림아파트40804716271620
7073720190116논현3단지하늘마을301동3963163148631480
7168620190116부광초등학교4043453175521751
1676220190104에코메트로1201동3970501010
3457220190108상인천초등학교39340381638160
255120190101신동아아파트116동4006073152731520