Overview

Dataset statistics

Number of variables14
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.3 MiB
Average record size in memory132.0 B

Variable types

Categorical1
Text2
Numeric11

Dataset

Description여객목적통행 OD
Author경기도교통정보센터
URLhttps://data.gg.go.kr/portal/data/service/selectServicePage.do?&infId=J3ZYX5A3T7GYE9TVLQW132783051&infSeq=1

Alerts

가정기반 통근 is highly overall correlated with 가정기반 기타 and 3 other fieldsHigh correlation
가정기반 통학 is highly overall correlated with 총목적High correlation
가정기반 학원 is highly overall correlated with 가정기반 쇼핑High correlation
가정기반 쇼핑 is highly overall correlated with 가정기반 학원 and 2 other fieldsHigh correlation
가정기반 기타 is highly overall correlated with 가정기반 통근 and 2 other fieldsHigh correlation
비가정기반 업무 is highly overall correlated with 가정기반 통근 and 2 other fieldsHigh correlation
비가정기반 기타 is highly overall correlated with 가정기반 통근 and 4 other fieldsHigh correlation
총목적 is highly overall correlated with 가정기반 통근 and 5 other fieldsHigh correlation
가정기반 통근 has 2869 (28.7%) zerosZeros
가정기반 통학 has 7481 (74.8%) zerosZeros
가정기반 학원 has 9023 (90.2%) zerosZeros
가정기반 쇼핑 has 8553 (85.5%) zerosZeros
가정기반 기타 has 3694 (36.9%) zerosZeros
비가정기반 업무 has 6383 (63.8%) zerosZeros
비가정기반 쇼핑 has 8985 (89.8%) zerosZeros
비가정기반 기타 has 6700 (67.0%) zerosZeros
총목적 has 1614 (16.1%) zerosZeros

Reproduction

Analysis started2023-12-10 21:27:00.267891
Analysis finished2023-12-10 21:27:14.821948
Duration14.55 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

년도
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2021
5019 
2020
4981 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020
2nd row2020
3rd row2021
4th row2020
5th row2021

Common Values

ValueCountFrequency (%)
2021 5019
50.2%
2020 4981
49.8%

Length

2023-12-11T06:27:14.877197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T06:27:14.960161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021 5019
50.2%
2020 4981
49.8%
Distinct77
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T06:27:15.165882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length8.8761
Min length7

Characters and Unicode

Total characters88761
Distinct characters86
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row서울특별시 서대문구
2nd row경기도 성남시 분당구
3rd row경기도 안양시 동안구
4th row경기도 수원시 팔달구
5th row경기도 안산시 상록구
ValueCountFrequency (%)
경기도 5417
24.3%
서울특별시 3402
 
15.3%
인천광역시 1181
 
5.3%
수원시 535
 
2.4%
성남시 406
 
1.8%
용인시 405
 
1.8%
고양시 399
 
1.8%
안산시 272
 
1.2%
안양시 265
 
1.2%
중구 207
 
0.9%
Other values (75) 9793
44.0%
2023-12-11T06:27:15.520620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
12282
13.8%
9842
 
11.1%
6940
 
7.8%
5555
 
6.3%
5546
 
6.2%
5417
 
6.1%
4068
 
4.6%
3402
 
3.8%
3402
 
3.8%
3402
 
3.8%
Other values (76) 28905
32.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 76479
86.2%
Space Separator 12282
 
13.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
9842
 
12.9%
6940
 
9.1%
5555
 
7.3%
5546
 
7.3%
5417
 
7.1%
4068
 
5.3%
3402
 
4.4%
3402
 
4.4%
3402
 
4.4%
2132
 
2.8%
Other values (75) 26773
35.0%
Space Separator
ValueCountFrequency (%)
12282
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 76479
86.2%
Common 12282
 
13.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
9842
 
12.9%
6940
 
9.1%
5555
 
7.3%
5546
 
7.3%
5417
 
7.1%
4068
 
5.3%
3402
 
4.4%
3402
 
4.4%
3402
 
4.4%
2132
 
2.8%
Other values (75) 26773
35.0%
Common
ValueCountFrequency (%)
12282
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 76479
86.2%
ASCII 12282
 
13.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
12282
100.0%
Hangul
ValueCountFrequency (%)
9842
 
12.9%
6940
 
9.1%
5555
 
7.3%
5546
 
7.3%
5417
 
7.1%
4068
 
5.3%
3402
 
4.4%
3402
 
4.4%
3402
 
4.4%
2132
 
2.8%
Other values (75) 26773
35.0%

기점지역코드
Real number (ℝ)

Distinct77
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2338006.8
Minimum1101000
Maximum3138000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T06:27:15.651510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1101000
5-th percentile1104000
Q11119000
median3101400
Q33114000
95-th percentile3127000
Maximum3138000
Range2037000
Interquartile range (IQR)1995000

Descriptive statistics

Standard deviation914748.72
Coefficient of variation (CV)0.39125153
Kurtosis-1.6250209
Mean2338006.8
Median Absolute Deviation (MAD)25600
Skewness-0.48688056
Sum2.3380068 × 1010
Variance8.3676522 × 1011
MonotonicityNot monotonic
2023-12-11T06:27:15.784966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1101000 146
 
1.5%
1103000 144
 
1.4%
1119000 143
 
1.4%
1116000 143
 
1.4%
3125000 142
 
1.4%
1121000 142
 
1.4%
1104000 140
 
1.4%
3110100 139
 
1.4%
1108000 139
 
1.4%
3107000 139
 
1.4%
Other values (67) 8583
85.8%
ValueCountFrequency (%)
1101000 146
1.5%
1102000 135
1.4%
1103000 144
1.4%
1104000 140
1.4%
1105000 134
1.3%
1106000 137
1.4%
1107000 129
1.3%
1108000 139
1.4%
1109000 137
1.4%
1110000 129
1.3%
ValueCountFrequency (%)
3138000 122
1.2%
3137000 97
1.0%
3135000 78
0.8%
3128000 115
1.1%
3127000 108
1.1%
3126000 117
1.2%
3125000 142
1.4%
3124000 137
1.4%
3123000 134
1.3%
3122000 124
1.2%
Distinct77
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T06:27:16.054552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length8.8694
Min length7

Characters and Unicode

Total characters88694
Distinct characters86
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row경기도 광주시
2nd row경기도 광주시
3rd row서울특별시 관악구
4th row경기도 수원시 팔달구
5th row경기도 수원시 영통구
ValueCountFrequency (%)
경기도 5359
24.1%
서울특별시 3401
 
15.3%
인천광역시 1240
 
5.6%
수원시 537
 
2.4%
고양시 400
 
1.8%
성남시 393
 
1.8%
용인시 392
 
1.8%
중구 269
 
1.2%
안양시 267
 
1.2%
안산시 260
 
1.2%
Other values (75) 9731
43.7%
2023-12-11T06:27:16.384629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
12249
13.8%
9865
 
11.1%
6965
 
7.9%
5493
 
6.2%
5491
 
6.2%
5359
 
6.0%
4076
 
4.6%
3401
 
3.8%
3401
 
3.8%
3401
 
3.8%
Other values (76) 28993
32.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 76445
86.2%
Space Separator 12249
 
13.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
9865
 
12.9%
6965
 
9.1%
5493
 
7.2%
5491
 
7.2%
5359
 
7.0%
4076
 
5.3%
3401
 
4.4%
3401
 
4.4%
3401
 
4.4%
2181
 
2.9%
Other values (75) 26812
35.1%
Space Separator
ValueCountFrequency (%)
12249
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 76445
86.2%
Common 12249
 
13.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
9865
 
12.9%
6965
 
9.1%
5493
 
7.2%
5491
 
7.2%
5359
 
7.0%
4076
 
5.3%
3401
 
4.4%
3401
 
4.4%
3401
 
4.4%
2181
 
2.9%
Other values (75) 26812
35.1%
Common
ValueCountFrequency (%)
12249
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 76445
86.2%
ASCII 12249
 
13.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
12249
100.0%
Hangul
ValueCountFrequency (%)
9865
 
12.9%
6965
 
9.1%
5493
 
7.2%
5491
 
7.2%
5359
 
7.0%
4076
 
5.3%
3401
 
4.4%
3401
 
4.4%
3401
 
4.4%
2181
 
2.9%
Other values (75) 26812
35.1%

종점지역코드
Real number (ℝ)

Distinct77
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2333395.9
Minimum1101000
Maximum3138000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T06:27:16.508125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1101000
5-th percentile1104000
Q11119000
median3101300
Q33113000
95-th percentile3127000
Maximum3138000
Range2037000
Interquartile range (IQR)1994000

Descriptive statistics

Standard deviation912689.23
Coefficient of variation (CV)0.39114204
Kurtosis-1.6260043
Mean2333395.9
Median Absolute Deviation (MAD)26700
Skewness-0.47840687
Sum2.3333959 × 1010
Variance8.3300162 × 1011
MonotonicityNot monotonic
2023-12-11T06:27:16.628422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1119000 143
 
1.4%
3113000 143
 
1.4%
1114000 143
 
1.4%
1108000 143
 
1.4%
1111000 142
 
1.4%
1122000 141
 
1.4%
2303000 141
 
1.4%
1105000 140
 
1.4%
1106000 140
 
1.4%
1101000 140
 
1.4%
Other values (67) 8584
85.8%
ValueCountFrequency (%)
1101000 140
1.4%
1102000 135
1.4%
1103000 135
1.4%
1104000 136
1.4%
1105000 140
1.4%
1106000 140
1.4%
1107000 124
1.2%
1108000 143
1.4%
1109000 135
1.4%
1110000 132
1.3%
ValueCountFrequency (%)
3138000 97
1.0%
3137000 104
1.0%
3135000 71
0.7%
3128000 118
1.2%
3127000 114
1.1%
3126000 124
1.2%
3125000 133
1.3%
3124000 132
1.3%
3123000 131
1.3%
3122000 129
1.3%

가정기반 통근
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct3591
Distinct (%)35.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4110.2654
Minimum0
Maximum885595
Zeros2869
Zeros (%)28.7%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T06:27:16.750065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median323
Q31671.5
95-th percentile13638.1
Maximum885595
Range885595
Interquartile range (IQR)1671.5

Descriptive statistics

Standard deviation23880.665
Coefficient of variation (CV)5.8100056
Kurtosis340.42152
Mean4110.2654
Median Absolute Deviation (MAD)323
Skewness15.569093
Sum41102654
Variance5.7028616 × 108
MonotonicityNot monotonic
2023-12-11T06:27:16.854210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2869
28.7%
1 51
 
0.5%
2 35
 
0.4%
3 30
 
0.3%
4 28
 
0.3%
5 26
 
0.3%
8 22
 
0.2%
12 18
 
0.2%
6 18
 
0.2%
10 18
 
0.2%
Other values (3581) 6885
68.8%
ValueCountFrequency (%)
0 2869
28.7%
1 51
 
0.5%
2 35
 
0.4%
3 30
 
0.3%
4 28
 
0.3%
5 26
 
0.3%
6 18
 
0.2%
7 17
 
0.2%
8 22
 
0.2%
9 13
 
0.1%
ValueCountFrequency (%)
885595 1
< 0.1%
565677 1
< 0.1%
495545 1
< 0.1%
435650 1
< 0.1%
432373 1
< 0.1%
408525 1
< 0.1%
391735 1
< 0.1%
382970 1
< 0.1%
366484 1
< 0.1%
364082 1
< 0.1%

가정기반 통학
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1083
Distinct (%)10.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean593.9374
Minimum0
Maximum126980
Zeros7481
Zeros (%)74.8%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T06:27:16.964874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile826.2
Maximum126980
Range126980
Interquartile range (IQR)1

Descriptive statistics

Standard deviation4879.3452
Coefficient of variation (CV)8.2152516
Kurtosis175.48063
Mean593.9374
Median Absolute Deviation (MAD)0
Skewness12.107064
Sum5939374
Variance23808010
MonotonicityNot monotonic
2023-12-11T06:27:17.076222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7481
74.8%
1 41
 
0.4%
2 38
 
0.4%
4 29
 
0.3%
3 26
 
0.3%
6 25
 
0.2%
8 22
 
0.2%
5 21
 
0.2%
10 20
 
0.2%
7 17
 
0.2%
Other values (1073) 2280
 
22.8%
ValueCountFrequency (%)
0 7481
74.8%
1 41
 
0.4%
2 38
 
0.4%
3 26
 
0.3%
4 29
 
0.3%
5 21
 
0.2%
6 25
 
0.2%
7 17
 
0.2%
8 22
 
0.2%
9 15
 
0.1%
ValueCountFrequency (%)
126980 1
< 0.1%
104184 1
< 0.1%
84404 1
< 0.1%
81899 1
< 0.1%
81064 1
< 0.1%
79392 1
< 0.1%
77098 1
< 0.1%
72780 1
< 0.1%
68824 1
< 0.1%
68121 1
< 0.1%

가정기반 학원
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct662
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean239.5502
Minimum0
Maximum46367
Zeros9023
Zeros (%)90.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T06:27:17.184448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile316.1
Maximum46367
Range46367
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2126.0187
Coefficient of variation (CV)8.8750445
Kurtosis201.88884
Mean239.5502
Median Absolute Deviation (MAD)0
Skewness13.142858
Sum2395502
Variance4519955.5
MonotonicityNot monotonic
2023-12-11T06:27:17.285249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 9023
90.2%
4 17
 
0.2%
6 14
 
0.1%
3 13
 
0.1%
1 11
 
0.1%
11 11
 
0.1%
5 9
 
0.1%
2 8
 
0.1%
15 6
 
0.1%
37 6
 
0.1%
Other values (652) 882
 
8.8%
ValueCountFrequency (%)
0 9023
90.2%
1 11
 
0.1%
2 8
 
0.1%
3 13
 
0.1%
4 17
 
0.2%
5 9
 
0.1%
6 14
 
0.1%
7 5
 
0.1%
8 4
 
< 0.1%
10 2
 
< 0.1%
ValueCountFrequency (%)
46367 1
< 0.1%
45581 1
< 0.1%
45181 1
< 0.1%
43461 1
< 0.1%
42277 1
< 0.1%
40929 1
< 0.1%
37518 1
< 0.1%
35367 1
< 0.1%
34039 1
< 0.1%
32607 1
< 0.1%

가정기반 쇼핑
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct911
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean352.657
Minimum0
Maximum54853
Zeros8553
Zeros (%)85.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T06:27:17.395130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile686.15
Maximum54853
Range54853
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2683.2674
Coefficient of variation (CV)7.6087172
Kurtosis150.56317
Mean352.657
Median Absolute Deviation (MAD)0
Skewness11.445352
Sum3526570
Variance7199923.8
MonotonicityNot monotonic
2023-12-11T06:27:17.499296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 8553
85.5%
1 23
 
0.2%
5 18
 
0.2%
6 16
 
0.2%
3 14
 
0.1%
8 13
 
0.1%
4 12
 
0.1%
2 11
 
0.1%
11 11
 
0.1%
7 10
 
0.1%
Other values (901) 1319
 
13.2%
ValueCountFrequency (%)
0 8553
85.5%
1 23
 
0.2%
2 11
 
0.1%
3 14
 
0.1%
4 12
 
0.1%
5 18
 
0.2%
6 16
 
0.2%
7 10
 
0.1%
8 13
 
0.1%
9 5
 
0.1%
ValueCountFrequency (%)
54853 1
< 0.1%
54127 1
< 0.1%
49009 1
< 0.1%
45730 1
< 0.1%
44972 1
< 0.1%
44273 1
< 0.1%
43238 1
< 0.1%
42722 1
< 0.1%
40841 1
< 0.1%
36350 1
< 0.1%

가정기반 기타
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct2775
Distinct (%)27.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3156.2492
Minimum0
Maximum669873
Zeros3694
Zeros (%)36.9%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T06:27:17.612346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median121.5
Q3802
95-th percentile8120.05
Maximum669873
Range669873
Interquartile range (IQR)802

Descriptive statistics

Standard deviation20884.479
Coefficient of variation (CV)6.6168663
Kurtosis259.3385
Mean3156.2492
Median Absolute Deviation (MAD)121.5
Skewness14.05818
Sum31562492
Variance4.3616146 × 108
MonotonicityNot monotonic
2023-12-11T06:27:17.733978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3694
36.9%
1 81
 
0.8%
2 56
 
0.6%
5 46
 
0.5%
3 44
 
0.4%
4 40
 
0.4%
6 26
 
0.3%
14 22
 
0.2%
136 21
 
0.2%
56 20
 
0.2%
Other values (2765) 5950
59.5%
ValueCountFrequency (%)
0 3694
36.9%
1 81
 
0.8%
2 56
 
0.6%
3 44
 
0.4%
4 40
 
0.4%
5 46
 
0.5%
6 26
 
0.3%
7 16
 
0.2%
8 19
 
0.2%
9 12
 
0.1%
ValueCountFrequency (%)
669873 1
< 0.1%
486385 1
< 0.1%
438277 1
< 0.1%
391138 1
< 0.1%
371954 1
< 0.1%
349018 1
< 0.1%
336221 1
< 0.1%
328293 1
< 0.1%
327452 1
< 0.1%
304921 1
< 0.1%

비가정기반 업무
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1539
Distinct (%)15.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean548.7566
Minimum0
Maximum110008
Zeros6383
Zeros (%)63.8%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T06:27:17.850375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3113.25
95-th percentile1768.2
Maximum110008
Range110008
Interquartile range (IQR)113.25

Descriptive statistics

Standard deviation3431.4235
Coefficient of variation (CV)6.2530884
Kurtosis349.56173
Mean548.7566
Median Absolute Deviation (MAD)0
Skewness16.035264
Sum5487566
Variance11774668
MonotonicityNot monotonic
2023-12-11T06:27:17.956155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6383
63.8%
2 98
 
1.0%
3 71
 
0.7%
4 60
 
0.6%
5 42
 
0.4%
1 41
 
0.4%
7 33
 
0.3%
6 28
 
0.3%
8 28
 
0.3%
11 21
 
0.2%
Other values (1529) 3195
31.9%
ValueCountFrequency (%)
0 6383
63.8%
1 41
 
0.4%
2 98
 
1.0%
3 71
 
0.7%
4 60
 
0.6%
5 42
 
0.4%
6 28
 
0.3%
7 33
 
0.3%
8 28
 
0.3%
9 17
 
0.2%
ValueCountFrequency (%)
110008 1
< 0.1%
96060 1
< 0.1%
92358 1
< 0.1%
81146 1
< 0.1%
75304 1
< 0.1%
71409 1
< 0.1%
61828 1
< 0.1%
61056 1
< 0.1%
60042 1
< 0.1%
54313 1
< 0.1%

비가정기반 쇼핑
Real number (ℝ)

ZEROS 

Distinct685
Distinct (%)6.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean272.8426
Minimum0
Maximum92774
Zeros8985
Zeros (%)89.8%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T06:27:18.061539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile389
Maximum92774
Range92774
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2927.3699
Coefficient of variation (CV)10.729153
Kurtosis313.20206
Mean272.8426
Median Absolute Deviation (MAD)0
Skewness16.371309
Sum2728426
Variance8569494.5
MonotonicityNot monotonic
2023-12-11T06:27:18.167553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 8985
89.8%
2 50
 
0.5%
3 29
 
0.3%
4 20
 
0.2%
5 12
 
0.1%
10 11
 
0.1%
7 9
 
0.1%
13 9
 
0.1%
6 9
 
0.1%
1 8
 
0.1%
Other values (675) 858
 
8.6%
ValueCountFrequency (%)
0 8985
89.8%
1 8
 
0.1%
2 50
 
0.5%
3 29
 
0.3%
4 20
 
0.2%
5 12
 
0.1%
6 9
 
0.1%
7 9
 
0.1%
8 5
 
0.1%
9 3
 
< 0.1%
ValueCountFrequency (%)
92774 1
< 0.1%
63999 1
< 0.1%
63890 1
< 0.1%
62800 1
< 0.1%
61669 1
< 0.1%
52660 1
< 0.1%
50638 1
< 0.1%
50536 1
< 0.1%
49011 1
< 0.1%
48373 1
< 0.1%

비가정기반 기타
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1165
Distinct (%)11.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean457.2792
Minimum0
Maximum81575
Zeros6700
Zeros (%)67.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T06:27:18.486626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q318
95-th percentile974.1
Maximum81575
Range81575
Interquartile range (IQR)18

Descriptive statistics

Standard deviation3371.023
Coefficient of variation (CV)7.3719142
Kurtosis181.07626
Mean457.2792
Median Absolute Deviation (MAD)0
Skewness12.31249
Sum4572792
Variance11363796
MonotonicityNot monotonic
2023-12-11T06:27:18.599498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6700
67.0%
2 132
 
1.3%
1 112
 
1.1%
3 77
 
0.8%
4 68
 
0.7%
5 67
 
0.7%
6 56
 
0.6%
7 49
 
0.5%
8 43
 
0.4%
10 28
 
0.3%
Other values (1155) 2668
 
26.7%
ValueCountFrequency (%)
0 6700
67.0%
1 112
 
1.1%
2 132
 
1.3%
3 77
 
0.8%
4 68
 
0.7%
5 67
 
0.7%
6 56
 
0.6%
7 49
 
0.5%
8 43
 
0.4%
9 24
 
0.2%
ValueCountFrequency (%)
81575 1
< 0.1%
73836 1
< 0.1%
65701 1
< 0.1%
61382 1
< 0.1%
58656 1
< 0.1%
58535 1
< 0.1%
57423 1
< 0.1%
52444 1
< 0.1%
51915 1
< 0.1%
51223 1
< 0.1%

총목적
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct4439
Distinct (%)44.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9731.5399
Minimum0
Maximum1931005
Zeros1614
Zeros (%)16.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T06:27:18.734344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q165
median705
Q33020.75
95-th percentile26549.45
Maximum1931005
Range1931005
Interquartile range (IQR)2955.75

Descriptive statistics

Standard deviation60067.137
Coefficient of variation (CV)6.1724185
Kurtosis243.97837
Mean9731.5399
Median Absolute Deviation (MAD)705
Skewness13.50207
Sum97315399
Variance3.6080609 × 109
MonotonicityNot monotonic
2023-12-11T06:27:18.859666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1614
 
16.1%
2 107
 
1.1%
4 60
 
0.6%
1 51
 
0.5%
3 48
 
0.5%
5 37
 
0.4%
6 35
 
0.4%
7 27
 
0.3%
8 26
 
0.3%
9 21
 
0.2%
Other values (4429) 7974
79.7%
ValueCountFrequency (%)
0 1614
16.1%
1 51
 
0.5%
2 107
 
1.1%
3 48
 
0.5%
4 60
 
0.6%
5 37
 
0.4%
6 35
 
0.4%
7 27
 
0.3%
8 26
 
0.3%
9 21
 
0.2%
ValueCountFrequency (%)
1931005 1
< 0.1%
1365294 1
< 0.1%
1159910 1
< 0.1%
1102416 1
< 0.1%
1037851 1
< 0.1%
1005276 1
< 0.1%
963994 1
< 0.1%
917258 1
< 0.1%
874770 1
< 0.1%
844884 1
< 0.1%

Interactions

2023-12-11T06:27:13.351331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:03.117404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:04.326534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:05.279060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:06.188371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:07.175752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:08.104836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:09.335838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:10.412742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:11.377840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:12.296401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:13.448783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:03.227401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:04.418591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:05.386081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:06.295347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:07.260297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:08.192590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:09.429787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:10.507817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:11.461705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:12.407146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:13.539695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:03.331213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:04.520327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:05.475422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:06.405928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:07.342590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:08.279428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:09.525262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:10.614896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:11.547953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:12.515395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:13.616657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:03.420432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:04.592882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:05.547855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:06.490537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:07.417096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:08.357085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:09.607233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:10.695997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:11.621652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:12.617333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:13.716471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:03.501563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:04.670216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:05.624388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:06.591641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:07.492788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:08.438645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:09.691418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:10.782570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:11.711370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:12.714370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:13.809807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:03.596017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:04.744287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:05.695085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:06.671550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:07.563016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:08.512759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:09.770789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:10.871620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:11.782113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:12.795723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:13.896273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:03.689747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:04.819772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:05.765335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:06.752015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:07.654866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:08.595323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:09.869577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:10.950372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:11.868886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:12.888621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:13.984902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:03.976599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:04.919970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:05.844555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:06.837764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:07.795536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:08.693372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:09.980048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:11.029301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:11.958640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:12.979355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:14.069038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:04.061495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:05.015830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:05.918303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:06.919193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:07.866493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:08.792563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:10.092274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:11.108142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:12.033976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:13.058747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:14.146754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:04.143507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:05.089571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:05.989865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:06.996643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:07.938506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:08.878715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:10.187836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:11.188951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:12.107582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:13.141436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:14.463923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:04.237911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:05.174904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:06.077814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:07.085108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:08.022113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:09.230780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:10.298638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:11.282840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:12.202058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:27:13.237536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T06:27:19.014296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년도기점지역기점지역코드종점지역종점지역코드가정기반 통근가정기반 통학가정기반 학원가정기반 쇼핑가정기반 기타비가정기반 업무비가정기반 쇼핑비가정기반 기타총목적
년도1.0000.0000.0000.0000.0000.0860.0130.0160.0000.0640.0030.0960.0670.058
기점지역0.0001.0001.0000.0000.0000.0000.0560.0000.0000.0000.0000.0000.0000.000
기점지역코드0.0001.0001.0000.0000.0680.0000.0000.0000.0220.0000.0000.0000.0000.000
종점지역0.0000.0000.0001.0001.0000.0000.0570.0000.0000.0000.0000.0000.0000.000
종점지역코드0.0000.0000.0681.0001.0000.0000.0000.0000.0210.0000.0000.0000.0000.000
가정기반 통근0.0860.0000.0000.0000.0001.0000.8110.7370.7570.9510.8310.4310.8630.949
가정기반 통학0.0130.0560.0000.0570.0000.8111.0000.8050.7930.9190.8130.7020.7900.924
가정기반 학원0.0160.0000.0000.0000.0000.7370.8051.0000.8700.7670.8910.6720.9070.788
가정기반 쇼핑0.0000.0000.0220.0000.0210.7570.7930.8701.0000.7970.8690.6270.8930.827
가정기반 기타0.0640.0000.0000.0000.0000.9510.9190.7670.7971.0000.8410.5540.8890.996
비가정기반 업무0.0030.0000.0000.0000.0000.8310.8130.8910.8690.8411.0000.6130.9220.858
비가정기반 쇼핑0.0960.0000.0000.0000.0000.4310.7020.6720.6270.5540.6131.0000.6550.567
비가정기반 기타0.0670.0000.0000.0000.0000.8630.7900.9070.8930.8890.9220.6551.0000.892
총목적0.0580.0000.0000.0000.0000.9490.9240.7880.8270.9960.8580.5670.8921.000
2023-12-11T06:27:19.164021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기점지역코드종점지역코드가정기반 통근가정기반 통학가정기반 학원가정기반 쇼핑가정기반 기타비가정기반 업무비가정기반 쇼핑비가정기반 기타총목적년도
기점지역코드1.000-0.015-0.166-0.076-0.057-0.062-0.155-0.103-0.075-0.068-0.1770.000
종점지역코드-0.0151.000-0.193-0.188-0.145-0.165-0.134-0.121-0.097-0.183-0.1870.000
가정기반 통근-0.166-0.1931.0000.4810.4190.4790.7760.5230.2230.5470.9360.065
가정기반 통학-0.076-0.1880.4811.0000.4180.4260.4610.3470.1300.4450.5060.013
가정기반 학원-0.057-0.1450.4190.4181.0000.5250.3970.4130.1970.4720.4260.012
가정기반 쇼핑-0.062-0.1650.4790.4260.5251.0000.4570.4730.3080.5070.5010.000
가정기반 기타-0.155-0.1340.7760.4610.3970.4571.0000.4690.1790.5120.8450.064
비가정기반 업무-0.103-0.1210.5230.3470.4130.4730.4691.0000.3080.5050.6150.002
비가정기반 쇼핑-0.075-0.0970.2230.1300.1970.3080.1790.3081.0000.2690.2820.072
비가정기반 기타-0.068-0.1830.5470.4450.4720.5070.5120.5050.2691.0000.5980.051
총목적-0.177-0.1870.9360.5060.4260.5010.8450.6150.2820.5981.0000.058
년도0.0000.0000.0650.0130.0120.0000.0640.0020.0720.0510.0581.000

Missing values

2023-12-11T06:27:14.594187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T06:27:14.753371image/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

년도기점지역기점지역코드종점지역종점지역코드가정기반 통근가정기반 통학가정기반 학원가정기반 쇼핑가정기반 기타비가정기반 업무비가정기반 쇼핑비가정기반 기타총목적
62212020서울특별시 서대문구1113000경기도 광주시31250005070005070001014
84432020경기도 성남시 분당구3102300경기도 광주시31250002873800122422893404453957
35472021경기도 안양시 동안구3104200서울특별시 관악구1121000267800034817222173426
76682020경기도 수원시 팔달구3101300경기도 수원시 팔달구31013009269917538498671807684310430018213227888
28832021경기도 안산시 상록구3109100경기도 수원시 영통구31014001977000031081998
87352020경기도 성남시 중원구3102200경기도 성남시 수정구3102100386552430179321353067331680182380676
7932021경기도 고양시 덕양구3110100경기도 수원시 장안구3101100000000000
3602021경기도 연천군3135000경기도 시흥시3115000000000000
15992021서울특별시 강남구1123000경기도 용인시 처인구31191003380001883100304363660
85192020경기도 평택시3107000서울특별시 서초구11220003795391114333022632
년도기점지역기점지역코드종점지역종점지역코드가정기반 통근가정기반 통학가정기반 학원가정기반 쇼핑가정기반 기타비가정기반 업무비가정기반 쇼핑비가정기반 기타총목적
17812021서울특별시 강남구1123000경기도 수원시 장안구310110000000275048323
4312021서울특별시 금천구1118000서울특별시 구로구11170009344364042561232426343079322905
67132020서울특별시 송파구1124000서울특별시 동대문구1106000490980400425144401210420
65722020서울특별시 서대문구1113000인천광역시 남구2303000108227001076002672451
103232020경기도 군포시3116000경기도 고양시 일산서구3110400395000236000630
86282020경기도 성남시 수정구3102100서울특별시 종로구1101000726000190003919
63672020서울특별시 강서구1116000경기도 수원시 영통구310140074910000013177
88502020경기도 안산시 단원구3109200경기도 안산시 단원구310920036408234793199881605927136881146034677822113
34742021서울특별시 강동구1125000서울특별시 강북구11090000000130400381342
46102021서울특별시 노원구1111000인천광역시 옹진군2332000000000000