Overview

Dataset statistics

Number of variables12
Number of observations500
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory50.9 KiB
Average record size in memory104.3 B

Variable types

Numeric8
Categorical3
Text1

Alerts

DEST_WDAYS(도착_요일) has constant value ""Constant
DEPRT_YM(출발_년월) is highly overall correlated with DEST_YM(도착_년월)High correlation
DEST_YM(도착_년월) is highly overall correlated with DEPRT_YM(출발_년월)High correlation
DEPRT_WDAYS(출발_요일) is highly imbalanced (87.0%)Imbalance

Reproduction

Analysis started2024-03-13 13:10:34.994298
Analysis finished2024-03-13 13:10:43.708020
Duration8.71 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

DEPRT_YM(출발_년월)
Real number (ℝ)

HIGH CORRELATION 

Distinct24
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean202062.72
Minimum202002
Maximum202201
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-03-13T22:10:43.787084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum202002
5-th percentile202003
Q1202007
median202101
Q3202108
95-th percentile202201
Maximum202201
Range199
Interquartile range (IQR)101

Descriptive statistics

Standard deviation59.351519
Coefficient of variation (CV)0.0002937282
Kurtosis-0.74753896
Mean202062.72
Median Absolute Deviation (MAD)89
Skewness0.50762529
Sum1.0103136 × 108
Variance3522.6028
MonotonicityNot monotonic
2024-03-13T22:10:43.937997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
202201 29
 
5.8%
202012 28
 
5.6%
202003 27
 
5.4%
202110 26
 
5.2%
202009 24
 
4.8%
202007 24
 
4.8%
202011 23
 
4.6%
202108 23
 
4.6%
202102 22
 
4.4%
202005 22
 
4.4%
Other values (14) 252
50.4%
ValueCountFrequency (%)
202002 22
4.4%
202003 27
5.4%
202004 20
4.0%
202005 22
4.4%
202006 19
3.8%
202007 24
4.8%
202008 19
3.8%
202009 24
4.8%
202010 20
4.0%
202011 23
4.6%
ValueCountFrequency (%)
202201 29
5.8%
202112 20
4.0%
202111 16
3.2%
202110 26
5.2%
202109 21
4.2%
202108 23
4.6%
202107 12
2.4%
202106 13
2.6%
202105 20
4.0%
202104 14
2.8%

DEPRT_WDAYS(출발_요일)
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
화요일
491 
월요일
 
9

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row화요일
2nd row화요일
3rd row화요일
4th row화요일
5th row화요일

Common Values

ValueCountFrequency (%)
화요일 491
98.2%
월요일 9
 
1.8%

Length

2024-03-13T22:10:44.090744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T22:10:44.189082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
화요일 491
98.2%
월요일 9
 
1.8%

DEPRT_HOUR(출발_시)
Real number (ℝ)

Distinct24
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.67
Minimum0
Maximum23
Zeros4
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-03-13T22:10:44.282852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q19
median14
Q318
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)9

Descriptive statistics

Standard deviation5.2505463
Coefficient of variation (CV)0.38409263
Kurtosis-0.71509212
Mean13.67
Median Absolute Deviation (MAD)4
Skewness-0.22015485
Sum6835
Variance27.568236
MonotonicityNot monotonic
2024-03-13T22:10:44.450888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
18 36
 
7.2%
14 33
 
6.6%
15 32
 
6.4%
12 32
 
6.4%
20 30
 
6.0%
8 29
 
5.8%
17 29
 
5.8%
16 29
 
5.8%
13 29
 
5.8%
9 28
 
5.6%
Other values (14) 193
38.6%
ValueCountFrequency (%)
0 4
 
0.8%
1 3
 
0.6%
2 2
 
0.4%
3 3
 
0.6%
4 3
 
0.6%
5 14
2.8%
6 15
3.0%
7 25
5.0%
8 29
5.8%
9 28
5.6%
ValueCountFrequency (%)
23 11
 
2.2%
22 17
3.4%
21 22
4.4%
20 30
6.0%
19 25
5.0%
18 36
7.2%
17 29
5.8%
16 29
5.8%
15 32
6.4%
14 33
6.6%

DEST_YM(도착_년월)
Real number (ℝ)

HIGH CORRELATION 

Distinct24
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean202062.72
Minimum202002
Maximum202201
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-03-13T22:10:44.599078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum202002
5-th percentile202003
Q1202007
median202101
Q3202108
95-th percentile202201
Maximum202201
Range199
Interquartile range (IQR)101

Descriptive statistics

Standard deviation59.351519
Coefficient of variation (CV)0.0002937282
Kurtosis-0.74753896
Mean202062.72
Median Absolute Deviation (MAD)89
Skewness0.50762529
Sum1.0103136 × 108
Variance3522.6028
MonotonicityNot monotonic
2024-03-13T22:10:44.774069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
202201 29
 
5.8%
202012 28
 
5.6%
202003 27
 
5.4%
202110 26
 
5.2%
202009 24
 
4.8%
202007 24
 
4.8%
202011 23
 
4.6%
202108 23
 
4.6%
202102 22
 
4.4%
202005 22
 
4.4%
Other values (14) 252
50.4%
ValueCountFrequency (%)
202002 22
4.4%
202003 27
5.4%
202004 20
4.0%
202005 22
4.4%
202006 19
3.8%
202007 24
4.8%
202008 19
3.8%
202009 24
4.8%
202010 20
4.0%
202011 23
4.6%
ValueCountFrequency (%)
202201 29
5.8%
202112 20
4.0%
202111 16
3.2%
202110 26
5.2%
202109 21
4.2%
202108 23
4.6%
202107 12
2.4%
202106 13
2.6%
202105 20
4.0%
202104 14
2.8%

DEST_WDAYS(도착_요일)
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
화요일
500 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row화요일
2nd row화요일
3rd row화요일
4th row화요일
5th row화요일

Common Values

ValueCountFrequency (%)
화요일 500
100.0%

Length

2024-03-13T22:10:44.967521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T22:10:45.059059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
화요일 500
100.0%

DEST_HOUR(도착_시)
Real number (ℝ)

Distinct24
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.686
Minimum0
Maximum23
Zeros4
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-03-13T22:10:45.174843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q111
median15
Q319
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)8

Descriptive statistics

Standard deviation5.1959021
Coefficient of variation (CV)0.35379968
Kurtosis-0.21467233
Mean14.686
Median Absolute Deviation (MAD)4
Skewness-0.57717342
Sum7343
Variance26.997399
MonotonicityNot monotonic
2024-03-13T22:10:45.315609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
19 41
 
8.2%
17 38
 
7.6%
18 38
 
7.6%
12 32
 
6.4%
21 32
 
6.4%
16 32
 
6.4%
15 32
 
6.4%
22 30
 
6.0%
14 29
 
5.8%
13 29
 
5.8%
Other values (14) 167
33.4%
ValueCountFrequency (%)
0 4
 
0.8%
1 7
 
1.4%
2 2
 
0.4%
3 3
 
0.6%
4 1
 
0.2%
5 7
 
1.4%
6 11
2.2%
7 10
2.0%
8 23
4.6%
9 21
4.2%
ValueCountFrequency (%)
23 7
 
1.4%
22 30
6.0%
21 32
6.4%
20 28
5.6%
19 41
8.2%
18 38
7.6%
17 38
7.6%
16 32
6.4%
15 32
6.4%
14 29
5.8%
Distinct332
Distinct (%)66.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1520122.2
Minimum1101053
Maximum3531032
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-03-13T22:10:45.453317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1101053
5-th percentile1103065.9
Q11110059.8
median1120064
Q31124080.2
95-th percentile3119390
Maximum3531032
Range2429979
Interquartile range (IQR)14020.5

Descriptive statistics

Standard deviation795717.59
Coefficient of variation (CV)0.52345633
Kurtosis0.31085678
Mean1520122.2
Median Absolute Deviation (MAD)7993.5
Skewness1.49648
Sum7.6006111 × 108
Variance6.3316649 × 1011
MonotonicityNot monotonic
2024-03-13T22:10:45.624545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1123064 9
 
1.8%
1123059 8
 
1.6%
1119054 7
 
1.4%
1123078 6
 
1.2%
1123075 5
 
1.0%
1122066 5
 
1.0%
1124080 5
 
1.0%
1116054 5
 
1.0%
1118051 5
 
1.0%
1114066 5
 
1.0%
Other values (322) 440
88.0%
ValueCountFrequency (%)
1101053 2
0.4%
1101054 1
 
0.2%
1101060 2
0.4%
1101061 2
0.4%
1101064 3
0.6%
1101072 1
 
0.2%
1102052 1
 
0.2%
1102054 1
 
0.2%
1102055 3
0.6%
1102057 1
 
0.2%
ValueCountFrequency (%)
3531032 1
0.2%
3501261 1
0.2%
3436013 1
0.2%
3401261 1
0.2%
3401260 1
0.2%
3304412 1
0.2%
3201011 1
0.2%
3126056 1
0.2%
3126052 1
0.2%
3126034 1
0.2%
Distinct323
Distinct (%)64.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1429539.9
Minimum1101053
Maximum3901061
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-03-13T22:10:45.782437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1101053
5-th percentile1102067.9
Q11109070.8
median1119054.5
Q31124061
95-th percentile3116101.1
Maximum3901061
Range2800008
Interquartile range (IQR)14990.25

Descriptive statistics

Standard deviation718882.84
Coefficient of variation (CV)0.50287709
Kurtosis1.8521024
Mean1429539.9
Median Absolute Deviation (MAD)6980.5
Skewness1.9179829
Sum7.1476994 × 108
Variance5.1679254 × 1011
MonotonicityNot monotonic
2024-03-13T22:10:45.958650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1122053 8
 
1.6%
1106091 5
 
1.0%
1123059 5
 
1.0%
1123064 5
 
1.0%
1124077 5
 
1.0%
1123072 4
 
0.8%
1104067 4
 
0.8%
1123078 4
 
0.8%
1114066 4
 
0.8%
1106081 4
 
0.8%
Other values (313) 452
90.4%
ValueCountFrequency (%)
1101053 1
 
0.2%
1101054 1
 
0.2%
1101056 1
 
0.2%
1101060 1
 
0.2%
1101061 3
0.6%
1101063 1
 
0.2%
1101064 1
 
0.2%
1101067 1
 
0.2%
1101073 1
 
0.2%
1102052 2
0.4%
ValueCountFrequency (%)
3901061 1
0.2%
3901031 1
0.2%
3401211 1
0.2%
3201038 1
0.2%
3128037 1
0.2%
3126052 1
0.2%
3125052 1
0.2%
3124052 1
0.2%
3123061 1
0.2%
3123056 1
0.2%

AGE_GR(연령대)
Real number (ℝ)

Distinct9
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.34
Minimum0
Maximum80
Zeros1
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-03-13T22:10:46.083005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10
Q130
median40
Q350
95-th percentile70
Maximum80
Range80
Interquartile range (IQR)20

Descriptive statistics

Standard deviation16.745304
Coefficient of variation (CV)0.43675806
Kurtosis-0.61027294
Mean38.34
Median Absolute Deviation (MAD)10
Skewness0.28200233
Sum19170
Variance280.40521
MonotonicityNot monotonic
2024-03-13T22:10:46.190124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
30 110
22.0%
40 96
19.2%
20 91
18.2%
50 76
15.2%
60 70
14.0%
10 30
 
6.0%
70 17
 
3.4%
80 9
 
1.8%
0 1
 
0.2%
ValueCountFrequency (%)
0 1
 
0.2%
10 30
 
6.0%
20 91
18.2%
30 110
22.0%
40 96
19.2%
50 76
15.2%
60 70
14.0%
70 17
 
3.4%
80 9
 
1.8%
ValueCountFrequency (%)
80 9
 
1.8%
70 17
 
3.4%
60 70
14.0%
50 76
15.2%
40 96
19.2%
30 110
22.0%
20 91
18.2%
10 30
 
6.0%
0 1
 
0.2%
Distinct9
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
WH
103 
EH
86 
HW
81 
HE
77 
EE
68 
Other values (4)
85 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWE
2nd rowWH
3rd rowHW
4th rowEE
5th rowWE

Common Values

ValueCountFrequency (%)
WH 103
20.6%
EH 86
17.2%
HW 81
16.2%
HE 77
15.4%
EE 68
13.6%
WE 35
 
7.0%
EW 28
 
5.6%
WW 11
 
2.2%
HH 11
 
2.2%

Length

2024-03-13T22:10:46.337508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T22:10:46.463175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
wh 103
20.6%
eh 86
17.2%
hw 81
16.2%
he 77
15.4%
ee 68
13.6%
we 35
 
7.0%
ew 28
 
5.6%
ww 11
 
2.2%
hh 11
 
2.2%
Distinct155
Distinct (%)31.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.746
Minimum2
Maximum475
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-03-13T22:10:46.623643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile9.95
Q126.75
median47
Q377
95-th percentile192.25
Maximum475
Range473
Interquartile range (IQR)50.25

Descriptive statistics

Standard deviation68.772879
Coefficient of variation (CV)1.046039
Kurtosis12.26077
Mean65.746
Median Absolute Deviation (MAD)23.5
Skewness3.1217245
Sum32873
Variance4729.7089
MonotonicityNot monotonic
2024-03-13T22:10:46.781370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24 11
 
2.2%
44 11
 
2.2%
12 10
 
2.0%
20 10
 
2.0%
56 10
 
2.0%
54 9
 
1.8%
28 9
 
1.8%
42 9
 
1.8%
13 8
 
1.6%
46 8
 
1.6%
Other values (145) 405
81.0%
ValueCountFrequency (%)
2 1
 
0.2%
3 2
 
0.4%
4 4
0.8%
5 3
0.6%
6 4
0.8%
7 5
1.0%
8 4
0.8%
9 2
 
0.4%
10 5
1.0%
11 5
1.0%
ValueCountFrequency (%)
475 1
0.2%
466 1
0.2%
445 1
0.2%
432 1
0.2%
417 1
0.2%
402 1
0.2%
373 1
0.2%
360 1
0.2%
340 1
0.2%
326 1
0.2%
Distinct216
Distinct (%)43.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2024-03-13T22:10:47.224832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length4
Mean length3.26
Min length1

Characters and Unicode

Total characters1630
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique154 ?
Unique (%)30.8%

Sample

1st row10.56
2nd row*
3rd row4.26
4th row*
5th row12.32
ValueCountFrequency (%)
130
26.0%
3.29 10
 
2.0%
3.3 10
 
2.0%
3.27 10
 
2.0%
3.28 8
 
1.6%
3.18 7
 
1.4%
3.31 7
 
1.4%
3.01 6
 
1.2%
4.08 6
 
1.2%
3.26 5
 
1.0%
Other values (206) 301
60.2%
2024-03-13T22:10:47.791969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 370
22.7%
3 246
15.1%
1 136
 
8.3%
* 130
 
8.0%
4 127
 
7.8%
6 125
 
7.7%
2 112
 
6.9%
5 98
 
6.0%
8 87
 
5.3%
9 68
 
4.2%
Other values (2) 131
 
8.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1130
69.3%
Other Punctuation 500
30.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 246
21.8%
1 136
12.0%
4 127
11.2%
6 125
11.1%
2 112
9.9%
5 98
 
8.7%
8 87
 
7.7%
9 68
 
6.0%
7 66
 
5.8%
0 65
 
5.8%
Other Punctuation
ValueCountFrequency (%)
. 370
74.0%
* 130
 
26.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1630
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 370
22.7%
3 246
15.1%
1 136
 
8.3%
* 130
 
8.0%
4 127
 
7.8%
6 125
 
7.7%
2 112
 
6.9%
5 98
 
6.0%
8 87
 
5.3%
9 68
 
4.2%
Other values (2) 131
 
8.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1630
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 370
22.7%
3 246
15.1%
1 136
 
8.3%
* 130
 
8.0%
4 127
 
7.8%
6 125
 
7.7%
2 112
 
6.9%
5 98
 
6.0%
8 87
 
5.3%
9 68
 
4.2%
Other values (2) 131
 
8.0%

Interactions

2024-03-13T22:10:42.399894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:10:35.457499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:10:36.384448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:10:37.184678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:10:38.175803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:10:39.099191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:10:40.023784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:10:41.032786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:10:42.543852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:10:35.573122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:10:36.494504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:10:37.306186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:10:38.289100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:10:39.215340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:10:40.168411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:10:41.183259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:10:42.642153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:10:35.682835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:10:36.575398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:10:37.417962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:10:38.396795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:10:39.324577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:10:40.284950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:10:41.304287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:10:42.775642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:10:35.809032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:10:36.685690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:10:37.563298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:10:38.555212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:10:39.452578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:10:40.432034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:10:41.425746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:10:42.883480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:10:35.920924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:10:36.783007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:10:37.659126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:10:38.647239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:10:39.550123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:10:40.551346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:10:41.843248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:10:43.013072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:10:36.030400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:10:36.877646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:10:37.755535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:10:38.763026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:10:39.684199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:10:40.681956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:10:41.976212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:10:43.155279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:10:36.131545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:10:36.973599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:10:37.874089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:10:38.886602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:10:39.794466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:10:40.806687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:10:42.120281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:10:43.256377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:10:36.266236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:10:37.082100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:10:38.065314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:10:39.007731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:10:39.923758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:10:40.924344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:10:42.252397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T22:10:47.913496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
DEPRT_YM(출발_년월)DEPRT_WDAYS(출발_요일)DEPRT_HOUR(출발_시)DEST_YM(도착_년월)DEST_HOUR(도착_시)DEPRTP_EMD_CD(출발지_행정동코드)DESTNTN_EMD_CD(도착지_행정동코드)AGE_GR(연령대)FLOW_TYPE(이동유형)AVG_TRVL_TIME(평균소요시간)
DEPRT_YM(출발_년월)1.0000.0000.0561.0000.1380.1570.1300.0000.0800.000
DEPRT_WDAYS(출발_요일)0.0001.0000.3430.0000.1220.2640.0000.0000.0000.000
DEPRT_HOUR(출발_시)0.0560.3431.0000.0560.0000.2810.0000.0640.0000.000
DEST_YM(도착_년월)1.0000.0000.0561.0000.1380.1570.1300.0000.0800.000
DEST_HOUR(도착_시)0.1380.1220.0000.1381.0000.1730.0000.1260.0000.117
DEPRTP_EMD_CD(출발지_행정동코드)0.1570.2640.2810.1570.1731.0000.0390.0000.0000.000
DESTNTN_EMD_CD(도착지_행정동코드)0.1300.0000.0000.1300.0000.0391.0000.0000.1400.286
AGE_GR(연령대)0.0000.0000.0640.0000.1260.0000.0001.0000.0000.000
FLOW_TYPE(이동유형)0.0800.0000.0000.0800.0000.0000.1400.0001.0000.000
AVG_TRVL_TIME(평균소요시간)0.0000.0000.0000.0000.1170.0000.2860.0000.0001.000
2024-03-13T22:10:48.068089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
DEPRT_WDAYS(출발_요일)FLOW_TYPE(이동유형)
DEPRT_WDAYS(출발_요일)1.0000.000
FLOW_TYPE(이동유형)0.0001.000
2024-03-13T22:10:48.177462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
DEPRT_YM(출발_년월)DEPRT_HOUR(출발_시)DEST_YM(도착_년월)DEST_HOUR(도착_시)DEPRTP_EMD_CD(출발지_행정동코드)DESTNTN_EMD_CD(도착지_행정동코드)AGE_GR(연령대)AVG_TRVL_TIME(평균소요시간)DEPRT_WDAYS(출발_요일)FLOW_TYPE(이동유형)
DEPRT_YM(출발_년월)1.0000.0781.000-0.0680.031-0.042-0.064-0.0270.0000.046
DEPRT_HOUR(출발_시)0.0781.0000.078-0.020-0.0900.0310.0320.0040.2610.000
DEST_YM(도착_년월)1.0000.0781.000-0.0680.031-0.042-0.064-0.0270.0000.046
DEST_HOUR(도착_시)-0.068-0.020-0.0681.000-0.0590.004-0.005-0.0170.0920.000
DEPRTP_EMD_CD(출발지_행정동코드)0.031-0.0900.031-0.0591.000-0.042-0.011-0.0460.1900.000
DESTNTN_EMD_CD(도착지_행정동코드)-0.0420.031-0.0420.004-0.0421.0000.028-0.0400.0000.080
AGE_GR(연령대)-0.0640.032-0.064-0.005-0.0110.0281.0000.0230.0000.000
AVG_TRVL_TIME(평균소요시간)-0.0270.004-0.027-0.017-0.046-0.0400.0231.0000.0000.000
DEPRT_WDAYS(출발_요일)0.0000.2610.0000.0920.1900.0000.0000.0001.0000.000
FLOW_TYPE(이동유형)0.0460.0000.0460.0000.0000.0800.0000.0000.0001.000

Missing values

2024-03-13T22:10:43.398638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T22:10:43.603266image/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

DEPRT_YM(출발_년월)DEPRT_WDAYS(출발_요일)DEPRT_HOUR(출발_시)DEST_YM(도착_년월)DEST_WDAYS(도착_요일)DEST_HOUR(도착_시)DEPRTP_EMD_CD(출발지_행정동코드)DESTNTN_EMD_CD(도착지_행정동코드)AGE_GR(연령대)FLOW_TYPE(이동유형)AVG_TRVL_TIME(평균소요시간)SUM_LIFE_FLPOP(인원합계)
0202107화요일20202107화요일121123059112307220WE2210.56
1202104화요일15202104화요일171108076110206060WH149*
2202010화요일9202010화요일141102052111407340HW1994.26
3202008화요일11202008화요일13125014110406830EE65*
4202004화요일11202004화요일131105067110707370WE7112.32
5202007화요일15202007화요일51103069111306920EE1103.55
6202003화요일20202003화요일211116071111907670HE823.17
7202005화요일19202005화요일13110153112408130WE733.15
8202112화요일5202112화요일141121065111907380EE163.51
9202002화요일6202002화요일71123067110505560EE523.29
DEPRT_YM(출발_년월)DEPRT_WDAYS(출발_요일)DEPRT_HOUR(출발_시)DEST_YM(도착_년월)DEST_WDAYS(도착_요일)DEST_HOUR(도착_시)DEPRTP_EMD_CD(출발지_행정동코드)DESTNTN_EMD_CD(도착지_행정동코드)AGE_GR(연령대)FLOW_TYPE(이동유형)AVG_TRVL_TIME(평균소요시간)SUM_LIFE_FLPOP(인원합계)
490202108화요일11202108화요일183119257112305960EH163.18
491202009화요일16202009화요일161106071112505450HW233.36
492202011화요일8202011화요일161105056112506760WE454.72
493202112화요일16202112화요일181121058112105760HE2253.11
494202104화요일11202104화요일221119065112505520WH785.73
495202004월요일9202004화요일223111056110506360EH603.3
496202111화요일16202111화요일91117054310505160EH129.41
497202007화요일13202007화요일133126056312305650WE369.48
498202011화요일19202011화요일231112056111107620HW863.85
499202009화요일5202009화요일131114066110106130WH573.57