Overview

Dataset statistics

Number of variables15
Number of observations500
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory64.6 KiB
Average record size in memory132.3 B

Variable types

Numeric10
Categorical4
Text1

Alerts

출발_년월일(DEPRT_YMD) is highly overall correlated with 도착_년월일(DEST_YMD) and 1 other fieldsHigh correlation
도착_년월일(DEST_YMD) is highly overall correlated with 출발_년월일(DEPRT_YMD) and 1 other fieldsHigh correlation
기준년월(YYYYMM) is highly overall correlated with 출발_년월일(DEPRT_YMD) and 1 other fieldsHigh correlation
출발_시(DEPRT_HOUR) has 8 (1.6%) zerosZeros

Reproduction

Analysis started2024-03-13 13:09:02.726410
Analysis finished2024-03-13 13:09:20.499449
Duration17.77 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

출발_년월일(DEPRT_YMD)
Real number (ℝ)

HIGH CORRELATION 

Distinct342
Distinct (%)68.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20204760
Minimum20200201
Maximum20210830
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-03-13T22:09:20.949458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20200201
5-th percentile20200306
Q120200619
median20201112
Q320210407
95-th percentile20210804
Maximum20210830
Range10629
Interquartile range (IQR)9788.5

Descriptive statistics

Standard deviation4815.3012
Coefficient of variation (CV)0.00023832509
Kurtosis-1.874204
Mean20204760
Median Absolute Deviation (MAD)785
Skewness0.34559874
Sum1.010238 × 1010
Variance23187126
MonotonicityNot monotonic
2024-03-13T22:09:21.245362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20210512 4
 
0.8%
20201129 3
 
0.6%
20201031 3
 
0.6%
20210204 3
 
0.6%
20200225 3
 
0.6%
20210824 3
 
0.6%
20200716 3
 
0.6%
20200425 3
 
0.6%
20200807 3
 
0.6%
20210712 3
 
0.6%
Other values (332) 469
93.8%
ValueCountFrequency (%)
20200201 2
0.4%
20200203 1
0.2%
20200205 1
0.2%
20200206 1
0.2%
20200207 2
0.4%
20200208 1
0.2%
20200209 1
0.2%
20200210 2
0.4%
20200211 1
0.2%
20200214 1
0.2%
ValueCountFrequency (%)
20210830 2
0.4%
20210829 1
 
0.2%
20210827 1
 
0.2%
20210826 1
 
0.2%
20210825 1
 
0.2%
20210824 3
0.6%
20210820 1
 
0.2%
20210819 2
0.4%
20210818 1
 
0.2%
20210815 1
 
0.2%

출발_시(DEPRT_HOUR)
Real number (ℝ)

ZEROS 

Distinct24
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.584
Minimum0
Maximum23
Zeros8
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-03-13T22:09:21.467192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q110
median14
Q317
95-th percentile21
Maximum23
Range23
Interquartile range (IQR)7

Descriptive statistics

Standard deviation5.0535156
Coefficient of variation (CV)0.3720197
Kurtosis-0.22696521
Mean13.584
Median Absolute Deviation (MAD)4
Skewness-0.43591197
Sum6792
Variance25.53802
MonotonicityNot monotonic
2024-03-13T22:09:21.625633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
15 40
 
8.0%
13 38
 
7.6%
10 37
 
7.4%
17 36
 
7.2%
14 34
 
6.8%
16 34
 
6.8%
20 31
 
6.2%
12 31
 
6.2%
18 30
 
6.0%
9 27
 
5.4%
Other values (14) 162
32.4%
ValueCountFrequency (%)
0 8
 
1.6%
1 5
 
1.0%
2 2
 
0.4%
3 1
 
0.2%
4 7
 
1.4%
5 9
 
1.8%
6 10
 
2.0%
7 14
2.8%
8 23
4.6%
9 27
5.4%
ValueCountFrequency (%)
23 2
 
0.4%
22 15
 
3.0%
21 20
4.0%
20 31
6.2%
19 24
4.8%
18 30
6.0%
17 36
7.2%
16 34
6.8%
15 40
8.0%
14 34
6.8%
Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0
170 
20
167 
40
163 

Length

Max length2
Median length2
Mean length1.66
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row40
4th row0
5th row20

Common Values

ValueCountFrequency (%)
0 170
34.0%
20 167
33.4%
40 163
32.6%

Length

2024-03-13T22:09:21.803111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T22:09:21.944548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 170
34.0%
20 167
33.4%
40 163
32.6%

도착_년월일(DEST_YMD)
Real number (ℝ)

HIGH CORRELATION 

Distinct342
Distinct (%)68.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20204760
Minimum20200201
Maximum20210830
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-03-13T22:09:22.149435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20200201
5-th percentile20200306
Q120200619
median20201112
Q320210407
95-th percentile20210804
Maximum20210830
Range10629
Interquartile range (IQR)9788.5

Descriptive statistics

Standard deviation4815.3012
Coefficient of variation (CV)0.00023832509
Kurtosis-1.874204
Mean20204760
Median Absolute Deviation (MAD)785
Skewness0.34559874
Sum1.010238 × 1010
Variance23187126
MonotonicityNot monotonic
2024-03-13T22:09:22.356313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20210512 4
 
0.8%
20201129 3
 
0.6%
20201031 3
 
0.6%
20210204 3
 
0.6%
20200225 3
 
0.6%
20210824 3
 
0.6%
20200716 3
 
0.6%
20200425 3
 
0.6%
20200807 3
 
0.6%
20210712 3
 
0.6%
Other values (332) 469
93.8%
ValueCountFrequency (%)
20200201 2
0.4%
20200203 1
0.2%
20200205 1
0.2%
20200206 1
0.2%
20200207 2
0.4%
20200208 1
0.2%
20200209 1
0.2%
20200210 2
0.4%
20200211 1
0.2%
20200214 1
0.2%
ValueCountFrequency (%)
20210830 2
0.4%
20210829 1
 
0.2%
20210827 1
 
0.2%
20210826 1
 
0.2%
20210825 1
 
0.2%
20210824 3
0.6%
20210820 1
 
0.2%
20210819 2
0.4%
20210818 1
 
0.2%
20210815 1
 
0.2%

도착_시(DEST_HOUR)
Real number (ℝ)

Distinct24
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.596
Minimum0
Maximum23
Zeros2
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-03-13T22:09:22.525991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q112
median15
Q318
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.9121321
Coefficient of variation (CV)0.33653961
Kurtosis-0.16602346
Mean14.596
Median Absolute Deviation (MAD)3
Skewness-0.47016276
Sum7298
Variance24.129042
MonotonicityNot monotonic
2024-03-13T22:09:22.685915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
13 45
 
9.0%
17 45
 
9.0%
15 40
 
8.0%
14 37
 
7.4%
18 35
 
7.0%
16 34
 
6.8%
20 28
 
5.6%
19 28
 
5.6%
11 27
 
5.4%
12 27
 
5.4%
Other values (14) 154
30.8%
ValueCountFrequency (%)
0 2
 
0.4%
1 2
 
0.4%
2 3
 
0.6%
3 4
 
0.8%
4 6
 
1.2%
5 7
1.4%
6 12
2.4%
7 12
2.4%
8 13
2.6%
9 15
3.0%
ValueCountFrequency (%)
23 17
 
3.4%
22 17
 
3.4%
21 24
4.8%
20 28
5.6%
19 28
5.6%
18 35
7.0%
17 45
9.0%
16 34
6.8%
15 40
8.0%
14 37
7.4%
Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
20
172 
0
170 
40
158 

Length

Max length2
Median length2
Mean length1.66
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row40
2nd row20
3rd row20
4th row20
5th row0

Common Values

ValueCountFrequency (%)
20 172
34.4%
0 170
34.0%
40 158
31.6%

Length

2024-03-13T22:09:22.826796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T22:09:22.935388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20 172
34.4%
0 170
34.0%
40 158
31.6%

출발지(DEPRTR)
Real number (ℝ)

Distinct437
Distinct (%)87.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean146143.22
Minimum110003
Maximum390179
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-03-13T22:09:23.055248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum110003
5-th percentile110162.9
Q1110608.5
median111187
Q3111725.5
95-th percentile312242.25
Maximum390179
Range280176
Interquartile range (IQR)1117

Descriptive statistics

Standard deviation76740.512
Coefficient of variation (CV)0.52510485
Kurtosis1.3022587
Mean146143.22
Median Absolute Deviation (MAD)541
Skewness1.7808321
Sum73071608
Variance5.8891061 × 109
MonotonicityNot monotonic
2024-03-13T22:09:23.200133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
110858 5
 
1.0%
111250 3
 
0.6%
111734 3
 
0.6%
310937 3
 
0.6%
311475 3
 
0.6%
110314 3
 
0.6%
111728 3
 
0.6%
111793 3
 
0.6%
110956 2
 
0.4%
313010 2
 
0.4%
Other values (427) 470
94.0%
ValueCountFrequency (%)
110003 1
0.2%
110028 2
0.4%
110031 1
0.2%
110033 2
0.4%
110034 1
0.2%
110042 1
0.2%
110054 1
0.2%
110060 2
0.4%
110062 1
0.2%
110064 1
0.2%
ValueCountFrequency (%)
390179 1
0.2%
380566 1
0.2%
370820 1
0.2%
360038 1
0.2%
350156 1
0.2%
341175 1
0.2%
341004 1
0.2%
340899 1
0.2%
340331 1
0.2%
320500 1
0.2%

도착지(DESTNTN)
Real number (ℝ)

Distinct428
Distinct (%)85.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean144932.46
Minimum110001
Maximum380748
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-03-13T22:09:23.465611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum110001
5-th percentile110090.95
Q1110566.5
median111074
Q3111706.5
95-th percentile311482.1
Maximum380748
Range270747
Interquartile range (IQR)1140

Descriptive statistics

Standard deviation75371.915
Coefficient of variation (CV)0.52004855
Kurtosis1.3557878
Mean144932.46
Median Absolute Deviation (MAD)547.5
Skewness1.8056043
Sum72466229
Variance5.6809255 × 109
MonotonicityNot monotonic
2024-03-13T22:09:23.726130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
110858 6
 
1.2%
111539 5
 
1.0%
111191 3
 
0.6%
110826 3
 
0.6%
111250 3
 
0.6%
110859 3
 
0.6%
110265 3
 
0.6%
111698 3
 
0.6%
110990 3
 
0.6%
110041 2
 
0.4%
Other values (418) 466
93.2%
ValueCountFrequency (%)
110001 1
0.2%
110004 1
0.2%
110013 1
0.2%
110014 2
0.4%
110016 1
0.2%
110023 1
0.2%
110034 1
0.2%
110041 2
0.4%
110042 1
0.2%
110044 1
0.2%
ValueCountFrequency (%)
380748 1
0.2%
370837 1
0.2%
360571 1
0.2%
360093 1
0.2%
330135 1
0.2%
320578 1
0.2%
313146 1
0.2%
313084 1
0.2%
312798 1
0.2%
312707 1
0.2%

성별(GENDER)
Categorical

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
M
280 
F
220 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd rowM
3rd rowM
4th rowF
5th rowF

Common Values

ValueCountFrequency (%)
M 280
56.0%
F 220
44.0%

Length

2024-03-13T22:09:23.904813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T22:09:24.039504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
m 280
56.0%
f 220
44.0%

연령대(AGE_GR)
Real number (ℝ)

Distinct16
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.68
Minimum0
Maximum80
Zeros1
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-03-13T22:09:24.197292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile15
Q125
median35
Q355
95-th percentile70
Maximum80
Range80
Interquartile range (IQR)30

Descriptive statistics

Standard deviation16.584562
Coefficient of variation (CV)0.41795771
Kurtosis-0.79250018
Mean39.68
Median Absolute Deviation (MAD)15
Skewness0.29454958
Sum19840
Variance275.0477
MonotonicityNot monotonic
2024-03-13T22:09:24.351845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
25 76
15.2%
30 58
11.6%
55 57
11.4%
35 48
9.6%
50 46
9.2%
20 43
8.6%
45 33
6.6%
40 32
6.4%
60 32
6.4%
15 18
 
3.6%
Other values (6) 57
11.4%
ValueCountFrequency (%)
0 1
 
0.2%
10 11
 
2.2%
15 18
 
3.6%
20 43
8.6%
25 76
15.2%
30 58
11.6%
35 48
9.6%
40 32
6.4%
45 33
6.6%
50 46
9.2%
ValueCountFrequency (%)
80 4
 
0.8%
75 11
 
2.2%
70 13
 
2.6%
65 17
 
3.4%
60 32
6.4%
55 57
11.4%
50 46
9.2%
45 33
6.6%
40 32
6.4%
35 48
9.6%
Distinct9
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
EH
152 
HE
116 
EE
89 
WH
45 
HW
39 
Other values (4)
59 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHE
2nd rowWH
3rd rowEH
4th rowHE
5th rowEH

Common Values

ValueCountFrequency (%)
EH 152
30.4%
HE 116
23.2%
EE 89
17.8%
WH 45
 
9.0%
HW 39
 
7.8%
WE 20
 
4.0%
EW 17
 
3.4%
HH 16
 
3.2%
WW 6
 
1.2%

Length

2024-03-13T22:09:24.566635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T22:09:24.716925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
eh 152
30.4%
he 116
23.2%
ee 89
17.8%
wh 45
 
9.0%
hw 39
 
7.8%
we 20
 
4.0%
ew 17
 
3.4%
hh 16
 
3.2%
ww 6
 
1.2%

소요시간(TRVL_TIME)
Real number (ℝ)

Distinct146
Distinct (%)29.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.312
Minimum1
Maximum367
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-03-13T22:09:24.894448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q116
median36
Q369.25
95-th percentile170.25
Maximum367
Range366
Interquartile range (IQR)53.25

Descriptive statistics

Standard deviation58.53049
Coefficient of variation (CV)1.0776714
Kurtosis7.7834929
Mean54.312
Median Absolute Deviation (MAD)23
Skewness2.4774823
Sum27156
Variance3425.8183
MonotonicityNot monotonic
2024-03-13T22:09:25.089444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16 14
 
2.8%
19 13
 
2.6%
5 13
 
2.6%
13 12
 
2.4%
17 12
 
2.4%
11 12
 
2.4%
20 11
 
2.2%
21 10
 
2.0%
9 10
 
2.0%
12 10
 
2.0%
Other values (136) 383
76.6%
ValueCountFrequency (%)
1 3
 
0.6%
2 1
 
0.2%
3 7
1.4%
4 5
 
1.0%
5 13
2.6%
6 4
 
0.8%
7 8
1.6%
8 8
1.6%
9 10
2.0%
10 6
1.2%
ValueCountFrequency (%)
367 1
0.2%
366 1
0.2%
351 1
0.2%
332 1
0.2%
330 1
0.2%
296 2
0.4%
272 1
0.2%
264 1
0.2%
262 1
0.2%
256 1
0.2%

이동거리(DIST)
Real number (ℝ)

Distinct451
Distinct (%)90.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18512.572
Minimum88
Maximum479798
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-03-13T22:09:25.252929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum88
5-th percentile330.7
Q11116
median3630.5
Q314942.5
95-th percentile65187.4
Maximum479798
Range479710
Interquartile range (IQR)13826.5

Descriptive statistics

Standard deviation50942.373
Coefficient of variation (CV)2.7517717
Kurtosis43.079384
Mean18512.572
Median Absolute Deviation (MAD)3163.5
Skewness6.057407
Sum9256286
Variance2.5951253 × 109
MonotonicityNot monotonic
2024-03-13T22:09:25.436815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
345 6
 
1.2%
239 5
 
1.0%
284 5
 
1.0%
177 4
 
0.8%
377 4
 
0.8%
142 4
 
0.8%
494 3
 
0.6%
1508 3
 
0.6%
417 3
 
0.6%
238 2
 
0.4%
Other values (441) 461
92.2%
ValueCountFrequency (%)
88 1
 
0.2%
142 4
0.8%
177 4
0.8%
222 2
 
0.4%
238 2
 
0.4%
239 5
1.0%
266 1
 
0.2%
284 5
1.0%
287 1
 
0.2%
333 1
 
0.2%
ValueCountFrequency (%)
479798 1
0.2%
470217 1
0.2%
446643 1
0.2%
322093 1
0.2%
309307 1
0.2%
286058 1
0.2%
285071 1
0.2%
253825 1
0.2%
189463 1
0.2%
188936 1
0.2%
Distinct103
Distinct (%)20.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2024-03-13T22:09:25.705063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length2.718
Min length1

Characters and Unicode

Total characters1359
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

Unique53 ?
Unique (%)10.6%

Sample

1st row3.53
2nd row3.28
3rd row3.27
4th row*
5th row3.04
ValueCountFrequency (%)
204
40.8%
3.3 16
 
3.2%
3.29 15
 
3.0%
3.01 14
 
2.8%
3.28 13
 
2.6%
3.27 10
 
2.0%
3.04 10
 
2.0%
3.31 10
 
2.0%
3.13 9
 
1.8%
3.02 8
 
1.6%
Other values (93) 191
38.2%
2024-03-13T22:09:26.260473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 296
21.8%
3 287
21.1%
* 204
15.0%
4 108
 
7.9%
2 77
 
5.7%
1 77
 
5.7%
5 74
 
5.4%
0 65
 
4.8%
8 47
 
3.5%
6 46
 
3.4%
Other values (2) 78
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 859
63.2%
Other Punctuation 500
36.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 287
33.4%
4 108
 
12.6%
2 77
 
9.0%
1 77
 
9.0%
5 74
 
8.6%
0 65
 
7.6%
8 47
 
5.5%
6 46
 
5.4%
9 41
 
4.8%
7 37
 
4.3%
Other Punctuation
ValueCountFrequency (%)
. 296
59.2%
* 204
40.8%

Most occurring scripts

ValueCountFrequency (%)
Common 1359
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 296
21.8%
3 287
21.1%
* 204
15.0%
4 108
 
7.9%
2 77
 
5.7%
1 77
 
5.7%
5 74
 
5.4%
0 65
 
4.8%
8 47
 
3.5%
6 46
 
3.4%
Other values (2) 78
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1359
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 296
21.8%
3 287
21.1%
* 204
15.0%
4 108
 
7.9%
2 77
 
5.7%
1 77
 
5.7%
5 74
 
5.4%
0 65
 
4.8%
8 47
 
3.5%
6 46
 
3.4%
Other values (2) 78
 
5.7%

기준년월(YYYYMM)
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean202047.44
Minimum202002
Maximum202108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-03-13T22:09:26.432185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum202002
5-th percentile202003
Q1202006
median202011
Q3202104
95-th percentile202108
Maximum202108
Range106
Interquartile range (IQR)98

Descriptive statistics

Standard deviation48.150801
Coefficient of variation (CV)0.00023831433
Kurtosis-1.8741818
Mean202047.44
Median Absolute Deviation (MAD)8
Skewness0.34560452
Sum1.0102372 × 108
Variance2318.4996
MonotonicityNot monotonic
2024-03-13T22:09:26.599797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
202105 36
 
7.2%
202005 33
 
6.6%
202008 30
 
6.0%
202108 29
 
5.8%
202010 29
 
5.8%
202011 28
 
5.6%
202004 28
 
5.6%
202103 27
 
5.4%
202003 26
 
5.2%
202102 26
 
5.2%
Other values (9) 208
41.6%
ValueCountFrequency (%)
202002 21
4.2%
202003 26
5.2%
202004 28
5.6%
202005 33
6.6%
202006 23
4.6%
202007 26
5.2%
202008 30
6.0%
202009 23
4.6%
202010 29
5.8%
202011 28
5.6%
ValueCountFrequency (%)
202108 29
5.8%
202107 26
5.2%
202106 17
3.4%
202105 36
7.2%
202104 24
4.8%
202103 27
5.4%
202102 26
5.2%
202101 22
4.4%
202012 26
5.2%
202011 28
5.6%

Interactions

2024-03-13T22:09:18.583151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:09:06.445030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:09:07.877031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:09:09.029047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:09:10.481016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:09:11.824947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:09:13.269293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:09:14.853284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:09:15.967430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:09:17.265373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:09:18.705500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:09:06.644296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:09:08.004169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:09:09.163913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:09:10.609512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:09:11.966531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:09:13.745097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:09:14.961961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:09:16.096648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:09:17.365298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:09:18.837894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:09:06.780401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:09:08.101591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:09:09.295633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:09:10.721517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:09:12.129666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:09:13.857539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:09:15.064878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:09:16.204723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:09:17.490025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:09:18.974006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:09:06.895967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:09:08.208090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:09:09.432743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:09:10.886795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:09:12.291771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:09:13.970806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:09:15.173113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:09:16.311491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:09:17.629624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:09:19.100741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:09:07.033939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:09:08.313282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:09:09.561504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:09:11.002521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:09:12.435385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:09:14.077188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:09:15.269846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:09:16.447915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:09:17.762737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:09:19.221295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:09:07.161999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:09:08.446460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:09:09.693784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:09:11.120595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:09:12.548546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:09:14.186462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:09:15.398925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:09:16.590962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:09:17.889398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:09:19.357202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:09:07.319762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:09:08.546695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:09:09.835351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:09:11.232281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:09:12.679900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:09:14.316507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:09:15.493988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:09:16.711635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:09:18.003332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:09:19.503616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:09:07.472310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:09:08.682087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:09:10.016257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:09:11.378623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:09:12.835476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:09:14.466700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:09:15.613558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:09:16.894964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:09:18.116766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:09:19.618235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:09:07.618795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:09:08.797112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:09:10.191800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:09:11.496445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:09:12.970257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:09:14.609760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:09:15.758455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:09:17.030827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:09:18.254712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:09:19.733873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:09:07.721963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:09:08.915277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:09:10.328311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:09:11.643473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:09:13.116258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:09:14.741466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:09:15.856810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:09:17.153715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:09:18.391046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T22:09:26.742283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
출발_년월일(DEPRT_YMD)출발_시(DEPRT_HOUR)출발_20분_단위(DEPRT_MINUTE)도착_년월일(DEST_YMD)도착_시(DEST_HOUR)도착_20분_단위(DEST_MINUTE)출발지(DEPRTR)도착지(DESTNTN)성별(GENDER)연령대(AGE_GR)이동유형(FLOW_TYPE)소요시간(TRVL_TIME)이동거리(DIST)기준년월(YYYYMM)
출발_년월일(DEPRT_YMD)1.0000.0000.0001.0000.0000.0480.0000.0000.0360.0000.0000.0990.0751.000
출발_시(DEPRT_HOUR)0.0001.0000.1750.0000.0000.0830.0000.2260.0000.0000.0000.0000.0000.000
출발_20분_단위(DEPRT_MINUTE)0.0000.1751.0000.0000.0700.1260.1140.0000.0000.0000.0640.0640.0000.000
도착_년월일(DEST_YMD)1.0000.0000.0001.0000.0000.0480.0000.0000.0360.0000.0000.0990.0751.000
도착_시(DEST_HOUR)0.0000.0000.0700.0001.0000.0000.0000.0000.1500.0000.0000.1480.1330.000
도착_20분_단위(DEST_MINUTE)0.0480.0830.1260.0480.0001.0000.0840.0490.0000.0320.2230.1080.0000.048
출발지(DEPRTR)0.0000.0000.1140.0000.0000.0841.0000.0000.0000.0000.0950.0000.0000.000
도착지(DESTNTN)0.0000.2260.0000.0000.0000.0490.0001.0000.0000.0000.0000.3540.0000.000
성별(GENDER)0.0360.0000.0000.0360.1500.0000.0000.0001.0000.0000.0000.0930.0000.000
연령대(AGE_GR)0.0000.0000.0000.0000.0000.0320.0000.0000.0001.0000.0980.1130.0000.000
이동유형(FLOW_TYPE)0.0000.0000.0640.0000.0000.2230.0950.0000.0000.0981.0000.1020.1470.000
소요시간(TRVL_TIME)0.0990.0000.0640.0990.1480.1080.0000.3540.0930.1130.1021.0000.0000.090
이동거리(DIST)0.0750.0000.0000.0750.1330.0000.0000.0000.0000.0000.1470.0001.0000.080
기준년월(YYYYMM)1.0000.0000.0001.0000.0000.0480.0000.0000.0000.0000.0000.0900.0801.000
2024-03-13T22:09:27.260899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
이동유형(FLOW_TYPE)성별(GENDER)도착_20분_단위(DEST_MINUTE)출발_20분_단위(DEPRT_MINUTE)
이동유형(FLOW_TYPE)1.0000.0000.0990.027
성별(GENDER)0.0001.0000.0000.000
도착_20분_단위(DEST_MINUTE)0.0990.0001.0000.038
출발_20분_단위(DEPRT_MINUTE)0.0270.0000.0381.000
2024-03-13T22:09:27.410979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
출발_년월일(DEPRT_YMD)출발_시(DEPRT_HOUR)도착_년월일(DEST_YMD)도착_시(DEST_HOUR)출발지(DEPRTR)도착지(DESTNTN)연령대(AGE_GR)소요시간(TRVL_TIME)이동거리(DIST)기준년월(YYYYMM)출발_20분_단위(DEPRT_MINUTE)도착_20분_단위(DEST_MINUTE)성별(GENDER)이동유형(FLOW_TYPE)
출발_년월일(DEPRT_YMD)1.0000.0361.000-0.003-0.044-0.026-0.028-0.019-0.0600.9990.0000.0760.0300.000
출발_시(DEPRT_HOUR)0.0361.0000.036-0.0530.017-0.041-0.0050.017-0.0340.0340.1050.0490.0000.000
도착_년월일(DEST_YMD)1.0000.0361.000-0.003-0.044-0.026-0.028-0.019-0.0600.9990.0000.0760.0300.000
도착_시(DEST_HOUR)-0.003-0.053-0.0031.0000.0480.0610.016-0.028-0.008-0.0050.0410.0000.1140.000
출발지(DEPRTR)-0.0440.017-0.0440.0481.000-0.0760.0390.0100.022-0.0480.0460.0350.0000.047
도착지(DESTNTN)-0.026-0.041-0.0260.061-0.0761.0000.0110.0280.018-0.0240.0000.0370.0000.000
연령대(AGE_GR)-0.028-0.005-0.0280.0160.0390.0111.000-0.074-0.017-0.0280.0000.0180.0000.044
소요시간(TRVL_TIME)-0.0190.017-0.019-0.0280.0100.028-0.0741.000-0.050-0.0160.0370.0630.0710.046
이동거리(DIST)-0.060-0.034-0.060-0.0080.0220.018-0.017-0.0501.000-0.0630.0000.0000.0000.077
기준년월(YYYYMM)0.9990.0340.999-0.005-0.048-0.024-0.028-0.016-0.0631.0000.0000.0760.0300.000
출발_20분_단위(DEPRT_MINUTE)0.0000.1050.0000.0410.0460.0000.0000.0370.0000.0001.0000.0380.0000.027
도착_20분_단위(DEST_MINUTE)0.0760.0490.0760.0000.0350.0370.0180.0630.0000.0760.0381.0000.0000.099
성별(GENDER)0.0300.0000.0300.1140.0000.0000.0000.0710.0000.0300.0000.0001.0000.000
이동유형(FLOW_TYPE)0.0000.0000.0000.0000.0470.0000.0440.0460.0770.0000.0270.0990.0001.000

Missing values

2024-03-13T22:09:19.951034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T22:09:20.333917image/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

출발_년월일(DEPRT_YMD)출발_시(DEPRT_HOUR)출발_20분_단위(DEPRT_MINUTE)도착_년월일(DEST_YMD)도착_시(DEST_HOUR)도착_20분_단위(DEST_MINUTE)출발지(DEPRTR)도착지(DESTNTN)성별(GENDER)연령대(AGE_GR)이동유형(FLOW_TYPE)소요시간(TRVL_TIME)이동거리(DIST)인원(LIFE_FLPOP)기준년월(YYYYMM)
02020050500202005052340111743111494M75HE845673.53202005
120200317220202003172020310193111025M20WH6219763.28202003
2202011031040202011032220110727111292M15EH77163.27202011
320210323190202103232120110962110752F45HE1455664*202103
420210407192020210407140230169110772F25EH33019383.04202104
52021070400202107041840111477110398F55EH523790*202107
6202107201120202107201620111797111382M35HE112396.06202107
720201021100202010211940111256110240F55EH5455213.13202010
820200318202020200318840111250110323M50EH9313223.17202003
9202006091740202006091740111790110264F35HE1127023.27202006
출발_년월일(DEPRT_YMD)출발_시(DEPRT_HOUR)출발_20분_단위(DEPRT_MINUTE)도착_년월일(DEST_YMD)도착_시(DEST_HOUR)도착_20분_단위(DEST_MINUTE)출발지(DEPRTR)도착지(DESTNTN)성별(GENDER)연령대(AGE_GR)이동유형(FLOW_TYPE)소요시간(TRVL_TIME)이동거리(DIST)인원(LIFE_FLPOP)기준년월(YYYYMM)
490202007291320202007291920110787111695F60EE189093.19202007
49120200408130202004081520370820111250F60EH3350883.29202004
49220210106174020210106140110858311135F55WH21430*202101
493202009262220202009261340111252111177M45EH745823.3202009
49420210208152020210208140111725311226M20HE54236103.14202102
49520201204162020201204540110399110546M25EH2722499*202012
4962021041517402021041560110504310343M40EH953453.83202104
49720210726174020210726120310389110850F35EH868584*202107
49820200220152020200220220310364320578F60HE232759*202002
4992021083013020210830200310922111283M60HW16130683.01202108