Overview

Dataset statistics

Number of variables10
Number of observations500
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory43.6 KiB
Average record size in memory89.3 B

Variable types

Text1
Numeric8
Categorical1

Alerts

주중보행인구수(WKDY_FLPOP_CNT) has 84 (16.8%) zerosZeros
주말보행인구수(WKND_FLPOP_CNT) has 235 (47.0%) zerosZeros

Reproduction

Analysis started2023-12-10 14:51:19.926259
Analysis finished2023-12-10 14:51:28.405316
Duration8.48 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Distinct199
Distinct (%)39.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-10T23:51:28.663024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters4000
Distinct characters11
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

Unique72 ?
Unique (%)14.4%

Sample

1st row4*7*0*1*
2nd row4*6*1*4*
3rd row4*5*2*7*
4th row4*4*7*5*
5th row4*7*7*7*
ValueCountFrequency (%)
4*5*0*8 7
 
1.4%
4*7*9*6 7
 
1.4%
4*4*3*6 7
 
1.4%
4*7*7*2 7
 
1.4%
4*7*7*9 7
 
1.4%
4*6*1*1 6
 
1.2%
4*6*7*4 6
 
1.2%
4*5*5*5 6
 
1.2%
4*7*3*0 6
 
1.2%
4*6*9*3 6
 
1.2%
Other values (189) 435
87.0%
2023-12-10T23:51:29.115991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
* 2000
50.0%
4 630
 
15.8%
7 238
 
5.9%
6 225
 
5.6%
5 169
 
4.2%
8 160
 
4.0%
9 127
 
3.2%
3 126
 
3.1%
1 111
 
2.8%
0 108
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Other Punctuation 2000
50.0%
Decimal Number 2000
50.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 630
31.5%
7 238
 
11.9%
6 225
 
11.2%
5 169
 
8.5%
8 160
 
8.0%
9 127
 
6.3%
3 126
 
6.3%
1 111
 
5.5%
0 108
 
5.4%
2 106
 
5.3%
Other Punctuation
ValueCountFrequency (%)
* 2000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
* 2000
50.0%
4 630
 
15.8%
7 238
 
5.9%
6 225
 
5.6%
5 169
 
4.2%
8 160
 
4.0%
9 127
 
3.2%
3 126
 
3.1%
1 111
 
2.8%
0 108
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 2000
50.0%
4 630
 
15.8%
7 238
 
5.9%
6 225
 
5.6%
5 169
 
4.2%
8 160
 
4.0%
9 127
 
3.2%
3 126
 
3.1%
1 111
 
2.8%
0 108
 
2.7%

x좌표(X_COORD)
Real number (ℝ)

Distinct431
Distinct (%)86.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean189922.59
Minimum188180
Maximum191344.59
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:51:29.306593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum188180
5-th percentile188732.07
Q1189424.45
median190022.41
Q3190444.66
95-th percentile190943.11
Maximum191344.59
Range3164.5911
Interquartile range (IQR)1020.2063

Descriptive statistics

Standard deviation688.3927
Coefficient of variation (CV)0.0036245961
Kurtosis-0.69065153
Mean189922.59
Median Absolute Deviation (MAD)471.44578
Skewness-0.3231909
Sum94961297
Variance473884.51
MonotonicityNot monotonic
2023-12-10T23:51:29.467763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
189371.406507057 3
 
0.6%
190436.173516034 3
 
0.6%
190445.189507806 3
 
0.6%
189372.998131676 3
 
0.6%
188969.848339079 3
 
0.6%
190649.415633978 3
 
0.6%
188622.000158713 3
 
0.6%
190893.032019304 3
 
0.6%
189937.275072476 3
 
0.6%
190416.545035712 2
 
0.4%
Other values (421) 471
94.2%
ValueCountFrequency (%)
188179.996135732 1
0.2%
188229.991842692 1
0.2%
188280.518013429 1
0.2%
188432.096526474 1
0.2%
188475.196086646 1
0.2%
188476.257096429 1
0.2%
188482.622697768 1
0.2%
188483.153129154 1
0.2%
188524.130793948 1
0.2%
188532.088000841 1
0.2%
ValueCountFrequency (%)
191344.587203335 1
0.2%
191246.716323185 2
0.4%
191245.125425751 1
0.2%
191243.53447931 1
0.2%
191197.780858172 2
0.4%
191196.720287255 2
0.4%
191196.189993629 1
0.2%
191194.599080073 1
0.2%
191146.193963218 1
0.2%
191144.07273454 1
0.2%

y좌표(Y_COORD)
Real number (ℝ)

Distinct428
Distinct (%)85.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean543906.38
Minimum542226.73
Maximum545524.34
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:51:29.625633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum542226.73
5-th percentile542621.94
Q1543369.08
median543862.27
Q3544568.85
95-th percentile545207.52
Maximum545524.34
Range3297.6122
Interquartile range (IQR)1199.769

Descriptive statistics

Standard deviation796.11375
Coefficient of variation (CV)0.0014636963
Kurtosis-0.89888568
Mean543906.38
Median Absolute Deviation (MAD)623.09314
Skewness-0.019044733
Sum2.7195319 × 108
Variance633797.11
MonotonicityNot monotonic
2023-12-10T23:51:29.801598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
543567.073095219 4
 
0.8%
544255.345016901 3
 
0.6%
544670.696099935 3
 
0.6%
543815.991652381 3
 
0.6%
543812.278679028 3
 
0.6%
543424.510799217 3
 
0.6%
543667.595267215 2
 
0.4%
542970.30450721 2
 
0.4%
543373.984463991 2
 
0.4%
545018.545300871 2
 
0.4%
Other values (418) 473
94.6%
ValueCountFrequency (%)
542226.728792782 1
0.2%
542276.724774327 1
0.2%
542327.251018902 1
0.2%
542329.372072035 1
0.2%
542374.595663709 1
0.2%
542377.24700575 1
0.2%
542377.77727437 1
0.2%
542426.182444602 1
0.2%
542426.712718528 1
0.2%
542427.773266591 1
0.2%
ValueCountFrequency (%)
545524.340976053 1
0.2%
545473.814415145 1
0.2%
545424.349065717 2
0.4%
545423.818465387 1
0.2%
545406.308694189 2
0.4%
545374.883705399 2
0.4%
545373.822515554 2
0.4%
545372.761325995 2
0.4%
545371.70013672 1
0.2%
545324.88774461 1
0.2%
Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
273 
2
227 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row2
5th row2

Common Values

ValueCountFrequency (%)
1 273
54.6%
2 227
45.4%

Length

2023-12-10T23:51:29.970248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:51:30.065287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 273
54.6%
2 227
45.4%

연령대(AGE_GR_SCTN_CD)
Real number (ℝ)

Distinct13
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4021.78
Minimum1014
Maximum7074
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:51:30.151561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1014
5-th percentile1519
Q12529
median4044
Q35559
95-th percentile6569
Maximum7074
Range6060
Interquartile range (IQR)3030

Descriptive statistics

Standard deviation1627.2311
Coefficient of variation (CV)0.40460471
Kurtosis-0.97845893
Mean4021.78
Median Absolute Deviation (MAD)1515
Skewness0.13254583
Sum2010890
Variance2647881.1
MonotonicityNot monotonic
2023-12-10T23:51:30.267213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
2529 58
11.6%
4549 54
10.8%
4044 53
10.6%
3539 52
10.4%
2024 50
10.0%
5559 44
8.8%
6064 36
7.2%
3034 35
7.0%
5054 33
6.6%
6569 25
5.0%
Other values (3) 60
12.0%
ValueCountFrequency (%)
1014 11
 
2.2%
1519 25
5.0%
2024 50
10.0%
2529 58
11.6%
3034 35
7.0%
3539 52
10.4%
4044 53
10.6%
4549 54
10.8%
5054 33
6.6%
5559 44
8.8%
ValueCountFrequency (%)
7074 24
4.8%
6569 25
5.0%
6064 36
7.2%
5559 44
8.8%
5054 33
6.6%
4549 54
10.8%
4044 53
10.6%
3539 52
10.4%
3034 35
7.0%
2529 58
11.6%

주중보행인구수(WKDY_FLPOP_CNT)
Real number (ℝ)

ZEROS 

Distinct378
Distinct (%)75.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.32911
Minimum0
Maximum8.0871
Zeros84
Zeros (%)16.8%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:51:30.421846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.0277
median0.10945
Q30.336175
95-th percentile1.42795
Maximum8.0871
Range8.0871
Interquartile range (IQR)0.308475

Descriptive statistics

Standard deviation0.68012711
Coefficient of variation (CV)2.0665647
Kurtosis57.66557
Mean0.32911
Median Absolute Deviation (MAD)0.1091
Skewness6.2350493
Sum164.555
Variance0.46257288
MonotonicityNot monotonic
2023-12-10T23:51:30.608138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 84
 
16.8%
0.35 4
 
0.8%
0.0554 3
 
0.6%
0.0996 3
 
0.6%
0.28 3
 
0.6%
0.087 2
 
0.4%
0.1555 2
 
0.4%
0.0109 2
 
0.4%
0.2352 2
 
0.4%
0.0006 2
 
0.4%
Other values (368) 393
78.6%
ValueCountFrequency (%)
0.0 84
16.8%
0.0001 1
 
0.2%
0.0006 2
 
0.4%
0.001 1
 
0.2%
0.0012 1
 
0.2%
0.0056 2
 
0.4%
0.0062 1
 
0.2%
0.0073 1
 
0.2%
0.0109 2
 
0.4%
0.012 1
 
0.2%
ValueCountFrequency (%)
8.0871 1
0.2%
7.3426 1
0.2%
3.3437 1
0.2%
2.9709 1
0.2%
2.9164 1
0.2%
2.712 1
0.2%
2.5693 1
0.2%
2.3913 1
0.2%
2.334 1
0.2%
2.2867 1
0.2%

주말보행인구수(WKND_FLPOP_CNT)
Real number (ℝ)

ZEROS 

Distinct235
Distinct (%)47.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1379456
Minimum0
Maximum3.9244
Zeros235
Zeros (%)47.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:51:30.792217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.0235
Q30.108725
95-th percentile0.663735
Maximum3.9244
Range3.9244
Interquartile range (IQR)0.108725

Descriptive statistics

Standard deviation0.357185
Coefficient of variation (CV)2.5893178
Kurtosis41.810333
Mean0.1379456
Median Absolute Deviation (MAD)0.0235
Skewness5.6468518
Sum68.9728
Variance0.12758112
MonotonicityNot monotonic
2023-12-10T23:51:30.983148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 235
47.0%
0.029 4
 
0.8%
0.0277 4
 
0.8%
0.0422 3
 
0.6%
0.1032 3
 
0.6%
0.126 2
 
0.4%
0.0274 2
 
0.4%
0.0932 2
 
0.4%
0.1108 2
 
0.4%
0.4451 2
 
0.4%
Other values (225) 241
48.2%
ValueCountFrequency (%)
0.0 235
47.0%
0.0014 1
 
0.2%
0.0017 1
 
0.2%
0.0021 1
 
0.2%
0.0041 1
 
0.2%
0.0094 1
 
0.2%
0.0113 1
 
0.2%
0.0132 1
 
0.2%
0.0164 1
 
0.2%
0.0193 1
 
0.2%
ValueCountFrequency (%)
3.9244 1
0.2%
2.8008 1
0.2%
2.7144 1
0.2%
2.1601 1
0.2%
2.0113 1
0.2%
1.9165 1
0.2%
1.9023 1
0.2%
1.4256 1
0.2%
1.2441 1
0.2%
1.1124 1
0.2%
Distinct294
Distinct (%)58.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7965238.2
Minimum26110
Maximum11740700
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:51:31.156445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum26110
5-th percentile28237
Q144195
median11440670
Q311545710
95-th percentile11682238
Maximum11740700
Range11714590
Interquartile range (IQR)11501515

Descriptive statistics

Standard deviation5294579
Coefficient of variation (CV)0.66471069
Kurtosis-1.3096427
Mean7965238.2
Median Absolute Deviation (MAD)179940
Skewness-0.8325622
Sum3.9826191 × 109
Variance2.8032567 × 1013
MonotonicityNot monotonic
2023-12-10T23:51:31.318913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11530530 8
 
1.6%
28170 6
 
1.2%
11545510 6
 
1.2%
11500530 5
 
1.0%
11560720 5
 
1.0%
11590550 5
 
1.0%
11530560 5
 
1.0%
11470560 5
 
1.0%
41281 4
 
0.8%
11560710 4
 
0.8%
Other values (284) 447
89.4%
ValueCountFrequency (%)
26110 1
 
0.2%
26200 2
 
0.4%
26230 1
 
0.2%
26320 1
 
0.2%
26710 1
 
0.2%
27170 1
 
0.2%
27260 1
 
0.2%
27290 1
 
0.2%
28140 2
 
0.4%
28170 6
1.2%
ValueCountFrequency (%)
11740700 2
0.4%
11740690 1
0.2%
11740650 1
0.2%
11740640 1
0.2%
11740600 2
0.4%
11740590 1
0.2%
11740570 1
0.2%
11740550 2
0.4%
11740530 1
0.2%
11740515 1
0.2%

행정동코드(ADMI_CD)
Real number (ℝ)

Distinct292
Distinct (%)58.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11429552
Minimum11110515
Maximum11740700
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:51:31.463143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11110515
5-th percentile11110700
Q111260655
median11440610
Q311620575
95-th percentile11712210
Maximum11740700
Range630185
Interquartile range (IQR)359920

Descriptive statistics

Standard deviation194946.45
Coefficient of variation (CV)0.017056351
Kurtosis-1.2545342
Mean11429552
Median Absolute Deviation (MAD)179965
Skewness0.016060571
Sum5.7147761 × 109
Variance3.800412 × 1010
MonotonicityNot monotonic
2023-12-10T23:51:31.631565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11470520 4
 
0.8%
11305660 4
 
0.8%
11140665 4
 
0.8%
11530750 4
 
0.8%
11470611 4
 
0.8%
11680510 4
 
0.8%
11110515 4
 
0.8%
11410710 4
 
0.8%
11110690 4
 
0.8%
11560560 3
 
0.6%
Other values (282) 461
92.2%
ValueCountFrequency (%)
11110515 4
0.8%
11110530 2
0.4%
11110550 2
0.4%
11110570 2
0.4%
11110580 1
 
0.2%
11110600 2
0.4%
11110615 2
0.4%
11110630 1
 
0.2%
11110640 1
 
0.2%
11110670 2
0.4%
ValueCountFrequency (%)
11740700 1
 
0.2%
11740690 3
0.6%
11740685 2
0.4%
11740660 1
 
0.2%
11740650 1
 
0.2%
11740600 3
0.6%
11740590 1
 
0.2%
11740580 3
0.6%
11740570 1
 
0.2%
11740560 1
 
0.2%

기준년월(STD_YM)
Real number (ℝ)

Distinct25
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean201656.75
Minimum201601
Maximum201801
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:51:31.784737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum201601
5-th percentile201602
Q1201607
median201612
Q3201706
95-th percentile201712
Maximum201801
Range200
Interquartile range (IQR)99

Descriptive statistics

Standard deviation56.123133
Coefficient of variation (CV)0.00027831021
Kurtosis-0.80550613
Mean201656.75
Median Absolute Deviation (MAD)11
Skewness0.53446141
Sum1.0082838 × 108
Variance3149.8061
MonotonicityNot monotonic
2023-12-10T23:51:31.920130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
201609 30
 
6.0%
201610 28
 
5.6%
201611 26
 
5.2%
201706 24
 
4.8%
201602 23
 
4.6%
201608 22
 
4.4%
201603 22
 
4.4%
201705 22
 
4.4%
201607 22
 
4.4%
201606 21
 
4.2%
Other values (15) 260
52.0%
ValueCountFrequency (%)
201601 19
3.8%
201602 23
4.6%
201603 22
4.4%
201604 13
2.6%
201605 20
4.0%
201606 21
4.2%
201607 22
4.4%
201608 22
4.4%
201609 30
6.0%
201610 28
5.6%
ValueCountFrequency (%)
201801 18
3.6%
201712 15
3.0%
201711 16
3.2%
201710 17
3.4%
201709 14
2.8%
201708 20
4.0%
201707 13
2.6%
201706 24
4.8%
201705 22
4.4%
201704 18
3.6%

Interactions

2023-12-10T23:51:26.871596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:20.303979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:21.267326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:22.330751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:23.174076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:24.116402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:25.011368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:25.951638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:26.996516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:20.414603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:21.363811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:22.431701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:23.285399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:24.223723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:25.115757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:26.067206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:27.123074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:20.529895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:21.454815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:22.533383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:23.393542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:24.333715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:25.240705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:26.181801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:27.241246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:20.693972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:21.548617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:22.645384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:23.493195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:24.452476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:25.366371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:26.295525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:27.378102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:20.844038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:21.648312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:22.754568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:23.619100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:24.569717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:25.490968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:26.429424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:27.501148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:20.966699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:21.747835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:22.859012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:23.761269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:24.677356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:25.620774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:26.542664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:27.606565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:21.069830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:22.133177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:22.969861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:23.888263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:24.783859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:25.724680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:26.647988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:27.723744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:21.162433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:22.225317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:23.065734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:23.994209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:24.892256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:25.834774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:26.748499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:51:32.038142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
x좌표(X_COORD)y좌표(Y_COORD)성별(AS_GNDR_CD)연령대(AGE_GR_SCTN_CD)주중보행인구수(WKDY_FLPOP_CNT)주말보행인구수(WKND_FLPOP_CNT)거주지코드(INFLOW_ADMIN_CD)행정동코드(ADMI_CD)기준년월(STD_YM)
x좌표(X_COORD)1.0000.0910.0570.0000.0000.0000.0100.0000.000
y좌표(Y_COORD)0.0911.0000.0000.0000.1900.0410.0000.0000.000
성별(AS_GNDR_CD)0.0570.0001.0000.1620.1120.0000.0630.1690.000
연령대(AGE_GR_SCTN_CD)0.0000.0000.1621.0000.0000.0000.0000.0000.138
주중보행인구수(WKDY_FLPOP_CNT)0.0000.1900.1120.0001.0000.0000.1220.0000.000
주말보행인구수(WKND_FLPOP_CNT)0.0000.0410.0000.0000.0001.0000.0000.0960.167
거주지코드(INFLOW_ADMIN_CD)0.0100.0000.0630.0000.1220.0001.0000.0000.000
행정동코드(ADMI_CD)0.0000.0000.1690.0000.0000.0960.0001.0000.161
기준년월(STD_YM)0.0000.0000.0000.1380.0000.1670.0000.1611.000
2023-12-10T23:51:32.205121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
x좌표(X_COORD)y좌표(Y_COORD)연령대(AGE_GR_SCTN_CD)주중보행인구수(WKDY_FLPOP_CNT)주말보행인구수(WKND_FLPOP_CNT)거주지코드(INFLOW_ADMIN_CD)행정동코드(ADMI_CD)기준년월(STD_YM)성별(AS_GNDR_CD)
x좌표(X_COORD)1.0000.0500.005-0.043-0.013-0.084-0.015-0.0330.054
y좌표(Y_COORD)0.0501.0000.003-0.0020.0040.0540.0610.0010.000
연령대(AGE_GR_SCTN_CD)0.0050.0031.000-0.037-0.012-0.038-0.0170.0290.118
주중보행인구수(WKDY_FLPOP_CNT)-0.043-0.002-0.0371.0000.015-0.032-0.0220.0160.080
주말보행인구수(WKND_FLPOP_CNT)-0.0130.004-0.0120.0151.0000.0440.0090.0460.000
거주지코드(INFLOW_ADMIN_CD)-0.0840.054-0.038-0.0320.0441.000-0.0380.0190.036
행정동코드(ADMI_CD)-0.0150.061-0.017-0.0220.009-0.0381.0000.0590.127
기준년월(STD_YM)-0.0330.0010.0290.0160.0460.0190.0591.0000.000
성별(AS_GNDR_CD)0.0540.0000.1180.0800.0000.0360.1270.0001.000

Missing values

2023-12-10T23:51:28.164291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:51:28.338837image/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

셀id(ID)x좌표(X_COORD)y좌표(Y_COORD)성별(AS_GNDR_CD)연령대(AGE_GR_SCTN_CD)주중보행인구수(WKDY_FLPOP_CNT)주말보행인구수(WKND_FLPOP_CNT)거주지코드(INFLOW_ADMIN_CD)행정동코드(ADMI_CD)기준년월(STD_YM)
04*7*0*1*190269.21015543463.368624140440.0330.10324111311500604201608
14*6*1*4*189785.165818543564.951504150540.03140.41841162072511560605201607
24*5*2*7*191096.728232544320.724671140440.04240.06181171068011410520201704
34*4*7*5*189142.11469542927.733185220240.00.01174060011590670201610
44*7*7*7*188876.753659543312.850723245490.00.02661162057511710720201611
54*7*7*2*189326.7159543657.517511220240.7240.07341156072011530540201609
64*3*9*0*189883.566337544715.917308140440.10740.34021168069011680700201608
74*5*6*2*189925.605238543117.110158240440.0150.01153073011170660201602
84*7*9*6*191095.137335545406.308694125290.01330.02620011545630201801
94*7*0*1*191243.534479543955.37045135390.06960.02025013011470520201704
셀id(ID)x좌표(X_COORD)y좌표(Y_COORD)성별(AS_GNDR_CD)연령대(AGE_GR_SCTN_CD)주중보행인구수(WKDY_FLPOP_CNT)주말보행인구수(WKND_FLPOP_CNT)거주지코드(INFLOW_ADMIN_CD)행정동코드(ADMI_CD)기준년월(STD_YM)
4904*4*3*8*190073.470873544370.190104150540.04690.01154551011470670201607
4914*6*2*7*188870.387333544149.51842220240.77520.06011156051511260600201610
4924*8*9*5*189887.809564543410.190495135390.03350.01141068511110670201706
4934*8*4*3*189675.095325544104.827278155590.00.01147056011530530201601
4944*6*2*5*190567.0635543910.679038250540.350.01162064511140605201705
4954*7*7*7*189833.570449544420.716508145490.13840.27364131011620645201603
4964*7*0*9*190466.540986544615.395058155590.00.0681156069011680656201702
4974*7*7*2*189375.120222543511.773292160640.01570.0294139011200590201704
4984*5*0*0*190996.205905542624.045165255590.21320.05461147068011560670201801
4994*5*9*5*190428.216922545074.377377240440.11622.80081162068511650531201710