Overview

Dataset statistics

Number of variables10
Number of observations500
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory42.6 KiB
Average record size in memory87.3 B

Variable types

Numeric7
Categorical3

Dataset

Description샘플 데이터
Author신한카드
URLhttps://bigdata.seoul.go.kr/data/selectSampleData.do?sample_data_seq=50

Reproduction

Analysis started2023-12-10 14:52:12.560001
Analysis finished2023-12-10 14:52:19.922850
Duration7.36 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Distinct268
Distinct (%)53.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1114783.1
Minimum1101056
Maximum1125074
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:52:20.024360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1101056
5-th percentile1103063.9
Q11109065
median1115062.5
Q31121070
95-th percentile1124065.1
Maximum1125074
Range24018
Interquartile range (IQR)12005

Descriptive statistics

Standard deviation6914.1041
Coefficient of variation (CV)0.0062021966
Kurtosis-1.1328431
Mean1114783.1
Median Absolute Deviation (MAD)6000.5
Skewness-0.23226926
Sum5.5739157 × 108
Variance47804836
MonotonicityNot monotonic
2023-12-10T23:52:20.209594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1123064 9
 
1.8%
1124071 7
 
1.4%
1119072 7
 
1.4%
1119056 6
 
1.2%
1111060 5
 
1.0%
1121062 5
 
1.0%
1111051 5
 
1.0%
1120072 5
 
1.0%
1106088 5
 
1.0%
1114076 4
 
0.8%
Other values (258) 442
88.4%
ValueCountFrequency (%)
1101056 1
 
0.2%
1101061 2
0.4%
1101063 1
 
0.2%
1101064 3
0.6%
1101067 1
 
0.2%
1102052 1
 
0.2%
1102054 4
0.8%
1102055 2
0.4%
1102059 1
 
0.2%
1102069 1
 
0.2%
ValueCountFrequency (%)
1125074 3
0.6%
1125073 1
 
0.2%
1125072 4
0.8%
1125067 3
0.6%
1125065 1
 
0.2%
1125052 1
 
0.2%
1124081 1
 
0.2%
1124077 1
 
0.2%
1124071 7
1.4%
1124069 2
 
0.4%

블록코드(BLOCK_ID)
Real number (ℝ)

Distinct479
Distinct (%)95.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1147393 × 1012
Minimum1.101053 × 1012
Maximum1.125074 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:52:20.429714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.101053 × 1012
5-th percentile1.1030627 × 1012
Q11.108083 × 1012
median1.1150625 × 1012
Q31.1210795 × 1012
95-th percentile1.1241305 × 1012
Maximum1.125074 × 1012
Range2.402102 × 1010
Interquartile range (IQR)1.299647 × 1010

Descriptive statistics

Standard deviation7.1969117 × 109
Coefficient of variation (CV)0.0064561386
Kurtosis-1.1962999
Mean1.1147393 × 1012
Median Absolute Deviation (MAD)6.01851 × 109
Skewness-0.23802217
Sum5.5736966 × 1014
Variance5.1795538 × 1019
MonotonicityNot monotonic
2023-12-10T23:52:20.652224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1116058010023 2
 
0.4%
1124071010010 2
 
0.4%
1125054010007 2
 
0.4%
1121081020006 2
 
0.4%
1120055010002 2
 
0.4%
1111056050003 2
 
0.4%
1123064030006 2
 
0.4%
1116069010006 2
 
0.4%
1124068020003 2
 
0.4%
1123079010001 2
 
0.4%
Other values (469) 480
96.0%
ValueCountFrequency (%)
1101053010005 1
0.2%
1101053020002 1
0.2%
1101053020004 1
0.2%
1101054010002 1
0.2%
1101055020002 1
0.2%
1101056020012 1
0.2%
1101058030001 1
0.2%
1101061030001 1
0.2%
1101064020004 1
0.2%
1101067010002 1
0.2%
ValueCountFrequency (%)
1125074030501 1
0.2%
1125074020203 1
0.2%
1125074020007 1
0.2%
1125073030301 1
0.2%
1125073010006 1
0.2%
1125072020030 1
0.2%
1125071022501 1
0.2%
1125071020026 1
0.2%
1125067020023 1
0.2%
1125067010012 1
0.2%
Distinct21
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
sb08
112 
sb01
71 
sb06
46 
sb19
37 
sb09
35 
Other values (16)
199 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowsb11
2nd rowsb08
3rd rowsb01
4th rowsb11
5th rowsb09

Common Values

ValueCountFrequency (%)
sb08 112
22.4%
sb01 71
14.2%
sb06 46
9.2%
sb19 37
 
7.4%
sb09 35
 
7.0%
sb03 32
 
6.4%
sb02 30
 
6.0%
sb11 21
 
4.2%
sb14 15
 
3.0%
sb04 15
 
3.0%
Other values (11) 86
17.2%

Length

2023-12-10T23:52:20.832870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sb08 112
22.4%
sb01 71
14.2%
sb06 46
9.2%
sb19 37
 
7.4%
sb09 35
 
7.0%
sb03 32
 
6.4%
sb02 30
 
6.0%
sb11 21
 
4.2%
sb14 15
 
3.0%
sb04 15
 
3.0%
Other values (11) 86
17.2%

이용년월(TS_YM)
Real number (ℝ)

Distinct24
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean201647.06
Minimum201512
Maximum201711
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:52:20.972738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum201512
5-th percentile201512
Q1201605
median201611
Q3201706
95-th percentile201710
Maximum201711
Range199
Interquartile range (IQR)101

Descriptive statistics

Standard deviation58.377242
Coefficient of variation (CV)0.00028950208
Kurtosis-0.83420072
Mean201647.06
Median Absolute Deviation (MAD)90
Skewness-0.36259214
Sum1.0082353 × 108
Variance3407.9024
MonotonicityNot monotonic
2023-12-10T23:52:21.145728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
201709 28
 
5.6%
201601 27
 
5.4%
201512 27
 
5.4%
201609 25
 
5.0%
201701 25
 
5.0%
201703 23
 
4.6%
201605 23
 
4.6%
201707 22
 
4.4%
201603 22
 
4.4%
201608 22
 
4.4%
Other values (14) 256
51.2%
ValueCountFrequency (%)
201512 27
5.4%
201601 27
5.4%
201602 15
3.0%
201603 22
4.4%
201604 18
3.6%
201605 23
4.6%
201606 20
4.0%
201607 21
4.2%
201608 22
4.4%
201609 25
5.0%
ValueCountFrequency (%)
201711 19
3.8%
201710 17
3.4%
201709 28
5.6%
201708 19
3.8%
201707 22
4.4%
201706 22
4.4%
201705 22
4.4%
201704 20
4.0%
201703 23
4.6%
201702 13
2.6%

요일코드(DAW_CCD)
Real number (ℝ)

Distinct7
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.076
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:52:21.287602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.9590558
Coefficient of variation (CV)0.48063195
Kurtosis-1.1948654
Mean4.076
Median Absolute Deviation (MAD)2
Skewness0.020106747
Sum2038
Variance3.8378998
MonotonicityNot monotonic
2023-12-10T23:52:21.419300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
4 85
17.0%
7 78
15.6%
3 77
15.4%
2 73
14.6%
6 68
13.6%
5 62
12.4%
1 57
11.4%
ValueCountFrequency (%)
1 57
11.4%
2 73
14.6%
3 77
15.4%
4 85
17.0%
5 62
12.4%
6 68
13.6%
7 78
15.6%
ValueCountFrequency (%)
7 78
15.6%
6 68
13.6%
5 62
12.4%
4 85
17.0%
3 77
15.4%
2 73
14.6%
1 57
11.4%

시간대(TM)
Categorical

Distinct4
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
t4
175 
t3
163 
t2
109 
t1
53 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowt4
2nd rowt2
3rd rowt4
4th rowt3
5th rowt1

Common Values

ValueCountFrequency (%)
t4 175
35.0%
t3 163
32.6%
t2 109
21.8%
t1 53
 
10.6%

Length

2023-12-10T23:52:21.586043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:52:21.733857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
t4 175
35.0%
t3 163
32.6%
t2 109
21.8%
t1 53
 
10.6%
Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
M
259 
F
241 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
M 259
51.8%
F 241
48.2%

Length

2023-12-10T23:52:21.878556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:52:21.996919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
m 259
51.8%
f 241
48.2%

연령대구분(AGE_GB)
Real number (ℝ)

Distinct6
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.528
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:52:22.088904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q35
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.4958915
Coefficient of variation (CV)0.42400553
Kurtosis-1.0263262
Mean3.528
Median Absolute Deviation (MAD)1
Skewness0.068340349
Sum1764
Variance2.2376914
MonotonicityNot monotonic
2023-12-10T23:52:22.210701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2 110
22.0%
4 110
22.0%
3 96
19.2%
5 83
16.6%
6 60
12.0%
1 41
 
8.2%
ValueCountFrequency (%)
1 41
 
8.2%
2 110
22.0%
3 96
19.2%
4 110
22.0%
5 83
16.6%
6 60
12.0%
ValueCountFrequency (%)
6 60
12.0%
5 83
16.6%
4 110
22.0%
3 96
19.2%
2 110
22.0%
1 41
 
8.2%

이용건수(USECT_CORR)
Real number (ℝ)

Distinct51
Distinct (%)10.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.71336
Minimum4.94
Maximum1274.52
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:52:22.397672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.94
5-th percentile4.94
Q14.94
median9.88
Q329.64
95-th percentile172.9
Maximum1274.52
Range1269.58
Interquartile range (IQR)24.7

Descriptive statistics

Standard deviation109.76434
Coefficient of variation (CV)2.6313953
Kurtosis65.1815
Mean41.71336
Median Absolute Deviation (MAD)4.94
Skewness7.2454981
Sum20856.68
Variance12048.21
MonotonicityNot monotonic
2023-12-10T23:52:22.602720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.94 189
37.8%
9.88 68
 
13.6%
19.76 40
 
8.0%
14.82 39
 
7.8%
29.64 26
 
5.2%
24.7 18
 
3.6%
34.58 17
 
3.4%
44.46 10
 
2.0%
39.52 10
 
2.0%
54.34 8
 
1.6%
Other values (41) 75
 
15.0%
ValueCountFrequency (%)
4.94 189
37.8%
9.88 68
 
13.6%
14.82 39
 
7.8%
19.76 40
 
8.0%
24.7 18
 
3.6%
29.64 26
 
5.2%
34.58 17
 
3.4%
39.52 10
 
2.0%
44.46 10
 
2.0%
49.4 6
 
1.2%
ValueCountFrequency (%)
1274.52 1
0.2%
1136.2 1
0.2%
978.12 1
0.2%
671.84 1
0.2%
602.68 1
0.2%
508.82 1
0.2%
405.08 1
0.2%
395.2 1
0.2%
316.16 1
0.2%
291.46 1
0.2%

이용금액(AMT_CORR)
Real number (ℝ)

Distinct382
Distinct (%)76.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1336130.8
Minimum2717
Maximum1.8257153 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:52:22.798507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2717
5-th percentile17759.3
Q166097.2
median170430
Q3502225.1
95-th percentile3517922.2
Maximum1.8257153 × 108
Range1.8256882 × 108
Interquartile range (IQR)436127.9

Descriptive statistics

Standard deviation8929220.8
Coefficient of variation (CV)6.6828941
Kurtosis343.49741
Mean1336130.8
Median Absolute Deviation (MAD)133380
Skewness17.388713
Sum6.6806541 × 108
Variance7.9730984 × 1013
MonotonicityNot monotonic
2023-12-10T23:52:23.012239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
74100.0 8
 
1.6%
49400.0 7
 
1.4%
113620.0 7
 
1.4%
14820.0 6
 
1.2%
34580.0 6
 
1.2%
83980.0 5
 
1.0%
123500.0 5
 
1.0%
22230.0 5
 
1.0%
44460.0 5
 
1.0%
39520.0 5
 
1.0%
Other values (372) 441
88.2%
ValueCountFrequency (%)
2717.0 1
 
0.2%
4940.0 2
0.4%
5434.0 1
 
0.2%
7904.0 1
 
0.2%
8645.0 1
 
0.2%
8892.0 1
 
0.2%
9880.0 4
0.8%
10868.0 1
 
0.2%
12350.0 2
0.4%
12844.0 1
 
0.2%
ValueCountFrequency (%)
182571532.0 1
0.2%
44801255.2 1
0.2%
36633271.48 1
0.2%
33023603.6 1
0.2%
32150310.4 1
0.2%
16257885.8 1
0.2%
15184078.0 1
0.2%
12854374.0 1
0.2%
12821770.0 1
0.2%
11005332.0 1
0.2%

Interactions

2023-12-10T23:52:18.573237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:13.145483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:13.954932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:14.760823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:15.619921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:16.478512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:17.409344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:18.712103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:13.251528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:14.059962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:14.895181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:15.734620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:16.601465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:17.853452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:18.844959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:13.379056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:14.165807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:15.022189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:15.856651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:16.718476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:17.965202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:18.988818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:13.492313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:14.295298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:15.138152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:16.000930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:16.850916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:18.103187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:19.120531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:13.632921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:14.413438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:15.262235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:16.122262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:16.979618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:18.230035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:19.256355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:13.740385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:14.514856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:15.374201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:16.242615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:17.137921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:18.341129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:19.406638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:13.850988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:14.633715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:15.499425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:16.358103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:17.264610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:18.446046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:52:23.159381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동코드(M_DONG_CD)블록코드(BLOCK_ID)업종코드(SE_UPJONG)이용년월(TS_YM)요일코드(DAW_CCD)시간대(TM)성별코드(SEX_CCD)연령대구분(AGE_GB)이용건수(USECT_CORR)이용금액(AMT_CORR)
행정동코드(M_DONG_CD)1.0000.0000.1500.0000.0950.0000.1090.1040.0940.000
블록코드(BLOCK_ID)0.0001.0000.1860.0000.1180.0810.0000.0000.0000.000
업종코드(SE_UPJONG)0.1500.1861.0000.0000.0550.0000.1960.2540.0000.218
이용년월(TS_YM)0.0000.0000.0001.0000.0000.0470.0000.0670.0000.000
요일코드(DAW_CCD)0.0950.1180.0550.0001.0000.0000.0000.0390.0820.000
시간대(TM)0.0000.0810.0000.0470.0001.0000.0000.0800.0000.000
성별코드(SEX_CCD)0.1090.0000.1960.0000.0000.0001.0000.0300.0700.000
연령대구분(AGE_GB)0.1040.0000.2540.0670.0390.0800.0301.0000.0000.077
이용건수(USECT_CORR)0.0940.0000.0000.0000.0820.0000.0700.0001.0000.000
이용금액(AMT_CORR)0.0000.0000.2180.0000.0000.0000.0000.0770.0001.000
2023-12-10T23:52:23.341917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시간대(TM)성별코드(SEX_CCD)업종코드(SE_UPJONG)
시간대(TM)1.0000.0000.000
성별코드(SEX_CCD)0.0001.0000.169
업종코드(SE_UPJONG)0.0000.1691.000
2023-12-10T23:52:23.462577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동코드(M_DONG_CD)블록코드(BLOCK_ID)이용년월(TS_YM)요일코드(DAW_CCD)연령대구분(AGE_GB)이용건수(USECT_CORR)이용금액(AMT_CORR)업종코드(SE_UPJONG)시간대(TM)성별코드(SEX_CCD)
행정동코드(M_DONG_CD)1.000-0.0030.0340.0460.0410.0890.0410.0560.0000.074
블록코드(BLOCK_ID)-0.0031.0000.013-0.047-0.065-0.0850.0590.0670.0450.000
이용년월(TS_YM)0.0340.0131.000-0.0380.016-0.013-0.0270.0870.0250.000
요일코드(DAW_CCD)0.046-0.047-0.0381.0000.0450.053-0.0540.0180.0000.000
연령대구분(AGE_GB)0.041-0.0650.0160.0451.000-0.015-0.1250.1150.0520.021
이용건수(USECT_CORR)0.089-0.085-0.0130.053-0.0151.0000.0120.0000.0000.070
이용금액(AMT_CORR)0.0410.059-0.027-0.054-0.1250.0121.0000.1170.0000.000
업종코드(SE_UPJONG)0.0560.0670.0870.0180.1150.0000.1171.0000.0000.169
시간대(TM)0.0000.0450.0250.0000.0520.0000.0000.0001.0000.000
성별코드(SEX_CCD)0.0740.0000.0000.0000.0210.0700.0000.1690.0001.000

Missing values

2023-12-10T23:52:19.606858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:52:19.831501image/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

행정동코드(M_DONG_CD)블록코드(BLOCK_ID)업종코드(SE_UPJONG)이용년월(TS_YM)요일코드(DAW_CCD)시간대(TM)성별코드(SEX_CCD)연령대구분(AGE_GB)이용건수(USECT_CORR)이용금액(AMT_CORR)
011160631118057040004sb112017115t4M69.8817784.0
111040651118054020017sb082017032t2M24.94123500.0
211200531108065010907sb012017017t4M3103.74782496.0
311190561114060020001sb112016081t3M229.64306280.0
411080641114059030303sb092015122t1M44.9447918.0
511230771103065020002sb062016064t2M44.94127946.0
611230751125067020023sb202016127t3F119.76256880.0
711170671111065030003sb062016083t2M534.58122512.0
811230641115063020010sb012016012t4F24.94296894.0
911020591118052040005sb122017091t3M559.2837050.0
행정동코드(M_DONG_CD)블록코드(BLOCK_ID)업종코드(SE_UPJONG)이용년월(TS_YM)요일코드(DAW_CCD)시간대(TM)성별코드(SEX_CCD)연령대구분(AGE_GB)이용건수(USECT_CORR)이용금액(AMT_CORR)
49011110601103066020004sb082016043t4F59.8844460.0
49111080781119068040003sb082016012t2M219.766514378.0
49211120571105064020022sb062017066t4F614.8283980.0
49311180541119072040008sb092017034t1F29.88429780.0
49411060871121063020015sb082017061t4F29.88674310.0
49511100511125053010003sb102016042t2M24.9466196.0
49611140761123064030006sb082016074t4F34.9433023603.6
49711140761108068010004sb022016044t3F3128.44985530.0
49811120711120055020021sb062016093t2M24.94107494.4
49911060911101056020012sb092016054t2M19.881066743.6