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

Numeric7
Text1
Categorical2

Dataset

Description샘플 데이터
Author롯데멤버스
URLhttps://bigdata.seoul.go.kr/data/selectSampleData.do?sample_data_seq=323

Alerts

구매_고객수(ACC_CNT) is highly imbalanced (83.3%)Imbalance

Reproduction

Analysis started2023-12-10 14:50:40.234209
Analysis finished2023-12-10 14:50:46.390834
Duration6.16 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기준년월(STD_YM)
Real number (ℝ)

Distinct24
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean201853.62
Minimum201801
Maximum201912
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:50:46.456115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum201801
5-th percentile201802
Q1201807
median201812
Q3201907
95-th percentile201911
Maximum201912
Range111
Interquartile range (IQR)100

Descriptive statistics

Standard deviation50.136278
Coefficient of variation (CV)0.00024837938
Kurtosis-1.9734067
Mean201853.62
Median Absolute Deviation (MAD)11
Skewness0.12746739
Sum1.0092681 × 108
Variance2513.6464
MonotonicityNot monotonic
2023-12-10T23:50:46.577032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
201809 32
 
6.4%
201808 30
 
6.0%
201812 29
 
5.8%
201907 28
 
5.6%
201908 26
 
5.2%
201910 25
 
5.0%
201801 24
 
4.8%
201807 23
 
4.6%
201904 23
 
4.6%
201909 23
 
4.6%
Other values (14) 237
47.4%
ValueCountFrequency (%)
201801 24
4.8%
201802 14
2.8%
201803 23
4.6%
201804 15
3.0%
201805 21
4.2%
201806 23
4.6%
201807 23
4.6%
201808 30
6.0%
201809 32
6.4%
201810 17
3.4%
ValueCountFrequency (%)
201912 22
4.4%
201911 15
3.0%
201910 25
5.0%
201909 23
4.6%
201908 26
5.2%
201907 28
5.6%
201906 13
2.6%
201905 14
2.8%
201904 23
4.6%
201903 13
2.6%
Distinct314
Distinct (%)62.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-10T23:50:46.911236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length5.75
Min length4

Characters and Unicode

Total characters2875
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

Unique204 ?
Unique (%)40.8%

Sample

1st row2*9*4*
2nd row4*7*9*
3rd row2*2*8*
4th row4*0*6*
5th row1*8*1*
ValueCountFrequency (%)
2*6*7 8
 
1.6%
2*7*7 7
 
1.4%
2*6*9 7
 
1.4%
3*5*1 6
 
1.2%
4*9*0 5
 
1.0%
2*1*6 5
 
1.0%
2*9*8 5
 
1.0%
2*2*2 5
 
1.0%
2*3*9 5
 
1.0%
2*9*4 5
 
1.0%
Other values (258) 442
88.4%
2023-12-10T23:50:47.362462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
* 1392
48.4%
2 302
 
10.5%
1 214
 
7.4%
3 178
 
6.2%
4 169
 
5.9%
9 123
 
4.3%
5 119
 
4.1%
7 114
 
4.0%
6 101
 
3.5%
8 96
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1483
51.6%
Other Punctuation 1392
48.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 302
20.4%
1 214
14.4%
3 178
12.0%
4 169
11.4%
9 123
8.3%
5 119
 
8.0%
7 114
 
7.7%
6 101
 
6.8%
8 96
 
6.5%
0 67
 
4.5%
Other Punctuation
ValueCountFrequency (%)
* 1392
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2875
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
* 1392
48.4%
2 302
 
10.5%
1 214
 
7.4%
3 178
 
6.2%
4 169
 
5.9%
9 123
 
4.3%
5 119
 
4.1%
7 114
 
4.0%
6 101
 
3.5%
8 96
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2875
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 1392
48.4%
2 302
 
10.5%
1 214
 
7.4%
3 178
 
6.2%
4 169
 
5.9%
9 123
 
4.3%
5 119
 
4.1%
7 114
 
4.0%
6 101
 
3.5%
8 96
 
3.3%
Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2
359 
1
141 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
2 359
71.8%
1 141
 
28.2%

Length

2023-12-10T23:50:47.849981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:50:47.951432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 359
71.8%
1 141
 
28.2%

연령대코드(AGE_CD)
Real number (ℝ)

Distinct7
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.276
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:50:48.032959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median4
Q35
95-th percentile6
Maximum7
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.2629386
Coefficient of variation (CV)0.29535516
Kurtosis-0.48796716
Mean4.276
Median Absolute Deviation (MAD)1
Skewness0.1463018
Sum2138
Variance1.595014
MonotonicityNot monotonic
2023-12-10T23:50:48.157405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
4 153
30.6%
5 120
24.0%
3 103
20.6%
6 65
13.0%
2 36
 
7.2%
7 22
 
4.4%
1 1
 
0.2%
ValueCountFrequency (%)
1 1
 
0.2%
2 36
 
7.2%
3 103
20.6%
4 153
30.6%
5 120
24.0%
6 65
13.0%
7 22
 
4.4%
ValueCountFrequency (%)
7 22
 
4.4%
6 65
13.0%
5 120
24.0%
4 153
30.6%
3 103
20.6%
2 36
 
7.2%
1 1
 
0.2%

상품코드(LMPH_CD)
Real number (ℝ)

Distinct216
Distinct (%)43.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1469531.5
Minimum1010102
Maximum10010104
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:50:48.374139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1010102
5-th percentile1010601
Q11011501
median1050302
Q31070201
95-th percentile5010302.1
Maximum10010104
Range9000002
Interquartile range (IQR)58700

Descriptive statistics

Standard deviation1275028.3
Coefficient of variation (CV)0.86764276
Kurtosis15.433516
Mean1469531.5
Median Absolute Deviation (MAD)34300
Skewness3.7207544
Sum7.3476573 × 108
Variance1.6256972 × 1012
MonotonicityNot monotonic
2023-12-10T23:50:48.545909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1070109 16
 
3.2%
1011107 15
 
3.0%
1060103 11
 
2.2%
1080101 11
 
2.2%
1070106 11
 
2.2%
1011601 10
 
2.0%
1010708 9
 
1.8%
1070201 8
 
1.6%
1060201 8
 
1.6%
1011106 7
 
1.4%
Other values (206) 394
78.8%
ValueCountFrequency (%)
1010102 1
0.2%
1010104 1
0.2%
1010202 1
0.2%
1010203 2
0.4%
1010204 1
0.2%
1010205 2
0.4%
1010206 2
0.4%
1010207 1
0.2%
1010212 2
0.4%
1010305 1
0.2%
ValueCountFrequency (%)
10010104 1
0.2%
9029999 2
0.4%
9029906 1
0.2%
8029902 1
0.2%
8020807 1
0.2%
7010202 1
0.2%
6111001 1
0.2%
6030307 1
0.2%
5030804 1
0.2%
5030701 1
0.2%

시간대코드(TIME_CD)
Real number (ℝ)

Distinct6
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.35
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:50:48.721643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q14
median5
Q35
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.0871356
Coefficient of variation (CV)0.24991622
Kurtosis-0.34635295
Mean4.35
Median Absolute Deviation (MAD)1
Skewness-0.49434392
Sum2175
Variance1.1818637
MonotonicityNot monotonic
2023-12-10T23:50:48.834169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
5 200
40.0%
4 126
25.2%
3 85
17.0%
6 60
 
12.0%
2 27
 
5.4%
1 2
 
0.4%
ValueCountFrequency (%)
1 2
 
0.4%
2 27
 
5.4%
3 85
17.0%
4 126
25.2%
5 200
40.0%
6 60
 
12.0%
ValueCountFrequency (%)
6 60
 
12.0%
5 200
40.0%
4 126
25.2%
3 85
17.0%
2 27
 
5.4%
1 2
 
0.4%

구매지역(PURH_AREA)
Real number (ℝ)

Distinct136
Distinct (%)27.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10026545
Minimum27260
Maximum11740610
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:50:48.957764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum27260
5-th percentile41284.4
Q111230570
median11380690
Q311575682
95-th percentile11710680
Maximum11740610
Range11713350
Interquartile range (IQR)345112.5

Descriptive statistics

Standard deviation3799425.2
Coefficient of variation (CV)0.37893665
Kurtosis3.1020102
Mean10026545
Median Absolute Deviation (MAD)179845
Skewness-2.2521704
Sum5.0132723 × 109
Variance1.4435632 × 1013
MonotonicityNot monotonic
2023-12-10T23:50:49.106796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11380690 27
 
5.4%
11545610 25
 
5.0%
11350621 19
 
3.8%
11230560 18
 
3.6%
11710680 17
 
3.4%
11710642 17
 
3.4%
11320690 16
 
3.2%
11560535 15
 
3.0%
11305534 15
 
3.0%
11530530 13
 
2.6%
Other values (126) 318
63.6%
ValueCountFrequency (%)
27260 1
 
0.2%
28185 2
0.4%
28237 3
0.6%
28260 1
 
0.2%
29140 2
0.4%
29200 1
 
0.2%
30200 3
0.6%
41113 1
 
0.2%
41117 2
0.4%
41131 1
 
0.2%
ValueCountFrequency (%)
11740610 1
 
0.2%
11710710 13
2.6%
11710690 2
 
0.4%
11710680 17
3.4%
11710670 4
 
0.8%
11710647 1
 
0.2%
11710646 1
 
0.2%
11710642 17
3.4%
11710620 2
 
0.4%
11710600 1
 
0.2%

구매_고객수(ACC_CNT)
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
481 
2
 
14
3
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 481
96.2%
2 14
 
2.8%
3 5
 
1.0%

Length

2023-12-10T23:50:49.250412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:50:49.364287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 481
96.2%
2 14
 
2.8%
3 5
 
1.0%

구매건수(PURH_CNT)
Real number (ℝ)

Distinct11
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.624
Minimum1
Maximum17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:50:49.448438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile4
Maximum17
Range16
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.5083681
Coefficient of variation (CV)0.9287981
Kurtosis35.099767
Mean1.624
Median Absolute Deviation (MAD)0
Skewness5.007691
Sum812
Variance2.2751743
MonotonicityNot monotonic
2023-12-10T23:50:49.544941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1 345
69.0%
2 98
 
19.6%
3 27
 
5.4%
4 10
 
2.0%
7 5
 
1.0%
6 5
 
1.0%
5 5
 
1.0%
9 2
 
0.4%
12 1
 
0.2%
13 1
 
0.2%
ValueCountFrequency (%)
1 345
69.0%
2 98
 
19.6%
3 27
 
5.4%
4 10
 
2.0%
5 5
 
1.0%
6 5
 
1.0%
7 5
 
1.0%
9 2
 
0.4%
12 1
 
0.2%
13 1
 
0.2%
ValueCountFrequency (%)
17 1
 
0.2%
13 1
 
0.2%
12 1
 
0.2%
9 2
 
0.4%
7 5
 
1.0%
6 5
 
1.0%
5 5
 
1.0%
4 10
 
2.0%
3 27
 
5.4%
2 98
19.6%

구매금액(PURH_AMT)
Real number (ℝ)

Distinct52
Distinct (%)10.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13114
Minimum1000
Maximum1739000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:50:49.666584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile1000
Q12000
median5000
Q39000
95-th percentile31050
Maximum1739000
Range1738000
Interquartile range (IQR)7000

Descriptive statistics

Standard deviation81032.62
Coefficient of variation (CV)6.1790926
Kurtosis415.50053
Mean13114
Median Absolute Deviation (MAD)3000
Skewness19.719575
Sum6557000
Variance6.5662856 × 109
MonotonicityNot monotonic
2023-12-10T23:50:49.804747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2000 73
14.6%
1000 63
12.6%
3000 54
10.8%
4000 47
9.4%
5000 44
8.8%
6000 38
 
7.6%
7000 27
 
5.4%
8000 25
 
5.0%
10000 15
 
3.0%
9000 13
 
2.6%
Other values (42) 101
20.2%
ValueCountFrequency (%)
1000 63
12.6%
2000 73
14.6%
3000 54
10.8%
4000 47
9.4%
5000 44
8.8%
6000 38
7.6%
7000 27
 
5.4%
8000 25
 
5.0%
9000 13
 
2.6%
10000 15
 
3.0%
ValueCountFrequency (%)
1739000 1
0.2%
381000 1
0.2%
284000 1
0.2%
155000 1
0.2%
110000 1
0.2%
66000 1
0.2%
65000 1
0.2%
54000 1
0.2%
52000 1
0.2%
51000 1
0.2%

Interactions

2023-12-10T23:50:45.343254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:40.684461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:41.440596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:42.284181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:43.013035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:43.813663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:44.556360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:45.449061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:40.777984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:41.565089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:42.406577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:43.099554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:43.910374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:44.654665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:45.558586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:40.915716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:41.697325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:42.525651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:43.214149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:44.021162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:44.760870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:45.673314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:41.025606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:41.821529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:42.624945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:43.312672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:44.119204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:44.867652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:45.786259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:41.126094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:41.920753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:42.722341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:43.410760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:44.216296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:44.971671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:45.886115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:41.220565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:42.030898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:42.822915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:43.572642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:44.306334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:45.089718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:45.989309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:41.322948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:42.157442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:42.913309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:43.686777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:44.425150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:50:45.224774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:50:49.936391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준년월(STD_YM)성별코드(SEX_CD)연령대코드(AGE_CD)상품코드(LMPH_CD)시간대코드(TIME_CD)구매지역(PURH_AREA)구매_고객수(ACC_CNT)구매건수(PURH_CNT)구매금액(PURH_AMT)
기준년월(STD_YM)1.0000.0000.0000.0000.0000.0000.0000.0000.014
성별코드(SEX_CD)0.0001.0000.0690.0000.0000.0000.0330.0440.119
연령대코드(AGE_CD)0.0000.0691.0000.0000.0330.0710.0000.0000.000
상품코드(LMPH_CD)0.0000.0000.0001.0000.0000.2470.0000.0000.000
시간대코드(TIME_CD)0.0000.0000.0330.0001.0000.1160.0000.0000.024
구매지역(PURH_AREA)0.0000.0000.0710.2470.1161.0000.0240.0000.000
구매_고객수(ACC_CNT)0.0000.0330.0000.0000.0000.0241.0000.0000.000
구매건수(PURH_CNT)0.0000.0440.0000.0000.0000.0000.0001.0000.000
구매금액(PURH_AMT)0.0140.1190.0000.0000.0240.0000.0000.0001.000
2023-12-10T23:50:50.090278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구매_고객수(ACC_CNT)성별코드(SEX_CD)
구매_고객수(ACC_CNT)1.0000.055
성별코드(SEX_CD)0.0551.000
2023-12-10T23:50:50.197584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준년월(STD_YM)연령대코드(AGE_CD)상품코드(LMPH_CD)시간대코드(TIME_CD)구매지역(PURH_AREA)구매건수(PURH_CNT)구매금액(PURH_AMT)성별코드(SEX_CD)구매_고객수(ACC_CNT)
기준년월(STD_YM)1.000-0.0080.031-0.0220.010-0.007-0.0040.0000.000
연령대코드(AGE_CD)-0.0081.0000.0140.0900.008-0.029-0.0480.0730.000
상품코드(LMPH_CD)0.0310.0141.000-0.0100.028-0.0550.0380.0000.000
시간대코드(TIME_CD)-0.0220.090-0.0101.000-0.049-0.0560.0600.0000.000
구매지역(PURH_AREA)0.0100.0080.028-0.0491.000-0.0170.0040.0000.041
구매건수(PURH_CNT)-0.007-0.029-0.055-0.056-0.0171.0000.0200.0330.000
구매금액(PURH_AMT)-0.004-0.0480.0380.0600.0040.0201.0000.0710.000
성별코드(SEX_CD)0.0000.0730.0000.0000.0000.0330.0711.0000.055
구매_고객수(ACC_CNT)0.0000.0000.0000.0000.0410.0000.0000.0551.000

Missing values

2023-12-10T23:50:46.147740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:50:46.319945image/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

기준년월(STD_YM)블록코드(BLCK_CD)성별코드(SEX_CD)연령대코드(AGE_CD)상품코드(LMPH_CD)시간대코드(TIME_CD)구매지역(PURH_AREA)구매_고객수(ACC_CNT)구매건수(PURH_CNT)구매금액(PURH_AMT)
02019082*9*4*241070104311350700115000
12019024*7*9*14107010931168053111381000
22019022*2*8*151020602511680545113000
32019104*0*6*1520102043115456101149000
42018071*8*1*1510601035117106801332000
52019045*2*1*2610701094112005601210000
62019064*9*8*131011899611140540114000
72019092*6*5131011106211650530115000
82019102*1*9*221060201441117122000
92019052*2*2*161011399411710642111000
기준년월(STD_YM)블록코드(BLCK_CD)성별코드(SEX_CD)연령대코드(AGE_CD)상품코드(LMPH_CD)시간대코드(TIME_CD)구매지역(PURH_AREA)구매_고객수(ACC_CNT)구매건수(PURH_CNT)구매금액(PURH_AMT)
4902018093*3*0*2210111015111405401138000
4912018013*7*6*151011601511350700116000
4922019074*1*2*241011107511530530121000
4932019114*3*6*151010901511305534123000
4942018032*6*7*2510508024114406301116000
4952018052*8*3*141070108511305534166000
4962018111*0*7*1610107085117107101121000
4972018092*7*6*261011603411545610151000
4982018033*4*72310506993415701133000
4992019044*6*3*2410902023117106421121000