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

Numeric8
Text1
Categorical1

Dataset

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

Reproduction

Analysis started2023-12-10 14:51:05.575759
Analysis finished2023-12-10 14:51:14.583452
Duration9.01 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%
Mean201858.44
Minimum201801
Maximum201912
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:51:14.652975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum201801
5-th percentile201802
Q1201807
median201901
Q3201907
95-th percentile201911
Maximum201912
Range111
Interquartile range (IQR)100

Descriptive statistics

Standard deviation50.084884
Coefficient of variation (CV)0.00024811885
Kurtosis-1.9841889
Mean201858.44
Median Absolute Deviation (MAD)11
Skewness-0.071737909
Sum1.0092922 × 108
Variance2508.4956
MonotonicityNot monotonic
2023-12-10T23:51:14.782680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
201907 30
 
6.0%
201910 28
 
5.6%
201808 27
 
5.4%
201911 26
 
5.2%
201811 25
 
5.0%
201905 25
 
5.0%
201803 24
 
4.8%
201904 23
 
4.6%
201806 23
 
4.6%
201812 22
 
4.4%
Other values (14) 247
49.4%
ValueCountFrequency (%)
201801 13
2.6%
201802 21
4.2%
201803 24
4.8%
201804 18
3.6%
201805 18
3.6%
201806 23
4.6%
201807 17
3.4%
201808 27
5.4%
201809 18
3.6%
201810 15
3.0%
ValueCountFrequency (%)
201912 18
3.6%
201911 26
5.2%
201910 28
5.6%
201909 14
2.8%
201908 21
4.2%
201907 30
6.0%
201906 15
3.0%
201905 25
5.0%
201904 23
4.6%
201903 18
3.6%

블록코드(BLCK_CD)
Text

Distinct132
Distinct (%)26.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-10T23:51:15.086831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length5.796
Min length4

Characters and Unicode

Total characters2898
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

Unique69 ?
Unique (%)13.8%

Sample

1st row1*8*6
2nd row1*5*0*
3rd row4*9*7*
4th row4*7*1*
5th row3*2*6*
ValueCountFrequency (%)
2*6*4 29
 
5.8%
1*5*0 22
 
4.4%
1*5*7 22
 
4.4%
2*7*3 20
 
4.0%
4*3*9 19
 
3.8%
3*2*6 19
 
3.8%
2*9*7 18
 
3.6%
3*5*2 18
 
3.6%
1*9*9 18
 
3.6%
1*2*3 17
 
3.4%
Other values (113) 298
59.6%
2023-12-10T23:51:15.533043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
* 1403
48.4%
2 308
 
10.6%
1 196
 
6.8%
4 191
 
6.6%
3 190
 
6.6%
5 154
 
5.3%
9 125
 
4.3%
7 115
 
4.0%
6 108
 
3.7%
8 56
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1495
51.6%
Other Punctuation 1403
48.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 308
20.6%
1 196
13.1%
4 191
12.8%
3 190
12.7%
5 154
10.3%
9 125
8.4%
7 115
 
7.7%
6 108
 
7.2%
8 56
 
3.7%
0 52
 
3.5%
Other Punctuation
ValueCountFrequency (%)
* 1403
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2898
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
* 1403
48.4%
2 308
 
10.6%
1 196
 
6.8%
4 191
 
6.6%
3 190
 
6.6%
5 154
 
5.3%
9 125
 
4.3%
7 115
 
4.0%
6 108
 
3.7%
8 56
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2898
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 1403
48.4%
2 308
 
10.6%
1 196
 
6.8%
4 191
 
6.6%
3 190
 
6.6%
5 154
 
5.3%
9 125
 
4.3%
7 115
 
4.0%
6 108
 
3.7%
8 56
 
1.9%

성별코드(SEX_CD)
Categorical

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2
341 
1
159 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
2 341
68.2%
1 159
31.8%

Length

2023-12-10T23:51:15.659050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:51:15.743302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 341
68.2%
1 159
31.8%

연령대코드(AGE_CD)
Real number (ℝ)

Distinct7
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.988
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:51:15.820133image/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 deviation1.2849741
Coefficient of variation (CV)0.32221014
Kurtosis-0.14222855
Mean3.988
Median Absolute Deviation (MAD)1
Skewness0.36383639
Sum1994
Variance1.6511583
MonotonicityNot monotonic
2023-12-10T23:51:15.915701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
4 182
36.4%
3 106
21.2%
5 85
17.0%
2 63
 
12.6%
6 39
 
7.8%
7 23
 
4.6%
1 2
 
0.4%
ValueCountFrequency (%)
1 2
 
0.4%
2 63
 
12.6%
3 106
21.2%
4 182
36.4%
5 85
17.0%
6 39
 
7.8%
7 23
 
4.6%
ValueCountFrequency (%)
7 23
 
4.6%
6 39
 
7.8%
5 85
17.0%
4 182
36.4%
3 106
21.2%
2 63
 
12.6%
1 2
 
0.4%

상품코드(LMPH_CD)
Real number (ℝ)

Distinct232
Distinct (%)46.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1514461.2
Minimum1010104
Maximum9030105
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:51:16.045460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1010104
5-th percentile1010601
Q11011603.8
median1050202
Q31080101
95-th percentile5010701.1
Maximum9030105
Range8020001
Interquartile range (IQR)68497.25

Descriptive statistics

Standard deviation1262879.3
Coefficient of variation (CV)0.83388024
Kurtosis10.590669
Mean1514461.2
Median Absolute Deviation (MAD)38348
Skewness3.1176389
Sum7.5723059 × 108
Variance1.594864 × 1012
MonotonicityNot monotonic
2023-12-10T23:51:16.176151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1070109 12
 
2.4%
1011107 11
 
2.2%
1070105 9
 
1.8%
1080101 9
 
1.8%
1050302 8
 
1.6%
1011501 7
 
1.4%
1020603 7
 
1.4%
1070201 7
 
1.4%
1011601 6
 
1.2%
1080102 6
 
1.2%
Other values (222) 418
83.6%
ValueCountFrequency (%)
1010104 1
 
0.2%
1010205 3
0.6%
1010206 2
0.4%
1010207 1
 
0.2%
1010209 1
 
0.2%
1010212 1
 
0.2%
1010213 3
0.6%
1010301 1
 
0.2%
1010302 4
0.8%
1010303 1
 
0.2%
ValueCountFrequency (%)
9030105 1
0.2%
9029906 1
0.2%
9020404 1
0.2%
8029901 1
0.2%
6110399 1
0.2%
6080299 1
0.2%
6030201 1
0.2%
5050108 1
0.2%
5040101 1
0.2%
5030805 1
0.2%

시간대코드(TIME_CD)
Real number (ℝ)

Distinct6
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.254
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:51:16.297052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median4
Q35
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.2084721
Coefficient of variation (CV)0.28407901
Kurtosis-0.31177632
Mean4.254
Median Absolute Deviation (MAD)1
Skewness-0.46343131
Sum2127
Variance1.4604048
MonotonicityNot monotonic
2023-12-10T23:51:16.397109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
5 158
31.6%
4 138
27.6%
3 86
17.2%
6 75
15.0%
2 34
 
6.8%
1 9
 
1.8%
ValueCountFrequency (%)
1 9
 
1.8%
2 34
 
6.8%
3 86
17.2%
4 138
27.6%
5 158
31.6%
6 75
15.0%
ValueCountFrequency (%)
6 75
15.0%
5 158
31.6%
4 138
27.6%
3 86
17.2%
2 34
 
6.8%
1 9
 
1.8%

구매자지역(BUYER_AREA)
Real number (ℝ)

Distinct287
Distinct (%)57.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9179990.2
Minimum26290
Maximum11740700
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:51:16.523179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum26290
5-th percentile41150
Q111170578
median11380570
Q311590632
95-th percentile11710647
Maximum11740700
Range11714410
Interquartile range (IQR)420055

Descriptive statistics

Standard deviation4577495
Coefficient of variation (CV)0.49863833
Kurtosis0.25803354
Mean9179990.2
Median Absolute Deviation (MAD)210005
Skewness-1.4996304
Sum4.5899951 × 109
Variance2.095346 × 1013
MonotonicityNot monotonic
2023-12-10T23:51:16.677078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11680700 5
 
1.0%
28260 5
 
1.0%
41360 5
 
1.0%
41281 5
 
1.0%
11680720 5
 
1.0%
11710570 5
 
1.0%
11410620 5
 
1.0%
11470550 4
 
0.8%
11650531 4
 
0.8%
11680670 4
 
0.8%
Other values (277) 453
90.6%
ValueCountFrequency (%)
26290 1
 
0.2%
26320 1
 
0.2%
26380 1
 
0.2%
27260 1
 
0.2%
28185 1
 
0.2%
28260 5
1.0%
29155 2
 
0.4%
29170 3
0.6%
29200 1
 
0.2%
41115 1
 
0.2%
ValueCountFrequency (%)
11740700 1
0.2%
11740685 1
0.2%
11740650 1
0.2%
11740640 1
0.2%
11740620 1
0.2%
11740580 1
0.2%
11740570 1
0.2%
11740560 1
0.2%
11740550 1
0.2%
11710720 1
0.2%

구매_고객수(ACC_CNT)
Real number (ℝ)

Distinct17
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.878
Minimum1
Maximum85
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:51:16.806609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile5
Maximum85
Range84
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4.3470576
Coefficient of variation (CV)2.3147272
Kurtosis271.18122
Mean1.878
Median Absolute Deviation (MAD)0
Skewness14.82982
Sum939
Variance18.89691
MonotonicityNot monotonic
2023-12-10T23:51:16.954453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
1 384
76.8%
2 54
 
10.8%
3 21
 
4.2%
4 13
 
2.6%
5 8
 
1.6%
6 4
 
0.8%
10 3
 
0.6%
7 3
 
0.6%
11 2
 
0.4%
9 1
 
0.2%
Other values (7) 7
 
1.4%
ValueCountFrequency (%)
1 384
76.8%
2 54
 
10.8%
3 21
 
4.2%
4 13
 
2.6%
5 8
 
1.6%
6 4
 
0.8%
7 3
 
0.6%
8 1
 
0.2%
9 1
 
0.2%
10 3
 
0.6%
ValueCountFrequency (%)
85 1
 
0.2%
24 1
 
0.2%
21 1
 
0.2%
18 1
 
0.2%
16 1
 
0.2%
14 1
 
0.2%
11 2
0.4%
10 3
0.6%
9 1
 
0.2%
8 1
 
0.2%

구매건수(PURH_CNT)
Real number (ℝ)

Distinct23
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.848
Minimum1
Maximum133
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:51:17.072208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile8
Maximum133
Range132
Interquartile range (IQR)1

Descriptive statistics

Standard deviation7.8615841
Coefficient of variation (CV)2.7603877
Kurtosis171.01809
Mean2.848
Median Absolute Deviation (MAD)0
Skewness11.826513
Sum1424
Variance61.804505
MonotonicityNot monotonic
2023-12-10T23:51:17.197668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
1 300
60.0%
2 84
 
16.8%
3 42
 
8.4%
4 24
 
4.8%
5 11
 
2.2%
7 9
 
1.8%
8 4
 
0.8%
9 4
 
0.8%
6 4
 
0.8%
13 2
 
0.4%
Other values (13) 16
 
3.2%
ValueCountFrequency (%)
1 300
60.0%
2 84
 
16.8%
3 42
 
8.4%
4 24
 
4.8%
5 11
 
2.2%
6 4
 
0.8%
7 9
 
1.8%
8 4
 
0.8%
9 4
 
0.8%
10 1
 
0.2%
ValueCountFrequency (%)
133 1
0.2%
77 1
0.2%
56 1
0.2%
36 1
0.2%
35 1
0.2%
19 1
0.2%
18 1
0.2%
17 1
0.2%
16 2
0.4%
15 2
0.4%

구매금액(PURH_AMT)
Real number (ℝ)

Distinct60
Distinct (%)12.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13444
Minimum1000
Maximum340000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:51:17.337322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile1000
Q13000
median6000
Q314000
95-th percentile44150
Maximum340000
Range339000
Interquartile range (IQR)11000

Descriptive statistics

Standard deviation27367.148
Coefficient of variation (CV)2.0356403
Kurtosis58.023978
Mean13444
Median Absolute Deviation (MAD)4000
Skewness6.5397775
Sum6722000
Variance7.4896079 × 108
MonotonicityNot monotonic
2023-12-10T23:51:17.516702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000 58
 
11.6%
2000 51
 
10.2%
4000 50
 
10.0%
5000 44
 
8.8%
3000 40
 
8.0%
6000 32
 
6.4%
7000 26
 
5.2%
10000 17
 
3.4%
9000 16
 
3.2%
8000 15
 
3.0%
Other values (50) 151
30.2%
ValueCountFrequency (%)
1000 58
11.6%
2000 51
10.2%
3000 40
8.0%
4000 50
10.0%
5000 44
8.8%
6000 32
6.4%
7000 26
5.2%
8000 15
 
3.0%
9000 16
 
3.2%
10000 17
 
3.4%
ValueCountFrequency (%)
340000 1
0.2%
255000 1
0.2%
159000 1
0.2%
145000 1
0.2%
140000 2
0.4%
133000 1
0.2%
132000 2
0.4%
107000 1
0.2%
100000 1
0.2%
81000 1
0.2%

Interactions

2023-12-10T23:51:13.148569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:06.004625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:07.209731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:08.118610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:09.002392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:09.744284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:10.872500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:12.277843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:13.251888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:06.101955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:07.323693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:08.221505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:09.103995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:09.837264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:11.177873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:12.385047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:13.359952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:06.209839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:07.418692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:08.326768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:09.192504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:09.933950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:11.514917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:12.501538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:13.473402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:06.318114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:07.520188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:08.447247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:09.286780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:10.029938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:11.706946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:12.617226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:13.568319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:06.416924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:07.611551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:08.547694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:09.371431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:10.124225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:11.793029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:12.733572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:13.683774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:06.519662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:07.735929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:08.667817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:09.456591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:10.226943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:11.962600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:12.839198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:13.778024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:06.623011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:07.835305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:08.769062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:09.537003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:10.328153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:12.046213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:12.939641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:14.190522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:07.109350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:07.979266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:08.895635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:09.631250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:10.581166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:12.151017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:13.041707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:51:17.634543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준년월(STD_YM)성별코드(SEX_CD)연령대코드(AGE_CD)상품코드(LMPH_CD)시간대코드(TIME_CD)구매자지역(BUYER_AREA)구매_고객수(ACC_CNT)구매건수(PURH_CNT)구매금액(PURH_AMT)
기준년월(STD_YM)1.0000.0000.0180.0000.0900.0000.0000.0000.125
성별코드(SEX_CD)0.0001.0000.0000.0000.1460.0000.1090.0210.000
연령대코드(AGE_CD)0.0180.0001.0000.0000.0000.0850.0000.0000.091
상품코드(LMPH_CD)0.0000.0000.0001.0000.0000.0990.0000.2530.000
시간대코드(TIME_CD)0.0900.1460.0000.0001.0000.0000.0000.0000.000
구매자지역(BUYER_AREA)0.0000.0000.0850.0990.0001.0000.1880.1910.062
구매_고객수(ACC_CNT)0.0000.1090.0000.0000.0000.1881.0000.0000.266
구매건수(PURH_CNT)0.0000.0210.0000.2530.0000.1910.0001.0000.188
구매금액(PURH_AMT)0.1250.0000.0910.0000.0000.0620.2660.1881.000
2023-12-10T23:51:17.777828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준년월(STD_YM)연령대코드(AGE_CD)상품코드(LMPH_CD)시간대코드(TIME_CD)구매자지역(BUYER_AREA)구매_고객수(ACC_CNT)구매건수(PURH_CNT)구매금액(PURH_AMT)성별코드(SEX_CD)
기준년월(STD_YM)1.0000.030-0.0270.040-0.0830.002-0.012-0.0330.000
연령대코드(AGE_CD)0.0301.0000.002-0.0070.034-0.023-0.029-0.0680.000
상품코드(LMPH_CD)-0.0270.0021.000-0.072-0.028-0.014-0.033-0.0410.000
시간대코드(TIME_CD)0.040-0.007-0.0721.000-0.0170.070-0.0260.0190.105
구매자지역(BUYER_AREA)-0.0830.034-0.028-0.0171.000-0.029-0.0260.0870.000
구매_고객수(ACC_CNT)0.002-0.023-0.0140.070-0.0291.000-0.0000.0230.072
구매건수(PURH_CNT)-0.012-0.029-0.033-0.026-0.026-0.0001.0000.0630.014
구매금액(PURH_AMT)-0.033-0.068-0.0410.0190.0870.0230.0631.0000.000
성별코드(SEX_CD)0.0000.0000.0000.1050.0000.0720.0140.0001.000

Missing values

2023-12-10T23:51:14.328023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:51:14.500998image/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)구매자지역(BUYER_AREA)구매_고객수(ACC_CNT)구매건수(PURH_CNT)구매금액(PURH_AMT)
02019021*8*62610509074112157503116000
12019101*5*0*231070201311350595125000
22019114*9*7*1410801023112158101144000
32018094*7*1*231080109411545610114000
42018123*2*6*221020302426320125000
52019115*2*4*244020206511230610122000
62019083*9*2*241070103511530770121000
72019052*7*0*251011107311470670116000
82018052*9*7*269020404411500550132000
92018062*7*31510506045115456701123000
기준년월(STD_YM)블록코드(BLCK_CD)성별코드(SEX_CD)연령대코드(AGE_CD)상품코드(LMPH_CD)시간대코드(TIME_CD)구매자지역(BUYER_AREA)구매_고객수(ACC_CNT)구매건수(PURH_CNT)구매금액(PURH_AMT)
4902018054*3*9*244010101411500615125000
4912019094*3*9*1330202025114106152133000
4922019114*7*1*141011805411650600135000
4932019083*2*6*238029901541820414000
4942019022*4*62320203034115006303181000
4952019022*1*5*1510901095114706701232000
4962019035*1*6*241100101247290119000
4972019112*0*6*141011104611230730119000
4982019011*5*0*241010709411620595618000
4992019113*9*6*161010999411200645221000