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
Categorical2

Dataset

Description샘플 데이터
Author서울시(신용보증재단)
URLhttps://bigdata.seoul.go.kr/data/selectSampleData.do?sample_data_seq=324

Reproduction

Analysis started2024-04-16 19:19:15.001824
Analysis finished2024-04-16 19:19:21.918923
Duration6.92 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%
Mean201856.77
Minimum201801
Maximum201912
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-04-17T04:19:21.975776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum201801
5-th percentile201802
Q1201806
median201901
Q3201906
95-th percentile201911
Maximum201912
Range111
Interquartile range (IQR)100

Descriptive statistics

Standard deviation50.209842
Coefficient of variation (CV)0.00024873995
Kurtosis-1.9887696
Mean201856.77
Median Absolute Deviation (MAD)11
Skewness-0.023681185
Sum1.0092838 × 108
Variance2521.0282
MonotonicityNot monotonic
2024-04-17T04:19:22.086665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
201803 31
 
6.2%
201910 28
 
5.6%
201909 28
 
5.6%
201904 26
 
5.2%
201903 25
 
5.0%
201902 24
 
4.8%
201806 24
 
4.8%
201804 24
 
4.8%
201805 24
 
4.8%
201809 22
 
4.4%
Other values (14) 244
48.8%
ValueCountFrequency (%)
201801 20
4.0%
201802 20
4.0%
201803 31
6.2%
201804 24
4.8%
201805 24
4.8%
201806 24
4.8%
201807 18
3.6%
201808 10
 
2.0%
201809 22
4.4%
201810 16
3.2%
ValueCountFrequency (%)
201912 14
2.8%
201911 19
3.8%
201910 28
5.6%
201909 28
5.6%
201908 11
 
2.2%
201907 12
2.4%
201906 22
4.4%
201905 22
4.4%
201904 26
5.2%
201903 25
5.0%
Distinct170
Distinct (%)34.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11450276
Minimum11110530
Maximum11740685
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-04-17T04:19:22.212178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11110530
5-th percentile11140540
Q111290555
median11440710
Q311650581
95-th percentile11710710
Maximum11740685
Range630155
Interquartile range (IQR)360026

Descriptive statistics

Standard deviation199382.21
Coefficient of variation (CV)0.017412874
Kurtosis-1.3610926
Mean11450276
Median Absolute Deviation (MAD)209850
Skewness-0.13295222
Sum5.7251379 × 109
Variance3.9753264 × 1010
MonotonicityNot monotonic
2024-04-17T04:19:22.352909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11140540 28
 
5.6%
11710710 24
 
4.8%
11380690 16
 
3.2%
11710642 16
 
3.2%
11230560 15
 
3.0%
11545610 12
 
2.4%
11710680 12
 
2.4%
11500620 11
 
2.2%
11350621 11
 
2.2%
11200570 10
 
2.0%
Other values (160) 345
69.0%
ValueCountFrequency (%)
11110530 2
 
0.4%
11110550 1
 
0.2%
11110560 1
 
0.2%
11110615 4
 
0.8%
11110650 1
 
0.2%
11140520 1
 
0.2%
11140540 28
5.6%
11140550 5
 
1.0%
11140590 1
 
0.2%
11140665 1
 
0.2%
ValueCountFrequency (%)
11740685 1
 
0.2%
11740660 1
 
0.2%
11740610 1
 
0.2%
11740550 1
 
0.2%
11740540 1
 
0.2%
11710710 24
4.8%
11710690 1
 
0.2%
11710680 12
2.4%
11710650 1
 
0.2%
11710646 1
 
0.2%
Distinct9
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
A
207 
E
89 
B
83 
L
69 
J
 
19
Other values (4)
33 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowE
2nd rowA
3rd rowA
4th rowC
5th rowA

Common Values

ValueCountFrequency (%)
A 207
41.4%
E 89
17.8%
B 83
16.6%
L 69
 
13.8%
J 19
 
3.8%
C 12
 
2.4%
I 12
 
2.4%
F 6
 
1.2%
G 3
 
0.6%

Length

2024-04-17T04:19:22.783578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T04:19:22.896963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 207
41.4%
e 89
17.8%
b 83
16.6%
l 69
 
13.8%
j 19
 
3.8%
c 12
 
2.4%
i 12
 
2.4%
f 6
 
1.2%
g 3
 
0.6%
Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2
316 
1
184 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
2 316
63.2%
1 184
36.8%

Length

2024-04-17T04:19:23.008935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T04:19:23.091397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 316
63.2%
1 184
36.8%

연령대코드(AGE_CD)
Real number (ℝ)

Distinct7
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.848
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-04-17T04:19:23.170843image/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 deviation1.3757476
Coefficient of variation (CV)0.35752276
Kurtosis-0.7009158
Mean3.848
Median Absolute Deviation (MAD)1
Skewness0.34032592
Sum1924
Variance1.8926814
MonotonicityNot monotonic
2024-04-17T04:19:23.269160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
3 140
28.0%
4 104
20.8%
5 101
20.2%
2 87
17.4%
6 49
 
9.8%
7 16
 
3.2%
1 3
 
0.6%
ValueCountFrequency (%)
1 3
 
0.6%
2 87
17.4%
3 140
28.0%
4 104
20.8%
5 101
20.2%
6 49
 
9.8%
7 16
 
3.2%
ValueCountFrequency (%)
7 16
 
3.2%
6 49
 
9.8%
5 101
20.2%
4 104
20.8%
3 140
28.0%
2 87
17.4%
1 3
 
0.6%

시간대코드(TIME_CD)
Real number (ℝ)

Distinct6
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.228
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-04-17T04:19:23.396556image/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 deviation1.2276115
Coefficient of variation (CV)0.29035277
Kurtosis-0.52545776
Mean4.228
Median Absolute Deviation (MAD)1
Skewness-0.43535391
Sum2114
Variance1.5070301
MonotonicityNot monotonic
2024-04-17T04:19:23.499961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
5 166
33.2%
4 123
24.6%
3 89
17.8%
6 72
14.4%
2 43
 
8.6%
1 7
 
1.4%
ValueCountFrequency (%)
1 7
 
1.4%
2 43
 
8.6%
3 89
17.8%
4 123
24.6%
5 166
33.2%
6 72
14.4%
ValueCountFrequency (%)
6 72
14.4%
5 166
33.2%
4 123
24.6%
3 89
17.8%
2 43
 
8.6%
1 7
 
1.4%
Distinct320
Distinct (%)64.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8095495.7
Minimum26260
Maximum11740700
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-04-17T04:19:23.633469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum26260
5-th percentile30148.5
Q145137.5
median11305582
Q311549401
95-th percentile11710670
Maximum11740700
Range11714440
Interquartile range (IQR)11504264

Descriptive statistics

Standard deviation5206085.3
Coefficient of variation (CV)0.64308419
Kurtosis-1.1831055
Mean8095495.7
Median Absolute Deviation (MAD)285058
Skewness-0.90390605
Sum4.0477478 × 109
Variance2.7103324 × 1013
MonotonicityNot monotonic
2024-04-17T04:19:23.785859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41210 6
 
1.2%
41281 5
 
1.0%
41570 5
 
1.0%
41173 5
 
1.0%
11470550 4
 
0.8%
41310 4
 
0.8%
11710641 4
 
0.8%
41271 4
 
0.8%
11710610 4
 
0.8%
11230610 4
 
0.8%
Other values (310) 455
91.0%
ValueCountFrequency (%)
26260 2
0.4%
26320 1
0.2%
26350 2
0.4%
26410 1
0.2%
26440 1
0.2%
26500 1
0.2%
27200 1
0.2%
27230 2
0.4%
27290 2
0.4%
28140 1
0.2%
ValueCountFrequency (%)
11740700 1
 
0.2%
11740685 3
0.6%
11740660 1
 
0.2%
11740650 1
 
0.2%
11740610 3
0.6%
11740590 1
 
0.2%
11740570 1
 
0.2%
11740560 1
 
0.2%
11740550 1
 
0.2%
11740540 3
0.6%

구매고객수(ACC_CNT)
Real number (ℝ)

Distinct50
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.15
Minimum1
Maximum1200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-04-17T04:19:23.914155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q35
95-th percentile34
Maximum1200
Range1199
Interquartile range (IQR)4

Descriptive statistics

Standard deviation69.172373
Coefficient of variation (CV)5.6931994
Kurtosis210.16204
Mean12.15
Median Absolute Deviation (MAD)1
Skewness13.624865
Sum6075
Variance4784.8171
MonotonicityNot monotonic
2024-04-17T04:19:24.048119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 203
40.6%
2 87
17.4%
3 49
 
9.8%
4 32
 
6.4%
5 19
 
3.8%
6 14
 
2.8%
8 10
 
2.0%
10 9
 
1.8%
7 7
 
1.4%
13 5
 
1.0%
Other values (40) 65
 
13.0%
ValueCountFrequency (%)
1 203
40.6%
2 87
17.4%
3 49
 
9.8%
4 32
 
6.4%
5 19
 
3.8%
6 14
 
2.8%
7 7
 
1.4%
8 10
 
2.0%
9 4
 
0.8%
10 9
 
1.8%
ValueCountFrequency (%)
1200 1
0.2%
808 1
0.2%
276 1
0.2%
273 1
0.2%
251 1
0.2%
181 1
0.2%
138 1
0.2%
128 1
0.2%
124 1
0.2%
101 1
0.2%

구매건수(PURH_CNT)
Real number (ℝ)

Distinct58
Distinct (%)11.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.874
Minimum1
Maximum3286
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-04-17T04:19:24.172734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median3
Q37
95-th percentile41.3
Maximum3286
Range3285
Interquartile range (IQR)6

Descriptive statistics

Standard deviation181.99678
Coefficient of variation (CV)7.6232211
Kurtosis234.73199
Mean23.874
Median Absolute Deviation (MAD)2
Skewness14.550249
Sum11937
Variance33122.828
MonotonicityNot monotonic
2024-04-17T04:19:24.299528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 158
31.6%
2 86
17.2%
3 45
 
9.0%
4 35
 
7.0%
5 26
 
5.2%
6 22
 
4.4%
8 15
 
3.0%
7 15
 
3.0%
9 9
 
1.8%
10 8
 
1.6%
Other values (48) 81
16.2%
ValueCountFrequency (%)
1 158
31.6%
2 86
17.2%
3 45
 
9.0%
4 35
 
7.0%
5 26
 
5.2%
6 22
 
4.4%
7 15
 
3.0%
8 15
 
3.0%
9 9
 
1.8%
10 8
 
1.6%
ValueCountFrequency (%)
3286 1
0.2%
1928 1
0.2%
1231 1
0.2%
510 1
0.2%
453 1
0.2%
228 1
0.2%
204 1
0.2%
174 1
0.2%
166 1
0.2%
142 1
0.2%

구매금액(PURH_AMT)
Real number (ℝ)

Distinct129
Distinct (%)25.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57972
Minimum1000
Maximum3333000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-04-17T04:19:24.424372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile1000
Q15000
median13000
Q341000
95-th percentile197350
Maximum3333000
Range3332000
Interquartile range (IQR)36000

Descriptive statistics

Standard deviation214927.19
Coefficient of variation (CV)3.7074311
Kurtosis140.08911
Mean57972
Median Absolute Deviation (MAD)10000
Skewness10.753895
Sum28986000
Variance4.6193699 × 1010
MonotonicityNot monotonic
2024-04-17T04:19:24.560087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5000 37
 
7.4%
1000 32
 
6.4%
3000 30
 
6.0%
2000 27
 
5.4%
4000 21
 
4.2%
6000 17
 
3.4%
15000 17
 
3.4%
9000 17
 
3.4%
10000 16
 
3.2%
7000 15
 
3.0%
Other values (119) 271
54.2%
ValueCountFrequency (%)
1000 32
6.4%
2000 27
5.4%
3000 30
6.0%
4000 21
4.2%
5000 37
7.4%
6000 17
3.4%
7000 15
3.0%
8000 13
 
2.6%
9000 17
3.4%
10000 16
3.2%
ValueCountFrequency (%)
3333000 1
0.2%
2366000 1
0.2%
1625000 1
0.2%
927000 1
0.2%
618000 1
0.2%
608000 1
0.2%
598000 1
0.2%
579000 1
0.2%
499000 1
0.2%
469000 1
0.2%

Interactions

2024-04-17T04:19:20.851301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:15.391101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:16.120977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:17.214676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:17.931016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:18.638179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:19.390484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:20.125247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:20.938481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:15.475903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:16.218132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:17.302159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:18.015232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:18.728163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:19.476568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:20.214759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:21.040413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:15.581279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:16.321165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:17.402004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:18.110498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:18.817544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:19.573395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:20.317001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:21.146811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:15.668502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:16.409836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:17.484096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:18.191787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:18.906095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:19.684125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:20.401857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:21.268683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:15.756214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:16.503774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:17.571463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:18.282295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:18.988348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:19.767687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:20.491590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:21.381767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:15.847435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:16.594983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:17.660519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:18.365699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:19.075017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:19.858601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:20.580816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:21.481113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:15.950945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:16.679735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:17.752971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:18.455359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:19.174574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:19.940665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:20.671651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:21.585480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:16.036715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:16.771521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:17.843609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:18.550117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:19.280348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:20.033642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:20.764588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-17T04:19:24.651207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준년월(STD_YM)행정동코드(ADSTRD_CD)통계청상품코드(STAT_CD)성별코드(SEX_CD)연령대코드(AGE_CD)시간대코드(TIME_CD)구매자지역(BUYER_AREA)구매고객수(ACC_CNT)구매건수(PURH_CNT)구매금액(PURH_AMT)
기준년월(STD_YM)1.0000.1220.0000.0000.0000.0000.0000.0000.0000.000
행정동코드(ADSTRD_CD)0.1221.0000.0000.1510.0000.0630.0000.0000.1000.000
통계청상품코드(STAT_CD)0.0000.0001.0000.0980.0000.0330.0000.0000.0000.000
성별코드(SEX_CD)0.0000.1510.0981.0000.0000.1010.0000.0000.0560.158
연령대코드(AGE_CD)0.0000.0000.0000.0001.0000.0000.0670.0950.0000.000
시간대코드(TIME_CD)0.0000.0630.0330.1010.0001.0000.0000.0770.0000.000
구매자지역(BUYER_AREA)0.0000.0000.0000.0000.0670.0001.0000.0540.0000.000
구매고객수(ACC_CNT)0.0000.0000.0000.0000.0950.0770.0541.0000.0000.000
구매건수(PURH_CNT)0.0000.1000.0000.0560.0000.0000.0000.0001.0000.000
구매금액(PURH_AMT)0.0000.0000.0000.1580.0000.0000.0000.0000.0001.000
2024-04-17T04:19:24.774146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
통계청상품코드(STAT_CD)성별코드(SEX_CD)
통계청상품코드(STAT_CD)1.0000.097
성별코드(SEX_CD)0.0971.000
2024-04-17T04:19:24.860287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준년월(STD_YM)행정동코드(ADSTRD_CD)연령대코드(AGE_CD)시간대코드(TIME_CD)구매자지역(BUYER_AREA)구매고객수(ACC_CNT)구매건수(PURH_CNT)구매금액(PURH_AMT)통계청상품코드(STAT_CD)성별코드(SEX_CD)
기준년월(STD_YM)1.0000.037-0.016-0.019-0.003-0.0890.0060.0100.0000.013
행정동코드(ADSTRD_CD)0.0371.0000.012-0.0280.064-0.0190.030-0.0180.0000.115
연령대코드(AGE_CD)-0.0160.0121.000-0.0240.0190.003-0.017-0.0390.0000.000
시간대코드(TIME_CD)-0.019-0.028-0.0241.0000.010-0.0200.0580.0420.0150.072
구매자지역(BUYER_AREA)-0.0030.0640.0190.0101.000-0.0170.0390.0260.0000.000
구매고객수(ACC_CNT)-0.089-0.0190.003-0.020-0.0171.000-0.0240.0130.0000.000
구매건수(PURH_CNT)0.0060.030-0.0170.0580.039-0.0241.0000.0170.0000.068
구매금액(PURH_AMT)0.010-0.018-0.0390.0420.0260.0130.0171.0000.0000.110
통계청상품코드(STAT_CD)0.0000.0000.0000.0150.0000.0000.0000.0001.0000.097
성별코드(SEX_CD)0.0130.1150.0000.0720.0000.0000.0680.1100.0971.000

Missing values

2024-04-17T04:19:21.705894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-17T04:19:21.844411image/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)행정동코드(ADSTRD_CD)통계청상품코드(STAT_CD)성별코드(SEX_CD)연령대코드(AGE_CD)시간대코드(TIME_CD)구매자지역(BUYER_AREA)구매고객수(ACC_CNT)구매건수(PURH_CNT)구매금액(PURH_AMT)
020190411530530E1444141021717000
120180511380530A13645130642000
220190611440660A25441390111000
320191011215860C12541287221000
420190111140540A16611500620211000
520190511710580A256116805901624000
620190511170685A2254146512246000
720190511410660E135437608117000
820191111500570A263117405603173000
920181011200550A234114106203249000
기준년월(STD_YM)행정동코드(ADSTRD_CD)통계청상품코드(STAT_CD)성별코드(SEX_CD)연령대코드(AGE_CD)시간대코드(TIME_CD)구매자지역(BUYER_AREA)구매고객수(ACC_CNT)구매건수(PURH_CNT)구매금액(PURH_AMT)
49020190911710680E224113055551425000
49120180611410620B23611215730195000
49220190611710642A2241171062073499000
49320190211140540E2554825016336000
49420181011680650F1361121577012110000
49520190111200570A126117406852210000
49620180411740610E243116806604110000
49720180311290725A122117106412814000
49820190211140540B124116805101187000
49920191211620565I124113055752114000