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 19:19:03.871174
Analysis finished2024-04-16 19:19:10.579375
Duration6.71 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%
Mean201853.75
Minimum201801
Maximum201912
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-04-17T04:19:10.944350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum201801
5-th percentile201802
Q1201806
median201812
Q3201906
95-th percentile201911
Maximum201912
Range111
Interquartile range (IQR)100

Descriptive statistics

Standard deviation50.20311
Coefficient of variation (CV)0.00024871031
Kurtosis-1.9773268
Mean201853.75
Median Absolute Deviation (MAD)11
Skewness0.11111396
Sum1.0092688 × 108
Variance2520.3522
MonotonicityNot monotonic
2024-04-17T04:19:11.059584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
201804 27
 
5.4%
201904 26
 
5.2%
201803 26
 
5.2%
201810 26
 
5.2%
201806 25
 
5.0%
201907 23
 
4.6%
201909 23
 
4.6%
201801 23
 
4.6%
201812 23
 
4.6%
201911 22
 
4.4%
Other values (14) 256
51.2%
ValueCountFrequency (%)
201801 23
4.6%
201802 18
3.6%
201803 26
5.2%
201804 27
5.4%
201805 18
3.6%
201806 25
5.0%
201807 20
4.0%
201808 21
4.2%
201809 18
3.6%
201810 26
5.2%
ValueCountFrequency (%)
201912 16
3.2%
201911 22
4.4%
201910 21
4.2%
201909 23
4.6%
201908 19
3.8%
201907 23
4.6%
201906 19
3.8%
201905 20
4.0%
201904 26
5.2%
201903 13
2.6%

블록코드(BLCK_CD)
Real number (ℝ)

Distinct270
Distinct (%)54.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean225331.65
Minimum8661
Maximum502407
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-04-17T04:19:11.191621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8661
5-th percentile17200.15
Q1149493.75
median218641.5
Q3356939.25
95-th percentile422185
Maximum502407
Range493746
Interquartile range (IQR)207445.5

Descriptive statistics

Standard deviation143774.8
Coefficient of variation (CV)0.63805859
Kurtosis-1.0033935
Mean225331.65
Median Absolute Deviation (MAD)134634
Skewness0.066258661
Sum1.1266582 × 108
Variance2.0671192 × 1010
MonotonicityNot monotonic
2024-04-17T04:19:11.337550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
206648 20
 
4.0%
155402 15
 
3.0%
502147 14
 
2.8%
19587 13
 
2.6%
122837 13
 
2.6%
362962 12
 
2.4%
409372 12
 
2.4%
413592 12
 
2.4%
229979 12
 
2.4%
422185 9
 
1.8%
Other values (260) 368
73.6%
ValueCountFrequency (%)
8661 7
1.4%
9291 1
 
0.2%
9478 1
 
0.2%
9911 1
 
0.2%
11478 1
 
0.2%
11689 1
 
0.2%
11749 1
 
0.2%
14142 1
 
0.2%
14274 1
 
0.2%
14374 1
 
0.2%
ValueCountFrequency (%)
502407 1
 
0.2%
502147 14
2.8%
500232 3
 
0.6%
499597 1
 
0.2%
422428 1
 
0.2%
422185 9
1.8%
422022 1
 
0.2%
422008 1
 
0.2%
421981 1
 
0.2%
421635 1
 
0.2%
Distinct9
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
A
240 
E
86 
B
78 
L
48 
I
 
17
Other values (4)
31 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowI
2nd rowE
3rd rowF
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 240
48.0%
E 86
 
17.2%
B 78
 
15.6%
L 48
 
9.6%
I 17
 
3.4%
J 15
 
3.0%
C 9
 
1.8%
F 4
 
0.8%
G 3
 
0.6%

Length

2024-04-17T04:19:11.456160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T04:19:11.561805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 240
48.0%
e 86
 
17.2%
b 78
 
15.6%
l 48
 
9.6%
i 17
 
3.4%
j 15
 
3.0%
c 9
 
1.8%
f 4
 
0.8%
g 3
 
0.6%
Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2
305 
1
195 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
2 305
61.0%
1 195
39.0%

Length

2024-04-17T04:19:11.674199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T04:19:11.770514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 305
61.0%
1 195
39.0%

연령대코드(AGE_CD)
Real number (ℝ)

Distinct7
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.864
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-04-17T04:19:11.843188image/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 deviation1.3435454
Coefficient of variation (CV)0.34770844
Kurtosis-0.40363872
Mean3.864
Median Absolute Deviation (MAD)1
Skewness0.33912226
Sum1932
Variance1.8051142
MonotonicityNot monotonic
2024-04-17T04:19:11.949117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
4 142
28.4%
3 114
22.8%
5 100
20.0%
2 87
17.4%
6 33
 
6.6%
7 21
 
4.2%
1 3
 
0.6%
ValueCountFrequency (%)
1 3
 
0.6%
2 87
17.4%
3 114
22.8%
4 142
28.4%
5 100
20.0%
6 33
 
6.6%
7 21
 
4.2%
ValueCountFrequency (%)
7 21
 
4.2%
6 33
 
6.6%
5 100
20.0%
4 142
28.4%
3 114
22.8%
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%
Mean4.188
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-04-17T04:19:12.062410image/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 deviation1.3145768
Coefficient of variation (CV)0.31389131
Kurtosis-0.63135505
Mean4.188
Median Absolute Deviation (MAD)1
Skewness-0.43547238
Sum2094
Variance1.7281122
MonotonicityNot monotonic
2024-04-17T04:19:12.182605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
5 155
31.0%
4 112
22.4%
3 89
17.8%
6 82
16.4%
2 50
 
10.0%
1 12
 
2.4%
ValueCountFrequency (%)
1 12
 
2.4%
2 50
 
10.0%
3 89
17.8%
4 112
22.4%
5 155
31.0%
6 82
16.4%
ValueCountFrequency (%)
6 82
16.4%
5 155
31.0%
4 112
22.4%
3 89
17.8%
2 50
 
10.0%
1 12
 
2.4%
Distinct299
Distinct (%)59.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7918351.2
Minimum26380
Maximum11740685
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-04-17T04:19:12.300207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum26380
5-th percentile28245
Q142130
median11305652
Q311560665
95-th percentile11710631
Maximum11740685
Range11714305
Interquartile range (IQR)11518535

Descriptive statistics

Standard deviation5289022.6
Coefficient of variation (CV)0.66794494
Kurtosis-1.3267514
Mean7918351.2
Median Absolute Deviation (MAD)315072.5
Skewness-0.82125057
Sum3.9591756 × 109
Variance2.797376 × 1013
MonotonicityNot monotonic
2024-04-17T04:19:12.436751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41360 12
 
2.4%
28237 7
 
1.4%
41310 7
 
1.4%
28200 6
 
1.2%
11710670 5
 
1.0%
45111 5
 
1.0%
41285 5
 
1.0%
11680720 4
 
0.8%
41590 4
 
0.8%
11215810 4
 
0.8%
Other values (289) 441
88.2%
ValueCountFrequency (%)
26380 1
 
0.2%
26410 1
 
0.2%
26440 1
 
0.2%
27260 3
0.6%
27710 1
 
0.2%
28110 1
 
0.2%
28140 1
 
0.2%
28185 2
 
0.4%
28200 6
1.2%
28237 7
1.4%
ValueCountFrequency (%)
11740685 2
0.4%
11740660 1
0.2%
11740640 2
0.4%
11740620 2
0.4%
11740580 1
0.2%
11740540 1
0.2%
11740520 1
0.2%
11710720 1
0.2%
11710710 1
0.2%
11710690 2
0.4%

구매고객수(ACC_CNT)
Real number (ℝ)

Distinct52
Distinct (%)10.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.38
Minimum1
Maximum819
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-04-17T04:19:12.574046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q35
95-th percentile30
Maximum819
Range818
Interquartile range (IQR)4

Descriptive statistics

Standard deviation48.826506
Coefficient of variation (CV)4.7039023
Kurtosis172.95163
Mean10.38
Median Absolute Deviation (MAD)1
Skewness12.028327
Sum5190
Variance2384.0277
MonotonicityNot monotonic
2024-04-17T04:19:12.707220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 224
44.8%
2 78
 
15.6%
3 37
 
7.4%
4 33
 
6.6%
5 23
 
4.6%
10 11
 
2.2%
6 10
 
2.0%
9 8
 
1.6%
7 7
 
1.4%
8 6
 
1.2%
Other values (42) 63
 
12.6%
ValueCountFrequency (%)
1 224
44.8%
2 78
 
15.6%
3 37
 
7.4%
4 33
 
6.6%
5 23
 
4.6%
6 10
 
2.0%
7 7
 
1.4%
8 6
 
1.2%
9 8
 
1.6%
10 11
 
2.2%
ValueCountFrequency (%)
819 1
0.2%
475 1
0.2%
357 1
0.2%
217 1
0.2%
214 1
0.2%
170 1
0.2%
114 1
0.2%
108 1
0.2%
99 1
0.2%
71 1
0.2%

구매건수(PURH_CNT)
Real number (ℝ)

Distinct66
Distinct (%)13.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.516
Minimum1
Maximum1276
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-04-17T04:19:12.839382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median3
Q37
95-th percentile61.4
Maximum1276
Range1275
Interquartile range (IQR)6

Descriptive statistics

Standard deviation78.178318
Coefficient of variation (CV)4.4632518
Kurtosis158.4386
Mean17.516
Median Absolute Deviation (MAD)2
Skewness11.368304
Sum8758
Variance6111.8494
MonotonicityNot monotonic
2024-04-17T04:19:13.006815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 154
30.8%
2 86
17.2%
3 49
 
9.8%
4 31
 
6.2%
6 23
 
4.6%
5 22
 
4.4%
8 13
 
2.6%
7 11
 
2.2%
10 10
 
2.0%
9 9
 
1.8%
Other values (56) 92
18.4%
ValueCountFrequency (%)
1 154
30.8%
2 86
17.2%
3 49
 
9.8%
4 31
 
6.2%
5 22
 
4.4%
6 23
 
4.6%
7 11
 
2.2%
8 13
 
2.6%
9 9
 
1.8%
10 10
 
2.0%
ValueCountFrequency (%)
1276 1
0.2%
811 1
0.2%
430 1
0.2%
404 1
0.2%
303 1
0.2%
256 1
0.2%
251 1
0.2%
212 1
0.2%
193 1
0.2%
191 1
0.2%

구매금액(PURH_AMT)
Real number (ℝ)

Distinct121
Distinct (%)24.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean106726
Minimum1000
Maximum8800000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-04-17T04:19:13.151656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile1000
Q14000
median11000
Q336000
95-th percentile252200
Maximum8800000
Range8799000
Interquartile range (IQR)32000

Descriptive statistics

Standard deviation588775.87
Coefficient of variation (CV)5.5167051
Kurtosis132.88035
Mean106726
Median Absolute Deviation (MAD)9000
Skewness10.780328
Sum53363000
Variance3.4665702 × 1011
MonotonicityNot monotonic
2024-04-17T04:19:13.279803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2000 40
 
8.0%
4000 37
 
7.4%
1000 34
 
6.8%
5000 32
 
6.4%
3000 29
 
5.8%
9000 22
 
4.4%
8000 15
 
3.0%
10000 14
 
2.8%
7000 14
 
2.8%
20000 14
 
2.8%
Other values (111) 249
49.8%
ValueCountFrequency (%)
1000 34
6.8%
2000 40
8.0%
3000 29
5.8%
4000 37
7.4%
5000 32
6.4%
6000 11
 
2.2%
7000 14
 
2.8%
8000 15
 
3.0%
9000 22
4.4%
10000 14
 
2.8%
ValueCountFrequency (%)
8800000 1
0.2%
6251000 1
0.2%
5328000 1
0.2%
3558000 1
0.2%
1838000 1
0.2%
1666000 1
0.2%
1628000 1
0.2%
1419000 1
0.2%
1318000 1
0.2%
1279000 1
0.2%

Interactions

2024-04-17T04:19:09.663927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:04.236195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:05.208190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:05.991513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:06.769774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:07.484292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:08.198988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:08.956297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:09.745186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:04.315361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:05.298903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:06.089439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:06.871087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:07.567511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:08.287269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:09.039659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:09.822128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:04.389878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:05.389211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:06.178293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:06.964325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:07.654617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:08.374692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:09.121942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:09.904972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:04.470606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:05.475900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:06.279026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:07.059504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:07.751264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:08.467625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:09.218036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:09.989280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:04.549542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:05.563938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:06.374876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:07.138197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:07.841512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:08.555483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:09.306040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:10.089844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:04.954947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:05.659137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:06.463238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:07.226943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:07.934511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:08.645697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:09.391960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:10.192222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:05.037004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:05.775956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:06.575082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:07.317445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:08.030586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:08.749835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:09.487334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:10.280667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:05.126040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:05.894923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:06.688853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:07.408568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:08.117639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:08.858095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:19:09.583549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-17T04:19:13.366471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준년월(STD_YM)블록코드(BLCK_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)1.0000.0000.0000.0190.0000.0000.0000.0730.0190.000
블록코드(BLCK_CD)0.0001.0000.0000.1130.1030.0000.0000.0000.0000.258
통계청상품코드(STAT_CD)0.0000.0001.0000.0350.0000.0000.0830.1400.2780.000
성별코드(SEX_CD)0.0190.1130.0351.0000.0320.0000.1610.0000.0000.047
연령대코드(AGE_CD)0.0000.1030.0000.0321.0000.0870.0510.0370.0000.000
시간대코드(TIME_CD)0.0000.0000.0000.0000.0871.0000.1400.3370.0000.000
구매자지역(BUYER_AREA)0.0000.0000.0830.1610.0510.1401.0000.0840.0000.015
구매고객수(ACC_CNT)0.0730.0000.1400.0000.0370.3370.0841.0000.3230.000
구매건수(PURH_CNT)0.0190.0000.2780.0000.0000.0000.0000.3231.0000.000
구매금액(PURH_AMT)0.0000.2580.0000.0470.0000.0000.0150.0000.0001.000
2024-04-17T04:19:13.487057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
통계청상품코드(STAT_CD)성별코드(SEX_CD)
통계청상품코드(STAT_CD)1.0000.034
성별코드(SEX_CD)0.0341.000
2024-04-17T04:19:13.572132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준년월(STD_YM)블록코드(BLCK_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)1.000-0.008-0.003-0.0470.0490.0060.0650.1080.0000.000
블록코드(BLCK_CD)-0.0081.0000.052-0.034-0.0470.0640.009-0.0260.0000.102
연령대코드(AGE_CD)-0.0030.0521.0000.0280.0810.0050.0480.0280.0000.034
시간대코드(TIME_CD)-0.047-0.0340.0281.000-0.025-0.016-0.0160.0080.0000.000
구매자지역(BUYER_AREA)0.049-0.0470.081-0.0251.000-0.0430.0230.0580.0880.107
구매고객수(ACC_CNT)0.0060.0640.005-0.016-0.0431.0000.065-0.0110.0690.000
구매건수(PURH_CNT)0.0650.0090.048-0.0160.0230.0651.0000.0220.1410.000
구매금액(PURH_AMT)0.108-0.0260.0280.0080.058-0.0110.0221.0000.0000.043
통계청상품코드(STAT_CD)0.0000.0000.0000.0000.0880.0690.1410.0001.0000.034
성별코드(SEX_CD)0.0000.1020.0340.0000.1070.0000.0000.0430.0341.000

Missing values

2024-04-17T04:19:10.386786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-17T04:19:10.520318image/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)통계청상품코드(STAT_CD)성별코드(SEX_CD)연령대코드(AGE_CD)시간대코드(TIME_CD)구매자지역(BUYER_AREA)구매고객수(ACC_CNT)구매건수(PURH_CNT)구매금액(PURH_AMT)
0201911206648I2261168072071277000
1201805225172E13611710520318000
2201905224659F1361171063211010000
3201805413188A2441171058039523000
4201803364511A12411680656611000
520180619587B155111706851257000
6201910362962L1631162056535741000
7201902189947E133282601131000
8201908413592B2641162071552240000
9201906417990B2741168072011021000
기준년월(STD_YM)블록코드(BLCK_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)
490201804275451L14611740520113558000
491201806153212B23411710532138000
492201805273675A1761171053266143000
493201906409372B15445111249000
49420190622870A125114407201110000
495201809274538I23211650621111000
496201804353409A2341147051042139000
497201806223026A145411901340000
4982019078661A24511650651324000
499201806207141L24641150122000