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

Alerts

구매_고객수(ACC_CNT) is highly skewed (γ1 = 21.64978997)Skewed

Reproduction

Analysis started2024-04-16 19:18:19.418150
Analysis finished2024-04-16 19:18:26.358510
Duration6.94 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%
Mean201857.62
Minimum201801
Maximum201912
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-04-17T04:18:26.422070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum201801
5-th percentile201802
Q1201807
median201901
Q3201906
95-th percentile201911
Maximum201912
Range111
Interquartile range (IQR)99

Descriptive statistics

Standard deviation50.086014
Coefficient of variation (CV)0.00024812546
Kurtosis-1.9879911
Mean201857.62
Median Absolute Deviation (MAD)11
Skewness-0.040308591
Sum1.0092881 × 108
Variance2508.6088
MonotonicityNot monotonic
2024-04-17T04:18:26.541548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
201810 32
 
6.4%
201905 27
 
5.4%
201903 25
 
5.0%
201806 24
 
4.8%
201811 24
 
4.8%
201906 24
 
4.8%
201911 24
 
4.8%
201803 23
 
4.6%
201909 23
 
4.6%
201904 22
 
4.4%
Other values (14) 252
50.4%
ValueCountFrequency (%)
201801 18
3.6%
201802 16
3.2%
201803 23
4.6%
201804 22
4.4%
201805 19
3.8%
201806 24
4.8%
201807 14
2.8%
201808 20
4.0%
201809 15
3.0%
201810 32
6.4%
ValueCountFrequency (%)
201912 17
3.4%
201911 24
4.8%
201910 22
4.4%
201909 23
4.6%
201908 20
4.0%
201907 18
3.6%
201906 24
4.8%
201905 27
5.4%
201904 22
4.4%
201903 25
5.0%
Distinct283
Distinct (%)56.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11461532
Minimum11110515
Maximum11740700
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-04-17T04:18:26.675154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11110515
5-th percentile11140680
Q111290694
median11500525
Q311650530
95-th percentile11710680
Maximum11740700
Range630185
Interquartile range (IQR)359836.5

Descriptive statistics

Standard deviation191466.07
Coefficient of variation (CV)0.016705103
Kurtosis-1.3041653
Mean11461532
Median Absolute Deviation (MAD)180011
Skewness-0.18383082
Sum5.730766 × 109
Variance3.6659254 × 1010
MonotonicityNot monotonic
2024-04-17T04:18:26.840584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11650621 6
 
1.2%
11710580 5
 
1.0%
11650530 5
 
1.0%
11560620 4
 
0.8%
11680565 4
 
0.8%
11545630 4
 
0.8%
11410615 4
 
0.8%
11170625 4
 
0.8%
11440660 4
 
0.8%
11260565 4
 
0.8%
Other values (273) 456
91.2%
ValueCountFrequency (%)
11110515 1
0.2%
11110530 1
0.2%
11110540 1
0.2%
11110550 2
0.4%
11110570 1
0.2%
11110580 2
0.4%
11110615 1
0.2%
11110650 2
0.4%
11110680 1
0.2%
11140540 1
0.2%
ValueCountFrequency (%)
11740700 2
0.4%
11740685 3
0.6%
11740660 1
 
0.2%
11740640 1
 
0.2%
11740600 2
0.4%
11740590 2
0.4%
11740580 1
 
0.2%
11740570 3
0.6%
11740560 1
 
0.2%
11740515 2
0.4%
Distinct9
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
A
217 
E
97 
B
93 
L
45 
I
22 
Other values (4)
26 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB
2nd rowA
3rd rowC
4th rowL
5th rowA

Common Values

ValueCountFrequency (%)
A 217
43.4%
E 97
19.4%
B 93
18.6%
L 45
 
9.0%
I 22
 
4.4%
J 11
 
2.2%
G 8
 
1.6%
C 5
 
1.0%
F 2
 
0.4%

Length

2024-04-17T04:18:26.978240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T04:18:27.117285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 217
43.4%
e 97
19.4%
b 93
18.6%
l 45
 
9.0%
i 22
 
4.4%
j 11
 
2.2%
g 8
 
1.6%
c 5
 
1.0%
f 2
 
0.4%
Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2
304 
1
196 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
2 304
60.8%
1 196
39.2%

Length

2024-04-17T04:18:27.235457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T04:18:27.318940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 304
60.8%
1 196
39.2%

연령대코드(AGE_CD)
Real number (ℝ)

Distinct7
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.894
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-04-17T04:18:27.402247image/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 deviation1.3365391
Coefficient of variation (CV)0.34323037
Kurtosis-0.57350426
Mean3.894
Median Absolute Deviation (MAD)1
Skewness0.28067975
Sum1947
Variance1.7863367
MonotonicityNot monotonic
2024-04-17T04:18:27.497822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
4 140
28.0%
3 119
23.8%
5 92
18.4%
2 81
16.2%
6 50
 
10.0%
7 15
 
3.0%
1 3
 
0.6%
ValueCountFrequency (%)
1 3
 
0.6%
2 81
16.2%
3 119
23.8%
4 140
28.0%
5 92
18.4%
6 50
 
10.0%
7 15
 
3.0%
ValueCountFrequency (%)
7 15
 
3.0%
6 50
 
10.0%
5 92
18.4%
4 140
28.0%
3 119
23.8%
2 81
16.2%
1 3
 
0.6%

시간대코드(TIME_CD)
Real number (ℝ)

Distinct6
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.174
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-04-17T04:18:27.624252image/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 deviation1.2786688
Coefficient of variation (CV)0.30634136
Kurtosis-0.68713646
Mean4.174
Median Absolute Deviation (MAD)1
Skewness-0.33445535
Sum2087
Variance1.634994
MonotonicityNot monotonic
2024-04-17T04:18:27.728409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
5 142
28.4%
4 126
25.2%
3 93
18.6%
6 81
16.2%
2 50
 
10.0%
1 8
 
1.6%
ValueCountFrequency (%)
1 8
 
1.6%
2 50
 
10.0%
3 93
18.6%
4 126
25.2%
5 142
28.4%
6 81
16.2%
ValueCountFrequency (%)
6 81
16.2%
5 142
28.4%
4 126
25.2%
3 93
18.6%
2 50
 
10.0%
1 8
 
1.6%

구매지역(PURH_AREA)
Real number (ℝ)

Distinct213
Distinct (%)42.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8244034.1
Minimum26290
Maximum11740700
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-04-17T04:18:27.852350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum26290
5-th percentile28260
Q144667.5
median11320710
Q311560610
95-th percentile11710710
Maximum11740700
Range11714410
Interquartile range (IQR)11515942

Descriptive statistics

Standard deviation5150260.4
Coefficient of variation (CV)0.62472575
Kurtosis-1.0614288
Mean8244034.1
Median Absolute Deviation (MAD)329820
Skewness-0.96823632
Sum4.122017 × 109
Variance2.6525182 × 1013
MonotonicityNot monotonic
2024-04-17T04:18:27.987850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11140540 21
 
4.2%
11710710 19
 
3.8%
11710680 13
 
2.6%
11545610 11
 
2.2%
11500620 11
 
2.2%
11350621 10
 
2.0%
11230560 10
 
2.0%
11560535 9
 
1.8%
11650530 8
 
1.6%
11710642 8
 
1.6%
Other values (203) 380
76.0%
ValueCountFrequency (%)
26290 1
 
0.2%
26710 1
 
0.2%
27140 1
 
0.2%
27230 1
 
0.2%
28110 3
0.6%
28185 4
0.8%
28237 6
1.2%
28245 5
1.0%
28260 6
1.2%
29140 2
 
0.4%
ValueCountFrequency (%)
11740700 1
 
0.2%
11740685 1
 
0.2%
11740640 1
 
0.2%
11740620 1
 
0.2%
11740610 1
 
0.2%
11740600 1
 
0.2%
11740550 1
 
0.2%
11740530 1
 
0.2%
11710710 19
3.8%
11710680 13
2.6%

구매_고객수(ACC_CNT)
Real number (ℝ)

SKEWED 

Distinct41
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.398
Minimum1
Maximum3324
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-04-17T04:18:28.123813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q34
95-th percentile20.05
Maximum3324
Range3323
Interquartile range (IQR)3

Descriptive statistics

Standard deviation150.01743
Coefficient of variation (CV)11.197002
Kurtosis478.06461
Mean13.398
Median Absolute Deviation (MAD)1
Skewness21.64979
Sum6699
Variance22505.23
MonotonicityNot monotonic
2024-04-17T04:18:28.251744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
1 221
44.2%
2 82
 
16.4%
3 47
 
9.4%
4 26
 
5.2%
5 21
 
4.2%
7 17
 
3.4%
6 14
 
2.8%
14 6
 
1.2%
18 6
 
1.2%
11 6
 
1.2%
Other values (31) 54
 
10.8%
ValueCountFrequency (%)
1 221
44.2%
2 82
 
16.4%
3 47
 
9.4%
4 26
 
5.2%
5 21
 
4.2%
6 14
 
2.8%
7 17
 
3.4%
8 5
 
1.0%
9 5
 
1.0%
10 4
 
0.8%
ValueCountFrequency (%)
3324 1
0.2%
284 1
0.2%
264 1
0.2%
161 1
0.2%
146 1
0.2%
110 1
0.2%
104 1
0.2%
95 1
0.2%
78 1
0.2%
60 1
0.2%

구매건수(PURH_CNT)
Real number (ℝ)

Distinct60
Distinct (%)12.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.098
Minimum1
Maximum1814
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-04-17T04:18:28.373335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median3
Q39
95-th percentile45.2
Maximum1814
Range1813
Interquartile range (IQR)8

Descriptive statistics

Standard deviation135.15104
Coefficient of variation (CV)5.851201
Kurtosis130.35414
Mean23.098
Median Absolute Deviation (MAD)2
Skewness10.941474
Sum11549
Variance18265.804
MonotonicityNot monotonic
2024-04-17T04:18:28.499494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 152
30.4%
2 62
12.4%
3 49
 
9.8%
5 33
 
6.6%
4 33
 
6.6%
10 18
 
3.6%
9 15
 
3.0%
6 15
 
3.0%
7 12
 
2.4%
8 12
 
2.4%
Other values (50) 99
19.8%
ValueCountFrequency (%)
1 152
30.4%
2 62
12.4%
3 49
 
9.8%
4 33
 
6.6%
5 33
 
6.6%
6 15
 
3.0%
7 12
 
2.4%
8 12
 
2.4%
9 15
 
3.0%
10 18
 
3.6%
ValueCountFrequency (%)
1814 1
0.2%
1756 1
0.2%
1249 1
0.2%
714 1
0.2%
610 1
0.2%
330 1
0.2%
326 1
0.2%
304 1
0.2%
289 1
0.2%
185 1
0.2%

구매금액(PURH_AMT)
Real number (ℝ)

Distinct143
Distinct (%)28.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean120564
Minimum1000
Maximum9484000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-04-17T04:18:28.635532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile1000
Q15000
median13000
Q343250
95-th percentile339050
Maximum9484000
Range9483000
Interquartile range (IQR)38250

Descriptive statistics

Standard deviation609304.08
Coefficient of variation (CV)5.0537812
Kurtosis133.79813
Mean120564
Median Absolute Deviation (MAD)11000
Skewness10.563016
Sum60282000
Variance3.7125146 × 1011
MonotonicityNot monotonic
2024-04-17T04:18:28.761639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2000 40
 
8.0%
1000 34
 
6.8%
4000 26
 
5.2%
3000 20
 
4.0%
7000 20
 
4.0%
8000 20
 
4.0%
9000 19
 
3.8%
6000 19
 
3.8%
5000 19
 
3.8%
15000 13
 
2.6%
Other values (133) 270
54.0%
ValueCountFrequency (%)
1000 34
6.8%
2000 40
8.0%
3000 20
4.0%
4000 26
5.2%
5000 19
3.8%
6000 19
3.8%
7000 20
4.0%
8000 20
4.0%
9000 19
3.8%
10000 10
 
2.0%
ValueCountFrequency (%)
9484000 1
0.2%
5078000 1
0.2%
4726000 1
0.2%
4692000 1
0.2%
2897000 1
0.2%
2457000 1
0.2%
2302000 1
0.2%
1416000 1
0.2%
1315000 1
0.2%
1191000 1
0.2%

Interactions

2024-04-17T04:18:25.381959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:19.772518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:20.530370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:21.356033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:22.086716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:22.785690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:23.544036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:24.262953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:25.493955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:19.863390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:20.646898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:21.444426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:22.169088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:22.898024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:23.635608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:24.353137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:25.588724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:19.955335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:20.765738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:21.549868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:22.256475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:22.992408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:23.731403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:24.454412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:25.676091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:20.053804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:20.858533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:21.633361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:22.342664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:23.081827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:23.818644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:24.549287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:25.764042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:20.150619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:20.942016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:21.733832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:22.420650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:23.169668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:23.908343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:24.648300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:25.849207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:20.241180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:21.037679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:21.817835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:22.512962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:23.264398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:23.998272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:24.756546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:25.935402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:20.327740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:21.134573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:21.906434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:22.613747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:23.364883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:24.085208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:24.865504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:26.027671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:20.445554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:21.270929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:22.006539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:22.702417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:23.461447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:24.179456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T04:18:25.297915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-17T04:18:28.848598image/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)구매지역(PURH_AREA)구매_고객수(ACC_CNT)구매건수(PURH_CNT)구매금액(PURH_AMT)
기준년월(STD_YM)1.0000.0000.1080.0000.0000.1000.1130.0000.0000.000
행정동코드(ADSTRD_CD)0.0001.0000.0000.0000.1400.1680.0000.0000.0000.128
통계청상품코드(STAT_CD)0.1080.0001.0000.0000.0000.0000.0610.0000.2970.000
성별코드(SEX_CD)0.0000.0000.0001.0000.0000.0000.0000.0000.0360.000
연령대코드(AGE_CD)0.0000.1400.0000.0001.0000.1260.0000.0000.0000.000
시간대코드(TIME_CD)0.1000.1680.0000.0000.1261.0000.0000.0000.0000.000
구매지역(PURH_AREA)0.1130.0000.0610.0000.0000.0001.0000.0000.0580.000
구매_고객수(ACC_CNT)0.0000.0000.0000.0000.0000.0000.0001.0000.0000.000
구매건수(PURH_CNT)0.0000.0000.2970.0360.0000.0000.0580.0001.0000.071
구매금액(PURH_AMT)0.0000.1280.0000.0000.0000.0000.0000.0000.0711.000
2024-04-17T04:18:28.988881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
통계청상품코드(STAT_CD)성별코드(SEX_CD)
통계청상품코드(STAT_CD)1.0000.000
성별코드(SEX_CD)0.0001.000
2024-04-17T04:18:29.077446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준년월(STD_YM)행정동코드(ADSTRD_CD)연령대코드(AGE_CD)시간대코드(TIME_CD)구매지역(PURH_AREA)구매_고객수(ACC_CNT)구매건수(PURH_CNT)구매금액(PURH_AMT)통계청상품코드(STAT_CD)성별코드(SEX_CD)
기준년월(STD_YM)1.0000.0180.0410.065-0.0390.044-0.067-0.0470.0810.000
행정동코드(ADSTRD_CD)0.0181.000-0.019-0.020-0.058-0.0360.019-0.0140.0000.000
연령대코드(AGE_CD)0.041-0.0191.0000.070-0.0170.0240.025-0.0670.0000.000
시간대코드(TIME_CD)0.065-0.0200.0701.000-0.0520.097-0.047-0.0740.0000.000
구매지역(PURH_AREA)-0.039-0.058-0.017-0.0521.0000.068-0.034-0.0030.0620.000
구매_고객수(ACC_CNT)0.044-0.0360.0240.0970.0681.0000.084-0.0130.0000.000
구매건수(PURH_CNT)-0.0670.0190.025-0.047-0.0340.0841.000-0.0040.1750.044
구매금액(PURH_AMT)-0.047-0.014-0.067-0.074-0.003-0.013-0.0041.0000.0000.000
통계청상품코드(STAT_CD)0.0810.0000.0000.0000.0620.0000.1750.0001.0000.000
성별코드(SEX_CD)0.0000.0000.0000.0000.0000.0000.0440.0000.0001.000

Missing values

2024-04-17T04:18:26.147259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-17T04:18:26.301201image/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)구매지역(PURH_AREA)구매_고객수(ACC_CNT)구매건수(PURH_CNT)구매금액(PURH_AMT)
020191211110650B24211710680193000
120180411620725A23511320690371000
220191111215840C24445180113000
320180711200580L24211290660111287000
420190611650530A1464136041814380000
520180311380690A23311560610121918000
620190411500615B23411680650429000
720181011710566E23411110640428205000
820190911350611B1354117362147000
920180411740570A23511380690927000
기준년월(STD_YM)행정동코드(ADSTRD_CD)통계청상품코드(STAT_CD)성별코드(SEX_CD)연령대코드(AGE_CD)시간대코드(TIME_CD)구매지역(PURH_AREA)구매_고객수(ACC_CNT)구매건수(PURH_CNT)구매금액(PURH_AMT)
49020190211680700C24641150717562000
49120181011230570I1431114055019716000
49220190811500540E146116806501150000
49320191011500560J22411230570147000
49420181211290525J264115456106787000
49520180411590660A211112307301330000
49620180511545700L246114706504399000
49720180311350640A25611260630418000
49820180811740515A2444139012151000
49920180911320514E24611350621252000