Overview

Dataset statistics

Number of variables10
Number of observations500
Missing cells75
Missing cells (%)1.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory43.1 KiB
Average record size in memory88.3 B

Variable types

Numeric8
Categorical2

Dataset

Description샘플 데이터
Author신한카드
URLhttps://bigdata.seoul.go.kr/data/selectSampleData.do?sample_data_seq=50

Alerts

시간대(TM) has constant value ""Constant
유입지_시군구코드(C_SGG_CD) has 75 (15.0%) missing valuesMissing

Reproduction

Analysis started2023-12-10 14:52:25.458157
Analysis finished2023-12-10 14:52:34.402851
Duration8.94 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Distinct252
Distinct (%)50.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1114635.4
Minimum1101053
Maximum1125074
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:52:34.508766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1101053
5-th percentile1102058
Q11108071.8
median1115070.5
Q31122053.2
95-th percentile1124071.3
Maximum1125074
Range24021
Interquartile range (IQR)13981.5

Descriptive statistics

Standard deviation7528.3759
Coefficient of variation (CV)0.006754115
Kurtosis-1.214018
Mean1114635.4
Median Absolute Deviation (MAD)6987
Skewness-0.33621625
Sum5.5731772 × 108
Variance56676444
MonotonicityNot monotonic
2023-12-10T23:52:34.674576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1123064 12
 
2.4%
1114077 9
 
1.8%
1114066 8
 
1.6%
1119074 7
 
1.4%
1118051 7
 
1.4%
1122067 5
 
1.0%
1123063 5
 
1.0%
1125067 5
 
1.0%
1113075 5
 
1.0%
1101072 4
 
0.8%
Other values (242) 433
86.6%
ValueCountFrequency (%)
1101053 2
0.4%
1101054 1
 
0.2%
1101055 1
 
0.2%
1101061 2
0.4%
1101068 1
 
0.2%
1101071 1
 
0.2%
1101072 4
0.8%
1101073 3
0.6%
1102052 2
0.4%
1102054 4
0.8%
ValueCountFrequency (%)
1125074 4
0.8%
1125073 1
 
0.2%
1125072 1
 
0.2%
1125071 2
 
0.4%
1125070 1
 
0.2%
1125067 5
1.0%
1125066 1
 
0.2%
1125065 1
 
0.2%
1125063 1
 
0.2%
1125058 1
 
0.2%

블록코드(BLOCK_ID)
Real number (ℝ)

Distinct474
Distinct (%)94.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.114411 × 1012
Minimum1.101053 × 1012
Maximum1.125073 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:52:34.828665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.101053 × 1012
5-th percentile1.102059 × 1012
Q11.1070698 × 1012
median1.116051 × 1012
Q31.121325 × 1012
95-th percentile1.124065 × 1012
Maximum1.125073 × 1012
Range2.402001 × 1010
Interquartile range (IQR)1.4255263 × 1010

Descriptive statistics

Standard deviation7.4415153 × 109
Coefficient of variation (CV)0.006677532
Kurtosis-1.2806913
Mean1.114411 × 1012
Median Absolute Deviation (MAD)6.015025 × 109
Skewness-0.28385241
Sum5.5720551 × 1014
Variance5.537615 × 1019
MonotonicityNot monotonic
2023-12-10T23:52:34.979835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1114066100004 3
 
0.6%
1123077040001 3
 
0.6%
1122053010010 2
 
0.4%
1123064030006 2
 
0.4%
1115060040011 2
 
0.4%
1123064070006 2
 
0.4%
1117056010002 2
 
0.4%
1111058060002 2
 
0.4%
1107060050005 2
 
0.4%
1123053010005 2
 
0.4%
Other values (464) 478
95.6%
ValueCountFrequency (%)
1101053020002 1
0.2%
1101056020001 1
0.2%
1101061020002 1
0.2%
1101061030004 1
0.2%
1101061030005 1
0.2%
1101063020001 1
0.2%
1101063030002 1
0.2%
1101071020004 1
0.2%
1101072010002 1
0.2%
1101072010004 1
0.2%
ValueCountFrequency (%)
1125073030012 1
0.2%
1125073030010 1
0.2%
1125073030006 1
0.2%
1125073020001 1
0.2%
1125073010001 1
0.2%
1125072010017 1
0.2%
1125067020013 1
0.2%
1125066020301 1
0.2%
1125066010008 1
0.2%
1125065010004 1
0.2%
Distinct21
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
sb08
117 
sb01
78 
sb06
65 
sb02
33 
sb19
30 
Other values (16)
177 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st rowsb04
2nd rowsb01
3rd rowsb01
4th rowsb02
5th rowsb01

Common Values

ValueCountFrequency (%)
sb08 117
23.4%
sb01 78
15.6%
sb06 65
13.0%
sb02 33
 
6.6%
sb19 30
 
6.0%
sb03 22
 
4.4%
sb09 19
 
3.8%
sb15 18
 
3.6%
sb14 18
 
3.6%
sb04 16
 
3.2%
Other values (11) 84
16.8%

Length

2023-12-10T23:52:35.117637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sb08 117
23.4%
sb01 78
15.6%
sb06 65
13.0%
sb02 33
 
6.6%
sb19 30
 
6.0%
sb03 22
 
4.4%
sb09 19
 
3.8%
sb15 18
 
3.6%
sb14 18
 
3.6%
sb11 16
 
3.2%
Other values (11) 84
16.8%

이용년월(TS_YM)
Real number (ℝ)

Distinct24
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean201642.56
Minimum201512
Maximum201711
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:52:35.247486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum201512
5-th percentile201512
Q1201604
median201610
Q3201705
95-th percentile201710
Maximum201711
Range199
Interquartile range (IQR)101

Descriptive statistics

Standard deviation59.773578
Coefficient of variation (CV)0.00029643335
Kurtosis-0.81167641
Mean201642.56
Median Absolute Deviation (MAD)9
Skewness-0.30851298
Sum1.0082128 × 108
Variance3572.8806
MonotonicityNot monotonic
2023-12-10T23:52:35.383495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
201512 34
 
6.8%
201609 29
 
5.8%
201604 29
 
5.8%
201602 24
 
4.8%
201601 24
 
4.8%
201708 22
 
4.4%
201706 22
 
4.4%
201603 22
 
4.4%
201710 22
 
4.4%
201707 21
 
4.2%
Other values (14) 251
50.2%
ValueCountFrequency (%)
201512 34
6.8%
201601 24
4.8%
201602 24
4.8%
201603 22
4.4%
201604 29
5.8%
201605 19
3.8%
201606 18
3.6%
201607 14
2.8%
201608 19
3.8%
201609 29
5.8%
ValueCountFrequency (%)
201711 16
3.2%
201710 22
4.4%
201709 20
4.0%
201708 22
4.4%
201707 21
4.2%
201706 22
4.4%
201705 21
4.2%
201704 17
3.4%
201703 20
4.0%
201702 15
3.0%

요일코드(DAW_CCD)
Real number (ℝ)

Distinct7
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.146
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:52:35.498471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.9078689
Coefficient of variation (CV)0.460171
Kurtosis-1.1422218
Mean4.146
Median Absolute Deviation (MAD)2
Skewness-0.10897173
Sum2073
Variance3.6399639
MonotonicityNot monotonic
2023-12-10T23:52:35.609220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
5 87
17.4%
4 83
16.6%
6 76
15.2%
2 74
14.8%
7 67
13.4%
3 60
12.0%
1 53
10.6%
ValueCountFrequency (%)
1 53
10.6%
2 74
14.8%
3 60
12.0%
4 83
16.6%
5 87
17.4%
6 76
15.2%
7 67
13.4%
ValueCountFrequency (%)
7 67
13.4%
6 76
15.2%
5 87
17.4%
4 83
16.6%
3 60
12.0%
2 74
14.8%
1 53
10.6%

시간대(TM)
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
t4
500 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowt4
2nd rowt4
3rd rowt4
4th rowt4
5th rowt4

Common Values

ValueCountFrequency (%)
t4 500
100.0%

Length

2023-12-10T23:52:35.740702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:52:35.840637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
t4 500
100.0%
Distinct15
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.004
Minimum11
Maximum48
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:52:35.926758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile11
Q111
median11
Q341
95-th percentile44.05
Maximum48
Range37
Interquartile range (IQR)30

Descriptive statistics

Standard deviation14.652145
Coefficient of variation (CV)0.61040431
Kurtosis-1.7684743
Mean24.004
Median Absolute Deviation (MAD)0
Skewness0.33012428
Sum12002
Variance214.68535
MonotonicityNot monotonic
2023-12-10T23:52:36.031398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
11 271
54.2%
41 147
29.4%
28 10
 
2.0%
27 10
 
2.0%
45 9
 
1.8%
30 8
 
1.6%
48 8
 
1.6%
29 7
 
1.4%
43 6
 
1.2%
42 5
 
1.0%
Other values (5) 19
 
3.8%
ValueCountFrequency (%)
11 271
54.2%
26 4
 
0.8%
27 10
 
2.0%
28 10
 
2.0%
29 7
 
1.4%
30 8
 
1.6%
31 3
 
0.6%
41 147
29.4%
42 5
 
1.0%
43 6
 
1.2%
ValueCountFrequency (%)
48 8
 
1.6%
47 3
 
0.6%
46 5
 
1.0%
45 9
 
1.8%
44 4
 
0.8%
43 6
 
1.2%
42 5
 
1.0%
41 147
29.4%
31 3
 
0.6%
30 8
 
1.6%

유입지_시군구코드(C_SGG_CD)
Real number (ℝ)

MISSING 

Distinct47
Distinct (%)11.1%
Missing75
Missing (%)15.0%
Infinite0
Infinite (%)0.0%
Mean40.268235
Minimum11
Maximum83
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:52:36.162673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile11
Q122
median38
Q357
95-th percentile71
Maximum83
Range72
Interquartile range (IQR)35

Descriptive statistics

Standard deviation19.35343
Coefficient of variation (CV)0.48061281
Kurtosis-1.1288186
Mean40.268235
Median Absolute Deviation (MAD)17
Skewness0.14462225
Sum17114
Variance374.55524
MonotonicityNot monotonic
2023-12-10T23:52:36.309533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
59 27
 
5.4%
11 25
 
5.0%
17 20
 
4.0%
68 17
 
3.4%
38 16
 
3.2%
56 15
 
3.0%
71 15
 
3.0%
21 15
 
3.0%
53 13
 
2.6%
50 13
 
2.6%
Other values (37) 249
49.8%
(Missing) 75
 
15.0%
ValueCountFrequency (%)
11 25
5.0%
13 10
 
2.0%
14 9
 
1.8%
15 7
 
1.4%
17 20
4.0%
19 8
 
1.6%
20 11
2.2%
21 15
3.0%
22 2
 
0.4%
23 11
2.2%
ValueCountFrequency (%)
83 3
 
0.6%
82 2
 
0.4%
74 11
2.2%
71 15
3.0%
68 17
3.4%
67 1
 
0.2%
65 9
1.8%
63 1
 
0.2%
62 8
1.6%
61 5
 
1.0%

이용건수(USECT_CORR)
Real number (ℝ)

Distinct44
Distinct (%)8.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.57928
Minimum4.94
Maximum2618.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:52:36.462527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.94
5-th percentile4.94
Q14.94
median9.88
Q319.76
95-th percentile144.001
Maximum2618.2
Range2613.26
Interquartile range (IQR)14.82

Descriptive statistics

Standard deviation156.45675
Coefficient of variation (CV)3.9529964
Kurtosis171.92044
Mean39.57928
Median Absolute Deviation (MAD)4.94
Skewness11.876466
Sum19789.64
Variance24478.715
MonotonicityNot monotonic
2023-12-10T23:52:36.596201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
4.94 217
43.4%
9.88 93
18.6%
14.82 42
 
8.4%
19.76 25
 
5.0%
24.7 14
 
2.8%
29.64 14
 
2.8%
34.58 13
 
2.6%
54.34 12
 
2.4%
39.52 9
 
1.8%
44.46 8
 
1.6%
Other values (34) 53
 
10.6%
ValueCountFrequency (%)
4.94 217
43.4%
9.88 93
18.6%
14.82 42
 
8.4%
19.76 25
 
5.0%
24.7 14
 
2.8%
29.64 14
 
2.8%
34.58 13
 
2.6%
39.52 9
 
1.8%
44.46 8
 
1.6%
49.4 4
 
0.8%
ValueCountFrequency (%)
2618.2 1
0.2%
1635.14 1
0.2%
963.3 1
0.2%
607.62 1
0.2%
553.28 1
0.2%
424.84 1
0.2%
365.56 1
0.2%
355.68 1
0.2%
350.74 1
0.2%
326.04 1
0.2%

이용금액(AMT_CORR)
Real number (ℝ)

Distinct353
Distinct (%)70.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean786786.64
Minimum2964
Maximum37026876
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:52:36.731049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2964
5-th percentile14701.44
Q145460.35
median126217
Q3402610
95-th percentile2924997.5
Maximum37026876
Range37023912
Interquartile range (IQR)357149.65

Descriptive statistics

Standard deviation3032595.3
Coefficient of variation (CV)3.8544062
Kurtosis92.498041
Mean786786.64
Median Absolute Deviation (MAD)103987
Skewness8.9766282
Sum3.9339332 × 108
Variance9.1966344 × 1012
MonotonicityNot monotonic
2023-12-10T23:52:36.862193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22230.0 11
 
2.2%
79040.0 9
 
1.8%
39520.0 8
 
1.6%
59280.0 8
 
1.6%
108680.0 6
 
1.2%
49400.0 6
 
1.2%
24700.0 5
 
1.0%
19760.0 5
 
1.0%
29640.0 5
 
1.0%
88920.0 5
 
1.0%
Other values (343) 432
86.4%
ValueCountFrequency (%)
2964.0 1
 
0.2%
4446.0 1
 
0.2%
4693.0 1
 
0.2%
4940.0 3
0.6%
5928.0 1
 
0.2%
6916.0 1
 
0.2%
7410.0 2
0.4%
7904.0 2
0.4%
8892.0 2
0.4%
9880.0 1
 
0.2%
ValueCountFrequency (%)
37026875.86 1
0.2%
35366497.4 1
0.2%
29246578.4 1
0.2%
17556611.8 1
0.2%
14828398.0 1
0.2%
14227200.0 1
0.2%
12003804.8 1
0.2%
7489040.0 1
0.2%
6518626.4 1
0.2%
5605368.6 1
0.2%

Interactions

2023-12-10T23:52:33.246604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:26.258438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:27.372946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:28.382419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:29.473824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:30.395034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:31.344955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:32.187240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:33.348315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:26.385979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:27.475881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:28.496983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:29.592482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:30.508070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:31.444336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:32.280625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:33.443487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:26.499999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:27.588386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:28.629358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:29.722892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:30.619839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:31.544157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:32.374301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:33.542314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:26.686461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:27.719801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:28.784081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:29.851922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:30.750249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:31.636633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:32.481489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:33.637071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:26.811596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:27.849749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:28.937997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:29.964437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:30.875265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:31.744108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:32.575197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:33.737558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:26.936249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:27.978538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:29.071456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:30.073844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:31.004197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:31.863411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:32.683935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:33.860928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:27.107574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:28.120149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:29.225500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:30.181530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:31.129807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:31.972679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:32.775628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:33.997084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:27.236168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:28.256948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:29.339981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:30.289695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:31.233418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:32.075577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:52:33.126668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:52:36.959642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동코드(M_DONG_CD)블록코드(BLOCK_ID)업종코드(SE_UPJONG)이용년월(TS_YM)요일코드(DAW_CCD)유입지_시도코드(C_SIDO_CD)유입지_시군구코드(C_SGG_CD)이용건수(USECT_CORR)이용금액(AMT_CORR)
행정동코드(M_DONG_CD)1.0000.0000.1060.0000.0000.0000.0000.1820.143
블록코드(BLOCK_ID)0.0001.0000.0000.0000.1320.0000.0000.0000.000
업종코드(SE_UPJONG)0.1060.0001.0000.0560.0810.0000.0000.0000.177
이용년월(TS_YM)0.0000.0000.0561.0000.0000.0000.0580.0000.000
요일코드(DAW_CCD)0.0000.1320.0810.0001.0000.0310.0000.0000.000
유입지_시도코드(C_SIDO_CD)0.0000.0000.0000.0000.0311.0000.0000.0000.000
유입지_시군구코드(C_SGG_CD)0.0000.0000.0000.0580.0000.0001.0000.0000.046
이용건수(USECT_CORR)0.1820.0000.0000.0000.0000.0000.0001.0000.000
이용금액(AMT_CORR)0.1430.0000.1770.0000.0000.0000.0460.0001.000
2023-12-10T23:52:37.100343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동코드(M_DONG_CD)블록코드(BLOCK_ID)이용년월(TS_YM)요일코드(DAW_CCD)유입지_시도코드(C_SIDO_CD)유입지_시군구코드(C_SGG_CD)이용건수(USECT_CORR)이용금액(AMT_CORR)업종코드(SE_UPJONG)
행정동코드(M_DONG_CD)1.0000.0190.0850.015-0.035-0.046-0.0120.0120.039
블록코드(BLOCK_ID)0.0191.0000.042-0.0470.0600.0320.003-0.0280.000
이용년월(TS_YM)0.0850.0421.000-0.0170.0100.011-0.068-0.0020.106
요일코드(DAW_CCD)0.015-0.047-0.0171.0000.011-0.023-0.0160.0400.028
유입지_시도코드(C_SIDO_CD)-0.0350.0600.0100.0111.0000.032-0.0390.0210.000
유입지_시군구코드(C_SGG_CD)-0.0460.0320.011-0.0230.0321.0000.034-0.0740.000
이용건수(USECT_CORR)-0.0120.003-0.068-0.016-0.0390.0341.0000.0510.000
이용금액(AMT_CORR)0.012-0.028-0.0020.0400.021-0.0740.0511.0000.066
업종코드(SE_UPJONG)0.0390.0000.1060.0280.0000.0000.0000.0661.000

Missing values

2023-12-10T23:52:34.166137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:52:34.332530image/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

행정동코드(M_DONG_CD)블록코드(BLOCK_ID)업종코드(SE_UPJONG)이용년월(TS_YM)요일코드(DAW_CCD)시간대(TM)유입지_시도코드(C_SIDO_CD)유입지_시군구코드(C_SGG_CD)이용건수(USECT_CORR)이용금액(AMT_CORR)
011210581112056020022sb042017083t441329.88186732.0
111220671106083020003sb012016046t411594.9449400.0
211230771123053010008sb012016022t411<NA>9.8818772.0
311140711103074040001sb022017042t4116224.758687.2
411040651101056020001sb012016015t4413519.769880.0
511230651111052011202sb022016092t429574.9449400.0
611150641122060020002sb012016092t4115319.7649400.0
711150641121052030005sb082016017t41128103.7450388.0
811070711113075040001sb062016055t411<NA>4.94103740.0
911150511122067010001sb052016035t411<NA>4.94113620.0
행정동코드(M_DONG_CD)블록코드(BLOCK_ID)업종코드(SE_UPJONG)이용년월(TS_YM)요일코드(DAW_CCD)시간대(TM)유입지_시도코드(C_SIDO_CD)유입지_시군구코드(C_SGG_CD)이용건수(USECT_CORR)이용금액(AMT_CORR)
49011160591107073030005sb082017056t441419.88347282.0
49111050611122054030003sb082017012t446574.9448412.0
49211030691124065020003sb082017014t411314.9458884.8
49311160711113073040001sb222017064t441234.94219928.8
49411200731124051010022sb032017075t4417119.7614820.0
49511100601123071020003sb082017086t441259.8834580.0
49611020681123080020001sb192016024t442214.9410868.0
49711200521122066030001sb102016052t4118219.76123500.0
49811200521124066010201sb062017111t411719.88537472.0
49911230791124061010009sb152016102t411384.942346747.0