Overview

Dataset statistics

Number of variables16
Number of observations680
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory93.8 KiB
Average record size in memory141.2 B

Variable types

DateTime1
Categorical2
Numeric13

Dataset

Description그랜드코리아레저(주)에서 운영 중인 3개 영업점(강남코엑스점, 서울드래곤시티점(구 강북힐튼점), 부산롯데점)의 2017년~2023년 7월 고객구분에 따른 국적별 입장객 현황
Author그랜드코리아레저(주)
URLhttps://www.data.go.kr/data/15021219/fileData.do

Alerts

일본 is highly overall correlated with 중국 and 6 other fieldsHigh correlation
중국 is highly overall correlated with 일본 and 8 other fieldsHigh correlation
홍콩 is highly overall correlated with 일본 and 3 other fieldsHigh correlation
대만 is highly overall correlated with 일본 and 6 other fieldsHigh correlation
동남아 is highly overall correlated with 일본 and 6 other fieldsHigh correlation
로컬 is highly overall correlated with 일본 and 5 other fieldsHigh correlation
러시아 is highly overall correlated with 일본 and 10 other fieldsHigh correlation
미국 is highly overall correlated with 베트남 and 4 other fieldsHigh correlation
베트남 is highly overall correlated with 러시아 and 5 other fieldsHigh correlation
태국 is highly overall correlated with 중국 and 6 other fieldsHigh correlation
몽골 is highly overall correlated with 중국 and 6 other fieldsHigh correlation
필리핀 is highly overall correlated with 러시아 and 5 other fieldsHigh correlation
기타 is highly overall correlated with 일본 and 11 other fieldsHigh correlation
방문고객구분 is highly overall correlated with 러시아 and 1 other fieldsHigh correlation
일본 has 80 (11.8%) zerosZeros
중국 has 87 (12.8%) zerosZeros
홍콩 has 212 (31.2%) zerosZeros
대만 has 96 (14.1%) zerosZeros
동남아 has 117 (17.2%) zerosZeros
로컬 has 136 (20.0%) zerosZeros
러시아 has 270 (39.7%) zerosZeros
미국 has 315 (46.3%) zerosZeros
베트남 has 333 (49.0%) zerosZeros
태국 has 380 (55.9%) zerosZeros
몽골 has 358 (52.6%) zerosZeros
필리핀 has 410 (60.3%) zerosZeros
기타 has 149 (21.9%) zerosZeros

Reproduction

Analysis started2023-12-12 15:03:36.654771
Analysis finished2023-12-12 15:03:55.158805
Duration18.5 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

년월
Date

Distinct77
Distinct (%)11.3%
Missing0
Missing (%)0.0%
Memory size5.4 KiB
Minimum2017-01-31 00:00:00
Maximum2023-07-31 00:00:00
2023-12-13T00:03:55.227722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:55.353352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

영업장
Categorical

Distinct4
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size5.4 KiB
부산롯데점
228 
강남코엑스점
227 
강북힐튼점
204 
서울드래곤시티점
 
21

Length

Max length8
Median length5
Mean length5.4264706
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row강남코엑스점
2nd row강남코엑스점
3rd row강남코엑스점
4th row강북힐튼점
5th row강북힐튼점

Common Values

ValueCountFrequency (%)
부산롯데점 228
33.5%
강남코엑스점 227
33.4%
강북힐튼점 204
30.0%
서울드래곤시티점 21
 
3.1%

Length

2023-12-13T00:03:55.483273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:03:55.590924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
부산롯데점 228
33.5%
강남코엑스점 227
33.4%
강북힐튼점 204
30.0%
서울드래곤시티점 21
 
3.1%

방문고객구분
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size5.4 KiB
단체관광객
227 
일반고객
227 
VIP
226 

Length

Max length5
Median length4
Mean length4.0014706
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVIP
2nd row단체관광객
3rd row일반고객
4th rowVIP
5th row단체관광객

Common Values

ValueCountFrequency (%)
단체관광객 227
33.4%
일반고객 227
33.4%
VIP 226
33.2%

Length

2023-12-13T00:03:56.035465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:03:56.139922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
단체관광객 227
33.4%
일반고객 227
33.4%
vip 226
33.2%

일본
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct477
Distinct (%)70.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1585.2809
Minimum0
Maximum12028
Zeros80
Zeros (%)11.8%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2023-12-13T00:03:56.289727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q133.75
median813.5
Q31629.75
95-th percentile7120.55
Maximum12028
Range12028
Interquartile range (IQR)1596

Descriptive statistics

Standard deviation2348.7111
Coefficient of variation (CV)1.4815741
Kurtosis3.4523567
Mean1585.2809
Median Absolute Deviation (MAD)784.5
Skewness2.0098239
Sum1077991
Variance5516444
MonotonicityNot monotonic
2023-12-13T00:03:56.471968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 80
 
11.8%
1 10
 
1.5%
2 8
 
1.2%
3 6
 
0.9%
34 6
 
0.9%
16 6
 
0.9%
14 5
 
0.7%
12 5
 
0.7%
25 5
 
0.7%
6 4
 
0.6%
Other values (467) 545
80.1%
ValueCountFrequency (%)
0 80
11.8%
1 10
 
1.5%
2 8
 
1.2%
3 6
 
0.9%
4 1
 
0.1%
5 1
 
0.1%
6 4
 
0.6%
7 2
 
0.3%
8 1
 
0.1%
10 1
 
0.1%
ValueCountFrequency (%)
12028 1
0.1%
11439 1
0.1%
11208 1
0.1%
11093 1
0.1%
9927 1
0.1%
9872 1
0.1%
9736 1
0.1%
9722 1
0.1%
9678 1
0.1%
9330 1
0.1%

중국
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct538
Distinct (%)79.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4397.8132
Minimum0
Maximum38757
Zeros87
Zeros (%)12.8%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2023-12-13T00:03:56.625829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1112
median1434
Q34329.75
95-th percentile22760.8
Maximum38757
Range38757
Interquartile range (IQR)4217.75

Descriptive statistics

Standard deviation7541.549
Coefficient of variation (CV)1.7148407
Kurtosis6.6205518
Mean4397.8132
Median Absolute Deviation (MAD)1395.5
Skewness2.5814069
Sum2990513
Variance56874962
MonotonicityNot monotonic
2023-12-13T00:03:56.809427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 87
 
12.8%
1 11
 
1.6%
5 4
 
0.6%
95 3
 
0.4%
157 3
 
0.4%
104 3
 
0.4%
22 3
 
0.4%
3 3
 
0.4%
8 2
 
0.3%
28 2
 
0.3%
Other values (528) 559
82.2%
ValueCountFrequency (%)
0 87
12.8%
1 11
 
1.6%
2 1
 
0.1%
3 3
 
0.4%
4 1
 
0.1%
5 4
 
0.6%
6 1
 
0.1%
7 1
 
0.1%
8 2
 
0.3%
9 2
 
0.3%
ValueCountFrequency (%)
38757 1
0.1%
37343 1
0.1%
36824 1
0.1%
36105 1
0.1%
36011 1
0.1%
34942 1
0.1%
34912 1
0.1%
34559 1
0.1%
34504 1
0.1%
34071 1
0.1%

홍콩
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct232
Distinct (%)34.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean105.97794
Minimum0
Maximum1901
Zeros212
Zeros (%)31.2%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2023-12-13T00:03:57.019212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median12
Q3133.5
95-th percentile448.45
Maximum1901
Range1901
Interquartile range (IQR)133.5

Descriptive statistics

Standard deviation219.51488
Coefficient of variation (CV)2.0713262
Kurtosis24.001924
Mean105.97794
Median Absolute Deviation (MAD)12
Skewness4.2321009
Sum72065
Variance48186.784
MonotonicityNot monotonic
2023-12-13T00:03:57.216133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 212
31.2%
1 30
 
4.4%
4 20
 
2.9%
2 19
 
2.8%
6 12
 
1.8%
5 10
 
1.5%
3 8
 
1.2%
13 8
 
1.2%
12 7
 
1.0%
15 7
 
1.0%
Other values (222) 347
51.0%
ValueCountFrequency (%)
0 212
31.2%
1 30
 
4.4%
2 19
 
2.8%
3 8
 
1.2%
4 20
 
2.9%
5 10
 
1.5%
6 12
 
1.8%
7 4
 
0.6%
8 6
 
0.9%
9 7
 
1.0%
ValueCountFrequency (%)
1901 1
0.1%
1842 1
0.1%
1691 1
0.1%
1612 1
0.1%
1433 1
0.1%
1272 1
0.1%
1209 1
0.1%
1096 1
0.1%
988 1
0.1%
983 1
0.1%

대만
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct385
Distinct (%)56.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean274.91176
Minimum0
Maximum1901
Zeros96
Zeros (%)14.1%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2023-12-13T00:03:57.392718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q139.5
median125
Q3349.25
95-th percentile1062.85
Maximum1901
Range1901
Interquartile range (IQR)309.75

Descriptive statistics

Standard deviation359.18043
Coefficient of variation (CV)1.3065299
Kurtosis3.7438713
Mean274.91176
Median Absolute Deviation (MAD)113
Skewness1.9441445
Sum186940
Variance129010.58
MonotonicityNot monotonic
2023-12-13T00:03:57.550425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 96
 
14.1%
3 5
 
0.7%
104 5
 
0.7%
32 5
 
0.7%
94 4
 
0.6%
82 4
 
0.6%
27 4
 
0.6%
13 4
 
0.6%
69 4
 
0.6%
123 4
 
0.6%
Other values (375) 545
80.1%
ValueCountFrequency (%)
0 96
14.1%
1 4
 
0.6%
3 5
 
0.7%
5 1
 
0.1%
6 2
 
0.3%
7 1
 
0.1%
8 1
 
0.1%
10 1
 
0.1%
12 3
 
0.4%
13 4
 
0.6%
ValueCountFrequency (%)
1901 1
0.1%
1864 1
0.1%
1823 1
0.1%
1815 1
0.1%
1802 1
0.1%
1795 1
0.1%
1668 1
0.1%
1482 1
0.1%
1442 1
0.1%
1417 1
0.1%

동남아
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct342
Distinct (%)50.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean449.72059
Minimum0
Maximum5873
Zeros117
Zeros (%)17.2%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2023-12-13T00:03:57.697965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17.75
median68
Q3298.25
95-th percentile2625.15
Maximum5873
Range5873
Interquartile range (IQR)290.5

Descriptive statistics

Standard deviation919.28515
Coefficient of variation (CV)2.0441251
Kurtosis7.6207011
Mean449.72059
Median Absolute Deviation (MAD)68
Skewness2.7599499
Sum305810
Variance845085.18
MonotonicityNot monotonic
2023-12-13T00:03:57.860352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 117
 
17.2%
2 13
 
1.9%
11 11
 
1.6%
1 11
 
1.6%
3 11
 
1.6%
9 8
 
1.2%
8 7
 
1.0%
23 6
 
0.9%
35 6
 
0.9%
13 6
 
0.9%
Other values (332) 484
71.2%
ValueCountFrequency (%)
0 117
17.2%
1 11
 
1.6%
2 13
 
1.9%
3 11
 
1.6%
4 6
 
0.9%
5 5
 
0.7%
6 5
 
0.7%
7 2
 
0.3%
8 7
 
1.0%
9 8
 
1.2%
ValueCountFrequency (%)
5873 1
0.1%
4995 1
0.1%
4554 1
0.1%
4429 1
0.1%
4411 1
0.1%
4339 1
0.1%
4301 1
0.1%
4230 1
0.1%
4187 1
0.1%
4162 1
0.1%

로컬
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct370
Distinct (%)54.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean389.34559
Minimum0
Maximum4765
Zeros136
Zeros (%)20.0%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2023-12-13T00:03:57.999607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median152.5
Q3506
95-th percentile1745.35
Maximum4765
Range4765
Interquartile range (IQR)504

Descriptive statistics

Standard deviation614.81319
Coefficient of variation (CV)1.5790938
Kurtosis9.4823478
Mean389.34559
Median Absolute Deviation (MAD)152.5
Skewness2.7316469
Sum264755
Variance377995.26
MonotonicityNot monotonic
2023-12-13T00:03:58.188217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 136
 
20.0%
2 19
 
2.8%
1 17
 
2.5%
4 13
 
1.9%
3 10
 
1.5%
13 8
 
1.2%
10 6
 
0.9%
5 6
 
0.9%
17 4
 
0.6%
9 4
 
0.6%
Other values (360) 457
67.2%
ValueCountFrequency (%)
0 136
20.0%
1 17
 
2.5%
2 19
 
2.8%
3 10
 
1.5%
4 13
 
1.9%
5 6
 
0.9%
6 2
 
0.3%
7 3
 
0.4%
8 4
 
0.6%
9 4
 
0.6%
ValueCountFrequency (%)
4765 1
0.1%
3809 1
0.1%
3530 1
0.1%
3221 1
0.1%
3209 1
0.1%
3030 1
0.1%
2870 1
0.1%
2836 1
0.1%
2796 1
0.1%
2770 1
0.1%

러시아
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct203
Distinct (%)29.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76.155882
Minimum0
Maximum975
Zeros270
Zeros (%)39.7%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2023-12-13T00:03:58.378695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q399.25
95-th percentile404.2
Maximum975
Range975
Interquartile range (IQR)99.25

Descriptive statistics

Standard deviation142.92792
Coefficient of variation (CV)1.8767811
Kurtosis7.2270919
Mean76.155882
Median Absolute Deviation (MAD)4
Skewness2.5854139
Sum51786
Variance20428.391
MonotonicityNot monotonic
2023-12-13T00:03:58.516590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 270
39.7%
1 39
 
5.7%
2 17
 
2.5%
3 10
 
1.5%
4 10
 
1.5%
5 10
 
1.5%
6 8
 
1.2%
12 7
 
1.0%
21 6
 
0.9%
8 5
 
0.7%
Other values (193) 298
43.8%
ValueCountFrequency (%)
0 270
39.7%
1 39
 
5.7%
2 17
 
2.5%
3 10
 
1.5%
4 10
 
1.5%
5 10
 
1.5%
6 8
 
1.2%
7 3
 
0.4%
8 5
 
0.7%
9 4
 
0.6%
ValueCountFrequency (%)
975 1
0.1%
788 1
0.1%
723 1
0.1%
709 1
0.1%
687 1
0.1%
669 1
0.1%
659 1
0.1%
627 1
0.1%
626 1
0.1%
612 1
0.1%

미국
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct288
Distinct (%)42.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean207.09265
Minimum0
Maximum2053
Zeros315
Zeros (%)46.3%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2023-12-13T00:03:58.644301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q3298.25
95-th percentile917.85
Maximum2053
Range2053
Interquartile range (IQR)298.25

Descriptive statistics

Standard deviation329.89175
Coefficient of variation (CV)1.592967
Kurtosis5.5779062
Mean207.09265
Median Absolute Deviation (MAD)4
Skewness2.2056604
Sum140823
Variance108828.57
MonotonicityNot monotonic
2023-12-13T00:03:58.799596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 315
46.3%
1 12
 
1.8%
2 8
 
1.2%
3 5
 
0.7%
35 4
 
0.6%
33 3
 
0.4%
248 3
 
0.4%
230 3
 
0.4%
43 3
 
0.4%
197 3
 
0.4%
Other values (278) 321
47.2%
ValueCountFrequency (%)
0 315
46.3%
1 12
 
1.8%
2 8
 
1.2%
3 5
 
0.7%
5 1
 
0.1%
6 1
 
0.1%
7 1
 
0.1%
19 1
 
0.1%
22 1
 
0.1%
24 1
 
0.1%
ValueCountFrequency (%)
2053 1
0.1%
1852 1
0.1%
1790 1
0.1%
1703 1
0.1%
1603 1
0.1%
1565 1
0.1%
1519 1
0.1%
1430 1
0.1%
1410 1
0.1%
1375 1
0.1%

베트남
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct222
Distinct (%)32.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean172.65147
Minimum0
Maximum3187
Zeros333
Zeros (%)49.0%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2023-12-13T00:03:58.946624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q3103
95-th percentile1277.6
Maximum3187
Range3187
Interquartile range (IQR)103

Descriptive statistics

Standard deviation450.78801
Coefficient of variation (CV)2.6109712
Kurtosis14.441698
Mean172.65147
Median Absolute Deviation (MAD)2
Skewness3.7029274
Sum117403
Variance203209.83
MonotonicityNot monotonic
2023-12-13T00:03:59.084874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 333
49.0%
12 7
 
1.0%
1 6
 
0.9%
70 6
 
0.9%
54 5
 
0.7%
111 4
 
0.6%
56 4
 
0.6%
23 4
 
0.6%
55 4
 
0.6%
205 4
 
0.6%
Other values (212) 303
44.6%
ValueCountFrequency (%)
0 333
49.0%
1 6
 
0.9%
2 4
 
0.6%
3 2
 
0.3%
4 2
 
0.3%
5 3
 
0.4%
7 1
 
0.1%
10 2
 
0.3%
11 2
 
0.3%
12 7
 
1.0%
ValueCountFrequency (%)
3187 1
0.1%
2973 1
0.1%
2771 1
0.1%
2678 1
0.1%
2590 1
0.1%
2388 1
0.1%
2258 1
0.1%
2180 1
0.1%
2117 1
0.1%
2101 1
0.1%

태국
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct126
Distinct (%)18.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.770588
Minimum0
Maximum835
Zeros380
Zeros (%)55.9%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2023-12-13T00:03:59.225727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q317
95-th percentile191.15
Maximum835
Range835
Interquartile range (IQR)17

Descriptive statistics

Standard deviation94.149305
Coefficient of variation (CV)2.7879084
Kurtosis20.598227
Mean33.770588
Median Absolute Deviation (MAD)0
Skewness4.2629707
Sum22964
Variance8864.0916
MonotonicityNot monotonic
2023-12-13T00:03:59.374558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 380
55.9%
2 19
 
2.8%
7 13
 
1.9%
4 12
 
1.8%
1 11
 
1.6%
5 9
 
1.3%
9 9
 
1.3%
3 8
 
1.2%
6 8
 
1.2%
13 7
 
1.0%
Other values (116) 204
30.0%
ValueCountFrequency (%)
0 380
55.9%
1 11
 
1.6%
2 19
 
2.8%
3 8
 
1.2%
4 12
 
1.8%
5 9
 
1.3%
6 8
 
1.2%
7 13
 
1.9%
8 3
 
0.4%
9 9
 
1.3%
ValueCountFrequency (%)
835 1
0.1%
632 1
0.1%
596 1
0.1%
560 1
0.1%
552 1
0.1%
509 1
0.1%
465 1
0.1%
447 1
0.1%
446 1
0.1%
445 1
0.1%

몽골
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct220
Distinct (%)32.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean203.46765
Minimum0
Maximum3441
Zeros358
Zeros (%)52.6%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2023-12-13T00:03:59.537132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q386.25
95-th percentile1949.7
Maximum3441
Range3441
Interquartile range (IQR)86.25

Descriptive statistics

Standard deviation570.13242
Coefficient of variation (CV)2.802079
Kurtosis12.889142
Mean203.46765
Median Absolute Deviation (MAD)0
Skewness3.669168
Sum138358
Variance325050.97
MonotonicityNot monotonic
2023-12-13T00:03:59.687865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 358
52.6%
1 11
 
1.6%
4 7
 
1.0%
3 7
 
1.0%
6 6
 
0.9%
9 5
 
0.7%
7 5
 
0.7%
5 4
 
0.6%
51 4
 
0.6%
56 4
 
0.6%
Other values (210) 269
39.6%
ValueCountFrequency (%)
0 358
52.6%
1 11
 
1.6%
2 2
 
0.3%
3 7
 
1.0%
4 7
 
1.0%
5 4
 
0.6%
6 6
 
0.9%
7 5
 
0.7%
8 2
 
0.3%
9 5
 
0.7%
ValueCountFrequency (%)
3441 1
0.1%
3025 1
0.1%
3000 1
0.1%
2990 1
0.1%
2969 1
0.1%
2946 1
0.1%
2923 1
0.1%
2913 1
0.1%
2844 1
0.1%
2788 1
0.1%

필리핀
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct128
Distinct (%)18.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean66.845588
Minimum0
Maximum2080
Zeros410
Zeros (%)60.3%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2023-12-13T00:03:59.831589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q38
95-th percentile352.6
Maximum2080
Range2080
Interquartile range (IQR)8

Descriptive statistics

Standard deviation239.44606
Coefficient of variation (CV)3.5820773
Kurtosis28.852754
Mean66.845588
Median Absolute Deviation (MAD)0
Skewness5.1202669
Sum45455
Variance57334.416
MonotonicityNot monotonic
2023-12-13T00:03:59.972106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 410
60.3%
1 30
 
4.4%
2 27
 
4.0%
3 13
 
1.9%
5 11
 
1.6%
4 8
 
1.2%
60 6
 
0.9%
8 6
 
0.9%
43 4
 
0.6%
6 4
 
0.6%
Other values (118) 161
 
23.7%
ValueCountFrequency (%)
0 410
60.3%
1 30
 
4.4%
2 27
 
4.0%
3 13
 
1.9%
4 8
 
1.2%
5 11
 
1.6%
6 4
 
0.6%
7 3
 
0.4%
8 6
 
0.9%
9 2
 
0.3%
ValueCountFrequency (%)
2080 1
0.1%
1941 1
0.1%
1840 1
0.1%
1504 1
0.1%
1496 1
0.1%
1421 1
0.1%
1420 1
0.1%
1281 1
0.1%
1271 1
0.1%
1259 1
0.1%

기타
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct356
Distinct (%)52.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean694.15
Minimum0
Maximum6665
Zeros149
Zeros (%)21.9%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2023-12-13T00:04:00.106480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median61
Q3806
95-th percentile3796.6
Maximum6665
Range6665
Interquartile range (IQR)804

Descriptive statistics

Standard deviation1251.0502
Coefficient of variation (CV)1.8022764
Kurtosis4.8422012
Mean694.15
Median Absolute Deviation (MAD)61
Skewness2.2909671
Sum472022
Variance1565126.5
MonotonicityNot monotonic
2023-12-13T00:04:00.620829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 149
 
21.9%
1 17
 
2.5%
2 16
 
2.4%
3 12
 
1.8%
8 9
 
1.3%
4 6
 
0.9%
29 6
 
0.9%
25 5
 
0.7%
12 5
 
0.7%
16 5
 
0.7%
Other values (346) 450
66.2%
ValueCountFrequency (%)
0 149
21.9%
1 17
 
2.5%
2 16
 
2.4%
3 12
 
1.8%
4 6
 
0.9%
5 4
 
0.6%
6 4
 
0.6%
7 1
 
0.1%
8 9
 
1.3%
9 1
 
0.1%
ValueCountFrequency (%)
6665 1
0.1%
6163 1
0.1%
5892 1
0.1%
5673 1
0.1%
5542 1
0.1%
5502 1
0.1%
5464 1
0.1%
5452 1
0.1%
5380 1
0.1%
5226 1
0.1%

Interactions

2023-12-13T00:03:53.547313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:37.577992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:38.884967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:40.266351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:41.616107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:42.981920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:44.684283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:45.895752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:47.058287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:48.266244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:49.741098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:51.106913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:52.381249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:53.655142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:37.678663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:38.993456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:40.383840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:41.720517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:43.398457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:44.771147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:46.028954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:47.143272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:48.380207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:50.081195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:51.192075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:52.481225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:53.749472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:37.779106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:39.101607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:40.482980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:41.824321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:43.506386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:44.868989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:46.118068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:47.227223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:48.514024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:50.167444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:51.285803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:52.581178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:53.850358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:37.891156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:39.209699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:40.575542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:41.942856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:43.627160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:44.962176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:46.204415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:47.321723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:48.623425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:50.259222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:51.405934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:52.669460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:53.981936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:38.004146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:39.337638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:40.687804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:42.051773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:43.746292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:45.056215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:46.307749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:47.420324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:48.727947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:50.362443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:51.545002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:52.779067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:54.082226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:38.122375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:39.428039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:40.785703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:42.159251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:43.854409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:45.147900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:46.406588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:47.521106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:48.876592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:50.444667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:51.633377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:52.871138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:54.168169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:38.218703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:39.558198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:40.870362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:42.260171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:43.953660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:45.249085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:46.488919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:47.601797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:48.995264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:50.537715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:51.730293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:52.964355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:54.259477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:38.312838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:39.665784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:40.959508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:42.360129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:44.085024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:45.369660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:46.570001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:47.685751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:49.136091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:50.630686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:51.814190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:53.056609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:54.348588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:38.405150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:39.761047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:41.092850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:42.467355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:44.189186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:45.463212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:46.657215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:47.763624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:49.256852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:50.699010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:51.900492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:53.129498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:54.457448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:38.493043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:39.860857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:41.189750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:42.561257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:44.300728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:45.560003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:46.741013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:47.854156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:49.372891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:50.779502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:52.023287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:53.212807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:54.581133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:38.585556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:39.956012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:41.288046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:42.657485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:44.384943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:45.648697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:46.816385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:47.934200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:49.469194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:50.859687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:52.112075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:53.302240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:54.686260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:38.692277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:40.076519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:41.398756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:42.776044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:44.483748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:45.736304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:46.895842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:48.033041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:49.580441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:50.955454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:52.203357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:53.390066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:54.759544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:38.786237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:40.166580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:41.506853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:42.883706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:44.590954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:45.810650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:46.966805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:48.159218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:49.659521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:51.029336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:52.291296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:53.464049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T00:04:00.756292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년월영업장방문고객구분일본중국홍콩대만동남아로컬러시아미국베트남태국몽골필리핀기타
년월1.0000.1390.0000.4720.0000.3470.2950.0000.0000.0000.0000.3360.1520.2550.3380.000
영업장0.1391.0000.0000.2460.4950.2210.2680.2820.4240.2380.4120.2130.1700.3400.2300.320
방문고객구분0.0000.0001.0000.6120.5920.4050.5640.5760.5920.7170.5890.4380.5540.4780.4110.766
일본0.4720.2460.6121.0000.7680.7130.7710.8030.4340.7890.6300.7890.6490.7340.8300.745
중국0.0000.4950.5920.7681.0000.6990.6620.8590.5250.6420.7250.7620.6830.7980.7940.852
홍콩0.3470.2210.4050.7130.6991.0000.8120.7140.7420.6170.7000.6110.5880.5750.6420.764
대만0.2950.2680.5640.7710.6620.8121.0000.7430.4840.7600.6110.6900.5420.6090.6530.713
동남아0.0000.2820.5760.8030.8590.7140.7431.0000.5820.7610.5180.7530.5470.6300.7320.845
로컬0.0000.4240.5920.4340.5250.7420.4840.5821.0000.1230.5190.2690.4730.3870.3050.682
러시아0.0000.2380.7170.7890.6420.6170.7600.7610.1231.0000.6360.7800.6850.7030.7730.759
미국0.0000.4120.5890.6300.7250.7000.6110.5180.5190.6361.0000.8030.7320.8400.8240.780
베트남0.3360.2130.4380.7890.7620.6110.6900.7530.2690.7800.8031.0000.8190.8840.9520.731
태국0.1520.1700.5540.6490.6830.5880.5420.5470.4730.6850.7320.8191.0000.7910.8410.621
몽골0.2550.3400.4780.7340.7980.5750.6090.6300.3870.7030.8400.8840.7911.0000.9120.764
필리핀0.3380.2300.4110.8300.7940.6420.6530.7320.3050.7730.8240.9520.8410.9121.0000.755
기타0.0000.3200.7660.7450.8520.7640.7130.8450.6820.7590.7800.7310.6210.7640.7551.000
2023-12-13T00:04:00.941706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
방문고객구분영업장
방문고객구분1.0000.000
영업장0.0001.000
2023-12-13T00:04:01.068753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일본중국홍콩대만동남아로컬러시아미국베트남태국몽골필리핀기타영업장방문고객구분
일본1.0000.6180.8100.7860.7880.5930.5050.1310.1930.2880.2140.1930.5470.1480.454
중국0.6181.0000.6090.5980.8040.7130.5940.4710.4880.5110.5150.4380.7630.3170.434
홍콩0.8100.6091.0000.7090.7080.4220.2900.0280.0670.1710.1110.0990.3870.1330.265
대만0.7860.5980.7091.0000.6930.6620.5690.3230.3440.4330.3850.3220.6220.1620.406
동남아0.7880.8040.7080.6931.0000.7040.6850.2890.3500.3980.3800.3750.7880.1710.417
로컬0.5930.7130.4220.6620.7041.0000.5610.4990.4240.4740.4690.3270.8360.2830.318
러시아0.5050.5940.2900.5690.6850.5611.0000.4880.5660.5120.5520.6610.8290.1430.575
미국0.1310.4710.0280.3230.2890.4990.4881.0000.8990.8760.9210.7590.5630.2570.430
베트남0.1930.4880.0670.3440.3500.4240.5660.8991.0000.8630.9030.8080.5700.1280.292
태국0.2880.5110.1710.4330.3980.4740.5120.8760.8631.0000.9030.7250.5460.1090.290
몽골0.2140.5150.1110.3850.3800.4690.5520.9210.9030.9031.0000.7730.5850.2080.326
필리핀0.1930.4380.0990.3220.3750.3270.6610.7590.8080.7250.7731.0000.5700.1390.270
기타0.5470.7630.3870.6220.7880.8360.8290.5630.5700.5460.5850.5701.0000.1960.640
영업장0.1480.3170.1330.1620.1710.2830.1430.2570.1280.1090.2080.1390.1961.0000.000
방문고객구분0.4540.4340.2650.4060.4170.3180.5750.4300.2920.2900.3260.2700.6400.0001.000

Missing values

2023-12-13T00:03:54.895499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T00:03:55.083744image/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

년월영업장방문고객구분일본중국홍콩대만동남아로컬러시아미국베트남태국몽골필리핀기타
02017-01-31강남코엑스점VIP13172669341892081812000000363
12017-01-31강남코엑스점단체관광객247267125188030000000
22017-01-31강남코엑스점일반고객2829112743423261815243283000004511
32017-01-31강북힐튼점VIP1931172018136313635000000199
42017-01-31강북힐튼점단체관광객127362725346119000000000
52017-01-31강북힐튼점일반고객86332391836586242301409124000004567
62017-01-31부산롯데점VIP162812202812458227270000026
72017-01-31부산롯데점단체관광객15642213311382600000000
82017-01-31부산롯데점일반고객57621815146204190733932000000986
92017-02-28강남코엑스점VIP11882515201942491671400000317
년월영업장방문고객구분일본중국홍콩대만동남아로컬러시아미국베트남태국몽골필리핀기타
6702023-06-30부산롯데점일반고객4022126649182411543271972262012670792
6712023-07-31강남코엑스점VIP1274157122776584935011818145162
6722023-07-31강남코엑스점단체관광객0000000000000
6732023-07-31강남코엑스점일반고객27336020553292176441498646779547521540
6742023-07-31서울드래곤시티점VIP1127230431653937710374604915076
6752023-07-31서울드래곤시티점단체관광객0000000000000
6762023-07-31서울드래곤시티점일반고객569817142896565063271286321958145950840
6772023-07-31부산롯데점VIP9602790771611686313101062
6782023-07-31부산롯데점단체관광객0000000000000
6792023-07-31부산롯데점일반고객4366137849666211142295233186196559905